def build_tree(self):
idxs = np.random.permutation(len(self.y))[:self.n_sample]
return DecisionTreeClassifier(self.x, self.y, idxs)
knn_classifier = KnnClassifier() knn_classifier.setKnn(knn) #CONFIGURACAO DO SVM svm = SvmModule() svm_classifier = SvmClassifier() svm_classifier.setSvm(svm) #CONFIGURACAO DO RF rf = RfModule() rf_classifier = RfClassifier() rf_classifier.setRf(rf) #CONFIGURACAO DO RF dt = DecisionTreeModule() dt_classifier = DecisionTreeClassifier() dt_classifier.setDecisionTree(dt) #CONFIGURACAO DA NAIVEBAYES naive_bayes = NaiveBayesModule() naive_bayes_classifier = NaiveBayesClassifier() naive_bayes_classifier.setNaiveBayes(naive_bayes) #CONFIGURACAO DO LSTM lstm = LstmModule() lstm.setInputLength(20) lstm.setNumberExamples(1000) lstm_classifier = LstmClassifier() lstm_classifier.setLstm(lstm) #CONFIGURACAO DA REDE NEURAL
from decision_tree_classifier import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
if __name__ == '__main__':
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
iris.data, iris.target,
test_size=0.2,
random_state=1234,
stratify=iris.target
)
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
clf.describe_tree()
y_pred = clf.predict(X_test)
print(classification_report(y_true=y_test, y_pred=y_pred, target_names=iris.target_names))