-
Notifications
You must be signed in to change notification settings - Fork 0
/
features_selection.py
63 lines (60 loc) · 2.34 KB
/
features_selection.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
import pandas as pd
from pathlib import Path
import seaborn as sns
import matplotlib.pyplot as plt
import argparse
data_file = Path("data/data.csv")
threshold = 0.9
target_col = 1
non_informative_col = [0, 11, 13, 16, 20, 21]
output_file = Path("data/data_cleaned.csv")
parser = argparse.ArgumentParser()
parser.add_argument("-v", "--verbose", type=int, choices=[0, 1, 2], default=1, help="Verbosity level")
args = parser.parse_args()
target_col_l = str(target_col) + "_l"
df = pd.read_csv(data_file, header=None, names=list(range(32)))
df[target_col] = pd.Categorical(df[target_col])
df[target_col_l] = df[target_col].cat.codes
corr = df.drop(columns=target_col).corr()
feature_corr = corr.drop(target_col_l)
feature_corr = feature_corr.drop(target_col_l, axis=1)
target_corr = corr[target_col_l].drop(target_col_l)
corr_list = []
for i, col in feature_corr.iteritems():
for j, val in col.iteritems():
if j >= i:
feature_corr[i][j] = 0
else:
corr_list.append(feature_corr[i][j])
top_cor = feature_corr.unstack().sort_values(ascending=False)
top_cor = top_cor[top_cor > threshold]
col2del = non_informative_col
for elm in top_cor.index:
if elm[0] not in col2del and elm[1] not in col2del:
if target_corr[elm[0]] < target_corr[elm[1]]:
col2del.append(elm[0])
else:
col2del.append(elm[1])
if args.verbose > 0:
print("Feature correlation to the target:\n", target_corr)
print("Correlated features:\n", top_cor)
print("List of column that should be deleted: {}".format(col2del))
if args.verbose > 1:
fig1 = plt.figure()
target_corr.sort_values().plot.bar()
plt.title("Features correlation to the target")
plt.xlabel("Features")
plt.ylabel("Correlation coeff value")
tmp = pd.DataFrame(corr_list)
tmp.plot.hist()
plt.title("Features cross correlation coefficient")
plt.xlabel("Correlation coeff value")
fig2 = plt.figure()
sns.heatmap(feature_corr, square=True, linewidths=0.5, linecolor="Black", fmt=".1f", annot=True, cbar_kws={"shrink":0.70}, vmax=1, center=0, vmin=-1, cmap="PiYG")
plt.title("Cross correlation matrix")
plt.show()
df_out = df.drop(columns=col2del)
df_out = df_out.drop(columns=target_col_l)
df_out.to_csv(output_file, sep=',', index=False)
if args.verbose > 0:
print("cleaned data saved to '{}'".format(output_file))