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def test_dt_increase_acc(dummy_titanic):
    X_train, y_train, X_test, y_test = dummy_titanic

    acc_list = []
    auc_list = []
    for num_trees in [1, 3, 7, 15]:
        rf = RandomForest(num_trees=num_trees, depth_limit=7, col_subsampling=0.7, row_subsampling=0.7)
        rf.fit(X_train, y_train)
        pred = rf.predict(X_test)
        pred_binary = np.round(pred)

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def test_dt_training_time(dummy_titanic):
    X_train, y_train, X_test, y_test = dummy_titanic

    # Standardize to use depth = 10
    dt = DecisionTree(depth_limit=10)
    latency_array = np.array([train_with_time(dt, X_train, y_train)[1] for i in range(100)])
    time_p95 = np.quantile(latency_array, 0.95)
    assert time_p95 < 1.0, 'Training time at 95th percentile should be < 1.0 sec'

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def test_dt_evaluation(dummy_titanic_dt, dummy_titanic):
    model = dummy_titanic_dt
    X_train, y_train, X_test, y_test = dummy_titanic
    pred_test = model.predict(X_test)
    pred_test_binary = np.round(pred_test)
    acc_test = accuracy_score(y_test, pred_test_binary)
    auc_test = roc_auc_score(y_test, pred_test)

    assert acc_test > 0.82, 'Accuracy on test should be > 0.82'
    assert auc_test > 0.84, 'AUC ROC on test should be > 0.84'

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def test_dt_directional_expectation(dummy_titanic_dt, dummy_passengers):
    model = dummy_titanic_dt
    _, p2 = dummy_passengers

    # Get original survival probability of passenger 2
    test_df = pd.DataFrame.from_dict([p2], orient='columns')
    X, y = get_feats_and_labels(prep_df(test_df))
    p2_prob = model.predict(X)[0]  # 1.0

    # Change gender from female to male

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@pytest.fixture
def dummy_passengers():
    # Based on passenger 2 (high passenger class female)
    passenger2 = {'PassengerId': 2,
                  'Pclass': 1,
                  'Name': ' Mrs. John',
                  'Sex': 'female',
                  'Age': 38.0,
                  'SibSp': 1,
                  'Parch': 0,

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def test_dt_increase_acc(dummy_titanic):
    X_train, y_train, _, _ = dummy_titanic
    acc_list = []
    auc_list = []
    for depth in range(1, 10):
        dt = DecisionTree(depth_limit=depth)
        dt.fit(X_train, y_train)
        pred = dt.predict(X_train)
        pred_binary = np.round(pred)
        acc_list.append(accuracy_score(y_train, pred_binary))

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@pytest.fixture
def dummy_feats_and_labels():
    feats = np.array([[0.7057, -5.4981, 8.3368, -2.8715],
                      [2.4391, 6.4417, -0.80743, -0.69139],
                      [-0.2062, 9.2207, -3.7044, -6.8103],
                      [4.2586, 11.2962, -4.0943, -4.3457],
                      [-2.343, 12.9516, 3.3285, -5.9426],
                      [-2.0545, -10.8679, 9.4926, -1.4116],
                      [2.2279, 4.0951, -4.8037, -2.1112],
                      [-6.1632, 8.7096, -0.21621, -3.6345],

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def test_data_leak_in_test_data(dummy_titanic_df):
    train, test = dummy_titanic_df

    concat_df = pd.concat([train, test])
    concat_df.drop_duplicates(inplace=True)

    assert concat_df.shape[0] == train.shape[0] + test.shape[0]

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def test_dt_output_range(dummy_titanic):
    X_train, y_train, X_test, y_test = dummy_titanic
    dt = DecisionTree()
    dt.fit(X_train, y_train)
    pred_train = dt.predict(X_train)
    pred_test = dt.predict(X_test)

    assert (pred_train <= 1).all() & (pred_train >= 0).all(), 'Decision tree output should range from 0 to 1 inclusive'
    assert (pred_test <= 1).all() & (pred_test >= 0).all(), 'Decision tree output should range from 0 to 1 inclusive'

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def test_dt_output_shape(dummy_titanic):
    X_train, y_train, X_test, y_test = dummy_titanic
    dt = DecisionTree()
    dt.fit(X_train, y_train)
    pred_train = dt.predict(X_train)
    pred_test = dt.predict(X_test)

    assert pred_train.shape == (X_train.shape[0],), 'DecisionTree output should be same as training labels.'
    assert pred_test.shape == (X_test.shape[0],), 'DecisionTree output should be same as testing labels.'