Code Duplication - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

Code Duplication Length = 146-146 lines in 2 locations

src/hyperactive/opt/hillclimbing/_repulsing.py 1 location


from hyperactive.opt._adapters._gfo import _BaseGFOadapter


class RepulsingHillClimbing(_BaseGFOadapter):
    """Repulsing hill climbing optimizer.

    Parameters
    ----------
    search_space : dict[str, list]
        The search space to explore. A dictionary with parameter
        names as keys and a numpy array as values.
        Optional, can be passed later via ``set_params``.
    initialize : dict[str, int], default={"grid": 4, "random": 2, "vertices": 4}
        The method to generate initial positions. A dictionary with
        the following key literals and the corresponding value type:
        {"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
    constraints : list[callable], default=[]
        A list of constraints, where each constraint is a callable.
        The callable returns `True` or `False` dependend on the input parameters.
    random_state : None, int, default=None
        If None, create a new random state. If int, create a new random state
        seeded with the value.
    rand_rest_p : float, default=0.1
        The probability of a random iteration during the the search process.
    epsilon : float, default=0.01
        The step-size for the climbing.
    distribution : str, default="normal"
        The type of distribution to sample from.
    n_neighbours : int, default=10
        The number of neighbours to sample and evaluate before moving to the best
        of those neighbours.
    repulsion_factor : float, default=5
        The factor to control the repulsion of the hill climbing process.
    n_iter : int, default=100
        The number of iterations to run the optimizer.
    verbose : bool, default=False
        If True, print the progress of the optimization process.
    experiment : BaseExperiment, optional
        The experiment to optimize parameters for.
        Optional, can be passed later via ``set_params``.

    Examples
    --------
    Hill climbing applied to scikit-learn parameter tuning:

    1. defining the experiment to optimize:
    >>> from hyperactive.experiment.integrations import SklearnCvExperiment
    >>> from sklearn.datasets import load_iris
    >>> from sklearn.svm import SVC
    >>>
    >>> X, y = load_iris(return_X_y=True)
    >>>
    >>> sklearn_exp = SklearnCvExperiment(
    ...     estimator=SVC(),
    ...     X=X,
    ...     y=y,
    ... )

    2. setting up the hill climbing optimizer:
    >>> from hyperactive.opt import RepulsingHillClimbing
    >>> import numpy as np
    >>> 
    >>> hc_config = {
    ...     "search_space": {
    ...         "C": np.array([0.01, 0.1, 1, 10]),
    ...         "gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
    ...     },
    ...     "n_iter": 100,
    ... }
    >>> hillclimbing = RepulsingHillClimbing(experiment=sklearn_exp, **hc_config)

    3. running the hill climbing search:
    >>> best_params = hillclimbing.run()

    Best parameters can also be accessed via the attributes:
    >>> best_params = hillclimbing.best_params_
    """

    _tags = {
        "info:name": "Repulsing Hill Climbing",
        "info:local_vs_global": "mixed",  # "local", "mixed", "global"
        "info:explore_vs_exploit": "exploit",  # "explore", "exploit", "mixed"
        "info:compute": "low",  # "low", "middle", "high"
    }

    def __init__(
        self,
        search_space=None,
        initialize=None,
        constraints=None,
        random_state=None,
        rand_rest_p=0.1,
        epsilon=0.01,
        distribution="normal",
        n_neighbours=10,
        repulsion_factor=5,
        n_iter=100,
        verbose=False,
        experiment=None,
    ):
        self.random_state = random_state
        self.rand_rest_p = rand_rest_p
        self.epsilon = epsilon
        self.distribution = distribution
        self.n_neighbours = n_neighbours
        self.search_space = search_space
        self.initialize = initialize
        self.constraints = constraints
        self.repulsion_factor = repulsion_factor
        self.n_iter = n_iter
        self.experiment = experiment
        self.verbose = verbose

        super().__init__()

    def _get_gfo_class(self):
        """Get the GFO class to use.

        Returns
        -------
        class
            The GFO class to use. One of the concrete GFO classes
        """
        from gradient_free_optimizers import RepulsingHillClimbingOptimizer

        return RepulsingHillClimbingOptimizer

    @classmethod
    def get_test_params(cls, parameter_set="default"):
        """Get the test parameters for the optimizer.

        Returns
        -------
        dict with str keys
            The test parameters dictionary.
        """
        import numpy as np

        params = super().get_test_params()
        more_params = {
            "repulsion_factor": 7,
            "search_space": {
                "C": np.array([0.01, 0.1, 1, 10]),
                "gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
            },
            "n_iter": 100,
        }
        params.update(more_params)
        return params


src/hyperactive/opt/hillclimbing/_stochastic.py 1 location


from hyperactive.opt._adapters._gfo import _BaseGFOadapter


class StochasticHillClimbing(_BaseGFOadapter):
    """Stochastic hill climbing optimizer.

    Parameters
    ----------
    search_space : dict[str, list]
        The search space to explore. A dictionary with parameter
        names as keys and a numpy array as values.
        Optional, can be passed later via ``set_params``.
    initialize : dict[str, int], default={"grid": 4, "random": 2, "vertices": 4}
        The method to generate initial positions. A dictionary with
        the following key literals and the corresponding value type:
        {"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
    constraints : list[callable], default=[]
        A list of constraints, where each constraint is a callable.
        The callable returns `True` or `False` dependend on the input parameters.
    random_state : None, int, default=None
        If None, create a new random state. If int, create a new random state
        seeded with the value.
    rand_rest_p : float, default=0.1
        The probability of a random iteration during the the search process.
    epsilon : float, default=0.01
        The step-size for the climbing.
    distribution : str, default="normal"
        The type of distribution to sample from.
    n_neighbours : int, default=10
        The number of neighbours to sample and evaluate before moving to the best
        of those neighbours.
    p_accept : float, default=0.5
        The probability of accepting a transition in the hill climbing process.
    n_iter : int, default=100
        The number of iterations to run the optimizer.
    verbose : bool, default=False
        If True, print the progress of the optimization process.
    experiment : BaseExperiment, optional
        The experiment to optimize parameters for.
        Optional, can be passed later via ``set_params``.

    Examples
    --------
    Hill climbing applied to scikit-learn parameter tuning:

    1. defining the experiment to optimize:
    >>> from hyperactive.experiment.integrations import SklearnCvExperiment
    >>> from sklearn.datasets import load_iris
    >>> from sklearn.svm import SVC
    >>>
    >>> X, y = load_iris(return_X_y=True)
    >>>
    >>> sklearn_exp = SklearnCvExperiment(
    ...     estimator=SVC(),
    ...     X=X,
    ...     y=y,
    ... )

    2. setting up the hill climbing optimizer:
    >>> from hyperactive.opt import StochasticHillClimbing
    >>> import numpy as np
    >>> 
    >>> hc_config = {
    ...     "search_space": {
    ...         "C": np.array([0.01, 0.1, 1, 10]),
    ...         "gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
    ...     },
    ...     "n_iter": 100,
    ... }
    >>> hillclimbing = StochasticHillClimbing(experiment=sklearn_exp, **hc_config)

    3. running the hill climbing search:
    >>> best_params = hillclimbing.run()

    Best parameters can also be accessed via the attributes:
    >>> best_params = hillclimbing.best_params_
    """

    _tags = {
        "info:name": "Hill Climbing",
        "info:local_vs_global": "local",  # "local", "mixed", "global"
        "info:explore_vs_exploit": "exploit",  # "explore", "exploit", "mixed"
        "info:compute": "low",  # "low", "middle", "high"
    }

    def __init__(
        self,
        search_space=None,
        initialize=None,
        constraints=None,
        random_state=None,
        rand_rest_p=0.1,
        epsilon=0.01,
        distribution="normal",
        n_neighbours=10,
        p_accept=0.5,
        n_iter=100,
        verbose=False,
        experiment=None,
    ):
        self.random_state = random_state
        self.rand_rest_p = rand_rest_p
        self.epsilon = epsilon
        self.distribution = distribution
        self.n_neighbours = n_neighbours
        self.search_space = search_space
        self.initialize = initialize
        self.constraints = constraints
        self.p_accept = p_accept
        self.n_iter = n_iter
        self.experiment = experiment
        self.verbose = verbose

        super().__init__()

    def _get_gfo_class(self):
        """Get the GFO class to use.

        Returns
        -------
        class
            The GFO class to use. One of the concrete GFO classes
        """
        from gradient_free_optimizers import StochasticHillClimbingOptimizer

        return StochasticHillClimbingOptimizer

    @classmethod
    def get_test_params(cls, parameter_set="default"):
        """Get the test parameters for the optimizer.

        Returns
        -------
        dict with str keys
            The test parameters dictionary.
        """
        import numpy as np

        params = super().get_test_params()
        more_params = {
            "p_accept": 0.33,
            "search_space": {
                "C": np.array([0.01, 0.1, 1, 10]),
                "gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
            },
            "n_iter": 100,
        }
        params.update(more_params)
        return params


		@@ 7-152 (lines=146) @@
4		from hyperactive.opt._adapters._gfo import _BaseGFOadapter
5
6
7		class RepulsingHillClimbing(_BaseGFOadapter):
8		"""Repulsing hill climbing optimizer.
9
10		Parameters
11		----------
12		search_space : dict[str, list]
13		The search space to explore. A dictionary with parameter
14		names as keys and a numpy array as values.
15		Optional, can be passed later via ``set_params``.
16		initialize : dict[str, int], default={"grid": 4, "random": 2, "vertices": 4}
17		The method to generate initial positions. A dictionary with
18		the following key literals and the corresponding value type:
19		{"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
20		constraints : list[callable], default=[]
21		A list of constraints, where each constraint is a callable.
22		The callable returns `True` or `False` dependend on the input parameters.
23		random_state : None, int, default=None
24		If None, create a new random state. If int, create a new random state
25		seeded with the value.
26		rand_rest_p : float, default=0.1
27		The probability of a random iteration during the the search process.
28		epsilon : float, default=0.01
29		The step-size for the climbing.
30		distribution : str, default="normal"
31		The type of distribution to sample from.
32		n_neighbours : int, default=10
33		The number of neighbours to sample and evaluate before moving to the best
34		of those neighbours.
35		repulsion_factor : float, default=5
36		The factor to control the repulsion of the hill climbing process.
37		n_iter : int, default=100
38		The number of iterations to run the optimizer.
39		verbose : bool, default=False
40		If True, print the progress of the optimization process.
41		experiment : BaseExperiment, optional
42		The experiment to optimize parameters for.
43		Optional, can be passed later via ``set_params``.
44
45		Examples
46		--------
47		Hill climbing applied to scikit-learn parameter tuning:
48
49		1. defining the experiment to optimize:
50		>>> from hyperactive.experiment.integrations import SklearnCvExperiment
51		>>> from sklearn.datasets import load_iris
52		>>> from sklearn.svm import SVC
53		>>>
54		>>> X, y = load_iris(return_X_y=True)
55		>>>
56		>>> sklearn_exp = SklearnCvExperiment(
57		... estimator=SVC(),
58		... X=X,
59		... y=y,
60		... )
61
62		2. setting up the hill climbing optimizer:
63		>>> from hyperactive.opt import RepulsingHillClimbing
64		>>> import numpy as np
65		>>>
66		>>> hc_config = {
67		... "search_space": {
68		... "C": np.array([0.01, 0.1, 1, 10]),
69		... "gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
70		... },
71		... "n_iter": 100,
72		... }
73		>>> hillclimbing = RepulsingHillClimbing(experiment=sklearn_exp, **hc_config)
74
75		3. running the hill climbing search:
76		>>> best_params = hillclimbing.run()
77
78		Best parameters can also be accessed via the attributes:
79		>>> best_params = hillclimbing.best_params_
80		"""
81
82		_tags = {
83		"info:name": "Repulsing Hill Climbing",
84		"info:local_vs_global": "mixed", # "local", "mixed", "global"
85		"info:explore_vs_exploit": "exploit", # "explore", "exploit", "mixed"
86		"info:compute": "low", # "low", "middle", "high"
87		}
88
89		def __init__(
90		self,
91		search_space=None,
92		initialize=None,
93		constraints=None,
94		random_state=None,
95		rand_rest_p=0.1,
96		epsilon=0.01,
97		distribution="normal",
98		n_neighbours=10,
99		repulsion_factor=5,
100		n_iter=100,
101		verbose=False,
102		experiment=None,
103		):
104		self.random_state = random_state
105		self.rand_rest_p = rand_rest_p
106		self.epsilon = epsilon
107		self.distribution = distribution
108		self.n_neighbours = n_neighbours
109		self.search_space = search_space
110		self.initialize = initialize
111		self.constraints = constraints
112		self.repulsion_factor = repulsion_factor
113		self.n_iter = n_iter
114		self.experiment = experiment
115		self.verbose = verbose
116
117		super().__init__()
118
119		def _get_gfo_class(self):
120		"""Get the GFO class to use.
121
122		Returns
123		-------
124		class
125		The GFO class to use. One of the concrete GFO classes
126		"""
127		from gradient_free_optimizers import RepulsingHillClimbingOptimizer
128
129		return RepulsingHillClimbingOptimizer
130
131		@classmethod
132		def get_test_params(cls, parameter_set="default"):
133		"""Get the test parameters for the optimizer.
134
135		Returns
136		-------
137		dict with str keys
138		The test parameters dictionary.
139		"""
140		import numpy as np
141
142		params = super().get_test_params()
143		more_params = {
144		"repulsion_factor": 7,
145		"search_space": {
146		"C": np.array([0.01, 0.1, 1, 10]),
147		"gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
148		},
149		"n_iter": 100,
150		}
151		params.update(more_params)
152		return params
153

		@@ 7-152 (lines=146) @@
4		from hyperactive.opt._adapters._gfo import _BaseGFOadapter
5
6
7		class StochasticHillClimbing(_BaseGFOadapter):
8		"""Stochastic hill climbing optimizer.
9
10		Parameters
11		----------
12		search_space : dict[str, list]
13		The search space to explore. A dictionary with parameter
14		names as keys and a numpy array as values.
15		Optional, can be passed later via ``set_params``.
16		initialize : dict[str, int], default={"grid": 4, "random": 2, "vertices": 4}
17		The method to generate initial positions. A dictionary with
18		the following key literals and the corresponding value type:
19		{"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
20		constraints : list[callable], default=[]
21		A list of constraints, where each constraint is a callable.
22		The callable returns `True` or `False` dependend on the input parameters.
23		random_state : None, int, default=None
24		If None, create a new random state. If int, create a new random state
25		seeded with the value.
26		rand_rest_p : float, default=0.1
27		The probability of a random iteration during the the search process.
28		epsilon : float, default=0.01
29		The step-size for the climbing.
30		distribution : str, default="normal"
31		The type of distribution to sample from.
32		n_neighbours : int, default=10
33		The number of neighbours to sample and evaluate before moving to the best
34		of those neighbours.
35		p_accept : float, default=0.5
36		The probability of accepting a transition in the hill climbing process.
37		n_iter : int, default=100
38		The number of iterations to run the optimizer.
39		verbose : bool, default=False
40		If True, print the progress of the optimization process.
41		experiment : BaseExperiment, optional
42		The experiment to optimize parameters for.
43		Optional, can be passed later via ``set_params``.
44
45		Examples
46		--------
47		Hill climbing applied to scikit-learn parameter tuning:
48
49		1. defining the experiment to optimize:
50		>>> from hyperactive.experiment.integrations import SklearnCvExperiment
51		>>> from sklearn.datasets import load_iris
52		>>> from sklearn.svm import SVC
53		>>>
54		>>> X, y = load_iris(return_X_y=True)
55		>>>
56		>>> sklearn_exp = SklearnCvExperiment(
57		... estimator=SVC(),
58		... X=X,
59		... y=y,
60		... )
61
62		2. setting up the hill climbing optimizer:
63		>>> from hyperactive.opt import StochasticHillClimbing
64		>>> import numpy as np
65		>>>
66		>>> hc_config = {
67		... "search_space": {
68		... "C": np.array([0.01, 0.1, 1, 10]),
69		... "gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
70		... },
71		... "n_iter": 100,
72		... }
73		>>> hillclimbing = StochasticHillClimbing(experiment=sklearn_exp, **hc_config)
74
75		3. running the hill climbing search:
76		>>> best_params = hillclimbing.run()
77
78		Best parameters can also be accessed via the attributes:
79		>>> best_params = hillclimbing.best_params_
80		"""
81
82		_tags = {
83		"info:name": "Hill Climbing",
84		"info:local_vs_global": "local", # "local", "mixed", "global"
85		"info:explore_vs_exploit": "exploit", # "explore", "exploit", "mixed"
86		"info:compute": "low", # "low", "middle", "high"
87		}
88
89		def __init__(
90		self,
91		search_space=None,
92		initialize=None,
93		constraints=None,
94		random_state=None,
95		rand_rest_p=0.1,
96		epsilon=0.01,
97		distribution="normal",
98		n_neighbours=10,
99		p_accept=0.5,
100		n_iter=100,
101		verbose=False,
102		experiment=None,
103		):
104		self.random_state = random_state
105		self.rand_rest_p = rand_rest_p
106		self.epsilon = epsilon
107		self.distribution = distribution
108		self.n_neighbours = n_neighbours
109		self.search_space = search_space
110		self.initialize = initialize
111		self.constraints = constraints
112		self.p_accept = p_accept
113		self.n_iter = n_iter
114		self.experiment = experiment
115		self.verbose = verbose
116
117		super().__init__()
118
119		def _get_gfo_class(self):
120		"""Get the GFO class to use.
121
122		Returns
123		-------
124		class
125		The GFO class to use. One of the concrete GFO classes
126		"""
127		from gradient_free_optimizers import StochasticHillClimbingOptimizer
128
129		return StochasticHillClimbingOptimizer
130
131		@classmethod
132		def get_test_params(cls, parameter_set="default"):
133		"""Get the test parameters for the optimizer.
134
135		Returns
136		-------
137		dict with str keys
138		The test parameters dictionary.
139		"""
140		import numpy as np
141
142		params = super().get_test_params()
143		more_params = {
144		"p_accept": 0.33,
145		"search_space": {
146		"C": np.array([0.01, 0.1, 1, 10]),
147		"gamma": np.array([0.0001, 0.01, 0.1, 1, 10]),
148		},
149		"n_iter": 100,
150		}
151		params.update(more_params)
152		return params
153

SimonBlanke / Hyperactive

Code Duplication Length = 146-146 lines in 2 locations

src/hyperactive/opt/hillclimbing/_repulsing.py 1 location

src/hyperactive/opt/hillclimbing/_stochastic.py 1 location