def helmert_encoder(self, df, configger):
"""
:param df: the train dataset.
:param configger: the json str of configger setting, the params means:
verbose: int
integer indicating verbosity of the output. 0 for none.
cols: list
a list of columns to encode, if None, all string columns will be encoded.
drop_invariant: bool
boolean for whether or not to drop columns with 0 variance.
return_df: bool
boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array).
handle_unknown: str
options are 'error', 'return_nan', 'value', and 'indicator'. The default is 'value'. Warning: if indicator is used,
an extra column will be added in if the transform matrix has unknown categories. This can cause
unexpected changes in dimension in some cases.
handle_missing: str
options are 'error', 'return_nan', 'value', and 'indicator'. The default is 'value'. Warning: if indicator is used,
an extra column will be added in if the transform matrix has nan values. This can cause
unexpected changes in dimension in some cases.
:return: the transform result
"""
X, y, encode_col = self.get_Xy(df, configger)
drop_invariant = set_default_vale("drop_invariant", configger, False, is_bool=True)
handle_missing = set_default_vale("handle_missing", configger, "value")
handle_unknown = set_default_vale("handle_unknown", configger, "value")
encoder = HelmertEncoder(verbose=1, cols=encode_col, drop_invariant=drop_invariant, return_df=True,
handle_unknown=handle_unknown, handle_missing=handle_missing)
res = encoder.fit_transform(X, y)
return res
def get_single_encoder(encoder_name: str, cat_cols: list):
"""
Get encoder by its name
:param encoder_name: Name of desired encoder
:param cat_cols: Cat columns for encoding
:return: Categorical encoder
"""
if encoder_name == "FrequencyEncoder":
encoder = FrequencyEncoder(cols=cat_cols)
if encoder_name == "WOEEncoder":
encoder = WOEEncoder(cols=cat_cols)
if encoder_name == "TargetEncoder":
encoder = TargetEncoder(cols=cat_cols)
if encoder_name == "SumEncoder":
encoder = SumEncoder(cols=cat_cols)
if encoder_name == "MEstimateEncoder":
encoder = MEstimateEncoder(cols=cat_cols)
if encoder_name == "LeaveOneOutEncoder":
encoder = LeaveOneOutEncoder(cols=cat_cols)
if encoder_name == "HelmertEncoder":
encoder = HelmertEncoder(cols=cat_cols)
if encoder_name == "BackwardDifferenceEncoder":
encoder = BackwardDifferenceEncoder(cols=cat_cols)
if encoder_name == "JamesSteinEncoder":
encoder = JamesSteinEncoder(cols=cat_cols)
if encoder_name == "OrdinalEncoder":
encoder = OrdinalEncoder(cols=cat_cols)
if encoder_name == "CatBoostEncoder":
encoder = CatBoostEncoder(cols=cat_cols)
if encoder_name == "MEstimateEncoder":
encoder = MEstimateEncoder(cols=cat_cols)
if encoder_name == "OneHotEncoder":
encoder = OneHotEncoder(cols=cat_cols)
if encoder is None:
raise NotImplementedError("To be implemented")
return encoder
def get_single_encoder(encoder_name: str, cat_cols: list):
if encoder_name == "FrequencyEncoder":
encoder = FrequencyEncoder(cols=cat_cols)
if encoder_name == "WOEEncoder":
encoder = WOEEncoder(cols=cat_cols)
if encoder_name == "TargetEncoder":
encoder = TargetEncoder(cols=cat_cols)
if encoder_name == "SumEncoder":
encoder = SumEncoder(cols=cat_cols)
if encoder_name == "MEstimateEncoder":
encoder = MEstimateEncoder(cols=cat_cols)
if encoder_name == "LeaveOneOutEncoder":
encoder = LeaveOneOutEncoder(cols=cat_cols)
if encoder_name == "HelmertEncoder":
encoder = HelmertEncoder(cols=cat_cols)
if encoder_name == "BackwardDifferenceEncoder":
encoder = BackwardDifferenceEncoder(cols=cat_cols)
if encoder_name == "JamesSteinEncoder":
encoder = JamesSteinEncoder(cols=cat_cols)
if encoder_name == "OrdinalEncoder":
encoder = OrdinalEncoder(cols=cat_cols)
if encoder_name == "CatBoostEncoder":
encoder = CatBoostEncoder(cols=cat_cols)
if encoder_name == "MEstimateEncoder":
encoder = MEstimateEncoder(cols=cat_cols)
if encoder_name == 'OneHotEncoder':
encoder = OneHotEncoder(cols=cat_cols)
# assert encoder is not None
return encoder