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Copy pathFinal Model HIVE.py
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Final Model HIVE.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
#Import libraries
import numpy as np
import numpy as np
import pandas as pd
import numpy as np
from sklearn.preprocessing import QuantileTransformer
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.datasets import load_breast_cancer
from numpy import int64
from sklearn import svm
from sklearn.svm import SVC
import matplotlib.pyplot as plt
from matplotlib import rcParams
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBClassifier
import xgboost
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import classification_report, accuracy_score
from sklearn.metrics import precision_score, recall_score
from sklearn.metrics import f1_score, matthews_corrcoef
from sklearn.metrics import confusion_matrix
from pyhive import hive
#from impala.dbapi import connect
from hdfs import InsecureClient
from pyhive import hive
import pandas as pd
from scipy import stats
from sklearn.utils import resample
import numpy as np
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import os # accessing directory structure
import pandas as pd # data processing
from pandas.plotting import scatter_matrix
#import library psycopyg2
import psycopg2
#import library pandas
import pandas as pd
#import library sqlio
import pandas.io.sql as sqlio
#from findspark import pyspark
from pyspark.sql import SparkSession
from pyspark.sql import SQLContext
# import the pandas
import pandas as pd
# from pyspark library import
# SparkSession
from pyspark.sql import SparkSession
from pyspark import SparkContext
import pyspark.sql
sc =SparkContext.getOrCreate()
#sc =SparkContext()
sqlContext = SQLContext(sc)
#from pyspark.sql import SQLContext
#sqlContext = SQLContext(sc)
# In[2]:
#from pyspark.context import SparkContext
#from pyspark.sql.session import SparkSession
#sc = SparkContext('local')
spark = SparkSession(sc)
# In[3]:
#hive
conn = hive.Connection(host='172.27.27.60', port=10000, password='Hive#Pa55', username='smicro', auth='CUSTOM')
dataset = pd.read_sql("SELECT * FROM sda_hdd_db.sda_hdd_all", conn)
##dataset = pd.read_sql("SELECT * FROM sda_hdd_db.ml_smart_data_view", conn)
#events1.disk_smartdata_dtls
# sda_hdd_db.ml_smart_data_view
#dataset = pd.read_sql("SELECT * FROM sda_hdd_db.sda_hdd_all limit 10", conn)
#dataset = pd.read_sql("SELECT * FROM sda_hdd_db.ml_smart_data_view limit 10", conn)
#dataset = pd.read_sql('''SELECT * FROM sda_hdd_db.ml_smart_data_view limit 10''', conn)
# In[4]:
from pyspark.sql import HiveContext
hive_context = HiveContext(sc)
#bank = hive_context.table("sda_hdd_db.ml_smart_data_view")
sqlContext = SQLContext(sc)
#bank1 = sqlContext.table("sda_hdd_db.ml_smart_data_view")
bank1 = sqlContext.table("sda_hdd_db.sda_hdd_all")
dataset = sqlContext.sql("SELECT * FROM sda_hdd_db.ml_smart_data_view")
dataset = sqlContext.sql("SELECT * FROM sda_hdd_db.sda_hdd_all")
# In[11]:
# FINAL DATASET
#dataset10 = pd.read_sql('''select process_date, serial_number, model from events1.disk_smartdata_dtls where extract (hour from process_date) || ":" || extract (minute from process_date) in ('1:0', '2:0', '3:0')''', conn)
#dataset10 = pd.read_sql('''select process_date, serial_number, model from events1.disk_smartdata_dtls where extract (minute from process_date) in ('0') limit 10''', conn)
dataset10 = pd.read_sql('''select process_date, serial_number, model from events1.disk_smartdata_dtls where extract (minute from process_date) in ('0')''', conn)
dataset10
# In[7]:
dataset10
# In[12]:
# loded table is not good
#dataframe = pd.read_csv('/home/smicro/dummy_data/sda_hdd_all.csv')
#dataframe.head()
##dataframe = pd.read_csv('/home/smicro/dummy_data/sda_hdd_all.csv', sep = '\t')
#dataframe = pd.read_csv('/home/smicro/dummy_data/sda_hdd_all.csv', sep = 't')
# In[13]:
#PRAS 111
#dataset = pd.read_sql("SELECT * FROM events1.disk_smartdata_dtls", conn)
#sda_hdd_db.sda_hdd_backblaze
#events1.disk_smartdata_dtls
#dataset.dtypes
# In[17]:
# data convertion in to float / int
#dataset['sda_hdd_backblaze.smart_252_raw'] = dataset['sda_hdd_backblaze.smart_252_raw'].fillna(0).astype(np.float64)
#dataset['sda_hdd_backblaze.smart_255_normalized'] = dataset['sda_hdd_backblaze.smart_255_normalized'].fillna(0).astype(np.float64)
#dataset['sda_hdd_backblaze.smart_252_raw'] = dataset['sda_hdd_backblaze.smart_252_raw'].fillna(0).astype(np.int64)
#dataset['disk_smartdata_dtls.smart_255_raw'] = dataset['disk_smartdata_dtls.smart_255_raw'].fillna(0).astype(np.int64)
#dataset['disk_smartdata_dtls.smart_254_normalized'] = dataset['disk_smartdata_dtls.smart_254_normalized'].fillna(0).astype(np.int64)
#dataset['disk_smartdata_dtls.smart_254_raw'] = dataset['disk_smartdata_dtls.smart_254_raw'].fillna(0).astype(np.int64)
#dataset['disk_smartdata_dtls.smart_255_normalized'] = dataset['disk_smartdata_dtls.smart_255_normalized'].fillna(0).astype(np.int64)
#dataset['disk_smartdata_dtls.smart_255_raw'] = dataset['disk_smartdata_dtls.smart_255_raw'].fillna(0).astype(np.int64)
# In[73]:
#final_colums1=dataset.describe()
#final_colums1=dataset.dtypes
#final_colums1.to_csv(r'E:\work\Supermicro\modeling\data_Q2_2019\sda_hdd_backblaze.csv')
# In[5]:
# PULL CURENT DATE REC
#dataframe2 = pd.read_sql("SELECT * FROM events1.disk_smartdata_mstr where date LIKE current_date||'%'", conn)
#dataset2 = pd.read_sql("SELECT * FROM events1.disk_smartdata_mstr where `date` LIKE current_date||'%'", conn)
# In[19]:
#final_colums1=dataset.dtypes
# Download descriptive analysis
#final_colums1=dataset.describe()
#final_colums1.to_csv(r'E:\work\Supermicro\modeling\data_Q2_2019\test.csv')
# In[66]:
dataset.rename(columns={'sda_hdd_all.date':'date',
'sda_hdd_all.serial_number':'serial_number',
'sda_hdd_all.model':'model',
'sda_hdd_all.capacity_bytes':'capacity_bytes',
'sda_hdd_all.failure':'failure',
'sda_hdd_all.smart_1_normalized':'smart_1_normalized',
'sda_hdd_all.smart_1_raw':'smart_1_raw',
'sda_hdd_all.smart_2_normalized':'smart_2_normalized',
'sda_hdd_all.smart_2_raw':'smart_2_raw',
'sda_hdd_all.smart_3_normalized':'smart_3_normalized',
'sda_hdd_all.smart_3_raw':'smart_3_raw',
'sda_hdd_all.smart_4_normalized':'smart_4_normalized',
'sda_hdd_all.smart_4_raw':'smart_4_raw',
'sda_hdd_all.smart_5_normalized':'smart_5_normalized',
'sda_hdd_all.smart_5_raw':'smart_5_raw',
'sda_hdd_all.smart_7_normalized':'smart_7_normalized',
'sda_hdd_all.smart_7_raw':'smart_7_raw',
'sda_hdd_all.smart_8_normalized':'smart_8_normalized',
'sda_hdd_all.smart_8_raw':'smart_8_raw',
'sda_hdd_all.smart_9_normalized':'smart_9_normalized',
'sda_hdd_all.smart_9_raw':'smart_9_raw',
'sda_hdd_all.smart_10_normalized':'smart_10_normalized',
'sda_hdd_all.smart_10_raw':'smart_10_raw',
'sda_hdd_all.smart_11_normalized':'smart_11_normalized',
'sda_hdd_all.smart_11_raw':'smart_11_raw',
'sda_hdd_all.smart_12_normalized':'smart_12_normalized',
'sda_hdd_all.smart_12_raw':'smart_12_raw',
'sda_hdd_all.smart_13_normalized':'smart_13_normalized',
'sda_hdd_all.smart_13_raw':'smart_13_raw',
'sda_hdd_all.smart_15_normalized':'smart_15_normalized',
'sda_hdd_all.smart_15_raw':'smart_15_raw',
'sda_hdd_all.smart_16_normalized':'smart_16_normalized',
'sda_hdd_all.smart_16_raw':'smart_16_raw',
'sda_hdd_all.smart_17_normalized':'smart_17_normalized',
'sda_hdd_all.smart_17_raw':'smart_17_raw',
'sda_hdd_all.smart_18_normalized':'smart_18_normalized',
'sda_hdd_all.smart_18_raw':'smart_18_raw',
'sda_hdd_all.smart_22_normalized':'smart_22_normalized',
'sda_hdd_all.smart_22_raw':'smart_22_raw',
'sda_hdd_all.smart_23_normalized':'smart_23_normalized',
'sda_hdd_all.smart_23_raw':'smart_23_raw',
'sda_hdd_all.smart_24_normalized':'smart_24_normalized',
'sda_hdd_all.smart_24_raw':'smart_24_raw',
'sda_hdd_all.smart_160_normalized':'smart_160_normalized',
'sda_hdd_all.smart_160_raw':'smart_160_raw',
'sda_hdd_all.smart_161_normalized':'smart_161_normalized',
'sda_hdd_all.smart_161_raw':'smart_161_raw',
'sda_hdd_all.smart_163_normalized':'smart_163_normalized',
'sda_hdd_all.smart_163_raw':'smart_163_raw',
'sda_hdd_all.smart_164_normalized':'smart_164_normalized',
'sda_hdd_all.smart_164_raw':'smart_164_raw',
'sda_hdd_all.smart_165_normalized':'smart_165_normalized',
'sda_hdd_all.smart_165_raw':'smart_165_raw',
'sda_hdd_all.smart_166_normalized':'smart_166_normalized',
'sda_hdd_all.smart_166_raw':'smart_166_raw',
'sda_hdd_all.smart_167_normalized':'smart_167_normalized',
'sda_hdd_all.smart_167_raw':'smart_167_raw',
'sda_hdd_all.smart_168_normalized':'smart_168_normalized',
'sda_hdd_all.smart_168_raw':'smart_168_raw',
'sda_hdd_all.smart_169_normalized':'smart_169_normalized',
'sda_hdd_all.smart_169_raw':'smart_169_raw',
'sda_hdd_all.smart_170_normalized':'smart_170_normalized',
'sda_hdd_all.smart_170_raw':'smart_170_raw',
'sda_hdd_all.smart_173_normalized':'smart_173_normalized',
'sda_hdd_all.smart_173_raw':'smart_173_raw',
'sda_hdd_all.smart_174_normalized':'smart_174_normalized',
'sda_hdd_all.smart_174_raw':'smart_174_raw',
'sda_hdd_all.smart_175_normalized':'smart_175_normalized',
'sda_hdd_all.smart_175_raw':'smart_175_raw',
'sda_hdd_all.smart_176_normalized':'smart_176_normalized',
'sda_hdd_all.smart_176_raw':'smart_176_raw',
'sda_hdd_all.smart_177_normalized':'smart_177_normalized',
'sda_hdd_all.smart_177_raw':'smart_177_raw',
'sda_hdd_all.smart_178_normalized':'smart_178_normalized',
'sda_hdd_all.smart_178_raw':'smart_178_raw',
'sda_hdd_all.smart_179_normalized':'smart_179_normalized',
'sda_hdd_all.smart_179_raw':'smart_179_raw',
'sda_hdd_all.smart_180_normalized':'smart_180_normalized',
'sda_hdd_all.smart_180_raw':'smart_180_raw',
'sda_hdd_all.smart_181_normalized':'smart_181_normalized',
'sda_hdd_all.smart_181_raw':'smart_181_raw',
'sda_hdd_all.smart_182_normalized':'smart_182_normalized',
'sda_hdd_all.smart_182_raw':'smart_182_raw',
'sda_hdd_all.smart_183_normalized':'smart_183_normalized',
'sda_hdd_all.smart_183_raw':'smart_183_raw',
'sda_hdd_all.smart_184_normalized':'smart_184_normalized',
'sda_hdd_all.smart_184_raw':'smart_184_raw',
'sda_hdd_all.smart_187_normalized':'smart_187_normalized',
'sda_hdd_all.smart_187_raw':'smart_187_raw',
'sda_hdd_all.smart_188_normalized':'smart_188_normalized',
'sda_hdd_all.smart_188_raw':'smart_188_raw',
'sda_hdd_all.smart_189_normalized':'smart_189_normalized',
'sda_hdd_all.smart_189_raw':'smart_189_raw',
'sda_hdd_all.smart_190_normalized':'smart_190_normalized',
'sda_hdd_all.smart_190_raw':'smart_190_raw',
'sda_hdd_all.smart_191_normalized':'smart_191_normalized',
'sda_hdd_all.smart_191_raw':'smart_191_raw',
'sda_hdd_all.smart_192_normalized':'smart_192_normalized',
'sda_hdd_all.smart_192_raw':'smart_192_raw',
'sda_hdd_all.smart_193_normalized':'smart_193_normalized',
'sda_hdd_all.smart_193_raw':'smart_193_raw',
'sda_hdd_all.smart_194_normalized':'smart_194_normalized',
'sda_hdd_all.smart_194_raw':'smart_194_raw',
'sda_hdd_all.smart_195_normalized':'smart_195_normalized',
'sda_hdd_all.smart_195_raw':'smart_195_raw',
'sda_hdd_all.smart_196_normalized':'smart_196_normalized',
'sda_hdd_all.smart_196_raw':'smart_196_raw',
'sda_hdd_all.smart_197_normalized':'smart_197_normalized',
'sda_hdd_all.smart_197_raw':'smart_197_raw',
'sda_hdd_all.smart_198_normalized':'smart_198_normalized',
'sda_hdd_all.smart_198_raw':'smart_198_raw',
'sda_hdd_all.smart_199_normalized':'smart_199_normalized',
'sda_hdd_all.smart_199_raw':'smart_199_raw',
'sda_hdd_all.smart_200_normalized':'smart_200_normalized',
'sda_hdd_all.smart_200_raw':'smart_200_raw',
'sda_hdd_all.smart_201_normalized':'smart_201_normalized',
'sda_hdd_all.smart_201_raw':'smart_201_raw',
'sda_hdd_all.smart_202_normalized':'smart_202_normalized',
'sda_hdd_all.smart_202_raw':'smart_202_raw',
'sda_hdd_all.smart_206_normalized':'smart_206_normalized',
'sda_hdd_all.smart_206_raw':'smart_206_raw',
'sda_hdd_all.smart_210_normalized':'smart_210_normalized',
'sda_hdd_all.smart_210_raw':'smart_210_raw',
'sda_hdd_all.smart_218_normalized':'smart_218_normalized',
'sda_hdd_all.smart_218_raw':'smart_218_raw',
'sda_hdd_all.smart_220_normalized':'smart_220_normalized',
'sda_hdd_all.smart_220_raw':'smart_220_raw',
'sda_hdd_all.smart_222_normalized':'smart_222_normalized',
'sda_hdd_all.smart_222_raw':'smart_222_raw',
'sda_hdd_all.smart_223_normalized':'smart_223_normalized',
'sda_hdd_all.smart_223_raw':'smart_223_raw',
'sda_hdd_all.smart_224_normalized':'smart_224_normalized',
'sda_hdd_all.smart_224_raw':'smart_224_raw',
'sda_hdd_all.smart_225_normalized':'smart_225_normalized',
'sda_hdd_all.smart_225_raw':'smart_225_raw',
'sda_hdd_all.smart_226_normalized':'smart_226_normalized',
'sda_hdd_all.smart_226_raw':'smart_226_raw',
'sda_hdd_all.smart_231_normalized':'smart_231_normalized',
'sda_hdd_all.smart_231_raw':'smart_231_raw',
'sda_hdd_all.smart_232_normalized':'smart_232_normalized',
'sda_hdd_all.smart_232_raw':'smart_232_raw',
'sda_hdd_all.smart_233_normalized':'smart_233_normalized',
'sda_hdd_all.smart_233_raw':'smart_233_raw',
'sda_hdd_all.smart_234_normalized':'smart_234_normalized',
'sda_hdd_all.smart_234_raw':'smart_234_raw',
'sda_hdd_all.smart_235_normalized':'smart_235_normalized',
'sda_hdd_all.smart_235_raw':'smart_235_raw',
'sda_hdd_all.smart_240_normalized':'smart_240_normalized',
'sda_hdd_all.smart_240_raw':'smart_240_raw',
'sda_hdd_all.smart_241_normalized':'smart_241_normalized',
'sda_hdd_all.smart_241_raw':'smart_241_raw',
'sda_hdd_all.smart_242_normalized':'smart_242_normalized',
'sda_hdd_all.smart_242_raw':'smart_242_raw',
'sda_hdd_all.smart_245_normalized':'smart_245_normalized',
'sda_hdd_all.smart_245_raw':'smart_245_raw',
'sda_hdd_all.smart_247_normalized':'smart_247_normalized',
'sda_hdd_all.smart_247_raw':'smart_247_raw',
'sda_hdd_all.smart_248_normalized':'smart_248_normalized',
'sda_hdd_all.smart_248_raw':'smart_248_raw',
'sda_hdd_all.smart_250_normalized':'smart_250_normalized',
'sda_hdd_all.smart_250_raw':'smart_250_raw',
'sda_hdd_all.smart_251_normalized':'smart_251_normalized',
'sda_hdd_all.smart_251_raw':'smart_251_raw',
'sda_hdd_all.smart_252_normalized':'smart_252_normalized',
'sda_hdd_all.smart_252_raw':'smart_252_raw',
'sda_hdd_all.smart_254_normalized':'smart_254_normalized',
'sda_hdd_all.smart_254_raw':'smart_254_raw',
'sda_hdd_all.smart_255_normalized':'smart_255_normalized',
'sda_hdd_all.smart_255_raw':'smart_255_raw'}, inplace=True)
# In[12]:
# fill missing with zero
dataset.fillna(0)
dataset.replace(np.nan, 0)
dataset.fillna(value = 0,
inplace = True)
dataset.replace(np.NaN, 0)
# In[13]:
#Missing value Imputation with model
#dataset['date']=dataset['date'].fillna(dataset['date'].mean())
#dataset['serial_number']=dataset['serial_number'].fillna(dataset['serial_number'].mean())
#dataset['model']=dataset['model'].fillna(dataset['model'].mean())
#dataset['capacity_bytes']=dataset['capacity_bytes'].fillna(dataset['capacity_bytes'].mean())
#dataset['failure']=dataset['failure'].fillna(dataset['failure'].mean())
dataset['smart_1_normalized']=dataset['smart_1_normalized'].fillna(dataset['smart_1_normalized'].mean())
dataset['smart_1_raw']=dataset['smart_1_raw'].fillna(dataset['smart_1_raw'].mean())
dataset['smart_2_normalized']=dataset['smart_2_normalized'].fillna(dataset['smart_2_normalized'].mean())
dataset['smart_2_raw']=dataset['smart_2_raw'].fillna(dataset['smart_2_raw'].mean())
dataset['smart_3_normalized']=dataset['smart_3_normalized'].fillna(dataset['smart_3_normalized'].mean())
dataset['smart_3_raw']=dataset['smart_3_raw'].fillna(dataset['smart_3_raw'].mean())
dataset['smart_4_normalized']=dataset['smart_4_normalized'].fillna(dataset['smart_4_normalized'].mean())
dataset['smart_4_raw']=dataset['smart_4_raw'].fillna(dataset['smart_4_raw'].mean())
dataset['smart_5_normalized']=dataset['smart_5_normalized'].fillna(dataset['smart_5_normalized'].mean())
dataset['smart_5_raw']=dataset['smart_5_raw'].fillna(dataset['smart_5_raw'].mean())
dataset['smart_7_normalized']=dataset['smart_7_normalized'].fillna(dataset['smart_7_normalized'].mean())
dataset['smart_7_raw']=dataset['smart_7_raw'].fillna(dataset['smart_7_raw'].mean())
dataset['smart_8_normalized']=dataset['smart_8_normalized'].fillna(dataset['smart_8_normalized'].mean())
dataset['smart_8_raw']=dataset['smart_8_raw'].fillna(dataset['smart_8_raw'].mean())
dataset['smart_9_normalized']=dataset['smart_9_normalized'].fillna(dataset['smart_9_normalized'].mean())
dataset['smart_9_raw']=dataset['smart_9_raw'].fillna(dataset['smart_9_raw'].mean())
dataset['smart_10_normalized']=dataset['smart_10_normalized'].fillna(dataset['smart_10_normalized'].mean())
dataset['smart_10_raw']=dataset['smart_10_raw'].fillna(dataset['smart_10_raw'].mean())
dataset['smart_11_normalized']=dataset['smart_11_normalized'].fillna(dataset['smart_11_normalized'].mean())
dataset['smart_11_raw']=dataset['smart_11_raw'].fillna(dataset['smart_11_raw'].mean())
dataset['smart_12_normalized']=dataset['smart_12_normalized'].fillna(dataset['smart_12_normalized'].mean())
dataset['smart_12_raw']=dataset['smart_12_raw'].fillna(dataset['smart_12_raw'].mean())
dataset['smart_13_normalized']=dataset['smart_13_normalized'].fillna(dataset['smart_13_normalized'].mean())
dataset['smart_13_raw']=dataset['smart_13_raw'].fillna(dataset['smart_13_raw'].mean())
dataset['smart_15_normalized']=dataset['smart_15_normalized'].fillna(dataset['smart_15_normalized'].mean())
dataset['smart_15_raw']=dataset['smart_15_raw'].fillna(dataset['smart_15_raw'].mean())
dataset['smart_16_normalized']=dataset['smart_16_normalized'].fillna(dataset['smart_16_normalized'].mean())
dataset['smart_16_raw']=dataset['smart_16_raw'].fillna(dataset['smart_16_raw'].mean())
dataset['smart_17_normalized']=dataset['smart_17_normalized'].fillna(dataset['smart_17_normalized'].mean())
dataset['smart_17_raw']=dataset['smart_17_raw'].fillna(dataset['smart_17_raw'].mean())
dataset['smart_18_normalized']=dataset['smart_18_normalized'].fillna(dataset['smart_18_normalized'].mean())
dataset['smart_18_raw']=dataset['smart_18_raw'].fillna(dataset['smart_18_raw'].mean())
dataset['smart_22_normalized']=dataset['smart_22_normalized'].fillna(dataset['smart_22_normalized'].mean())
dataset['smart_22_raw']=dataset['smart_22_raw'].fillna(dataset['smart_22_raw'].mean())
dataset['smart_23_normalized']=dataset['smart_23_normalized'].fillna(dataset['smart_23_normalized'].mean())
dataset['smart_23_raw']=dataset['smart_23_raw'].fillna(dataset['smart_23_raw'].mean())
dataset['smart_24_normalized']=dataset['smart_24_normalized'].fillna(dataset['smart_24_normalized'].mean())
dataset['smart_24_raw']=dataset['smart_24_raw'].fillna(dataset['smart_24_raw'].mean())
dataset['smart_168_normalized']=dataset['smart_168_normalized'].fillna(dataset['smart_168_normalized'].mean())
dataset['smart_168_raw']=dataset['smart_168_raw'].fillna(dataset['smart_168_raw'].mean())
dataset['smart_170_normalized']=dataset['smart_170_normalized'].fillna(dataset['smart_170_normalized'].mean())
dataset['smart_170_raw']=dataset['smart_170_raw'].fillna(dataset['smart_170_raw'].mean())
dataset['smart_173_normalized']=dataset['smart_173_normalized'].fillna(dataset['smart_173_normalized'].mean())
dataset['smart_173_raw']=dataset['smart_173_raw'].fillna(dataset['smart_173_raw'].mean())
dataset['smart_174_normalized']=dataset['smart_174_normalized'].fillna(dataset['smart_174_normalized'].mean())
dataset['smart_174_raw']=dataset['smart_174_raw'].fillna(dataset['smart_174_raw'].mean())
dataset['smart_175_normalized']=dataset['smart_175_normalized'].fillna(dataset['smart_175_normalized'].mean())
dataset['smart_175_raw']=dataset['smart_175_raw'].fillna(dataset['smart_175_raw'].mean())
dataset['smart_177_normalized']=dataset['smart_177_normalized'].fillna(dataset['smart_177_normalized'].mean())
dataset['smart_177_raw']=dataset['smart_177_raw'].fillna(dataset['smart_177_raw'].mean())
dataset['smart_179_normalized']=dataset['smart_179_normalized'].fillna(dataset['smart_179_normalized'].mean())
dataset['smart_179_raw']=dataset['smart_179_raw'].fillna(dataset['smart_179_raw'].mean())
dataset['smart_180_normalized']=dataset['smart_180_normalized'].fillna(dataset['smart_180_normalized'].mean())
dataset['smart_180_raw']=dataset['smart_180_raw'].fillna(dataset['smart_180_raw'].mean())
dataset['smart_181_normalized']=dataset['smart_181_normalized'].fillna(dataset['smart_181_normalized'].mean())
dataset['smart_181_raw']=dataset['smart_181_raw'].fillna(dataset['smart_181_raw'].mean())
dataset['smart_182_normalized']=dataset['smart_182_normalized'].fillna(dataset['smart_182_normalized'].mean())
dataset['smart_182_raw']=dataset['smart_182_raw'].fillna(dataset['smart_182_raw'].mean())
dataset['smart_183_normalized']=dataset['smart_183_normalized'].fillna(dataset['smart_183_normalized'].mean())
dataset['smart_183_raw']=dataset['smart_183_raw'].fillna(dataset['smart_183_raw'].mean())
dataset['smart_184_normalized']=dataset['smart_184_normalized'].fillna(dataset['smart_184_normalized'].mean())
dataset['smart_184_raw']=dataset['smart_184_raw'].fillna(dataset['smart_184_raw'].mean())
dataset['smart_187_normalized']=dataset['smart_187_normalized'].fillna(dataset['smart_187_normalized'].mean())
dataset['smart_187_raw']=dataset['smart_187_raw'].fillna(dataset['smart_187_raw'].mean())
dataset['smart_188_normalized']=dataset['smart_188_normalized'].fillna(dataset['smart_188_normalized'].mean())
dataset['smart_188_raw']=dataset['smart_188_raw'].fillna(dataset['smart_188_raw'].mean())
dataset['smart_189_normalized']=dataset['smart_189_normalized'].fillna(dataset['smart_189_normalized'].mean())
dataset['smart_189_raw']=dataset['smart_189_raw'].fillna(dataset['smart_189_raw'].mean())
dataset['smart_190_normalized']=dataset['smart_190_normalized'].fillna(dataset['smart_190_normalized'].mean())
dataset['smart_190_raw']=dataset['smart_190_raw'].fillna(dataset['smart_190_raw'].mean())
dataset['smart_191_normalized']=dataset['smart_191_normalized'].fillna(dataset['smart_191_normalized'].mean())
dataset['smart_191_raw']=dataset['smart_191_raw'].fillna(dataset['smart_191_raw'].mean())
dataset['smart_192_normalized']=dataset['smart_192_normalized'].fillna(dataset['smart_192_normalized'].mean())
dataset['smart_192_raw']=dataset['smart_192_raw'].fillna(dataset['smart_192_raw'].mean())
dataset['smart_193_normalized']=dataset['smart_193_normalized'].fillna(dataset['smart_193_normalized'].mean())
dataset['smart_193_raw']=dataset['smart_193_raw'].fillna(dataset['smart_193_raw'].mean())
dataset['smart_194_normalized']=dataset['smart_194_normalized'].fillna(dataset['smart_194_normalized'].mean())
dataset['smart_194_raw']=dataset['smart_194_raw'].fillna(dataset['smart_194_raw'].mean())
dataset['smart_195_normalized']=dataset['smart_195_normalized'].fillna(dataset['smart_195_normalized'].mean())
dataset['smart_195_raw']=dataset['smart_195_raw'].fillna(dataset['smart_195_raw'].mean())
dataset['smart_196_normalized']=dataset['smart_196_normalized'].fillna(dataset['smart_196_normalized'].mean())
dataset['smart_196_raw']=dataset['smart_196_raw'].fillna(dataset['smart_196_raw'].mean())
dataset['smart_197_normalized']=dataset['smart_197_normalized'].fillna(dataset['smart_197_normalized'].mean())
dataset['smart_197_raw']=dataset['smart_197_raw'].fillna(dataset['smart_197_raw'].mean())
dataset['smart_198_normalized']=dataset['smart_198_normalized'].fillna(dataset['smart_198_normalized'].mean())
dataset['smart_198_raw']=dataset['smart_198_raw'].fillna(dataset['smart_198_raw'].mean())
dataset['smart_199_normalized']=dataset['smart_199_normalized'].fillna(dataset['smart_199_normalized'].mean())
dataset['smart_199_raw']=dataset['smart_199_raw'].fillna(dataset['smart_199_raw'].mean())
dataset['smart_200_normalized']=dataset['smart_200_normalized'].fillna(dataset['smart_200_normalized'].mean())
dataset['smart_200_raw']=dataset['smart_200_raw'].fillna(dataset['smart_200_raw'].mean())
dataset['smart_201_normalized']=dataset['smart_201_normalized'].fillna(dataset['smart_201_normalized'].mean())
dataset['smart_201_raw']=dataset['smart_201_raw'].fillna(dataset['smart_201_raw'].mean())
dataset['smart_202_normalized']=dataset['smart_202_normalized'].fillna(dataset['smart_202_normalized'].mean())
dataset['smart_202_raw']=dataset['smart_202_raw'].fillna(dataset['smart_202_raw'].mean())
dataset['smart_206_normalized']=dataset['smart_206_normalized'].fillna(dataset['smart_206_normalized'].mean())
dataset['smart_206_raw']=dataset['smart_206_raw'].fillna(dataset['smart_206_raw'].mean())
dataset['smart_210_normalized']=dataset['smart_210_normalized'].fillna(dataset['smart_210_normalized'].mean())
dataset['smart_210_raw']=dataset['smart_210_raw'].fillna(dataset['smart_210_raw'].mean())
dataset['smart_218_normalized']=dataset['smart_218_normalized'].fillna(dataset['smart_218_normalized'].mean())
dataset['smart_218_raw']=dataset['smart_218_raw'].fillna(dataset['smart_218_raw'].mean())
dataset['smart_220_normalized']=dataset['smart_220_normalized'].fillna(dataset['smart_220_normalized'].mean())
dataset['smart_220_raw']=dataset['smart_220_raw'].fillna(dataset['smart_220_raw'].mean())
dataset['smart_222_normalized']=dataset['smart_222_normalized'].fillna(dataset['smart_222_normalized'].mean())
dataset['smart_222_raw']=dataset['smart_222_raw'].fillna(dataset['smart_222_raw'].mean())
dataset['smart_223_normalized']=dataset['smart_223_normalized'].fillna(dataset['smart_223_normalized'].mean())
dataset['smart_223_raw']=dataset['smart_223_raw'].fillna(dataset['smart_223_raw'].mean())
dataset['smart_224_normalized']=dataset['smart_224_normalized'].fillna(dataset['smart_224_normalized'].mean())
dataset['smart_224_raw']=dataset['smart_224_raw'].fillna(dataset['smart_224_raw'].mean())
dataset['smart_225_normalized']=dataset['smart_225_normalized'].fillna(dataset['smart_225_normalized'].mean())
dataset['smart_225_raw']=dataset['smart_225_raw'].fillna(dataset['smart_225_raw'].mean())
dataset['smart_226_normalized']=dataset['smart_226_normalized'].fillna(dataset['smart_226_normalized'].mean())
dataset['smart_226_raw']=dataset['smart_226_raw'].fillna(dataset['smart_226_raw'].mean())
dataset['smart_231_normalized']=dataset['smart_231_normalized'].fillna(dataset['smart_231_normalized'].mean())
dataset['smart_231_raw']=dataset['smart_231_raw'].fillna(dataset['smart_231_raw'].mean())
dataset['smart_232_normalized']=dataset['smart_232_normalized'].fillna(dataset['smart_232_normalized'].mean())
dataset['smart_232_raw']=dataset['smart_232_raw'].fillna(dataset['smart_232_raw'].mean())
dataset['smart_233_normalized']=dataset['smart_233_normalized'].fillna(dataset['smart_233_normalized'].mean())
dataset['smart_233_raw']=dataset['smart_233_raw'].fillna(dataset['smart_233_raw'].mean())
dataset['smart_234_normalized']=dataset['smart_234_normalized'].fillna(dataset['smart_234_normalized'].mean())
dataset['smart_234_raw']=dataset['smart_234_raw'].fillna(dataset['smart_234_raw'].mean())
dataset['smart_235_normalized']=dataset['smart_235_normalized'].fillna(dataset['smart_235_normalized'].mean())
dataset['smart_235_raw']=dataset['smart_235_raw'].fillna(dataset['smart_235_raw'].mean())
dataset['smart_240_normalized']=dataset['smart_240_normalized'].fillna(dataset['smart_240_normalized'].mean())
dataset['smart_240_raw']=dataset['smart_240_raw'].fillna(dataset['smart_240_raw'].mean())
dataset['smart_241_normalized']=dataset['smart_241_normalized'].fillna(dataset['smart_241_normalized'].mean())
dataset['smart_241_raw']=dataset['smart_241_raw'].fillna(dataset['smart_241_raw'].mean())
dataset['smart_242_normalized']=dataset['smart_242_normalized'].fillna(dataset['smart_242_normalized'].mean())
dataset['smart_242_raw']=dataset['smart_242_raw'].fillna(dataset['smart_242_raw'].mean())
dataset['smart_245_normalized']=dataset['smart_245_normalized'].fillna(dataset['smart_245_normalized'].mean())
dataset['smart_245_raw']=dataset['smart_245_raw'].fillna(dataset['smart_245_raw'].mean())
dataset['smart_247_normalized']=dataset['smart_247_normalized'].fillna(dataset['smart_247_normalized'].mean())
dataset['smart_247_raw']=dataset['smart_247_raw'].fillna(dataset['smart_247_raw'].mean())
dataset['smart_248_normalized']=dataset['smart_248_normalized'].fillna(dataset['smart_248_normalized'].mean())
dataset['smart_248_raw']=dataset['smart_248_raw'].fillna(dataset['smart_248_raw'].mean())
dataset['smart_250_normalized']=dataset['smart_250_normalized'].fillna(dataset['smart_250_normalized'].mean())
dataset['smart_250_raw']=dataset['smart_250_raw'].fillna(dataset['smart_250_raw'].mean())
dataset['smart_251_normalized']=dataset['smart_251_normalized'].fillna(dataset['smart_251_normalized'].mean())
dataset['smart_251_raw']=dataset['smart_251_raw'].fillna(dataset['smart_251_raw'].mean())
dataset['smart_252_normalized']=dataset['smart_252_normalized'].fillna(dataset['smart_252_normalized'].mean())
dataset['smart_252_raw']=dataset['smart_252_raw'].fillna(dataset['smart_252_raw'].mean())
dataset['smart_254_normalized']=dataset['smart_254_normalized'].fillna(dataset['smart_254_normalized'].mean())
dataset['smart_254_raw']=dataset['smart_254_raw'].fillna(dataset['smart_254_raw'].mean())
dataset['smart_255_normalized']=dataset['smart_255_normalized'].fillna(dataset['smart_255_normalized'].mean())
dataset['smart_255_raw']=dataset['smart_255_raw'].fillna(dataset['smart_255_raw'].mean())
#dataset['failure']=dataset['failure'].fillna(dataset['failure'].mean())
#dataset['capacity_bytes']=dataset['capacity_bytes'].fillna(dataset['capacity_bytes'].mean())
#dataset['model']=dataset['model'].fillna(dataset['model'].mean())
#dataset['serial_number']=dataset['serial_number'].fillna(dataset['serial_number'].mean())
#dataset['date']=dataset['date'].fillna(dataset['date'].mean())
# In[14]:
#dataset=dataframe
#Know the Missing data
total = dataframe.isnull().sum().sort_values(ascending=False)
percent = (dataframe.isnull().sum()/dataframe.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data
missing_data.to_csv(r'E:\work\Supermicro\modeling\data_Q2_2019\missing_data1.csv')
# In[15]:
# Remove CORRILATION VARIYABLE
# smart_226_raw - correlated with capacity_bytes
# smart_8_normalized - correlated withsmart_2_normalized
# smart_254_normalized - correlated with smart_3_normalized
# smart_12_raw correlated withsmart_4_raw
# smart_192_raw correlated with smart_4_raw
# smart_196_normalized correlated with smart_5_normalized
# smart_222_normalized correlated with smart_9_normalized
# smart_175_raw correlated with smart_9_normalized
# smart_190_normalized correlated with smart_11_raw
# smart_192_raw correlated with smart_12_raw
# smart_13_raw - correlated with capacity_bytes
# smart_177_normalized - correlated with smart_22_normalized
# smart_164_raw - correlated with smart_165_raw
# smart_167_raw - correlated withsmart_165_raw
# smart_175_raw - many
# smart_190_raw - correlated with smart_194_normalized and smart_196_normalized
# smart_202_raw - correlated with smart_180_raw and smart_194_normalized
# smart_226_raw - correlated with smart_3_raw
# smart_254_normalized - correlated with many
dataset = dataset.drop(['smart_226_raw'], 1)
dataset = dataset.drop(['smart_8_normalized'], 1)
dataset = dataset.drop(['smart_254_normalized'], 1)
dataset = dataset.drop(['smart_12_raw'], 1)
dataset = dataset.drop(['smart_192_raw'], 1)
dataset = dataset.drop(['smart_196_normalized'], 1)
dataset = dataset.drop(['smart_222_normalized'], 1)
dataset = dataset.drop(['smart_175_raw'], 1)
dataset = dataset.drop(['smart_190_normalized'], 1)
dataset = dataset.drop(['smart_13_raw'], 1)
dataset = dataset.drop(['smart_177_normalized'], 1)
#dataset = dataset.drop(['smart_164_raw'], 1)
#dataset = dataset.drop(['smart_167_raw'], 1)
dataset = dataset.drop(['smart_190_raw'], 1)
dataset = dataset.drop(['smart_202_raw'], 1)
# In[16]:
# drop date
dataset = dataset.drop(['date'], 1)
# In[47]:
# Univariate analysis & describe analysis
#univariate_analysis=dataset.describe()
#univariate_analysis.to_csv(r'E:\work\Supermicro\modeling\data_Q2_2019\univariate_analysis.csv')
# In[17]:
# remove top 10 missing variables
dataset = dataset.drop(['smart_255_raw'], 1)
dataset = dataset.drop(['smart_15_normalized'], 1)
dataset = dataset.drop(['smart_234_raw'], 1)
dataset = dataset.drop(['smart_255_normalized'], 1)
dataset = dataset.drop(['smart_15_raw'], 1)
dataset = dataset.drop(['smart_234_normalized'], 1)
dataset = dataset.drop(['smart_206_normalized'], 1)
dataset = dataset.drop(['smart_206_raw'], 1)
dataset = dataset.drop(['smart_248_raw'], 1)
dataset = dataset.drop(['smart_248_normalized'], 1)
# In[18]:
# Remove below variable as it has only one value
dataset = dataset.drop(['smart_210_raw'], 1)
dataset = dataset.drop(['smart_224_raw'], 1)
dataset = dataset.drop(['smart_18_raw'], 1)
dataset = dataset.drop(['smart_23_raw'], 1)
dataset = dataset.drop(['smart_24_raw'], 1)
#dataset = dataset.drop(['smart_166_raw'], 1)
#dataset = dataset.drop(['smart_176_raw'], 1)
dataset = dataset.drop(['smart_179_raw'], 1)
dataset = dataset.drop(['smart_181_raw'], 1)
dataset = dataset.drop(['smart_182_raw'], 1)
dataset = dataset.drop(['smart_251_normalized'], 1)
dataset = dataset.drop(['smart_250_normalized'], 1)
dataset = dataset.drop(['smart_254_raw'], 1)
# In[19]:
# normalize the data
dataset['normalized_capacity_bytes'] = (dataset['capacity_bytes'] - dataset['capacity_bytes'].min()) / (dataset['capacity_bytes'].max() - dataset['capacity_bytes'].min())
dataset['normalized_smart_1_raw'] = (dataset['smart_1_raw'] - dataset['smart_1_raw'].min()) / (dataset['smart_1_raw'].max() - dataset['smart_1_raw'].min())
dataset['normalized_smart_241_raw'] = (dataset['smart_241_raw'] - dataset['smart_241_raw'].min()) / (dataset['smart_241_raw'].max() - dataset['smart_241_raw'].min())
dataset['normalized_smart_242_raw'] = (dataset['smart_242_raw'] - dataset['smart_242_raw'].min()) / (dataset['smart_242_raw'].max() - dataset['smart_242_raw'].min())
dataset['normalized_smart_7_raw'] = (dataset['smart_7_raw'] - dataset['smart_7_raw'].min()) / (dataset['smart_7_raw'].max() - dataset['smart_7_raw'].min())
dataset['normalized_smart_9_raw'] = (dataset['smart_9_raw'] - dataset['smart_9_raw'].min()) / (dataset['smart_9_raw'].max() - dataset['smart_9_raw'].min())
dataset['normalized_smart_11_raw'] = (dataset['smart_11_raw'] - dataset['smart_11_raw'].min()) / (dataset['smart_11_raw'].max() - dataset['smart_11_raw'].min())
dataset['normalized_smart_173_raw'] = (dataset['smart_173_raw'] - dataset['smart_173_raw'].min()) / (dataset['smart_173_raw'].max() - dataset['smart_173_raw'].min())
dataset['normalized_smart_188_raw'] = (dataset['smart_188_raw'] - dataset['smart_188_raw'].min()) / (dataset['smart_188_raw'].max() - dataset['smart_188_raw'].min())
dataset['normalized_smart_193_raw'] = (dataset['smart_193_raw'] - dataset['smart_193_raw'].min()) / (dataset['smart_193_raw'].max() - dataset['smart_193_raw'].min())
dataset['normalized_smart_195_raw'] = (dataset['smart_195_raw'] - dataset['smart_195_raw'].min()) / (dataset['smart_195_raw'].max() - dataset['smart_195_raw'].min())
dataset['normalized_smart_225_raw'] = (dataset['smart_225_raw'] - dataset['smart_225_raw'].min()) / (dataset['smart_225_raw'].max() - dataset['smart_225_raw'].min())
dataset['normalized_smart_232_raw'] = (dataset['smart_232_raw'] - dataset['smart_232_raw'].min()) / (dataset['smart_232_raw'].max() - dataset['smart_232_raw'].min())
dataset['normalized_smart_233_raw'] = (dataset['smart_233_raw'] - dataset['smart_233_raw'].min()) / (dataset['smart_233_raw'].max() - dataset['smart_233_raw'].min())
dataset['normalized_smart_235_raw'] = (dataset['smart_235_raw'] - dataset['smart_235_raw'].min()) / (dataset['smart_235_raw'].max() - dataset['smart_235_raw'].min())
dataset['normalized_smart_240_raw'] = (dataset['smart_240_raw'] - dataset['smart_240_raw'].min()) / (dataset['smart_240_raw'].max() - dataset['smart_240_raw'].min())
dataset['normalized_smart_247_raw'] = (dataset['smart_247_raw'] - dataset['smart_247_raw'].min()) / (dataset['smart_247_raw'].max() - dataset['smart_247_raw'].min())
dataset['normalized_smart_251_raw'] = (dataset['smart_251_raw'] - dataset['smart_251_raw'].min()) / (dataset['smart_251_raw'].max() - dataset['smart_251_raw'].min())
dataset['normalized_smart_252_raw'] = (dataset['smart_252_raw'] - dataset['smart_252_raw'].min()) / (dataset['smart_252_raw'].max() - dataset['smart_252_raw'].min())
dataset['normalized_smart_5_raw'] = (dataset['smart_5_raw'] - dataset['smart_5_raw'].min()) / (dataset['smart_5_raw'].max() - dataset['smart_5_raw'].min())
dataset['normalized_smart_197_raw'] = (dataset['smart_197_raw'] - dataset['smart_197_raw'].min()) / (dataset['smart_197_raw'].max() - dataset['smart_197_raw'].min())
dataset['normalized_smart_10_raw'] = (dataset['smart_10_raw'] - dataset['smart_10_raw'].min()) / (dataset['smart_10_raw'].max() - dataset['smart_10_raw'].min())
dataset['normalized_smart_198_raw'] = (dataset['smart_198_raw'] - dataset['smart_198_raw'].min()) / (dataset['smart_198_raw'].max() - dataset['smart_198_raw'].min())
# In[20]:
# drop after normalization
dataset = dataset.drop(['capacity_bytes'], 1)
dataset = dataset.drop(['smart_1_raw'], 1)
dataset = dataset.drop(['smart_241_raw'], 1)
dataset = dataset.drop(['smart_242_raw'], 1)
dataset = dataset.drop(['smart_7_raw'], 1)
dataset = dataset.drop(['smart_9_raw'], 1)
dataset = dataset.drop(['smart_11_raw'], 1)
dataset = dataset.drop(['smart_173_raw'], 1)
dataset = dataset.drop(['smart_188_raw'], 1)
dataset = dataset.drop(['smart_193_raw'], 1)
dataset = dataset.drop(['smart_195_raw'], 1)
dataset = dataset.drop(['smart_225_raw'], 1)
dataset = dataset.drop(['smart_232_raw'], 1)
dataset = dataset.drop(['smart_233_raw'], 1)
dataset = dataset.drop(['smart_235_raw'], 1)
dataset = dataset.drop(['smart_240_raw'], 1)
dataset = dataset.drop(['smart_247_raw'], 1)
dataset = dataset.drop(['smart_251_raw'], 1)
dataset = dataset.drop(['smart_252_raw'], 1)
dataset = dataset.drop(['smart_5_raw'], 1)
dataset = dataset.drop(['smart_197_raw'], 1)
dataset = dataset.drop(['smart_10_raw'], 1)
dataset = dataset.drop(['smart_198_raw'], 1)
# In[21]:
# one hot encoding or label encoder or creating dummies for all the categorical data
# Label Encoding model
dataset.model = pd.Categorical(dataset.model)
dataset["model"] = dataset["model"].cat.codes
# In[22]:
# fill missing with zero
dataset.fillna(0)
dataset.replace(np.nan, 0)
dataset.fillna(value = 0,
inplace = True)
dataset.replace(np.NaN, 0)
# In[23]:
# data conversion run only if requred
#dataset.failure = pd.Categorical(dataset.failure)
# In[24]:
# define the y variable
target = 'failure' #defining a global variable
# In[25]:
# Machine learning algorithms require that the distribution of our data is uniform or normal
qtrans = QuantileTransformer(output_distribution='normal', random_state=0)
dataset['smart_1_normalized'] = qtrans.fit_transform(dataset[['smart_1_normalized']])
dataset['smart_2_normalized'] = qtrans.fit_transform(dataset[['smart_2_normalized']])
dataset['smart_2_raw'] = qtrans.fit_transform(dataset[['smart_2_raw']])
dataset['smart_3_normalized'] = qtrans.fit_transform(dataset[['smart_3_normalized']])
dataset['smart_3_raw'] = qtrans.fit_transform(dataset[['smart_3_raw']])
dataset['smart_4_normalized'] = qtrans.fit_transform(dataset[['smart_4_normalized']])
dataset['smart_4_raw'] = qtrans.fit_transform(dataset[['smart_4_raw']])
dataset['smart_5_normalized'] = qtrans.fit_transform(dataset[['smart_5_normalized']])
dataset['smart_7_normalized'] = qtrans.fit_transform(dataset[['smart_7_normalized']])
dataset['smart_8_raw'] = qtrans.fit_transform(dataset[['smart_8_raw']])
dataset['smart_9_normalized'] = qtrans.fit_transform(dataset[['smart_9_normalized']])
dataset['smart_10_normalized'] = qtrans.fit_transform(dataset[['smart_10_normalized']])
dataset['smart_11_normalized'] = qtrans.fit_transform(dataset[['smart_11_normalized']])
dataset['smart_12_normalized'] = qtrans.fit_transform(dataset[['smart_12_normalized']])
dataset['smart_13_normalized'] = qtrans.fit_transform(dataset[['smart_13_normalized']])
dataset['smart_16_normalized'] = qtrans.fit_transform(dataset[['smart_16_normalized']])
dataset['smart_16_raw'] = qtrans.fit_transform(dataset[['smart_16_raw']])
dataset['smart_17_normalized'] = qtrans.fit_transform(dataset[['smart_17_normalized']])
dataset['smart_17_raw'] = qtrans.fit_transform(dataset[['smart_17_raw']])
dataset['smart_18_normalized'] = qtrans.fit_transform(dataset[['smart_18_normalized']])
dataset['smart_22_normalized'] = qtrans.fit_transform(dataset[['smart_22_normalized']])
dataset['smart_22_raw'] = qtrans.fit_transform(dataset[['smart_22_raw']])
dataset['smart_23_normalized'] = qtrans.fit_transform(dataset[['smart_23_normalized']])
dataset['smart_24_normalized'] = qtrans.fit_transform(dataset[['smart_24_normalized']])
dataset['smart_168_normalized'] = qtrans.fit_transform(dataset[['smart_168_normalized']])
dataset['smart_168_raw'] = qtrans.fit_transform(dataset[['smart_168_raw']])
dataset['smart_170_normalized'] = qtrans.fit_transform(dataset[['smart_170_normalized']])
dataset['smart_170_raw'] = qtrans.fit_transform(dataset[['smart_170_raw']])
dataset['smart_173_normalized'] = qtrans.fit_transform(dataset[['smart_173_normalized']])
dataset['smart_174_normalized'] = qtrans.fit_transform(dataset[['smart_174_normalized']])
dataset['smart_174_raw'] = qtrans.fit_transform(dataset[['smart_174_raw']])
dataset['smart_175_normalized'] = qtrans.fit_transform(dataset[['smart_175_normalized']])
dataset['smart_177_raw'] = qtrans.fit_transform(dataset[['smart_177_raw']])
dataset['smart_179_normalized'] = qtrans.fit_transform(dataset[['smart_179_normalized']])
dataset['smart_180_normalized'] = qtrans.fit_transform(dataset[['smart_180_normalized']])
dataset['smart_180_raw'] = qtrans.fit_transform(dataset[['smart_180_raw']])
dataset['smart_181_normalized'] = qtrans.fit_transform(dataset[['smart_181_normalized']])
dataset['smart_182_normalized'] = qtrans.fit_transform(dataset[['smart_182_normalized']])
dataset['smart_183_normalized'] = qtrans.fit_transform(dataset[['smart_183_normalized']])
dataset['smart_183_raw'] = qtrans.fit_transform(dataset[['smart_183_raw']])
dataset['smart_184_normalized'] = qtrans.fit_transform(dataset[['smart_184_normalized']])
dataset['smart_184_raw'] = qtrans.fit_transform(dataset[['smart_184_raw']])
dataset['smart_187_normalized'] = qtrans.fit_transform(dataset[['smart_187_normalized']])
dataset['smart_187_raw'] = qtrans.fit_transform(dataset[['smart_187_raw']])
dataset['smart_188_normalized'] = qtrans.fit_transform(dataset[['smart_188_normalized']])
dataset['smart_189_normalized'] = qtrans.fit_transform(dataset[['smart_189_normalized']])
dataset['smart_189_raw'] = qtrans.fit_transform(dataset[['smart_189_raw']])
dataset['smart_191_normalized'] = qtrans.fit_transform(dataset[['smart_191_normalized']])
dataset['smart_191_raw'] = qtrans.fit_transform(dataset[['smart_191_raw']])
dataset['smart_192_normalized'] = qtrans.fit_transform(dataset[['smart_192_normalized']])
dataset['smart_193_normalized'] = qtrans.fit_transform(dataset[['smart_193_normalized']])
dataset['smart_194_normalized'] = qtrans.fit_transform(dataset[['smart_194_normalized']])
dataset['smart_194_raw'] = qtrans.fit_transform(dataset[['smart_194_raw']])
dataset['smart_195_normalized'] = qtrans.fit_transform(dataset[['smart_195_normalized']])
dataset['smart_196_raw'] = qtrans.fit_transform(dataset[['smart_196_raw']])
dataset['smart_197_normalized'] = qtrans.fit_transform(dataset[['smart_197_normalized']])
dataset['smart_198_normalized'] = qtrans.fit_transform(dataset[['smart_198_normalized']])
dataset['smart_199_normalized'] = qtrans.fit_transform(dataset[['smart_199_normalized']])
dataset['smart_199_raw'] = qtrans.fit_transform(dataset[['smart_199_raw']])
dataset['smart_200_normalized'] = qtrans.fit_transform(dataset[['smart_200_normalized']])
dataset['smart_200_raw'] = qtrans.fit_transform(dataset[['smart_200_raw']])
dataset['smart_201_normalized'] = qtrans.fit_transform(dataset[['smart_201_normalized']])
dataset['smart_201_raw'] = qtrans.fit_transform(dataset[['smart_201_raw']])
dataset['smart_202_normalized'] = qtrans.fit_transform(dataset[['smart_202_normalized']])
dataset['smart_210_normalized'] = qtrans.fit_transform(dataset[['smart_210_normalized']])
dataset['smart_218_normalized'] = qtrans.fit_transform(dataset[['smart_218_normalized']])
dataset['smart_218_raw'] = qtrans.fit_transform(dataset[['smart_218_raw']])
dataset['smart_220_normalized'] = qtrans.fit_transform(dataset[['smart_220_normalized']])
dataset['smart_220_raw'] = qtrans.fit_transform(dataset[['smart_220_raw']])
dataset['smart_222_raw'] = qtrans.fit_transform(dataset[['smart_222_raw']])
dataset['smart_223_normalized'] = qtrans.fit_transform(dataset[['smart_223_normalized']])
dataset['smart_223_raw'] = qtrans.fit_transform(dataset[['smart_223_raw']])
dataset['smart_224_normalized'] = qtrans.fit_transform(dataset[['smart_224_normalized']])
dataset['smart_225_normalized'] = qtrans.fit_transform(dataset[['smart_225_normalized']])
dataset['smart_226_normalized'] = qtrans.fit_transform(dataset[['smart_226_normalized']])
dataset['smart_231_normalized'] = qtrans.fit_transform(dataset[['smart_231_normalized']])
dataset['smart_231_raw'] = qtrans.fit_transform(dataset[['smart_231_raw']])
dataset['smart_232_normalized'] = qtrans.fit_transform(dataset[['smart_232_normalized']])
dataset['smart_233_normalized'] = qtrans.fit_transform(dataset[['smart_233_normalized']])
dataset['smart_235_normalized'] = qtrans.fit_transform(dataset[['smart_235_normalized']])
dataset['smart_240_normalized'] = qtrans.fit_transform(dataset[['smart_240_normalized']])
dataset['smart_241_normalized'] = qtrans.fit_transform(dataset[['smart_241_normalized']])
dataset['smart_242_normalized'] = qtrans.fit_transform(dataset[['smart_242_normalized']])
dataset['smart_245_normalized'] = qtrans.fit_transform(dataset[['smart_245_normalized']])
dataset['smart_245_raw'] = qtrans.fit_transform(dataset[['smart_245_raw']])
dataset['smart_247_normalized'] = qtrans.fit_transform(dataset[['smart_247_normalized']])
dataset['smart_250_raw'] = qtrans.fit_transform(dataset[['smart_250_raw']])
dataset['smart_252_normalized'] = qtrans.fit_transform(dataset[['smart_252_normalized']])
# In[26]:
# remove max VIF variables
dataset = dataset.drop(['smart_170_raw'], 1)
dataset = dataset.drop(['smart_180_normalized'], 1)
dataset = dataset.drop(['smart_180_raw'], 1)
dataset = dataset.drop(['smart_201_normalized'], 1)
dataset = dataset.drop(['smart_201_raw'], 1)
dataset = dataset.drop(['smart_218_raw'], 1)
dataset = dataset.drop(['smart_16_raw'], 1)
dataset = dataset.drop(['smart_17_raw'], 1)
dataset = dataset.drop(['smart_245_raw'], 1)
#dataset = dataset.drop(['smart_169_raw'], 1)
# In[13]:
# Download descriptive analysis
#final_colums1=dataset.describe()
#final_colums1.to_csv(r'E:\work\Supermicro\modeling\data_Q2_2019\final_colums1.csv')
# In[65]:
# missing value treatment wth Dropping columns or rows with missing value rate higher than threshold
#threshold = 0.7
#Dropping columns with missing value rate higher than threshold
#dataset = dataset[dataset.columns[dataset.isnull().mean() < threshold]]
# In[27]:
# validation2
validation2=dataset.head(50)
#validation5
# In[28]:
# SPLIT DATA IN TRAIN TEST VALIDAATION
import numpy as np
import pandas as pd
def train_validate_test_split(dataset, train_percent=.7, validate_percent=.1, seed=None):
np.random.seed(seed)
perm = np.random.permutation(dataset.index)
m = len(dataset.index)
train_end = int(train_percent * m)
validate_end = int(validate_percent * m) + train_end
train = dataset.iloc[perm[:train_end]]
validate = dataset.iloc[perm[train_end:validate_end]]
test = dataset.iloc[perm[validate_end:]]
return train, validate, test
df_train, df_validate, df_test = train_validate_test_split(dataset)
# In[20]:
# define the y variyable
#from sklearn.model_selection import train_test_split
#df_train=dataset.sample(frac=0.8,random_state=200) #random state is a seed value
#df_test=dataset.drop(df_train.index)
# In[29]:
# delete
sns.countplot(df_train['failure'])
# In[30]:
# Up-sampling the minority class for train data
valid_train = df_train[df_train['failure'] == 0] #data of HDDs which do not indicate failure
failed_train = df_train[df_train['failure'] == 1] #data of HDDs likely to fail
# In[31]:
print("valid hdds:",len(valid_train)) #storing the total number of valid HDDs
print("failing hdds:",len(failed_train))
# In[32]:
# up-sample # as same length of valid
failed_up = resample(failed_train,replace=True,n_samples=len(valid_train),random_state=27)
# In[33]:
# resample with train data
df_train = pd.concat([valid_train,failed_up])
# In[34]:
#features selection
features = ['serial_number',
'model',
'failure',
'smart_1_normalized',
'smart_2_normalized',
'smart_2_raw',
'smart_3_normalized',
'smart_3_raw',
'smart_4_normalized',
'smart_4_raw',
'smart_5_normalized',
'smart_7_normalized',
'smart_8_raw',
'smart_9_normalized',
'smart_10_normalized',
'smart_11_normalized',
'smart_12_normalized',
'smart_13_normalized',
'smart_16_normalized',
'smart_17_normalized',
'smart_18_normalized',
'smart_22_normalized',
'smart_22_raw',
'smart_23_normalized',
'smart_24_normalized',
'smart_160_normalized',
'smart_160_raw',
'smart_161_normalized',
'smart_161_raw',
'smart_163_normalized',
'smart_163_raw',
'smart_164_normalized',
'smart_165_normalized',
'smart_165_raw',
'smart_166_normalized',
'smart_167_normalized',
'smart_168_normalized',
'smart_168_raw',
'smart_169_normalized',
'smart_170_normalized',
'smart_173_normalized',
'smart_174_normalized',
'smart_174_raw',
'smart_175_normalized',
'smart_176_normalized',
'smart_177_raw',
'smart_178_normalized',
'smart_178_raw',
'smart_179_normalized',
'smart_181_normalized',
'smart_182_normalized',
'smart_183_normalized',
'smart_183_raw',