Feature-Merge/360_mul_algorithm.py at master · crystal-tensor/Feature-Merge · GitHub

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
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import accuracy_score, log_loss
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression

#data_test = pd.read_csv("F:\\金融算法挑战\\360金融算法挑战赛\\open_data_train_valid\\valid.txt", sep='\t')
df1 = pd.read_csv("data/train_1.txt", sep='\t')
aa = list(df1.columns.values)[0:6749]
df2 = pd.read_csv("data/train_2.txt", sep='\t', names=aa)
df3 = pd.read_csv("data/train_3.txt", sep='\t', names=aa)
df4 = pd.read_csv("data/train_4.txt", sep='\t', names=aa)
df5 = pd.read_csv("data/train_5.txt", sep='\t', names=aa)

#data_train.head(5)
frames = [df1, df2, df3, df4, df5]
#frames = [df1]
data_train = pd.concat(frames)
data_train.info()

bb=data_train.iloc[:, 4:6749]
#dd=data_train.iloc[:, 3:4]
xx = bb.apply(lambda x: x.fillna(x.mean()), axis=0)
yy = data_train.iloc[:, 3:4]
# #

classifiers = [
    KNeighborsClassifier(3),
    SVC(probability=True),
    DecisionTreeClassifier(),
    RandomForestClassifier(),
    AdaBoostClassifier(),
    GradientBoostingClassifier(),
    GaussianNB(),
    LinearDiscriminantAnalysis(),
    QuadraticDiscriminantAnalysis(),
    LogisticRegression()]

log_cols = ["Classifier", "Accuracy"]
log = pd.DataFrame(columns=log_cols)

sss = StratifiedShuffleSplit(n_splits=12, test_size=0.1, random_state=0)

xx = data_train.iloc[:, 4:6749].apply(lambda x: x.fillna(x.mean()), axis=0)
yy = data_train.iloc[:, 3:4]
trainx = xx.values
trainy = yy.values
X = trainx[0::, 1::]
y = trainy[0::, 0]


#
print(X[0:, 0:10])

acc_dict = {}

for train_index, test_index in sss.split(X, y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

    for clf in classifiers:
        name = clf.__class__.__name__
        clf.fit(X_train, y_train)
        train_predictions = clf.predict(X_test)
        acc = accuracy_score(y_test, train_predictions)
        if name in acc_dict:
            acc_dict[name] += acc
        else:
            acc_dict[name] = acc
        print('Accuracy=', acc)

for clf in acc_dict:
    acc_dict[clf] = acc_dict[clf] / 10.0
    log_entry = pd.DataFrame([[clf, acc_dict[clf]]], columns=log_cols)
    log = log.append(log_entry)
print('log=', log)