ds_utils

Install

pip install ds_utils

Development notes

This library has been developed using nbdev. Any change or PR has to be made directly to the notebooks that creates each module. All tests must pass.

ml.RegressorCV Class

Overview

The RegressorCV class in the ml.py module of the ds_utils library is designed to train an estimator using cross-validation and record metrics for each fold. It also stores each model that is trained on each fold of the cross-validation process. The final prediction is made as the median value of the predictions from each stored model.

Initialization

RegressorCV(base_reg, cv=5, groups=None, verbose=False, n_bins_stratify=None)

Parameters

• base_reg : Estimator object implementing ‘fit’. The object to use to fit the data.
• cv : int or cross-validation generator, default=5. Determines the cross-validation splitting strategy.
• groups : array-like of shape (n_samples,), default=None. Group labels for the samples used while splitting the dataset into train/test set.
• n_bins_stratify : int, default=None. Number of bins to use for stratification.
• verbose : bool, default=False. Whether or not to print metrics.

Attributes

• cv_results_ : Dictionary containing the results of the cross-validation, including the fold number, the regressor, and the metrics.
• oof_train_ : Series containing the out-of-fold predictions on the training set.
• oof_score_ : The R2 score calculated using the out-of-fold predictions.
• oof_mape_ : The Mean Absolute Percentage Error calculated using the out-of-fold predictions.
• oof_rmse_ : The Root Mean Squared Error calculated using the out-of-fold predictions.
• metrics_ : The summary of metrics calculated during the fitting process.

Methods

fit(self, X, y, **kwargs)

Trains the base regressor on the provided data using cross-validation and stores the results.

predict(self, X)

Predicts the target variable for the provided features using the median value of the predictions from the models trained during cross-validation.

Example

from ds_utils.ml import RegressorCV
from sklearn.ensemble import RandomForestRegressor

# Initialize the RegressorCV with a base regressor and cross-validation strategy
reg_cv = RegressorCV(base_reg=RandomForestRegressor(), cv=5, verbose=True)

# Fit the RegressorCV to the training data
reg_cv.fit(X_train, y_train)

# Predict the target variable for new data
predictions = reg_cv.predict(X_new)

# Get the summary of recorded metrics
metrics = reg_cv.metrics_

ml.RegressorTimeSeriesCV Class

Overview

The RegressorTimeSeriesCV class in the ml.py module of the ds_utils library is designed to train a base regressor using time series cross-validation and record metrics for each fold.

Initialization

RegressorTimeSeriesCV(base_reg, cv=5, verbose=False, catboost_use_eval_set=False)

Parameters

• base_reg : Estimator object implementing ‘fit’. The object to use to fit the data.
• cv : int, default=5. Determines the cross-validation splitting strategy.
• verbose : bool, default=False. Whether or not to print metrics.
• catboost_use_eval_set : bool, default=False. Whether or not to use eval_set in CatBoostRegressor.

Attributes

• cv_results_ : List containing the results of the cross-validation, including fold number, regressor, train and test indices, and metrics.
• metrics_ : The summary of metrics calculated during the fitting process.
• y_test_last_fold_ : The true target variable values of the last fold.
• y_pred_last_fold_ : The predicted target variable values of the last fold.

Methods

fit(self, X, y, sample_weight=None)

Trains the base regressor on the provided data using time series cross-validation and stores the results.

predict(self, X)

Predicts the target variable for the provided features using the base regressor trained on the full data.

Example

from ds_utils.ml import RegressorTimeSeriesCV
from sklearn.ensemble import RandomForestRegressor

# Initialize the RegressorTimeSeriesCV with a base regressor and cross-validation strategy
reg_tscv = RegressorTimeSeriesCV(base_reg=RandomForestRegressor(), cv=5, verbose=True)

# Fit the RegressorTimeSeriesCV to the training data
reg_tscv.fit(X_train, y_train)

# Predict the target variable for new data
predictions = reg_tscv.predict(X_new)

# Get the summary of recorded metrics
metrics = reg_tscv.metrics_

ml.KNNRegressor Class

Overview

The KNNRegressor class in the ml.py module of the ds_utils library is an extension of the KNeighborsRegressor from scikit-learn, with modifications to the predict method to allow different calculations for predictions and to optionally return the indices of the nearest neighbors.

Initialization

The initialization parameters are the same as those of the KNeighborsRegressor from scikit-learn. Refer to the official documentation for details on the parameters.

Methods

predict(self, X, return_match_index=False, pred_calc=‘mean’)

Predicts the target variable for the provided features and allows different calculations for predictions.

Parameters

• X : array-like of shape (n_samples, n_features). Test samples.
• return_match_index : bool, default=False. Whether to return the index of the nearest matched neighbor.
• pred_calc : str, default=‘mean’. The calculation to use for predictions. Possible values are ‘mean’ and ‘median’.

Returns

• y_pred : array of shape (n_samples,) or (n_samples, n_outputs). The predicted target variable.
• nearest_matched_index : array of shape (n_samples,). The index of the nearest matched neighbor. Returned only if return_match_index=True.
• neigh_ind : array of shape (n_samples, n_neighbors). Indices of the neighbors in the training set. Returned only if return_match_index=True.

Example

from ds_utils.ml import KNNRegressor

# Initialize the KNNRegressor with specific parameters
knn_reg = KNNRegressor(n_neighbors=3)

# Fit the KNNRegressor to the training data
knn_reg.fit(X_train, y_train)

# Predict the target variable for new data and return the index of the nearest matched neighbor
predictions, nearest_matched_index, neigh_ind = knn_reg.predict(X_new, return_match_index=True, pred_calc='median')

ml.AutoRegressor Class

Overview

The AutoRegressor class is designed for performing automated regression tasks, including preprocessing and model fitting. It supports several regression algorithms and allows for easy comparison of their performance on a given dataset. The class provides various methods for model evaluation, feature importance, and visualization.

Initialization

ar = AutoRegressor(
    num_cols,
    cat_cols,
    target_col,
    data=None,
    train=None,
    test=None,
    random_st=42,
    log_target=False,
    estimator='catboost',
    imputer_strategy='simple',
    use_catboost_native_cat_features=False,
    ohe_min_freq=0.05,
    scale_numeric_data=False,
    scale_categoric_data=False,
    scale_target=False
)

Parameters

• num_cols: list
  • List of numerical columns in the dataset.
• cat_cols: list
  • List of categorical columns in the dataset.
• target_col: str
  • Target column name in the dataset.
• data: pd.DataFrame, optional (default=None)
  • Input dataset containing both features and target column.
• train: pd.DataFrame, optional (default=None)
  • Training dataset containing both features and target column. Used if data is not provided.
• test: pd.DataFrame, optional (default=None)
  • Testing dataset containing both features and target column. Used if data is not provided.
• random_st: int, optional (default=42)
  • Random state for reproducibility.
• log_target: bool, optional (default=False)
  • If the logarithm of the target variable should be used.
• estimator: str or estimator object, optional (default=‘catboost’)
  • String or estimator object. Options are ‘catboost’, ‘random_forest’, and ‘linear’.
• imputer_strategy: str, optional (default=‘simple’)
  • Imputation strategy for missing values. Options are ‘simple’ and ‘knn’.
• use_catboost_native_cat_features: bool, optional (default=False)
  • If the native CatBoost categorical feature handling should be used.
• ohe_min_freq: float, optional (default=0.05)
  • Minimum frequency for OneHotEncoder to consider a category in categorical columns.
• scale_numeric_data: bool, optional (default=False)
  • If numeric data should be scaled using StandardScaler.
• scale_categoric_data: bool, optional (default=False)
  • If categorical data (after one-hot encoding) should be scaled using StandardScaler.
• scale_target: bool, optional (default=False)
  • If the target variable should be scaled using StandardScaler.

Methods

fit_report(self)

Fits the model to the training data and prints the R2 Score, RMSE, and MAPE on the test data.

test_binary_column(self, binary_column)

Tests the significance of a binary column on the target variable and returns the p-value for Mann–Whitney U test.

get_coefficients(self)

Returns the coefficients of the model if the estimator is linear, otherwise returns feature importances.

get_feature_importances(self)

Returns the feature importances of the model.

get_shap(self, return_shap_values=False)

Generates and plots SHAP values for the model and returns SHAP values if return_shap_values is True.

plot_importance(self, feat_imp, graph_title=“Model feature importance”)

Plots the feature importances provided in feat_imp with the specified graph_title.

Example Usage

# Initialize AutoRegressor
ar = AutoRegressor(num_cols, cat_cols, target_col, data)

# Fit the model and print the report
ar.fit_report()

# Get and plot feature importances
feat_imp = ar.get_feature_importances()
ar.plot_importance(feat_imp)