# Copyright 2019 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import lale.helpers
import lale.operators
import pandas as pd
import sklearn.preprocessing
[docs]class OneHotEncoderImpl():
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
[docs] def fit(self, X, y=None):
self._sklearn_model = sklearn.preprocessing.OneHotEncoder(**self._hyperparams)
self._sklearn_model.fit(X, y)
if isinstance(X, pd.DataFrame):
cols_X = [str(c) for c in X.columns]
self._feature_names = self._sklearn_model.get_feature_names(cols_X)
else:
self._feature_names = self._sklearn_model.get_feature_names()
return self
[docs] def get_feature_names(self, input_features=None):
"""Return feature names for output features after this transformation.
This uses the output features obtained from scikit-learn's
OneHotEncoder's get_feature_names method.
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder.get_feature_names
The precedence of input feature names used is as follows:
1. input_features passed as an argument to this function.
This is a list string of length n_features.
2. feature_names obtained at the time of training.
They are input data's column names if it was a Pandas dataframe.
3. feature_names obtained at the time of training.
If training data was not a Pandas DataFrame, this method returns
the output of scikit's get_feature_names(None).
Returns
-------
output_feature_names : array of string, length n_output_features
"""
try:
trained_sklearn_model = self._sklearn_model
except AttributeError:
raise ValueError('Can only call get_feature_names on a trained operator. Please call fit to get a trained operator.')
if input_features is not None:
return trained_sklearn_model.get_feature_names(input_features)
elif self._feature_names is not None:
return self._feature_names
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Hyperparameter schema for the OneHotEncoder model from scikit-learn.',
'allOf': [
{ 'description': 'This first object lists all constructor arguments with their types, but omits constraints for conditional hyperparameters.',
'type': 'object',
'additionalProperties': False,
'required': ['categories', 'sparse', 'dtype', 'handle_unknown'],
'relevantToOptimizer': [],
'properties': {
'categories': {
'anyOf': [
{ 'description':
'Determine categories automatically from training data.',
'enum': ['auto', None]},
{ 'description': 'The ith list element holds the categories expected in the ith column.',
'type': 'array',
'items': {
'anyOf': [
{ 'type': 'array',
'items': {
'type': 'string'},
}, {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Should be sorted.'}]}}],
'default': None},
'sparse': {
'description':
'Will return sparse matrix if set true, else array.',
'type': 'boolean',
'default': True},
'dtype': {
'description': 'Desired dtype of output, must be number. See https://docs.scipy.org/doc/numpy-1.14.0/reference/arrays.scalars.html#arrays-scalars-built-in',
'enum': ['byte', 'short', 'intc', 'int_', 'longlong', 'intp', 'int8', 'int16', 'int32', 'int64', 'ubyte', 'ushort', 'uintc', 'uint', 'ulonglong', 'uintp', 'uint16', 'uint32', 'uint64', 'half', 'single', 'double', 'float_', 'longfloat', 'float16', 'float32', 'float64', 'float96', 'float128'],
'default': 'float64'},
'handle_unknown': {
'description': 'Whether to raise an error or ignore if an unknown categorical feature is present during transform.',
'enum': ['error', 'ignore'],
'default': 'error'},
}}]}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Input data schema for training the OneHotEncoder model from scikit-learn.',
'type': 'object',
'required': ['X'],
'additionalProperties': False,
'properties': {
'X': {
'description': 'Features; the outer array is over samples.',
'type': 'array',
'items': {
'type': 'array',
'items': {
'anyOf':[{'type': 'number'}, {'type':'string'}]},
}},
'y': {
'description': 'Target class labels; the array is over samples.'}}}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Input data schema for predictions using the OneHotEncoder model from scikit-learn.',
'type': 'object',
'required': ['X'],
'additionalProperties': False,
'properties': {
'X': {
'description': 'Features; the outer array is over samples.',
'type': 'array',
'items': {
'type': 'array',
'items': {
'anyOf':[{'type': 'number'}, {'type':'string'}]}}}}}
_output_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Output data schema for predictions (projected data) using the OneHotEncoder model from scikit-learn. See the official documentation for details: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html\n',
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'}}}
_combined_schemas = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Combined schema for expected data and hyperparameters.',
'documentation_url': 'https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html',
'type': 'object',
'tags': {
'pre': ['categoricals'],
'op': ['transformer'],
'post': []},
'properties': {
'hyperparams': _hyperparams_schema,
'input_fit': _input_fit_schema,
'input_predict': _input_predict_schema,
'output': _output_schema }}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
OneHotEncoder = lale.operators.make_operator(OneHotEncoderImpl, _combined_schemas)