classifierpromax.ClassifierTrainer

Functions

validate_preprocessor(preprocessor)	Validate if the preprocessor has the required fit and transform methods.
validate_data(X_train, y_train)	Ensure that X_train and y_train have the same number of samples.
get_scoring_metrics()	Return a dictionary of default scoring metrics for model evaluation.
define_models(preprocessor, seed)	Define a set of machine learning models with preprocessing pipelines.
train_and_evaluate(models, X_train, y_train, cv, metrics)	Train models using cross-validation and return trained models with their evaluation metrics.
ClassifierTrainer(preprocessor, X_train, y_train, seed)	Train multiple machine learning classifiers using cross-validation.

Module Contents

classifierpromax.ClassifierTrainer.validate_preprocessor(preprocessor)

Validate if the preprocessor has the required fit and transform methods.

Raises:

TypeError – If the preprocessor does not have ‘fit’ and ‘transform’ methods.

Example

>>> from sklearn.preprocessing import StandardScaler
>>> validate_preprocessor(StandardScaler())  # No error
>>> validate_preprocessor("invalid")  # Raises TypeError

classifierpromax.ClassifierTrainer.validate_data(X_train, y_train)

Ensure that X_train and y_train have the same number of samples.

Raises:

ValueError – If the number of samples in X_train and y_train do not match.

Example

>>> import numpy as np
>>> X_train = np.random.rand(100, 5)
>>> y_train = np.random.randint(0, 2, size=100)
>>> validate_data(X_train, y_train)  # No error
>>> y_train = np.random.randint(0, 2, size=50)
>>> validate_data(X_train, y_train)  # Raises ValueError

classifierpromax.ClassifierTrainer.get_scoring_metrics()

Return a dictionary of default scoring metrics for model evaluation.

Returns:

A dictionary containing accuracy, precision, recall, and F1-score.

Return type:

dict

Example

>>> metrics = get_scoring_metrics()
>>> print(metrics.keys())  # dict_keys(['accuracy', 'precision', 'recall', 'f1'])

classifierpromax.ClassifierTrainer.define_models(preprocessor, seed)

Define a set of machine learning models with preprocessing pipelines.

Parameters:

• preprocessor (sklearn.pipeline.Pipeline or transformer) – Preprocessing pipeline applied before training the models.

• seed (int) – Random seed for reproducibility.

Returns:

A dictionary containing model names as keys and pipelines as values.

Return type:

dict

Example

>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.pipeline import Pipeline
>>> preprocessor = Pipeline([("scaler", StandardScaler())])
>>> models = define_models(preprocessor, seed=42)
>>> print(models.keys())  # dict_keys(['dummy', 'logreg', 'svc', 'random_forest'])

classifierpromax.ClassifierTrainer.train_and_evaluate(models, X_train, y_train, cv, metrics)

Train models using cross-validation and return trained models with their evaluation metrics.

Parameters:

• models (dict) – Dictionary of model pipelines to train.

• X_train (array-like) – Training input data.

• y_train (array-like) – Training target labels.

• cv (int) – Number of cross-validation folds.

• metrics (dict) – Scoring metrics for evaluation.

Returns:

• trained_models (dict) – Dictionary containing trained models.

• scoring_results (dict) – Dictionary of DataFrames summarizing mean and std of cross-validation scores.

Raises:

ValueError – If training fails due to misaligned data.

Example

>>> from sklearn.datasets import make_classification
>>> X_train, y_train = make_classification(n_samples=100, n_features=5, random_state=42)
>>> preprocessor = Pipeline([("scaler", StandardScaler())])
>>> models = define_models(preprocessor, seed=42)
>>> trained_models, scoring_results = train_and_evaluate(models, X_train, y_train, cv=5, metrics=get_scoring_metrics())
>>> print(scoring_results['logreg'])  # DataFrame with mean/std scores

classifierpromax.ClassifierTrainer.ClassifierTrainer(preprocessor, X_train, y_train, seed, cv=5, metrics=None)

Train multiple machine learning classifiers using cross-validation.

Parameters:

• preprocessor (sklearn.pipeline.Pipeline or transformer) – A preprocessing pipeline or transformer object with fit and transform methods.

• X_train (array-like) – Training input data.

• y_train (array-like) – Target labels corresponding to the training data.

• seed (int) – Random seed for reproducibility.

• cv (int, default=5) – Number of folds for cross-validation.

• metrics (dict, optional) – Dictionary of scoring metrics. If None, default metrics are used.

Returns:

• trained_models (dict) – Dictionary of trained model instances.

• scoring_results (dict) – Dictionary of evaluation results as pandas DataFrames.

Raises:

• TypeError – If preprocessor is not a valid transformer.

• ValueError – If X_train and y_train have inconsistent sample sizes.

Example

>>> from sklearn.datasets import make_classification
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> X_train, y_train = make_classification(n_samples=100, n_features=5, random_state=42)
>>> preprocessor = Pipeline([("scaler", StandardScaler())])
>>> seed = 42
>>> trained_models, scoring_results = ClassifierTrainer(preprocessor, X_train, y_train, seed)
>>> print(scoring_results['logreg'])  # DataFrame with mean/std scores