def fit(self, X, y, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = util.convert_input(X)
if isinstance(y, pd.DataFrame):
y = y.iloc[:, 0]
else:
y = pd.Series(y, name='target')
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) +
" but length of y is " + str(y.shape[0]) + ".")
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
_, self.mapping = self.target_encode(
X,
y,
mapping=None,
cols=self.cols,
impute_missing=self.impute_missing,
handle_unknown=self.handle_unknown,
smoothing_in=self.smoothing,
min_samples_leaf=self.min_samples_leaf)
X_temp = self.transform(X, override_return_df=True)
self.feature_names = list(X_temp.columns)
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# unite the input into pandas types
X = util.convert_input(X)
y = util.convert_input_vector(y, X.index).astype(float)
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) +
" but length of y is " + str(y.shape[0]) + ".")
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.use_default_cols:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().bool():
raise ValueError('Columns to be encoded can not contain null')
categories = self.fit_leave_one_out(X, y, cols=self.cols)
self.mapping = categories
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# unite the input into pandas types
X = util.convert_input(X)
y = util.convert_input_vector(y, X.index).astype(float)
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) + " but length of y is " + str(y.shape[0]) + ".")
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.use_default_cols:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().bool():
raise ValueError('Columns to be encoded can not contain null')
categories = self.fit_leave_one_out(
X, y,
cols=self.cols
)
self.mapping = categories
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().bool():
raise ValueError('Columns to be encoded can not contain null')
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
handle_unknown='value',
handle_missing='value'
)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
self.mapping = self.generate_mapping()
X_temp = self.transform(X, override_return_df=True)
self.feature_names = list(X_temp.columns)
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = get_obj_cols(X)
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
impute_missing=self.impute_missing,
handle_unknown=self.handle_unknown)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
ordinal_mapping = self.ordinal_encoder.category_mapping
mappings_out = []
for switch in ordinal_mapping:
values = [x[1] for x in switch.get('mapping')]
column_mapping = self.fit_helmert_coding(values)
mappings_out.append({
'col': switch.get('col'),
'mapping': column_mapping,
})
self.mapping = mappings_out
if self.drop_invariant:
self.drop_cols = []
X_temp = self.transform(X)
generated_cols = get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().bool():
raise ValueError('Columns to be encoded can not contain null')
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
handle_unknown='value',
handle_missing='value'
)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
self.mapping = self.generate_mapping()
X_temp = self.transform(X, override_return_df=True)
self.feature_names = list(X_temp.columns)
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
_, categories = self.ordinal_encoding(
X,
mapping=self.mapping,
cols=self.cols,
impute_missing=self.impute_missing,
handle_unknown=self.handle_unknown)
self.mapping = categories
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y=None, **kwargs):
"""Fits an ordinal encoder to produce a consistent mapping across applications and optionally finds
generally invariant columns to drop consistently.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# if the input dataset isn't already a dataframe, convert it to one (using default column names)
# first check the type
X = convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = get_obj_cols(X)
# train an ordinal pre-encoder
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
impute_missing=self.impute_missing,
handle_unknown=self.handle_unknown
)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
ordinal_mapping = self.ordinal_encoder.category_mapping
mappings_out = []
for switch in ordinal_mapping:
values = [x[1] for x in switch.get('mapping')]
column_mapping = self.fit_backward_difference_coding(values)
mappings_out.append({'col': switch.get('col'), 'mapping': column_mapping, })
self.mapping = mappings_out
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
X_temp = self.transform(X)
generated_cols = get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# if the input dataset isn't already a dataframe, convert it to one (using default column names)
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
# train an ordinal pre-encoder
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
impute_missing=self.impute_missing,
handle_unknown=self.handle_unknown)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
for col in self.cols:
self.digits_per_col[col] = self.calc_required_digits(X, col)
# do a transform on the training data to get a column list
X_t = self.transform(X, override_return_df=True)
self._encoded_columns = X_t.columns.values
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
X_temp = self.transform(X)
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# unite the input into pandas types
X = util.convert_input(X)
# y = util.convert_input_vector(y, X.index)
if X.shape[0] != y.shape[0]:
raise ValueError(
"The length of X is {} but length of y is {}.".format(
X.shape[0], y.shape[0]))
self._dim = X.shape[1]
if self.handle_missing == "error":
if X[self.cols].isnull().any().any():
raise ValueError("Columns to be encoded can not contain null")
self._fit_cooc_encode(self, X, y=None)
# TODO: after transform
if self.drop_invariant:
self.drop_cols = []
X_temp = self.transform(X)
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
self._check_set_create_dict_attrs()
self._fit_count_encode(X, y)
X_temp = self.transform(X, override_return_df=True)
self.feature_names = list(X_temp.columns)
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = convert_input(X)
if isinstance(y, pd.DataFrame):
y = y.iloc[:, 0]
else:
y = pd.Series(y, name='target')
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) +
" but length of y is " + str(y.shape[0]) + ".")
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = get_obj_cols(X)
_, categories = self.target_encode(
X,
y,
mapping=self.mapping,
cols=self.cols,
impute_missing=self.impute_missing,
handle_unknown=self.handle_unknown,
smoothing_in=self.smoothing,
min_samples_leaf=self.min_samples_leaf)
self.mapping = categories
if self.drop_invariant:
self.drop_cols = []
X_temp = self.transform(X)
generated_cols = get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = util.convert_input(X)
if isinstance(y, pd.DataFrame):
y = y.iloc[:, 0].astype(float)
else:
y = pd.Series(y, name='target', dtype=float)
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) +
" but length of y is " + str(y.shape[0]) + ".")
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.use_default_cols:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
categories = self.fit_leave_one_out(X, y, cols=self.cols)
self.mapping = categories
if self.drop_invariant:
self.drop_cols = []
X_temp = self.transform(X)
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
_, categories = self.ordinal_encoding(
X,
mapping=self.mapping,
cols=self.cols,
impute_missing=self.impute_missing,
handle_unknown=self.handle_unknown)
self.mapping = categories
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
X_temp = self.transform(X)
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
return self
def transform(self, X, override_return_df=False):
"""Perform the transformation to new categorical data.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
p : array, shape = [n_samples, n_numeric + N]
Transformed values with encoding applied.
"""
if self._dim is None:
raise ValueError(
'Must train encoder before it can be used to transform data.')
# first check the type
X = util.convert_input(X)
# then make sure that it is the right size
if X.shape[1] != self._dim:
raise ValueError('Unexpected input dimension %d, expected %d' % (
X.shape[1],
self._dim,
))
if not self.cols:
return X
X_out = self.ordinal_encoder.transform(X)
X_out = self.basen_encode(X_out, cols=self.cols)
if self.drop_invariant:
for col in self.drop_cols:
X_out.drop(col, 1, inplace=True)
# impute missing values only in the generated columns
generated_cols = util.get_generated_cols(X, X_out, self.cols)
X_out[generated_cols] = X_out[generated_cols].fillna(value=0.0)
if self.return_df or override_return_df:
return X_out
else:
return X_out.values
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
self._check_set_create_dict_attrs()
self._fit_count_encode(X, y)
if self.drop_invariant:
self.drop_cols = []
X_temp = self.transform(X)
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
return self
def fit(self, X, y=None, **kwargs):
"""Fits an ordinal encoder to produce a consistent mapping across applications and optionally finds
generally invariant columns to drop consistently.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# if the input dataset isn't already a dataframe, convert it to one (using default column names)
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
# train an ordinal pre-encoder
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
impute_missing=self.impute_missing,
handle_unknown=self.handle_unknown)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
ordinal_mapping = self.ordinal_encoder.category_mapping
mappings_out = []
for switch in ordinal_mapping:
values = switch.get('mapping').get_values()
column_mapping = self.fit_backward_difference_coding(values)
mappings_out.append({
'col': switch.get('col'),
'mapping': column_mapping,
})
self.mapping = mappings_out
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and binary y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Binary target values.
Returns
-------
self : encoder
Returns self.
"""
# Unite parameters into pandas types
X = util.convert_input(X)
y = util.convert_input_vector(y, X.index).astype(float)
# The lengths must be equal
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) +
" but length of y is " + str(y.shape[0]) + ".")
self._dim = X.shape[1]
# If columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().any():
raise ValueError('Columns to be encoded can not contain null')
self.ordinal_encoder = OrdinalEncoder(verbose=self.verbose,
cols=self.cols,
handle_unknown='value',
handle_missing='value')
self.ordinal_encoder = self.ordinal_encoder.fit(X)
X_ordinal = self.ordinal_encoder.transform(X)
# Training
self.mapping = self._train(X_ordinal, y)
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# Store column names with approximately constant variance on the training data
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# if the input dataset isn't already a dataframe, convert it to one (using default column names)
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().bool():
raise ValueError('Columns to be encoded can not contain null')
# train an ordinal pre-encoder
self.ordinal_encoder = OrdinalEncoder(verbose=self.verbose,
cols=self.cols,
handle_unknown='value',
handle_missing='value')
self.ordinal_encoder = self.ordinal_encoder.fit(X)
ordinal_mapping = self.ordinal_encoder.category_mapping
mappings_out = []
for switch in ordinal_mapping:
values = switch.get('mapping')
col = switch.get('col')
column_mapping = self.fit_sum_coding(col, values,
self.handle_missing,
self.handle_unknown)
mappings_out.append({
'col': switch.get('col'),
'mapping': column_mapping,
})
self.mapping = mappings_out
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# if the input dataset isn't already a dataframe, convert it to one (using default column names)
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
# train an ordinal pre-encoder
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
impute_missing=self.impute_missing,
handle_unknown=self.handle_unknown)
X = X.drop_duplicates(subset=self.cols) if self.cols else X
self.ordinal_encoder = self.ordinal_encoder.fit(X)
for col in self.cols:
self.digits_per_col[col] = self.calc_required_digits(X, col)
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and binary y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Binary target values.
Returns
-------
self : encoder
Returns self.
"""
# Unite parameters into pandas types
X = util.convert_input(X)
y = util.convert_input_vector(y, X.index).astype(float)
# The lengths must be equal
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) +
" but length of y is " + str(y.shape[0]) + ".")
self._dim = X.shape[1]
# If columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().bool():
raise ValueError('Columns to be encoded can not contain null')
self.ordinal_encoder = OrdinalEncoder(verbose=self.verbose,
cols=self.cols,
handle_unknown='value',
handle_missing='value')
self.ordinal_encoder = self.ordinal_encoder.fit(X)
X_ordinal = self.ordinal_encoder.transform(X)
# Training
if self.model == 'independent':
self.mapping = self._train_independent(X_ordinal, y)
elif self.model == 'pooled':
self.mapping = self._train_pooled(X_ordinal, y)
elif self.model == 'beta':
self.mapping = self._train_beta(X_ordinal, y)
elif self.model == 'binary':
# The label must be binary with values {0,1}
unique = y.unique()
if len(unique) != 2:
raise ValueError(
"The target column y must be binary. But the target contains "
+ str(len(unique)) + " unique value(s).")
if y.isnull().any():
raise ValueError(
"The target column y must not contain missing values.")
if np.max(unique) < 1:
raise ValueError(
"The target column y must be binary with values {0, 1}. Value 1 was not found in the target."
)
if np.min(unique) > 0:
raise ValueError(
"The target column y must be binary with values {0, 1}. Value 0 was not found in the target."
)
# Perform the training
self.mapping = self._train_log_odds_ratio(X_ordinal, y)
else:
raise ValueError("model='" + str(self.model) +
"' is not a recognized option")
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# Store column names with approximately constant variance on the training data
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and binary y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Binary target values.
Returns
-------
self : encoder
Returns self.
"""
# Unite parameters into pandas types
X = util.convert_input(X)
y = util.convert_input_vector(y, X.index).astype(float)
# The lengths must be equal
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) + " but length of y is " + str(y.shape[0]) + ".")
self._dim = X.shape[1]
# If columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().bool():
raise ValueError('Columns to be encoded can not contain null')
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
handle_unknown='value',
handle_missing='value'
)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
X_ordinal = self.ordinal_encoder.transform(X)
# Training
self.mapping = self._train(X_ordinal, y)
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# Store column names with approximately constant variance on the training data
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y=None, **kwargs):
"""Fit encoder according to X and y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# if the input dataset isn't already a dataframe, convert it to one (using default column names)
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().bool():
raise ValueError('Columns to be encoded can not contain null')
# train an ordinal pre-encoder
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
handle_unknown='value',
handle_missing='value'
)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
ordinal_mapping = self.ordinal_encoder.category_mapping
mappings_out = []
for switch in ordinal_mapping:
values = switch.get('mapping')
col = switch.get('col')
column_mapping = self.fit_sum_coding(col, values, self.handle_missing, self.handle_unknown)
mappings_out.append({'col': switch.get('col'), 'mapping': column_mapping, })
self.mapping = mappings_out
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and binary y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Binary target values.
Returns
-------
self : encoder
Returns self.
"""
# Create parent encoder and fit it
self.parent_cols = list(self.feature_mapping.values())
self.parent_encoder = MEstimateEncoder(
verbose=self.verbose,
cols=self.parent_cols,
drop_invariant=self.drop_invariant,
return_df=self.return_df,
handle_unknown=self.handle_unknown,
handle_missing=self.handle_missing,
random_state=self.random_state,
randomized=self.randomized,
sigma=self.sigma,
m=self.m_prior,
)
self.parent_encoder.fit(X, y)
# Unite parameters into pandas types
X = util.convert_input(X)
y = util.convert_input_vector(y, X.index).astype(float)
# The lengths must be equal
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) +
" but length of y is " + str(y.shape[0]) + ".")
self._dim = X.shape[1]
# If columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == "error":
if X[self.cols].isnull().any().any():
raise ValueError("Columns to be encoded can not contain null")
# Check that children and parents are disjoint
children = set(self.feature_mapping.keys())
parents = set(self.feature_mapping.values())
if len(children.intersection(parents)) > 0:
raise ValueError("No column should be a child and a parent")
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
handle_unknown="value",
handle_missing="value",
)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
X_ordinal = self.ordinal_encoder.transform(X)
# Training
self.mapping = self._train(X_ordinal, y)
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# Store column names with approximately constant variance on the training data
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
def fit(self, X, y, **kwargs):
"""Fit encoder according to X and binary y.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Binary target values.
Returns
-------
self : encoder
Returns self.
"""
# Unite parameters into pandas types
X = convert_input(X)
if isinstance(y, pd.DataFrame):
y = y.iloc[:, 0]
else:
y = pd.Series(y, name='target')
# The lengths must be equal
if X.shape[0] != y.shape[0]:
raise ValueError("The length of X is " + str(X.shape[0]) +
" but length of y is " + str(y.shape[0]) + ".")
# The label must be binary with values {0,1}
unique = y.unique()
if len(unique) != 2:
raise ValueError(
"The target column y must be binary. But the target contains "
+ str(len(unique)) + " unique value(s).")
if y.isnull().any():
raise ValueError(
"The target column y must not contain missing values.")
if np.max(unique) < 1:
raise ValueError(
"The target column y must be binary with values {0, 1}. Value 1 was not found in the target."
)
if np.min(unique) > 0:
raise ValueError(
"The target column y must be binary with values {0, 1}. Value 0 was not found in the target."
)
self._dim = X.shape[1]
# If columns aren't passed, just use every string column
if self.cols is None:
self.cols = get_obj_cols(X)
# Training
self.mapping = self._train(X, y, cols=self.cols)
# Store column names with approximately constant variance on the training data
if self.drop_invariant:
self.drop_cols = []
X_temp = self.transform(X)
generated_cols = get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [
x for x in generated_cols if X_temp[x].var() <= 10e-5
]
return self
