Tucker tensor regression

import matplotlib.pyplot as plt from tensorly.base import tensor_to_vec, partial_tensor_to_vec from tensorly.datasets.synthetic import gen_image from tensorly.regression.tucker_regression import TuckerRegressor import tensorly as tl # Parameter of the experiment image_height = 25 image_width = 25 # shape of the images patterns = ["rectangle", "swiss", "circle"] # ranks to test ranks = [1, 2, 3, 4, 5] # Generate random samples rng = tl.check_random_state(1) X = tl.tensor(rng.normal(size=(1000, image_height, image_width), loc=0, scale=1)) # Parameters of the plot, deduced from the data n_rows = len(patterns) n_columns = len(ranks) + 1 # Plot the three images fig = plt.figure() for i, pattern in enumerate(patterns): print(f"fitting pattern n.{i}") # Generate the original image weight_img = gen_image( region=pattern, image_height=image_height, image_width=image_width ) weight_img = tl.tensor(weight_img) # Generate the labels y = tl.dot(partial_tensor_to_vec(X, skip_begin=1), tensor_to_vec(weight_img)) # Plot the original weights ax = fig.add_subplot(n_rows, n_columns, i * n_columns + 1) ax.imshow(tl.to_numpy(weight_img), cmap=plt.cm.OrRd, interpolation="nearest") ax.set_axis_off() if i == 0: ax.set_title("Original\nweights") for j, rank in enumerate(ranks): print(f"fitting for rank = {rank}") # Create a tensor Regressor estimator estimator = TuckerRegressor( weight_ranks=[rank, rank], tol=10e-7, n_iter_max=100, reg_W=1, verbose=0 ) # Fit the estimator to the data estimator.fit(X, y) ax = fig.add_subplot(n_rows, n_columns, i * n_columns + j + 2) ax.imshow( tl.to_numpy(estimator.weight_tensor_), cmap=plt.cm.OrRd, interpolation="nearest", ) ax.set_axis_off() if i == 0: ax.set_title(f"Learned\nrank = {rank}") plt.suptitle("Tucker tensor regression") plt.show()