From b7e8b11b97c8b384c758dfbfff668772a375538a Mon Sep 17 00:00:00 2001 From: RektPunk Date: Thu, 2 Apr 2026 10:28:04 +0900 Subject: [PATCH 01/40] remove optimizer --- .pre-commit-config.yaml | 42 +- .python-version | 1 + examples/mqoptimizer.py | 74 --- mqboost/__init__.py | 1 - mqboost/optimize.py | 283 --------- pyproject.toml | 38 +- tests/test_optimize.py | 209 ------- uv.lock | 1318 +++++++++++++++++++++++++++++++++++++++ 8 files changed, 1353 insertions(+), 613 deletions(-) create mode 100644 .python-version delete mode 100644 examples/mqoptimizer.py delete mode 100644 mqboost/optimize.py delete mode 100644 tests/test_optimize.py create mode 100644 uv.lock diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 58701df..cbce740 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,24 +1,22 @@ repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v5.0.0 - hooks: - - id: trailing-whitespace - - id: end-of-file-fixer - - id: check-yaml - - id: check-json - - id: check-toml - - id: check-added-large-files - - id: check-merge-conflict + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-json + - id: check-toml + - id: check-added-large-files + - id: check-merge-conflict -- repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.7.4 - hooks: - - id: ruff - args: [ --fix ] - - id: ruff-format - -- repo: https://github.com/pycqa/isort - rev: 5.13.2 - hooks: - - id: isort - args: ["--profile", "black"] + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.8 + hooks: + - id: ruff + name: ruff check + args: [--fix] + - id: ruff + name: ruff isort + args: [--select, I, --fix] + - id: ruff-format diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..6324d40 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.14 diff --git a/examples/mqoptimizer.py b/examples/mqoptimizer.py deleted file mode 100644 index 0d2d5d7..0000000 --- a/examples/mqoptimizer.py +++ /dev/null @@ -1,74 +0,0 @@ -import numpy as np -from optuna import Trial - -from mqboost import MQDataset, MQOptimizer, MQRegressor - -# Generate sample data -sample_size = 500 -x = np.linspace(-10, 10, sample_size) -y = np.sin(x) + np.random.uniform(-0.4, 0.4, sample_size) -x_valid = np.linspace(-10, 10, sample_size) -y_valid = np.sin(x_valid) + np.random.uniform(-0.4, 0.4, sample_size) -x_test = np.linspace(-10, 10, sample_size) -y_test = np.sin(x_test) + np.random.uniform(-0.4, 0.4, sample_size) - -# Define target quantiles -alphas = [0.3, 0.4, 0.5, 0.6, 0.7] - -# Specify model type -model = "lightgbm" # Options: "lightgbm" or "xgboost" - -# Set objective function -objective = "check" # Options: "check", "huber", or "approx" - -# Set dataset -train_dataset = MQDataset(data=x, label=y, alphas=alphas, model=model) -valid_dataset = MQDataset( - data=x_valid, label=y_valid, alphas=alphas, model=model, reference=train_dataset -) -test_dataset = MQDataset( - data=x_test, label=y_test, alphas=alphas, model=model, reference=train_dataset -) - -# Initialize the optimizer -mq_optimizer = MQOptimizer( - model=model, - objective=objective, -) - -# Optimize params using Optuna -mq_optimizer.optimize_params( - dataset=train_dataset, - n_trials=10, -) - - -# Moreover, you can optimize parameters by implementing functions manually -# Also, you can manually set the validation set -def get_params(trial: Trial): - return { - "verbose": -1, - "learning_rate": trial.suggest_float("learning_rate", 1e-2, 1.0, log=True), - "max_depth": trial.suggest_int("max_depth", 1, 10), - "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), - "num_leaves": trial.suggest_int("num_leaves", 2, 256), - "feature_fraction": trial.suggest_float("feature_fraction", 0.4, 1.0), - "bagging_fraction": trial.suggest_float("bagging_fraction", 0.4, 1.0), - "bagging_freq": trial.suggest_int("bagging_freq", 1, 7), - } - - -mq_optimizer.optimize_params( - dataset=train_dataset, - n_trials=10, - get_params_func=get_params, - valid_set=valid_dataset, -) - -# Init MQRegressor with best params -mq_regressor = MQRegressor(**mq_optimizer.best_params) -mq_regressor.fit(dataset=train_dataset, eval_set=valid_dataset) - -# Predict using the trained model -mq_regressor.predict(dataset=test_dataset) diff --git a/mqboost/__init__.py b/mqboost/__init__.py index 023b075..ecc220c 100644 --- a/mqboost/__init__.py +++ b/mqboost/__init__.py @@ -1,6 +1,5 @@ # ruff: noqa from mqboost.dataset import MQDataset -from mqboost.optimize import MQOptimizer from mqboost.regressor import MQRegressor __version__ = "0.2.10" diff --git a/mqboost/optimize.py b/mqboost/optimize.py deleted file mode 100644 index 9b96eea..0000000 --- a/mqboost/optimize.py +++ /dev/null @@ -1,283 +0,0 @@ -from typing import Callable - -import lightgbm as lgb -import numpy as np -import optuna -import pandas as pd -import xgboost as xgb -from optuna import Trial -from sklearn.model_selection import train_test_split - -from mqboost.base import ( - DtrainLike, - FittingException, - ModelName, - ObjectiveName, - ParamsLike, -) -from mqboost.constraints import set_monotone_constraints -from mqboost.dataset import MQDataset -from mqboost.objective import MQObjective -from mqboost.utils import delta_validate, epsilon_validate, params_validate - -__all__ = ["MQOptimizer"] - - -def _lgb_get_params(trial: Trial): - return { - "verbose": -1, - "learning_rate": trial.suggest_float("learning_rate", 1e-2, 1.0), - "max_depth": trial.suggest_int("max_depth", 1, 10), - "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0), - "num_leaves": trial.suggest_int("num_leaves", 2, 256), - "feature_fraction": trial.suggest_float("feature_fraction", 0.4, 1.0), - "bagging_fraction": trial.suggest_float("bagging_fraction", 0.4, 1.0), - "bagging_freq": trial.suggest_int("bagging_freq", 1, 7), - } - - -def _xgb_get_params(trial: Trial): - return { - "learning_rate": trial.suggest_float("learning_rate", 1e-2, 1.0), - "max_depth": trial.suggest_int("max_depth", 1, 10), - "reg_lambda": trial.suggest_float("reg_lambda", 1e-8, 100.0), - "reg_alpha": trial.suggest_float("reg_alpha", 1e-8, 100.0), - "subsample": trial.suggest_float("subsample", 0.1, 1.0), - "colsample_bytree": trial.suggest_float("colsample_bytree", 0.1, 1.0), - } - - -_GET_PARAMS_FUNC = { - ModelName.lightgbm: _lgb_get_params, - ModelName.xgboost: _xgb_get_params, -} - - -def _train_valid_split( - x_train: pd.DataFrame, - y_train: np.ndarray, - weight: np.ndarray | None, -) -> tuple[ - pd.DataFrame, - pd.DataFrame, - np.ndarray, - np.ndarray, - np.ndarray | None, - np.ndarray | None, -]: - if weight is not None: - _x_train, _x_valid, _y_train, _y_valid, _w_train, _w_valid = train_test_split( - x_train, - y_train, - weight, - test_size=0.2, - random_state=42, - stratify=x_train["_tau"], - ) - else: - _x_train, _x_valid, _y_train, _y_valid = train_test_split( - x_train, - y_train, - test_size=0.2, - random_state=42, - stratify=x_train["_tau"], - ) - _w_train = None - _w_valid = None - - return _x_train, _x_valid, _y_train, _y_valid, _w_train, _w_valid - - -class MQOptimizer: - """ - MQOptimizer is designed to optimize hyperparameters for MQRegressor with Optuna. - - Attributes: - model (str): The model type (either 'lightgbm' or 'xgboost'). Default is 'lightgbm'. - objective (str): - The objective function for the quantile regression ('check', 'huber', or 'phuber'). Default is 'check'. - delta (float): Delta parameter for the 'huber' objective function. Default is 0.01. - epsilon (float): Epsilon parameter for the 'apptox' objective function. Default is 1e-5. - - Methods: - optimize_params(dataset, n_trials, get_params_func, valid_set): - Optimizes the hyperparameters for the specified dataset using Optuna. - - Property - MQObj: Returns the MQObjective instance. - study: Returns the Optuna study instance. - best_params: Returns the best hyperparameters found by the optimization process. - """ - - def __init__( - self, - model: str = ModelName.lightgbm.value, - objective: str = ObjectiveName.check.value, - delta: float = 0.01, - epsilon: float = 1e-5, - ) -> None: - """Initialize the MQOptimizer.""" - delta_validate(delta=delta) - epsilon_validate(epsilon=epsilon) - - self._model = ModelName.get(model) - self._objective = ObjectiveName.get(objective) - self._delta = delta - self._epsilon = epsilon - self._get_params = _GET_PARAMS_FUNC.get(self._model) - - def optimize_params( - self, - dataset: MQDataset, - n_trials: int, - get_params_func: Callable[[Trial], ParamsLike] | None = None, - valid_set: MQDataset | None = None, - ) -> ParamsLike: - """ - Optimize hyperparameters. - Args: - dataset (MQDataset): The dataset to be used for optimization. - n_trials (int): The number of trials for the hyperparameter optimization. - get_params_func (Callable, optional): A custom function to get the parameters for the model. - For example, - def get_params(trial: Trial) -> dict[str, Any]: - return { - "learning_rate": trial.suggest_float("learning_rate", 1e-2, 1.0), - "max_depth": trial.suggest_int("max_depth", 1, 10), - "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0), - } - valid_set (MQDataset, optional): The validation dataset. Defaults to None. - Returns: - dict[str, Any]: The best hyperparameters found by the optimization process. - """ - self._dataset = dataset - self._label_mean = dataset.label_mean - self._MQObj = MQObjective( - alphas=dataset.alphas, - objective=self._objective, - weight=dataset.weight, - model=self._model, - delta=self._delta, - epsilon=self._epsilon, - ) - if valid_set is None: - x_train, x_valid, y_train, y_valid, weight_train, weight_valid = ( - _train_valid_split( - x_train=self._dataset.data, - y_train=self._dataset.label, - weight=dataset.weight, - ) - ) - dtrain = self._dataset.train_dtype( - data=x_train, label=y_train, weight=weight_train - ) - dvalid = self._dataset.train_dtype( - data=x_valid, label=y_valid, weight=weight_valid - ) - deval = self._dataset.predict_dtype(data=x_valid) - - else: - dtrain = self._dataset.dtrain - dvalid = valid_set.dtrain - deval = valid_set.dpredict - - if get_params_func is None: - get_params_func = _GET_PARAMS_FUNC.get(self._model) - - def _study_func(trial: optuna.Trial) -> float: - return self.__optuna_objective( - trial=trial, - dtrain=dtrain, - dvalid=dvalid, - deval=deval, - get_params_func=get_params_func, - ) - - self._study = optuna.create_study( - study_name=f"MQBoost_{self._model}", - direction="minimize", - load_if_exists=True, - ) - self._study.optimize(_study_func, n_trials=n_trials) - self._is_optimized = True - return self._study.best_params - - def __optuna_objective( - self, - trial: optuna.Trial, - dtrain: DtrainLike, - dvalid: DtrainLike, - deval: DtrainLike | pd.DataFrame, - get_params_func: Callable[[Trial], ParamsLike], - ) -> float: - """Objective function for Optuna to minimize.""" - params = get_params_func(trial=trial) - params_validate(params=params) - params = set_monotone_constraints( - params=params, - columns=self._dataset.columns, - model_name=self._model, - ) - if self.__is_lgb: - model_params = dict( - params=params, - train_set=dtrain, - valid_sets=dvalid, - ) - _gbm = lgb.train(**model_params) - _preds = ( - _gbm.predict(data=deval, num_iteration=_gbm.best_iteration) - + self._label_mean - ) - _, loss, _ = self._MQObj.feval(y_pred=_preds, dtrain=dvalid) - elif self.__is_xgb: - model_params = dict( - params=params, - dtrain=dtrain, - evals=[(dvalid, "valid")], - num_boost_round=100, - ) - _gbm = xgb.train(**model_params) - _preds = _gbm.predict(data=deval) + self._label_mean - _, loss = self._MQObj.feval(y_pred=_preds, dtrain=dvalid) - else: - raise FittingException("Model name is invalid") - return loss - - @property - def MQObj(self) -> MQObjective: - """Get the MQObjective instance.""" - return self._MQObj - - @property - def study(self) -> optuna.Study: - """Get the Optuna study instance.""" - return getattr(self, "_study", None) - - @property - def best_params(self) -> ParamsLike: - """Get the best hyperparameters found by the optimization process.""" - self.__is_optimized() - return { - "params": self._study.best_params, - "model": self._model.value, - "objective": self._objective.value, - "delta": self._delta, - } - - @property - def __is_lgb(self) -> bool: - """Check if the model is LightGBM.""" - return self._model == ModelName.lightgbm - - @property - def __is_xgb(self) -> bool: - """Check if the model is XGBoost.""" - return self._model == ModelName.xgboost - - def __is_optimized(self) -> None: - """Check if the optimization process has been completed.""" - if not getattr(self, "_is_optimized", False): - raise FittingException("Optimization is not completed.") diff --git a/pyproject.toml b/pyproject.toml index 7678ab1..a6e77d0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,29 +1,19 @@ -[tool.poetry] +[project] name = "mqboost" -version = "0.2.10" +version = "1.0.0" description = "Monotonic composite quantile gradient boost regressor" -authors = ["RektPunk "] readme = "README.md" -repository = "https://github.com/RektPunk/MQBoost" -classifiers = [ - "Topic :: Software Development :: Libraries :: Python Modules" +requires-python = ">=3.9" +dependencies = [ + "lightgbm>=4.6.0", + "numpy>=2.0.2", + "pandas>=2.3.3", + "scikit-learn>=1.6.1", + "xgboost>=2.1.4", ] -[tool.poetry.dependencies] -python = "^3.9" -numpy = "^2.0.0" -pandas = "^2.2.2" -lightgbm = "^4.3.0" -xgboost = "^2.1.0" -optuna = "^3.6.1" -scikit-learn = "^1.5.1" - - -[tool.poetry.group.dev.dependencies] -pre-commit = "^3.7.1" -pytest = "^8.3.2" -pytest-cov = "^5.0.0" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +[dependency-groups] +dev = [ + "pytest>=8.4.2", + "pytest-cov>=7.1.0", +] diff --git a/tests/test_optimize.py b/tests/test_optimize.py deleted file mode 100644 index eb1fee8..0000000 --- a/tests/test_optimize.py +++ /dev/null @@ -1,209 +0,0 @@ -import numpy as np -import optuna -import pandas as pd -import pytest - -from mqboost.base import FittingException, ModelName, ObjectiveName, ValidationException -from mqboost.dataset import MQDataset -from mqboost.objective import MQObjective -from mqboost.optimize import MQOptimizer, _lgb_get_params, _xgb_get_params - - -@pytest.fixture -def sample_data(): - """Generates sample data for testing.""" - X = pd.DataFrame(np.random.rand(100, 5), columns=[f"feature_{i}" for i in range(5)]) - y = np.random.rand(100) - alphas = [0.1, 0.5, 0.9] - dataset = MQDataset(alphas=alphas, data=X, label=y, model=ModelName.lightgbm.value) - return dataset - - -@pytest.fixture -def sample_valid_data(): - """Generates sample validation data.""" - X_valid = pd.DataFrame( - np.random.rand(20, 5), columns=[f"feature_{i}" for i in range(5)] - ) - y_valid = np.random.rand(20) - alphas = [0.1, 0.5, 0.9] - valid_set = MQDataset( - alphas=alphas, data=X_valid, label=y_valid, model=ModelName.lightgbm.value - ) - return valid_set - - -# Test MQOptimizer Initialization -def test_mqoptimizer_initialization(): - """Test initialization with valid parameters.""" - optimizer = MQOptimizer( - model=ModelName.lightgbm.value, - objective=ObjectiveName.check.value, - delta=0.01, - epsilon=1e-5, - ) - assert optimizer._model == ModelName.lightgbm - assert optimizer._objective == ObjectiveName.check - assert optimizer._delta == 0.01 - assert optimizer._epsilon == 1e-5 - - -def test_mqoptimizer_invalid_model(): - """Test initialization with an invalid model.""" - with pytest.raises(ValueError): - MQOptimizer(model="invalid_model") - - -def test_mqoptimizer_invalid_objective(): - """Test initialization with an invalid objective.""" - with pytest.raises(ValueError): - MQOptimizer(objective="invalid_objective") - - -def test_mqoptimizer_invalid_delta(): - """Test initialization with invalid delta value.""" - with pytest.raises(ValidationException): - MQOptimizer(delta=-0.01) - - -def test_mqoptimizer_invalid_epsilon(): - """Test initialization with invalid epsilon value.""" - with pytest.raises(ValidationException): - MQOptimizer(epsilon=-1e-5) - - -# Test optimize_params method -def test_optimize_params_with_default_get_params(sample_data): - """Test optimize_params with default get_params function.""" - optimizer = MQOptimizer() - best_params = optimizer.optimize_params(dataset=sample_data, n_trials=1) - assert isinstance(best_params, dict) - assert optimizer._is_optimized - - -def test_optimize_params_with_custom_get_params(sample_data): - """Test optimize_params with a custom get_params function.""" - optimizer = MQOptimizer() - - def custom_get_params(trial): - return { - "learning_rate": trial.suggest_float("learning_rate", 0.01, 0.1), - "num_leaves": trial.suggest_int("num_leaves", 10, 50), - } - - best_params = optimizer.optimize_params( - dataset=sample_data, n_trials=1, get_params_func=custom_get_params - ) - assert "learning_rate" in best_params - assert "num_leaves" in best_params - - -def test_optimize_params_with_valid_set(sample_data, sample_valid_data): - """Test optimize_params with a provided validation set.""" - optimizer = MQOptimizer() - best_params = optimizer.optimize_params( - dataset=sample_data, n_trials=1, valid_set=sample_valid_data - ) - assert isinstance(best_params, dict) - assert optimizer._is_optimized - - -def test_optimize_params_without_optimization(): - """Test accessing best_params before optimization is completed.""" - optimizer = MQOptimizer() - with pytest.raises(FittingException, match="Optimization is not completed."): - _ = optimizer.best_params - - -def test_study_property_before_optimization(): - """Test accessing study property before optimization.""" - optimizer = MQOptimizer() - assert optimizer.study is None - - -def test_study_property_after_optimization(sample_data): - """Test accessing study property after optimization.""" - optimizer = MQOptimizer() - optimizer.optimize_params(dataset=sample_data, n_trials=1) - assert isinstance(optimizer.study, optuna.Study) - - -def test_mqobjective_property(sample_data): - """Test MQObj property after initialization.""" - optimizer = MQOptimizer() - optimizer._MQObj = MQObjective( - alphas=sample_data.alphas, - objective=optimizer._objective, - weight=None, - model=optimizer._model, - delta=optimizer._delta, - epsilon=optimizer._epsilon, - ) - assert isinstance(optimizer.MQObj, MQObjective) - - -def test_optimize_params_invalid_dataset(): - """Test optimize_params with an invalid dataset.""" - optimizer = MQOptimizer() - with pytest.raises(AttributeError): - optimizer.optimize_params(dataset=None, n_trials=1) - - -def test_optimize_params_invalid_n_trials(sample_data): - """Test optimize_params with invalid n_trials.""" - optimizer = MQOptimizer() - with pytest.raises(ValueError): - optimizer.optimize_params(dataset=sample_data, n_trials=0) - - -def test_lgb_get_params(): - """Test the default LightGBM get_params function.""" - trial = optuna.trial.FixedTrial( - { - "learning_rate": 0.05, - "max_depth": 5, - "lambda_l1": 0.1, - "lambda_l2": 0.1, - "num_leaves": 31, - "feature_fraction": 0.8, - "bagging_fraction": 0.8, - 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mqboost/regressor.py | 9 ++++---- mqboost/utils.py | 3 +++ 7 files changed, 55 insertions(+), 70 deletions(-) diff --git a/mqboost/base.py b/mqboost/base.py index 827da11..0b01ed7 100644 --- a/mqboost/base.py +++ b/mqboost/base.py @@ -1,53 +1,37 @@ -from enum import Enum +from enum import StrEnum from typing import Callable import lightgbm as lgb -import numpy as np +import numpy.typing as npt import pandas as pd import xgboost as xgb - -class BaseEnum(Enum): - @classmethod - def get(cls, text: str) -> "BaseEnum": - cls._isin(text) - return cls[text] - - @classmethod - def _isin(cls, text: str) -> None: - if text not in cls._member_names_: - valid_members = ", ".join(cls._member_names_) - raise ValueError( - f"Invalid value: '{text}'. Expected one of: {valid_members}." - ) - - # Type -XdataLike = pd.DataFrame | pd.Series | np.ndarray -YdataLike = pd.Series | np.ndarray +XdataLike = pd.DataFrame | pd.Series | npt.NDArray +YdataLike = pd.Series | npt.NDArray AlphaLike = list[float] | float ModelLike = lgb.basic.Booster | xgb.Booster DtrainLike = lgb.basic.Dataset | xgb.DMatrix -ParamsLike = dict[str, float | int | str | bool] -WeightLike = list[float] | list[int] | np.ndarray | pd.Series +ParamsLike = dict[str, float | int | str | bool | list[int]] +WeightLike = list[float] | list[int] | npt.NDArray | pd.Series # Name -class ModelName(BaseEnum): - lightgbm: str = "lightgbm" - xgboost: str = "xgboost" +class ModelName(StrEnum): + lightgbm = "lightgbm" + xgboost = "xgboost" -class ObjectiveName(BaseEnum): - check: str = "check" - huber: str = "huber" - approx: str = "approx" +class ObjectiveName(StrEnum): + check = "check" + huber = "huber" + approx = "approx" -class TypeName(BaseEnum): - train_dtype: str = "train_dtype" - predict_dtype: str = "predict_dtype" - constraints_type: str = "constraints_type" +class TypeName(StrEnum): + train_dtype = "train_dtype" + predict_dtype = "predict_dtype" + constraints_type = "constraints_type" # Functions diff --git a/mqboost/constraints.py b/mqboost/constraints.py index 45d0fa6..02e0771 100644 --- a/mqboost/constraints.py +++ b/mqboost/constraints.py @@ -21,10 +21,13 @@ def set_monotone_constraints( """ MONOTONE_CONSTRAINTS: str = "monotone_constraints" - constraints_fucs = FUNC_TYPE.get(model_name).get(TypeName.constraints_type) + constraints_fucs = FUNC_TYPE[model_name][TypeName.constraints_type] _params = params.copy() if MONOTONE_CONSTRAINTS in _params: - _monotone_constraints = list(_params[MONOTONE_CONSTRAINTS]) + _monotone_constraints = _params.get(MONOTONE_CONSTRAINTS) + if not isinstance(_monotone_constraints, list): + raise TypeError(f"{MONOTONE_CONSTRAINTS} must be a list") + _monotone_constraints.append(1) _params.update({MONOTONE_CONSTRAINTS: constraints_fucs(_monotone_constraints)}) else: diff --git a/mqboost/dataset.py b/mqboost/dataset.py index e792bf4..45e9ce5 100644 --- a/mqboost/dataset.py +++ b/mqboost/dataset.py @@ -1,6 +1,7 @@ -from typing import Callable, Optional +from typing import Callable import numpy as np +import numpy.typing as npt import pandas as pd from mqboost.base import ( @@ -24,8 +25,7 @@ def _compare_datasets(data: pd.DataFrame, reference_data: pd.DataFrame) -> None: class MQDataset: - """ - MQDataset encapsulates the dataset used for training and predicting with the MQRegressor. + """MQDataset encapsulates the dataset used for training and predicting with the MQRegressor. It supports both LightGBM and XGBoost models, handling data preparation, validation, and conversion for training and prediction. Attributes: @@ -36,7 +36,6 @@ class MQDataset: label (pd.Series | np.ndarray): The target labels (if provided). weight (list[float] | list[int] | np.ndarray | pd.Series): Weight for each instance (if provided). model (str): The model type (LightGBM or XGBoost). - reference (MQBoost | None): Reference dataset for label encoding and label mean. Property: train_dtype: Returns the data type function for training data. @@ -58,35 +57,31 @@ def __init__( label: YdataLike | None = None, weight: WeightLike | None = None, model: str = ModelName.lightgbm.value, - reference: Optional["MQDataset"] = None, ) -> None: """Initialize the MQDataset.""" - self._model = ModelName.get(model) + self._model = ModelName[model] self._nrow = len(data) self._alphas = alpha_validate(alphas) - _funcs = FUNC_TYPE.get(self._model) - self._train_dtype: Callable = _funcs.get(TypeName.train_dtype) - self._predict_dtype: Callable = _funcs.get(TypeName.predict_dtype) + _funcs = FUNC_TYPE[self._model] + self._train_dtype: Callable = _funcs[TypeName.train_dtype] + self._predict_dtype: Callable = _funcs[TypeName.predict_dtype] _data = to_dataframe(data) - if reference is None: - self.encoders: dict[str, MQLabelEncoder] = {} - for col in _data.columns: - if _data[col].dtype == "object": - _encoder = MQLabelEncoder() - _data[col] = _encoder.fit_transform(_data[col]) - self.encoders.update({col: _encoder}) - else: - _compare_datasets(_data, reference.data) - self.encoders = reference.encoders.copy() - for col, _encoder in self.encoders.items(): - _data[col] = _encoder.transform(_data[col]) + self.encoders: dict[str, MQLabelEncoder] = {} + for col in _data.columns: + _series = _data[col] + if not isinstance(_series, pd.Series): + continue + if _series.dtype == "object": + _encoder = MQLabelEncoder() + _data[col] = _encoder.fit_transform(_series) + self.encoders.update({col: _encoder}) self._data = prepare_x(x=_data, alphas=self._alphas) self._columns = self._data.columns if label is not None: - self._label_mean = reference.label_mean if reference else label.mean() + self._label_mean = label.mean() self._label = prepare_y(y=label - self._label_mean, alphas=self._alphas) self._is_none_label = False @@ -125,7 +120,7 @@ def alphas(self) -> list[float]: return self._alphas @property - def label(self) -> pd.DataFrame: + def label(self) -> npt.NDArray: """Get the raw target labels.""" self.__label_available() return self._label @@ -137,7 +132,7 @@ def label_mean(self) -> float: return self._label_mean @property - def weight(self) -> np.ndarray | None: + def weight(self) -> npt.NDArray | None: """Get the weights.""" return getattr(self, "_weight", None) diff --git a/mqboost/encoder.py b/mqboost/encoder.py index e45433c..500e09c 100644 --- a/mqboost/encoder.py +++ b/mqboost/encoder.py @@ -1,17 +1,16 @@ import numpy as np +import pandas as pd from sklearn.preprocessing import LabelEncoder -from mqboost.base import XdataLike - class MQLabelEncoder: def __init__(self) -> None: self.label_encoder = LabelEncoder() - def fit(self, series: XdataLike) -> None: + def fit(self, series: pd.Series) -> None: self.label_encoder.fit(list(series[~series.isna()]) + ["Unseen", "NaN"]) - def transform(self, series: XdataLike) -> XdataLike: + def transform(self, series: pd.Series) -> pd.Series: return self.label_encoder.transform( np.select( [series.isna(), ~series.isin(self.label_encoder.classes_)], @@ -20,6 +19,6 @@ def transform(self, series: XdataLike) -> XdataLike: ) ) - def fit_transform(self, series: XdataLike) -> XdataLike: + def fit_transform(self, series: pd.Series) -> pd.Series: self.fit(series=series) return self.transform(series=series) diff --git a/mqboost/objective.py b/mqboost/objective.py index 4d8666c..547575e 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -61,7 +61,9 @@ def _train_pred_reshape( len_alpha: int, ) -> tuple[np.ndarray, np.ndarray]: """Reshape training predictions and labels to match the number of quantile levels.""" - _y_train: np.ndarray = dtrain.get_label() + _y_train = dtrain.get_label() + if not isinstance(_y_train, np.ndarray): + _y_train = np.array(_y_train) return _y_train.reshape(len_alpha, -1), y_pred.reshape(len_alpha, -1) diff --git a/mqboost/regressor.py b/mqboost/regressor.py index f13ffb9..bb92fbf 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -12,8 +12,7 @@ class MQRegressor: - """ - MQRegressor is a custom multiple quantile estimator that supports LightGBM and XGBoost models with + """MQRegressor is a custom multiple quantile estimator that supports LightGBM and XGBoost models with preserving monotonicity among quantiles. Attributes: @@ -49,8 +48,8 @@ def __init__( """Initialize the MQRegressor.""" params_validate(params=params) self._params = params - self._model = ModelName.get(model) - self._objective = ObjectiveName.get(objective) + self._model = ModelName[model] + self._objective = ObjectiveName[objective] self._delta = delta self._epsilon = epsilon @@ -138,7 +137,7 @@ def MQObj(self) -> MQObjective: return self._MQObj @property - def feature_importance(self) -> dict[str, float]: + def feature_importance(self) -> dict[str, int]: self.__predict_available() importances = {str(k): 0 for k in self._colnames} if self.__is_lgb: diff --git a/mqboost/utils.py b/mqboost/utils.py index 6b80146..908fd55 100644 --- a/mqboost/utils.py +++ b/mqboost/utils.py @@ -20,6 +20,9 @@ def alpha_validate( if isinstance(alphas, float): alphas = [alphas] + if not isinstance(alphas, list): + raise ValidationException("Alpha must be a list or float") + if 0.0 in alphas or 1.0 in alphas: raise ValidationException("Alpha cannot be 0 or 1") From 9f8bbf0e66f95bf16c96ed66e7d1525ecfe00747 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 00:09:17 +0900 Subject: [PATCH 03/40] update test and simplify --- .github/workflows/pypi_release.yaml | 27 +- .github/workflows/test.yaml | 46 +- mqboost/__init__.py | 4 +- mqboost/base.py | 17 +- mqboost/constraints.py | 12 +- mqboost/dataset.py | 41 +- mqboost/objective.py | 65 +- mqboost/regressor.py | 32 +- mqboost/utils.py | 20 +- poetry.lock | 1071 --------------------------- pyproject.toml | 9 + tests/test_base.py | 18 +- tests/test_dataset.py | 53 +- tests/test_regressor.py | 31 +- tests/test_utils.py | 10 - 15 files changed, 165 insertions(+), 1291 deletions(-) delete mode 100644 poetry.lock diff --git a/.github/workflows/pypi_release.yaml b/.github/workflows/pypi_release.yaml index 3c175af..c95825d 100644 --- a/.github/workflows/pypi_release.yaml +++ b/.github/workflows/pypi_release.yaml @@ -1,21 +1,26 @@ -name: Publish Python Package +name: Publish to PyPI on: push: tags: - - 'v*.*.*' + - "v*.*.*" jobs: - release: + build-and-publish: runs-on: ubuntu-latest steps: - - name: Checkout repository - uses: actions/checkout@v4 + - name: Checkout code + uses: actions/checkout@v6 - # poetry publish - - name: Publish module - uses: JRubics/poetry-publish@v2.0 - with: - pypi_token: ${{ secrets.PYPI_TOKEN }} - poetry_install_options: "--without dev" + - name: Setup python + uses: actions/setup-python@v6 + + - name: Install uv + uses: astral-sh/setup-uv@v7 + + - name: Build distributions + run: uv build --sdist --wheel + + - name: Publish to PyPI + run: uv publish --token ${{ secrets.PYPI_TOKEN }} diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index fe0fc56..81f29a3 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -9,28 +9,30 @@ on: jobs: run-pytest: - runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - - name: Set up python - uses: actions/setup-python@v5 - with: - python-version: '3.12' - - - name: Install Poetry - uses: snok/install-poetry@v1 - - - name: Install dependencies - run: poetry install --no-interaction --with dev - - - name: Run tests - run: poetry run pytest --junitxml=pytest.xml --cov-report=term-missing:skip-covered --cov=app tests/ | tee pytest-coverage.txt - - - name: Pytest coverage comment - uses: MishaKav/pytest-coverage-comment@main - with: - pytest-coverage-path: ./pytest-coverage.txt - junitxml-path: ./pytest.xml + - name: Checkout code + uses: actions/checkout@v6 + + - name: Set up python + uses: actions/setup-python@v6 + with: + python-version: "3.12" + + - name: Install uv + uses: astral-sh/setup-uv@v7 + + - name: Install dependencies + run: | + uv sync --all-extras + uv pip install -e . + + - name: Run tests + run: uv run pytest --junitxml=pytest.xml --cov-report=term-missing:skip-covered --cov=app tests/ | tee pytest-coverage.txt + + - name: Pytest coverage comment + uses: MishaKav/pytest-coverage-comment@main + with: + pytest-coverage-path: ./pytest-coverage.txt + junitxml-path: ./pytest.xml diff --git a/mqboost/__init__.py b/mqboost/__init__.py index ecc220c..34e3237 100644 --- a/mqboost/__init__.py +++ b/mqboost/__init__.py @@ -1,5 +1,5 @@ -# ruff: noqa from mqboost.dataset import MQDataset from mqboost.regressor import MQRegressor -__version__ = "0.2.10" +__version__ = "1.0.0" +__all__ = ["MQDataset", "MQRegressor"] diff --git a/mqboost/base.py b/mqboost/base.py index 0b01ed7..f6255f6 100644 --- a/mqboost/base.py +++ b/mqboost/base.py @@ -1,20 +1,9 @@ from enum import StrEnum -from typing import Callable +from typing import Any import lightgbm as lgb -import numpy.typing as npt -import pandas as pd import xgboost as xgb -# Type -XdataLike = pd.DataFrame | pd.Series | npt.NDArray -YdataLike = pd.Series | npt.NDArray -AlphaLike = list[float] | float -ModelLike = lgb.basic.Booster | xgb.Booster -DtrainLike = lgb.basic.Dataset | xgb.DMatrix -ParamsLike = dict[str, float | int | str | bool | list[int]] -WeightLike = list[float] | list[int] | npt.NDArray | pd.Series - # Name class ModelName(StrEnum): @@ -35,11 +24,11 @@ class TypeName(StrEnum): # Functions -def _lgb_predict_dtype(data: XdataLike): +def _lgb_predict_dtype(data: Any): return data -FUNC_TYPE: dict[ModelName, dict[TypeName, Callable]] = { +FUNC_TYPE: dict[ModelName, dict[TypeName, Any]] = { ModelName.lightgbm: { TypeName.train_dtype: lgb.Dataset, TypeName.predict_dtype: _lgb_predict_dtype, diff --git a/mqboost/constraints.py b/mqboost/constraints.py index 02e0771..8b8d517 100644 --- a/mqboost/constraints.py +++ b/mqboost/constraints.py @@ -1,23 +1,25 @@ +from typing import Any + import pandas as pd -from mqboost.base import FUNC_TYPE, ModelName, ParamsLike, TypeName +from mqboost.base import FUNC_TYPE, ModelName, TypeName def set_monotone_constraints( - params: ParamsLike, + params: dict[str, Any], columns: pd.Index, model_name: ModelName, -) -> ParamsLike: +) -> dict[str, Any]: """ Set monotone constraints in params Args: - params (ParamsLike) + params (dict) columns (pd.Index) model_name (ModelName) Raises: ValidationException: when "objective" is in params.keys() Returns: - ParamsLike + dict[str, Any] """ MONOTONE_CONSTRAINTS: str = "monotone_constraints" diff --git a/mqboost/dataset.py b/mqboost/dataset.py index 45e9ce5..824512c 100644 --- a/mqboost/dataset.py +++ b/mqboost/dataset.py @@ -1,29 +1,21 @@ from typing import Callable +import lightgbm as lgb import numpy as np import numpy.typing as npt import pandas as pd +import xgboost as xgb from mqboost.base import ( FUNC_TYPE, - AlphaLike, - DtrainLike, FittingException, ModelName, TypeName, - WeightLike, - XdataLike, - YdataLike, ) from mqboost.encoder import MQLabelEncoder from mqboost.utils import alpha_validate, prepare_x, prepare_y, to_dataframe -def _compare_datasets(data: pd.DataFrame, reference_data: pd.DataFrame) -> None: - if data.shape[1] != reference_data.shape[1] - 1: - raise ValueError("Number of columns do not match") - - class MQDataset: """MQDataset encapsulates the dataset used for training and predicting with the MQRegressor. It supports both LightGBM and XGBoost models, handling data preparation, validation, and conversion for training and prediction. @@ -52,10 +44,10 @@ class MQDataset: def __init__( self, - alphas: AlphaLike, - data: XdataLike, - label: YdataLike | None = None, - weight: WeightLike | None = None, + alphas: list[float] | float, + data: pd.DataFrame | pd.Series | npt.NDArray, + label: pd.Series | npt.NDArray | None = None, + weight: list[float] | list[int] | npt.NDArray | pd.Series | None = None, model: str = ModelName.lightgbm.value, ) -> None: """Initialize the MQDataset.""" @@ -64,19 +56,18 @@ def __init__( self._alphas = alpha_validate(alphas) _funcs = FUNC_TYPE[self._model] - self._train_dtype: Callable = _funcs[TypeName.train_dtype] - self._predict_dtype: Callable = _funcs[TypeName.predict_dtype] + self._train_dtype = _funcs[TypeName.train_dtype] + self._predict_dtype = _funcs[TypeName.predict_dtype] _data = to_dataframe(data) self.encoders: dict[str, MQLabelEncoder] = {} - for col in _data.columns: + for col in _data.select_dtypes(exclude="number").columns: _series = _data[col] if not isinstance(_series, pd.Series): continue - if _series.dtype == "object": - _encoder = MQLabelEncoder() - _data[col] = _encoder.fit_transform(_series) - self.encoders.update({col: _encoder}) + _encoder = MQLabelEncoder() + _data[col] = _encoder.fit_transform(_series) + self.encoders.update({col: _encoder}) self._data = prepare_x(x=_data, alphas=self._alphas) self._columns = self._data.columns @@ -90,12 +81,12 @@ def __init__( self._weight = prepare_y(y=_weight, alphas=self._alphas) @property - def train_dtype(self) -> Callable: + def train_dtype(self): """Get the data type function for training data.""" return self._train_dtype @property - def predict_dtype(self) -> Callable: + def predict_dtype(self): """Get the data type function for prediction data.""" return self._predict_dtype @@ -137,13 +128,13 @@ def weight(self) -> npt.NDArray | None: return getattr(self, "_weight", None) @property - def dtrain(self) -> DtrainLike: + def dtrain(self) -> lgb.Dataset | xgb.DMatrix: """Get the training data in the required format for the model.""" self.__label_available() return self._train_dtype(data=self._data, label=self._label, weight=self.weight) @property - def dpredict(self) -> DtrainLike | Callable: + def dpredict(self) -> lgb.Dataset | xgb.DMatrix | Callable: """Get the prediction data in the required format for the model.""" return self._predict_dtype(data=self._data) diff --git a/mqboost/objective.py b/mqboost/objective.py index 547575e..9c77e21 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -1,37 +1,30 @@ from functools import partial from typing import Any, Callable +import lightgbm as lgb import numpy as np +import numpy.typing as npt +import xgboost as xgb -from mqboost.base import DtrainLike, ModelName, ObjectiveName +from mqboost.base import ModelName, ObjectiveName from mqboost.utils import delta_validate, epsilon_validate -CHECK_LOSS: str = "check_loss" -GradFnLike = Callable[[np.ndarray, float, Any], np.ndarray] -HessFnLike = Callable[[np.ndarray, float, Any], np.ndarray] -ObjLike = Callable[ - [np.ndarray, DtrainLike, list[float], Any], tuple[np.ndarray, np.ndarray] -] -EvalLike = Callable[ - [np.ndarray, DtrainLike, list[float]], tuple[str, float, bool] | tuple[str, float] -] - # check loss -def _grad_rho(error: np.ndarray, alpha: float) -> np.ndarray: +def _grad_rho(error: npt.NDArray, alpha: float) -> npt.NDArray: return (error < 0).astype(int) - alpha -def _rho(error: np.ndarray, alpha: float) -> np.ndarray: +def _rho(error: npt.NDArray, alpha: float) -> npt.NDArray: return -error * _grad_rho(error=error, alpha=alpha) -def _hess_rho(error: np.ndarray, **kwargs) -> np.ndarray: +def _hess_rho(error: npt.NDArray, **kwargs) -> npt.NDArray: return np.ones_like(error) # Huber loss -def _grad_huber(error: np.ndarray, alpha: float, delta: float) -> np.ndarray: +def _grad_huber(error: npt.NDArray, alpha: float, delta: float) -> npt.NDArray: _abs_error = np.abs(error) _smaller_delta = (_abs_error <= delta).astype(int) _bigger_delta = (_abs_error > delta).astype(int) @@ -40,26 +33,26 @@ def _grad_huber(error: np.ndarray, alpha: float, delta: float) -> np.ndarray: return _r * _smaller_delta + _grad * _bigger_delta -def _hess_huber(error: np.ndarray, **kwargs) -> np.ndarray: +def _hess_huber(error: npt.NDArray, **kwargs) -> npt.NDArray: return np.ones_like(error) # Approx loss (MM loss) -def _grad_approx(error: np.ndarray, alpha: float, epsilon: float) -> np.ndarray: +def _grad_approx(error: npt.NDArray, alpha: float, epsilon: float) -> npt.NDArray: _grad = 0.5 * (1 - 2 * alpha - error / (epsilon + np.abs(error))) return _grad -def _hess_approx(error: np.ndarray, epsilon: float, **kwargs) -> np.ndarray: +def _hess_approx(error: npt.NDArray, epsilon: float, **kwargs) -> npt.NDArray: _hess = 1 / (2 * (epsilon + np.abs(error))) return _hess def _train_pred_reshape( - y_pred: np.ndarray, - dtrain: DtrainLike, + y_pred: npt.NDArray, + dtrain: lgb.Dataset | xgb.DMatrix, len_alpha: int, -) -> tuple[np.ndarray, np.ndarray]: +) -> tuple[npt.NDArray, npt.NDArray]: """Reshape training predictions and labels to match the number of quantile levels.""" _y_train = dtrain.get_label() if not isinstance(_y_train, np.ndarray): @@ -68,16 +61,16 @@ def _train_pred_reshape( # Compute gradient hessian logic -def compute_grad_hess(grad_fn: GradFnLike, hess_fn: HessFnLike) -> ObjLike: +def compute_grad_hess(grad_fn: Callable, hess_fn: Callable) -> Callable: """Return computing gradient hessian function.""" def _compute_grads_hess( - y_pred: np.ndarray, - dtrain: DtrainLike, + y_pred: npt.NDArray, + dtrain: lgb.Dataset | xgb.DMatrix, alphas: list[float], - weight: np.ndarray | None, + weight: npt.NDArray | None, **kwargs: Any, - ) -> tuple[np.ndarray, np.ndarray]: + ) -> tuple[npt.NDArray, npt.NDArray]: _len_alpha = len(alphas) _y_train, _y_pred = _train_pred_reshape( y_pred=y_pred, dtrain=dtrain, len_alpha=_len_alpha @@ -108,7 +101,7 @@ def _compute_grads_hess( def _eval_check_loss( y_pred: np.ndarray, - dtrain: DtrainLike, + dtrain: lgb.Dataset | xgb.DMatrix, alphas: list[float], ) -> float: """Evaluate the check loss function.""" @@ -120,26 +113,26 @@ def _eval_check_loss( for alpha_inx in range(_len_alpha): _err_for_alpha = _y_train[alpha_inx] - _y_pred[alpha_inx] _loss = _rho(error=_err_for_alpha, alpha=alphas[alpha_inx]) - loss += np.mean(_loss) + loss += float(np.mean(_loss)) return loss def _xgb_eval_loss( y_pred: np.ndarray, - dtrain: DtrainLike, + dtrain: lgb.Dataset | xgb.DMatrix, alphas: list[float], ) -> tuple[str, float]: loss = _eval_check_loss(y_pred=y_pred, dtrain=dtrain, alphas=alphas) - return CHECK_LOSS, loss + return "check_loss", loss def _lgb_eval_loss( y_pred: np.ndarray, - dtrain: DtrainLike, + dtrain: lgb.Dataset | xgb.DMatrix, alphas: list[float], ) -> tuple[str, float, bool]: loss = _eval_check_loss(y_pred=y_pred, dtrain=dtrain, alphas=alphas) - return CHECK_LOSS, loss, False + return "check_loss", loss, False def validate_parameters(objective: ObjectiveName, delta: float, epsilon: float) -> None: @@ -155,8 +148,8 @@ def get_fobj_function( alphas: list[float], delta: float, epsilon: float, -) -> ObjLike: - objective_mapping: dict[ObjectiveName, ObjLike] = { +) -> Callable: + objective_mapping: dict[ObjectiveName, Callable] = { ObjectiveName.check: partial( check_loss_grad_hess, weight=weight, alphas=alphas ), @@ -170,8 +163,8 @@ def get_fobj_function( return objective_mapping[objective] -def get_feval_function(model: ModelName, alphas: list[float]) -> EvalLike: - model_mapping: dict[ModelName, EvalLike] = { +def get_feval_function(model: ModelName, alphas: list[float]) -> Callable: + model_mapping: dict[ModelName, Callable] = { ModelName.lightgbm: partial(_lgb_eval_loss, alphas=alphas), ModelName.xgboost: partial(_xgb_eval_loss, alphas=alphas), } diff --git a/mqboost/regressor.py b/mqboost/regressor.py index bb92fbf..223fd80 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -1,8 +1,11 @@ +from typing import Any + import lightgbm as lgb import numpy as np +import numpy.typing as npt import xgboost as xgb -from mqboost.base import FittingException, ModelName, ObjectiveName, ParamsLike +from mqboost.base import FittingException, ModelName, ObjectiveName from mqboost.constraints import set_monotone_constraints from mqboost.dataset import MQDataset from mqboost.objective import MQObjective @@ -39,7 +42,7 @@ class MQRegressor: def __init__( self, - params: ParamsLike, + params: dict[str, Any], model: str = ModelName.lightgbm.value, objective: str = ObjectiveName.check.value, delta: float = 0.01, @@ -90,6 +93,12 @@ def fit( ) if self.__is_lgb: params.update({"objective": self._MQObj.fobj}) + if not ( + isinstance(dataset.dtrain, lgb.Dataset) + and isinstance(_eval_set, lgb.Dataset) + ): + raise ValueError("dtrain must be a lightgbm Dataset") + self.model = lgb.train( train_set=dataset.dtrain, params=params, @@ -113,7 +122,7 @@ def fit( def predict( self, dataset: MQDataset, - ) -> np.ndarray: + ) -> npt.NDArray: """ Predict quantiles for the dataset. Args: @@ -122,7 +131,9 @@ def predict( np.ndarray: The predicted quantiles. """ self.__predict_available() - _pred = self.model.predict(data=dataset.dpredict) + self._label_mean + _pred = ( + np.asanyarray(self.model.predict(data=dataset.dpredict)) + self._label_mean + ) _pred = _pred.reshape(len(dataset.alphas), dataset.nrow) return _pred @@ -132,19 +143,18 @@ def __predict_available(self) -> None: raise FittingException("Fit must be executed first.") @property - def MQObj(self) -> MQObjective: - """Get the MQObjective instance.""" - return self._MQObj - - @property - def feature_importance(self) -> dict[str, int]: + def feature_importance(self) -> dict[str, Any]: self.__predict_available() - importances = {str(k): 0 for k in self._colnames} + importances: dict[str, Any] = {str(k): 0 for k in self._colnames} if self.__is_lgb: + if not isinstance(self.model, lgb.Booster): + raise TypeError("model must be a lightgbm Booster") _importance = self.model.feature_importance(importance_type="gain").tolist() importances.update({str(k): v for k, v in zip(self._colnames, _importance)}) return importances else: + if not isinstance(self.model, xgb.Booster): + raise TypeError("model must be a xgboost Booster") importances.update(self.model.get_score(importance_type="gain")) return importances diff --git a/mqboost/utils.py b/mqboost/utils.py index 908fd55..3b28b1c 100644 --- a/mqboost/utils.py +++ b/mqboost/utils.py @@ -1,20 +1,16 @@ import warnings from itertools import chain, repeat +from typing import Any import numpy as np +import numpy.typing as npt import pandas as pd -from mqboost.base import ( - AlphaLike, - ParamsLike, - ValidationException, - XdataLike, - YdataLike, -) +from mqboost.base import ValidationException def alpha_validate( - alphas: AlphaLike, + alphas: list[float] | float, ) -> list[float]: """Validates the list of alphas ensuring they are in ascending order and contain no duplicates.""" if isinstance(alphas, float): @@ -41,7 +37,7 @@ def alpha_validate( return alphas -def to_dataframe(x: XdataLike) -> pd.DataFrame: +def to_dataframe(x: pd.DataFrame | pd.Series | npt.NDArray) -> pd.DataFrame: if isinstance(x, np.ndarray) or isinstance(x, pd.Series): _x = pd.DataFrame(x.copy()) else: @@ -71,9 +67,9 @@ def prepare_x( def prepare_y( - y: YdataLike, + y: pd.Series | npt.NDArray, alphas: list[float], -) -> np.ndarray: +) -> npt.NDArray: """Prepares and returns a stacked array of target values repeated for each alpha.""" return np.concatenate(list(repeat(y, len(alphas)))) @@ -101,7 +97,7 @@ def epsilon_validate(epsilon: float) -> None: raise ValidationException("Epsilon must be positive") -def params_validate(params: ParamsLike) -> None: +def params_validate(params: dict[str, Any]) -> None: """Validates the model parameter ensuring its key dosen't contain 'objective'.""" if "objective" in params: raise ValidationException( diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 3e94bd2..0000000 --- a/poetry.lock +++ /dev/null @@ -1,1071 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - 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assert ModelName.get("lightgbm") == ModelName.lightgbm - assert ModelName.get("xgboost") == ModelName.xgboost + assert ModelName["lightgbm"] == ModelName.lightgbm + assert ModelName["xgboost"] == ModelName.xgboost - with pytest.raises(ValueError): - ModelName.get("invalid_model") + with pytest.raises(KeyError): + ModelName["invalid_model"] def test_objective_name_enum(): - assert ObjectiveName.get("check") == ObjectiveName.check - assert ObjectiveName.get("huber") == ObjectiveName.huber - assert ObjectiveName.get("approx") == ObjectiveName.approx + assert ObjectiveName["check"] == ObjectiveName.check + assert ObjectiveName["huber"] == ObjectiveName.huber + assert ObjectiveName["approx"] == ObjectiveName.approx - with pytest.raises(ValueError): - ObjectiveName.get("invalid_objective") + with pytest.raises(KeyError): + ObjectiveName["invalid_objective"] # Test FUNC_TYPE diff --git a/tests/test_dataset.py b/tests/test_dataset.py index ec582ca..de38511 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -1,6 +1,8 @@ +import lightgbm as lgb import numpy as np import pandas as pd import pytest +import xgboost as xgb from mqboost.base import FittingException, ModelName, ValidationException from mqboost.dataset import MQDataset @@ -108,7 +110,7 @@ def test_mqdataset_dtype_lgb(): dtrain = dataset.dtrain dpredict = dataset.dpredict - assert isinstance(dtrain, dataset.train_dtype) + assert isinstance(dtrain, lgb.Dataset) assert isinstance(dpredict, pd.DataFrame) @@ -122,11 +124,11 @@ def test_mqdataset_dtype_xgb(): dtrain = dataset.dtrain dpredict = dataset.dpredict - assert isinstance(dtrain, dataset.train_dtype) - assert isinstance(dpredict, dataset.predict_dtype) + assert isinstance(dtrain, xgb.DMatrix) + assert isinstance(dpredict, xgb.DMatrix) -def test_MQDataset_reference(): +def test_mqdataset_encoders(): data = pd.DataFrame( { "col1": ["A", "B", "C"], @@ -149,46 +151,3 @@ def test_MQDataset_reference(): } ) pd.testing.assert_frame_equal(dataset.data, transformed_data) - new_data = pd.DataFrame( - { - "col1": ["A", "C", "B"], - "col2": [1, 3, 2], - "col3": ["X", "Y", "X"], - } - ) - - new_dataset = MQDataset(data=new_data, alphas=alphas, reference=dataset) - transformed_new_data = pd.DataFrame( - { - "col1": [0, 2, 1] * 2, - "col2": [1, 3, 2] * 2, - "col3": [4, 4, 4] * 2, - "_tau": [0.1, 0.1, 0.1, 0.2, 0.2, 0.2], - } - ) - print(new_dataset.data) - pd.testing.assert_frame_equal(new_dataset.data, transformed_new_data) - assert new_dataset.encoders == dataset.encoders - - -def test_MQDataset_reference_with_missing_columns(): - data = pd.DataFrame( - { - "col1": ["A", "B", "C"], - "col2": [1, 2, 3], - "col3": ["2", "3", "1"], - } - ) - label = pd.Series([0, 1, 0]) - alphas = [0.1, 0.2] - dataset = MQDataset(data=data, label=label, alphas=alphas) - - new_data = pd.DataFrame( - { - "col1": ["A", "C"], - "col2": [1, 3], # col3 is missing - } - ) - - with pytest.raises(ValueError): - MQDataset(data=new_data, alphas=alphas, reference=dataset) diff --git a/tests/test_regressor.py b/tests/test_regressor.py index 348af98..196ce3d 100644 --- a/tests/test_regressor.py +++ b/tests/test_regressor.py @@ -6,9 +6,8 @@ from mqboost.base import FittingException, ModelName, ObjectiveName from mqboost.regressor import MQDataset, MQRegressor -# Test data and helper functions - +# Test data and helper functions @pytest.fixture def dummy_dataset_lgb(): X = np.random.rand(100, 10) @@ -63,7 +62,7 @@ def test_mqregressor_initialization(): def test_invalid_model_initialization(): params = {"learning_rate": 0.1, "num_leaves": 31} - with pytest.raises(ValueError): + with pytest.raises(KeyError): MQRegressor( params=params, model="invalid_model", @@ -75,7 +74,7 @@ def test_invalid_model_initialization(): def test_invalid_objective_initialization(): params = {"learning_rate": 0.1, "num_leaves": 31} - with pytest.raises(ValueError): + with pytest.raises(KeyError): MQRegressor( params=params, model=ModelName.lightgbm.value, @@ -191,16 +190,16 @@ def test_feature_importance_after_fit(dummy_dataset_lgb): gbm_model.fit(dataset=dummy_dataset_lgb) feature_importances = gbm_model.feature_importance - assert isinstance( - feature_importances, dict - ), "Feature importances should be a dictionary" - assert len(feature_importances) == len( - dummy_dataset_lgb.columns - ), "Feature importance length mismatch" + assert isinstance(feature_importances, dict), ( + "Feature importances should be a dictionary" + ) + assert len(feature_importances) == len(dummy_dataset_lgb.columns), ( + "Feature importance length mismatch" + ) for feature in dummy_dataset_lgb.columns: - assert ( - str(feature) in feature_importances - ), f"Feature {feature} not found in importance" + assert str(feature) in feature_importances, ( + f"Feature {feature} not found in importance" + ) def test_feature_importance_positive(dummy_dataset_lgb): @@ -210,6 +209,6 @@ def test_feature_importance_positive(dummy_dataset_lgb): gbm_model.fit(dataset=dummy_dataset_lgb) feature_importances = gbm_model.feature_importance - assert all( - [importance >= 0 for importance in feature_importances.values()] - ), "All importance should be positive." + assert all([importance >= 0 for importance in feature_importances.values()]), ( + "All importance should be positive." + ) diff --git a/tests/test_utils.py b/tests/test_utils.py index 52dd8e9..f6fec28 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -139,16 +139,6 @@ def test_prepare_x_raises_on_invalid_column_name(): # Test for prepare_y -def test_prepare_y_with_list(): - y = [1, 2, 3] - alphas = [0.1, 0.2] - result = prepare_y(y, alphas) - - expected = np.array([1, 2, 3, 1, 2, 3]) - - np.testing.assert_array_equal(result, expected) - - def test_prepare_y_with_array(): y = np.array([1, 2, 3]) alphas = [0.1, 0.2] From 4ed0c84e36afc30256333b731c3e71f819290188 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 14:12:49 +0900 Subject: [PATCH 04/40] remove property --- .gitignore | 1 + mqboost/constraints.py | 2 -- mqboost/dataset.py | 62 +++++++----------------------------------- mqboost/objective.py | 20 ++------------ mqboost/regressor.py | 3 -- tests/test_dataset.py | 15 ++++++---- 6 files changed, 24 insertions(+), 79 deletions(-) diff --git a/.gitignore b/.gitignore index 2646699..288eecf 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,4 @@ __pycache__/ .vscode/ .pytest_cache/ .mypy_cache/ +*.egg-info diff --git a/mqboost/constraints.py b/mqboost/constraints.py index 8b8d517..ca0615c 100644 --- a/mqboost/constraints.py +++ b/mqboost/constraints.py @@ -16,8 +16,6 @@ def set_monotone_constraints( params (dict) columns (pd.Index) model_name (ModelName) - Raises: - ValidationException: when "objective" is in params.keys() Returns: dict[str, Any] """ diff --git a/mqboost/dataset.py b/mqboost/dataset.py index 824512c..dbefd87 100644 --- a/mqboost/dataset.py +++ b/mqboost/dataset.py @@ -28,18 +28,6 @@ class MQDataset: label (pd.Series | np.ndarray): The target labels (if provided). weight (list[float] | list[int] | np.ndarray | pd.Series): Weight for each instance (if provided). model (str): The model type (LightGBM or XGBoost). - - Property: - train_dtype: Returns the data type function for training data. - predict_dtype: Returns the data type function for prediction data. - columns: Returns the column names of the input features. - nrow: Returns the number of rows in the dataset. - data: Returns the input features. - label: Returns the target labels. - alphas: Returns the list of quantile levels. - weight: Returns the weight vector for each instance. - dtrain: Returns the training data in the required format for the model. - dpredict: Returns the prediction data in the required format for the model. """ def __init__( @@ -52,12 +40,12 @@ def __init__( ) -> None: """Initialize the MQDataset.""" self._model = ModelName[model] - self._nrow = len(data) - self._alphas = alpha_validate(alphas) + self.nrow = len(data) + self.alphas = alpha_validate(alphas) _funcs = FUNC_TYPE[self._model] - self._train_dtype = _funcs[TypeName.train_dtype] - self._predict_dtype = _funcs[TypeName.predict_dtype] + self.train_dtype = _funcs[TypeName.train_dtype] + self.predict_dtype = _funcs[TypeName.predict_dtype] _data = to_dataframe(data) self.encoders: dict[str, MQLabelEncoder] = {} @@ -69,46 +57,16 @@ def __init__( _data[col] = _encoder.fit_transform(_series) self.encoders.update({col: _encoder}) - self._data = prepare_x(x=_data, alphas=self._alphas) - self._columns = self._data.columns + self.data = prepare_x(x=_data, alphas=self.alphas) + self.columns = self.data.columns if label is not None: self._label_mean = label.mean() - self._label = prepare_y(y=label - self._label_mean, alphas=self._alphas) + self._label = prepare_y(y=label - self._label_mean, alphas=self.alphas) self._is_none_label = False if weight is not None: _weight = np.array(weight) if not isinstance(weight, np.ndarray) else weight - self._weight = prepare_y(y=_weight, alphas=self._alphas) - - @property - def train_dtype(self): - """Get the data type function for training data.""" - return self._train_dtype - - @property - def predict_dtype(self): - """Get the data type function for prediction data.""" - return self._predict_dtype - - @property - def columns(self) -> pd.Index: - """Get the column names of the input features.""" - return self._columns - - @property - def nrow(self) -> int: - """Get the number of rows in the dataset.""" - return self._nrow - - @property - def data(self) -> pd.DataFrame: - """Get the raw input features.""" - return self._data - - @property - def alphas(self) -> list[float]: - """Get the list of quantile levels.""" - return self._alphas + self._weight = prepare_y(y=_weight, alphas=self.alphas) @property def label(self) -> npt.NDArray: @@ -131,12 +89,12 @@ def weight(self) -> npt.NDArray | None: def dtrain(self) -> lgb.Dataset | xgb.DMatrix: """Get the training data in the required format for the model.""" self.__label_available() - return self._train_dtype(data=self._data, label=self._label, weight=self.weight) + return self.train_dtype(data=self.data, label=self._label, weight=self.weight) @property def dpredict(self) -> lgb.Dataset | xgb.DMatrix | Callable: """Get the prediction data in the required format for the model.""" - return self._predict_dtype(data=self._data) + return self.predict_dtype(data=self.data) def __label_available(self) -> None: """Check if the label is available, raises an exception if not.""" diff --git a/mqboost/objective.py b/mqboost/objective.py index 9c77e21..27e01a6 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -172,8 +172,7 @@ def get_feval_function(model: ModelName, alphas: list[float]) -> Callable: class MQObjective: - """ - MQObjective provides a monotone quantile objective and evaluation function for models. + """MQObjective provides a monotone quantile objective and evaluation function for models. Attributes: alphas (list[float]): List of quantile levels for the model. @@ -182,9 +181,6 @@ class MQObjective: delta (float): The delta parameter used for the 'huber' loss. epsilon (float): The epsilon parameter used for the 'approx' loss. weight (np.ndarray): The weight for each instance (if provided). - Properties: - fobj (Callable): The objective function to be minimized. - feval (Callable): The evaluation function used during training. """ def __init__( @@ -198,21 +194,11 @@ def __init__( ) -> None: """Initialize the MQObjective.""" validate_parameters(objective=objective, delta=delta, epsilon=epsilon) - self._fobj = get_fobj_function( + self.fobj = get_fobj_function( objective=objective, weight=weight, alphas=alphas, delta=delta, epsilon=epsilon, ) - self._feval = get_feval_function(model=model, alphas=alphas) - - @property - def fobj(self) -> Callable: - """Get the objective function to be minimized.""" - return self._fobj - - @property - def feval(self) -> Callable: - """Get the evaluation function used during training.""" - return self._feval + self.feval = get_feval_function(model=model, alphas=alphas) diff --git a/mqboost/regressor.py b/mqboost/regressor.py index 223fd80..ef7ecf8 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -35,9 +35,6 @@ class MQRegressor: Fits the regressor to the provided dataset, optionally evaluating on a separate validation set. predict(dataset): Predicts quantiles for the given dataset. - - Property: - MQObj: Returns the MQObjective instance. """ def __init__( diff --git a/tests/test_dataset.py b/tests/test_dataset.py index de38511..4fdf1b9 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -4,7 +4,12 @@ import pytest import xgboost as xgb -from mqboost.base import FittingException, ModelName, ValidationException +from mqboost.base import ( + FittingException, + ModelName, + ValidationException, + _lgb_predict_dtype, +) from mqboost.dataset import MQDataset from mqboost.encoder import MQLabelEncoder @@ -80,12 +85,12 @@ def test_mqdataset_train_predict_dtype(): data = pd.DataFrame({"feature_1": [1, 2, 3], "feature_2": [4, 5, 6]}) alphas = [0.1, 0.2] dataset = MQDataset(alphas=alphas, data=data, model=ModelName.lightgbm.value) - assert dataset.train_dtype == dataset._train_dtype - assert dataset.predict_dtype == dataset._predict_dtype + assert dataset.train_dtype == lgb.Dataset + assert dataset.predict_dtype == _lgb_predict_dtype dataset = MQDataset(alphas=alphas, data=data, model=ModelName.xgboost.value) - assert dataset.train_dtype == dataset._train_dtype - assert dataset.predict_dtype == dataset._predict_dtype + assert dataset.train_dtype == xgb.DMatrix + assert dataset.predict_dtype == xgb.DMatrix def test_mqdataset_columns_property(): From 2759c493d943d5b1415d61bce6fd71eda991e195 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 14:14:26 +0900 Subject: [PATCH 05/40] add eof --- .github/CODEOWNERS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 1b910e4..1872300 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,2 +1,2 @@ # ALL -@RektPunk \ No newline at end of file +@RektPunk From c8b0b8e777e2793c081559d2753ba0df519253ff Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 15:31:05 +0900 Subject: [PATCH 06/40] refactor objective and regressor --- mqboost/objective.py | 230 ++++++++++++++++++++++--------------------- mqboost/regressor.py | 46 ++++----- 2 files changed, 142 insertions(+), 134 deletions(-) diff --git a/mqboost/objective.py b/mqboost/objective.py index 27e01a6..0b9dfd2 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -1,4 +1,3 @@ -from functools import partial from typing import Any, Callable import lightgbm as lgb @@ -10,58 +9,65 @@ from mqboost.utils import delta_validate, epsilon_validate -# check loss -def _grad_rho(error: npt.NDArray, alpha: float) -> npt.NDArray: - return (error < 0).astype(int) - alpha - - -def _rho(error: npt.NDArray, alpha: float) -> npt.NDArray: - return -error * _grad_rho(error=error, alpha=alpha) +def calc_rho(error: npt.NDArray, alpha: float) -> npt.NDArray: + return (alpha - (error < 0).astype(int)) * error -def _hess_rho(error: npt.NDArray, **kwargs) -> npt.NDArray: - return np.ones_like(error) +# check loss +def calc_check_grad_hess( + error: npt.NDArray, alpha: float +) -> tuple[npt.NDArray, npt.NDArray]: + return (error < 0).astype(int) - alpha, np.ones_like(error) # Huber loss -def _grad_huber(error: npt.NDArray, alpha: float, delta: float) -> npt.NDArray: - _abs_error = np.abs(error) - _smaller_delta = (_abs_error <= delta).astype(int) - _bigger_delta = (_abs_error > delta).astype(int) - _r = _rho(error=error, alpha=alpha) - _grad = _grad_rho(error=error, alpha=alpha) - return _r * _smaller_delta + _grad * _bigger_delta - - -def _hess_huber(error: npt.NDArray, **kwargs) -> npt.NDArray: - return np.ones_like(error) +def calc_huber_grad_hess( + error: npt.NDArray, alpha: float, delta: float +) -> tuple[npt.NDArray, npt.NDArray]: + abs_error = np.abs(error) + smaller_delta = (abs_error <= delta).astype(int) + bigger_delta = (abs_error > delta).astype(int) + rho_val = calc_rho(error=error, alpha=alpha) + check_grad, check_hess = calc_check_grad_hess(error=error, alpha=alpha) + return rho_val * smaller_delta + check_grad * bigger_delta, check_hess # Approx loss (MM loss) -def _grad_approx(error: npt.NDArray, alpha: float, epsilon: float) -> npt.NDArray: - _grad = 0.5 * (1 - 2 * alpha - error / (epsilon + np.abs(error))) - return _grad - - -def _hess_approx(error: npt.NDArray, epsilon: float, **kwargs) -> npt.NDArray: - _hess = 1 / (2 * (epsilon + np.abs(error))) - return _hess +def calc_approx_grad_hess( + error: npt.NDArray, alpha: float, epsilon: float +) -> tuple[npt.NDArray, npt.NDArray]: + approx_grad = 0.5 * (1 - 2 * alpha - error / (epsilon + np.abs(error))) + approx_hess = 1 / (2 * (epsilon + np.abs(error))) + return approx_grad, approx_hess -def _train_pred_reshape( - y_pred: npt.NDArray, +def train_pred_reshape( dtrain: lgb.Dataset | xgb.DMatrix, + y_pred: npt.NDArray, len_alpha: int, ) -> tuple[npt.NDArray, npt.NDArray]: """Reshape training predictions and labels to match the number of quantile levels.""" - _y_train = dtrain.get_label() - if not isinstance(_y_train, np.ndarray): - _y_train = np.array(_y_train) - return _y_train.reshape(len_alpha, -1), y_pred.reshape(len_alpha, -1) + y_train = dtrain.get_label() + if not isinstance(y_train, np.ndarray): + y_train = np.array(y_train) + return y_train.reshape(len_alpha, -1), y_pred.reshape(len_alpha, -1) # Compute gradient hessian logic -def compute_grad_hess(grad_fn: Callable, hess_fn: Callable) -> Callable: +def compute_grad_hess_single_alpha( + y_true: npt.NDArray, + y_pred: npt.NDArray, + alpha: float, + calc_grad_hess_fn: Callable, + n: int, + **kwargs, +) -> tuple[npt.NDArray, npt.NDArray]: + error = y_true - y_pred + grad, hess = calc_grad_hess_fn(error=error, alpha=alpha, **kwargs) + return grad / n, hess / n + + +def compute_grad_hess(calc_grad_hess_fn: Callable) -> Callable: """Return computing gradient hessian function.""" def _compute_grads_hess( @@ -71,19 +77,25 @@ def _compute_grads_hess( weight: npt.NDArray | None, **kwargs: Any, ) -> tuple[npt.NDArray, npt.NDArray]: - _len_alpha = len(alphas) - _y_train, _y_pred = _train_pred_reshape( - y_pred=y_pred, dtrain=dtrain, len_alpha=_len_alpha + len_alpha = len(alphas) + y_train_reshaped, y_pred_reshaped = train_pred_reshape( + y_pred=y_pred, dtrain=dtrain, len_alpha=len_alpha ) + grads: list[np.ndarray] = [] hess: list[np.ndarray] = [] - _len_y = len(_y_train[0]) + len_y = len(y_train_reshaped[0]) for alpha_inx in range(len(alphas)): - _err_for_alpha: np.ndarray = _y_train[alpha_inx] - _y_pred[alpha_inx] - _grad = grad_fn(error=_err_for_alpha, alpha=alphas[alpha_inx], **kwargs) - _hess = hess_fn(error=_err_for_alpha, alpha=alphas[alpha_inx], **kwargs) - grads.append(_grad / _len_y) - hess.append(_hess / _len_y) + _grad, _hess = compute_grad_hess_single_alpha( + y_train_reshaped[alpha_inx], + y_pred_reshaped[alpha_inx], + alphas[alpha_inx], + calc_grad_hess_fn, + len_y, + **kwargs, + ) + grads.append(_grad) + hess.append(_hess) if isinstance(weight, np.ndarray): return np.concatenate(grads) * weight, np.concatenate(hess) * weight @@ -94,81 +106,84 @@ def _compute_grads_hess( # Gradient and Hessian functions -check_loss_grad_hess = compute_grad_hess(grad_fn=_grad_rho, hess_fn=_hess_rho) -huber_loss_grad_hess = compute_grad_hess(grad_fn=_grad_huber, hess_fn=_hess_huber) -approx_loss_grad_hess = compute_grad_hess(grad_fn=_grad_approx, hess_fn=_hess_approx) +check_loss_grad_hess = compute_grad_hess(calc_grad_hess_fn=calc_check_grad_hess) +huber_loss_grad_hess = compute_grad_hess(calc_grad_hess_fn=calc_huber_grad_hess) +approx_loss_grad_hess = compute_grad_hess(calc_grad_hess_fn=calc_approx_grad_hess) -def _eval_check_loss( +def eval_check_loss( y_pred: np.ndarray, dtrain: lgb.Dataset | xgb.DMatrix, alphas: list[float], -) -> float: + return_is_higher_better: bool, +) -> tuple[str, float, bool] | tuple[str, float]: """Evaluate the check loss function.""" - _len_alpha = len(alphas) - _y_train, _y_pred = _train_pred_reshape( - y_pred=y_pred, dtrain=dtrain, len_alpha=_len_alpha + len_alpha = len(alphas) + y_train_reshaped, y_pred_reshaped = train_pred_reshape( + y_pred=y_pred, dtrain=dtrain, len_alpha=len_alpha ) loss: float = 0.0 - for alpha_inx in range(_len_alpha): - _err_for_alpha = _y_train[alpha_inx] - _y_pred[alpha_inx] - _loss = _rho(error=_err_for_alpha, alpha=alphas[alpha_inx]) + for alpha_inx in range(len_alpha): + _err_for_alpha = y_train_reshaped[alpha_inx] - y_pred_reshaped[alpha_inx] + _loss = calc_rho(error=_err_for_alpha, alpha=alphas[alpha_inx]) loss += float(np.mean(_loss)) - return loss - -def _xgb_eval_loss( - y_pred: np.ndarray, - dtrain: lgb.Dataset | xgb.DMatrix, - alphas: list[float], -) -> tuple[str, float]: - loss = _eval_check_loss(y_pred=y_pred, dtrain=dtrain, alphas=alphas) + if return_is_higher_better: + return "check_loss", loss, False return "check_loss", loss -def _lgb_eval_loss( - y_pred: np.ndarray, - dtrain: lgb.Dataset | xgb.DMatrix, +def build_fobj( alphas: list[float], -) -> tuple[str, float, bool]: - loss = _eval_check_loss(y_pred=y_pred, dtrain=dtrain, alphas=alphas) - return "check_loss", loss, False - - -def validate_parameters(objective: ObjectiveName, delta: float, epsilon: float) -> None: - if objective == ObjectiveName.huber: - delta_validate(delta=delta) - elif objective == ObjectiveName.approx: - epsilon_validate(epsilon=epsilon) - - -def get_fobj_function( objective: ObjectiveName, - weight: np.ndarray | None, - alphas: list[float], delta: float, epsilon: float, + weight: np.ndarray | None, ) -> Callable: - objective_mapping: dict[ObjectiveName, Callable] = { - ObjectiveName.check: partial( - check_loss_grad_hess, weight=weight, alphas=alphas - ), - ObjectiveName.huber: partial( - huber_loss_grad_hess, weight=weight, alphas=alphas, delta=delta - ), - ObjectiveName.approx: partial( - approx_loss_grad_hess, weight=weight, alphas=alphas, epsilon=epsilon - ), - } - return objective_mapping[objective] - - -def get_feval_function(model: ModelName, alphas: list[float]) -> Callable: - model_mapping: dict[ModelName, Callable] = { - ModelName.lightgbm: partial(_lgb_eval_loss, alphas=alphas), - ModelName.xgboost: partial(_xgb_eval_loss, alphas=alphas), - } - return model_mapping[model] + def fobj(y_pred, dtrain): + if objective == ObjectiveName.check: + return check_loss_grad_hess( + y_pred=y_pred, + dtrain=dtrain, + alphas=alphas, + weight=weight, + ) + + elif objective == ObjectiveName.huber: + delta_validate(delta) + return huber_loss_grad_hess( + y_pred=y_pred, + dtrain=dtrain, + alphas=alphas, + weight=weight, + delta=delta, + ) + + elif objective == ObjectiveName.approx: + epsilon_validate(epsilon) + return approx_loss_grad_hess( + y_pred=y_pred, + dtrain=dtrain, + alphas=alphas, + weight=weight, + epsilon=epsilon, + ) + else: + raise ValueError(f"Unsupported objective: {objective}") + + return fobj + + +def build_feval(model: ModelName, alphas: list[float]) -> Callable: + def feval(y_pred, dtrain): + if model == ModelName.lightgbm: + return eval_check_loss(y_pred, dtrain, alphas, return_is_higher_better=True) + elif model == ModelName.xgboost: + return eval_check_loss( + y_pred, dtrain, alphas, return_is_higher_better=False + ) + + return feval class MQObjective: @@ -193,12 +208,5 @@ def __init__( weight: np.ndarray | None, ) -> None: """Initialize the MQObjective.""" - validate_parameters(objective=objective, delta=delta, epsilon=epsilon) - self.fobj = get_fobj_function( - objective=objective, - weight=weight, - alphas=alphas, - delta=delta, - epsilon=epsilon, - ) - self.feval = get_feval_function(model=model, alphas=alphas) + self.fobj = build_fobj(alphas, objective, delta, epsilon, weight) + self.feval = build_feval(model, alphas) diff --git a/mqboost/regressor.py b/mqboost/regressor.py index ef7ecf8..f7aa4fd 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -47,11 +47,11 @@ def __init__( ) -> None: """Initialize the MQRegressor.""" params_validate(params=params) - self._params = params - self._model = ModelName[model] - self._objective = ObjectiveName[objective] - self._delta = delta - self._epsilon = epsilon + self.params = params + self.model_name = ModelName[model] + self.objective = ObjectiveName[objective] + self.delta = delta + self.epsilon = epsilon def fit( self, @@ -69,38 +69,38 @@ def fit( train parameters. """ if eval_set: - _eval_set = eval_set.dtrain + eval_set_dtrain = eval_set.dtrain else: - _eval_set = dataset.dtrain + eval_set_dtrain = dataset.dtrain self._label_mean = dataset.label_mean params = set_monotone_constraints( - params=self._params, + params=self.params, columns=dataset.columns, - model_name=self._model, + model_name=self.model_name, ) - self._MQObj = MQObjective( + self.MQObj = MQObjective( alphas=dataset.alphas, - objective=self._objective, + objective=self.objective, weight=dataset.weight, - model=self._model, - delta=self._delta, - epsilon=self._epsilon, + model=self.model_name, + delta=self.delta, + epsilon=self.epsilon, ) if self.__is_lgb: - params.update({"objective": self._MQObj.fobj}) + params.update({"objective": self.MQObj.fobj}) if not ( isinstance(dataset.dtrain, lgb.Dataset) - and isinstance(_eval_set, lgb.Dataset) + and isinstance(eval_set_dtrain, lgb.Dataset) ): raise ValueError("dtrain must be a lightgbm Dataset") self.model = lgb.train( train_set=dataset.dtrain, params=params, - feval=self._MQObj.feval, - valid_sets=[_eval_set], + feval=self.MQObj.feval, + valid_sets=[eval_set_dtrain], **kwargs, ) elif self.__is_xgb: @@ -108,9 +108,9 @@ def fit( dtrain=dataset.dtrain, verbose_eval=False, params=params, - obj=self._MQObj.fobj, - custom_metric=self._MQObj.feval, - evals=[(_eval_set, "eval")], + obj=self.MQObj.fobj, + custom_metric=self.MQObj.feval, + evals=[(eval_set_dtrain, "eval")], **kwargs, ) self._colnames = dataset.columns.to_list() @@ -158,9 +158,9 @@ def feature_importance(self) -> dict[str, Any]: @property def __is_lgb(self) -> bool: """Check if the model is LightGBM.""" - return self._model == ModelName.lightgbm + return self.model_name == ModelName.lightgbm @property def __is_xgb(self) -> bool: """Check if the model is XGBoost.""" - return self._model == ModelName.xgboost + return self.model_name == ModelName.xgboost From 363b0b1dad28f61fa934f2d1c8217258fb67e799 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 15:49:49 +0900 Subject: [PATCH 07/40] fix tests --- mqboost/objective.py | 25 +++++++++++-------------- tests/test_objective.py | 13 +++++-------- tests/test_regressor.py | 10 +++++----- 3 files changed, 21 insertions(+), 27 deletions(-) diff --git a/mqboost/objective.py b/mqboost/objective.py index 0b9dfd2..259fd40 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -115,8 +115,7 @@ def eval_check_loss( y_pred: np.ndarray, dtrain: lgb.Dataset | xgb.DMatrix, alphas: list[float], - return_is_higher_better: bool, -) -> tuple[str, float, bool] | tuple[str, float]: +) -> float: """Evaluate the check loss function.""" len_alpha = len(alphas) y_train_reshaped, y_pred_reshaped = train_pred_reshape( @@ -127,10 +126,7 @@ def eval_check_loss( _err_for_alpha = y_train_reshaped[alpha_inx] - y_pred_reshaped[alpha_inx] _loss = calc_rho(error=_err_for_alpha, alpha=alphas[alpha_inx]) loss += float(np.mean(_loss)) - - if return_is_higher_better: - return "check_loss", loss, False - return "check_loss", loss + return loss def build_fobj( @@ -140,6 +136,12 @@ def build_fobj( epsilon: float, weight: np.ndarray | None, ) -> Callable: + if objective == ObjectiveName.approx: + epsilon_validate(epsilon) + + if objective == ObjectiveName.huber: + delta_validate(delta) + def fobj(y_pred, dtrain): if objective == ObjectiveName.check: return check_loss_grad_hess( @@ -150,7 +152,6 @@ def fobj(y_pred, dtrain): ) elif objective == ObjectiveName.huber: - delta_validate(delta) return huber_loss_grad_hess( y_pred=y_pred, dtrain=dtrain, @@ -160,7 +161,6 @@ def fobj(y_pred, dtrain): ) elif objective == ObjectiveName.approx: - epsilon_validate(epsilon) return approx_loss_grad_hess( y_pred=y_pred, dtrain=dtrain, @@ -168,20 +168,17 @@ def fobj(y_pred, dtrain): weight=weight, epsilon=epsilon, ) - else: - raise ValueError(f"Unsupported objective: {objective}") return fobj def build_feval(model: ModelName, alphas: list[float]) -> Callable: def feval(y_pred, dtrain): + loss = eval_check_loss(y_pred, dtrain, alphas) if model == ModelName.lightgbm: - return eval_check_loss(y_pred, dtrain, alphas, return_is_higher_better=True) + return "check_loss", loss, False elif model == ModelName.xgboost: - return eval_check_loss( - y_pred, dtrain, alphas, return_is_higher_better=False - ) + return "check_loss", loss return feval diff --git a/tests/test_objective.py b/tests/test_objective.py index 223bb9d..c6952f8 100644 --- a/tests/test_objective.py +++ b/tests/test_objective.py @@ -4,11 +4,10 @@ from mqboost.base import ModelName, ObjectiveName, ValidationException from mqboost.objective import ( MQObjective, - _eval_check_loss, - _lgb_eval_loss, - _xgb_eval_loss, approx_loss_grad_hess, + build_feval, check_loss_grad_hess, + eval_check_loss, huber_loss_grad_hess, ) @@ -153,7 +152,7 @@ def test_approx_loss_grad_hess(dummy_data, epsilon, expected_grads, expected_hes def test_eval_check_loss(dummy_data): """Test evaluation of the check loss.""" dtrain = dummy_data(y_true) - loss = _eval_check_loss(y_pred=y_pred, dtrain=dtrain, alphas=alphas) + loss = eval_check_loss(y_pred=y_pred, dtrain=dtrain, alphas=alphas) np.testing.assert_almost_equal(loss, 0.036) assert isinstance(loss, float) assert loss > 0 @@ -162,7 +161,7 @@ def test_eval_check_loss(dummy_data): def test_xgb_eval_loss(dummy_data): """Test XGBoost evaluation function.""" dtrain = dummy_data(y_true) - metric_name, loss = _xgb_eval_loss(y_pred=y_pred, dtrain=dtrain, alphas=alphas) + metric_name, loss = build_feval(ModelName.xgboost, alphas)(y_pred, dtrain) assert metric_name == "check_loss" assert isinstance(loss, float) @@ -170,9 +169,7 @@ def test_xgb_eval_loss(dummy_data): def test_lgb_eval_loss(dummy_data): """Test LightGBM evaluation function.""" dtrain = dummy_data(y_true) - metric_name, loss, higher_better = _lgb_eval_loss( - y_pred=y_pred, dtrain=dtrain, alphas=alphas - ) + metric_name, loss, higher_better = build_feval(ModelName.lightgbm, alphas)(y_pred, dtrain) assert metric_name == "check_loss" assert isinstance(loss, float) assert higher_better is False diff --git a/tests/test_regressor.py b/tests/test_regressor.py index 196ce3d..aa359dc 100644 --- a/tests/test_regressor.py +++ b/tests/test_regressor.py @@ -53,11 +53,11 @@ def test_mqregressor_initialization(): delta=0.01, epsilon=1e-5, ) - assert regressor._params == params - assert regressor._model == ModelName.lightgbm - assert regressor._objective == ObjectiveName.check - assert regressor._delta == 0.01 - assert regressor._epsilon == 1e-5 + assert regressor.params == params + assert regressor.model_name == ModelName.lightgbm + assert regressor.objective == ObjectiveName.check + assert regressor.delta == 0.01 + assert regressor.epsilon == 1e-5 def test_invalid_model_initialization(): From f379ebc0c1e529448de26d55bd6c7ab287fdcdf2 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 15:54:03 +0900 Subject: [PATCH 08/40] remove encoding --- README.md | 4 - mqboost/dataset.py | 10 --- mqboost/encoder.py | 24 ------ pyproject.toml | 1 - tests/test_dataset.py | 26 ------- tests/test_encoder.py | 42 ---------- uv.lock | 173 ------------------------------------------ 7 files changed, 280 deletions(-) delete mode 100644 mqboost/encoder.py delete mode 100644 tests/test_encoder.py diff --git a/README.md b/README.md index 6980308..32beaa2 100644 --- a/README.md +++ b/README.md @@ -22,8 +22,6 @@ **MQBoost** introduces an advanced model for estimating multiple quantiles while ensuring the non-crossing condition (monotone quantile condition). This model harnesses the capabilities of both [LightGBM](https://github.com/microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost), two leading gradient boosting frameworks. -By implementing the hyperparameter optimization prowess of [Optuna](https://github.com/optuna/optuna), the model achieves great performance. Optuna's optimization algorithms fine-tune the hyperparameters, ensuring the model operates efficiently. - # Installation Install using pip: ```bash @@ -34,8 +32,6 @@ pip install mqboost ## Features - **MQDataset**: Encapsulates the dataset used for MQRegressor and MQOptimizer. - **MQRegressor**: Custom multiple quantile estimator with preserving monotonicity among quantiles. -- **MQOptimizer**: Optimize hyperparameters for MQRegressor with Optuna. - ## Example Please refer to the [**Examples**](https://github.com/RektPunk/MQBoost/tree/main/examples) provided for further clarification. diff --git a/mqboost/dataset.py b/mqboost/dataset.py index dbefd87..6fa2800 100644 --- a/mqboost/dataset.py +++ b/mqboost/dataset.py @@ -12,7 +12,6 @@ ModelName, TypeName, ) -from mqboost.encoder import MQLabelEncoder from mqboost.utils import alpha_validate, prepare_x, prepare_y, to_dataframe @@ -48,15 +47,6 @@ def __init__( self.predict_dtype = _funcs[TypeName.predict_dtype] _data = to_dataframe(data) - self.encoders: dict[str, MQLabelEncoder] = {} - for col in _data.select_dtypes(exclude="number").columns: - _series = _data[col] - if not isinstance(_series, pd.Series): - continue - _encoder = MQLabelEncoder() - _data[col] = _encoder.fit_transform(_series) - self.encoders.update({col: _encoder}) - self.data = prepare_x(x=_data, alphas=self.alphas) self.columns = self.data.columns if label is not None: diff --git a/mqboost/encoder.py b/mqboost/encoder.py deleted file mode 100644 index 500e09c..0000000 --- a/mqboost/encoder.py +++ /dev/null @@ -1,24 +0,0 @@ -import numpy as np -import pandas as pd -from sklearn.preprocessing import LabelEncoder - - -class MQLabelEncoder: - def __init__(self) -> None: - self.label_encoder = LabelEncoder() - - def fit(self, series: pd.Series) -> None: - self.label_encoder.fit(list(series[~series.isna()]) + ["Unseen", "NaN"]) - - def transform(self, series: pd.Series) -> pd.Series: - return self.label_encoder.transform( - np.select( - [series.isna(), ~series.isin(self.label_encoder.classes_)], - ["NaN", "Unseen"], - series, - ) - ) - - def fit_transform(self, series: pd.Series) -> pd.Series: - self.fit(series=series) - return self.transform(series=series) diff --git a/pyproject.toml b/pyproject.toml index ba6fd15..b902bf7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,6 @@ dependencies = [ "lightgbm>=4.6.0", "numpy>=2.0.2", "pandas>=2.3.3", - "scikit-learn>=1.6.1", "xgboost>=2.1.4", ] diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 4fdf1b9..5fa4c82 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -11,7 +11,6 @@ _lgb_predict_dtype, ) from mqboost.dataset import MQDataset -from mqboost.encoder import MQLabelEncoder def _concat(df: pd.DataFrame, concat_count: int): @@ -131,28 +130,3 @@ def test_mqdataset_dtype_xgb(): dpredict = dataset.dpredict assert isinstance(dtrain, xgb.DMatrix) assert isinstance(dpredict, xgb.DMatrix) - - -def test_mqdataset_encoders(): - data = pd.DataFrame( - { - "col1": ["A", "B", "C"], - "col2": [1, 2, 3], - "col3": ["2", "3", "1"], - } - ) - label = pd.Series([0, 1, 0]) - alphas = [0.1, 0.2] - dataset = MQDataset(data=data, label=label, alphas=alphas) - - assert isinstance(dataset.encoders["col1"], MQLabelEncoder) - assert isinstance(dataset.encoders["col3"], MQLabelEncoder) - transformed_data = pd.DataFrame( - { - "col1": [0, 1, 2] * 2, - "col2": [1, 2, 3] * 2, - "col3": [1, 2, 0] * 2, - "_tau": [0.1, 0.1, 0.1, 0.2, 0.2, 0.2], - } - ) - pd.testing.assert_frame_equal(dataset.data, transformed_data) diff --git a/tests/test_encoder.py b/tests/test_encoder.py deleted file mode 100644 index 00bf709..0000000 --- a/tests/test_encoder.py +++ /dev/null @@ -1,42 +0,0 @@ -import numpy as np -import pandas as pd -import pytest - -from mqboost.encoder import MQLabelEncoder - - -# Test data for categorical variables -@pytest.fixture -def sample_data(): - return pd.Series(["apple", "banana", "orange", None, "kiwi", np.nan]) - - -# Test data for label encoding -@pytest.fixture -def sample_label_data(): - 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[[package]] name = "tomli" version = "2.4.1" From d615ac2ee2f05ee693b9b05b805a7d9074fa6616 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 15:55:55 +0900 Subject: [PATCH 09/40] update example --- examples/mqregressor.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/examples/mqregressor.py b/examples/mqregressor.py index 021e162..2dfa811 100644 --- a/examples/mqregressor.py +++ b/examples/mqregressor.py @@ -40,7 +40,5 @@ mq_regressor.fit(dataset=train_dataset) # Predict using the fitted model -test_dataset = MQDataset( - data=x_test, alphas=alphas, model=model, reference=train_dataset -) +test_dataset = MQDataset(data=x_test, alphas=alphas, model=model) preds_lgb = mq_regressor.predict(test_dataset) From 88e01d46f1360366580b440295ca7cceaba6e649 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 15:59:56 +0900 Subject: [PATCH 10/40] revert workflow name --- .github/workflows/pypi_release.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi_release.yaml b/.github/workflows/pypi_release.yaml index c95825d..df52c85 100644 --- a/.github/workflows/pypi_release.yaml +++ b/.github/workflows/pypi_release.yaml @@ -1,4 +1,4 @@ -name: Publish to PyPI +name: Publish Python Package on: push: From 817f028536409cdf92cb076b64f3c171a2177641 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 16:02:02 +0900 Subject: [PATCH 11/40] update pytest workflow --- .github/workflows/test.yaml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 81f29a3..633e2db 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -17,8 +17,6 @@ jobs: - name: Set up python uses: actions/setup-python@v6 - with: - python-version: "3.12" - name: Install uv uses: astral-sh/setup-uv@v7 From cb36020d18a9d10b4135ba5456f6cf0a4f32d6c0 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 16:10:00 +0900 Subject: [PATCH 12/40] update readme --- README.md | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 32beaa2..2b7d6ac 100644 --- a/README.md +++ b/README.md @@ -19,8 +19,18 @@

+**MQBoost** is a gradient boosting-based framework for simultaneous multi-quantile regression with monotonicity constraints (non-crossing quantiles). +It is built on top of [LightGBM](https://github.com/microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost), two leading gradient boosting frameworks, enabling efficient and scalable training while ensuring valid quantile estimates. -**MQBoost** introduces an advanced model for estimating multiple quantiles while ensuring the non-crossing condition (monotone quantile condition). This model harnesses the capabilities of both [LightGBM](https://github.com/microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost), two leading gradient boosting frameworks. +# Why MQBoost? +Standard quantile regression models often suffer from: +- Quantile crossing (e.g., 90% quantile < 50% quantile) +- Independent training per quantile → inconsistent predictions + +**MQBoost** solves this by: +[x] Learning multiple quantiles jointly +[x] Enforcing monotonicity across quantiles +[x] Leveraging efficient boosting frameworks # Installation Install using pip: @@ -30,7 +40,7 @@ pip install mqboost # Usage ## Features -- **MQDataset**: Encapsulates the dataset used for MQRegressor and MQOptimizer. +- **MQDataset**: Encapsulates the dataset used for MQRegressor. - **MQRegressor**: Custom multiple quantile estimator with preserving monotonicity among quantiles. ## Example From 7b7174831a0fd51105f393bed9b1bc45df844c52 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 16:10:43 +0900 Subject: [PATCH 13/40] update readme --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 2b7d6ac..f409261 100644 --- a/README.md +++ b/README.md @@ -28,9 +28,9 @@ Standard quantile regression models often suffer from: - Independent training per quantile → inconsistent predictions **MQBoost** solves this by: -[x] Learning multiple quantiles jointly -[x] Enforcing monotonicity across quantiles -[x] Leveraging efficient boosting frameworks +- Learning multiple quantiles jointly +- Enforcing monotonicity across quantiles +- Leveraging efficient boosting frameworks # Installation Install using pip: From e22acac8720bf2991341b65634f28f22645dff60 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 16:10:58 +0900 Subject: [PATCH 14/40] update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f409261..2d1fe20 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ **MQBoost** is a gradient boosting-based framework for simultaneous multi-quantile regression with monotonicity constraints (non-crossing quantiles). It is built on top of [LightGBM](https://github.com/microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost), two leading gradient boosting frameworks, enabling efficient and scalable training while ensuring valid quantile estimates. -# Why MQBoost? +### Why MQBoost? Standard quantile regression models often suffer from: - Quantile crossing (e.g., 90% quantile < 50% quantile) - Independent training per quantile → inconsistent predictions From f6e759de768f88d87d692c83f8f4dcb7020a0f48 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Fri, 3 Apr 2026 23:49:59 +0900 Subject: [PATCH 15/40] update test and objective --- mqboost/base.py | 8 +------- mqboost/constraints.py | 3 +-- mqboost/objective.py | 41 ++++++++++++++++++++--------------------- mqboost/regressor.py | 6 ++---- tests/test_base.py | 18 ++++++++---------- tests/test_dataset.py | 3 +-- tests/test_regressor.py | 12 ------------ tests/test_utils.py | 7 ------- 8 files changed, 33 insertions(+), 65 deletions(-) diff --git a/mqboost/base.py b/mqboost/base.py index f6255f6..7967b63 100644 --- a/mqboost/base.py +++ b/mqboost/base.py @@ -5,7 +5,6 @@ import xgboost as xgb -# Name class ModelName(StrEnum): lightgbm = "lightgbm" xgboost = "xgboost" @@ -23,15 +22,10 @@ class TypeName(StrEnum): constraints_type = "constraints_type" -# Functions -def _lgb_predict_dtype(data: Any): - return data - - FUNC_TYPE: dict[ModelName, dict[TypeName, Any]] = { ModelName.lightgbm: { TypeName.train_dtype: lgb.Dataset, - TypeName.predict_dtype: _lgb_predict_dtype, + TypeName.predict_dtype: lambda data: data, TypeName.constraints_type: list, }, ModelName.xgboost: { diff --git a/mqboost/constraints.py b/mqboost/constraints.py index ca0615c..5080a64 100644 --- a/mqboost/constraints.py +++ b/mqboost/constraints.py @@ -10,8 +10,7 @@ def set_monotone_constraints( columns: pd.Index, model_name: ModelName, ) -> dict[str, Any]: - """ - Set monotone constraints in params + """Set monotone constraints in params Args: params (dict) columns (pd.Index) diff --git a/mqboost/objective.py b/mqboost/objective.py index 259fd40..746359a 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -10,20 +10,21 @@ def calc_rho(error: npt.NDArray, alpha: float) -> npt.NDArray: + """Compute rho for the given error and alpha.""" return (alpha - (error < 0).astype(int)) * error -# check loss def calc_check_grad_hess( error: npt.NDArray, alpha: float ) -> tuple[npt.NDArray, npt.NDArray]: + """Compute gradient and Hessian for the check loss.""" return (error < 0).astype(int) - alpha, np.ones_like(error) -# Huber loss def calc_huber_grad_hess( error: npt.NDArray, alpha: float, delta: float ) -> tuple[npt.NDArray, npt.NDArray]: + """Compute gradient and Hessian for the Huber loss.""" abs_error = np.abs(error) smaller_delta = (abs_error <= delta).astype(int) bigger_delta = (abs_error > delta).astype(int) @@ -32,10 +33,10 @@ def calc_huber_grad_hess( return rho_val * smaller_delta + check_grad * bigger_delta, check_hess -# Approx loss (MM loss) def calc_approx_grad_hess( error: npt.NDArray, alpha: float, epsilon: float ) -> tuple[npt.NDArray, npt.NDArray]: + """Compute gradient and Hessian for the approximate loss (MM loss).""" approx_grad = 0.5 * (1 - 2 * alpha - error / (epsilon + np.abs(error))) approx_hess = 1 / (2 * (epsilon + np.abs(error))) return approx_grad, approx_hess @@ -53,7 +54,6 @@ def train_pred_reshape( return y_train.reshape(len_alpha, -1), y_pred.reshape(len_alpha, -1) -# Compute gradient hessian logic def compute_grad_hess_single_alpha( y_true: npt.NDArray, y_pred: npt.NDArray, @@ -62,13 +62,16 @@ def compute_grad_hess_single_alpha( n: int, **kwargs, ) -> tuple[npt.NDArray, npt.NDArray]: + """Compute gradient and Hessian using the given function for a single alpha value.""" error = y_true - y_pred grad, hess = calc_grad_hess_fn(error=error, alpha=alpha, **kwargs) return grad / n, hess / n -def compute_grad_hess(calc_grad_hess_fn: Callable) -> Callable: - """Return computing gradient hessian function.""" +def compute_grad_hess( + calc_grad_hess_fn: Callable, +) -> Callable[...,]: + """Return a function that computes gradient and Hessian for a given calc_grad_hess_fn.""" def _compute_grads_hess( y_pred: npt.NDArray, @@ -135,14 +138,17 @@ def build_fobj( delta: float, epsilon: float, weight: np.ndarray | None, -) -> Callable: +) -> Callable[..., tuple[npt.NDArray, npt.NDArray]]: + """Return fobj function.""" if objective == ObjectiveName.approx: epsilon_validate(epsilon) if objective == ObjectiveName.huber: delta_validate(delta) - def fobj(y_pred, dtrain): + def fobj( + y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix + ) -> tuple[npt.NDArray, npt.NDArray]: if objective == ObjectiveName.check: return check_loss_grad_hess( y_pred=y_pred, @@ -172,8 +178,12 @@ def fobj(y_pred, dtrain): return fobj -def build_feval(model: ModelName, alphas: list[float]) -> Callable: - def feval(y_pred, dtrain): +def build_feval( + model: ModelName, alphas: list[float] +) -> Callable[[npt.NDArray, lgb.Dataset | xgb.DMatrix], tuple]: + """Return feval function.""" + + def feval(y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix) -> tuple: loss = eval_check_loss(y_pred, dtrain, alphas) if model == ModelName.lightgbm: return "check_loss", loss, False @@ -184,17 +194,6 @@ def feval(y_pred, dtrain): class MQObjective: - """MQObjective provides a monotone quantile objective and evaluation function for models. - - Attributes: - alphas (list[float]): List of quantile levels for the model. - objective (ObjectiveName): The objective function type (either 'huber' or 'check'). - model (ModelName): The model type (either 'lightgbm' or 'xgboost'). - delta (float): The delta parameter used for the 'huber' loss. - epsilon (float): The epsilon parameter used for the 'approx' loss. - weight (np.ndarray): The weight for each instance (if provided). - """ - def __init__( self, alphas: list[float], diff --git a/mqboost/regressor.py b/mqboost/regressor.py index f7aa4fd..ac9f3fb 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -59,8 +59,7 @@ def fit( eval_set: MQDataset | None = None, **kwargs, ) -> None: - """ - Fit the regressor to the dataset. + """Fit the regressor to the dataset. Args: dataset (MQDataset): The dataset to fit the model on. eval_set (Optional[MQDataset]): @@ -120,8 +119,7 @@ def predict( self, dataset: MQDataset, ) -> npt.NDArray: - """ - Predict quantiles for the dataset. + """Predict quantiles for the dataset. Args: dataset (MQDataset): The dataset to make predictions on. Returns: diff --git a/tests/test_base.py b/tests/test_base.py index dc435c1..d8d5edf 100644 --- a/tests/test_base.py +++ b/tests/test_base.py @@ -11,7 +11,6 @@ ObjectiveName, TypeName, ValidationException, - _lgb_predict_dtype, ) @@ -50,6 +49,14 @@ def test_func_type_for_lgb(): ) assert isinstance(FUNC_TYPE[ModelName.lightgbm][TypeName.constraints_type](), list) + data = pd.DataFrame([1, 2, 3]) + assert FUNC_TYPE[ModelName.lightgbm][TypeName.predict_dtype](data) is data + + array_data = np.array([1, 2, 3]) + assert ( + FUNC_TYPE[ModelName.lightgbm][TypeName.predict_dtype](array_data) is array_data + ) + def test_func_type_for_xgb(): assert FUNC_TYPE[ModelName.xgboost][TypeName.train_dtype] == xgb.DMatrix @@ -57,15 +64,6 @@ def test_func_type_for_xgb(): assert isinstance(FUNC_TYPE[ModelName.xgboost][TypeName.constraints_type](), tuple) -# Test _lgb_predict_dtype -def test_lgb_predict_dtype(): - data = pd.DataFrame([1, 2, 3]) - assert _lgb_predict_dtype(data) is data - - array_data = np.array([1, 2, 3]) - assert _lgb_predict_dtype(array_data) is array_data - - # Test custom exceptions def test_custom_exceptions(): with pytest.raises(FittingException): diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 5fa4c82..f07c7cd 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -8,7 +8,6 @@ FittingException, ModelName, ValidationException, - _lgb_predict_dtype, ) from mqboost.dataset import MQDataset @@ -85,7 +84,7 @@ def test_mqdataset_train_predict_dtype(): alphas = [0.1, 0.2] dataset = MQDataset(alphas=alphas, data=data, model=ModelName.lightgbm.value) assert dataset.train_dtype == lgb.Dataset - assert dataset.predict_dtype == _lgb_predict_dtype + pd.testing.assert_frame_equal(dataset.predict_dtype(data), data) dataset = MQDataset(alphas=alphas, data=data, model=ModelName.xgboost.value) assert dataset.train_dtype == xgb.DMatrix diff --git a/tests/test_regressor.py b/tests/test_regressor.py index aa359dc..901e9b0 100644 --- a/tests/test_regressor.py +++ b/tests/test_regressor.py @@ -89,7 +89,6 @@ def test_mqregressor_fit_lgb(dummy_dataset_lgb, dummy_eval_set_lgb): params = {"learning_rate": 0.1, "num_leaves": 31} regressor = MQRegressor(params=params, model=ModelName.lightgbm.value) regressor.fit(dataset=dummy_dataset_lgb, eval_set=dummy_eval_set_lgb) - assert regressor._fitted is True assert isinstance(regressor.model, lgb.Booster) @@ -98,7 +97,6 @@ def test_mqregressor_fit_xgb(dummy_dataset_xgb, dummy_eval_set_xgb): params = {"learning_rate": 0.1, "max_depth": 6} regressor = MQRegressor(params=params, model=ModelName.xgboost.value) regressor.fit(dataset=dummy_dataset_xgb, eval_set=dummy_eval_set_xgb) - assert regressor._fitted is True assert isinstance(regressor.model, xgb.Booster) @@ -106,7 +104,6 @@ def test_mqregressor_fit_xgb(dummy_dataset_xgb, dummy_eval_set_xgb): def test_fit_without_eval_set_lgb(dummy_dataset_lgb): params = {"learning_rate": 0.1, "num_leaves": 31} regressor = MQRegressor(params=params, model=ModelName.lightgbm.value) - regressor.fit(dataset=dummy_dataset_lgb) assert regressor._fitted is True assert isinstance(regressor.model, lgb.Booster) @@ -116,7 +113,6 @@ def test_fit_without_eval_set_xgb(dummy_dataset_xgb): params = {"learning_rate": 0.1, "max_depth": 6} regressor = MQRegressor(params=params, model=ModelName.xgboost.value) regressor.fit(dataset=dummy_dataset_xgb) - assert regressor._fitted is True assert isinstance(regressor.model, xgb.Booster) @@ -125,7 +121,6 @@ def test_fit_without_eval_set_xgb(dummy_dataset_xgb): def test_predict_lgb(dummy_dataset_lgb): params = {"learning_rate": 0.1, "num_leaves": 31} regressor = MQRegressor(params=params, model=ModelName.lightgbm.value) - regressor.fit(dataset=dummy_dataset_lgb) predictions = regressor.predict(dataset=dummy_dataset_lgb) assert predictions.shape == (len(dummy_dataset_lgb.alphas), dummy_dataset_lgb.nrow) @@ -134,17 +129,14 @@ def test_predict_lgb(dummy_dataset_lgb): def test_predict_xgb(dummy_dataset_xgb): params = {"learning_rate": 0.1, "max_depth": 6} regressor = MQRegressor(params=params, model=ModelName.xgboost.value) - regressor.fit(dataset=dummy_dataset_xgb) predictions = regressor.predict(dataset=dummy_dataset_xgb) - assert predictions.shape == (len(dummy_dataset_xgb.alphas), dummy_dataset_xgb.nrow) def test_predict_without_fit(dummy_dataset_lgb): params = {"learning_rate": 0.1, "num_leaves": 31} regressor = MQRegressor(params=params, model=ModelName.lightgbm.value) - with pytest.raises(FittingException): regressor.predict(dataset=dummy_dataset_lgb) @@ -153,7 +145,6 @@ def test_predict_without_fit(dummy_dataset_lgb): def test_monotone_constraints_called_lgb(dummy_dataset_lgb): params = {"learning_rate": 0.1, "num_leaves": 31} regressor = MQRegressor(params=params, model=ModelName.lightgbm.value) - regressor.fit(dataset=dummy_dataset_lgb) predictions = regressor.predict(dataset=dummy_dataset_lgb) assert np.all( @@ -167,7 +158,6 @@ def test_monotone_constraints_called_lgb(dummy_dataset_lgb): def test_monotone_constraints_called_xgb(dummy_dataset_xgb): params = {"learning_rate": 0.1, "max_depth": 6} regressor = MQRegressor(params=params, model=ModelName.xgboost.value) - regressor.fit(dataset=dummy_dataset_xgb) predictions = regressor.predict(dataset=dummy_dataset_xgb) assert np.all( @@ -189,7 +179,6 @@ def test_feature_importance_after_fit(dummy_dataset_lgb): gbm_model = MQRegressor(params=params) gbm_model.fit(dataset=dummy_dataset_lgb) feature_importances = gbm_model.feature_importance - assert isinstance(feature_importances, dict), ( "Feature importances should be a dictionary" ) @@ -208,7 +197,6 @@ def test_feature_importance_positive(dummy_dataset_lgb): gbm_model = MQRegressor(params=params) gbm_model.fit(dataset=dummy_dataset_lgb) feature_importances = gbm_model.feature_importance - assert all([importance >= 0 for importance in feature_importances.values()]), ( "All importance should be positive." ) diff --git a/tests/test_utils.py b/tests/test_utils.py index f6fec28..9d0daa3 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -111,7 +111,6 @@ def test_prepare_x_with_series(): "_tau": [0.1, 0.1, 0.1, 0.2, 0.2, 0.2], } ) - pd.testing.assert_frame_equal(result, expected) @@ -126,14 +125,12 @@ def test_prepare_x_with_array(): "_tau": [0.1, 0.1, 0.2, 0.2], } ) - pd.testing.assert_frame_equal(result, expected) def test_prepare_x_raises_on_invalid_column_name(): x = pd.DataFrame({"_tau": [1, 2], "feature_1": [3, 4]}) alphas = [0.1, 0.2] - with pytest.raises(ValidationException, match="Column name '_tau' is not allowed."): prepare_x(x, alphas) @@ -143,9 +140,7 @@ def test_prepare_y_with_array(): y = np.array([1, 2, 3]) alphas = [0.1, 0.2] result = prepare_y(y, alphas) - expected = np.array([1, 2, 3, 1, 2, 3]) - np.testing.assert_array_equal(result, expected) @@ -153,9 +148,7 @@ def test_prepare_y_with_series(): y = pd.Series([1, 2, 3]) alphas = [0.1, 0.2] result = prepare_y(y, alphas) - expected = np.array([1, 2, 3, 1, 2, 3]) - np.testing.assert_array_equal(result, expected) From 952336dfcc815cb4f8b955e0a0687f8d3ff2b727 Mon Sep 17 00:00:00 2001 From: RektPunk Date: Sat, 4 Apr 2026 16:33:43 +0900 Subject: [PATCH 16/40] update readme --- README.md | 9 --------- 1 file changed, 9 deletions(-) diff --git a/README.md b/README.md index 2d1fe20..42d7000 100644 --- a/README.md +++ b/README.md @@ -8,15 +8,6 @@ Pythonv - - License - - - Lint - - - Test -

**MQBoost** is a gradient boosting-based framework for simultaneous multi-quantile regression with monotonicity constraints (non-crossing quantiles). From 391882a439b3db54a6fafbcdfb9f9a99fd1c070c Mon Sep 17 00:00:00 2001 From: unknown Date: Fri, 10 Apr 2026 10:59:01 +0900 Subject: [PATCH 17/40] remove utils.py --- mqboost/constraints.py | 9 +- mqboost/dataset.py | 72 +++++++++++++++- mqboost/objective.py | 31 ++++++- mqboost/regressor.py | 13 ++- mqboost/utils.py | 105 ---------------------- tests/test_dataset.py | 147 ++++++++++++++++++++++++++++++- tests/test_objective.py | 38 ++++++++ tests/test_regressor.py | 17 +++- tests/test_utils.py | 187 ---------------------------------------- 9 files changed, 306 insertions(+), 313 deletions(-) delete mode 100644 mqboost/utils.py diff --git a/mqboost/constraints.py b/mqboost/constraints.py index 5080a64..5e29d14 100644 --- a/mqboost/constraints.py +++ b/mqboost/constraints.py @@ -10,14 +10,7 @@ def set_monotone_constraints( columns: pd.Index, model_name: ModelName, ) -> dict[str, Any]: - """Set monotone constraints in params - Args: - params (dict) - columns (pd.Index) - model_name (ModelName) - Returns: - dict[str, Any] - """ + """Set monotone constraints in params""" MONOTONE_CONSTRAINTS: str = "monotone_constraints" constraints_fucs = FUNC_TYPE[model_name][TypeName.constraints_type] diff --git a/mqboost/dataset.py b/mqboost/dataset.py index 6fa2800..0c9685d 100644 --- a/mqboost/dataset.py +++ b/mqboost/dataset.py @@ -1,3 +1,4 @@ +from itertools import chain, repeat from typing import Callable import lightgbm as lgb @@ -11,8 +12,73 @@ FittingException, ModelName, TypeName, + ValidationException, ) -from mqboost.utils import alpha_validate, prepare_x, prepare_y, to_dataframe + + +def validate_alpha( + alphas: list[float] | float, +) -> list[float]: + """Validates the list of alphas ensuring they are in ascending order and contain no duplicates.""" + if isinstance(alphas, float): + alphas = [alphas] + + if not isinstance(alphas, list): + raise ValidationException("Alpha must be a list or float") + + if 0.0 in alphas or 1.0 in alphas: + raise ValidationException("Alpha cannot be 0 or 1") + + _len_alphas = len(alphas) + if _len_alphas == 0: + raise ValidationException("Input alpha is not valid") + + if _len_alphas >= 2 and any( + alphas[i] > alphas[i + 1] for i in range(_len_alphas - 1) + ): + raise ValidationException("Alpha is not ascending order") + + if _len_alphas != len(set(alphas)): + raise ValidationException("Duplicated alpha exists") + + return alphas + + +def prepare_x( + x: pd.DataFrame, + alphas: list[float], +) -> pd.DataFrame: + """Prepares and returns a stacked DataFrame of features repeated for each alpha, with an additional column indicating the alpha value. + Raises: + ValidationException: If the input data contains a column named '_tau'. + """ + if "_tau" in x.columns: + raise ValidationException("Column name '_tau' is not allowed.") + + _alpha_repeat_count_list = [list(repeat(alpha, len(x))) for alpha in alphas] + _alpha_repeat_list = list(chain.from_iterable(_alpha_repeat_count_list)) + + _repeated_x = pd.concat([x] * len(alphas), axis=0).reset_index(drop=True) + _repeated_x = _repeated_x.assign( + _tau=_alpha_repeat_list, + ) + return _repeated_x + + +def prepare_y( + y: pd.Series | npt.NDArray, + alphas: list[float], +) -> npt.NDArray: + """Prepares and returns a stacked array of target values repeated for each alpha.""" + return np.concatenate(list(repeat(y, len(alphas)))) + + +def to_dataframe(x: pd.DataFrame | pd.Series | npt.NDArray) -> pd.DataFrame: + if isinstance(x, np.ndarray) or isinstance(x, pd.Series): + _x = pd.DataFrame(x.copy()) + else: + _x = x.copy() + return _x class MQDataset: @@ -40,7 +106,7 @@ def __init__( """Initialize the MQDataset.""" self._model = ModelName[model] self.nrow = len(data) - self.alphas = alpha_validate(alphas) + self.alphas = validate_alpha(alphas) _funcs = FUNC_TYPE[self._model] self.train_dtype = _funcs[TypeName.train_dtype] @@ -68,7 +134,7 @@ def label(self) -> npt.NDArray: def label_mean(self) -> float: """Get the label mean.""" self.__label_available() - return self._label_mean + return float(self._label_mean) @property def weight(self) -> npt.NDArray | None: diff --git a/mqboost/objective.py b/mqboost/objective.py index 746359a..29b9651 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -1,3 +1,4 @@ +import warnings from typing import Any, Callable import lightgbm as lgb @@ -5,8 +6,7 @@ import numpy.typing as npt import xgboost as xgb -from mqboost.base import ModelName, ObjectiveName -from mqboost.utils import delta_validate, epsilon_validate +from mqboost.base import ModelName, ObjectiveName, ValidationException def calc_rho(error: npt.NDArray, alpha: float) -> npt.NDArray: @@ -132,6 +132,29 @@ def eval_check_loss( return loss +def validate_epsilon(epsilon: float) -> None: + """Validate epsilon parameter ensuring it is positive float""" + if not isinstance(epsilon, float): + raise ValidationException("Epsilon is not float type") + + if epsilon <= 0: + raise ValidationException("Epsilon must be positive") + + +def validate_delta(delta: float) -> None: + """Validates the delta parameter ensuring it is a positive float and less than or equal to 0.05.""" + _delta_upper_bound: float = 0.05 + + if not isinstance(delta, float): + raise ValidationException("Delta is not float type") + + if delta <= 0: + raise ValidationException("Delta must be positive") + + if delta > _delta_upper_bound: + warnings.warn("Delta should be 0.05 or less.") + + def build_fobj( alphas: list[float], objective: ObjectiveName, @@ -141,10 +164,10 @@ def build_fobj( ) -> Callable[..., tuple[npt.NDArray, npt.NDArray]]: """Return fobj function.""" if objective == ObjectiveName.approx: - epsilon_validate(epsilon) + validate_epsilon(epsilon) if objective == ObjectiveName.huber: - delta_validate(delta) + validate_delta(delta) def fobj( y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix diff --git a/mqboost/regressor.py b/mqboost/regressor.py index ac9f3fb..f28e217 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -5,15 +5,22 @@ import numpy.typing as npt import xgboost as xgb -from mqboost.base import FittingException, ModelName, ObjectiveName +from mqboost.base import FittingException, ModelName, ObjectiveName, ValidationException from mqboost.constraints import set_monotone_constraints from mqboost.dataset import MQDataset from mqboost.objective import MQObjective -from mqboost.utils import params_validate __all__ = ["MQRegressor"] +def validate_params(params: dict[str, Any]) -> None: + """Validates the model parameter ensuring its key dosen't contain 'objective'.""" + if "objective" in params: + raise ValidationException( + "The parameter named 'objective' must be excluded in params" + ) + + class MQRegressor: """MQRegressor is a custom multiple quantile estimator that supports LightGBM and XGBoost models with preserving monotonicity among quantiles. @@ -46,7 +53,7 @@ def __init__( epsilon: float = 1e-5, ) -> None: """Initialize the MQRegressor.""" - params_validate(params=params) + validate_params(params=params) self.params = params self.model_name = ModelName[model] self.objective = ObjectiveName[objective] diff --git a/mqboost/utils.py b/mqboost/utils.py deleted file mode 100644 index 3b28b1c..0000000 --- a/mqboost/utils.py +++ /dev/null @@ -1,105 +0,0 @@ -import warnings -from itertools import chain, repeat -from typing import Any - -import numpy as np -import numpy.typing as npt -import pandas as pd - -from mqboost.base import ValidationException - - -def alpha_validate( - alphas: list[float] | float, -) -> list[float]: - """Validates the list of alphas ensuring they are in ascending order and contain no duplicates.""" - if isinstance(alphas, float): - alphas = [alphas] - - if not isinstance(alphas, list): - raise ValidationException("Alpha must be a list or float") - - if 0.0 in alphas or 1.0 in alphas: - raise ValidationException("Alpha cannot be 0 or 1") - - _len_alphas = len(alphas) - if _len_alphas == 0: - raise ValidationException("Input alpha is not valid") - - if _len_alphas >= 2 and any( - alphas[i] > alphas[i + 1] for i in range(_len_alphas - 1) - ): - raise ValidationException("Alpha is not ascending order") - - if _len_alphas != len(set(alphas)): - raise ValidationException("Duplicated alpha exists") - - return alphas - - -def to_dataframe(x: pd.DataFrame | pd.Series | npt.NDArray) -> pd.DataFrame: - if isinstance(x, np.ndarray) or isinstance(x, pd.Series): - _x = pd.DataFrame(x.copy()) - else: - _x = x.copy() - return _x - - -def prepare_x( - x: pd.DataFrame, - alphas: list[float], -) -> pd.DataFrame: - """Prepares and returns a stacked DataFrame of features repeated for each alpha, with an additional column indicating the alpha value. - Raises: - ValidationException: If the input data contains a column named '_tau'. - """ - if "_tau" in x.columns: - raise ValidationException("Column name '_tau' is not allowed.") - - _alpha_repeat_count_list = [list(repeat(alpha, len(x))) for alpha in alphas] - _alpha_repeat_list = list(chain.from_iterable(_alpha_repeat_count_list)) - - _repeated_x = pd.concat([x] * len(alphas), axis=0).reset_index(drop=True) - _repeated_x = _repeated_x.assign( - _tau=_alpha_repeat_list, - ) - return _repeated_x - - -def prepare_y( - y: pd.Series | npt.NDArray, - alphas: list[float], -) -> npt.NDArray: - """Prepares and returns a stacked array of target values repeated for each alpha.""" - return np.concatenate(list(repeat(y, len(alphas)))) - - -def delta_validate(delta: float) -> None: - """Validates the delta parameter ensuring it is a positive float and less than or equal to 0.05.""" - _delta_upper_bound: float = 0.05 - - if not isinstance(delta, float): - raise ValidationException("Delta is not float type") - - if delta <= 0: - raise ValidationException("Delta must be positive") - - if delta > _delta_upper_bound: - warnings.warn("Delta should be 0.05 or less.") - - -def epsilon_validate(epsilon: float) -> None: - """Validate epsilon parameter ensuring it is positive float""" - if not isinstance(epsilon, float): - raise ValidationException("Epsilon is not float type") - - if epsilon <= 0: - raise ValidationException("Epsilon must be positive") - - -def params_validate(params: dict[str, Any]) -> None: - """Validates the model parameter ensuring its key dosen't contain 'objective'.""" - if "objective" in params: - raise ValidationException( - "The parameter named 'objective' must be excluded in params" - ) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index f07c7cd..b96133d 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -9,7 +9,152 @@ ModelName, ValidationException, ) -from mqboost.dataset import MQDataset +from mqboost.dataset import ( + MQDataset, + prepare_x, + prepare_y, + to_dataframe, + validate_alpha, +) + + +# Test for to_dataframe +def test_to_dataframe_with_dataframe(): + x = pd.DataFrame( + { + "feature_1": [1, 2], + "feature_2": [3, 4], + } + ) + pd.testing.assert_frame_equal(x, to_dataframe(x)) + + +def test_to_dataframe_with_series(): + x = pd.Series([1, 2, 3]) + expected = pd.DataFrame( + { + 0: [1, 2, 3], + } + ) + pd.testing.assert_frame_equal(expected, to_dataframe(x)) + + +def test_to_dataframe_with_array(): + x = np.array([[1, 2], [3, 4]]) + expected = pd.DataFrame( + { + 0: [1, 3], + 1: [2, 4], + } + ) + pd.testing.assert_frame_equal(expected, to_dataframe(x)) + + +# Test for prepare_x +def test_prepare_x_with_dataframe(): + x = pd.DataFrame( + { + "feature_1": [1, 2], + "feature_2": [3, 4], + } + ) + alphas = [0.1, 0.2] + result = prepare_x(x, alphas) + expected = pd.DataFrame( + { + "feature_1": [1, 2, 1, 2], + "feature_2": [3, 4, 3, 4], + "_tau": [0.1, 0.1, 0.2, 0.2], + } + ) + + pd.testing.assert_frame_equal(result, expected) + + +def test_prepare_x_with_series(): + x = pd.Series([1, 2, 3]) + alphas = [0.1, 0.2] + result = prepare_x(to_dataframe(x), alphas) + expected = pd.DataFrame( + { + 0: [1, 2, 3, 1, 2, 3], + "_tau": [0.1, 0.1, 0.1, 0.2, 0.2, 0.2], + } + ) + pd.testing.assert_frame_equal(result, expected) + + +def test_prepare_x_with_array(): + x = np.array([[1, 2], [3, 4]]) + alphas = [0.1, 0.2] + result = prepare_x(to_dataframe(x), alphas) + expected = pd.DataFrame( + { + 0: [1, 3, 1, 3], + 1: [2, 4, 2, 4], + "_tau": [0.1, 0.1, 0.2, 0.2], + } + ) + pd.testing.assert_frame_equal(result, expected) + + +def test_prepare_x_raises_on_invalid_column_name(): + x = pd.DataFrame({"_tau": [1, 2], "feature_1": [3, 4]}) + alphas = [0.1, 0.2] + with pytest.raises(ValidationException, match="Column name '_tau' is not allowed."): + prepare_x(x, alphas) + + +# Test for prepare_y +def test_prepare_y_with_array(): + y = np.array([1, 2, 3]) + alphas = [0.1, 0.2] + result = prepare_y(y, alphas) + expected = np.array([1, 2, 3, 1, 2, 3]) + np.testing.assert_array_equal(result, expected) + + +def test_prepare_y_with_series(): + y = pd.Series([1, 2, 3]) + alphas = [0.1, 0.2] + result = prepare_y(y, alphas) + expected = np.array([1, 2, 3, 1, 2, 3]) + np.testing.assert_array_equal(result, expected) + + +# Test for validate_alpha +def test_validate_alpha_single_alpha(): + alphas = 0.3 + result = validate_alpha(alphas) + assert result == [0.3] + + +def test_validate_alpha_multiple_alphas(): + alphas = [0.1, 0.2, 0.3] + result = validate_alpha(alphas) + assert result == alphas + + +def test_validate_alpha_raises_on_zero_or_one_alpha(): + with pytest.raises(ValidationException, match="Alpha cannot be 0 or 1"): + validate_alpha([0.0, 0.3]) + with pytest.raises(ValidationException, match="Alpha cannot be 0 or 1"): + validate_alpha([0.3, 1.0]) + + +def test_validate_alpha_raises_on_non_ascending_alphas(): + with pytest.raises(ValidationException, match="Alpha is not ascending order"): + validate_alpha([0.3, 0.2, 0.1]) + + +def test_validate_alpha_raises_on_duplicate_alphas(): + with pytest.raises(ValidationException, match="Duplicated alpha exists"): + validate_alpha([0.1, 0.2, 0.2]) + + +def test_validate_alpha_raises_on_empty_alphas(): + with pytest.raises(ValidationException, match="Input alpha is not valid"): + validate_alpha([]) def _concat(df: pd.DataFrame, concat_count: int): diff --git a/tests/test_objective.py b/tests/test_objective.py index c6952f8..ac3a39f 100644 --- a/tests/test_objective.py +++ b/tests/test_objective.py @@ -9,6 +9,8 @@ check_loss_grad_hess, eval_check_loss, huber_loss_grad_hess, + validate_delta, + validate_epsilon, ) @@ -202,3 +204,39 @@ def test_invalid_epsilon_for_approx(): delta=0.0, epsilon=-0.01, # Invalid epsilon (negative) ) + +# Test for validate_delta +def test_validate_delta_valid_delta(): + delta = 0.04 + assert validate_delta(delta) is None + + +def test_validate_delta_invalid_type(): + with pytest.raises(ValidationException, match="Delta is not float type"): + validate_delta(1) + + +def test_validate_delta_negative_delta(): + with pytest.raises(ValidationException, match="Delta must be positive"): + validate_delta(-0.01) + + +def test_validate_delta_exceeds_upper_bound(): + delta = 0.06 + with pytest.warns(UserWarning, match="Delta should be 0.05 or less."): + validate_delta(delta) + +# Test for validate_epsilon +def test_validate_epsilon_valid_epsilon(): + epsilon = 0.01 + assert validate_epsilon(epsilon) is None + + +def test_validate_epsilon_invalid_type(): + with pytest.raises(ValidationException, match="Epsilon is not float type"): + validate_epsilon(1) + + +def test_validate_epsilon_negative_epsilon(): + with pytest.raises(ValidationException, match="Epsilon must be positive"): + validate_epsilon(-0.01) diff --git a/tests/test_regressor.py b/tests/test_regressor.py index 901e9b0..cea079a 100644 --- a/tests/test_regressor.py +++ b/tests/test_regressor.py @@ -3,8 +3,8 @@ import pytest import xgboost as xgb -from mqboost.base import FittingException, ModelName, ObjectiveName -from mqboost.regressor import MQDataset, MQRegressor +from mqboost.base import FittingException, ModelName, ObjectiveName, ValidationException +from mqboost.regressor import MQDataset, MQRegressor, validate_params # Test data and helper functions @@ -200,3 +200,16 @@ def test_feature_importance_positive(dummy_dataset_lgb): assert all([importance >= 0 for importance in feature_importances.values()]), ( "All importance should be positive." ) + + +# Test for params validate +def test_set_validate_params_raises_validation_exception(): + params = { + "objective": "regression", + "monotone_constraints": [1, -1], + } + with pytest.raises( + ValidationException, + match="The parameter named 'objective' must be excluded in params", + ): + validate_params(params) diff --git a/tests/test_utils.py b/tests/test_utils.py index 9d0daa3..e69de29 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,187 +0,0 @@ -import numpy as np -import pandas as pd -import pytest - -from mqboost.base import ValidationException -from mqboost.utils import ( - alpha_validate, - delta_validate, - params_validate, - prepare_x, - prepare_y, - to_dataframe, -) - - -# Test for alpha_validate -def test_alpha_validate_single_alpha(): - alphas = 0.3 - result = alpha_validate(alphas) - assert result == [0.3] - - -def test_alpha_validate_multiple_alphas(): - alphas = [0.1, 0.2, 0.3] - result = alpha_validate(alphas) - assert result == alphas - - -def test_alpha_validate_raises_on_zero_or_one_alpha(): - with pytest.raises(ValidationException, match="Alpha cannot be 0 or 1"): - alpha_validate([0.0, 0.3]) - with pytest.raises(ValidationException, match="Alpha cannot be 0 or 1"): - alpha_validate([0.3, 1.0]) - - -def test_alpha_validate_raises_on_non_ascending_alphas(): - with pytest.raises(ValidationException, match="Alpha is not ascending order"): - alpha_validate([0.3, 0.2, 0.1]) - - -def test_alpha_validate_raises_on_duplicate_alphas(): - with pytest.raises(ValidationException, match="Duplicated alpha exists"): - alpha_validate([0.1, 0.2, 0.2]) - - -def test_alpha_validate_raises_on_empty_alphas(): - with pytest.raises(ValidationException, match="Input alpha is not valid"): - alpha_validate([]) - - -# Test for to_dataframe -def test_to_dataframe_with_dataframe(): - x = pd.DataFrame( - { - "feature_1": [1, 2], - "feature_2": [3, 4], - } - ) - pd.testing.assert_frame_equal(x, to_dataframe(x)) - - -def test_to_dataframe_with_series(): - x = pd.Series([1, 2, 3]) - expected = pd.DataFrame( - { - 0: [1, 2, 3], - } - ) - pd.testing.assert_frame_equal(expected, to_dataframe(x)) - - -def test_to_dataframe_with_array(): - x = np.array([[1, 2], [3, 4]]) - expected = pd.DataFrame( - { - 0: [1, 3], - 1: [2, 4], - } - ) - pd.testing.assert_frame_equal(expected, to_dataframe(x)) - - -# Test for prepare_x -def test_prepare_x_with_dataframe(): - x = pd.DataFrame( - { - "feature_1": [1, 2], - "feature_2": [3, 4], - } - ) - alphas = [0.1, 0.2] - result = prepare_x(x, alphas) - expected = pd.DataFrame( - { - "feature_1": [1, 2, 1, 2], - "feature_2": [3, 4, 3, 4], - "_tau": [0.1, 0.1, 0.2, 0.2], - } - ) - - pd.testing.assert_frame_equal(result, expected) - - -def test_prepare_x_with_series(): - x = pd.Series([1, 2, 3]) - alphas = [0.1, 0.2] - result = prepare_x(to_dataframe(x), alphas) - expected = pd.DataFrame( - { - 0: [1, 2, 3, 1, 2, 3], - "_tau": [0.1, 0.1, 0.1, 0.2, 0.2, 0.2], - } - ) - pd.testing.assert_frame_equal(result, expected) - - -def test_prepare_x_with_array(): - x = np.array([[1, 2], [3, 4]]) - alphas = [0.1, 0.2] - result = prepare_x(to_dataframe(x), alphas) - expected = pd.DataFrame( - { - 0: [1, 3, 1, 3], - 1: [2, 4, 2, 4], - "_tau": [0.1, 0.1, 0.2, 0.2], - } - ) - pd.testing.assert_frame_equal(result, expected) - - -def test_prepare_x_raises_on_invalid_column_name(): - x = pd.DataFrame({"_tau": [1, 2], "feature_1": [3, 4]}) - alphas = [0.1, 0.2] - with pytest.raises(ValidationException, match="Column name '_tau' is not allowed."): - prepare_x(x, alphas) - - -# Test for prepare_y -def test_prepare_y_with_array(): - y = np.array([1, 2, 3]) - alphas = [0.1, 0.2] - result = prepare_y(y, alphas) - expected = np.array([1, 2, 3, 1, 2, 3]) - np.testing.assert_array_equal(result, expected) - - -def test_prepare_y_with_series(): - y = pd.Series([1, 2, 3]) - alphas = [0.1, 0.2] - result = prepare_y(y, alphas) - expected = np.array([1, 2, 3, 1, 2, 3]) - np.testing.assert_array_equal(result, expected) - - -# Test for delta_validate -def test_delta_validate_valid_delta(): - delta = 0.04 - assert delta_validate(delta) is None - - -def test_delta_validate_invalid_type(): - with pytest.raises(ValidationException, match="Delta is not float type"): - delta_validate(1) - - -def test_delta_validate_negative_delta(): - with pytest.raises(ValidationException, match="Delta must be positive"): - delta_validate(-0.01) - - -def test_delta_validate_exceeds_upper_bound(): - delta = 0.06 - with pytest.warns(UserWarning, match="Delta should be 0.05 or less."): - delta_validate(delta) - - -# Test for params validate -def test_set_params_validate_raises_validation_exception(): - params = { - "objective": "regression", - "monotone_constraints": [1, -1], - } - with pytest.raises( - ValidationException, - match="The parameter named 'objective' must be excluded in params", - ): - params_validate(params) From d8e911cb001ef081c9f3ccb4ad9eff83f40d5d7f Mon Sep 17 00:00:00 2001 From: unknown Date: Tue, 28 Apr 2026 09:35:54 +0900 Subject: [PATCH 18/40] set examples --- README.md | 8 +++- examples/README.md | 12 ----- examples/mqregressor.ipynb | 92 ++++++++++++++++++++++++++++++++++++++ examples/mqregressor.py | 6 +++ 4 files changed, 105 insertions(+), 13 deletions(-) delete mode 100644 examples/README.md create mode 100644 examples/mqregressor.ipynb diff --git a/README.md b/README.md index 42d7000..f79c375 100644 --- a/README.md +++ b/README.md @@ -35,4 +35,10 @@ pip install mqboost - **MQRegressor**: Custom multiple quantile estimator with preserving monotonicity among quantiles. ## Example -Please refer to the [**Examples**](https://github.com/RektPunk/MQBoost/tree/main/examples) provided for further clarification. +Please refer to the [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RektPunk/MQBoost/tree/main/examples/mqregressor.ipynb) or [**Examples**](https://github.com/RektPunk/MQBoost/tree/main/examples/mqregressor.py) provided for further clarification. + +# Citation +If you use MQBoost in your research or project, please cite it as follows: +```md + +``` diff --git a/examples/README.md b/examples/README.md deleted file mode 100644 index 06a327c..0000000 --- a/examples/README.md +++ /dev/null @@ -1,12 +0,0 @@ -# MQBoost Example - -These following examples show how to set up and use `mqboost` for quantile regression with both parameter optimization and fixed parameters. -Adjust the parameters and settings as needed for your specific use case. To use the code, make sure you have `mqboost` and other required dependencies installed. - -- `mqregressor.py` - - Training with Fixed Parameters. - - This example demonstrates how to train and evaluate the model with fixed parameters. - -- `mqoptimizer.py` - - Parameter Optimization with Optuna. - - This example demonstrates how to optimize parameters using Optuna and then train and evaluate the model. diff --git a/examples/mqregressor.ipynb b/examples/mqregressor.ipynb new file mode 100644 index 0000000..94d4fb4 --- /dev/null +++ b/examples/mqregressor.ipynb @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6e97aaab", + "metadata": {}, + "source": [ + "These following example show how to set up and use `mqboost` for quantile regression with fixed parameters.\n", + "Adjust the parameters and settings as needed for your specific use case.\n", + "To use the code, make sure you have `mqboost` and other required dependencies installed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c73ade31", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "!uv pip install mqboost" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e67ae6e", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from mqboost import MQDataset, MQRegressor\n", + "\n", + "# Generate sample data\n", + "sample_size = 500\n", + "x = np.linspace(-10, 10, sample_size)\n", + "y = np.sin(x) + np.random.uniform(-0.4, 0.4, sample_size)\n", + "x_test = np.linspace(-10, 10, sample_size)\n", + "y_test = np.sin(x_test) + np.random.uniform(-0.4, 0.4, sample_size)\n", + "\n", + "# Define target quantiles\n", + "alphas = [0.3, 0.4, 0.5, 0.6, 0.7]\n", + "\n", + "# Specify model type\n", + "model = \"lightgbm\" # Options: \"lightgbm\" or \"xgboost\"\n", + "\n", + "# Set objective function\n", + "objective = \"huber\" # Options: \"check\", \"huber\", or \"approx\"\n", + "delta = 0.01 # Set when objective is \"huber\", default is 0.01\n", + "\n", + "# Train the model with fixed parameters\n", + "# Initialize the LightGBM-based quantile regressor\n", + "lgb_params = {\n", + " \"max_depth\": 4,\n", + " \"num_leaves\": 15,\n", + " \"learning_rate\": 0.1,\n", + " \"boosting_type\": \"gbdt\",\n", + "}\n", + "\n", + "mq_regressor = MQRegressor(\n", + " params=lgb_params,\n", + " objective=objective,\n", + " model=model,\n", + " delta=delta,\n", + ")\n", + "\n", + "# Fit the model\n", + "train_dataset = MQDataset(data=x, label=y, alphas=alphas, model=model)\n", + "mq_regressor.fit(dataset=train_dataset)\n", + "\n", + "# Predict using the fitted model\n", + "test_dataset = MQDataset(data=x_test, alphas=alphas, model=model)\n", + "preds_lgb = mq_regressor.predict(test_dataset)\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/mqregressor.py b/examples/mqregressor.py index 2dfa811..db16010 100644 --- a/examples/mqregressor.py +++ b/examples/mqregressor.py @@ -1,3 +1,9 @@ +""" +These following example show how to set up and use `mqboost` for quantile regression with fixed parameters. +Adjust the parameters and settings as needed for your specific use case. +To use the code, make sure you have `mqboost` and other required dependencies installed. +""" + import numpy as np from mqboost import MQDataset, MQRegressor From 5933d2d64d2fcbd05b87cdd812221f4a3670cec0 Mon Sep 17 00:00:00 2001 From: unknown Date: Tue, 28 Apr 2026 09:39:05 +0900 Subject: [PATCH 19/40] fix example colab link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f79c375..2b852e0 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ pip install mqboost - **MQRegressor**: Custom multiple quantile estimator with preserving monotonicity among quantiles. ## Example -Please refer to the [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RektPunk/MQBoost/tree/main/examples/mqregressor.ipynb) or [**Examples**](https://github.com/RektPunk/MQBoost/tree/main/examples/mqregressor.py) provided for further clarification. +Please refer to the [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/RektPunk/MQBoost/blob/main/examples/mqregressor.ipynb) or [**Examples**](https://github.com/RektPunk/MQBoost/tree/main/examples/mqregressor.py) provided for further clarification. # Citation If you use MQBoost in your research or project, please cite it as follows: From e529230987b787ed0efe0911a45725e75e29075f Mon Sep 17 00:00:00 2001 From: unknown Date: Wed, 6 May 2026 11:06:32 +0900 Subject: [PATCH 20/40] autoupdate pre-commit --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index cbce740..7d92114 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,7 +11,7 @@ repos: - id: check-merge-conflict - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.15.8 + rev: v0.15.12 hooks: - id: ruff name: ruff check From 62ac860021bb38faa2f2d217538b2feb0b714287 Mon Sep 17 00:00:00 2001 From: unknown Date: Wed, 6 May 2026 11:15:40 +0900 Subject: [PATCH 21/40] set base proper to py39 --- mqboost/base.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mqboost/base.py b/mqboost/base.py index 7967b63..eee8d8c 100644 --- a/mqboost/base.py +++ b/mqboost/base.py @@ -1,22 +1,22 @@ -from enum import StrEnum +from enum import Enum from typing import Any import lightgbm as lgb import xgboost as xgb -class ModelName(StrEnum): +class ModelName(str, Enum): lightgbm = "lightgbm" xgboost = "xgboost" -class ObjectiveName(StrEnum): +class ObjectiveName(str, Enum): check = "check" huber = "huber" approx = "approx" -class TypeName(StrEnum): +class TypeName(str, Enum): train_dtype = "train_dtype" predict_dtype = "predict_dtype" constraints_type = "constraints_type" From 3ab8b0d74b698b49cd97433dfeb5adbed078466a Mon Sep 17 00:00:00 2001 From: unknown Date: Wed, 6 May 2026 13:07:53 +0900 Subject: [PATCH 22/40] fix dataset and regressor --- mqboost/dataset.py | 34 +++++++++++++++++++++++++--------- mqboost/regressor.py | 6 +++--- tests/test_utils.py | 0 3 files changed, 28 insertions(+), 12 deletions(-) delete mode 100644 tests/test_utils.py diff --git a/mqboost/dataset.py b/mqboost/dataset.py index 0c9685d..012b167 100644 --- a/mqboost/dataset.py +++ b/mqboost/dataset.py @@ -1,4 +1,3 @@ -from itertools import chain, repeat from typing import Callable import lightgbm as lgb @@ -55,13 +54,13 @@ def prepare_x( if "_tau" in x.columns: raise ValidationException("Column name '_tau' is not allowed.") - _alpha_repeat_count_list = [list(repeat(alpha, len(x))) for alpha in alphas] - _alpha_repeat_list = list(chain.from_iterable(_alpha_repeat_count_list)) + num_alphas = len(alphas) + num_rows = len(x) + + _alpha_repeat_list = np.repeat(alphas, num_rows) + _repeated_x = pd.concat([x] * num_alphas, axis=0).reset_index(drop=True) + _repeated_x["_tau"] = _alpha_repeat_list - _repeated_x = pd.concat([x] * len(alphas), axis=0).reset_index(drop=True) - _repeated_x = _repeated_x.assign( - _tau=_alpha_repeat_list, - ) return _repeated_x @@ -70,12 +69,12 @@ def prepare_y( alphas: list[float], ) -> npt.NDArray: """Prepares and returns a stacked array of target values repeated for each alpha.""" - return np.concatenate(list(repeat(y, len(alphas)))) + return np.tile(y, len(alphas)) def to_dataframe(x: pd.DataFrame | pd.Series | npt.NDArray) -> pd.DataFrame: if isinstance(x, np.ndarray) or isinstance(x, pd.Series): - _x = pd.DataFrame(x.copy()) + _x = pd.DataFrame(x) else: _x = x.copy() return _x @@ -115,15 +114,30 @@ def __init__( _data = to_dataframe(data) self.data = prepare_x(x=_data, alphas=self.alphas) self.columns = self.data.columns + + self._label_raw = label + self._label_mean = None if label is not None: self._label_mean = label.mean() self._label = prepare_y(y=label - self._label_mean, alphas=self.alphas) self._is_none_label = False + else: + self._is_none_label = True + self._weight_raw = weight if weight is not None: _weight = np.array(weight) if not isinstance(weight, np.ndarray) else weight self._weight = prepare_y(y=_weight, alphas=self.alphas) + def set_label_mean(self, label_mean: float) -> None: + """Re-center labels using a new mean.""" + if self._label_raw is None: + raise ValidationException("Cannot set label mean when labels are None") + self._label_mean = label_mean + self._label = prepare_y( + y=self._label_raw - self._label_mean, alphas=self.alphas + ) + @property def label(self) -> npt.NDArray: """Get the raw target labels.""" @@ -134,6 +148,8 @@ def label(self) -> npt.NDArray: def label_mean(self) -> float: """Get the label mean.""" self.__label_available() + if self._label_mean is None: + raise ValidationException("Label mean is None") return float(self._label_mean) @property diff --git a/mqboost/regressor.py b/mqboost/regressor.py index f28e217..01e0e15 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -14,7 +14,7 @@ def validate_params(params: dict[str, Any]) -> None: - """Validates the model parameter ensuring its key dosen't contain 'objective'.""" + """Validates the model parameter ensuring its key doesn't contain 'objective'.""" if "objective" in params: raise ValidationException( "The parameter named 'objective' must be excluded in params" @@ -74,13 +74,13 @@ def fit( **kwargs: train parameters. """ + self._label_mean = dataset.label_mean if eval_set: + eval_set.set_label_mean(self._label_mean) eval_set_dtrain = eval_set.dtrain else: eval_set_dtrain = dataset.dtrain - self._label_mean = dataset.label_mean - params = set_monotone_constraints( params=self.params, columns=dataset.columns, diff --git a/tests/test_utils.py b/tests/test_utils.py deleted file mode 100644 index e69de29..0000000 From 2accd6342605fc3adcaadfb0693a385bc24be67b Mon Sep 17 00:00:00 2001 From: unknown Date: Wed, 6 May 2026 13:09:29 +0900 Subject: [PATCH 23/40] fix typo in constraints --- mqboost/constraints.py | 29 +++++++++++++++++++---------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/mqboost/constraints.py b/mqboost/constraints.py index 5e29d14..0246136 100644 --- a/mqboost/constraints.py +++ b/mqboost/constraints.py @@ -13,21 +13,30 @@ def set_monotone_constraints( """Set monotone constraints in params""" MONOTONE_CONSTRAINTS: str = "monotone_constraints" - constraints_fucs = FUNC_TYPE[model_name][TypeName.constraints_type] + constraints_funcs = FUNC_TYPE[model_name][TypeName.constraints_type] _params = params.copy() + num_columns = len(columns) + if MONOTONE_CONSTRAINTS in _params: _monotone_constraints = _params.get(MONOTONE_CONSTRAINTS) if not isinstance(_monotone_constraints, list): raise TypeError(f"{MONOTONE_CONSTRAINTS} must be a list") - _monotone_constraints.append(1) - _params.update({MONOTONE_CONSTRAINTS: constraints_fucs(_monotone_constraints)}) + # If user provided constraints for all columns including _tau + if len(_monotone_constraints) == num_columns: + pass + # If user provided constraints for original columns only + elif len(_monotone_constraints) == num_columns - 1: + _monotone_constraints.append(1) + else: + raise ValueError( + f"Length of {MONOTONE_CONSTRAINTS} must be {num_columns} or {num_columns - 1}" + ) + + _params.update({MONOTONE_CONSTRAINTS: constraints_funcs(_monotone_constraints)}) else: - _params.update( - { - MONOTONE_CONSTRAINTS: constraints_fucs( - [1 if "_tau" == col else 0 for col in columns] - ) - } - ) + # Default: only _tau is monotonic (1) + _constraints = [1 if col == "_tau" else 0 for col in columns] + _params.update({MONOTONE_CONSTRAINTS: constraints_funcs(_constraints)}) + return _params From 06698118ff0c2c99afda4204c958b7ae00f5d23a Mon Sep 17 00:00:00 2001 From: unknown Date: Wed, 6 May 2026 14:00:19 +0900 Subject: [PATCH 24/40] fix huber loss error and refactor objective --- mqboost/objective.py | 269 ++++++++++++++++------------------------ mqboost/regressor.py | 4 +- tests/test_objective.py | 30 ++--- 3 files changed, 124 insertions(+), 179 deletions(-) diff --git a/mqboost/objective.py b/mqboost/objective.py index 29b9651..2949924 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -1,5 +1,4 @@ import warnings -from typing import Any, Callable import lightgbm as lgb import numpy as np @@ -9,131 +8,83 @@ from mqboost.base import ModelName, ObjectiveName, ValidationException -def calc_rho(error: npt.NDArray, alpha: float) -> npt.NDArray: - """Compute rho for the given error and alpha.""" +def calc_rho(error: npt.NDArray, alpha: npt.NDArray | float) -> npt.NDArray: + """Compute rho (pinball loss) for the given error and alpha.""" + # L = (alpha - I(error < 0)) * error return (alpha - (error < 0).astype(int)) * error def calc_check_grad_hess( - error: npt.NDArray, alpha: float + error: npt.NDArray, alpha: npt.NDArray | float ) -> tuple[npt.NDArray, npt.NDArray]: """Compute gradient and Hessian for the check loss.""" + # dL/dp = I(error < 0) - alpha + # d2L/dp2 = 1 as a proxy for Hessian return (error < 0).astype(int) - alpha, np.ones_like(error) def calc_huber_grad_hess( - error: npt.NDArray, alpha: float, delta: float + error: npt.NDArray, alpha: npt.NDArray | float, delta: float ) -> tuple[npt.NDArray, npt.NDArray]: - """Compute gradient and Hessian for the Huber loss.""" + """Compute gradient and Hessian for the Huber loss (Smooth Quantile Loss).""" abs_error = np.abs(error) - smaller_delta = (abs_error <= delta).astype(int) - bigger_delta = (abs_error > delta).astype(int) - rho_val = calc_rho(error=error, alpha=alpha) + mask = (abs_error <= delta).astype(float) + + # Gradient for linear part check_grad, check_hess = calc_check_grad_hess(error=error, alpha=alpha) - return rho_val * smaller_delta + check_grad * bigger_delta, check_hess + # Gradient for Huber part + # dL/dp = check_grad * (abs_error / delta) + huber_grad = check_grad * (abs_error / delta) + grad = mask * huber_grad + (1 - mask) * check_grad + + # Hessian for Huber part + # d2L/dp2 = |check_grad| / delta + huber_hess = np.abs(check_grad) / delta + # For linear part, we use check_hess as a proxy for Hessian + hess = mask * huber_hess + (1 - mask) * check_hess + + return grad, hess def calc_approx_grad_hess( - error: npt.NDArray, alpha: float, epsilon: float + error: npt.NDArray, alpha: npt.NDArray | float, epsilon: float ) -> tuple[npt.NDArray, npt.NDArray]: """Compute gradient and Hessian for the approximate loss (MM loss).""" + # dL/dp = 0.5 * (1 - 2 * alpha - error / (epsilon + |error|)) approx_grad = 0.5 * (1 - 2 * alpha - error / (epsilon + np.abs(error))) + + # d2L/dp2 = 1 / (2 * (epsilon + |error|)) approx_hess = 1 / (2 * (epsilon + np.abs(error))) return approx_grad, approx_hess -def train_pred_reshape( - dtrain: lgb.Dataset | xgb.DMatrix, - y_pred: npt.NDArray, - len_alpha: int, -) -> tuple[npt.NDArray, npt.NDArray]: - """Reshape training predictions and labels to match the number of quantile levels.""" - y_train = dtrain.get_label() - if not isinstance(y_train, np.ndarray): - y_train = np.array(y_train) - return y_train.reshape(len_alpha, -1), y_pred.reshape(len_alpha, -1) - - -def compute_grad_hess_single_alpha( - y_true: npt.NDArray, - y_pred: npt.NDArray, - alpha: float, - calc_grad_hess_fn: Callable, - n: int, - **kwargs, -) -> tuple[npt.NDArray, npt.NDArray]: - """Compute gradient and Hessian using the given function for a single alpha value.""" - error = y_true - y_pred - grad, hess = calc_grad_hess_fn(error=error, alpha=alpha, **kwargs) - return grad / n, hess / n - - -def compute_grad_hess( - calc_grad_hess_fn: Callable, -) -> Callable[...,]: - """Return a function that computes gradient and Hessian for a given calc_grad_hess_fn.""" - - def _compute_grads_hess( - y_pred: npt.NDArray, - dtrain: lgb.Dataset | xgb.DMatrix, - alphas: list[float], - weight: npt.NDArray | None, - **kwargs: Any, - ) -> tuple[npt.NDArray, npt.NDArray]: - len_alpha = len(alphas) - y_train_reshaped, y_pred_reshaped = train_pred_reshape( - y_pred=y_pred, dtrain=dtrain, len_alpha=len_alpha - ) - - grads: list[np.ndarray] = [] - hess: list[np.ndarray] = [] - len_y = len(y_train_reshaped[0]) - for alpha_inx in range(len(alphas)): - _grad, _hess = compute_grad_hess_single_alpha( - y_train_reshaped[alpha_inx], - y_pred_reshaped[alpha_inx], - alphas[alpha_inx], - calc_grad_hess_fn, - len_y, - **kwargs, - ) - grads.append(_grad) - hess.append(_hess) - - if isinstance(weight, np.ndarray): - return np.concatenate(grads) * weight, np.concatenate(hess) * weight - else: - return np.concatenate(grads), np.concatenate(hess) - - return _compute_grads_hess - - -# Gradient and Hessian functions -check_loss_grad_hess = compute_grad_hess(calc_grad_hess_fn=calc_check_grad_hess) -huber_loss_grad_hess = compute_grad_hess(calc_grad_hess_fn=calc_huber_grad_hess) -approx_loss_grad_hess = compute_grad_hess(calc_grad_hess_fn=calc_approx_grad_hess) +def _get_alpha_expanded(alphas: list[float], total_len: int) -> tuple[npt.NDArray, int]: + """Helper to expand alphas and get original dataset size.""" + n = total_len // len(alphas) + return np.repeat(alphas, n), n def eval_check_loss( - y_pred: np.ndarray, + y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix, alphas: list[float], ) -> float: - """Evaluate the check loss function.""" - len_alpha = len(alphas) - y_train_reshaped, y_pred_reshaped = train_pred_reshape( - y_pred=y_pred, dtrain=dtrain, len_alpha=len_alpha - ) - loss: float = 0.0 - for alpha_inx in range(len_alpha): - _err_for_alpha = y_train_reshaped[alpha_inx] - y_pred_reshaped[alpha_inx] - _loss = calc_rho(error=_err_for_alpha, alpha=alphas[alpha_inx]) - loss += float(np.mean(_loss)) - return loss + """Evaluate the check loss function using vectorized operations.""" + y_true = dtrain.get_label() + if not isinstance(y_true, np.ndarray): + y_true = np.array(y_true) + + alphas_expanded, n = _get_alpha_expanded(alphas, len(y_true)) + error = y_true - y_pred + loss_all = calc_rho(error=error, alpha=alphas_expanded) + + # Return the sum of mean losses across all quantiles + loss_reshaped = loss_all.reshape(len(alphas), n) + return float(np.sum(np.mean(loss_reshaped, axis=1))) def validate_epsilon(epsilon: float) -> None: - """Validate epsilon parameter ensuring it is positive float""" + """Validate epsilon parameter ensuring it is a positive float.""" if not isinstance(epsilon, float): raise ValidationException("Epsilon is not float type") @@ -142,7 +93,7 @@ def validate_epsilon(epsilon: float) -> None: def validate_delta(delta: float) -> None: - """Validates the delta parameter ensuring it is a positive float and less than or equal to 0.05.""" + """Validate the delta parameter ensuring it is a positive float and less than or equal to 0.05.""" _delta_upper_bound: float = 0.05 if not isinstance(delta, float): @@ -155,68 +106,9 @@ def validate_delta(delta: float) -> None: warnings.warn("Delta should be 0.05 or less.") -def build_fobj( - alphas: list[float], - objective: ObjectiveName, - delta: float, - epsilon: float, - weight: np.ndarray | None, -) -> Callable[..., tuple[npt.NDArray, npt.NDArray]]: - """Return fobj function.""" - if objective == ObjectiveName.approx: - validate_epsilon(epsilon) - - if objective == ObjectiveName.huber: - validate_delta(delta) - - def fobj( - y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix - ) -> tuple[npt.NDArray, npt.NDArray]: - if objective == ObjectiveName.check: - return check_loss_grad_hess( - y_pred=y_pred, - dtrain=dtrain, - alphas=alphas, - weight=weight, - ) - - elif objective == ObjectiveName.huber: - return huber_loss_grad_hess( - y_pred=y_pred, - dtrain=dtrain, - alphas=alphas, - weight=weight, - delta=delta, - ) - - elif objective == ObjectiveName.approx: - return approx_loss_grad_hess( - y_pred=y_pred, - dtrain=dtrain, - alphas=alphas, - weight=weight, - epsilon=epsilon, - ) - - return fobj - - -def build_feval( - model: ModelName, alphas: list[float] -) -> Callable[[npt.NDArray, lgb.Dataset | xgb.DMatrix], tuple]: - """Return feval function.""" - - def feval(y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix) -> tuple: - loss = eval_check_loss(y_pred, dtrain, alphas) - if model == ModelName.lightgbm: - return "check_loss", loss, False - elif model == ModelName.xgboost: - return "check_loss", loss - - return feval - - class MQObjective: + """MQObjective encapsulates the objective and evaluation functions for the MQRegressor.""" + def __init__( self, alphas: list[float], @@ -224,8 +116,67 @@ def __init__( model: ModelName, delta: float, epsilon: float, - weight: np.ndarray | None, + weight: npt.NDArray | None = None, ) -> None: """Initialize the MQObjective.""" - self.fobj = build_fobj(alphas, objective, delta, epsilon, weight) - self.feval = build_feval(model, alphas) + self.alphas = alphas + self.objective = objective + self.model = model + self.delta = delta + self.epsilon = epsilon + self.weight = weight + + # Pre-validate parameters + if self.objective == ObjectiveName.approx: + validate_epsilon(self.epsilon) + if self.objective == ObjectiveName.huber: + validate_delta(self.delta) + + def fobj( + self, y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix + ) -> tuple[npt.NDArray, npt.NDArray]: + """Custom objective function for LightGBM and XGBoost.""" + y_true = dtrain.get_label() + if not isinstance(y_true, np.ndarray): + y_true = np.array(y_true) + + alphas_expanded, n = _get_alpha_expanded(self.alphas, len(y_true)) + error = y_true - y_pred + + # Calculate gradients and Hessians based on objective + if self.objective == ObjectiveName.check: + grads, hess = calc_check_grad_hess(error, alphas_expanded) + elif self.objective == ObjectiveName.huber: + grads, hess = calc_huber_grad_hess(error, alphas_expanded, self.delta) + elif self.objective == ObjectiveName.approx: + grads, hess = calc_approx_grad_hess(error, alphas_expanded, self.epsilon) + else: + raise ValueError(f"Unknown objective: {self.objective}") + + # Normalize and apply weights + grads /= n + hess /= n + + if isinstance(self.weight, np.ndarray): + return grads * self.weight, hess * self.weight + return grads, hess + + def feval( + self, y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix + ) -> tuple[str, float, bool] | tuple[str, float]: + """Custom evaluation function for LightGBM and XGBoost.""" + if self.model == ModelName.lightgbm: + return self.lgb_feval(y_pred, dtrain) # type: ignore + return self.xgb_feval(y_pred, dtrain) # type: ignore + + def lgb_feval( + self, y_pred: npt.NDArray, dtrain: lgb.Dataset + ) -> tuple[str, float, bool]: + """Custom evaluation function for LightGBM.""" + loss = eval_check_loss(y_pred, dtrain, self.alphas) + return "check_loss", loss, False + + def xgb_feval(self, y_pred: npt.NDArray, dtrain: xgb.DMatrix) -> tuple[str, float]: + """Custom evaluation function for XGBoost.""" + loss = eval_check_loss(y_pred, dtrain, self.alphas) + return "check_loss", loss diff --git a/mqboost/regressor.py b/mqboost/regressor.py index 01e0e15..d097764 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -105,7 +105,7 @@ def fit( self.model = lgb.train( train_set=dataset.dtrain, params=params, - feval=self.MQObj.feval, + feval=self.MQObj.lgb_feval, valid_sets=[eval_set_dtrain], **kwargs, ) @@ -115,7 +115,7 @@ def fit( verbose_eval=False, params=params, obj=self.MQObj.fobj, - custom_metric=self.MQObj.feval, + custom_metric=self.MQObj.xgb_feval, evals=[(eval_set_dtrain, "eval")], **kwargs, ) diff --git a/tests/test_objective.py b/tests/test_objective.py index ac3a39f..a150e0e 100644 --- a/tests/test_objective.py +++ b/tests/test_objective.py @@ -4,11 +4,7 @@ from mqboost.base import ModelName, ObjectiveName, ValidationException from mqboost.objective import ( MQObjective, - approx_loss_grad_hess, - build_feval, - check_loss_grad_hess, eval_check_loss, - huber_loss_grad_hess, validate_delta, validate_epsilon, ) @@ -83,9 +79,8 @@ def test_mqobjective_approx_loss_initialization(): def test_check_loss_grad_hess(dummy_data): """Test check loss gradient and Hessian calculation.""" dtrain = dummy_data(y_true) - grads, hess = check_loss_grad_hess( - y_pred=y_pred, dtrain=dtrain, weight=None, alphas=alphas - ) + obj = MQObjective(alphas, ObjectiveName.check, ModelName.lightgbm, 0.01, 1e-5) + grads, hess = obj.fobj(y_pred, dtrain) # fmt: off expected_grads = [-0.02, -0.02, 0.18, -0.02, -0.02, -0.1, -0.1, 0.1, -0.1, -0.1, -0.18, -0.18, 0.02, -0.18, -0.18] # fmt: on @@ -99,18 +94,16 @@ def test_check_loss_grad_hess(dummy_data): "delta, expected_grads", [ (0.01, [-0.02, -0.02, 0.18, -0.02, -0.02, -0.1, -0.1, 0.1, -0.1, -0.1, -0.18, -0.18, 0.02, -0.18, -0.18]), - (0.02, [0.0002, 0.0002, 0.18, -0.02, -0.02, 0.001, 0.001, 0.1, -0.1, -0.1, 0.0018, 0.0018, 0.02, -0.18, -0.18]), - (0.05, [0.0002, 0.0002, 0.0036, 0.0006, 0.001, 0.001, 0.001, 0.002, 0.003, 0.005, 0.0018, 0.0018, 0.0004, 0.0054, 0.009]), + (0.02, [-0.01, -0.01, 0.18, -0.02, -0.02, -0.05, -0.05, 0.1, -0.1, -0.1, -0.09, -0.09, 0.02, -0.18, -0.18]), + (0.05, [-0.004, -0.004, 0.072, -0.012, -0.02, -0.02, -0.02, 0.04, -0.06, -0.1, -0.036, -0.036, 0.008, -0.108, -0.18]), ], ) # fmt: on def test_huber_loss_grad_hess(dummy_data, delta, expected_grads): """Test huber loss gradient and Hessian calculation with multiple datasets and deltas.""" dtrain = dummy_data(y_true) - grads, hess = huber_loss_grad_hess( - y_pred=y_pred, dtrain=dtrain, weight = None,alphas=alphas, delta=delta - ) - + obj = MQObjective(alphas, ObjectiveName.huber, ModelName.lightgbm, delta, 1e-5) + grads, hess = obj.fobj(y_pred, dtrain) np.testing.assert_almost_equal(grads, np.array(expected_grads)) assert grads.shape == hess.shape assert len(grads) == len(y_pred) @@ -141,9 +134,8 @@ def test_huber_loss_grad_hess(dummy_data, delta, expected_grads): def test_approx_loss_grad_hess(dummy_data, epsilon, expected_grads, expected_hess): """Test approx loss gradient and Hessian calculation.""" dtrain = dummy_data(y_true) - grads, hess = approx_loss_grad_hess( - y_pred=y_pred, dtrain=dtrain, weight = None, alphas=alphas, epsilon=epsilon - ) + obj = MQObjective(alphas, ObjectiveName.approx, ModelName.lightgbm, 0.01, epsilon) + grads, hess = obj.fobj(y_pred, dtrain) np.testing.assert_almost_equal(grads, np.array(expected_grads), decimal=4) np.testing.assert_almost_equal(hess, np.array(expected_hess), decimal=4) assert grads.shape == hess.shape @@ -163,7 +155,8 @@ def test_eval_check_loss(dummy_data): def test_xgb_eval_loss(dummy_data): """Test XGBoost evaluation function.""" dtrain = dummy_data(y_true) - metric_name, loss = build_feval(ModelName.xgboost, alphas)(y_pred, dtrain) + obj = MQObjective(alphas, ObjectiveName.check, ModelName.xgboost, 0.01, 1e-5, None) + metric_name, loss = obj.xgb_feval(y_pred, dtrain) assert metric_name == "check_loss" assert isinstance(loss, float) @@ -171,7 +164,8 @@ def test_xgb_eval_loss(dummy_data): def test_lgb_eval_loss(dummy_data): """Test LightGBM evaluation function.""" dtrain = dummy_data(y_true) - metric_name, loss, higher_better = build_feval(ModelName.lightgbm, alphas)(y_pred, dtrain) + obj = MQObjective(alphas, ObjectiveName.check, ModelName.lightgbm, 0.01, 1e-5, None) + metric_name, loss, higher_better = obj.lgb_feval(y_pred, dtrain) assert metric_name == "check_loss" assert isinstance(loss, float) assert higher_better is False From 44a8d4e0c03e735998f31c6deabc138c63ca4c36 Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 7 May 2026 12:18:11 +0900 Subject: [PATCH 25/40] make test simple --- .github/workflows/test.yaml | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 633e2db..8af8a77 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -2,7 +2,7 @@ name: Tests on: pull_request: - types: [opened, reopened, ready_for_review, review_requested] + types: [opened, reopened, synchronize] push: branches: - main @@ -27,10 +27,4 @@ jobs: uv pip install -e . - name: Run tests - run: uv run pytest --junitxml=pytest.xml --cov-report=term-missing:skip-covered --cov=app tests/ | tee pytest-coverage.txt - - - name: Pytest coverage comment - uses: MishaKav/pytest-coverage-comment@main - with: - pytest-coverage-path: ./pytest-coverage.txt - junitxml-path: ./pytest.xml + run: uv run pytest From cca24cc9b782c3bbdd55c4f96fd087a7cbc101d8 Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 7 May 2026 17:27:38 +0900 Subject: [PATCH 26/40] set license --- LICENSE | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..087cab5 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 RektPunk + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. From 67661f172cefdfbf61bf7179e61a000d79f38e70 Mon Sep 17 00:00:00 2001 From: unknown Date: Fri, 8 May 2026 10:31:05 +0900 Subject: [PATCH 27/40] remove badge --- README.md | 8 -------- 1 file changed, 8 deletions(-) diff --git a/README.md b/README.md index 2b852e0..6dd15da 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,6 @@
-

- - release - - - Pythonv - -

**MQBoost** is a gradient boosting-based framework for simultaneous multi-quantile regression with monotonicity constraints (non-crossing quantiles). It is built on top of [LightGBM](https://github.com/microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost), two leading gradient boosting frameworks, enabling efficient and scalable training while ensuring valid quantile estimates. From 2849ce750be5b681a8f62fbdef35e94a8ff56216 Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 14 May 2026 15:41:21 +0900 Subject: [PATCH 28/40] fix actions --- .github/workflows/lint.yaml | 10 ++++++---- .github/workflows/pypi_release.yaml | 3 --- .github/workflows/test.yaml | 3 --- 3 files changed, 6 insertions(+), 10 deletions(-) diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index 97c1f18..88b4424 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -3,7 +3,7 @@ name: Lint on: push: paths: - - '**.py' + - "**.py" jobs: run-linters: @@ -12,7 +12,9 @@ jobs: steps: - name: Check out Git repository - uses: actions/checkout@v4 + uses: actions/checkout@v6 - - name: Lint check ruff - uses: chartboost/ruff-action@v1 + - name: Lint with Ruff + uses: astral-sh/ruff-action@v4 + with: + args: "check && ruff format --check --diff" diff --git a/.github/workflows/pypi_release.yaml b/.github/workflows/pypi_release.yaml index df52c85..89ed1e8 100644 --- a/.github/workflows/pypi_release.yaml +++ b/.github/workflows/pypi_release.yaml @@ -13,9 +13,6 @@ jobs: - name: Checkout code uses: actions/checkout@v6 - - name: Setup python - uses: actions/setup-python@v6 - - name: Install uv uses: astral-sh/setup-uv@v7 diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 8af8a77..522dde3 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -15,9 +15,6 @@ jobs: - name: Checkout code uses: actions/checkout@v6 - - name: Set up python - uses: actions/setup-python@v6 - - name: Install uv uses: astral-sh/setup-uv@v7 From b0991cb2ba7a83a43a31e5b2d0f0b330c9d71484 Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 14 May 2026 15:52:13 +0900 Subject: [PATCH 29/40] specific args --- .github/workflows/lint.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index 88b4424..fa18dc6 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -17,4 +17,4 @@ jobs: - name: Lint with Ruff uses: astral-sh/ruff-action@v4 with: - args: "check && ruff format --check --diff" + args: "check . && ruff format --check --diff ." From a40d29c437e67be5bf9d3f2cb8cfea03a602c637 Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 14 May 2026 16:21:01 +0900 Subject: [PATCH 30/40] unify delta and epsilon to epsilon --- examples/mqregressor.ipynb | 8 +++--- examples/mqregressor.py | 6 ++-- mqboost/objective.py | 36 ++++++------------------ mqboost/regressor.py | 8 +----- tests/test_objective.py | 57 +++++++++++--------------------------- tests/test_regressor.py | 4 --- 6 files changed, 32 insertions(+), 87 deletions(-) diff --git a/examples/mqregressor.ipynb b/examples/mqregressor.ipynb index 94d4fb4..7917375 100644 --- a/examples/mqregressor.ipynb +++ b/examples/mqregressor.ipynb @@ -53,8 +53,8 @@ "model = \"lightgbm\" # Options: \"lightgbm\" or \"xgboost\"\n", "\n", "# Set objective function\n", - "objective = \"huber\" # Options: \"check\", \"huber\", or \"approx\"\n", - "delta = 0.01 # Set when objective is \"huber\", default is 0.01\n", + "objective = \"approx\" # Options: \"approx\", \"check\", or \"huber\"\n", + "epsilon = 1e-5 # Set when objective is \"approx\" or \"huber\", default is 1e-5\n", "\n", "# Train the model with fixed parameters\n", "# Initialize the LightGBM-based quantile regressor\n", @@ -69,7 +69,7 @@ " params=lgb_params,\n", " objective=objective,\n", " model=model,\n", - " delta=delta,\n", + " epsilon=epsilon,\n", ")\n", "\n", "# Fit the model\n", @@ -78,7 +78,7 @@ "\n", "# Predict using the fitted model\n", "test_dataset = MQDataset(data=x_test, alphas=alphas, model=model)\n", - "preds_lgb = mq_regressor.predict(test_dataset)\n" + "preds_lgb = mq_regressor.predict(test_dataset)" ] } ], diff --git a/examples/mqregressor.py b/examples/mqregressor.py index db16010..4edefb6 100644 --- a/examples/mqregressor.py +++ b/examples/mqregressor.py @@ -22,8 +22,8 @@ model = "lightgbm" # Options: "lightgbm" or "xgboost" # Set objective function -objective = "huber" # Options: "check", "huber", or "approx" -delta = 0.01 # Set when objective is "huber", default is 0.01 +objective = "approx" # Options: "approx", "check", or "huber" +epsilon = 1e-5 # Set when objective is "approx" or "huber", default is 1e-5 # Train the model with fixed parameters # Initialize the LightGBM-based quantile regressor @@ -38,7 +38,7 @@ params=lgb_params, objective=objective, model=model, - delta=delta, + epsilon=epsilon, ) # Fit the model diff --git a/mqboost/objective.py b/mqboost/objective.py index 2949924..7b8ffd1 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -1,5 +1,3 @@ -import warnings - import lightgbm as lgb import numpy as np import numpy.typing as npt @@ -24,22 +22,22 @@ def calc_check_grad_hess( def calc_huber_grad_hess( - error: npt.NDArray, alpha: npt.NDArray | float, delta: float + error: npt.NDArray, alpha: npt.NDArray | float, epsilon: float ) -> tuple[npt.NDArray, npt.NDArray]: """Compute gradient and Hessian for the Huber loss (Smooth Quantile Loss).""" abs_error = np.abs(error) - mask = (abs_error <= delta).astype(float) + mask = (abs_error <= epsilon).astype(float) # Gradient for linear part check_grad, check_hess = calc_check_grad_hess(error=error, alpha=alpha) # Gradient for Huber part - # dL/dp = check_grad * (abs_error / delta) - huber_grad = check_grad * (abs_error / delta) + # dL/dp = check_grad * (abs_error / epsilon) + huber_grad = check_grad * (abs_error / epsilon) grad = mask * huber_grad + (1 - mask) * check_grad # Hessian for Huber part - # d2L/dp2 = |check_grad| / delta - huber_hess = np.abs(check_grad) / delta + # d2L/dp2 = |check_grad| / epsilon + huber_hess = np.abs(check_grad) / epsilon # For linear part, we use check_hess as a proxy for Hessian hess = mask * huber_hess + (1 - mask) * check_hess @@ -92,20 +90,6 @@ def validate_epsilon(epsilon: float) -> None: raise ValidationException("Epsilon must be positive") -def validate_delta(delta: float) -> None: - """Validate the delta parameter ensuring it is a positive float and less than or equal to 0.05.""" - _delta_upper_bound: float = 0.05 - - if not isinstance(delta, float): - raise ValidationException("Delta is not float type") - - if delta <= 0: - raise ValidationException("Delta must be positive") - - if delta > _delta_upper_bound: - warnings.warn("Delta should be 0.05 or less.") - - class MQObjective: """MQObjective encapsulates the objective and evaluation functions for the MQRegressor.""" @@ -114,7 +98,6 @@ def __init__( alphas: list[float], objective: ObjectiveName, model: ModelName, - delta: float, epsilon: float, weight: npt.NDArray | None = None, ) -> None: @@ -122,15 +105,12 @@ def __init__( self.alphas = alphas self.objective = objective self.model = model - self.delta = delta self.epsilon = epsilon self.weight = weight # Pre-validate parameters - if self.objective == ObjectiveName.approx: + if self.objective in (ObjectiveName.approx, ObjectiveName.huber): validate_epsilon(self.epsilon) - if self.objective == ObjectiveName.huber: - validate_delta(self.delta) def fobj( self, y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix @@ -147,7 +127,7 @@ def fobj( if self.objective == ObjectiveName.check: grads, hess = calc_check_grad_hess(error, alphas_expanded) elif self.objective == ObjectiveName.huber: - grads, hess = calc_huber_grad_hess(error, alphas_expanded, self.delta) + grads, hess = calc_huber_grad_hess(error, alphas_expanded, self.epsilon) elif self.objective == ObjectiveName.approx: grads, hess = calc_approx_grad_hess(error, alphas_expanded, self.epsilon) else: diff --git a/mqboost/regressor.py b/mqboost/regressor.py index d097764..ebcf790 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -31,11 +31,8 @@ class MQRegressor: Any params related to model can be used except "objective". model (str): The model type (either 'lightgbm' or 'xgboost'). Default is 'lightgbm'. objective (str): The objective function (either 'check', 'huber', or 'approx'). Default is 'check'. - delta (float): - Parameter for the 'huber' objective function. - Default is 0.01 and must be smaller than 0.05. epsilon (float): - Parameter for the 'smooth approximated check' objective function. + Parameter for the 'smooth approximated check' or 'huber' objective function. Default is 1e-5. Methods: fit(dataset, eval_set): @@ -49,7 +46,6 @@ def __init__( params: dict[str, Any], model: str = ModelName.lightgbm.value, objective: str = ObjectiveName.check.value, - delta: float = 0.01, epsilon: float = 1e-5, ) -> None: """Initialize the MQRegressor.""" @@ -57,7 +53,6 @@ def __init__( self.params = params self.model_name = ModelName[model] self.objective = ObjectiveName[objective] - self.delta = delta self.epsilon = epsilon def fit( @@ -91,7 +86,6 @@ def fit( objective=self.objective, weight=dataset.weight, model=self.model_name, - delta=self.delta, epsilon=self.epsilon, ) if self.__is_lgb: diff --git a/tests/test_objective.py b/tests/test_objective.py index a150e0e..83760cf 100644 --- a/tests/test_objective.py +++ b/tests/test_objective.py @@ -5,7 +5,6 @@ from mqboost.objective import ( MQObjective, eval_check_loss, - validate_delta, validate_epsilon, ) @@ -36,7 +35,6 @@ def test_mqobjective_check_loss_initialization(): objective=ObjectiveName.check, weight=None, model=ModelName.xgboost, - delta=0.0, epsilon=0.0, ) assert mq_objective.fobj is not None @@ -47,14 +45,13 @@ def test_mqobjective_check_loss_initialization(): def test_mqobjective_huber_loss_initialization(): """Test MQObjective initialization with huber loss.""" - delta = 0.05 + epsilon = 0.05 mq_objective = MQObjective( alphas=alphas, objective=ObjectiveName.huber, weight=None, model=ModelName.lightgbm, - delta=delta, - epsilon=0.0, + epsilon=epsilon, ) assert mq_objective.fobj is not None assert callable(mq_objective.fobj) @@ -68,7 +65,6 @@ def test_mqobjective_approx_loss_initialization(): objective=ObjectiveName.approx, weight=None, model=ModelName.xgboost, - delta=0.0, epsilon=epsilon, ) assert mq_objective.fobj is not None @@ -79,7 +75,7 @@ def test_mqobjective_approx_loss_initialization(): def test_check_loss_grad_hess(dummy_data): """Test check loss gradient and Hessian calculation.""" dtrain = dummy_data(y_true) - obj = MQObjective(alphas, ObjectiveName.check, ModelName.lightgbm, 0.01, 1e-5) + obj = MQObjective(alphas, ObjectiveName.check, ModelName.lightgbm, 1e-5) grads, hess = obj.fobj(y_pred, dtrain) # fmt: off expected_grads = [-0.02, -0.02, 0.18, -0.02, -0.02, -0.1, -0.1, 0.1, -0.1, -0.1, -0.18, -0.18, 0.02, -0.18, -0.18] @@ -91,7 +87,7 @@ def test_check_loss_grad_hess(dummy_data): # fmt: off @pytest.mark.parametrize( - "delta, expected_grads", + "epsilon, expected_grads", [ (0.01, [-0.02, -0.02, 0.18, -0.02, -0.02, -0.1, -0.1, 0.1, -0.1, -0.1, -0.18, -0.18, 0.02, -0.18, -0.18]), (0.02, [-0.01, -0.01, 0.18, -0.02, -0.02, -0.05, -0.05, 0.1, -0.1, -0.1, -0.09, -0.09, 0.02, -0.18, -0.18]), @@ -99,10 +95,10 @@ def test_check_loss_grad_hess(dummy_data): ], ) # fmt: on -def test_huber_loss_grad_hess(dummy_data, delta, expected_grads): - """Test huber loss gradient and Hessian calculation with multiple datasets and deltas.""" +def test_huber_loss_grad_hess(dummy_data, epsilon, expected_grads): + """Test huber loss gradient and Hessian calculation with multiple datasets and epsilon values.""" dtrain = dummy_data(y_true) - obj = MQObjective(alphas, ObjectiveName.huber, ModelName.lightgbm, delta, 1e-5) + obj = MQObjective(alphas, ObjectiveName.huber, ModelName.lightgbm, epsilon) grads, hess = obj.fobj(y_pred, dtrain) np.testing.assert_almost_equal(grads, np.array(expected_grads)) assert grads.shape == hess.shape @@ -134,7 +130,7 @@ def test_huber_loss_grad_hess(dummy_data, delta, expected_grads): def test_approx_loss_grad_hess(dummy_data, epsilon, expected_grads, expected_hess): """Test approx loss gradient and Hessian calculation.""" dtrain = dummy_data(y_true) - obj = MQObjective(alphas, ObjectiveName.approx, ModelName.lightgbm, 0.01, epsilon) + obj = MQObjective(alphas, ObjectiveName.approx, ModelName.lightgbm, epsilon) grads, hess = obj.fobj(y_pred, dtrain) np.testing.assert_almost_equal(grads, np.array(expected_grads), decimal=4) np.testing.assert_almost_equal(hess, np.array(expected_hess), decimal=4) @@ -155,7 +151,7 @@ def test_eval_check_loss(dummy_data): def test_xgb_eval_loss(dummy_data): """Test XGBoost evaluation function.""" dtrain = dummy_data(y_true) - obj = MQObjective(alphas, ObjectiveName.check, ModelName.xgboost, 0.01, 1e-5, None) + obj = MQObjective(alphas, ObjectiveName.check, ModelName.xgboost, 1e-5, None) metric_name, loss = obj.xgb_feval(y_pred, dtrain) assert metric_name == "check_loss" assert isinstance(loss, float) @@ -164,7 +160,7 @@ def test_xgb_eval_loss(dummy_data): def test_lgb_eval_loss(dummy_data): """Test LightGBM evaluation function.""" dtrain = dummy_data(y_true) - obj = MQObjective(alphas, ObjectiveName.check, ModelName.lightgbm, 0.01, 1e-5, None) + obj = MQObjective(alphas, ObjectiveName.check, ModelName.lightgbm, 1e-5, None) metric_name, loss, higher_better = obj.lgb_feval(y_pred, dtrain) assert metric_name == "check_loss" assert isinstance(loss, float) @@ -172,8 +168,8 @@ def test_lgb_eval_loss(dummy_data): # Test error handling for invalid parameters -def test_invalid_delta_for_huber(): - """Test that invalid delta for Huber loss raises an exception.""" +def test_invalid_epsilon_for_huber(): + """Test that invalid epsilon for Huber loss raises an exception.""" alphas = [0.1, 0.5, 0.9] with pytest.raises(ValidationException): MQObjective( @@ -181,8 +177,7 @@ def test_invalid_delta_for_huber(): objective=ObjectiveName.huber, weight=None, model=ModelName.xgboost, - delta=-0.1, # Invalid delta (negative) - epsilon=0.0, + epsilon=-0.1, # Invalid epsilon (negative) ) @@ -195,36 +190,16 @@ def test_invalid_epsilon_for_approx(): objective=ObjectiveName.approx, weight=None, model=ModelName.xgboost, - delta=0.0, epsilon=-0.01, # Invalid epsilon (negative) ) -# Test for validate_delta -def test_validate_delta_valid_delta(): - delta = 0.04 - assert validate_delta(delta) is None - - -def test_validate_delta_invalid_type(): - with pytest.raises(ValidationException, match="Delta is not float type"): - validate_delta(1) - - -def test_validate_delta_negative_delta(): - with pytest.raises(ValidationException, match="Delta must be positive"): - validate_delta(-0.01) - - -def test_validate_delta_exceeds_upper_bound(): - delta = 0.06 - with pytest.warns(UserWarning, match="Delta should be 0.05 or less."): - validate_delta(delta) - # Test for validate_epsilon def test_validate_epsilon_valid_epsilon(): - epsilon = 0.01 + epsilon = 0.04 assert validate_epsilon(epsilon) is None + epsilon = 0.01 + assert validate_epsilon(epsilon) is None def test_validate_epsilon_invalid_type(): with pytest.raises(ValidationException, match="Epsilon is not float type"): diff --git a/tests/test_regressor.py b/tests/test_regressor.py index cea079a..0b7ed5e 100644 --- a/tests/test_regressor.py +++ b/tests/test_regressor.py @@ -50,13 +50,11 @@ def test_mqregressor_initialization(): params=params, model=ModelName.lightgbm.value, objective=ObjectiveName.check.value, - delta=0.01, epsilon=1e-5, ) assert regressor.params == params assert regressor.model_name == ModelName.lightgbm assert regressor.objective == ObjectiveName.check - assert regressor.delta == 0.01 assert regressor.epsilon == 1e-5 @@ -67,7 +65,6 @@ def test_invalid_model_initialization(): params=params, model="invalid_model", objective=ObjectiveName.check.value, - delta=0.01, epsilon=1e-5, ) @@ -79,7 +76,6 @@ def test_invalid_objective_initialization(): params=params, model=ModelName.lightgbm.value, objective="invalid_objective", - delta=0.01, epsilon=1e-5, ) From 88dd49faa0dab34d938fd1ad981ffc2b8da1941c Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 14 May 2026 16:25:12 +0900 Subject: [PATCH 31/40] fix lint action version --- .github/workflows/lint.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index fa18dc6..90b0e2a 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -15,6 +15,6 @@ jobs: uses: actions/checkout@v6 - name: Lint with Ruff - uses: astral-sh/ruff-action@v4 + uses: astral-sh/ruff-action@v4.0.0 with: args: "check . && ruff format --check --diff ." From 938a7e01669d0ea75931c8e016a86b1cb85a988c Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 14 May 2026 16:43:50 +0900 Subject: [PATCH 32/40] add comments --- mqboost/base.py | 4 +++ mqboost/constraints.py | 7 ++++- mqboost/dataset.py | 15 +++++----- mqboost/objective.py | 63 +++++++++++++++++++++++++---------------- mqboost/regressor.py | 44 +++++++--------------------- tests/test_objective.py | 8 ++++-- 6 files changed, 72 insertions(+), 69 deletions(-) diff --git a/mqboost/base.py b/mqboost/base.py index eee8d8c..27888da 100644 --- a/mqboost/base.py +++ b/mqboost/base.py @@ -38,8 +38,12 @@ class TypeName(str, Enum): # Exception class FittingException(Exception): + """Raised when an operation requiring a fitted model is called on an unfitted model.""" + pass class ValidationException(Exception): + """Raised when input parameters or data fail validation checks.""" + pass diff --git a/mqboost/constraints.py b/mqboost/constraints.py index 0246136..5b672e3 100644 --- a/mqboost/constraints.py +++ b/mqboost/constraints.py @@ -10,7 +10,12 @@ def set_monotone_constraints( columns: pd.Index, model_name: ModelName, ) -> dict[str, Any]: - """Set monotone constraints in params""" + """Configure monotone constraints for the GBDT model. + + To ensure that predicted quantiles are non-decreasing with respect to the + quantile level (alpha), a monotone constraint of '1' is applied to the + special '_tau' feature. + """ MONOTONE_CONSTRAINTS: str = "monotone_constraints" constraints_funcs = FUNC_TYPE[model_name][TypeName.constraints_type] diff --git a/mqboost/dataset.py b/mqboost/dataset.py index 012b167..884bc2e 100644 --- a/mqboost/dataset.py +++ b/mqboost/dataset.py @@ -18,7 +18,7 @@ def validate_alpha( alphas: list[float] | float, ) -> list[float]: - """Validates the list of alphas ensuring they are in ascending order and contain no duplicates.""" + """Validate target quantiles (alphas). Ensures alphas are in (0, 1), in ascending order, and contain no duplicates.""" if isinstance(alphas, float): alphas = [alphas] @@ -47,10 +47,8 @@ def prepare_x( x: pd.DataFrame, alphas: list[float], ) -> pd.DataFrame: - """Prepares and returns a stacked DataFrame of features repeated for each alpha, with an additional column indicating the alpha value. - Raises: - ValidationException: If the input data contains a column named '_tau'. - """ + """Prepare the feature matrix for multi-quantile training by stacking the dataset + and adding a '_tau' column to indicate the quantile level.""" if "_tau" in x.columns: raise ValidationException("Column name '_tau' is not allowed.") @@ -68,11 +66,12 @@ def prepare_y( y: pd.Series | npt.NDArray, alphas: list[float], ) -> npt.NDArray: - """Prepares and returns a stacked array of target values repeated for each alpha.""" + """Prepare the target vector by repeating it for each target quantile.""" return np.tile(y, len(alphas)) def to_dataframe(x: pd.DataFrame | pd.Series | npt.NDArray) -> pd.DataFrame: + """Convert numpy array or pandas Series to a pandas DataFrame.""" if isinstance(x, np.ndarray) or isinstance(x, pd.Series): _x = pd.DataFrame(x) else: @@ -81,8 +80,8 @@ def to_dataframe(x: pd.DataFrame | pd.Series | npt.NDArray) -> pd.DataFrame: class MQDataset: - """MQDataset encapsulates the dataset used for training and predicting with the MQRegressor. - It supports both LightGBM and XGBoost models, handling data preparation, validation, and conversion for training and prediction. + """A container for multi-quantile datasets, handling the transformation into + a stacked format suitable for LightGBM and XGBoost training. Attributes: alphas (list[float] | float): diff --git a/mqboost/objective.py b/mqboost/objective.py index 7b8ffd1..ca65bca 100644 --- a/mqboost/objective.py +++ b/mqboost/objective.py @@ -7,38 +7,44 @@ def calc_rho(error: npt.NDArray, alpha: npt.NDArray | float) -> npt.NDArray: - """Compute rho (pinball loss) for the given error and alpha.""" - # L = (alpha - I(error < 0)) * error + """Compute the pinball loss (check loss) for a given error and quantile level alpha. + + The pinball loss is defined as: L(error, alpha) = (alpha - I(error < 0)) * error.""" return (alpha - (error < 0).astype(int)) * error def calc_check_grad_hess( error: npt.NDArray, alpha: npt.NDArray | float ) -> tuple[npt.NDArray, npt.NDArray]: - """Compute gradient and Hessian for the check loss.""" - # dL/dp = I(error < 0) - alpha - # d2L/dp2 = 1 as a proxy for Hessian + """Compute the gradient and Hessian for the standard check loss. + + The gradient is dL/dp = I(error < 0) - alpha. + A constant proxy of 1.0 is used for the Hessian to facilitate optimization.""" return (error < 0).astype(int) - alpha, np.ones_like(error) def calc_huber_grad_hess( error: npt.NDArray, alpha: npt.NDArray | float, epsilon: float ) -> tuple[npt.NDArray, npt.NDArray]: - """Compute gradient and Hessian for the Huber loss (Smooth Quantile Loss).""" + """Compute the gradient and Hessian for the Huber-like Smooth Quantile Loss. + + This objective provides a smooth approximation to the check loss near zero, controlled by the epsilon parameter. + It behaves quadratically for |error| <= epsilon and linearly for |error| > epsilon.""" abs_error = np.abs(error) mask = (abs_error <= epsilon).astype(float) - # Gradient for linear part + # Gradient for the linear part (Standard Check Loss) check_grad, check_hess = calc_check_grad_hess(error=error, alpha=alpha) - # Gradient for Huber part - # dL/dp = check_grad * (abs_error / epsilon) + + # Gradient for the Huber part (Quadratic approximation) + # dL/dp = check_grad * (|error| / epsilon) huber_grad = check_grad * (abs_error / epsilon) grad = mask * huber_grad + (1 - mask) * check_grad - # Hessian for Huber part + # Hessian for the Huber part # d2L/dp2 = |check_grad| / epsilon huber_hess = np.abs(check_grad) / epsilon - # For linear part, we use check_hess as a proxy for Hessian + # For the linear part, we use check_hess (1.0) as a proxy hess = mask * huber_hess + (1 - mask) * check_hess return grad, hess @@ -47,7 +53,10 @@ def calc_huber_grad_hess( def calc_approx_grad_hess( error: npt.NDArray, alpha: npt.NDArray | float, epsilon: float ) -> tuple[npt.NDArray, npt.NDArray]: - """Compute gradient and Hessian for the approximate loss (MM loss).""" + """Compute the gradient and Hessian for the Smooth Quantile Approximation. + + This uses a smooth approximation derived from the Majorization-Minimization + approach for quantile regression.""" # dL/dp = 0.5 * (1 - 2 * alpha - error / (epsilon + |error|)) approx_grad = 0.5 * (1 - 2 * alpha - error / (epsilon + np.abs(error))) @@ -57,7 +66,7 @@ def calc_approx_grad_hess( def _get_alpha_expanded(alphas: list[float], total_len: int) -> tuple[npt.NDArray, int]: - """Helper to expand alphas and get original dataset size.""" + """Expand the list of alphas to match the stacked dataset size.""" n = total_len // len(alphas) return np.repeat(alphas, n), n @@ -67,7 +76,7 @@ def eval_check_loss( dtrain: lgb.Dataset | xgb.DMatrix, alphas: list[float], ) -> float: - """Evaluate the check loss function using vectorized operations.""" + """Evaluate the mean check loss across all quantiles.""" y_true = dtrain.get_label() if not isinstance(y_true, np.ndarray): y_true = np.array(y_true) @@ -82,7 +91,7 @@ def eval_check_loss( def validate_epsilon(epsilon: float) -> None: - """Validate epsilon parameter ensuring it is a positive float.""" + """Ensure epsilon is a positive float.""" if not isinstance(epsilon, float): raise ValidationException("Epsilon is not float type") @@ -91,7 +100,8 @@ def validate_epsilon(epsilon: float) -> None: class MQObjective: - """MQObjective encapsulates the objective and evaluation functions for the MQRegressor.""" + """Encapsulates custom objective and evaluation functions for Multi-Quantile regression. + This class handles the interface with LightGBM and XGBoost, providing the gradients and Hessians required for training.""" def __init__( self, @@ -101,7 +111,7 @@ def __init__( epsilon: float, weight: npt.NDArray | None = None, ) -> None: - """Initialize the MQObjective.""" + """Initialize the multi-quantile objective.""" self.alphas = alphas self.objective = objective self.model = model @@ -115,7 +125,7 @@ def __init__( def fobj( self, y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix ) -> tuple[npt.NDArray, npt.NDArray]: - """Custom objective function for LightGBM and XGBoost.""" + """Standard interface for custom objective functions in LightGBM and XGBoost.""" y_true = dtrain.get_label() if not isinstance(y_true, np.ndarray): y_true = np.array(y_true) @@ -133,7 +143,7 @@ def fobj( else: raise ValueError(f"Unknown objective: {self.objective}") - # Normalize and apply weights + # Normalize by original sample size grads /= n hess /= n @@ -144,19 +154,22 @@ def fobj( def feval( self, y_pred: npt.NDArray, dtrain: lgb.Dataset | xgb.DMatrix ) -> tuple[str, float, bool] | tuple[str, float]: - """Custom evaluation function for LightGBM and XGBoost.""" - if self.model == ModelName.lightgbm: - return self.lgb_feval(y_pred, dtrain) # type: ignore - return self.xgb_feval(y_pred, dtrain) # type: ignore + """Unified interface for custom evaluation functions.""" + if self.model == ModelName.lightgbm and isinstance(dtrain, lgb.Dataset): + return self.lgb_feval(y_pred, dtrain) + elif self.model == ModelName.xgboost and isinstance(dtrain, xgb.DMatrix): + return self.xgb_feval(y_pred, dtrain) + else: + raise ValueError(f"Cannot evaluate {self.model}, got type {type(dtrain)}") def lgb_feval( self, y_pred: npt.NDArray, dtrain: lgb.Dataset ) -> tuple[str, float, bool]: - """Custom evaluation function for LightGBM.""" + """Specific evaluation function for LightGBM.""" loss = eval_check_loss(y_pred, dtrain, self.alphas) return "check_loss", loss, False def xgb_feval(self, y_pred: npt.NDArray, dtrain: xgb.DMatrix) -> tuple[str, float]: - """Custom evaluation function for XGBoost.""" + """Specific evaluation function for XGBoost.""" loss = eval_check_loss(y_pred, dtrain, self.alphas) return "check_loss", loss diff --git a/mqboost/regressor.py b/mqboost/regressor.py index ebcf790..19c17d0 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -14,7 +14,7 @@ def validate_params(params: dict[str, Any]) -> None: - """Validates the model parameter ensuring its key doesn't contain 'objective'.""" + """Validate that model parameters do not contain an 'objective' key.""" if "objective" in params: raise ValidationException( "The parameter named 'objective' must be excluded in params" @@ -22,23 +22,12 @@ def validate_params(params: dict[str, Any]) -> None: class MQRegressor: - """MQRegressor is a custom multiple quantile estimator that supports LightGBM and XGBoost models with - preserving monotonicity among quantiles. - - Attributes: - params (dict[str, Any]): - Parameters for the model. - Any params related to model can be used except "objective". - model (str): The model type (either 'lightgbm' or 'xgboost'). Default is 'lightgbm'. - objective (str): The objective function (either 'check', 'huber', or 'approx'). Default is 'check'. - epsilon (float): - Parameter for the 'smooth approximated check' or 'huber' objective function. - Default is 1e-5. - Methods: - fit(dataset, eval_set): - Fits the regressor to the provided dataset, optionally evaluating on a separate validation set. - predict(dataset): - Predicts quantiles for the given dataset. + """ + Multiple Quantile Regressor using GBDT (LightGBM or XGBoost). + + This regressor implements a multi-quantile estimation strategy by stacking + the dataset and using monotone constraints on the special '_tau' feature + to ensure non-crossing quantiles. """ def __init__( @@ -48,7 +37,7 @@ def __init__( objective: str = ObjectiveName.check.value, epsilon: float = 1e-5, ) -> None: - """Initialize the MQRegressor.""" + """Initialize the MQRegressor with specified model parameters and objective.""" validate_params(params=params) self.params = params self.model_name = ModelName[model] @@ -61,14 +50,7 @@ def fit( eval_set: MQDataset | None = None, **kwargs, ) -> None: - """Fit the regressor to the dataset. - Args: - dataset (MQDataset): The dataset to fit the model on. - eval_set (Optional[MQDataset]): - The validation dataset. If None, the dataset is used for evaluation. - **kwargs: - train parameters. - """ + """Fit the multi-quantile regressor to the dataset.""" self._label_mean = dataset.label_mean if eval_set: eval_set.set_label_mean(self._label_mean) @@ -120,12 +102,7 @@ def predict( self, dataset: MQDataset, ) -> npt.NDArray: - """Predict quantiles for the dataset. - Args: - dataset (MQDataset): The dataset to make predictions on. - Returns: - np.ndarray: The predicted quantiles. - """ + """Predict multiple quantiles for the given dataset.""" self.__predict_available() _pred = ( np.asanyarray(self.model.predict(data=dataset.dpredict)) + self._label_mean @@ -140,6 +117,7 @@ def __predict_available(self) -> None: @property def feature_importance(self) -> dict[str, Any]: + """Get feature importance scores from the fitted model.""" self.__predict_available() importances: dict[str, Any] = {str(k): 0 for k in self._colnames} if self.__is_lgb: diff --git a/tests/test_objective.py b/tests/test_objective.py index 83760cf..eb46672 100644 --- a/tests/test_objective.py +++ b/tests/test_objective.py @@ -195,16 +195,20 @@ def test_invalid_epsilon_for_approx(): # Test for validate_epsilon def test_validate_epsilon_valid_epsilon(): - epsilon = 0.04 + epsilon = 0.01 assert validate_epsilon(epsilon) is None - epsilon = 0.01 + epsilon = 0.04 assert validate_epsilon(epsilon) is None + def test_validate_epsilon_invalid_type(): with pytest.raises(ValidationException, match="Epsilon is not float type"): validate_epsilon(1) + with pytest.raises(ValidationException, match="Epsilon is not float type"): + validate_epsilon(2) + def test_validate_epsilon_negative_epsilon(): with pytest.raises(ValidationException, match="Epsilon must be positive"): From 0fc76dfbc55e9f0a8836fb45e2a9dc27bd5e1a4c Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 14 May 2026 16:48:28 +0900 Subject: [PATCH 33/40] lint action test --- .github/workflows/lint.yaml | 10 ++++++++++ mqboost/constraints.py | 3 +-- mqboost/dataset.py | 3 +-- mqboost/regressor.py | 6 ++---- 4 files changed, 14 insertions(+), 8 deletions(-) diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index 90b0e2a..328b25d 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -18,3 +18,13 @@ jobs: uses: astral-sh/ruff-action@v4.0.0 with: args: "check . && ruff format --check --diff ." + + - name: Install Ruff + uses: astral-sh/ruff-action@v4.0.0 + with: + args: "--version" + + - name: Run Ruff Check & Format + run: | + ruff check . + ruff format --check --diff . diff --git a/mqboost/constraints.py b/mqboost/constraints.py index 5b672e3..2b9eb63 100644 --- a/mqboost/constraints.py +++ b/mqboost/constraints.py @@ -14,8 +14,7 @@ def set_monotone_constraints( To ensure that predicted quantiles are non-decreasing with respect to the quantile level (alpha), a monotone constraint of '1' is applied to the - special '_tau' feature. - """ + special '_tau' feature.""" MONOTONE_CONSTRAINTS: str = "monotone_constraints" constraints_funcs = FUNC_TYPE[model_name][TypeName.constraints_type] diff --git a/mqboost/dataset.py b/mqboost/dataset.py index 884bc2e..c75cbfd 100644 --- a/mqboost/dataset.py +++ b/mqboost/dataset.py @@ -90,8 +90,7 @@ class MQDataset: data (pd.DataFrame | pd.Series | np.ndarray): The input features. label (pd.Series | np.ndarray): The target labels (if provided). weight (list[float] | list[int] | np.ndarray | pd.Series): Weight for each instance (if provided). - model (str): The model type (LightGBM or XGBoost). - """ + model (str): The model type (LightGBM or XGBoost).""" def __init__( self, diff --git a/mqboost/regressor.py b/mqboost/regressor.py index 19c17d0..3f5611b 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -22,13 +22,11 @@ def validate_params(params: dict[str, Any]) -> None: class MQRegressor: - """ - Multiple Quantile Regressor using GBDT (LightGBM or XGBoost). + """Multiple Quantile Regressor using GBDT (LightGBM or XGBoost). This regressor implements a multi-quantile estimation strategy by stacking the dataset and using monotone constraints on the special '_tau' feature - to ensure non-crossing quantiles. - """ + to ensure non-crossing quantiles.""" def __init__( self, From b2832091fbb64f3c214706463e141a747da59cfc Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 14 May 2026 16:49:40 +0900 Subject: [PATCH 34/40] fix action --- .github/workflows/lint.yaml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index 328b25d..13f2d0e 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -14,11 +14,6 @@ jobs: - name: Check out Git repository uses: actions/checkout@v6 - - name: Lint with Ruff - uses: astral-sh/ruff-action@v4.0.0 - with: - args: "check . && ruff format --check --diff ." - - name: Install Ruff uses: astral-sh/ruff-action@v4.0.0 with: From 4e217424eaa0107438a2e33abad0d3c18a4a8d49 Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 14 May 2026 16:51:53 +0900 Subject: [PATCH 35/40] test action --- mqboost/base.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/mqboost/base.py b/mqboost/base.py index 27888da..8fe9a1b 100644 --- a/mqboost/base.py +++ b/mqboost/base.py @@ -38,12 +38,10 @@ class TypeName(str, Enum): # Exception class FittingException(Exception): - """Raised when an operation requiring a fitted model is called on an unfitted model.""" - + # Raised when an operation requiring a fitted model is called on an unfitted model. pass class ValidationException(Exception): - """Raised when input parameters or data fail validation checks.""" - + # Raised when input parameters or data fail validation checks. pass From d8b5778c726ff1a77f4622075e2784b2f2c3fb44 Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 14 May 2026 17:16:02 +0900 Subject: [PATCH 36/40] add ipynb to linter too --- .github/workflows/lint.yaml | 1 + examples/mqregressor.ipynb | 33 ++++++++++++++++++++++++++++++--- examples/mqregressor.py | 27 ++++++++++++++++++++------- mqboost/regressor.py | 5 +---- 4 files changed, 52 insertions(+), 14 deletions(-) diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml index 13f2d0e..f3edc57 100644 --- a/.github/workflows/lint.yaml +++ b/.github/workflows/lint.yaml @@ -4,6 +4,7 @@ on: push: paths: - "**.py" + - "**.ipynb" jobs: run-linters: diff --git a/examples/mqregressor.ipynb b/examples/mqregressor.ipynb index 7917375..da60796 100644 --- a/examples/mqregressor.ipynb +++ b/examples/mqregressor.ipynb @@ -35,6 +35,7 @@ }, "outputs": [], "source": [ + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from mqboost import MQDataset, MQRegressor\n", @@ -53,8 +54,8 @@ "model = \"lightgbm\" # Options: \"lightgbm\" or \"xgboost\"\n", "\n", "# Set objective function\n", - "objective = \"approx\" # Options: \"approx\", \"check\", or \"huber\"\n", - "epsilon = 1e-5 # Set when objective is \"approx\" or \"huber\", default is 1e-5\n", + "objective = \"check\" # Options: \"check\", \"approx\", or \"huber\"\n", + "# epsilon = 1e-5 # Set when objective is \"approx\" or \"huber\", default is 1e-5\n", "\n", "# Train the model with fixed parameters\n", "# Initialize the LightGBM-based quantile regressor\n", @@ -69,7 +70,6 @@ " params=lgb_params,\n", " objective=objective,\n", " model=model,\n", - " epsilon=epsilon,\n", ")\n", "\n", "# Fit the model\n", @@ -80,6 +80,33 @@ "test_dataset = MQDataset(data=x_test, alphas=alphas, model=model)\n", "preds_lgb = mq_regressor.predict(test_dataset)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f9e16a2", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "# For visualization of predictions vs. actual values\n", + "plt.figure(figsize=(12, 6))\n", + "plt.scatter(x_test, y_test, label=\"Actual y_test\", alpha=0.5, s=10)\n", + "\n", + "# Plot each quantile prediction\n", + "for i, alpha in enumerate(alphas):\n", + " plt.plot(x_test, preds_lgb[i, :], label=f\"Quantile {alpha}\")\n", + "\n", + "plt.title(\"Quantile Regression Predictions vs. Actual Values\")\n", + "plt.xlabel(\"x_test\")\n", + "plt.ylabel(\"y_values\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] } ], "metadata": { diff --git a/examples/mqregressor.py b/examples/mqregressor.py index 4edefb6..7f8b7c2 100644 --- a/examples/mqregressor.py +++ b/examples/mqregressor.py @@ -1,9 +1,8 @@ -""" -These following example show how to set up and use `mqboost` for quantile regression with fixed parameters. +"""These following example show how to set up and use `mqboost` for quantile regression with fixed parameters. Adjust the parameters and settings as needed for your specific use case. -To use the code, make sure you have `mqboost` and other required dependencies installed. -""" +To use the code, make sure you have `mqboost` and other required dependencies installed.""" +# import matplotlib.pyplot as plt import numpy as np from mqboost import MQDataset, MQRegressor @@ -22,8 +21,8 @@ model = "lightgbm" # Options: "lightgbm" or "xgboost" # Set objective function -objective = "approx" # Options: "approx", "check", or "huber" -epsilon = 1e-5 # Set when objective is "approx" or "huber", default is 1e-5 +objective = "check" # Options: "check", "approx", or "huber" +# epsilon = 1e-5 # Set when objective is "approx" or "huber", default is 1e-5 # Train the model with fixed parameters # Initialize the LightGBM-based quantile regressor @@ -38,7 +37,6 @@ params=lgb_params, objective=objective, model=model, - epsilon=epsilon, ) # Fit the model @@ -48,3 +46,18 @@ # Predict using the fitted model test_dataset = MQDataset(data=x_test, alphas=alphas, model=model) preds_lgb = mq_regressor.predict(test_dataset) + +# # For visualization of predictions vs. actual values +# plt.figure(figsize=(12, 6)) +# plt.scatter(x_test, y_test, label="Actual y_test", alpha=0.5, s=10) + +# # Plot each quantile prediction +# for i, alpha in enumerate(alphas): +# plt.plot(x_test, preds_lgb[i, :], label=f"Quantile {alpha}") + +# plt.title("Quantile Regression Predictions vs. Actual Values") +# plt.xlabel("x_test") +# plt.ylabel("y_values") +# plt.legend() +# plt.grid(True) +# plt.show() diff --git a/mqboost/regressor.py b/mqboost/regressor.py index 3f5611b..df65ce7 100644 --- a/mqboost/regressor.py +++ b/mqboost/regressor.py @@ -23,10 +23,7 @@ def validate_params(params: dict[str, Any]) -> None: class MQRegressor: """Multiple Quantile Regressor using GBDT (LightGBM or XGBoost). - - This regressor implements a multi-quantile estimation strategy by stacking - the dataset and using monotone constraints on the special '_tau' feature - to ensure non-crossing quantiles.""" + This regressor implements a multi-quantile estimation while ensuring non-crossing quantiles.""" def __init__( self, From 559e579a21141bb09483b64a81beef77d33f7e23 Mon Sep 17 00:00:00 2001 From: unknown Date: Tue, 19 May 2026 14:06:58 +0900 Subject: [PATCH 37/40] citation --- README.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 6dd15da..9dc51e3 100644 --- a/README.md +++ b/README.md @@ -32,5 +32,17 @@ Please refer to the [![Open In Colab](https://colab.research.google.com/assets/c # Citation If you use MQBoost in your research or project, please cite it as follows: ```md - +@article{Moon2026, + title={Monotone Composite Quantile Regression via Second-Order Gradient Boosting Framework}, + author={Moon, Sangjun and Hong, Sungchul and Park, Beomjin}, + journal={Machine Learning}, + volume={115}, + number={6}, + pages={127}, + year={2026}, + month={may}, + issn={1573-0565}, + doi={10.1007/s10994-026-07058-2}, + url={https://doi.org/10.1007/s10994-026-07058-2} +} ``` From 9e3583cdbaa70c720f8f0f58d2f9b2552df37573 Mon Sep 17 00:00:00 2001 From: unknown Date: Tue, 19 May 2026 14:10:03 +0900 Subject: [PATCH 38/40] fix readme --- README.md | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/README.md b/README.md index 9dc51e3..c60d5fc 100644 --- a/README.md +++ b/README.md @@ -5,12 +5,7 @@ **MQBoost** is a gradient boosting-based framework for simultaneous multi-quantile regression with monotonicity constraints (non-crossing quantiles). It is built on top of [LightGBM](https://github.com/microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost), two leading gradient boosting frameworks, enabling efficient and scalable training while ensuring valid quantile estimates. -### Why MQBoost? -Standard quantile regression models often suffer from: -- Quantile crossing (e.g., 90% quantile < 50% quantile) -- Independent training per quantile → inconsistent predictions - -**MQBoost** solves this by: +Standard quantile regression models often suffer from Quantile crossing (e.g., 90% quantile < 50% quantile) and independent training per quantile → inconsistent predictions. **MQBoost** solves this by: - Learning multiple quantiles jointly - Enforcing monotonicity across quantiles - Leveraging efficient boosting frameworks From dace3c494d06050fb7683f96e57c3c4a17ccb63d Mon Sep 17 00:00:00 2001 From: unknown Date: Tue, 19 May 2026 14:17:48 +0900 Subject: [PATCH 39/40] fix bibtex --- README.md | 23 ++++++++++------------- 1 file changed, 10 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index c60d5fc..f9b49cb 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ **MQBoost** is a gradient boosting-based framework for simultaneous multi-quantile regression with monotonicity constraints (non-crossing quantiles). It is built on top of [LightGBM](https://github.com/microsoft/LightGBM) and [XGBoost](https://github.com/dmlc/xgboost), two leading gradient boosting frameworks, enabling efficient and scalable training while ensuring valid quantile estimates. -Standard quantile regression models often suffer from Quantile crossing (e.g., 90% quantile < 50% quantile) and independent training per quantile → inconsistent predictions. **MQBoost** solves this by: +Standard quantile regression models often suffer from quantile crossing (e.g., 90% quantile < 50% quantile) and independent training per quantile → inconsistent predictions. We solve this by: - Learning multiple quantiles jointly - Enforcing monotonicity across quantiles - Leveraging efficient boosting frameworks @@ -26,18 +26,15 @@ Please refer to the [![Open In Colab](https://colab.research.google.com/assets/c # Citation If you use MQBoost in your research or project, please cite it as follows: -```md +```bibtex @article{Moon2026, - title={Monotone Composite Quantile Regression via Second-Order Gradient Boosting Framework}, - author={Moon, Sangjun and Hong, Sungchul and Park, Beomjin}, - journal={Machine Learning}, - volume={115}, - number={6}, - pages={127}, - year={2026}, - month={may}, - issn={1573-0565}, - doi={10.1007/s10994-026-07058-2}, - url={https://doi.org/10.1007/s10994-026-07058-2} + title={Monotone Composite Quantile Regression via Second-Order Gradient Boosting Framework}, + author={Moon, Sangjun and Hong, Sungchul and Park, Beomjin}, + journal={Machine Learning}, + volume={115}, + number={6}, + pages={127}, + year={2026}, + publisher={Springer} } ``` From 846cea3f68dfca64667a611280d2227c49f91336 Mon Sep 17 00:00:00 2001 From: unknown Date: Tue, 19 May 2026 14:23:36 +0900 Subject: [PATCH 40/40] fix readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f9b49cb..01ecd13 100644 --- a/README.md +++ b/README.md @@ -10,13 +10,13 @@ Standard quantile regression models often suffer from quantile crossing (e.g., 9 - Enforcing monotonicity across quantiles - Leveraging efficient boosting frameworks -# Installation +# Usage +## Installation Install using pip: ```bash pip install mqboost ``` -# Usage ## Features - **MQDataset**: Encapsulates the dataset used for MQRegressor. - **MQRegressor**: Custom multiple quantile estimator with preserving monotonicity among quantiles.