-
Notifications
You must be signed in to change notification settings - Fork 78
/
preprocessing.py
executable file
·110 lines (85 loc) · 3.52 KB
/
preprocessing.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
#!/usr/bin/env python
import numpy as np
import pandas as pd
from pandas_ml.core.accessor import _AccessorMethods, _attach_methods
from pandas_ml.compat import _SKLEARN_INSTALLED, _SKLEARN_ge_017
if _SKLEARN_INSTALLED:
import sklearn.preprocessing as pp
if _SKLEARN_ge_017:
_keep_col_classes = [pp.Binarizer,
pp.FunctionTransformer,
pp.Imputer,
pp.KernelCenterer,
pp.LabelEncoder,
pp.MaxAbsScaler,
pp.MinMaxScaler,
pp.Normalizer,
pp.RobustScaler,
pp.StandardScaler]
else:
_keep_col_classes = [pp.Binarizer,
pp.Imputer,
pp.KernelCenterer,
pp.LabelEncoder,
pp.MinMaxScaler,
pp.Normalizer,
pp.StandardScaler]
else:
_keep_col_classes = []
class PreprocessingMethods(_AccessorMethods):
"""
Accessor to ``sklearn.preprocessing``.
"""
_module_name = 'sklearn.preprocessing'
def _keep_existing_columns(self, estimator):
"""
Check whether estimator should preserve existing column names
"""
return estimator.__class__ in _keep_col_classes
def add_dummy_feature(self, value=1.0):
"""
Call ``sklearn.preprocessing.add_dummy_feature`` using automatic mapping.
- ``X``: ``ModelFrame.data``
"""
from pandas_ml.core.series import ModelSeries
from pandas_ml.core.frame import ModelFrame
func = self._module.add_dummy_feature
if isinstance(self._df, ModelSeries):
data = self._df.to_frame()
constructor = ModelFrame
else:
data = self._data
constructor = self._constructor
result = func(data.values, value=value)
result = constructor(result, index=data.index)
columns = result.columns[:-len(data.columns)].append(data.columns)
result.columns = columns
return result
_preprocessing_methods = ['binarize', 'normalize', 'scale']
def _wrap_func(func, func_name):
def f(self, *args, **kwargs):
from pandas_ml.core.frame import ModelFrame
if isinstance(self._df, ModelFrame):
values = self._data.values
if pd.core.common.is_integer_dtype(values):
# integer raises an error in normalize
values = values.astype(np.float)
result = func(values, *args, **kwargs)
result = self._constructor(result, index=self._data.index,
columns=self._data.columns)
else:
# ModelSeries
values = np.atleast_2d(self._df.values)
if pd.core.common.is_integer_dtype(values):
values = values.astype(np.float)
result = func(values, *args, **kwargs)
result = self._constructor(result[0], index=self._df.index,
name=self._df.name)
return result
f.__doc__ = (
"""
Call ``%s`` using automatic mapping.
- ``X``: ``ModelFrame.data``
""" % func_name)
return f
_attach_methods(PreprocessingMethods, _wrap_func, _preprocessing_methods)