--- /dev/null
+# 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)
Code Review / thoth.git / blobdiff