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.
RegressorCV(base_reg, cv=5, groups=None, verbose=False, n_bins_stratify=None)
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.
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.
Trains the base regressor on the provided data using cross-validation and stores the results.
Predicts the target variable for the provided features using the median value of the predictions from the models trained during cross-validation.
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_
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.
RegressorTimeSeriesCV(base_reg, cv=5, verbose=False, catboost_use_eval_set=False)
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.
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.
Trains the base regressor on the provided data using time series cross-validation and stores the results.
Predicts the target variable for the provided features using the base regressor trained on the full data.
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_
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.
The initialization parameters are the same as those of the KNeighborsRegressor
from scikit-learn. Refer to the official documentation for details on the parameters.
Predicts the target variable for the provided features and allows different calculations for predictions.
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'.
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 ifreturn_match_index=True
.neigh_ind
: array of shape (n_samples, n_neighbors). Indices of the neighbors in the training set. Returned only ifreturn_match_index=True
.
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')
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.
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
)
- 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.
- Training dataset containing both features and target column. Used if
- test: pd.DataFrame, optional (default=None)
- Testing dataset containing both features and target column. Used if
data
is not provided.
- Testing dataset containing both features and target column. Used if
- 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.
Fits the model to the training data and prints the R2 Score, RMSE, and MAPE on the test data.
Tests the significance of a binary column on the target variable and returns the p-value for Mann–Whitney U test.
Returns the coefficients of the model if the estimator is linear, otherwise returns feature importances.
Returns the feature importances of the model.
Generates and plots SHAP values for the model and returns SHAP values if return_shap_values
is True.
Plots the feature importances provided in feat_imp
with the specified graph_title
.
# 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)