4960685499

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Merge 7b7c1d7ba into d1de71ffa

Pull Request Pull Request #34: Preparation for JOSS submission

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/coolest/template/classes/parameter.py

# Single parameter of a profile

from typing import List

from coolest.template.classes.base import APIBaseObject
from coolest.template.classes.probabilities import Prior, PosteriorStatistics
from coolest.template.classes.grid import PixelatedRegularGrid, IrregularGrid

import numpy as np


__all__ = [
    'Parameter',
    'NonLinearParameter', 
    'LinearParameter', 
    'HyperParameter',
    'LinearParameterSet',
    'NonLinearParameterSet',
    'PixelatedRegularGridParameter',
    'IrregularGridParameter',
]


class DefinitionRange(APIBaseObject):
    """Defines the interval over which a parameter is defined.

    Parameters
    ----------
    min_value : (int, float), optional
        Lower bound of the interval (inclusive), by default None
    max_value : (int, float), optional
        Upper bound of the interval (inclusive), by default None
    """
    
    def __init__(self, min_value=None, max_value=None):
        self.min_value = min_value
        self.max_value = max_value


class PointEstimate(APIBaseObject):
    """Define a point in the parameter space.

    Parameters
    ----------
    value : float, optional
        Value of the parameter, by default None
    """
    
    def __init__(self, value=None):
        self.value = value


class Parameter(APIBaseObject):
    """Base class of a generic model parameter.

    Parameters
    ----------
    documentation : str
        Short description of the parameter.
    definition_range : DefinitionRange, optional
        Interval over which the parameter is defined, by default None
    units : str, optional
        Unit of the parameter, if any, by default None
    fixed : bool, optional
        If True, the parameter is considered fixed 
        (i.e. should not be, or has not be optimized), by default False
    point_estimate : PointEstimate, optional
        Point-estimate value of the parameter, by default None
    posterior_stats : PosteriorStatistics, optional
        Summary statistics of the marginalized posterior 
        distribution of the parameter, by default None
    prior : Prior, optional
        Prior assigned the parameter, if any, by default None
    latex_str : str, optional
        LaTeX representation of the parameter, by default None
    """
    def __init__(self, 
                 documentation: str, 
                 definition_range: DefinitionRange = None,
                 units: str = None,
                 fixed: bool = False,
                 point_estimate: PointEstimate = None,
                 posterior_stats: PosteriorStatistics = None,
                 prior: Prior = None,
                 latex_str: str = None) -> None:
        self.documentation = documentation
        self.units = units
        self.definition_range = definition_range
        self.fixed = fixed
        if not isinstance(point_estimate, PointEstimate):
            self.point_estimate = PointEstimate(point_estimate)
        else:
            self.point_estimate = point_estimate
        if posterior_stats is None:
            posterior_stats = PosteriorStatistics()
        self.posterior_stats = posterior_stats
        if prior is None:
            prior = Prior()
        self.prior = prior
        self.latex_str = latex_str
        self.id = None
        super().__init__()
        
    def set_point_estimate(self, point_estimate):
        """Set the point estimate value of the parameter.

        Parameters
        ----------
        point_estimate : int, float, list, PointEstimate
            Parameter value, or directly a PointEstimate instance.

        Raises
        ------
        ValueError
            If the provided point_estimate has not a supported type.
        ValueError
            If the parameter value is below its minimum allowed value.
        ValueError
            If the parameter value is above its maximum allowed value.
        """
        if isinstance(point_estimate, (float, int, list)):
            self.point_estimate = PointEstimate(value=point_estimate)
        elif isinstance(point_estimate, tuple):
            self.point_estimate = PointEstimate(value=list(point_estimate))
        elif isinstance(point_estimate, np.ndarray):
            self.point_estimate = PointEstimate(value=point_estimate.tolist())
        elif isinstance(point_estimate, PointEstimate):
            self.point_estimate = point_estimate
        else:
            raise ValueError("Parameter prior must be either a PointEstimate instance "
                             "or a single number (float or int) or an array (tuple, list or ndarray).")
        if self.point_estimate.value is not None:
            val = self.point_estimate.value
            min_val = self.definition_range.min_value
            max_val = self.definition_range.max_value
            if min_val is not None and np.any(np.asarray(val) < np.asarray(min_val)):
                raise ValueError(f"Value cannot be smaller than {self.definition_range.min_value}.")
            if max_val is not None and np.any(np.asarray(val) > np.asarray(max_val)):
                raise ValueError(f"Value cannot be larger than {self.definition_range.max_value}.")

    def remove_point_estimate(self):
        """Remove the current point estimate of the parameter.
        """
        self.point_estimate = PointEstimate()

    def set_posterior(self, posterior_stats):
        """Set the posterior statistics of the parameter.

        Parameters
        ----------
        posterior_stats : PosteriorStatistics
            Instance of the PosteriorStatistics object.

        Raises
        ------
        ValueError
            If the argument is not a PosteriorStatistics instance.
        """
        if not isinstance(posterior_stats, PosteriorStatistics):
            raise ValueError("Parameter prior must be a PosteriorStatistics instance.")
        self.posterior_stats = posterior_stats

    def remove_posterior(self):
        """Remove the current posterior statistics of the parameter.
        """
        self.posterior_stats = PosteriorStatistics()

    def set_prior(self, prior):
        """Associate a prior distribution to the parameter.

        Parameters
        ----------
        prior : Prior
            Instance of Prior object.

        Raises
        ------
        ValueError
            If the argument is not a Prior instance.
        """
        if not isinstance(prior, Prior):
            raise ValueError("Parameter prior must be a Prior instance.")
        self.prior = prior

    def remove_prior(self):
        """Remove the current posterior statistics of the parameter.
        """
        self.prior = Prior()

    def fix(self):
        """Set the fixed attribute to True, marking it as fixed."""
        if self.point_estimate.value is None:
            raise ValueError("Cannot fix parameter as no point estimate value has been set.")
        self.fixed = True

    def unfix(self):
        """Set the fixed attribute to False, marking as free to vary"""
        self.fixed = False


class NonLinearParameter(Parameter):
    """Define a non-linear parameter of a lens model
    
    Warning: this class may be removed in the future.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)


class LinearParameter(Parameter):
    """Define a hyper-parameter of a lens model
    
    Warning: this class may be removed in the future, as it has adds unncessary abstraction level.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)


class HyperParameter(Parameter):
    """Define a hyper-parameter of a model"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)        


class ParameterSet(Parameter):
    """Typically for analytical basis sets"""

    def __init__(self, *args, **kwargs) -> None:
        if 'point_estimate' not in kwargs or kwargs['point_estimate'] is None:
            kwargs['point_estimate'] = []
        if not isinstance(kwargs['point_estimate'], list):
            raise ValueError("For any ParameterSet, `point_estimate` must be a list of values.")
        super().__init__(*args, **kwargs)
        self.num_values = len(self.point_estimate.value)


class LinearParameterSet(ParameterSet):
    """Typically for analytical basis sets.

    Warning: this class may be removed in the future, as it has adds unncessary abstraction level.
    """

    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        
        
class NonLinearParameterSet(ParameterSet):
    """Typically for position of point sources.
    
    Warning: this class may be removed in the future, as it has adds unncessary abstraction level."""

    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)


class PixelatedRegularGridParameter(PixelatedRegularGrid):
    """Typically for pixelated profiles"""

    def __init__(self, documentation, **kwargs_grid) -> None:
        self.documentation = documentation
        super().__init__(**kwargs_grid)


class IrregularGridParameter(IrregularGrid):
    """Typically for pixelated profiles"""

    def __init__(self, documentation, **kwargs_grid) -> None:
        self.documentation = documentation
        super().__init__(**kwargs_grid)
        

1	# Single parameter of a profile
2
3	from typing import List	1✔
4
5	from coolest.template.classes.base import APIBaseObject	1✔
6	from coolest.template.classes.probabilities import Prior, PosteriorStatistics	1✔
7	from coolest.template.classes.grid import PixelatedRegularGrid, IrregularGrid	1✔
8
9	import numpy as np	1✔
10
11
12	__all__ = [	1✔
13	'Parameter',
14	'NonLinearParameter',
15	'LinearParameter',
16	'HyperParameter',
17	'LinearParameterSet',
18	'NonLinearParameterSet',
19	'PixelatedRegularGridParameter',
20	'IrregularGridParameter',
21	]
22
23
24	class DefinitionRange(APIBaseObject):	1✔
25	"""Defines the interval over which a parameter is defined.	1✔
26
27	Parameters
28	----------
29	min_value : (int, float), optional
30	Lower bound of the interval (inclusive), by default None
31	max_value : (int, float), optional
32	Upper bound of the interval (inclusive), by default None
33	"""
34
35	def __init__(self, min_value=None, max_value=None):	1✔
36	self.min_value = min_value	1✔
37	self.max_value = max_value	1✔
38
39
40	class PointEstimate(APIBaseObject):	1✔
41	"""Define a point in the parameter space.	1✔
42
43	Parameters
44	----------
45	value : float, optional
46	Value of the parameter, by default None
47	"""
48
49	def __init__(self, value=None):	1✔
50	self.value = value	1✔
51
52
53	class Parameter(APIBaseObject):	1✔
54	"""Base class of a generic model parameter.	1✔
55
56	Parameters
57	----------
58	documentation : str
59	Short description of the parameter.
60	definition_range : DefinitionRange, optional
61	Interval over which the parameter is defined, by default None
62	units : str, optional
63	Unit of the parameter, if any, by default None
64	fixed : bool, optional
65	If True, the parameter is considered fixed
66	(i.e. should not be, or has not be optimized), by default False
67	point_estimate : PointEstimate, optional
68	Point-estimate value of the parameter, by default None
69	posterior_stats : PosteriorStatistics, optional
70	Summary statistics of the marginalized posterior
71	distribution of the parameter, by default None
72	prior : Prior, optional
73	Prior assigned the parameter, if any, by default None
74	latex_str : str, optional
75	LaTeX representation of the parameter, by default None
76	"""
77	def __init__(self,	1✔
78	documentation: str,
79	definition_range: DefinitionRange = None,
80	units: str = None,
81	fixed: bool = False,
82	point_estimate: PointEstimate = None,
83	posterior_stats: PosteriorStatistics = None,
84	prior: Prior = None,
85	latex_str: str = None) -> None:
86	self.documentation = documentation	1✔
87	self.units = units	1✔
88	self.definition_range = definition_range	1✔
89	self.fixed = fixed	1✔
90	if not isinstance(point_estimate, PointEstimate):	1✔
91	self.point_estimate = PointEstimate(point_estimate)	1✔
92	else:
93	self.point_estimate = point_estimate	×
94	if posterior_stats is None:	1✔
95	posterior_stats = PosteriorStatistics()	1✔
96	self.posterior_stats = posterior_stats	1✔
97	if prior is None:	1✔
98	prior = Prior()	1✔
99	self.prior = prior	1✔
100	self.latex_str = latex_str	1✔
101	self.id = None	1✔
102	super().__init__()	1✔
103
104	def set_point_estimate(self, point_estimate):	1✔
105	"""Set the point estimate value of the parameter.
106
107	Parameters
108	----------
109	point_estimate : int, float, list, PointEstimate
110	Parameter value, or directly a PointEstimate instance.
111
112	Raises
113	------
114	ValueError
115	If the provided point_estimate has not a supported type.
116	ValueError
117	If the parameter value is below its minimum allowed value.
118	ValueError
119	If the parameter value is above its maximum allowed value.
120	"""
121	if isinstance(point_estimate, (float, int, list)):	1✔
122	self.point_estimate = PointEstimate(value=point_estimate)	1✔
123	elif isinstance(point_estimate, tuple):	1✔
124	self.point_estimate = PointEstimate(value=list(point_estimate))	×
125	elif isinstance(point_estimate, np.ndarray):	1✔
126	self.point_estimate = PointEstimate(value=point_estimate.tolist())	×
127	elif isinstance(point_estimate, PointEstimate):	1✔
128	self.point_estimate = point_estimate	1✔
129	else:
130	raise ValueError("Parameter prior must be either a PointEstimate instance "	×
131	"or a single number (float or int) or an array (tuple, list or ndarray).")
132	if self.point_estimate.value is not None:	1✔
133	val = self.point_estimate.value	1✔
134	min_val = self.definition_range.min_value	1✔
135	max_val = self.definition_range.max_value	1✔
136	if min_val is not None and np.any(np.asarray(val) < np.asarray(min_val)):	1✔
137	raise ValueError(f"Value cannot be smaller than {self.definition_range.min_value}.")	×
138	if max_val is not None and np.any(np.asarray(val) > np.asarray(max_val)):	1✔
139	raise ValueError(f"Value cannot be larger than {self.definition_range.max_value}.")	×
140
141	def remove_point_estimate(self):	1✔
142	"""Remove the current point estimate of the parameter.
143	"""
144	self.point_estimate = PointEstimate()	×
145
146	def set_posterior(self, posterior_stats):	1✔
147	"""Set the posterior statistics of the parameter.
148
149	Parameters
150	----------
151	posterior_stats : PosteriorStatistics
152	Instance of the PosteriorStatistics object.
153
154	Raises
155	------
156	ValueError
157	If the argument is not a PosteriorStatistics instance.
158	"""
159	if not isinstance(posterior_stats, PosteriorStatistics):	1✔
160	raise ValueError("Parameter prior must be a PosteriorStatistics instance.")	×
161	self.posterior_stats = posterior_stats	1✔
162
163	def remove_posterior(self):	1✔
164	"""Remove the current posterior statistics of the parameter.
165	"""
166	self.posterior_stats = PosteriorStatistics()	×
167
168	def set_prior(self, prior):	1✔
169	"""Associate a prior distribution to the parameter.
170
171	Parameters
172	----------
173	prior : Prior
174	Instance of Prior object.
175
176	Raises
177	------
178	ValueError
179	If the argument is not a Prior instance.
180	"""
181	if not isinstance(prior, Prior):	1✔
182	raise ValueError("Parameter prior must be a Prior instance.")	×
183	self.prior = prior	1✔
184
185	def remove_prior(self):	1✔
186	"""Remove the current posterior statistics of the parameter.
187	"""
188	self.prior = Prior()	×
189
190	def fix(self):	1✔
191	"""Set the fixed attribute to True, marking it as fixed."""
192	if self.point_estimate.value is None:	×
193	raise ValueError("Cannot fix parameter as no point estimate value has been set.")	×
194	self.fixed = True	×
195
196	def unfix(self):	1✔
197	"""Set the fixed attribute to False, marking as free to vary"""
198	self.fixed = False	×
199
200
201	class NonLinearParameter(Parameter):	1✔
202	"""Define a non-linear parameter of a lens model	1✔
203
204	Warning: this class may be removed in the future.
205	"""
206
207	def __init__(self, args, *kwargs):	1✔
208	super().__init__(args, *kwargs)	1✔
209
210
211	class LinearParameter(Parameter):	1✔
212	"""Define a hyper-parameter of a lens model	1✔
213
214	Warning: this class may be removed in the future, as it has adds unncessary abstraction level.
215	"""
216
217	def __init__(self, args, *kwargs):	1✔
218	super().__init__(args, *kwargs)	1✔
219
220
221	class HyperParameter(Parameter):	1✔
222	"""Define a hyper-parameter of a model"""	1✔
223
224	def __init__(self, args, *kwargs):	1✔
225	super().__init__(args, *kwargs)	×
226
227
228	class ParameterSet(Parameter):	1✔
229	"""Typically for analytical basis sets"""	1✔
230
231	def __init__(self, args, *kwargs) -> None:	1✔
232	if 'point_estimate' not in kwargs or kwargs['point_estimate'] is None:	1✔
233	kwargs['point_estimate'] = []	1✔
234	if not isinstance(kwargs['point_estimate'], list):	1✔
235	raise ValueError("For any ParameterSet, `point_estimate` must be a list of values.")	×
236	super().__init__(args, *kwargs)	1✔
237	self.num_values = len(self.point_estimate.value)	1✔
238
239
240	class LinearParameterSet(ParameterSet):	1✔
241	"""Typically for analytical basis sets.	1✔
242
243	Warning: this class may be removed in the future, as it has adds unncessary abstraction level.
244	"""
245
246	def __init__(self, args, *kwargs) -> None:	1✔
247	super().__init__(args, *kwargs)	1✔
248
249
250	class NonLinearParameterSet(ParameterSet):	1✔
251	"""Typically for position of point sources.	1✔
252
253	Warning: this class may be removed in the future, as it has adds unncessary abstraction level."""
254
255	def __init__(self, args, *kwargs) -> None:	1✔
256	super().__init__(args, *kwargs)	×
257
258
259	class PixelatedRegularGridParameter(PixelatedRegularGrid):	1✔
260	"""Typically for pixelated profiles"""	1✔
261
262	def __init__(self, documentation, **kwargs_grid) -> None:	1✔
263	self.documentation = documentation	1✔
264	super().__init__(**kwargs_grid)	1✔
265
266
267	class IrregularGridParameter(IrregularGrid):	1✔
268	"""Typically for pixelated profiles"""	1✔
269
270	def __init__(self, documentation, **kwargs_grid) -> None:	1✔
271	self.documentation = documentation	1✔
272	super().__init__(**kwargs_grid)	1✔
273

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