Create sthelper.py

sthelper.py

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+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import VotingClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import  GaussianNB
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+class StHelper:
+
+    def __init__(self,X,y):
+        self.X = X
+        self.y = y
+        # Apply train test split
+        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=42)
+
+    def create_base_estimators(self,estimators,voting_type):
+
+        algos = []
+
+        if 'KNN' in estimators:
+            knn_clf = KNeighborsClassifier()
+            algos.append(('knn', knn_clf))
+        if 'Logistic Regression' in estimators:
+            log_clf = LogisticRegression(solver="lbfgs", random_state=42)
+            algos.append(('lr', log_clf))
+        if 'Gaussian Naive Bayes' in estimators:
+            gnb_clf = GaussianNB()
+            algos.append(('gnb', gnb_clf))
+        if 'SVM' in estimators:
+            if voting_type == "hard":
+                svm_clf = SVC(gamma="scale", random_state=42)
+            else:
+                svm_clf = SVC(gamma="scale", probability=True, random_state=42)
+            algos.append(('svc', svm_clf))
+        if 'Random Forest' in estimators:
+            rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)
+            algos.append(('rf', rnd_clf))
+
+        return algos
+
+    def train_voting_classifier(self,algos, voting_type):
+
+        voting_clf = VotingClassifier(
+            estimators=algos,
+            voting=voting_type)
+
+        voting_clf.fit(self.X_train, self.y_train)
+        y_pred = voting_clf.predict(self.X_test)
+
+        accuracy = accuracy_score(self.y_test, y_pred)
+
+        return voting_clf, accuracy
+
+    def draw_main_graph(self,voting_clf,ax):
+
+        XX, YY, input_array = self.draw_meshgrid()
+        labels = voting_clf.predict(input_array)
+        ax.contourf(XX, YY, labels.reshape(XX.shape), alpha=0.5, cmap='rainbow')
+
+    def plot_other_graphs(self,algos):
+
+        figs = []
+        XX, YY, input_array = self.draw_meshgrid()
+
+        for estimator in algos:
+            estimator[1].fit(self.X_train, self.y_train)
+            labels = estimator[1].predict(input_array)
+            fig1, ax1 = plt.subplots()
+            ax1.contourf(XX, YY, labels.reshape(XX.shape), alpha=0.5, cmap='rainbow')
+            figs.append(fig1)
+
+        return figs
+
+    def calculate_base_model_accuracy(self,algos):
+
+        accuracy_scores = []
+
+        for model in algos:
+            model[1].fit(self.X_train, self.y_train)
+            y_pred = model[1].predict(self.X_test)
+            accuracy_scores.append(accuracy_score(self.y_test, y_pred))
+
+        return accuracy_scores
+
+    def draw_meshgrid(self):
+        a = np.arange(start=self.X[:, 0].min() - 1, stop=self.X[:, 0].max() + 1, step=0.01)
+        b = np.arange(start=self.X[:, 1].min() - 1, stop=self.X[:, 1].max() + 1, step=0.01)
+
+        XX, YY = np.meshgrid(a, b)
+
+        input_array = np.array([XX.ravel(), YY.ravel()]).T
+
+        return XX, YY, input_array
+
+        labels = voting_clf.predict(input_array)