def transform(self, X):
"""
Replaces missing data with the learned parameters.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The input samples.
Returns
-------
X_transformed : pandas dataframe of shape = [n_samples, n_features]
The dataframe without missing values in the selected variables.
"""
# Check method fit has been called
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
# Check that input data contains same number of columns than
# the dataframe used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
# replaces missing data with the learned parameters
for variable in self.imputer_dict_:
X[variable].fillna(self.imputer_dict_[variable], inplace=True)
return X
def transform(self, X):
"""
Apply the transformation to the dataframe. Only the selected features will be modified.
If transformer is OneHotEncoder, dummy features are concatenated to the source dataset.
Note that the original categorical variables will not be removed from the dataset
after encoding. If this is the desired effect, please use Feature-engine's
OneHotCategoricalEncoder instead.
"""
# check that input is a dataframe
X = _is_dataframe(X)
# Check that input data contains same number of columns than
# the dataframe used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
if isinstance(self.transformer, OneHotEncoder):
ohe_results_as_df = pd.DataFrame(
data=self.transformer.transform(X[self.variables]),
columns=self.transformer.get_feature_names(self.variables)
)
X = pd.concat([X, ohe_results_as_df], axis=1)
else:
X[self.variables] = self.transformer.transform(X[self.variables])
return X
def transform(self, X):
"""
Drops the variable or list of variables indicated by the user from the original dataframe
and returns a new dataframe with the remaining subset of variables.
Parameters
----------
X: pandas dataframe
The input dataframe from which features will be dropped
Returns
-------
X_transformed: pandas dataframe of shape = [n_samples, n_features - len(features_to_drop)]
The transformed dataframe with the remaining subset of variables.
"""
# check if fit is called prior
check_is_fitted(self)
# check input dataframe
X = _is_dataframe(X)
# check for input consistency
_check_input_matches_training_df(X, self.input_shape_[1])
X = X.drop(columns=self.features_to_drop)
return X
def transform(self, X):
"""
Groups rare labels under separate group 'Rare' or any other name provided
by the user.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The input samples.
Returns
-------
X_transformed : pandas dataframe of shape = [n_samples, n_features]
The dataframe where rare categories have been grouped.
"""
# Check method fit has been called
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
# check if dataset contains na
_check_contains_na(X, self.variables)
# Check that the dataframe contains the same number of columns than the dataframe
# used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
for feature in self.variables:
X[feature] = np.where(X[feature].isin(self.encoder_dict_[feature]),
X[feature], self.replace_with)
return X
def fit(self, X, y=None):
"""
Verifies that the input X is a pandas dataframe
Parameters
----------
X: pandas dataframe of shape = [n_samples, n_features]
The input dataframe
y: None
y is not needed for this transformer. You can pass y or None.
"""
# check input dataframe
X = _is_dataframe(X)
# check for non existent columns
non_existent = [x for x in self.features_to_drop if x not in X.columns]
if non_existent:
raise KeyError(
f"Columns '{', '.join(non_existent)}' not present in the input dataframe, "
f"please check the columns and enter a new list of features to drop"
)
# check that user does not drop all columns returning empty dataframe
if len(self.features_to_drop) == len(X.columns):
raise ValueError(
"The resulting dataframe will have no columns after dropping all existing variables"
)
# add input shape
self.input_shape_ = X.shape
return self
def transform(self, X):
"""
Adds the binary missing indicators.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The dataframe to be transformed.
Returns
-------
X_transformed : pandas dataframe of shape = [n_samples, n_features]
The dataframe containing the additional binary variables.
Binary variables are named with the original variable name plus
'_na'.
"""
# Check method fit has been called
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
# Check that the dataframe contains the same number of columns than the dataframe
# used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
X = X.copy()
for feature in self.variables_:
X[feature + '_na'] = np.where(X[feature].isnull(), 1, 0)
return X
def transform(self, X):
"""
Returns the predictions of the decision tree based of the variable's original value.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The input samples.
Returns
-------
X_transformed : pandas dataframe of shape = [n_samples, n_features].
Dataframe with variables encoded with decision tree predictions.
"""
# Check method fit has been called
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
# check if dataset contains na
_check_contains_na(X, self.variables)
# Check that the dataframe contains the same number of columns than the dataframe
# used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
X = self.encoder_.transform(X)
return X
def fit(self, X, y=None):
"""
The `fit` method allows Scikit-learn transformers to learn the required parameters
from the training data set.
If transformer is OneHotEncoder, OrdinalEncoder or SimpleImputer, all variables indicated
in the variables parameter will be transformed. When the variables parameter is None, the
SklearnWrapper will automatically select and transform all features in the dataset,
numerical or otherwise.
For all other Scikit-learn transformers only numerical variables will be transformed.
The SklearnWrapper will check that the variables indicated in the variables parameter
are numerical, or alternatively, if variables is None, it will automatically select
the numerical variables in the data set.
"""
# check input dataframe
X = _is_dataframe(X)
if isinstance(self.transformer, (OneHotEncoder, OrdinalEncoder, SimpleImputer)):
self.variables = _find_all_variables(X, self.variables)
else:
self.variables = _find_numerical_variables(X, self.variables)
self.transformer.fit(X[self.variables])
self.input_shape_ = X.shape
return self
def fit(self, X, y=None):
"""
Learns the values at the end of the variable distribution.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
The user can pass the entire dataframe, not just the variables that need imputation.
y : None
y is not needed in this imputation. You can pass None or y.
Attributes
----------
imputer_dict_: dictionary
The dictionary containing the values at the end of the distribution
per variable. These values will be used by the imputer to replace missing
data.
"""
# check input dataframe
X = _is_dataframe(X)
# find or check for numerical variables
self.variables = _find_numerical_variables(X, self.variables)
# estimate imputation values
if self.distribution == 'max':
self.imputer_dict_ = (X[self.variables].max() *
self.fold).to_dict()
elif self.distribution == 'gaussian':
if self.tail == 'right':
self.imputer_dict_ = (
X[self.variables].mean() +
self.fold * X[self.variables].std()).to_dict()
elif self.tail == 'left':
self.imputer_dict_ = (
X[self.variables].mean() -
self.fold * X[self.variables].std()).to_dict()
elif self.distribution == 'skewed':
IQR = X[self.variables].quantile(0.75) - X[
self.variables].quantile(0.25)
if self.tail == 'right':
self.imputer_dict_ = (X[self.variables].quantile(0.75) +
(IQR * self.fold)).to_dict()
elif self.tail == 'left':
self.imputer_dict_ = (X[self.variables].quantile(0.25) -
(IQR * self.fold)).to_dict()
self.input_shape_ = X.shape
return self
def fit(self, X, y=None):
""" Learns the numbers to be used to replace the categories in each
variable.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
Can be the entire dataframe, not just the variables to be
encoded.
y : pandas series, default=None
The Target. Can be None if encoding_method = 'arbitrary'.
Otherwise, y needs to be passed when fitting the transformer.
"""
# check input dataframe
X = _is_dataframe(X)
# find categorical variables or check that those entered by the user
# are of type object
self.variables = _find_categorical_variables(X, self.variables)
# check if dataset contains na
_check_contains_na(X, self.variables)
# join target to predictor variables
if self.encoding_method == 'ordered':
if y is None:
raise ValueError(
'Please provide a target y for this encoding method')
temp = pd.concat([X, y], axis=1)
temp.columns = list(X.columns) + ['target']
# find mappings
self.encoder_dict_ = {}
for var in self.variables:
if self.encoding_method == 'ordered':
t = temp.groupby(
[var])['target'].mean().sort_values(ascending=True).index
elif self.encoding_method == 'arbitrary':
t = X[var].unique()
self.encoder_dict_[var] = {k: i for i, k in enumerate(t, 0)}
self.encoder_dict_ = _check_encoding_dictionary(self.encoder_dict_)
self.input_shape_ = X.shape
return self
def fit(self, X, y=None):
"""
Learns the unique categories per variable. If top_categories is indicated,
it will learn the most popular categories. Alternatively, it learns all
unique categories per variable.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
Can be the entire dataframe, not just seleted variables.
y : pandas series, default=None
Target. It is not needed in this encoded. You can pass y or
None.
Attributes
----------
encoder_dict_: dictionary
The dictionary containing the categories for which dummy variables
will be created.
"""
# check input dataframe
X = _is_dataframe(X)
# find categorical variables or check that those entered by the user
# are of type object
self.variables = _find_categorical_variables(X, self.variables)
# check if dataset contains na
_check_contains_na(X, self.variables)
self.encoder_dict_ = {}
for var in self.variables:
if not self.top_categories:
if self.drop_last:
category_ls = [x for x in X[var].unique()]
self.encoder_dict_[var] = category_ls[:-1]
else:
self.encoder_dict_[var] = X[var].unique()
else:
self.encoder_dict_[var] = [
x for x in X[var].value_counts().sort_values(
ascending=False).head(self.top_categories).index
]
self.encoder_dict_ = _check_encoding_dictionary(self.encoder_dict_)
self.input_shape_ = X.shape
return self
def fit(self, X, y=None):
# check input dataframe
X = _is_dataframe(X)
# find or check for numerical variables
self.variables = _find_numerical_variables(X, self.variables)
# check if dataset contains na
_check_contains_na(X, self.variables)
return X
def fit(self, X, y=None):
"""
Learns the most frequent category if the imputation method is set to frequent.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
Can be the entire dataframe, not just the selected variables.
y : None
y is not needed in this imputation. You can pass None or y.
Attributes
----------
imputer_dict_: dictionary
The dictionary mapping each variable to the most frequent category, or to
the value 'Missing' depending on the imputation_method. The most frequent
category is calculated when fitting the transformer.
"""
# check input dataframe
X = _is_dataframe(X)
# find or check for categorical variables
self.variables = _find_categorical_variables(X, self.variables)
if self.imputation_method == 'missing':
self.imputer_dict_ = {
var: self.fill_value
for var in self.variables
}
elif self.imputation_method == 'frequent':
self.imputer_dict_ = {}
for var in self.variables:
mode_vals = X[var].mode()
# careful: some variables contain multiple modes
if len(mode_vals) == 1:
self.imputer_dict_[var] = mode_vals[0]
else:
raise ValueError(
'The variable {} contains multiple frequent categories.'
.format(var))
self.input_shape_ = X.shape
return self
def fit(self, X, y=None):
"""
Learns the counts or frequencies which will be used to replace the categories.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
The user can pass the entire dataframe.
y : None
y is not needed in this encoder. You can pass y or None.
Attributes
----------
encoder_dict_: dictionary
Dictionary containing the {category: count / frequency} pairs for
each variable.
"""
# check input dataframe
X = _is_dataframe(X)
# find categorical variables or check that those entered by the user
# are of type object
self.variables = _find_categorical_variables(X, self.variables)
# check if dataset contains na
_check_contains_na(X, self.variables)
self.encoder_dict_ = {}
# learn encoding maps
for var in self.variables:
if self.encoding_method == 'count':
self.encoder_dict_[var] = X[var].value_counts().to_dict()
elif self.encoding_method == 'frequency':
n_obs = np.float(len(X))
self.encoder_dict_[var] = (X[var].value_counts() /
n_obs).to_dict()
self.encoder_dict_ = _check_encoding_dictionary(self.encoder_dict_)
self.input_shape_ = X.shape
return self
def fit(self, X, y):
"""
Learns the mean value of the target for each category of the variable.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
Can be the entire dataframe, not just the variables to be encoded.
y : pandas series
The target.
Attributes
----------
encoder_dict_: dictionary
The dictionary containing the {category: target mean} pairs used
to replace categories in every variable.
"""
# check input dataframe
X = _is_dataframe(X)
# find categorical variables or check that those entered by the user
# are of type object
self.variables = _find_categorical_variables(X, self.variables)
# check if dataset contains na
_check_contains_na(X, self.variables)
if y is None:
raise ValueError(
'Please provide a target y for this encoding method')
temp = pd.concat([X, y], axis=1)
temp.columns = list(X.columns) + ['target']
self.encoder_dict_ = {}
for var in self.variables:
self.encoder_dict_[var] = temp.groupby(
var)['target'].mean().to_dict()
self.encoder_dict_ = _check_encoding_dictionary(self.encoder_dict_)
self.input_shape_ = X.shape
return self
def transform(self, X):
# Check method fit has been called
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
# check if dataset contains na
_check_contains_na(X, self.variables)
# Check that the dataframe contains the same number of columns
# than the dataframe used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
return X
def fit(self, X, y=None):
"""
Learns the numbers that should be used to replace the categories in each
variable.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
Can be the entire dataframe, not just the categorical variables.
y : pandas series.
The target variable. Required to train the decision tree and for
ordered ordinal encoding.
"""
# check input dataframe
X = _is_dataframe(X)
# find categorical variables or check that those entered by the user
# are of type object
self.variables = _find_categorical_variables(X, self.variables)
# check if dataset contains na
_check_contains_na(X, self.variables)
# initialize categorical encoder
cat_encoder = OrdinalCategoricalEncoder(
encoding_method=self.encoding_method, variables=self.variables)
# initialize decision tree discretiser
tree_discretiser = DecisionTreeDiscretiser(
cv=self.cv,
scoring=self.scoring,
variables=self.variables,
param_grid=self.param_grid,
regression=self.regression,
random_state=self.random_state)
# pipeline for the encoder
self.encoder_ = Pipeline([('categorical_encoder', cat_encoder),
('tree_discretiser', tree_discretiser)])
self.encoder_.fit(X, y)
self.input_shape_ = X.shape
return self
def fit(self, X, y=None):
"""
Makes a copy of the variables to impute in the training dataframe from
which it will randomly extract the values to fill the missing data
during transform.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
Can be the entire dataframe, not just he variables to impute.
y : None
y is not needed in this imputation. You can pass None or y.
Attributes
----------
X_ : dataframe.
Copy of the training dataframe from which to extract the random samples.
"""
# check input dataframe
X = _is_dataframe(X)
# find variables to impute
if not self.variables:
self.variables = [var for var in X.columns]
else:
self.variables = self.variables
# take a copy of the selected variables
self.X_ = X[self.variables].copy()
# check the variables assigned to the random state
if self.seed == 'observation':
self.random_state = _define_variables(self.random_state)
if len([var
for var in self.random_state if var not in X.columns]) > 0:
raise ValueError(
"There are variables assigned as random state which are not part of the training "
"dataframe.")
self.input_shape_ = X.shape
return self
def transform(self, X):
""" Replaces categories with the learned parameters.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features].
The input samples.
Returns
-------
X_transformed : pandas dataframe of shape = [n_samples, n_features].
The dataframe containing categories replaced by numbers.
"""
# Check method fit has been called
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
# check if dataset contains na
_check_contains_na(X, self.variables)
# Check that the dataframe contains the same number of columns
# than the dataframe
# used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
# replace categories by the learned parameters
for feature in self.encoder_dict_.keys():
X[feature] = X[feature].map(self.encoder_dict_[feature])
# check if NaN values were introduced by the encoding
if X[self.encoder_dict_.keys()].isnull().sum().sum() > 0:
warnings.warn(
"NaN values were introduced in the returned dataframe by the encoder."
"This means that some of the categories in the input dataframe were "
"not present in the training set used when the fit method was called. "
"Thus, mappings for those categories does not exist. Try using the "
"RareLabelCategoricalEncoder to remove infrequent categories before "
"calling this encoder."
)
return X
def fit(self, X, y=None):
"""
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
y : None
y is not needed in this transformer. You can pass y or None.
Attributes
----------
right_tail_caps_: dictionary
The dictionary containing the maximum values at which variables
will be capped.
left_tail_caps_ : dictionary
The dictionary containing the minimum values at which variables
will be capped.
"""
X = _is_dataframe(X)
if self.missing_values == 'raise':
# check if dataset contains na
_check_contains_na(X, self.variables)
# find or check for numerical variables
self.variables = _find_numerical_variables(X, self.variables)
if self.max_capping_dict is not None:
self.right_tail_caps_ = self.max_capping_dict
else:
self.right_tail_caps_ = {}
if self.min_capping_dict is not None:
self.left_tail_caps_ = self.min_capping_dict
else:
self.left_tail_caps_ = {}
self.input_shape_ = X.shape
return self
def fit(self, X, y=None):
"""
Learns the variables for which the missing indicators will be created.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
y : None
y is not needed in this imputation. You can pass None or y.
Attributes
----------
variables_: list
the lit of variables for which the missing indicator will be created.
"""
# check input dataframe
X = _is_dataframe(X)
# find variables for which indicator should be added
if self.how == 'missing_only':
if not self.variables:
self.variables_ = [
var for var in X.columns if X[var].isnull().sum() > 0
]
else:
self.variables_ = [
var for var in self.variables if X[var].isnull().sum() > 0
]
elif self.how == 'all':
if not self.variables:
self.variables_ = [var for var in X.columns]
else:
self.variables_ = self.variables
self.input_shape_ = X.shape
return self
def fit(self, X, y=None):
"""
Checks that the variables are numerical.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
User can pass the entire dataframe, not just the variables to impute.
y : None
y is not needed in this imputation. You can pass None or y.
Attributes
----------
imputer_dict_: dictionary
The dictionary containing the values that will be used to replace each variable.
"""
# check input dataframe
X = _is_dataframe(X)
# find or check for numerical variables
if self.imputer_dict:
self.variables = _find_numerical_variables(
X, self.imputer_dict.keys())
else:
self.variables = _find_numerical_variables(X, self.variables)
# create the imputer dictionary
if self.imputer_dict:
self.imputer_dict_ = self.imputer_dict
else:
self.imputer_dict_ = {
var: self.arbitrary_number
for var in self.variables
}
self.input_shape_ = X.shape
return self
def transform(self, X):
"""
Removes observations with outliers from the dataframe.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The data to be transformed.
Returns
-------
X_transformed : pandas dataframe of shape = [n_samples, n_features]
The dataframe without outlier observations.
"""
# Check method fit has been called
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
if self.missing_values == 'raise':
# check if dataset contains na
_check_contains_na(X, self.variables)
# Check that the dataframe contains the same number of columns than the dataframe
# used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
for feature in self.right_tail_caps_.keys():
outliers = np.where(X[feature] > self.right_tail_caps_[feature],
True, False)
X = X.loc[~outliers]
for feature in self.left_tail_caps_.keys():
outliers = np.where(X[feature] < self.left_tail_caps_[feature],
True, False)
X = X.loc[~outliers]
return X
def transform(self, X):
"""
Caps the variable values, that is, censors outliers.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The data to be transformed.
Returns
-------
X_transformed : pandas dataframe of shape = [n_samples, n_features]
The dataframe with the capped variables.
"""
# check if class was fitted
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
if self.missing_values == 'raise':
# check if dataset contains na
_check_contains_na(X, self.variables)
# Check that the dataframe contains the same number of columns
# than the dataframe used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
# replace outliers
for feature in self.right_tail_caps_.keys():
X[feature] = np.where(X[feature] > self.right_tail_caps_[feature],
self.right_tail_caps_[feature], X[feature])
for feature in self.left_tail_caps_.keys():
X[feature] = np.where(X[feature] < self.left_tail_caps_[feature],
self.left_tail_caps_[feature], X[feature])
return X
def transform(self, X):
"""
Creates the dummy / binary variables.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The data to transform.
Returns
-------
X_transformed : pandas dataframe.
The shape of the dataframe will be different from the original as it includes the dummy variables.
"""
# Check method fit has been called
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
# check if dataset contains na
_check_contains_na(X, self.variables)
# Check that the dataframe contains the same number of columns than the dataframe
# used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
for feature in self.variables:
for category in self.encoder_dict_[feature]:
X[str(feature) + '_' + str(category)] = np.where(
X[feature] == category, 1, 0)
# drop the original non-encoded variables.
X.drop(labels=self.variables, axis=1, inplace=True)
return X
def fit(self, X, y=None):
"""
Learns the mean or median values.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
User can pass the entire dataframe, not just the variables that need imputation.
y : pandas series or None, default=None
y is not needed in this imputation. You can pass None or y.
Attributes
----------
imputer_dict_ : dictionary
The dictionary containing the mean / median values per variable. These
values will be used by the imputer to replace missing data.
"""
# check input dataframe
X = _is_dataframe(X)
# find or check for numerical variables
self.variables = _find_numerical_variables(X, self.variables)
# find imputation parameters: mean or median
if self.imputation_method == 'mean':
self.imputer_dict_ = X[self.variables].mean().to_dict()
elif self.imputation_method == 'median':
self.imputer_dict_ = X[self.variables].median().to_dict()
self.input_shape_ = X.shape
return self
def inverse_transform(self, X):
""" Convert the data back to the original representation.
Parameters
----------
X_transformed : pandas dataframe of shape = [n_samples, n_features].
The transformed dataframe.
Returns
-------
X : pandas dataframe of shape = [n_samples, n_features].
The un-transformed dataframe, that is, containing the original values
of the categorical variables.
"""
# Check method fit has been called
check_is_fitted(self)
# check that input is a dataframe
X = _is_dataframe(X)
# check if dataset contains na
_check_contains_na(X, self.variables)
# Check that the dataframe contains the same number of columns
# than the dataframe
# used to fit the imputer.
_check_input_matches_training_df(X, self.input_shape_[1])
# replace encoded categories by the original values
for feature in self.encoder_dict_.keys():
inv_map = {v: k for k, v in self.encoder_dict_[feature].items()}
X[feature] = X[feature].map(inv_map)
return X
def test_is_dataframe(dataframe_vartypes):
assert_frame_equal(_is_dataframe(dataframe_vartypes), dataframe_vartypes)
with pytest.raises(TypeError):
assert _is_dataframe([1, 2, 4])
def fit(self, X, y):
"""
Learns the numbers that should be used to replace the categories in each
variable. That is the WoE or ratio of probability.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
Can be the entire dataframe, not just the categorical variables.
y : pandas series.
Target, must be binary [0,1].
Attributes
----------
encoder_dict_: dictionary
The dictionary containing the {category: WoE / ratio} pairs per variable.
"""
# check input dataframe
X = _is_dataframe(X)
# find categorical variables or check that those entered by the user
# are of type object
self.variables = _find_categorical_variables(X, self.variables)
# check if dataset contains na
_check_contains_na(X, self.variables)
if y is None:
raise ValueError(
'Please provide a target y for this encoding method')
# check that y is binary
if len([x for x in y.unique() if x not in [0, 1]]) > 0:
raise ValueError(
"This encoder is only designed for binary classification, values of y can be only 0 or 1"
)
temp = pd.concat([X, y], axis=1)
temp.columns = list(X.columns) + ['target']
self.encoder_dict_ = {}
if self.encoding_method == 'woe':
total_pos = temp['target'].sum()
total_neg = len(temp) - total_pos
temp['non_target'] = np.where(temp['target'] == 1, 0, 1)
for var in self.variables:
pos = temp.groupby([var])['target'].sum() / total_pos
neg = temp.groupby([var])['non_target'].sum() / total_neg
t = pd.concat([pos, neg], axis=1)
t['woe'] = np.log(t['target'] / t['non_target'])
if not t.loc[t['target'] == 0, :].empty or not t.loc[
t['non_target'] == 0, :].empty:
raise ValueError(
"The proportion of 1 of the classes for a category in variable {} is zero, and log of zero is "
"not defined".format(var))
self.encoder_dict_[var] = t['woe'].to_dict()
else:
for var in self.variables:
t = temp.groupby(var)['target'].mean()
t = pd.concat([t, 1 - t], axis=1)
t.columns = ['p1', 'p0']
if self.encoding_method == 'log_ratio':
if not t.loc[t['p0'] == 0, :].empty or not t.loc[
t['p1'] == 0, :].empty:
raise ValueError(
"p(0) or p(1) for a category in variable {} is zero, log of zero is not defined"
.format(var))
else:
self.encoder_dict_[var] = (np.log(t.p1 /
t.p0)).to_dict()
elif self.encoding_method == 'ratio':
if not t.loc[t['p0'] == 0, :].empty:
raise ValueError(
"p(0) for a category in variable {} is zero, division by 0 is not defined"
.format(var))
else:
self.encoder_dict_[var] = (t.p1 / t.p0).to_dict()
self.encoder_dict_ = _check_encoding_dictionary(self.encoder_dict_)
self.input_shape_ = X.shape
return self
def fit(self, X, y=None):
"""
Learns the frequent categories for each variable.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
Can be the entire dataframe, not just selected variables
y : None
y is not required. You can pass y or None.
Attributes
----------
encoder_dict_: dictionary
The dictionary containing the frequent categories (that will be kept)
for each variable. Categories not present in this list will be replaced
by 'Rare' or by the user defined value.
"""
# check input dataframe
X = _is_dataframe(X)
# find categorical variables or check that those entered by the user
# are of type object
self.variables = _find_categorical_variables(X, self.variables)
# check if dataset contains na
_check_contains_na(X, self.variables)
self.encoder_dict_ = {}
for var in self.variables:
if len(X[var].unique()) > self.n_categories:
# if the variable has more than the indicated number of categories
# the encoder will learn the most frequent categories
t = pd.Series(X[var].value_counts() / np.float(len(X)))
# non-rare labels:
freq_idx = t[t >= self.tol].index
if self.max_n_categories:
self.encoder_dict_[var] = freq_idx[:self.max_n_categories]
else:
self.encoder_dict_[var] = freq_idx
else:
# if the total number of categories is smaller than the indicated
# the encoder will consider all categories as frequent.
warnings.warn(
"The number of unique categories for variable {} is less than that indicated in "
"n_categories. Thus, all categories will be considered frequent"
.format(var))
self.encoder_dict_[var] = X[var].unique()
self.encoder_dict_ = _check_encoding_dictionary(self.encoder_dict_)
self.input_shape_ = X.shape
return self
