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Classifier.py
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Sat, May 4, 20:27

Classifier.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report,confusion_matrix
import itertools
import os
from sklearn import metrics
import pydot
import collections
import pydotplus
import os
import pydotplus
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import RandomizedSearchCV
from sklearn.feature_selection import SelectFromModel
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.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from Plots import *
#%%
def LR(X_train, X_test, y_train, y_test):
model = LogisticRegression(max_iter=1000, random_state=123)
model.fit(X_train,y_train)
predictions = model.predict(X_test)
print("LogisticRegression Accuracy:",metrics.accuracy_score(y_test, predictions))
print(classification_report(y_test,predictions))
print(confusion_matrix(y_test,predictions))
graph_name1= 'LR'+'_without normalization w/o Opt'
graph_name2= 'Logistic Regression'
graph_1= 'LR'+'_Confusion_Matrix'+'_'+'No_Opt'+'.png'
graph_2= 'LR'+'_Confusion_Matrix'+'_'+'Opt'+'.png'
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 = plot_confusion_matrix(model, X_test, y_test,
display_labels=['P1','P2','P3','P4','P5','P6'],
cmap=plt.cm.Reds,xticks_rotation='vertical',
normalize=normalize,values_format = '.2f')
plt.title(title, size = 12)
plt.savefig(graphname,bbox_inches='tight',dpi=400)
savemodel= 'LR'+'_model'+'.sav'
joblib.dump(model, savemodel)
#%%
train_embeddings = 'train_embeddings'+'_'+ '.npy'
train_labelsname = 'train_labels'+'_'+'.npy'
test_embeddings = 'test_embeddings'+'_'+ '.npy'
test_labelsname = 'test_labels'+'_'+'.npy'
X_train = np.load(train_embeddings).astype(np.float64)
y_train = np.load(train_labelsname).astype(np.float64)
y_train=Dataframe_Manipulation_Classifier(y_train)
X_test = np.load(test_embeddings).astype(np.float64)
y_test = np.load(test_labelsname).astype(np.float64)
y_test=Dataframe_Manipulation_Classifier(y_test)
#%%
LR(X_train, X_test, y_train, y_test)

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