Exemplo n.º 1
0
def test_My_Random_Forest_Classifier_predict():
    # Object Declarations
    # Tests with N = 3, M = 2, F = 2 and seed = 1
    rand_forest_test = MyRandomForestClassifier(3, 2, 2, 1)
    table = MyPyTable()

    # Variable Assignment and Declaration
    table.data = interview_table
    table.column_names = interview_header

    y_train, X_train = [], []
    for inst in interview_table:
        y_train.append(inst[-1])
        X_train.append(inst[:-1])

    # Sets X_test
    X_test = [["Junior", "Java", "yes", "no"],
              ["Junior", "Java", "yes", "yes"]]

    # Tests on the Interview Dataset
    rand_forest_test.header = interview_header[:-1]
    rand_forest_test.fit(X_train, y_train)
    y_predicted = rand_forest_test.predict(X_test)

    print("y_predicted:", y_predicted)

    # Trace Test

    assert y_predicted == ['True', 'False']
Exemplo n.º 2
0
def test_My_Random_Forest_Classifier_fit():
    # Object Declarations
    # Tests with N = 3, M = 2, F = 2 and seed = 0
    rand_forest_test = MyRandomForestClassifier(3, 2, 2, 0)
    table = MyPyTable()

    # Variable Assignment and Declaration
    table.data = interview_table
    table.column_names = interview_header

    X_test = interview_table
    y_train = table.get_column("interviewed_well")

    # Tests on the Interview Dataset
    rand_forest_test.header = interview_header
    rand_forest_test.fit(X_test, y_train)

    trees = rand_forest_test.trees