def test_knn_functional_response(self):
knnr = KNeighborsRegressor(n_neighbors=1)
knnr.fit(self.X, self.X)
res = knnr.predict(self.X)
np.testing.assert_array_almost_equal(res.data_matrix,
self.X.data_matrix)
def test_functional_response_basis(self):
knnr = KNeighborsRegressor(weights='distance', n_neighbors=5)
response = self.X.to_basis(Fourier(domain_range=(-1, 1), n_basis=10))
knnr.fit(self.X, response)
res = knnr.predict(self.X)
np.testing.assert_array_almost_equal(res.coefficients,
response.coefficients)
def test_knn_functional_response_sklearn(self):
# Check sklearn metric
knnr = KNeighborsRegressor(n_neighbors=1, metric='euclidean',
multivariate_metric=True)
knnr.fit(self.X, self.X)
res = knnr.predict(self.X)
np.testing.assert_array_almost_equal(res.data_matrix,
self.X.data_matrix)
def test_knn_functional_response_precomputed(self):
knnr = KNeighborsRegressor(n_neighbors=4, weights='distance',
metric='precomputed')
d = pairwise_distance(lp_distance)
distances = d(self.X[:4], self.X[:4])
knnr.fit(distances, self.X[:4])
res = knnr.predict(distances)
np.testing.assert_array_almost_equal(res.data_matrix,
self.X[:4].data_matrix)
def test_functional_response_custom_weights(self):
def weights(weights):
return np.array([w == 0 for w in weights], dtype=float)
knnr = KNeighborsRegressor(weights=weights, n_neighbors=5)
response = self.X.to_basis(Fourier(domain_range=(-1, 1), n_basis=10))
knnr.fit(self.X, response)
res = knnr.predict(self.X)
np.testing.assert_array_almost_equal(res.coefficients,
response.coefficients)
def test_predict_regressor(self):
"""Test scalar regression, predics mode location"""
# Dummy test, with weight = distance, only the sample with distance 0
# will be returned, obtaining the exact location
knnr = KNeighborsRegressor(weights='distance')
rnnr = RadiusNeighborsRegressor(weights='distance', radius=.1)
knnr.fit(self.X, self.modes_location)
rnnr.fit(self.X, self.modes_location)
np.testing.assert_array_almost_equal(knnr.predict(self.X),
self.modes_location)
np.testing.assert_array_almost_equal(rnnr.predict(self.X),
self.modes_location)
def test_functional_regression_distance_weights(self):
knnr = KNeighborsRegressor(
weights='distance', n_neighbors=10)
knnr.fit(self.X[:10], self.X[:10])
res = knnr.predict(self.X[11])
d = pairwise_distance(lp_distance)
distances = d(self.X[:10], self.X[11]).flatten()
weights = 1 / distances
weights /= weights.sum()
response = self.X[:10].mean(weights=weights)
np.testing.assert_array_almost_equal(res.data_matrix,
response.data_matrix)
# the response we will use a mean of the response, weighted by their distance
# to the test sample.
#
knn = KNeighborsRegressor(n_neighbors=5, weights='distance')
knn.fit(X_train, y_train)
##############################################################################
#
# We can predict values for the test partition using
# :meth:`~skfda.ml.regression.KNeighborsFunctionalRegressor.predict`. The
# following figure shows the real precipitation curves, in dashed line, and
# the predicted ones.
#
y_pred = knn.predict(X_test)
# Plot prediction
fig = y_pred.plot()
fig.axes[0].set_prop_cycle(None) # Reset colors
y_test.plot(fig=fig, linestyle='--')
##############################################################################
#
# We can quantify how much variability it is explained by the model
# using the
# :meth:`~skfda.ml.regression.KNeighborsFunctionalRegressor.score` method,
# which computes the value
#
# .. math::
# 1 - \frac{\sum_{i=1}^{n}\int (y_i(t) - \hat{y}_i(t))^2dt}