def test_random_visualization_plot_feature(self):
"""`visualization.Visualization.plot_feature`: Randomized Validator.
Tests the behavior of `Visualization.plot_feature` by feeding it
randomly generated arguments.
Raises:
AssertionError: If `Visualization.plot_feature` needs debugging.
"""
for i in range(self.n_tests):
for ModelWrapper in self.wrappers.values():
X = _random_matrix(self.data_shape)
"""np.matrix: Random-valued feature set."""
Y = _random_matrix((self.data_shape[0], 1))
"""np.matrix: Random-valued observation set."""
v = Visualization("Title", (3, 3))
"""Visualization: Plotter instance."""
# Intialize model parameters to random values.
ModelWrapper.model = dict(X=X)
for i in range(9):
x, y, error = v._plot_feature(X, Y, i, ModelWrapper)
"""(list of float, :obj:`list of float`): X- and y-values to
plot."""
# `x` should be a list of floats.
self.assertIsInstance(x, list)
map(_appendargs(self.assertIsInstance, float), x)
# Number of `x` values should match number of data points in
# `X`.
self.assertEqual(len(x), X.shape[0])
# `x` values should match all values in `X`.
self.assertEqual(*map(_np.linalg.norm, [x, X[:, i]]))
# `y` should be a dict.
self.assertIsInstance(y, dict)
for j, values in _compose(enumerate, y.values)():
# `values` should be a list of floats.
self.assertIsInstance(values, list)
map(_appendargs(self.assertIsInstance, float), values)
# Number of values in `values` should match number of
# data points in `Y`.
self.assertEqual(len(values), Y.shape[0])
if j == 0:
# Observation values should match all values in `Y`.
self.assertEqual(
*map(_np.linalg.norm, [values, Y[:, 0]]))
v.close()
def test_edge_cases_visualization_plot_feature(self):
"""`visualization.Visualization.plot_feature`: Edge Case Validator.
Tests the behavior of `Visualization.plot_feature` with edge cases.
Raises:
Exception: If at least one `Exception` raised is not of the expected
kind.
"""
X = _random_matrix(self.data_shape)
"""np.matrix: Random-valued feature set."""
Y = _random_matrix((self.data_shape[0], 1))
"""np.matrix: Random-valued observation set."""
v = Visualization("Title", (1, 1))
"""Visualization: Plotter instance."""
for ModelWrapper in self.wrappers.values():
with self.assertRaises(_InvalidFeatureSetError):
# Empty feature set.
v._plot_feature(_np.matrix([[]]), Y, 0, ModelWrapper)
with self.assertRaises(_InvalidObservationSetError):
# Empty observation set.
v._plot_feature(X, _np.matrix([[]]), 0, ModelWrapper)
with self.assertRaises(IndexError):
# Feature index out of range.
v._plot_feature(X, Y, self.data_shape[1], ModelWrapper)
v.close()
def test_invalid_args_visualization_plot_feature(self):
"""`visualization.Visualization.plot_feature`: Argument Validator.
Tests the behavior of `Visualization.plot_feature` with invalid
argument counts and values.
Raises:
Exception: If at least one `Exception` raised is not of the expected
kind.
"""
X = _random_matrix(self.data_shape)
"""np.matrix: Random-valued feature set."""
Y = _random_matrix((self.data_shape[0], 1))
"""np.matrix: Random-valued observation set."""
v = Visualization("Title", (1, 1))
"""Visualization: Plotter instance."""
with self.assertRaises(TypeError):
# No arguments.
v._plot_feature()
with self.assertRaises(TypeError):
# Only one argument.
v._plot_feature(X)
with self.assertRaises(TypeError):
# Only two arguments.
v._plot_feature(X, Y)
with self.assertRaises(TypeError):
# Only three arguments.
v._plot_feature(X, Y, 0)
with self.assertRaises(TypeError):
# `None` instead of model wrapper.
v._plot_feature(X, Y, 0, None)
with self.assertRaises(TypeError):
# `Visualization` instead of `ModelWrapper`.
v._plot_feature(X, Y, 0, v)
for ModelWrapper in self.wrappers.values():
with self.assertRaises(TypeError):
# Too many arguments.
v._plot_feature(X, Y, 0, ModelWrapper, "extra")
with self.assertRaises(TypeError):
# With keyword.
v._plot_feature(X, Y, 0, ModelWrapper, key="value")
with self.assertRaises(_InvalidFeatureSetError):
# Non-matrix feature set `X`.
v._plot_feature(None, Y, 0, ModelWrapper)
with self.assertRaises(_InvalidFeatureSetError):
# ndarray instead of feature set `X`.
v._plot_feature(_np.zeros(self.data_shape), Y, 0, ModelWrapper)
with self.assertRaises(_InvalidObservationSetError):
# Non-matrix feature set `X`.
v._plot_feature(X, None, 0, ModelWrapper)
with self.assertRaises(_InvalidObservationSetError):
# ndarray instead of feature set `X`.
v._plot_feature(X, _np.zeros((self.data_shape[0], 1)), 0,
ModelWrapper)
with self.assertRaises(_IncompatibleDataSetsError):
# Incompatible datasets.
v._plot_feature(X, _random_matrix((self.data_shape[0] + 1, 1)),
0, ModelWrapper)
with self.assertRaises(TypeError):
# Non-integer index `feature`.
v._plot_feature(X, Y, None, ModelWrapper)
with self.assertRaises(ValueError):
# Negative integer instead of `feature`.
v._plot_feature(X, Y, -2, ModelWrapper)
v.close()
