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classification.py
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190 lines (160 loc) · 10.8 KB
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# Clear any created variables
#%reset -f
import sys
import os
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.metrics import precision_recall_fscore_support as score, accuracy_score, classification_report
from sklearn.model_selection import StratifiedKFold
def main(dataset_processed_path):
random_state = 0
output_range = range(0,6)
dataset = pd.read_csv(dataset_processed_path)
dataset = dataset.dropna()
#Features & Output Split
X = dataset.iloc[:, 0: 10].values
y = dataset.iloc[:, 10: 16].values
for i in output_range:
index_of_y_to_classify = i
if index_of_y_to_classify == 0:
folder_name = '1-is_normal/'
elif index_of_y_to_classify == 1:
folder_name = '2-affected_component/'
elif index_of_y_to_classify == 2:
folder_name = '3-scenario/'
elif index_of_y_to_classify == 3:
folder_name = '4-operational-scenario/'
elif index_of_y_to_classify == 4:
folder_name = '5-combined_affected_component/'
elif index_of_y_to_classify == 5:
folder_name = '6-combined_scenario/'
kfold = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 0)
counter = 0
for train, test in kfold.split(X, y[:, index_of_y_to_classify]):
X_train = X[train]
y_train = y[train, index_of_y_to_classify]
X_test = X[test]
y_test = y[test, index_of_y_to_classify]
#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state = random_state)
#Normalization
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
# Begin Classification
y_current_train = y_train
y_current_test = y_test
# 1- linear regression
linear_classifier = LogisticRegression(random_state = random_state)
linear_classifier.fit(X_train, y_current_train)
cm_linear = pd.crosstab(y_current_test, linear_classifier.predict(X_test))
accuracy_linear = pd.DataFrame(classification_report(y_current_test, linear_classifier.predict(X_test), output_dict = True)).transpose()
# accuracies_linear = cross_val_score(estimator=linear_classifier, X = X_new, y = y_new, cv = k_cross_val)
l1, l2, l3, l4 = score(y_current_test, linear_classifier.predict(X_test))
# 2- KNN
knn_classifier = KNeighborsClassifier()
knn_classifier.fit(X_train, y_current_train)
cm_knn = pd.crosstab(y_current_test, knn_classifier.predict(X_test))
accuracy_knn = pd.DataFrame(classification_report(y_current_test, knn_classifier.predict(X_test), output_dict = True)).transpose()
# accuracies_KNN = cross_val_score(estimator=knn_classifier, X = X_new, y = y_new, cv = k_cross_val)
k1, k2, k3, k4 = score(y_current_test, knn_classifier.predict(X_test))
# 3- SVM
svm_classifier = SVC(kernel = 'linear', random_state = random_state)
svm_classifier.fit(X_train, y_current_train)
cm_svm = pd.crosstab(y_current_test, svm_classifier.predict(X_test))
accuracy_svm = pd.DataFrame(classification_report(y_current_test, svm_classifier.predict(X_test), output_dict = True)).transpose()
# accuracies_svm = cross_val_score(estimator=svm_classifier, X = X_new, y = y_new, cv = k_cross_val)
s1, s2, s3, s4 = score(y_current_test, svm_classifier.predict(X_test))
#4- Kernel SVM
kernel_svm_classifier = SVC(kernel = 'rbf', random_state = random_state)
kernel_svm_classifier.fit(X_train, y_current_train)
cm_kernel_svm = pd.crosstab(y_current_test, kernel_svm_classifier.predict(X_test))
accuracy_kernel_svm = pd.DataFrame(classification_report(y_current_test, kernel_svm_classifier.predict(X_test), output_dict = True)).transpose()
# accuracies_kernel_svm = cross_val_score(estimator=kernel_svm_classifier, X = X_new, y = y_new, cv = k_cross_val)
sv1, sv2, sv3, sv4 = score(y_current_test, kernel_svm_classifier.predict(X_test))
#5- Naive Bayes
naive_classifier = GaussianNB()
naive_classifier.fit(X_train, y_current_train)
cm_naive = pd.crosstab(y_current_test, naive_classifier.predict(X_test))
accuracy_naive =pd.DataFrame(classification_report(y_current_test, naive_classifier.predict(X_test), output_dict = True)).transpose()
# accuracies_naive = cross_val_score(estimator=naive_classifier, X = X_new, y = y_new, cv = k_cross_val)
n1, n2, n3, n4 = score(y_current_test, naive_classifier.predict(X_test))
#6- Decision Tree
decision_tree_classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = random_state)
decision_tree_classifier.fit(X_train, y_current_train)
cm_decision_tree = pd.crosstab(y_current_test, decision_tree_classifier.predict(X_test))
accuracy_decision_tree = pd.DataFrame(classification_report(y_current_test, decision_tree_classifier.predict(X_test), output_dict = True)).transpose()
# accuracies_decision_tree = cross_val_score(estimator=decision_tree_classifier, X = X_new, y = y_new, cv = k_cross_val)
d1, d2, d3, d4 = score(y_current_test, decision_tree_classifier.predict(X_test))
#7- Random Forest
random_forest_classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = random_state)
random_forest_classifier.fit(X_train, y_current_train)
cm_random_forest = pd.crosstab(y_current_test, random_forest_classifier.predict(X_test))
accuracy_random_forest = pd.DataFrame(classification_report(y_current_test, random_forest_classifier.predict(X_test), output_dict = True)).transpose()
# accuracies_random_forest = cross_val_score(estimator=random_forest_classifier, X = X_new, y = y_new, cv = k_cross_val)
r1, r2, r3, r4 = score(y_current_test, random_forest_classifier.predict(X_test))
if os.path.isdir(folder_name) == False:
os.mkdir(folder_name)
cm_linear.to_csv(folder_name + 'cm_linear.csv')
cm_knn.to_csv(folder_name + 'cm_knn.csv')
cm_svm.to_csv(folder_name + 'cm_svm.csv')
cm_kernel_svm.to_csv(folder_name + 'cm_kernel_svm.csv')
cm_naive.to_csv(folder_name + 'cm_naive.csv')
cm_decision_tree.to_csv(folder_name + 'cm_decision_tree.csv')
cm_random_forest.to_csv(folder_name + 'cm_random_forest.csv')
with open(folder_name + 'Accuracies.csv', 'a') as cv_file:
cv_file.write('\n{}\n'.format(counter))
cv_file.write('\n{}\n'.format('Linear'))
accuracy_linear.to_csv(cv_file, header=True)
cv_file.write('\n{}\n'.format('Naive'))
accuracy_naive.to_csv(cv_file, header=False)
cv_file.write('\n{}\n'.format('KNN'))
accuracy_knn.to_csv(cv_file, header=False)
cv_file.write('\n{}\n'.format('SVM'))
accuracy_svm.to_csv(cv_file, header=False)
cv_file.write('\n{}\n'.format('Kern SVM'))
accuracy_kernel_svm.to_csv(cv_file, header=False)
cv_file.write('\n{}\n'.format('DT'))
accuracy_decision_tree.to_csv(cv_file, header=False)
cv_file.write('\n{}\n'.format('RF'))
accuracy_random_forest.to_csv(cv_file, header=False)
# df.to_csv(f, header=False)
# cv_file.write('\nLinear , ' + str(accuracy_linear))
# cv_file.write('\nNaive , ' + str(accuracy_naive))
# cv_file.write('\nKNN , ' + str(accuracy_knn))
# cv_file.write('\nSVM , ' + str(accuracy_svm))
# cv_file.write('\nKern SVM , ' + str(accuracy_kernel_svm))
# cv_file.write('\nDecision Trees , ' + str(accuracy_decision_tree))
# cv_file.write('\nRandom Forest , ' + str(accuracy_random_forest))
#
# with open(folder_name + 'CrossValidation.csv', 'a') as cv_file:
# cv_file.write('\nLinear , ' + str(accuracies_linear.mean()) + " , " + str(accuracies_linear.std()))
# cv_file.write('\nNaive , ' + str(accuracies_naive.mean()) + " , " + str(accuracies_naive.std()))
# cv_file.write('\nKNN , ' + str(accuracies_KNN.mean()) + " , " + str(accuracies_KNN.std()))
# cv_file.write('\nSVM , ' + str(accuracies_svm.mean()) + " , " + str(accuracies_svm.std()))
# cv_file.write('\nKern SVM , ' + str(accuracies_kernel_svm.mean()) + " , " + str(accuracies_kernel_svm.std()))
# cv_file.write('\nDecision Trees , ' + str(accuracies_decision_tree.mean()) + ", " + str(accuracies_decision_tree.std()))
# cv_file.write('\nRandom Forest , ' + str(accuracies_random_forest.mean()) + " , " + str(accuracies_random_forest.std()))
#
with open(folder_name + 'all_scores.csv', 'a') as cv_file:
cv_file.write('\n{}\n'.format(counter))
cv_file.write('\nLinear , ' + 'percision {} \nrecall {}\n {}\nSupport {}'.format(l1, l2, l3, l4))
cv_file.write('\nNaive , ' + 'percision {} \nrecall {}\n {}\nSupport {}'.format(n1, n2, n3, n4))
cv_file.write('\nKNN , ' + 'percision {} \nrecall {}\n {}\nSupport {}'.format(k1, k2, k3, k4))
cv_file.write('\nSVM , ' + 'percision {} \nrecall {}\n {}\nSupport {}'.format(s1, s2, s3, s4))
cv_file.write('\nKern SVM , ' + 'percision {} \nrecall {}\n {}\nSupport {}'.format(sv1, sv2, sv3, sv4))
cv_file.write('\nDecision Trees , ' + 'percision {} \nrecall {}\n {}\nSupport {}'.format(d1, d2, d3, d4))
cv_file.write('\nRandom Forest , ' + 'percision {} \nrecall {}\n {}\nSupport {}'.format(r1, r2, r3, r4))
counter += 1
if __name__ == "__main__":
if len(sys.argv) > 1:
path = sys.argv[1]
else:
path = os.getcwd() + '/dataset_processed.csv'
main(path)