import numpy as np

from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raises

from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.decomposition import RandomizedPCA
from sklearn.decomposition.pca import _assess_dimension_
from sklearn.decomposition.pca import _infer_dimension_

iris = datasets.load_iris()

def test_pca():
    # PCA on dense arrays
    pca = PCA(n_components=2)
    X = iris.data
    X_r = pca.fit(X).transform(X)
    np.testing.assert_equal(X_r.shape[1], 2)

    X_r2 = pca.fit_transform(X)
    assert_array_almost_equal(X_r, X_r2)

    pca = PCA()
    pca.fit(X)
    assert_almost_equal(pca.explained_variance_ratio_.sum(), 1.0, 3)

    X_r = pca.transform(X)
    X_r2 = pca.fit_transform(X)

    assert_array_almost_equal(X_r, X_r2)

    # Test get_covariance and get_precision with n_components == n_features
    # with n_components < n_features and with n_components == 0
    for n_components in [0, 2, X.shape[1]]:
        pca.n_components = n_components
        pca.fit(X)
        cov = pca.get_covariance()
        precision = pca.get_precision()
        assert_array_almost_equal(np.dot(cov, precision),
                                  np.eye(X.shape[1]), 12)

def test_whitening():
    # Check that PCA output has unit-variance
    rng = np.random.RandomState(0)
    n_samples = 100
    n_features = 80
    n_components = 30
    rank = 50

    # some low rank data with correlated features
    X = np.dot(rng.randn(n_samples, rank),
               np.dot(np.diag(np.linspace(10.0, 1.0, rank)),
                      rng.randn(rank, n_features)))
    # the component-wise variance of the first 50 features is 3 times the
    # mean component-wise variance of the remaingin 30 features
    X[:, :50] *= 3

    assert_equal(X.shape, (n_samples, n_features))

    # the component-wise variance is thus highly varying:
    assert_almost_equal(X.std(axis=0).std(), 43.9, 1)

    for this_PCA, copy in [(x, y) for x in (PCA, RandomizedPCA)
                           for y in (True, False)]:
        # whiten the data while projecting to the lower dim subspace
        X_ = X.copy()  # make sure we keep an original across iterations.
        pca = this_PCA(n_components=n_components, whiten=True, copy=copy)
        # test fit_transform
        X_whitened = pca.fit_transform(X_.copy())
        assert_equal(X_whitened.shape, (n_samples, n_components))
        X_whitened2 = pca.transform(X_)
        assert_array_almost_equal(X_whitened, X_whitened2)

        assert_almost_equal(X_whitened.std(axis=0), np.ones(n_components))
        assert_almost_equal(X_whitened.mean(axis=0), np.zeros(n_components))

        X_ = X.copy()
        pca = this_PCA(n_components=n_components, whiten=False,
                       copy=copy).fit(X_)
        X_unwhitened = pca.transform(X_)
        assert_equal(X_unwhitened.shape, (n_samples, n_components))

        # in that case the output components still have varying variances
        assert_almost_equal(X_unwhitened.std(axis=0).std(), 74.1, 1)
        # we always center, so no test for non-centering.

def test_explained_variance():
    # Check that PCA output has unit-variance
    rng = np.random.RandomState(0)
    n_samples = 100
    n_features = 80

    X = rng.randn(n_samples, n_features)

    pca = PCA(n_components=2).fit(X)
    rpca = RandomizedPCA(n_components=2, random_state=42).fit(X)
    assert_array_almost_equal(pca.explained_variance_,
                              rpca.explained_variance_, 1)
    assert_array_almost_equal(pca.explained_variance_ratio_,
                              rpca.explained_variance_ratio_, 3)

    # compare to empirical variances
    X_pca = pca.transform(X)
    assert_array_almost_equal(pca.explained_variance_,
                              np.var(X_pca, axis=0))

    X_rpca = rpca.transform(X)
    assert_array_almost_equal(rpca.explained_variance_,
                              np.var(X_rpca, axis=0))

def test_pca_check_projection():
    # Test that the projection of data is correct
    rng = np.random.RandomState(0)
    n, p = 100, 3
    X = rng.randn(n, p) * .1
    X[:10] += np.array([3, 4, 5])
    Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])

    Yt = PCA(n_components=2).fit(X).transform(Xt)
    Yt /= np.sqrt((Yt ** 2).sum())

    assert_almost_equal(np.abs(Yt[0][0]), 1., 1)

def test_pca_inverse():
    # Test that the projection of data can be inverted
    rng = np.random.RandomState(0)
    n, p = 50, 3
    X = rng.randn(n, p)  # spherical data
    X[:, 1] *= .00001  # make middle component relatively small
    X += [5, 4, 3]  # make a large mean

    # same check that we can find the original data from the transformed
    # signal (since the data is almost of rank n_components)
    pca = PCA(n_components=2).fit(X)
    Y = pca.transform(X)
    Y_inverse = pca.inverse_transform(Y)
    assert_almost_equal(X, Y_inverse, decimal=3)

    # same as above with whitening (approximate reconstruction)
    pca = PCA(n_components=2, whiten=True)
    pca.fit(X)
    Y = pca.transform(X)
    Y_inverse = pca.inverse_transform(Y)
    assert_almost_equal(X, Y_inverse, decimal=3)

def test_pca_validation():
    X = [[0, 1], [1, 0]]
    for n_components in [-1, 3]:
        assert_raises(ValueError, PCA(n_components).fit, X)

def test_randomized_pca_check_projection():
    # Test that the projection by RandomizedPCA on dense data is correct
    rng = np.random.RandomState(0)
    n, p = 100, 3
    X = rng.randn(n, p) * .1
    X[:10] += np.array([3, 4, 5])
    Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])

    Yt = RandomizedPCA(n_components=2, random_state=0).fit(X).transform(Xt)
    Yt /= np.sqrt((Yt ** 2).sum())

    assert_almost_equal(np.abs(Yt[0][0]), 1., 1)

def test_randomized_pca_check_list():
    # Test that the projection by RandomizedPCA on list data is correct
    X = [[1.0, 0.0], [0.0, 1.0]]
    X_transformed = RandomizedPCA(n_components=1,
                                  random_state=0).fit(X).transform(X)
    assert_equal(X_transformed.shape, (2, 1))
    assert_almost_equal(X_transformed.mean(), 0.00, 2)
    assert_almost_equal(X_transformed.std(), 0.71, 2)

def test_randomized_pca_inverse():
    # Test that RandomizedPCA is inversible on dense data
    rng = np.random.RandomState(0)
    n, p = 50, 3
    X = rng.randn(n, p)  # spherical data
    X[:, 1] *= .00001  # make middle component relatively small
    X += [5, 4, 3]  # make a large mean

    # same check that we can find the original data from the transformed signal
    # (since the data is almost of rank n_components)
    pca = RandomizedPCA(n_components=2, random_state=0).fit(X)
    Y = pca.transform(X)
    Y_inverse = pca.inverse_transform(Y)
    assert_almost_equal(X, Y_inverse, decimal=2)

    # same as above with whitening (approximate reconstruction)
    pca = RandomizedPCA(n_components=2, whiten=True,
                        random_state=0).fit(X)
    Y = pca.transform(X)
    Y_inverse = pca.inverse_transform(Y)
    relative_max_delta = (np.abs(X - Y_inverse) / np.abs(X).mean()).max()
    assert_almost_equal(relative_max_delta, 0.11, decimal=2)

def test_pca_dim():
    # Check automated dimensionality setting
    rng = np.random.RandomState(0)
    n, p = 100, 5
    X = rng.randn(n, p) * .1
    X[:10] += np.array([3, 4, 5, 1, 2])
    pca = PCA(n_components='mle').fit(X)
    assert_equal(pca.n_components, 'mle')
    assert_equal(pca.n_components_, 1)

def test_infer_dim_1():
    # TODO: explain what this is testing
    # Or at least use explicit variable names...
    n, p = 1000, 5
    rng = np.random.RandomState(0)
    X = (rng.randn(n, p) * .1 + rng.randn(n, 1) * np.array([3, 4, 5, 1, 2])
         + np.array([1, 0, 7, 4, 6]))
    pca = PCA(n_components=p)
    pca.fit(X)
    spect = pca.explained_variance_
    ll = []
    for k in range(p):
        ll.append(_assess_dimension_(spect, k, n, p))
    ll = np.array(ll)
    assert_greater(ll[1], ll.max() - .01 * n)

def test_infer_dim_2():
    # TODO: explain what this is testing
    # Or at least use explicit variable names...
    n, p = 1000, 5
    rng = np.random.RandomState(0)
    X = rng.randn(n, p) * .1
    X[:10] += np.array([3, 4, 5, 1, 2])
    X[10:20] += np.array([6, 0, 7, 2, -1])
    pca = PCA(n_components=p)
    pca.fit(X)
    spect = pca.explained_variance_
    assert_greater(_infer_dimension_(spect, n, p), 1)

def test_infer_dim_3():
    n, p = 100, 5
    rng = np.random.RandomState(0)
    X = rng.randn(n, p) * .1
    X[:10] += np.array([3, 4, 5, 1, 2])
    X[10:20] += np.array([6, 0, 7, 2, -1])
    X[30:40] += 2 * np.array([-1, 1, -1, 1, -1])
    pca = PCA(n_components=p)
    pca.fit(X)
    spect = pca.explained_variance_
    assert_greater(_infer_dimension_(spect, n, p), 2)

def test_infer_dim_by_explained_variance():
    X = iris.data
    pca = PCA(n_components=0.95)
    pca.fit(X)
    assert_equal(pca.n_components, 0.95)
    assert_equal(pca.n_components_, 2)

    pca = PCA(n_components=0.01)
    pca.fit(X)
    assert_equal(pca.n_components, 0.01)
    assert_equal(pca.n_components_, 1)

    rng = np.random.RandomState(0)
    # more features than samples
    X = rng.rand(5, 20)
    pca = PCA(n_components=.5).fit(X)
    assert_equal(pca.n_components, 0.5)
    assert_equal(pca.n_components_, 2)

def test_pca_score():
    # Test that probabilistic PCA scoring yields a reasonable score
    n, p = 1000, 3
    rng = np.random.RandomState(0)
    X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
    pca = PCA(n_components=2)
    pca.fit(X)
    ll1 = pca.score(X)
    h = -0.5 * np.log(2 * np.pi * np.exp(1) * 0.1 ** 2) * p
    np.testing.assert_almost_equal(ll1 / h, 1, 0)

def test_pca_score2():
    # Test that probabilistic PCA correctly separated different datasets
    n, p = 100, 3
    rng = np.random.RandomState(0)
    X = rng.randn(n, p) * .1 + np.array([3, 4, 5])
    pca = PCA(n_components=2)
    pca.fit(X)
    ll1 = pca.score(X)
    ll2 = pca.score(rng.randn(n, p) * .2 + np.array([3, 4, 5]))
    assert_greater(ll1, ll2)

    # Test that it gives the same scores if whiten=True
    pca = PCA(n_components=2, whiten=True)
    pca.fit(X)
    ll2 = pca.score(X)
    assert_almost_equal(ll1, ll2)

def test_pca_score3():
    # Check that probabilistic PCA selects the right model
    n, p = 200, 3
    rng = np.random.RandomState(0)
    Xl = (rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5])
          + np.array([1, 0, 7]))
    Xt = (rng.randn(n, p) + rng.randn(n, 1) * np.array([3, 4, 5])
          + np.array([1, 0, 7]))
    ll = np.zeros(p)
    for k in range(p):
        pca = PCA(n_components=k)
        pca.fit(Xl)
        ll[k] = pca.score(Xt)

    assert_true(ll.argmax() == 1)


"""
A module for parsing and generating fontconfig patterns.

See the `fontconfig pattern specification
<http://www.fontconfig.org/fontconfig-user.html>`_ for more
information.
"""

# Author : Michael Droettboom <mdroe@stsci.edu>
# License : matplotlib license (PSF compatible)

# This class is defined here because it must be available in:
#   - The old-style config framework (:file:`rcsetup.py`)
#   - The traits-based config framework (:file:`mpltraits.py`)
#   - The font manager (:file:`font_manager.py`)

# It probably logically belongs in :file:`font_manager.py`, but
# placing it in any of these places would have created cyclical
# dependency problems, or an undesired dependency on traits even
# when the traits-based config framework is not used.

import re
from matplotlib.pyparsing import Literal, ZeroOrMore, \
    Optional, Regex, StringEnd, ParseException, Suppress

family_punc = r'\\\-:,'
family_unescape = re.compile(r'\\([%s])' % family_punc).sub
family_escape = re.compile(r'([%s])' % family_punc).sub

value_punc = r'\\=_:,'
value_unescape = re.compile(r'\\([%s])' % value_punc).sub
value_escape = re.compile(r'([%s])' % value_punc).sub

class FontconfigPatternParser:
    """A simple pyparsing-based parser for fontconfig-style patterns.

    See the `fontconfig pattern specification
    <http://www.fontconfig.org/fontconfig-user.html>`_ for more
    information.
    """

    _constants = {
        'thin'           : ('weight', 'light'),
        'extralight'     : ('weight', 'light'),
        'ultralight'     : ('weight', 'light'),
        'light'          : ('weight', 'light'),
        'book'           : ('weight', 'book'),
        'regular'        : ('weight', 'regular'),
        'normal'         : ('weight', 'normal'),
        'medium'         : ('weight', 'medium'),
        'demibold'       : ('weight', 'demibold'),
        'semibold'       : ('weight', 'semibold'),
        'bold'           : ('weight', 'bold'),
        'extrabold'      : ('weight', 'extra bold'),
        'black'          : ('weight', 'black'),
        'heavy'          : ('weight', 'heavy'),
        'roman'          : ('slant', 'normal'),
        'italic'         : ('slant', 'italic'),
        'oblique'        : ('slant', 'oblique'),
        'ultracondensed' : ('width', 'ultra-condensed'),
        'extracondensed' : ('width', 'extra-condensed'),
        'condensed'      : ('width', 'condensed'),
        'semicondensed'  : ('width', 'semi-condensed'),
        'expanded'       : ('width', 'expanded'),
        'extraexpanded'  : ('width', 'extra-expanded'),
        'ultraexpanded'  : ('width', 'ultra-expanded')
        }

    def __init__(self):
        family      = Regex(r'([^%s]|(\\[%s]))*' %
                            (family_punc, family_punc)) \
                      .setParseAction(self._family)
        size        = Regex(r"([0-9]+\.?[0-9]*|\.[0-9]+)") \
                      .setParseAction(self._size)
        name        = Regex(r'[a-z]+') \
                      .setParseAction(self._name)
        value       = Regex(r'([^%s]|(\\[%s]))*' %
                            (value_punc, value_punc)) \
                      .setParseAction(self._value)

        families    =(family
                    + ZeroOrMore(
                        Literal(',')
                      + family)
                    ).setParseAction(self._families)

        point_sizes =(size
                    + ZeroOrMore(
                        Literal(',')
                      + size)
                    ).setParseAction(self._point_sizes)

        property    =( (name
                      + Suppress(Literal('='))
                      + value
                      + ZeroOrMore(
                          Suppress(Literal(','))
                        + value)
                      )
                     |  name
                    ).setParseAction(self._property)

        pattern     =(Optional(
                        families)
                    + Optional(
                        Literal('-')
                      + point_sizes)
                    + ZeroOrMore(
                        Literal(':')
                      + property)
                    + StringEnd()
                    )

        self._parser = pattern
        self.ParseException = ParseException

    def parse(self, pattern):
        """
        Parse the given fontconfig *pattern* and return a dictionary
        of key/value pairs useful for initializing a
        :class:`font_manager.FontProperties` object.
        """
        props = self._properties = {}
        try:
            self._parser.parseString(pattern)
        except self.ParseException, e:
            raise ValueError("Could not parse font string: '%s'\n%s" % (pattern, e))

        self._properties = None
        return props

    def _family(self, s, loc, tokens):
        return [family_unescape(r'\1', str(tokens[0]))]

    def _size(self, s, loc, tokens):
        return [float(tokens[0])]

    def _name(self, s, loc, tokens):
        return [str(tokens[0])]

    def _value(self, s, loc, tokens):
        return [value_unescape(r'\1', str(tokens[0]))]

    def _families(self, s, loc, tokens):
        self._properties['family'] = [str(x) for x in tokens]
        return []

    def _point_sizes(self, s, loc, tokens):
        self._properties['size'] = [str(x) for x in tokens]
        return []

    def _property(self, s, loc, tokens):
        if len(tokens) == 1:
            if tokens[0] in self._constants:
                key, val = self._constants[tokens[0]]
                self._properties.setdefault(key, []).append(val)
        else:
            key = tokens[0]
            val = tokens[1:]
            self._properties.setdefault(key, []).extend(val)
        return []

parse_fontconfig_pattern = FontconfigPatternParser().parse

def generate_fontconfig_pattern(d):
    """
    Given a dictionary of key/value pairs, generates a fontconfig
    pattern string.
    """
    props = []
    families = ''
    size = ''
    for key in 'family style variant weight stretch file size'.split():
        val = getattr(d, 'get_' + key)()
        if val is not None and val != []:
            if type(val) == list:
                val = [value_escape(r'\\\1', str(x)) for x in val if x is not None]
                if val != []:
                    val = ','.join(val)
            props.append(":%s=%s" % (key, val))
    return ''.join(props)


# -*- coding: utf-8 -*-
"""
Created on Wed Aug 30 10:54:21 2017

@author: ishort
"""

#plotting:
import matplotlib
import matplotlib.pyplot as plt
#%matplotlib inline
import pylab

#General file for printing ad hoc quantities
#dbgHandle = open("debug.out", 'w')

#Get the data
dataPath = "SolFluxAtlas2005/"
#outPath = absPath + "Outputs/"

numStr = ""
num = 0.0
wavStr = ""
flxStr = ""
inLine = ""
fields = [" " for i in range(2)]

#with open("", 'r', encoding='utf-8') as inputHandle:
inFile = dataPath + "fluxspliced.2005"
with open(inFile, 'r') as inputHandle:

    #Expects number of records on first lines, then white space delimited columns of
    #wavelengths in nm and continuum rectified fluxes
    inLine = inputHandle.readline()   #Special one-line header
    print(inLine)
    fields = inLine.split()
    numStr = fields[0].strip()   #first field is number of following records
    num = int(numStr)
    waveSun = [0.0 for i in range(num)]
    fluxSun = [0.0 for i in range(num)]

    for i in range(num):
        inLine = inputHandle.readline()
        fields = inLine.split()
        wavStr = fields[0].strip(); flxStr = fields[1].strip()
        waveSun[i] = float(wavStr); fluxSun[i] = float(flxStr)

pylab.plot(waveSun, fluxSun, color='black')

#Now get the synthetic spectrum pre-computed with ChromaStarPy
modelPath = "Outputs/"
#outPath = absPath + "Outputs/"

numStr = ""
num = 0.0
wavStr = ""
flxStr = ""
inLine = "   "
#fields = [" " for i in range(2)]
"""
runVers = "pyLoop"
#Model atmosphere
teffStr = "5777.0"
loggStr = "4.44"
logZStr = "0.0"
massStarStr = "1.0"
xiTStr = "1.0"
logHeFeStr = "0.0"
logCOStr = "0.0"
logAlphaFeStr = "0.0"
#Spectrum synthesis
lambdaStartStr = "390.0"
lambdaStopStr = "400.0"
lineThreshStr = "-3.0"
voigtThreshStr = "-3.0"
logGammaColStr = "0.5"
logKapFudgeStr = "0.0"
macroVStr = "1.0"
rotVStr = "2.0"
rotIStr = "90.0"
RVStr = "0.0"

strucStem = "Teff" + teffStr + "Logg" + loggStr + "Z" + logZStr + "M" + massStarStr+"xiT"+xiTStr + \
"HeFe" + logHeFeStr + "CO" + logCOStr + "AlfFe" + logAlphaFeStr + "v" + runVers
strucFile = "struc." + strucStem + ".out"
specFile = "spec." + strucStem + "L"+lambdaStartStr+"-"+lambdaStopStr+"xiT"+xiTStr+"LThr"+lineThreshStr+ \
"GamCol"+logGammaColStr+"Mac"+macroVStr+"Rot"+rotVStr+"-"+rotIStr+"RV"+RVStr + ".out"
#with open("", 'r', encoding='utf-8') as inputHandle:
inFile = modelPath + specFile;
"""

project = "Project"
runVers = "Run"
teff = 5777.0
logg = 4.44
log10ZScale = 0.0
lambdaStart = 390.0
lambdaStop = 400.0
fileStem = project + "-"\
 + str(round(teff, 7)) + "-" + str(round(logg, 3)) + "-" + str(round(log10ZScale, 3))\
 + "-" + str(round(lambdaStart, 5)) + "-" + str(round(lambdaStop, 5))\
 + "-" + runVers
inFile = modelPath + fileStem + ".spec.txt"

invnAir = 1.0 / 1.000277 #// reciprocal of refractive index of air at STP

#numStr = fields[0].strip()   #first field is number of following records
#num = int(numStr)
waveMod = []
fluxMod = []
wav = 0.0 #//initialization
wavStr = ""
lblStr = ""

with open(inFile, 'r') as inputHandle:

    #Expects number of records on first lines, then white space delimited columns of
    #wavelengths in nm and continuum rectified fluxes
    inLine = inputHandle.readline()   #line of header
    print(inLine)
    inLine = inputHandle.readline()
    print(inLine)
    fields = inLine.split()
    #number of line IDs is last field:
    numLineIdsStr = fields[len(fields)-1]
    numLineIds = int(numLineIdsStr) - 1  # to be on safe side
    print("Recovered that there are " +  numLineIdsStr + " lines to ID")
    inLine = inputHandle.readline()
    print(inLine)
    fields = inLine.split()
    #number of wavelengths in spectrum is last field:
    numWavsStr = fields[len(fields)-1]
    numWavs = int(numWavsStr)  # to be on safe side
    print("Recovered that there are " +  numWavsStr + " wavelengths")
    #One more line of header
    inLine = inputHandle.readline()   #line of header
    print(inLine)

    waveMod = [0.0 for i in range(numWavs)]
    fluxMod = [0.0 for i in range(numWavs)]

    #Get the synthetic spectrum
    for i in range(numWavs):
        inLine = inputHandle.readline()
        fields = inLine.split()
        wavStr = fields[0].strip(); flxStr = fields[1].strip()
        wav = invnAir * float(wavStr)
        waveMod[i] = wav
        fluxMod[i] = float(flxStr)

    waveIds = [0.0 for i in range(numLineIds)]
    lblIds = ["" for i in range(numLineIds)]

    #Get the line IDs
    #Expects four white-space-delimited fields:
    # wavelength, element, ion. stage, and rounded wavelength
    #Another line of header for line id section
    inLine = inputHandle.readline()   #line of header
    print(inLine)

    for i in range(numLineIds):
        inLine = inputHandle.readline()
        fields = inLine.split()
        wavStr = fields[0].strip()
        wav = invnAir * float(wavStr)
        waveIds[i] = wav
        lblStr = fields[1].strip() + " " + fields[2].strip() + " " + fields[3].strip()
        lblIds[i] = lblStr

    """
    #If we do NOT know number of records:
    #for i in inputHandle: #doesn't work - 0 iterations
    while (inLine != ""):
        inLine = inputHandle.readline()
        if not inLine:
            break
        #print(inLine)
        fields = inLine.split()
        wavStr = fields[0].strip(); flxStr = fields[1].strip()
        wav = invnAir * float(wavStr)
        waveMod.append(wav)
        fluxMod.append(float(flxStr))
    """

#plot the spectrum
#plt.title('Synthetic spectrum')
plt.ylabel('$F_\lambda/F^C_\lambda$')
plt.xlabel('$\lambda$ (nm)')
xMin = min(waveMod)
xMax = max(waveMod)
pylab.xlim(xMin, xMax)
pylab.ylim(0.0, 1.6)
pylab.plot(waveMod, fluxMod, color="gray")

#add the line IDs
for i in range(numLineIds):
    if "Ca II" in lblIds[i]:
        thisLam = waveIds[i]
        thisLbl = lblIds[i]
        xPoint = [thisLam, thisLam]
        yPoint = [1.05, 1.1]
        pylab.plot(xPoint, yPoint, color='black')
        pylab.text(thisLam, 1.5, thisLbl, rotation=270)

#Save as encapsulated postscript (eps) for LaTex
epsName = fileStem + ".eps"
plt.savefig(epsName, format='eps', dpi=1000)


from itertools import product
import numpy as np
from scipy.sparse import (bsr_matrix, coo_matrix, csc_matrix, csr_matrix,
                          dok_matrix, lil_matrix)

from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert_greater
from sklearn.utils.validation import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn import neighbors, datasets

rng = np.random.RandomState(0)
# load and shuffle iris dataset
iris = datasets.load_iris()
perm = rng.permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]

# load and shuffle digits
digits = datasets.load_digits()
perm = rng.permutation(digits.target.size)
digits.data = digits.data[perm]
digits.target = digits.target[perm]

SPARSE_TYPES = (bsr_matrix, coo_matrix, csc_matrix, csr_matrix, dok_matrix,
                lil_matrix)
SPARSE_OR_DENSE = SPARSE_TYPES + (np.asarray,)

ALGORITHMS = ('ball_tree', 'brute', 'kd_tree', 'auto')
P = (1, 2, 3, 4, np.inf)

# Filter deprecation warnings.
neighbors.kneighbors_graph = ignore_warnings(neighbors.kneighbors_graph)
neighbors.radius_neighbors_graph = ignore_warnings(
    neighbors.radius_neighbors_graph)

def _weight_func(dist):
    """ Weight function to replace lambda d: d ** -2.
    The lambda function is not valid because:
    if d==0 then 0^-2 is not valid. """

    # Dist could be multidimensional, flatten it so all values
    # can be looped
    with np.errstate(divide='ignore'):
        retval = 1. / dist
    return retval ** 2

def test_unsupervised_kneighbors(n_samples=20, n_features=5,
                                 n_query_pts=2, n_neighbors=5):
    # Test unsupervised neighbors methods
    X = rng.rand(n_samples, n_features)

    test = rng.rand(n_query_pts, n_features)

    for p in P:
        results_nodist = []
        results = []

        for algorithm in ALGORITHMS:
            neigh = neighbors.NearestNeighbors(n_neighbors=n_neighbors,
                                               algorithm=algorithm,
                                               p=p)
            neigh.fit(X)

            results_nodist.append(neigh.kneighbors(test,
                                                   return_distance=False))
            results.append(neigh.kneighbors(test, return_distance=True))

        for i in range(len(results) - 1):
            assert_array_almost_equal(results_nodist[i], results[i][1])
            assert_array_almost_equal(results[i][0], results[i + 1][0])
            assert_array_almost_equal(results[i][1], results[i + 1][1])

def test_unsupervised_inputs():
    # test the types of valid input into NearestNeighbors
    X = rng.random_sample((10, 3))

    nbrs_fid = neighbors.NearestNeighbors(n_neighbors=1)
    nbrs_fid.fit(X)

    dist1, ind1 = nbrs_fid.kneighbors(X)

    nbrs = neighbors.NearestNeighbors(n_neighbors=1)

    for input in (nbrs_fid, neighbors.BallTree(X), neighbors.KDTree(X)):
        nbrs.fit(input)
        dist2, ind2 = nbrs.kneighbors(X)

        assert_array_almost_equal(dist1, dist2)
        assert_array_almost_equal(ind1, ind2)

def test_precomputed(random_state=42):
    """Tests unsupervised NearestNeighbors with a distance matrix."""
    # Note: smaller samples may result in spurious test success
    rng = np.random.RandomState(random_state)
    X = rng.random_sample((10, 4))
    Y = rng.random_sample((3, 4))
    DXX = metrics.pairwise_distances(X, metric='euclidean')
    DYX = metrics.pairwise_distances(Y, X, metric='euclidean')
    for method in ['kneighbors']:
        # TODO: also test radius_neighbors, but requires different assertion

        # As a feature matrix (n_samples by n_features)
        nbrs_X = neighbors.NearestNeighbors(n_neighbors=3)
        nbrs_X.fit(X)
        dist_X, ind_X = getattr(nbrs_X, method)(Y)

        # As a dense distance matrix (n_samples by n_samples)
        nbrs_D = neighbors.NearestNeighbors(n_neighbors=3, algorithm='brute',
                                            metric='precomputed')
        nbrs_D.fit(DXX)
        dist_D, ind_D = getattr(nbrs_D, method)(DYX)
        assert_array_almost_equal(dist_X, dist_D)
        assert_array_almost_equal(ind_X, ind_D)

        # Check auto works too
        nbrs_D = neighbors.NearestNeighbors(n_neighbors=3, algorithm='auto',
                                            metric='precomputed')
        nbrs_D.fit(DXX)
        dist_D, ind_D = getattr(nbrs_D, method)(DYX)
        assert_array_almost_equal(dist_X, dist_D)
        assert_array_almost_equal(ind_X, ind_D)

        # Check X=None in prediction
        dist_X, ind_X = getattr(nbrs_X, method)(None)
        dist_D, ind_D = getattr(nbrs_D, method)(None)
        assert_array_almost_equal(dist_X, dist_D)
        assert_array_almost_equal(ind_X, ind_D)

        # Must raise a ValueError if the matrix is not of correct shape
        assert_raises(ValueError, getattr(nbrs_D, method), X)

    target = np.arange(X.shape[0])
    for Est in (neighbors.KNeighborsClassifier,
                neighbors.RadiusNeighborsClassifier,
                neighbors.KNeighborsRegressor,
                neighbors.RadiusNeighborsRegressor):
        print(Est)
        est = Est(metric='euclidean')
        est.radius = est.n_neighbors = 1
        pred_X = est.fit(X, target).predict(Y)
        est.metric = 'precomputed'
        pred_D = est.fit(DXX, target).predict(DYX)
        assert_array_almost_equal(pred_X, pred_D)

def test_precomputed_cross_validation():
    # Ensure array is split correctly
    rng = np.random.RandomState(0)
    X = rng.rand(20, 2)
    D = pairwise_distances(X, metric='euclidean')
    y = rng.randint(3, size=20)
    for Est in (neighbors.KNeighborsClassifier,
                neighbors.RadiusNeighborsClassifier,
                neighbors.KNeighborsRegressor,
                neighbors.RadiusNeighborsRegressor):
        metric_score = cross_val_score(Est(), X, y)
        precomp_score = cross_val_score(Est(metric='precomputed'), D, y)
        assert_array_equal(metric_score, precomp_score)

def test_unsupervised_radius_neighbors(n_samples=20, n_features=5,
                                       n_query_pts=2, radius=0.5,
                                       random_state=0):
    # Test unsupervised radius-based query
    rng = np.random.RandomState(random_state)

    X = rng.rand(n_samples, n_features)

    test = rng.rand(n_query_pts, n_features)

    for p in P:
        results = []

        for algorithm in ALGORITHMS:
            neigh = neighbors.NearestNeighbors(radius=radius,
                                               algorithm=algorithm,
                                               p=p)
            neigh.fit(X)

            ind1 = neigh.radius_neighbors(test, return_distance=False)

            # sort the results: this is not done automatically for
            # radius searches
            dist, ind = neigh.radius_neighbors(test, return_distance=True)
            for (d, i, i1) in zip(dist, ind, ind1):
                j = d.argsort()
                d[:] = d[j]
                i[:] = i[j]
                i1[:] = i1[j]
            results.append((dist, ind))

            assert_array_almost_equal(np.concatenate(list(ind)),
                                      np.concatenate(list(ind1)))

        for i in range(len(results) - 1):
            assert_array_almost_equal(np.concatenate(list(results[i][0])),
                                      np.concatenate(list(results[i + 1][0]))),
            assert_array_almost_equal(np.concatenate(list(results[i][1])),
                                      np.concatenate(list(results[i + 1][1])))

def test_kneighbors_classifier(n_samples=40,
                               n_features=5,
                               n_test_pts=10,
                               n_neighbors=5,
                               random_state=0):
    # Test k-neighbors classification
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    y = ((X ** 2).sum(axis=1) < .5).astype(np.int)
    y_str = y.astype(str)

    weight_func = _weight_func

    for algorithm in ALGORITHMS:
        for weights in ['uniform', 'distance', weight_func]:
            knn = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors,
                                                 weights=weights,
                                                 algorithm=algorithm)
            knn.fit(X, y)
            epsilon = 1e-5 * (2 * rng.rand(1, n_features) - 1)
            y_pred = knn.predict(X[:n_test_pts] + epsilon)
            assert_array_equal(y_pred, y[:n_test_pts])
            # Test prediction with y_str
            knn.fit(X, y_str)
            y_pred = knn.predict(X[:n_test_pts] + epsilon)
            assert_array_equal(y_pred, y_str[:n_test_pts])

def test_kneighbors_classifier_float_labels(n_samples=40, n_features=5,
                                            n_test_pts=10, n_neighbors=5,
                                            random_state=0):
    # Test k-neighbors classification
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    y = ((X ** 2).sum(axis=1) < .5).astype(np.int)

    knn = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors)
    knn.fit(X, y.astype(np.float))
    epsilon = 1e-5 * (2 * rng.rand(1, n_features) - 1)
    y_pred = knn.predict(X[:n_test_pts] + epsilon)
    assert_array_equal(y_pred, y[:n_test_pts])

def test_kneighbors_classifier_predict_proba():
    # Test KNeighborsClassifier.predict_proba() method
    X = np.array([[0, 2, 0],
                  [0, 2, 1],
                  [2, 0, 0],
                  [2, 2, 0],
                  [0, 0, 2],
                  [0, 0, 1]])
    y = np.array([4, 4, 5, 5, 1, 1])
    cls = neighbors.KNeighborsClassifier(n_neighbors=3, p=1)  # cityblock dist
    cls.fit(X, y)
    y_prob = cls.predict_proba(X)
    real_prob = np.array([[0, 2. / 3, 1. / 3],
                          [1. / 3, 2. / 3, 0],
                          [1. / 3, 0, 2. / 3],
                          [0, 1. / 3, 2. / 3],
                          [2. / 3, 1. / 3, 0],
                          [2. / 3, 1. / 3, 0]])
    assert_array_equal(real_prob, y_prob)
    # Check that it also works with non integer labels
    cls.fit(X, y.astype(str))
    y_prob = cls.predict_proba(X)
    assert_array_equal(real_prob, y_prob)
    # Check that it works with weights='distance'
    cls = neighbors.KNeighborsClassifier(
        n_neighbors=2, p=1, weights='distance')
    cls.fit(X, y)
    y_prob = cls.predict_proba(np.array([[0, 2, 0], [2, 2, 2]]))
    real_prob = np.array([[0, 1, 0], [0, 0.4, 0.6]])
    assert_array_almost_equal(real_prob, y_prob)

def test_radius_neighbors_classifier(n_samples=40,
                                     n_features=5,
                                     n_test_pts=10,
                                     radius=0.5,
                                     random_state=0):
    # Test radius-based classification
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    y = ((X ** 2).sum(axis=1) < .5).astype(np.int)
    y_str = y.astype(str)

    weight_func = _weight_func

    for algorithm in ALGORITHMS:
        for weights in ['uniform', 'distance', weight_func]:
            neigh = neighbors.RadiusNeighborsClassifier(radius=radius,
                                                        weights=weights,
                                                        algorithm=algorithm)
            neigh.fit(X, y)
            epsilon = 1e-5 * (2 * rng.rand(1, n_features) - 1)
            y_pred = neigh.predict(X[:n_test_pts] + epsilon)
            assert_array_equal(y_pred, y[:n_test_pts])
            neigh.fit(X, y_str)
            y_pred = neigh.predict(X[:n_test_pts] + epsilon)
            assert_array_equal(y_pred, y_str[:n_test_pts])

def test_radius_neighbors_classifier_when_no_neighbors():
    # Test radius-based classifier when no neighbors found.
    # In this case it should rise an informative exception

    X = np.array([[1.0, 1.0], [2.0, 2.0]])
    y = np.array([1, 2])
    radius = 0.1

    z1 = np.array([[1.01, 1.01], [2.01, 2.01]])  # no outliers
    z2 = np.array([[1.01, 1.01], [1.4, 1.4]])    # one outlier

    weight_func = _weight_func

    for outlier_label in [0, -1, None]:
        for algorithm in ALGORITHMS:
            for weights in ['uniform', 'distance', weight_func]:
                rnc = neighbors.RadiusNeighborsClassifier
                clf = rnc(radius=radius, weights=weights, algorithm=algorithm,
                          outlier_label=outlier_label)
                clf.fit(X, y)
                assert_array_equal(np.array([1, 2]),
                                   clf.predict(z1))
                if outlier_label is None:
                    assert_raises(ValueError, clf.predict, z2)
                elif False:
                    assert_array_equal(np.array([1, outlier_label]),
                                       clf.predict(z2))

def test_radius_neighbors_classifier_outlier_labeling():
    # Test radius-based classifier when no neighbors found and outliers
    # are labeled.

    X = np.array([[1.0, 1.0], [2.0, 2.0]])
    y = np.array([1, 2])
    radius = 0.1

    z1 = np.array([[1.01, 1.01], [2.01, 2.01]])  # no outliers
    z2 = np.array([[1.01, 1.01], [1.4, 1.4]])    # one outlier
    correct_labels1 = np.array([1, 2])
    correct_labels2 = np.array([1, -1])

    weight_func = _weight_func

    for algorithm in ALGORITHMS:
        for weights in ['uniform', 'distance', weight_func]:
            clf = neighbors.RadiusNeighborsClassifier(radius=radius,
                                                      weights=weights,
                                                      algorithm=algorithm,
                                                      outlier_label=-1)
            clf.fit(X, y)
            assert_array_equal(correct_labels1, clf.predict(z1))
            assert_array_equal(correct_labels2, clf.predict(z2))

def test_radius_neighbors_classifier_zero_distance():
    # Test radius-based classifier, when distance to a sample is zero.

    X = np.array([[1.0, 1.0], [2.0, 2.0]])
    y = np.array([1, 2])
    radius = 0.1

    z1 = np.array([[1.01, 1.01], [2.0, 2.0]])
    correct_labels1 = np.array([1, 2])

    weight_func = _weight_func

    for algorithm in ALGORITHMS:
        for weights in ['uniform', 'distance', weight_func]:
            clf = neighbors.RadiusNeighborsClassifier(radius=radius,
                                                      weights=weights,
                                                      algorithm=algorithm)
            clf.fit(X, y)
            assert_array_equal(correct_labels1, clf.predict(z1))

def test_neighbors_regressors_zero_distance():
    # Test radius-based regressor, when distance to a sample is zero.

    X = np.array([[1.0, 1.0], [1.0, 1.0], [2.0, 2.0], [2.5, 2.5]])
    y = np.array([1.0, 1.5, 2.0, 0.0])
    radius = 0.2
    z = np.array([[1.1, 1.1], [2.0, 2.0]])

    rnn_correct_labels = np.array([1.25, 2.0])

    knn_correct_unif = np.array([1.25, 1.0])
    knn_correct_dist = np.array([1.25, 2.0])

    for algorithm in ALGORITHMS:
        # we don't test for weights=_weight_func since user will be expected
        # to handle zero distances themselves in the function.
        for weights in ['uniform', 'distance']:
            rnn = neighbors.RadiusNeighborsRegressor(radius=radius,
                                                     weights=weights,
                                                     algorithm=algorithm)
            rnn.fit(X, y)
            assert_array_almost_equal(rnn_correct_labels, rnn.predict(z))

        for weights, corr_labels in zip(['uniform', 'distance'],
                                        [knn_correct_unif, knn_correct_dist]):
            knn = neighbors.KNeighborsRegressor(n_neighbors=2,
                                                weights=weights,
                                                algorithm=algorithm)
            knn.fit(X, y)
            assert_array_almost_equal(corr_labels, knn.predict(z))

def test_radius_neighbors_boundary_handling():
    """Test whether points lying on boundary are handled consistently

    Also ensures that even with only one query point, an object array
    is returned rather than a 2d array.
    """

    X = np.array([[1.5], [3.0], [3.01]])
    radius = 3.0

    for algorithm in ALGORITHMS:
        nbrs = neighbors.NearestNeighbors(radius=radius,
                                          algorithm=algorithm).fit(X)
        results = nbrs.radius_neighbors([[0.0]], return_distance=False)
        assert_equal(results.shape, (1,))
        assert_equal(results.dtype, object)
        assert_array_equal(results[0], [0, 1])

def test_RadiusNeighborsClassifier_multioutput():
    # Test k-NN classifier on multioutput data
    rng = check_random_state(0)
    n_features = 2
    n_samples = 40
    n_output = 3

    X = rng.rand(n_samples, n_features)
    y = rng.randint(0, 3, (n_samples, n_output))

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    weights = [None, 'uniform', 'distance', _weight_func]

    for algorithm, weights in product(ALGORITHMS, weights):
        # Stack single output prediction
        y_pred_so = []
        for o in range(n_output):
            rnn = neighbors.RadiusNeighborsClassifier(weights=weights,
                                                      algorithm=algorithm)
            rnn.fit(X_train, y_train[:, o])
            y_pred_so.append(rnn.predict(X_test))

        y_pred_so = np.vstack(y_pred_so).T
        assert_equal(y_pred_so.shape, y_test.shape)

        # Multioutput prediction
        rnn_mo = neighbors.RadiusNeighborsClassifier(weights=weights,
                                                     algorithm=algorithm)
        rnn_mo.fit(X_train, y_train)
        y_pred_mo = rnn_mo.predict(X_test)

        assert_equal(y_pred_mo.shape, y_test.shape)
        assert_array_almost_equal(y_pred_mo, y_pred_so)

def test_kneighbors_classifier_sparse(n_samples=40,
                                      n_features=5,
                                      n_test_pts=10,
                                      n_neighbors=5,
                                      random_state=0):
    # Test k-NN classifier on sparse matrices
    # Like the above, but with various types of sparse matrices
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    X *= X > .2
    y = ((X ** 2).sum(axis=1) < .5).astype(np.int)

    for sparsemat in SPARSE_TYPES:
        knn = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors,
                                             algorithm='auto')
        knn.fit(sparsemat(X), y)
        epsilon = 1e-5 * (2 * rng.rand(1, n_features) - 1)
        for sparsev in SPARSE_TYPES + (np.asarray,):
            X_eps = sparsev(X[:n_test_pts] + epsilon)
            y_pred = knn.predict(X_eps)
            assert_array_equal(y_pred, y[:n_test_pts])

def test_KNeighborsClassifier_multioutput():
    # Test k-NN classifier on multioutput data
    rng = check_random_state(0)
    n_features = 5
    n_samples = 50
    n_output = 3

    X = rng.rand(n_samples, n_features)
    y = rng.randint(0, 3, (n_samples, n_output))

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    weights = [None, 'uniform', 'distance', _weight_func]

    for algorithm, weights in product(ALGORITHMS, weights):
        # Stack single output prediction
        y_pred_so = []
        y_pred_proba_so = []
        for o in range(n_output):
            knn = neighbors.KNeighborsClassifier(weights=weights,
                                                 algorithm=algorithm)
            knn.fit(X_train, y_train[:, o])
            y_pred_so.append(knn.predict(X_test))
            y_pred_proba_so.append(knn.predict_proba(X_test))

        y_pred_so = np.vstack(y_pred_so).T
        assert_equal(y_pred_so.shape, y_test.shape)
        assert_equal(len(y_pred_proba_so), n_output)

        # Multioutput prediction
        knn_mo = neighbors.KNeighborsClassifier(weights=weights,
                                                algorithm=algorithm)
        knn_mo.fit(X_train, y_train)
        y_pred_mo = knn_mo.predict(X_test)

        assert_equal(y_pred_mo.shape, y_test.shape)
        assert_array_almost_equal(y_pred_mo, y_pred_so)

        # Check proba
        y_pred_proba_mo = knn_mo.predict_proba(X_test)
        assert_equal(len(y_pred_proba_mo), n_output)

        for proba_mo, proba_so in zip(y_pred_proba_mo, y_pred_proba_so):
            assert_array_almost_equal(proba_mo, proba_so)

def test_kneighbors_regressor(n_samples=40,
                              n_features=5,
                              n_test_pts=10,
                              n_neighbors=3,
                              random_state=0):
    # Test k-neighbors regression
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    y = np.sqrt((X ** 2).sum(1))
    y /= y.max()

    y_target = y[:n_test_pts]

    weight_func = _weight_func

    for algorithm in ALGORITHMS:
        for weights in ['uniform', 'distance', weight_func]:
            knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors,
                                                weights=weights,
                                                algorithm=algorithm)
            knn.fit(X, y)
            epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1)
            y_pred = knn.predict(X[:n_test_pts] + epsilon)
            assert_true(np.all(abs(y_pred - y_target) < 0.3))

def test_KNeighborsRegressor_multioutput_uniform_weight():
    # Test k-neighbors in multi-output regression with uniform weight
    rng = check_random_state(0)
    n_features = 5
    n_samples = 40
    n_output = 4

    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples, n_output)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    for algorithm, weights in product(ALGORITHMS, [None, 'uniform']):
        knn = neighbors.KNeighborsRegressor(weights=weights,
                                            algorithm=algorithm)
        knn.fit(X_train, y_train)

        neigh_idx = knn.kneighbors(X_test, return_distance=False)
        y_pred_idx = np.array([np.mean(y_train[idx], axis=0)
                               for idx in neigh_idx])

        y_pred = knn.predict(X_test)

        assert_equal(y_pred.shape, y_test.shape)
        assert_equal(y_pred_idx.shape, y_test.shape)
        assert_array_almost_equal(y_pred, y_pred_idx)

def test_kneighbors_regressor_multioutput(n_samples=40,
                                          n_features=5,
                                          n_test_pts=10,
                                          n_neighbors=3,
                                          random_state=0):
    # Test k-neighbors in multi-output regression
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    y = np.sqrt((X ** 2).sum(1))
    y /= y.max()
    y = np.vstack([y, y]).T

    y_target = y[:n_test_pts]

    weights = ['uniform', 'distance', _weight_func]
    for algorithm, weights in product(ALGORITHMS, weights):
        knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors,
                                            weights=weights,
                                            algorithm=algorithm)
        knn.fit(X, y)
        epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1)
        y_pred = knn.predict(X[:n_test_pts] + epsilon)
        assert_equal(y_pred.shape, y_target.shape)

        assert_true(np.all(np.abs(y_pred - y_target) < 0.3))

def test_radius_neighbors_regressor(n_samples=40,
                                    n_features=3,
                                    n_test_pts=10,
                                    radius=0.5,
                                    random_state=0):
    # Test radius-based neighbors regression
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    y = np.sqrt((X ** 2).sum(1))
    y /= y.max()

    y_target = y[:n_test_pts]

    weight_func = _weight_func

    for algorithm in ALGORITHMS:
        for weights in ['uniform', 'distance', weight_func]:
            neigh = neighbors.RadiusNeighborsRegressor(radius=radius,
                                                       weights=weights,
                                                       algorithm=algorithm)
            neigh.fit(X, y)
            epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1)
            y_pred = neigh.predict(X[:n_test_pts] + epsilon)
            assert_true(np.all(abs(y_pred - y_target) < radius / 2))

def test_RadiusNeighborsRegressor_multioutput_with_uniform_weight():
    # Test radius neighbors in multi-output regression (uniform weight)

    rng = check_random_state(0)
    n_features = 5
    n_samples = 40
    n_output = 4

    X = rng.rand(n_samples, n_features)
    y = rng.rand(n_samples, n_output)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    for algorithm, weights in product(ALGORITHMS, [None, 'uniform']):

        rnn = neighbors. RadiusNeighborsRegressor(weights=weights,
                                                  algorithm=algorithm)
        rnn.fit(X_train, y_train)

        neigh_idx = rnn.radius_neighbors(X_test, return_distance=False)
        y_pred_idx = np.array([np.mean(y_train[idx], axis=0)
                               for idx in neigh_idx])

        y_pred_idx = np.array(y_pred_idx)
        y_pred = rnn.predict(X_test)

        assert_equal(y_pred_idx.shape, y_test.shape)
        assert_equal(y_pred.shape, y_test.shape)
        assert_array_almost_equal(y_pred, y_pred_idx)

def test_RadiusNeighborsRegressor_multioutput(n_samples=40,
                                              n_features=5,
                                              n_test_pts=10,
                                              n_neighbors=3,
                                              random_state=0):
    # Test k-neighbors in multi-output regression with various weight
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    y = np.sqrt((X ** 2).sum(1))
    y /= y.max()
    y = np.vstack([y, y]).T

    y_target = y[:n_test_pts]
    weights = ['uniform', 'distance', _weight_func]

    for algorithm, weights in product(ALGORITHMS, weights):
        rnn = neighbors.RadiusNeighborsRegressor(n_neighbors=n_neighbors,
                                                 weights=weights,
                                                 algorithm=algorithm)
        rnn.fit(X, y)
        epsilon = 1E-5 * (2 * rng.rand(1, n_features) - 1)
        y_pred = rnn.predict(X[:n_test_pts] + epsilon)

        assert_equal(y_pred.shape, y_target.shape)
        assert_true(np.all(np.abs(y_pred - y_target) < 0.3))

def test_kneighbors_regressor_sparse(n_samples=40,
                                     n_features=5,
                                     n_test_pts=10,
                                     n_neighbors=5,
                                     random_state=0):
    # Test radius-based regression on sparse matrices
    # Like the above, but with various types of sparse matrices
    rng = np.random.RandomState(random_state)
    X = 2 * rng.rand(n_samples, n_features) - 1
    y = ((X ** 2).sum(axis=1) < .25).astype(np.int)

    for sparsemat in SPARSE_TYPES:
        knn = neighbors.KNeighborsRegressor(n_neighbors=n_neighbors,
                                            algorithm='auto')
        knn.fit(sparsemat(X), y)
        for sparsev in SPARSE_OR_DENSE:
            X2 = sparsev(X)
            assert_true(np.mean(knn.predict(X2).round() == y) > 0.95)

def test_neighbors_iris():
    # Sanity checks on the iris dataset
    # Puts three points of each label in the plane and performs a
    # nearest neighbor query on points near the decision boundary.

    for algorithm in ALGORITHMS:
        clf = neighbors.KNeighborsClassifier(n_neighbors=1,
                                             algorithm=algorithm)
        clf.fit(iris.data, iris.target)
        assert_array_equal(clf.predict(iris.data), iris.target)

        clf.set_params(n_neighbors=9, algorithm=algorithm)
        clf.fit(iris.data, iris.target)
        assert_true(np.mean(clf.predict(iris.data) == iris.target) > 0.95)

        rgs = neighbors.KNeighborsRegressor(n_neighbors=5, algorithm=algorithm)
        rgs.fit(iris.data, iris.target)
        assert_greater(np.mean(rgs.predict(iris.data).round() == iris.target),
                       0.95)

def test_neighbors_digits():
    # Sanity check on the digits dataset
    # the 'brute' algorithm has been observed to fail if the input
    # dtype is uint8 due to overflow in distance calculations.

    X = digits.data.astype('uint8')
    Y = digits.target
    (n_samples, n_features) = X.shape
    train_test_boundary = int(n_samples * 0.8)
    train = np.arange(0, train_test_boundary)
    test = np.arange(train_test_boundary, n_samples)
    (X_train, Y_train, X_test, Y_test) = X[train], Y[train], X[test], Y[test]

    clf = neighbors.KNeighborsClassifier(n_neighbors=1, algorithm='brute')
    score_uint8 = clf.fit(X_train, Y_train).score(X_test, Y_test)
    score_float = clf.fit(X_train.astype(float), Y_train).score(
        X_test.astype(float), Y_test)
    assert_equal(score_uint8, score_float)

def test_kneighbors_graph():
    # Test kneighbors_graph to build the k-Nearest Neighbor graph.
    X = np.array([[0, 1], [1.01, 1.], [2, 0]])

    # n_neighbors = 1
    A = neighbors.kneighbors_graph(X, 1, mode='connectivity',
                                   include_self=True)
    assert_array_equal(A.toarray(), np.eye(A.shape[0]))

    A = neighbors.kneighbors_graph(X, 1, mode='distance')
    assert_array_almost_equal(
        A.toarray(),
        [[0.00, 1.01, 0.],
         [1.01, 0., 0.],
         [0.00, 1.40716026, 0.]])

    # n_neighbors = 2
    A = neighbors.kneighbors_graph(X, 2, mode='connectivity',
                                   include_self=True)
    assert_array_equal(
        A.toarray(),
        [[1., 1., 0.],
         [1., 1., 0.],
         [0., 1., 1.]])

    A = neighbors.kneighbors_graph(X, 2, mode='distance')
    assert_array_almost_equal(
        A.toarray(),
        [[0., 1.01, 2.23606798],
         [1.01, 0., 1.40716026],
         [2.23606798, 1.40716026, 0.]])

    # n_neighbors = 3
    A = neighbors.kneighbors_graph(X, 3, mode='connectivity',
                                   include_self=True)
    assert_array_almost_equal(
        A.toarray(),
        [[1, 1, 1], [1, 1, 1], [1, 1, 1]])

def test_kneighbors_graph_sparse(seed=36):
    # Test kneighbors_graph to build the k-Nearest Neighbor graph
    # for sparse input.
    rng = np.random.RandomState(seed)
    X = rng.randn(10, 10)
    Xcsr = csr_matrix(X)

    for n_neighbors in [1, 2, 3]:
        for mode in ["connectivity", "distance"]:
            assert_array_almost_equal(
                neighbors.kneighbors_graph(X,
                                           n_neighbors,
                                           mode=mode).toarray(),
                neighbors.kneighbors_graph(Xcsr,
                                           n_neighbors,
                                           mode=mode).toarray())

def test_radius_neighbors_graph():
    # Test radius_neighbors_graph to build the Nearest Neighbor graph.
    X = np.array([[0, 1], [1.01, 1.], [2, 0]])

    A = neighbors.radius_neighbors_graph(X, 1.5, mode='connectivity',
                                         include_self=True)
    assert_array_equal(
        A.toarray(),
        [[1., 1., 0.],
         [1., 1., 1.],
         [0., 1., 1.]])

    A = neighbors.radius_neighbors_graph(X, 1.5, mode='distance')
    assert_array_almost_equal(
        A.toarray(),
        [[0., 1.01, 0.],
         [1.01, 0., 1.40716026],
         [0., 1.40716026, 0.]])

def test_radius_neighbors_graph_sparse(seed=36):
    # Test radius_neighbors_graph to build the Nearest Neighbor graph
    # for sparse input.
    rng = np.random.RandomState(seed)
    X = rng.randn(10, 10)
    Xcsr = csr_matrix(X)

    for n_neighbors in [1, 2, 3]:
        for mode in ["connectivity", "distance"]:
            assert_array_almost_equal(
                neighbors.radius_neighbors_graph(X,
                                                 n_neighbors,
                                                 mode=mode).toarray(),
                neighbors.radius_neighbors_graph(Xcsr,
                                                 n_neighbors,
                                                 mode=mode).toarray())

def test_neighbors_badargs():
    # Test bad argument values: these should all raise ValueErrors
    assert_raises(ValueError,
                  neighbors.NearestNeighbors,
                  algorithm='blah')

    X = rng.random_sample((10, 2))
    Xsparse = csr_matrix(X)
    y = np.ones(10)

    for cls in (neighbors.KNeighborsClassifier,
                neighbors.RadiusNeighborsClassifier,
                neighbors.KNeighborsRegressor,
                neighbors.RadiusNeighborsRegressor):
        assert_raises(ValueError,
                      cls,
                      weights='blah')
        assert_raises(ValueError,
                      cls, p=-1)
        assert_raises(ValueError,
                      cls, algorithm='blah')
        nbrs = cls(algorithm='ball_tree', metric='haversine')
        assert_raises(ValueError,
                      nbrs.predict,
                      X)
        assert_raises(ValueError,
                      ignore_warnings(nbrs.fit),
                      Xsparse, y)
        nbrs = cls()
        assert_raises(ValueError,
                      nbrs.fit,
                      np.ones((0, 2)), np.ones(0))
        assert_raises(ValueError,
                      nbrs.fit,
                      X[:, :, None], y)
        nbrs.fit(X, y)
        assert_raises(ValueError,
                      nbrs.predict,
                      [[]])
        if (isinstance(cls, neighbors.KNeighborsClassifier) or
                isinstance(cls, neighbors.KNeighborsRegressor)):
            nbrs = cls(n_neighbors=-1)
            assert_raises(ValueError, nbrs.fit, X, y)

    nbrs = neighbors.NearestNeighbors().fit(X)

    assert_raises(ValueError, nbrs.kneighbors_graph, X, mode='blah')
    assert_raises(ValueError, nbrs.radius_neighbors_graph, X, mode='blah')

def test_neighbors_metrics(n_samples=20, n_features=3,
                           n_query_pts=2, n_neighbors=5):
    # Test computing the neighbors for various metrics
    # create a symmetric matrix
    V = rng.rand(n_features, n_features)
    VI = np.dot(V, V.T)

    metrics = [('euclidean', {}),
               ('manhattan', {}),
               ('minkowski', dict(p=1)),
               ('minkowski', dict(p=2)),
               ('minkowski', dict(p=3)),
               ('minkowski', dict(p=np.inf)),
               ('chebyshev', {}),
               ('seuclidean', dict(V=rng.rand(n_features))),
               ('wminkowski', dict(p=3, w=rng.rand(n_features))),
               ('mahalanobis', dict(VI=VI))]
    algorithms = ['brute', 'ball_tree', 'kd_tree']
    X = rng.rand(n_samples, n_features)

    test = rng.rand(n_query_pts, n_features)

    for metric, metric_params in metrics:
        results = []
        p = metric_params.pop('p', 2)
        for algorithm in algorithms:
            # KD tree doesn't support all metrics
            if (algorithm == 'kd_tree' and
                    metric not in neighbors.KDTree.valid_metrics):
                assert_raises(ValueError,
                              neighbors.NearestNeighbors,
                              algorithm=algorithm,
                              metric=metric, metric_params=metric_params)
                continue

            neigh = neighbors.NearestNeighbors(n_neighbors=n_neighbors,
                                               algorithm=algorithm,
                                               metric=metric, p=p,
                                               metric_params=metric_params)
            neigh.fit(X)
            results.append(neigh.kneighbors(test, return_distance=True))

        assert_array_almost_equal(results[0][0], results[1][0])
        assert_array_almost_equal(results[0][1], results[1][1])

def test_callable_metric():

    def custom_metric(x1, x2):
        return np.sqrt(np.sum(x1 ** 2 + x2 ** 2))

    X = np.random.RandomState(42).rand(20, 2)
    nbrs1 = neighbors.NearestNeighbors(3, algorithm='auto',
                                       metric=custom_metric)
    nbrs2 = neighbors.NearestNeighbors(3, algorithm='brute',
                                       metric=custom_metric)

    nbrs1.fit(X)
    nbrs2.fit(X)

    dist1, ind1 = nbrs1.kneighbors(X)
    dist2, ind2 = nbrs2.kneighbors(X)

    assert_array_almost_equal(dist1, dist2)

def test_metric_params_interface():
    assert_warns(SyntaxWarning, neighbors.KNeighborsClassifier,
                 metric_params={'p': 3})

def test_predict_sparse_ball_kd_tree():
    rng = np.random.RandomState(0)
    X = rng.rand(5, 5)
    y = rng.randint(0, 2, 5)
    nbrs1 = neighbors.KNeighborsClassifier(1, algorithm='kd_tree')
    nbrs2 = neighbors.KNeighborsRegressor(1, algorithm='ball_tree')
    for model in [nbrs1, nbrs2]:
        model.fit(X, y)
        assert_raises(ValueError, model.predict, csr_matrix(X))

def test_non_euclidean_kneighbors():
    rng = np.random.RandomState(0)
    X = rng.rand(5, 5)

    # Find a reasonable radius.
    dist_array = pairwise_distances(X).flatten()
    np.sort(dist_array)
    radius = dist_array[15]

    # Test kneighbors_graph
    for metric in ['manhattan', 'chebyshev']:
        nbrs_graph = neighbors.kneighbors_graph(
            X, 3, metric=metric, mode='connectivity',
            include_self=True).toarray()
        nbrs1 = neighbors.NearestNeighbors(3, metric=metric).fit(X)
        assert_array_equal(nbrs_graph, nbrs1.kneighbors_graph(X).toarray())

    # Test radiusneighbors_graph
    for metric in ['manhattan', 'chebyshev']:
        nbrs_graph = neighbors.radius_neighbors_graph(
            X, radius, metric=metric, mode='connectivity',
            include_self=True).toarray()
        nbrs1 = neighbors.NearestNeighbors(metric=metric, radius=radius).fit(X)
        assert_array_equal(nbrs_graph, nbrs1.radius_neighbors_graph(X).A)

    # Raise error when wrong parameters are supplied,
    X_nbrs = neighbors.NearestNeighbors(3, metric='manhattan')
    X_nbrs.fit(X)
    assert_raises(ValueError, neighbors.kneighbors_graph, X_nbrs, 3,
                  metric='euclidean')
    X_nbrs = neighbors.NearestNeighbors(radius=radius, metric='manhattan')
    X_nbrs.fit(X)
    assert_raises(ValueError, neighbors.radius_neighbors_graph, X_nbrs,
                  radius, metric='euclidean')

def check_object_arrays(nparray, list_check):
    for ind, ele in enumerate(nparray):
        assert_array_equal(ele, list_check[ind])

def test_k_and_radius_neighbors_train_is_not_query():
    # Test kneighbors et.al when query is not training data

    for algorithm in ALGORITHMS:

        nn = neighbors.NearestNeighbors(n_neighbors=1, algorithm=algorithm)

        X = [[0], [1]]
        nn.fit(X)
        test_data = [[2], [1]]

        # Test neighbors.
        dist, ind = nn.kneighbors(test_data)
        assert_array_equal(dist, [[1], [0]])
        assert_array_equal(ind, [[1], [1]])
        dist, ind = nn.radius_neighbors([[2], [1]], radius=1.5)
        check_object_arrays(dist, [[1], [1, 0]])
        check_object_arrays(ind, [[1], [0, 1]])

        # Test the graph variants.
        assert_array_equal(
            nn.kneighbors_graph(test_data).A, [[0., 1.], [0., 1.]])
        assert_array_equal(
            nn.kneighbors_graph([[2], [1]], mode='distance').A,
            np.array([[0., 1.], [0., 0.]]))
        rng = nn.radius_neighbors_graph([[2], [1]], radius=1.5)
        assert_array_equal(rng.A, [[0, 1], [1, 1]])

def test_k_and_radius_neighbors_X_None():
    # Test kneighbors et.al when query is None
    for algorithm in ALGORITHMS:

        nn = neighbors.NearestNeighbors(n_neighbors=1, algorithm=algorithm)

        X = [[0], [1]]
        nn.fit(X)

        dist, ind = nn.kneighbors()
        assert_array_equal(dist, [[1], [1]])
        assert_array_equal(ind, [[1], [0]])
        dist, ind = nn.radius_neighbors(None, radius=1.5)
        check_object_arrays(dist, [[1], [1]])
        check_object_arrays(ind, [[1], [0]])

        # Test the graph variants.
        rng = nn.radius_neighbors_graph(None, radius=1.5)
        kng = nn.kneighbors_graph(None)
        for graph in [rng, kng]:
            assert_array_equal(rng.A, [[0, 1], [1, 0]])
            assert_array_equal(rng.data, [1, 1])
            assert_array_equal(rng.indices, [1, 0])

        X = [[0, 1], [0, 1], [1, 1]]
        nn = neighbors.NearestNeighbors(n_neighbors=2, algorithm=algorithm)
        nn.fit(X)
        assert_array_equal(
            nn.kneighbors_graph().A,
            np.array([[0., 1., 1.], [1., 0., 1.], [1., 1., 0]]))

def test_k_and_radius_neighbors_duplicates():
    # Test behavior of kneighbors when duplicates are present in query

    for algorithm in ALGORITHMS:
        nn = neighbors.NearestNeighbors(n_neighbors=1, algorithm=algorithm)
        nn.fit([[0], [1]])

        # Do not do anything special to duplicates.
        kng = nn.kneighbors_graph([[0], [1]], mode='distance')
        assert_array_equal(
            kng.A,
            np.array([[0., 0.], [0., 0.]]))
        assert_array_equal(kng.data, [0., 0.])
        assert_array_equal(kng.indices, [0, 1])

        dist, ind = nn.radius_neighbors([[0], [1]], radius=1.5)
        check_object_arrays(dist, [[0, 1], [1, 0]])
        check_object_arrays(ind, [[0, 1], [0, 1]])

        rng = nn.radius_neighbors_graph([[0], [1]], radius=1.5)
        assert_array_equal(rng.A, np.ones((2, 2)))

        rng = nn.radius_neighbors_graph([[0], [1]], radius=1.5,
                                        mode='distance')
        assert_array_equal(rng.A, [[0, 1], [1, 0]])
        assert_array_equal(rng.indices, [0, 1, 0, 1])
        assert_array_equal(rng.data, [0, 1, 1, 0])

        # Mask the first duplicates when n_duplicates > n_neighbors.
        X = np.ones((3, 1))
        nn = neighbors.NearestNeighbors(n_neighbors=1)
        nn.fit(X)
        dist, ind = nn.kneighbors()
        assert_array_equal(dist, np.zeros((3, 1)))
        assert_array_equal(ind, [[1], [0], [1]])

        # Test that zeros are explicitly marked in kneighbors_graph.
        kng = nn.kneighbors_graph(mode='distance')
        assert_array_equal(
            kng.A, np.zeros((3, 3)))
        assert_array_equal(kng.data, np.zeros(3))
        assert_array_equal(kng.indices, [1., 0., 1.])
        assert_array_equal(
            nn.kneighbors_graph().A,
            np.array([[0., 1., 0.], [1., 0., 0.], [0., 1., 0.]]))

def test_include_self_neighbors_graph():
    # Test include_self parameter in neighbors_graph
    X = [[2, 3], [4, 5]]
    kng = neighbors.kneighbors_graph(X, 1, include_self=True).A
    kng_not_self = neighbors.kneighbors_graph(X, 1, include_self=False).A
    assert_array_equal(kng, [[1., 0.], [0., 1.]])
    assert_array_equal(kng_not_self, [[0., 1.], [1., 0.]])

    rng = neighbors.radius_neighbors_graph(X, 5.0, include_self=True).A
    rng_not_self = neighbors.radius_neighbors_graph(
        X, 5.0, include_self=False).A
    assert_array_equal(rng, [[1., 1.], [1., 1.]])
    assert_array_equal(rng_not_self, [[0., 1.], [1., 0.]])

def test_same_knn_parallel():
    X, y = datasets.make_classification(n_samples=30, n_features=5,
                                        n_redundant=0, random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y)

    def check_same_knn_parallel(algorithm):
        clf = neighbors.KNeighborsClassifier(n_neighbors=3,
                                             algorithm=algorithm)
        clf.fit(X_train, y_train)
        y = clf.predict(X_test)
        dist, ind = clf.kneighbors(X_test)
        graph = clf.kneighbors_graph(X_test, mode='distance').toarray()

        clf.set_params(n_jobs=3)
        clf.fit(X_train, y_train)
        y_parallel = clf.predict(X_test)
        dist_parallel, ind_parallel = clf.kneighbors(X_test)
        graph_parallel = \
            clf.kneighbors_graph(X_test, mode='distance').toarray()

        assert_array_equal(y, y_parallel)
        assert_array_almost_equal(dist, dist_parallel)
        assert_array_equal(ind, ind_parallel)
        assert_array_almost_equal(graph, graph_parallel)

    for algorithm in ALGORITHMS:
        yield check_same_knn_parallel, algorithm

def test_dtype_convert():
    classifier = neighbors.KNeighborsClassifier(n_neighbors=1)
    CLASSES = 15
    X = np.eye(CLASSES)
    y = [ch for ch in 'ABCDEFGHIJKLMNOPQRSTU'[:CLASSES]]

    result = classifier.fit(X, y).predict(X)
    assert_array_equal(result, y)


#!/usr/bin/env python
"""
Atlas of all graphs of 6 nodes or less.

"""
__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
#    Copyright (C) 2004 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.

import networkx as nx
from networkx.generators.atlas import *
from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic as isomorphic
import random

def atlas6():
    """ Return the atlas of all connected graphs of 6 nodes or less.
        Attempt to check for isomorphisms and remove.
    """

    Atlas=graph_atlas_g()[0:208] # 208
    # remove isolated nodes, only connected graphs are left
    U=nx.Graph() # graph for union of all graphs in atlas
    for G in Atlas:
        zerodegree=[n for n in G if G.degree(n)==0]
        for n in zerodegree:
            G.remove_node(n)
        U=nx.disjoint_union(U,G)

    # list of graphs of all connected components
    C=nx.connected_component_subgraphs(U)

    UU=nx.Graph()
    # do quick isomorphic-like check, not a true isomorphism checker
    nlist=[] # list of nonisomorphic graphs
    for G in C:
        # check against all nonisomorphic graphs so far
        if not iso(G,nlist):
            nlist.append(G)
            UU=nx.disjoint_union(UU,G) # union the nonisomorphic graphs
    return UU

def iso(G1, glist):
    """Quick and dirty nonisomorphism checker used to check isomorphisms."""
    for G2 in glist:
        if isomorphic(G1,G2):
            return True
    return False

if __name__ == '__main__':

    import networkx as nx

    G=atlas6()

    print("graph has %d nodes with %d edges"\
          %(nx.number_of_nodes(G),nx.number_of_edges(G)))
    print(nx.number_connected_components(G),"connected components")

    try:
        from networkx import graphviz_layout
    except ImportError:
        raise ImportError("This example needs Graphviz and either PyGraphviz or Pydot")

    import matplotlib.pyplot as plt
    plt.figure(1,figsize=(8,8))
    # layout graphs with positions using graphviz neato
    pos=nx.graphviz_layout(G,prog="neato")
    # color nodes the same in each connected subgraph
    C=nx.connected_component_subgraphs(G)
    for g in C:
        c=[random.random()]*nx.number_of_nodes(g) # random color...
        nx.draw(g,
             pos,
             node_size=40,
             node_color=c,
             vmin=0.0,
             vmax=1.0,
             with_labels=False
             )
    plt.savefig("atlas.png",dpi=75)


"""Tests for input validation functions"""

import warnings

from tempfile import NamedTemporaryFile
from itertools import product

import numpy as np
from numpy.testing import assert_array_equal
import scipy.sparse as sp
from nose.tools import assert_raises, assert_true, assert_false, assert_equal

from sklearn.utils.testing import assert_raises_regexp
from sklearn.utils.testing import assert_no_warnings
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import assert_warns
from sklearn.utils import as_float_array, check_array, check_symmetric
from sklearn.utils import check_X_y
from sklearn.utils.mocking import MockDataFrame
from sklearn.utils.estimator_checks import NotAnArray
from sklearn.random_projection import sparse_random_matrix
from sklearn.linear_model import ARDRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.datasets import make_blobs
from sklearn.utils.validation import (
    NotFittedError,
    has_fit_parameter,
    check_is_fitted,
    check_consistent_length,
    DataConversionWarning,
)

from sklearn.utils.testing import assert_raise_message

def test_as_float_array():
    # Test function for as_float_array
    X = np.ones((3, 10), dtype=np.int32)
    X = X + np.arange(10, dtype=np.int32)
    # Checks that the return type is ok
    X2 = as_float_array(X, copy=False)
    np.testing.assert_equal(X2.dtype, np.float32)
    # Another test
    X = X.astype(np.int64)
    X2 = as_float_array(X, copy=True)
    # Checking that the array wasn't overwritten
    assert_true(as_float_array(X, False) is not X)
    # Checking that the new type is ok
    np.testing.assert_equal(X2.dtype, np.float64)
    # Here, X is of the right type, it shouldn't be modified
    X = np.ones((3, 2), dtype=np.float32)
    assert_true(as_float_array(X, copy=False) is X)
    # Test that if X is fortran ordered it stays
    X = np.asfortranarray(X)
    assert_true(np.isfortran(as_float_array(X, copy=True)))

    # Test the copy parameter with some matrices
    matrices = [
        np.matrix(np.arange(5)),
        sp.csc_matrix(np.arange(5)).toarray(),
        sparse_random_matrix(10, 10, density=0.10).toarray()
    ]
    for M in matrices:
        N = as_float_array(M, copy=True)
        N[0, 0] = np.nan
        assert_false(np.isnan(M).any())

def test_np_matrix():
    # Confirm that input validation code does not return np.matrix
    X = np.arange(12).reshape(3, 4)

    assert_false(isinstance(as_float_array(X), np.matrix))
    assert_false(isinstance(as_float_array(np.matrix(X)), np.matrix))
    assert_false(isinstance(as_float_array(sp.csc_matrix(X)), np.matrix))

def test_memmap():
    # Confirm that input validation code doesn't copy memory mapped arrays

    asflt = lambda x: as_float_array(x, copy=False)

    with NamedTemporaryFile(prefix='sklearn-test') as tmp:
        M = np.memmap(tmp, shape=100, dtype=np.float32)
        M[:] = 0

        for f in (check_array, np.asarray, asflt):
            X = f(M)
            X[:] = 1
            assert_array_equal(X.ravel(), M)
            X[:] = 0

def test_ordering():
    # Check that ordering is enforced correctly by validation utilities.
    # We need to check each validation utility, because a 'copy' without
    # 'order=K' will kill the ordering.
    X = np.ones((10, 5))
    for A in X, X.T:
        for copy in (True, False):
            B = check_array(A, order='C', copy=copy)
            assert_true(B.flags['C_CONTIGUOUS'])
            B = check_array(A, order='F', copy=copy)
            assert_true(B.flags['F_CONTIGUOUS'])
            if copy:
                assert_false(A is B)

    X = sp.csr_matrix(X)
    X.data = X.data[::-1]
    assert_false(X.data.flags['C_CONTIGUOUS'])

def test_check_array():
    # accept_sparse == None
    # raise error on sparse inputs
    X = [[1, 2], [3, 4]]
    X_csr = sp.csr_matrix(X)
    assert_raises(TypeError, check_array, X_csr)
    # ensure_2d
    X_array = check_array([0, 1, 2])
    assert_equal(X_array.ndim, 2)
    X_array = check_array([0, 1, 2], ensure_2d=False)
    assert_equal(X_array.ndim, 1)
    # don't allow ndim > 3
    X_ndim = np.arange(8).reshape(2, 2, 2)
    assert_raises(ValueError, check_array, X_ndim)
    check_array(X_ndim, allow_nd=True)  # doesn't raise
    # force_all_finite
    X_inf = np.arange(4).reshape(2, 2).astype(np.float)
    X_inf[0, 0] = np.inf
    assert_raises(ValueError, check_array, X_inf)
    check_array(X_inf, force_all_finite=False)  # no raise
    # nan check
    X_nan = np.arange(4).reshape(2, 2).astype(np.float)
    X_nan[0, 0] = np.nan
    assert_raises(ValueError, check_array, X_nan)
    check_array(X_inf, force_all_finite=False)  # no raise

    # dtype and order enforcement.
    X_C = np.arange(4).reshape(2, 2).copy("C")
    X_F = X_C.copy("F")
    X_int = X_C.astype(np.int)
    X_float = X_C.astype(np.float)
    Xs = [X_C, X_F, X_int, X_float]
    dtypes = [np.int32, np.int, np.float, np.float32, None, np.bool, object]
    orders = ['C', 'F', None]
    copys = [True, False]

    for X, dtype, order, copy in product(Xs, dtypes, orders, copys):
        X_checked = check_array(X, dtype=dtype, order=order, copy=copy)
        if dtype is not None:
            assert_equal(X_checked.dtype, dtype)
        else:
            assert_equal(X_checked.dtype, X.dtype)
        if order == 'C':
            assert_true(X_checked.flags['C_CONTIGUOUS'])
            assert_false(X_checked.flags['F_CONTIGUOUS'])
        elif order == 'F':
            assert_true(X_checked.flags['F_CONTIGUOUS'])
            assert_false(X_checked.flags['C_CONTIGUOUS'])
        if copy:
            assert_false(X is X_checked)
        else:
            # doesn't copy if it was already good
            if (X.dtype == X_checked.dtype and
                    X_checked.flags['C_CONTIGUOUS'] == X.flags['C_CONTIGUOUS']
                    and X_checked.flags['F_CONTIGUOUS'] == X.flags['F_CONTIGUOUS']):
                assert_true(X is X_checked)

    # allowed sparse != None
    X_csc = sp.csc_matrix(X_C)
    X_coo = X_csc.tocoo()
    X_dok = X_csc.todok()
    X_int = X_csc.astype(np.int)
    X_float = X_csc.astype(np.float)

    Xs = [X_csc, X_coo, X_dok, X_int, X_float]
    accept_sparses = [['csr', 'coo'], ['coo', 'dok']]
    for X, dtype, accept_sparse, copy in product(Xs, dtypes, accept_sparses,
                                                 copys):
        with warnings.catch_warnings(record=True) as w:
            X_checked = check_array(X, dtype=dtype,
                                    accept_sparse=accept_sparse, copy=copy)
        if (dtype is object or sp.isspmatrix_dok(X)) and len(w):
            message = str(w[0].message)
            messages = ["object dtype is not supported by sparse matrices",
                        "Can't check dok sparse matrix for nan or inf."]
            assert_true(message in messages)
        else:
            assert_equal(len(w), 0)
        if dtype is not None:
            assert_equal(X_checked.dtype, dtype)
        else:
            assert_equal(X_checked.dtype, X.dtype)
        if X.format in accept_sparse:
            # no change if allowed
            assert_equal(X.format, X_checked.format)
        else:
            # got converted
            assert_equal(X_checked.format, accept_sparse[0])
        if copy:
            assert_false(X is X_checked)
        else:
            # doesn't copy if it was already good
            if (X.dtype == X_checked.dtype and X.format == X_checked.format):
                assert_true(X is X_checked)

    # other input formats
    # convert lists to arrays
    X_dense = check_array([[1, 2], [3, 4]])
    assert_true(isinstance(X_dense, np.ndarray))
    # raise on too deep lists
    assert_raises(ValueError, check_array, X_ndim.tolist())
    check_array(X_ndim.tolist(), allow_nd=True)  # doesn't raise
    # convert weird stuff to arrays
    X_no_array = NotAnArray(X_dense)
    result = check_array(X_no_array)
    assert_true(isinstance(result, np.ndarray))

def test_check_array_pandas_dtype_object_conversion():
    # test that data-frame like objects with dtype object
    # get converted
    X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.object)
    X_df = MockDataFrame(X)
    assert_equal(check_array(X_df).dtype.kind, "f")
    assert_equal(check_array(X_df, ensure_2d=False).dtype.kind, "f")
    # smoke-test against dataframes with column named "dtype"
    X_df.dtype = "Hans"
    assert_equal(check_array(X_df, ensure_2d=False).dtype.kind, "f")

def test_check_array_dtype_stability():
    # test that lists with ints don't get converted to floats
    X = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    assert_equal(check_array(X).dtype.kind, "i")
    assert_equal(check_array(X, ensure_2d=False).dtype.kind, "i")

def test_check_array_dtype_warning():
    X_int_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    X_float64 = np.asarray(X_int_list, dtype=np.float64)
    X_float32 = np.asarray(X_int_list, dtype=np.float32)
    X_int64 = np.asarray(X_int_list, dtype=np.int64)
    X_csr_float64 = sp.csr_matrix(X_float64)
    X_csr_float32 = sp.csr_matrix(X_float32)
    X_csc_float32 = sp.csc_matrix(X_float32)
    X_csc_int32 = sp.csc_matrix(X_int64, dtype=np.int32)
    y = [0, 0, 1]
    integer_data = [X_int64, X_csc_int32]
    float64_data = [X_float64, X_csr_float64]
    float32_data = [X_float32, X_csr_float32, X_csc_float32]
    for X in integer_data:
        X_checked = assert_no_warnings(check_array, X, dtype=np.float64,
                                       accept_sparse=True)
        assert_equal(X_checked.dtype, np.float64)

        X_checked = assert_warns(DataConversionWarning, check_array, X,
                                 dtype=np.float64,
                                 accept_sparse=True, warn_on_dtype=True)
        assert_equal(X_checked.dtype, np.float64)

        # Check that the warning message includes the name of the Estimator
        X_checked = assert_warns_message(DataConversionWarning,
                                         'SomeEstimator',
                                         check_array, X,
                                         dtype=[np.float64, np.float32],
                                         accept_sparse=True,
                                         warn_on_dtype=True,
                                         estimator='SomeEstimator')
        assert_equal(X_checked.dtype, np.float64)

        X_checked, y_checked = assert_warns_message(
            DataConversionWarning, 'KNeighborsClassifier',
            check_X_y, X, y, dtype=np.float64, accept_sparse=True,
            warn_on_dtype=True, estimator=KNeighborsClassifier())

        assert_equal(X_checked.dtype, np.float64)

    for X in float64_data:
        X_checked = assert_no_warnings(check_array, X, dtype=np.float64,
                                       accept_sparse=True, warn_on_dtype=True)
        assert_equal(X_checked.dtype, np.float64)
        X_checked = assert_no_warnings(check_array, X, dtype=np.float64,
                                       accept_sparse=True, warn_on_dtype=False)
        assert_equal(X_checked.dtype, np.float64)

    for X in float32_data:
        X_checked = assert_no_warnings(check_array, X,
                                       dtype=[np.float64, np.float32],
                                       accept_sparse=True)
        assert_equal(X_checked.dtype, np.float32)
        assert_true(X_checked is X)

        X_checked = assert_no_warnings(check_array, X,
                                       dtype=[np.float64, np.float32],
                                       accept_sparse=['csr', 'dok'],
                                       copy=True)
        assert_equal(X_checked.dtype, np.float32)
        assert_false(X_checked is X)

    X_checked = assert_no_warnings(check_array, X_csc_float32,
                                   dtype=[np.float64, np.float32],
                                   accept_sparse=['csr', 'dok'],
                                   copy=False)
    assert_equal(X_checked.dtype, np.float32)
    assert_false(X_checked is X_csc_float32)
    assert_equal(X_checked.format, 'csr')

def test_check_array_min_samples_and_features_messages():
    # empty list is considered 2D by default:
    msg = "0 feature(s) (shape=(1, 0)) while a minimum of 1 is required."
    assert_raise_message(ValueError, msg, check_array, [])

    # If considered a 1D collection when ensure_2d=False, then the minimum
    # number of samples will break:
    msg = "0 sample(s) (shape=(0,)) while a minimum of 1 is required."
    assert_raise_message(ValueError, msg, check_array, [], ensure_2d=False)

    # Invalid edge case when checking the default minimum sample of a scalar
    msg = "Singleton array array(42) cannot be considered a valid collection."
    assert_raise_message(TypeError, msg, check_array, 42, ensure_2d=False)

    # But this works if the input data is forced to look like a 2 array with
    # one sample and one feature:
    X_checked = check_array(42, ensure_2d=True)
    assert_array_equal(np.array([[42]]), X_checked)

    # Simulate a model that would need at least 2 samples to be well defined
    X = np.ones((1, 10))
    y = np.ones(1)
    msg = "1 sample(s) (shape=(1, 10)) while a minimum of 2 is required."
    assert_raise_message(ValueError, msg, check_X_y, X, y,
                         ensure_min_samples=2)

    # The same message is raised if the data has 2 dimensions even if this is
    # not mandatory
    assert_raise_message(ValueError, msg, check_X_y, X, y,
                         ensure_min_samples=2, ensure_2d=False)

    # Simulate a model that would require at least 3 features (e.g. SelectKBest
    # with k=3)
    X = np.ones((10, 2))
    y = np.ones(2)
    msg = "2 feature(s) (shape=(10, 2)) while a minimum of 3 is required."
    assert_raise_message(ValueError, msg, check_X_y, X, y,
                         ensure_min_features=3)

    # Only the feature check is enabled whenever the number of dimensions is 2
    # even if allow_nd is enabled:
    assert_raise_message(ValueError, msg, check_X_y, X, y,
                         ensure_min_features=3, allow_nd=True)

    # Simulate a case where a pipeline stage as trimmed all the features of a
    # 2D dataset.
    X = np.empty(0).reshape(10, 0)
    y = np.ones(10)
    msg = "0 feature(s) (shape=(10, 0)) while a minimum of 1 is required."
    assert_raise_message(ValueError, msg, check_X_y, X, y)

    # nd-data is not checked for any minimum number of features by default:
    X = np.ones((10, 0, 28, 28))
    y = np.ones(10)
    X_checked, y_checked = check_X_y(X, y, allow_nd=True)
    assert_array_equal(X, X_checked)
    assert_array_equal(y, y_checked)

def test_has_fit_parameter():
    assert_false(has_fit_parameter(KNeighborsClassifier, "sample_weight"))
    assert_true(has_fit_parameter(RandomForestRegressor, "sample_weight"))
    assert_true(has_fit_parameter(SVR, "sample_weight"))
    assert_true(has_fit_parameter(SVR(), "sample_weight"))

def test_check_symmetric():
    arr_sym = np.array([[0, 1], [1, 2]])
    arr_bad = np.ones(2)
    arr_asym = np.array([[0, 2], [0, 2]])

    test_arrays = {'dense': arr_asym,
                   'dok': sp.dok_matrix(arr_asym),
                   'csr': sp.csr_matrix(arr_asym),
                   'csc': sp.csc_matrix(arr_asym),
                   'coo': sp.coo_matrix(arr_asym),
                   'lil': sp.lil_matrix(arr_asym),
                   'bsr': sp.bsr_matrix(arr_asym)}

    # check error for bad inputs
    assert_raises(ValueError, check_symmetric, arr_bad)

    # check that asymmetric arrays are properly symmetrized
    for arr_format, arr in test_arrays.items():
        # Check for warnings and errors
        assert_warns(UserWarning, check_symmetric, arr)
        assert_raises(ValueError, check_symmetric, arr, raise_exception=True)

        output = check_symmetric(arr, raise_warning=False)
        if sp.issparse(output):
            assert_equal(output.format, arr_format)
            assert_array_equal(output.toarray(), arr_sym)
        else:
            assert_array_equal(output, arr_sym)

def test_check_is_fitted():
    # Check is ValueError raised when non estimator instance passed
    assert_raises(ValueError, check_is_fitted, ARDRegression, "coef_")
    assert_raises(TypeError, check_is_fitted, "SVR", "support_")

    ard = ARDRegression()
    svr = SVR()

    try:
        assert_raises(NotFittedError, check_is_fitted, ard, "coef_")
        assert_raises(NotFittedError, check_is_fitted, svr, "support_")
    except ValueError:
        assert False, "check_is_fitted failed with ValueError"

    # NotFittedError is a subclass of both ValueError and AttributeError
    try:
        check_is_fitted(ard, "coef_", "Random message %(name)s, %(name)s")
    except ValueError as e:
        assert_equal(str(e), "Random message ARDRegression, ARDRegression")

    try:
        check_is_fitted(svr, "support_", "Another message %(name)s, %(name)s")
    except AttributeError as e:
        assert_equal(str(e), "Another message SVR, SVR")

    ard.fit(*make_blobs())
    svr.fit(*make_blobs())

    assert_equal(None, check_is_fitted(ard, "coef_"))
    assert_equal(None, check_is_fitted(svr, "support_"))

def test_check_consistent_length():
    check_consistent_length([1], [2], [3], [4], [5])
    check_consistent_length([[1, 2], [[1, 2]]], [1, 2], ['a', 'b'])
    check_consistent_length([1], (2,), np.array([3]), sp.csr_matrix((1, 2)))
    assert_raises_regexp(ValueError, 'inconsistent numbers of samples',
                         check_consistent_length, [1, 2], [1])
    assert_raises_regexp(TypeError, 'got <\w+ \'int\'>',
                         check_consistent_length, [1, 2], 1)
    assert_raises_regexp(TypeError, 'got <\w+ \'object\'>',
                         check_consistent_length, [1, 2], object())

    assert_raises(TypeError, check_consistent_length, [1, 2], np.array(1))
    # Despite ensembles having __len__ they must raise TypeError
    assert_raises_regexp(TypeError, 'estimator', check_consistent_length,
                         [1, 2], RandomForestRegressor())
    # XXX: We should have a test with a string, but what is correct behaviour?


"""
===========================================
Comparison of F-test and mutual information
===========================================

This example illustrates the differences between univariate F-test statistics
and mutual information.

We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the
target depends on them as follows:

y = x_1 + sin(6 * pi * x_2) + 0.1 * N(0, 1), that is the third features is
completely irrelevant.

The code below plots the dependency of y against individual x_i and normalized
values of univariate F-tests statistics and mutual information.

As F-test captures only linear dependency, it rates x_1 as the most
discriminative feature. On the other hand, mutual information can capture any
kind of dependency between variables and it rates x_2 as the most
discriminative feature, which probably agrees better with our intuitive
perception for this example. Both methods correctly marks x_3 as irrelevant.
"""
print(__doc__)

import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_selection import f_regression, mutual_info_regression

np.random.seed(0)
X = np.random.rand(1000, 3)
y = X[:, 0] + np.sin(6 * np.pi * X[:, 1]) + 0.1 * np.random.randn(1000)

f_test, _ = f_regression(X, y)
f_test /= np.max(f_test)

mi = mutual_info_regression(X, y)
mi /= np.max(mi)

plt.figure(figsize=(15, 5))
for i in range(3):
    plt.subplot(1, 3, i + 1)
    plt.scatter(X[:, i], y, edgecolor='black', s=20)
    plt.xlabel("$x_{}$".format(i + 1), fontsize=14)
    if i == 0:
        plt.ylabel("$y$", fontsize=14)
    plt.title("F-test={:.2f}, MI={:.2f}".format(f_test[i], mi[i]),
              fontsize=16)
plt.show()


"""
A little example that shows how the various indexing into the font
tables relate to one another.  Mainly for mpl developers....

"""
import matplotlib
from matplotlib.ft2font import FT2Font, KERNING_DEFAULT, KERNING_UNFITTED, KERNING_UNSCALED

#fname = '/usr/share/fonts/sfd/FreeSans.ttf'
fname = matplotlib.get_data_path() + '/fonts/ttf/Vera.ttf'
font = FT2Font(fname)
font.set_charmap(0)

codes = font.get_charmap().items()
#dsu = [(ccode, glyphind) for ccode, glyphind in codes]
#dsu.sort()
#for ccode, glyphind in dsu:
#    try: name = font.get_glyph_name(glyphind)
#    except RuntimeError: pass
#    else: print '% 4d % 4d %s %s'%(glyphind, ccode, hex(int(ccode)), name)

# make a charname to charcode and glyphind dictionary
coded = {}
glyphd = {}
for ccode, glyphind in codes:
    name = font.get_glyph_name(glyphind)
    coded[name] = ccode
    glyphd[name] = glyphind

code =  coded['A']
glyph = font.load_char(code)
#print glyph.bbox
print glyphd['A'], glyphd['V'], coded['A'], coded['V']
print 'AV', font.get_kerning(glyphd['A'], glyphd['V'], KERNING_DEFAULT)
print 'AV', font.get_kerning(glyphd['A'], glyphd['V'], KERNING_UNFITTED)
print 'AV', font.get_kerning(glyphd['A'], glyphd['V'], KERNING_UNSCALED)
print 'AV', font.get_kerning(glyphd['A'], glyphd['T'], KERNING_UNSCALED)