From dcc4cb1dfadba29a79dfa9a8d6e06a6f68e1a31d Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Fri, 3 May 2024 16:01:44 -0400
Subject: [PATCH 01/25] prompter pipeline

---
 ...reprocessing.rolling_window_sequences.json |  42 +++++
 sigllm/primitives/postprocessing.py           |   0
 sigllm/primitives/prompting/anomalies.py      | 163 ++++++++++--------
 sigllm/primitives/prompting/data.py           |  81 ---------
 sigllm/primitives/prompting/gpt.py            | 128 +++++++++++++-
 sigllm/primitives/prompting/gpt_messages.json |   4 +
 sigllm/primitives/prompting/huggingface.py    | 134 ++++++++++++++
 .../prompting/huggingface_messages.json       |   4 +
 sigllm/primitives/timeseries_preprocessing.py |  43 +++++
 tutorials/HFPipeline.ipynb                    |   3 +
 tutorials/prompter.ipynb                      |  58 ++++++-
 11 files changed, 502 insertions(+), 158 deletions(-)
 create mode 100644 sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json
 delete mode 100644 sigllm/primitives/postprocessing.py
 delete mode 100644 sigllm/primitives/prompting/data.py
 create mode 100644 sigllm/primitives/prompting/gpt_messages.json
 create mode 100644 sigllm/primitives/prompting/huggingface.py
 create mode 100644 sigllm/primitives/prompting/huggingface_messages.json
 create mode 100644 sigllm/primitives/timeseries_preprocessing.py

diff --git a/sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json b/sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json
new file mode 100644
index 0000000..be85b35
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json
@@ -0,0 +1,42 @@
+{
+    "name": "sigllm.primitives.timeseries_preprocessing.rolling_window_sequences",
+    "contributors": ["Linh Nguyen <linhnk@mit.edu>"],
+    "description": "Create rolling windows",
+    "classifiers": {
+        "type": "preprocessor",
+        "subtype": "rolling windows"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.timeseries_preprocessing.rolling_window_sequences",
+    "produce": {
+        "method": "detect",
+        "args": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            },
+            {
+                "name": "index",
+                "type": "ndarray"
+            },
+            {
+                "name": "window_size",
+                "type": "int"
+            }, 
+            {
+                "name": "step_size",
+                "type": "int"
+            }
+        ],
+        "output": [
+            {
+                "name": "out_X",
+                "type": "ndarray"
+            },
+            {
+                "name": "X_index",
+                "type": "ndarray"
+            }
+        ]
+    }
+}
\ No newline at end of file
diff --git a/sigllm/primitives/postprocessing.py b/sigllm/primitives/postprocessing.py
deleted file mode 100644
index e69de29..0000000
diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index c23abb4..9fa4405 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -1,107 +1,91 @@
 # -*- coding: utf-8 -*-
 
 """
-Result post-processing module.
+Prompter post-processing module
 
-This module contains functions that help convert model responses back to indices and timestamps.
+This module contains functions that help filter LLMs results to get the final anomalies.
 """
-import numpy as np
-
-
-def str2sig(text, sep=',', decimal=0):
-    """Convert a text string to a signal.
-
-    Convert a string containing digits into an array of numbers.
-
-    Args:
-        text (str):
-            A string containing signal values.
-        sep (str):
-            String that was used to separate each element in text, Default to `","`.
-        decimal (int):
-            Number of decimal points to shift each element in text to. Default to `0`.
-
-    Returns:
-        numpy.ndarray:
-            A 1-dimensional array containing parsed elements in `text`.
-    """
-    # Remove all characters from text except the digits and sep and decimal point
-    text = ''.join(i for i in text if (i.isdigit() or i == sep or i == '.'))
-    values = np.fromstring(text, dtype=float, sep=sep)
-    return values * 10**(-decimal)
-
-
-def str2idx(text, len_seq, sep=','):
-    """Convert a text string to indices.
-
-    Convert a string containing digits into an array of indices.
 
-    Args:
-        text (str):
-            A string containing indices values.
-        len_seq (int):
-            The length of processed sequence
-        sep (str):
-            String that was used to separate each element in text, Default to `","`.
+import numpy as np
 
-    Returns:
-        numpy.ndarray:
-            A 1-dimensional array containing parsed elements in `text`.
+def val2idx(vals, windows): 
+    """Convert detected anomalies values into indices.
+    
+    Convert windows of detected anomalies values into an array of all indices
+    in the input sequence that have those values. 
+    
+    Args: 
+        vals (List[ndarray]]): 
+            A list nd array containing detected anomalous values from different
+            responses of one window in one sample response. 
+        windows (ndarray):
+            rolling window sequences.      
+    Returns: 
+        List([ndarray]):
+            A list of nd array containing detected anomalous indices from different
+            responses of one window in one sample response.
     """
-    # Remove all characters from text except the digits and sep
-    text = ''.join(i for i in text if (i.isdigit() or i == sep))
-
-    values = np.fromstring(text, dtype=int, sep=sep)
-
-    # Remove indices that exceed the length of sequence
-    values = values[values < len_seq]
-    return values
-
 
-def get_anomaly_list_within_seq(res_list, alpha=0.5):
+    idx_list = []
+    for anomalies_list, seq in zip(vals, windows): 
+        idx_win_list = []
+        for anomalies in anomalies_list:
+            mask = np.isin(seq, anomalies)
+            indices = np.where(mask)[0]
+            idx_win_list.append(indices)
+        idx_win_list = np.array(idx_win_list)
+        idx_list.append(idx_win_list)
+    return idx_list
+
+def ano_within_windows(idx_win_list, alpha=0.5):
     """Get the final list of anomalous indices of a sequence
 
     Choose anomalous index in the sequence based on multiple LLM responses
 
     Args:
-        res_list (List[numpy.ndarray]):
-            A list of 1-dimensional array containing anomous indices output by LLM
+        idx_win_list (List[List[numpy.ndarray]]):
+            A list of lists of 1d array containing detected anomalous indices of
+            one window in one sample response.
         alpha (float):
-            Percentage of votes needed for an index to be deemed anomalous. Default: 0.5
+            Percentage of votes needed for an index to be deemed anomalous. Default to `0.5`.
 
     Returns:
-        numpy.ndarray:
-            A 1-dimensional array containing final anomalous indices
+        List[numpy.ndarray]:
+            A list of 1-dimensional array containing final anomalous indices of each windows.
     """
-    min_vote = np.ceil(alpha * len(res_list))
+    
+    idx_list = []
+    for samples in idx_win_list:
+        min_vote = np.ceil(alpha * len(samples))
 
-    flattened_res = np.concatenate(res_list)
+        flattened_res = np.flatten(samples)
 
-    unique_elements, counts = np.unique(flattened_res, return_counts=True)
+        unique_elements, counts = np.unique(flattened_res, return_counts=True)
 
-    final_list = unique_elements[counts >= min_vote]
+        final_list = unique_elements[counts >= min_vote]
 
-    return final_list
+        idx_list.append(final_list)
 
+    return idx_list
 
 def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5):
     """Get the final list of anomalous indices of a sequence when merging all rolling windows
 
     Args:
         anomalies (List[numpy.ndarray]):
-            A list of 1-dimensional array containing anomous indices of each window
+            A list of 1-dimensional array containing anomous indices of each window.
         start_indices (numpy.ndarray):
-            A 1-dimensional array contaning the first index of each window
+            A 1-dimensional array contaning the first index of each window.
         window_size (int):
-            Length of each window
+            Length of each window.
         step_size (int):
             Indicating the number of steps the window moves forward each round.
         beta (float):
-            Percentage of containing windows needed for index to be deemed anomalous. Default: 0.5
+            Percentage of containing windows needed for index to be deemed anomalous. Default to `0.5`.
 
     Return:
         numpy.ndarray:
-            A 1-dimensional array containing final anomalous indices
+            A 1-dimensional array containing final anomalous indices.
     """
     anomalies = [arr + first_idx for (arr, first_idx) in zip(anomalies, start_indices)]
 
@@ -115,18 +99,55 @@ def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5
 
     return np.sort(final_list)
 
-
 def idx2time(sequence, idx_list):
     """Convert list of indices into list of timestamp
 
     Args:
         sequence (pandas.Dataframe):
-            Signal with timestamps and values
+            Signal with timestamps and values.
         idx_list (numpy.ndarray):
-            A 1-dimensional array of indices
+            A 1-dimensional array of indices.
 
     Returns:
         numpy.ndarray:
-            A 1-dimensional array containing timestamps
+            A 1-dimensional array containing timestamps.
     """
     return sequence.iloc[idx_list].timestamp.to_numpy()
+
+def timestamp2interval(timestamp_list, interval, start, end, padding_size = 50): 
+    """Convert list of timestamps to list of intervals by padding to both sides
+    and merge overlapping 
+    
+    Args: 
+        timestamp_list (List[timestamp]): 
+            A list of point timestamps.
+        interval (int):
+            The fixed gap between two consecutive timestamps of the time series.
+        start (timestamp): 
+            The start timestamp of the time series.
+        end (timestamp): 
+            The end timestamp of the time series.
+        padding_size (int): 
+            Number of steps to pad on both sides of a timestamp point.
+    """
+    intervals = []
+    for timestamp in timestamp_list: 
+        intervals.append((max(start, timestamp-padding_size*interval), min(end, timestamp+padding_size*interval)))
+
+    if not intervals:
+        return []
+
+    intervals.sort(key=lambda x: x[0])  # Sort intervals based on start time
+    merged_intervals = [intervals[0]]  # Initialize merged intervals with the first interval
+
+    for current_interval in intervals[1:]:
+        previous_interval = merged_intervals[-1]
+        
+        # If the current interval overlaps with the previous one, merge them
+        if current_interval[0] <= previous_interval[1]:
+            previous_interval = (previous_interval[0], max(previous_interval[1], current_interval[1]))
+            merged_intervals[-1] = previous_interval
+        else:
+            merged_intervals.append(current_interval)  # Append the current interval if no overlap
+
+    return merged_intervals
diff --git a/sigllm/primitives/prompting/data.py b/sigllm/primitives/prompting/data.py
deleted file mode 100644
index 7d28dd8..0000000
--- a/sigllm/primitives/prompting/data.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-Data preprocessing module.
-
-This module contains functions that prepare timeseries for a language model.
-"""
-
-import numpy as np
-
-
-def rolling_window_sequences(X, index, window_size, step_size):
-    """Create rolling window sequences out of time series data.
-
-    The function creates an array of sequences by rolling over the input sequence.
-
-    Args:
-        X (ndarray):
-            The sequence to iterate over.
-        index (ndarray):
-            Array containing the index values of X.
-        window_size (int):
-            Length of window.
-        step_size (int):
-            Indicating the number of steps to move the window forward each round.
-
-    Returns:
-        ndarray, ndarray:
-            * rolling window sequences.
-            * first index value of each input sequence.
-    """
-    out_X = list()
-    X_index = list()
-
-    start = 0
-    max_start = len(X) - window_size + 1
-    while start < max_start:
-        end = start + window_size
-        out_X.append(X[start:end])
-        X_index.append(index[start])
-        start = start + step_size
-
-    return np.asarray(out_X), np.asarray(X_index)
-
-
-def sig2str(values, sep=',', space=False, decimal=0, rescale=True):
-    """Convert a signal to a string.
-
-    Convert a 1-dimensional time series into text by casting and rescaling it
-    to nonnegative integer values then into a string (optional).
-
-    Args:
-        values (numpy.ndarray):
-            A sequence of signal values.
-        sep (str):
-            String to separate each element in values. Default to `","`.
-        space (bool):
-            Whether to add space between each digit in the result. Default to `False`.
-        decimal (int):
-            Number of decimal points to keep from the float representation. Default to `0`.
-        rescale(bool):
-            Whether to rescale the time series. Default to `True`
-
-    Returns:
-        str:
-            Text containing the elements of `values`.
-    """
-    sign = 1 * (values >= 0) - 1 * (values < 0)
-    values = np.abs(values)
-
-    sequence = sign * (values * 10**decimal).astype(int)
-
-    # Rescale all elements to be nonnegative
-    if rescale:
-        sequence = sequence - min(sequence)
-
-    res = sep.join([str(num) for num in sequence])
-    if space:
-        res = ' '.join(res)
-
-    return res
diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index 3778336..9d8be21 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -1,10 +1,130 @@
 # -*- coding: utf-8 -*-
 
-"""
-GPT model module.
+import json
+import os
+
+import openai
+import tiktoken
+
+PROMPT_PATH = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    'gpt_messages.json'
+)
+
+PROMPTS = json.load(open(PROMPT_PATH))
+
+VALID_NUMBERS = list("0123456789 ")
+BIAS = 30
+
+
+class GPT:
+    """Prompt GPT models to detect anomalies in a time series.
+
+    Args:
+        name (str):
+            Model name. Default to `'gpt-3.5-turbo'`.
+        sep (str):
+            String to separate each element in values. Default to `','`.
+    """
+
+    def __init__(self, name='gpt-3.5-turbo', sep=','):
+        self.name = name
+        self.sep = sep
+
+        self.tokenizer = tiktoken.encoding_for_model(self.name)
+
+        valid_tokens = []
+        for number in VALID_NUMBERS:
+            token = self.tokenizer.encode(number)
+            valid_tokens.append(token)
+
+        valid_tokens.append(self.tokenizer.encode(self.sep))
+        self.logit_bias = {token: BIAS for token in valid_tokens}
+
+    def detect(self, text, anomalous_percent = 0.5, temp=1, top_p=1, logprobs=False, top_logprobs=None,
+                 samples=10, seed=None):
+        """Use GPT to forecast a signal.
+
+        Args:
+            text (str):
+                A string containing signal values.
+            anomalous_percent (float): 
+                Expected percentage of time series that are anomalous. Default to `0.5`.
+            temp (float):
+                Sampling temperature to use, between 0 and 2. Higher values like 0.8 will
+                make the output more random, while lower values like 0.2 will make it
+                more focused and deterministic. Do not use with `top_p`. Default to `1`.
+            top_p (float):
+                Alternative to sampling with temperature, called nucleus sampling, where the
+                model considers the results of the tokens with top_p probability mass.
+                So 0.1 means only the tokens comprising the top 10% probability mass are
+                considered. Do not use with `temp`. Default to `1`.
+            logprobs (bool):
+                Whether to return the log probabilities of the output tokens or not.
+                Defaults to `False`.
+            top_logprobs (int):
+                An integer between 0 and 20 specifying the number of most likely tokens
+                to return at each token position. Default to `None`.
+            samples (int):
+                Number of responses to generate for each input message. Default to `10`.
+            seed (int):
+                Beta feature by OpenAI to sample deterministically. Default to `None`.
+
+        Returns:
+            list, list:
+                * List of detected anomalous values.
+                * Optionally, a list of the output tokens' log probabilities.
+        """
+        input_length = len(self.tokenizer.encode(text))
+        max_tokens = input_length * anomalous_percent
+
+        message = ' '.join(PROMPTS['user_message'], text, self.sep)
+        response = openai.ChatCompletion.create(
+            model=self.name,
+            messages=[
+                {"role": "system", "content": PROMPTS['system_message']},
+                {"role": "user", "content": message}
+            ],
+            max_tokens=max_tokens,
+            temperature=temp,
+            logprobs=logprobs,
+            top_logprobs=top_logprobs,
+            n=samples,
+        )
+        responses = [choice.message.content for choice in response.choices]
+        if logprobs:
+            probs = [choice.logprobs for choice in response.choices]
+            return responses, probs
+
+        return responses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 
-This module contains functions that are specifically used for GPT models
-"""
 import os
 
 from openai import OpenAI
diff --git a/sigllm/primitives/prompting/gpt_messages.json b/sigllm/primitives/prompting/gpt_messages.json
new file mode 100644
index 0000000..064b6e0
--- /dev/null
+++ b/sigllm/primitives/prompting/gpt_messages.json
@@ -0,0 +1,4 @@
+{
+    "system_message": "You are an exceptionally intelligent assistant that detect anomalies in time series data by listing all the anomalies. Below is a sequence, please return the anomalies in that sequence in. Do not say anything like 'the anomalies in the sequence are', just return the numbers.",
+    "user_message": "Sequence:\n"
+}
\ No newline at end of file
diff --git a/sigllm/primitives/prompting/huggingface.py b/sigllm/primitives/prompting/huggingface.py
new file mode 100644
index 0000000..5e9e3e8
--- /dev/null
+++ b/sigllm/primitives/prompting/huggingface.py
@@ -0,0 +1,134 @@
+# -*- coding: utf-8 -*-
+
+import json
+import os
+import logging
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+PROMPT_PATH = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)),
+    'huggingface_messages.json'
+)
+
+PROMPTS = json.load(open(PROMPT_PATH))
+
+LOGGER = logging.getLogger(__name__)
+
+DEFAULT_BOS_TOKEN = "<s>"
+DEFAULT_EOS_TOKEN = "</s>"
+DEFAULT_UNK_TOKEN = "<unk>"
+DEFAULT_PAD_TOKEN = "<pad>"
+
+VALID_NUMBERS = list("0123456789")
+
+DEFAULT_MODEL = 'mistralai/Mistral-7B-Instruct-v0.2'
+
+
+class HF:
+    """Prompt Pretrained models on HuggingFace to detect anomalies in a time series.
+
+    Args:
+        name (str):
+            Model name. Default to `'mistralai/Mistral-7B-Instruct-v0.2'`.
+        sep (str):
+            String to separate each element in values. Default to `','`.
+    """
+
+    def __init__(self, name=DEFAULT_MODEL, sep=','):
+        self.name = name
+        self.sep = sep
+
+        self.tokenizer = AutoTokenizer.from_pretrained(self.name, use_fast=False)
+
+        # special tokens
+        special_tokens_dict = dict()
+        if self.tokenizer.eos_token is None:
+            special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN
+        if self.tokenizer.bos_token is None:
+            special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN
+        if self.tokenizer.unk_token is None:
+            special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN
+        if self.tokenizer.pad_token is None:
+            special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN
+
+        self.tokenizer.add_special_tokens(special_tokens_dict)
+        self.tokenizer.pad_token = self.tokenizer.eos_token  # indicate the end of the time series
+
+        # invalid tokens
+        valid_tokens = []
+        for number in VALID_NUMBERS:
+            token = self.tokenizer.convert_tokens_to_ids(number)
+            valid_tokens.append(token)
+
+        valid_tokens.append(self.tokenizer.convert_tokens_to_ids(self.sep))
+        self.invalid_tokens = [[i]
+                               for i in range(len(self.tokenizer) - 1) if i not in valid_tokens]
+
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.name,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+
+        self.model.eval()
+
+    def detect(self, text, anomalous_percent = 0.5, temp=1, top_p=1, raw=False, samples=10, padding=0):
+        """Use GPT to forecast a signal.
+
+        Args:
+            text (str):
+                A string containing signal values.
+            anomalous_percent (float): 
+                Expected percentage of time series that are anomalous. Default to `0.5`.
+            temp (float):
+                The value used to modulate the next token probabilities. Default to `1`.
+            top_p (float):
+                 If set to float < 1, only the smallest set of most probable tokens with
+                 probabilities that add up to `top_p` or higher are kept for generation.
+                 Default to `1`.
+            raw (bool):
+                Whether to return the raw output or not. Defaults to `False`.
+            samples (int):
+                Number of responsed to generate for each input message. Default to `10`.
+            padding (int):
+                Additional padding token to forecast to reduce short horizon predictions.
+                Default to `0`.
+
+        Returns:
+            list, list:
+                * List of forecasted signal values.
+                * Optionally, a list of dictionaries for raw output.
+        """
+        input_length = len(self.tokenizer.encode(text))
+        max_tokens = input_length * anomalous_percent
+
+        message = ' '.join((PROMPTS['system_message'], PROMPTS['user_message'], text, '[RESPONSE]'))
+
+        tokenized_input = self.tokenizer(
+            [message],
+            return_tensors="pt"
+        ).to("cuda")
+
+        generate_ids = self.model.generate(
+            **tokenized_input,
+            do_sample=True,
+            max_new_tokens=max_tokens,
+            temperature=temp,
+            top_p=top_p,
+            bad_words_ids=self.invalid_tokens,
+            renormalize_logits=True,
+            num_return_sequences=samples
+        )
+
+        responses = self.tokenizer.batch_decode(
+            generate_ids[:, input_length:],
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=False
+        )
+
+        if raw:
+            return responses, generate_ids
+
+        return responses
\ No newline at end of file
diff --git a/sigllm/primitives/prompting/huggingface_messages.json b/sigllm/primitives/prompting/huggingface_messages.json
new file mode 100644
index 0000000..3ad1dad
--- /dev/null
+++ b/sigllm/primitives/prompting/huggingface_messages.json
@@ -0,0 +1,4 @@
+{
+    "system_message": "You are an exceptionally intelligent assistant that detect anomalies in time series data by listing all the anomalies.",
+    "user_message": "Below is a [SEQUENCE], please return the anomalies in that sequence in [RESPONSE]. Only return the numbers. [SEQUENCE]"
+}
\ No newline at end of file
diff --git a/sigllm/primitives/timeseries_preprocessing.py b/sigllm/primitives/timeseries_preprocessing.py
new file mode 100644
index 0000000..494a7ca
--- /dev/null
+++ b/sigllm/primitives/timeseries_preprocessing.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+
+"""
+Data preprocessing module.
+
+This module contains functions that prepare timeseries for a language model.
+"""
+
+import numpy as np
+
+
+def rolling_window_sequences(X, index, window_size, step_size):
+    """Create rolling window sequences out of time series data.
+
+    This function creates an array of sequences by rolling over the input sequence.
+
+    Args:
+        X (ndarray):
+            The sequence to iterate over.
+        index (ndarray):
+            Array containing the index values of X.
+        window_size (int):
+            Length of window.
+        step_size (int):
+            Indicating the number of steps to move the window forward each round.
+
+    Returns:
+        ndarray, ndarray:
+            * rolling window sequences.
+            * first index value of each input sequence.
+    """
+    out_X = list()
+    X_index = list()
+
+    start = 0
+    max_start = len(X) - window_size + 1
+    while start < max_start:
+        end = start + window_size
+        out_X.append(X[start:end])
+        X_index.append(index[start])
+        start = start + step_size
+
+    return np.asarray(out_X), np.asarray(X_index)
\ No newline at end of file
diff --git a/tutorials/HFPipeline.ipynb b/tutorials/HFPipeline.ipynb
index 42ce204..df786f0 100644
--- a/tutorials/HFPipeline.ipynb
+++ b/tutorials/HFPipeline.ipynb
@@ -15,6 +15,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "98a74d37-d0f3-4970-a3eb-e30ea578dc11",
    "metadata": {},
@@ -54,6 +55,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a1be8f93-1f69-48a7-91cc-f7070f355a40",
    "metadata": {},
@@ -223,6 +225,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6909acb9-ecac-4bb6-b342-d32e6121b21b",
    "metadata": {},
diff --git a/tutorials/prompter.ipynb b/tutorials/prompter.ipynb
index 45ba355..b026989 100644
--- a/tutorials/prompter.ipynb
+++ b/tutorials/prompter.ipynb
@@ -117,10 +117,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "fd1a9ba6",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "PROMPTS = {\n",
+    "    \"system_message\": \"You are an exceptionally intelligent assistant that detect anomalies in time series data by listing all the anomalies.\",\n",
+    "    \"user_message\": \"Below is a [SEQUENCE], please return the anomalies in that sequence in [RESPONSE]. Only return the numbers. [SEQUENCE]\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bd160c3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = '0, 11, 3'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ab08a9a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "message = ' '.join((PROMPTS['system_message'], PROMPTS['user_message'], text, '[RESPONSE]'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "11eb949c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'You are an exceptionally intelligent assistant that detect anomalies in time series data by listing all the anomalies. Below is a [SEQUENCE], please return the anomalies in that sequence in [RESPONSE]. Only return the numbers. [SEQUENCE] 0, 11, 3 [RESPONSE]'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b7faf94",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
@@ -140,7 +194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

From 6a76b69ad5654b8ec6b656848c9684e125904bce Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Sat, 4 May 2024 11:56:41 -0400
Subject: [PATCH 02/25] support 3-D array

---
 sigllm/primitives/prompting/anomalies.py | 29 ++++++++++++------------
 tests/primitives/test_transformation.py  |  1 -
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index 9fa4405..2237786 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -15,15 +15,15 @@ def val2idx(vals, windows):
     in the input sequence that have those values. 
     
     Args: 
-        vals (List[ndarray]]): 
-            A list nd array containing detected anomalous values from different
-            responses of one window in one sample response. 
+        vals (ndarray): 
+            A 3d array containing detected anomalous values from different
+            responses of each window.
         windows (ndarray):
             rolling window sequences.      
     Returns: 
         List([ndarray]):
-            A list of nd array containing detected anomalous indices from different
-            responses of one window in one sample response.
+            A 3d array containing detected anomalous indices from different
+            responses of each window.
     """
 
     idx_list = []
@@ -33,8 +33,9 @@ def val2idx(vals, windows):
             mask = np.isin(seq, anomalies)
             indices = np.where(mask)[0]
             idx_win_list.append(indices)
-        idx_win_list = np.array(idx_win_list)
+        #idx_win_list = np.array(idx_win_list)
         idx_list.append(idx_win_list)
+        idx_list = np.array(idx_list)
     return idx_list
 
 def ano_within_windows(idx_win_list, alpha=0.5):
@@ -43,15 +44,15 @@ def ano_within_windows(idx_win_list, alpha=0.5):
     Choose anomalous index in the sequence based on multiple LLM responses
 
     Args:
-        idx_win_list (List[List[numpy.ndarray]]):
-            A list of lists of 1d array containing detected anomalous indices of
-            one window in one sample response.
+        idx_win_list (ndarray):
+            A 3d array containing detected anomalous values from different
+            responses of each window.
         alpha (float):
             Percentage of votes needed for an index to be deemed anomalous. Default to `0.5`.
 
     Returns:
-        List[numpy.ndarray]:
-            A list of 1-dimensional array containing final anomalous indices of each windows.
+        ndarray:
+            A 2-dimensional array containing final anomalous indices of each windows.
     """
     
     idx_list = []
@@ -65,15 +66,15 @@ def ano_within_windows(idx_win_list, alpha=0.5):
         final_list = unique_elements[counts >= min_vote]
 
         idx_list.append(final_list)
-
+    idx_list = np.vstack(idx_list)
     return idx_list
 
 def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5):
     """Get the final list of anomalous indices of a sequence when merging all rolling windows
 
     Args:
-        anomalies (List[numpy.ndarray]):
-            A list of 1-dimensional array containing anomous indices of each window.
+        anomalies (ndarray):
+            A 2-dimensional array containing anomous indices of each window.
         start_indices (numpy.ndarray):
             A 1-dimensional array contaning the first index of each window.
         window_size (int):
diff --git a/tests/primitives/test_transformation.py b/tests/primitives/test_transformation.py
index 906d8eb..9f81e05 100644
--- a/tests/primitives/test_transformation.py
+++ b/tests/primitives/test_transformation.py
@@ -202,7 +202,6 @@ def test_format_as_integer_2d_trunc():
 
     np.testing.assert_equal(output, expected)
 
-
 class Float2ScalarTest(unittest.TestCase):
 
     def test_transform_default(self):

From a3c7d888d2e2664ce7e1e645ecc7fa66635b06fe Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Wed, 29 May 2024 17:32:28 -0400
Subject: [PATCH 03/25] incomplete pipeline

---
 ...rompting.anomalies.ano_within_windows.json |  37 +++++
 ...imitives.prompting.anomalies.idx2time.json |  33 +++++
 ...prompting.anomalies.merge_anomaly_seq.json |  53 +++++++
 ...rompting.anomalies.timestamp2interval.json |  49 +++++++
 ...rimitives.prompting.anomalies.val2idx.json |  33 +++++
 ...reprocessing.rolling_window_sequences.json |   7 +-
 ...rimitives.transformation.Float2Scalar.json |   4 +-
 sigllm/primitives/prompting/anomalies.py      |  33 +++--
 sigllm/primitives/prompting/gpt.py            |  92 +++---------
 tests/primitives/prompting/test_anomalies.py  |  96 ++++++------
 tests/primitives/prompting/test_data.py       | 138 ------------------
 .../test_timeseries_preprocessing.py          |  38 +++++
 12 files changed, 329 insertions(+), 284 deletions(-)
 create mode 100644 sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
 create mode 100644 sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
 create mode 100644 sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
 create mode 100644 sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
 create mode 100644 sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json
 delete mode 100644 tests/primitives/prompting/test_data.py
 create mode 100644 tests/primitives/test_timeseries_preprocessing.py

diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
new file mode 100644
index 0000000..1e77986
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
@@ -0,0 +1,37 @@
+{
+    "name": "sigllm.primitives.prompting.anomalies.ano_within_windows",
+    "contributors": [
+        "Sarah Alnegheimish <smish@mit.edu>",
+        "Linh Nguyen <linhnk@mit.edu>"
+    ],
+    "description": "Get the final list of anomalous indices of each window",
+    "classifiers": {
+        "type": "postprocessor",
+        "subtype": "merger"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.prompting.anomalies.ano_within_windows",
+    "produce": {
+        "method": "ano_within_windows",
+        "args": [
+            {
+                "name": "idx_win_list",
+                "type": "ndarray"
+            }
+        ],
+        "output": [
+            {
+                "name": "idx_list",
+                "type": "ndarray"
+            }
+        ]
+    }, 
+    "hyperparameters": {
+        "fixed": {
+            "alpha": {
+                "type": "float",
+                "default": 0.5
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
new file mode 100644
index 0000000..3338c1e
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
@@ -0,0 +1,33 @@
+{
+    "name": "sigllm.primitives.prompting.anomalies.idx2time",
+    "contributors": [
+        "Sarah Alnegheimish <smish@mit.edu>",
+        "Linh Nguyen <linhnk@mit.edu>"
+    ],
+    "description": "Convert list of indices into list of timestamp",
+    "classifiers": {
+        "type": "postprocessor",
+        "subtype": "converter"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.prompting.anomalies.idx2time",
+    "produce": {
+        "method": "idx2time",
+        "args": [
+            {
+                "name": "sequence",
+                "type": "DataFrame"
+            }, 
+            {
+                "name": "idx_list",
+                "type": "ndarray"
+            }
+        ],
+        "output": [
+            {
+                "name": "timestamp_list",
+                "type": "ndarray"
+            }
+        ]
+    }
+}
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
new file mode 100644
index 0000000..220e485
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
@@ -0,0 +1,53 @@
+{
+    "name": "sigllm.primitives.prompting.anomalies.merge_anomaly_seq",
+    "contributors": [
+        "Sarah Alnegheimish <smish@mit.edu>",
+        "Linh Nguyen <linhnk@mit.edu>"
+    ],
+    "description": "Get the final list of anomalous indices of a sequence when merging all rolling windows",
+    "classifiers": {
+        "type": "postprocessor",
+        "subtype": "merger"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.prompting.anomalies.merge_anomaly_seq",
+    "produce": {
+        "method": "merge_anomaly_seq",
+        "args": [
+            {
+                "name": "anomalies",
+                "type": "ndarray"
+            }, 
+            {
+                "name": "start_indices",
+                "type": "ndarray"
+            },
+            {
+                "name": "window_size",
+                "type": "int"
+            },
+            {
+                "name": "step_size",
+                "type": "int"
+            },
+            {
+                "name": "beta",
+                "type": "float"
+            }
+        ],
+        "output": [
+            {
+                "name": "final_list",
+                "type": "ndarray"
+            }
+        ]
+    }, 
+    "hyperparameters": {
+        "fixed": {
+            "beta": {
+                "type": "float",
+                "default": 0.5
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
new file mode 100644
index 0000000..a2eab50
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
@@ -0,0 +1,49 @@
+{
+    "name": "sigllm.primitives.prompting.anomalies.timestamp2interval",
+    "contributors": [
+        "Sarah Alnegheimish <smish@mit.edu>",
+        "Linh Nguyen <linhnk@mit.edu>"
+    ],
+    "description": "Convert list of timestamps to list of intervals by padding to both sides and merge overlapping",
+    "classifiers": {
+        "type": "postprocessor",
+        "subtype": "converter"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.prompting.anomalies.timestamp2interval",
+    "produce": {
+        "method": "timestamp2interval",
+        "args": [
+            {
+                "name": "timestamp_list",
+                "type": "ndarray"
+            },
+            {
+                "name": "interval",
+                "type": "int"
+            },
+            {
+                "name": "start",
+                "type": "timestamp"
+            },
+            {
+                "name": "end",
+                "type": "timestamp"
+            }
+        ],
+        "output": [
+            {
+                "name": "merged_intervals",
+                "type": "List[Tuple(start, end)]"
+            }
+        ]
+    }, 
+    "hyperparameters": {
+        "fixed": {
+            "padding_size": {
+                "type": "int",
+                "default": 50
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json
new file mode 100644
index 0000000..8ebee88
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json
@@ -0,0 +1,33 @@
+{
+    "name": "sigllm.primitives.prompting.anomalies.val2idx",
+    "contributors": [
+        "Sarah Alnegheimish <smish@mit.edu>",
+        "Linh Nguyen <linhnk@mit.edu>"
+    ],
+    "description": "Convert detected anomalous values into indices",
+    "classifiers": {
+        "type": "postprocessor",
+        "subtype": "converter"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.prompting.anomalies.val2idx",
+    "produce": {
+        "method": "val2idx",
+        "args": [
+            {
+                "name": "vals",
+                "type": "ndarray"
+            },
+            {
+                "name": "windows",
+                "type": "ndarray"
+            }
+        ],
+        "output": [
+            {
+                "name": "idx_list",
+                "type": "ndarray"
+            }
+        ]
+    }
+}
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json b/sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json
index be85b35..5195062 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json
@@ -1,6 +1,9 @@
 {
     "name": "sigllm.primitives.timeseries_preprocessing.rolling_window_sequences",
-    "contributors": ["Linh Nguyen <linhnk@mit.edu>"],
+    "contributors": [
+        "Sarah Alnegheimish <smish@mit.edu>",
+        "Linh Nguyen <linhnk@mit.edu>"
+    ],
     "description": "Create rolling windows",
     "classifiers": {
         "type": "preprocessor",
@@ -9,7 +12,7 @@
     "modalities": [],
     "primitive": "sigllm.primitives.timeseries_preprocessing.rolling_window_sequences",
     "produce": {
-        "method": "detect",
+        "method": "rolling_window_sequences",
         "args": [
             {
                 "name": "X",
diff --git a/sigllm/primitives/jsons/sigllm.primitives.transformation.Float2Scalar.json b/sigllm/primitives/jsons/sigllm.primitives.transformation.Float2Scalar.json
index b608362..bcb6a9c 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.transformation.Float2Scalar.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.transformation.Float2Scalar.json
@@ -1,5 +1,5 @@
 {
-    "name": "sigllm.primitives.transformation.Float2Scaler",
+    "name": "sigllm.primitives.transformation.Float2Scalar",
     "contributors": [
         "Sarah Alnegheimish <smish@mit.edu>",
         "Linh Nguyen <linhnk@mit.edu>"
@@ -10,7 +10,7 @@
         "subtype": "transformer"
     },
     "modalities": [],
-    "primitive": "sigllm.primitives.transformation.Float2Scaler",
+    "primitive": "sigllm.primitives.transformation.Float2Scalar",
     "fit": {
         "method": "fit",
         "args": [
diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index 2237786..067bae0 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -35,11 +35,11 @@ def val2idx(vals, windows):
             idx_win_list.append(indices)
         #idx_win_list = np.array(idx_win_list)
         idx_list.append(idx_win_list)
-        idx_list = np.array(idx_list)
+    idx_list = np.array(idx_list, dtype=object)
     return idx_list
 
 def ano_within_windows(idx_win_list, alpha=0.5):
-    """Get the final list of anomalous indices of a sequence
+    """Get the final list of anomalous indices of each window
 
     Choose anomalous index in the sequence based on multiple LLM responses
 
@@ -58,15 +58,16 @@ def ano_within_windows(idx_win_list, alpha=0.5):
     idx_list = []
     for samples in idx_win_list:
         min_vote = np.ceil(alpha * len(samples))
+        #print(type(samples.tolist()))
 
-        flattened_res = np.flatten(samples)
+        flattened_res = np.concatenate(samples.tolist())
 
         unique_elements, counts = np.unique(flattened_res, return_counts=True)
 
         final_list = unique_elements[counts >= min_vote]
 
         idx_list.append(final_list)
-    idx_list = np.vstack(idx_list)
+    idx_list = np.array(idx_list, dtype = object)
     return idx_list
 
 def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5):
@@ -75,7 +76,7 @@ def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5
     Args:
         anomalies (ndarray):
             A 2-dimensional array containing anomous indices of each window.
-        start_indices (numpy.ndarray):
+        start_indices (ndarray):
             A 1-dimensional array contaning the first index of each window.
         window_size (int):
             Length of each window.
@@ -85,7 +86,7 @@ def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5
             Percentage of containing windows needed for index to be deemed anomalous. Default to `0.5`.
 
     Return:
-        numpy.ndarray:
+        ndarray:
             A 1-dimensional array containing final anomalous indices.
     """
     anomalies = [arr + first_idx for (arr, first_idx) in zip(anomalies, start_indices)]
@@ -104,24 +105,25 @@ def idx2time(sequence, idx_list):
     """Convert list of indices into list of timestamp
 
     Args:
-        sequence (pandas.Dataframe):
+        sequence (DataFrame):
             Signal with timestamps and values.
-        idx_list (numpy.ndarray):
+        idx_list (ndarray):
             A 1-dimensional array of indices.
 
     Returns:
-        numpy.ndarray:
+        ndarray:
             A 1-dimensional array containing timestamps.
     """
-    return sequence.iloc[idx_list].timestamp.to_numpy()
+    timestamp_list = sequence.iloc[idx_list].timestamp.to_numpy()
+    return timestamp_list
 
 def timestamp2interval(timestamp_list, interval, start, end, padding_size = 50): 
     """Convert list of timestamps to list of intervals by padding to both sides
     and merge overlapping 
     
     Args: 
-        timestamp_list (List[timestamp]): 
-            A list of point timestamps.
+        timestamp_list (ndarray): 
+            A 1d array of point timestamps.
         interval (int):
             The fixed gap between two consecutive timestamps of the time series.
         start (timestamp): 
@@ -129,12 +131,15 @@ def timestamp2interval(timestamp_list, interval, start, end, padding_size = 50):
         end (timestamp): 
             The end timestamp of the time series.
         padding_size (int): 
-            Number of steps to pad on both sides of a timestamp point.
+            Number of steps to pad on both sides of a timestamp point. Default to `50`.
+             
+    Returns:
+        List[Tuple(start, end)]:
+            A list of intervals.
     """
     intervals = []
     for timestamp in timestamp_list: 
         intervals.append((max(start, timestamp-padding_size*interval), min(end, timestamp+padding_size*interval)))
-
     if not intervals:
         return []
 
diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index 9d8be21..b9b5a18 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -27,9 +27,18 @@ class GPT:
             String to separate each element in values. Default to `','`.
     """
 
-    def __init__(self, name='gpt-3.5-turbo', sep=','):
+    def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent = 0.5, temp=1, top_p=1, logprobs=False, top_logprobs=None,
+                 samples=10, seed=None):
         self.name = name
         self.sep = sep
+        self.anomalous_percent = anomalous_percent
+        self.temp = temp
+        self.top_p = top_p
+        self.logprobs = logprobs
+        self.top_logprobs = top_logprobs
+        self.samples = samples
+        self.seed = seed
+
 
         self.tokenizer = tiktoken.encoding_for_model(self.name)
 
@@ -41,8 +50,7 @@ def __init__(self, name='gpt-3.5-turbo', sep=','):
         valid_tokens.append(self.tokenizer.encode(self.sep))
         self.logit_bias = {token: BIAS for token in valid_tokens}
 
-    def detect(self, text, anomalous_percent = 0.5, temp=1, top_p=1, logprobs=False, top_logprobs=None,
-                 samples=10, seed=None):
+    def detect(self, X, **kwargs):
         """Use GPT to forecast a signal.
 
         Args:
@@ -75,8 +83,8 @@ def detect(self, text, anomalous_percent = 0.5, temp=1, top_p=1, logprobs=False,
                 * List of detected anomalous values.
                 * Optionally, a list of the output tokens' log probabilities.
         """
-        input_length = len(self.tokenizer.encode(text))
-        max_tokens = input_length * anomalous_percent
+        input_length = len(self.tokenizer.encode(X[0]))
+        max_tokens = input_length * self.anomalous_percent
 
         message = ' '.join(PROMPTS['user_message'], text, self.sep)
         response = openai.ChatCompletion.create(
@@ -86,13 +94,13 @@ def detect(self, text, anomalous_percent = 0.5, temp=1, top_p=1, logprobs=False,
                 {"role": "user", "content": message}
             ],
             max_tokens=max_tokens,
-            temperature=temp,
-            logprobs=logprobs,
-            top_logprobs=top_logprobs,
-            n=samples,
+            temperature=self.temp,
+            logprobs=self.logprobs,
+            top_logprobs=self.top_logprobs,
+            n=self.samples,
         )
         responses = [choice.message.content for choice in response.choices]
-        if logprobs:
+        if self.logprobs:
             probs = [choice.logprobs for choice in response.choices]
             return responses, probs
 
@@ -115,67 +123,3 @@ def detect(self, text, anomalous_percent = 0.5, temp=1, top_p=1, logprobs=False,
 
 
 
-
-
-
-
-
-
-
-
-
-
-import os
-
-from openai import OpenAI
-
-
-def load_system_prompt(file_path):
-    with open(file_path) as f:
-        system_prompt = f.read()
-    return system_prompt
-
-
-CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
-
-ZERO_SHOT_FILE = 'gpt_system_prompt_zero_shot.txt'
-ONE_SHOT_FILE = 'gpt_system_prompt_one_shot.txt'
-
-ZERO_SHOT_DIR = os.path.join(CURRENT_DIR, "..", "template", ZERO_SHOT_FILE)
-ONE_SHOT_DIR = os.path.join(CURRENT_DIR, "..", "template", ONE_SHOT_FILE)
-
-
-GPT_model = "gpt-4"  # "gpt-4-0125-preview" #  #  #"gpt-3.5-turbo" #
-client = OpenAI()
-
-
-def get_gpt_model_response(message, gpt_model=GPT_model):
-    completion = client.chat.completions.create(
-        model=gpt_model,
-        messages=message,
-    )
-    return completion.choices[0].message.content
-
-
-def create_message_zero_shot(seq_query, system_prompt_file=ZERO_SHOT_DIR):
-    messages = []
-
-    messages.append({"role": "system", "content": load_system_prompt(system_prompt_file)})
-
-    # final prompt
-    messages.append({"role": "user", "content": f"Sequence: {seq_query}"})
-    return messages
-
-
-def create_message_one_shot(seq_query, seq_ex, ano_idx_ex, system_prompt_file=ONE_SHOT_DIR):
-    messages = []
-
-    messages.append({"role": "system", "content": load_system_prompt(system_prompt_file)})
-
-    # one shot
-    messages.append({"role": "user", "content": f"Sequence: {seq_ex}"})
-    messages.append({"role": "assistant", "content": ano_idx_ex})
-
-    # final prompt
-    messages.append({"role": "user", "content": f"Sequence: {seq_query}"})
-    return messages
diff --git a/tests/primitives/prompting/test_anomalies.py b/tests/primitives/prompting/test_anomalies.py
index 7ca4d22..2fbdf0e 100644
--- a/tests/primitives/prompting/test_anomalies.py
+++ b/tests/primitives/prompting/test_anomalies.py
@@ -5,39 +5,25 @@
 from pytest import fixture
 
 from sigllm.primitives.prompting.anomalies import (
-    get_anomaly_list_within_seq, idx2time, merge_anomaly_seq, str2idx, str2sig,)
+    val2idx, ano_within_windows, merge_anomaly_seq, idx2time, timestamp2interval,)
 
 
-@fixture
-def text():
-    return '1,2,3,4,5,6,7,8,9'
-
-
-@fixture
-def text_1():
-    return 'Result: 1 2 3, 2 3 4,'
-
-
-@fixture
-def text_float():
-    return 'Result: 1.23, 2.34,'
 
 
 @fixture
 def anomaly_list_within_seq():
-    return [np.array([2, 3, 7, 9]),
-            np.array([5]),
-            np.array([2, 5]),
-            np.array([8, 9])]
+    return np.array([[np.array([0, 3]), np.array([1]), np.array([1, 2])],
+                     [np.array([0]), np.array([1, 4]), np.array([2, 3])],
+                     [np.array([0, 2]), np.array([]), np.array([0, 1])]] , dtype = object)
 
 
 @fixture
 def anomaly_list_across_seq():
-    return [np.array([0]),
+    return np.array([np.array([0]),
             np.array([1, 2]),
             np.array([0, 2]),
             np.array([1, 2]),
-            np.array([1])]
+            np.array([1])], dtype=object)
 
 
 @fixture
@@ -66,45 +52,29 @@ def signal():
 def idx_list():
     return np.array([0, 1, 3])
 
+@fixture
+def anomalous_val(): 
+    return np.array([[np.array([0, 3]), np.array([])],
+                    [np.array([2]), np.array([4])]], dtype=object)
 
-def test_str2sig(text_float):
-    expected = np.array([0.123, 0.234])
-
-    result = str2sig(text_float, decimal=1)
-
-    np.testing.assert_allclose(result, expected, rtol=1e-15, atol=0)
-
-
-def test_str2idx(text):
-    expected = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
-
-    result = str2idx(text, len_seq=20)
-
-    np.testing.assert_equal(result, expected)
-
-
-def test_str2idx_spurious(text_1):
-    expected = np.array([123, 234])
-
-    result = str2idx(text_1, len_seq=500)
-
-    np.testing.assert_equal(result, expected)
-
-
-def test_str2idx_overflow(text):
-    expected = np.array([1, 2, 3, 4, 5, 6, 7])
-
-    result = str2idx(text, len_seq=8)
-
-    np.testing.assert_equal(result, expected)
+@fixture
+def windows(): 
+    return np.array([[0, 1, 0, 3],
+                     [3, 2, 6, 2]])
 
+@fixture
+def point_timestamp(): 
+    return np.array([1320, 6450, 7890, 12030, 12340])
 
-def test_get_anomaly_list_within_seq(anomaly_list_within_seq):
-    expected = np.array([2, 5, 9])
+def test_ano_within_windows(anomaly_list_within_seq):
+    expected = np.array([np.array([1]),
+                         np.array([]),
+                         np.array([0])], dtype = object)
 
-    result = get_anomaly_list_within_seq(anomaly_list_within_seq)
+    result = ano_within_windows(anomaly_list_within_seq)
 
-    np.testing.assert_equal(result, expected)
+    for r, e in zip(result, expected):
+        np.testing.assert_equal(r, e)
 
 
 def test_merge_anomaly_seq(anomaly_list_across_seq, first_indices, window_size, step_size):
@@ -121,3 +91,21 @@ def test_idx2time(signal, idx_list):
     result = idx2time(signal, idx_list)
 
     np.testing.assert_equal(result, expected)
+
+
+#val2idx
+def test_val2idx(anomalous_val, windows):
+    expected = np.array([[np.array([0, 2, 3]), np.array([])],
+                    [np.array([1, 3]), np.array([])]], dtype=object)
+    result = val2idx(anomalous_val, windows)
+
+    for r_list, e_list in zip(result, expected):
+        for r, e in zip(r_list, e_list):
+            np.testing.assert_equal(r, e)
+
+#timestamp2interval
+def test_timestamp2interval(point_timestamp): 
+    expected = [(1000, 1820), (5950, 6950), (7390, 8390), (11530, 12840)]
+    result = timestamp2interval(point_timestamp, 10, 1000, 13000)
+
+    assert result == expected
\ No newline at end of file
diff --git a/tests/primitives/prompting/test_data.py b/tests/primitives/prompting/test_data.py
deleted file mode 100644
index d4f2c56..0000000
--- a/tests/primitives/prompting/test_data.py
+++ /dev/null
@@ -1,138 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import numpy as np
-from pytest import fixture
-
-from sigllm.primitives.prompting.data import rolling_window_sequences, sig2str
-
-
-@fixture
-def integers():
-    return np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
-
-
-@fixture
-def floats():
-    return np.array([
-        1.283,
-        2.424,
-        3.213,
-        4.583,
-        5.486,
-        6.284,
-        7.297,
-        8.023,
-        9.786
-    ])
-
-
-@fixture
-def negatives():
-    return np.array([
-        -2.5,
-        -1.5,
-        0,
-        1.5,
-        2.5,
-    ])
-
-
-@fixture
-def indices():
-    return np.array([0, 1, 2, 3, 4, 5, 6])
-
-
-@fixture
-def values():
-    return np.array([0.555, 2.345, 1.501, 5.903, 9.116, 3.068, 4.678])
-
-
-@fixture
-def window_size():
-    return 3
-
-
-@fixture
-def step_size():
-    return 1
-
-
-def test_sig2str(integers):
-    expected = '0,1,2,3,4,5,6,7,8'
-
-    result = sig2str(integers)
-
-    assert result == expected
-
-
-def test_sig2str_noscale(integers):
-    expected = '1,2,3,4,5,6,7,8,9'
-
-    result = sig2str(integers, rescale=False)
-
-    assert result == expected
-
-
-def test_sig2str_decimal(integers):
-    expected = '0,100,200,300,400,500,600,700,800'
-
-    result = sig2str(integers, decimal=2)
-
-    assert result == expected
-
-
-def test_sig2str_sep(integers):
-    expected = '0|1|2|3|4|5|6|7|8'
-
-    result = sig2str(integers, sep='|')
-
-    assert result == expected
-
-
-def test_sig2str_space(integers):
-    expected = '0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8'
-
-    result = sig2str(integers, space=True)
-
-    assert result == expected
-
-
-def test_sig2str_float(floats):
-    expected = '0,1,2,3,4,5,6,7,8'
-
-    result = sig2str(floats)
-
-    assert result == expected
-
-
-def test_sig2str_float_decimal(floats):
-    expected = '0,114,193,330,420,500,601,674,850'
-
-    result = sig2str(floats, decimal=2)
-
-    assert result == expected
-
-
-def test_sig2str_negative_decimal(negatives):
-    expected = '0,10,25,40,50'
-
-    result = sig2str(negatives, decimal=1)
-
-    assert result == expected
-
-
-def test_rolling_window_sequences(values, indices, window_size, step_size):
-    expected = (np.array([[0.555, 2.345, 1.501],
-                          [2.345, 1.501, 5.903],
-                          [1.501, 5.903, 9.116],
-                          [5.903, 9.116, 3.068],
-                          [9.116, 3.068, 4.678], ]),
-                np.array([0, 1, 2, 3, 4]))
-
-    result = rolling_window_sequences(values, indices, window_size, step_size)
-
-    if len(result) != len(expected):
-        raise AssertionError("Tuples has different length")
-
-    for arr1, arr2 in zip(result, expected):
-        np.testing.assert_equal(arr1, arr2)
diff --git a/tests/primitives/test_timeseries_preprocessing.py b/tests/primitives/test_timeseries_preprocessing.py
new file mode 100644
index 0000000..c0191a7
--- /dev/null
+++ b/tests/primitives/test_timeseries_preprocessing.py
@@ -0,0 +1,38 @@
+import numpy as np
+from pytest import fixture
+from sigllm.primitives.timeseries_preprocessing import rolling_window_sequences
+
+@fixture
+def indices():
+    return np.array([0, 1, 2, 3, 4, 5, 6])
+
+
+@fixture
+def values():
+    return np.array([0.555, 2.345, 1.501, 5.903, 9.116, 3.068, 4.678])
+
+
+@fixture
+def window_size():
+    return 3
+
+
+@fixture
+def step_size():
+    return 1
+
+def test_rolling_window_sequences(values, indices, window_size, step_size):
+    expected = (np.array([[0.555, 2.345, 1.501],
+                          [2.345, 1.501, 5.903],
+                          [1.501, 5.903, 9.116],
+                          [5.903, 9.116, 3.068],
+                          [9.116, 3.068, 4.678]]),
+                np.array([0, 1, 2, 3, 4]))
+
+    result = rolling_window_sequences(values, indices, window_size, step_size)
+
+    if len(result) != len(expected):
+        raise AssertionError("Tuples has different length")
+
+    for arr1, arr2 in zip(result, expected):
+        np.testing.assert_equal(arr1, arr2)
\ No newline at end of file

From 94812ddfedd9d18e2bcda980d44801cf7647a3b9 Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Wed, 18 Sep 2024 11:03:43 -0400
Subject: [PATCH 04/25] mistral prompter pipeline

---
 .../pipelines/prompter/mistral_prompter.json  |  68 +++++++++++
 ...rompting.anomalies.ano_within_windows.json |   4 +-
 ...imitives.prompting.anomalies.idx2time.json |   8 +-
 ...prompting.anomalies.merge_anomaly_seq.json |  10 +-
 ...rompting.anomalies.timestamp2interval.json |  12 +-
 ...rimitives.prompting.anomalies.val2idx.json |   6 +-
 .../sigllm.primitives.prompting.gpt.GPT.json  |  73 +++++++++++
 ...m.primitives.prompting.huggingface.HF.json |  69 +++++++++++
 ...eprocessing.rolling_window_sequences.json} |  34 +++---
 sigllm/primitives/prompting/anomalies.py      |  18 ++-
 sigllm/primitives/prompting/gpt.py            |  89 ++++++++------
 sigllm/primitives/prompting/huggingface.py    |  70 ++++++-----
 .../timeseries_preprocessing.py               |   9 +-
 .../test_timeseries_preprocessing.py          |   2 +-
 tutorials/prompter.ipynb                      | 114 ++++++++++++++++--
 15 files changed, 450 insertions(+), 136 deletions(-)
 create mode 100644 sigllm/pipelines/prompter/mistral_prompter.json
 create mode 100644 sigllm/primitives/jsons/sigllm.primitives.prompting.gpt.GPT.json
 create mode 100644 sigllm/primitives/jsons/sigllm.primitives.prompting.huggingface.HF.json
 rename sigllm/primitives/jsons/{sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json => sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences.json} (51%)
 rename sigllm/primitives/{ => prompting}/timeseries_preprocessing.py (83%)

diff --git a/sigllm/pipelines/prompter/mistral_prompter.json b/sigllm/pipelines/prompter/mistral_prompter.json
new file mode 100644
index 0000000..b29d657
--- /dev/null
+++ b/sigllm/pipelines/prompter/mistral_prompter.json
@@ -0,0 +1,68 @@
+{
+    "primitives": [
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate",
+        "sklearn.impute.SimpleImputer",
+        "sigllm.primitives.transformation.Float2Scalar",
+        "sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences",
+	"sigllm.primitives.transformation.format_as_string",
+        "sigllm.primitives.prompting.huggingface.HF",
+        "sigllm.primitives.transformation.format_as_integer",
+        "sigllm.primitives.prompting.anomalies.val2idx",
+        "sigllm.primitives.prompting.anomalies.ano_within_windows",
+        "sigllm.primitives.prompting.anomalies.merge_anomaly_seq",
+        "sigllm.primitives.prompting.anomalies.idx2time",
+        "sigllm.primitives.prompting.anomalies.timestamp2interval"
+    ],
+    "init_params": {
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
+            "time_column": "timestamp",
+            "interval": 21600,
+            "method": "mean"
+        },
+        "sigllm.primitives.transformation.Float2Scalar#1": {
+            "decimal": 2,
+            "rescale": true
+        },
+        "sigllm.primitives.transformation.format_as_string#1": {
+            "space": false
+        },
+        "sigllm.primitives.prompting.huggingface.HF#1": {
+            "name": "mistralai/Mistral-7B-Instruct-v0.2",
+            "samples": 10
+        },
+        "sigllm.primitives.prompting.anomalies.ano_within_windows": {
+            "alpha": 0.4
+        },
+        "orion.primitives.prompting.anomalies.merge_anomaly_seq": {
+            "beta": 0.5
+        }
+    },
+    "input_names": {
+	"sigllm.primitives.transformation.Float2Scalar#1": {
+	    "X": "y"
+	},
+    "sigllm.primitives.prompting.huggingface.HF#1": {
+        "X": "X_str"
+    },
+    "sigllm.primitives.transformation.format_as_integer#1":{
+        "X": "y_hat"
+    }
+    },
+    "output_names": {
+    "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
+        "index": "timestamp"
+    },
+	"sklearn.impute.SimpleImputer#1": {
+	    "X": "y"
+	},
+    "sigllm.primitives.transformation.format_as_string#1": {
+        "X": "X_str"
+    },
+    "sigllm.primitives.prompting.huggingface.HF#1": {
+        "y": "y_hat"
+    },
+    "sigllm.primitives.transformation.format_as_integer#1":{
+        "X": "y"
+    }
+    }
+}
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
index 1e77986..9085c29 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
@@ -15,13 +15,13 @@
         "method": "ano_within_windows",
         "args": [
             {
-                "name": "idx_win_list",
+                "name": "y",
                 "type": "ndarray"
             }
         ],
         "output": [
             {
-                "name": "idx_list",
+                "name": "y",
                 "type": "ndarray"
             }
         ]
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
index 3338c1e..d4e4dd0 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
@@ -15,17 +15,17 @@
         "method": "idx2time",
         "args": [
             {
-                "name": "sequence",
-                "type": "DataFrame"
+                "name": "timestamp",
+                "type": "ndarray"
             }, 
             {
-                "name": "idx_list",
+                "name": "y",
                 "type": "ndarray"
             }
         ],
         "output": [
             {
-                "name": "timestamp_list",
+                "name": "y",
                 "type": "ndarray"
             }
         ]
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
index 220e485..22a72d6 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
@@ -15,11 +15,11 @@
         "method": "merge_anomaly_seq",
         "args": [
             {
-                "name": "anomalies",
+                "name": "y",
                 "type": "ndarray"
             }, 
             {
-                "name": "start_indices",
+                "name": "first_index",
                 "type": "ndarray"
             },
             {
@@ -29,15 +29,11 @@
             {
                 "name": "step_size",
                 "type": "int"
-            },
-            {
-                "name": "beta",
-                "type": "float"
             }
         ],
         "output": [
             {
-                "name": "final_list",
+                "name": "y",
                 "type": "ndarray"
             }
         ]
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
index a2eab50..1a33e0a 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
@@ -15,7 +15,7 @@
         "method": "timestamp2interval",
         "args": [
             {
-                "name": "timestamp_list",
+                "name": "y",
                 "type": "ndarray"
             },
             {
@@ -23,17 +23,13 @@
                 "type": "int"
             },
             {
-                "name": "start",
-                "type": "timestamp"
-            },
-            {
-                "name": "end",
-                "type": "timestamp"
+                "name": "timestamp",
+                "type": "ndarray"
             }
         ],
         "output": [
             {
-                "name": "merged_intervals",
+                "name": "anomalies",
                 "type": "List[Tuple(start, end)]"
             }
         ]
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json
index 8ebee88..27199fc 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json
@@ -15,17 +15,17 @@
         "method": "val2idx",
         "args": [
             {
-                "name": "vals",
+                "name": "y",
                 "type": "ndarray"
             },
             {
-                "name": "windows",
+                "name": "X",
                 "type": "ndarray"
             }
         ],
         "output": [
             {
-                "name": "idx_list",
+                "name": "y",
                 "type": "ndarray"
             }
         ]
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.gpt.GPT.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.gpt.GPT.json
new file mode 100644
index 0000000..e951194
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.gpt.GPT.json
@@ -0,0 +1,73 @@
+{
+    "name": "sigllm.primitives.prompting.gpt.GPT",
+    "contributors": [
+        "Linh Nguyen <linhnk@mit.edu>"
+    ],
+    "description": "Prompt openai GPT model to detect time series anomalies.",
+    "classifiers": {
+        "type": "estimator",
+        "subtype": "detector"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.prompting.huggingface.HF",
+    "produce": {
+        "method": "detect",
+        "args": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            }
+        ],
+        "output": [
+            {
+                "name": "y",
+                "type": "ndarray"
+            },
+            {
+                "name": "logprob",
+                "type": "ndarray",
+                "default": null
+            }
+        ]
+    },
+    "hyperparameters": {
+        "fixed": {
+            "name": {
+                "type": "str",
+                "default": "gpt-3.5-turbo"
+            },
+            "sep": {
+                "type": "str",
+                "default": ","
+            },
+            "anomalous_percent": {
+                "type": "float",
+                "default": "0.5"
+            },
+            "temp": {
+                "type": "float",
+                "default": 1
+            },
+            "top_p": {
+                "type": "float",
+                "default": 1
+            },
+            "logprobs": {
+                "type": "bool",
+                "default": false
+            },
+            "top_logprobs": {
+                "type": "int",
+                "default": null
+            },
+            "samples": {
+                "type": "int",
+                "default": 1
+            },
+            "seed": {
+                "type": "int",
+                "default": null
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.huggingface.HF.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.huggingface.HF.json
new file mode 100644
index 0000000..91d0530
--- /dev/null
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.huggingface.HF.json
@@ -0,0 +1,69 @@
+{
+    "name": "sigllm.primitives.prompting.huggingface.HF",
+    "contributors": [
+        "Linh Nguyen <linhnk@mit.edu>"
+    ],
+    "description": "Prompt any HF model to detect time series anomalies.",
+    "classifiers": {
+        "type": "estimator",
+        "subtype": "detector"
+    },
+    "modalities": [],
+    "primitive": "sigllm.primitives.prompting.huggingface.HF",
+    "produce": {
+        "method": "detect",
+        "args": [
+            {
+                "name": "X",
+                "type": "ndarray"
+            }
+        ],
+        "output": [
+            {
+                "name": "y",
+                "type": "ndarray"
+            },
+            {
+                "name": "logprob",
+                "type": "ndarray",
+                "default": null
+            }
+        ]
+    },
+    "hyperparameters": {
+        "fixed": {
+            "name": {
+                "type": "str",
+                "default": "mistralai/Mistral-7B-Instruct-v0.2"
+            },
+            "sep": {
+                "type": "str",
+                "default": ","
+            },
+            "anomalous_percent": {
+                "type": "float",
+                "default": "0.5"
+            },
+            "temp": {
+                "type": "float",
+                "default": 1
+            },
+            "top_p": {
+                "type": "float",
+                "default": 1
+            },
+            "raw": {
+                "type": "bool",
+                "default": false
+            },
+            "samples": {
+                "type": "int",
+                "default": 1
+            },
+            "padding": {
+                "type": "int",
+                "default": 0
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences.json
similarity index 51%
rename from sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json
rename to sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences.json
index 5195062..6c97cb8 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.timeseries_preprocessing.rolling_window_sequences.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences.json
@@ -1,5 +1,5 @@
 {
-    "name": "sigllm.primitives.timeseries_preprocessing.rolling_window_sequences",
+    "name": "sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences",
     "contributors": [
         "Sarah Alnegheimish <smish@mit.edu>",
         "Linh Nguyen <linhnk@mit.edu>"
@@ -10,36 +10,36 @@
         "subtype": "rolling windows"
     },
     "modalities": [],
-    "primitive": "sigllm.primitives.timeseries_preprocessing.rolling_window_sequences",
+    "primitive": "sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences",
     "produce": {
         "method": "rolling_window_sequences",
         "args": [
             {
                 "name": "X",
                 "type": "ndarray"
-            },
-            {
-                "name": "index",
-                "type": "ndarray"
-            },
-            {
-                "name": "window_size",
-                "type": "int"
-            }, 
-            {
-                "name": "step_size",
-                "type": "int"
             }
         ],
         "output": [
             {
-                "name": "out_X",
+                "name": "X",
                 "type": "ndarray"
             },
             {
-                "name": "X_index",
+                "name": "first_index",
                 "type": "ndarray"
             }
-        ]
+        ], 
+        "hyperparameters": {
+            "fixed": {
+                "window_size": {
+                    "type": "int",
+                    "default": 500
+                },
+                "step_size": {
+                    "type": "int",
+                    "default": 100
+                }
+            }
+        }
     }
 }
\ No newline at end of file
diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index 067bae0..5606900 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -75,16 +75,15 @@ def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5
 
     Args:
         anomalies (ndarray):
-            A 2-dimensional array containing anomous indices of each window.
+            A 2-dimensional array containing anomalous indices of each window.
         start_indices (ndarray):
             A 1-dimensional array contaning the first index of each window.
         window_size (int):
-            Length of each window.
+            Length of each window
         step_size (int):
             Indicating the number of steps the window moves forward each round.
         beta (float):
             Percentage of containing windows needed for index to be deemed anomalous. Default to `0.5`.
-
     Return:
         ndarray:
             A 1-dimensional array containing final anomalous indices.
@@ -101,7 +100,7 @@ def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5
 
     return np.sort(final_list)
 
-def idx2time(sequence, idx_list):
+def idx2time(timestamp, idx_list):
     """Convert list of indices into list of timestamp
 
     Args:
@@ -114,10 +113,10 @@ def idx2time(sequence, idx_list):
         ndarray:
             A 1-dimensional array containing timestamps.
     """
-    timestamp_list = sequence.iloc[idx_list].timestamp.to_numpy()
+    timestamp_list = timestamp[idx_list]
     return timestamp_list
 
-def timestamp2interval(timestamp_list, interval, start, end, padding_size = 50): 
+def timestamp2interval(timestamp_list, interval, timestamp, padding_size = 50): 
     """Convert list of timestamps to list of intervals by padding to both sides
     and merge overlapping 
     
@@ -126,10 +125,8 @@ def timestamp2interval(timestamp_list, interval, start, end, padding_size = 50):
             A 1d array of point timestamps.
         interval (int):
             The fixed gap between two consecutive timestamps of the time series.
-        start (timestamp): 
-            The start timestamp of the time series.
-        end (timestamp): 
-            The end timestamp of the time series.
+        timestamp (ndarray):
+            List of full timestamp of the signal
         padding_size (int): 
             Number of steps to pad on both sides of a timestamp point. Default to `50`.
              
@@ -137,6 +134,7 @@ def timestamp2interval(timestamp_list, interval, start, end, padding_size = 50):
         List[Tuple(start, end)]:
             A list of intervals.
     """
+    start, end = timestamp[0], timestamp[-1]
     intervals = []
     for timestamp in timestamp_list: 
         intervals.append((max(start, timestamp-padding_size*interval), min(end, timestamp+padding_size*interval)))
diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index b9b5a18..b6a7ba9 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -5,6 +5,7 @@
 
 import openai
 import tiktoken
+from tqdm import tqdm
 
 PROMPT_PATH = os.path.join(
     os.path.dirname(os.path.abspath(__file__)),
@@ -25,6 +26,27 @@ class GPT:
             Model name. Default to `'gpt-3.5-turbo'`.
         sep (str):
             String to separate each element in values. Default to `','`.
+        anomalous_percent (float): 
+                Expected percentage of time series that are anomalous. Default to `0.5`.
+        temp (float):
+            Sampling temperature to use, between 0 and 2. Higher values like 0.8 will
+            make the output more random, while lower values like 0.2 will make it
+            more focused and deterministic. Do not use with `top_p`. Default to `1`.
+        top_p (float):
+            Alternative to sampling with temperature, called nucleus sampling, where the
+            model considers the results of the tokens with top_p probability mass.
+            So 0.1 means only the tokens comprising the top 10% probability mass are
+            considered. Do not use with `temp`. Default to `1`.
+        logprobs (bool):
+            Whether to return the log probabilities of the output tokens or not.
+            Defaults to `False`.
+        top_logprobs (int):
+            An integer between 0 and 20 specifying the number of most likely tokens
+            to return at each token position. Default to `None`.
+        samples (int):
+            Number of responses to generate for each input message. Default to `10`.
+        seed (int):
+            Beta feature by OpenAI to sample deterministically. Default to `None`.
     """
 
     def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent = 0.5, temp=1, top_p=1, logprobs=False, top_logprobs=None,
@@ -54,29 +76,8 @@ def detect(self, X, **kwargs):
         """Use GPT to forecast a signal.
 
         Args:
-            text (str):
-                A string containing signal values.
-            anomalous_percent (float): 
-                Expected percentage of time series that are anomalous. Default to `0.5`.
-            temp (float):
-                Sampling temperature to use, between 0 and 2. Higher values like 0.8 will
-                make the output more random, while lower values like 0.2 will make it
-                more focused and deterministic. Do not use with `top_p`. Default to `1`.
-            top_p (float):
-                Alternative to sampling with temperature, called nucleus sampling, where the
-                model considers the results of the tokens with top_p probability mass.
-                So 0.1 means only the tokens comprising the top 10% probability mass are
-                considered. Do not use with `temp`. Default to `1`.
-            logprobs (bool):
-                Whether to return the log probabilities of the output tokens or not.
-                Defaults to `False`.
-            top_logprobs (int):
-                An integer between 0 and 20 specifying the number of most likely tokens
-                to return at each token position. Default to `None`.
-            samples (int):
-                Number of responses to generate for each input message. Default to `10`.
-            seed (int):
-                Beta feature by OpenAI to sample deterministically. Default to `None`.
+            X (ndarray):
+                Input sequences of strings containing signal values.
 
         Returns:
             list, list:
@@ -86,25 +87,33 @@ def detect(self, X, **kwargs):
         input_length = len(self.tokenizer.encode(X[0]))
         max_tokens = input_length * self.anomalous_percent
 
-        message = ' '.join(PROMPTS['user_message'], text, self.sep)
-        response = openai.ChatCompletion.create(
-            model=self.name,
-            messages=[
-                {"role": "system", "content": PROMPTS['system_message']},
-                {"role": "user", "content": message}
-            ],
-            max_tokens=max_tokens,
-            temperature=self.temp,
-            logprobs=self.logprobs,
-            top_logprobs=self.top_logprobs,
-            n=self.samples,
-        )
-        responses = [choice.message.content for choice in response.choices]
+        all_responses, all_probs = [], []
+        for text in tqdm(X):
+            message = ' '.join(PROMPTS['user_message'], text, self.sep)
+            response = openai.ChatCompletion.create(
+                model=self.name,
+                messages=[
+                    {"role": "system", "content": PROMPTS['system_message']},
+                    {"role": "user", "content": message}
+                ],
+                max_tokens=max_tokens,
+                temperature=self.temp,
+                logprobs=self.logprobs,
+                top_logprobs=self.top_logprobs,
+                n=self.samples,
+                seed = self.seed
+            )
+            responses = [choice.message.content for choice in response.choices]
+            if self.logprobs:
+                probs = [choice.logprobs for choice in response.choices]
+                all_probs.append(probs)
+
+            all_responses.append(responses)
+        
         if self.logprobs:
-            probs = [choice.logprobs for choice in response.choices]
-            return responses, probs
+            return all_responses, all_probs
 
-        return responses
+        return all_responses
 
 
 
diff --git a/sigllm/primitives/prompting/huggingface.py b/sigllm/primitives/prompting/huggingface.py
index 5e9e3e8..319039b 100644
--- a/sigllm/primitives/prompting/huggingface.py
+++ b/sigllm/primitives/prompting/huggingface.py
@@ -34,11 +34,33 @@ class HF:
             Model name. Default to `'mistralai/Mistral-7B-Instruct-v0.2'`.
         sep (str):
             String to separate each element in values. Default to `','`.
+        anomalous_percent (float): 
+            Expected percentage of time series that are anomalous. Default to `0.5`.
+        temp (float):
+            The value used to modulate the next token probabilities. Default to `1`.
+        top_p (float):
+            If set to float < 1, only the smallest set of most probable tokens with
+            probabilities that add up to `top_p` or higher are kept for generation.
+            Default to `1`.
+        raw (bool):
+            Whether to return the raw output or not. Defaults to `False`.
+        samples (int):
+            Number of responsed to generate for each input message. Default to `10`.
+        padding (int):
+            Additional padding token to forecast to reduce short horizon predictions.
+            Default to `0`.
     """
 
-    def __init__(self, name=DEFAULT_MODEL, sep=','):
+    def __init__(self, name=DEFAULT_MODEL, sep=',', anomalous_percent = 0.5, temp=1, top_p=1, 
+                 raw=False, samples=10, padding=0):
         self.name = name
         self.sep = sep
+        self.anomalous_percent = anomalous_percent
+        self.temp = temp
+        self.top_p = top_p
+        self.raw = raw
+        self.samples = samples
+        self.padding = padding
 
         self.tokenizer = AutoTokenizer.from_pretrained(self.name, use_fast=False)
 
@@ -74,40 +96,28 @@ def __init__(self, name=DEFAULT_MODEL, sep=','):
 
         self.model.eval()
 
-    def detect(self, text, anomalous_percent = 0.5, temp=1, top_p=1, raw=False, samples=10, padding=0):
-        """Use GPT to forecast a signal.
+    def detect(self, X, **kwargs):
+        """Use HF to detect anomalies of a signal.
 
         Args:
-            text (str):
-                A string containing signal values.
-            anomalous_percent (float): 
-                Expected percentage of time series that are anomalous. Default to `0.5`.
-            temp (float):
-                The value used to modulate the next token probabilities. Default to `1`.
-            top_p (float):
-                 If set to float < 1, only the smallest set of most probable tokens with
-                 probabilities that add up to `top_p` or higher are kept for generation.
-                 Default to `1`.
-            raw (bool):
-                Whether to return the raw output or not. Defaults to `False`.
-            samples (int):
-                Number of responsed to generate for each input message. Default to `10`.
-            padding (int):
-                Additional padding token to forecast to reduce short horizon predictions.
-                Default to `0`.
-
+            X (ndarray):
+                Input sequences of strings containing signal values
+            
         Returns:
             list, list:
-                * List of forecasted signal values.
+                * List of detected anomalous values.
                 * Optionally, a list of dictionaries for raw output.
         """
-        input_length = len(self.tokenizer.encode(text))
-        max_tokens = input_length * anomalous_percent
 
-        message = ' '.join((PROMPTS['system_message'], PROMPTS['user_message'], text, '[RESPONSE]'))
+        input_length = len(self.tokenizer.encode(X[0]))
+        max_tokens = input_length * self.anomalous_percent
+
+        message = []
+        for text in X: 
+            message.append(' '.join((PROMPTS['system_message'], PROMPTS['user_message'], text, '[RESPONSE]')))
 
         tokenized_input = self.tokenizer(
-            [message],
+            message,
             return_tensors="pt"
         ).to("cuda")
 
@@ -115,11 +125,11 @@ def detect(self, text, anomalous_percent = 0.5, temp=1, top_p=1, raw=False, samp
             **tokenized_input,
             do_sample=True,
             max_new_tokens=max_tokens,
-            temperature=temp,
-            top_p=top_p,
+            temperature=self.temp,
+            top_p=self.top_p,
             bad_words_ids=self.invalid_tokens,
             renormalize_logits=True,
-            num_return_sequences=samples
+            num_return_sequences=self.samples
         )
 
         responses = self.tokenizer.batch_decode(
@@ -128,7 +138,7 @@ def detect(self, text, anomalous_percent = 0.5, temp=1, top_p=1, raw=False, samp
             clean_up_tokenization_spaces=False
         )
 
-        if raw:
+        if self.raw:
             return responses, generate_ids
 
         return responses
\ No newline at end of file
diff --git a/sigllm/primitives/timeseries_preprocessing.py b/sigllm/primitives/prompting/timeseries_preprocessing.py
similarity index 83%
rename from sigllm/primitives/timeseries_preprocessing.py
rename to sigllm/primitives/prompting/timeseries_preprocessing.py
index 494a7ca..03cad06 100644
--- a/sigllm/primitives/timeseries_preprocessing.py
+++ b/sigllm/primitives/prompting/timeseries_preprocessing.py
@@ -9,7 +9,7 @@
 import numpy as np
 
 
-def rolling_window_sequences(X, index, window_size, step_size):
+def rolling_window_sequences(X, window_size = 500, step_size = 100):
     """Create rolling window sequences out of time series data.
 
     This function creates an array of sequences by rolling over the input sequence.
@@ -17,18 +17,17 @@ def rolling_window_sequences(X, index, window_size, step_size):
     Args:
         X (ndarray):
             The sequence to iterate over.
-        index (ndarray):
-            Array containing the index values of X.
         window_size (int):
-            Length of window.
+            Length of window. Defaults to 500
         step_size (int):
-            Indicating the number of steps to move the window forward each round.
+            Indicating the number of steps to move the window forward each round. Defaults to 100
 
     Returns:
         ndarray, ndarray:
             * rolling window sequences.
             * first index value of each input sequence.
     """
+    index = range(len(X))
     out_X = list()
     X_index = list()
 
diff --git a/tests/primitives/test_timeseries_preprocessing.py b/tests/primitives/test_timeseries_preprocessing.py
index c0191a7..59336f0 100644
--- a/tests/primitives/test_timeseries_preprocessing.py
+++ b/tests/primitives/test_timeseries_preprocessing.py
@@ -1,6 +1,6 @@
 import numpy as np
 from pytest import fixture
-from sigllm.primitives.timeseries_preprocessing import rolling_window_sequences
+from sigllm.primitives.prompting.timeseries_preprocessing import rolling_window_sequences
 
 @fixture
 def indices():
diff --git a/tutorials/prompter.ipynb b/tutorials/prompter.ipynb
index b026989..a33e46d 100644
--- a/tutorials/prompter.ipynb
+++ b/tutorials/prompter.ipynb
@@ -2,17 +2,113 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "e6707064",
+   "execution_count": 1,
+   "id": "c4cc3835",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mlblocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "32c83a5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "primitives = [\n",
+    "    'sklearn.impute.SimpleImputer',\n",
+    "   'xgboost.XGBClassifier',\n",
+    "] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8ae34e69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = mlblocks.MLPipeline(primitives)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "262441fe-841b-4555-bf57-249305b59f92",
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "be80a076",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.24.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "print(np.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2e548714",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Name: numpy\r\n",
+      "Version: 1.18.5\r\n",
+      "Summary: NumPy is the fundamental package for array computing with Python.\r\n",
+      "Home-page: https://www.numpy.org\r\n",
+      "Author: Travis E. Oliphant et al.\r\n",
+      "Author-email: \r\n",
+      "License: BSD\r\n",
+      "Location: /opt/anaconda3/lib/python3.8/site-packages\r\n",
+      "Requires: \r\n",
+      "Required-by: accelerate, awkward, blueqat, cirq-core, cvxpy, daal4py, dimod, dwave-neal, ecos, fastdtw, gensim, gwpy, h5py, imbalanced-learn, Keras, Keras-Preprocessing, lightgbm, lmfit, maggma, matminer, matplotlib, mendeleev, mkl-fft, mkl-random, ml-stars, mlblocks, mplhep, numba, opencv-python, opt-einsum, orion-ml, osqp, pandas, patsy, pymatgen, pyqsp, pyquil, pyts, qdldl, qiskit-aer, qiskit-aqua, qiskit-ibmq-provider, qiskit-ignis, qiskit-terra, Quandl, retworkx, robocrys, scikit-learn, scipy, scs, seaborn, spglib, statsmodels, tensorboard, tensorflow, transformers, uhi, uproot, xgboost, yfinance\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip show numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5c14f5c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1624, 2)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "from data import rolling_window_sequences\n",
-    "from orion.data import load_signal, load_anomalies\n",
-    "from sigllm import get_anomalies\n",
-    "from gpt import get_gpt_model_response, create_message_zero_shot\n",
-    "from anomalies import merge_anomaly_seq\n",
-    "import numpy as np"
+    "from orion.data import load_signal\n",
+    "\n",
+    "data = load_signal('exchange-2_cpm_results')\n",
+    "data.shape"
    ]
   },
   {
@@ -194,7 +290,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.8.19"
   }
  },
  "nbformat": 4,

From d10a9953e1bed2bc16168db677d31cf494b97e28 Mon Sep 17 00:00:00 2001
From: Linh Nguyen <linhnk@dai-desk34.lids.mit.edu>
Date: Thu, 19 Sep 2024 13:52:59 -0400
Subject: [PATCH 05/25] mistral_pipeline

---
 setup.py                                      |    2 +-
 sigllm/core.py                                |   58 +-
 .../pipelines/prompter/mistral_prompter.json  |   16 +-
 ...rompting.anomalies.ano_within_windows.json |    1 -
 ...imitives.prompting.anomalies.idx2time.json |    1 -
 ...prompting.anomalies.merge_anomaly_seq.json |    1 -
 ...rompting.anomalies.timestamp2interval.json |    9 +-
 ...rimitives.prompting.anomalies.val2idx.json |    1 -
 ...reprocessing.rolling_window_sequences.json |   37 +-
 ...ives.transformation.format_as_integer.json |    3 +-
 ...tives.transformation.format_as_string.json |    1 -
 sigllm/primitives/prompting/anomalies.py      |   44 +-
 sigllm/primitives/prompting/gpt.py            |    6 +-
 sigllm/primitives/prompting/huggingface.py    |   64 +-
 .../prompting/timeseries_preprocessing.py     |    2 +-
 sigllm/primitives/transformation.py           |    8 +-
 .../test_timeseries_preprocessing.py          |   12 +-
 tutorials/prompter.ipynb                      | 1416 +++++++++++++++--
 18 files changed, 1431 insertions(+), 251 deletions(-)

diff --git a/setup.py b/setup.py
index d2a1690..610f5fa 100644
--- a/setup.py
+++ b/setup.py
@@ -12,7 +12,7 @@
     history = history_file.read()
 
 install_requires = [
-    'numpy>=1.17.5,<2.15',
+    'numpy>=1.17.5,<2',
     'openai',
     'pandas>=1,<2',
     'orion-ml>=0.5,<0.8',
diff --git a/sigllm/core.py b/sigllm/core.py
index 4df80f6..362be5e 100644
--- a/sigllm/core.py
+++ b/sigllm/core.py
@@ -3,38 +3,40 @@
 """
 Main module.
 
-This module contains functions that get LLM's anomaly detection results.
+This is an extension to Orion's core module
 """
-from sigllm.primitives.prompting.anomalies import get_anomaly_list_within_seq, str2idx
-from sigllm.primitives.prompting.data import sig2str
+from typing import Union
 
+from mlblocks import MLPipeline
+from orion import Orion
 
-def get_anomalies(seq, msg_func, model_func, num_iters=1, alpha=0.5):
-    """Get LLM anomaly detection results.
 
-    The function get the LLM's anomaly detection and converts them into an 1D array
+class SigLLM(Orion):
+    """SigLLM Class.
+
+    The SigLLM Class provides the main anomaly detection functionalities
+    of SigLLM and is responsible for the interaction with the underlying
+    MLBlocks pipelines.
 
     Args:
-        seq (ndarray):
-            The sequence to detect anomalies.
-        msg_func (func):
-            Function to create message prompt.
-        model_func (func):
-            Function to get LLM answer.
-        num_iters (int):
-            Number of times to run the same query.
-        alpha (float):
-            Percentage of total number of votes that an index needs to have to be
-            considered anomalous. Default: 0.5
-
-    Returns:
-        ndarray:
-            1D array containing anomalous indices of the sequence.
+        pipeline (str, dict or MLPipeline):
+            Pipeline to use. It can be passed as:
+                * An ``str`` with a path to a JSON file.
+                * An ``str`` with the name of a registered pipeline.
+                * An ``MLPipeline`` instance.
+                * A ``dict`` with an ``MLPipeline`` specification.
+        window_size (int):
+            Size of the input window.
+        steps (int):
+            Number of steps ahead to forecast.
+
+        hyperparameters (dict):
+            Additional hyperparameters to set to the Pipeline.
     """
-    message = msg_func(sig2str(seq, space=True))
-    res_list = []
-    for i in range(num_iters):
-        res = model_func(message)
-        ano_ind = str2idx(res, len(seq))
-        res_list.append(ano_ind)
-    return get_anomaly_list_within_seq(res_list, alpha=alpha)
+
+    def __init__(self, pipeline: Union[str, dict, MLPipeline] = None,
+                 hyperparameters: dict = None):
+        self._pipeline = pipeline or self.DEFAULT_PIPELINE
+        self._hyperparameters = hyperparameters
+        self._mlpipeline = self._get_mlpipeline()
+        self._fitted = False
diff --git a/sigllm/pipelines/prompter/mistral_prompter.json b/sigllm/pipelines/prompter/mistral_prompter.json
index b29d657..35e2c39 100644
--- a/sigllm/pipelines/prompter/mistral_prompter.json
+++ b/sigllm/pipelines/prompter/mistral_prompter.json
@@ -4,7 +4,7 @@
         "sklearn.impute.SimpleImputer",
         "sigllm.primitives.transformation.Float2Scalar",
         "sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences",
-	"sigllm.primitives.transformation.format_as_string",
+	    "sigllm.primitives.transformation.format_as_string",
         "sigllm.primitives.prompting.huggingface.HF",
         "sigllm.primitives.transformation.format_as_integer",
         "sigllm.primitives.prompting.anomalies.val2idx",
@@ -23,6 +23,10 @@
             "decimal": 2,
             "rescale": true
         },
+        "sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences#1": {
+            "window_size": 200,
+            "step_size": 40
+        },
         "sigllm.primitives.transformation.format_as_string#1": {
             "space": false
         },
@@ -30,17 +34,14 @@
             "name": "mistralai/Mistral-7B-Instruct-v0.2",
             "samples": 10
         },
-        "sigllm.primitives.prompting.anomalies.ano_within_windows": {
+        "sigllm.primitives.prompting.anomalies.ano_within_windows#1": {
             "alpha": 0.4
         },
-        "orion.primitives.prompting.anomalies.merge_anomaly_seq": {
+        "sigllm.primitives.prompting.anomalies.merge_anomaly_seq#1": {
             "beta": 0.5
         }
     },
     "input_names": {
-	"sigllm.primitives.transformation.Float2Scalar#1": {
-	    "X": "y"
-	},
     "sigllm.primitives.prompting.huggingface.HF#1": {
         "X": "X_str"
     },
@@ -52,9 +53,6 @@
     "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
         "index": "timestamp"
     },
-	"sklearn.impute.SimpleImputer#1": {
-	    "X": "y"
-	},
     "sigllm.primitives.transformation.format_as_string#1": {
         "X": "X_str"
     },
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
index 9085c29..0a46a8c 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
@@ -12,7 +12,6 @@
     "modalities": [],
     "primitive": "sigllm.primitives.prompting.anomalies.ano_within_windows",
     "produce": {
-        "method": "ano_within_windows",
         "args": [
             {
                 "name": "y",
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
index d4e4dd0..9c2a563 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
@@ -12,7 +12,6 @@
     "modalities": [],
     "primitive": "sigllm.primitives.prompting.anomalies.idx2time",
     "produce": {
-        "method": "idx2time",
         "args": [
             {
                 "name": "timestamp",
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
index 22a72d6..7a9c45b 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
@@ -12,7 +12,6 @@
     "modalities": [],
     "primitive": "sigllm.primitives.prompting.anomalies.merge_anomaly_seq",
     "produce": {
-        "method": "merge_anomaly_seq",
         "args": [
             {
                 "name": "y",
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
index 1a33e0a..2188727 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
@@ -12,16 +12,11 @@
     "modalities": [],
     "primitive": "sigllm.primitives.prompting.anomalies.timestamp2interval",
     "produce": {
-        "method": "timestamp2interval",
         "args": [
             {
                 "name": "y",
                 "type": "ndarray"
             },
-            {
-                "name": "interval",
-                "type": "int"
-            },
             {
                 "name": "timestamp",
                 "type": "ndarray"
@@ -29,8 +24,8 @@
         ],
         "output": [
             {
-                "name": "anomalies",
-                "type": "List[Tuple(start, end)]"
+                "name": "df",
+                "type": "Dataframe"
             }
         ]
     }, 
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json
index 27199fc..5a07bf1 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.val2idx.json
@@ -12,7 +12,6 @@
     "modalities": [],
     "primitive": "sigllm.primitives.prompting.anomalies.val2idx",
     "produce": {
-        "method": "val2idx",
         "args": [
             {
                 "name": "y",
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences.json
index 6c97cb8..23658e8 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences.json
@@ -7,12 +7,13 @@
     "description": "Create rolling windows",
     "classifiers": {
         "type": "preprocessor",
-        "subtype": "rolling windows"
+        "subtype": "feature_extractor"
     },
-    "modalities": [],
+    "modalities": [
+        "timeseries"
+    ],
     "primitive": "sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences",
     "produce": {
-        "method": "rolling_window_sequences",
         "args": [
             {
                 "name": "X",
@@ -27,18 +28,26 @@
             {
                 "name": "first_index",
                 "type": "ndarray"
+            }, 
+            {
+                "name": "window_size", 
+                "type": "int"
+            },
+            {
+                "name": "step_size", 
+                "type": "int"
             }
-        ], 
-        "hyperparameters": {
-            "fixed": {
-                "window_size": {
-                    "type": "int",
-                    "default": 500
-                },
-                "step_size": {
-                    "type": "int",
-                    "default": 100
-                }
+        ]
+    }, 
+    "hyperparameters": {
+        "fixed": {
+            "window_size": {
+                "type": "int",
+                "default": 500
+            },
+            "step_size": {
+                "type": "int",
+                "default": 100
             }
         }
     }
diff --git a/sigllm/primitives/jsons/sigllm.primitives.transformation.format_as_integer.json b/sigllm/primitives/jsons/sigllm.primitives.transformation.format_as_integer.json
index 48637aa..62bca33 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.transformation.format_as_integer.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.transformation.format_as_integer.json
@@ -4,7 +4,7 @@
         "Sarah Alnegheimish <smish@mit.edu>",
         "Linh Nguyen <linhnk@mit.edu>"
     ],
-    "description": "Transform an ndarray of scalar values to an ndarray of string.",
+    "description": "Transform an ndarray of string values to an ndarray of integers.",
     "classifiers": {
         "type": "preprocessor",
         "subtype": "transformer"
@@ -12,7 +12,6 @@
     "modalities": [],
     "primitive": "sigllm.primitives.transformation.format_as_integer",
     "produce": {
-        "method": "format_as_integer",
         "args": [
             {
                 "name": "X",
diff --git a/sigllm/primitives/jsons/sigllm.primitives.transformation.format_as_string.json b/sigllm/primitives/jsons/sigllm.primitives.transformation.format_as_string.json
index bedb6ff..89d18f5 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.transformation.format_as_string.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.transformation.format_as_string.json
@@ -12,7 +12,6 @@
     "modalities": [],
     "primitive": "sigllm.primitives.transformation.format_as_string",
     "produce": {
-        "method": "format_as_string",
         "args": [
             {
                 "name": "X",
diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index 5606900..f0a816c 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -7,18 +7,19 @@
 """
 
 import numpy as np
+import pandas as pd
 
-def val2idx(vals, windows): 
+def val2idx(y, X): 
     """Convert detected anomalies values into indices.
     
     Convert windows of detected anomalies values into an array of all indices
     in the input sequence that have those values. 
     
     Args: 
-        vals (ndarray): 
+        y (ndarray): 
             A 3d array containing detected anomalous values from different
             responses of each window.
-        windows (ndarray):
+        X (ndarray):
             rolling window sequences.      
     Returns: 
         List([ndarray]):
@@ -27,7 +28,7 @@ def val2idx(vals, windows):
     """
 
     idx_list = []
-    for anomalies_list, seq in zip(vals, windows): 
+    for anomalies_list, seq in zip(y, X): 
         idx_win_list = []
         for anomalies in anomalies_list:
             mask = np.isin(seq, anomalies)
@@ -38,13 +39,13 @@ def val2idx(vals, windows):
     idx_list = np.array(idx_list, dtype=object)
     return idx_list
 
-def ano_within_windows(idx_win_list, alpha=0.5):
+def ano_within_windows(y, alpha=0.5):
     """Get the final list of anomalous indices of each window
 
     Choose anomalous index in the sequence based on multiple LLM responses
 
     Args:
-        idx_win_list (ndarray):
+        y (ndarray):
             A 3d array containing detected anomalous values from different
             responses of each window.
         alpha (float):
@@ -56,7 +57,7 @@ def ano_within_windows(idx_win_list, alpha=0.5):
     """
     
     idx_list = []
-    for samples in idx_win_list:
+    for samples in y:
         min_vote = np.ceil(alpha * len(samples))
         #print(type(samples.tolist()))
 
@@ -70,13 +71,13 @@ def ano_within_windows(idx_win_list, alpha=0.5):
     idx_list = np.array(idx_list, dtype = object)
     return idx_list
 
-def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5):
+def merge_anomaly_seq(y, first_index, window_size, step_size, beta=0.5):
     """Get the final list of anomalous indices of a sequence when merging all rolling windows
 
     Args:
-        anomalies (ndarray):
+        y (ndarray):
             A 2-dimensional array containing anomalous indices of each window.
-        start_indices (ndarray):
+        first_index (ndarray):
             A 1-dimensional array contaning the first index of each window.
         window_size (int):
             Length of each window
@@ -88,7 +89,7 @@ def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5
         ndarray:
             A 1-dimensional array containing final anomalous indices.
     """
-    anomalies = [arr + first_idx for (arr, first_idx) in zip(anomalies, start_indices)]
+    anomalies = [arr + first_idx for (arr, first_idx) in zip(y, first_index)]
 
     min_vote = np.ceil(beta * window_size / step_size)
 
@@ -100,31 +101,29 @@ def merge_anomaly_seq(anomalies, start_indices, window_size, step_size, beta=0.5
 
     return np.sort(final_list)
 
-def idx2time(timestamp, idx_list):
+def idx2time(timestamp, y):
     """Convert list of indices into list of timestamp
 
     Args:
         sequence (DataFrame):
             Signal with timestamps and values.
-        idx_list (ndarray):
+        y (ndarray):
             A 1-dimensional array of indices.
 
     Returns:
         ndarray:
             A 1-dimensional array containing timestamps.
     """
-    timestamp_list = timestamp[idx_list]
+    timestamp_list = timestamp[y]
     return timestamp_list
 
-def timestamp2interval(timestamp_list, interval, timestamp, padding_size = 50): 
+def timestamp2interval(y, timestamp, padding_size = 50): 
     """Convert list of timestamps to list of intervals by padding to both sides
     and merge overlapping 
     
     Args: 
-        timestamp_list (ndarray): 
+        y (ndarray): 
             A 1d array of point timestamps.
-        interval (int):
-            The fixed gap between two consecutive timestamps of the time series.
         timestamp (ndarray):
             List of full timestamp of the signal
         padding_size (int): 
@@ -135,8 +134,9 @@ def timestamp2interval(timestamp_list, interval, timestamp, padding_size = 50):
             A list of intervals.
     """
     start, end = timestamp[0], timestamp[-1]
+    interval = timestamp[1] - timestamp[0]
     intervals = []
-    for timestamp in timestamp_list: 
+    for timestamp in y: 
         intervals.append((max(start, timestamp-padding_size*interval), min(end, timestamp+padding_size*interval)))
     if not intervals:
         return []
@@ -153,5 +153,7 @@ def timestamp2interval(timestamp_list, interval, timestamp, padding_size = 50):
             merged_intervals[-1] = previous_interval
         else:
             merged_intervals.append(current_interval)  # Append the current interval if no overlap
-
-    return merged_intervals
+            
+    df = pd.DataFrame(merged_intervals, columns=['start', 'end'])
+    df['score'] = 0
+    return df
diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index b6a7ba9..236995d 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -84,12 +84,12 @@ def detect(self, X, **kwargs):
                 * List of detected anomalous values.
                 * Optionally, a list of the output tokens' log probabilities.
         """
-        input_length = len(self.tokenizer.encode(X[0]))
-        max_tokens = input_length * self.anomalous_percent
+        input_length = len(self.tokenizer.encode(X[0][0]))
+        max_tokens = input_length * float(self.anomalous_percent)
 
         all_responses, all_probs = [], []
         for text in tqdm(X):
-            message = ' '.join(PROMPTS['user_message'], text, self.sep)
+            message = ' '.join(PROMPTS['user_message'], text[0], self.sep)
             response = openai.ChatCompletion.create(
                 model=self.name,
                 messages=[
diff --git a/sigllm/primitives/prompting/huggingface.py b/sigllm/primitives/prompting/huggingface.py
index 319039b..4153bdc 100644
--- a/sigllm/primitives/prompting/huggingface.py
+++ b/sigllm/primitives/prompting/huggingface.py
@@ -3,6 +3,7 @@
 import json
 import os
 import logging
+from tqdm import tqdm
 
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -109,36 +110,41 @@ def detect(self, X, **kwargs):
                 * Optionally, a list of dictionaries for raw output.
         """
 
-        input_length = len(self.tokenizer.encode(X[0]))
-        max_tokens = input_length * self.anomalous_percent
-
-        message = []
-        for text in X: 
-            message.append(' '.join((PROMPTS['system_message'], PROMPTS['user_message'], text, '[RESPONSE]')))
-
-        tokenized_input = self.tokenizer(
-            message,
-            return_tensors="pt"
-        ).to("cuda")
-
-        generate_ids = self.model.generate(
-            **tokenized_input,
-            do_sample=True,
-            max_new_tokens=max_tokens,
-            temperature=self.temp,
-            top_p=self.top_p,
-            bad_words_ids=self.invalid_tokens,
-            renormalize_logits=True,
-            num_return_sequences=self.samples
-        )
+        input_length = len(self.tokenizer.encode(X[0].flatten().tolist()[0]))
+        max_tokens = input_length * float(self.anomalous_percent)
+        all_responses, all_generate_ids = [], []
 
-        responses = self.tokenizer.batch_decode(
-            generate_ids[:, input_length:],
-            skip_special_tokens=True,
-            clean_up_tokenization_spaces=False
-        )
+        for text in tqdm(X): 
+            text = text.flatten().tolist()
+            message = [' '.join((PROMPTS['system_message'], PROMPTS['user_message'], x, '[RESPONSE]')) for x in text]
+
+            input_length = len(self.tokenizer.encode(message[0]))
+
+            tokenized_input = self.tokenizer(
+                message,
+                return_tensors="pt"
+                    ).to("cuda")
+            
+            generate_ids = self.model.generate(
+                **tokenized_input,
+                do_sample=True,
+                max_new_tokens=max_tokens,
+                temperature=self.temp,
+                top_p=self.top_p,
+                bad_words_ids=self.invalid_tokens,
+                renormalize_logits=True,
+                num_return_sequences=self.samples
+            )
+
+            responses = self.tokenizer.batch_decode(
+                generate_ids[:, input_length:],
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=False
+            )
+            all_responses.append(responses)
+            all_generate_ids.append(generate_ids)
 
         if self.raw:
-            return responses, generate_ids
+            return all_responses, all_generate_ids
 
-        return responses
\ No newline at end of file
+        return all_responses
\ No newline at end of file
diff --git a/sigllm/primitives/prompting/timeseries_preprocessing.py b/sigllm/primitives/prompting/timeseries_preprocessing.py
index 03cad06..0f8d759 100644
--- a/sigllm/primitives/prompting/timeseries_preprocessing.py
+++ b/sigllm/primitives/prompting/timeseries_preprocessing.py
@@ -39,4 +39,4 @@ def rolling_window_sequences(X, window_size = 500, step_size = 100):
         X_index.append(index[start])
         start = start + step_size
 
-    return np.asarray(out_X), np.asarray(X_index)
\ No newline at end of file
+    return np.asarray(out_X), np.asarray(X_index), window_size, step_size
\ No newline at end of file
diff --git a/sigllm/primitives/transformation.py b/sigllm/primitives/transformation.py
index b1dcde3..b267c8f 100644
--- a/sigllm/primitives/transformation.py
+++ b/sigllm/primitives/transformation.py
@@ -10,7 +10,7 @@
 import numpy as np
 
 
-def format_as_string(values, sep=',', space=False):
+def format_as_string(X, sep=',', space=False):
     """Format values to a list of string.
 
     Transform a 2-D array of integers to a list of strings,
@@ -34,7 +34,7 @@ def _as_string(x):
 
         return text
 
-    return np.apply_along_axis(_as_string, axis=1, arr=values)
+    return np.apply_along_axis(_as_string, axis=1, arr=X)
 
 
 def _from_string_to_integer(text, sep=',', trunc=None, errors='ignore'):
@@ -71,7 +71,7 @@ def _from_string_to_integer(text, sep=',', trunc=None, errors='ignore'):
     return clean
 
 
-def format_as_integer(strings, sep=',', trunc=None, errors='ignore'):
+def format_as_integer(X, sep=',', trunc=None, errors='ignore'):
     """Format a nested list of text into an array of integers.
 
     Transforms a list of list of string input as 3-D array of integers,
@@ -96,7 +96,7 @@ def format_as_integer(strings, sep=',', trunc=None, errors='ignore'):
             An array of digits values.
     """
     result = list()
-    for string_list in strings:
+    for string_list in X:
         sample = list()
         if not isinstance(string_list, list):
             raise ValueError("Input is not a list of lists.")
diff --git a/tests/primitives/test_timeseries_preprocessing.py b/tests/primitives/test_timeseries_preprocessing.py
index 59336f0..a3f1189 100644
--- a/tests/primitives/test_timeseries_preprocessing.py
+++ b/tests/primitives/test_timeseries_preprocessing.py
@@ -2,10 +2,6 @@
 from pytest import fixture
 from sigllm.primitives.prompting.timeseries_preprocessing import rolling_window_sequences
 
-@fixture
-def indices():
-    return np.array([0, 1, 2, 3, 4, 5, 6])
-
 
 @fixture
 def values():
@@ -21,15 +17,17 @@ def window_size():
 def step_size():
     return 1
 
-def test_rolling_window_sequences(values, indices, window_size, step_size):
+def test_rolling_window_sequences(values, window_size, step_size):
     expected = (np.array([[0.555, 2.345, 1.501],
                           [2.345, 1.501, 5.903],
                           [1.501, 5.903, 9.116],
                           [5.903, 9.116, 3.068],
                           [9.116, 3.068, 4.678]]),
-                np.array([0, 1, 2, 3, 4]))
+                np.array([0, 1, 2, 3, 4]), 
+               3,
+               1)
 
-    result = rolling_window_sequences(values, indices, window_size, step_size)
+    result = rolling_window_sequences(values, window_size, step_size)
 
     if len(result) != len(expected):
         raise AssertionError("Tuples has different length")
diff --git a/tutorials/prompter.ipynb b/tutorials/prompter.ipynb
index a33e46d..0f15839 100644
--- a/tutorials/prompter.ipynb
+++ b/tutorials/prompter.ipynb
@@ -7,268 +7,1444 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import mlblocks"
+    "import warnings; warnings.simplefilter('ignore')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "32c83a5a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1624, 2)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "primitives = [\n",
-    "    'sklearn.impute.SimpleImputer',\n",
-    "   'xgboost.XGBClassifier',\n",
-    "] "
+    "from orion.data import load_signal\n",
+    "\n",
+    "data = load_signal('exchange-2_cpm_results')\n",
+    "data.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "8ae34e69",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "pipeline = mlblocks.MLPipeline(primitives)"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(data['value']);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "262441fe-841b-4555-bf57-249305b59f92",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe1fc2429b6a49fcb0d059b40d131a57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mlblocks import MLPipeline, add_pipelines_path, add_primitives_path\n",
+    "pipeline = MLPipeline('mistral_prompter')"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "be80a076",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "hyperparameters = {\n",
+    "    \"mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1\": {\n",
+    "        \"interval\": 3600\n",
+    "    }, \n",
+    "    \"sigllm.primitives.prompting.anomalies.ano_within_windows#1\": {\n",
+    "        \"alpha\": 1.0\n",
+    "    },\n",
+    "    \"sigllm.primitives.prompting.anomalies.merge_anomaly_seq#1\": {\n",
+    "        \"beta\": 1.0\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "pipeline.set_hyperparameters(hyperparameters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2e548714",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['mlstars.custom.timeseries_preprocessing.time_segments_aggregate',\n",
+       " 'sklearn.impute.SimpleImputer',\n",
+       " 'sigllm.primitives.transformation.Float2Scalar',\n",
+       " 'sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences',\n",
+       " 'sigllm.primitives.transformation.format_as_string',\n",
+       " 'sigllm.primitives.prompting.huggingface.HF',\n",
+       " 'sigllm.primitives.transformation.format_as_integer',\n",
+       " 'sigllm.primitives.prompting.anomalies.val2idx',\n",
+       " 'sigllm.primitives.prompting.anomalies.ano_within_windows',\n",
+       " 'sigllm.primitives.prompting.anomalies.merge_anomaly_seq',\n",
+       " 'sigllm.primitives.prompting.anomalies.idx2time',\n",
+       " 'sigllm.primitives.prompting.anomalies.timestamp2interval']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline.primitives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af16a62a-c4cb-424f-bcdc-cdfaa0a51977",
+   "metadata": {},
+   "source": [
+    "time segment aggerate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f683c7f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['X', 'timestamp'])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 0\n",
+    "context = pipeline.fit(data, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "533566d5",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.24.2\n"
+      "entry at 1309471201 has value [0.4010481]\n",
+      "entry at 1309474801 has value [0.39271888]\n",
+      "entry at 1309478401 has value [0.30999612]\n",
+      "entry at 1309482001 has value [0.21293855]\n",
+      "entry at 1309485601 has value [0.20580091]\n"
      ]
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "print(np.__version__)"
+    "for i, x in list(zip(context['timestamp'], context['X']))[:5]:\n",
+    "    print(\"entry at {} has value {}\".format(i, x))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "2e548714",
+   "id": "a488bc32",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Name: numpy\r\n",
-      "Version: 1.18.5\r\n",
-      "Summary: NumPy is the fundamental package for array computing with Python.\r\n",
-      "Home-page: https://www.numpy.org\r\n",
-      "Author: Travis E. Oliphant et al.\r\n",
-      "Author-email: \r\n",
-      "License: BSD\r\n",
-      "Location: /opt/anaconda3/lib/python3.8/site-packages\r\n",
-      "Requires: \r\n",
-      "Required-by: accelerate, awkward, blueqat, cirq-core, cvxpy, daal4py, dimod, dwave-neal, ecos, fastdtw, gensim, gwpy, h5py, imbalanced-learn, Keras, Keras-Preprocessing, lightgbm, lmfit, maggma, matminer, matplotlib, mendeleev, mkl-fft, mkl-random, ml-stars, mlblocks, mplhep, numba, opencv-python, opt-einsum, orion-ml, osqp, pandas, patsy, pymatgen, pyqsp, pyquil, pyts, qdldl, qiskit-aer, qiskit-aqua, qiskit-ibmq-provider, qiskit-ignis, qiskit-terra, Quandl, retworkx, robocrys, scikit-learn, scipy, scs, seaborn, spglib, statsmodels, tensorboard, tensorflow, transformers, uhi, uproot, xgboost, yfinance\r\n"
-     ]
+     "data": {
+      "text/plain": [
+       "(1648, 1)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "!pip show numpy"
+    "context['X'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7e8110b-6d0a-4e67-9346-5317f137b05c",
+   "metadata": {},
+   "source": [
+    "Single Imputer"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "5c14f5c7",
+   "execution_count": 10,
+   "id": "35c41874",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1624, 2)"
+       "dict_keys(['timestamp', 'X'])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from orion.data import load_signal\n",
-    "\n",
-    "data = load_signal('exchange-2_cpm_results')\n",
-    "data.shape"
+    "step = 1\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4aa81d9-f6ee-49bd-894b-ec64445b7edb",
+   "metadata": {},
+   "source": [
+    "Float2Scalar"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "f683c7f7",
+   "execution_count": 11,
+   "id": "b49c4fbf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'X', 'minimum'])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#Load the signal and true anomalies\n",
-    "signal_name = 'M-7'\n",
-    "signal = load_signal(signal_name)\n",
-    "true_ano = load_anomalies(signal_name)"
+    "step = 2\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "533566d5",
+   "execution_count": 12,
+   "id": "f7571fa1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry at 1309471201 has value [40]\n",
+      "entry at 1309474801 has value [39]\n",
+      "entry at 1309478401 has value [30]\n",
+      "entry at 1309482001 has value [21]\n",
+      "entry at 1309485601 has value [20]\n"
+     ]
+    }
+   ],
    "source": [
-    "#Get values and indices of signal \n",
-    "values = signal['value'].values\n",
-    "indices = signal.index.values"
+    "for i, x in list(zip(context['timestamp'], context['X']))[:5]:\n",
+    "    print(\"entry at {} has value {}\".format(i, x))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "a488bc32",
+   "execution_count": 13,
+   "id": "fd1a9ba6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.000385004945833"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['minimum']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3914c439-0452-4151-93d2-9aa0ec0d3442",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#make rolling windows\n",
-    "window_size = 2500\n",
-    "step_size = 500\n",
-    "windows, start_indices = rolling_window_sequences(values, indices, window_size, step_size)"
+    "Rolling Window"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "35c41874",
+   "execution_count": 14,
+   "id": "bd160c3e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'X', 'first_index', 'window_size', 'step_size'])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#rolling window anomaly detection\n",
-    "final_ano = []\n",
-    "i = 0\n",
-    "for seq in windows: \n",
-    "    #to print out where the error was thrown out\n",
-    "    i+= 1\n",
-    "    try:\n",
-    "        final_ano.append(get_anomalies(seq, create_message_zero_shot, get_gpt_model_response))\n",
-    "    except Exception as error:\n",
-    "        print(error)\n",
-    "        print(i)\n",
-    "        break"
+    "step = 3\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "b49c4fbf",
+   "execution_count": 15,
+   "id": "ab08a9a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape = (37, 200, 1)\n",
+      "Timestamp shape = (1648,)\n",
+      "First index shape = (37,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# after slicing X into multiple sub-sequences\n",
+    "# we obtain a 3 dimensional matrix X where\n",
+    "# the shape indicates (# slices, window size, 1)\n",
+    "\n",
+    "print(\"X shape = {}\\nTimestamp shape = {}\\nFirst index shape = {}\".format(\n",
+    "    context['X'].shape, context['timestamp'].shape, context['first_index'].shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f201cbc8-0c88-4489-a7b0-b5060ac785a1",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "final_res = merge_anomaly_seq(final_ano, start_indices, window_size, step_size, beta = 0)"
+    "Format as string"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "f7571fa1",
+   "execution_count": 16,
+   "id": "3a1836db-cd6f-4a39-8f00-6a09c620c5f0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 215,  224,  225,  227,  247,  248,  249,  250,  252,  253,  254,\n",
-       "        256,  562,  564,  566,  567,  568,  569,  570,  571,  572,  573,\n",
-       "        574,  575,  576,  577,  578,  579,  580,  581,  738,  739,  743,\n",
-       "       1444, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533,\n",
-       "       1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544,\n",
-       "       1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555,\n",
-       "       1691, 1692, 1693, 1694, 1695, 2068, 2069, 2070, 2071, 2072, 2073,\n",
-       "       2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084,\n",
-       "       2085, 2086, 2087, 2088, 2091, 2092, 2093, 2094, 2095, 2096, 2097,\n",
-       "       2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108,\n",
-       "       2109, 2110])"
+       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X', 'X_str'])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "final_res"
+    "step = 4\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "fd1a9ba6",
+   "execution_count": 17,
+   "id": "1259df2c-d656-42a8-973c-b15cf8e031d4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'40,39,30,21,20,25,30,29,53,78,74,73,69,68,51,51,51,41,24,30,27,26,23,26,32,25,21,16,21,30,28,39,58,59,71,78,73,68,72,52,40,34,27,27,34,28,32,25,20,20,17,13,17,27,24,34,67,62,60,59,71,63,56,43,36,30,26,24,24,20,20,23,17,19,16,14,12,16,21,28,47,54,50,53,60,51,52,42,32,34,24,24,21,21,22,25,22,16,17,12,13,17,22,27,44,47,54,66,54,58,42,39,36,32,27,23,21,21,19,24,22,19,13,11,15,20,22,28,47,64,52,57,57,51,40,44,36,35,28,24,20,29,21,22,21,16,12,11,12,17,19,24,40,53,54,43,46,43,34,38,32,25,22,15,18,17,17,15,16,14,14,10,11,14,15,31,52,43,49,45,44,36,30,32,22,24,22,19,18,20,19,17,19,15,12,11,17,23,22,29'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "PROMPTS = {\n",
-    "    \"system_message\": \"You are an exceptionally intelligent assistant that detect anomalies in time series data by listing all the anomalies.\",\n",
-    "    \"user_message\": \"Below is a [SEQUENCE], please return the anomalies in that sequence in [RESPONSE]. Only return the numbers. [SEQUENCE]\"\n",
-    "}"
+    "context['X_str'].flatten().tolist()[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "bd160c3e",
+   "execution_count": 18,
+   "id": "d05e85ce-6111-494f-88c0-4fc566386b43",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.str_"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "text = '0, 11, 3'"
+    "type(context['X_str'][0][0])"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ab08a9a9",
+   "cell_type": "markdown",
+   "id": "7f403aca-ba56-42d3-bcae-b665a234c710",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "message = ' '.join((PROMPTS['system_message'], PROMPTS['user_message'], text, '[RESPONSE]'))"
+    "HF"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "11eb949c",
+   "execution_count": 19,
+   "id": "b4711e98-c522-4464-b645-607f76e89063",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                    | 0/37 [00:00<?, ?it/s]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "2024-09-19 13:22:37.977012: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2024-09-19 13:22:37.977047: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2024-09-19 13:22:37.977082: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-09-19 13:22:37.983817: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2024-09-19 13:22:38.716582: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+      "  3%|█▏                                          | 1/37 [00:24<14:50, 24.75s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "  5%|██▍                                         | 2/37 [00:47<13:52, 23.80s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "  8%|███▌                                        | 3/37 [01:10<13:14, 23.38s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 11%|████▊                                       | 4/37 [01:33<12:45, 23.19s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 14%|█████▉                                      | 5/37 [01:56<12:19, 23.10s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 16%|███████▏                                    | 6/37 [02:19<11:53, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 19%|████████▎                                   | 7/37 [02:42<11:29, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 22%|█████████▌                                  | 8/37 [03:05<11:06, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 24%|██████████▋                                 | 9/37 [03:28<10:43, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 27%|███████████▌                               | 10/37 [03:51<10:20, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 30%|████████████▊                              | 11/37 [04:14<09:56, 22.96s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 32%|█████████████▉                             | 12/37 [04:37<09:33, 22.95s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 35%|███████████████                            | 13/37 [05:00<09:10, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 38%|████████████████▎                          | 14/37 [05:22<08:47, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 41%|█████████████████▍                         | 15/37 [05:45<08:24, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 43%|██████████████████▌                        | 16/37 [06:08<08:01, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 46%|███████████████████▊                       | 17/37 [06:31<07:38, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 49%|████████████████████▉                      | 18/37 [06:54<07:15, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 51%|██████████████████████                     | 19/37 [07:17<06:52, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 54%|███████████████████████▏                   | 20/37 [07:40<06:29, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 57%|████████████████████████▍                  | 21/37 [08:03<06:07, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 59%|█████████████████████████▌                 | 22/37 [08:26<05:44, 22.95s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 62%|██████████████████████████▋                | 23/37 [08:49<05:21, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 65%|███████████████████████████▉               | 24/37 [09:12<04:58, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 68%|█████████████████████████████              | 25/37 [09:35<04:35, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 70%|██████████████████████████████▏            | 26/37 [09:58<04:12, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 73%|███████████████████████████████▍           | 27/37 [10:21<03:49, 22.95s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 76%|████████████████████████████████▌          | 28/37 [10:44<03:26, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 78%|█████████████████████████████████▋         | 29/37 [11:07<03:03, 22.92s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 81%|██████████████████████████████████▊        | 30/37 [11:29<02:40, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 84%|████████████████████████████████████       | 31/37 [11:52<02:17, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 86%|█████████████████████████████████████▏     | 32/37 [12:15<01:54, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 89%|██████████████████████████████████████▎    | 33/37 [12:38<01:31, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 92%|███████████████████████████████████████▌   | 34/37 [13:01<01:08, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 95%|████████████████████████████████████████▋  | 35/37 [13:24<00:45, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 97%|█████████████████████████████████████████▊ | 36/37 [13:47<00:23, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "100%|███████████████████████████████████████████| 37/37 [14:10<00:00, 23.00s/it]\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "'You are an exceptionally intelligent assistant that detect anomalies in time series data by listing all the anomalies. Below is a [SEQUENCE], please return the anomalies in that sequence in [RESPONSE]. Only return the numbers. [SEQUENCE] 0, 11, 3 [RESPONSE]'"
+       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'X', 'y_hat'])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "message"
+    "step = 5\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d587d1d3-9325-4d45-9e15-d69d2d16bb6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['16,13,17,12,11,15,14,14,10,11,14,15,31,52,53,43,46,43,34,38,32,25,22,18,15,17,19,15,17,12,11,17,23,22,29,51,51,51,41,34,27,27,34,28,32,25,20,20,23,17,19,16,14,12,16,21,28,47,54,50,53,60,51,52,42,32,34,24,24,21,21,22,25,22,16,17,12,13,17,22,27,44,47,54,66,54,58,42,39,36,32,27,23,21,21,19,24,22,19,13,1',\n",
+       "  '16,13,17,24,21,14,12,11,15,18,14,10,11,14,15,31,52,59,44,47,67,62,60,59,71,73,78,72,73,72,68,63,56,53,43,36,34,32,35,38,25,22,21,22,16,12,11,12,17,19,27,44,47,54,66,58,34,39,36,32,23,21,19,24,22,13,11,15,15,20,22,28,47,54,50,53,60,51,52,42,34,32,21,22,25,22,19,17,19,15,12,11,17,23,22,29,47,53,49,45,4',\n",
+       "  '30,25,23,32,41,16,13,17,53,78,51,51,51,24,19,34,67,62,59,71,63,56,43,36,30,17,13,17,24,44,47,54,66,54,58,39,32,15,18,17,15,10,11,15,31,52,49,45,38,22,19,18,17,19,15,12,17,23,31,52,40,44,35,38,25,15,18,17,11,11,15,14,14,10,11,22,25,34,54,43,49,45,36,35,28,24,29,21,12,11,12,17,19,24,40,53,46,43,36,24,3',\n",
+       "  '16,13,17,12,11,15,14,14,10,11,14,15,23,19,18,20,19,15,11,17,22,31,52,43,49,45,44,36,35,32,25,22,18,19,20,17,19,15,12,22,40,53,44,36,38,32,24,17,12,11,12,17,19,27,44,64,57,57,51,42,39,36,27,23,21,19,24,22,19,13,11,15,20,22,34,47,54,47,66,54,58,43,39,36,32,27,23,19,24,22,19,13,11,15,14,12,16,19,47,64,5',\n",
+       "  '30,21,26,53,78,74,51,41,24,16,14,12,15,19,47,44,59,36,23,19,11,15,31,40,34,28,17,13,11,15,14,14,10,11,14,34,67,62,59,71,63,56,35,30,32,22,19,18,20,17,19,15,12,11,17,23,22,38,54,57,66,54,43,38,32,25,18,17,15,14,14,10,11,14,34,47,53,43,36,34,21,25,21,22,19,16,12,13,17,22,27,44,47,57,64,51,39,36,27,23,2',\n",
+       "  '13,11,15,14,10,11,14,15,53,78,73,51,51,51,41,24,16,13,17,27,34,67,62,59,71,63,56,43,36,30,24,20,20,17,13,19,34,62,14,12,16,47,54,47,44,36,38,32,21,15,18,17,15,16,14,14,10,11,14,15,31,52,49,38,35,34,24,18,20,19,17,15,17,22,40,53,46,34,38,32,24,19,13,11,17,23,22,29',\n",
+       "  '16,13,11,15,14,14,10,11,14,15,31,52,49,45,38,32,22,18,19,17,15,12,17,23,22,29,53,71,78,73,58,59,71,78,66,54,36,30,26,24,24,20,20,23,22,16,17,12,13,17,22,44,47,54,66,54,34,32,25,22,15,18,17,17,15,16,14,14,10,11,14,15,34,47,54,67,52,57,57,51,44,43,36,39,36,32,27,23,21,19,24,22,19,13,11,15,20,22,28,47,6',\n",
+       "  '16,13,17,12,11,15,14,10,11,14,15,31,52,43,49,45,44,36,35,28,24,20,29,21,22,21,16,12,11,17,23,44,47,54,66,54,58,42,39,36,32,23,21,19,24,22,19,13,11,15,20,22,28,47,64,52,57,57,51,43,44,36,38,25,22,15,18,17,17,15,16,14,14,9,11,14,15,34,28,32,25,20,20,23,19,19,27,40,53,54,43,46,43,34,38,32,25,22,15,18,17',\n",
+       "  '16,13,11,15,14,14,10,11,14,15,51,51,51,41,17,19,16,14,12,16,32,19,13,11,15,24,23,19,18,20,19,15,12,17,23,22,29,44,47,54,66,54,58,36,36,28,23,21,19,24,34,38,32,25,22,15,18,17,17,15,14,10,11,14,15,31,52,49,43,35,30,32,22,24,22,19,18,20,19,17,19,15,12,11,17,23,22,29,34,34,32,25,22,15,18,17,17,15,14,10,1',\n",
+       "  '21,23,34,51,51,51,41,16,13,17,24,34,12,11,15,31,67,40,34,38,25,18,22,19,13,11,15,20,22,31,47,54,43,46,35,30,24,17,19,16,14,12,16,47,54,44,36,32,25,22,19,18,20,19,17,19,15,20,10,11,14,15,34,62,59,59,71,78,73,68,72,52,27,27,34,17,19,27,24,27,34,52,57,57,51,40,36,24,21,13,17,12,17,22,27,44,47,54,66,54,4'],\n",
+       " ['13,11,15,11,13,15,14,10,11,14,15,31,52,43,49,35,18,18,18,14,14,10,22,26,54,47,44,49,49,41,37,34,22,19,19,17,17,15,12,11,17,23,22,29,47,46,47,57,51,39,35,21,19,18,20,22,20,16,11,11,13,22,19,31,47,47,39,49,49,44,41,37,34,32,22,19,19,17,17,15,12,11,17,23,22,29,49,46,47,54,57,51,40,39,35,28,21,23,19,18,2',\n",
+       "  '13,11,15,12,11,12,17,19,14,14,10,11,14,15,22,26,31,44,47,53,54,51,43,38,32,21,18,17,17,15,16,14,14,10,11,14,15,38,49,46,49,42,41,37,34,32,21,19,19,17,17,15,22,20,16,11,11,13,22,19,47,52,43,49,45,44,36,30,32,22,20,16,11,11,13,22,19,47,57,51,40',\n",
+       "  '13,11,11,12,15,14,14,10,11,14,15,31,52,43,49,45,44,36,30,32,22,19,18,20,19,17,19,15,12,11,17,23,22,29,49,46,47,54,57,51,40,39,35,28,21,23,19,18,20,22,20,16,11,11,13,22,19,26,47,47,44,49,49,42,41,37,34,32,22,19,19,17,17,15,22,67,62,60,59,71,63,56,43,36,30,26,24,24,20,20,23,17,19,16,14,12,16,21,28,47,5',\n",
+       "  '13,11,12,13,11,12,17,19,13,11,15,20,14,12,16,14,14,14,10,11,14,15,31,52,43,36,35,28,21,23,18,18,22,29,49,46,47,44,49,42,37,34,32,22,19,15,13,17,23,22,29,44,53,43,46,43,38,32,25,22,15,18,17,15,14,10,11,14,19,31,66,54,51,58,57,51,44,40,36,35,24,29,21,21,16,11,11,13,22,19,26,47,47,41,49,49,37,41,34,32,2',\n",
+       "  '13,11,15,12,13,17,19,16,14,12,16,21,31,44,47,54,66,47,47,49,49,41,38,32,34,35,19,18,20,19,15,11,13,19,26,40,53,43,49,36,30,32,18,17,17,15,16,14,14,10,11,14,15,22,29,49,57,51,57,51,40,39,35,28,21,23,19,18,20,22,20,16,11,11,13,22,19,26,47,44,42,46,43,34,38,32,25,22,15,18,17,17,15,16,14,14,10,11,14,15,3',\n",
+       "  '13,11,12,13,11,15,20,19,14,11,15,22,18,17,15,14,10,11,14,15,31,16,11,13,22,19,26,44,18,14,46,49,41,38,32,19,18,22,20,15,11,17,23,22,29,47,15,12,17,22,13,37,47,44,41,35,28,23,19,18,20,22,10,11,13,22,19,47,47,42,43,34,35,32,21,19,17,15,12,11,17,15,14,14,10,11,14,15,31,52,43,49,45,45,44,36,30,32,22,24,2',\n",
+       "  '13,11,11,12,15,14,14,10,11,14,15,31,52,41,49,16,22,26,29,44,47,51,57,58,54,53,38,35,32,23,18,22,19,15,12,17,23,41,46,43,36,34,21,21,19,18,20,17,15,12,13,17,19,27,47,54,57,51,40,39,35,28,23,19,18,20,22,20,16,11,17,22,19,26,47,47,44,49,49,41,37,34,32,22,19,19,15,17,15,16,14,14,10,11,14,15,36,54,43,49,4',\n",
+       "  '13,11,15,11,13,11,22,19,16,11,13,17,22,27,19,18,29,49,14,12,17,23,15,18,17,15,16,14,14,10,11,14,15,31,52,43,49,45,44,36,30,32,22,19,18,20,19,17,19,15,12,11,17,23,44,47,54,66,36,35,28,21,23,19,18,20,22,20,16,11,11,13,22,19,26,47,47,44,49,49,41,41,37,34,32,22,19,19,17,17,15,15,13,11,14,15,38,32,25,22,2',\n",
+       "  '13,11,15,11,13,15,15,13,11,12,12,11,13,10,12,39,6,14,10,3,28,18,18,5,17,38,26,33,7,24,4,25,25,5,24,10,11,18,7,12,17,16,15,15,10,20,24,42,40,41,29,33,32,26,25,18,18,22,8,10,29,49,31,40,27,14,18,32,14,32,12,23,32,15,25,31,30,18,16,26,29,15,9,11,17,15,15,11,13,19,26,45,16,37,31,14,18,29,12,20,17,25,32,2',\n",
+       "  '13,11,12,15,14,14,10,11,13,15,31,52,43,49,45,39,23,18,18,17,17,15,12,11,17,23,44,47,54,66,54,58,42,39,36,32,23,15,18,17,17,15,16,14,11,12,13,19,26,47,47,44,49,49,41,37,34,32,22,19,19,17,17,15,11,11,13,22,19,26,47,47,44,49,49,41,37,34,32,22,19,22,21,16,11,11,13,22,19,26,40,39,35,28,21,23,19,18,20,22,2']]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y_hat'][:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc70e55b-4a3e-43d8-83f8-b998fa88ee29",
+   "metadata": {},
+   "source": [
+    "format as integer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5ac4fc58-055d-4210-b330-0832508c1cf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 6\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c5948478-0acd-4daf-b4b9-ef4ce83f3693",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[array([16., 13., 17., 12., 11., 15., 14., 14., 10., 11., 14., 15., 31.,\n",
+       "               52., 53., 43., 46., 43., 34., 38., 32., 25., 22., 18., 15., 17.,\n",
+       "               19., 15., 17., 12., 11., 17., 23., 22., 29., 51., 51., 51., 41.,\n",
+       "               34., 27., 27., 34., 28., 32., 25., 20., 20., 23., 17., 19., 16.,\n",
+       "               14., 12., 16., 21., 28., 47., 54., 50., 53., 60., 51., 52., 42.,\n",
+       "               32., 34., 24., 24., 21., 21., 22., 25., 22., 16., 17., 12., 13.,\n",
+       "               17., 22., 27., 44., 47., 54., 66., 54., 58., 42., 39., 36., 32.,\n",
+       "               27., 23., 21., 21., 19., 24., 22., 19., 13.,  1.])              ,\n",
+       "        array([16., 13., 17., 24., 21., 14., 12., 11., 15., 18., 14., 10., 11.,\n",
+       "               14., 15., 31., 52., 59., 44., 47., 67., 62., 60., 59., 71., 73.,\n",
+       "               78., 72., 73., 72., 68., 63., 56., 53., 43., 36., 34., 32., 35.,\n",
+       "               38., 25., 22., 21., 22., 16., 12., 11., 12., 17., 19., 27., 44.,\n",
+       "               47., 54., 66., 58., 34., 39., 36., 32., 23., 21., 19., 24., 22.,\n",
+       "               13., 11., 15., 15., 20., 22., 28., 47., 54., 50., 53., 60., 51.,\n",
+       "               52., 42., 34., 32., 21., 22., 25., 22., 19., 17., 19., 15., 12.,\n",
+       "               11., 17., 23., 22., 29., 47., 53., 49., 45.,  4.])              ,\n",
+       "        array([30., 25., 23., 32., 41., 16., 13., 17., 53., 78., 51., 51., 51.,\n",
+       "               24., 19., 34., 67., 62., 59., 71., 63., 56., 43., 36., 30., 17.,\n",
+       "               13., 17., 24., 44., 47., 54., 66., 54., 58., 39., 32., 15., 18.,\n",
+       "               17., 15., 10., 11., 15., 31., 52., 49., 45., 38., 22., 19., 18.,\n",
+       "               17., 19., 15., 12., 17., 23., 31., 52., 40., 44., 35., 38., 25.,\n",
+       "               15., 18., 17., 11., 11., 15., 14., 14., 10., 11., 22., 25., 34.,\n",
+       "               54., 43., 49., 45., 36., 35., 28., 24., 29., 21., 12., 11., 12.,\n",
+       "               17., 19., 24., 40., 53., 46., 43., 36., 24.,  3.])              ,\n",
+       "        array([16., 13., 17., 12., 11., 15., 14., 14., 10., 11., 14., 15., 23.,\n",
+       "               19., 18., 20., 19., 15., 11., 17., 22., 31., 52., 43., 49., 45.,\n",
+       "               44., 36., 35., 32., 25., 22., 18., 19., 20., 17., 19., 15., 12.,\n",
+       "               22., 40., 53., 44., 36., 38., 32., 24., 17., 12., 11., 12., 17.,\n",
+       "               19., 27., 44., 64., 57., 57., 51., 42., 39., 36., 27., 23., 21.,\n",
+       "               19., 24., 22., 19., 13., 11., 15., 20., 22., 34., 47., 54., 47.,\n",
+       "               66., 54., 58., 43., 39., 36., 32., 27., 23., 19., 24., 22., 19.,\n",
+       "               13., 11., 15., 14., 12., 16., 19., 47., 64.,  5.])              ,\n",
+       "        array([30., 21., 26., 53., 78., 74., 51., 41., 24., 16., 14., 12., 15.,\n",
+       "               19., 47., 44., 59., 36., 23., 19., 11., 15., 31., 40., 34., 28.,\n",
+       "               17., 13., 11., 15., 14., 14., 10., 11., 14., 34., 67., 62., 59.,\n",
+       "               71., 63., 56., 35., 30., 32., 22., 19., 18., 20., 17., 19., 15.,\n",
+       "               12., 11., 17., 23., 22., 38., 54., 57., 66., 54., 43., 38., 32.,\n",
+       "               25., 18., 17., 15., 14., 14., 10., 11., 14., 34., 47., 53., 43.,\n",
+       "               36., 34., 21., 25., 21., 22., 19., 16., 12., 13., 17., 22., 27.,\n",
+       "               44., 47., 57., 64., 51., 39., 36., 27., 23.,  2.])              ,\n",
+       "        array([13., 11., 15., 14., 10., 11., 14., 15., 53., 78., 73., 51., 51.,\n",
+       "               51., 41., 24., 16., 13., 17., 27., 34., 67., 62., 59., 71., 63.,\n",
+       "               56., 43., 36., 30., 24., 20., 20., 17., 13., 19., 34., 62., 14.,\n",
+       "               12., 16., 47., 54., 47., 44., 36., 38., 32., 21., 15., 18., 17.,\n",
+       "               15., 16., 14., 14., 10., 11., 14., 15., 31., 52., 49., 38., 35.,\n",
+       "               34., 24., 18., 20., 19., 17., 15., 17., 22., 40., 53., 46., 34.,\n",
+       "               38., 32., 24., 19., 13., 11., 17., 23., 22., 29.])              ,\n",
+       "        array([16., 13., 11., 15., 14., 14., 10., 11., 14., 15., 31., 52., 49.,\n",
+       "               45., 38., 32., 22., 18., 19., 17., 15., 12., 17., 23., 22., 29.,\n",
+       "               53., 71., 78., 73., 58., 59., 71., 78., 66., 54., 36., 30., 26.,\n",
+       "               24., 24., 20., 20., 23., 22., 16., 17., 12., 13., 17., 22., 44.,\n",
+       "               47., 54., 66., 54., 34., 32., 25., 22., 15., 18., 17., 17., 15.,\n",
+       "               16., 14., 14., 10., 11., 14., 15., 34., 47., 54., 67., 52., 57.,\n",
+       "               57., 51., 44., 43., 36., 39., 36., 32., 27., 23., 21., 19., 24.,\n",
+       "               22., 19., 13., 11., 15., 20., 22., 28., 47.,  6.])              ,\n",
+       "        array([16., 13., 17., 12., 11., 15., 14., 10., 11., 14., 15., 31., 52.,\n",
+       "               43., 49., 45., 44., 36., 35., 28., 24., 20., 29., 21., 22., 21.,\n",
+       "               16., 12., 11., 17., 23., 44., 47., 54., 66., 54., 58., 42., 39.,\n",
+       "               36., 32., 23., 21., 19., 24., 22., 19., 13., 11., 15., 20., 22.,\n",
+       "               28., 47., 64., 52., 57., 57., 51., 43., 44., 36., 38., 25., 22.,\n",
+       "               15., 18., 17., 17., 15., 16., 14., 14.,  9., 11., 14., 15., 34.,\n",
+       "               28., 32., 25., 20., 20., 23., 19., 19., 27., 40., 53., 54., 43.,\n",
+       "               46., 43., 34., 38., 32., 25., 22., 15., 18., 17.])              ,\n",
+       "        array([16., 13., 11., 15., 14., 14., 10., 11., 14., 15., 51., 51., 51.,\n",
+       "               41., 17., 19., 16., 14., 12., 16., 32., 19., 13., 11., 15., 24.,\n",
+       "               23., 19., 18., 20., 19., 15., 12., 17., 23., 22., 29., 44., 47.,\n",
+       "               54., 66., 54., 58., 36., 36., 28., 23., 21., 19., 24., 34., 38.,\n",
+       "               32., 25., 22., 15., 18., 17., 17., 15., 14., 10., 11., 14., 15.,\n",
+       "               31., 52., 49., 43., 35., 30., 32., 22., 24., 22., 19., 18., 20.,\n",
+       "               19., 17., 19., 15., 12., 11., 17., 23., 22., 29., 34., 34., 32.,\n",
+       "               25., 22., 15., 18., 17., 17., 15., 14., 10.,  1.])              ,\n",
+       "        array([21., 23., 34., 51., 51., 51., 41., 16., 13., 17., 24., 34., 12.,\n",
+       "               11., 15., 31., 67., 40., 34., 38., 25., 18., 22., 19., 13., 11.,\n",
+       "               15., 20., 22., 31., 47., 54., 43., 46., 35., 30., 24., 17., 19.,\n",
+       "               16., 14., 12., 16., 47., 54., 44., 36., 32., 25., 22., 19., 18.,\n",
+       "               20., 19., 17., 19., 15., 20., 10., 11., 14., 15., 34., 62., 59.,\n",
+       "               59., 71., 78., 73., 68., 72., 52., 27., 27., 34., 17., 19., 27.,\n",
+       "               24., 27., 34., 52., 57., 57., 51., 40., 36., 24., 21., 13., 17.,\n",
+       "               12., 17., 22., 27., 44., 47., 54., 66., 54.,  4.])              ],\n",
+       "       [array([13., 11., 15., 11., 13., 15., 14., 10., 11., 14., 15., 31., 52.,\n",
+       "               43., 49., 35., 18., 18., 18., 14., 14., 10., 22., 26., 54., 47.,\n",
+       "               44., 49., 49., 41., 37., 34., 22., 19., 19., 17., 17., 15., 12.,\n",
+       "               11., 17., 23., 22., 29., 47., 46., 47., 57., 51., 39., 35., 21.,\n",
+       "               19., 18., 20., 22., 20., 16., 11., 11., 13., 22., 19., 31., 47.,\n",
+       "               47., 39., 49., 49., 44., 41., 37., 34., 32., 22., 19., 19., 17.,\n",
+       "               17., 15., 12., 11., 17., 23., 22., 29., 49., 46., 47., 54., 57.,\n",
+       "               51., 40., 39., 35., 28., 21., 23., 19., 18.,  2.])              ,\n",
+       "        array([13., 11., 15., 12., 11., 12., 17., 19., 14., 14., 10., 11., 14.,\n",
+       "               15., 22., 26., 31., 44., 47., 53., 54., 51., 43., 38., 32., 21.,\n",
+       "               18., 17., 17., 15., 16., 14., 14., 10., 11., 14., 15., 38., 49.,\n",
+       "               46., 49., 42., 41., 37., 34., 32., 21., 19., 19., 17., 17., 15.,\n",
+       "               22., 20., 16., 11., 11., 13., 22., 19., 47., 52., 43., 49., 45.,\n",
+       "               44., 36., 30., 32., 22., 20., 16., 11., 11., 13., 22., 19., 47.,\n",
+       "               57., 51., 40.])                                                 ,\n",
+       "        array([13., 11., 11., 12., 15., 14., 14., 10., 11., 14., 15., 31., 52.,\n",
+       "               43., 49., 45., 44., 36., 30., 32., 22., 19., 18., 20., 19., 17.,\n",
+       "               19., 15., 12., 11., 17., 23., 22., 29., 49., 46., 47., 54., 57.,\n",
+       "               51., 40., 39., 35., 28., 21., 23., 19., 18., 20., 22., 20., 16.,\n",
+       "               11., 11., 13., 22., 19., 26., 47., 47., 44., 49., 49., 42., 41.,\n",
+       "               37., 34., 32., 22., 19., 19., 17., 17., 15., 22., 67., 62., 60.,\n",
+       "               59., 71., 63., 56., 43., 36., 30., 26., 24., 24., 20., 20., 23.,\n",
+       "               17., 19., 16., 14., 12., 16., 21., 28., 47.,  5.])              ,\n",
+       "        array([13., 11., 12., 13., 11., 12., 17., 19., 13., 11., 15., 20., 14.,\n",
+       "               12., 16., 14., 14., 14., 10., 11., 14., 15., 31., 52., 43., 36.,\n",
+       "               35., 28., 21., 23., 18., 18., 22., 29., 49., 46., 47., 44., 49.,\n",
+       "               42., 37., 34., 32., 22., 19., 15., 13., 17., 23., 22., 29., 44.,\n",
+       "               53., 43., 46., 43., 38., 32., 25., 22., 15., 18., 17., 15., 14.,\n",
+       "               10., 11., 14., 19., 31., 66., 54., 51., 58., 57., 51., 44., 40.,\n",
+       "               36., 35., 24., 29., 21., 21., 16., 11., 11., 13., 22., 19., 26.,\n",
+       "               47., 47., 41., 49., 49., 37., 41., 34., 32.,  2.])              ,\n",
+       "        array([13., 11., 15., 12., 13., 17., 19., 16., 14., 12., 16., 21., 31.,\n",
+       "               44., 47., 54., 66., 47., 47., 49., 49., 41., 38., 32., 34., 35.,\n",
+       "               19., 18., 20., 19., 15., 11., 13., 19., 26., 40., 53., 43., 49.,\n",
+       "               36., 30., 32., 18., 17., 17., 15., 16., 14., 14., 10., 11., 14.,\n",
+       "               15., 22., 29., 49., 57., 51., 57., 51., 40., 39., 35., 28., 21.,\n",
+       "               23., 19., 18., 20., 22., 20., 16., 11., 11., 13., 22., 19., 26.,\n",
+       "               47., 44., 42., 46., 43., 34., 38., 32., 25., 22., 15., 18., 17.,\n",
+       "               17., 15., 16., 14., 14., 10., 11., 14., 15.,  3.])              ,\n",
+       "        array([13., 11., 12., 13., 11., 15., 20., 19., 14., 11., 15., 22., 18.,\n",
+       "               17., 15., 14., 10., 11., 14., 15., 31., 16., 11., 13., 22., 19.,\n",
+       "               26., 44., 18., 14., 46., 49., 41., 38., 32., 19., 18., 22., 20.,\n",
+       "               15., 11., 17., 23., 22., 29., 47., 15., 12., 17., 22., 13., 37.,\n",
+       "               47., 44., 41., 35., 28., 23., 19., 18., 20., 22., 10., 11., 13.,\n",
+       "               22., 19., 47., 47., 42., 43., 34., 35., 32., 21., 19., 17., 15.,\n",
+       "               12., 11., 17., 15., 14., 14., 10., 11., 14., 15., 31., 52., 43.,\n",
+       "               49., 45., 45., 44., 36., 30., 32., 22., 24.,  2.])              ,\n",
+       "        array([13., 11., 11., 12., 15., 14., 14., 10., 11., 14., 15., 31., 52.,\n",
+       "               41., 49., 16., 22., 26., 29., 44., 47., 51., 57., 58., 54., 53.,\n",
+       "               38., 35., 32., 23., 18., 22., 19., 15., 12., 17., 23., 41., 46.,\n",
+       "               43., 36., 34., 21., 21., 19., 18., 20., 17., 15., 12., 13., 17.,\n",
+       "               19., 27., 47., 54., 57., 51., 40., 39., 35., 28., 23., 19., 18.,\n",
+       "               20., 22., 20., 16., 11., 17., 22., 19., 26., 47., 47., 44., 49.,\n",
+       "               49., 41., 37., 34., 32., 22., 19., 19., 15., 17., 15., 16., 14.,\n",
+       "               14., 10., 11., 14., 15., 36., 54., 43., 49.,  4.])              ,\n",
+       "        array([13., 11., 15., 11., 13., 11., 22., 19., 16., 11., 13., 17., 22.,\n",
+       "               27., 19., 18., 29., 49., 14., 12., 17., 23., 15., 18., 17., 15.,\n",
+       "               16., 14., 14., 10., 11., 14., 15., 31., 52., 43., 49., 45., 44.,\n",
+       "               36., 30., 32., 22., 19., 18., 20., 19., 17., 19., 15., 12., 11.,\n",
+       "               17., 23., 44., 47., 54., 66., 36., 35., 28., 21., 23., 19., 18.,\n",
+       "               20., 22., 20., 16., 11., 11., 13., 22., 19., 26., 47., 47., 44.,\n",
+       "               49., 49., 41., 41., 37., 34., 32., 22., 19., 19., 17., 17., 15.,\n",
+       "               15., 13., 11., 14., 15., 38., 32., 25., 22.,  2.])              ,\n",
+       "        array([13., 11., 15., 11., 13., 15., 15., 13., 11., 12., 12., 11., 13.,\n",
+       "               10., 12., 39.,  6., 14., 10.,  3., 28., 18., 18.,  5., 17., 38.,\n",
+       "               26., 33.,  7., 24.,  4., 25., 25.,  5., 24., 10., 11., 18.,  7.,\n",
+       "               12., 17., 16., 15., 15., 10., 20., 24., 42., 40., 41., 29., 33.,\n",
+       "               32., 26., 25., 18., 18., 22.,  8., 10., 29., 49., 31., 40., 27.,\n",
+       "               14., 18., 32., 14., 32., 12., 23., 32., 15., 25., 31., 30., 18.,\n",
+       "               16., 26., 29., 15.,  9., 11., 17., 15., 15., 11., 13., 19., 26.,\n",
+       "               45., 16., 37., 31., 14., 18., 29., 12., 20., 17., 25., 32.,  2.]),\n",
+       "        array([13., 11., 12., 15., 14., 14., 10., 11., 13., 15., 31., 52., 43.,\n",
+       "               49., 45., 39., 23., 18., 18., 17., 17., 15., 12., 11., 17., 23.,\n",
+       "               44., 47., 54., 66., 54., 58., 42., 39., 36., 32., 23., 15., 18.,\n",
+       "               17., 17., 15., 16., 14., 11., 12., 13., 19., 26., 47., 47., 44.,\n",
+       "               49., 49., 41., 37., 34., 32., 22., 19., 19., 17., 17., 15., 11.,\n",
+       "               11., 13., 22., 19., 26., 47., 47., 44., 49., 49., 41., 37., 34.,\n",
+       "               32., 22., 19., 22., 21., 16., 11., 11., 13., 22., 19., 26., 40.,\n",
+       "               39., 35., 28., 21., 23., 19., 18., 20., 22.,  2.])              ]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'][:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a934005e-5bce-4a47-831a-3cf1f0c92f28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(37, 10)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b1ff549-c823-4a31-b324-19ee21a8c193",
+   "metadata": {},
+   "source": [
+    "Val2idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "32b4ccf9-d8fa-4110-bc22-717d07e3ae0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 7\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "339db91e-cd35-4ae8-abc5-3ba52b6c0bbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[array([  1,   3,   4,   5,   7,   8,  14,  15,  16,  17,  18,  20,  22,\n",
+       "                24,  25,  26,  27,  28,  30,  31,  32,  39,  41,  42,  43,  44,\n",
+       "                45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  58,  63,\n",
+       "                64,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
+       "                79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
+       "                92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
+       "               105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n",
+       "               118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 130, 133,\n",
+       "               135, 136, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,\n",
+       "               149, 150, 151, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,\n",
+       "               163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,\n",
+       "               176, 177, 180, 181, 183, 184, 185, 186, 187, 188, 189, 190, 191,\n",
+       "               192, 193, 194, 195, 196, 197, 198, 199])                        ,\n",
+       "        array([  1,   3,   4,   5,   7,   8,   9,  11,  13,  14,  15,  16,  18,\n",
+       "                20,  22,  24,  25,  26,  27,  28,  30,  31,  32,  33,  34,  35,\n",
+       "                36,  37,  38,  39,  41,  42,  43,  44,  45,  46,  47,  48,  49,\n",
+       "                50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,\n",
+       "                63,  64,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
+       "                78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
+       "                91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
+       "               104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
+       "               117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 130,\n",
+       "               133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
+       "               147, 148, 149, 150, 151, 153, 154, 155, 157, 158, 159, 160, 161,\n",
+       "               162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,\n",
+       "               175, 176, 177, 178, 179, 180, 181, 183, 184, 185, 186, 187, 188,\n",
+       "               189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])         ,\n",
+       "        array([  0,   1,   2,   3,   5,   6,   7,   8,   9,  14,  15,  16,  17,\n",
+       "                18,  19,  22,  24,  25,  26,  27,  28,  29,  30,  31,  32,  33,\n",
+       "                34,  35,  39,  40,  41,  44,  45,  46,  47,  50,  51,  52,  54,\n",
+       "                55,  56,  57,  59,  60,  61,  62,  63,  64,  65,  67,  68,  71,\n",
+       "                72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  83,  85,  86,\n",
+       "                88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100,\n",
+       "               101, 102, 104, 105, 106, 107, 108, 109, 111, 112, 113, 115, 116,\n",
+       "               117, 118, 119, 120, 121, 122, 123, 124, 126, 127, 128, 130, 133,\n",
+       "               134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147,\n",
+       "               148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160,\n",
+       "               161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173,\n",
+       "               174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186,\n",
+       "               187, 188, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])    ,\n",
+       "        array([  0,   1,   3,   4,   5,   8,  14,  15,  16,  18,  20,  22,  24,\n",
+       "                25,  26,  27,  28,  31,  32,  39,  40,  41,  42,  43,  44,  46,\n",
+       "                47,  48,  49,  50,  51,  52,  53,  54,  55,  63,  64,  67,  68,\n",
+       "                69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  80,  81,  83,\n",
+       "                85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
+       "                98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,\n",
+       "               111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123,\n",
+       "               124, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,\n",
+       "               139, 140, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152,\n",
+       "               153, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166,\n",
+       "               167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,\n",
+       "               180, 181, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
+       "               194, 195, 196, 197, 198])                                       ,\n",
+       "        array([  0,   1,   2,   3,   4,   5,   6,   8,   9,  10,  14,  15,  16,\n",
+       "                17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,  29,\n",
+       "                30,  31,  33,  34,  35,  40,  41,  42,  43,  44,  45,  46,  47,\n",
+       "                48,  49,  50,  51,  52,  53,  54,  55,  56,  57,  59,  60,  61,\n",
+       "                62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,\n",
+       "                75,  76,  77,  78,  79,  80,  81,  83,  85,  88,  89,  90,  91,\n",
+       "                92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
+       "               105, 106, 107, 108, 111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
+       "               120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 132, 133,\n",
+       "               134, 135, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147,\n",
+       "               148, 149, 150, 151, 152, 153, 154, 155, 157, 158, 159, 160, 161,\n",
+       "               162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,\n",
+       "               175, 177, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,\n",
+       "               191, 192, 193, 194, 195, 196, 197, 198])                        ,\n",
+       "        array([  0,   2,   3,   4,   6,   7,   8,   9,  11,  14,  15,  16,  17,\n",
+       "                18,  19,  20,  22,  24,  26,  27,  28,  29,  33,  34,  35,  36,\n",
+       "                39,  40,  41,  42,  43,  44,  46,  48,  49,  50,  51,  52,  53,\n",
+       "                54,  55,  56,  57,  59,  60,  61,  62,  63,  64,  65,  67,  68,\n",
+       "                69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  80,  81,  83,\n",
+       "                85,  86,  88,  89,  90,  91,  92,  93,  94,  96,  97,  98,  99,\n",
+       "               100, 101, 102, 103, 104, 105, 106, 108, 112, 113, 114, 115, 116,\n",
+       "               117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 128, 130, 133,\n",
+       "               134, 135, 136, 137, 139, 140, 141, 142, 143, 144, 145, 146, 147,\n",
+       "               148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160,\n",
+       "               162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,\n",
+       "               175, 176, 177, 178, 180, 181, 182, 183, 184, 185, 186, 187, 188,\n",
+       "               189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])         ,\n",
+       "        array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  11,  14,  15,  16,\n",
+       "                18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,  29,  30,\n",
+       "                31,  32,  33,  34,  35,  36,  39,  41,  42,  43,  44,  45,  46,\n",
+       "                47,  48,  49,  50,  51,  52,  53,  54,  55,  56,  59,  60,  63,\n",
+       "                64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,\n",
+       "                77,  78,  79,  80,  81,  83,  85,  86,  88,  89,  90,  91,  92,\n",
+       "                93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105,\n",
+       "               106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
+       "               120, 121, 122, 123, 124, 125, 126, 127, 128, 130, 131, 132, 133,\n",
+       "               135, 136, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,\n",
+       "               149, 150, 151, 153, 154, 155, 157, 158, 159, 160, 161, 162, 163,\n",
+       "               164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,\n",
+       "               177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,\n",
+       "               190, 191, 192, 193, 194, 195, 196, 197, 198, 199])              ,\n",
+       "        array([  0,   1,   3,   4,   5,   7,   8,  14,  15,  16,  18,  20,  22,\n",
+       "                24,  25,  26,  27,  28,  30,  31,  32,  39,  40,  41,  42,  43,\n",
+       "                44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  63,\n",
+       "                64,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
+       "                79,  80,  81,  83,  85,  86,  87,  88,  89,  90,  91,  92,  93,\n",
+       "                94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106,\n",
+       "               107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
+       "               120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132,\n",
+       "               133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,\n",
+       "               146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158,\n",
+       "               159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,\n",
+       "               172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 183, 184, 185,\n",
+       "               186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
+       "               199])                                                           ,\n",
+       "        array([  2,   3,   4,   5,   6,   7,  14,  15,  16,  17,  18,  19,  22,\n",
+       "                24,  25,  26,  27,  28,  29,  30,  32,  39,  41,  44,  45,  46,\n",
+       "                47,  48,  49,  50,  51,  52,  54,  55,  63,  64,  65,  67,  68,\n",
+       "                69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,\n",
+       "                85,  86,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,\n",
+       "                99, 100, 101, 102, 104, 105, 106, 107, 108, 109, 112, 113, 115,\n",
+       "               116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,\n",
+       "               130, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,\n",
+       "               146, 147, 148, 149, 150, 151, 154, 155, 157, 158, 159, 160, 161,\n",
+       "               162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,\n",
+       "               175, 176, 177, 178, 180, 181, 182, 183, 184, 185, 186, 187, 188,\n",
+       "               189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])         ,\n",
+       "        array([  0,   2,   3,   4,   5,   6,   9,  11,  13,  14,  15,  16,  17,\n",
+       "                18,  19,  20,  22,  24,  25,  26,  27,  28,  29,  33,  34,  35,\n",
+       "                36,  37,  38,  39,  40,  41,  42,  43,  44,  46,  47,  48,  49,\n",
+       "                50,  51,  52,  53,  54,  55,  56,  57,  59,  60,  63,  64,  65,\n",
+       "                67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  80,\n",
+       "                81,  85,  86,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
+       "                98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 112, 113,\n",
+       "               114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n",
+       "               128, 130, 131, 132, 133, 134, 135, 136, 137, 139, 140, 142, 143,\n",
+       "               144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157,\n",
+       "               158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,\n",
+       "               171, 172, 173, 174, 175, 176, 177, 180, 181, 182, 183, 184, 185,\n",
+       "               186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198])],\n",
+       "       [array([  0,   1,   4,   5,   6,   8,   9,  10,  11,  12,  15,  23,  26,\n",
+       "                29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
+       "                45,  46,  48,  49,  52,  53,  54,  56,  57,  58,  59,  60,  61,\n",
+       "                62,  64,  65,  66,  68,  71,  73,  75,  76,  77,  78,  80,  81,\n",
+       "                82,  83,  84,  85,  86,  87,  88,  90,  91,  92,  93,  94,  95,\n",
+       "                97,  98, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,\n",
+       "               112, 114, 115, 116, 117, 118, 120, 122, 123, 124, 125, 126, 127,\n",
+       "               128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 140, 143,\n",
+       "               144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,\n",
+       "               158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,\n",
+       "               171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,\n",
+       "               184, 185, 186, 187, 188, 190, 191, 192, 193, 194, 195, 196, 197,\n",
+       "               198, 199])                                                      ,\n",
+       "        array([  0,   1,   4,   6,   8,   9,  10,  11,  12,  15,  23,  24,  25,\n",
+       "                26,  29,  30,  32,  33,  34,  35,  36,  37,  38,  40,  41,  43,\n",
+       "                45,  46,  47,  48,  49,  52,  53,  54,  56,  57,  58,  59,  60,\n",
+       "                61,  62,  64,  65,  66,  68,  70,  72,  73,  76,  77,  78,  80,\n",
+       "                81,  82,  83,  84,  85,  86,  88,  90,  91,  92,  93,  94,  95,\n",
+       "                96, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113,\n",
+       "               114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127,\n",
+       "               128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,\n",
+       "               141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
+       "               155, 156, 158, 160, 161, 162, 163, 164, 165, 166, 170, 172, 173,\n",
+       "               174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186,\n",
+       "               187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199]),\n",
+       "        array([  0,   1,   4,   5,   6,   8,   9,  10,  11,  12,  14,  15,  16,\n",
+       "                17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,  29,\n",
+       "                30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  44,\n",
+       "                45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  56,  57,  58,\n",
+       "                59,  60,  61,  62,  64,  65,  66,  68,  70,  71,  72,  73,  75,\n",
+       "                76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,\n",
+       "                90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,\n",
+       "               103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 114, 115, 116,\n",
+       "               117, 118, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,\n",
+       "               132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,\n",
+       "               145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,\n",
+       "               158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,\n",
+       "               171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,\n",
+       "               184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,\n",
+       "               197, 198, 199])                                                 ,\n",
+       "        array([  0,   1,   4,   5,   6,   7,   8,   9,  10,  11,  12,  14,  15,\n",
+       "                23,  24,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,\n",
+       "                37,  38,  39,  40,  41,  43,  45,  46,  47,  48,  49,  50,  51,\n",
+       "                52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  64,  65,\n",
+       "                66,  67,  68,  69,  70,  72,  73,  75,  76,  77,  78,  79,  80,\n",
+       "                81,  82,  83,  84,  85,  86,  87,  88,  90,  91,  92,  93,  94,\n",
+       "                95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107,\n",
+       "               108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,\n",
+       "               121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
+       "               134, 135, 136, 137, 138, 140, 141, 143, 144, 145, 146, 147, 148,\n",
+       "               149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161,\n",
+       "               162, 163, 164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175,\n",
+       "               176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188,\n",
+       "               189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])         ,\n",
+       "        array([  0,   1,   4,   5,   6,   7,   8,   9,  10,  11,  12,  15,  23,\n",
+       "                24,  25,  26,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
+       "                39,  40,  41,  43,  45,  47,  48,  49,  52,  53,  54,  55,  56,\n",
+       "                57,  58,  59,  60,  61,  62,  64,  65,  66,  67,  68,  70,  71,\n",
+       "                72,  73,  75,  76,  77,  78,  80,  81,  82,  83,  84,  85,  86,\n",
+       "                87,  88,  91,  92,  93,  94,  95,  96,  97,  98, 100, 101, 102,\n",
+       "               103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 115, 116,\n",
+       "               117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
+       "               130, 131, 132, 133, 134, 135, 137, 138, 140, 141, 142, 143, 144,\n",
+       "               146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158,\n",
+       "               159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,\n",
+       "               172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n",
+       "               185, 186, 187, 188, 189, 190, 192, 193, 194, 195, 196, 197, 198,\n",
+       "               199])                                                           ,\n",
+       "        array([  1,   4,   5,   6,   8,   9,  10,  11,  12,  14,  15,  23,  24,\n",
+       "                25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,\n",
+       "                38,  39,  40,  46,  47,  48,  49,  50,  51,  52,  53,  54,  56,\n",
+       "                57,  58,  59,  60,  61,  62,  64,  65,  70,  72,  73,  75,  76,\n",
+       "                77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  90,\n",
+       "                95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107,\n",
+       "               108, 109, 110, 111, 115, 116, 117, 118, 119, 120, 122, 123, 124,\n",
+       "               125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,\n",
+       "               138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,\n",
+       "               151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 168,\n",
+       "               169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
+       "               182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
+       "               195, 196, 197, 198, 199])                                       ,\n",
+       "        array([  0,   1,   2,   3,   4,   5,   6,   8,   9,  10,  11,  12,  13,\n",
+       "                15,  23,  24,  26,  29,  30,  31,  32,  33,  34,  35,  36,  37,\n",
+       "                38,  39,  40,  41,  43,  45,  46,  48,  49,  52,  53,  54,  56,\n",
+       "                57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  68,  69,  71,\n",
+       "                72,  73,  74,  75,  76,  77,  78,  80,  81,  82,  83,  84,  85,\n",
+       "                86,  87,  88,  90,  91,  92,  93,  94,  95,  96,  97,  98, 100,\n",
+       "               101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114,\n",
+       "               115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128,\n",
+       "               129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 140, 141, 143,\n",
+       "               144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,\n",
+       "               158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,\n",
+       "               171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,\n",
+       "               184, 185, 186, 187, 188, 190, 191, 192, 193, 194, 195, 196, 197,\n",
+       "               198, 199])                                                      ,\n",
+       "        array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
+       "                15,  23,  24,  25,  26,  29,  30,  31,  32,  33,  34,  35,  36,\n",
+       "                37,  38,  39,  40,  41,  46,  48,  49,  52,  53,  54,  55,  56,\n",
+       "                57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  72,\n",
+       "                73,  74,  75,  76,  77,  78,  80,  81,  82,  83,  84,  85,  86,\n",
+       "                87,  88,  90,  95,  96,  97,  98, 100, 101, 102, 103, 104, 105,\n",
+       "               106, 107, 108, 109, 110, 114, 115, 117, 118, 119, 120, 121, 122,\n",
+       "               123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,\n",
+       "               136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149,\n",
+       "               150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 162, 163,\n",
+       "               168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
+       "               181, 182, 183, 184, 185, 186, 187, 188, 190, 191, 192, 193, 194,\n",
+       "               195, 196, 197, 198, 199])                                       ,\n",
+       "        array([  0,   2,   3,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
+       "                25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,\n",
+       "                39,  47,  48,  50,  51,  54,  55,  56,  57,  58,  59,  60,  61,\n",
+       "                62,  63,  70,  71,  73,  74,  75,  78,  79,  80,  81,  82,  83,\n",
+       "                84,  85,  86,  87,  94,  98,  99, 100, 101, 103, 105, 106, 107,\n",
+       "               108, 109, 110, 111, 112, 119, 120, 121, 122, 123, 124, 125, 126,\n",
+       "               127, 128, 129, 130, 131, 132, 133, 134, 135, 138, 139, 142, 143,\n",
+       "               144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n",
+       "               157, 158, 159, 160, 166, 167, 169, 171, 172, 173, 174, 175, 176,\n",
+       "               177, 178, 179, 180, 181, 182, 183, 187, 188, 189, 190, 191, 193,\n",
+       "               194, 195, 196, 197, 198, 199])                                  ,\n",
+       "        array([  0,   1,   4,   5,   6,   8,   9,  10,  11,  12,  15,  23,  24,\n",
+       "                26,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,\n",
+       "                41,  46,  47,  48,  49,  52,  53,  54,  56,  57,  58,  59,  60,\n",
+       "                61,  62,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  75,\n",
+       "                76,  77,  78,  80,  81,  82,  83,  84,  85,  86,  87,  88,  90,\n",
+       "                94,  95,  96,  97,  98, 100, 102, 103, 104, 105, 106, 107, 108,\n",
+       "               109, 110, 112, 114, 115, 117, 118, 120, 122, 123, 124, 125, 126,\n",
+       "               127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
+       "               140, 141, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
+       "               155, 156, 157, 158, 160, 162, 163, 166, 167, 168, 169, 170, 171,\n",
+       "               172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n",
+       "               185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197,\n",
+       "               198, 199])                                                      ]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'][:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbcf3479-7ff2-4a81-86c0-e678a8f735c6",
+   "metadata": {},
+   "source": [
+    "Ano_within_window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "17ce8982-2ba3-4508-a526-8a9960e0ecd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 8\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "fa61503a-81af-4252-805c-19009cff7e52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([array([  3,  14,  15,  16,  18,  22,  24,  26,  27,  28,  41,  44,  46,\n",
+       "               50,  51,  52,  54,  55,  63,  64,  67,  68,  71,  72,  73,  74,\n",
+       "               75,  76,  77,  78,  80,  81,  85,  88,  89,  90,  91,  92,  93,\n",
+       "               94,  96,  97,  98,  99, 100, 101, 102, 104, 105, 106, 108, 112,\n",
+       "              113, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 126, 128,\n",
+       "              133, 135, 136, 139, 142, 143, 144, 145, 146, 147, 148, 149, 150,\n",
+       "              151, 154, 155, 157, 158, 159, 160, 162, 163, 164, 165, 166, 167,\n",
+       "              168, 169, 170, 171, 172, 173, 174, 175, 177, 180, 181, 183, 184,\n",
+       "              185, 186, 187, 188, 190, 191, 192, 193, 194, 195, 196, 197, 198]),\n",
+       "       array([  6,   8,   9,  10,  11,  12,  26,  29,  30,  32,  33,  34,  35,\n",
+       "               36,  37,  48,  54,  56,  57,  58,  59,  60,  61,  62,  73,  78,\n",
+       "               80,  81,  82,  83,  84,  85,  86, 100, 103, 105, 106, 107, 108,\n",
+       "              109, 110, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,\n",
+       "              132, 133, 134, 135, 138, 143, 144, 146, 147, 148, 149, 150, 151,\n",
+       "              152, 153, 154, 155, 156, 158, 160, 172, 173, 174, 175, 176, 177,\n",
+       "              178, 179, 180, 181, 182, 183, 187, 188, 190, 193, 194, 195, 196,\n",
+       "              197, 198, 199])                                                 ],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'][:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "5274e7a0-acf5-4b9f-9cb4-070357fc7c10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(37,)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03002457-e136-445d-811a-97c20eb47d5d",
+   "metadata": {},
+   "source": [
+    "Merge anomaly seq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "bd219c08-29b2-4809-934b-287dfac4e4fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 9\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "57f900db-d712-4ff1-986b-7f4dc1accda0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(17,)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e41f194-47ee-4ce2-b9bf-5596cd99b810",
+   "metadata": {},
+   "source": [
+    "Index2time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "6488a15c-74b6-49ec-b98c-817614919700",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 10\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f8f419d4-34a8-4234-b73d-16916bf8243d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(17,)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eeac9a9-613a-43b8-abd9-6e455bf82a62",
+   "metadata": {},
+   "source": [
+    "timestamp2interval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "7de109bd-8306-4753-8446-07f438e8c3e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y', 'df'])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 11\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a37daebf-7091-4dab-93dc-928c7163778b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1310119201</td>\n",
+       "      <td>1310796001</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1310936401</td>\n",
+       "      <td>1311379201</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1311415201</td>\n",
+       "      <td>1312736401</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        start         end  score\n",
+       "0  1310119201  1310796001      0\n",
+       "1  1310936401  1311379201      0\n",
+       "2  1311415201  1312736401      0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['df']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "98b221ef-ff0c-4705-9697-e2d240ff756e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "index, anomalies = list(map(context.get, ['timestamp', 'df']))\n",
+    "\n",
+    "plt.plot(data['timestamp'], data['value'], label='original')\n",
+    "\n",
+    "plt.axvspan(anomalies.iloc[0]['start'].item(), anomalies.iloc[0]['end'].item(), color='r', alpha=0.2, label='detected anomalies')\n",
+    "plt.axvspan(anomalies.iloc[1]['start'].item(), anomalies.iloc[1]['end'].item(), color='r', alpha=0.2, label='detected anomalies')\n",
+    "plt.axvspan(anomalies.iloc[2]['start'].item(), anomalies.iloc[2]['end'].item(), color='r', alpha=0.2, label='detected anomalies')\n",
+    "\n",
+    "plt.legend();"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7b7faf94",
+   "id": "ee002d85-571a-4ecd-8f9d-99cb84808d7f",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -276,9 +1452,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "prompter",
    "language": "python",
-   "name": "python3"
+   "name": "prompter"
   },
   "language_info": {
    "codemirror_mode": {
@@ -290,7 +1466,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.19"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,

From 58c18e17c55ddeece1fb672fd01f99bf481cc098 Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Sat, 21 Sep 2024 13:25:10 -0400
Subject: [PATCH 06/25] fix test

---
 sigllm/primitives/prompting/anomalies.py     |  4 ++--
 tests/primitives/prompting/test_anomalies.py | 21 +++++++++++---------
 tutorials/prompter.ipynb                     | 18 ++++++++++++++---
 3 files changed, 29 insertions(+), 14 deletions(-)

diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index f0a816c..319ff49 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -130,8 +130,8 @@ def timestamp2interval(y, timestamp, padding_size = 50):
             Number of steps to pad on both sides of a timestamp point. Default to `50`.
              
     Returns:
-        List[Tuple(start, end)]:
-            A list of intervals.
+        Dataframe:
+            Dataframe of interval (start, end, score).
     """
     start, end = timestamp[0], timestamp[-1]
     interval = timestamp[1] - timestamp[0]
diff --git a/tests/primitives/prompting/test_anomalies.py b/tests/primitives/prompting/test_anomalies.py
index 2fbdf0e..518cdb8 100644
--- a/tests/primitives/prompting/test_anomalies.py
+++ b/tests/primitives/prompting/test_anomalies.py
@@ -42,9 +42,8 @@ def step_size():
 
 
 @fixture
-def signal():
-    d = {'timestamp': [1222819200, 1222840800, 1222862400, 1222884000,
-                       1222905600], 'value': [-1.0, -1.0, -1.0, -1.0, -1.0]}
+def timestamp():
+    d = [1222819200, 1222840800, 1222862400, 1222884000, 1222905600]
     return pd.DataFrame(data=d)
 
 
@@ -66,6 +65,10 @@ def windows():
 def point_timestamp(): 
     return np.array([1320, 6450, 7890, 12030, 12340])
 
+@fixture
+def timestamp1():
+    return np.array(range(1000, 13000, 10))
+
 def test_ano_within_windows(anomaly_list_within_seq):
     expected = np.array([np.array([1]),
                          np.array([]),
@@ -85,10 +88,10 @@ def test_merge_anomaly_seq(anomaly_list_across_seq, first_indices, window_size,
     np.testing.assert_equal(result, expected)
 
 
-def test_idx2time(signal, idx_list):
+def test_idx2time(timestamp, idx_list):
     expected = np.array([1222819200, 1222840800, 1222884000])
 
-    result = idx2time(signal, idx_list)
+    result = idx2time(timestamp, idx_list)
 
     np.testing.assert_equal(result, expected)
 
@@ -104,8 +107,8 @@ def test_val2idx(anomalous_val, windows):
             np.testing.assert_equal(r, e)
 
 #timestamp2interval
-def test_timestamp2interval(point_timestamp): 
-    expected = [(1000, 1820), (5950, 6950), (7390, 8390), (11530, 12840)]
-    result = timestamp2interval(point_timestamp, 10, 1000, 13000)
+def test_timestamp2interval(point_timestamp, timestamp1): 
+    expected = pd.DataFrame([(1000, 1820, 0), (5950, 6950, 0), (7390, 8390, 0), (11530, 12840, 0)], columns = ['start', 'end', 'score'])
+    result = timestamp2interval(point_timestamp, timestamp1)
 
-    assert result == expected
\ No newline at end of file
+    assert result.equals(expected)
\ No newline at end of file
diff --git a/tutorials/prompter.ipynb b/tutorials/prompter.ipynb
index 0f15839..36c0ab0 100644
--- a/tutorials/prompter.ipynb
+++ b/tutorials/prompter.ipynb
@@ -138,6 +138,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "af16a62a-c4cb-424f-bcdc-cdfaa0a51977",
    "metadata": {},
@@ -213,6 +214,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "d7e8110b-6d0a-4e67-9346-5317f137b05c",
    "metadata": {},
@@ -244,6 +246,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "d4aa81d9-f6ee-49bd-894b-ec64445b7edb",
    "metadata": {},
@@ -319,6 +322,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3914c439-0452-4151-93d2-9aa0ec0d3442",
    "metadata": {},
@@ -375,6 +379,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "f201cbc8-0c88-4489-a7b0-b5060ac785a1",
    "metadata": {},
@@ -448,6 +453,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7f403aca-ba56-42d3-bcae-b665a234c710",
    "metadata": {},
@@ -569,6 +575,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "dc70e55b-4a3e-43d8-83f8-b998fa88ee29",
    "metadata": {},
@@ -800,6 +807,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6b1ff549-c823-4a31-b324-19ee21a8c193",
    "metadata": {},
@@ -1117,6 +1125,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "bbcf3479-7ff2-4a81-86c0-e678a8f735c6",
    "metadata": {},
@@ -1207,6 +1216,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "03002457-e136-445d-811a-97c20eb47d5d",
    "metadata": {},
@@ -1259,6 +1269,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "5e41f194-47ee-4ce2-b9bf-5596cd99b810",
    "metadata": {},
@@ -1311,6 +1322,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "2eeac9a9-613a-43b8-abd9-6e455bf82a62",
    "metadata": {},
@@ -1452,9 +1464,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "prompter",
+   "display_name": "sigllm1-venv",
    "language": "python",
-   "name": "prompter"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1466,7 +1478,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

From d082d192268443d1ab0a88d0ab29c920ba56af4b Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Sat, 21 Sep 2024 14:36:14 -0400
Subject: [PATCH 07/25] fix lint

---
 sigllm/primitives/prompting/anomalies.py      | 56 +++++++++++--------
 sigllm/primitives/prompting/gpt.py            | 27 ++-------
 sigllm/primitives/prompting/huggingface.py    | 25 +++++----
 .../prompting/timeseries_preprocessing.py     |  4 +-
 tests/primitives/prompting/test_anomalies.py  | 45 ++++++++-------
 .../test_timeseries_preprocessing.py          | 10 ++--
 6 files changed, 84 insertions(+), 83 deletions(-)

diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index 319ff49..949cff1 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -9,26 +9,27 @@
 import numpy as np
 import pandas as pd
 
-def val2idx(y, X): 
+
+def val2idx(y, X):
     """Convert detected anomalies values into indices.
-    
+
     Convert windows of detected anomalies values into an array of all indices
-    in the input sequence that have those values. 
-    
-    Args: 
-        y (ndarray): 
+    in the input sequence that have those values.
+
+    Args:
+        y (ndarray):
             A 3d array containing detected anomalous values from different
             responses of each window.
         X (ndarray):
-            rolling window sequences.      
-    Returns: 
+            rolling window sequences.
+    Returns:
         List([ndarray]):
             A 3d array containing detected anomalous indices from different
             responses of each window.
     """
 
     idx_list = []
-    for anomalies_list, seq in zip(y, X): 
+    for anomalies_list, seq in zip(y, X):
         idx_win_list = []
         for anomalies in anomalies_list:
             mask = np.isin(seq, anomalies)
@@ -39,6 +40,7 @@ def val2idx(y, X):
     idx_list = np.array(idx_list, dtype=object)
     return idx_list
 
+
 def ano_within_windows(y, alpha=0.5):
     """Get the final list of anomalous indices of each window
 
@@ -55,11 +57,11 @@ def ano_within_windows(y, alpha=0.5):
         ndarray:
             A 2-dimensional array containing final anomalous indices of each windows.
     """
-    
+
     idx_list = []
     for samples in y:
         min_vote = np.ceil(alpha * len(samples))
-        #print(type(samples.tolist()))
+        # print(type(samples.tolist()))
 
         flattened_res = np.concatenate(samples.tolist())
 
@@ -68,9 +70,10 @@ def ano_within_windows(y, alpha=0.5):
         final_list = unique_elements[counts >= min_vote]
 
         idx_list.append(final_list)
-    idx_list = np.array(idx_list, dtype = object)
+    idx_list = np.array(idx_list, dtype=object)
     return idx_list
 
+
 def merge_anomaly_seq(y, first_index, window_size, step_size, beta=0.5):
     """Get the final list of anomalous indices of a sequence when merging all rolling windows
 
@@ -101,6 +104,7 @@ def merge_anomaly_seq(y, first_index, window_size, step_size, beta=0.5):
 
     return np.sort(final_list)
 
+
 def idx2time(timestamp, y):
     """Convert list of indices into list of timestamp
 
@@ -117,18 +121,19 @@ def idx2time(timestamp, y):
     timestamp_list = timestamp[y]
     return timestamp_list
 
-def timestamp2interval(y, timestamp, padding_size = 50): 
+
+def timestamp2interval(y, timestamp, padding_size=50):
     """Convert list of timestamps to list of intervals by padding to both sides
-    and merge overlapping 
-    
-    Args: 
-        y (ndarray): 
+    and merge overlapping
+
+    Args:
+        y (ndarray):
             A 1d array of point timestamps.
         timestamp (ndarray):
             List of full timestamp of the signal
-        padding_size (int): 
+        padding_size (int):
             Number of steps to pad on both sides of a timestamp point. Default to `50`.
-             
+
     Returns:
         Dataframe:
             Dataframe of interval (start, end, score).
@@ -136,8 +141,9 @@ def timestamp2interval(y, timestamp, padding_size = 50):
     start, end = timestamp[0], timestamp[-1]
     interval = timestamp[1] - timestamp[0]
     intervals = []
-    for timestamp in y: 
-        intervals.append((max(start, timestamp-padding_size*interval), min(end, timestamp+padding_size*interval)))
+    for timestamp in y:
+        intervals.append((max(start, timestamp - padding_size * interval),
+                         min(end, timestamp + padding_size * interval)))
     if not intervals:
         return []
 
@@ -146,14 +152,16 @@ def timestamp2interval(y, timestamp, padding_size = 50):
 
     for current_interval in intervals[1:]:
         previous_interval = merged_intervals[-1]
-        
+
         # If the current interval overlaps with the previous one, merge them
         if current_interval[0] <= previous_interval[1]:
-            previous_interval = (previous_interval[0], max(previous_interval[1], current_interval[1]))
+            previous_interval = (
+                previous_interval[0], max(
+                    previous_interval[1], current_interval[1]))
             merged_intervals[-1] = previous_interval
         else:
             merged_intervals.append(current_interval)  # Append the current interval if no overlap
-            
+
     df = pd.DataFrame(merged_intervals, columns=['start', 'end'])
     df['score'] = 0
     return df
diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index 236995d..e83b5bd 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -26,7 +26,7 @@ class GPT:
             Model name. Default to `'gpt-3.5-turbo'`.
         sep (str):
             String to separate each element in values. Default to `','`.
-        anomalous_percent (float): 
+        anomalous_percent (float):
                 Expected percentage of time series that are anomalous. Default to `0.5`.
         temp (float):
             Sampling temperature to use, between 0 and 2. Higher values like 0.8 will
@@ -49,7 +49,7 @@ class GPT:
             Beta feature by OpenAI to sample deterministically. Default to `None`.
     """
 
-    def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent = 0.5, temp=1, top_p=1, logprobs=False, top_logprobs=None,
+    def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent=0.5, temp=1, top_p=1, logprobs=False, top_logprobs=None,
                  samples=10, seed=None):
         self.name = name
         self.sep = sep
@@ -61,7 +61,6 @@ def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent = 0.5, temp=
         self.samples = samples
         self.seed = seed
 
-
         self.tokenizer = tiktoken.encoding_for_model(self.name)
 
         valid_tokens = []
@@ -101,7 +100,7 @@ def detect(self, X, **kwargs):
                 logprobs=self.logprobs,
                 top_logprobs=self.top_logprobs,
                 n=self.samples,
-                seed = self.seed
+                seed=self.seed
             )
             responses = [choice.message.content for choice in response.choices]
             if self.logprobs:
@@ -109,26 +108,8 @@ def detect(self, X, **kwargs):
                 all_probs.append(probs)
 
             all_responses.append(responses)
-        
+
         if self.logprobs:
             return all_responses, all_probs
 
         return all_responses
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/sigllm/primitives/prompting/huggingface.py b/sigllm/primitives/prompting/huggingface.py
index 4153bdc..0d6b984 100644
--- a/sigllm/primitives/prompting/huggingface.py
+++ b/sigllm/primitives/prompting/huggingface.py
@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 
 import json
-import os
 import logging
-from tqdm import tqdm
+import os
 
 import torch
+from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 PROMPT_PATH = os.path.join(
@@ -35,7 +35,7 @@ class HF:
             Model name. Default to `'mistralai/Mistral-7B-Instruct-v0.2'`.
         sep (str):
             String to separate each element in values. Default to `','`.
-        anomalous_percent (float): 
+        anomalous_percent (float):
             Expected percentage of time series that are anomalous. Default to `0.5`.
         temp (float):
             The value used to modulate the next token probabilities. Default to `1`.
@@ -52,7 +52,7 @@ class HF:
             Default to `0`.
     """
 
-    def __init__(self, name=DEFAULT_MODEL, sep=',', anomalous_percent = 0.5, temp=1, top_p=1, 
+    def __init__(self, name=DEFAULT_MODEL, sep=',', anomalous_percent=0.5, temp=1, top_p=1,
                  raw=False, samples=10, padding=0):
         self.name = name
         self.sep = sep
@@ -103,7 +103,7 @@ def detect(self, X, **kwargs):
         Args:
             X (ndarray):
                 Input sequences of strings containing signal values
-            
+
         Returns:
             list, list:
                 * List of detected anomalous values.
@@ -114,17 +114,22 @@ def detect(self, X, **kwargs):
         max_tokens = input_length * float(self.anomalous_percent)
         all_responses, all_generate_ids = [], []
 
-        for text in tqdm(X): 
+        for text in tqdm(X):
             text = text.flatten().tolist()
-            message = [' '.join((PROMPTS['system_message'], PROMPTS['user_message'], x, '[RESPONSE]')) for x in text]
+            message = [
+                ' '.join(
+                    (PROMPTS['system_message'],
+                     PROMPTS['user_message'],
+                        x,
+                        '[RESPONSE]')) for x in text]
 
             input_length = len(self.tokenizer.encode(message[0]))
 
             tokenized_input = self.tokenizer(
                 message,
                 return_tensors="pt"
-                    ).to("cuda")
-            
+            ).to("cuda")
+
             generate_ids = self.model.generate(
                 **tokenized_input,
                 do_sample=True,
@@ -147,4 +152,4 @@ def detect(self, X, **kwargs):
         if self.raw:
             return all_responses, all_generate_ids
 
-        return all_responses
\ No newline at end of file
+        return all_responses
diff --git a/sigllm/primitives/prompting/timeseries_preprocessing.py b/sigllm/primitives/prompting/timeseries_preprocessing.py
index 0f8d759..b1aec5f 100644
--- a/sigllm/primitives/prompting/timeseries_preprocessing.py
+++ b/sigllm/primitives/prompting/timeseries_preprocessing.py
@@ -9,7 +9,7 @@
 import numpy as np
 
 
-def rolling_window_sequences(X, window_size = 500, step_size = 100):
+def rolling_window_sequences(X, window_size=500, step_size=100):
     """Create rolling window sequences out of time series data.
 
     This function creates an array of sequences by rolling over the input sequence.
@@ -39,4 +39,4 @@ def rolling_window_sequences(X, window_size = 500, step_size = 100):
         X_index.append(index[start])
         start = start + step_size
 
-    return np.asarray(out_X), np.asarray(X_index), window_size, step_size
\ No newline at end of file
+    return np.asarray(out_X), np.asarray(X_index), window_size, step_size
diff --git a/tests/primitives/prompting/test_anomalies.py b/tests/primitives/prompting/test_anomalies.py
index 518cdb8..f314ffc 100644
--- a/tests/primitives/prompting/test_anomalies.py
+++ b/tests/primitives/prompting/test_anomalies.py
@@ -5,25 +5,23 @@
 from pytest import fixture
 
 from sigllm.primitives.prompting.anomalies import (
-    val2idx, ano_within_windows, merge_anomaly_seq, idx2time, timestamp2interval,)
-
-
+    ano_within_windows, idx2time, merge_anomaly_seq, timestamp2interval, val2idx,)
 
 
 @fixture
 def anomaly_list_within_seq():
     return np.array([[np.array([0, 3]), np.array([1]), np.array([1, 2])],
                      [np.array([0]), np.array([1, 4]), np.array([2, 3])],
-                     [np.array([0, 2]), np.array([]), np.array([0, 1])]] , dtype = object)
+                     [np.array([0, 2]), np.array([]), np.array([0, 1])]], dtype=object)
 
 
 @fixture
 def anomaly_list_across_seq():
     return np.array([np.array([0]),
-            np.array([1, 2]),
-            np.array([0, 2]),
-            np.array([1, 2]),
-            np.array([1])], dtype=object)
+                     np.array([1, 2]),
+                     np.array([0, 2]),
+                     np.array([1, 2]),
+                     np.array([1])], dtype=object)
 
 
 @fixture
@@ -43,36 +41,40 @@ def step_size():
 
 @fixture
 def timestamp():
-    d = [1222819200, 1222840800, 1222862400, 1222884000, 1222905600]
-    return pd.DataFrame(data=d)
+    return np.array([1222819200, 1222840800, 1222862400, 1222884000, 1222905600])
 
 
 @fixture
 def idx_list():
     return np.array([0, 1, 3])
 
+
 @fixture
-def anomalous_val(): 
+def anomalous_val():
     return np.array([[np.array([0, 3]), np.array([])],
                     [np.array([2]), np.array([4])]], dtype=object)
 
+
 @fixture
-def windows(): 
+def windows():
     return np.array([[0, 1, 0, 3],
                      [3, 2, 6, 2]])
 
+
 @fixture
-def point_timestamp(): 
+def point_timestamp():
     return np.array([1320, 6450, 7890, 12030, 12340])
 
+
 @fixture
 def timestamp1():
     return np.array(range(1000, 13000, 10))
 
+
 def test_ano_within_windows(anomaly_list_within_seq):
     expected = np.array([np.array([1]),
                          np.array([]),
-                         np.array([0])], dtype = object)
+                         np.array([0])], dtype=object)
 
     result = ano_within_windows(anomaly_list_within_seq)
 
@@ -96,19 +98,22 @@ def test_idx2time(timestamp, idx_list):
     np.testing.assert_equal(result, expected)
 
 
-#val2idx
+# val2idx
 def test_val2idx(anomalous_val, windows):
     expected = np.array([[np.array([0, 2, 3]), np.array([])],
-                    [np.array([1, 3]), np.array([])]], dtype=object)
+                         [np.array([1, 3]), np.array([])]], dtype=object)
     result = val2idx(anomalous_val, windows)
 
     for r_list, e_list in zip(result, expected):
         for r, e in zip(r_list, e_list):
             np.testing.assert_equal(r, e)
 
-#timestamp2interval
-def test_timestamp2interval(point_timestamp, timestamp1): 
-    expected = pd.DataFrame([(1000, 1820, 0), (5950, 6950, 0), (7390, 8390, 0), (11530, 12840, 0)], columns = ['start', 'end', 'score'])
+# timestamp2interval
+
+
+def test_timestamp2interval(point_timestamp, timestamp1):
+    expected = pd.DataFrame([(1000, 1820, 0), (5950, 6950, 0), (7390, 8390, 0),
+                            (11530, 12840, 0)], columns=['start', 'end', 'score'])
     result = timestamp2interval(point_timestamp, timestamp1)
 
-    assert result.equals(expected)
\ No newline at end of file
+    assert result.equals(expected)
diff --git a/tests/primitives/test_timeseries_preprocessing.py b/tests/primitives/test_timeseries_preprocessing.py
index a3f1189..d16754a 100644
--- a/tests/primitives/test_timeseries_preprocessing.py
+++ b/tests/primitives/test_timeseries_preprocessing.py
@@ -1,5 +1,6 @@
 import numpy as np
 from pytest import fixture
+
 from sigllm.primitives.prompting.timeseries_preprocessing import rolling_window_sequences
 
 
@@ -17,15 +18,16 @@ def window_size():
 def step_size():
     return 1
 
+
 def test_rolling_window_sequences(values, window_size, step_size):
     expected = (np.array([[0.555, 2.345, 1.501],
                           [2.345, 1.501, 5.903],
                           [1.501, 5.903, 9.116],
                           [5.903, 9.116, 3.068],
                           [9.116, 3.068, 4.678]]),
-                np.array([0, 1, 2, 3, 4]), 
-               3,
-               1)
+                np.array([0, 1, 2, 3, 4]),
+                3,
+                1)
 
     result = rolling_window_sequences(values, window_size, step_size)
 
@@ -33,4 +35,4 @@ def test_rolling_window_sequences(values, window_size, step_size):
         raise AssertionError("Tuples has different length")
 
     for arr1, arr2 in zip(result, expected):
-        np.testing.assert_equal(arr1, arr2)
\ No newline at end of file
+        np.testing.assert_equal(arr1, arr2)

From a0762da5098dd149ef20b12a60398cde01630413 Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Sat, 21 Sep 2024 14:51:45 -0400
Subject: [PATCH 08/25] fix test

---
 tests/primitives/prompting/test_anomalies.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/primitives/prompting/test_anomalies.py b/tests/primitives/prompting/test_anomalies.py
index f314ffc..fbf45e0 100644
--- a/tests/primitives/prompting/test_anomalies.py
+++ b/tests/primitives/prompting/test_anomalies.py
@@ -116,4 +116,4 @@ def test_timestamp2interval(point_timestamp, timestamp1):
                             (11530, 12840, 0)], columns=['start', 'end', 'score'])
     result = timestamp2interval(point_timestamp, timestamp1)
 
-    assert result.equals(expected)
+    pd.testing.assert_frame_equal(expected, result)

From 0054d11016612b14b89fa064ddc4b3e5c2353985 Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Mon, 23 Sep 2024 09:58:35 -0400
Subject: [PATCH 09/25] fix test dtype

---
 tests/primitives/prompting/test_anomalies.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/primitives/prompting/test_anomalies.py b/tests/primitives/prompting/test_anomalies.py
index fbf45e0..8da809a 100644
--- a/tests/primitives/prompting/test_anomalies.py
+++ b/tests/primitives/prompting/test_anomalies.py
@@ -116,4 +116,4 @@ def test_timestamp2interval(point_timestamp, timestamp1):
                             (11530, 12840, 0)], columns=['start', 'end', 'score'])
     result = timestamp2interval(point_timestamp, timestamp1)
 
-    pd.testing.assert_frame_equal(expected, result)
+    pd.testing.assert_frame_equal(expected, result, check_dtype = False)

From e5d683de48b5b4ec699a6a35406ab28ed2e767af Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Mon, 23 Sep 2024 15:36:58 -0400
Subject: [PATCH 10/25] fix lint

---
 sigllm/core.py                               | 12 ++++++------
 tests/primitives/prompting/test_anomalies.py |  2 +-
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/sigllm/core.py b/sigllm/core.py
index ed0580d..27918cb 100644
--- a/sigllm/core.py
+++ b/sigllm/core.py
@@ -10,7 +10,7 @@
 
 from mlblocks import MLPipeline
 from orion import Orion
-=======
+== == == =
 SigLLM is an extension to Orion's core module
 """
 import logging
@@ -36,19 +36,19 @@ class SigLLM(Orion):
     MLBlocks pipelines.
 
     Args:
-        pipeline (str, dict or MLPipeline):
+        pipeline(str, dict or MLPipeline):
             Pipeline to use. It can be passed as:
                 * An ``str`` with a path to a JSON file.
                 * An ``str`` with the name of a registered pipeline.
                 * An ``MLPipeline`` instance.
                 * A ``dict`` with an ``MLPipeline`` specification.
-<<<<<<< HEAD
-        window_size (int):
+<< << << < HEAD
+        window_size(int):
             Size of the input window.
-        steps (int):
+        steps(int):
             Number of steps ahead to forecast.
 
-        hyperparameters (dict):
+        hyperparameters(dict):
             Additional hyperparameters to set to the Pipeline.
     """
 
diff --git a/tests/primitives/prompting/test_anomalies.py b/tests/primitives/prompting/test_anomalies.py
index 8da809a..f572ebd 100644
--- a/tests/primitives/prompting/test_anomalies.py
+++ b/tests/primitives/prompting/test_anomalies.py
@@ -116,4 +116,4 @@ def test_timestamp2interval(point_timestamp, timestamp1):
                             (11530, 12840, 0)], columns=['start', 'end', 'score'])
     result = timestamp2interval(point_timestamp, timestamp1)
 
-    pd.testing.assert_frame_equal(expected, result, check_dtype = False)
+    pd.testing.assert_frame_equal(expected, result, check_dtype=False)

From 1d1c0facfc737d1e4264ecc98f521e7a21c293b0 Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Mon, 23 Sep 2024 15:54:36 -0400
Subject: [PATCH 11/25] fix lint

---
 sigllm/primitives/prompting/anomalies.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index 949cff1..0133115 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -35,7 +35,6 @@ def val2idx(y, X):
             mask = np.isin(seq, anomalies)
             indices = np.where(mask)[0]
             idx_win_list.append(indices)
-        #idx_win_list = np.array(idx_win_list)
         idx_list.append(idx_win_list)
     idx_list = np.array(idx_list, dtype=object)
     return idx_list
@@ -51,7 +50,8 @@ def ano_within_windows(y, alpha=0.5):
             A 3d array containing detected anomalous values from different
             responses of each window.
         alpha (float):
-            Percentage of votes needed for an index to be deemed anomalous. Default to `0.5`.
+            Percentage of votes needed for an index to be deemed anomalous. 
+            Default to `0.5`.
 
     Returns:
         ndarray:
@@ -87,7 +87,8 @@ def merge_anomaly_seq(y, first_index, window_size, step_size, beta=0.5):
         step_size (int):
             Indicating the number of steps the window moves forward each round.
         beta (float):
-            Percentage of containing windows needed for index to be deemed anomalous. Default to `0.5`.
+            Percentage of containing windows needed for index to be deemed anomalous. 
+            Default to `0.5`.
     Return:
         ndarray:
             A 1-dimensional array containing final anomalous indices.

From e8594624be46463afb52b8e7ada5d7f73bc05aa4 Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Mon, 23 Sep 2024 16:01:02 -0400
Subject: [PATCH 12/25] fix long lines

---
 sigllm/primitives/prompting/anomalies.py | 7 +++----
 sigllm/primitives/prompting/gpt.py       | 4 ++--
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index 0133115..c2ef02f 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -50,8 +50,7 @@ def ano_within_windows(y, alpha=0.5):
             A 3d array containing detected anomalous values from different
             responses of each window.
         alpha (float):
-            Percentage of votes needed for an index to be deemed anomalous. 
-            Default to `0.5`.
+            Percent of votes needed for an index to be anomalous. Default to `0.5`.
 
     Returns:
         ndarray:
@@ -87,8 +86,8 @@ def merge_anomaly_seq(y, first_index, window_size, step_size, beta=0.5):
         step_size (int):
             Indicating the number of steps the window moves forward each round.
         beta (float):
-            Percentage of containing windows needed for index to be deemed anomalous. 
-            Default to `0.5`.
+            Percent of windows needed for index to be anomalous. Default to `0.5`.
+            
     Return:
         ndarray:
             A 1-dimensional array containing final anomalous indices.
diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index e83b5bd..2aca9e6 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -49,8 +49,8 @@ class GPT:
             Beta feature by OpenAI to sample deterministically. Default to `None`.
     """
 
-    def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent=0.5, temp=1, top_p=1, logprobs=False, top_logprobs=None,
-                 samples=10, seed=None):
+    def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent=0.5, temp=1, 
+                 top_p=1, logprobs=False, top_logprobs=None, samples=10, seed=None):
         self.name = name
         self.sep = sep
         self.anomalous_percent = anomalous_percent

From cbc0d0b9c2c1461240304c7bfac0bac2fb2aabcb Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Mon, 23 Sep 2024 16:09:18 -0400
Subject: [PATCH 13/25] fix trailing whitespace

---
 sigllm/primitives/prompting/gpt.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index 2aca9e6..9a92377 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -49,7 +49,7 @@ class GPT:
             Beta feature by OpenAI to sample deterministically. Default to `None`.
     """
 
-    def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent=0.5, temp=1, 
+    def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent=0.5, temp=1,
                  top_p=1, logprobs=False, top_logprobs=None, samples=10, seed=None):
         self.name = name
         self.sep = sep

From 3973f61f562178eaf314332c79ebd126bf32a0d6 Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Tue, 24 Sep 2024 11:03:26 -0400
Subject: [PATCH 14/25] change core.py

---
 sigllm/core.py                           | 171 ++++-------------------
 sigllm/primitives/prompting/anomalies.py |   2 +-
 2 files changed, 29 insertions(+), 144 deletions(-)

diff --git a/sigllm/core.py b/sigllm/core.py
index 27918cb..4df80f6 100644
--- a/sigllm/core.py
+++ b/sigllm/core.py
@@ -3,153 +3,38 @@
 """
 Main module.
 
-<<<<<<< HEAD
-This is an extension to Orion's core module
+This module contains functions that get LLM's anomaly detection results.
 """
-from typing import Union
+from sigllm.primitives.prompting.anomalies import get_anomaly_list_within_seq, str2idx
+from sigllm.primitives.prompting.data import sig2str
 
-from mlblocks import MLPipeline
-from orion import Orion
-== == == =
-SigLLM is an extension to Orion's core module
-"""
-import logging
-from typing import Union
-
-import pandas as pd
-from mlblocks import MLPipeline
-from orion import Orion
-
-LOGGER = logging.getLogger(__name__)
-
-INTERVAL_PRIMITIVE = "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1"
-DECIMAL_PRIMITIVE = "sigllm.primitives.transformation.Float2Scalar#1"
-WINDOW_SIZE_PRIMITIVE = "sigllm.primitives.forecasting.custom.rolling_window_sequences#1"
->>>>>>> b764a0da83edbcf790d61cfe880c80dc1d043a07
 
+def get_anomalies(seq, msg_func, model_func, num_iters=1, alpha=0.5):
+    """Get LLM anomaly detection results.
 
-class SigLLM(Orion):
-    """SigLLM Class.
-
-    The SigLLM Class provides the main anomaly detection functionalities
-    of SigLLM and is responsible for the interaction with the underlying
-    MLBlocks pipelines.
+    The function get the LLM's anomaly detection and converts them into an 1D array
 
     Args:
-        pipeline(str, dict or MLPipeline):
-            Pipeline to use. It can be passed as:
-                * An ``str`` with a path to a JSON file.
-                * An ``str`` with the name of a registered pipeline.
-                * An ``MLPipeline`` instance.
-                * A ``dict`` with an ``MLPipeline`` specification.
-<< << << < HEAD
-        window_size(int):
-            Size of the input window.
-        steps(int):
-            Number of steps ahead to forecast.
-
-        hyperparameters(dict):
-            Additional hyperparameters to set to the Pipeline.
+        seq (ndarray):
+            The sequence to detect anomalies.
+        msg_func (func):
+            Function to create message prompt.
+        model_func (func):
+            Function to get LLM answer.
+        num_iters (int):
+            Number of times to run the same query.
+        alpha (float):
+            Percentage of total number of votes that an index needs to have to be
+            considered anomalous. Default: 0.5
+
+    Returns:
+        ndarray:
+            1D array containing anomalous indices of the sequence.
     """
-
-    def __init__(self, pipeline: Union[str, dict, MLPipeline] = None,
-                 hyperparameters: dict = None):
-=======
-        interval (int):
-            Number of time points between one sample and another.
-        decimal (int):
-            Number of decimal points to keep from the float representation.
-        window_size (int):
-            Size of the input window.
-        hyperparameters (dict):
-            Additional hyperparameters to set to the Pipeline.
-    """
-    DEFAULT_PIPELINE = 'mistral_detector'
-
-    def _augment_hyperparameters(self, primitive, key, value):
-        if not value:
-            return
-
-        if self._hyperparameters is None:
-            self._hyperparameters = {
-                primitive: {}
-            }
-        else:
-            if primitive not in self._hyperparameters:
-                self._hyperparameters[primitive] = {}
-
-        self._hyperparameters[primitive][key] = value
-
-    def __init__(self, pipeline: Union[str, dict, MLPipeline] = None, interval: int = None,
-                 decimal: int = None, window_size: int = None, hyperparameters: dict = None):
->>>>>>> b764a0da83edbcf790d61cfe880c80dc1d043a07
-        self._pipeline = pipeline or self.DEFAULT_PIPELINE
-        self._hyperparameters = hyperparameters
-        self._mlpipeline = self._get_mlpipeline()
-        self._fitted = False
-<<<<<<< HEAD
-=======
-
-        self.interval = interval
-        self.decimal = decimal
-        self.window_size = window_size
-
-        self._augment_hyperparameters(INTERVAL_PRIMITIVE, 'interval', interval)
-        self._augment_hyperparameters(DECIMAL_PRIMITIVE, 'decimal', decimal)
-        self._augment_hyperparameters(WINDOW_SIZE_PRIMITIVE, 'window_size', window_size)
-
-    def __repr__(self):
-        if isinstance(self._pipeline, MLPipeline):
-            pipeline = '\n'.join(
-                '    {}'.format(primitive) for primitive in self._pipeline.to_dict()['primitives'])
-
-        elif isinstance(self._pipeline, dict):
-            pipeline = '\n'.join(
-                '    {}'.format(primitive) for primitive in self._pipeline['primitives'])
-
-        else:
-            pipeline = '    {}'.format(self._pipeline)
-
-        hyperparameters = None
-        if self._hyperparameters is not None:
-            hyperparameters = '\n'.join(
-                '    {}: {}'.format(step, value) for step, value in self._hyperparameters.items())
-
-        return (
-            'SigLLM:\n{}\n'
-            'hyperparameters:\n{}\n'
-        ).format(
-            pipeline,
-            hyperparameters
-        )
-
-    def detect(self, data: pd.DataFrame, visualization: bool = False, **kwargs) -> pd.DataFrame:
-        """Detect anomalies in the given data..
-
-        If ``visualization=True``, also return the visualization
-        outputs from the MLPipeline object.
-
-        Args:
-            data (DataFrame):
-                Input data, passed as a ``pandas.DataFrame`` containing
-                exactly two columns: timestamp and value.
-            visualization (bool):
-                If ``True``, also capture the ``visualization`` named
-                output from the ``MLPipeline`` and return it as a second
-                output.
-
-        Returns:
-            DataFrame or tuple:
-                If visualization is ``False``, it returns the events
-                DataFrame. If visualization is ``True``, it returns a
-                tuple containing the events DataFrame followed by the
-                visualization outputs dict.
-        """
-        if not self._fitted:
-            self._mlpipeline = self._get_mlpipeline()
-
-        result = self._detect(self._mlpipeline.fit, data, visualization, **kwargs)
-        self._fitted = True
-
-        return result
->>>>>>> b764a0da83edbcf790d61cfe880c80dc1d043a07
+    message = msg_func(sig2str(seq, space=True))
+    res_list = []
+    for i in range(num_iters):
+        res = model_func(message)
+        ano_ind = str2idx(res, len(seq))
+        res_list.append(ano_ind)
+    return get_anomaly_list_within_seq(res_list, alpha=alpha)
diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index c2ef02f..3600289 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -87,7 +87,7 @@ def merge_anomaly_seq(y, first_index, window_size, step_size, beta=0.5):
             Indicating the number of steps the window moves forward each round.
         beta (float):
             Percent of windows needed for index to be anomalous. Default to `0.5`.
-            
+
     Return:
         ndarray:
             A 1-dimensional array containing final anomalous indices.

From 461afb0b71a9b802f50c8fb65660d07ba4846236 Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Tue, 24 Sep 2024 11:20:30 -0400
Subject: [PATCH 15/25] fix core

---
 sigllm/core.py                                | 145 ++++++++++---
 .../test_timeseries_preprocessing.py          |   0
 tests/readme_test/README.md                   | 120 +++++++++++
 tests/readme_test/data.csv                    | 201 ++++++++++++++++++
 4 files changed, 438 insertions(+), 28 deletions(-)
 rename tests/primitives/{ => prompting}/test_timeseries_preprocessing.py (100%)
 create mode 100644 tests/readme_test/README.md
 create mode 100644 tests/readme_test/data.csv

diff --git a/sigllm/core.py b/sigllm/core.py
index 4df80f6..fdf767d 100644
--- a/sigllm/core.py
+++ b/sigllm/core.py
@@ -3,38 +3,127 @@
 """
 Main module.
 
-This module contains functions that get LLM's anomaly detection results.
+SigLLM is an extension to Orion's core module
 """
-from sigllm.primitives.prompting.anomalies import get_anomaly_list_within_seq, str2idx
-from sigllm.primitives.prompting.data import sig2str
+import logging
+from typing import Union
 
+import pandas as pd
+from mlblocks import MLPipeline
+from orion import Orion
 
-def get_anomalies(seq, msg_func, model_func, num_iters=1, alpha=0.5):
-    """Get LLM anomaly detection results.
+LOGGER = logging.getLogger(__name__)
 
-    The function get the LLM's anomaly detection and converts them into an 1D array
+INTERVAL_PRIMITIVE = "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1"
+DECIMAL_PRIMITIVE = "sigllm.primitives.transformation.Float2Scalar#1"
+WINDOW_SIZE_PRIMITIVE = "sigllm.primitives.forecasting.custom.rolling_window_sequences#1"
+
+
+class SigLLM(Orion):
+    """SigLLM Class.
+
+    The SigLLM Class provides the main anomaly detection functionalities
+    of SigLLM and is responsible for the interaction with the underlying
+    MLBlocks pipelines.
 
     Args:
-        seq (ndarray):
-            The sequence to detect anomalies.
-        msg_func (func):
-            Function to create message prompt.
-        model_func (func):
-            Function to get LLM answer.
-        num_iters (int):
-            Number of times to run the same query.
-        alpha (float):
-            Percentage of total number of votes that an index needs to have to be
-            considered anomalous. Default: 0.5
-
-    Returns:
-        ndarray:
-            1D array containing anomalous indices of the sequence.
+        pipeline (str, dict or MLPipeline):
+            Pipeline to use. It can be passed as:
+                * An ``str`` with a path to a JSON file.
+                * An ``str`` with the name of a registered pipeline.
+                * An ``MLPipeline`` instance.
+                * A ``dict`` with an ``MLPipeline`` specification.
+        interval (int):
+            Number of time points between one sample and another.
+        decimal (int):
+            Number of decimal points to keep from the float representation.
+        window_size (int):
+            Size of the input window.
+        hyperparameters (dict):
+            Additional hyperparameters to set to the Pipeline.
     """
-    message = msg_func(sig2str(seq, space=True))
-    res_list = []
-    for i in range(num_iters):
-        res = model_func(message)
-        ano_ind = str2idx(res, len(seq))
-        res_list.append(ano_ind)
-    return get_anomaly_list_within_seq(res_list, alpha=alpha)
+    DEFAULT_PIPELINE = 'mistral_detector'
+
+    def _augment_hyperparameters(self, primitive, key, value):
+        if not value:
+            return
+
+        if self._hyperparameters is None:
+            self._hyperparameters = {
+                primitive: {}
+            }
+        else:
+            if primitive not in self._hyperparameters:
+                self._hyperparameters[primitive] = {}
+
+        self._hyperparameters[primitive][key] = value
+
+    def __init__(self, pipeline: Union[str, dict, MLPipeline] = None, interval: int = None,
+                 decimal: int = None, window_size: int = None, hyperparameters: dict = None):
+        self._pipeline = pipeline or self.DEFAULT_PIPELINE
+        self._hyperparameters = hyperparameters
+        self._mlpipeline = self._get_mlpipeline()
+        self._fitted = False
+
+        self.interval = interval
+        self.decimal = decimal
+        self.window_size = window_size
+
+        self._augment_hyperparameters(INTERVAL_PRIMITIVE, 'interval', interval)
+        self._augment_hyperparameters(DECIMAL_PRIMITIVE, 'decimal', decimal)
+        self._augment_hyperparameters(WINDOW_SIZE_PRIMITIVE, 'window_size', window_size)
+
+    def __repr__(self):
+        if isinstance(self._pipeline, MLPipeline):
+            pipeline = '\n'.join(
+                '    {}'.format(primitive) for primitive in self._pipeline.to_dict()['primitives'])
+
+        elif isinstance(self._pipeline, dict):
+            pipeline = '\n'.join(
+                '    {}'.format(primitive) for primitive in self._pipeline['primitives'])
+
+        else:
+            pipeline = '    {}'.format(self._pipeline)
+
+        hyperparameters = None
+        if self._hyperparameters is not None:
+            hyperparameters = '\n'.join(
+                '    {}: {}'.format(step, value) for step, value in self._hyperparameters.items())
+
+        return (
+            'SigLLM:\n{}\n'
+            'hyperparameters:\n{}\n'
+        ).format(
+            pipeline,
+            hyperparameters
+        )
+
+    def detect(self, data: pd.DataFrame, visualization: bool = False, **kwargs) -> pd.DataFrame:
+        """Detect anomalies in the given data..
+
+        If ``visualization=True``, also return the visualization
+        outputs from the MLPipeline object.
+
+        Args:
+            data (DataFrame):
+                Input data, passed as a ``pandas.DataFrame`` containing
+                exactly two columns: timestamp and value.
+            visualization (bool):
+                If ``True``, also capture the ``visualization`` named
+                output from the ``MLPipeline`` and return it as a second
+                output.
+
+        Returns:
+            DataFrame or tuple:
+                If visualization is ``False``, it returns the events
+                DataFrame. If visualization is ``True``, it returns a
+                tuple containing the events DataFrame followed by the
+                visualization outputs dict.
+        """
+        if not self._fitted:
+            self._mlpipeline = self._get_mlpipeline()
+
+        result = self._detect(self._mlpipeline.fit, data, visualization, **kwargs)
+        self._fitted = True
+
+        return result
\ No newline at end of file
diff --git a/tests/primitives/test_timeseries_preprocessing.py b/tests/primitives/prompting/test_timeseries_preprocessing.py
similarity index 100%
rename from tests/primitives/test_timeseries_preprocessing.py
rename to tests/primitives/prompting/test_timeseries_preprocessing.py
diff --git a/tests/readme_test/README.md b/tests/readme_test/README.md
new file mode 100644
index 0000000..5d125cf
--- /dev/null
+++ b/tests/readme_test/README.md
@@ -0,0 +1,120 @@
+<p align="left">
+<img width=15% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt=“DAI-Lab” />
+<i>An open source project from Data to AI Lab at MIT.</i>
+</p>
+
+[![Development Status](https://img.shields.io/badge/Development%20Status-2%20--%20Pre--Alpha-yellow)](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
+[![Python](https://img.shields.io/badge/Python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)](https://badge.fury.io/py/sigllm) 
+[![PyPi Shield](https://img.shields.io/pypi/v/sigllm.svg)](https://pypi.python.org/pypi/sigllm)
+[![Run Tests](https://github.com/sintel-dev/sigllm/actions/workflows/tests.yml/badge.svg)](https://github.com/sintel-dev/sigllm/actions/workflows/tests.yml)
+[![Downloads](https://pepy.tech/badge/sigllm)](https://pepy.tech/project/sigllm)
+
+
+# SigLLM
+
+Using Large Language Models (LLMs) for time series anomaly detection.
+
+<!-- - Documentation: https://sintel-dev.github.io/sigllm -->
+- Homepage: https://github.com/sintel-dev/sigllm
+
+# Overview
+
+SigLLM is an extension of the Orion library, built to detect anomalies in time series data using LLMs.
+We provide two types of pipelines for anomaly detection:
+* **Prompter**: directly prompting LLMs to find anomalies in time series.
+* **Detector**: using LLMs to forecast time series and finding anomalies through by comparing the real and forecasted signals.
+
+For more details on our pipelines, please read our [paper](https://arxiv.org/pdf/2405.14755).
+
+# Quickstart
+
+## Install with pip
+
+The easiest and recommended way to install **SigLLM** is using [pip](https://pip.pypa.io/en/stable/):
+
+```bash
+pip install sigllm
+```
+This will pull and install the latest stable release from [PyPi](https://pypi.org/).
+
+
+In the following example we show how to use one of the **SigLLM Pipelines**.
+
+# Detect anomalies using a SigLLM pipeline
+
+We will load a demo data located in `tutorials/data.csv` for this example:
+
+```python3
+import pandas as pd
+
+data = pd.read_csv('data.csv')
+data.head()
+```
+
+which should show a signal with `timestamp` and `value`.
+```
+     timestamp      value
+0   1222840800   6.357008
+1   1222862400  12.763547
+2   1222884000  18.204697
+3   1222905600  21.972602
+4   1222927200  23.986643
+5   1222948800  24.906765
+```
+
+In this example we use `gpt_detector` pipeline and set some hyperparameters. In this case, we set the thresholding strategy to dynamic. The hyperparameters are optional and can be removed.
+
+In addtion, the `SigLLM` object takes in a `decimal` argument to determine how many digits from the float value include. Here, we don't want to keep any decimal values, so we set it to zero.
+
+```python3
+from sigllm import SigLLM
+
+hyperparameters = {
+    "orion.primitives.timeseries_anomalies.find_anomalies#1": {
+        "fixed_threshold": False
+    }
+}
+
+sigllm = SigLLM(
+    pipeline='gpt_detector',
+    decimal=0,
+    hyperparameters=hyperparameters
+)
+```
+
+Now that we have initialized the pipeline, we are ready to use it to detect anomalies:
+
+```python3
+anomalies = sigllm.detect(data)
+```
+> :warning: Depending on the length of your timeseries, this might take time to run.
+
+The output of the previous command will be a ``pandas.DataFrame`` containing a table of detected anomalies:
+
+```
+        start         end  severity
+0  1225864800  1227139200  0.625879
+```
+
+# Resources
+
+Additional resources that might be of interest:
+* Learn about [Orion](https://github.com/sintel-dev/Orion).
+* Read our [paper](https://arxiv.org/pdf/2405.14755).
+
+
+# Citation
+
+If you use **SigLLM** for your research, please consider citing the following paper:
+
+Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille, Kalyan Veeramachaneni. [Can Large Language Models be Anomaly Detectors for Time Series?](https://arxiv.org/pdf/2405.14755).
+
+```
+@inproceedings{alnegheimish2024sigllm,
+  title={Can Large Language Models be Anomaly Detectors for Time Series?},
+  author={Alnegheimish, Sarah and Nguyen, Linh and Berti-Equille, Laure and Veeramachaneni, Kalyan},
+  booktitle={2024 IEEE International Conferencze on Data Science and Advanced Analytics (IEEE DSAA)},
+  organization={IEEE},
+  year={2024}
+}
+```
\ No newline at end of file
diff --git a/tests/readme_test/data.csv b/tests/readme_test/data.csv
new file mode 100644
index 0000000..e09e7c6
--- /dev/null
+++ b/tests/readme_test/data.csv
@@ -0,0 +1,201 @@
+timestamp,value
+1222840800,6.357008100494576
+1222862400,12.763546581352072
+1222884000,18.204696564815272
+1222905600,21.972602361114156
+1222927200,23.986642642785114
+1222948800,24.90676506875653
+1222970400,25.989320595137087
+1222992000,28.716431070627678
+1223013600,34.30506674613711
+1223035200,43.24737910800088
+1223056800,55.034423935929546
+1223078400,68.16905746141728
+1223100000,80.4921989796059
+1223121600,89.75244394286932
+1223143200,94.26952941914647
+1223164800,93.5017314050363
+1223186400,88.34007856895734
+1223208000,81.01778802042743
+1223229600,74.62585774349444
+1223251200,72.33814720068355
+1223272800,76.54011355267909
+1223294400,88.09758081162164
+1223316000,105.98060943348528
+1223337600,127.37474497756938
+1223359200,148.2869095456845
+1223380800,164.51795927519777
+1223402400,172.76503742626085
+1223424000,171.5656480692757
+1223445600,161.81915287334337
+1223467200,146.71799781894887
+1223488800,131.0684345926863
+1223510400,120.1419904963696
+1223532000,118.33198080422939
+1223553600,127.95693178234077
+1223575200,148.53348852101556
+1223596800,176.73586061300654
+1223618400,207.0906381853098
+1223640000,233.26610945828563
+1223661600,249.65283874180827
+1223683200,252.8410925016408
+1223704800,242.60791530701061
+1223726400,222.13490788909508
+1223748000,197.3634328487735
+1223769600,175.6122660298582
+1223791200,163.77843813506888
+1223812800,166.56354247260586
+1223834400,185.1805958672306
+1223856000,216.89283097585792
+1223877600,255.53754623830937
+1223899200,292.942553088285
+1223920800,320.9110467703906
+1223942400,333.2928441860287
+1223964000,327.61911646502824
+1223985600,305.868804462216
+1224007200,274.1394184581488
+1224028800,241.26479563168095
+1224050400,216.69114779327512
+1224072000,208.12189799368588
+1224093600,219.51831346047348
+1224115200,249.97318954768332
+1224136800,293.77165293559335
+1224158400,341.6636921738509
+1224180000,383.0678688846789
+1224201600,408.6816026718849
+1224223200,412.85414714653876
+1224244800,395.1179174503577
+1224266400,360.4670535080087
+1224288000,318.2748439708085
+1224309600,280.0807462952766
+1224331200,256.76875877886397
+1224352800,255.82708783616715
+1224374400,279.37853689892256
+1224396000,323.49720200097903
+1224417600,379.01881797553744
+1224439200,433.68328808802346
+1224460800,475.10960892950766
+1224482400,493.8813805029361
+1224504000,485.97418885423394
+1224525600,453.9007031757384
+1224547200,406.254007906167
+1224568800,355.7221665496005
+1224590400,316.0311204010601
+1224612000,298.5523174446173
+1224633600,309.4134235160652
+1224655200,347.845261675828
+1224676800,406.20711606471434
+1224698400,471.7241173418954
+1224720000,529.5439805702732
+1224741600,566.3820817231293
+1224763200,573.8602962016663
+1224784800,550.7010061849473
+1224806400,503.2041570542908
+1224828000,443.85034846253427
+1224849600,388.3363394378638
+1224871200,351.74645248312936
+1224892800,344.7936959238848
+1224914400,371.0669968072905
+1224936000,425.9903093164482
+1224957600,497.78815676380424
+1224979200,570.2591174788184
+1225000800,626.7050329896276
+1225022400,654.0631040110301
+1225044000,646.218468177966
+1225065600,605.6582283694571
+1225087200,543.023554005779
+1225108800,474.63009058078984
+1225130400,418.5329823792372
+1225152000,390.0853207748052
+1225173600,398.0826119567664
+1225195200,442.46087411936696
+1225216800,514.1482974446757
+1225238400,597.1469398311953
+1225260000,672.368916561776
+1225281600,722.3076003727401
+1225303200,735.4006929301665
+1225324800,708.9981228898694
+1225346400,650.174572392425
+1225368000,574.1476185312686
+1225389600,500.65199249832983
+1225411200,449.13316781791525
+1225432800,433.93085110056245
+1225454400,460.64523247450745
+1225476000,524.6057519011459
+1225497600,611.8564817436211
+1225519200,702.4555861457212
+1225540800,775.3097875175549
+1225562400,813.3719438548361
+1225584000,807.9210583841716
+1225605600,760.8508061391368
+1225627200,684.3687276727511
+1225648800,598.1398709696585
+1225670400,524.5417834812782
+1225692000,483.17553722979517
+1225713600,485.97916964551314
+1225735200,534.1613714059761
+1225756800,617.7412864857179
+1225778400,717.8473165174294
+1225800000,811.2475680995937
+1225821600,876.0247613322366
+1225843200,897.0093838501864
+1225864800,869.624451145587
+1225886400,801.1685333625392
+1225908000,709.1831524440421
+1225929600,617.2664795262941
+1225951200,549.3317273039922
+1225972800,523.7071574332649
+1225994400,548.5332944762629
+1226016000,619.6125200416544
+1226037600,721.2760592722431
+1226059200,830.0954451472978
+1226080800,920.5603196607155
+1226102400,971.3467231368722
+1226124000,970.635675881821
+1226145600,919.1559495348253
+1226167200,830.1695533228165
+1226188800,726.3633565878374
+1226210400,634.3739465873152
+1226232000,578.2667887512415
+1226253600,573.566040686077
+1226275200,623.3158563580123
+1226296800,717.1712587472132
+1226318400,833.7812740150428
+1226340000,945.9174989754079
+1226361600,1027.116662771763
+1226383200,1058.2167038036155
+1226404800,1032.172160126205
+1226426400,955.9395751535849
+1226448000,848.9320265136402
+1226469600,738.3825446783692
+1226491200,652.7231152317531
+1226512800,122.91778142825031
+1226534400,127.03380291678923
+1226556000,142.26262759412742
+1226577600,165.22805091768024
+1226599200,190.60801463693895
+1226620800,212.42395984229324
+1226642400,225.50691103802396
+1226664000,226.78077364684677
+1226685600,216.04430264453876
+1226707200,196.05322970896995
+1226728800,171.8743971580046
+1226750400,748.3148492497778
+1226772000,675.7888000290897
+1226793600,661.3124647751896
+1226815200,710.3185851343285
+1226836800,812.6612602064542
+1226858400,944.9461793166995
+1226880000,1076.1509876660386
+1226901600,1175.1860130915284
+1226923200,1218.5527023888787
+1226944800,1196.2133055128206
+1226966400,1114.2062008595062
+1226988000,993.3279200066103
+1227009600,864.165279271635
+1227031200,759.6636032843628
+1227052800,707.0366570127511
+1227074400,721.0048694428842
+1227096000,800.0415811489435
+1227117600,926.5830116645803
+1227139200,1071.190646235585

From 5360d3b5b9b07cddb06bf042514c4f07a01b333e Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Tue, 24 Sep 2024 11:25:30 -0400
Subject: [PATCH 16/25] new line end of file

---
 sigllm/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sigllm/core.py b/sigllm/core.py
index fdf767d..a4217d5 100644
--- a/sigllm/core.py
+++ b/sigllm/core.py
@@ -126,4 +126,4 @@ def detect(self, data: pd.DataFrame, visualization: bool = False, **kwargs) -> p
         result = self._detect(self._mlpipeline.fit, data, visualization, **kwargs)
         self._fitted = True
 
-        return result
\ No newline at end of file
+        return result

From 7e5712768d2e6fe27e47b8d2041ae0eceb2cb50c Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Tue, 1 Oct 2024 11:33:39 -0400
Subject: [PATCH 17/25] fix PR comments

---
 .../pipelines/prompter/mistral_prompter.json  |  47 ++--
 ....anomalies.find_anomalies_in_windows.json} |   4 +-
 ...prompting.anomalies.format_anomalies.json} |  10 +-
 ...imitives.prompting.anomalies.idx2time.json |  32 ---
 ....anomalies.merge_anomalous_sequences.json} |   4 +-
 sigllm/primitives/prompting/anomalies.py      |  38 +---
 sigllm/primitives/transformation.py           |   8 +-
 tests/primitives/prompting/test_anomalies.py  |  43 ++--
 tests/readme_test/README.md                   | 120 -----------
 tests/readme_test/data.csv                    | 201 ------------------
 .../mistral-prompter-pipeline.ipynb}          |   0
 11 files changed, 61 insertions(+), 446 deletions(-)
 rename sigllm/primitives/jsons/{sigllm.primitives.prompting.anomalies.ano_within_windows.json => sigllm.primitives.prompting.anomalies.find_anomalies_in_windows.json} (81%)
 rename sigllm/primitives/jsons/{sigllm.primitives.prompting.anomalies.timestamp2interval.json => sigllm.primitives.prompting.anomalies.format_anomalies.json} (67%)
 delete mode 100644 sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
 rename sigllm/primitives/jsons/{sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json => sigllm.primitives.prompting.anomalies.merge_anomalous_sequences.json} (91%)
 delete mode 100644 tests/readme_test/README.md
 delete mode 100644 tests/readme_test/data.csv
 rename tutorials/{prompter.ipynb => pipelines/mistral-prompter-pipeline.ipynb} (100%)

diff --git a/sigllm/pipelines/prompter/mistral_prompter.json b/sigllm/pipelines/prompter/mistral_prompter.json
index 35e2c39..79081ab 100644
--- a/sigllm/pipelines/prompter/mistral_prompter.json
+++ b/sigllm/pipelines/prompter/mistral_prompter.json
@@ -8,10 +8,9 @@
         "sigllm.primitives.prompting.huggingface.HF",
         "sigllm.primitives.transformation.format_as_integer",
         "sigllm.primitives.prompting.anomalies.val2idx",
-        "sigllm.primitives.prompting.anomalies.ano_within_windows",
-        "sigllm.primitives.prompting.anomalies.merge_anomaly_seq",
-        "sigllm.primitives.prompting.anomalies.idx2time",
-        "sigllm.primitives.prompting.anomalies.timestamp2interval"
+        "sigllm.primitives.prompting.anomalies.find_anomalies_in_window",
+        "sigllm.primitives.prompting.anomalies.merge_anomalous_sequences",
+        "sigllm.primitives.prompting.anomalies.format_anomalies"
     ],
     "init_params": {
         "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
@@ -34,33 +33,33 @@
             "name": "mistralai/Mistral-7B-Instruct-v0.2",
             "samples": 10
         },
-        "sigllm.primitives.prompting.anomalies.ano_within_windows#1": {
+        "sigllm.primitives.prompting.anomalies.find_anomalies_in_window#1": {
             "alpha": 0.4
         },
-        "sigllm.primitives.prompting.anomalies.merge_anomaly_seq#1": {
+        "sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1": {
             "beta": 0.5
         }
     },
     "input_names": {
-    "sigllm.primitives.prompting.huggingface.HF#1": {
-        "X": "X_str"
-    },
-    "sigllm.primitives.transformation.format_as_integer#1":{
-        "X": "y_hat"
-    }
+        "sigllm.primitives.prompting.huggingface.HF#1": {
+            "X": "X_str"
+        },
+        "sigllm.primitives.transformation.format_as_integer#1":{
+            "X": "y_hat"
+        }
     },
     "output_names": {
-    "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
-        "index": "timestamp"
-    },
-    "sigllm.primitives.transformation.format_as_string#1": {
-        "X": "X_str"
-    },
-    "sigllm.primitives.prompting.huggingface.HF#1": {
-        "y": "y_hat"
-    },
-    "sigllm.primitives.transformation.format_as_integer#1":{
-        "X": "y"
-    }
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
+            "index": "timestamp"
+        },
+        "sigllm.primitives.transformation.format_as_string#1": {
+            "X": "X_str"
+        },
+        "sigllm.primitives.prompting.huggingface.HF#1": {
+            "y": "y_hat"
+        },
+        "sigllm.primitives.transformation.format_as_integer#1":{
+            "X": "y"
+        }
     }
 }
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.find_anomalies_in_windows.json
similarity index 81%
rename from sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
rename to sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.find_anomalies_in_windows.json
index 0a46a8c..be11b49 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.ano_within_windows.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.find_anomalies_in_windows.json
@@ -1,5 +1,5 @@
 {
-    "name": "sigllm.primitives.prompting.anomalies.ano_within_windows",
+    "name": "sigllm.primitives.prompting.anomalies.find_anomalies_in_window",
     "contributors": [
         "Sarah Alnegheimish <smish@mit.edu>",
         "Linh Nguyen <linhnk@mit.edu>"
@@ -10,7 +10,7 @@
         "subtype": "merger"
     },
     "modalities": [],
-    "primitive": "sigllm.primitives.prompting.anomalies.ano_within_windows",
+    "primitive": "sigllm.primitives.prompting.anomalies.find_anomalies_in_window",
     "produce": {
         "args": [
             {
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.format_anomalies.json
similarity index 67%
rename from sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
rename to sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.format_anomalies.json
index 2188727..aa54eb0 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.timestamp2interval.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.format_anomalies.json
@@ -1,16 +1,16 @@
 {
-    "name": "sigllm.primitives.prompting.anomalies.timestamp2interval",
+    "name": "sigllm.primitives.prompting.anomalies.format_anomalies",
     "contributors": [
         "Sarah Alnegheimish <smish@mit.edu>",
         "Linh Nguyen <linhnk@mit.edu>"
     ],
-    "description": "Convert list of timestamps to list of intervals by padding to both sides and merge overlapping",
+    "description": "Convert list of indices to list of intervals by padding to both sides and merge overlapping",
     "classifiers": {
         "type": "postprocessor",
         "subtype": "converter"
     },
     "modalities": [],
-    "primitive": "sigllm.primitives.prompting.anomalies.timestamp2interval",
+    "primitive": "sigllm.primitives.prompting.anomalies.format_anomalies",
     "produce": {
         "args": [
             {
@@ -24,8 +24,8 @@
         ],
         "output": [
             {
-                "name": "df",
-                "type": "Dataframe"
+                "name": "merged_intervals",
+                "type": "List"
             }
         ]
     }, 
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
deleted file mode 100644
index 9c2a563..0000000
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.idx2time.json
+++ /dev/null
@@ -1,32 +0,0 @@
-{
-    "name": "sigllm.primitives.prompting.anomalies.idx2time",
-    "contributors": [
-        "Sarah Alnegheimish <smish@mit.edu>",
-        "Linh Nguyen <linhnk@mit.edu>"
-    ],
-    "description": "Convert list of indices into list of timestamp",
-    "classifiers": {
-        "type": "postprocessor",
-        "subtype": "converter"
-    },
-    "modalities": [],
-    "primitive": "sigllm.primitives.prompting.anomalies.idx2time",
-    "produce": {
-        "args": [
-            {
-                "name": "timestamp",
-                "type": "ndarray"
-            }, 
-            {
-                "name": "y",
-                "type": "ndarray"
-            }
-        ],
-        "output": [
-            {
-                "name": "y",
-                "type": "ndarray"
-            }
-        ]
-    }
-}
\ No newline at end of file
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomalous_sequences.json
similarity index 91%
rename from sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
rename to sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomalous_sequences.json
index 7a9c45b..384293c 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomaly_seq.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.merge_anomalous_sequences.json
@@ -1,5 +1,5 @@
 {
-    "name": "sigllm.primitives.prompting.anomalies.merge_anomaly_seq",
+    "name": "sigllm.primitives.prompting.anomalies.merge_anomalous_sequences",
     "contributors": [
         "Sarah Alnegheimish <smish@mit.edu>",
         "Linh Nguyen <linhnk@mit.edu>"
@@ -10,7 +10,7 @@
         "subtype": "merger"
     },
     "modalities": [],
-    "primitive": "sigllm.primitives.prompting.anomalies.merge_anomaly_seq",
+    "primitive": "sigllm.primitives.prompting.anomalies.merge_anomalous_sequences",
     "produce": {
         "args": [
             {
diff --git a/sigllm/primitives/prompting/anomalies.py b/sigllm/primitives/prompting/anomalies.py
index 3600289..4c1f497 100644
--- a/sigllm/primitives/prompting/anomalies.py
+++ b/sigllm/primitives/prompting/anomalies.py
@@ -7,7 +7,6 @@
 """
 
 import numpy as np
-import pandas as pd
 
 
 def val2idx(y, X):
@@ -40,7 +39,7 @@ def val2idx(y, X):
     return idx_list
 
 
-def ano_within_windows(y, alpha=0.5):
+def find_anomalies_in_windows(y, alpha=0.5):
     """Get the final list of anomalous indices of each window
 
     Choose anomalous index in the sequence based on multiple LLM responses
@@ -73,7 +72,7 @@ def ano_within_windows(y, alpha=0.5):
     return idx_list
 
 
-def merge_anomaly_seq(y, first_index, window_size, step_size, beta=0.5):
+def merge_anomalous_sequences(y, first_index, window_size, step_size, beta=0.5):
     """Get the final list of anomalous indices of a sequence when merging all rolling windows
 
     Args:
@@ -105,39 +104,23 @@ def merge_anomaly_seq(y, first_index, window_size, step_size, beta=0.5):
     return np.sort(final_list)
 
 
-def idx2time(timestamp, y):
-    """Convert list of indices into list of timestamp
-
-    Args:
-        sequence (DataFrame):
-            Signal with timestamps and values.
-        y (ndarray):
-            A 1-dimensional array of indices.
-
-    Returns:
-        ndarray:
-            A 1-dimensional array containing timestamps.
-    """
-    timestamp_list = timestamp[y]
-    return timestamp_list
-
-
-def timestamp2interval(y, timestamp, padding_size=50):
-    """Convert list of timestamps to list of intervals by padding to both sides
+def format_anomalies(y, timestamp, padding_size=50):
+    """Convert list of anomalous indices to list of intervals by padding to both sides
     and merge overlapping
 
     Args:
         y (ndarray):
-            A 1d array of point timestamps.
+            A 1-dimensional array of indices.
         timestamp (ndarray):
             List of full timestamp of the signal
         padding_size (int):
             Number of steps to pad on both sides of a timestamp point. Default to `50`.
 
     Returns:
-        Dataframe:
-            Dataframe of interval (start, end, score).
+        List[Tuple]:
+            List of intervals (start, end, score).
     """
+    y = timestamp[y]  # Convert list of indices into list of timestamps
     start, end = timestamp[0], timestamp[-1]
     interval = timestamp[1] - timestamp[0]
     intervals = []
@@ -162,6 +145,5 @@ def timestamp2interval(y, timestamp, padding_size=50):
         else:
             merged_intervals.append(current_interval)  # Append the current interval if no overlap
 
-    df = pd.DataFrame(merged_intervals, columns=['start', 'end'])
-    df['score'] = 0
-    return df
+    merged_intervals = [(interval[0], interval[1], 0) for interval in merged_intervals]
+    return merged_intervals
diff --git a/sigllm/primitives/transformation.py b/sigllm/primitives/transformation.py
index ac4eb2e..574974d 100644
--- a/sigllm/primitives/transformation.py
+++ b/sigllm/primitives/transformation.py
@@ -10,8 +10,8 @@
 import numpy as np
 
 
-def format_as_string(values, sep=',', space=False):
-    """Format values to a list of string.
+def format_as_string(X, sep=',', space=False):
+    """Format X to a list of string.
 
     Transform a 2-D array of integers to a list of strings,
     seperated by the indicated seperator and space.
@@ -34,7 +34,9 @@ def _as_string(x):
 
         return text
 
-    return np.apply_along_axis(_as_string, axis=1, arr=values)
+    results = list(map(_as_string, X))
+
+    return np.array(results)
 
 
 def _from_string_to_integer(text, sep=',', trunc=None, errors='ignore'):
diff --git a/tests/primitives/prompting/test_anomalies.py b/tests/primitives/prompting/test_anomalies.py
index f572ebd..bd117c6 100644
--- a/tests/primitives/prompting/test_anomalies.py
+++ b/tests/primitives/prompting/test_anomalies.py
@@ -1,11 +1,10 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-import pandas as pd
 from pytest import fixture
 
 from sigllm.primitives.prompting.anomalies import (
-    ano_within_windows, idx2time, merge_anomaly_seq, timestamp2interval, val2idx,)
+    find_anomalies_in_windows, format_anomalies, merge_anomalous_sequences, val2idx,)
 
 
 @fixture
@@ -39,14 +38,9 @@ def step_size():
     return 1
 
 
-@fixture
-def timestamp():
-    return np.array([1222819200, 1222840800, 1222862400, 1222884000, 1222905600])
-
-
 @fixture
 def idx_list():
-    return np.array([0, 1, 3])
+    return np.array([32, 545, 689, 1103, 1134])
 
 
 @fixture
@@ -62,12 +56,7 @@ def windows():
 
 
 @fixture
-def point_timestamp():
-    return np.array([1320, 6450, 7890, 12030, 12340])
-
-
-@fixture
-def timestamp1():
+def timestamp():
     return np.array(range(1000, 13000, 10))
 
 
@@ -76,7 +65,7 @@ def test_ano_within_windows(anomaly_list_within_seq):
                          np.array([]),
                          np.array([0])], dtype=object)
 
-    result = ano_within_windows(anomaly_list_within_seq)
+    result = find_anomalies_in_windows(anomaly_list_within_seq)
 
     for r, e in zip(result, expected):
         np.testing.assert_equal(r, e)
@@ -85,15 +74,11 @@ def test_ano_within_windows(anomaly_list_within_seq):
 def test_merge_anomaly_seq(anomaly_list_across_seq, first_indices, window_size, step_size):
     expected = np.array([2, 4, 5])
 
-    result = merge_anomaly_seq(anomaly_list_across_seq, first_indices, window_size, step_size)
-
-    np.testing.assert_equal(result, expected)
-
-
-def test_idx2time(timestamp, idx_list):
-    expected = np.array([1222819200, 1222840800, 1222884000])
-
-    result = idx2time(timestamp, idx_list)
+    result = merge_anomalous_sequences(
+        anomaly_list_across_seq,
+        first_indices,
+        window_size,
+        step_size)
 
     np.testing.assert_equal(result, expected)
 
@@ -111,9 +96,9 @@ def test_val2idx(anomalous_val, windows):
 # timestamp2interval
 
 
-def test_timestamp2interval(point_timestamp, timestamp1):
-    expected = pd.DataFrame([(1000, 1820, 0), (5950, 6950, 0), (7390, 8390, 0),
-                            (11530, 12840, 0)], columns=['start', 'end', 'score'])
-    result = timestamp2interval(point_timestamp, timestamp1)
+def test_format_anomalies(idx_list, timestamp):
+    expected = [(1000, 1820, 0), (5950, 6950, 0), (7390, 8390, 0),
+                (11530, 12840, 0)]
+    result = format_anomalies(idx_list, timestamp)
 
-    pd.testing.assert_frame_equal(expected, result, check_dtype=False)
+    assert expected == result
diff --git a/tests/readme_test/README.md b/tests/readme_test/README.md
deleted file mode 100644
index 5d125cf..0000000
--- a/tests/readme_test/README.md
+++ /dev/null
@@ -1,120 +0,0 @@
-<p align="left">
-<img width=15% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt=“DAI-Lab” />
-<i>An open source project from Data to AI Lab at MIT.</i>
-</p>
-
-[![Development Status](https://img.shields.io/badge/Development%20Status-2%20--%20Pre--Alpha-yellow)](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
-[![Python](https://img.shields.io/badge/Python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)](https://badge.fury.io/py/sigllm) 
-[![PyPi Shield](https://img.shields.io/pypi/v/sigllm.svg)](https://pypi.python.org/pypi/sigllm)
-[![Run Tests](https://github.com/sintel-dev/sigllm/actions/workflows/tests.yml/badge.svg)](https://github.com/sintel-dev/sigllm/actions/workflows/tests.yml)
-[![Downloads](https://pepy.tech/badge/sigllm)](https://pepy.tech/project/sigllm)
-
-
-# SigLLM
-
-Using Large Language Models (LLMs) for time series anomaly detection.
-
-<!-- - Documentation: https://sintel-dev.github.io/sigllm -->
-- Homepage: https://github.com/sintel-dev/sigllm
-
-# Overview
-
-SigLLM is an extension of the Orion library, built to detect anomalies in time series data using LLMs.
-We provide two types of pipelines for anomaly detection:
-* **Prompter**: directly prompting LLMs to find anomalies in time series.
-* **Detector**: using LLMs to forecast time series and finding anomalies through by comparing the real and forecasted signals.
-
-For more details on our pipelines, please read our [paper](https://arxiv.org/pdf/2405.14755).
-
-# Quickstart
-
-## Install with pip
-
-The easiest and recommended way to install **SigLLM** is using [pip](https://pip.pypa.io/en/stable/):
-
-```bash
-pip install sigllm
-```
-This will pull and install the latest stable release from [PyPi](https://pypi.org/).
-
-
-In the following example we show how to use one of the **SigLLM Pipelines**.
-
-# Detect anomalies using a SigLLM pipeline
-
-We will load a demo data located in `tutorials/data.csv` for this example:
-
-```python3
-import pandas as pd
-
-data = pd.read_csv('data.csv')
-data.head()
-```
-
-which should show a signal with `timestamp` and `value`.
-```
-     timestamp      value
-0   1222840800   6.357008
-1   1222862400  12.763547
-2   1222884000  18.204697
-3   1222905600  21.972602
-4   1222927200  23.986643
-5   1222948800  24.906765
-```
-
-In this example we use `gpt_detector` pipeline and set some hyperparameters. In this case, we set the thresholding strategy to dynamic. The hyperparameters are optional and can be removed.
-
-In addtion, the `SigLLM` object takes in a `decimal` argument to determine how many digits from the float value include. Here, we don't want to keep any decimal values, so we set it to zero.
-
-```python3
-from sigllm import SigLLM
-
-hyperparameters = {
-    "orion.primitives.timeseries_anomalies.find_anomalies#1": {
-        "fixed_threshold": False
-    }
-}
-
-sigllm = SigLLM(
-    pipeline='gpt_detector',
-    decimal=0,
-    hyperparameters=hyperparameters
-)
-```
-
-Now that we have initialized the pipeline, we are ready to use it to detect anomalies:
-
-```python3
-anomalies = sigllm.detect(data)
-```
-> :warning: Depending on the length of your timeseries, this might take time to run.
-
-The output of the previous command will be a ``pandas.DataFrame`` containing a table of detected anomalies:
-
-```
-        start         end  severity
-0  1225864800  1227139200  0.625879
-```
-
-# Resources
-
-Additional resources that might be of interest:
-* Learn about [Orion](https://github.com/sintel-dev/Orion).
-* Read our [paper](https://arxiv.org/pdf/2405.14755).
-
-
-# Citation
-
-If you use **SigLLM** for your research, please consider citing the following paper:
-
-Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille, Kalyan Veeramachaneni. [Can Large Language Models be Anomaly Detectors for Time Series?](https://arxiv.org/pdf/2405.14755).
-
-```
-@inproceedings{alnegheimish2024sigllm,
-  title={Can Large Language Models be Anomaly Detectors for Time Series?},
-  author={Alnegheimish, Sarah and Nguyen, Linh and Berti-Equille, Laure and Veeramachaneni, Kalyan},
-  booktitle={2024 IEEE International Conferencze on Data Science and Advanced Analytics (IEEE DSAA)},
-  organization={IEEE},
-  year={2024}
-}
-```
\ No newline at end of file
diff --git a/tests/readme_test/data.csv b/tests/readme_test/data.csv
deleted file mode 100644
index e09e7c6..0000000
--- a/tests/readme_test/data.csv
+++ /dev/null
@@ -1,201 +0,0 @@
-timestamp,value
-1222840800,6.357008100494576
-1222862400,12.763546581352072
-1222884000,18.204696564815272
-1222905600,21.972602361114156
-1222927200,23.986642642785114
-1222948800,24.90676506875653
-1222970400,25.989320595137087
-1222992000,28.716431070627678
-1223013600,34.30506674613711
-1223035200,43.24737910800088
-1223056800,55.034423935929546
-1223078400,68.16905746141728
-1223100000,80.4921989796059
-1223121600,89.75244394286932
-1223143200,94.26952941914647
-1223164800,93.5017314050363
-1223186400,88.34007856895734
-1223208000,81.01778802042743
-1223229600,74.62585774349444
-1223251200,72.33814720068355
-1223272800,76.54011355267909
-1223294400,88.09758081162164
-1223316000,105.98060943348528
-1223337600,127.37474497756938
-1223359200,148.2869095456845
-1223380800,164.51795927519777
-1223402400,172.76503742626085
-1223424000,171.5656480692757
-1223445600,161.81915287334337
-1223467200,146.71799781894887
-1223488800,131.0684345926863
-1223510400,120.1419904963696
-1223532000,118.33198080422939
-1223553600,127.95693178234077
-1223575200,148.53348852101556
-1223596800,176.73586061300654
-1223618400,207.0906381853098
-1223640000,233.26610945828563
-1223661600,249.65283874180827
-1223683200,252.8410925016408
-1223704800,242.60791530701061
-1223726400,222.13490788909508
-1223748000,197.3634328487735
-1223769600,175.6122660298582
-1223791200,163.77843813506888
-1223812800,166.56354247260586
-1223834400,185.1805958672306
-1223856000,216.89283097585792
-1223877600,255.53754623830937
-1223899200,292.942553088285
-1223920800,320.9110467703906
-1223942400,333.2928441860287
-1223964000,327.61911646502824
-1223985600,305.868804462216
-1224007200,274.1394184581488
-1224028800,241.26479563168095
-1224050400,216.69114779327512
-1224072000,208.12189799368588
-1224093600,219.51831346047348
-1224115200,249.97318954768332
-1224136800,293.77165293559335
-1224158400,341.6636921738509
-1224180000,383.0678688846789
-1224201600,408.6816026718849
-1224223200,412.85414714653876
-1224244800,395.1179174503577
-1224266400,360.4670535080087
-1224288000,318.2748439708085
-1224309600,280.0807462952766
-1224331200,256.76875877886397
-1224352800,255.82708783616715
-1224374400,279.37853689892256
-1224396000,323.49720200097903
-1224417600,379.01881797553744
-1224439200,433.68328808802346
-1224460800,475.10960892950766
-1224482400,493.8813805029361
-1224504000,485.97418885423394
-1224525600,453.9007031757384
-1224547200,406.254007906167
-1224568800,355.7221665496005
-1224590400,316.0311204010601
-1224612000,298.5523174446173
-1224633600,309.4134235160652
-1224655200,347.845261675828
-1224676800,406.20711606471434
-1224698400,471.7241173418954
-1224720000,529.5439805702732
-1224741600,566.3820817231293
-1224763200,573.8602962016663
-1224784800,550.7010061849473
-1224806400,503.2041570542908
-1224828000,443.85034846253427
-1224849600,388.3363394378638
-1224871200,351.74645248312936
-1224892800,344.7936959238848
-1224914400,371.0669968072905
-1224936000,425.9903093164482
-1224957600,497.78815676380424
-1224979200,570.2591174788184
-1225000800,626.7050329896276
-1225022400,654.0631040110301
-1225044000,646.218468177966
-1225065600,605.6582283694571
-1225087200,543.023554005779
-1225108800,474.63009058078984
-1225130400,418.5329823792372
-1225152000,390.0853207748052
-1225173600,398.0826119567664
-1225195200,442.46087411936696
-1225216800,514.1482974446757
-1225238400,597.1469398311953
-1225260000,672.368916561776
-1225281600,722.3076003727401
-1225303200,735.4006929301665
-1225324800,708.9981228898694
-1225346400,650.174572392425
-1225368000,574.1476185312686
-1225389600,500.65199249832983
-1225411200,449.13316781791525
-1225432800,433.93085110056245
-1225454400,460.64523247450745
-1225476000,524.6057519011459
-1225497600,611.8564817436211
-1225519200,702.4555861457212
-1225540800,775.3097875175549
-1225562400,813.3719438548361
-1225584000,807.9210583841716
-1225605600,760.8508061391368
-1225627200,684.3687276727511
-1225648800,598.1398709696585
-1225670400,524.5417834812782
-1225692000,483.17553722979517
-1225713600,485.97916964551314
-1225735200,534.1613714059761
-1225756800,617.7412864857179
-1225778400,717.8473165174294
-1225800000,811.2475680995937
-1225821600,876.0247613322366
-1225843200,897.0093838501864
-1225864800,869.624451145587
-1225886400,801.1685333625392
-1225908000,709.1831524440421
-1225929600,617.2664795262941
-1225951200,549.3317273039922
-1225972800,523.7071574332649
-1225994400,548.5332944762629
-1226016000,619.6125200416544
-1226037600,721.2760592722431
-1226059200,830.0954451472978
-1226080800,920.5603196607155
-1226102400,971.3467231368722
-1226124000,970.635675881821
-1226145600,919.1559495348253
-1226167200,830.1695533228165
-1226188800,726.3633565878374
-1226210400,634.3739465873152
-1226232000,578.2667887512415
-1226253600,573.566040686077
-1226275200,623.3158563580123
-1226296800,717.1712587472132
-1226318400,833.7812740150428
-1226340000,945.9174989754079
-1226361600,1027.116662771763
-1226383200,1058.2167038036155
-1226404800,1032.172160126205
-1226426400,955.9395751535849
-1226448000,848.9320265136402
-1226469600,738.3825446783692
-1226491200,652.7231152317531
-1226512800,122.91778142825031
-1226534400,127.03380291678923
-1226556000,142.26262759412742
-1226577600,165.22805091768024
-1226599200,190.60801463693895
-1226620800,212.42395984229324
-1226642400,225.50691103802396
-1226664000,226.78077364684677
-1226685600,216.04430264453876
-1226707200,196.05322970896995
-1226728800,171.8743971580046
-1226750400,748.3148492497778
-1226772000,675.7888000290897
-1226793600,661.3124647751896
-1226815200,710.3185851343285
-1226836800,812.6612602064542
-1226858400,944.9461793166995
-1226880000,1076.1509876660386
-1226901600,1175.1860130915284
-1226923200,1218.5527023888787
-1226944800,1196.2133055128206
-1226966400,1114.2062008595062
-1226988000,993.3279200066103
-1227009600,864.165279271635
-1227031200,759.6636032843628
-1227052800,707.0366570127511
-1227074400,721.0048694428842
-1227096000,800.0415811489435
-1227117600,926.5830116645803
-1227139200,1071.190646235585
diff --git a/tutorials/prompter.ipynb b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
similarity index 100%
rename from tutorials/prompter.ipynb
rename to tutorials/pipelines/mistral-prompter-pipeline.ipynb

From 9ecd5fafdc15f2f945fbfefbcd4d014a80ed5929 Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Tue, 1 Oct 2024 11:45:14 -0400
Subject: [PATCH 18/25] change message

---
 sigllm/primitives/prompting/gpt.py         |  2 +-
 sigllm/primitives/prompting/huggingface.py | 11 ++++-------
 2 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index 9a92377..f8e8a20 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -88,7 +88,7 @@ def detect(self, X, **kwargs):
 
         all_responses, all_probs = [], []
         for text in tqdm(X):
-            message = ' '.join(PROMPTS['user_message'], text[0], self.sep)
+            message = ' '.join(PROMPTS['user_message'], text, self.sep)
             response = openai.ChatCompletion.create(
                 model=self.name,
                 messages=[
diff --git a/sigllm/primitives/prompting/huggingface.py b/sigllm/primitives/prompting/huggingface.py
index 0d6b984..6842a9b 100644
--- a/sigllm/primitives/prompting/huggingface.py
+++ b/sigllm/primitives/prompting/huggingface.py
@@ -115,13 +115,10 @@ def detect(self, X, **kwargs):
         all_responses, all_generate_ids = [], []
 
         for text in tqdm(X):
-            text = text.flatten().tolist()
-            message = [
-                ' '.join(
-                    (PROMPTS['system_message'],
-                     PROMPTS['user_message'],
-                        x,
-                        '[RESPONSE]')) for x in text]
+            message = ' '.join(PROMPTS['system_message'],
+                                PROMPTS['user_message'],
+                                text,
+                                '[RESPONSE]')
 
             input_length = len(self.tokenizer.encode(message[0]))
 

From 5b171d91e1f9c7bdb439569abb91b6d08298fe0b Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Tue, 1 Oct 2024 11:56:53 -0400
Subject: [PATCH 19/25] fix lint

---
 sigllm/primitives/prompting/huggingface.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/sigllm/primitives/prompting/huggingface.py b/sigllm/primitives/prompting/huggingface.py
index 6842a9b..75e7e51 100644
--- a/sigllm/primitives/prompting/huggingface.py
+++ b/sigllm/primitives/prompting/huggingface.py
@@ -115,10 +115,9 @@ def detect(self, X, **kwargs):
         all_responses, all_generate_ids = [], []
 
         for text in tqdm(X):
-            message = ' '.join(PROMPTS['system_message'],
-                                PROMPTS['user_message'],
-                                text,
-                                '[RESPONSE]')
+            system_message = PROMPTS['system_message']
+            user_message = PROMPTS['user_message']
+            message = ' '.join(system_message, user_message, text, '[RESPONSE]')
 
             input_length = len(self.tokenizer.encode(message[0]))
 

From d4b57beacd885200c146c4da95b532143818cc7b Mon Sep 17 00:00:00 2001
From: Linh Nguyen <linhnk@dai-desk34.lids.mit.edu>
Date: Wed, 2 Oct 2024 10:14:58 -0400
Subject: [PATCH 20/25] tutorial

---
 .../pipelines/prompter/mistral_prompter.json  |   4 +-
 ...g.anomalies.find_anomalies_in_windows.json |   4 +-
 ....prompting.anomalies.format_anomalies.json |   2 +-
 sigllm/primitives/prompting/huggingface.py    |   2 +-
 .../pipelines/mistral-prompter-pipeline.ipynb | 810 ++++--------------
 5 files changed, 156 insertions(+), 666 deletions(-)

diff --git a/sigllm/pipelines/prompter/mistral_prompter.json b/sigllm/pipelines/prompter/mistral_prompter.json
index 79081ab..0bc3e10 100644
--- a/sigllm/pipelines/prompter/mistral_prompter.json
+++ b/sigllm/pipelines/prompter/mistral_prompter.json
@@ -8,7 +8,7 @@
         "sigllm.primitives.prompting.huggingface.HF",
         "sigllm.primitives.transformation.format_as_integer",
         "sigllm.primitives.prompting.anomalies.val2idx",
-        "sigllm.primitives.prompting.anomalies.find_anomalies_in_window",
+        "sigllm.primitives.prompting.anomalies.find_anomalies_in_windows",
         "sigllm.primitives.prompting.anomalies.merge_anomalous_sequences",
         "sigllm.primitives.prompting.anomalies.format_anomalies"
     ],
@@ -33,7 +33,7 @@
             "name": "mistralai/Mistral-7B-Instruct-v0.2",
             "samples": 10
         },
-        "sigllm.primitives.prompting.anomalies.find_anomalies_in_window#1": {
+        "sigllm.primitives.prompting.anomalies.find_anomalies_in_windows#1": {
             "alpha": 0.4
         },
         "sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1": {
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.find_anomalies_in_windows.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.find_anomalies_in_windows.json
index be11b49..64648aa 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.find_anomalies_in_windows.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.find_anomalies_in_windows.json
@@ -1,5 +1,5 @@
 {
-    "name": "sigllm.primitives.prompting.anomalies.find_anomalies_in_window",
+    "name": "sigllm.primitives.prompting.anomalies.find_anomalies_in_windows",
     "contributors": [
         "Sarah Alnegheimish <smish@mit.edu>",
         "Linh Nguyen <linhnk@mit.edu>"
@@ -10,7 +10,7 @@
         "subtype": "merger"
     },
     "modalities": [],
-    "primitive": "sigllm.primitives.prompting.anomalies.find_anomalies_in_window",
+    "primitive": "sigllm.primitives.prompting.anomalies.find_anomalies_in_windows",
     "produce": {
         "args": [
             {
diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.format_anomalies.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.format_anomalies.json
index aa54eb0..76990d0 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.format_anomalies.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.anomalies.format_anomalies.json
@@ -24,7 +24,7 @@
         ],
         "output": [
             {
-                "name": "merged_intervals",
+                "name": "anomalies",
                 "type": "List"
             }
         ]
diff --git a/sigllm/primitives/prompting/huggingface.py b/sigllm/primitives/prompting/huggingface.py
index 75e7e51..6143dbe 100644
--- a/sigllm/primitives/prompting/huggingface.py
+++ b/sigllm/primitives/prompting/huggingface.py
@@ -117,7 +117,7 @@ def detect(self, X, **kwargs):
         for text in tqdm(X):
             system_message = PROMPTS['system_message']
             user_message = PROMPTS['user_message']
-            message = ' '.join(system_message, user_message, text, '[RESPONSE]')
+            message = ' '.join([system_message, user_message, text, '[RESPONSE]'])
 
             input_length = len(self.tokenizer.encode(message[0]))
 
diff --git a/tutorials/pipelines/mistral-prompter-pipeline.ipynb b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
index 36c0ab0..8799719 100644
--- a/tutorials/pipelines/mistral-prompter-pipeline.ipynb
+++ b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
@@ -2,14 +2,22 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "c4cc3835",
+   "execution_count": null,
+   "id": "76f73dbe-645a-4ed5-b042-ab14a1e330ea",
    "metadata": {},
    "outputs": [],
    "source": [
     "import warnings; warnings.simplefilter('ignore')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "67b19cca-149e-4ec1-8cff-11e712c34c29",
+   "metadata": {},
+   "source": [
+    "1. Data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -57,6 +65,37 @@
     "plt.plot(data['value']);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6b16f040-63b1-4171-8b1c-90c4d721d641",
+   "metadata": {},
+   "source": [
+    "if you want a quick test of how this pipeline works, uncomment the cell below to save time. We will look at a small segment of the time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1029c7ee-8a42-4452-8bc0-20c0fb45b8d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# start = 900\n",
+    "# end = start + 200\n",
+    "\n",
+    "# data = data.iloc[start: end]\n",
+    "\n",
+    "# plt.plot(data['value']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "409dabf0-be06-41fc-8793-01872c2a3055",
+   "metadata": {},
+   "source": [
+    "2. Pipeline"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -66,7 +105,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe1fc2429b6a49fcb0d059b40d131a57",
+       "model_id": "c579f8c14788475d88502bdd9d3937f7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -94,10 +133,10 @@
     "    \"mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1\": {\n",
     "        \"interval\": 3600\n",
     "    }, \n",
-    "    \"sigllm.primitives.prompting.anomalies.ano_within_windows#1\": {\n",
+    "    \"sigllm.primitives.prompting.anomalies.find_anomalies_in_windows#1\": {\n",
     "        \"alpha\": 1.0\n",
     "    },\n",
-    "    \"sigllm.primitives.prompting.anomalies.merge_anomaly_seq#1\": {\n",
+    "    \"sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1\": {\n",
     "        \"beta\": 1.0\n",
     "    }\n",
     "}\n",
@@ -122,10 +161,9 @@
        " 'sigllm.primitives.prompting.huggingface.HF',\n",
        " 'sigllm.primitives.transformation.format_as_integer',\n",
        " 'sigllm.primitives.prompting.anomalies.val2idx',\n",
-       " 'sigllm.primitives.prompting.anomalies.ano_within_windows',\n",
-       " 'sigllm.primitives.prompting.anomalies.merge_anomaly_seq',\n",
-       " 'sigllm.primitives.prompting.anomalies.idx2time',\n",
-       " 'sigllm.primitives.prompting.anomalies.timestamp2interval']"
+       " 'sigllm.primitives.prompting.anomalies.find_anomalies_in_windows',\n",
+       " 'sigllm.primitives.prompting.anomalies.merge_anomalous_sequences',\n",
+       " 'sigllm.primitives.prompting.anomalies.format_anomalies']"
       ]
      },
      "execution_count": 6,
@@ -263,7 +301,7 @@
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'X', 'minimum'])"
+       "dict_keys(['timestamp', 'X', 'minimum', 'decimal'])"
       ]
      },
      "execution_count": 11,
@@ -339,7 +377,7 @@
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'X', 'first_index', 'window_size', 'step_size'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'X', 'first_index', 'window_size', 'step_size'])"
       ]
      },
      "execution_count": 14,
@@ -396,7 +434,7 @@
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X', 'X_str'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X', 'X_str'])"
       ]
      },
      "execution_count": 16,
@@ -440,7 +478,7 @@
     {
      "data": {
       "text/plain": [
-       "numpy.str_"
+       "str"
       ]
      },
      "execution_count": 18,
@@ -472,55 +510,55 @@
      "output_type": "stream",
      "text": [
       "  0%|                                                    | 0/37 [00:00<?, ?it/s]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "2024-09-19 13:22:37.977012: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "2024-09-19 13:22:37.977047: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "2024-09-19 13:22:37.977082: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-09-19 13:22:37.983817: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "2024-10-01 20:27:58.821861: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2024-10-01 20:27:58.821998: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2024-10-01 20:27:58.823828: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-01 20:27:58.992915: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
       "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "2024-09-19 13:22:38.716582: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
-      "  3%|█▏                                          | 1/37 [00:24<14:50, 24.75s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "  5%|██▍                                         | 2/37 [00:47<13:52, 23.80s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "  8%|███▌                                        | 3/37 [01:10<13:14, 23.38s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 11%|████▊                                       | 4/37 [01:33<12:45, 23.19s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 14%|█████▉                                      | 5/37 [01:56<12:19, 23.10s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 16%|███████▏                                    | 6/37 [02:19<11:53, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 19%|████████▎                                   | 7/37 [02:42<11:29, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 22%|█████████▌                                  | 8/37 [03:05<11:06, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 24%|██████████▋                                 | 9/37 [03:28<10:43, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 27%|███████████▌                               | 10/37 [03:51<10:20, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 30%|████████████▊                              | 11/37 [04:14<09:56, 22.96s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 32%|█████████████▉                             | 12/37 [04:37<09:33, 22.95s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 35%|███████████████                            | 13/37 [05:00<09:10, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 38%|████████████████▎                          | 14/37 [05:22<08:47, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 41%|█████████████████▍                         | 15/37 [05:45<08:24, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 43%|██████████████████▌                        | 16/37 [06:08<08:01, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 46%|███████████████████▊                       | 17/37 [06:31<07:38, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 49%|████████████████████▉                      | 18/37 [06:54<07:15, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 51%|██████████████████████                     | 19/37 [07:17<06:52, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 54%|███████████████████████▏                   | 20/37 [07:40<06:29, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 57%|████████████████████████▍                  | 21/37 [08:03<06:07, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 59%|█████████████████████████▌                 | 22/37 [08:26<05:44, 22.95s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 62%|██████████████████████████▋                | 23/37 [08:49<05:21, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 65%|███████████████████████████▉               | 24/37 [09:12<04:58, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 68%|█████████████████████████████              | 25/37 [09:35<04:35, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 70%|██████████████████████████████▏            | 26/37 [09:58<04:12, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 73%|███████████████████████████████▍           | 27/37 [10:21<03:49, 22.95s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 76%|████████████████████████████████▌          | 28/37 [10:44<03:26, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 78%|█████████████████████████████████▋         | 29/37 [11:07<03:03, 22.92s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 81%|██████████████████████████████████▊        | 30/37 [11:29<02:40, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 84%|████████████████████████████████████       | 31/37 [11:52<02:17, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 86%|█████████████████████████████████████▏     | 32/37 [12:15<01:54, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 89%|██████████████████████████████████████▎    | 33/37 [12:38<01:31, 22.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 92%|███████████████████████████████████████▌   | 34/37 [13:01<01:08, 22.93s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 95%|████████████████████████████████████████▋  | 35/37 [13:24<00:45, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 97%|█████████████████████████████████████████▊ | 36/37 [13:47<00:23, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "100%|███████████████████████████████████████████| 37/37 [14:10<00:00, 23.00s/it]\n"
+      "2024-10-01 20:28:00.135504: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+      "  3%|█▏                                          | 1/37 [00:27<16:12, 27.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "  5%|██▍                                         | 2/37 [00:49<14:21, 24.63s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "  8%|███▌                                        | 3/37 [01:12<13:31, 23.85s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 11%|████▊                                       | 4/37 [01:35<12:55, 23.49s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 14%|█████▉                                      | 5/37 [01:58<12:25, 23.29s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 16%|███████▏                                    | 6/37 [02:21<11:58, 23.17s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 19%|████████▎                                   | 7/37 [02:44<11:33, 23.10s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 22%|█████████▌                                  | 8/37 [03:07<11:08, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 24%|██████████▋                                 | 9/37 [03:30<10:44, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 27%|███████████▌                               | 10/37 [03:53<10:20, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 30%|████████████▊                              | 11/37 [04:16<09:58, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 32%|█████████████▉                             | 12/37 [04:39<09:35, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 35%|███████████████                            | 13/37 [05:02<09:11, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 38%|████████████████▎                          | 14/37 [05:25<08:48, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 41%|█████████████████▍                         | 15/37 [05:48<08:25, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 43%|██████████████████▌                        | 16/37 [06:11<08:02, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 46%|███████████████████▊                       | 17/37 [06:34<07:39, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 49%|████████████████████▉                      | 18/37 [06:57<07:16, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 51%|██████████████████████                     | 19/37 [07:20<06:53, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 54%|███████████████████████▏                   | 20/37 [07:43<06:30, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 57%|████████████████████████▍                  | 21/37 [08:06<06:07, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 59%|█████████████████████████▌                 | 22/37 [08:29<05:44, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 62%|██████████████████████████▋                | 23/37 [08:52<05:22, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 65%|███████████████████████████▉               | 24/37 [09:15<04:59, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 68%|█████████████████████████████              | 25/37 [09:38<04:36, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 70%|██████████████████████████████▏            | 26/37 [10:01<04:13, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 73%|███████████████████████████████▍           | 27/37 [10:24<03:50, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 76%|████████████████████████████████▌          | 28/37 [10:47<03:27, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 78%|█████████████████████████████████▋         | 29/37 [11:10<03:04, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 81%|██████████████████████████████████▊        | 30/37 [11:33<02:41, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 84%|████████████████████████████████████       | 31/37 [11:56<02:18, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 86%|█████████████████████████████████████▏     | 32/37 [12:19<01:55, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 89%|██████████████████████████████████████▎    | 33/37 [12:42<01:32, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 92%|███████████████████████████████████████▌   | 34/37 [13:05<01:09, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 95%|████████████████████████████████████████▋  | 35/37 [13:28<00:46, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 97%|█████████████████████████████████████████▊ | 36/37 [13:51<00:23, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "100%|███████████████████████████████████████████| 37/37 [14:14<00:00, 23.10s/it]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'X', 'y_hat'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'X', 'y_hat'])"
       ]
      },
      "execution_count": 19,
@@ -536,42 +574,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "id": "d587d1d3-9325-4d45-9e15-d69d2d16bb6d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[['16,13,17,12,11,15,14,14,10,11,14,15,31,52,53,43,46,43,34,38,32,25,22,18,15,17,19,15,17,12,11,17,23,22,29,51,51,51,41,34,27,27,34,28,32,25,20,20,23,17,19,16,14,12,16,21,28,47,54,50,53,60,51,52,42,32,34,24,24,21,21,22,25,22,16,17,12,13,17,22,27,44,47,54,66,54,58,42,39,36,32,27,23,21,21,19,24,22,19,13,1',\n",
-       "  '16,13,17,24,21,14,12,11,15,18,14,10,11,14,15,31,52,59,44,47,67,62,60,59,71,73,78,72,73,72,68,63,56,53,43,36,34,32,35,38,25,22,21,22,16,12,11,12,17,19,27,44,47,54,66,58,34,39,36,32,23,21,19,24,22,13,11,15,15,20,22,28,47,54,50,53,60,51,52,42,34,32,21,22,25,22,19,17,19,15,12,11,17,23,22,29,47,53,49,45,4',\n",
-       "  '30,25,23,32,41,16,13,17,53,78,51,51,51,24,19,34,67,62,59,71,63,56,43,36,30,17,13,17,24,44,47,54,66,54,58,39,32,15,18,17,15,10,11,15,31,52,49,45,38,22,19,18,17,19,15,12,17,23,31,52,40,44,35,38,25,15,18,17,11,11,15,14,14,10,11,22,25,34,54,43,49,45,36,35,28,24,29,21,12,11,12,17,19,24,40,53,46,43,36,24,3',\n",
-       "  '16,13,17,12,11,15,14,14,10,11,14,15,23,19,18,20,19,15,11,17,22,31,52,43,49,45,44,36,35,32,25,22,18,19,20,17,19,15,12,22,40,53,44,36,38,32,24,17,12,11,12,17,19,27,44,64,57,57,51,42,39,36,27,23,21,19,24,22,19,13,11,15,20,22,34,47,54,47,66,54,58,43,39,36,32,27,23,19,24,22,19,13,11,15,14,12,16,19,47,64,5',\n",
-       "  '30,21,26,53,78,74,51,41,24,16,14,12,15,19,47,44,59,36,23,19,11,15,31,40,34,28,17,13,11,15,14,14,10,11,14,34,67,62,59,71,63,56,35,30,32,22,19,18,20,17,19,15,12,11,17,23,22,38,54,57,66,54,43,38,32,25,18,17,15,14,14,10,11,14,34,47,53,43,36,34,21,25,21,22,19,16,12,13,17,22,27,44,47,57,64,51,39,36,27,23,2',\n",
-       "  '13,11,15,14,10,11,14,15,53,78,73,51,51,51,41,24,16,13,17,27,34,67,62,59,71,63,56,43,36,30,24,20,20,17,13,19,34,62,14,12,16,47,54,47,44,36,38,32,21,15,18,17,15,16,14,14,10,11,14,15,31,52,49,38,35,34,24,18,20,19,17,15,17,22,40,53,46,34,38,32,24,19,13,11,17,23,22,29',\n",
-       "  '16,13,11,15,14,14,10,11,14,15,31,52,49,45,38,32,22,18,19,17,15,12,17,23,22,29,53,71,78,73,58,59,71,78,66,54,36,30,26,24,24,20,20,23,22,16,17,12,13,17,22,44,47,54,66,54,34,32,25,22,15,18,17,17,15,16,14,14,10,11,14,15,34,47,54,67,52,57,57,51,44,43,36,39,36,32,27,23,21,19,24,22,19,13,11,15,20,22,28,47,6',\n",
-       "  '16,13,17,12,11,15,14,10,11,14,15,31,52,43,49,45,44,36,35,28,24,20,29,21,22,21,16,12,11,17,23,44,47,54,66,54,58,42,39,36,32,23,21,19,24,22,19,13,11,15,20,22,28,47,64,52,57,57,51,43,44,36,38,25,22,15,18,17,17,15,16,14,14,9,11,14,15,34,28,32,25,20,20,23,19,19,27,40,53,54,43,46,43,34,38,32,25,22,15,18,17',\n",
-       "  '16,13,11,15,14,14,10,11,14,15,51,51,51,41,17,19,16,14,12,16,32,19,13,11,15,24,23,19,18,20,19,15,12,17,23,22,29,44,47,54,66,54,58,36,36,28,23,21,19,24,34,38,32,25,22,15,18,17,17,15,14,10,11,14,15,31,52,49,43,35,30,32,22,24,22,19,18,20,19,17,19,15,12,11,17,23,22,29,34,34,32,25,22,15,18,17,17,15,14,10,1',\n",
-       "  '21,23,34,51,51,51,41,16,13,17,24,34,12,11,15,31,67,40,34,38,25,18,22,19,13,11,15,20,22,31,47,54,43,46,35,30,24,17,19,16,14,12,16,47,54,44,36,32,25,22,19,18,20,19,17,19,15,20,10,11,14,15,34,62,59,59,71,78,73,68,72,52,27,27,34,17,19,27,24,27,34,52,57,57,51,40,36,24,21,13,17,12,17,22,27,44,47,54,66,54,4'],\n",
-       " ['13,11,15,11,13,15,14,10,11,14,15,31,52,43,49,35,18,18,18,14,14,10,22,26,54,47,44,49,49,41,37,34,22,19,19,17,17,15,12,11,17,23,22,29,47,46,47,57,51,39,35,21,19,18,20,22,20,16,11,11,13,22,19,31,47,47,39,49,49,44,41,37,34,32,22,19,19,17,17,15,12,11,17,23,22,29,49,46,47,54,57,51,40,39,35,28,21,23,19,18,2',\n",
-       "  '13,11,15,12,11,12,17,19,14,14,10,11,14,15,22,26,31,44,47,53,54,51,43,38,32,21,18,17,17,15,16,14,14,10,11,14,15,38,49,46,49,42,41,37,34,32,21,19,19,17,17,15,22,20,16,11,11,13,22,19,47,52,43,49,45,44,36,30,32,22,20,16,11,11,13,22,19,47,57,51,40',\n",
-       "  '13,11,11,12,15,14,14,10,11,14,15,31,52,43,49,45,44,36,30,32,22,19,18,20,19,17,19,15,12,11,17,23,22,29,49,46,47,54,57,51,40,39,35,28,21,23,19,18,20,22,20,16,11,11,13,22,19,26,47,47,44,49,49,42,41,37,34,32,22,19,19,17,17,15,22,67,62,60,59,71,63,56,43,36,30,26,24,24,20,20,23,17,19,16,14,12,16,21,28,47,5',\n",
-       "  '13,11,12,13,11,12,17,19,13,11,15,20,14,12,16,14,14,14,10,11,14,15,31,52,43,36,35,28,21,23,18,18,22,29,49,46,47,44,49,42,37,34,32,22,19,15,13,17,23,22,29,44,53,43,46,43,38,32,25,22,15,18,17,15,14,10,11,14,19,31,66,54,51,58,57,51,44,40,36,35,24,29,21,21,16,11,11,13,22,19,26,47,47,41,49,49,37,41,34,32,2',\n",
-       "  '13,11,15,12,13,17,19,16,14,12,16,21,31,44,47,54,66,47,47,49,49,41,38,32,34,35,19,18,20,19,15,11,13,19,26,40,53,43,49,36,30,32,18,17,17,15,16,14,14,10,11,14,15,22,29,49,57,51,57,51,40,39,35,28,21,23,19,18,20,22,20,16,11,11,13,22,19,26,47,44,42,46,43,34,38,32,25,22,15,18,17,17,15,16,14,14,10,11,14,15,3',\n",
-       "  '13,11,12,13,11,15,20,19,14,11,15,22,18,17,15,14,10,11,14,15,31,16,11,13,22,19,26,44,18,14,46,49,41,38,32,19,18,22,20,15,11,17,23,22,29,47,15,12,17,22,13,37,47,44,41,35,28,23,19,18,20,22,10,11,13,22,19,47,47,42,43,34,35,32,21,19,17,15,12,11,17,15,14,14,10,11,14,15,31,52,43,49,45,45,44,36,30,32,22,24,2',\n",
-       "  '13,11,11,12,15,14,14,10,11,14,15,31,52,41,49,16,22,26,29,44,47,51,57,58,54,53,38,35,32,23,18,22,19,15,12,17,23,41,46,43,36,34,21,21,19,18,20,17,15,12,13,17,19,27,47,54,57,51,40,39,35,28,23,19,18,20,22,20,16,11,17,22,19,26,47,47,44,49,49,41,37,34,32,22,19,19,15,17,15,16,14,14,10,11,14,15,36,54,43,49,4',\n",
-       "  '13,11,15,11,13,11,22,19,16,11,13,17,22,27,19,18,29,49,14,12,17,23,15,18,17,15,16,14,14,10,11,14,15,31,52,43,49,45,44,36,30,32,22,19,18,20,19,17,19,15,12,11,17,23,44,47,54,66,36,35,28,21,23,19,18,20,22,20,16,11,11,13,22,19,26,47,47,44,49,49,41,41,37,34,32,22,19,19,17,17,15,15,13,11,14,15,38,32,25,22,2',\n",
-       "  '13,11,15,11,13,15,15,13,11,12,12,11,13,10,12,39,6,14,10,3,28,18,18,5,17,38,26,33,7,24,4,25,25,5,24,10,11,18,7,12,17,16,15,15,10,20,24,42,40,41,29,33,32,26,25,18,18,22,8,10,29,49,31,40,27,14,18,32,14,32,12,23,32,15,25,31,30,18,16,26,29,15,9,11,17,15,15,11,13,19,26,45,16,37,31,14,18,29,12,20,17,25,32,2',\n",
-       "  '13,11,12,15,14,14,10,11,13,15,31,52,43,49,45,39,23,18,18,17,17,15,12,11,17,23,44,47,54,66,54,58,42,39,36,32,23,15,18,17,17,15,16,14,11,12,13,19,26,47,47,44,49,49,41,37,34,32,22,19,19,17,17,15,11,11,13,22,19,26,47,47,44,49,49,41,37,34,32,22,19,22,21,16,11,11,13,22,19,26,40,39,35,28,21,23,19,18,20,22,2']]"
+       "(37, 10)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "context['y_hat'][:2]"
+    "len(context['y_hat']), len(context['y_hat'][0])"
    ]
   },
   {
@@ -585,17 +604,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "id": "5ac4fc58-055d-4210-b330-0832508c1cf4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -608,186 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "c5948478-0acd-4daf-b4b9-ef4ce83f3693",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[array([16., 13., 17., 12., 11., 15., 14., 14., 10., 11., 14., 15., 31.,\n",
-       "               52., 53., 43., 46., 43., 34., 38., 32., 25., 22., 18., 15., 17.,\n",
-       "               19., 15., 17., 12., 11., 17., 23., 22., 29., 51., 51., 51., 41.,\n",
-       "               34., 27., 27., 34., 28., 32., 25., 20., 20., 23., 17., 19., 16.,\n",
-       "               14., 12., 16., 21., 28., 47., 54., 50., 53., 60., 51., 52., 42.,\n",
-       "               32., 34., 24., 24., 21., 21., 22., 25., 22., 16., 17., 12., 13.,\n",
-       "               17., 22., 27., 44., 47., 54., 66., 54., 58., 42., 39., 36., 32.,\n",
-       "               27., 23., 21., 21., 19., 24., 22., 19., 13.,  1.])              ,\n",
-       "        array([16., 13., 17., 24., 21., 14., 12., 11., 15., 18., 14., 10., 11.,\n",
-       "               14., 15., 31., 52., 59., 44., 47., 67., 62., 60., 59., 71., 73.,\n",
-       "               78., 72., 73., 72., 68., 63., 56., 53., 43., 36., 34., 32., 35.,\n",
-       "               38., 25., 22., 21., 22., 16., 12., 11., 12., 17., 19., 27., 44.,\n",
-       "               47., 54., 66., 58., 34., 39., 36., 32., 23., 21., 19., 24., 22.,\n",
-       "               13., 11., 15., 15., 20., 22., 28., 47., 54., 50., 53., 60., 51.,\n",
-       "               52., 42., 34., 32., 21., 22., 25., 22., 19., 17., 19., 15., 12.,\n",
-       "               11., 17., 23., 22., 29., 47., 53., 49., 45.,  4.])              ,\n",
-       "        array([30., 25., 23., 32., 41., 16., 13., 17., 53., 78., 51., 51., 51.,\n",
-       "               24., 19., 34., 67., 62., 59., 71., 63., 56., 43., 36., 30., 17.,\n",
-       "               13., 17., 24., 44., 47., 54., 66., 54., 58., 39., 32., 15., 18.,\n",
-       "               17., 15., 10., 11., 15., 31., 52., 49., 45., 38., 22., 19., 18.,\n",
-       "               17., 19., 15., 12., 17., 23., 31., 52., 40., 44., 35., 38., 25.,\n",
-       "               15., 18., 17., 11., 11., 15., 14., 14., 10., 11., 22., 25., 34.,\n",
-       "               54., 43., 49., 45., 36., 35., 28., 24., 29., 21., 12., 11., 12.,\n",
-       "               17., 19., 24., 40., 53., 46., 43., 36., 24.,  3.])              ,\n",
-       "        array([16., 13., 17., 12., 11., 15., 14., 14., 10., 11., 14., 15., 23.,\n",
-       "               19., 18., 20., 19., 15., 11., 17., 22., 31., 52., 43., 49., 45.,\n",
-       "               44., 36., 35., 32., 25., 22., 18., 19., 20., 17., 19., 15., 12.,\n",
-       "               22., 40., 53., 44., 36., 38., 32., 24., 17., 12., 11., 12., 17.,\n",
-       "               19., 27., 44., 64., 57., 57., 51., 42., 39., 36., 27., 23., 21.,\n",
-       "               19., 24., 22., 19., 13., 11., 15., 20., 22., 34., 47., 54., 47.,\n",
-       "               66., 54., 58., 43., 39., 36., 32., 27., 23., 19., 24., 22., 19.,\n",
-       "               13., 11., 15., 14., 12., 16., 19., 47., 64.,  5.])              ,\n",
-       "        array([30., 21., 26., 53., 78., 74., 51., 41., 24., 16., 14., 12., 15.,\n",
-       "               19., 47., 44., 59., 36., 23., 19., 11., 15., 31., 40., 34., 28.,\n",
-       "               17., 13., 11., 15., 14., 14., 10., 11., 14., 34., 67., 62., 59.,\n",
-       "               71., 63., 56., 35., 30., 32., 22., 19., 18., 20., 17., 19., 15.,\n",
-       "               12., 11., 17., 23., 22., 38., 54., 57., 66., 54., 43., 38., 32.,\n",
-       "               25., 18., 17., 15., 14., 14., 10., 11., 14., 34., 47., 53., 43.,\n",
-       "               36., 34., 21., 25., 21., 22., 19., 16., 12., 13., 17., 22., 27.,\n",
-       "               44., 47., 57., 64., 51., 39., 36., 27., 23.,  2.])              ,\n",
-       "        array([13., 11., 15., 14., 10., 11., 14., 15., 53., 78., 73., 51., 51.,\n",
-       "               51., 41., 24., 16., 13., 17., 27., 34., 67., 62., 59., 71., 63.,\n",
-       "               56., 43., 36., 30., 24., 20., 20., 17., 13., 19., 34., 62., 14.,\n",
-       "               12., 16., 47., 54., 47., 44., 36., 38., 32., 21., 15., 18., 17.,\n",
-       "               15., 16., 14., 14., 10., 11., 14., 15., 31., 52., 49., 38., 35.,\n",
-       "               34., 24., 18., 20., 19., 17., 15., 17., 22., 40., 53., 46., 34.,\n",
-       "               38., 32., 24., 19., 13., 11., 17., 23., 22., 29.])              ,\n",
-       "        array([16., 13., 11., 15., 14., 14., 10., 11., 14., 15., 31., 52., 49.,\n",
-       "               45., 38., 32., 22., 18., 19., 17., 15., 12., 17., 23., 22., 29.,\n",
-       "               53., 71., 78., 73., 58., 59., 71., 78., 66., 54., 36., 30., 26.,\n",
-       "               24., 24., 20., 20., 23., 22., 16., 17., 12., 13., 17., 22., 44.,\n",
-       "               47., 54., 66., 54., 34., 32., 25., 22., 15., 18., 17., 17., 15.,\n",
-       "               16., 14., 14., 10., 11., 14., 15., 34., 47., 54., 67., 52., 57.,\n",
-       "               57., 51., 44., 43., 36., 39., 36., 32., 27., 23., 21., 19., 24.,\n",
-       "               22., 19., 13., 11., 15., 20., 22., 28., 47.,  6.])              ,\n",
-       "        array([16., 13., 17., 12., 11., 15., 14., 10., 11., 14., 15., 31., 52.,\n",
-       "               43., 49., 45., 44., 36., 35., 28., 24., 20., 29., 21., 22., 21.,\n",
-       "               16., 12., 11., 17., 23., 44., 47., 54., 66., 54., 58., 42., 39.,\n",
-       "               36., 32., 23., 21., 19., 24., 22., 19., 13., 11., 15., 20., 22.,\n",
-       "               28., 47., 64., 52., 57., 57., 51., 43., 44., 36., 38., 25., 22.,\n",
-       "               15., 18., 17., 17., 15., 16., 14., 14.,  9., 11., 14., 15., 34.,\n",
-       "               28., 32., 25., 20., 20., 23., 19., 19., 27., 40., 53., 54., 43.,\n",
-       "               46., 43., 34., 38., 32., 25., 22., 15., 18., 17.])              ,\n",
-       "        array([16., 13., 11., 15., 14., 14., 10., 11., 14., 15., 51., 51., 51.,\n",
-       "               41., 17., 19., 16., 14., 12., 16., 32., 19., 13., 11., 15., 24.,\n",
-       "               23., 19., 18., 20., 19., 15., 12., 17., 23., 22., 29., 44., 47.,\n",
-       "               54., 66., 54., 58., 36., 36., 28., 23., 21., 19., 24., 34., 38.,\n",
-       "               32., 25., 22., 15., 18., 17., 17., 15., 14., 10., 11., 14., 15.,\n",
-       "               31., 52., 49., 43., 35., 30., 32., 22., 24., 22., 19., 18., 20.,\n",
-       "               19., 17., 19., 15., 12., 11., 17., 23., 22., 29., 34., 34., 32.,\n",
-       "               25., 22., 15., 18., 17., 17., 15., 14., 10.,  1.])              ,\n",
-       "        array([21., 23., 34., 51., 51., 51., 41., 16., 13., 17., 24., 34., 12.,\n",
-       "               11., 15., 31., 67., 40., 34., 38., 25., 18., 22., 19., 13., 11.,\n",
-       "               15., 20., 22., 31., 47., 54., 43., 46., 35., 30., 24., 17., 19.,\n",
-       "               16., 14., 12., 16., 47., 54., 44., 36., 32., 25., 22., 19., 18.,\n",
-       "               20., 19., 17., 19., 15., 20., 10., 11., 14., 15., 34., 62., 59.,\n",
-       "               59., 71., 78., 73., 68., 72., 52., 27., 27., 34., 17., 19., 27.,\n",
-       "               24., 27., 34., 52., 57., 57., 51., 40., 36., 24., 21., 13., 17.,\n",
-       "               12., 17., 22., 27., 44., 47., 54., 66., 54.,  4.])              ],\n",
-       "       [array([13., 11., 15., 11., 13., 15., 14., 10., 11., 14., 15., 31., 52.,\n",
-       "               43., 49., 35., 18., 18., 18., 14., 14., 10., 22., 26., 54., 47.,\n",
-       "               44., 49., 49., 41., 37., 34., 22., 19., 19., 17., 17., 15., 12.,\n",
-       "               11., 17., 23., 22., 29., 47., 46., 47., 57., 51., 39., 35., 21.,\n",
-       "               19., 18., 20., 22., 20., 16., 11., 11., 13., 22., 19., 31., 47.,\n",
-       "               47., 39., 49., 49., 44., 41., 37., 34., 32., 22., 19., 19., 17.,\n",
-       "               17., 15., 12., 11., 17., 23., 22., 29., 49., 46., 47., 54., 57.,\n",
-       "               51., 40., 39., 35., 28., 21., 23., 19., 18.,  2.])              ,\n",
-       "        array([13., 11., 15., 12., 11., 12., 17., 19., 14., 14., 10., 11., 14.,\n",
-       "               15., 22., 26., 31., 44., 47., 53., 54., 51., 43., 38., 32., 21.,\n",
-       "               18., 17., 17., 15., 16., 14., 14., 10., 11., 14., 15., 38., 49.,\n",
-       "               46., 49., 42., 41., 37., 34., 32., 21., 19., 19., 17., 17., 15.,\n",
-       "               22., 20., 16., 11., 11., 13., 22., 19., 47., 52., 43., 49., 45.,\n",
-       "               44., 36., 30., 32., 22., 20., 16., 11., 11., 13., 22., 19., 47.,\n",
-       "               57., 51., 40.])                                                 ,\n",
-       "        array([13., 11., 11., 12., 15., 14., 14., 10., 11., 14., 15., 31., 52.,\n",
-       "               43., 49., 45., 44., 36., 30., 32., 22., 19., 18., 20., 19., 17.,\n",
-       "               19., 15., 12., 11., 17., 23., 22., 29., 49., 46., 47., 54., 57.,\n",
-       "               51., 40., 39., 35., 28., 21., 23., 19., 18., 20., 22., 20., 16.,\n",
-       "               11., 11., 13., 22., 19., 26., 47., 47., 44., 49., 49., 42., 41.,\n",
-       "               37., 34., 32., 22., 19., 19., 17., 17., 15., 22., 67., 62., 60.,\n",
-       "               59., 71., 63., 56., 43., 36., 30., 26., 24., 24., 20., 20., 23.,\n",
-       "               17., 19., 16., 14., 12., 16., 21., 28., 47.,  5.])              ,\n",
-       "        array([13., 11., 12., 13., 11., 12., 17., 19., 13., 11., 15., 20., 14.,\n",
-       "               12., 16., 14., 14., 14., 10., 11., 14., 15., 31., 52., 43., 36.,\n",
-       "               35., 28., 21., 23., 18., 18., 22., 29., 49., 46., 47., 44., 49.,\n",
-       "               42., 37., 34., 32., 22., 19., 15., 13., 17., 23., 22., 29., 44.,\n",
-       "               53., 43., 46., 43., 38., 32., 25., 22., 15., 18., 17., 15., 14.,\n",
-       "               10., 11., 14., 19., 31., 66., 54., 51., 58., 57., 51., 44., 40.,\n",
-       "               36., 35., 24., 29., 21., 21., 16., 11., 11., 13., 22., 19., 26.,\n",
-       "               47., 47., 41., 49., 49., 37., 41., 34., 32.,  2.])              ,\n",
-       "        array([13., 11., 15., 12., 13., 17., 19., 16., 14., 12., 16., 21., 31.,\n",
-       "               44., 47., 54., 66., 47., 47., 49., 49., 41., 38., 32., 34., 35.,\n",
-       "               19., 18., 20., 19., 15., 11., 13., 19., 26., 40., 53., 43., 49.,\n",
-       "               36., 30., 32., 18., 17., 17., 15., 16., 14., 14., 10., 11., 14.,\n",
-       "               15., 22., 29., 49., 57., 51., 57., 51., 40., 39., 35., 28., 21.,\n",
-       "               23., 19., 18., 20., 22., 20., 16., 11., 11., 13., 22., 19., 26.,\n",
-       "               47., 44., 42., 46., 43., 34., 38., 32., 25., 22., 15., 18., 17.,\n",
-       "               17., 15., 16., 14., 14., 10., 11., 14., 15.,  3.])              ,\n",
-       "        array([13., 11., 12., 13., 11., 15., 20., 19., 14., 11., 15., 22., 18.,\n",
-       "               17., 15., 14., 10., 11., 14., 15., 31., 16., 11., 13., 22., 19.,\n",
-       "               26., 44., 18., 14., 46., 49., 41., 38., 32., 19., 18., 22., 20.,\n",
-       "               15., 11., 17., 23., 22., 29., 47., 15., 12., 17., 22., 13., 37.,\n",
-       "               47., 44., 41., 35., 28., 23., 19., 18., 20., 22., 10., 11., 13.,\n",
-       "               22., 19., 47., 47., 42., 43., 34., 35., 32., 21., 19., 17., 15.,\n",
-       "               12., 11., 17., 15., 14., 14., 10., 11., 14., 15., 31., 52., 43.,\n",
-       "               49., 45., 45., 44., 36., 30., 32., 22., 24.,  2.])              ,\n",
-       "        array([13., 11., 11., 12., 15., 14., 14., 10., 11., 14., 15., 31., 52.,\n",
-       "               41., 49., 16., 22., 26., 29., 44., 47., 51., 57., 58., 54., 53.,\n",
-       "               38., 35., 32., 23., 18., 22., 19., 15., 12., 17., 23., 41., 46.,\n",
-       "               43., 36., 34., 21., 21., 19., 18., 20., 17., 15., 12., 13., 17.,\n",
-       "               19., 27., 47., 54., 57., 51., 40., 39., 35., 28., 23., 19., 18.,\n",
-       "               20., 22., 20., 16., 11., 17., 22., 19., 26., 47., 47., 44., 49.,\n",
-       "               49., 41., 37., 34., 32., 22., 19., 19., 15., 17., 15., 16., 14.,\n",
-       "               14., 10., 11., 14., 15., 36., 54., 43., 49.,  4.])              ,\n",
-       "        array([13., 11., 15., 11., 13., 11., 22., 19., 16., 11., 13., 17., 22.,\n",
-       "               27., 19., 18., 29., 49., 14., 12., 17., 23., 15., 18., 17., 15.,\n",
-       "               16., 14., 14., 10., 11., 14., 15., 31., 52., 43., 49., 45., 44.,\n",
-       "               36., 30., 32., 22., 19., 18., 20., 19., 17., 19., 15., 12., 11.,\n",
-       "               17., 23., 44., 47., 54., 66., 36., 35., 28., 21., 23., 19., 18.,\n",
-       "               20., 22., 20., 16., 11., 11., 13., 22., 19., 26., 47., 47., 44.,\n",
-       "               49., 49., 41., 41., 37., 34., 32., 22., 19., 19., 17., 17., 15.,\n",
-       "               15., 13., 11., 14., 15., 38., 32., 25., 22.,  2.])              ,\n",
-       "        array([13., 11., 15., 11., 13., 15., 15., 13., 11., 12., 12., 11., 13.,\n",
-       "               10., 12., 39.,  6., 14., 10.,  3., 28., 18., 18.,  5., 17., 38.,\n",
-       "               26., 33.,  7., 24.,  4., 25., 25.,  5., 24., 10., 11., 18.,  7.,\n",
-       "               12., 17., 16., 15., 15., 10., 20., 24., 42., 40., 41., 29., 33.,\n",
-       "               32., 26., 25., 18., 18., 22.,  8., 10., 29., 49., 31., 40., 27.,\n",
-       "               14., 18., 32., 14., 32., 12., 23., 32., 15., 25., 31., 30., 18.,\n",
-       "               16., 26., 29., 15.,  9., 11., 17., 15., 15., 11., 13., 19., 26.,\n",
-       "               45., 16., 37., 31., 14., 18., 29., 12., 20., 17., 25., 32.,  2.]),\n",
-       "        array([13., 11., 12., 15., 14., 14., 10., 11., 13., 15., 31., 52., 43.,\n",
-       "               49., 45., 39., 23., 18., 18., 17., 17., 15., 12., 11., 17., 23.,\n",
-       "               44., 47., 54., 66., 54., 58., 42., 39., 36., 32., 23., 15., 18.,\n",
-       "               17., 17., 15., 16., 14., 11., 12., 13., 19., 26., 47., 47., 44.,\n",
-       "               49., 49., 41., 37., 34., 32., 22., 19., 19., 17., 17., 15., 11.,\n",
-       "               11., 13., 22., 19., 26., 47., 47., 44., 49., 49., 41., 37., 34.,\n",
-       "               32., 22., 19., 22., 21., 16., 11., 11., 13., 22., 19., 26., 40.,\n",
-       "               39., 35., 28., 21., 23., 19., 18., 20., 22.,  2.])              ]],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "context['y'][:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "a934005e-5bce-4a47-831a-3cf1f0c92f28",
    "metadata": {},
    "outputs": [
@@ -797,7 +637,7 @@
        "(37, 10)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -817,17 +657,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "32b4ccf9-d8fa-4110-bc22-717d07e3ae0b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -840,288 +680,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "339db91e-cd35-4ae8-abc5-3ba52b6c0bbd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[array([  1,   3,   4,   5,   7,   8,  14,  15,  16,  17,  18,  20,  22,\n",
-       "                24,  25,  26,  27,  28,  30,  31,  32,  39,  41,  42,  43,  44,\n",
-       "                45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  58,  63,\n",
-       "                64,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
-       "                79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
-       "                92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
-       "               105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n",
-       "               118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 130, 133,\n",
-       "               135, 136, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,\n",
-       "               149, 150, 151, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162,\n",
-       "               163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175,\n",
-       "               176, 177, 180, 181, 183, 184, 185, 186, 187, 188, 189, 190, 191,\n",
-       "               192, 193, 194, 195, 196, 197, 198, 199])                        ,\n",
-       "        array([  1,   3,   4,   5,   7,   8,   9,  11,  13,  14,  15,  16,  18,\n",
-       "                20,  22,  24,  25,  26,  27,  28,  30,  31,  32,  33,  34,  35,\n",
-       "                36,  37,  38,  39,  41,  42,  43,  44,  45,  46,  47,  48,  49,\n",
-       "                50,  51,  52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,\n",
-       "                63,  64,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
-       "                78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
-       "                91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
-       "               104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
-       "               117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 130,\n",
-       "               133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146,\n",
-       "               147, 148, 149, 150, 151, 153, 154, 155, 157, 158, 159, 160, 161,\n",
-       "               162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,\n",
-       "               175, 176, 177, 178, 179, 180, 181, 183, 184, 185, 186, 187, 188,\n",
-       "               189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])         ,\n",
-       "        array([  0,   1,   2,   3,   5,   6,   7,   8,   9,  14,  15,  16,  17,\n",
-       "                18,  19,  22,  24,  25,  26,  27,  28,  29,  30,  31,  32,  33,\n",
-       "                34,  35,  39,  40,  41,  44,  45,  46,  47,  50,  51,  52,  54,\n",
-       "                55,  56,  57,  59,  60,  61,  62,  63,  64,  65,  67,  68,  71,\n",
-       "                72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  83,  85,  86,\n",
-       "                88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100,\n",
-       "               101, 102, 104, 105, 106, 107, 108, 109, 111, 112, 113, 115, 116,\n",
-       "               117, 118, 119, 120, 121, 122, 123, 124, 126, 127, 128, 130, 133,\n",
-       "               134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147,\n",
-       "               148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160,\n",
-       "               161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173,\n",
-       "               174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186,\n",
-       "               187, 188, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])    ,\n",
-       "        array([  0,   1,   3,   4,   5,   8,  14,  15,  16,  18,  20,  22,  24,\n",
-       "                25,  26,  27,  28,  31,  32,  39,  40,  41,  42,  43,  44,  46,\n",
-       "                47,  48,  49,  50,  51,  52,  53,  54,  55,  63,  64,  67,  68,\n",
-       "                69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  80,  81,  83,\n",
-       "                85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
-       "                98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,\n",
-       "               111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123,\n",
-       "               124, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,\n",
-       "               139, 140, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152,\n",
-       "               153, 154, 155, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166,\n",
-       "               167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179,\n",
-       "               180, 181, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,\n",
-       "               194, 195, 196, 197, 198])                                       ,\n",
-       "        array([  0,   1,   2,   3,   4,   5,   6,   8,   9,  10,  14,  15,  16,\n",
-       "                17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,  29,\n",
-       "                30,  31,  33,  34,  35,  40,  41,  42,  43,  44,  45,  46,  47,\n",
-       "                48,  49,  50,  51,  52,  53,  54,  55,  56,  57,  59,  60,  61,\n",
-       "                62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,\n",
-       "                75,  76,  77,  78,  79,  80,  81,  83,  85,  88,  89,  90,  91,\n",
-       "                92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
-       "               105, 106, 107, 108, 111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
-       "               120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 131, 132, 133,\n",
-       "               134, 135, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147,\n",
-       "               148, 149, 150, 151, 152, 153, 154, 155, 157, 158, 159, 160, 161,\n",
-       "               162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,\n",
-       "               175, 177, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,\n",
-       "               191, 192, 193, 194, 195, 196, 197, 198])                        ,\n",
-       "        array([  0,   2,   3,   4,   6,   7,   8,   9,  11,  14,  15,  16,  17,\n",
-       "                18,  19,  20,  22,  24,  26,  27,  28,  29,  33,  34,  35,  36,\n",
-       "                39,  40,  41,  42,  43,  44,  46,  48,  49,  50,  51,  52,  53,\n",
-       "                54,  55,  56,  57,  59,  60,  61,  62,  63,  64,  65,  67,  68,\n",
-       "                69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  80,  81,  83,\n",
-       "                85,  86,  88,  89,  90,  91,  92,  93,  94,  96,  97,  98,  99,\n",
-       "               100, 101, 102, 103, 104, 105, 106, 108, 112, 113, 114, 115, 116,\n",
-       "               117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 128, 130, 133,\n",
-       "               134, 135, 136, 137, 139, 140, 141, 142, 143, 144, 145, 146, 147,\n",
-       "               148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160,\n",
-       "               162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,\n",
-       "               175, 176, 177, 178, 180, 181, 182, 183, 184, 185, 186, 187, 188,\n",
-       "               189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])         ,\n",
-       "        array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  11,  14,  15,  16,\n",
-       "                18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,  29,  30,\n",
-       "                31,  32,  33,  34,  35,  36,  39,  41,  42,  43,  44,  45,  46,\n",
-       "                47,  48,  49,  50,  51,  52,  53,  54,  55,  56,  59,  60,  63,\n",
-       "                64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,\n",
-       "                77,  78,  79,  80,  81,  83,  85,  86,  88,  89,  90,  91,  92,\n",
-       "                93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105,\n",
-       "               106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
-       "               120, 121, 122, 123, 124, 125, 126, 127, 128, 130, 131, 132, 133,\n",
-       "               135, 136, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148,\n",
-       "               149, 150, 151, 153, 154, 155, 157, 158, 159, 160, 161, 162, 163,\n",
-       "               164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,\n",
-       "               177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189,\n",
-       "               190, 191, 192, 193, 194, 195, 196, 197, 198, 199])              ,\n",
-       "        array([  0,   1,   3,   4,   5,   7,   8,  14,  15,  16,  18,  20,  22,\n",
-       "                24,  25,  26,  27,  28,  30,  31,  32,  39,  40,  41,  42,  43,\n",
-       "                44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  63,\n",
-       "                64,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
-       "                79,  80,  81,  83,  85,  86,  87,  88,  89,  90,  91,  92,  93,\n",
-       "                94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106,\n",
-       "               107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
-       "               120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132,\n",
-       "               133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,\n",
-       "               146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158,\n",
-       "               159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,\n",
-       "               172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 183, 184, 185,\n",
-       "               186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,\n",
-       "               199])                                                           ,\n",
-       "        array([  2,   3,   4,   5,   6,   7,  14,  15,  16,  17,  18,  19,  22,\n",
-       "                24,  25,  26,  27,  28,  29,  30,  32,  39,  41,  44,  45,  46,\n",
-       "                47,  48,  49,  50,  51,  52,  54,  55,  63,  64,  65,  67,  68,\n",
-       "                69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,\n",
-       "                85,  86,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,\n",
-       "                99, 100, 101, 102, 104, 105, 106, 107, 108, 109, 112, 113, 115,\n",
-       "               116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,\n",
-       "               130, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,\n",
-       "               146, 147, 148, 149, 150, 151, 154, 155, 157, 158, 159, 160, 161,\n",
-       "               162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174,\n",
-       "               175, 176, 177, 178, 180, 181, 182, 183, 184, 185, 186, 187, 188,\n",
-       "               189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])         ,\n",
-       "        array([  0,   2,   3,   4,   5,   6,   9,  11,  13,  14,  15,  16,  17,\n",
-       "                18,  19,  20,  22,  24,  25,  26,  27,  28,  29,  33,  34,  35,\n",
-       "                36,  37,  38,  39,  40,  41,  42,  43,  44,  46,  47,  48,  49,\n",
-       "                50,  51,  52,  53,  54,  55,  56,  57,  59,  60,  63,  64,  65,\n",
-       "                67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,  80,\n",
-       "                81,  85,  86,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
-       "                98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 112, 113,\n",
-       "               114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n",
-       "               128, 130, 131, 132, 133, 134, 135, 136, 137, 139, 140, 142, 143,\n",
-       "               144, 145, 146, 147, 148, 149, 150, 151, 152, 154, 155, 156, 157,\n",
-       "               158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,\n",
-       "               171, 172, 173, 174, 175, 176, 177, 180, 181, 182, 183, 184, 185,\n",
-       "               186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198])],\n",
-       "       [array([  0,   1,   4,   5,   6,   8,   9,  10,  11,  12,  15,  23,  26,\n",
-       "                29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
-       "                45,  46,  48,  49,  52,  53,  54,  56,  57,  58,  59,  60,  61,\n",
-       "                62,  64,  65,  66,  68,  71,  73,  75,  76,  77,  78,  80,  81,\n",
-       "                82,  83,  84,  85,  86,  87,  88,  90,  91,  92,  93,  94,  95,\n",
-       "                97,  98, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110,\n",
-       "               112, 114, 115, 116, 117, 118, 120, 122, 123, 124, 125, 126, 127,\n",
-       "               128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 140, 143,\n",
-       "               144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,\n",
-       "               158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,\n",
-       "               171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,\n",
-       "               184, 185, 186, 187, 188, 190, 191, 192, 193, 194, 195, 196, 197,\n",
-       "               198, 199])                                                      ,\n",
-       "        array([  0,   1,   4,   6,   8,   9,  10,  11,  12,  15,  23,  24,  25,\n",
-       "                26,  29,  30,  32,  33,  34,  35,  36,  37,  38,  40,  41,  43,\n",
-       "                45,  46,  47,  48,  49,  52,  53,  54,  56,  57,  58,  59,  60,\n",
-       "                61,  62,  64,  65,  66,  68,  70,  72,  73,  76,  77,  78,  80,\n",
-       "                81,  82,  83,  84,  85,  86,  88,  90,  91,  92,  93,  94,  95,\n",
-       "                96, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113,\n",
-       "               114, 115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127,\n",
-       "               128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140,\n",
-       "               141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
-       "               155, 156, 158, 160, 161, 162, 163, 164, 165, 166, 170, 172, 173,\n",
-       "               174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186,\n",
-       "               187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199]),\n",
-       "        array([  0,   1,   4,   5,   6,   8,   9,  10,  11,  12,  14,  15,  16,\n",
-       "                17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,  29,\n",
-       "                30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  44,\n",
-       "                45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  56,  57,  58,\n",
-       "                59,  60,  61,  62,  64,  65,  66,  68,  70,  71,  72,  73,  75,\n",
-       "                76,  77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,\n",
-       "                90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,\n",
-       "               103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 114, 115, 116,\n",
-       "               117, 118, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,\n",
-       "               132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,\n",
-       "               145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,\n",
-       "               158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,\n",
-       "               171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,\n",
-       "               184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,\n",
-       "               197, 198, 199])                                                 ,\n",
-       "        array([  0,   1,   4,   5,   6,   7,   8,   9,  10,  11,  12,  14,  15,\n",
-       "                23,  24,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,\n",
-       "                37,  38,  39,  40,  41,  43,  45,  46,  47,  48,  49,  50,  51,\n",
-       "                52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  64,  65,\n",
-       "                66,  67,  68,  69,  70,  72,  73,  75,  76,  77,  78,  79,  80,\n",
-       "                81,  82,  83,  84,  85,  86,  87,  88,  90,  91,  92,  93,  94,\n",
-       "                95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107,\n",
-       "               108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,\n",
-       "               121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,\n",
-       "               134, 135, 136, 137, 138, 140, 141, 143, 144, 145, 146, 147, 148,\n",
-       "               149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161,\n",
-       "               162, 163, 164, 165, 166, 168, 169, 170, 171, 172, 173, 174, 175,\n",
-       "               176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188,\n",
-       "               189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199])         ,\n",
-       "        array([  0,   1,   4,   5,   6,   7,   8,   9,  10,  11,  12,  15,  23,\n",
-       "                24,  25,  26,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
-       "                39,  40,  41,  43,  45,  47,  48,  49,  52,  53,  54,  55,  56,\n",
-       "                57,  58,  59,  60,  61,  62,  64,  65,  66,  67,  68,  70,  71,\n",
-       "                72,  73,  75,  76,  77,  78,  80,  81,  82,  83,  84,  85,  86,\n",
-       "                87,  88,  91,  92,  93,  94,  95,  96,  97,  98, 100, 101, 102,\n",
-       "               103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 115, 116,\n",
-       "               117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
-       "               130, 131, 132, 133, 134, 135, 137, 138, 140, 141, 142, 143, 144,\n",
-       "               146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158,\n",
-       "               159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171,\n",
-       "               172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n",
-       "               185, 186, 187, 188, 189, 190, 192, 193, 194, 195, 196, 197, 198,\n",
-       "               199])                                                           ,\n",
-       "        array([  1,   4,   5,   6,   8,   9,  10,  11,  12,  14,  15,  23,  24,\n",
-       "                25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,\n",
-       "                38,  39,  40,  46,  47,  48,  49,  50,  51,  52,  53,  54,  56,\n",
-       "                57,  58,  59,  60,  61,  62,  64,  65,  70,  72,  73,  75,  76,\n",
-       "                77,  78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  90,\n",
-       "                95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106, 107,\n",
-       "               108, 109, 110, 111, 115, 116, 117, 118, 119, 120, 122, 123, 124,\n",
-       "               125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,\n",
-       "               138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,\n",
-       "               151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 168,\n",
-       "               169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
-       "               182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
-       "               195, 196, 197, 198, 199])                                       ,\n",
-       "        array([  0,   1,   2,   3,   4,   5,   6,   8,   9,  10,  11,  12,  13,\n",
-       "                15,  23,  24,  26,  29,  30,  31,  32,  33,  34,  35,  36,  37,\n",
-       "                38,  39,  40,  41,  43,  45,  46,  48,  49,  52,  53,  54,  56,\n",
-       "                57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  68,  69,  71,\n",
-       "                72,  73,  74,  75,  76,  77,  78,  80,  81,  82,  83,  84,  85,\n",
-       "                86,  87,  88,  90,  91,  92,  93,  94,  95,  96,  97,  98, 100,\n",
-       "               101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114,\n",
-       "               115, 116, 117, 118, 119, 120, 122, 123, 124, 125, 126, 127, 128,\n",
-       "               129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 140, 141, 143,\n",
-       "               144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,\n",
-       "               158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,\n",
-       "               171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,\n",
-       "               184, 185, 186, 187, 188, 190, 191, 192, 193, 194, 195, 196, 197,\n",
-       "               198, 199])                                                      ,\n",
-       "        array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
-       "                15,  23,  24,  25,  26,  29,  30,  31,  32,  33,  34,  35,  36,\n",
-       "                37,  38,  39,  40,  41,  46,  48,  49,  52,  53,  54,  55,  56,\n",
-       "                57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  72,\n",
-       "                73,  74,  75,  76,  77,  78,  80,  81,  82,  83,  84,  85,  86,\n",
-       "                87,  88,  90,  95,  96,  97,  98, 100, 101, 102, 103, 104, 105,\n",
-       "               106, 107, 108, 109, 110, 114, 115, 117, 118, 119, 120, 121, 122,\n",
-       "               123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,\n",
-       "               136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149,\n",
-       "               150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 162, 163,\n",
-       "               168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,\n",
-       "               181, 182, 183, 184, 185, 186, 187, 188, 190, 191, 192, 193, 194,\n",
-       "               195, 196, 197, 198, 199])                                       ,\n",
-       "        array([  0,   2,   3,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
-       "                25,  26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,\n",
-       "                39,  47,  48,  50,  51,  54,  55,  56,  57,  58,  59,  60,  61,\n",
-       "                62,  63,  70,  71,  73,  74,  75,  78,  79,  80,  81,  82,  83,\n",
-       "                84,  85,  86,  87,  94,  98,  99, 100, 101, 103, 105, 106, 107,\n",
-       "               108, 109, 110, 111, 112, 119, 120, 121, 122, 123, 124, 125, 126,\n",
-       "               127, 128, 129, 130, 131, 132, 133, 134, 135, 138, 139, 142, 143,\n",
-       "               144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n",
-       "               157, 158, 159, 160, 166, 167, 169, 171, 172, 173, 174, 175, 176,\n",
-       "               177, 178, 179, 180, 181, 182, 183, 187, 188, 189, 190, 191, 193,\n",
-       "               194, 195, 196, 197, 198, 199])                                  ,\n",
-       "        array([  0,   1,   4,   5,   6,   8,   9,  10,  11,  12,  15,  23,  24,\n",
-       "                26,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,\n",
-       "                41,  46,  47,  48,  49,  52,  53,  54,  56,  57,  58,  59,  60,\n",
-       "                61,  62,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  75,\n",
-       "                76,  77,  78,  80,  81,  82,  83,  84,  85,  86,  87,  88,  90,\n",
-       "                94,  95,  96,  97,  98, 100, 102, 103, 104, 105, 106, 107, 108,\n",
-       "               109, 110, 112, 114, 115, 117, 118, 120, 122, 123, 124, 125, 126,\n",
-       "               127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
-       "               140, 141, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154,\n",
-       "               155, 156, 157, 158, 160, 162, 163, 166, 167, 168, 169, 170, 171,\n",
-       "               172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n",
-       "               185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197,\n",
-       "               198, 199])                                                      ]],\n",
-       "      dtype=object)"
+       "(37, 10)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "context['y'][:2]"
+    "context['y'].shape"
    ]
   },
   {
@@ -1130,22 +705,22 @@
    "id": "bbcf3479-7ff2-4a81-86c0-e678a8f735c6",
    "metadata": {},
    "source": [
-    "Ano_within_window"
+    "find_anomalies_in_windows"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "17ce8982-2ba3-4508-a526-8a9960e0ecd3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1158,45 +733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "fa61503a-81af-4252-805c-19009cff7e52",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([array([  3,  14,  15,  16,  18,  22,  24,  26,  27,  28,  41,  44,  46,\n",
-       "               50,  51,  52,  54,  55,  63,  64,  67,  68,  71,  72,  73,  74,\n",
-       "               75,  76,  77,  78,  80,  81,  85,  88,  89,  90,  91,  92,  93,\n",
-       "               94,  96,  97,  98,  99, 100, 101, 102, 104, 105, 106, 108, 112,\n",
-       "              113, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 126, 128,\n",
-       "              133, 135, 136, 139, 142, 143, 144, 145, 146, 147, 148, 149, 150,\n",
-       "              151, 154, 155, 157, 158, 159, 160, 162, 163, 164, 165, 166, 167,\n",
-       "              168, 169, 170, 171, 172, 173, 174, 175, 177, 180, 181, 183, 184,\n",
-       "              185, 186, 187, 188, 190, 191, 192, 193, 194, 195, 196, 197, 198]),\n",
-       "       array([  6,   8,   9,  10,  11,  12,  26,  29,  30,  32,  33,  34,  35,\n",
-       "               36,  37,  48,  54,  56,  57,  58,  59,  60,  61,  62,  73,  78,\n",
-       "               80,  81,  82,  83,  84,  85,  86, 100, 103, 105, 106, 107, 108,\n",
-       "              109, 110, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,\n",
-       "              132, 133, 134, 135, 138, 143, 144, 146, 147, 148, 149, 150, 151,\n",
-       "              152, 153, 154, 155, 156, 158, 160, 172, 173, 174, 175, 176, 177,\n",
-       "              178, 179, 180, 181, 182, 183, 187, 188, 190, 193, 194, 195, 196,\n",
-       "              197, 198, 199])                                                 ],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "context['y'][:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "id": "5274e7a0-acf5-4b9f-9cb4-070357fc7c10",
    "metadata": {},
    "outputs": [
@@ -1206,7 +743,7 @@
        "(37,)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1221,22 +758,22 @@
    "id": "03002457-e136-445d-811a-97c20eb47d5d",
    "metadata": {},
    "source": [
-    "Merge anomaly seq"
+    "merge_anomalous_sequences"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "bd219c08-29b2-4809-934b-287dfac4e4fa",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1249,17 +786,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "id": "57f900db-d712-4ff1-986b-7f4dc1accda0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(17,)"
+       "(1319,)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1271,25 +808,25 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "5e41f194-47ee-4ce2-b9bf-5596cd99b810",
+   "id": "2eeac9a9-613a-43b8-abd9-6e455bf82a62",
    "metadata": {},
    "source": [
-    "Index2time"
+    "format_anomalies"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "6488a15c-74b6-49ec-b98c-817614919700",
+   "execution_count": 32,
+   "id": "7de109bd-8306-4753-8446-07f438e8c3e8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y', 'merged_intervals'])"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1302,61 +839,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "f8f419d4-34a8-4234-b73d-16916bf8243d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(17,)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "context['y'].shape"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "2eeac9a9-613a-43b8-abd9-6e455bf82a62",
-   "metadata": {},
-   "source": [
-    "timestamp2interval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "7de109bd-8306-4753-8446-07f438e8c3e8",
+   "execution_count": 34,
+   "id": "a37daebf-7091-4dab-93dc-928c7163778b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y', 'df'])"
+       "[(1309867201, 1314975601, 0)]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "step = 11\n",
-    "context = pipeline.fit(**context, start_=step, output_=step)\n",
-    "context.keys()"
+    "context['anomalies']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "a37daebf-7091-4dab-93dc-928c7163778b",
+   "execution_count": 37,
+   "id": "60064037-5ed6-49f3-ae13-5f4952f5eabf",
    "metadata": {},
    "outputs": [
     {
@@ -1388,20 +893,8 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1310119201</td>\n",
-       "      <td>1310796001</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1310936401</td>\n",
-       "      <td>1311379201</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1311415201</td>\n",
-       "      <td>1312736401</td>\n",
+       "      <td>1309867201</td>\n",
+       "      <td>1314975601</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1410,29 +903,29 @@
       ],
       "text/plain": [
        "        start         end  score\n",
-       "0  1310119201  1310796001      0\n",
-       "1  1310936401  1311379201      0\n",
-       "2  1311415201  1312736401      0"
+       "0  1309867201  1314975601      0"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "context['df']"
+    "import pandas as pd\n",
+    "\n",
+    "pd.DataFrame(context['anomalies'], columns=['start', 'end', 'score'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 41,
    "id": "98b221ef-ff0c-4705-9697-e2d240ff756e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1442,14 +935,11 @@
     }
    ],
    "source": [
-    "index, anomalies = list(map(context.get, ['timestamp', 'df']))\n",
+    "index, anomalies = list(map(context.get, ['timestamp', 'merged_intervals']))\n",
     "\n",
     "plt.plot(data['timestamp'], data['value'], label='original')\n",
     "\n",
-    "plt.axvspan(anomalies.iloc[0]['start'].item(), anomalies.iloc[0]['end'].item(), color='r', alpha=0.2, label='detected anomalies')\n",
-    "plt.axvspan(anomalies.iloc[1]['start'].item(), anomalies.iloc[1]['end'].item(), color='r', alpha=0.2, label='detected anomalies')\n",
-    "plt.axvspan(anomalies.iloc[2]['start'].item(), anomalies.iloc[2]['end'].item(), color='r', alpha=0.2, label='detected anomalies')\n",
-    "\n",
+    "plt.axvspan(*anomalies[0][:2], color='r', alpha=0.2, label='detected anomalies')\n",
     "plt.legend();"
    ]
   },
@@ -1464,9 +954,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "sigllm1-venv",
+   "display_name": "prompter",
    "language": "python",
-   "name": "python3"
+   "name": "prompter"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1478,7 +968,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,

From 0252b8be5a7fd01b7253c0d0b38f4e328860dd0b Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Thu, 3 Oct 2024 10:43:36 -0400
Subject: [PATCH 21/25] tutorial

---
 .../pipelines/mistral-prompter-pipeline.ipynb | 100 +++++++++++++++---
 1 file changed, 87 insertions(+), 13 deletions(-)

diff --git a/tutorials/pipelines/mistral-prompter-pipeline.ipynb b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
index 8799719..f65408d 100644
--- a/tutorials/pipelines/mistral-prompter-pipeline.ipynb
+++ b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
@@ -11,11 +11,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "67b19cca-149e-4ec1-8cff-11e712c34c29",
    "metadata": {},
    "source": [
-    "1. Data"
+    "This notebook requires **gpu** to run. See [mistral documentation](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for memory requirements.\n",
+    "## 1. Data"
    ]
   },
   {
@@ -66,6 +68,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6b16f040-63b1-4171-8b1c-90c4d721d641",
    "metadata": {},
@@ -89,11 +92,12 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "409dabf0-be06-41fc-8793-01872c2a3055",
    "metadata": {},
    "source": [
-    "2. Pipeline"
+    "## 2. Pipeline"
    ]
   },
   {
@@ -144,6 +148,19 @@
     "pipeline.set_hyperparameters(hyperparameters)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "d1190b42",
+   "metadata": {},
+   "source": [
+    "### step-by-step execution\n",
+    "\n",
+    "MLPipelines are compose of a squence of primitives, these primitives apply tranformation and calculation operations to the data and updates the variables within the pipeline. To view the primitives used by the pipeline, we access its primtivies attribute.\n",
+    "\n",
+    "The mistral-detector contains 10 primitives. we will observe how the context (which are the variables held within the pipeline) are updated after the execution of each primitive."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -181,7 +198,13 @@
    "id": "af16a62a-c4cb-424f-bcdc-cdfaa0a51977",
    "metadata": {},
    "source": [
-    "time segment aggerate"
+    "#### time segment aggerate\n",
+    "this primitive creates an equi-spaced time series by aggregating values over fixed specified interval.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**:\n",
+    "    * `X` sequence of aggregated values, one column for each aggregation method.\n",
+    "    * `timestamp` sequence of timestamp values."
    ]
   },
   {
@@ -257,7 +280,11 @@
    "id": "d7e8110b-6d0a-4e67-9346-5317f137b05c",
    "metadata": {},
    "source": [
-    "Single Imputer"
+    "#### Single Imputer\n",
+    "this primitive is an imputation transformer for filling missing values.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**: `y` which is a transformed version of `X`."
    ]
   },
   {
@@ -289,7 +316,11 @@
    "id": "d4aa81d9-f6ee-49bd-894b-ec64445b7edb",
    "metadata": {},
    "source": [
-    "Float2Scalar"
+    "#### Float2Scalar\n",
+    "this primitive converts float values into scalar up to certain decimal points.\n",
+    "\n",
+    "* **input**: `y` which is an n-dimensional sequence of values in float type.\n",
+    "* **output**: `X` which is a transformed version of `y` in scalar."
    ]
   },
   {
@@ -365,7 +396,12 @@
    "id": "3914c439-0452-4151-93d2-9aa0ec0d3442",
    "metadata": {},
    "source": [
-    "Rolling Window"
+    "#### Rolling Window\n",
+    "this primitive generates many sub-sequences of the original sequence. it uses a rolling window approach to create the sub-sequences out of time series data.\n",
+    "* **input**: `X` which is an 1-dimensional sequence to iterate over\n",
+    "* **output**: \n",
+    "    * `X` input sequences\n",
+    "    * `first_index`: first index value of each input sequences"
    ]
   },
   {
@@ -422,7 +458,10 @@
    "id": "f201cbc8-0c88-4489-a7b0-b5060ac785a1",
    "metadata": {},
    "source": [
-    "Format as string"
+    "#### Format as string\n",
+    "this primitive converts each sequence of scalar values into string. \n",
+    "* **input**: `X` which is an n-dimensional sequence of values\n",
+    "* **output**: `X_str` which is a string representation version of X"
    ]
   },
   {
@@ -490,13 +529,25 @@
     "type(context['X_str'][0][0])"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "a2411064",
+   "metadata": {},
+   "source": [
+    "when inspecting the time series, we can see that we have a single list consisting of 200 values (according the set `window_size`) and it is now of string type, ready to be an input to an LLM."
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "id": "7f403aca-ba56-42d3-bcae-b665a234c710",
    "metadata": {},
    "source": [
-    "HF"
+    "#### HF\n",
+    "this primitive prompts a huggingface model to detect the anomalies\n",
+    "* **input**: `X_str` input sequence\n",
+    "* **output**: `y_hat` detected anomalous values"
    ]
   },
   {
@@ -599,7 +650,10 @@
    "id": "dc70e55b-4a3e-43d8-83f8-b998fa88ee29",
    "metadata": {},
    "source": [
-    "format as integer"
+    "#### format as integer\n",
+    "this primitive converts each sequences of string values into integers.\n",
+    "* **input**: `y_hat` which is a sequence of string values\n",
+    "* **output**: `y` which is an integer representation version of `y_hat`"
    ]
   },
   {
@@ -652,7 +706,13 @@
    "id": "6b1ff549-c823-4a31-b324-19ee21a8c193",
    "metadata": {},
    "source": [
-    "Val2idx"
+    "#### Val2Idx\n",
+    "this primitive converts integer values into indices they appear in the sequence\n",
+    "* **input**: \n",
+    "    * `y` sequences of anomalous values\n",
+    "    * `X` input sequences\n",
+    "* **output**: \n",
+    "    * `y` sequences of anomalous indices"
    ]
   },
   {
@@ -705,7 +765,9 @@
    "id": "bbcf3479-7ff2-4a81-86c0-e678a8f735c6",
    "metadata": {},
    "source": [
-    "find_anomalies_in_windows"
+    "#### find_anomalies_in_windows\n",
+    "* **input**: `y` n-dimensional array of multiple anomalous indices sequences\n",
+    "* **output**: `y` array of each window's anomalous indices sequences"
    ]
   },
   {
@@ -758,7 +820,14 @@
    "id": "03002457-e136-445d-811a-97c20eb47d5d",
    "metadata": {},
    "source": [
-    "merge_anomalous_sequences"
+    "#### merge_anomalous_sequences\n",
+    "* **input**: \n",
+    "    * `y` array of each window's anomalous indices sequences\n",
+    "    * `first_index` first indices of input sequences\n",
+    "    * `window_size` size of each window\n",
+    "    * `step_size` step of rolling windows\n",
+    "* **output**: \n",
+    "    * `y` anomalous indices of the input timeseries"
    ]
   },
   {
@@ -811,7 +880,12 @@
    "id": "2eeac9a9-613a-43b8-abd9-6e455bf82a62",
    "metadata": {},
    "source": [
-    "format_anomalies"
+    "#### format_anomalies\n",
+    "* **input**: \n",
+    "    * `y` sequence of anomalous indices\n",
+    "    * `timestamp` sequence of timestamp of the input series\n",
+    "* **output**:\n",
+    "    * `anomalies` array containing start-index, end-index, score for each anomalous sequence that was found"
    ]
   },
   {

From f017d17b73b35005ee430343eabffa8d22545a6d Mon Sep 17 00:00:00 2001
From: "linhnk@mit.edu" <klinh152001@gmail.com>
Date: Fri, 18 Oct 2024 08:49:46 -0400
Subject: [PATCH 22/25] gpt

---
 sigllm/pipelines/prompter/gpt_prompter.json   |   65 +
 .../pipelines/gpt-prompter-pipeline.ipynb     | 1050 +++++++++++++++++
 .../pipelines/mistral-prompter-pipeline.ipynb |    2 +-
 3 files changed, 1116 insertions(+), 1 deletion(-)
 create mode 100644 sigllm/pipelines/prompter/gpt_prompter.json
 create mode 100644 tutorials/pipelines/gpt-prompter-pipeline.ipynb

diff --git a/sigllm/pipelines/prompter/gpt_prompter.json b/sigllm/pipelines/prompter/gpt_prompter.json
new file mode 100644
index 0000000..381dd5b
--- /dev/null
+++ b/sigllm/pipelines/prompter/gpt_prompter.json
@@ -0,0 +1,65 @@
+{
+    "primitives": [
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate",
+        "sklearn.impute.SimpleImputer",
+        "sigllm.primitives.transformation.Float2Scalar",
+        "sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences",
+	    "sigllm.primitives.transformation.format_as_string",
+        "sigllm.primitives.prompting.gpt.GPT",
+        "sigllm.primitives.transformation.format_as_integer",
+        "sigllm.primitives.prompting.anomalies.val2idx",
+        "sigllm.primitives.prompting.anomalies.find_anomalies_in_windows",
+        "sigllm.primitives.prompting.anomalies.merge_anomalous_sequences",
+        "sigllm.primitives.prompting.anomalies.format_anomalies"
+    ],
+    "init_params": {
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
+            "time_column": "timestamp",
+            "interval": 21600,
+            "method": "mean"
+        },
+        "sigllm.primitives.transformation.Float2Scalar#1": {
+            "decimal": 2,
+            "rescale": true
+        },
+        "sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences#1": {
+            "window_size": 200,
+            "step_size": 40
+        },
+        "sigllm.primitives.transformation.format_as_string#1": {
+            "space": true
+        },
+        "sigllm.primitives.prompting.gpt.GPT#1": {
+            "name": "gpt-3.5-turbo",
+            "samples": 10
+        },
+        "sigllm.primitives.prompting.anomalies.find_anomalies_in_windows#1": {
+            "alpha": 0.4
+        },
+        "sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1": {
+            "beta": 0.5
+        }
+    },
+    "input_names": {
+        "sigllm.primitives.prompting.gpt.GPT#1": {
+            "X": "X_str"
+        },
+        "sigllm.primitives.transformation.format_as_integer#1":{
+            "X": "y_hat"
+        }
+    },
+    "output_names": {
+        "mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1": {
+            "index": "timestamp"
+        },
+        "sigllm.primitives.transformation.format_as_string#1": {
+            "X": "X_str"
+        },
+        "sigllm.primitives.prompting.gpt.GPT#1": {
+            "y": "y_hat"
+        },
+        "sigllm.primitives.transformation.format_as_integer#1":{
+            "X": "y"
+        }
+    }
+}
\ No newline at end of file
diff --git a/tutorials/pipelines/gpt-prompter-pipeline.ipynb b/tutorials/pipelines/gpt-prompter-pipeline.ipynb
new file mode 100644
index 0000000..7c7f76e
--- /dev/null
+++ b/tutorials/pipelines/gpt-prompter-pipeline.ipynb
@@ -0,0 +1,1050 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76f73dbe-645a-4ed5-b042-ab14a1e330ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings; warnings.simplefilter('ignore')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "67b19cca-149e-4ec1-8cff-11e712c34c29",
+   "metadata": {},
+   "source": [
+    "This notebook requires **gpu** to run. See [mistral documentation](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for memory requirements.\n",
+    "## 1. Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "32c83a5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1624, 2)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from orion.data import load_signal\n",
+    "\n",
+    "data = load_signal('exchange-2_cpm_results')\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8ae34e69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(data['value']);"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "6b16f040-63b1-4171-8b1c-90c4d721d641",
+   "metadata": {},
+   "source": [
+    "if you want a quick test of how this pipeline works, uncomment the cell below to save time. We will look at a small segment of the time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1029c7ee-8a42-4452-8bc0-20c0fb45b8d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# start = 900\n",
+    "# end = start + 200\n",
+    "\n",
+    "# data = data.iloc[start: end]\n",
+    "\n",
+    "# plt.plot(data['value']);"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "409dabf0-be06-41fc-8793-01872c2a3055",
+   "metadata": {},
+   "source": [
+    "## 2. Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "262441fe-841b-4555-bf57-249305b59f92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c579f8c14788475d88502bdd9d3937f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mlblocks import MLPipeline\n",
+    "pipeline = MLPipeline('gpt_prompter')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "be80a076",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hyperparameters = {\n",
+    "    \"mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1\": {\n",
+    "        \"interval\": 3600\n",
+    "    }, \n",
+    "    \"sigllm.primitives.prompting.anomalies.find_anomalies_in_windows#1\": {\n",
+    "        \"alpha\": 1.0\n",
+    "    },\n",
+    "    \"sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1\": {\n",
+    "        \"beta\": 1.0\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "pipeline.set_hyperparameters(hyperparameters)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "d1190b42",
+   "metadata": {},
+   "source": [
+    "### step-by-step execution\n",
+    "\n",
+    "MLPipelines are compose of a squence of primitives, these primitives apply tranformation and calculation operations to the data and updates the variables within the pipeline. To view the primitives used by the pipeline, we access its primtivies attribute.\n",
+    "\n",
+    "The mistral-detector contains 10 primitives. we will observe how the context (which are the variables held within the pipeline) are updated after the execution of each primitive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2e548714",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['mlstars.custom.timeseries_preprocessing.time_segments_aggregate',\n",
+       " 'sklearn.impute.SimpleImputer',\n",
+       " 'sigllm.primitives.transformation.Float2Scalar',\n",
+       " 'sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences',\n",
+       " 'sigllm.primitives.transformation.format_as_string',\n",
+       " 'sigllm.primitives.prompting.huggingface.HF',\n",
+       " 'sigllm.primitives.transformation.format_as_integer',\n",
+       " 'sigllm.primitives.prompting.anomalies.val2idx',\n",
+       " 'sigllm.primitives.prompting.anomalies.find_anomalies_in_windows',\n",
+       " 'sigllm.primitives.prompting.anomalies.merge_anomalous_sequences',\n",
+       " 'sigllm.primitives.prompting.anomalies.format_anomalies']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline.primitives"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "af16a62a-c4cb-424f-bcdc-cdfaa0a51977",
+   "metadata": {},
+   "source": [
+    "#### time segment aggerate\n",
+    "this primitive creates an equi-spaced time series by aggregating values over fixed specified interval.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**:\n",
+    "    * `X` sequence of aggregated values, one column for each aggregation method.\n",
+    "    * `timestamp` sequence of timestamp values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f683c7f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['X', 'timestamp'])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 0\n",
+    "context = pipeline.fit(data, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "533566d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry at 1309471201 has value [0.4010481]\n",
+      "entry at 1309474801 has value [0.39271888]\n",
+      "entry at 1309478401 has value [0.30999612]\n",
+      "entry at 1309482001 has value [0.21293855]\n",
+      "entry at 1309485601 has value [0.20580091]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, x in list(zip(context['timestamp'], context['X']))[:5]:\n",
+    "    print(\"entry at {} has value {}\".format(i, x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a488bc32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1648, 1)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['X'].shape"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "d7e8110b-6d0a-4e67-9346-5317f137b05c",
+   "metadata": {},
+   "source": [
+    "#### Single Imputer\n",
+    "this primitive is an imputation transformer for filling missing values.\n",
+    "\n",
+    "* **input**: `X` which is an n-dimensional sequence of values.\n",
+    "* **output**: `y` which is a transformed version of `X`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "35c41874",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'X'])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 1\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "d4aa81d9-f6ee-49bd-894b-ec64445b7edb",
+   "metadata": {},
+   "source": [
+    "#### Float2Scalar\n",
+    "this primitive converts float values into scalar up to certain decimal points.\n",
+    "\n",
+    "* **input**: `y` which is an n-dimensional sequence of values in float type.\n",
+    "* **output**: `X` which is a transformed version of `y` in scalar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b49c4fbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'X', 'minimum', 'decimal'])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 2\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f7571fa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry at 1309471201 has value [40]\n",
+      "entry at 1309474801 has value [39]\n",
+      "entry at 1309478401 has value [30]\n",
+      "entry at 1309482001 has value [21]\n",
+      "entry at 1309485601 has value [20]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, x in list(zip(context['timestamp'], context['X']))[:5]:\n",
+    "    print(\"entry at {} has value {}\".format(i, x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "fd1a9ba6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.000385004945833"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['minimum']"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "3914c439-0452-4151-93d2-9aa0ec0d3442",
+   "metadata": {},
+   "source": [
+    "#### Rolling Window\n",
+    "this primitive generates many sub-sequences of the original sequence. it uses a rolling window approach to create the sub-sequences out of time series data.\n",
+    "* **input**: `X` which is an 1-dimensional sequence to iterate over\n",
+    "* **output**: \n",
+    "    * `X` input sequences\n",
+    "    * `first_index`: first index value of each input sequences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bd160c3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'X', 'first_index', 'window_size', 'step_size'])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 3\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ab08a9a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape = (37, 200, 1)\n",
+      "Timestamp shape = (1648,)\n",
+      "First index shape = (37,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# after slicing X into multiple sub-sequences\n",
+    "# we obtain a 3 dimensional matrix X where\n",
+    "# the shape indicates (# slices, window size, 1)\n",
+    "\n",
+    "print(\"X shape = {}\\nTimestamp shape = {}\\nFirst index shape = {}\".format(\n",
+    "    context['X'].shape, context['timestamp'].shape, context['first_index'].shape))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "f201cbc8-0c88-4489-a7b0-b5060ac785a1",
+   "metadata": {},
+   "source": [
+    "#### Format as string\n",
+    "this primitive converts each sequence of scalar values into string. \n",
+    "* **input**: `X` which is an n-dimensional sequence of values\n",
+    "* **output**: `X_str` which is a string representation version of X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3a1836db-cd6f-4a39-8f00-6a09c620c5f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X', 'X_str'])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 4\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1259df2c-d656-42a8-973c-b15cf8e031d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'40,39,30,21,20,25,30,29,53,78,74,73,69,68,51,51,51,41,24,30,27,26,23,26,32,25,21,16,21,30,28,39,58,59,71,78,73,68,72,52,40,34,27,27,34,28,32,25,20,20,17,13,17,27,24,34,67,62,60,59,71,63,56,43,36,30,26,24,24,20,20,23,17,19,16,14,12,16,21,28,47,54,50,53,60,51,52,42,32,34,24,24,21,21,22,25,22,16,17,12,13,17,22,27,44,47,54,66,54,58,42,39,36,32,27,23,21,21,19,24,22,19,13,11,15,20,22,28,47,64,52,57,57,51,40,44,36,35,28,24,20,29,21,22,21,16,12,11,12,17,19,24,40,53,54,43,46,43,34,38,32,25,22,15,18,17,17,15,16,14,14,10,11,14,15,31,52,43,49,45,44,36,30,32,22,24,22,19,18,20,19,17,19,15,12,11,17,23,22,29'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['X_str'].flatten().tolist()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d05e85ce-6111-494f-88c0-4fc566386b43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(context['X_str'][0][0])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "a2411064",
+   "metadata": {},
+   "source": [
+    "when inspecting the time series, we can see that we have a single list consisting of 200 values (according the set `window_size`) and it is now of string type, ready to be an input to an LLM."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "7f403aca-ba56-42d3-bcae-b665a234c710",
+   "metadata": {},
+   "source": [
+    "#### GPT\n",
+    "this primitive prompts a gpt model to detect the anomalies\n",
+    "* **input**: `X_str` input sequence\n",
+    "* **output**: `y_hat` detected anomalous values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b4711e98-c522-4464-b645-607f76e89063",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                    | 0/37 [00:00<?, ?it/s]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "2024-10-01 20:27:58.821861: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2024-10-01 20:27:58.821998: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2024-10-01 20:27:58.823828: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-01 20:27:58.992915: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2024-10-01 20:28:00.135504: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+      "  3%|█▏                                          | 1/37 [00:27<16:12, 27.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "  5%|██▍                                         | 2/37 [00:49<14:21, 24.63s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "  8%|███▌                                        | 3/37 [01:12<13:31, 23.85s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 11%|████▊                                       | 4/37 [01:35<12:55, 23.49s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 14%|█████▉                                      | 5/37 [01:58<12:25, 23.29s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 16%|███████▏                                    | 6/37 [02:21<11:58, 23.17s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 19%|████████▎                                   | 7/37 [02:44<11:33, 23.10s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 22%|█████████▌                                  | 8/37 [03:07<11:08, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 24%|██████████▋                                 | 9/37 [03:30<10:44, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 27%|███████████▌                               | 10/37 [03:53<10:20, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 30%|████████████▊                              | 11/37 [04:16<09:58, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 32%|█████████████▉                             | 12/37 [04:39<09:35, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 35%|███████████████                            | 13/37 [05:02<09:11, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 38%|████████████████▎                          | 14/37 [05:25<08:48, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 41%|█████████████████▍                         | 15/37 [05:48<08:25, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 43%|██████████████████▌                        | 16/37 [06:11<08:02, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 46%|███████████████████▊                       | 17/37 [06:34<07:39, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 49%|████████████████████▉                      | 18/37 [06:57<07:16, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 51%|██████████████████████                     | 19/37 [07:20<06:53, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 54%|███████████████████████▏                   | 20/37 [07:43<06:30, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 57%|████████████████████████▍                  | 21/37 [08:06<06:07, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 59%|█████████████████████████▌                 | 22/37 [08:29<05:44, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 62%|██████████████████████████▋                | 23/37 [08:52<05:22, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 65%|███████████████████████████▉               | 24/37 [09:15<04:59, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 68%|█████████████████████████████              | 25/37 [09:38<04:36, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 70%|██████████████████████████████▏            | 26/37 [10:01<04:13, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 73%|███████████████████████████████▍           | 27/37 [10:24<03:50, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 76%|████████████████████████████████▌          | 28/37 [10:47<03:27, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 78%|█████████████████████████████████▋         | 29/37 [11:10<03:04, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 81%|██████████████████████████████████▊        | 30/37 [11:33<02:41, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 84%|████████████████████████████████████       | 31/37 [11:56<02:18, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 86%|█████████████████████████████████████▏     | 32/37 [12:19<01:55, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 89%|██████████████████████████████████████▎    | 33/37 [12:42<01:32, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 92%|███████████████████████████████████████▌   | 34/37 [13:05<01:09, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 95%|████████████████████████████████████████▋  | 35/37 [13:28<00:46, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 97%|█████████████████████████████████████████▊ | 36/37 [13:51<00:23, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "100%|███████████████████████████████████████████| 37/37 [14:14<00:00, 23.10s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'X', 'y_hat'])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 5\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d587d1d3-9325-4d45-9e15-d69d2d16bb6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(37, 10)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(context['y_hat']), len(context['y_hat'][0])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "dc70e55b-4a3e-43d8-83f8-b998fa88ee29",
+   "metadata": {},
+   "source": [
+    "#### format as integer\n",
+    "this primitive converts each sequences of string values into integers.\n",
+    "* **input**: `y_hat` which is a sequence of string values\n",
+    "* **output**: `y` which is an integer representation version of `y_hat`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5ac4fc58-055d-4210-b330-0832508c1cf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 6\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a934005e-5bce-4a47-831a-3cf1f0c92f28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(37, 10)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'].shape"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "6b1ff549-c823-4a31-b324-19ee21a8c193",
+   "metadata": {},
+   "source": [
+    "#### Val2Idx\n",
+    "this primitive converts integer values into indices they appear in the sequence\n",
+    "* **input**: \n",
+    "    * `y` sequences of anomalous values\n",
+    "    * `X` input sequences\n",
+    "* **output**: \n",
+    "    * `y` sequences of anomalous indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "32b4ccf9-d8fa-4110-bc22-717d07e3ae0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 7\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "339db91e-cd35-4ae8-abc5-3ba52b6c0bbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(37, 10)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'].shape"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "bbcf3479-7ff2-4a81-86c0-e678a8f735c6",
+   "metadata": {},
+   "source": [
+    "#### find_anomalies_in_windows\n",
+    "* **input**: `y` n-dimensional array of multiple anomalous indices sequences\n",
+    "* **output**: `y` array of each window's anomalous indices sequences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "17ce8982-2ba3-4508-a526-8a9960e0ecd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 8\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "5274e7a0-acf5-4b9f-9cb4-070357fc7c10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(37,)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'].shape"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "03002457-e136-445d-811a-97c20eb47d5d",
+   "metadata": {},
+   "source": [
+    "#### merge_anomalous_sequences\n",
+    "* **input**: \n",
+    "    * `y` array of each window's anomalous indices sequences\n",
+    "    * `first_index` first indices of input sequences\n",
+    "    * `window_size` size of each window\n",
+    "    * `step_size` step of rolling windows\n",
+    "* **output**: \n",
+    "    * `y` anomalous indices of the input timeseries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "bd219c08-29b2-4809-934b-287dfac4e4fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 9\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "57f900db-d712-4ff1-986b-7f4dc1accda0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1319,)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['y'].shape"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "2eeac9a9-613a-43b8-abd9-6e455bf82a62",
+   "metadata": {},
+   "source": [
+    "#### format_anomalies\n",
+    "* **input**: \n",
+    "    * `y` sequence of anomalous indices\n",
+    "    * `timestamp` sequence of timestamp of the input series\n",
+    "* **output**:\n",
+    "    * `anomalies` array containing start-index, end-index, score for each anomalous sequence that was found"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "7de109bd-8306-4753-8446-07f438e8c3e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y', 'merged_intervals'])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 10\n",
+    "context = pipeline.fit(**context, start_=step, output_=step)\n",
+    "context.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a37daebf-7091-4dab-93dc-928c7163778b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1309867201, 1314975601, 0)]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "context['anomalies']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "60064037-5ed6-49f3-ae13-5f4952f5eabf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>start</th>\n",
+       "      <th>end</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1309867201</td>\n",
+       "      <td>1314975601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        start         end  score\n",
+       "0  1309867201  1314975601      0"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.DataFrame(context['anomalies'], columns=['start', 'end', 'score'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "98b221ef-ff0c-4705-9697-e2d240ff756e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "index, anomalies = list(map(context.get, ['timestamp', 'merged_intervals']))\n",
+    "\n",
+    "plt.plot(data['timestamp'], data['value'], label='original')\n",
+    "\n",
+    "plt.axvspan(*anomalies[0][:2], color='r', alpha=0.2, label='detected anomalies')\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee002d85-571a-4ecd-8f9d-99cb84808d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "prompter",
+   "language": "python",
+   "name": "prompter"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/pipelines/mistral-prompter-pipeline.ipynb b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
index f65408d..8406d51 100644
--- a/tutorials/pipelines/mistral-prompter-pipeline.ipynb
+++ b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
@@ -122,7 +122,7 @@
     }
    ],
    "source": [
-    "from mlblocks import MLPipeline, add_pipelines_path, add_primitives_path\n",
+    "from mlblocks import MLPipeline\n",
     "pipeline = MLPipeline('mistral_prompter')"
    ]
   },

From d21163b0b7de897a3b8d68b4a4db9a9c9880f47f Mon Sep 17 00:00:00 2001
From: Linh-nk <linhnk@dai-desk34.lids.mit.edu>
Date: Fri, 18 Oct 2024 08:52:38 -0400
Subject: [PATCH 23/25] merge

---
 .../pipelines/mistral-prompter-pipeline.ipynb | 256 +++++++++++-------
 1 file changed, 155 insertions(+), 101 deletions(-)

diff --git a/tutorials/pipelines/mistral-prompter-pipeline.ipynb b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
index f65408d..0306573 100644
--- a/tutorials/pipelines/mistral-prompter-pipeline.ipynb
+++ b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "76f73dbe-645a-4ed5-b042-ab14a1e330ea",
    "metadata": {},
    "outputs": [],
@@ -109,7 +109,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c579f8c14788475d88502bdd9d3937f7",
+       "model_id": "066d82461cfb4ea18358bade4ce0337d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -142,12 +142,66 @@
     "    },\n",
     "    \"sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1\": {\n",
     "        \"beta\": 1.0\n",
+    "    },\n",
+    "    \"sigllm.primitives.prompting.anomalies.format_anomalies#1\": {\n",
+    "        \"padding_size\": 10\n",
     "    }\n",
     "}\n",
     "\n",
+    "## reduce padding, reduce overlapping windows\n",
+    "\n",
     "pipeline.set_hyperparameters(hyperparameters)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9292817b-75d5-4526-a1b8-7475bcb787c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mlstars.custom.timeseries_preprocessing.time_segments_aggregate#1': {'interval': 3600,\n",
+       "  'time_column': 'timestamp',\n",
+       "  'method': 'mean'},\n",
+       " 'sklearn.impute.SimpleImputer#1': {'missing_values': nan,\n",
+       "  'fill_value': None,\n",
+       "  'verbose': False,\n",
+       "  'copy': True,\n",
+       "  'strategy': 'mean'},\n",
+       " 'sigllm.primitives.transformation.Float2Scalar#1': {'decimal': 2,\n",
+       "  'rescale': True},\n",
+       " 'sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences#1': {'window_size': 200,\n",
+       "  'step_size': 40},\n",
+       " 'sigllm.primitives.transformation.format_as_string#1': {'sep': ',',\n",
+       "  'space': False},\n",
+       " 'sigllm.primitives.prompting.huggingface.HF#1': {'name': 'mistralai/Mistral-7B-Instruct-v0.2',\n",
+       "  'sep': ',',\n",
+       "  'anomalous_percent': '0.5',\n",
+       "  'temp': 1,\n",
+       "  'top_p': 1,\n",
+       "  'raw': False,\n",
+       "  'samples': 10,\n",
+       "  'padding': 0},\n",
+       " 'sigllm.primitives.transformation.format_as_integer#1': {'sep': ',',\n",
+       "  'trunc': None,\n",
+       "  'errors': 'ignore'},\n",
+       " 'sigllm.primitives.prompting.anomalies.val2idx#1': {},\n",
+       " 'sigllm.primitives.prompting.anomalies.find_anomalies_in_windows#1': {'alpha': 1.0},\n",
+       " 'sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1': {'beta': 1.0},\n",
+       " 'sigllm.primitives.prompting.anomalies.format_anomalies#1': {'padding_size': 10}}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline.get_hyperparameters()"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -163,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "2e548714",
    "metadata": {},
    "outputs": [
@@ -183,7 +237,7 @@
        " 'sigllm.primitives.prompting.anomalies.format_anomalies']"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "f683c7f7",
    "metadata": {},
    "outputs": [
@@ -219,7 +273,7 @@
        "dict_keys(['X', 'timestamp'])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -232,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "533566d5",
    "metadata": {},
    "outputs": [
@@ -255,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "a488bc32",
    "metadata": {},
    "outputs": [
@@ -265,7 +319,7 @@
        "(1648, 1)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -289,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "35c41874",
    "metadata": {},
    "outputs": [
@@ -299,7 +353,7 @@
        "dict_keys(['timestamp', 'X'])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "b49c4fbf",
    "metadata": {},
    "outputs": [
@@ -335,7 +389,7 @@
        "dict_keys(['timestamp', 'X', 'minimum', 'decimal'])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -348,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "f7571fa1",
    "metadata": {},
    "outputs": [
@@ -371,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "fd1a9ba6",
    "metadata": {},
    "outputs": [
@@ -381,7 +435,7 @@
        "0.000385004945833"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -406,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "bd160c3e",
    "metadata": {},
    "outputs": [
@@ -416,7 +470,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'X', 'first_index', 'window_size', 'step_size'])"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -429,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "ab08a9a9",
    "metadata": {},
    "outputs": [
@@ -466,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "3a1836db-cd6f-4a39-8f00-6a09c620c5f0",
    "metadata": {},
    "outputs": [
@@ -476,7 +530,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X', 'X_str'])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -489,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "1259df2c-d656-42a8-973c-b15cf8e031d4",
    "metadata": {},
    "outputs": [
@@ -499,7 +553,7 @@
        "'40,39,30,21,20,25,30,29,53,78,74,73,69,68,51,51,51,41,24,30,27,26,23,26,32,25,21,16,21,30,28,39,58,59,71,78,73,68,72,52,40,34,27,27,34,28,32,25,20,20,17,13,17,27,24,34,67,62,60,59,71,63,56,43,36,30,26,24,24,20,20,23,17,19,16,14,12,16,21,28,47,54,50,53,60,51,52,42,32,34,24,24,21,21,22,25,22,16,17,12,13,17,22,27,44,47,54,66,54,58,42,39,36,32,27,23,21,21,19,24,22,19,13,11,15,20,22,28,47,64,52,57,57,51,40,44,36,35,28,24,20,29,21,22,21,16,12,11,12,17,19,24,40,53,54,43,46,43,34,38,32,25,22,15,18,17,17,15,16,14,14,10,11,14,15,31,52,43,49,45,44,36,30,32,22,24,22,19,18,20,19,17,19,15,12,11,17,23,22,29'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -510,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "d05e85ce-6111-494f-88c0-4fc566386b43",
    "metadata": {},
    "outputs": [
@@ -520,7 +574,7 @@
        "str"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -552,7 +606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "b4711e98-c522-4464-b645-607f76e89063",
    "metadata": {},
    "outputs": [
@@ -561,48 +615,48 @@
      "output_type": "stream",
      "text": [
       "  0%|                                                    | 0/37 [00:00<?, ?it/s]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "2024-10-01 20:27:58.821861: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "2024-10-01 20:27:58.821998: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "2024-10-01 20:27:58.823828: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-10-01 20:27:58.992915: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "2024-10-03 11:37:28.645348: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2024-10-03 11:37:28.645377: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2024-10-03 11:37:28.645402: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-03 11:37:28.652931: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
       "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "2024-10-01 20:28:00.135504: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
-      "  3%|█▏                                          | 1/37 [00:27<16:12, 27.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "  5%|██▍                                         | 2/37 [00:49<14:21, 24.63s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "  8%|███▌                                        | 3/37 [01:12<13:31, 23.85s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 11%|████▊                                       | 4/37 [01:35<12:55, 23.49s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 14%|█████▉                                      | 5/37 [01:58<12:25, 23.29s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 16%|███████▏                                    | 6/37 [02:21<11:58, 23.17s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 19%|████████▎                                   | 7/37 [02:44<11:33, 23.10s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 22%|█████████▌                                  | 8/37 [03:07<11:08, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 24%|██████████▋                                 | 9/37 [03:30<10:44, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 27%|███████████▌                               | 10/37 [03:53<10:20, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 30%|████████████▊                              | 11/37 [04:16<09:58, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 32%|█████████████▉                             | 12/37 [04:39<09:35, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 35%|███████████████                            | 13/37 [05:02<09:11, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 38%|████████████████▎                          | 14/37 [05:25<08:48, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 41%|█████████████████▍                         | 15/37 [05:48<08:25, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 43%|██████████████████▌                        | 16/37 [06:11<08:02, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 46%|███████████████████▊                       | 17/37 [06:34<07:39, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 49%|████████████████████▉                      | 18/37 [06:57<07:16, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 51%|██████████████████████                     | 19/37 [07:20<06:53, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 54%|███████████████████████▏                   | 20/37 [07:43<06:30, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 57%|████████████████████████▍                  | 21/37 [08:06<06:07, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 59%|█████████████████████████▌                 | 22/37 [08:29<05:44, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 62%|██████████████████████████▋                | 23/37 [08:52<05:22, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 65%|███████████████████████████▉               | 24/37 [09:15<04:59, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 68%|█████████████████████████████              | 25/37 [09:38<04:36, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 70%|██████████████████████████████▏            | 26/37 [10:01<04:13, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 73%|███████████████████████████████▍           | 27/37 [10:24<03:50, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 76%|████████████████████████████████▌          | 28/37 [10:47<03:27, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 78%|█████████████████████████████████▋         | 29/37 [11:10<03:04, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 81%|██████████████████████████████████▊        | 30/37 [11:33<02:41, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 84%|████████████████████████████████████       | 31/37 [11:56<02:18, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 86%|█████████████████████████████████████▏     | 32/37 [12:19<01:55, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 89%|██████████████████████████████████████▎    | 33/37 [12:42<01:32, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 92%|███████████████████████████████████████▌   | 34/37 [13:05<01:09, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 95%|████████████████████████████████████████▋  | 35/37 [13:28<00:46, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 97%|█████████████████████████████████████████▊ | 36/37 [13:51<00:23, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "2024-10-03 11:37:29.421095: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
+      "  3%|█▏                                          | 1/37 [00:24<14:57, 24.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "  5%|██▍                                         | 2/37 [00:47<13:53, 23.81s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "  8%|███▌                                        | 3/37 [01:10<13:16, 23.43s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 11%|████▊                                       | 4/37 [01:33<12:47, 23.27s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 14%|█████▉                                      | 5/37 [01:56<12:21, 23.18s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 16%|███████▏                                    | 6/37 [02:20<11:57, 23.13s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 19%|████████▎                                   | 7/37 [02:43<11:32, 23.08s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 22%|█████████▌                                  | 8/37 [03:06<11:09, 23.09s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 24%|██████████▋                                 | 9/37 [03:29<10:46, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 27%|███████████▌                               | 10/37 [03:52<10:22, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 30%|████████████▊                              | 11/37 [04:15<09:59, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 32%|█████████████▉                             | 12/37 [04:38<09:36, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 35%|███████████████                            | 13/37 [05:01<09:12, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 38%|████████████████▎                          | 14/37 [05:24<08:49, 23.04s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 41%|█████████████████▍                         | 15/37 [05:47<08:26, 23.04s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 43%|██████████████████▌                        | 16/37 [06:10<08:03, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 46%|███████████████████▊                       | 17/37 [06:33<07:40, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 49%|████████████████████▉                      | 18/37 [06:56<07:17, 23.04s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 51%|██████████████████████                     | 19/37 [07:19<06:54, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 54%|███████████████████████▏                   | 20/37 [07:42<06:31, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 57%|████████████████████████▍                  | 21/37 [08:05<06:08, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 59%|█████████████████████████▌                 | 22/37 [08:28<05:45, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 62%|██████████████████████████▋                | 23/37 [08:51<05:22, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 65%|███████████████████████████▉               | 24/37 [09:14<04:59, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 68%|█████████████████████████████              | 25/37 [09:37<04:36, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 70%|██████████████████████████████▏            | 26/37 [10:00<04:13, 23.08s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 73%|███████████████████████████████▍           | 27/37 [10:24<03:50, 23.09s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 76%|████████████████████████████████▌          | 28/37 [10:47<03:27, 23.09s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 78%|█████████████████████████████████▋         | 29/37 [11:10<03:04, 23.08s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 81%|██████████████████████████████████▊        | 30/37 [11:33<02:41, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 84%|████████████████████████████████████       | 31/37 [11:56<02:18, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 86%|█████████████████████████████████████▏     | 32/37 [12:19<01:55, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 89%|██████████████████████████████████████▎    | 33/37 [12:42<01:32, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 92%|███████████████████████████████████████▌   | 34/37 [13:05<01:09, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 95%|████████████████████████████████████████▋  | 35/37 [13:28<00:46, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 97%|█████████████████████████████████████████▊ | 36/37 [13:51<00:23, 23.08s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
       "100%|███████████████████████████████████████████| 37/37 [14:14<00:00, 23.10s/it]\n"
      ]
     },
@@ -612,7 +666,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'X', 'y_hat'])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -625,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "d587d1d3-9325-4d45-9e15-d69d2d16bb6d",
    "metadata": {},
    "outputs": [
@@ -635,7 +689,7 @@
        "(37, 10)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -658,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "5ac4fc58-055d-4210-b330-0832508c1cf4",
    "metadata": {},
    "outputs": [
@@ -668,7 +722,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -681,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "a934005e-5bce-4a47-831a-3cf1f0c92f28",
    "metadata": {},
    "outputs": [
@@ -691,7 +745,7 @@
        "(37, 10)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -717,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "32b4ccf9-d8fa-4110-bc22-717d07e3ae0b",
    "metadata": {},
    "outputs": [
@@ -727,7 +781,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -740,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "339db91e-cd35-4ae8-abc5-3ba52b6c0bbd",
    "metadata": {},
    "outputs": [
@@ -750,7 +804,7 @@
        "(37, 10)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -772,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "17ce8982-2ba3-4508-a526-8a9960e0ecd3",
    "metadata": {},
    "outputs": [
@@ -782,7 +836,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -795,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 27,
    "id": "5274e7a0-acf5-4b9f-9cb4-070357fc7c10",
    "metadata": {},
    "outputs": [
@@ -805,7 +859,7 @@
        "(37,)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -832,7 +886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 28,
    "id": "bd219c08-29b2-4809-934b-287dfac4e4fa",
    "metadata": {},
    "outputs": [
@@ -842,7 +896,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -855,7 +909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "id": "57f900db-d712-4ff1-986b-7f4dc1accda0",
    "metadata": {},
    "outputs": [
@@ -865,7 +919,7 @@
        "(1319,)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -890,17 +944,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "id": "7de109bd-8306-4753-8446-07f438e8c3e8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y', 'merged_intervals'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y', 'anomalies'])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -913,17 +967,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 31,
    "id": "a37daebf-7091-4dab-93dc-928c7163778b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[(1309867201, 1314975601, 0)]"
+       "[(1310011201, 1314831601, 0)]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -934,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 32,
    "id": "60064037-5ed6-49f3-ae13-5f4952f5eabf",
    "metadata": {},
    "outputs": [
@@ -967,8 +1021,8 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1309867201</td>\n",
-       "      <td>1314975601</td>\n",
+       "      <td>1310011201</td>\n",
+       "      <td>1314831601</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -977,10 +1031,10 @@
       ],
       "text/plain": [
        "        start         end  score\n",
-       "0  1309867201  1314975601      0"
+       "0  1310011201  1314831601      0"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -993,13 +1047,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 33,
    "id": "98b221ef-ff0c-4705-9697-e2d240ff756e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1009,11 +1063,11 @@
     }
    ],
    "source": [
-    "index, anomalies = list(map(context.get, ['timestamp', 'merged_intervals']))\n",
+    "index, anomalies = list(map(context.get, ['timestamp', 'anomalies']))\n",
     "\n",
     "plt.plot(data['timestamp'], data['value'], label='original')\n",
     "\n",
-    "plt.axvspan(*anomalies[0][:2], color='r', alpha=0.2, label='detected anomalies')\n",
+    "plt.axvspan(*anomalies[0], color='r', alpha=0.2, label='detected anomalies')\n",
     "plt.legend();"
    ]
   },

From cc185fefadb00927cc0e6b7e0d4fcaaa8e8d9df3 Mon Sep 17 00:00:00 2001
From: Linh-nk <linhnk@dai-desk34.lids.mit.edu>
Date: Fri, 18 Oct 2024 09:36:47 -0400
Subject: [PATCH 24/25] gpt

---
 .../sigllm.primitives.prompting.gpt.GPT.json  |   2 +-
 sigllm/primitives/prompting/gpt.py            |  16 +-
 .../pipelines/gpt-prompter-pipeline.ipynb     | 254 ++++++++---------
 .../pipelines/mistral-prompter-pipeline.ipynb | 268 ++++++++++--------
 4 files changed, 273 insertions(+), 267 deletions(-)

diff --git a/sigllm/primitives/jsons/sigllm.primitives.prompting.gpt.GPT.json b/sigllm/primitives/jsons/sigllm.primitives.prompting.gpt.GPT.json
index e951194..9c07924 100644
--- a/sigllm/primitives/jsons/sigllm.primitives.prompting.gpt.GPT.json
+++ b/sigllm/primitives/jsons/sigllm.primitives.prompting.gpt.GPT.json
@@ -9,7 +9,7 @@
         "subtype": "detector"
     },
     "modalities": [],
-    "primitive": "sigllm.primitives.prompting.huggingface.HF",
+    "primitive": "sigllm.primitives.prompting.gpt.GPT",
     "produce": {
         "method": "detect",
         "args": [
diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index f8e8a20..b2ae007 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -6,6 +6,7 @@
 import openai
 import tiktoken
 from tqdm import tqdm
+from openai import OpenAI
 
 PROMPT_PATH = os.path.join(
     os.path.dirname(os.path.abspath(__file__)),
@@ -63,14 +64,17 @@ def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent=0.5, temp=1,
 
         self.tokenizer = tiktoken.encoding_for_model(self.name)
 
+
         valid_tokens = []
         for number in VALID_NUMBERS:
             token = self.tokenizer.encode(number)
-            valid_tokens.append(token)
+            valid_tokens.extend(token)
 
-        valid_tokens.append(self.tokenizer.encode(self.sep))
+        valid_tokens.extend(self.tokenizer.encode(self.sep))
         self.logit_bias = {token: BIAS for token in valid_tokens}
 
+        self.client = OpenAI()
+
     def detect(self, X, **kwargs):
         """Use GPT to forecast a signal.
 
@@ -83,13 +87,13 @@ def detect(self, X, **kwargs):
                 * List of detected anomalous values.
                 * Optionally, a list of the output tokens' log probabilities.
         """
-        input_length = len(self.tokenizer.encode(X[0][0]))
-        max_tokens = input_length * float(self.anomalous_percent)
+        input_length = len(self.tokenizer.encode(X[0]))
+        max_tokens = int(input_length * float(self.anomalous_percent))
 
         all_responses, all_probs = [], []
         for text in tqdm(X):
-            message = ' '.join(PROMPTS['user_message'], text, self.sep)
-            response = openai.ChatCompletion.create(
+            message = ' '.join([PROMPTS['user_message'], text, self.sep])
+            response = self.client.chat.completions.create(
                 model=self.name,
                 messages=[
                     {"role": "system", "content": PROMPTS['system_message']},
diff --git a/tutorials/pipelines/gpt-prompter-pipeline.ipynb b/tutorials/pipelines/gpt-prompter-pipeline.ipynb
index 7c7f76e..ed4ac5d 100644
--- a/tutorials/pipelines/gpt-prompter-pipeline.ipynb
+++ b/tutorials/pipelines/gpt-prompter-pipeline.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "76f73dbe-645a-4ed5-b042-ab14a1e330ea",
    "metadata": {},
    "outputs": [],
@@ -16,13 +16,13 @@
    "id": "67b19cca-149e-4ec1-8cff-11e712c34c29",
    "metadata": {},
    "source": [
-    "This notebook requires **gpu** to run. See [mistral documentation](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) for memory requirements.\n",
+    "This notebook requires access to OpenAI API to run.\n",
     "## 1. Data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 35,
    "id": "32c83a5a",
    "metadata": {},
    "outputs": [
@@ -32,7 +32,7 @@
        "(1624, 2)"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 36,
    "id": "8ae34e69",
    "metadata": {},
    "outputs": [
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "1029c7ee-8a42-4452-8bc0-20c0fb45b8d9",
    "metadata": {},
    "outputs": [],
@@ -102,25 +102,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 38,
    "id": "262441fe-841b-4555-bf57-249305b59f92",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c579f8c14788475d88502bdd9d3937f7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mlblocks import MLPipeline\n",
     "pipeline = MLPipeline('gpt_prompter')"
@@ -128,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 39,
    "id": "be80a076",
    "metadata": {},
    "outputs": [],
@@ -138,10 +123,10 @@
     "        \"interval\": 3600\n",
     "    }, \n",
     "    \"sigllm.primitives.prompting.anomalies.find_anomalies_in_windows#1\": {\n",
-    "        \"alpha\": 1.0\n",
+    "        \"alpha\": 0.5\n",
     "    },\n",
     "    \"sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1\": {\n",
-    "        \"beta\": 1.0\n",
+    "        \"beta\": 0.5\n",
     "    }\n",
     "}\n",
     "\n",
@@ -163,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 40,
    "id": "2e548714",
    "metadata": {},
    "outputs": [
@@ -175,7 +160,7 @@
        " 'sigllm.primitives.transformation.Float2Scalar',\n",
        " 'sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences',\n",
        " 'sigllm.primitives.transformation.format_as_string',\n",
-       " 'sigllm.primitives.prompting.huggingface.HF',\n",
+       " 'sigllm.primitives.prompting.gpt.GPT',\n",
        " 'sigllm.primitives.transformation.format_as_integer',\n",
        " 'sigllm.primitives.prompting.anomalies.val2idx',\n",
        " 'sigllm.primitives.prompting.anomalies.find_anomalies_in_windows',\n",
@@ -183,7 +168,7 @@
        " 'sigllm.primitives.prompting.anomalies.format_anomalies']"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 41,
    "id": "f683c7f7",
    "metadata": {},
    "outputs": [
@@ -219,7 +204,7 @@
        "dict_keys(['X', 'timestamp'])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -232,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 42,
    "id": "533566d5",
    "metadata": {},
    "outputs": [
@@ -255,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 43,
    "id": "a488bc32",
    "metadata": {},
    "outputs": [
@@ -265,7 +250,7 @@
        "(1648, 1)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -289,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 44,
    "id": "35c41874",
    "metadata": {},
    "outputs": [
@@ -299,7 +284,7 @@
        "dict_keys(['timestamp', 'X'])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 45,
    "id": "b49c4fbf",
    "metadata": {},
    "outputs": [
@@ -335,7 +320,7 @@
        "dict_keys(['timestamp', 'X', 'minimum', 'decimal'])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -348,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 46,
    "id": "f7571fa1",
    "metadata": {},
    "outputs": [
@@ -371,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 47,
    "id": "fd1a9ba6",
    "metadata": {},
    "outputs": [
@@ -381,7 +366,7 @@
        "0.000385004945833"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -406,7 +391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 48,
    "id": "bd160c3e",
    "metadata": {},
    "outputs": [
@@ -416,7 +401,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'X', 'first_index', 'window_size', 'step_size'])"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -429,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 49,
    "id": "ab08a9a9",
    "metadata": {},
    "outputs": [
@@ -466,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 50,
    "id": "3a1836db-cd6f-4a39-8f00-6a09c620c5f0",
    "metadata": {},
    "outputs": [
@@ -476,7 +461,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X', 'X_str'])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -489,17 +474,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 51,
    "id": "1259df2c-d656-42a8-973c-b15cf8e031d4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'40,39,30,21,20,25,30,29,53,78,74,73,69,68,51,51,51,41,24,30,27,26,23,26,32,25,21,16,21,30,28,39,58,59,71,78,73,68,72,52,40,34,27,27,34,28,32,25,20,20,17,13,17,27,24,34,67,62,60,59,71,63,56,43,36,30,26,24,24,20,20,23,17,19,16,14,12,16,21,28,47,54,50,53,60,51,52,42,32,34,24,24,21,21,22,25,22,16,17,12,13,17,22,27,44,47,54,66,54,58,42,39,36,32,27,23,21,21,19,24,22,19,13,11,15,20,22,28,47,64,52,57,57,51,40,44,36,35,28,24,20,29,21,22,21,16,12,11,12,17,19,24,40,53,54,43,46,43,34,38,32,25,22,15,18,17,17,15,16,14,14,10,11,14,15,31,52,43,49,45,44,36,30,32,22,24,22,19,18,20,19,17,19,15,12,11,17,23,22,29'"
+       "'4 0 , 3 9 , 3 0 , 2 1 , 2 0 , 2 5 , 3 0 , 2 9 , 5 3 , 7 8 , 7 4 , 7 3 , 6 9 , 6 8 , 5 1 , 5 1 , 5 1 , 4 1 , 2 4 , 3 0 , 2 7 , 2 6 , 2 3 , 2 6 , 3 2 , 2 5 , 2 1 , 1 6 , 2 1 , 3 0 , 2 8 , 3 9 , 5 8 , 5 9 , 7 1 , 7 8 , 7 3 , 6 8 , 7 2 , 5 2 , 4 0 , 3 4 , 2 7 , 2 7 , 3 4 , 2 8 , 3 2 , 2 5 , 2 0 , 2 0 , 1 7 , 1 3 , 1 7 , 2 7 , 2 4 , 3 4 , 6 7 , 6 2 , 6 0 , 5 9 , 7 1 , 6 3 , 5 6 , 4 3 , 3 6 , 3 0 , 2 6 , 2 4 , 2 4 , 2 0 , 2 0 , 2 3 , 1 7 , 1 9 , 1 6 , 1 4 , 1 2 , 1 6 , 2 1 , 2 8 , 4 7 , 5 4 , 5 0 , 5 3 , 6 0 , 5 1 , 5 2 , 4 2 , 3 2 , 3 4 , 2 4 , 2 4 , 2 1 , 2 1 , 2 2 , 2 5 , 2 2 , 1 6 , 1 7 , 1 2 , 1 3 , 1 7 , 2 2 , 2 7 , 4 4 , 4 7 , 5 4 , 6 6 , 5 4 , 5 8 , 4 2 , 3 9 , 3 6 , 3 2 , 2 7 , 2 3 , 2 1 , 2 1 , 1 9 , 2 4 , 2 2 , 1 9 , 1 3 , 1 1 , 1 5 , 2 0 , 2 2 , 2 8 , 4 7 , 6 4 , 5 2 , 5 7 , 5 7 , 5 1 , 4 0 , 4 4 , 3 6 , 3 5 , 2 8 , 2 4 , 2 0 , 2 9 , 2 1 , 2 2 , 2 1 , 1 6 , 1 2 , 1 1 , 1 2 , 1 7 , 1 9 , 2 4 , 4 0 , 5 3 , 5 4 , 4 3 , 4 6 , 4 3 , 3 4 , 3 8 , 3 2 , 2 5 , 2 2 , 1 5 , 1 8 , 1 7 , 1 7 , 1 5 , 1 6 , 1 4 , 1 4 , 1 0 , 1 1 , 1 4 , 1 5 , 3 1 , 5 2 , 4 3 , 4 9 , 4 5 , 4 4 , 3 6 , 3 0 , 3 2 , 2 2 , 2 4 , 2 2 , 1 9 , 1 8 , 2 0 , 1 9 , 1 7 , 1 9 , 1 5 , 1 2 , 1 1 , 1 7 , 2 3 , 2 2 , 2 9'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -510,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 52,
    "id": "d05e85ce-6111-494f-88c0-4fc566386b43",
    "metadata": {},
    "outputs": [
@@ -520,7 +505,7 @@
        "str"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -552,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 53,
    "id": "b4711e98-c522-4464-b645-607f76e89063",
    "metadata": {},
    "outputs": [
@@ -560,50 +545,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                    | 0/37 [00:00<?, ?it/s]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "2024-10-01 20:27:58.821861: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "2024-10-01 20:27:58.821998: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "2024-10-01 20:27:58.823828: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-10-01 20:27:58.992915: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "2024-10-01 20:28:00.135504: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
-      "  3%|█▏                                          | 1/37 [00:27<16:12, 27.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "  5%|██▍                                         | 2/37 [00:49<14:21, 24.63s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "  8%|███▌                                        | 3/37 [01:12<13:31, 23.85s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 11%|████▊                                       | 4/37 [01:35<12:55, 23.49s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 14%|█████▉                                      | 5/37 [01:58<12:25, 23.29s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 16%|███████▏                                    | 6/37 [02:21<11:58, 23.17s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 19%|████████▎                                   | 7/37 [02:44<11:33, 23.10s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 22%|█████████▌                                  | 8/37 [03:07<11:08, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 24%|██████████▋                                 | 9/37 [03:30<10:44, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 27%|███████████▌                               | 10/37 [03:53<10:20, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 30%|████████████▊                              | 11/37 [04:16<09:58, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 32%|█████████████▉                             | 12/37 [04:39<09:35, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 35%|███████████████                            | 13/37 [05:02<09:11, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 38%|████████████████▎                          | 14/37 [05:25<08:48, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 41%|█████████████████▍                         | 15/37 [05:48<08:25, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 43%|██████████████████▌                        | 16/37 [06:11<08:02, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 46%|███████████████████▊                       | 17/37 [06:34<07:39, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 49%|████████████████████▉                      | 18/37 [06:57<07:16, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 51%|██████████████████████                     | 19/37 [07:20<06:53, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 54%|███████████████████████▏                   | 20/37 [07:43<06:30, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 57%|████████████████████████▍                  | 21/37 [08:06<06:07, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 59%|█████████████████████████▌                 | 22/37 [08:29<05:44, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 62%|██████████████████████████▋                | 23/37 [08:52<05:22, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 65%|███████████████████████████▉               | 24/37 [09:15<04:59, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 68%|█████████████████████████████              | 25/37 [09:38<04:36, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 70%|██████████████████████████████▏            | 26/37 [10:01<04:13, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 73%|███████████████████████████████▍           | 27/37 [10:24<03:50, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 76%|████████████████████████████████▌          | 28/37 [10:47<03:27, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 78%|█████████████████████████████████▋         | 29/37 [11:10<03:04, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 81%|██████████████████████████████████▊        | 30/37 [11:33<02:41, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 84%|████████████████████████████████████       | 31/37 [11:56<02:18, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 86%|█████████████████████████████████████▏     | 32/37 [12:19<01:55, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 89%|██████████████████████████████████████▎    | 33/37 [12:42<01:32, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 92%|███████████████████████████████████████▌   | 34/37 [13:05<01:09, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 95%|████████████████████████████████████████▋  | 35/37 [13:28<00:46, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 97%|█████████████████████████████████████████▊ | 36/37 [13:51<00:23, 23.01s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "100%|███████████████████████████████████████████| 37/37 [14:14<00:00, 23.10s/it]\n"
+      "100%|███████████████████████████████████████████| 37/37 [01:04<00:00,  1.74s/it]\n"
      ]
     },
     {
@@ -612,7 +554,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'X', 'y_hat'])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -625,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 54,
    "id": "d587d1d3-9325-4d45-9e15-d69d2d16bb6d",
    "metadata": {},
    "outputs": [
@@ -635,7 +577,7 @@
        "(37, 10)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -658,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 55,
    "id": "5ac4fc58-055d-4210-b330-0832508c1cf4",
    "metadata": {},
    "outputs": [
@@ -668,7 +610,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -681,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 56,
    "id": "a934005e-5bce-4a47-831a-3cf1f0c92f28",
    "metadata": {},
    "outputs": [
@@ -691,7 +633,7 @@
        "(37, 10)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -717,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 57,
    "id": "32b4ccf9-d8fa-4110-bc22-717d07e3ae0b",
    "metadata": {},
    "outputs": [
@@ -727,7 +669,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -740,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 58,
    "id": "339db91e-cd35-4ae8-abc5-3ba52b6c0bbd",
    "metadata": {},
    "outputs": [
@@ -750,7 +692,7 @@
        "(37, 10)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -772,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 59,
    "id": "17ce8982-2ba3-4508-a526-8a9960e0ecd3",
    "metadata": {},
    "outputs": [
@@ -782,7 +724,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -795,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 60,
    "id": "5274e7a0-acf5-4b9f-9cb4-070357fc7c10",
    "metadata": {},
    "outputs": [
@@ -805,7 +747,7 @@
        "(37,)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -832,7 +774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 61,
    "id": "bd219c08-29b2-4809-934b-287dfac4e4fa",
    "metadata": {},
    "outputs": [
@@ -842,7 +784,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -855,17 +797,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 62,
    "id": "57f900db-d712-4ff1-986b-7f4dc1accda0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1319,)"
+       "(11,)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -890,17 +832,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 63,
    "id": "7de109bd-8306-4753-8446-07f438e8c3e8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y', 'merged_intervals'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y', 'anomalies'])"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -913,17 +855,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 64,
    "id": "a37daebf-7091-4dab-93dc-928c7163778b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[(1309867201, 1314975601, 0)]"
+       "[(1310162401, 1310529601, 0),\n",
+       " (1311472801, 1312142401, 0),\n",
+       " (1312178401, 1312538401, 0),\n",
+       " (1312948801, 1313308801, 0),\n",
+       " (1313877601, 1314241201, 0),\n",
+       " (1314399601, 1314759601, 0)]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -934,7 +881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 65,
    "id": "60064037-5ed6-49f3-ae13-5f4952f5eabf",
    "metadata": {},
    "outputs": [
@@ -967,8 +914,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1309867201</td>\n",
-       "      <td>1314975601</td>\n",
+       "      <td>1310162401</td>\n",
+       "      <td>1310529601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1311472801</td>\n",
+       "      <td>1312142401</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1312178401</td>\n",
+       "      <td>1312538401</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1312948801</td>\n",
+       "      <td>1313308801</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1313877601</td>\n",
+       "      <td>1314241201</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1314399601</td>\n",
+       "      <td>1314759601</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -977,10 +954,15 @@
       ],
       "text/plain": [
        "        start         end  score\n",
-       "0  1309867201  1314975601      0"
+       "0  1310162401  1310529601      0\n",
+       "1  1311472801  1312142401      0\n",
+       "2  1312178401  1312538401      0\n",
+       "3  1312948801  1313308801      0\n",
+       "4  1313877601  1314241201      0\n",
+       "5  1314399601  1314759601      0"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -993,13 +975,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 72,
    "id": "98b221ef-ff0c-4705-9697-e2d240ff756e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1009,21 +991,15 @@
     }
    ],
    "source": [
-    "index, anomalies = list(map(context.get, ['timestamp', 'merged_intervals']))\n",
+    "index, anomalies = list(map(context.get, ['timestamp', 'anomalies']))\n",
     "\n",
     "plt.plot(data['timestamp'], data['value'], label='original')\n",
     "\n",
-    "plt.axvspan(*anomalies[0][:2], color='r', alpha=0.2, label='detected anomalies')\n",
+    "for ano in anomalies:\n",
+    "\n",
+    "    plt.axvspan(*ano[:2], color='r', alpha=0.2, label='detected anomalies')\n",
     "plt.legend();"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ee002d85-571a-4ecd-8f9d-99cb84808d7f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/tutorials/pipelines/mistral-prompter-pipeline.ipynb b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
index 20ad16e..c6b7928 100644
--- a/tutorials/pipelines/mistral-prompter-pipeline.ipynb
+++ b/tutorials/pipelines/mistral-prompter-pipeline.ipynb
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "1029c7ee-8a42-4452-8bc0-20c0fb45b8d9",
    "metadata": {},
    "outputs": [],
@@ -102,14 +102,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "262441fe-841b-4555-bf57-249305b59f92",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "066d82461cfb4ea18358bade4ce0337d",
+       "model_id": "9be9a9c4412a47fba8a20063766571f5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 65,
    "id": "be80a076",
    "metadata": {},
    "outputs": [],
@@ -143,19 +143,21 @@
     "    \"sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1\": {\n",
     "        \"beta\": 1.0\n",
     "    },\n",
+    "    \"sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences#1\": {\n",
+    "            \"window_size\": 200,\n",
+    "            \"step_size\": 150\n",
+    "    },\n",
     "    \"sigllm.primitives.prompting.anomalies.format_anomalies#1\": {\n",
-    "        \"padding_size\": 10\n",
+    "        \"padding_size\": 5\n",
     "    }\n",
     "}\n",
     "\n",
-    "## reduce padding, reduce overlapping windows\n",
-    "\n",
     "pipeline.set_hyperparameters(hyperparameters)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 36,
    "id": "9292817b-75d5-4526-a1b8-7475bcb787c5",
    "metadata": {},
    "outputs": [
@@ -173,7 +175,7 @@
        " 'sigllm.primitives.transformation.Float2Scalar#1': {'decimal': 2,\n",
        "  'rescale': True},\n",
        " 'sigllm.primitives.prompting.timeseries_preprocessing.rolling_window_sequences#1': {'window_size': 200,\n",
-       "  'step_size': 40},\n",
+       "  'step_size': 150},\n",
        " 'sigllm.primitives.transformation.format_as_string#1': {'sep': ',',\n",
        "  'space': False},\n",
        " 'sigllm.primitives.prompting.huggingface.HF#1': {'name': 'mistralai/Mistral-7B-Instruct-v0.2',\n",
@@ -190,10 +192,10 @@
        " 'sigllm.primitives.prompting.anomalies.val2idx#1': {},\n",
        " 'sigllm.primitives.prompting.anomalies.find_anomalies_in_windows#1': {'alpha': 1.0},\n",
        " 'sigllm.primitives.prompting.anomalies.merge_anomalous_sequences#1': {'beta': 1.0},\n",
-       " 'sigllm.primitives.prompting.anomalies.format_anomalies#1': {'padding_size': 10}}"
+       " 'sigllm.primitives.prompting.anomalies.format_anomalies#1': {'padding_size': 0}}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -217,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 37,
    "id": "2e548714",
    "metadata": {},
    "outputs": [
@@ -237,7 +239,7 @@
        " 'sigllm.primitives.prompting.anomalies.format_anomalies']"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 38,
    "id": "f683c7f7",
    "metadata": {},
    "outputs": [
@@ -273,7 +275,7 @@
        "dict_keys(['X', 'timestamp'])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -286,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 39,
    "id": "533566d5",
    "metadata": {},
    "outputs": [
@@ -309,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 40,
    "id": "a488bc32",
    "metadata": {},
    "outputs": [
@@ -319,7 +321,7 @@
        "(1648, 1)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -343,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 41,
    "id": "35c41874",
    "metadata": {},
    "outputs": [
@@ -353,7 +355,7 @@
        "dict_keys(['timestamp', 'X'])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 42,
    "id": "b49c4fbf",
    "metadata": {},
    "outputs": [
@@ -389,7 +391,7 @@
        "dict_keys(['timestamp', 'X', 'minimum', 'decimal'])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -402,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 43,
    "id": "f7571fa1",
    "metadata": {},
    "outputs": [
@@ -425,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 44,
    "id": "fd1a9ba6",
    "metadata": {},
    "outputs": [
@@ -435,7 +437,7 @@
        "0.000385004945833"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -460,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 45,
    "id": "bd160c3e",
    "metadata": {},
    "outputs": [
@@ -470,7 +472,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'X', 'first_index', 'window_size', 'step_size'])"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -483,7 +485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 46,
    "id": "ab08a9a9",
    "metadata": {},
    "outputs": [
@@ -491,9 +493,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "X shape = (37, 200, 1)\n",
+      "X shape = (10, 200, 1)\n",
       "Timestamp shape = (1648,)\n",
-      "First index shape = (37,)\n"
+      "First index shape = (10,)\n"
      ]
     }
    ],
@@ -520,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 47,
    "id": "3a1836db-cd6f-4a39-8f00-6a09c620c5f0",
    "metadata": {},
    "outputs": [
@@ -530,7 +532,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X', 'X_str'])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -543,7 +545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 48,
    "id": "1259df2c-d656-42a8-973c-b15cf8e031d4",
    "metadata": {},
    "outputs": [
@@ -553,7 +555,7 @@
        "'40,39,30,21,20,25,30,29,53,78,74,73,69,68,51,51,51,41,24,30,27,26,23,26,32,25,21,16,21,30,28,39,58,59,71,78,73,68,72,52,40,34,27,27,34,28,32,25,20,20,17,13,17,27,24,34,67,62,60,59,71,63,56,43,36,30,26,24,24,20,20,23,17,19,16,14,12,16,21,28,47,54,50,53,60,51,52,42,32,34,24,24,21,21,22,25,22,16,17,12,13,17,22,27,44,47,54,66,54,58,42,39,36,32,27,23,21,21,19,24,22,19,13,11,15,20,22,28,47,64,52,57,57,51,40,44,36,35,28,24,20,29,21,22,21,16,12,11,12,17,19,24,40,53,54,43,46,43,34,38,32,25,22,15,18,17,17,15,16,14,14,10,11,14,15,31,52,43,49,45,44,36,30,32,22,24,22,19,18,20,19,17,19,15,12,11,17,23,22,29'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -564,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 49,
    "id": "d05e85ce-6111-494f-88c0-4fc566386b43",
    "metadata": {},
    "outputs": [
@@ -574,7 +576,7 @@
        "str"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -606,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 50,
    "id": "b4711e98-c522-4464-b645-607f76e89063",
    "metadata": {},
    "outputs": [
@@ -614,50 +616,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                    | 0/37 [00:00<?, ?it/s]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "2024-10-03 11:37:28.645348: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
-      "2024-10-03 11:37:28.645377: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
-      "2024-10-03 11:37:28.645402: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-10-03 11:37:28.652931: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
-      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-      "2024-10-03 11:37:29.421095: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
-      "  3%|█▏                                          | 1/37 [00:24<14:57, 24.94s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "  5%|██▍                                         | 2/37 [00:47<13:53, 23.81s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "  8%|███▌                                        | 3/37 [01:10<13:16, 23.43s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 11%|████▊                                       | 4/37 [01:33<12:47, 23.27s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 14%|█████▉                                      | 5/37 [01:56<12:21, 23.18s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 16%|███████▏                                    | 6/37 [02:20<11:57, 23.13s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 19%|████████▎                                   | 7/37 [02:43<11:32, 23.08s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 22%|█████████▌                                  | 8/37 [03:06<11:09, 23.09s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 24%|██████████▋                                 | 9/37 [03:29<10:46, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 27%|███████████▌                               | 10/37 [03:52<10:22, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 30%|████████████▊                              | 11/37 [04:15<09:59, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 32%|█████████████▉                             | 12/37 [04:38<09:36, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 35%|███████████████                            | 13/37 [05:01<09:12, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 38%|████████████████▎                          | 14/37 [05:24<08:49, 23.04s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 41%|█████████████████▍                         | 15/37 [05:47<08:26, 23.04s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 43%|██████████████████▌                        | 16/37 [06:10<08:03, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 46%|███████████████████▊                       | 17/37 [06:33<07:40, 23.02s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 49%|████████████████████▉                      | 18/37 [06:56<07:17, 23.04s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 51%|██████████████████████                     | 19/37 [07:19<06:54, 23.03s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 54%|███████████████████████▏                   | 20/37 [07:42<06:31, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 57%|████████████████████████▍                  | 21/37 [08:05<06:08, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 59%|█████████████████████████▌                 | 22/37 [08:28<05:45, 23.05s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 62%|██████████████████████████▋                | 23/37 [08:51<05:22, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 65%|███████████████████████████▉               | 24/37 [09:14<04:59, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 68%|█████████████████████████████              | 25/37 [09:37<04:36, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 70%|██████████████████████████████▏            | 26/37 [10:00<04:13, 23.08s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 73%|███████████████████████████████▍           | 27/37 [10:24<03:50, 23.09s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 76%|████████████████████████████████▌          | 28/37 [10:47<03:27, 23.09s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 78%|█████████████████████████████████▋         | 29/37 [11:10<03:04, 23.08s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 81%|██████████████████████████████████▊        | 30/37 [11:33<02:41, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 84%|████████████████████████████████████       | 31/37 [11:56<02:18, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 86%|█████████████████████████████████████▏     | 32/37 [12:19<01:55, 23.06s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 89%|██████████████████████████████████████▎    | 33/37 [12:42<01:32, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 92%|███████████████████████████████████████▌   | 34/37 [13:05<01:09, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 95%|████████████████████████████████████████▋  | 35/37 [13:28<00:46, 23.07s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      " 97%|█████████████████████████████████████████▊ | 36/37 [13:51<00:23, 23.08s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
-      "100%|███████████████████████████████████████████| 37/37 [14:14<00:00, 23.10s/it]\n"
+      "  0%|                                                    | 0/10 [00:00<?, ?it/s]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 10%|████▍                                       | 1/10 [00:23<03:27, 23.09s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 20%|████████▊                                   | 2/10 [00:46<03:04, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 30%|█████████████▏                              | 3/10 [01:08<02:40, 22.98s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 40%|█████████████████▌                          | 4/10 [01:31<02:17, 22.97s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 50%|██████████████████████                      | 5/10 [01:54<01:54, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 60%|██████████████████████████▍                 | 6/10 [02:17<01:31, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 70%|██████████████████████████████▊             | 7/10 [02:40<01:08, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 80%|███████████████████████████████████▏        | 8/10 [03:03<00:45, 22.99s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      " 90%|███████████████████████████████████████▌    | 9/10 [03:26<00:22, 23.00s/it]Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+      "100%|███████████████████████████████████████████| 10/10 [03:49<00:00, 23.00s/it]\n"
      ]
     },
     {
@@ -666,7 +635,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'X', 'y_hat'])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -679,17 +648,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 51,
    "id": "d587d1d3-9325-4d45-9e15-d69d2d16bb6d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(37, 10)"
+       "(10, 10)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -712,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 52,
    "id": "5ac4fc58-055d-4210-b330-0832508c1cf4",
    "metadata": {},
    "outputs": [
@@ -722,7 +691,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -735,17 +704,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 53,
    "id": "a934005e-5bce-4a47-831a-3cf1f0c92f28",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(37, 10)"
+       "(10, 10)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -771,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 54,
    "id": "32b4ccf9-d8fa-4110-bc22-717d07e3ae0b",
    "metadata": {},
    "outputs": [
@@ -781,7 +750,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -794,17 +763,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 55,
    "id": "339db91e-cd35-4ae8-abc5-3ba52b6c0bbd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(37, 10)"
+       "(10, 10)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -826,7 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 56,
    "id": "17ce8982-2ba3-4508-a526-8a9960e0ecd3",
    "metadata": {},
    "outputs": [
@@ -836,7 +805,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -849,17 +818,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 57,
    "id": "5274e7a0-acf5-4b9f-9cb4-070357fc7c10",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(37,)"
+       "(10,)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -886,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 58,
    "id": "bd219c08-29b2-4809-934b-287dfac4e4fa",
    "metadata": {},
    "outputs": [
@@ -896,7 +865,7 @@
        "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y'])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -909,17 +878,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 59,
    "id": "57f900db-d712-4ff1-986b-7f4dc1accda0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1319,)"
+       "(449,)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -944,17 +913,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 66,
    "id": "7de109bd-8306-4753-8446-07f438e8c3e8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'X', 'y', 'anomalies'])"
+       "dict_keys(['timestamp', 'minimum', 'decimal', 'first_index', 'window_size', 'step_size', 'X_str', 'y_hat', 'anomalies', 'X', 'y'])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -967,28 +936,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 67,
    "id": "a37daebf-7091-4dab-93dc-928c7163778b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[(1310011201, 1314831601, 0)]"
+       "9"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "context['anomalies']"
+    "len(context['anomalies'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 68,
    "id": "60064037-5ed6-49f3-ae13-5f4952f5eabf",
    "metadata": {},
    "outputs": [
@@ -1021,8 +990,56 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1310011201</td>\n",
-       "      <td>1314831601</td>\n",
+       "      <td>1309993201</td>\n",
+       "      <td>1310205601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1310536801</td>\n",
+       "      <td>1310745601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1311073201</td>\n",
+       "      <td>1311285601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1311613201</td>\n",
+       "      <td>1311825601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1312153201</td>\n",
+       "      <td>1312365601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1312693201</td>\n",
+       "      <td>1312905601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1313233201</td>\n",
+       "      <td>1313445601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1313773201</td>\n",
+       "      <td>1313985601</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1314313201</td>\n",
+       "      <td>1314525601</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1031,10 +1048,18 @@
       ],
       "text/plain": [
        "        start         end  score\n",
-       "0  1310011201  1314831601      0"
+       "0  1309993201  1310205601      0\n",
+       "1  1310536801  1310745601      0\n",
+       "2  1311073201  1311285601      0\n",
+       "3  1311613201  1311825601      0\n",
+       "4  1312153201  1312365601      0\n",
+       "5  1312693201  1312905601      0\n",
+       "6  1313233201  1313445601      0\n",
+       "7  1313773201  1313985601      0\n",
+       "8  1314313201  1314525601      0"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1047,13 +1072,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 71,
    "id": "98b221ef-ff0c-4705-9697-e2d240ff756e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1067,8 +1092,9 @@
     "\n",
     "plt.plot(data['timestamp'], data['value'], label='original')\n",
     "\n",
-    "plt.axvspan(*anomalies[0], color='r', alpha=0.2, label='detected anomalies')\n",
-    "plt.legend();"
+    "for ano in anomalies:\n",
+    "    plt.axvspan(*ano[:2], color='r', alpha=0.2, label='detected anomalies')\n",
+    "plt.legend(['original', 'detected anomalies']);"
    ]
   },
   {

From 1b07add089283d289ad7904cddadb1511af60714 Mon Sep 17 00:00:00 2001
From: Linh-nk <linhnk@dai-desk34.lids.mit.edu>
Date: Fri, 18 Oct 2024 09:47:19 -0400
Subject: [PATCH 25/25] fix-lint

---
 sigllm/primitives/prompting/gpt.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/sigllm/primitives/prompting/gpt.py b/sigllm/primitives/prompting/gpt.py
index b2ae007..f78bbe0 100644
--- a/sigllm/primitives/prompting/gpt.py
+++ b/sigllm/primitives/prompting/gpt.py
@@ -3,10 +3,9 @@
 import json
 import os
 
-import openai
 import tiktoken
-from tqdm import tqdm
 from openai import OpenAI
+from tqdm import tqdm
 
 PROMPT_PATH = os.path.join(
     os.path.dirname(os.path.abspath(__file__)),
@@ -64,7 +63,6 @@ def __init__(self, name='gpt-3.5-turbo', sep=',', anomalous_percent=0.5, temp=1,
 
         self.tokenizer = tiktoken.encoding_for_model(self.name)
 
-
         valid_tokens = []
         for number in VALID_NUMBERS:
             token = self.tokenizer.encode(number)