def test_random_forest_classifier_fit():
mp_table = MyPyTable(interview_header, interview_table)
# Formulate X_train and y_train
y_train = mp_table.get_column('interviewed_well')
X_train_col_names = ["level", "lang", "tweets", "phd"]
X_train = mp_table.get_rows(X_train_col_names)
myRF = MyRandomForestClassifier(N=4, M=2, F=4)
myRF.fit(X_train, y_train)
assert len(myRF.M_attr_sets) == myRF.M
def test_random_forest_classifier_predict():
X_test = [["Mid", "Python", "no", "no", "True"],
["Mid", "R", "yes", "yes", "True"],
["Mid", "Python", "no", "yes", "True"]]
y_test = ["True", "True", "True"]
mp_table = MyPyTable(interview_header, interview_table)
# Formulate X_train and y_train
y_train = mp_table.get_column('interviewed_well')
X_train_col_names = ["level", "lang", "tweets", "phd"]
X_train = mp_table.get_rows(X_train_col_names)
myRF = MyRandomForestClassifier(N=4, M=2, F=4)
myRF.fit(X_train, y_train)
predictions = myRF.predict(X_test)
for i in range(0, len(predictions)):
assert predictions[i] == y_test[i]