metaperceptron.helpers package¶

metaperceptron.helpers.act_util module¶

metaperceptron.helpers.act_util.celu(x, alpha=1.0)[source]¶

metaperceptron.helpers.act_util.elu(x, alpha=1)[source]¶

metaperceptron.helpers.act_util.gelu(x, alpha=0.044715)[source]¶

metaperceptron.helpers.act_util.hard_shrink(x, alpha=0.5)[source]¶

metaperceptron.helpers.act_util.hard_sigmoid(x, lower=-2.5, upper=2.5)[source]¶

metaperceptron.helpers.act_util.hard_swish(x, lower=-3.0, upper=3.0)[source]¶

metaperceptron.helpers.act_util.hard_tanh(x, lower=-1.0, upper=1.0)[source]¶

metaperceptron.helpers.act_util.leaky_relu(x, alpha=0.01)[source]¶

metaperceptron.helpers.act_util.log_sigmoid(x)[source]¶

metaperceptron.helpers.act_util.log_softmax(x)[source]¶

metaperceptron.helpers.act_util.mish(x, beta=1.0)[source]¶

metaperceptron.helpers.act_util.none(x)[source]¶

metaperceptron.helpers.act_util.prelu(x, alpha=0.5)[source]¶

metaperceptron.helpers.act_util.relu(x)[source]¶

metaperceptron.helpers.act_util.rrelu(x, lower=0.125, upper=0.3333333333333333)[source]¶

metaperceptron.helpers.act_util.selu(x, alpha=1.67326324, scale=1.05070098)[source]¶

metaperceptron.helpers.act_util.sigmoid(x)[source]¶

metaperceptron.helpers.act_util.silu(x)¶

metaperceptron.helpers.act_util.soft_plus(x, beta=1.0)[source]¶

metaperceptron.helpers.act_util.soft_shrink(x, alpha=0.5)[source]¶

metaperceptron.helpers.act_util.soft_sign(x)[source]¶

metaperceptron.helpers.act_util.softmax(x)[source]¶

metaperceptron.helpers.act_util.softmin(x)[source]¶

metaperceptron.helpers.act_util.swish(x)[source]¶

metaperceptron.helpers.act_util.tanh(x)[source]¶

metaperceptron.helpers.act_util.tanh_shrink(x)[source]¶

metaperceptron.helpers.metric_util module¶

metaperceptron.helpers.metric_util.get_all_classification_metrics()[source]¶

metaperceptron.helpers.metric_util.get_all_regression_metrics()[source]¶

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

metaperceptron.helpers.preprocessor module¶

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

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)[source]¶

static encode_label(y)[source]¶

static scale(X, scaling_methods=('standard',), list_dict_paras=None)[source]¶

set_train_test(X_train=None, y_train=None, X_test=None, y_test=None)[source]¶

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)[source]¶: The wrapper of the split_train_test function in scikit-learn library.

class metaperceptron.helpers.preprocessor.FeatureEngineering[source]¶

Bases: object

create_threshold_binary_features(X, threshold)[source]¶

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[source]¶

Bases: object

Encode categorical features as integer labels.

static check_y(y)[source]¶

fit(y)[source]¶

Fit label encoder to a given set of labels.

Parameters:¶

yarray-like: Labels to encode.

fit_transform(y)[source]¶

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)[source]¶

Transform integer labels to original labels.

Parameters:¶

yarray-like: Encoded integer labels.

Returns:¶

original_labelsarray-like: Original labels.

transform(y)[source]¶

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)[source]¶

Bases: object

difference(X)[source]¶

inverse_difference(diff_data)[source]¶

metaperceptron.helpers.preprocessor.get_dataset(dataset_name)[source]¶

Helper function to retrieve the data

Parameters:: dataset_name (str) – Name of the dataset
Returns:: data – The instance of Data class, that hold X and y variables.
Return type:: Data

metaperceptron.helpers.scaler_util module¶

class metaperceptron.helpers.scaler_util.BoxCoxScaler(lmbda=None)[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.DataTransformer(scaling_methods=('standard',), list_dict_paras=None)[source]¶

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)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.Log1pScaler[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.LogeScaler[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.ObjectiveScaler(obj_name='sigmoid', ohe_scaler=None)[source]¶

Bases: object

For label scaler in classification (binary and multiple classification)

inverse_transform(data)[source]¶

transform(data)[source]¶

class metaperceptron.helpers.scaler_util.SinhArcSinhScaler(epsilon=0.1, delta=1.0)[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.SqrtScaler[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.YeoJohnsonScaler(lmbda=None)[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

metaperceptron.helpers.validator module¶

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

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

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

metaperceptron.helpers.validator.check_str(name: str, value: str, bound=None)[source]¶

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

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

metaperceptron.helpers.validator.is_in_bound(value, bound)[source]¶

metaperceptron.helpers.validator.is_str_in_list(value: str, my_list: list)[source]¶