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hypersklearn

HyperSklearn

Hyperparameter Tuning for Sklearn.

Parameters:

Name Type Description Default
seed int

seed. See Numpy Random Sampling

 126
log_level int

log level for logger. Default is 50.

 50

Attributes:

Name Type Description
seed int

seed for random number generator.
rng Generator

random number generator.
fun_control dict

dictionary containing control parameters for the function.
log_level int

log level for logger.

Examples:

>>> from spotPython.fun.hypersklearn import HyperSklearn
>>> hyper_sklearn = HyperSklearn(seed=126, log_level=50)
>>> print(hyper_sklearn.seed)
126
Source code in spotPython/fun/hypersklearn.py 
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class HyperSklearn:
    """
    Hyperparameter Tuning for Sklearn.

    Args:
        seed (int): seed.
            See Numpy Random Sampling
        log_level (int): log level for logger. Default is 50.

    Attributes:
        seed (int): seed for random number generator.
        rng (Generator): random number generator.
        fun_control (dict): dictionary containing control parameters for the function.
        log_level (int): log level for logger.

    Examples:
        >>> from spotPython.fun.hypersklearn import HyperSklearn
        >>> hyper_sklearn = HyperSklearn(seed=126, log_level=50)
        >>> print(hyper_sklearn.seed)
        126
    """

    def __init__(self, seed: int = 126, log_level: int = 50):
        self.seed = seed
        self.rng = default_rng(seed=self.seed)
        self.fun_control = {
            "seed": None,
            "data": None,
            "step": 10_000,
            "horizon": None,
            "grace_period": None,
            "metric_river": None,
            "metric_sklearn": mean_absolute_error,
            "weights": array([1, 0, 0]),
            "weight_coeff": 0.0,
            "log_level": log_level,
            "var_name": [],
            "var_type": [],
            "prep_model": None,
            "predict_proba": False,
        }
        self.log_level = self.fun_control["log_level"]
        logger.setLevel(self.log_level)
        logger.info(f"Starting the logger at level {self.log_level} for module {__name__}:")

    def check_X_shape(self, X: np.ndarray) -> None:
        """
        Check the shape of the input array X.

        Args:
            X (np.ndarray): input array.

        Raises:
            Exception: if the second dimension of X does not match the length of var_name in fun_control.

        Examples:
            >>> from spotPython.fun.hypersklearn import HyperSklearn
            >>> hyper_sklearn = HyperSklearn(seed=126, log_level=50)
            >>> hyper_sklearn.fun_control["var_name"] = ["a", "b", "c"]
            >>> hyper_sklearn.check_X_shape(X=np.array([[1, 2, 3]]))
            >>> hyper_sklearn.check_X_shape(X=np.array([[1, 2]]))
            Traceback (most recent call last):
            ...
            Exception

        """
        try:
            X.shape[1]
        except ValueError:
            X = np.array([X])
        if X.shape[1] != len(self.fun_control["var_name"]):
            raise Exception

    def get_sklearn_df_eval_preds(self, model) -> tuple:
        """
        Get evaluation and prediction dataframes for a given model.
        Args:
            model (sklearn model): sklearn model.

        Returns:
            (tuple): tuple containing evaluation and prediction dataframes.

        Raises:
            Exception: if call to evaluate_model fails.

        """
        try:
            df_eval, df_preds = self.evaluate_model(model, self.fun_control)
        except Exception as err:
            print(f"Error in get_sklearn_df_eval_preds(). Call to evaluate_model failed. {err=}, {type(err)=}")
            print("Setting df_eval and df.preds to np.nan")
            df_eval = np.nan
            df_preds = np.nan
        return df_eval, df_preds

    def fun_sklearn(self, X: np.ndarray, fun_control: dict = None) -> np.ndarray:
        """
        Evaluate a sklearn model using hyperparameters specified in X.

        Args:
            X (np.ndarray): input array containing hyperparameters.
            fun_control (dict): dictionary containing control parameters for the function. Default is None.

        Returns:
            (np.ndarray): array containing evaluation results.

        Raises:
            Exception: if call to evaluate_model fails.

        """
        z_res = np.array([], dtype=float)
        self.fun_control.update(fun_control)
        self.check_X_shape(X)
        var_dict = assign_values(X, self.fun_control["var_name"])
        for config in generate_one_config_from_var_dict(var_dict, self.fun_control):
            # config_id = generate_config_id(config)
            if self.fun_control["prep_model"] is not None:
                model = make_pipeline(self.fun_control["prep_model"], self.fun_control["core_model"](**config))
            else:
                model = self.fun_control["core_model"](**config)
            try:
                eval_type = fun_control["eval"]
                if eval_type == "eval_oob_score":
                    df_eval, _ = evaluate_model_oob(model, self.fun_control)
                elif eval_type == "train_cv":
                    df_eval, _ = evaluate_cv(model, self.fun_control)
                else:  # eval_type == "train_hold_out":
                    df_eval, _ = evaluate_hold_out(model, self.fun_control)
            except Exception as err:
                print(f"Error in fun_sklearn(). Call to evaluate_model failed. {err=}, {type(err)=}")
                print("Setting df_eval to np.nan")
                df_eval = np.nan
            z_res = np.append(z_res, fun_control["weights"] * df_eval)
        return z_res

check_X_shape(X)

Check the shape of the input array X.

Parameters:

Name Type Description Default
X ndarray

input array.

 required

Raises:

Type Description
Exception

if the second dimension of X does not match the length of var_name in fun_control.

Examples:

>>> from spotPython.fun.hypersklearn import HyperSklearn
>>> hyper_sklearn = HyperSklearn(seed=126, log_level=50)
>>> hyper_sklearn.fun_control["var_name"] = ["a", "b", "c"]
>>> hyper_sklearn.check_X_shape(X=np.array([[1, 2, 3]]))
>>> hyper_sklearn.check_X_shape(X=np.array([[1, 2]]))
Traceback (most recent call last):
...
Exception
Source code in spotPython/fun/hypersklearn.py 
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def check_X_shape(self, X: np.ndarray) -> None:
    """
    Check the shape of the input array X.

    Args:
        X (np.ndarray): input array.

    Raises:
        Exception: if the second dimension of X does not match the length of var_name in fun_control.

    Examples:
        >>> from spotPython.fun.hypersklearn import HyperSklearn
        >>> hyper_sklearn = HyperSklearn(seed=126, log_level=50)
        >>> hyper_sklearn.fun_control["var_name"] = ["a", "b", "c"]
        >>> hyper_sklearn.check_X_shape(X=np.array([[1, 2, 3]]))
        >>> hyper_sklearn.check_X_shape(X=np.array([[1, 2]]))
        Traceback (most recent call last):
        ...
        Exception

    """
    try:
        X.shape[1]
    except ValueError:
        X = np.array([X])
    if X.shape[1] != len(self.fun_control["var_name"]):
        raise Exception

fun_sklearn(X, fun_control=None)

Evaluate a sklearn model using hyperparameters specified in X.

Parameters:

Name Type Description Default
X ndarray

input array containing hyperparameters.

 required
fun_control dict

dictionary containing control parameters for the function. Default is None.

 None

Returns:

Type Description
ndarray

array containing evaluation results.

Raises:

Type Description
Exception

if call to evaluate_model fails.
Source code in spotPython/fun/hypersklearn.py 
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def fun_sklearn(self, X: np.ndarray, fun_control: dict = None) -> np.ndarray:
    """
    Evaluate a sklearn model using hyperparameters specified in X.

    Args:
        X (np.ndarray): input array containing hyperparameters.
        fun_control (dict): dictionary containing control parameters for the function. Default is None.

    Returns:
        (np.ndarray): array containing evaluation results.

    Raises:
        Exception: if call to evaluate_model fails.

    """
    z_res = np.array([], dtype=float)
    self.fun_control.update(fun_control)
    self.check_X_shape(X)
    var_dict = assign_values(X, self.fun_control["var_name"])
    for config in generate_one_config_from_var_dict(var_dict, self.fun_control):
        # config_id = generate_config_id(config)
        if self.fun_control["prep_model"] is not None:
            model = make_pipeline(self.fun_control["prep_model"], self.fun_control["core_model"](**config))
        else:
            model = self.fun_control["core_model"](**config)
        try:
            eval_type = fun_control["eval"]
            if eval_type == "eval_oob_score":
                df_eval, _ = evaluate_model_oob(model, self.fun_control)
            elif eval_type == "train_cv":
                df_eval, _ = evaluate_cv(model, self.fun_control)
            else:  # eval_type == "train_hold_out":
                df_eval, _ = evaluate_hold_out(model, self.fun_control)
        except Exception as err:
            print(f"Error in fun_sklearn(). Call to evaluate_model failed. {err=}, {type(err)=}")
            print("Setting df_eval to np.nan")
            df_eval = np.nan
        z_res = np.append(z_res, fun_control["weights"] * df_eval)
    return z_res

get_sklearn_df_eval_preds(model)

Get evaluation and prediction dataframes for a given model. Args: model (sklearn model): sklearn model.

Returns:

Type Description
tuple

tuple containing evaluation and prediction dataframes.

Raises:

Type Description
Exception

if call to evaluate_model fails.
Source code in spotPython/fun/hypersklearn.py 
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def get_sklearn_df_eval_preds(self, model) -> tuple:
    """
    Get evaluation and prediction dataframes for a given model.
    Args:
        model (sklearn model): sklearn model.

    Returns:
        (tuple): tuple containing evaluation and prediction dataframes.

    Raises:
        Exception: if call to evaluate_model fails.

    """
    try:
        df_eval, df_preds = self.evaluate_model(model, self.fun_control)
    except Exception as err:
        print(f"Error in get_sklearn_df_eval_preds(). Call to evaluate_model failed. {err=}, {type(err)=}")
        print("Setting df_eval and df.preds to np.nan")
        df_eval = np.nan
        df_preds = np.nan
    return df_eval, df_preds