test_kruskal_regression.py
import numpy as np
from ..kruskal_regression import KruskalRegressor
from ...base import tensor_to_vec, partial_tensor_to_vec
from ...metrics.regression import RMSE
from ...random import check_random_state
from ... import backend as T
def test_KruskalRegressor():
"""Test for KruskalRegressor"""
# Parameter of the experiment
image_height = 8
image_width = 8
n_channels = 3
tol = 0.05
# Generate random samples
rng = check_random_state(1234)
X = T.tensor(rng.normal(size=(1200, image_height, image_width, n_channels), loc=0, scale=1))
regression_weights = np.zeros((image_height, image_width, n_channels))
regression_weights[2:-2, 2:-2, 0] = 1
regression_weights[2:-2, 2:-2, 1] = 2
regression_weights[2:-2, 2:-2, 2] = -1
regression_weights = T.tensor(regression_weights)
y = T.dot(partial_tensor_to_vec(X, skip_begin=1), tensor_to_vec(regression_weights))
X_train = X[:1000, :, :]
X_test = X[1000:, : ,:]
y_train = y[:1000]
y_test = y[1000:]
estimator = KruskalRegressor(weight_rank=4, tol=10e-8, reg_W=1, n_iter_max=200, verbose=True)
estimator.fit(X_train, y_train)
y_pred = estimator.predict(X_test)
error = RMSE(y_test, y_pred)
T.assert_(error <= tol, msg='Kruskal Regressor : RMSE is too large, {} > {}'.format(error, tol))
params = estimator.get_params()
T.assert_(params['weight_rank'] == 4, msg='get_params did not return the correct parameters')
params['weight_rank'] = 5
estimator.set_params(**params)
T.assert_(estimator.weight_rank == 5, msg='set_params did not correctly set the given parameters')