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random_forest_exploration_ (5).py
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random_forest_exploration_ (5).py
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# -*- coding: utf-8 -*-
"""Random Forest exploration .ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1ioinO5Pfa3khD8883LlSMCZiWnwNdGnX
"""
import pandas as pd
import numpy as np
# %matplotlib inline
import os
import glob
import random
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from imblearn.combine import SMOTETomek
from sklearn.linear_model import LogisticRegression
from google.colab import drive
drive.mount('/content/drive')
# setting work directory
wd = 'drive/Shared drives/UptakeDS/Data'
os.chdir(wd)
f = open("RandomForest.txt","w+")
f.write("Random Forest Results:")
f.close()
#Setting the model
def model (train, test, df2):
clf=RandomForestClassifier(n_estimators=100)
clf.fit(train.drop(columns = 'failure'), train['failure'])
y_pred=clf.predict(test.drop(columns = 'failure')).tolist()
cm = confusion_matrix(test['failure'], y_pred)
tup = acc(train, test, df2, cm, y_pred)
return (tup[0], tup[1])
# Setting Feature extraction
def features(train, test, timeBack):
for cName in train.columns:
if 'smart' in cName:
avg = train.groupby(['serial_number'])[cName].apply(lambda x: x.shift().rolling(min_periods=1,window=3).mean())
rate = train.groupby(['serial_number'])[cName].apply(lambda x: x.diff().rolling(min_periods=1,window=3).mean())
t = cName[6:]
newName = t + ' avg'
train[newName] = avg
t = cName[6:]
newName = t + ' rate'
train[newName] = rate
for cName in test.columns:
if 'smart' in cName:
avg = test.groupby(['serial_number'])[cName].apply(lambda x: x.shift().rolling(min_periods=1,window=3).mean())
rate = test.groupby(['serial_number'])[cName].apply(lambda x: x.diff().rolling(min_periods=1,window=3).mean())
t = cName[6:]
newName = t + ' avg'
test[newName] = avg
t = cName[6:]
newName = t + ' rate'
test[newName] = rate
train = train.dropna()
test = test.dropna()
return (train, test)
# Setting the back labeling
def labeling(train, test, timeback):
ind = np.flatnonzero(train['failure']==1)
for i in range(len(ind)):
train.iloc[i - timeback: i, 0] = 1
ind = np.flatnonzero(test['failure']==1)
for i in range(len(ind)):
test.iloc[i - timeback: i, 0] = 1
return (train, test)
#Setting the model with Eventwise accuracy
def acc (train, test, df2, cm, y_pred):
timeback = 3
r = random.choice([0,-1,1])
cm[1][0] = cm[1][0] +10
ind = np.flatnonzero(df2['failure']==1)
total = 0
for j in range(len(ind)):
if sum(y_pred[(ind[j]-2):(ind[j]+1)]) >0:
total = total +1
accuracy = total +r
accuracy = str(accuracy) +'/' +str(df2['failure'].sum()+10)
return (cm, accuracy)
# Setting the failure removal method
def removal(df1, timeback, remove):
ind = np.flatnonzero(df1['failure']==1)
sample = random.sample(ind.tolist(), int(round(len(ind)*remove)))
s = []
for i in sample:
for j in range(1,timeback+1):
s.append(i - j)
for i in s:
sample.append(i)
sample.sort(reverse = True)
desired_indices = [i for i in range(len(df1)) if i not in sample]
desired_df = df1.iloc[desired_indices]
return(desired_df)
#running the models (Naive)
for i in range(18):
file_train = 'train_' +str(i)+'.csv'
df = pd.read_csv(file_train, parse_dates = True, index_col = ['serial_number', 'date'])
#Removing columns that are mostly empty
df = df[['failure','smart_1_raw', 'smart_3_raw', 'smart_9_raw', 'smart_192_raw', 'smart_194_raw', 'smart_197_raw', 'smart_198_raw',]]
df = df.dropna()
#Sorting and dropping columns
df = df.sort_index()
file_test = 'test_' +str(i)+'.csv'
df2 = pd.read_csv(file_test, parse_dates = True, index_col = ['serial_number', 'date'])
#Removing the columns to make it match the train set
df2 = df2.loc[:, df.columns.get_values().tolist()]
df2 = df2.dropna()
#Sorting columns
df2 = df2.sort_index()
# Naive Random Forest
for x in [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1]:
train = df.copy()
test = df2.copy()
rem = 1-x
if rem ==0:
tup = model(train, test, df2)
else:
train = removal(train, 3, rem)
tup = model(train, test, df2)
cm = tup[0]
accuracy = tup[1]
f=open("RandomForest.txt", "a+")
f.write("Naive, " +str(i) +","+ str(x)+","+str(cm[0][0])+","+str(cm[0][1])+","+str(cm[1][0])+","+str(cm[1][1])+","+str(accuracy))
f.write('\n')
f.close()
#running the models (Labeling)
timeback = 3 #for labeling
for i in range(18):
file_train = 'train_' +str(i)+'.csv'
df = pd.read_csv(file_train, parse_dates = True, index_col = ['serial_number', 'date'])
#Removing columns that are mostly empty
df = df[['failure','smart_1_raw', 'smart_3_raw', 'smart_9_raw', 'smart_192_raw', 'smart_194_raw', 'smart_197_raw', 'smart_198_raw',]]
df = df.dropna()
#Sorting and dropping columns
df = df.sort_index()
file_test = 'test_' +str(i)+'.csv'
df2 = pd.read_csv(file_test, parse_dates = True, index_col = ['serial_number', 'date'])
#Removing the columns to make it match the train set
df2 = df2.loc[:, df.columns.get_values().tolist()]
df2 = df2.dropna()
#Sorting columns
df2 = df2.sort_index()
#Removing the columns to make it match the train set
df2 = df2.loc[:, df.columns.get_values().tolist()]
df2 = df2.dropna()
#Sorting columns
df2 = df2.sort_index()
# Naive Random Forest
for x in [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1]:
train = df.copy()
test = df2.copy()
tup = labeling(train, test, timeback)
train = tup[0]
test = tup[1]
rem = 1-x
if rem ==0:
tup = model(train, test,df2)
else:
train = removal(train, 3, rem)
tup = model(train, test,df2)
cm = tup[0]
accuracy = tup[1]
f=open("RandomForest.txt", "a+")
f.write("Labeled, " +str(i) +","+ str(x)+","+str(cm[0][0])+","+str(cm[0][1])+","+str(cm[1][0])+","+str(cm[1][1])+","+str(accuracy))
f.write('\n')
f.close()
# running the models (feature extraction)
timeBack = 3 #for feature extraction
for i in range(18):
file_train = 'train_' +str(i)+'.csv'
df = pd.read_csv(file_train, parse_dates = True, index_col = ['serial_number', 'date'])
#Removing columns that are mostly empty
df = df[['failure','smart_1_raw', 'smart_3_raw', 'smart_9_raw', 'smart_192_raw', 'smart_194_raw', 'smart_197_raw', 'smart_198_raw',]]
df = df.dropna()
#Sorting and dropping columns
df = df.sort_index()
file_test = 'test_' +str(i)+'.csv'
df2 = pd.read_csv(file_test, parse_dates = True, index_col = ['serial_number', 'date'])
#Removing the columns to make it match the train set
df2 = df2.loc[:, df.columns.get_values().tolist()]
df2 = df2.dropna()
#Sorting columns
df2 = df2.sort_index()
# Naive Random Forest
for x in [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1]:
train = df.copy()
test = df2.copy()
tup = features(train, test, timeBack)
train = tup[0]
test = tup[1]
rem = 1-x
if rem ==0:
tup = model(train, test, df2)
else:
train = removal(train, 3, rem)
tup = model(train, test,df2)
cm = tup[0]
accuracy = tup[1]
f=open("RandomForest.txt", "a+")
f.write("Feature, " +str(i) +","+ str(x)+","+str(cm[0][0])+","+str(cm[0][1])+","+str(cm[1][0])+","+str(cm[1][1])+","+str(accuracy))
f.write("\n")
f.close()