minor/test.py at main · PatibandlaRithvik/minor · 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
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
131
132
133
134
135
136
137
138
139
#https://github.com/etas/SynCAN/blob/master/train_2.zip
import numpy as np
import pandas as pd
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from genetic_selection import GeneticSelectionCV
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix

import SwarmPackagePy
from SwarmPackagePy import testFunctions as tf

def nearest_spider(spider, spiders):

        spudis = list(spiders)

        try:
            pos = spudis.index(spider)
            spudis.pop(pos)
        except ValueError:
            pass

        dists = np.array([np.linalg.norm(spider - s) for s in spudis])
        m = dists.argmin()
        d = dists[m]

        return d, m

def main():
    train = pd.read_csv('dataset/CAN.csv',nrows=14000)
    train.fillna(0,inplace=True)
    print(train)
    print(train.shape)

    le = LabelEncoder()
    train['ID'] = pd.Series(le.fit_transform(train['ID']))
    print(train)

    X = train.values[:, 1:7]
    Y = train.values[:, 0]
    print(Y)
    #X = tf.easom_function(X[0].astype(int))
    #print(X)
    alh = SwarmPackagePy.ssa(10, tf.easom_function, -10, 6, 2, 20,0.4)
    print(nearest_spider(0, X[0]))
    print(nearest_spider(1, X[1]))
    print(nearest_spider(2, X[2]))
    print(nearest_spider(3, X[3]))

    '''
    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)
    estimator = svm.SVC(C=2.0,gamma='scale',kernel = 'rbf', random_state = 0)
    estimator.fit(X_train, y_train)
    y_pred = estimator.predict(X_test)
    hr = accuracy_score(y_test,y_pred)*100
    mr = precision_score(y_test, y_pred,average='macro') * 100
    fr = recall_score(y_test, y_pred,average='macro') * 100
    cr = f1_score(y_test, y_pred,average='macro') * 100
    print(str(hr)+" "+str(mr)+" "+str(fr)+" "+str(cr))
    tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
    print(str(fp)+" "+str(fn))

    X = train.values[:, 3:7]
    Y = train.values[:, 0]
    print(Y)


    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

    estimator = KNeighborsClassifier()
    estimator.fit(X_train, y_train)
    y_pred = estimator.predict(X_test)
    hr = accuracy_score(y_test,y_pred)*100
    mr = precision_score(y_test, y_pred,average='macro') * 100
    fr = recall_score(y_test, y_pred,average='macro') * 100
    cr = f1_score(y_test, y_pred,average='macro') * 100
    print(str(hr)+" "+str(mr)+" "+str(fr)+" "+str(cr))
    tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
    print(str(fp)+" "+str(fn))

    X = train.values[:, 4:7]
    Y = train.values[:, 0]
    print(Y)


    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)
    estimator = DecisionTreeClassifier(max_features=2)
    estimator.fit(X_train, y_train)
    y_pred = estimator.predict(X_test)
    hr = accuracy_score(y_test,y_pred)*100
    mr = precision_score(y_test, y_pred,average='macro') * 100
    fr = recall_score(y_test, y_pred,average='macro') * 100
    cr = f1_score(y_test, y_pred,average='macro') * 100
    print(str(hr)+" "+str(mr)+" "+str(fr)+" "+str(cr))
    tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
    print(str(fp)+" "+str(fn))

    X = train.values[:, 1:7]
    Y = train.values[:, 0]
    print(Y)


    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

    estimator = svm.SVC(C=2.0,gamma='scale',kernel = 'rbf', random_state = 0)
    selector = GeneticSelectionCV(estimator,
                                  cv=5,
                                  verbose=1,
                                  scoring="accuracy",
                                  max_features=6,
                                  n_population=5,
                                  crossover_proba=0.5,
                                  mutation_proba=0.2,
                                  n_generations=5,
                                  crossover_independent_proba=0.5,
                                  mutation_independent_proba=0.05,
                                  tournament_size=3,
                                  n_gen_no_change=2,
                                  caching=True,
                                  n_jobs=-1)
    selector = selector.fit(X_train, y_train)
    y_pred = selector.predict(X_test)
    hr = accuracy_score(y_pred,y_pred)*100
    mr = precision_score(y_pred, y_pred,average='macro') * 100
    fr = recall_score(y_pred, y_pred,average='macro') * 100
    cr = f1_score(y_pred, y_pred,average='macro') * 100
    print(str(hr)+" "+str(mr)+" "+str(fr)+" "+str(cr))
    tn, fp, fn, tp = confusion_matrix(y_pred, y_pred).ravel()
    print(str(fp)+" "+str(fn))
'''
if __name__ == "__main__":
    main()