def test_plot_violins(pca, kwargs, df_norm):
from flotilla.visualize.decomposition import DecompositionViz
kw = kwargs.copy()
kw.pop('singles')
dv = DecompositionViz(pca.reduced_space, pca.components_,
pca.explained_variance_ratio_,
singles=df_norm, **kw)
dv.plot(plot_violins=True)
ncols = 4
nrows = 1
top_features = pd.Index(dv.top_features)
vector_labels = list(set(dv.magnitudes[:dv.n_vectors].index.union(
top_features)))
while ncols * nrows < len(vector_labels):
nrows += 1
pdt.assert_equal(len(dv.fig_violins.axes), nrows * ncols)
# for i in np.arange(len(top_features)):
# ax = dv.fig_violins.axes[i]
# pdt.assert_equal(len(ax.collections), len(dv.grouped.groups))
plt.close('all')
def test_plot_violins(pca, kwargs, df_norm):
from flotilla.visualize.decomposition import DecompositionViz
kw = kwargs.copy()
kw.pop('singles')
dv = DecompositionViz(pca.reduced_space,
pca.components_,
pca.explained_variance_ratio_,
singles=df_norm,
**kw)
dv.plot(plot_violins=True)
ncols = 4
nrows = 1
top_features = pd.Index(dv.top_features)
vector_labels = list(
set(dv.magnitudes[:dv.n_vectors].index.union(top_features)))
while ncols * nrows < len(vector_labels):
nrows += 1
pdt.assert_equal(len(dv.fig_violins.axes), nrows * ncols)
# for i in np.arange(len(top_features)):
# ax = dv.fig_violins.axes[i]
# pdt.assert_equal(len(ax.collections), len(dv.grouped.groups))
plt.close('all')
def test_plot_loadings_scatter(pca, kwargs):
from flotilla.visualize.decomposition import DecompositionViz
dv = DecompositionViz(pca.reduced_space, pca.components_,
pca.explained_variance_ratio_, **kwargs)
dv.plot(plot_loadings='scatter')
pdt.assert_equal(len(dv.fig_reduced.axes), 3)
pdt.assert_equal(len(dv.ax_loading1.collections), 1)
pdt.assert_equal(len(dv.ax_loading1.collections), 1)
plt.close('all')
def test_plot_loadings_scatter(pca, kwargs):
from flotilla.visualize.decomposition import DecompositionViz
dv = DecompositionViz(pca.reduced_space, pca.components_,
pca.explained_variance_ratio_, **kwargs)
dv.plot(plot_loadings='scatter')
pdt.assert_equal(len(dv.fig_reduced.axes), 3)
pdt.assert_equal(len(dv.ax_loading1.collections), 1)
pdt.assert_equal(len(dv.ax_loading1.collections), 1)
plt.close('all')
def test_plot(pca, kwargs):
from flotilla.visualize.decomposition import DecompositionViz
dv = DecompositionViz(pca.reduced_space, pca.components_,
pca.explained_variance_ratio_, **kwargs)
dv.plot()
pdt.assert_equal(len(dv.fig_reduced.axes), 5)
pdt.assert_equal(len(dv.ax_components.lines), kwargs['n_vectors'] + 1)
pdt.assert_equal(len(dv.ax_explained_variance.lines), 1)
pdt.assert_equal(len(dv.ax_explained_variance.collections), 1)
pdt.assert_equal(len(dv.ax_empty.collections), 0)
pdt.assert_equal(len(dv.ax_pcs_heatmap.collections), 1)
pdt.assert_equal(len(dv.ax_pcs_colorbar.collections), 1)
assert not hasattr(dv, 'ax_loading1')
assert not hasattr(dv, 'ax_loading2')
plt.close('all')
def test_plot(pca, kwargs):
from flotilla.visualize.decomposition import DecompositionViz
dv = DecompositionViz(pca.reduced_space, pca.components_,
pca.explained_variance_ratio_, **kwargs)
dv.plot()
pdt.assert_equal(len(dv.fig_reduced.axes), 5)
pdt.assert_equal(len(dv.ax_components.lines),
kwargs['n_vectors']+1)
pdt.assert_equal(len(dv.ax_explained_variance.lines), 1)
pdt.assert_equal(len(dv.ax_explained_variance.collections), 1)
pdt.assert_equal(len(dv.ax_empty.collections), 0)
pdt.assert_equal(len(dv.ax_pcs_heatmap.collections), 1)
pdt.assert_equal(len(dv.ax_pcs_colorbar.collections), 1)
assert not hasattr(dv, 'ax_loading1')
assert not hasattr(dv, 'ax_loading2')
plt.close('all')
