Week1+2 #1
Week1+2 #1
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WalkthroughThe pull request introduces several changes, including the addition of new files for data processing and model training specific to hotel reservations, updates to configuration files, and modifications to existing files for formatting and structure. Key additions include a Changes
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Actionable comments posted: 21
🧹 Outside diff range and nitpick comments (30)
src/hotel_reservations/utils.py (3)
1-1: Enhance module docstring with more detailsConsider expanding the docstring to include:
- Brief description of available functions
- Example usage
- Any dependencies or requirements
-"""Utility functions for the hotel reservations project.""" +"""Utility functions for the hotel reservations project. + +This module provides visualization utilities for model evaluation: + - visualize_results: Creates a normalized confusion matrix visualization + +Example: + >>> y_test = [0, 1, 0, 1] + >>> y_pred = [0, 1, 1, 1] + >>> visualize_results(y_test, y_pred) +"""
12-25: Add option to save the plotIn non-interactive environments (e.g., production),
plt.show()might not work as expected. Consider adding an option to save the plot to a file.-def visualize_results(y_test, y_pred): +def visualize_results(y_test: np.ndarray, y_pred: np.ndarray, save_path: Optional[str] = None) -> None: # ... existing code ... plt.ylabel("Actual", fontsize=12) plt.xlabel("Prediction", fontsize=12) + if save_path: + plt.savefig(save_path, bbox_inches='tight', dpi=300) plt.show() + plt.close() # Clean up resources
13-21: Consider adding customization optionsThe visualization parameters (figure size, colors, font sizes) could be made configurable for better reusability.
- plt.figure(figsize=(10, 6)) + def visualize_results( + y_test: np.ndarray, + y_pred: np.ndarray, + figsize: Tuple[int, int] = (10, 6), + cmap: str = "viridis", + font_size: int = 12, + save_path: Optional[str] = None + ) -> None: + plt.figure(figsize=figsize) sns.heatmap( conf_matrix, annot=True, fmt=".2f", vmin=0.0, vmax=1.0, + cmap=cmap, xticklabels=["Not Cancelled", "Cancelled"], yticklabels=["Not Cancelled", "Cancelled"], )src/hotel_reservations/config.py (3)
1-1: Enhance module docstring with more detailsConsider expanding the module docstring to include:
- Purpose and usage of the configuration module
- Example usage
- Description of the configuration structure
-"""Configuration module for the project.""" +"""Configuration module for the hotel reservations project. + +This module provides a Pydantic-based configuration management system that handles: +- Feature lists (numerical and categorical) +- Target variable specifications +- Database configuration (catalog and schema) +- Model parameters +- A/B test parameters + +Example: + config = ProjectConfig.from_yaml("config.yml") + num_features = config.num_features +"""
9-11: Enhance class docstring with validation detailsThe class docstring should describe the validation behavior provided by Pydantic and document the expected configuration structure.
- """Class to store the configuration for the project.""" + """Configuration class with Pydantic validation for the hotel reservations project. + + This class provides runtime validation for all configuration parameters and + ensures type safety through Pydantic's validation system. + + Attributes: + num_features (List[str]): List of numerical feature names + cat_features (List[str]): List of categorical feature names + original_target (str): Name of the original target variable + target (str): Name of the processed target variable + catalog_name (str): Name of the data catalog + schema_name (str): Name of the database schema + parameters (Dict[str, Any]): Model-related parameters + ab_test (Dict[str, Any]): A/B test configuration parameters + """
1-26: Consider architectural improvements for ML pipeline integrationThe configuration structure suggests this is part of a machine learning pipeline. Consider these architectural improvements:
- Version the configuration schema to support backward compatibility
- Add validation for feature name conflicts between num_features and cat_features
- Consider adding configuration for:
- Feature preprocessing steps
- Model evaluation metrics
- Model versioning strategy
- Monitoring parameters
Would you like assistance in implementing any of these architectural improvements?
notebooks/week_2/01_preparation.py (2)
2-2: Consider using a more robust package installation approachThe relative path to the wheel file (
../hotel_reservations-2.2.0-py3-none-any.whl) could break if notebook locations change. Consider:
- Using an absolute path from a known location
- Installing from a package repository instead
- Using environment variables or project config for the version number
19-19: Fix incorrect dataset commentThe comment refers to "house prices" but this is a hotel reservations dataset.
-# Load the house prices dataset +# Load the hotel reservations datasetproject_config.yml (5)
1-2: Consider environment-specific configuration filesThe current configuration appears to be development-specific (
dev). Consider creating separate configuration files for different environments (dev, staging, prod) to prevent accidental usage of development settings in production.🧰 Tools
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17-17: Remove trailing whitespaceRemove the trailing space after
no_of_adults.🧰 Tools
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16-45: Improve feature organization and preprocessingSeveral suggestions for the feature configuration:
- Separate one-hot encoded features from original numerical features
- Consider standardizing date handling:
arrival_year,arrival_month,arrival_datecould be combined- Add derived features like day_of_week, is_weekend
- Consider adding feature groups for better organization
num_features: + # Guest information - no_of_adults - no_of_children + # Stay duration - no_of_weekend_nights - no_of_week_nights + # Booking history - repeated_guest - no_of_previous_cancellations - no_of_previous_bookings_not_canceled + # Additional services - required_car_parking_space - no_of_special_requests + # Financial - avg_price_per_room + # Temporal features - arrival_year - arrival_month - arrival_date + # One-hot encoded features - type_of_meal_plan_meal_plan_1 # ... rest of one-hot features🧰 Tools
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51-53: Document target variable transformationPlease add comments explaining:
- The relationship between
booking_statusandbooking_status_canceled- The transformation logic
- Expected values for both variables
1-53: Consider adding version control for configurationAs this configuration file is central to the project, consider:
- Adding a version field to track configuration changes
- Implementing configuration validation
- Adding a changelog section or comments for major changes
Example:
+version: '1.0.0' # Semantic versioning catalog_name: dev🧰 Tools
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src/hotel_reservations/reservations_model.py (5)
1-1: Enhance the module docstring with more details.Consider expanding the docstring to include:
- Purpose and use cases
- Key features and capabilities
- Example usage
-"""Model for predicting hotel cancellations.""" +"""Model for predicting hotel cancellations. + +This module provides a scikit-learn compatible classifier for predicting hotel reservation +cancellations using XGBoost. It includes features for: +- Data preprocessing with StandardScaler +- Model training and prediction +- Performance evaluation using accuracy and precision metrics + +Example: + >>> from hotel_reservations.config import ProjectConfig + >>> model = ReservationsModel(config=ProjectConfig()) + >>> model.fit(X_train, y_train) + >>> predictions = model.predict(X_test) +"""
12-14: Enhance the class docstring with implementation details.The sklearn integration is well-implemented. Consider expanding the docstring to document the inheritance and parameters.
- """Class to train and evaluate a model for predicting cancellation on hotel reservations.""" + """A sklearn-compatible classifier for predicting hotel reservation cancellations. + + This class implements a wrapper around XGBoost classifier with standardized feature scaling. + It inherits from sklearn's BaseEstimator and ClassifierMixin to ensure compatibility with + sklearn pipelines and cross-validation utilities. + + Parameters + ---------- + config : ProjectConfig + Configuration object containing model parameters including: + - eta: XGBoost learning rate + - n_estimators: Number of boosting rounds + - max_depth: Maximum tree depth + """
15-23: Add parameter validation in the constructor.While the implementation is correct, consider adding validation for the config parameters to fail fast if invalid values are provided.
def __init__(self, config: ProjectConfig): + if not isinstance(config, ProjectConfig): + raise TypeError("config must be an instance of ProjectConfig") + + required_params = ["eta", "n_estimators", "max_depth"] + missing_params = [p for p in required_params if p not in config.parameters] + if missing_params: + raise ValueError(f"Missing required parameters: {missing_params}") + self.config = config self.scaler = StandardScaler() self.model = XGBClassifier(
35-39: Enhance evaluation metrics and add validation.Consider adding more evaluation metrics and proper input validation.
def evaluate(self, y: pd.DataFrame, y_pred: pd.DataFrame): """Evaluate the model on the test data.""" + if not isinstance(y, pd.DataFrame) or not isinstance(y_pred, pd.DataFrame): + raise TypeError("Inputs must be pandas DataFrames") + if y.empty or y_pred.empty: + raise ValueError("Empty evaluation data provided") + if y.shape != y_pred.shape: + raise ValueError("Mismatched shapes between true and predicted labels") + + try: accuracy = accuracy_score(y_true=y, y_pred=y_pred) precision = precision_score(y_true=y, y_pred=y_pred) - return accuracy, precision + recall = recall_score(y_true=y, y_pred=y_pred) + f1 = f1_score(y_true=y, y_pred=y_pred) + + return { + 'accuracy': accuracy, + 'precision': precision, + 'recall': recall, + 'f1': f1 + } + except Exception as e: + raise RuntimeError(f"Error computing metrics: {str(e)}") from eDon't forget to add the required imports:
from sklearn.metrics import accuracy_score, precision_score +from sklearn.metrics import recall_score, f1_score
12-39: Consider adding logging and model persistence capabilities.To enhance the production readiness of this class, consider:
- Adding logging to track training progress, predictions, and evaluation metrics
- Implementing model persistence methods (save/load) for the entire pipeline (both scaler and model)
- Adding a method to extract feature importance from the XGBoost model
This will improve monitoring and maintainability in production.
notebooks/week_1/01_preprocessing.py (3)
2-2: Consider using an absolute path or environment variable for the wheel file.The relative path
../hotel_reservations-2.2.0-py3-none-any.whlmight break if the notebook location changes. Consider using an absolute path or environment variable to make it more robust.-# MAGIC %pip install ../hotel_reservations-2.2.0-py3-none-any.whl +# MAGIC %pip install ${WHEEL_PATH}/hotel_reservations-2.2.0-py3-none-any.whl
15-15: Use absolute path or environment variable for config file.Similar to the wheel file, the relative path for the config file could break if notebook location changes.
-config = ProjectConfig.from_yaml(config_path="../../project_config.yml") +config = ProjectConfig.from_yaml(config_path="${CONFIG_PATH}/project_config.yml")
22-26: Consider performance implications of Spark to Pandas conversion.Loading data as a Spark DataFrame and immediately converting to pandas might be inefficient for large datasets. Consider either:
- Using pandas directly if the data size is manageable
- Keeping it as a Spark DataFrame if you need to handle large-scale data
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22-22: Undefined name
spark(F821)
notebooks/week_2/02_logging_and_registering.py (6)
8-8: Remove unused importpandas as pdThe import statement
import pandas as pdis not used anywhere in the code. Removing unused imports improves code readability and reduces clutter.Apply this diff to remove the unused import:
-import pandas as pd🧰 Tools
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pandasimported but unusedRemove unused import:
pandas(F401)
8-15: Consider reorganizing imports to comply with PEP 8 standardsPEP 8 recommends placing all import statements at the top of the file. Currently, the imports are placed after restarting the Python environment. While this might be intentional due to the notebook context, consider reorganizing the code to have imports at the top for better readability and to comply with standard conventions.
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pandasimported but unusedRemove unused import:
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59-59: Avoid hardcoding the Git SHAThe
GIT_SHAis hardcoded as"ffa63b430205ff7". Hardcoding can lead to inconsistencies if the code changes without updating the SHA. Consider retrieving the Git SHA dynamically to ensure it reflects the current commit.Possible Implementation: Retrieve Git SHA dynamically
-GIT_SHA = "ffa63b430205ff7" +import os +GIT_SHA = os.popen('git rev-parse HEAD').read().strip()Note: Ensure that the environment where this code runs has access to Git commands.
67-72: Clarify preprocessing of target variabley_trainIn the preprocessing step for
y_train, the methodpreprocess_datais called withX=y_train, which could be confusing sinceXtypically represents features. Consider renaming the parameter or providing additional comments to improve clarity.Suggestion: Rename parameter for clarity
If
preprocess_datais intended to preprocess the target variable, adjust the parameter name accordingly.- X=y_train, + y=y_train,And modify the
preprocess_datamethod signature if necessary.
75-80: Clarify preprocessing of target variabley_testSimilar to the previous comment on
y_train, ensure that the preprocessing ofy_testis clearly documented and parameter names accurately reflect their purpose.
117-119: Ensure proper usage of MLflow data loadingThe code retrieves dataset information and attempts to load it using
dataset_source.load(). Confirm that this step is necessary and that it functions as intended. If the loaded data is not used subsequently, consider removing this step to streamline the code.src/hotel_reservations/data_processor.py (3)
60-65: Simplify column renaming using Pandas built-in methods.The current loop for renaming columns can be replaced with Pandas string methods for better readability and efficiency.
Apply this diff to simplify the code:
- col_names = X.columns.to_list() - for i, col in enumerate(col_names): - col = col.replace(" ", "_").lower() - col_names[i] = col - X.columns = col_names + X.columns = X.columns.str.replace(" ", "_").str.lower()
81-86: Maketest_sizeandrandom_stateconfigurable throughProjectConfig.Hardcoding
test_sizeandrandom_statereduces flexibility. Fetching these values from the configuration enhances adaptability.Apply this diff to retrieve values from
self.config:- def split_data( - self, X: pd.DataFrame, test_size: float = 0.2, random_state: int = 42 - ) -> tuple[pd.DataFrame, pd.DataFrame]: + def split_data( + self, X: pd.DataFrame, test_size: float = None, random_state: int = None + ) -> tuple[pd.DataFrame, pd.DataFrame]: + if test_size is None: + test_size = self.config.test_size + if random_state is None: + random_state = self.config.random_state train_set, test_set = train_test_split( X, test_size=test_size, random_state=random_state )Ensure that
test_sizeandrandom_stateare defined inProjectConfig.
28-31: Allowinclude_fe_featuresto be configurable in thetransformmethod.Hardcoding
include_fe_featuresasTruelimits flexibility. Consider making it a parameter to control feature inclusion during transformation.Modify the method signature and usage:
- def transform(self, X: pd.DataFrame) -> pd.DataFrame: + def transform(self, X: pd.DataFrame, include_fe_features: bool = True) -> pd.DataFrame: """Preprocess data with One-Hot Encoding and relevant feature extraction.""" X = self.one_hot_encode(X=X, features="cat_features") return self.extract_features( - X=X, features="num_features", include_fe_features=True + X=X, features="num_features", include_fe_features=include_fe_features )
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⛔ Files ignored due to path filters (2)
data/hotel_reservations.csvis excluded by!**/*.csvuv.lockis excluded by!**/*.lock
📒 Files selected for processing (17)
.github/CODEOWNERS(1 hunks).github/workflows/ci.yml(1 hunks).gitignore(1 hunks).pre-commit-config.yaml(1 hunks)README.md(1 hunks)databricks.yml(1 hunks)notebooks/week_1/01_preprocessing.py(1 hunks)notebooks/week_2/01_preparation.py(1 hunks)notebooks/week_2/02_logging_and_registering.py(1 hunks)notebooks/week_2/03_feature_engineering.py(1 hunks)project_config.yml(1 hunks)pyproject.toml(1 hunks)pytest.ini(1 hunks)src/hotel_reservations/config.py(1 hunks)src/hotel_reservations/data_processor.py(1 hunks)src/hotel_reservations/reservations_model.py(1 hunks)src/hotel_reservations/utils.py(1 hunks)
✅ Files skipped from review due to trivial changes (4)
- .github/CODEOWNERS
- .gitignore
- databricks.yml
- pytest.ini
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notebooks/week_1/01_preprocessing.py
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src/hotel_reservations/data_processor.py
16-16: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
🔇 Additional comments (15)
src/hotel_reservations/utils.py (1)
8-25: Verify function usage in preprocessing notebook
Let's verify the function is properly imported and used in the preprocessing notebook.
✅ Verification successful
Function is correctly imported and used
The visualize_results function is properly imported and used in notebooks/week_1/01_preprocessing.py. The function is called with the correct parameters (y_test and y_pred) as defined in the function signature, where y_pred is obtained from model predictions and passed along with the actual test labels (y_test).
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The following scripts were executed for the analysis:
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notebooks/week_2/01_preparation.py (3)
8-11: LGTM! Imports are appropriate for the data processing task
The import order warning from static analysis can be ignored as this is a Databricks notebook where imports commonly follow command cell markers.
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13-13: LGTM! Standard Spark session initialization
27-29: Verify DataProcessor implementation
Let's verify the implementation of the mentioned methods in the DataProcessor class.
✅ Verification successful
Let me search for the specific methods mentioned in the review.
Both methods are properly implemented in DataProcessor class
The verification confirms that both split_data and save_to_catalog methods are properly implemented in the DataProcessor class with appropriate type hints and functionality:
split_data: Correctly splits data using train_test_split with configurable test_size and random_statesave_to_catalog: Properly saves train and test sets as Databricks tables with timestamp columns
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pyproject.toml (5)
6-6: LGTM: Improved Python version constraint
The change from "==3.11" to "==3.11.*" is a good improvement as it allows patch-level updates while maintaining compatibility.
20-24: LGTM: Well-structured dev dependencies
The development dependencies are well-structured with appropriate version constraints and include all necessary tools for Databricks integration.
26-39: LGTM: Comprehensive Ruff configuration
The Ruff configuration includes a good selection of linting rules covering code style, potential bugs, and import sorting.
2-3: Verify version numbering strategy
The version number jumped from 0.0.1 to 2.2.0, which is a significant change. Please ensure this aligns with your versioning strategy and consider documenting the versioning scheme in the README.
7-18: Verify impact of ML library changes
The switch from lightgbm to xgboost suggests a change in the ML approach. Please ensure all existing model code has been updated accordingly.
✅ Verification successful
The ML library switch is properly implemented
The codebase has been correctly updated to use XGBoost instead of LightGBM. The model implementation in src/hotel_reservations/reservations_model.py shows:
- Clean XGBoost integration with no remaining LightGBM references
- Proper model configuration using XGBClassifier
- Complete implementation including training, prediction, and evaluation methods
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The following scripts were executed for the analysis:
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#!/bin/bash
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rg "^(import|from.*import)" src/hotel_reservations/reservations_model.py
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notebooks/week_1/01_preprocessing.py (2)
17-18: Review sensitive information in config dump.
Printing the entire configuration might expose sensitive information. Consider filtering out sensitive fields before printing.
✅ Verification successful
Config dump appears safe, no sensitive information exposed
The configuration file only contains non-sensitive model parameters, feature names, and database schema information. There are no passwords, tokens, keys, or other sensitive credentials being dumped to the output.
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31-34: Verify data splitting strategy.
Consider the following aspects:
- Is the split ratio appropriate for your use case?
- Should stratification be used if the target variable is imbalanced?
- Is the random state set for reproducibility?
notebooks/week_2/02_logging_and_registering.py (2)
5-5: Confirm availability of dbutils in the execution environment
The dbutils module is used but not imported. In Databricks notebooks, dbutils is available by default; however, if this script is run outside of a Databricks notebook, it will raise a NameError. Ensure that this code is intended to run within a Databricks environment or modify it to handle external execution.
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83-83: Validate the use of the evaluate method
Ensure that the evaluate method in ReservationsModel correctly calculates accuracy and precision. Verify that it handles input types appropriately and that the evaluation metrics are correctly implemented.
notebooks/week_2/03_feature_engineering.py (2)
161-165: Verify if preprocessing the target variable is appropriate
You're applying the preprocess_data method to y_train, which is the target variable. Typically, preprocessing is applied to features (X) rather than the target (y). Please verify if this is intentional and necessary.
169-174: Verify if preprocessing the target variable is appropriate
Similarly, you are preprocessing y_test with the data preprocessor. Ensure that this step is required for your target variable.
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Actionable comments posted: 5
🧹 Outside diff range and nitpick comments (17)
.github/workflows/ci.yml (4)
12-12: Consider pinning the checkout action to a commit SHAWhile using
actions/checkout@v4is good for getting updates, for maximum security, consider pinning to a specific commit SHA.- - uses: actions/checkout@v4 + - uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
14-15: Consider pinning the uv setup action to a commit SHASimilar to the checkout action, consider pinning
astral-sh/setup-uv@v3to a specific commit SHA for enhanced security.- uses: astral-sh/setup-uv@v3 + uses: astral-sh/setup-uv@e5f837a6d67ddb0b4d6eef68a50b12c5d39b3d73 # v3
24-26: Consider caching pre-commit for faster CI runsThe pre-commit installation could benefit from caching to speed up CI runs.
+ - name: Cache pre-commit + uses: actions/cache@v4 + with: + path: ~/.cache/pre-commit + key: pre-commit-${{ hashFiles('.pre-commit-config.yaml') }} - name: Run pre-commit checks run: | uv pip install pre-commit uv run pre-commit run --all-files
1-26: Fix line endings in the YAML fileThe static analysis tool detected incorrect line endings in the file.
Please ensure all line endings are Unix-style (
\n) rather than Windows-style (\r\n). You can fix this by:
- Setting
core.autocrlf=inputin your git config- Using a text editor that preserves Unix line endings
- Running
dos2unixon the file🧰 Tools
🪛 yamllint
[error] 1-1: wrong new line character: expected \n
(new-lines)
README.md (3)
11-11: Minor grammatical improvement neededAdd the article "a" before "repository" for better readability.
-To submit the deliverable, create a feature branch in that repository, and a PR to main branch. +To submit the deliverable, create a feature branch in a repository, and a PR to the main branch.🧰 Tools
🪛 LanguageTool
[uncategorized] ~11-~11: Possible missing article found.
Context: ... branch in that repository, and a PR to main branch. The code can be merged after we...(AI_HYDRA_LEO_MISSING_THE)
17-17: Format documentation link using proper markdown syntaxConvert the bare URL to a proper markdown link for better readability and maintainability.
-In our examples, we use UV. Check out the documentation on how to install it: https://docs.astral.sh/uv/getting-started/installation/ +In our examples, we use UV. Check out the [installation documentation](https://docs.astral.sh/uv/getting-started/installation/).🧰 Tools
🪛 Markdownlint
17-17: null
Bare URL used(MD034, no-bare-urls)
21-26: Add language specification to code block and enhance setup instructionsThe code block should specify the shell language, and it would be helpful to add some context about the expected outcome.
-``` +```shell uv venv -p 3.11.0 venv source venv/bin/activate uv pip install -r pyproject.toml --all-extras uv lock
+This will:
+1. Create a new Python 3.11 virtual environment
+2. Activate the environment
+3. Install all dependencies including optional extras
+4. Generate a lockfile for reproducible installations<details> <summary>🧰 Tools</summary> <details> <summary>🪛 Markdownlint</summary> 21-21: null Fenced code blocks should have a language specified (MD040, fenced-code-language) </details> </details> </blockquote></details> <details> <summary>project_config.yml (3)</summary><blockquote> `1-2`: **Consider environment-specific configuration management** Environment-specific values like `catalog_name` and `schema_name` should be externalized to support different environments (dev, staging, prod). Consider: 1. Using environment variables 2. Creating separate config files per environment (e.g., `config.dev.yml`, `config.prod.yml`) 3. Using a configuration management system <details> <summary>🧰 Tools</summary> <details> <summary>🪛 yamllint</summary> [error] 1-1: wrong new line character: expected \n (new-lines) </details> </details> --- `51-53`: **Document target variable transformation** The configuration defines two targets: - `original_target: booking_status` - `target: booking_status_canceled` Please document: 1. The relationship between these targets 2. The transformation applied 3. The rationale for using the transformed target --- `1-53`: **Fix newline character format** The file uses incorrect newline characters. Please ensure consistent use of Unix-style newlines (\n). <details> <summary>🧰 Tools</summary> <details> <summary>🪛 yamllint</summary> [error] 1-1: wrong new line character: expected \n (new-lines) </details> </details> </blockquote></details> <details> <summary>src/hotel_reservations/reservations_model.py (3)</summary><blockquote> `12-14`: **Enhance class documentation with parameters and usage examples.** While the class purpose is clear, consider adding more detailed documentation: - Parameters section describing the config - Returns section for methods - Example usage section Example improvement: ```diff - """Class to train and evaluate a model for predicting cancellation on hotel reservations.""" + """Class to train and evaluate a model for predicting cancellation on hotel reservations. + + Parameters + ---------- + config : ProjectConfig + Configuration object containing model parameters like eta, n_estimators, and max_depth + + Examples + -------- + >>> from hotel_reservations.config import ProjectConfig + >>> config = ProjectConfig() + >>> model = ReservationsModel(config) + >>> model.fit(X_train, y_train) + >>> predictions = model.predict(X_test) + """
15-22: Add parameter validation and type hints for class attributes.Consider adding validation for config parameters and type hints for better code maintainability.
def __init__(self, config: ProjectConfig): + if not isinstance(config, ProjectConfig): + raise TypeError("config must be an instance of ProjectConfig") + required_params = ["eta", "n_estimators", "max_depth"] + missing_params = [param for param in required_params if param not in config.parameters] + if missing_params: + raise ValueError(f"Missing required parameters: {missing_params}") + self.config = config - self.scaler = StandardScaler() - self.model = XGBClassifier( + self.scaler: StandardScaler = StandardScaler() + self.model: XGBClassifier = XGBClassifier( eta=self.config.parameters["eta"], n_estimators=self.config.parameters["n_estimators"], max_depth=self.config.parameters["max_depth"], )
12-39: Consider making the model implementation more flexible.The current implementation is tightly coupled with XGBoost. Consider implementing a more flexible architecture that allows easy switching between different model types.
Suggestions:
- Create a base model interface
- Use factory pattern for model creation
- Move model-specific parameters to separate configuration classes
Example architecture:
from abc import ABC, abstractmethod class BaseModel(ABC): @abstractmethod def fit(self, X, y): pass @abstractmethod def predict(self, X): pass class XGBoostModel(BaseModel): def __init__(self, config: XGBoostConfig): # XGBoost specific implementation class LightGBMModel(BaseModel): def __init__(self, config: LightGBMConfig): # LightGBM specific implementation class ModelFactory: @staticmethod def create_model(model_type: str, config: ModelConfig) -> BaseModel: if model_type == "xgboost": return XGBoostModel(config) elif model_type == "lightgbm": return LightGBMModel(config)notebooks/week_2/03_feature_engineering.py (4)
1-2: Consider using a package registry for production deploymentsInstalling from a local wheel file might cause issues in production environments where the file might not be available or may have different versions. Consider publishing the package to a private PyPI repository or using Databricks package management features.
65-73: Add error handling for data insertionThe INSERT operations should handle potential duplicate key violations or data integrity issues. Consider using MERGE or INSERT IF NOT EXISTS operations.
-spark.sql( - f"INSERT INTO {catalog_name}.{schema_name}.hotel_features " - f"SELECT Booking_ID, no_of_previous_cancellations, avg_price_per_room FROM {catalog_name}.{schema_name}.train_set_vs" -) +spark.sql( + f""" + MERGE INTO {catalog_name}.{schema_name}.hotel_features AS target + USING {catalog_name}.{schema_name}.train_set_vs AS source + ON target.Booking_ID = source.Booking_ID + WHEN NOT MATCHED THEN + INSERT (Booking_ID, no_of_previous_cancellations, avg_price_per_room) + VALUES (source.Booking_ID, source.no_of_previous_cancellations, source.avg_price_per_room) + """ +)
76-86: Add input validation and documentation to the UDFThe function should include input validation and documentation for better maintainability and reliability.
spark.sql( f""" CREATE OR REPLACE FUNCTION {function_name}(no_of_week_nights INT, no_of_weekend_nights INT) RETURNS INT LANGUAGE PYTHON AS $$ +# Calculate total stay length by summing weekday and weekend nights +if no_of_week_nights < 0 or no_of_weekend_nights < 0: + raise ValueError("Number of nights cannot be negative") return no_of_week_nights + no_of_weekend_nights $$ """ )
195-198: Implement a versioning strategy for model registrationConsider adding a version strategy to avoid overwriting existing models and to maintain proper versioning:
+from mlflow.tracking.client import MlflowClient + +client = MlflowClient() +latest_version = client.get_latest_versions( + name=f"{catalog_name}.{schema_name}.hotel_reservations_model_fe", + stages=["None"] +)[0].version + mlflow.register_model( model_uri=model_uri, name=f"{catalog_name}.{schema_name}.hotel_reservations_model_fe", + tags={"version_comment": f"Feature engineering pipeline v{int(latest_version) + 1}"} )
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
⛔ Files ignored due to path filters (1)
uv.lockis excluded by!**/*.lock
📒 Files selected for processing (11)
.github/workflows/ci.yml(1 hunks)README.md(1 hunks)notebooks/week_1/01_preprocessing.py(1 hunks)notebooks/week_2/01_preparation.py(1 hunks)notebooks/week_2/02_logging_and_registering.py(1 hunks)notebooks/week_2/03_feature_engineering.py(1 hunks)project_config.yml(1 hunks)pyproject.toml(1 hunks)src/hotel_reservations/data_processor.py(1 hunks)src/hotel_reservations/reservations_model.py(1 hunks)src/hotel_reservations/utils.py(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (5)
- notebooks/week_1/01_preprocessing.py
- notebooks/week_2/01_preparation.py
- notebooks/week_2/02_logging_and_registering.py
- src/hotel_reservations/data_processor.py
- src/hotel_reservations/utils.py
🧰 Additional context used
🪛 yamllint
.github/workflows/ci.yml
[error] 1-1: wrong new line character: expected \n
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project_config.yml
[error] 1-1: wrong new line character: expected \n
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🪛 LanguageTool
README.md
[uncategorized] ~11-~11: Possible missing article found.
Context: ... branch in that repository, and a PR to main branch. The code can be merged after we...
(AI_HYDRA_LEO_MISSING_THE)
🪛 Markdownlint
README.md
17-17: null
Bare URL used
(MD034, no-bare-urls)
21-21: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
🔇 Additional comments (9)
.github/workflows/ci.yml (1)
17-22: LGTM: Python setup and dependency installation
The Python setup and dependency installation using uv looks good. Using uv sync is efficient as it installs dependencies based on the lock file.
pyproject.toml (3)
20-24: LGTM! Development dependencies are well-defined
The development dependencies include all necessary tools for Databricks development with appropriate version constraints.
7-18: Verify impact of switching from lightgbm to xgboost
The change from lightgbm to xgboost represents a significant shift in the model framework choice. While both are gradient boosting implementations, they have different characteristics and performance profiles.
Let's check for model-related files that might need updates:
✅ Verification successful
The switch from lightgbm to xgboost is properly implemented
The codebase has been correctly updated to use XGBoost instead of LightGBM. The implementation in reservations_model.py shows a clean integration of XGBClassifier with proper hyperparameter configuration through the project config. The model training pipeline in the notebook also demonstrates correct usage of the XGBoost-based model with MLflow tracking.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for files containing model training code or lightgbm references
rg -l "lightgbm|LGBMRegressor|LGBMClassifier" || echo "No lightgbm references found"
Length of output: 114
Script:
#!/bin/bash
# Let's check for xgboost usage to ensure the transition is properly implemented
rg -l "xgboost|XGBRegressor|XGBClassifier"
# Also search for any model training related files
rg -l "model.*train|train.*model"
Length of output: 361
Script:
#!/bin/bash
# Let's examine the model implementation in reservations_model.py
cat src/hotel_reservations/reservations_model.py
# Also check the training code in notebooks
cat notebooks/week_2/02_logging_and_registering.py
Length of output: 5456
35-39: Review new linting ignore rules
The addition of two new ignore rules raises some concerns:
F821(undefined-name): This could mask undefined variable referencesE402(module-level-import-not-at-top): This might indicate circular dependency issues
Consider addressing the underlying issues instead of suppressing these warnings.
Let's check for potential issues:
project_config.yml (2)
4-7: Previous review comment about learning rate is still applicable
9-14: Review A/B test experimental design
The current A/B test design:
- Varies
max_depth(6 vs 10) - Keeps other parameters constant
- Uses a much lower learning rate than the main model
Consider documenting:
- The hypothesis being tested
- Why max_depth was chosen as the variable parameter
- Expected impact on model performance
src/hotel_reservations/reservations_model.py (1)
1-10: LGTM! Well-organized imports and clear module documentation.
The imports are logically organized and the module's purpose is clearly documented.
notebooks/week_2/03_feature_engineering.py (2)
124-125: **** The previous review comment about ensuring consistent feature engineering for the test set is still valid.
150-150: **** The previous review comment about replacing hardcoded GIT_SHA with dynamic retrieval is still valid.
| # Evaluate the model performance | ||
| accuracy, precision = pipeline.named_steps["classifier"].evaluate(y_test, y_pred) | ||
|
|
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🛠️ Refactor suggestion
Enhance model evaluation with cross-validation and additional metrics
The current evaluation is limited to accuracy and precision on a single test set. Consider:
- Adding k-fold cross-validation
- Including additional metrics like recall, F1-score, and ROC-AUC
- Adding confusion matrix visualization
Summary by CodeRabbit
Release Notes
New Features
Bug Fixes
Chores
.gitignorefile to better reflect current development needs.