Abhi001vj / testing-ml

🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance.

testing-ml

How to test machine learning code. In this example, we'll test a numpy implementation of DecisionTree and RandomForest via:

• Pre-train tests to ensure correct implementation
• Post-train tests to ensure expected learned behaviour
• Evaluation to ensure satisfactory model performance

Accompanying article: How to Test Machine Learning Code and Systems. Inspired by @jeremyjordan's Effective Testing for Machine Learning Systems.

Quick Start

# Clone and setup environment
git clone https://github.com/eugeneyan/testing-ml.git
cd testing-ml
make setup

# Run test suite
make check

Standard software habits

• Unit test fixture reuse, exceptions testing with pytest
• Code coverage with Coverage.py and pytest-cov
• Linting to ensure code consistency with pylint
• Type checks to verify type correctness with mypy

More details here: How to Set Up a Python Project For Automation and Collaboration (GitHub repo)

Pre-train tests to ensure correct implementation

• Test implementation of Gini impurity and gain

def test_gini_impurity():
    assert round(gini_impurity([1, 1, 1, 1, 1, 1, 1, 1]), 3) == 0
    assert round(gini_impurity([1, 1, 1, 1, 1, 1, 0, 0]), 3) == 0.375
    assert round(gini_impurity([1, 1, 1, 1, 0, 0, 0, 0]), 3) == 0.500


def test_gini_gain():
    assert round(gini_gain([1, 1, 1, 1, 0, 0, 0, 0], [[1, 1, 1, 1], [0, 0, 0, 0]]), 3) == 0.5
    assert round(gini_gain([1, 1, 1, 1, 0, 0, 0, 0], [[1, 1, 1, 0], [0, 0, 0, 1]]), 3) == 0.125
    assert round(gini_gain([1, 1, 1, 1, 0, 0, 0, 0], [[1, 1, 0, 0], [0, 0, 1, 1]]), 3) == 0.0

• Test output shape

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, pred_test = dt.predict(X_train), 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.'

• Test data leak between train and test set

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]

• Test output range

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, pred_test = dt.predict(X_train), 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'

• Test model able to overfit on perfectly separable data

def test_dt_overfit(dummy_feats_and_labels):
    feats, labels = dummy_feats_and_labels
    dt = DecisionTree()
    dt.fit(feats, labels)
    pred = np.round(dt.predict(feats))

    assert np.array_equal(labels, pred), 'DecisionTree should fit data perfectly and prediction should == labels.'

• Test additional tree depth increases training accuracy and AUC ROC

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))
        auc_list.append(roc_auc_score(y_train, pred))

    assert sorted(acc_list) == acc_list, 'Accuracy should increase as tree depth increases.'
    assert sorted(auc_list) == auc_list, 'AUC ROC should increase as tree depth increases.'

• Test additional trees in RandomForest improves validation accuracy and AUC ROC

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)
        acc_list.append(accuracy_score(y_test, pred_binary))
        auc_list.append(roc_auc_score(y_test, pred))

    assert sorted(acc_list) == acc_list, 'Accuracy should increase as number of trees increases.'
    assert sorted(auc_list) == auc_list, 'AUC ROC should increase as number of trees increases.'

• Test RandomForest outperforms DecisionTree given the same tree depth

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

    dt = DecisionTree(depth_limit=10)
    dt.fit(X_train, y_train)

    rf = RandomForest(depth_limit=10, num_trees=7, col_subsampling=0.8, row_subsampling=0.8)
    rf.fit(X_train, y_train)

    pred_test_dt = dt.predict(X_test)
    pred_test_binary_dt = np.round(pred_test_dt)
    acc_test_dt = accuracy_score(y_test, pred_test_binary_dt)
    auc_test_dt = roc_auc_score(y_test, pred_test_dt)

    pred_test_rf = rf.predict(X_test)
    pred_test_binary_rf = np.round(pred_test_rf)
    acc_test_rf = accuracy_score(y_test, pred_test_binary_rf)
    auc_test_rf = roc_auc_score(y_test, pred_test_rf)

    assert acc_test_rf > acc_test_dt, 'RandomForest should have higher accuracy than DecisionTree on test set.'
    assert auc_test_rf > auc_test_dt, 'RandomForest should have higher AUC ROC than DecisionTree on test set.'

Post-train tests to ensure expected learned behaviour

• Test invariance (e.g., ticket number should not affect survival probability)

def test_dt_invariance(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 ticket number from 'PC 17599' to 'A/5 21171'
    p2_ticket = p2.copy()
    p2_ticket['ticket'] = 'A/5 21171'
    test_df = pd.DataFrame.from_dict([p2_ticket], orient='columns')
    X, y = get_feats_and_labels(prep_df(test_df))
    p2_ticket_prob = model.predict(X)[0]  # 1.0

    assert p2_prob == p2_ticket_prob

• Test directional expectation (e.g., females should have higher survival probability than males)

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
    p2_male = p2.copy()
    p2_male['Name'] = ' Mr. John'
    p2_male['Sex'] = 'male'
    test_df = pd.DataFrame.from_dict([p2_male], orient='columns')
    X, y = get_feats_and_labels(prep_df(test_df))
    p2_male_prob = model.predict(X)[0]  # 0.56

    # Change class from 1 to 3
    p2_class = p2.copy()
    p2_class['Pclass'] = 3
    test_df = pd.DataFrame.from_dict([p2_class], orient='columns')
    X, y = get_feats_and_labels(prep_df(test_df))
    p2_class_prob = model.predict(X)[0]  # 0.0

    assert p2_prob > p2_male_prob, 'Changing gender from female to male should decrease survival probability.'
    assert p2_prob > p2_class_prob, 'Changing class from 1 to 3 should decrease survival probability.'

Evaluation to ensure satisfactory model performance

• Evaluation on accuracy and AUC ROC

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'

• Evaluation on training and inference times

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'


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

    # Standardize to use depth = 10
    dt = DecisionTree(depth_limit=10)
    dt.fit(X_train, y_train)

    latency_array = np.array([predict_with_time(dt, X_test)[1] for i in range(500)])
    latency_p99 = np.quantile(latency_array, 0.99)
    assert latency_p99 < 0.004, 'Serving latency at 99th percentile should be < 0.004 sec'