-
Notifications
You must be signed in to change notification settings - Fork 0
/
model.py
164 lines (142 loc) · 5.91 KB
/
model.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
from sklearn import preprocessing,metrics
from sklearn.svm import OneClassSVM
import matplotlib.pyplot as plt
import custom_function as cf
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
from win_preprocess import processData
from lin_preprocess import processLData
def collection_values_to_array(dataset):
dataset = np.array(dataset)
new_dataset = []
for row in dataset:
row_array = np.array(eval(row[0]))
new_dataset.append(row_array)
return np.array(new_dataset)
def print_accuracy(title, datasetY, predictions):
print(title)
print datasetY.shape
print predictions.shape
print("accuracy: ", metrics.accuracy_score(datasetY, predictions))
print("precision: ", metrics.precision_score(datasetY, predictions))
print("recall: ", metrics.recall_score(datasetY, predictions))
print("f1: ", metrics.f1_score(datasetY, predictions))
print("area under curve (auc): ", metrics.roc_auc_score(datasetY, predictions))
def applySVM():
dftrain, dftest = load_features()
dftrain = dftrain.fillna(0)
dftest = dftest.fillna(0)
data_train = dftrain.drop(["eventTime"], axis=1)
data_test = dftest.drop(["eventTime"], axis=1)
data_train = data_train[(data_train.T != 0).any()]
data_test = data_test[(data_test.T != 0).any()]
min_max_scaler = preprocessing.StandardScaler()
np_scaled_train = min_max_scaler.fit_transform(data_train)
np_scaled_test = min_max_scaler.fit_transform(data_test)
clf = OneClassSVM(nu=0.01,kernel='rbf',verbose=True,gamma=0.1,random_state=False)
clf.fit(np_scaled_train)
y_pred_train = clf.predict(np_scaled_train)
y_pred_test = clf.predict(np_scaled_test)
n_error_test = y_pred_test[y_pred_test == -1].size
#collect all the anomalous points
for index, rows in data_test.iterrows():
if y_pred_test[index] == -1:
data_test.iloc[index].to_csv("data/anomalous.csv")
# Visualize
plt.title("Novelty Detection")
plt.figure(1)
plt.subplot(211)
plt.plot(np_scaled_train, 'bo', np_scaled_test, 'y^')
plt.subplot(212)
plt.plot(y_pred_train, 'go', y_pred_test, 'r^')
plt.ylabel("Labels")
plt.xlabel(
"Anomalies in test set: %s;"
% (str(n_error_test*100/data_test.shape[0])+"%"))
plt.show()
def applyPCAModel():
dftrain, dftest = load_features()
dftrain = dftrain.fillna(0)
data_train = dftrain.drop(["eventTime"], axis=1)
# print ("X_train: ",data.head(5))
min_max_scaler = preprocessing.StandardScaler()
np_scaled = min_max_scaler.fit_transform(data_train)
data_train = pd.DataFrame(np_scaled)
# reduce to 2 importants features
pca = PCA(n_components=2)
data_train = pca.fit_transform(data_train)
# standardize these 2 new features
min_max_scaler = preprocessing.StandardScaler()
np_scaled = min_max_scaler.fit_transform(data_train)
data_train = pd.DataFrame(np_scaled)
#After processing the data fit the model for our testing logs
print ("find the best value of clusters using elbow loss curve")
n_cluster = range(1, 20)
kmeans = [KMeans(n_clusters=i).fit(data_train) for i in n_cluster]
scores = [kmeans[i].score(data_train) for i in range(len(kmeans))]
fig, ax = plt.subplots()
ax.plot(n_cluster, scores)
plt.show()
dftrain['cluster'] = kmeans[10].predict(data_train)
dftrain['principal_feature1'] = data_train[0]
dftrain['principal_feature2'] = data_train[1]
print (dftrain['cluster'].value_counts())
fig, ax = plt.subplots()
colors = {0: 'red', 1: 'blue', 2: 'green', 3: 'pink', 4: 'black', 5: 'orange', 6: 'cyan', 7: 'yellow', 8: 'brown',
9: 'purple', 10: 'white', 11: 'grey', 12: 'lightblue', 13: 'lightgreen', 14: 'darkgrey'}
ax.scatter(dftrain['principal_feature1'], dftrain['principal_feature2'], c=dftrain["cluster"].apply(lambda x: colors[x]))
plt.show()
distance = cf.getDistanceByPoint(data_train, kmeans[10])
outlier_percentage = 0.01
number_of_outliers = int(outlier_percentage*len(distance))
threshold = distance.nlargest(number_of_outliers).min()
dftrain['anomaly_PCA'] = (distance >= threshold).astype(int)
print(dftrain['anomaly_PCA'].value_counts())
fig, ax = plt.subplots()
colors = {0: 'blue', 1: 'red'}
print ("the outliers in red")
ax.scatter(dftrain['principal_feature1'], dftrain['principal_feature2'], c=dftrain["anomaly_PCA"].apply(lambda x: colors[x]))
plt.show()
def create_and_save_win():
#creating the train feature vector
train_data = processData()
train_data.processLogs('train.log')
train_vec = train_data.dataframe
#creating the test feature vector
test_data = processData()
test_data.processLogs('test.log')
test_vec = test_data.dataframe
#save the test and train features
print ("Saving train and test feature vectors")
train_vec.to_csv('data/train_vec.csv', index=False)
test_vec.to_csv('data/test_vec.csv', index=False)
def create_and_save_lin():
#creating the train feature vector
train_data = processLData()
train_data.processLogs('train_lin.log')
train_vec = train_data.dataframe
#creating the test feature vector
test_data = processLData()
test_data.processLogs('test_lin.log')
test_vec = test_data.dataframe
#save the test and train features
print ("Saving train and test feature vectors")
train_vec.to_csv('data/train_vec.csv', index=False)
test_vec.to_csv('data/test_vec.csv', index=False)
def load_features():
print ("Loading train features")
train_features = pd.read_csv('data/train_vec.csv')
print ("Loaded train features")
print ("Loading test features")
test_features = pd.read_csv('data/test_vec.csv')
print ("Loaded test features")
return train_features, test_features
def predict_anomalies(algo):
algo = int(algo)
options = {
1 : applySVM,
2 : applyPCAModel,
}
options[algo]()