SIENTIAPDE-1430: Add unit tests for Silverman radius and RCE drift metrics.
This commit is contained in:
Bruno Domingues
@@ -1,8 +1,17 @@
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"""Unit tests for sientia metrics module."""
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import numpy as np
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import pandas as pd
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from model_manager.sientia.metrics import mae, mse, r2
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from model_manager.sientia.metrics import (
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mae,
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mse,
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r2,
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rce_drift,
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rce_test,
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rce_train,
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silverman_radius,
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)
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def test_mse_perfect_predictions():
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@@ -200,3 +209,273 @@ def test_r2_with_mixed_positive_negative():
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result = r2(real_data, predictions)
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assert result == 1.0
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# ============================================================================
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# Tests for silverman_radius
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# ============================================================================
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def test_silverman_radius_basic():
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"""Test silverman_radius returns a positive float."""
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data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
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result = silverman_radius(data)
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assert isinstance(result, float)
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assert result > 0
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def test_silverman_radius_uniform_data():
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"""Test silverman_radius with uniformly distributed data."""
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data = np.linspace(0, 100, 50)
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result = silverman_radius(data)
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assert result > 0
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assert np.isfinite(result)
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def test_silverman_radius_normal_distribution():
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"""Test silverman_radius with normally distributed data."""
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np.random.seed(42)
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data = np.random.normal(loc=50, scale=10, size=100)
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result = silverman_radius(data)
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assert result > 0
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assert np.isfinite(result)
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def test_silverman_radius_small_dataset():
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"""Test silverman_radius with small dataset."""
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data = np.array([1.0, 2.0, 3.0])
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result = silverman_radius(data)
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assert result > 0
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# ============================================================================
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# Tests for rce_train
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# ============================================================================
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def test_rce_train_returns_dataframe():
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"""Test rce_train returns a DataFrame."""
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training_set = pd.DataFrame({'a': [1.0, 2.0, 3.0, 4.0, 5.0], 'b': [2.0, 3.0, 4.0, 5.0, 6.0]})
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result = rce_train(training_set, 0.1)
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assert isinstance(result, pd.DataFrame)
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def test_rce_train_includes_first_vector():
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"""Test rce_train always includes the first vector as a prototype."""
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training_set = pd.DataFrame({'a': [1.0, 2.0, 3.0], 'b': [1.0, 2.0, 3.0]})
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result = rce_train(training_set, 0.1)
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assert len(result) >= 1
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assert result.iloc[0].tolist() == [1.0, 1.0]
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def test_rce_train_with_identical_vectors():
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"""Test rce_train with identical vectors returns single prototype."""
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training_set = pd.DataFrame({'a': [1.0, 1.0, 1.0], 'b': [2.0, 2.0, 2.0]})
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result = rce_train(training_set, 0.1)
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# All vectors are identical, so only one prototype should be created
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assert len(result) == 1
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def test_rce_train_with_distant_vectors():
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"""Test rce_train with very distant vectors creates multiple prototypes."""
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training_set = pd.DataFrame({'a': [0.0, 100.0, 200.0], 'b': [0.0, 100.0, 200.0]})
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result = rce_train(training_set, 0.1)
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# Distant vectors should create multiple prototypes
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assert len(result) >= 1
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# ============================================================================
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# Tests for rce_test
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# ============================================================================
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def test_rce_test_returns_series():
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"""Test rce_test returns a pandas Series."""
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test_set = pd.DataFrame({'a': [1.5, 2.5], 'b': [1.5, 2.5]})
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prototypes = pd.DataFrame({'a': [1.0, 3.0], 'b': [1.0, 3.0]})
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result = rce_test(test_set, prototypes)
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assert isinstance(result, pd.Series)
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assert len(result) == len(test_set)
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def test_rce_test_with_exact_match():
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"""Test rce_test with test vector matching a prototype."""
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test_set = pd.DataFrame({'a': [1.0], 'b': [2.0]})
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prototypes = pd.DataFrame({'a': [1.0], 'b': [2.0]})
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result = rce_test(test_set, prototypes)
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# Distance should be 0 for exact match
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assert result.iloc[0] == 0.0
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def test_rce_test_multiple_prototypes():
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"""Test rce_test finds closest prototype."""
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test_set = pd.DataFrame({'a': [1.1], 'b': [1.1]})
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prototypes = pd.DataFrame({'a': [1.0, 10.0], 'b': [1.0, 10.0]})
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result = rce_test(test_set, prototypes)
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# Should find the closest prototype (1.0, 1.0)
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assert len(result) == 1
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assert np.isfinite(result.iloc[0])
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def test_rce_test_signed_distances():
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"""Test rce_test returns signed distances."""
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test_set = pd.DataFrame({'a': [0.0, 5.0], 'b': [0.0, 5.0]})
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prototypes = pd.DataFrame({'a': [2.0], 'b': [2.0]})
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result = rce_test(test_set, prototypes)
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assert len(result) == 2
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# First test vector (0,0) is less than prototype (2,2) - should be negative
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# Second test vector (5,5) is greater than prototype (2,2) - should be positive
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assert result.iloc[0] < 0
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assert result.iloc[1] > 0
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# ============================================================================
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# Tests for rce_drift
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# ============================================================================
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def test_rce_drift_returns_series():
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"""Test rce_drift returns a pandas Series."""
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reference_data = pd.DataFrame(
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{
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'feature1': [1.0, 2.0, 3.0, 4.0, 5.0],
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'feature2': [2.0, 3.0, 4.0, 5.0, 6.0],
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'target': [10.0, 20.0, 30.0, 40.0, 50.0],
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'prediction': [11.0, 21.0, 31.0, 41.0, 51.0],
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}
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)
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real_data = pd.DataFrame(
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{
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'feature1': [1.5, 2.5],
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'feature2': [2.5, 3.5],
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'target': [15.0, 25.0],
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'prediction': [16.0, 26.0],
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}
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)
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result = rce_drift(reference_data, real_data, 'target')
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assert isinstance(result, pd.Series)
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assert len(result) == len(real_data)
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def test_rce_drift_with_target_column():
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"""Test rce_drift using target column (drops prediction)."""
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reference_data = pd.DataFrame(
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{
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'feature1': [1.0, 2.0, 3.0],
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'target': [10.0, 20.0, 30.0],
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'prediction': [11.0, 21.0, 31.0],
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}
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)
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real_data = pd.DataFrame(
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{
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'feature1': [1.5],
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'target': [15.0],
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'prediction': [16.0],
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}
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)
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result = rce_drift(reference_data, real_data, 'target')
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assert isinstance(result, pd.Series)
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assert len(result) == 1
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def test_rce_drift_with_prediction_column():
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"""Test rce_drift using prediction column (drops target)."""
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reference_data = pd.DataFrame(
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{
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'feature1': [1.0, 2.0, 3.0],
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'target': [10.0, 20.0, 30.0],
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'prediction': [11.0, 21.0, 31.0],
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}
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)
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real_data = pd.DataFrame(
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{
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'feature1': [1.5],
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'target': [15.0],
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'prediction': [16.0],
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}
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)
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result = rce_drift(reference_data, real_data, 'prediction')
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assert isinstance(result, pd.Series)
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assert len(result) == 1
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def test_rce_drift_normalized_output():
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"""Test rce_drift returns normalized distances."""
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reference_data = pd.DataFrame(
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{
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'feature1': [1.0, 2.0, 3.0, 4.0, 5.0],
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'target': [10.0, 20.0, 30.0, 40.0, 50.0],
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'prediction': [10.0, 20.0, 30.0, 40.0, 50.0],
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}
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)
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real_data = pd.DataFrame(
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{
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'feature1': [2.5, 3.5],
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'target': [25.0, 35.0],
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'prediction': [25.0, 35.0],
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}
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)
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result = rce_drift(reference_data, real_data, 'target')
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# Result should be a Series with same length as real_data
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assert isinstance(result, pd.Series)
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assert len(result) == len(real_data)
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def test_rce_drift_handles_common_columns():
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"""Test rce_drift correctly handles common columns between datasets."""
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reference_data = pd.DataFrame(
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{
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'feature1': [1.0, 2.0, 3.0],
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'feature2': [2.0, 3.0, 4.0],
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'extra_ref': [100.0, 200.0, 300.0],
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'target': [10.0, 20.0, 30.0],
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'prediction': [11.0, 21.0, 31.0],
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}
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)
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real_data = pd.DataFrame(
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{
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'feature1': [1.5],
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'feature2': [2.5],
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'extra_real': [150.0],
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'target': [15.0],
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'prediction': [16.0],
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}
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)
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result = rce_drift(reference_data, real_data, 'target')
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# Should work with only common columns
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assert isinstance(result, pd.Series)
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assert len(result) == 1
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