metaperceptron.helpers package

metaperceptron.helpers.metric_util module

metaperceptron.helpers.metric_util.get_all_classification_metrics()

Gets a dictionary of all supported classification metrics.

This function returns a dictionary where keys are metric names and values are their optimization types (“min” or “max”).

Returns:

A dictionary containing all supported classification metrics.

Return type:

dict

metaperceptron.helpers.metric_util.get_all_regression_metrics()

Gets a dictionary of all supported regression metrics.

This function returns a dictionary where keys are metric names and values are their optimization types (“min” or “max”).

Returns:

A dictionary containing all supported regression metrics.

Return type:

dict

metaperceptron.helpers.metric_util.get_metric_sklearn(task='classification', metric_names=None)

Creates a dictionary of scorers for scikit-learn cross-validation.

This function takes the task type (classification or regression) and a list of metric names. It creates an appropriate metrics instance (ClassificationMetric or RegressionMetric) and iterates through the provided metric names. For each metric name, it checks if it exists in the metrics instance and retrieves the corresponding method. Finally, it uses make_scorer to convert the method to a scorer and adds it to a dictionary.

Parameters:

• task (str, optional) – The task type, either “classification” or “regression”. Defaults to “classification”.

• metric_names (list, optional) – A list of metric names. Defaults to None.

Returns:

A dictionary of scorers for scikit-learn cross-validation.

Return type:

dict

metaperceptron.helpers.metric_util.get_metrics(problem, y_true, y_pred, metrics=None, testcase='test')

Calculates metrics for regression or classification tasks.

This function takes the true labels (y_true), predicted labels (y_pred), problem type (regression or classification), a dictionary or list of metrics to calculate, and an optional test case name. It returns a dictionary containing the calculated metrics with descriptive names.

Parameters:

• problem (str) – The type of problem, either “regression” or “classification”.

• y_true (array-like) – The true labels.

• y_pred (array-like) – The predicted labels.

• metrics (dict or list, optional) – A dictionary or list of metrics to calculate. Defaults to None.

• testcase (str, optional) – An optional test case name to prepend to the metric names. Defaults to “test”.

Returns:

A dictionary containing the calculated metrics with descriptive names.

Return type:

dict

Raises:

ValueError – If the metrics parameter is not a list or dictionary.

metaperceptron.helpers.preprocessor module

class metaperceptron.helpers.preprocessor.Data(X=None, y=None, name='Unknown')

Bases: object

The structure of our supported Data class

Parameters:

• X (np.ndarray) – The features of your data

• y (np.ndarray) – The labels of your data

SUPPORT = {'scaler': ['standard', 'minmax', 'max-abs', 'log1p', 'loge', 'sqrt', 'sinh-arc-sinh', 'robust', 'box-cox', 'yeo-johnson']}

static check_y(y)

static encode_label(y)

static scale(X, scaling_methods=('standard',), list_dict_paras=None)

set_train_test(X_train=None, y_train=None, X_test=None, y_test=None)

Function use to set your own X_train, y_train, X_test, y_test in case you don’t want to use our split function

Parameters:

• X_train (np.ndarray) –

• y_train (np.ndarray) –

• X_test (np.ndarray) –

• y_test (np.ndarray) –

split_train_test(test_size=0.2, train_size=None, random_state=41, shuffle=True, stratify=None, inplace=True)

The wrapper of the split_train_test function in scikit-learn library.

class metaperceptron.helpers.preprocessor.FeatureEngineering

Bases: object

create_threshold_binary_features(X, threshold)

Perform feature engineering to add binary indicator columns for values below the threshold. Add each new column right after the corresponding original column.

Args: X (numpy.ndarray): The input 2D matrix of shape (n_samples, n_features). threshold (float): The threshold value for identifying low values.

Returns: numpy.ndarray: The updated 2D matrix with binary indicator columns.

class metaperceptron.helpers.preprocessor.LabelEncoder

Bases: object

Encode categorical features as integer labels.

static check_y(y)

fit(y)

Fit label encoder to a given set of labels.

Parameters:

yarray-like

Labels to encode.

fit_transform(y)

Fit label encoder and return encoded labels.

Parameters:

y (array-like of shape (n_samples,)) – Target values.

Returns:

y – Encoded labels.

Return type:

array-like of shape (n_samples,)

inverse_transform(y)

Transform integer labels to original labels.

Parameters:

yarray-like

Encoded integer labels.

Returns:

original_labelsarray-like

Original labels.

transform(y)

Transform labels to encoded integer labels.

Parameters:

yarray-like (1-D vector)

Labels to encode.

Returns:

encoded_labelsarray-like

Encoded integer labels.

class metaperceptron.helpers.preprocessor.TimeSeriesDifferencer(interval=1)

Bases: object

difference(X)

inverse_difference(diff_data)

metaperceptron.helpers.scaler_util module

class metaperceptron.helpers.scaler_util.BoxCoxScaler(lmbda=None)

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class metaperceptron.helpers.scaler_util.DataTransformer(scaling_methods=('standard',), list_dict_paras=None)

Bases: BaseEstimator, TransformerMixin

SUPPORTED_SCALERS = {'box-cox': <class 'metaperceptron.helpers.scaler_util.BoxCoxScaler'>, 'log1p': <class 'metaperceptron.helpers.scaler_util.Log1pScaler'>, 'loge': <class 'metaperceptron.helpers.scaler_util.LogeScaler'>, 'max-abs': <class 'sklearn.preprocessing._data.MaxAbsScaler'>, 'minmax': <class 'sklearn.preprocessing._data.MinMaxScaler'>, 'robust': <class 'sklearn.preprocessing._data.RobustScaler'>, 'sinh-arc-sinh': <class 'metaperceptron.helpers.scaler_util.SinhArcSinhScaler'>, 'sqrt': <class 'metaperceptron.helpers.scaler_util.SqrtScaler'>, 'standard': <class 'sklearn.preprocessing._data.StandardScaler'>, 'yeo-johnson': <class 'metaperceptron.helpers.scaler_util.YeoJohnsonScaler'>}

fit(X, y=None)

inverse_transform(X)

transform(X)

class metaperceptron.helpers.scaler_util.Log1pScaler

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class metaperceptron.helpers.scaler_util.LogeScaler

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class metaperceptron.helpers.scaler_util.ObjectiveScaler(obj_name='sigmoid', ohe_scaler=None)

Bases: object

For label scaler in classification (binary and multiple classification)

inverse_transform(data)

transform(data)

class metaperceptron.helpers.scaler_util.SinhArcSinhScaler(epsilon=0.1, delta=1.0)

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class metaperceptron.helpers.scaler_util.SqrtScaler

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class metaperceptron.helpers.scaler_util.YeoJohnsonScaler(lmbda=None)

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

metaperceptron.helpers.validator module

metaperceptron.helpers.validator.check_bool(name: str, value: bool, bound=(True, False))

metaperceptron.helpers.validator.check_float(name: str, value: int, bound=None)

metaperceptron.helpers.validator.check_int(name: str, value: int, bound=None)

metaperceptron.helpers.validator.check_str(name: str, value: str, bound=None)

metaperceptron.helpers.validator.check_tuple_float(name: str, values: tuple, bounds=None)

metaperceptron.helpers.validator.check_tuple_int(name: str, values: tuple, bounds=None)

metaperceptron.helpers.validator.is_in_bound(value, bound)

metaperceptron.helpers.validator.is_str_in_list(value: str, my_list: list)