Ejemplo n.º 1
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    def test_setitem(self):
        df = DataFrame()
        rows = "row/"
        cols = "col/"
        M = np.arange(6).reshape(2, 3)
        df.__setitem__((slice(None, None, None), slice(None, None, None)), M)
        assert (df.get_matrix() == M).all()

        df = DataFrame()
        df.__setitem__((slice(None, None, None), slice(None, None, None)),
                       M,
                       rows=["a", "b"],
                       cols=["c", "d", "e"])
        assert (df.get_matrix() == M).all()

        df = DataFrame()
        df["x/", "y/"].__setitem__(
            (slice(None, None, None), slice(None, None, None)),
            M,
            rows=["a", "b"],
            cols=["c", "d", "e"])
        assert (df.get_matrix() == M).all()
        assert (df["x/", "y/"].get_matrix() == M).all()

        df["x/", "y/"].__setitem__(
            (slice(None, None, None), slice(None, None, None)), 2)
        assert (df.get_matrix() == 2).all()
Ejemplo n.º 2
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    def test_gd(self):
        df = DataFrame()
        M1_path = ("row1/", "col1/")
        M2_path = ("row2/", "col2/")
        batch1_path = ("row1/", "col1/batch1/")
        batch2_path = ("row1/", "col1/batch2/")
        x0_path = ("x0/", "y0/")
        M1 = nprand.rand(3, 5)
        df[batch1_path].set_matrix(M1)

        M2 = np.zeros((3, 5))

        df[M2_path] = GD(SquareTest, M2, df[M1_path], step_size=1)
        sleep(1)
        # df[M2_path].stop()
        assert np.allclose(df[M2_path].get_matrix(), df[M1_path].get_matrix())

        # Assert that the input structure has been replicated
        assert (
            df["row2/",
               "col2/batch1/"].get_matrix() == df[M2_path].get_matrix()).all()

        # Now attempt to extend the parameter matrix
        M3 = nprand.rand(3, 4)
        df[batch2_path].set_matrix(M3)

        sleep(1)
        assert df[M2_path].shape == df[M1_path].shape
        assert df[M2_path].shape == (3, 9)
        assert np.allclose(df[M2_path].get_matrix(), df[M1_path].get_matrix())

        df[M2_path].stop()
Ejemplo n.º 3
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    def test_softmax_reg_loss(self):
        df = DataFrame()
        epsilon = 1e-4
        y_path = ("y/", "y/")
        theta_path = ("theta/", "theta/")
        X_path = ("X/", "X/")

        k = 10
        n, m = 5, 8
        df[X_path] = DataFrame.from_matrix(nprand.rand(n, m))
        df[theta_path] = DataFrame.from_matrix(nprand.rand(k, m))
        y = np.zeros((n, k), dtype=bool)
        for i in range(n):
            j = nprand.randint(k)
            y[i, j] = True
        df[y_path] = DataFrame.from_matrix(y)
        reg = 0.0001

        softmax = lambda theta_df: SoftmaxRegression(theta_df, df[X_path], df[
            y_path], reg).f()

        g_central = self.central_diff(softmax, epsilon, df[theta_path])
        g1 = SoftmaxRegression(df[theta_path], df[X_path], df[y_path], reg).g()

        # print g_central
        assert (np.allclose(g_central, g1))
Ejemplo n.º 4
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 def test_zero_mean(self):
     df = DataFrame()
     M1_path = ("row1/", "col1/")
     M2_path = ("row2/", "col2/")
     M1 = nprand.rand(3, 5)
     M1_zm = M1 - np.mean(M1, axis=0)
     df[M1_path].set_matrix(M1)
     df[M2_path] = ZeroMean(df[M1_path])
     assert (df[M2_path].get_matrix() == M1_zm).all()
Ejemplo n.º 5
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 def test_permutation(self):
     df = DataFrame()
     M1_path = ("row1/", "col1/")
     permute_path1 = ("row2/", "col1/")
     M1 = nprand.rand(3, 5)
     df[M1_path] = DataFrame.from_matrix(M1)
     df[permute_path1] = Permute(df[M1_path])
     p_df = df["auto/row1/", "auto/permutation/"]
     p = p_df.get_matrix().ravel()
     assert (df[permute_path1].get_matrix() == M1[p, :]).all()
Ejemplo n.º 6
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 def test_dot(self):
     df = DataFrame()
     M1_path = ("row1/", "col1/")
     M2_path = ("row2/", "col2/")
     dot_path1 = ("row1/", "col2/")
     M1 = nprand.rand(3, 5)
     M2 = nprand.rand(5, 8)
     df[M1_path] = DataFrame.from_matrix(M1)
     df[M2_path].set_matrix(M2)
     df[dot_path1] = Dot(df[M1_path], df[M2_path])
     assert (df[dot_path1].get_matrix() == M1.dot(M2)).all()
Ejemplo n.º 7
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 def test_linear(self):
     df = DataFrame()
     M1_path = ("row1/", "col1/")
     M2_path = ("row2/", "col2/")
     linear_path1 = ("row1/", "col2/")
     M1 = nprand.rand(3, 5)
     M2 = nprand.rand(3, 5)
     df[M1_path] = DataFrame.from_matrix(M1)
     df[M2_path].set_matrix(M2)
     a = 2
     b = -3
     df[linear_path1] = Linear(a, df[M1_path], b, df[M2_path])
     assert (df[linear_path1].get_matrix() == a * M1 + b * M2).all()
Ejemplo n.º 8
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    def test_tuple_to_query(self):
        df = DataFrame()
        # Test conversion of hashable elements to their actual queries
        string = "randomstring"
        slice_hash, slice_actual = (slice, (2, 4, 1)), slice(2, 4, 1)
        list_hash, list_actual = (list, (1, 2, 3, 4, 5, 6)), [1, 2, 3, 4, 5, 6]

        assert df._tuple_element_to_query(string) == string
        assert df._tuple_element_to_query(slice_hash) == slice_actual
        assert df._tuple_element_to_query(list_hash) == list_actual

        assert df._query_to_tuple_element(string) == string
        assert df._query_to_tuple_element(slice_actual) == slice_hash
        assert df._query_to_tuple_element(list_actual) == list_hash
Ejemplo n.º 9
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    def test_PCA_basis(self):
        df = DataFrame()
        M1_path = ("row1/", "col1/")
        M2_path = ("row2/", "col2/")
        n = 10
        m = 5
        d = 3
        M1 = nprand.rand(n, m)
        M1 = M1 - np.mean(M1, axis=0)
        # print M1
        df[M1_path].set_matrix(M1)

        df[M2_path] = PCABasis(df[M1_path], d)

        u, s, v_T = numpy.linalg.svd(M1, full_matrices=False)
        s[d + 1:] = 0

        v = v_T.T[:, :d]

        M1_reconstructed = u.dot(np.diag(s).dot(v_T))
        # print M1
        # print M1_reconstructed

        M1_reconstructed2 = M1.dot(v).dot(v.T)
        # print M1_reconstructed2
        # print M1.dot(v.dot(v.T))

        covmat = (1. / (n - 1)) * M1.T.dot(M1)
        evs, evmat = scipy.linalg.eig(covmat)
        p = np.argsort(evs)[::-1]
        evmat_sorted = evmat[:, p][:, :d]
        M1_reconstructed3 = M1.dot(evmat_sorted).dot(evmat_sorted.T)

        basis = df[M2_path].get_matrix()
        for i in range(evmat_sorted.shape[1]):
            assert np.isclose(basis[:,i], evmat_sorted[:,i]).all() or \
                   np.isclose(basis[:,i],-evmat_sorted[:,i]).all()

        M3_path = ("row3/", "col3/")
        M3 = nprand.rand(2 * n, m)
        M3 = M3 - np.mean(M1, axis=0)
        df[M3_path].set_matrix(M3)
        pca_path = ("pca/", "pca/")
        df[pca_path] = PCA(df[M1_path], df[M3_path], d)
        pca = df[pca_path].get_matrix()
        proj = M3.dot(evmat_sorted)

        for i in range(pca.shape[1]):
            assert np.isclose(pca[:,i], proj[:,i]).all() or \
                   np.isclose(pca[:,i],-proj[:,i]).all()
Ejemplo n.º 10
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    def test_one_hot_encoding(self):
        df = DataFrame()
        M1_path = ("row1/", "col1/")
        M2_path = ("row2/", "col2/")
        n = 10
        m = 5
        M1 = np.vstack(
            [nprand.randint(0, m, (n, 1)),
             np.arange(m).reshape(m, 1)])
        M2 = np.zeros((n + m, m))
        for i in range(n + m):
            M2[i, M1[i]] = 1

        df[M1_path].set_matrix(M1)
        df[M2_path] = OneHotEncoding(df[M1_path])

        assert (df[M2_path].get_matrix() == M2).all()
Ejemplo n.º 11
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    def test_sgd(self):
        # Also test sgd
        close = np.array([[-44.25076083, 38.62854577],
                          [-38.41473092, 36.29945225],
                          [-31.43300105, 30.79620632],
                          [-21.27706071, 24.08638079],
                          [-14.00259076, 6.54438641],
                          [11.52354442, -6.07783327],
                          [48.69374796, -38.64696136],
                          [95.49682071, -84.38906967]])

        df = DataFrame()
        path = "row/", "col/"
        df["xrow/",
           "xcol/"] = DataFrame.from_matrix(np.arange(16).reshape(8, 2))
        df["yrow/",
           "ycol/"] = DataFrame.from_matrix(np.arange(8).reshape(8, 1))
        X_df = df["xrow/", "xcol/"]
        y_df = df["yrow/", "ycol/"]
        df[path] = SGD(SquareTest, close, y_df, batch_size=8, step_size=0.5)
        sleep(1)
        df[path].stop()
        assert np.allclose(df[path].get_matrix(), y_df.get_matrix())