Spaces:
Sleeping
Sleeping
File size: 11,716 Bytes
f4be780 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 |
#!/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"])
|