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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import classification_report, confusion_matrix
import joblib
import os
def preprocess(dataset, x_iloc_list, y_iloc, testSize):
# dataset = pd.read_csv(csv_file)
X = dataset.iloc[:, x_iloc_list].values
y = dataset.iloc[:, y_iloc].values
# split into training and testing set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = testSize, random_state = 0)
# standardization of values
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
return X_train, X_test, y_train, y_test
class classification:
def __init__(self, X_train, X_test, y_train, y_test):
self.X_train = X_train
self.X_test = X_test
self.y_train = y_train
self.y_test = y_test
# Contour Graph if no. of feature is 2
def classification_view(self, X_train, y_train, classifier):
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.figure(figsize=(16,8))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.4, cmap = ListedColormap(('#F5716C', '#39A861')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('#F5716C', '#39A861'))(i), label = j)
plt.title('Visualization of how the classification is made: errors are identified where the color of the\
point and the background are different')
plt.legend()
plt.show()
def accuracy(self, confusion_matrix):
sum, total = 0,0
for i in range(len(confusion_matrix)):
for j in range(len(confusion_matrix[0])):
if i == j:
sum += confusion_matrix[i,j]
total += confusion_matrix[i,j]
return sum/total
def classification_report_plot(self, clf_report, filename):
folder = "clf_plots"
if not os.path.isdir(folder):
os.mkdir(folder)
out_file_name = folder + "/" + filename + ".png"
fig=plt.figure(figsize=(16,10))
sns.set(font_scale=4)
sns.heatmap(pd.DataFrame(clf_report).iloc[:-1, :].T, annot=True, cmap="Greens")
fig.savefig(out_file_name, bbox_inches="tight")
def LR(self):
from sklearn.linear_model import LogisticRegression
lr_classifier = LogisticRegression()
lr_classifier.fit(self.X_train, self.y_train)
joblib.dump(lr_classifier, "model/lr.sav")
y_pred = lr_classifier.predict(self.X_test)
print("\n")
print("### Logistic Regression Classifier ###")
print('Classification Report: ')
print(classification_report(self.y_test, y_pred),'\n')
print('Confusion Matrix: ')
print(confusion_matrix(self.y_test, y_pred),'\n')
print('Precision: ', self.accuracy(confusion_matrix(self.y_test, y_pred))*100,'%')
self.classification_report_plot(classification_report(self.y_test, y_pred, \
output_dict=True), "LR")
if len(self.X_train[0]) == 2:
self.classification_view(self.X_train, self.y_train, lr_classifier)
def KNN(self):
from sklearn.neighbors import KNeighborsClassifier
knn_classifier = KNeighborsClassifier()
knn_classifier.fit(self.X_train, self.y_train)
joblib.dump(knn_classifier, "model/knn.sav")
y_pred = knn_classifier.predict(self.X_test)
print("\n")
print("### K-Neighbors Classifier ###")
print('Classification Report: ')
print(classification_report(self.y_test, y_pred),'\n')
print('Confusion Matrix: ')
print(confusion_matrix(self.y_test, y_pred),'\n')
print('Precision: ', self.accuracy(confusion_matrix(self.y_test, y_pred))*100,'%')
self.classification_report_plot(classification_report(self.y_test, y_pred, \
output_dict=True), "KNN")
if len(self.X_train[0]) == 2:
self.classification_view(self.X_train, self.y_train, knn_classifier)
# kernel type could be 'linear' or 'rbf' (Gaussian)
def SVM(self, kernel_type):
from sklearn.svm import SVC
svm_classifier = SVC(kernel = kernel_type)
svm_classifier.fit(self.X_train, self.y_train)
joblib.dump(svm_classifier, "model/svm.sav")
y_pred = svm_classifier.predict(self.X_test)
print("\n")
print("### Support Vector Classifier (" + kernel_type + ") ###")
print('Classification Report: ')
print(classification_report(self.y_test, y_pred),'\n')
print('Confusion Matrix: ')
print(confusion_matrix(self.y_test, y_pred),'\n')
print('Precision: ', self.accuracy(confusion_matrix(self.y_test, y_pred))*100,'%')
self.classification_report_plot(classification_report(self.y_test, y_pred, \
output_dict=True), "SVC"+kernel_type)
if len(self.X_train[0]) == 2:
self.classification_view(self.X_train, self.y_train, svm_classifier)
def NB(self):
from sklearn.naive_bayes import GaussianNB
nb_classifier = GaussianNB()
nb_classifier.fit(self.X_train, self.y_train)
joblib.dump(nb_classifier, "model/nb.sav")
y_pred = nb_classifier.predict(self.X_test)
print("\n")
print("### Naive Bayes Classifier ###")
print('Classification Report: ')
print(classification_report(self.y_test, y_pred),'\n')
print('Confusion Matrix: ')
print(confusion_matrix(self.y_test, y_pred),'\n')
print('Precision: ', self.accuracy(confusion_matrix(self.y_test, y_pred))*100,'%')
self.classification_report_plot(classification_report(self.y_test, y_pred, \
output_dict=True), "NB")
if len(self.X_train[0]) == 2:
self.classification_view(self.X_train, self.y_train, nb_classifier)
def DT(self):
from sklearn.tree import DecisionTreeClassifier
tree_classifier = DecisionTreeClassifier()
tree_classifier.fit(self.X_train, self.y_train)
joblib.dump(tree_classifier, "model/tree.sav")
y_pred = tree_classifier.predict(self.X_test)
print("\n")
print("### Decision Tree Classifier ###")
print('Classification Report: ')
print(classification_report(self.y_test, y_pred),'\n')
print('Confusion Matrix: ')
print(confusion_matrix(self.y_test, y_pred),'\n')
print('Precision: ', self.accuracy(confusion_matrix(self.y_test, y_pred))*100,'%')
self.classification_report_plot(classification_report(self.y_test, y_pred, \
output_dict=True), "DT")
if len(self.X_train[0]) == 2:
self.classification_view(self.X_train, self.y_train, tree_classifier)
def RF(self):
from sklearn.ensemble import RandomForestClassifier
rf_classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy')
rf_classifier.fit(self.X_train, self.y_train)
joblib.dump(rf_classifier, "model/rf.sav")
y_pred = rf_classifier.predict(self.X_test)
print("\n")
print("### Random Forest Classifier ###")
print('Classification Report: ')
print(classification_report(self.y_test, y_pred),'\n')
print('Confusion Matrix: ')
print(confusion_matrix(self.y_test, y_pred),'\n')
print('Precision: ', self.accuracy(confusion_matrix(self.y_test, y_pred))*100,'%')
self.classification_report_plot(classification_report(self.y_test, y_pred, \
output_dict=True), "RF")
if len(self.X_train[0]) == 2:
self.classification_view(self.X_train, self.y_train, rf_classifier)
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