Fixed a bug in SVD

Added coverage tests for decomposition module

Signed-off-by: Timothy Click <tcthepoet@yahoo.com>

src/fluctmatch/decomposition/svd.py

@@ -343,10 +343,10 @@ def _fit_truncated(self, X, n_components, svd_solver):
             U, S, V = randomized_svd(X, n_components=n_components,
                                      n_iter=self.iterated_power,
                                      flip_sign=True, random_state=random_state)
-            U = U[:, :self.n_components_]
+            U = U[:, :n_components]
 
             # X_new = X * V = U * S * V^T * V = U * S
-            U *= S[:self.n_components_]
+            U *= S[:n_components]
 
         components_: np.ndarray = V
 

tests/decomposition/__init__.py

@@ -0,0 +1,52 @@
+# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding: utf-8 -*-
+# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
+#
+# pysca --- https://github.com/tclick/python-pysca
+# Copyright (c) 2015-2017 The pySCA Development Team and contributors
+# (see the file AUTHORS for the full list of names)
+#
+# Released under the New BSD license.
+#
+# Please cite your use of fluctmatch in published work:
+#
+# Timothy H. Click, Nixon Raj, and Jhih-Wei Chu.
+# Calculation of Enzyme Fluctuograms from All-Atom Molecular Dynamics
+# Simulation. Meth Enzymology. 578 (2016), 327-342,
+# doi:10.1016/bs.mie.2016.05.024.
+#
+from __future__ import (
+    absolute_import,
+    division,
+    print_function,
+    unicode_literals,
+)
+
+from future.utils import (
+    native_str,
+    raise_from,
+    with_metaclass,
+)
+from future.builtins import (
+    ascii,
+    bytes,
+    chr,
+    dict,
+    filter,
+    hex,
+    input,
+    map,
+    next,
+    oct,
+    open,
+    pow,
+    range,
+    round,
+    str,
+    super,
+    zip,
+)
+
+import numpy as np
+import pandas as pd
+
+

tests/decomposition/test_eigh.py

@@ -0,0 +1,41 @@
+# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding: utf-8 -*-
+# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
+#
+# fluctmatch --- https://github.com/tclick/python-fluctmatch
+# Copyright (c) 2015-2017 The fluctmatch Development Team and contributors
+# (see the file AUTHORS for the full list of names)
+#
+# Released under the New BSD license.
+#
+# Please cite your use of fluctmatch in published work:
+#
+# Timothy H. Click, Nixon Raj, and Jhih-Wei Chu.
+# Calculation of Enzyme Fluctuograms from All-Atom Molecular Dynamics
+# Simulation. Meth Enzymology. 578 (2016), 327-342,
+# doi:10.1016/bs.mie.2016.05.024.
+#
+
+import numpy as np
+from numpy import testing
+from sklearn.utils.extmath import svd_flip
+
+from fluctmatch.decomposition.eigh import Eigh
+
+# Constants
+X: np.ndarray = np.array([
+    [1, 2, 0],
+    [2, 4, 0],
+    [0, 0, 3]
+])
+L: np.ndarray = np.array([5., 3., 0.])
+V: np.ndarray = np.array([
+    [np.sqrt(0.2), 0, np.sqrt(0.8)],
+    [np.sqrt(0.8), 0, -np.sqrt(0.2)],
+    [0, 1, 0]
+])
+
+def test_eigh():
+    eigh = Eigh()
+    Vtest = eigh.fit_transform(X)
+    testing.assert_almost_equal(Vtest, V, decimal=6)
+    testing.assert_almost_equal(eigh.eigenvalues_, L, decimal=6)

tests/decomposition/test_svd.py

@@ -0,0 +1,76 @@
+# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding: utf-8 -*-
+# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
+#
+# fluctmatch --- https://github.com/tclick/python-fluctmatch
+# Copyright (c) 2015-2017 The fluctmatch Development Team and contributors
+# (see the file AUTHORS for the full list of names)
+#
+# Released under the New BSD license.
+#
+# Please cite your use of fluctmatch in published work:
+#
+# Timothy H. Click, Nixon Raj, and Jhih-Wei Chu.
+# Calculation of Enzyme Fluctuograms from All-Atom Molecular Dynamics
+# Simulation. Meth Enzymology. 578 (2016), 327-342,
+# doi:10.1016/bs.mie.2016.05.024.
+#
+import numpy as np
+from numpy import testing
+from sklearn.utils.extmath import svd_flip
+from fluctmatch.decomposition.svd import SVD
+
+# Constants
+X: np.ndarray = np.array([
+    [1, 0, 0, 0, 2],
+    [0, 0, 3, 0, 0],
+    [0, 0, 0, 0, 0],
+    [0, 2, 0, 0, 0]
+])
+U: np.ndarray = np.array([
+    [0, 1, 0, 0],
+    [1, 0, 0, 0],
+    [0, 0, 0, -1],
+    [0, 0, 1, 0]
+])
+S: np.ndarray = np.array([3, np.sqrt(5), 2, 0])
+VT: np.ndarray = np.array([
+    [0, 0, 1, 0, 0],
+    [np.sqrt(0.2), 0, 0, 0, np.sqrt(0.8)],
+    [0, 1, 0, 0, 0],
+    [0, 0, 0, 1, 0]
+])
+U, VT = svd_flip(U, VT)
+US: np.ndarray = U * S
+N_COMPONENTS_ = 3
+
+
+def test_full():
+    svd = SVD(svd_solver="full")
+    Utest = svd.fit_transform(X)
+    testing.assert_array_almost_equal(Utest, US, decimal=6)
+    testing.assert_array_almost_equal(svd.singular_values_, S, decimal=6)
+    testing.assert_array_almost_equal(svd.components_, VT, decimal=6)
+
+
+def test_randomized():
+    svd = SVD(svd_solver="randomized")
+    Utest = svd.fit_transform(X)
+    testing.assert_array_almost_equal(Utest, US, decimal=6)
+    testing.assert_array_almost_equal(svd.singular_values_, S, decimal=6)
+    testing.assert_array_almost_equal(svd.components_, VT, decimal=6)
+
+
+def test_trunc_randomized():
+    svd = SVD(n_components=N_COMPONENTS_, svd_solver="randomized")
+    Utest = svd.fit_transform(X)
+    testing.assert_array_almost_equal(Utest, US[:, :N_COMPONENTS_], decimal=6)
+    testing.assert_array_almost_equal(svd.singular_values_, S[:N_COMPONENTS_], decimal=6)
+    testing.assert_array_almost_equal(svd.components_, VT[:N_COMPONENTS_], decimal=6)
+
+
+def test_trunc_arpack():
+    svd = SVD(n_components=N_COMPONENTS_, svd_solver="arpack")
+    Utest = svd.fit_transform(X)
+    testing.assert_array_almost_equal(Utest, US[:, :N_COMPONENTS_], decimal=6)
+    testing.assert_array_almost_equal(svd.singular_values_, S[:N_COMPONENTS_], decimal=6)
+    testing.assert_array_almost_equal(svd.components_, VT[:N_COMPONENTS_], decimal=6)