def main(): print("-- Classification Tree --") data = datasets.load_iris() X = data.data y = data.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4) clf = ClassificationTree(max_features=2) clf.fit(X_train, y_train) y_pred = clf.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print("Our DTClassifier Accuracy:", accuracy) clf = tree.DecisionTreeClassifier() clf.fit(X_train, y_train) y_pred = clf.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print("sklearn DTClassifier Accuracy:", accuracy)
def main(): print('-- Classification Tree') data = datasets.load_iris() X = data.data y = data.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4) clf = ClassificationTree() clf.fit(X_train, y_train) y_pred = clf.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print('Accuracy: ', accuracy) dt = DecisionTreeClassifier() dt.fit(X_train, y_train) y_val = dt.predict(X_test) acc = accuracy_score(y_test, y_val) print('sklearn score:', acc)
# dataset.append(Instance( [ncfr, dur, dfl, a3fl, a1fl, gr], [target] )) except Exception as e: pass model = ClassificationTree() X = standardize(X) # X = RobustScaler(quantile_range=(25, 75)).fit_transform(X) # X = MinMaxScaler().fit_transform(X) # # # print y datasets = k_fold_cross_validation_sets(X, y, 3) for data in datasets: X_train, X_test, y_train, y_test = data model.fit(X_train, y_train) y_pred = model.predict(X_test) print(accuracy_score(y_test, y_pred)) # print accuracy_score(y_test, y_pred) mse = mean_squared_error(y_test, y_pred) print("Mean Squared Error:", mse) # print (model.predict(np.array([[178, 0, 1000, 936]]))) print(model.predict(np.array([[178, 0, 1000, 936, 855, 237000000, 0]]))) # model.print_tree() filename = 'finalized_model.pikle' pickle.dump(model, open(filename, 'wb')) model_a = pickle.load(open(filename, 'rb')) # print (model_a.predict(np.array([[178, 0, 1000, 936, 855, 237000000, 0]])))
random_forest.fit(X_train, y_train) print "\tSupport Vector Machine" support_vector_machine.fit(X_train, rescaled_y_train) # ......... # PREDICT # ......... y_pred = {} y_pred["Adaboost"] = adaboost.predict(X_test) y_pred["Naive Bayes"] = naive_bayes.predict(X_test) y_pred["K Nearest Neighbors"] = knn.predict(X_test, X_train, y_train) y_pred["Logistic Regression"] = logistic_regression.predict(X_test) y_pred["LDA"] = lda.predict(X_test) y_pred["Multilayer Perceptron"] = mlp.predict(X_test) y_pred["Perceptron"] = perceptron.predict(X_test) y_pred["Decision Tree"] = decision_tree.predict(X_test) y_pred["Random Forest"] = random_forest.predict(X_test) y_pred["Support Vector Machine"] = support_vector_machine.predict(X_test) # .......... # ACCURACY # .......... print print "Accuracy:" for clf in y_pred: if clf == "Adaboost" or clf == "Support Vector Machine": print "\t%-23s: %.5f" % (clf, accuracy_score(rescaled_y_test, y_pred[clf])) else: print "\t%-23s: %.5f" % (clf, accuracy_score(y_test, y_pred[clf])) print
# ......... # PREDICT # ......... y_pred = {} y_pred["Adaboost"] = adaboost.predict(X_test) y_pred["Gradient Boosting"] = gbc.predict(X_test) y_pred["Naive Bayes"] = naive_bayes.predict(X_test) y_pred["K Nearest Neighbors"] = knn.predict(X_test, X_train, y_train) y_pred["Logistic Regression"] = logistic_regression.predict(X_test) y_pred["LDA"] = lda.predict(X_test) y_pred["Multilayer Perceptron"] = mlp.predict(X_test) y_pred["Perceptron"] = perceptron.predict(X_test) y_pred["Decision Tree"] = decision_tree.predict(X_test) y_pred["Random Forest"] = random_forest.predict(X_test) y_pred["Support Vector Machine"] = support_vector_machine.predict(X_test) y_pred["XGBoost"] = xgboost.predict(X_test) # .......... # ACCURACY # .......... print ("Accuracy:") for clf in y_pred: if clf == "Adaboost" or clf == "Support Vector Machine": print ("\t%-23s: %.5f" %(clf, accuracy_score(rescaled_y_test, y_pred[clf]))) else: print ("\t%-23s: %.5f" %(clf, accuracy_score(y_test, y_pred[clf])))