From 0aec5cd837af4503605707a560687a2b5b106fd4 Mon Sep 17 00:00:00 2001 From: RohitRathore1 Date: Thu, 19 May 2022 17:29:04 +0530 Subject: Python_Code: Added python code after running pylint This patch contains required python code Removed vims_visulization and a duplicate between lstm_attention Signed-off-by: Rohit Singh Rathaur Change-Id: I4a9b70a186498b24ba258a6ac303c827d18a4765 --- .../failure_prediction/python/featurecreation.py | 114 +++++++++++++++++++++ 1 file changed, 114 insertions(+) create mode 100644 models/failure_prediction/python/featurecreation.py (limited to 'models/failure_prediction/python/featurecreation.py') diff --git a/models/failure_prediction/python/featurecreation.py b/models/failure_prediction/python/featurecreation.py new file mode 100644 index 0000000..7ed5cf3 --- /dev/null +++ b/models/failure_prediction/python/featurecreation.py @@ -0,0 +1,114 @@ +# pylint: disable=C0103, C0116, W0621, E0401, W0104, W0105, R0913, E1136, W0612, E0102, C0301, W0611, C0411, W0311, C0326, C0330, W0106, C0412 +# -*- coding: utf-8 -*- +"""FeatureCreation.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1UQzgn71tYU7WHgr-CL1CRNM9q9Ajr2Kx + +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. +""" + +# Commented out IPython magic to ensure Python compatibility. +# Import libraries use for visualization and analysis +import pandas as pd +import numpy as np + +# %matplotlib inline +import matplotlib +import matplotlib.pyplot as plt + +from pandas import Series, DataFrame +import seaborn as sns +from sklearn.preprocessing import scale +from sklearn.decomposition import PCA +from sklearn.discriminant_analysis import LinearDiscriminantAnalysis +from scipy import stats +from IPython.display import display, HTML + +from google.colab import drive +drive.mount('/gdrive') + +"""# **Loading the Data**""" + +df_Ellis = pd.read_csv( + "/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/Final.csv") +#df_Bono = pd.read_csv("/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Bono.csv", error_bad_lines=False) +#df_Sprout = pd.read_csv("/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Sprout.csv", error_bad_lines=False) +#df_Homer = pd.read_csv("/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Homer.csv", error_bad_lines=False) +#df_Homestead = pd.read_csv("/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Homestead.csv", error_bad_lines=False) +#df_Ralf = pd.read_csv("/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Ralf.csv", error_bad_lines=False) + +df_Ellis.head() + +df_Ellis.describe() + +#df_Ellis['SLO1'] = 0 +#print('Column names are: ',list(df_Ellis.columns)) + +df4 = df_Ellis["ellis-load.avg_1_min"] > 2.45 +df4 +df4.to_csv( + '/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/EllisLoadAvgLabel_lessthan0198.csv') +df4.head(50) + +df3 = df_Ellis["ellis-cpu.wait_perc"] > 5 +df3 +df3.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/ellis-cpu>5.csv') +df3.head(50) + +df5 = df_Ellis["ellis-net.out_packets_sec"] > 1000 +df5 +df5.to_csv( + '/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/ellis-net.in_bytes_sec21139.csv') +df5.head(50) + +# We are applying Logical OR Operator between df4 and df3 +df6 = (df4[0:176999]) | (df3[0:176999]) +df6.head(50) + +df6.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/OR_TwoCondition(2).csv') +df6.head(50) + +df7 = (df6[0:176999]) | (df5[0:176999]) +df7.head(50) + +df7.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/FinalORLabel8.5.csv') +df7.head(50) + +df_Ellis.insert(7, "Label", df7) + +#df_Ellis.insert (8, "Label", df7) + +# We applied Logical OR operator in two features only known as and df3 +# and df4 and stored result in df6 which is known as Final Label after +# applying OR condition +df_Ellis +df_Ellis.to_csv( + '/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/Ellis_FinalTwoConditionwithOR.csv') + +df_Ellis.head(100) + +# pandas count distinct values in column +df_Ellis['Label'].value_counts() + +#final.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/New/FinalLabel.csv') + +#df_Ellis.loc[(df_Ellis["ellis-cpu.wait_perc"] > 5) & (df_Ellis["ellis-load.avg_1_min"] > 2)] + +"""# **Creating New Features**""" -- cgit 1.2.3-korg