-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathClassifiersTest.py
168 lines (129 loc) · 5.29 KB
/
ClassifiersTest.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
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
import time
import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
import matplotlib
matplotlib.use('TKAgg')
from matplotlib import pyplot
import matplotlib.pyplot as plt
from sklearn.metrics import auc, roc_curve
from sklearn import metrics, svm
from sklearn.metrics import precision_recall_fscore_support
import pandas as pd
from sklearn import preprocessing
import warnings
warnings.filterwarnings('ignore')
from sklearn.utils import shuffle
from sklearn import tree
from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
################################################################################
def dataProc(X1,y1):
X1 = pd.DataFrame(X1)
X1 = X1.fillna(X1.mean())
X1=X1.to_numpy()
y1 = y1.to_numpy()
min_max_scaler = preprocessing.MinMaxScaler()
X1 = min_max_scaler.fit_transform(X1)
X1, y1 = shuffle(X1, y1, random_state=0)
return X1,y1
def evaluateClassifier (clf, Xt, yt):
accuracy = []
f1 = []
precision = []
recall = []
falsePositives = []
for i in range(10):
y_pred = clf.predict(Xt[i])
CM = confusion_matrix(yt[i], y_pred)
TN = CM[0][0]
FN = CM[1][0]
TP = CM[1][1]
FP = CM[0][1]
accuracy.append( ((TP+TN)/(TP+FN+TN+FP)) )
precision.append( (TP/(TP+FP)) )
recall.append( (TP/(TP+FN)) )
f1.append ( ( (2*precision[i]*recall[i])/(precision[i]+recall[i]) ) )
falsePositives.append( (FP/(TN+FP)) )
return accuracy, f1, precision, recall, falsePositives
#################################################################################
sysName = "IEEE57"
#sysName2 = "IEEE57_2"
testType = "ClassifiersTest"
X = []
Xv = []
Xt = []
y = []
yv = []
yt = []
for i in range(1,11,1):
X.append(pd.read_csv("Data/{}/{}Data_10k_{}Sparsity.csv".format(sysName, sysName, i/10 ), header=None))
Xt.append(pd.read_csv("Data/{}/{}Data_2k_{}Sparsity.csv".format(sysName, sysName, i/10 ), header=None))
y.append(pd.read_csv("Data/{}/{}Labels_10k_{}Sparsity.csv".format(sysName, sysName, i/10 ), header=None))
yt.append(pd.read_csv("Data/{}/{}Labels_2k_{}Sparsity.csv".format(sysName, sysName, i/10 ), header=None))
X[i-1],y[i-1] = dataProc(X[i-1],y[i-1])
Xt[i-1],yt[i-1] = dataProc(Xt[i-1],yt[i-1])
numFeatures = len(X[0])
X_train = np.concatenate((X[0],X[1]), axis=0)
X_test = np.concatenate((Xt[0],Xt[1]), axis=0)
y_train = np.concatenate((y[0],y[1]), axis=0)
y_test = np.concatenate((yt[0],yt[1]), axis=0)
for i in range(2,10,1):
X_train = np.concatenate((X_train,X[i]), axis=0)
X_test = np.concatenate((X_test,Xt[i]), axis=0)
y_train = np.concatenate((y_train,y[i]), axis=0)
y_test = np.concatenate((y_test,yt[i]), axis=0)
X_train, y_train = shuffle(X_train, y_train, random_state=0)
X_test, y_test = shuffle(X_test, y_test, random_state=0)
#######################################################################################
#Classifiers
gnb = GaussianNB()
knn = KNeighborsClassifier(n_neighbors=20)
dtc = tree.DecisionTreeClassifier()
print("Initializing training")
#Training Classifiers
time1= time.time()
dtc.fit(X_train,y_train)
time2 = time.time()
dctTime = time2 - time1;
print("DT DONE")
result_dct, f1_dct, precision_dct, recall_dct, fp_dct = evaluateClassifier(dtc, Xt,yt)
time1 = time.time()
knn.fit(X_train,y_train)
time2 = time.time()
knnTime = time2 - time1;
print("KNN DONE")
result_knn, f1_knn, precision_knn, recall_knn, fp_knn = evaluateClassifier(knn, Xt,yt)
time1 = time.time()
gnb.fit(X_train,y_train)
time2 = time.time()
gnbTime = time2 - time1;
print("GNB DONE")
result_gnb, f1_gnb, precision_gnb, recall_gnb, fp_gnb = evaluateClassifier(gnb, Xt,yt)
finalTimes = [dctTime,
knnTime,
gnbTime,
]
finalModels = ['DCT',
'KNN',
'NB',
]
sparsity = np.arange(0.1,1.1,0.1)
################ DATA OUTPUT (Saving in Excel) ###############
# Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd.ExcelWriter('output/RESULTS_'+sysName+'_'+testType+'.xlsx', engine='xlsxwriter') #CHANGE THE NAME OF THE OUTPUT EXCEL FILE HERE
Results_dct = pd.DataFrame({'Sparsity': sparsity, 'Accuracy': result_dct, 'F1 score': f1_dct, 'Precision': precision_dct, 'Recall': recall_dct, 'False Positive Rate': fp_dct})
Results_gnb = pd.DataFrame({'Sparsity': sparsity, 'Accuracy': result_gnb, 'F1 score': f1_gnb, 'Precision': precision_gnb, 'Recall': recall_gnb, 'False Positive Rate': fp_gnb})
Results_knn = pd.DataFrame({'Sparsity': sparsity, 'Accuracy': result_knn, 'F1 score': f1_knn, 'Precision': precision_knn, 'Recall': recall_knn, 'False Positive Rate': fp_knn})
Results_times = pd.DataFrame({'Model': finalModels, 'Training Time': finalTimes})
# Convert the dataframe to an XlsxWriter Excel object.
Results_dct.to_excel(writer, sheet_name="DCT")
Results_gnb.to_excel(writer, sheet_name="GNB")
Results_knn.to_excel(writer, sheet_name="KNN")
Results_times.to_excel(writer, sheet_name="Training Time")
# Close the Pandas Excel writer and output the Excel file.
writer.save()
print("PROGRAM IS COMPLETE !!!!! ")