103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
"""
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©AngelaMos | 2026
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test_scaler.py
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"""
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import json
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from pathlib import Path
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import numpy as np
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import pytest
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from ml.scaler import FeatureScaler
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@pytest.fixture
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def sample_data() -> np.ndarray:
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rng = np.random.default_rng(42)
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return rng.standard_normal((200, 35)).astype(np.float32)
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class TestFeatureScaler:
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def test_fit_sets_n_features(self, sample_data: np.ndarray) -> None:
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scaler = FeatureScaler()
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scaler.fit(sample_data)
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assert scaler.n_features == 35
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def test_transform_preserves_shape(self, sample_data: np.ndarray) -> None:
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scaler = FeatureScaler()
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scaler.fit(sample_data)
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transformed = scaler.transform(sample_data)
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assert transformed.shape == sample_data.shape
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def test_transform_dtype_float32(self, sample_data: np.ndarray) -> None:
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scaler = FeatureScaler()
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scaler.fit(sample_data)
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transformed = scaler.transform(sample_data)
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assert transformed.dtype == np.float32
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def test_transformed_median_near_zero(self, sample_data: np.ndarray) -> None:
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scaler = FeatureScaler()
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scaler.fit(sample_data)
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transformed = scaler.transform(sample_data)
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medians = np.median(transformed, axis=0)
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assert np.allclose(medians, 0.0, atol=0.15)
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def test_inverse_transform_recovers_original(
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self, sample_data: np.ndarray
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) -> None:
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scaler = FeatureScaler()
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scaler.fit(sample_data)
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transformed = scaler.transform(sample_data)
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recovered = scaler.inverse_transform(transformed)
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np.testing.assert_allclose(recovered, sample_data, atol=1e-5)
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def test_save_json_creates_file(
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self, sample_data: np.ndarray, tmp_path: Path
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) -> None:
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scaler = FeatureScaler()
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scaler.fit(sample_data)
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path = tmp_path / "scaler.json"
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scaler.save_json(path)
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assert path.exists()
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assert path.stat().st_size > 0
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def test_save_json_is_valid_json(
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self, sample_data: np.ndarray, tmp_path: Path
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) -> None:
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scaler = FeatureScaler()
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scaler.fit(sample_data)
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path = tmp_path / "scaler.json"
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scaler.save_json(path)
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data = json.loads(path.read_text())
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assert "center" in data
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assert "scale" in data
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assert "n_features" in data
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def test_load_json_round_trip(
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self, sample_data: np.ndarray, tmp_path: Path
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) -> None:
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scaler = FeatureScaler()
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scaler.fit(sample_data)
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path = tmp_path / "scaler.json"
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scaler.save_json(path)
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loaded = FeatureScaler.load_json(path)
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assert loaded.n_features == scaler.n_features
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original_out = scaler.transform(sample_data)
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loaded_out = loaded.transform(sample_data)
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np.testing.assert_allclose(original_out, loaded_out, atol=1e-6)
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def test_transform_before_fit_raises(self) -> None:
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scaler = FeatureScaler()
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with pytest.raises(RuntimeError):
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scaler.transform(np.zeros((5, 35), dtype=np.float32))
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def test_fit_transform_convenience(self, sample_data: np.ndarray) -> None:
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scaler = FeatureScaler()
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result = scaler.fit_transform(sample_data)
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assert result.shape == sample_data.shape
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assert scaler.n_features == 35
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