reflame.utils package

reflame.utils.activation module

reflame.utils.activation.celu(x, alpha=1.0)

reflame.utils.activation.elu(x, alpha=1)

reflame.utils.activation.gelu(x, alpha=0.044715)

reflame.utils.activation.hard_shrink(x, alpha=0.5)

reflame.utils.activation.hard_sigmoid(x, lower=- 2.5, upper=2.5)

reflame.utils.activation.hard_swish(x, lower=- 3.0, upper=3.0)

reflame.utils.activation.hard_tanh(x, lower=- 1.0, upper=1.0)

reflame.utils.activation.leaky_relu(x, alpha=0.01)

reflame.utils.activation.log_sigmoid(x)

reflame.utils.activation.log_softmax(x)

reflame.utils.activation.mish(x, beta=1.0)

reflame.utils.activation.prelu(x, alpha=0.5)

reflame.utils.activation.relu(x)

reflame.utils.activation.rrelu(x, lower=0.125, upper=0.3333333333333333)

reflame.utils.activation.selu(x, alpha=1.67326324, scale=1.05070098)

reflame.utils.activation.sigmoid(x)

reflame.utils.activation.silu(x)

reflame.utils.activation.soft_plus(x, beta=1.0)

reflame.utils.activation.soft_shrink(x, alpha=0.5)

reflame.utils.activation.soft_sign(x)

reflame.utils.activation.softmax(x)

reflame.utils.activation.softmin(x)

reflame.utils.activation.swish(x)

reflame.utils.activation.tanh(x)

reflame.utils.activation.tanh_shrink(x)

reflame.utils.data_toolkit module

class reflame.utils.data_toolkit.BoxCoxScaler(lmbda=None)

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class reflame.utils.data_toolkit.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 reflame.utils.data_toolkit.DataTransformer(scaling_methods=('standard',), list_dict_paras=None)

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

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

fit(X, y=None)

inverse_transform(X)

transform(X)

class reflame.utils.data_toolkit.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 reflame.utils.data_toolkit.LabelEncoder

Bases: object

Encode categorical features as integer labels.

static check_y(y)

fit(y)

Fit label encoder to a given set of labels.

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.

yarray-like

Encoded integer labels.

original_labelsarray-like

Original labels.

transform(y)

Transform labels to encoded integer labels.

yarray-like (1-D vector)

Labels to encode.

encoded_labelsarray-like

Encoded integer labels.

class reflame.utils.data_toolkit.Log1pScaler

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class reflame.utils.data_toolkit.LogeScaler

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class reflame.utils.data_toolkit.ObjectiveScaler(obj_name='sigmoid', ohe_scaler=None)

Bases: object

For label scaler in classification (binary and multiple classification)

inverse_transform(data)

transform(data)

class reflame.utils.data_toolkit.SinhArcSinhScaler(epsilon=0.1, delta=1.0)

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class reflame.utils.data_toolkit.SqrtScaler

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

class reflame.utils.data_toolkit.TimeSeriesDifferencer(interval=1)

Bases: object

difference(X)

inverse_difference(diff_data)

class reflame.utils.data_toolkit.YeoJohnsonScaler(lmbda=None)

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)

inverse_transform(X)

transform(X)

reflame.utils.evaluator module

reflame.utils.evaluator.get_all_classification_metrics()

reflame.utils.evaluator.get_all_regression_metrics()

reflame.utils.evaluator.get_metrics(problem, y_true, y_pred, metrics=None, testcase='test')

reflame.utils.expand_util module

reflame.utils.expand_util.expand_chebyshev(x, n_funcs=5)

reflame.utils.expand_util.expand_gegenbauer(x, n_funcs=5, a=1.0)

reflame.utils.expand_util.expand_hermite(x, n_funcs=5)

reflame.utils.expand_util.expand_laguerre(x, n_funcs=5)

reflame.utils.expand_util.expand_legendre(x, n_funcs=5)

reflame.utils.expand_util.expand_power(x, n_funcs=5)

reflame.utils.expand_util.expand_trigonometric(x, n_funcs=5, a0=1.0)

reflame.utils.validator module

reflame.utils.validator.check_bool(name: str, value: bool, bound=(True, False))

reflame.utils.validator.check_float(name: str, value: int, bound=None)

reflame.utils.validator.check_int(name: str, value: int, bound=None)

reflame.utils.validator.check_str(name: str, value: str, bound=None)

reflame.utils.validator.check_tuple_float(name: str, values: tuple, bounds=None)

reflame.utils.validator.check_tuple_int(name: str, values: tuple, bounds=None)

reflame.utils.validator.is_in_bound(value, bound)

reflame.utils.validator.is_str_in_list(value: str, my_list: list)