Github action: auto-update.

Author: github-actions[bot]

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 
 """
 Demonstration of PARAFAC2

dev/_downloads/31ebe5f65fa406fbccca74ea8deb8a69/plot_IL2.zip

@@ -67,7 +67,7 @@
         ax.set_axis_off()
 
         if i == 0:
-            ax.set_title("Learned\nrank = {}".format(rank))
+            ax.set_title(f"Learned\nrank = {rank}")
 
 plt.suptitle("CP tensor regression")
 plt.show()

dev/_downloads/32f8d624f9d1f7b561ad62798d8861ca/plot_parafac2.py

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom tensorly.base import tensor_to_vec, partial_tensor_to_vec\nfrom tensorly.datasets.synthetic import gen_image\nfrom tensorly.regression.cp_regression import CPRegressor\nimport tensorly as tl\n\n# Parameter of the experiment\nimage_height = 25\nimage_width = 25\n# shape of the images\npatterns = [\"rectangle\", \"swiss\", \"circle\"]\n# ranks to test\nranks = [1, 2, 3, 4, 5]\n\n# Generate random samples\nrng = tl.check_random_state(1)\nX = tl.tensor(rng.normal(size=(1000, image_height, image_width), loc=0, scale=1))\n\n\n# Parameters of the plot, deduced from the data\nn_rows = len(patterns)\nn_columns = len(ranks) + 1\n# Plot the three images\nfig = plt.figure()\n\nfor i, pattern in enumerate(patterns):\n\n    # Generate the original image\n    weight_img = gen_image(\n        region=pattern, image_height=image_height, image_width=image_width\n    )\n    weight_img = tl.tensor(weight_img)\n\n    # Generate the labels\n    y = tl.dot(partial_tensor_to_vec(X, skip_begin=1), tensor_to_vec(weight_img))\n\n    # Plot the original weights\n    ax = fig.add_subplot(n_rows, n_columns, i * n_columns + 1)\n    ax.imshow(tl.to_numpy(weight_img), cmap=plt.cm.OrRd, interpolation=\"nearest\")\n    ax.set_axis_off()\n    if i == 0:\n        ax.set_title(\"Original\\nweights\")\n\n    for j, rank in enumerate(ranks):\n\n        # Create a tensor Regressor estimator\n        estimator = CPRegressor(\n            weight_rank=rank, tol=10e-7, n_iter_max=100, reg_W=1, verbose=0\n        )\n\n        # Fit the estimator to the data\n        estimator.fit(X, y)\n\n        ax = fig.add_subplot(n_rows, n_columns, i * n_columns + j + 2)\n        ax.imshow(\n            tl.to_numpy(estimator.weight_tensor_),\n            cmap=plt.cm.OrRd,\n            interpolation=\"nearest\",\n        )\n        ax.set_axis_off()\n\n        if i == 0:\n            ax.set_title(\"Learned\\nrank = {}\".format(rank))\n\nplt.suptitle(\"CP tensor regression\")\nplt.show()"
+        "import matplotlib.pyplot as plt\nfrom tensorly.base import tensor_to_vec, partial_tensor_to_vec\nfrom tensorly.datasets.synthetic import gen_image\nfrom tensorly.regression.cp_regression import CPRegressor\nimport tensorly as tl\n\n# Parameter of the experiment\nimage_height = 25\nimage_width = 25\n# shape of the images\npatterns = [\"rectangle\", \"swiss\", \"circle\"]\n# ranks to test\nranks = [1, 2, 3, 4, 5]\n\n# Generate random samples\nrng = tl.check_random_state(1)\nX = tl.tensor(rng.normal(size=(1000, image_height, image_width), loc=0, scale=1))\n\n\n# Parameters of the plot, deduced from the data\nn_rows = len(patterns)\nn_columns = len(ranks) + 1\n# Plot the three images\nfig = plt.figure()\n\nfor i, pattern in enumerate(patterns):\n\n    # Generate the original image\n    weight_img = gen_image(\n        region=pattern, image_height=image_height, image_width=image_width\n    )\n    weight_img = tl.tensor(weight_img)\n\n    # Generate the labels\n    y = tl.dot(partial_tensor_to_vec(X, skip_begin=1), tensor_to_vec(weight_img))\n\n    # Plot the original weights\n    ax = fig.add_subplot(n_rows, n_columns, i * n_columns + 1)\n    ax.imshow(tl.to_numpy(weight_img), cmap=plt.cm.OrRd, interpolation=\"nearest\")\n    ax.set_axis_off()\n    if i == 0:\n        ax.set_title(\"Original\\nweights\")\n\n    for j, rank in enumerate(ranks):\n\n        # Create a tensor Regressor estimator\n        estimator = CPRegressor(\n            weight_rank=rank, tol=10e-7, n_iter_max=100, reg_W=1, verbose=0\n        )\n\n        # Fit the estimator to the data\n        estimator.fit(X, y)\n\n        ax = fig.add_subplot(n_rows, n_columns, i * n_columns + j + 2)\n        ax.imshow(\n            tl.to_numpy(estimator.weight_tensor_),\n            cmap=plt.cm.OrRd,\n            interpolation=\"nearest\",\n        )\n        ax.set_axis_off()\n\n        if i == 0:\n            ax.set_title(f\"Learned\\nrank = {rank}\")\n\nplt.suptitle(\"CP tensor regression\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/3ca037908294af324764e4c88851b187/plot_covid.zip

@@ -109,9 +109,9 @@
 # To compare the various methods, first we may look at each algorithm
 # processing time:
 
-print("time for tensorly nntucker:" + " " + str("{:.2f}".format(time_mu)))
-print("time for HALS with fista:" + " " + str("{:.2f}".format(time_fista)))
-print("time for HALS with as:" + " " + str("{:.2f}".format(time_as)))
+print("time for tensorly nntucker:" + " " + str(f"{time_mu:.2f}"))
+print("time for HALS with fista:" + " " + str(f"{time_fista:.2f}"))
+print("time for HALS with as:" + " " + str(f"{time_as:.2f}"))
 
 ##############################################################################
 # All algorithms should run with about the same number of iterations on our

dev/_downloads/42966e24b5f81902fc31165ec0d52e6c/plot_cp_regression.py

@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "tensor = tl.tensor(np.arange(24).reshape((3, 4, 2)))\nprint(\"* original tensor:\\n{}\".format(tensor))"
+        "tensor = tl.tensor(np.arange(24).reshape((3, 4, 2)))\nprint(f\"* original tensor:\\n{tensor}\")"
       ]
     },
     {
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "for mode in range(tensor.ndim):\n    print(\"* mode-{} unfolding:\\n{}\".format(mode, tl.unfold(tensor, mode)))"
+        "for mode in range(tensor.ndim):\n    print(f\"* mode-{mode} unfolding:\\n{tl.unfold(tensor, mode)}\")"
       ]
     },
     {