diff --git a/abl/reasoning/kb.py b/abl/reasoning/kb.py index 7463346..5534e83 100644 --- a/abl/reasoning/kb.py +++ b/abl/reasoning/kb.py @@ -470,8 +470,10 @@ class PrologKB(KBBase): try: import pyswip - except IndexError: - print("A Prolog-based knowledge base is used. You need to install Swi-Prolog from https://www.swi-prolog.org/Download.html") + except (IndexError, ImportError): + print("A Prolog-based knowledge base is in use. Please install Swi-Prolog \ + using the command 'sudo apt-get install swi-prolog' for Linux users, \ + or download it from https://www.swi-prolog.org/Download.html for Windows and Mac users.") self.prolog = pyswip.Prolog() self.pl_file = pl_file diff --git a/docs/Examples/HED.rst b/docs/Examples/HED.rst index 2cce245..f27d7ed 100644 --- a/docs/Examples/HED.rst +++ b/docs/Examples/HED.rst @@ -101,28 +101,28 @@ As illustrations, we show four equations in the training dataset: for i, x in enumerate(true_train_equation_with_length_5[0]): plt.subplot(1, 5, i+1) plt.axis('off') - plt.imshow(x.transpose(1, 2, 0)) + plt.imshow(x.squeeze(), cmap='gray') plt.show() print(f"First true equation with length 8 in the training dataset:") for i, x in enumerate(true_train_equation_with_length_8[0]): plt.subplot(1, 8, i+1) plt.axis('off') - plt.imshow(x.transpose(1, 2, 0)) + plt.imshow(x.squeeze(), cmap='gray') plt.show() - + false_train_equation_with_length_5 = false_train_equation[5] false_train_equation_with_length_8 = false_train_equation[8] print(f"First false equation with length 5 in the training dataset:") for i, x in enumerate(false_train_equation_with_length_5[0]): plt.subplot(1, 5, i+1) plt.axis('off') - plt.imshow(x.transpose(1, 2, 0)) + plt.imshow(x.squeeze(), cmap='gray') plt.show() print(f"First false equation with length 8 in the training dataset:") for i, x in enumerate(false_train_equation_with_length_8[0]): plt.subplot(1, 8, i+1) plt.axis('off') - plt.imshow(x.transpose(1, 2, 0)) + plt.imshow(x.squeeze(), cmap='gray') plt.show() @@ -135,8 +135,6 @@ Out: .. image:: ../img/hed_dataset1.png :width: 300px - -Out: .. code:: none :class: code-out @@ -145,8 +143,6 @@ Out: .. image:: ../img/hed_dataset2.png :width: 480px - -Out: .. code:: none :class: code-out @@ -155,8 +151,6 @@ Out: .. image:: ../img/hed_dataset3.png :width: 300px - -Out: .. code:: none :class: code-out diff --git a/docs/Examples/HWF.rst b/docs/Examples/HWF.rst index ad25717..613c2e7 100644 --- a/docs/Examples/HWF.rst +++ b/docs/Examples/HWF.rst @@ -102,9 +102,9 @@ illstrations: for i, x in enumerate(X_1000): plt.subplot(1, len(X_1000), i+1) plt.axis('off') - plt.imshow(x.numpy().transpose(1, 2, 0)) + plt.imshow(x.squeeze(), cmap='gray') plt.show() - print(f"gt_pseudo_label in the 1001st data example (a list of pseudo-labels): {gt_pseudo_label_1000}") + print(f"gt_pseudo_label in the 1001st data example (a list of ground truth pseudo-labels): {gt_pseudo_label_1000}") print(f"Y in the 1001st data example (the computed result): {Y_1000}") print() X_3000, gt_pseudo_label_3000, Y_3000 = train_X[3000], train_gt_pseudo_label[3000], train_Y[3000] @@ -112,9 +112,9 @@ illstrations: for i, x in enumerate(X_3000): plt.subplot(1, len(X_3000), i+1) plt.axis('off') - plt.imshow(x.numpy().transpose(1, 2, 0)) + plt.imshow(x.squeeze(), cmap='gray') plt.show() - print(f"gt_pseudo_label in the 3001st data example (a list of pseudo-labels): {gt_pseudo_label_3000}") + print(f"gt_pseudo_label in the 3001st data example (a list of ground truth pseudo-labels): {gt_pseudo_label_3000}") print(f"Y in the 3001st data example (the computed result): {Y_3000}") @@ -125,7 +125,7 @@ Out: X in the 1001st data example (a list of images): .. image:: ../img/hwf_dataset1.png - :width: 300px + :width: 210px .. code:: none :class: code-out @@ -139,7 +139,7 @@ Out: X in the 3001st data example (a list of images): .. image:: ../img/hwf_dataset2.png - :width: 500px + :width: 350px .. code:: none :class: code-out diff --git a/docs/Examples/MNISTAdd.rst b/docs/Examples/MNISTAdd.rst index 425a324..148d213 100644 --- a/docs/Examples/MNISTAdd.rst +++ b/docs/Examples/MNISTAdd.rst @@ -104,10 +104,10 @@ training set, we have: print(f"X in the first data example (a list of two images):") plt.subplot(1,2,1) plt.axis('off') - plt.imshow(X_0[0].numpy().transpose(1, 2, 0)) + plt.imshow(X_0[0].squeeze(), cmap='gray') plt.subplot(1,2,2) plt.axis('off') - plt.imshow(X_0[1].numpy().transpose(1, 2, 0)) + plt.imshow(X_0[1].squeeze(), cmap='gray') plt.show() print(f"gt_pseudo_label in the first data example (a list of two ground truth pseudo-labels): {gt_pseudo_label_0}") print(f"Y in the first data example (their sum result): {Y_0}") @@ -120,10 +120,11 @@ Out: X in the first data example (a list of two images): .. image:: ../img/mnist_add_datasets.png - :width: 400px + :width: 200px - .. parsed-literal:: + .. code:: none + :class: code-out gt_pseudo_label in the first data example (a list of two ground truth pseudo-labels): [7, 5] Y in the first data example (their sum result): 12 diff --git a/docs/Overview/Installation.rst b/docs/Overview/Installation.rst index 860ec97..78b664f 100644 --- a/docs/Overview/Installation.rst +++ b/docs/Overview/Installation.rst @@ -23,6 +23,12 @@ sequentially run following commands in your terminal/command line. $ cd ABL-Package $ pip install -v -e . -(Optional) If the use of a `Prolog-based knowledge base `_ is necessary, you will also need to install Swi-Prolog. +(Optional) If the use of a :ref:`Prolog-based knowledge base ` is necessary, the installation of `Swi-Prolog `_ is also required: -`http://www.swi-prolog.org/build/unix.html `_ \ No newline at end of file +For Linux users: + +.. code:: console + + $ sudo apt-get install swi-prolog + +For Windows and Mac users, please refer to the `Swi-Prolog Download Page `_. \ No newline at end of file diff --git a/docs/README.rst b/docs/README.rst index cfd44a6..9e5494e 100644 --- a/docs/README.rst +++ b/docs/README.rst @@ -34,3 +34,13 @@ sequentially run following commands in your terminal/command line. $ git clone https://github.com/AbductiveLearning/ABL-Package.git $ cd ABL-Package $ pip install -v -e . + +(Optional) If the use of a :ref:`Prolog-based knowledge base ` is necessary, the installation of `Swi-Prolog `_ is also required: + +For Linux users: + +.. code:: console + + $ sudo apt-get install swi-prolog + +For Windows and Mac users, please refer to the `Swi-Prolog Download Page `_. diff --git a/docs/img/ABL.png b/docs/img/ABL.png index d8dc12d..2cedb4a 100644 Binary files a/docs/img/ABL.png and b/docs/img/ABL.png differ diff --git a/docs/img/hed_dataset1.png b/docs/img/hed_dataset1.png index f0d6be3..4908bcb 100644 Binary files a/docs/img/hed_dataset1.png and b/docs/img/hed_dataset1.png differ diff --git a/docs/img/hed_dataset2.png b/docs/img/hed_dataset2.png index f8e203a..46de604 100644 Binary files a/docs/img/hed_dataset2.png and b/docs/img/hed_dataset2.png differ diff --git a/docs/img/hed_dataset3.png b/docs/img/hed_dataset3.png index 5ed16c2..7e46295 100644 Binary files a/docs/img/hed_dataset3.png and b/docs/img/hed_dataset3.png differ diff --git a/docs/img/hed_dataset4.png b/docs/img/hed_dataset4.png index a4f74b4..39b3dcb 100644 Binary files a/docs/img/hed_dataset4.png and b/docs/img/hed_dataset4.png differ diff --git a/docs/img/hwf_dataset1.png b/docs/img/hwf_dataset1.png index 58008f4..ddb623d 100644 Binary files a/docs/img/hwf_dataset1.png and b/docs/img/hwf_dataset1.png differ diff --git a/docs/img/hwf_dataset2.png b/docs/img/hwf_dataset2.png index 0ca7038..f3b6e93 100644 Binary files a/docs/img/hwf_dataset2.png and b/docs/img/hwf_dataset2.png differ diff --git a/docs/img/mnist_add_datasets.png b/docs/img/mnist_add_datasets.png index 485e1f7..8e8d8b4 100644 Binary files a/docs/img/mnist_add_datasets.png and b/docs/img/mnist_add_datasets.png differ diff --git a/examples/hed/hed.ipynb b/examples/hed/hed.ipynb index 4fb0686..27bf086 100644 --- a/examples/hed/hed.ipynb +++ b/examples/hed/hed.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -132,7 +132,7 @@ }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -199,13 +199,13 @@ "for i, x in enumerate(true_train_equation_with_length_5[0]):\n", " plt.subplot(1, 5, i+1)\n", " plt.axis('off') \n", - " plt.imshow(x.transpose(1, 2, 0))\n", + " plt.imshow(x.squeeze(), cmap='gray')\n", "plt.show()\n", "print(f\"First true equation with length 8 in the training dataset:\")\n", "for i, x in enumerate(true_train_equation_with_length_8[0]):\n", " plt.subplot(1, 8, i+1)\n", " plt.axis('off') \n", - " plt.imshow(x.transpose(1, 2, 0))\n", + " plt.imshow(x.squeeze(), cmap='gray')\n", "plt.show()\n", "\n", "false_train_equation_with_length_5 = false_train_equation[5]\n", @@ -214,13 +214,13 @@ "for i, x in enumerate(false_train_equation_with_length_5[0]):\n", " plt.subplot(1, 5, i+1)\n", " plt.axis('off') \n", - " plt.imshow(x.transpose(1, 2, 0))\n", + " plt.imshow(x.squeeze(), cmap='gray')\n", "plt.show()\n", "print(f\"First false equation with length 8 in the training dataset:\")\n", "for i, x in enumerate(false_train_equation_with_length_8[0]):\n", " plt.subplot(1, 8, i+1)\n", " plt.axis('off') \n", - " plt.imshow(x.transpose(1, 2, 0))\n", + " plt.imshow(x.squeeze(), cmap='gray')\n", "plt.show()" ] }, @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -365,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ diff --git a/examples/hwf/hwf.ipynb b/examples/hwf/hwf.ipynb index 106daab..67f6a47 100644 --- a/examples/hwf/hwf.ipynb +++ b/examples/hwf/hwf.ipynb @@ -122,7 +122,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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", 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", 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" ] @@ -165,7 +165,7 @@ "for i, x in enumerate(X_1000):\n", " plt.subplot(1, len(X_1000), i+1)\n", " plt.axis('off') \n", - " plt.imshow(x.numpy().transpose(1, 2, 0))\n", + " plt.imshow(x.squeeze(), cmap='gray')\n", "plt.show()\n", "print(f\"gt_pseudo_label in the 1001st data example (a list of ground truth pseudo-labels): {gt_pseudo_label_1000}\")\n", "print(f\"Y in the 1001st data example (the computed result): {Y_1000}\")\n", @@ -175,7 +175,7 @@ "for i, x in enumerate(X_3000):\n", " plt.subplot(1, len(X_3000), i+1)\n", " plt.axis('off') \n", - " plt.imshow(x.numpy().transpose(1, 2, 0))\n", + " plt.imshow(x.squeeze(), cmap='gray')\n", "plt.show()\n", "print(f\"gt_pseudo_label in the 3001st data example (a list of ground truth pseudo-labels): {gt_pseudo_label_3000}\")\n", "print(f\"Y in the 3001st data example (the computed result): {Y_3000}\")" diff --git a/examples/mnist_add/mnist_add.ipynb b/examples/mnist_add/mnist_add.ipynb index eb2bedf..27ec738 100644 --- a/examples/mnist_add/mnist_add.ipynb +++ b/examples/mnist_add/mnist_add.ipynb @@ -125,7 +125,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -147,10 +147,10 @@ "print(f\"X in the first data example (a list of two images):\")\n", "plt.subplot(1,2,1)\n", "plt.axis('off') \n", - "plt.imshow(X_0[0].numpy().transpose(1, 2, 0))\n", + "plt.imshow(X_0[0].squeeze(), cmap='gray')\n", "plt.subplot(1,2,2)\n", "plt.axis('off') \n", - "plt.imshow(X_0[1].numpy().transpose(1, 2, 0))\n", + "plt.imshow(X_0[1].squeeze(), cmap='gray')\n", "plt.show()\n", "print(f\"gt_pseudo_label in the first data example (a list of two ground truth pseudo-labels): {gt_pseudo_label_0}\")\n", "print(f\"Y in the first data example (their sum result): {Y_0}\")"