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
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
|
# pylint: disable=C0103, C0116, W0621, E0401, W0104, W0105, R0913, E1136, W0612, E0102, C0301, W0611, C0411, W0311, C0326, C0330, W0106, C0412
# -*- coding: utf-8 -*-
"""Decision_Tree.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1TdQCHMWu8lPA53-jFhxXDUPQdjqufrL1
Contributors: **Rohit Singh Rathaur, Girish L.**
Copyright [2021](2021) [*Rohit Singh Rathaur, BIT Mesra and Girish L., CIT GUBBI, Karnataka*]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
We mounted the drive to access the data
"""
import sklearn.metrics as metrics
from sklearn.metrics import classification_report
import seaborn as sns
from sklearn import tree
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import tensorflow as tf
from google.colab import drive
drive.mount('/content/drive')
"""We are importing libraries to read the CSV and to train the models"""
# Importing libraries
"""We are reading CSV file using `read_csv` function and dropping the `Timestamp` column and storing it in a DataFrame called `df_Ellis`."""
df_Ellis = pd.read_csv(
"/content/drive/MyDrive/Failure/lstm/Ellis_FinalTwoConditionwithOR.csv")
df_Ellis = df_Ellis.drop(columns='Timestamp')
df_Ellis
"""First we stored the `feature_cols` and defined the `X` matrix and `y` vector where `X` is a matrix and containing all the feature matrix and `y` is a vector which is having target value."""
# define X and y
feature_cols = [
'ellis-cpu.wait_perc',
'ellis-load.avg_1_min',
'ellis-net.in_bytes_sec',
'ellis-cpu.system_perc',
'ellis-mem.free_mb']
# X is a matrix, hence we use [] to access the features we want in feature_cols
X = df_Ellis[feature_cols]
# y is a vector, hence we use dot to access 'label'
y = df_Ellis.Label
"""We splitted `X` and `y` into `X_train`, `X_test`, `y_train`, and `y_test` using `train_test_split` function."""
# split X and y into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.30, random_state=5)
"""We are training the model with Decision Tree."""
# train a logistic regression model on the training set
# instantiate model
logreg = tree.DecisionTreeClassifier()
# fit model
logreg.fit(X_train, y_train)
"""We are making predictions for test set"""
# make class predictions for the testing set
y_pred_class = logreg.predict(X_test)
"""Here, we are calculating the accuracy using `sklearn` library"""
# calculate accuracy
print(metrics.accuracy_score(y_test, y_pred_class))
"""We are examining the class distribution of the testing set using a `pandas` series method"""
# examine the class distribution of the testing set (using a Pandas Series
# method)
y_test.value_counts()
"""We counted the value for each lables"""
y_train.value_counts()
"""We are calculating the percentage of ones because `y_test` only contains ones and zeroes, we can simply calculate the mean = percentage of ones"""
# calculate the percentage of ones
# because y_test only contains ones and zeros, we can simply calculate the
# mean = percentage of ones
y_test.mean()
"""We are calculating the percentage of zeros"""
# calculate the percentage of zeros
1 - y_test.mean()
# calculate null accuracy in a single line of code
# only for binary classification problems coded as 0/1
max(y_test.mean(), 1 - y_test.mean())
# calculate null accuracy (for multi-class classification problems)
y_test.value_counts().head(1) / len(y_test)
# print the first 25 true and predicted responses
print('True:', y_test.values[0:50])
print('False:', y_pred_class[0:50])
# IMPORTANT: first argument is true values, second argument is predicted values
# this produces a 2x2 numpy array (matrix)
print(metrics.confusion_matrix(y_test, y_pred_class))
# save confusion matrix and slice into four pieces
confusion = metrics.confusion_matrix(y_test, y_pred_class)
print(confusion)
#[row, column]
TP = confusion[1, 1]
TN = confusion[0, 0]
FP = confusion[0, 1]
FN = confusion[1, 0]
# use float to perform true division, not integer division
print((TP + TN) / float(TP + TN + FP + FN))
print(metrics.accuracy_score(y_test, y_pred_class))
"""We are defining a function `print_results` to print the result of `y_test` and `y_pred`."""
def print_results(y_test, y_pred):
# f1-score
f1 = metrics.f1_score(y_test, y_pred)
print("F1 Score: ", f1)
print(classification_report(y_test, y_pred))
conf_matrix = metrics.confusion_matrix(y_test, y_pred)
plt.figure(figsize=(12, 12))
plt.subplot(221)
sns.heatmap(conf_matrix, fmt="d", annot=True, cmap='Blues')
b, t = plt.ylim()
plt.ylim(b + 0.5, t - 0.5)
plt.title('Confuion Matrix')
plt.ylabel('True Values')
plt.xlabel('Predicted Values')
# roc_auc_score
model_roc_auc = metrics.roc_auc_score(y_test, y_pred)
print("Area under curve : ", model_roc_auc, "\n")
fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred)
gmeans = np.sqrt(tpr * (1 - fpr))
ix = np.argmax(gmeans)
threshold = np.round(thresholds[ix], 3)
plt.subplot(222)
plt.plot(
fpr,
tpr,
color='darkorange',
lw=1,
label="Auc : %.3f" %
model_roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.scatter(
fpr[ix],
tpr[ix],
marker='o',
color='black',
label='Best Threshold:' +
str(threshold))
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
print_results(y_test, y_pred_class)
|
