-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathMetadata.py
130 lines (105 loc) · 5.01 KB
/
Metadata.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
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
import numpy as np
import pandas as pd
test_dataframe = pd.DataFrame({'float64': [1.0, 3.0, 4, -5, 6],
'float64_null': [1.0, -3.0, np.nan, np.inf, None],
'int64': [1, 2, 3, -5, 6],
'int64_null_const': [1, -2, np.nan, np.inf, None],
'int64_const': [1, 1, 1, 1, 1],
'int64_null_const': [1, 1, np.nan, np.inf, None],
'bool': [True, False, True, False, True],
'bool_null': [True, False, np.nan, np.inf, None],
'datetime64': [pd.Timestamp('20180310'), pd.Timestamp('20190310'), pd.Timestamp('20200310'),
pd.Timestamp('20210310'), pd.Timestamp('20220310')],
'datetime64_time': [pd.Timestamp('20180310 13:00:15'), pd.Timestamp('20190310 13:00:15'),
pd.Timestamp('20200310 13:00:15'), pd.Timestamp('20210310 12:00'),
pd.Timestamp('20220310 12:00')],
'datetime64_null': [pd.Timestamp('20180310'), pd.Timestamp('20190310'), np.nan, np.nan, None],
'object': ['foo','buzz', 'buzz','buzz', 'buzz'],
'object_null': ['foo','buzz', np.nan, np.inf, None],
'nans_inf_nones': [np.nan, np.inf, None, np.nan, None],
'nans_nones': [np.nan, np.nan, None, np.nan, None],
'nones': [None, None, None, None, None]
})
catCol = np.array(list('ABCD'))[np.random.randint(4, size=100)]
catCol2 = pd.Categorical(catCol)
numCol = np.random.random(size=100)
intCol = np.random.randint(5, size=100)
intCol2 = np.random.randint(30, size=100)
datCol = pd.date_range(
'2018-01-01', '2018-01-31')[np.random.randint(31, size=100)]
boolCol = np.array(list([True, False]))[np.random.randint(2, size=100)]
constCol = np.ones(100)
test_dataframe2 = pd.DataFrame({
'catCol': catCol,
'catCol2': catCol2,
'intCol': intCol,
'intCol2': intCol2,
'numCol': numCol,
'datCol': datCol,
'boolCol': boolCol,
'constCol_longlonglonglonglonglongName': constCol
})
def remove_all_by_values(list_obj, values):
list_obj_rem = list_obj.copy()
for value in set(values):
while value in list_obj_rem:
list_obj_rem.remove(value)
return list_obj_rem
def dataframe_metadata(data):
if isinstance(data, pd.DataFrame):
master = pd.DataFrame()
for column in data.columns:
datatype = str(data.dtypes[column])
total = data.shape[0]
null = data[column].isna().sum()
non_null = total - null
proc_null = round((null/total)*100, 2)
unique = len(data[column].unique())
name_count = column + '_count'
val = data.groupby(column, dropna = False).size().reset_index(name = name_count)
val.sort_values(name_count, ascending = False, inplace = True)
val_list = val[column].to_list()
max_elem = 20
n_elem = len(val_list)
min_elem = min(max_elem, n_elem)
data_list = list()
for index in range(0, min_elem):
data_list.append(val_list[index])
val_str = str(data_list)
data_dict = {'name': [column], 'type': [datatype],
'total': [total], 'non_null': [non_null], 'null': [null], 'proc_null': [proc_null],
'unique': [unique],
'data': val_str
}
row = pd.DataFrame.from_dict(data_dict)
master = pd.concat([master, row])
master = master.reset_index(drop = True)
return(master)
else:
return None
def calculate_woe_iv(dataset, feature, target):
if isinstance(dataset, pd.DataFrame):
lst = []
values = dataset[feature].unique()
for count, value in enumerate(values):
lst.append({
'Name' : feature,
'Value': value,
'Total': dataset[dataset[feature] == value].shape[0],
'Good': dataset[(dataset[feature] == value) & (dataset[target] == 0)].shape[0],
'Bad': dataset[(dataset[feature] == value) & (dataset[target] == 1)].shape[0]
})
dset = pd.DataFrame(lst)
dset['Share_Total'] = dset['Total'] / dset['Total'].sum()
dset['Share_Good'] = dset['Good'] / dset['Total']
dset['Share_Bad'] = dset['Bad'] / dset['Total']
dset['Distr_Good'] = dset['Good'] / dset['Good'].sum()
dset['Distr_Bad'] = dset['Bad'] / dset['Bad'].sum()
dset['WoE'] = np.log(dset['Distr_Good'] / dset['Distr_Bad'])
dset = dset.replace({'WoE': {np.inf: 0, -np.inf: 0}})
dset['IV'] = (dset['Distr_Good'] - dset['Distr_Bad']) * dset['WoE']
iv = dset['IV'].sum()
dset = dset.sort_values(by='WoE')
return dset, iv
else:
return None