# 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)