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import numpy as np
import pandas as pd
from sklearn.base import TransformerMixin
import warnings
def _get_mask(X, value):
"""
Compute the boolean mask X == missing_values.
"""
if value == "NaN" or \
value is None or \
(isinstance(value, float) and np.isnan(value)):
return pd.isnull(X)
else:
return X == value
class NumericalTransformer(TransformerMixin):
"""
Provides commonly used numerical transformers.
"""
SUPPORTED_FUNCTIONS = ['log', 'log1p']
def __init__(self, func):
"""
Params
func function to apply to input columns. The function will be
applied to each value. Supported functions are defined
in SUPPORTED_FUNCTIONS variable. Throws assertion error if the
not supported.
"""
warnings.warn("""
NumericalTransformer will be deprecated in 3.0 version.
Please use Sklearn.base.TransformerMixin to write
customer transformers
""", DeprecationWarning)
assert func in self.SUPPORTED_FUNCTIONS, \
f"Only following func are supported: {self.SUPPORTED_FUNCTIONS}"
super(NumericalTransformer, self).__init__()
self.__func = func
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
if self.__func == 'log1p':
return np.vectorize(np.log1p)(X)
elif self.__func == 'log':
return np.vectorize(np.log)(X)
raise ValueError(f"Invalid function name: {self.__func}")
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