RegularizedCannonModel.__init__() - Code Metrics - Inspection of "Addressed ~13/~31 issues from the referee for pape..." - andycasey/AnniesLasso - Measure and Improve Code Quality continuously with Scrutinizer

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created 2016-04-25 15:23 UTC

RegularizedCannonModel.init() A

↳ Parent: RegularizedCannonModel

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
A regularized (compressed sensing) version of The Cannon.
"""

from __future__ import (division, print_function, absolute_import,
                        unicode_literals)

__all__ = ["RegularizedCannonModel"]

import logging
import numpy as np
import scipy.optimize as op

from . import (cannon, model, utils)

logger = logging.getLogger(__name__)


class RegularizedCannonModel(cannon.CannonModel):
    """
    A L1-regularized edition of The Cannon model for the estimation of arbitrary
    stellar labels.

    :param labels:
        A table with columns as labels, and stars as rows.

    :type labels:
        :class:`~astropy.table.Table` or numpy structured array

    :param fluxes:
        An array of fluxes for stars in the training set, given as shape
        `(num_stars, num_pixels)`. The `num_stars` should match the number of
        rows in `labels`.

    :type fluxes:
        :class:`np.ndarray`

    :param flux_uncertainties:
        An array of 1-sigma flux uncertainties for stars in the training set,
        The shape of the `flux_uncertainties` should match `fluxes`. 

    :type flux_uncertainties:
        :class:`np.ndarray`

    :param dispersion: [optional]
        The dispersion values corresponding to the given pixels. If provided, 
        this should have length `num_pixels`.

    :param live_dangerously: [optional]
        If enabled then no checks will be made on the label names, prohibiting
        the user to input human-readable forms of the label vector.
    """

    _descriptive_attributes = ["_label_vector", "_regularization"]
    
    def __init__(self, *args, **kwargs):
        super(RegularizedCannonModel, self).__init__(*args, **kwargs)


    @property
    def regularization(self):
        return self._regularization


    @regularization.setter
    def regularization(self, regularization):
        if regularization is None:
            self._regularization = None
            return None
        
        # Can be positive float, or positive values for all pixels.
        try:
            regularization = float(regularization)
        except (TypeError, ValueError):
            regularization = np.array(regularization).flatten()

            if regularization.size != len(self.dispersion):
                raise ValueError("regularization must be a positive value or "
                                 "an array of positive values for each pixel "
                                 "({0} != {1})".format(
                                    regularization.size,
                                    len(self.dispersion)))

            if any(0 > regularization) \
            or not np.all(np.isfinite(regularization)):
                raise ValueError("regularization terms must be "
                                 "positive and finite")
        else:
            if 0 > regularization or not np.isfinite(regularization):
                raise ValueError("regularization term must be "
                                 "positive and finite")
            regularization = np.ones_like(self.dispersion) * regularization
        self._regularization = regularization
        return None


    # windows to specify zero coefficients for a given label (or terms comprising)
    # that label.


    @model.requires_model_description
    def train(self, **kwargs):
        """
        Train the model based on the training set and the description of the
        label vector, and enforce regularization.
        """
        
        # Initialise the scatter and coefficient arrays.
        N_pixels = len(self.dispersion)
        scatter = np.nan * np.ones(N_pixels)
        label_vector_array = self.label_vector_array
        theta = np.nan * np.ones((N_pixels, label_vector_array.shape[0]))

        # Details for the progressbar.
        pb_kwds = {
            "message": "Training regularized Cannon model from {0} stars with "\
                       "{1} pixels each".format(
                           len(self.training_labels), N_pixels),
            "size": 100 if kwargs.pop("progressbar", True) else -1
        }
        
        if self.pool is None:
            for pixel in utils.progressbar(range(N_pixels), **pb_kwds):
                theta[pixel, :], scatter[pixel] = _fit_pixel(
                    self.training_fluxes[:, pixel], 
                    self.training_flux_uncertainties[:, pixel],
                    label_vector_array, self.regularization[pixel],
                    **kwargs)

        else:
            # Not as nice as just mapping, but necessary for a progress bar.
            process = { pixel: self.pool.apply_async(_fit_pixel, args=(
                    self.training_fluxes[:, pixel], 
                    self.training_flux_uncertainties[:, pixel],
                    label_vector_array, self.regularization[pixel]
                ), kwds=kwargs) \
                for pixel in range(N_pixels) }

            for pixel, proc in utils.progressbar(process.items(), **pb_kwds):
                theta[pixel, :], scatter[pixel] = proc.get()

        self.coefficients, self.scatter = (theta, scatter)
        self._trained = True

        return (theta, scatter)


    def conservative_cross_validation(self, **kwargs):
        """
        Perform conservative cross-validation using cyclic training, validation,
        and test subsets of the labelled data.
        """

        """
        Assign integer probabilities for each star.

        0: for choosing the regularization term.
        1-8 inclusive: training set.
        9: for prediction.
        """

        subset_index = np.random.randint(0, 10, size=len(self.training_labels))

        # Start with an initial value of the regularization.

        raise NotImplementedError



def _fit_pixel(fluxes, flux_uncertainties, label_vector_array, 

    regularization, **kwargs):
    """
    Return the optimal label vector coefficients and scatter for a pixel, given
    the fluxes, uncertainties, and the label vector array.

    :param fluxes:
        The fluxes for the given pixel, from all stars.

    :param flux_uncertainties:
        The 1-sigma flux uncertainties for the given pixel, from all stars.

    :param label_vector_array:
        The label vector array. This should have shape `(N_stars, N_terms + 1)`.

    :param regularization:
        The regularization term.

    :returns:
        The optimised label vector coefficients and scatter for this pixel.
    """

    _ = kwargs.get("max_uncertainty", 1)
    failed_response = (np.nan * np.ones(label_vector_array.shape[0]), _)
    if np.all(flux_uncertainties >= _):
        return failed_response

    # Get an initial guess of the scatter.
    scatter = np.var(fluxes) - np.median(flux_uncertainties)**2
    scatter = np.sqrt(scatter) if scatter >= 0 else np.std(fluxes)
    
    # Optimise the scatter, and at each scatter value we will calculate the
    # optimal vector coefficients.
    op_scatter, fopt, direc, n_iter, n_funcs, warnflag = op.fmin_powell(
        _pixel_scatter_nll, scatter,
        args=(fluxes, flux_uncertainties, label_vector_array, regularization),
        disp=False, full_output=True)

    if warnflag > 0:
        logger.warning("Warning: {}".format([
            "Maximum number of function evaluations made during optimisation.",
            "Maximum number of iterations made during optimisation."
            ][warnflag - 1]))

    # Calculate the coefficients at the optimal scatter value.
    # Note that if we can't solve for the coefficients, we should just set them
    # as zero and send back a giant variance.
    try:
        coefficients, ATCiAinv, variance = cannon._fit_coefficients(
            fluxes, flux_uncertainties, op_scatter, label_vector_array)

    except np.linalg.linalg.LinAlgError:
        logger.exception("Failed to calculate coefficients")
        if kwargs.get("debug", False): raise

        return failed_response

    else:
        return (coefficients, op_scatter)


def _pixel_scatter_nll(scatter, fluxes, flux_uncertainties, label_vector_array,
    regularization, **kwargs):
    """
    Return the negative log-likelihood for the scatter in a single pixel.

    :param scatter:
        The model scatter in the pixel.

    :param fluxes:
        The fluxes for a given pixel (in many stars).

    :param flux_uncertainties:
        The 1-sigma uncertainties in the fluxes for a given pixel. This should
        have the same shape as `fluxes`.

    :param label_vector_array:
        The label vector array for each star, for the given pixel.

    :param regularization:
        A regularization term.

    :returns:
        The log-likelihood of the log scatter, given the fluxes and the label
        vector array.

    :raises np.linalg.linalg.LinAlgError:
        If there was an error in inverting a matrix, and `debug` is set to True.
    """

    if 0 > scatter:
        return np.inf

    try:
        # Calculate the coefficients for the given level of scatter.
        theta, ATCiAinv, variance = cannon._fit_coefficients(
            fluxes, flux_uncertainties, scatter, label_vector_array)

    except np.linalg.linalg.LinAlgError:
        if kwargs.get("debug", False): raise
        return np.inf

    model = np.dot(theta, label_vector_array)
    
    return np.sum((fluxes - model)**2 / variance) \
        +  np.sum(np.log(variance)) \
        +  regularization * np.abs(theta).sum()


1		#!/usr/bin/env python
2		# -- coding: utf-8 --
3
4		"""
5		A regularized (compressed sensing) version of The Cannon.
6		"""
7
8		from __future__ import (division, print_function, absolute_import,
9		unicode_literals)
10
11		__all__ = ["RegularizedCannonModel"]
12
13		import logging
14		import numpy as np
15		import scipy.optimize as op
16
17		from . import (cannon, model, utils)
18
19		logger = logging.getLogger(__name__)
20
21
22		class RegularizedCannonModel(cannon.CannonModel):
23		"""
24		A L1-regularized edition of The Cannon model for the estimation of arbitrary
25		stellar labels.
26
27		:param labels:
28		A table with columns as labels, and stars as rows.
29
30		:type labels:
31		:class:`~astropy.table.Table` or numpy structured array
32
33		:param fluxes:
34		An array of fluxes for stars in the training set, given as shape
35		`(num_stars, num_pixels)`. The `num_stars` should match the number of
36		rows in `labels`.
37
38		:type fluxes:
39		:class:`np.ndarray`
40
41		:param flux_uncertainties:
42		An array of 1-sigma flux uncertainties for stars in the training set,
43		The shape of the `flux_uncertainties` should match `fluxes`.
44
45		:type flux_uncertainties:
46		:class:`np.ndarray`
47
48		:param dispersion: [optional]
49		The dispersion values corresponding to the given pixels. If provided,
50		this should have length `num_pixels`.
51
52		:param live_dangerously: [optional]
53		If enabled then no checks will be made on the label names, prohibiting
54		the user to input human-readable forms of the label vector.
55		"""
56
57		_descriptive_attributes = ["_label_vector", "_regularization"]
58
59		def __init__(self, args, *kwargs):
60		super(RegularizedCannonModel, self).__init__(args, *kwargs)
61
62
63		@property
64		def regularization(self):
65		return self._regularization
66
67
68		@regularization.setter
69		def regularization(self, regularization):
70		if regularization is None:
71		self._regularization = None
72		return None
73
74		# Can be positive float, or positive values for all pixels.
75		try:
76		regularization = float(regularization)
77		except (TypeError, ValueError):
78		regularization = np.array(regularization).flatten()
79
80		if regularization.size != len(self.dispersion):
81		raise ValueError("regularization must be a positive value or "
82		"an array of positive values for each pixel "
83		"({0} != {1})".format(
84		regularization.size,
85		len(self.dispersion)))
86
87		if any(0 > regularization) \
88		or not np.all(np.isfinite(regularization)):
89		raise ValueError("regularization terms must be "
90		"positive and finite")
91		else:
92		if 0 > regularization or not np.isfinite(regularization):
93		raise ValueError("regularization term must be "
94		"positive and finite")
95		regularization = np.ones_like(self.dispersion) * regularization
96		self._regularization = regularization
97		return None
98
99
100		# windows to specify zero coefficients for a given label (or terms comprising)
101		# that label.
102
103
104		@model.requires_model_description
105		def train(self, **kwargs):
106		"""
107		Train the model based on the training set and the description of the
108		label vector, and enforce regularization.
109		"""
110
111		# Initialise the scatter and coefficient arrays.
112		N_pixels = len(self.dispersion)
113		scatter = np.nan * np.ones(N_pixels)
114		label_vector_array = self.label_vector_array
115		theta = np.nan * np.ones((N_pixels, label_vector_array.shape[0]))
116
117		# Details for the progressbar.
118		pb_kwds = {
119		"message": "Training regularized Cannon model from {0} stars with "\
120		"{1} pixels each".format(
121		len(self.training_labels), N_pixels),
122		"size": 100 if kwargs.pop("progressbar", True) else -1
123		}
124
125		if self.pool is None:
126		for pixel in utils.progressbar(range(N_pixels), **pb_kwds):
127		theta[pixel, :], scatter[pixel] = _fit_pixel(
128		self.training_fluxes[:, pixel],
129		self.training_flux_uncertainties[:, pixel],
130		label_vector_array, self.regularization[pixel],
131		**kwargs)
132
133		else:
134		# Not as nice as just mapping, but necessary for a progress bar.
135		process = { pixel: self.pool.apply_async(_fit_pixel, args=(
136		self.training_fluxes[:, pixel],
137		self.training_flux_uncertainties[:, pixel],
138		label_vector_array, self.regularization[pixel]
139		), kwds=kwargs) \
140		for pixel in range(N_pixels) }
141
142		for pixel, proc in utils.progressbar(process.items(), **pb_kwds):
143		theta[pixel, :], scatter[pixel] = proc.get()
144
145		self.coefficients, self.scatter = (theta, scatter)
146		self._trained = True
147
148		return (theta, scatter)
149
150
151		def conservative_cross_validation(self, **kwargs):
152		"""
153		Perform conservative cross-validation using cyclic training, validation,
154		and test subsets of the labelled data.
155		"""
156
157		"""
158		Assign integer probabilities for each star.
159
160		0: for choosing the regularization term.
161		1-8 inclusive: training set.
162		9: for prediction.
163		"""
164
165		subset_index = np.random.randint(0, 10, size=len(self.training_labels))
166
167		# Start with an initial value of the regularization.
168
169		raise NotImplementedError
170
171
172
173	View Code Duplication	def _fit_pixel(fluxes, flux_uncertainties, label_vector_array,
		0 ignored issues – show Duplication introduced 2016-04-25 15:24 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
174		regularization, **kwargs):
175		"""
176		Return the optimal label vector coefficients and scatter for a pixel, given
177		the fluxes, uncertainties, and the label vector array.
178
179		:param fluxes:
180		The fluxes for the given pixel, from all stars.
181
182		:param flux_uncertainties:
183		The 1-sigma flux uncertainties for the given pixel, from all stars.
184
185		:param label_vector_array:
186		The label vector array. This should have shape `(N_stars, N_terms + 1)`.
187
188		:param regularization:
189		The regularization term.
190
191		:returns:
192		The optimised label vector coefficients and scatter for this pixel.
193		"""
194
195		_ = kwargs.get("max_uncertainty", 1)
196		failed_response = (np.nan * np.ones(label_vector_array.shape[0]), _)
197		if np.all(flux_uncertainties >= _):
198		return failed_response
199
200		# Get an initial guess of the scatter.
201		scatter = np.var(fluxes) - np.median(flux_uncertainties)**2
202		scatter = np.sqrt(scatter) if scatter >= 0 else np.std(fluxes)
203
204		# Optimise the scatter, and at each scatter value we will calculate the
205		# optimal vector coefficients.
206		op_scatter, fopt, direc, n_iter, n_funcs, warnflag = op.fmin_powell(
207		_pixel_scatter_nll, scatter,
208		args=(fluxes, flux_uncertainties, label_vector_array, regularization),
209		disp=False, full_output=True)
210
211		if warnflag > 0:
212		logger.warning("Warning: {}".format([
213		"Maximum number of function evaluations made during optimisation.",
214		"Maximum number of iterations made during optimisation."
215		][warnflag - 1]))
216
217		# Calculate the coefficients at the optimal scatter value.
218		# Note that if we can't solve for the coefficients, we should just set them
219		# as zero and send back a giant variance.
220		try:
221		coefficients, ATCiAinv, variance = cannon._fit_coefficients(
222		fluxes, flux_uncertainties, op_scatter, label_vector_array)
223
224		except np.linalg.linalg.LinAlgError:
225		logger.exception("Failed to calculate coefficients")
226		if kwargs.get("debug", False): raise
227
228		return failed_response
229
230		else:
231		return (coefficients, op_scatter)
232
233
234		def _pixel_scatter_nll(scatter, fluxes, flux_uncertainties, label_vector_array,
235		regularization, **kwargs):
236		"""
237		Return the negative log-likelihood for the scatter in a single pixel.
238
239		:param scatter:
240		The model scatter in the pixel.
241
242		:param fluxes:
243		The fluxes for a given pixel (in many stars).
244
245		:param flux_uncertainties:
246		The 1-sigma uncertainties in the fluxes for a given pixel. This should
247		have the same shape as `fluxes`.
248
249		:param label_vector_array:
250		The label vector array for each star, for the given pixel.
251
252		:param regularization:
253		A regularization term.
254
255		:returns:
256		The log-likelihood of the log scatter, given the fluxes and the label
257		vector array.
258
259		:raises np.linalg.linalg.LinAlgError:
260		If there was an error in inverting a matrix, and `debug` is set to True.
261		"""
262
263		if 0 > scatter:
264		return np.inf
265
266		try:
267		# Calculate the coefficients for the given level of scatter.
268		theta, ATCiAinv, variance = cannon._fit_coefficients(
269		fluxes, flux_uncertainties, scatter, label_vector_array)
270
271		except np.linalg.linalg.LinAlgError:
272		if kwargs.get("debug", False): raise
273		return np.inf
274
275		model = np.dot(theta, label_vector_array)
276
277		return np.sum((fluxes - model)**2 / variance) \
278		+ np.sum(np.log(variance)) \
279		+ regularization * np.abs(theta).sum()
280

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