[Feature] simplify code for readability

mqboost/base.py

@@ -49,12 +49,6 @@ class TypeName(BaseEnum):
     constraints_type: str = "constraints_type"
 
 
-class MQStr(BaseEnum):
-    mono: str = "monotone_constraints"
-    obj: str = "objective"
-    valid: str = "valid"
-
-
 # Functions
 def _lgb_predict_dtype(data: XdataLike):
     return data

mqboost/constraints.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-from mqboost.base import FUNC_TYPE, ModelName, MQStr, ParamsLike, TypeName
+from mqboost.base import FUNC_TYPE, ModelName, ParamsLike, TypeName
 
 
 def set_monotone_constraints(
@@ -19,16 +19,18 @@ def set_monotone_constraints(
     Returns:
         ParamsLike
     """
+    MONOTONE_CONSTRAINTS: str = "monotone_constraints"
+
     constraints_fucs = FUNC_TYPE.get(model_name).get(TypeName.constraints_type)
     _params = params.copy()
-    if MQStr.mono.value in _params:
-        _monotone_constraints = list(_params[MQStr.mono.value])
+    if MONOTONE_CONSTRAINTS in _params:
+        _monotone_constraints = list(_params[MONOTONE_CONSTRAINTS])
         _monotone_constraints.append(1)
-        _params.update({MQStr.mono.value: constraints_fucs(_monotone_constraints)})
+        _params.update({MONOTONE_CONSTRAINTS: constraints_fucs(_monotone_constraints)})
     else:
         _params.update(
             {
-                MQStr.mono.value: constraints_fucs(
+                MONOTONE_CONSTRAINTS: constraints_fucs(
                     [1 if "_tau" == col else 0 for col in columns]
                 )
             }

mqboost/objective.py

@@ -7,6 +7,11 @@
 from mqboost.utils import delta_validate, epsilon_validate
 
 CHECK_LOSS: str = "check_loss"
+GradFnLike = Callable[[Any], np.ndarray]
+HessFnLike = Callable[[Any], np.ndarray]
+CallGradHessLike = Callable[
+    [np.ndarray, DtrainLike, list[float], Any], tuple[np.ndarray, np.ndarray]
+]
 
 
 # check loss
@@ -42,13 +47,13 @@ def _hess_huber(error: np.ndarray, **kwargs) -> np.ndarray:
 
 
 # Approx loss (MM loss)
-def _grad_approx(error: np.ndarray, alpha: float, epsilon: float):
+def _grad_approx(error: np.ndarray, alpha: float, epsilon: float) -> np.ndarray:
     """Compute the gradient of the approx of the smooth approximated check loss function."""
     _grad = 0.5 * (1 - 2 * alpha - error / (epsilon + np.abs(error)))
     return _grad
 
 
-def _hess_approx(error: np.ndarray, epsilon: float, **kwargs):
+def _hess_approx(error: np.ndarray, epsilon: float, **kwargs) -> np.ndarray:
     """Compute the Hessian of the approx of the smooth approximated check loss function."""
     _hess = 1 / (2 * (epsilon + np.abs(error)))
     return _hess
@@ -64,40 +69,38 @@ def _train_pred_reshape(
     return _y_train.reshape(len_alpha, -1), y_pred.reshape(len_alpha, -1)
 
 
-def _compute_grads_hess(
-    y_pred: np.ndarray,
-    dtrain: DtrainLike,
-    alphas: list[float],
-    grad_fn: Callable[[np.ndarray, float, Any], np.ndarray],
-    hess_fn: Callable[[np.ndarray, float, Any], np.ndarray],
-    **kwargs: dict[str, float],
-) -> tuple[np.ndarray, np.ndarray]:
-    """Compute gradients and hessians for the given loss function."""
-    _len_alpha = len(alphas)
-    _y_train, _y_pred = _train_pred_reshape(
-        y_pred=y_pred, dtrain=dtrain, len_alpha=_len_alpha
-    )
-    grads = []
-    hess = []
-    for alpha_inx in range(len(alphas)):
-        _err_for_alpha = _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)
-        hess.append(_hess)
-
-    return np.concatenate(grads), np.concatenate(hess)
-
-
-check_loss_grad_hess: Callable = partial(
-    _compute_grads_hess, grad_fn=_grad_rho, hess_fn=_hess_rho
-)
-huber_loss_grad_hess: Callable = partial(
-    _compute_grads_hess, grad_fn=_grad_huber, hess_fn=_hess_huber
-)
-approx_loss_grad_hess: Callable = partial(
-    _compute_grads_hess, grad_fn=_grad_approx, hess_fn=_hess_approx
-)
+# Compute gradient hessian logic
+def compute_grad_hess(grad_fn: GradFnLike, hess_fn: HessFnLike) -> CallGradHessLike:
+    """Return computing gradient hessian function."""
+
+    def _compute_grads_hess(
+        y_pred: np.ndarray,
+        dtrain: DtrainLike,
+        alphas: list[float],
+        **kwargs: Any,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Compute gradients and hessians for the given gradient and hessian function."""
+        _len_alpha = len(alphas)
+        _y_train, _y_pred = _train_pred_reshape(
+            y_pred=y_pred, dtrain=dtrain, len_alpha=_len_alpha
+        )
+        grads = []
+        hess = []
+        for alpha_inx in range(len(alphas)):
+            _err_for_alpha = _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)
+            hess.append(_hess)
+
+        return np.concatenate(grads), np.concatenate(hess)
+
+    return _compute_grads_hess
+
+
+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)
 
 
 def _eval_check_loss(

mqboost/optimize.py

@@ -12,7 +12,6 @@
     DtrainLike,
     FittingException,
     ModelName,
-    MQStr,
     ObjectiveName,
     ParamsLike,
 )
@@ -200,7 +199,7 @@ def __optuna_objective(
             model_params = dict(
                 params=params,
                 dtrain=dtrain,
-                evals=[(dvalid, MQStr.valid.value)],
+                evals=[(dvalid, "valid")],
             )
             _gbm = xgb.train(**model_params)
             _preds = _gbm.predict(data=deval)

mqboost/regressor.py

@@ -2,7 +2,7 @@
 import numpy as np
 import xgboost as xgb
 
-from mqboost.base import FittingException, ModelName, MQStr, ObjectiveName, ParamsLike
+from mqboost.base import FittingException, ModelName, ObjectiveName, ParamsLike
 from mqboost.constraints import set_monotone_constraints
 from mqboost.dataset import MQDataset
 from mqboost.objective import MQObjective
@@ -84,7 +84,7 @@ def fit(
             epsilon=self._epsilon,
         )
         if self.__is_lgb:
-            params.update({MQStr.obj.value: self._MQObj.fobj})
+            params.update({"objective": self._MQObj.fobj})
             self.model = lgb.train(
                 train_set=dataset.dtrain,
                 params=params,

mqboost/utils.py

@@ -6,7 +6,6 @@
 
 from mqboost.base import (
     AlphaLike,
-    MQStr,
     ParamsLike,
     ValidationException,
     XdataLike,
@@ -98,7 +97,7 @@ def epsilon_validate(epsilon: float) -> None:
 
 def params_validate(params: ParamsLike) -> None:
     """Validates the model parameter ensuring its key dosen't contain 'objective'."""
-    if MQStr.obj.value in params:
+    if "objective" in params:
         raise ValidationException(
             "The parameter named 'objective' must be excluded in params"
         )

pyproject.toml

@@ -19,8 +19,6 @@ scikit-learn = "^1.5.1"
 
 [tool.poetry.group.dev.dependencies]
 pre-commit = "^3.7.1"
-isort = "^5.13.2"
-black = "^24.4.2"
 pytest = "^8.3.2"
 pytest-cov = "^5.0.0"