tests/test_feature_availability.py

#!/usr/bin/env python3
"""
Unit Tests for Feature Availability Module
Tests feature categorization, availability masking, and validation.
"""

import pytest
import numpy as np
import polars as pl
from datasets import load_dataset
from src.forecasting.feature_availability import FeatureAvailability


@pytest.fixture(scope="module")
def sample_columns():
    """Load actual dataset columns for testing."""
    # Use HF token for private dataset access
    hf_token = "<HF_TOKEN>"

    dataset = load_dataset(
        "evgueni-p/fbmc-features-24month",
        split="train",
        token=hf_token
    )

    return list(dataset.features.keys())


@pytest.fixture(scope="module")
def categories(sample_columns):
    """Categorize features once for all tests."""
    return FeatureAvailability.categorize_features(sample_columns)


class TestFeatureCategorization:
    """Test feature categorization logic."""

    def test_total_feature_count(self, categories):
        """Test total feature count matches expected."""
        total = sum(len(v) for v in categories.values())
        # 2,553 columns - 1 timestamp - 38 targets = 2,514 features
        assert total == 2514, f"Expected 2,514 features, got {total}"

    def test_no_uncategorized_features(self, categories):
        """Test all features are categorized."""
        uncategorized = categories['uncategorized']
        assert len(uncategorized) == 0, (
            f"Found {len(uncategorized)} uncategorized features: "
            f"{uncategorized[:10]}"
        )

    def test_full_horizon_count(self, categories):
        """Test full-horizon D+14 feature count."""
        full_d14 = len(categories['full_horizon_d14'])
        # Expected: temporal (12) + weather (375) + outages (176) + LTA (40) = 603
        assert 580 <= full_d14 <= 620, (
            f"Expected ~603 full-horizon features, got {full_d14}"
        )

    def test_partial_d1_count(self, categories):
        """Test partial D+1 feature count."""
        partial = len(categories['partial_d1'])
        # Expected: load forecasts (12)
        assert partial == 12, f"Expected 12 partial D+1 features, got {partial}"

    def test_historical_count(self, categories):
        """Test historical feature count."""
        historical = len(categories['historical'])
        # Expected: ~1,899 (prices, generation, demand, lags, etc.)
        assert 1800 <= historical <= 2000, (
            f"Expected ~1,899 historical features, got {historical}"
        )

    def test_temporal_features_in_full_horizon(self, categories):
        """Test temporal features are in full_horizon_d14."""
        full_d14 = categories['full_horizon_d14']

        temporal_patterns = [
            'hour_sin', 'hour_cos',
            'day_sin', 'day_cos',
            'month_sin', 'month_cos',
            'weekday_sin', 'weekday_cos',
            'is_weekend'
        ]

        for pattern in temporal_patterns:
            matching = [f for f in full_d14 if pattern in f]
            assert len(matching) > 0, f"No temporal features matching '{pattern}'"

    def test_weather_features_in_full_horizon(self, categories):
        """Test weather features are in full_horizon_d14."""
        full_d14 = categories['full_horizon_d14']

        weather_prefixes = ['temp_', 'wind_', 'solar_', 'cloud_', 'pressure_']

        for prefix in weather_prefixes:
            matching = [f for f in full_d14 if f.startswith(prefix)]
            assert len(matching) > 0, f"No weather features starting with '{prefix}'"

    def test_outage_features_in_full_horizon(self, categories):
        """Test CNEC outage features are in full_horizon_d14."""
        full_d14 = categories['full_horizon_d14']

        outage_features = [f for f in full_d14 if f.startswith('outage_cnec_')]
        assert len(outage_features) == 176, (
            f"Expected 176 CNEC outage features, got {len(outage_features)}"
        )

    def test_lta_features_in_full_horizon(self, categories):
        """Test LTA features are in full_horizon_d14."""
        full_d14 = categories['full_horizon_d14']

        lta_features = [f for f in full_d14 if f.startswith('lta_')]
        assert len(lta_features) == 40, (
            f"Expected 40 LTA features, got {len(lta_features)}"
        )

    def test_load_forecast_in_partial(self, categories):
        """Test load forecast features are in partial_d1."""
        partial = categories['partial_d1']

        load_forecasts = [f for f in partial if f.startswith('load_forecast_')]
        assert len(load_forecasts) == 12, (
            f"Expected 12 load forecast features, got {len(load_forecasts)}"
        )

    def test_price_features_in_historical(self, categories):
        """Test price features are in historical."""
        historical = categories['historical']

        price_features = [f for f in historical if f.startswith('price_')]
        assert len(price_features) > 0, "No price features found in historical"

    def test_generation_features_in_historical(self, categories):
        """Test generation features are in historical."""
        historical = categories['historical']

        gen_features = [f for f in historical if f.startswith('gen_')]
        assert len(gen_features) > 0, "No generation features found in historical"

    def test_demand_features_in_historical(self, categories):
        """Test demand features are in historical."""
        historical = categories['historical']

        demand_features = [f for f in historical if f.startswith('demand_')]
        assert len(demand_features) > 0, "No demand features found in historical"

    def test_no_duplicates_across_categories(self, categories):
        """Test features are not duplicated across categories."""
        full_set = set(categories['full_horizon_d14'])
        partial_set = set(categories['partial_d1'])
        historical_set = set(categories['historical'])

        # Check for overlaps
        full_partial = full_set & partial_set
        full_historical = full_set & historical_set
        partial_historical = partial_set & historical_set

        assert len(full_partial) == 0, f"Overlap between full and partial: {full_partial}"
        assert len(full_historical) == 0, f"Overlap between full and historical: {full_historical}"
        assert len(partial_historical) == 0, f"Overlap between partial and historical: {partial_historical}"


class TestAvailabilityMasking:
    """Test availability mask creation."""

    def test_full_horizon_mask(self):
        """Test mask for full-horizon features."""
        mask = FeatureAvailability.create_availability_mask('temp_DE_LU', 336)
        assert mask.shape == (336,), f"Expected shape (336,), got {mask.shape}"
        assert np.all(mask == 1.0), "Full-horizon mask should be all ones"

    def test_partial_d1_mask(self):
        """Test mask for partial D+1 features."""
        mask = FeatureAvailability.create_availability_mask('load_forecast_DE', 336)
        assert mask.shape == (336,), f"Expected shape (336,), got {mask.shape}"
        assert np.sum(mask) == 24, f"Expected 24 ones (D+1), got {np.sum(mask)}"
        assert np.all(mask[:24] == 1.0), "First 24 hours should be available"
        assert np.all(mask[24:] == 0.0), "Hours 25-336 should be masked"

    def test_temporal_mask(self):
        """Test mask for temporal features (always available)."""
        mask = FeatureAvailability.create_availability_mask('hour_sin', 336)
        assert mask.shape == (336,), f"Expected shape (336,), got {mask.shape}"
        assert np.all(mask == 1.0), "Temporal mask should be all ones"

    def test_lta_mask(self):
        """Test mask for LTA features (forward-filled)."""
        mask = FeatureAvailability.create_availability_mask('lta_AT_CZ', 336)
        assert mask.shape == (336,), f"Expected shape (336,), got {mask.shape}"
        assert np.all(mask == 1.0), "LTA mask should be all ones (forward-filled)"

    def test_historical_mask(self):
        """Test mask for historical features."""
        mask = FeatureAvailability.create_availability_mask('price_DE', 336)
        assert mask.shape == (336,), f"Expected shape (336,), got {mask.shape}"
        assert np.all(mask == 0.0), "Historical mask should be all zeros"

    def test_mask_different_horizons(self):
        """Test mask with different forecast horizons."""
        # Test 168-hour horizon (7 days)
        mask_168 = FeatureAvailability.create_availability_mask('load_forecast_DE', 168)
        assert mask_168.shape == (168,)
        assert np.sum(mask_168) == 24

        # Test 720-hour horizon (30 days)
        mask_720 = FeatureAvailability.create_availability_mask('load_forecast_DE', 720)
        assert mask_720.shape == (720,)
        assert np.sum(mask_720) == 24


class TestValidation:
    """Test validation functions."""

    def test_validation_passes(self, categories):
        """Test validation passes for correct categorization."""
        is_valid, warnings = FeatureAvailability.validate_categorization(
            categories, verbose=False
        )

        assert is_valid, f"Validation failed with warnings: {warnings}"
        assert len(warnings) == 0, f"Unexpected warnings: {warnings}"

    def test_category_summary_generation(self, categories):
        """Test category summary table generation."""
        summary = FeatureAvailability.get_category_summary(categories)

        assert 'Category' in summary.columns
        assert 'Count' in summary.columns
        assert 'Availability' in summary.columns
        assert len(summary) >= 3  # At least 3 categories (full, partial, historical)


class TestPatternMatching:
    """Test internal pattern matching logic."""

    def test_temporal_pattern_matching(self):
        """Test temporal feature pattern matching."""
        test_cols = ['hour_sin', 'day_cos', 'month', 'weekday', 'is_weekend']
        categories = FeatureAvailability.categorize_features(test_cols)

        assert len(categories['full_horizon_d14']) == 5
        assert len(categories['partial_d1']) == 0
        assert len(categories['historical']) == 0

    def test_weather_prefix_matching(self):
        """Test weather feature prefix matching."""
        test_cols = ['temp_DE', 'wind_FR', 'solar_AT', 'cloud_NL', 'pressure_BE']
        categories = FeatureAvailability.categorize_features(test_cols)

        assert len(categories['full_horizon_d14']) == 5

    def test_load_forecast_matching(self):
        """Test load forecast prefix matching."""
        test_cols = ['load_forecast_DE', 'load_forecast_FR', 'load_forecast_AT']
        categories = FeatureAvailability.categorize_features(test_cols)

        assert len(categories['partial_d1']) == 3

    def test_price_matching(self):
        """Test price feature matching."""
        test_cols = ['price_DE', 'price_FR', 'price_AT']
        categories = FeatureAvailability.categorize_features(test_cols)

        assert len(categories['historical']) == 3

    def test_mixed_features(self):
        """Test categorization with mixed feature types."""
        test_cols = [
            'hour_sin',  # temporal -> full
            'temp_DE',  # weather -> full
            'load_forecast_DE',  # load -> partial
            'price_DE',  # price -> historical
            'gen_FR_nuclear',  # generation -> historical
        ]
        categories = FeatureAvailability.categorize_features(test_cols)

        assert len(categories['full_horizon_d14']) == 2  # hour_sin, temp_DE
        assert len(categories['partial_d1']) == 1  # load_forecast_DE
        assert len(categories['historical']) == 2  # price_DE, gen_FR_nuclear


if __name__ == "__main__":
    pytest.main([__file__, "-v", "-s"])