def test_magnitude_shape_plot(self):
fd = fetch_weather()["data"]
fd_temperatures = FDataGrid(data_matrix=fd.data_matrix[:, :, 0],
sample_points=fd.sample_points,
dataset_label=fd.dataset_label,
axes_labels=fd.axes_labels[0:2])
msplot = MagnitudeShapePlot(fd_temperatures,
depth_method=modified_band_depth)
np.testing.assert_allclose(
msplot.points,
np.array([[0.2591055, 3.15861149], [1.3811996, 0.91806814],
[0.94648379, 2.75695426], [2.11346208, 7.24045853],
[0.82557436, 0.82727771], [1.23249759, 0.22004329],
[-2.66259589, 0.96925352], [0.15827963, 1.00235557],
[-0.43751765, 0.66236714], [0.70695162, 0.66482897],
[0.73095525, 0.33165117], [3.48445368, 12.59745018],
[3.15539264, 13.85234879], [3.52759979, 10.49810783],
[3.95518808, 15.28317686], [-0.48486514, 0.5035343],
[0.64492781, 6.83385521], [-0.83185751, 0.81125541],
[-3.47125418, 1.10683451], [0.22241054, 1.76783493],
[-0.54402406, 0.95229119], [-1.71310618, 0.61875513],
[-0.44161441, 0.77815135], [0.13851408, 1.02560672],
[7.59909246, 40.82126568], [7.57868277, 36.03923856],
[7.12930634, 45.96866318], [0.05746528, 1.75817588],
[1.53092075, 8.85227], [-1.48696387, 0.22472872],
[-2.853082, 4.50814844], [-2.42297615, 1.46926902],
[-5.8873129, 5.35742609], [-5.44346193, 5.18338576],
[-16.38949483, 0.94027717]]))
np.testing.assert_array_almost_equal(
msplot.outliers,
np.array([
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1
]))
def test_magnitude_shape_plot(self):
fd = fetch_weather()["data"]
fd_temperatures = fd.coordinates[0]
msplot = MagnitudeShapePlot(
fd_temperatures, multivariate_depth=SimplicialDepth())
np.testing.assert_allclose(msplot.points,
np.array([[0.2112587, 3.0322570],
[1.2823448, 0.8272850],
[0.8646544, 1.8619370],
[1.9862512, 5.5287354],
[0.7534918, 0.7203502],
[1.1325291, 0.2808455],
[-2.650529, 0.9702889],
[0.1434387, 0.9159834],
[-0.402844, 0.6413531],
[0.6354411, 0.6934311],
[0.5727553, 0.4628254],
[3.0524899, 8.8008899],
[2.7355803, 10.338497],
[3.1179374, 7.0686220],
[3.4944047, 11.479432],
[-0.402532, 0.5253690],
[0.5782190, 5.5400704],
[-0.839887, 0.7350041],
[-3.456470, 1.1156415],
[0.2260207, 1.5071672],
[-0.561562, 0.8836978],
[-1.690263, 0.6392155],
[-0.385394, 0.7401909],
[0.1467050, 0.9090058],
[7.1811993, 39.003407],
[6.8943132, 30.968126],
[6.6227164, 41.448548],
[0.0726709, 1.5960063],
[1.4450617, 8.7183435],
[-1.459836, 0.2719813],
[-2.824349, 4.5729382],
[-2.390462, 1.5464775],
[-5.869571, 5.3517279],
[-5.426019, 5.1817219],
[-16.34459, 0.9397117]]), rtol=1e-5)
np.testing.assert_array_almost_equal(msplot.outliers,
np.array(
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 1, 0,
0, 0, 0, 0, 1]))
label_names = dataset["target_names"]
nlabels = len(label_names)
label_colors = colormap(np.arange(nlabels) / (nlabels - 1))
fd_temperatures.plot(group=dataset["target"],
group_colors=label_colors,
group_names=label_names)
##############################################################################
# The MS-Plot is generated. In order to show the results, the
# :func:`~skfda.exploratory.visualization.MagnitudeShapePlot.plot` method
# is used. Note that the colors have been specified before to distinguish
# between outliers or not. In particular the tones of the default colormap,
# (which is 'seismic' and can be customized), are assigned.
msplot = MagnitudeShapePlot(fdatagrid=fd_temperatures,
depth_method=modified_band_depth)
color = 0.3
outliercol = 0.7
msplot.color = color
msplot.outliercol = outliercol
msplot.plot()
##############################################################################
# To show the utility of the plot, the curves are plotted according to the
# distinction made by the MS-Plot (outliers or not) with the same colors.
fd_temperatures.plot(group=msplot.outliers.astype(int),
group_colors=msplot.colormap([color, outliercol]),
group_names=['nonoutliers', 'outliers'])
shape_outlier_peak, shape_outlier_sin,
shape_outlier_slope, magnitude_shape_outlier)
##############################################################################
# The data is plotted to show the curves we are working with.
labels = [0] * n_samples + [1] * 6
fd.plot(sample_labels=labels,
label_colors=['lightgrey', 'black'])
##############################################################################
# The MS-Plot is generated. In order to show the results, the
# :func:`~skfda.exploratory.visualization.MagnitudeShapePlot.plot`
# method is used.
msplot = MagnitudeShapePlot(fdatagrid=fd)
msplot.plot()
##############################################################################
# To show the utility of the plot, the curves are plotted showing each outlier
# in a different color
labels = [0] * n_samples + [1, 2, 3, 4, 5, 6]
colors = ['lightgrey', 'orange', 'blue', 'black',
'green', 'brown', 'lightblue']
fd.plot(sample_labels=labels,
label_colors=colors)
##############################################################################
label_names = target.categories
nlabels = len(label_names)
label_colors = colormap(np.arange(nlabels) / (nlabels - 1))
fd_temperatures.plot(group=target.codes,
group_colors=label_colors,
group_names=label_names)
##############################################################################
# The MS-Plot is generated. In order to show the results, the
# :func:`~skfda.exploratory.visualization.MagnitudeShapePlot.plot` method
# is used. Note that the colors have been specified before to distinguish
# between outliers or not. In particular the tones of the default colormap,
# (which is 'seismic' and can be customized), are assigned.
msplot = MagnitudeShapePlot(fdatagrid=fd_temperatures,
multivariate_depth=SimplicialDepth())
color = 0.3
outliercol = 0.7
msplot.color = color
msplot.outliercol = outliercol
msplot.plot()
##############################################################################
# To show the utility of the plot, the curves are plotted according to the
# distinction made by the MS-Plot (outliers or not) with the same colors.
fd_temperatures.plot(group=msplot.outliers.astype(int),
group_colors=msplot.colormap([color, outliercol]),
group_names=['nonoutliers', 'outliers'])