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Classifier.py
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Classifier.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Nov 10 11:47:42 2022
@author: srpv
"""
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.model_selection import RepeatedStratifiedKFold
import joblib
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np
from sklearn import metrics
import pandas as pd
import joblib
from sklearn.model_selection import cross_val_score
from IPython.display import Image
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split# implementing train-test-split
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.neural_network import MLPClassifier
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
import os
#%%
def Dataframe_Manipulation(Distance,target):
df1 = pd.DataFrame(Distance)
df1.columns = ['Distance']
df2 = pd.DataFrame(target)
df2.columns = ['Categorical']
df2=df2['Categorical'].replace(0,'P1')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(1,'P2')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(2,'P3')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(3,'P4')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(4,'P5')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(5,'P6')
df2 = pd.DataFrame(df2)
df=pd.concat([df1,df2], axis=1)
new_columns = list(df.columns)
new_columns[-1] = 'Target'
df.columns = new_columns
df.Target.value_counts()
df = df.sample(frac=1.0)
print(df.shape)
return df
def Dataframe_Manipulation_Classifier(target):
df2 = pd.DataFrame(target)
df2.columns = ['Categorical']
df2=df2['Categorical'].replace(0,'P1')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(1,'P2')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(2,'P3')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(3,'P4')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(4,'P5')
df2 = pd.DataFrame(df2)
df2=df2['Categorical'].replace(5,'P6')
df2 = pd.DataFrame(df2)
return df2
#%%
def classifier(X_train, X_test, y_train, y_test,modelname,CNN):
plt.rcParams.update(plt.rcParamsDefault)
folder = os.path.join('Figures/', 'MLclassifier')
try:
os.makedirs(folder, exist_ok = True)
print("Directory created....")
except OSError as error:
print("Directory already exists....")
print(folder)
folder=folder+'/'
y_train=Dataframe_Manipulation_Classifier(y_train)
y_test=Dataframe_Manipulation_Classifier(y_test)
if modelname == 'LogisticRegression':
model = LogisticRegression(max_iter=1000, random_state=123)
print("this will do the calculation")
elif modelname == 'SVM':
model = SVC(kernel='rbf', probability=True)
elif modelname == 'RF':
model = RandomForestClassifier(n_estimators=100 , oob_score=True)
elif modelname == 'GaussianNB':
model = GaussianNB()
else:
exit()
standard_scaler = StandardScaler()
X_train=standard_scaler.fit_transform(X_train)
X_test=standard_scaler.fit_transform(X_test)
model.fit(X_train,y_train)
predictions = model.predict(X_test)
print(str(modelname))
print(metrics.accuracy_score(y_test, predictions))
print(classification_report(y_test,predictions))
print(confusion_matrix(y_test,predictions))
graph_name1= str(CNN)+'_'+str(modelname)+'_without normalization w/o Opt'
graph_name2= str(CNN)+'_'+str(modelname)
graph_1= str(CNN)+'_'+str(modelname)+'_Confusion_Matrix'+'_'+'No_Opt'+'.png'
graph_2= str(CNN)+'_'+str(modelname)+'_Confusion_Matrix'+'_'+'Opt'+'.png'
plt.rcParams.update(plt.rcParamsDefault)
titles_options = [(graph_name1, None, graph_1),
(graph_name2, 'true', graph_2)]
for title, normalize ,graphname in titles_options:
plt.figure(figsize = (20, 10),dpi=400)
disp = ConfusionMatrixDisplay.from_estimator(model, X_test, y_test,
display_labels=['P1','P2','P3','P4'],
cmap=plt.cm.Reds,xticks_rotation='vertical',
normalize=normalize,values_format = '.2f')
plt.title(title, size = 12)
plt.savefig(os.path.join(folder, graphname),bbox_inches='tight',dpi=400)
savemodel= folder+str(CNN)+'_'+str(modelname)+'_LR'+'_model'+'.sav'
joblib.dump(model, savemodel)
#%%

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