Add TFT benchmark

Author: wendili-cs

examples/benchmarks/TFT/data_formatters/__init__.py

@@ -0,0 +1,15 @@
+# coding=utf-8
+# Copyright 2020 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+

examples/benchmarks/TFT/data_formatters/base.py

@@ -0,0 +1,235 @@
+# coding=utf-8
+# Copyright 2020 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""Default data formatting functions for experiments.
+
+For new datasets, inherit form GenericDataFormatter and implement
+all abstract functions.
+
+These dataset-specific methods:
+1) Define the column and input types for tabular dataframes used by model
+2) Perform the necessary input feature engineering & normalisation steps
+3) Reverts the normalisation for predictions
+4) Are responsible for train, validation and test splits
+
+
+"""
+
+import abc
+import enum
+
+
+# Type defintions
+class DataTypes(enum.IntEnum):
+  """Defines numerical types of each column."""
+  REAL_VALUED = 0
+  CATEGORICAL = 1
+  DATE = 2
+
+
+class InputTypes(enum.IntEnum):
+  """Defines input types of each column."""
+  TARGET = 0
+  OBSERVED_INPUT = 1
+  KNOWN_INPUT = 2
+  STATIC_INPUT = 3
+  ID = 4  # Single column used as an entity identifier
+  TIME = 5  # Single column exclusively used as a time index
+
+
+class GenericDataFormatter(abc.ABC):
+  """Abstract base class for all data formatters.
+
+  User can implement the abstract methods below to perform dataset-specific
+  manipulations.
+
+  """
+
+  @abc.abstractmethod
+  def set_scalers(self, df):
+    """Calibrates scalers using the data supplied."""
+    raise NotImplementedError()
+
+  @abc.abstractmethod
+  def transform_inputs(self, df):
+    """Performs feature transformation."""
+    raise NotImplementedError()
+
+  @abc.abstractmethod
+  def format_predictions(self, df):
+    """Reverts any normalisation to give predictions in original scale."""
+    raise NotImplementedError()
+
+  @abc.abstractmethod
+  def split_data(self, df):
+    """Performs the default train, validation and test splits."""
+    raise NotImplementedError()
+
+  @property
+  @abc.abstractmethod
+  def _column_definition(self):
+    """Defines order, input type and data type of each column."""
+    raise NotImplementedError()
+
+  @abc.abstractmethod
+  def get_fixed_params(self):
+    """Defines the fixed parameters used by the model for training.
+
+    Requires the following keys:
+      'total_time_steps': Defines the total number of time steps used by TFT
+      'num_encoder_steps': Determines length of LSTM encoder (i.e. history)
+      'num_epochs': Maximum number of epochs for training
+      'early_stopping_patience': Early stopping param for keras
+      'multiprocessing_workers': # of cpus for data processing
+
+
+    Returns:
+      A dictionary of fixed parameters, e.g.:
+
+      fixed_params = {
+          'total_time_steps': 252 + 5,
+          'num_encoder_steps': 252,
+          'num_epochs': 100,
+          'early_stopping_patience': 5,
+          'multiprocessing_workers': 5,
+      }
+    """
+    raise NotImplementedError
+
+  # Shared functions across data-formatters
+  @property
+  def num_classes_per_cat_input(self):
+    """Returns number of categories per relevant input.
+
+    This is seqeuently required for keras embedding layers.
+    """
+    return self._num_classes_per_cat_input
+
+  def get_num_samples_for_calibration(self):
+    """Gets the default number of training and validation samples.
+
+    Use to sub-sample the data for network calibration and a value of -1 uses
+    all available samples.
+
+    Returns:
+      Tuple of (training samples, validation samples)
+    """
+    return -1, -1
+
+  def get_column_definition(self):
+    """"Returns formatted column definition in order expected by the TFT."""
+
+    column_definition = self._column_definition
+
+    # Sanity checks first.
+    # Ensure only one ID and time column exist
+    def _check_single_column(input_type):
+
+      length = len([tup for tup in column_definition if tup[2] == input_type])
+
+      if length != 1:
+        raise ValueError('Illegal number of inputs ({}) of type {}'.format(
+            length, input_type))
+
+    _check_single_column(InputTypes.ID)
+    _check_single_column(InputTypes.TIME)
+
+    identifier = [tup for tup in column_definition if tup[2] == InputTypes.ID]
+    time = [tup for tup in column_definition if tup[2] == InputTypes.TIME]
+    real_inputs = [
+        tup for tup in column_definition if tup[1] == DataTypes.REAL_VALUED and
+        tup[2] not in {InputTypes.ID, InputTypes.TIME}
+    ]
+    categorical_inputs = [
+        tup for tup in column_definition if tup[1] == DataTypes.CATEGORICAL and
+        tup[2] not in {InputTypes.ID, InputTypes.TIME}
+    ]
+
+    return identifier + time + real_inputs + categorical_inputs
+
+  def _get_input_columns(self):
+    """Returns names of all input columns."""
+    return [
+        tup[0]
+        for tup in self.get_column_definition()
+        if tup[2] not in {InputTypes.ID, InputTypes.TIME}
+    ]
+
+  def _get_tft_input_indices(self):
+    """Returns the relevant indexes and input sizes required by TFT."""
+
+    # Functions
+    def _extract_tuples_from_data_type(data_type, defn):
+      return [
+          tup for tup in defn if tup[1] == data_type and
+          tup[2] not in {InputTypes.ID, InputTypes.TIME}
+      ]
+
+    def _get_locations(input_types, defn):
+      return [i for i, tup in enumerate(defn) if tup[2] in input_types]
+
+    # Start extraction
+    column_definition = [
+        tup for tup in self.get_column_definition()
+        if tup[2] not in {InputTypes.ID, InputTypes.TIME}
+    ]
+
+    categorical_inputs = _extract_tuples_from_data_type(DataTypes.CATEGORICAL,
+                                                        column_definition)
+    real_inputs = _extract_tuples_from_data_type(DataTypes.REAL_VALUED,
+                                                 column_definition)
+
+    locations = {
+        'input_size':
+            len(self._get_input_columns()),
+        'output_size':
+            len(_get_locations({InputTypes.TARGET}, column_definition)),
+        'category_counts':
+            self.num_classes_per_cat_input,
+        'input_obs_loc':
+            _get_locations({InputTypes.TARGET}, column_definition),
+        'static_input_loc':
+            _get_locations({InputTypes.STATIC_INPUT}, column_definition),
+        'known_regular_inputs':
+            _get_locations({InputTypes.STATIC_INPUT, InputTypes.KNOWN_INPUT},
+                           real_inputs),
+        'known_categorical_inputs':
+            _get_locations({InputTypes.STATIC_INPUT, InputTypes.KNOWN_INPUT},
+                           categorical_inputs),
+    }
+
+    return locations
+
+  def get_experiment_params(self):
+    """Returns fixed model parameters for experiments."""
+
+    required_keys = [
+        'total_time_steps', 'num_encoder_steps', 'num_epochs',
+        'early_stopping_patience', 'multiprocessing_workers'
+    ]
+
+    fixed_params = self.get_fixed_params()
+
+    for k in required_keys:
+      if k not in fixed_params:
+        raise ValueError('Field {}'.format(k) +
+                         ' missing from fixed parameter definitions!')
+
+    fixed_params['column_definition'] = self.get_column_definition()
+
+    fixed_params.update(self._get_tft_input_indices())
+
+    return fixed_params