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14 changes: 14 additions & 0 deletions docs/ML_PARAMETER_COVERAGE.md
Original file line number Diff line number Diff line change
Expand Up @@ -66,4 +66,18 @@ python collect_ml_search_data.py --list

---

---

## LightGBM Classifier Function

**Function ID:** `lightgbm_classifier`

ℹ️ **Status:** Database exists but contains no data

## LightGBM Regressor Function

**Function ID:** `lightgbm_regressor`

ℹ️ **Status:** Database exists but contains no data

*Generated on 2025-08-09 14:19:51*
2 changes: 1 addition & 1 deletion docs/_generators/generate_diagrams.py
Original file line number Diff line number Diff line change
Expand Up @@ -640,7 +640,7 @@ def main():
for filename, generator in generators.items():
content = generator()
output_path = DIAGRAMS_DIR / filename
output_path.write_text(content)
output_path.write_text(content, encoding="utf-8")
print(f" Generated: {filename}")


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154 changes: 154 additions & 0 deletions docs/ml_parameter_coverage.json
Original file line number Diff line number Diff line change
Expand Up @@ -235,6 +235,160 @@
]
}
}
}
}
,
"lightgbm_classifier": {
"default_ranges": {
"n_estimators": {
"type": "numeric",
"default_count": 29,
"default_values": ["10", "50", "100", "200", "500"],
"is_truncated": true
},
"learning_rate": {
"type": "numeric",
"default_count": 4,
"default_values": ["0.01", "0.05", "0.1", "0.3"],
"is_truncated": false
},
"num_leaves": {
"type": "numeric",
"default_count": 18,
"default_values": ["20", "31", "40", "50"],
"is_truncated": true
},
"max_depth": {
"type": "numeric",
"default_count": 18,
"default_values": ["-1", "5", "10", "15"],
"is_truncated": true
},
"min_child_samples": {
"type": "numeric",
"default_count": 19,
"default_values": ["10", "20", "30"],
"is_truncated": true
},
"subsample": {
"type": "numeric",
"default_count": 10,
"default_values": ["0.8", "0.9", "1.0"],
"is_truncated": false
},
"colsample_bytree": {
"type": "numeric",
"default_count": 10,
"default_values": ["0.8", "0.9", "1.0"],
"is_truncated": false
},
"reg_alpha": {
"type": "numeric",
"default_count": 6,
"default_values": ["0.0", "0.1", "0.5"],
"is_truncated": false
},
"reg_lambda": {
"type": "numeric",
"default_count": 5,
"default_values": ["0.0", "0.1", "0.5"],
"is_truncated": false
},
"dataset": {
"type": "categorical",
"default_count": 5,
"default_values": [
"<function digits_data at 0x77efe95d23e0>",
"<function wine_data at 0x77efe95d22a0>",
"<function iris_data at 0x77efe9257d80>"
],
"is_truncated": false
},
"cv": {
"type": "numeric",
"default_count": 4,
"default_values": [2, 3, 4, 5],
"is_truncated": false
}
},
"stored_ranges": {
"exists": true,
"message": "Database exists but contains no data"
}
},
"lightgbm_regressor": {
"default_ranges": {
"n_estimators": {
"type": "numeric",
"default_count": 29,
"default_values": ["10", "50", "100", "200", "500"],
"is_truncated": true
},
"learning_rate": {
"type": "numeric",
"default_count": 4,
"default_values": ["0.01", "0.05", "0.1", "0.3"],
"is_truncated": false
},
"num_leaves": {
"type": "numeric",
"default_count": 18,
"default_values": ["20", "31", "40", "50"],
"is_truncated": true
},
"max_depth": {
"type": "numeric",
"default_count": 18,
"default_values": ["-1", "5", "10", "15"],
"is_truncated": true
},
"min_child_samples": {
"type": "numeric",
"default_count": 19,
"default_values": ["10", "20", "30"],
"is_truncated": true
},
"subsample": {
"type": "numeric",
"default_values": ["0.8", "0.9", "1.0"],
"is_truncated": false
},
"colsample_bytree": {
"type": "numeric",
"default_count": 10,
"default_values": ["0.8", "0.9", "1.0"],
"is_truncated": false
},
"reg_alpha": {
"type": "numeric",
"default_count": 6,
"default_values": ["0.0", "0.1", "0.5"],
"is_truncated": false
},
"reg_lambda": {
"type": "numeric",
"default_count": 5,
"default_values": ["0.0", "0.1", "0.5"],
"is_truncated": false
},
"dataset": {
"type": "categorical",
"default_count": 5,
"default_values": [
"<function diabetes_data at 0x77efe92c8680>"
],
"is_truncated": false
},
"cv": {
"type": "numeric",
"default_count": 4,
"default_values": [2, 3, 4, 5],
"is_truncated": false
}
},
"stored_ranges": {
"exists": true,
"message": "Database exists but contains no data"
}
}
}
1 change: 1 addition & 0 deletions docs/requirements.txt
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@ numpy
pandas
scikit-learn
matplotlib
lightgbm

# For plot generation
plotly>=5.0
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2 changes: 2 additions & 0 deletions src/surfaces/test_functions/algebraic/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,7 @@
standard_functions_1d,
standard_functions_2d,
standard_functions_nd,
ShekelFunction,
)

__all__ = [
Expand Down Expand Up @@ -108,6 +109,7 @@
"RosenbrockFunction",
"SphereFunction",
"StyblinskiTangFunction",
"ShekelFunction"
# Constrained
"CantileverBeamFunction",
"PressureVesselFunction",
Expand Down
4 changes: 4 additions & 0 deletions src/surfaces/test_functions/algebraic/standard/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,7 @@
RosenbrockFunction,
SphereFunction,
StyblinskiTangFunction,
ShekelFunction
)

__all__ = [
Expand Down Expand Up @@ -76,6 +77,7 @@
"RosenbrockFunction",
"SphereFunction",
"StyblinskiTangFunction",
"ShekelFunction"
]

standard_functions = [
Expand Down Expand Up @@ -110,6 +112,7 @@
RosenbrockFunction,
SphereFunction,
StyblinskiTangFunction,
ShekelFunction
]

standard_functions_1d = [
Expand Down Expand Up @@ -147,4 +150,5 @@
RosenbrockFunction,
SphereFunction,
StyblinskiTangFunction,
ShekelFunction
]
Original file line number Diff line number Diff line change
Expand Up @@ -8,11 +8,13 @@
from .rosenbrock_function import RosenbrockFunction
from .sphere_function import SphereFunction
from .styblinski_tang_function import StyblinskiTangFunction
from .shekel_function import ShekelFunction

__all__ = [
"RastriginFunction",
"RosenbrockFunction",
"SphereFunction",
"StyblinskiTangFunction",
"GriewankFunction",
"ShekelFunction",
]
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