Skip to content

hyperlight

HyperLight

Hyperparameter Tuning for Lightning.

Parameters:

Name Type Description Default
seed int

seed for the random number generator. See Numpy Random Sampling.

126
log_level int

log level for the logger.

50

Attributes:

Name Type Description
seed int

seed for the random number generator.

rng Generator

random number generator.

fun_control dict

dictionary containing control parameters for the hyperparameter tuning.

log_level int

log level for the logger.

Examples:

>>> hyper_light = HyperLight(seed=126, log_level=50)
>>> print(hyper_light.seed)
    126
Source code in spotpython/fun/hyperlight.py
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
class HyperLight:
    """
    Hyperparameter Tuning for Lightning.

    Args:
        seed (int): seed for the random number generator. See Numpy Random Sampling.
        log_level (int): log level for the logger.

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

    Examples:
        >>> hyper_light = HyperLight(seed=126, log_level=50)
        >>> print(hyper_light.seed)
            126
    """

    def __init__(self, seed: int = 126, log_level: int = 50) -> None:
        self.seed = seed
        self.rng = default_rng(seed=self.seed)
        self.log_level = log_level
        logger.setLevel(log_level)
        logger.info(f"Starting the logger at level {log_level} for module {__name__}:")

    def check_X_shape(self, X: np.ndarray, fun_control: dict) -> np.ndarray:
        """
        Checks the shape of the input array X and raises an exception if it is not valid.

        Args:
            X (np.ndarray):
                input array.
            fun_control (dict):
                dictionary containing control parameters for the hyperparameter tuning.

        Returns:
            np.ndarray:
                input array with valid shape.

        Raises:
            Exception:
                if the shape of the input array is not valid.

        Examples:
            >>> import numpy as np
                from spotpython.utils.init import fun_control_init
                from spotpython.light.regression.netlightregression import NetLightRegression
                from spotpython.hyperdict.light_hyper_dict import LightHyperDict
                from spotpython.hyperparameters.values import add_core_model_to_fun_control
                from spotpython.fun.hyperlight import HyperLight
                from spotpython.hyperparameters.values import get_var_name
                fun_control = fun_control_init()
                add_core_model_to_fun_control(core_model=NetLightRegression,
                                            fun_control=fun_control,
                                            hyper_dict=LightHyperDict)
                hyper_light = HyperLight(seed=126, log_level=50)
                n_hyperparams = len(get_var_name(fun_control))
                # generate a random np.array X with shape (2, n_hyperparams)
                X = np.random.rand(2, n_hyperparams)
                X == hyper_light.check_X_shape(X, fun_control)
                array([[ True,  True,  True,  True,  True,  True,  True,  True,  True],
                [ True,  True,  True,  True,  True,  True,  True,  True,  True]])

        """
        try:
            X.shape[1]
        except ValueError:
            X = np.array([X])
        if X.shape[1] != len(get_var_name(fun_control)):
            raise Exception("Invalid shape of input array X.")
        return X

    def fun(self, X: np.ndarray, fun_control: dict = None) -> np.ndarray:
        """
        Evaluates the function for the given input array X and control parameters.

        Args:
            X (np.ndarray):
                input array.
            fun_control (dict):
                dictionary containing control parameters for the hyperparameter tuning.

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

        Examples:
            >>> from math import inf
                import numpy as np
                from spotpython.data.diabetes import Diabetes
                from spotpython.hyperdict.light_hyper_dict import LightHyperDict
                from spotpython.fun.hyperlight import HyperLight
                from spotpython.utils.init import fun_control_init
                from spotpython.utils.eda import gen_design_table
                from spotpython.spot import spot
                from spotpython.hyperparameters.values import get_default_hyperparameters_as_array
                PREFIX="000"
                data_set = Diabetes()
                fun_control = fun_control_init(
                    PREFIX=PREFIX,
                    save_experiment=True,
                    fun_evals=inf,
                    max_time=1,
                    data_set = data_set,
                    core_model_name="light.regression.NNLinearRegressor",
                    hyperdict=LightHyperDict,
                    _L_in=10,
                    _L_out=1,
                    TENSORBOARD_CLEAN=True,
                    tensorboard_log=True,
                    seed=42,)
                print(gen_design_table(fun_control))
                X = get_default_hyperparameters_as_array(fun_control)
                # set epochs to 2^8:
                X[0, 1] = 8
                # set patience to 2^10:
                X[0, 7] = 10
                print(f"X: {X}")
                # combine X and X to a np.array with shape (2, n_hyperparams)
                # so that two values are returned
                X = np.vstack((X, X))
                hyper_light = HyperLight(seed=125, log_level=50)
                hyper_light.fun(X, fun_control)
        """
        z_res = np.array([], dtype=float)
        self.check_X_shape(X=X, fun_control=fun_control)
        var_dict = assign_values(X, get_var_name(fun_control))
        # type information and transformations are considered in generate_one_config_from_var_dict:
        for config in generate_one_config_from_var_dict(var_dict, fun_control):
            if fun_control["verbosity"] > 0:
                print("\nIn fun(): config:")
                pprint.pprint(config)
            logger.debug(f"\nconfig: {config}")
            # extract parameters like epochs, batch_size, lr, etc. from config
            # config_id = generate_config_id(config)
            try:
                logger.debug("fun: Calling train_model")
                df_eval = train_model(config, fun_control)
                logger.debug("fun: train_model returned")
            except Exception as err:
                if fun_control["verbosity"] > 0:
                    print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                    pprint.pprint(fun_control)
                    print(f"Error in fun(). Call to train_model failed. {err=}, {type(err)=}")
                    print("Setting df_eval to np.nan\n")
                logger.error(f"Error in fun(). Call to train_model failed. {err=}, {type(err)=}")
                logger.error("Setting df_eval to np.nan")
                df_eval = np.nan
            z_val = fun_control["weights"] * df_eval
            z_res = np.append(z_res, z_val)
        return z_res

check_X_shape(X, fun_control)

Checks the shape of the input array X and raises an exception if it is not valid.

Parameters:

Name Type Description Default
X ndarray

input array.

required
fun_control dict

dictionary containing control parameters for the hyperparameter tuning.

required

Returns:

Type Description
ndarray

np.ndarray: input array with valid shape.

Raises:

Type Description
Exception

if the shape of the input array is not valid.

Examples:

>>> import numpy as np
    from spotpython.utils.init import fun_control_init
    from spotpython.light.regression.netlightregression import NetLightRegression
    from spotpython.hyperdict.light_hyper_dict import LightHyperDict
    from spotpython.hyperparameters.values import add_core_model_to_fun_control
    from spotpython.fun.hyperlight import HyperLight
    from spotpython.hyperparameters.values import get_var_name
    fun_control = fun_control_init()
    add_core_model_to_fun_control(core_model=NetLightRegression,
                                fun_control=fun_control,
                                hyper_dict=LightHyperDict)
    hyper_light = HyperLight(seed=126, log_level=50)
    n_hyperparams = len(get_var_name(fun_control))
    # generate a random np.array X with shape (2, n_hyperparams)
    X = np.random.rand(2, n_hyperparams)
    X == hyper_light.check_X_shape(X, fun_control)
    array([[ True,  True,  True,  True,  True,  True,  True,  True,  True],
    [ True,  True,  True,  True,  True,  True,  True,  True,  True]])
Source code in spotpython/fun/hyperlight.py
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
def check_X_shape(self, X: np.ndarray, fun_control: dict) -> np.ndarray:
    """
    Checks the shape of the input array X and raises an exception if it is not valid.

    Args:
        X (np.ndarray):
            input array.
        fun_control (dict):
            dictionary containing control parameters for the hyperparameter tuning.

    Returns:
        np.ndarray:
            input array with valid shape.

    Raises:
        Exception:
            if the shape of the input array is not valid.

    Examples:
        >>> import numpy as np
            from spotpython.utils.init import fun_control_init
            from spotpython.light.regression.netlightregression import NetLightRegression
            from spotpython.hyperdict.light_hyper_dict import LightHyperDict
            from spotpython.hyperparameters.values import add_core_model_to_fun_control
            from spotpython.fun.hyperlight import HyperLight
            from spotpython.hyperparameters.values import get_var_name
            fun_control = fun_control_init()
            add_core_model_to_fun_control(core_model=NetLightRegression,
                                        fun_control=fun_control,
                                        hyper_dict=LightHyperDict)
            hyper_light = HyperLight(seed=126, log_level=50)
            n_hyperparams = len(get_var_name(fun_control))
            # generate a random np.array X with shape (2, n_hyperparams)
            X = np.random.rand(2, n_hyperparams)
            X == hyper_light.check_X_shape(X, fun_control)
            array([[ True,  True,  True,  True,  True,  True,  True,  True,  True],
            [ True,  True,  True,  True,  True,  True,  True,  True,  True]])

    """
    try:
        X.shape[1]
    except ValueError:
        X = np.array([X])
    if X.shape[1] != len(get_var_name(fun_control)):
        raise Exception("Invalid shape of input array X.")
    return X

fun(X, fun_control=None)

Evaluates the function for the given input array X and control parameters.

Parameters:

Name Type Description Default
X ndarray

input array.

required
fun_control dict

dictionary containing control parameters for the hyperparameter tuning.

None

Returns:

Type Description
ndarray

array containing the evaluation results.

Examples:

>>> from math import inf
    import numpy as np
    from spotpython.data.diabetes import Diabetes
    from spotpython.hyperdict.light_hyper_dict import LightHyperDict
    from spotpython.fun.hyperlight import HyperLight
    from spotpython.utils.init import fun_control_init
    from spotpython.utils.eda import gen_design_table
    from spotpython.spot import spot
    from spotpython.hyperparameters.values import get_default_hyperparameters_as_array
    PREFIX="000"
    data_set = Diabetes()
    fun_control = fun_control_init(
        PREFIX=PREFIX,
        save_experiment=True,
        fun_evals=inf,
        max_time=1,
        data_set = data_set,
        core_model_name="light.regression.NNLinearRegressor",
        hyperdict=LightHyperDict,
        _L_in=10,
        _L_out=1,
        TENSORBOARD_CLEAN=True,
        tensorboard_log=True,
        seed=42,)
    print(gen_design_table(fun_control))
    X = get_default_hyperparameters_as_array(fun_control)
    # set epochs to 2^8:
    X[0, 1] = 8
    # set patience to 2^10:
    X[0, 7] = 10
    print(f"X: {X}")
    # combine X and X to a np.array with shape (2, n_hyperparams)
    # so that two values are returned
    X = np.vstack((X, X))
    hyper_light = HyperLight(seed=125, log_level=50)
    hyper_light.fun(X, fun_control)
Source code in spotpython/fun/hyperlight.py
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
def fun(self, X: np.ndarray, fun_control: dict = None) -> np.ndarray:
    """
    Evaluates the function for the given input array X and control parameters.

    Args:
        X (np.ndarray):
            input array.
        fun_control (dict):
            dictionary containing control parameters for the hyperparameter tuning.

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

    Examples:
        >>> from math import inf
            import numpy as np
            from spotpython.data.diabetes import Diabetes
            from spotpython.hyperdict.light_hyper_dict import LightHyperDict
            from spotpython.fun.hyperlight import HyperLight
            from spotpython.utils.init import fun_control_init
            from spotpython.utils.eda import gen_design_table
            from spotpython.spot import spot
            from spotpython.hyperparameters.values import get_default_hyperparameters_as_array
            PREFIX="000"
            data_set = Diabetes()
            fun_control = fun_control_init(
                PREFIX=PREFIX,
                save_experiment=True,
                fun_evals=inf,
                max_time=1,
                data_set = data_set,
                core_model_name="light.regression.NNLinearRegressor",
                hyperdict=LightHyperDict,
                _L_in=10,
                _L_out=1,
                TENSORBOARD_CLEAN=True,
                tensorboard_log=True,
                seed=42,)
            print(gen_design_table(fun_control))
            X = get_default_hyperparameters_as_array(fun_control)
            # set epochs to 2^8:
            X[0, 1] = 8
            # set patience to 2^10:
            X[0, 7] = 10
            print(f"X: {X}")
            # combine X and X to a np.array with shape (2, n_hyperparams)
            # so that two values are returned
            X = np.vstack((X, X))
            hyper_light = HyperLight(seed=125, log_level=50)
            hyper_light.fun(X, fun_control)
    """
    z_res = np.array([], dtype=float)
    self.check_X_shape(X=X, fun_control=fun_control)
    var_dict = assign_values(X, get_var_name(fun_control))
    # type information and transformations are considered in generate_one_config_from_var_dict:
    for config in generate_one_config_from_var_dict(var_dict, fun_control):
        if fun_control["verbosity"] > 0:
            print("\nIn fun(): config:")
            pprint.pprint(config)
        logger.debug(f"\nconfig: {config}")
        # extract parameters like epochs, batch_size, lr, etc. from config
        # config_id = generate_config_id(config)
        try:
            logger.debug("fun: Calling train_model")
            df_eval = train_model(config, fun_control)
            logger.debug("fun: train_model returned")
        except Exception as err:
            if fun_control["verbosity"] > 0:
                print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
                pprint.pprint(fun_control)
                print(f"Error in fun(). Call to train_model failed. {err=}, {type(err)=}")
                print("Setting df_eval to np.nan\n")
            logger.error(f"Error in fun(). Call to train_model failed. {err=}, {type(err)=}")
            logger.error("Setting df_eval to np.nan")
            df_eval = np.nan
        z_val = fun_control["weights"] * df_eval
        z_res = np.append(z_res, z_val)
    return z_res