Remove mlab.PCA

dstansby · dstansby · commit b17eccaa698e · 2018-09-29T11:05:05.000+01:00
diff --git a/doc/api/next_api_changes/2018-09-18-DS.rst b/doc/api/next_api_changes/2018-09-18-DS.rst
@@ -11,4 +11,5 @@ in Matplotlib 2.2 has been removed. See below for a list:
 - `mlab.find` (use ``np.nonzero(np.ravel(condition))`` instead)
 - `mlab.longest_contiguous_ones`
 - `mlab.longest_ones`
+- `mlab.PCA`
 - `mlab.donothing_callback`
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
@@ -1194,142 +1194,6 @@ def cohere(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning,
     return Cxy, f
 
 
-@cbook.deprecated('2.2')
-class PCA(object):
-    def __init__(self, a, standardize=True):
-        """
-        compute the SVD of a and store data for PCA.  Use project to
-        project the data onto a reduced set of dimensions
-
-        Parameters
-        ----------
-        a : np.ndarray
-            A numobservations x numdims array
-        standardize : bool
-            True if input data are to be standardized. If False, only centering
-            will be carried out.
-
-        Attributes
-        ----------
-        a
-            A centered unit sigma version of input ``a``.
-
-        numrows, numcols
-            The dimensions of ``a``.
-
-        mu
-            A numdims array of means of ``a``. This is the vector that points
-            to the origin of PCA space.
-
-        sigma
-            A numdims array of standard deviation of ``a``.
-
-        fracs
-            The proportion of variance of each of the principal components.
-
-        s
-            The actual eigenvalues of the decomposition.
-
-        Wt
-            The weight vector for projecting a numdims point or array into
-            PCA space.
-
-        Y
-            A projected into PCA space.
-
-        Notes
-        -----
-        The factor loadings are in the ``Wt`` factor, i.e., the factor loadings
-        for the first principal component are given by ``Wt[0]``. This row is
-        also the first eigenvector.
-
-        """
-        n, m = a.shape
-        if n < m:
-            raise RuntimeError('we assume data in a is organized with '
-                               'numrows>numcols')
-
-        self.numrows, self.numcols = n, m
-        self.mu = a.mean(axis=0)
-        self.sigma = a.std(axis=0)
-        self.standardize = standardize
-
-        a = self.center(a)
-
-        self.a = a
-
-        U, s, Vh = np.linalg.svd(a, full_matrices=False)
-
-        # Note: .H indicates the conjugate transposed / Hermitian.
-
-        # The SVD is commonly written as a = U s V.H.
-        # If U is a unitary matrix, it means that it satisfies U.H = inv(U).
-
-        # The rows of Vh are the eigenvectors of a.H a.
-        # The columns of U are the eigenvectors of a a.H.
-        # For row i in Vh and column i in U, the corresponding eigenvalue is
-        # s[i]**2.
-
-        self.Wt = Vh
-
-        # save the transposed coordinates
-        Y = np.dot(Vh, a.T).T
-        self.Y = Y
-
-        # save the eigenvalues
-        self.s = s**2
-
-        # and now the contribution of the individual components
-        vars = self.s / len(s)
-        self.fracs = vars/vars.sum()
-
-    def project(self, x, minfrac=0.):
-        '''
-        project x onto the principle axes, dropping any axes where fraction
-        of variance<minfrac
-        '''
-        x = np.asarray(x)
-        if x.shape[-1] != self.numcols:
-            raise ValueError('Expected an array with dims[-1]==%d' %
-                             self.numcols)
-        Y = np.dot(self.Wt, self.center(x).T).T
-        mask = self.fracs >= minfrac
-        if x.ndim == 2:
-            Yreduced = Y[:, mask]
-        else:
-            Yreduced = Y[mask]
-        return Yreduced
-
-    def center(self, x):
-        '''
-        center and optionally standardize the data using the mean and sigma
-        from training set a
-        '''
-        if self.standardize:
-            return (x - self.mu)/self.sigma
-        else:
-            return (x - self.mu)
-
-    @staticmethod
-    def _get_colinear():
-        c0 = np.array([
-            0.19294738,  0.6202667,   0.45962655,  0.07608613,  0.135818,
-            0.83580842,  0.07218851,  0.48318321,  0.84472463,  0.18348462,
-            0.81585306,  0.96923926,  0.12835919,  0.35075355,  0.15807861,
-            0.837437,    0.10824303,  0.1723387,   0.43926494,  0.83705486])
-
-        c1 = np.array([
-            -1.17705601, -0.513883,   -0.26614584,  0.88067144,  1.00474954,
-            -1.1616545,   0.0266109,   0.38227157,  1.80489433,  0.21472396,
-            -1.41920399, -2.08158544, -0.10559009,  1.68999268,  0.34847107,
-            -0.4685737,   1.23980423, -0.14638744, -0.35907697,  0.22442616])
-
-        c2 = c0 + 2*c1
-        c3 = -3*c0 + 4*c1
-        a = np.array([c3, c0, c1, c2]).T
-        return a
-
-
 @cbook.deprecated('2.2', 'numpy.percentile')
 def prctile(x, p=(0.0, 25.0, 50.0, 75.0, 100.0)):
     """
diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
@@ -20,15 +20,6 @@
 '''
 
 
-def test_colinear_pca():
-    with pytest.warns(MatplotlibDeprecationWarning):
-        a = mlab.PCA._get_colinear()
-        pca = mlab.PCA(a)
-
-    assert_allclose(pca.fracs[2:], 0., atol=1e-8)
-    assert_allclose(pca.Y[:, 2:], 0., atol=1e-8)
-
-
 @pytest.mark.parametrize('input', [
     # test odd lengths
     [1, 2, 3],
