From d15b1fb60e87c13f4324cc87a478e9958d29c048 Mon Sep 17 00:00:00 2001 From: avk256 Date: Tue, 28 May 2024 12:39:55 +0300 Subject: [PATCH] update results and ipynb --- .../StackNet_matv0_2.ipynb | 4719 ++++++++--------- .../matbench_v0.1_StackNet-mat_v0.2/info.json | 4 +- .../results.json.gz | Bin 4725 -> 4694 bytes 3 files changed, 2215 insertions(+), 2508 deletions(-) diff --git a/benchmarks/matbench_v0.1_StackNet-mat_v0.2/StackNet_matv0_2.ipynb b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/StackNet_matv0_2.ipynb index 7f37b5b5..eaf6fec9 100644 --- a/benchmarks/matbench_v0.1_StackNet-mat_v0.2/StackNet_matv0_2.ipynb +++ b/benchmarks/matbench_v0.1_StackNet-mat_v0.2/StackNet_matv0_2.ipynb @@ -15,7 +15,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "11b4244294aa4d42b38911fd5fc49918": { + "5951387c8ad44e6e8c3bc221d26f95a7": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -30,14 +30,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - 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"layout": "IPY_MODEL_4c8e5e58d83a4b8eae9d70b4c3edcc2b", + "layout": "IPY_MODEL_a7ca0f0d40974f2a88f2ac074cfa8632", "placeholder": "​", - "style": "IPY_MODEL_b3696cc227f347218a44775e2a9ac8e9", - "value": " 62/62 [00:00<00:00,  6.82it/s]" + "style": "IPY_MODEL_a3f432d74847438a9ef7fa9948ba1d12", + "value": " 62/62 [00:00<00:00,  7.30it/s]" } }, - "95a64b3bb8d343d39ac19de385e6b1fd": { + "337099bd22c5463586a9b8c4a38e1c9b": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -18281,7 +18281,7 @@ "width": null } }, - "dbe768fea0d441f291efed84ff58e926": { + "9b3a9fe11d8b46678e33231d25ee641a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -18333,7 +18333,7 @@ "width": null } }, - "44ee46d342fa440391065eb8b7e8078e": { + "50738fc09b11451fb7c02feb3e03495d": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -18348,7 +18348,7 @@ "description_width": "" } }, - 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"IPY_MODEL_9beb2c27834c47f58d0828a56fc0c4eb", - "IPY_MODEL_73316c6ca8624080838ac6a759cb9b50", - "IPY_MODEL_66bb8213fa8e454e9a124f6d2ba033a6" + "IPY_MODEL_5668dd1f1b5943ea9119f32cb2da4986", + "IPY_MODEL_4bd9030c572c4594a5e344c5982eadd9", + "IPY_MODEL_c85eb2e60ecc4803a798275132856457" ], - "layout": "IPY_MODEL_0748b98cd73146eda7459d0860147fcf" + "layout": "IPY_MODEL_4b41b23c14754556a02a28c5ed653636" } }, - "9beb2c27834c47f58d0828a56fc0c4eb": { + "5668dd1f1b5943ea9119f32cb2da4986": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -18520,13 +18520,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_41d31b0bd5134e198aac234022f700b2", + "layout": "IPY_MODEL_fd1668d9e47f4c53882d670975ce061e", "placeholder": "​", - "style": "IPY_MODEL_d8e3374ff2cd45ada94f99a17102bd32", + "style": "IPY_MODEL_50b75f18541240d28a4b10f245b594f8", "value": "Meredig: 100%" } }, - "73316c6ca8624080838ac6a759cb9b50": { + "4bd9030c572c4594a5e344c5982eadd9": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -18542,15 +18542,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_578bd888db2c42e2ac808e0b93bce3d8", + "layout": "IPY_MODEL_192940c1da884c4a96952c0d6e1aae67", "max": 62, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_f4244b5a535e44e8a331b77bd38183a4", + "style": "IPY_MODEL_0e7cea53c882436a86cc94cb6246da96", "value": 62 } }, - "66bb8213fa8e454e9a124f6d2ba033a6": { + "c85eb2e60ecc4803a798275132856457": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -18565,13 +18565,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_e1c10f56a9144c1faa30063e207f3774", + "layout": "IPY_MODEL_742d302cfc074d399dc0cb243949fd16", "placeholder": "​", - "style": "IPY_MODEL_a38f7dd92d704a2aad67bb2947600b8f", - "value": " 62/62 [00:04<00:00, 11.38it/s]" + "style": "IPY_MODEL_2af170da050f47e5baa6c2bbafe130ac", + "value": " 62/62 [00:00<00:00,  6.78it/s]" } }, - "0748b98cd73146eda7459d0860147fcf": { + "4b41b23c14754556a02a28c5ed653636": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -18623,7 +18623,7 @@ "width": null } }, - "41d31b0bd5134e198aac234022f700b2": { + "fd1668d9e47f4c53882d670975ce061e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -18675,7 +18675,7 @@ "width": null } }, - "d8e3374ff2cd45ada94f99a17102bd32": { + "50b75f18541240d28a4b10f245b594f8": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -18690,7 +18690,7 @@ "description_width": "" } }, - "578bd888db2c42e2ac808e0b93bce3d8": { + "192940c1da884c4a96952c0d6e1aae67": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -18742,7 +18742,7 @@ "width": null } }, - "f4244b5a535e44e8a331b77bd38183a4": { + "0e7cea53c882436a86cc94cb6246da96": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -18758,7 +18758,7 @@ "description_width": "" } }, - "e1c10f56a9144c1faa30063e207f3774": { + "742d302cfc074d399dc0cb243949fd16": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -18810,7 +18810,7 @@ "width": null } }, - "a38f7dd92d704a2aad67bb2947600b8f": { + "2af170da050f47e5baa6c2bbafe130ac": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -18861,7 +18861,7 @@ "base_uri": "https://localhost:8080/" }, "id": "ltnsYDADJMe0", - "outputId": "fd594291-156e-42f1-b46c-d79c5417d9bc" + "outputId": "63956c0f-73d6-47ff-fd57-8482100191bf" }, "execution_count": 2, "outputs": [ @@ -18872,44 +18872,12 @@ "Cloning into 'pystacknet'...\n", "remote: Enumerating objects: 42, done.\u001b[K\n", "remote: Total 42 (delta 0), reused 0 (delta 0), pack-reused 42\u001b[K\n", - "Receiving objects: 100% (42/42), 1.57 MiB | 19.38 MiB/s, done.\n", + "Receiving objects: 100% (42/42), 1.57 MiB | 19.86 MiB/s, done.\n", "Resolving deltas: 100% (10/10), done.\n" ] } ] }, - { - "cell_type": "code", - "source": [ - "\n", - "\n", - "os.getcwd()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "JCQng-CZYL6a", - "outputId": "c0fae35e-7b73-45c3-8b32-d9e96e0b8b7a" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'/content'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 3 - } - ] - }, { "cell_type": "code", "source": [ @@ -18931,7 +18899,7 @@ "base_uri": "https://localhost:8080/" }, "id": "rffuTw_bZfAD", - "outputId": "634797a3-038e-4c24-fb5e-19970e3cbf0c" + "outputId": "81520e67-32ce-4921-b2fd-e58e942891db" }, "execution_count": 5, "outputs": [ @@ -19086,7 +19054,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "cfd47fc7-2bec-418d-899b-3e9c208aeffb" + "outputId": "b7e3830b-f1d4-49a9-bcd8-7468794a33bd" }, "execution_count": 7, "outputs": [ @@ -19099,9 +19067,8 @@ "remote: Counting objects: 100% (1043/1043), done.\u001b[K\n", "remote: Compressing objects: 100% (279/279), done.\u001b[K\n", "remote: Total 10388 (delta 584), reused 963 (delta 523), pack-reused 9345\u001b[K\n", - "Receiving objects: 100% (10388/10388), 367.01 MiB | 32.27 MiB/s, done.\n", - "Resolving deltas: 100% (6749/6749), done.\n", - "Updating files: 100% (382/382), done.\n" + "Receiving objects: 100% (10388/10388), 367.01 MiB | 21.28 MiB/s, done.\n", + "Resolving deltas: 100% (6749/6749), done.\n" ] } ] @@ -19116,7 +19083,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "506b14a5-8905-4b23-d139-dc1884c16ff7" + "outputId": "78a3052e-98f5-41a4-c791-e1d07217ef41" }, "execution_count": 8, "outputs": [ @@ -19130,11 +19097,11 @@ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "Collecting matminer>=0.7.4 (from matbench==0.6)\n", " Downloading matminer-0.9.2-py3-none-any.whl (1.4 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: scipy>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from matbench==0.6) (1.11.4)\n", "Collecting monty>=2022.4.26 (from matbench==0.6)\n", " Downloading monty-2024.5.24-py3-none-any.whl (67 kB)\n", - 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" Stored in directory: /tmp/pip-ephem-wheel-cache-_27kz6xs/wheels/0d/71/69/39ae3b7c60edd18e46ed290a09bc2fe0c344372520cd165c70\n", + " Created wheel for matbench: filename=matbench-0.6-py3-none-any.whl size=5449480 sha256=21118a50e093168124303d796628ed4d15a24ef227a5125d3c15d93dcf56ecc9\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-o8vzp4k6/wheels/0d/71/69/39ae3b7c60edd18e46ed290a09bc2fe0c344372520cd165c70\n", "Successfully built matbench\n", "Installing collected packages: uncertainties, spglib, ruamel.yaml.clib, palettable, monty, latexcodec, dnspython, scikit-learn, ruamel.yaml, pymongo, pybtex, pymatgen, matminer, matbench\n", "\u001b[33m WARNING: The scripts pybtex, pybtex-convert and pybtex-format are installed in '/root/.local/bin' which is not on PATH.\n", @@ -19234,7 +19201,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "efef4a6d-1539-4b9b-c843-dfc247ba3395" + "outputId": "19c35786-9fa1-4ddd-8011-5e86a3c121c1" }, "execution_count": 10, "outputs": [ @@ -19250,21 +19217,21 @@ "Requirement already satisfied: pytest in /usr/local/lib/python3.10/dist-packages (from -r requirements-dev.txt (line 1)) (7.4.4)\n", "Collecting coverage==6.4.3 (from -r requirements-dev.txt (line 2))\n", " Downloading coverage-6.4.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n", - 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" Created wheel for matbench: filename=matbench-0.6-0.editable-py3-none-any.whl size=3622 sha256=9faa20e2edea9128712a28871e4f9de0b8a9dd24c7a49a3aeb03a74cdb88ac3e\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-id9pupbj/wheels/0d/71/69/39ae3b7c60edd18e46ed290a09bc2fe0c344372520cd165c70\n", + " Created wheel for matbench: filename=matbench-0.6-0.editable-py3-none-any.whl size=3622 sha256=2543740d086d28759c38528a7a709a1f9380a26a1f71387e128484223259d533\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-8dxz848k/wheels/0d/71/69/39ae3b7c60edd18e46ed290a09bc2fe0c344372520cd165c70\n", " Building wheel for pymatgen (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for pymatgen: filename=pymatgen-2024.1.27-cp310-cp310-linux_x86_64.whl size=8767375 sha256=52f748f354052fc925f62a0dc53b0197421299982ff51b46efbf7880fb6d99db\n", + " Created wheel for pymatgen: filename=pymatgen-2024.1.27-cp310-cp310-linux_x86_64.whl size=8767378 sha256=9fe1528bfeb13e80a7c4e417544d291be54286e3c6891b56f4305b648270faf3\n", " Stored in directory: /root/.cache/pip/wheels/ee/58/93/14b5660717b7cf0223df12fcdf754c2ee6aa4daba441b337f1\n", "Successfully built matbench pymatgen\n", "Installing collected packages: mccabe, wheel, pyflakes, pycodestyle, pathspec, mypy-extensions, monty, isort, coverage, pandas, flake8, black, pymatgen, matminer, matbench\n", @@ -19415,7 +19382,7 @@ "base_uri": "https://localhost:8080/" }, "id": "AbCvuaICLFaw", - "outputId": "961ab1c0-b9da-44ca-cc72-586ef399cd0f" + "outputId": "30c87ce5-79ff-4b10-9633-4d9d9935d63c" }, "execution_count": 11, "outputs": [ @@ -19444,7 +19411,7 @@ "base_uri": "https://localhost:8080/" }, "id": "h3DfACJ1LPK9", - "outputId": "b4cffa60-aa35-4013-e766-c90adee1a715" + "outputId": "ee9f24e5-9342-4dfb-c126-dea6002f0a8c" }, "execution_count": 12, "outputs": [ @@ -19518,9 +19485,9 @@ "base_uri": "https://localhost:8080/", "height": 658 }, - "outputId": "a028548b-c35e-41f5-df46-ab31aaed9b2e" + "outputId": "05c48a03-777d-49a8-8b58-27bec5e2e562" }, - "execution_count": 16, + "execution_count": 18, "outputs": [ { "output_type": "stream", @@ -19529,16 +19496,13 @@ "Collecting scikit-learn==1.5\n", " Using cached scikit_learn-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n", "Collecting numpy>=1.19.5 (from scikit-learn==1.5)\n", - " Downloading numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting scipy>=1.6.0 (from scikit-learn==1.5)\n", - " Downloading scipy-1.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.6/38.6 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting joblib>=1.2.0 (from scikit-learn==1.5)\n", - " Downloading joblib-1.4.2-py3-none-any.whl (301 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.8/301.8 kB\u001b[0m \u001b[31m35.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting threadpoolctl>=3.1.0 (from scikit-learn==1.5)\n", - " Downloading threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n", + " Using cached numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n", + "Collecting scipy>=1.6.0 (from scikit-learn==1.5)\n", + " Using cached scipy-1.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB)\n", + "Collecting joblib>=1.2.0 (from scikit-learn==1.5)\n", + " Using cached joblib-1.4.2-py3-none-any.whl (301 kB)\n", + "Collecting threadpoolctl>=3.1.0 (from scikit-learn==1.5)\n", + " Using cached threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n", "Installing collected packages: threadpoolctl, numpy, joblib, scipy, scikit-learn\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "cudf-cu12 24.4.1 requires pandas<2.2.2dev0,>=2.0, but you have pandas 1.5.3 which is incompatible.\u001b[0m\u001b[31m\n", @@ -19551,7 +19515,10 @@ "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/sklearn already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/threadpoolctl.py already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/joblib-1.4.2.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/scikit_learn-1.5.0.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/scipy already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/scipy-1.13.1.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/numpy-1.26.4.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/numpy already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/scipy.libs already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Target directory /usr/local/lib/python3.10/dist-packages/bin already exists. Specify --upgrade to force replacement.\u001b[0m\u001b[33m\n", @@ -19567,7 +19534,7 @@ "joblib" ] }, - "id": "abc79fed5601455dba6602de390012fd" + "id": "b10b4d1bee714f3f8d9d2cc94cf00a2f" } }, "metadata": {} @@ -19609,7 +19576,7 @@ "metadata": { "id": "mmcr8nDFBpIV" }, - "execution_count": 13, + "execution_count": 1, "outputs": [] }, { @@ -19622,7 +19589,7 @@ "metadata": { "id": "tUYFfnGjDHwn" }, - "execution_count": null, + "execution_count": 2, "outputs": [] }, { @@ -19633,7 +19600,7 @@ "metadata": { "id": "WNgvKuttDPL-" }, - "execution_count": 1, + "execution_count": 3, "outputs": [] }, { @@ -19655,7 +19622,7 @@ "metadata": { "id": "94r1T6YADSzO" }, - "execution_count": 2, + "execution_count": 4, "outputs": [] }, { @@ -19702,9 +19669,9 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "3386b7f8-7319-4ef8-b3f9-1f9e36455309" + "outputId": "8f63b269-742c-4ed2-84a1-ee35feb92bf6" }, - "execution_count": null, + "execution_count": 5, "outputs": [ { "output_type": "stream", @@ -19717,7 +19684,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "Fetching https://ml.materialsproject.org/projects/matbench_steels.json.gz in MB: 0.010239999999999999MB [00:00, 3.64MB/s] \n" + "Fetching https://ml.materialsproject.org/projects/matbench_steels.json.gz in MB: 0.010239999999999999MB [00:00, 3.07MB/s] \n" ] } ] @@ -19737,9 +19704,9 @@ "base_uri": "https://localhost:8080/" }, "id": "jtSDRvkJbQhv", - "outputId": "995fbec2-87c7-43c2-cedf-4d56069b44c3" + "outputId": "071daed9-ce7e-4625-d125-c0f946242f4e" }, - "execution_count": null, + "execution_count": 6, "outputs": [ { "output_type": "stream", @@ -19773,9 +19740,9 @@ "base_uri": "https://localhost:8080/", "height": 206 }, - "outputId": "96c7f4a6-763e-4e79-b193-d063fbdafc58" + "outputId": "30f6dab2-3004-4e4c-b687-f503ea440879" }, - "execution_count": null, + "execution_count": 7, "outputs": [ { "output_type": "execute_result", @@ -19790,7 +19757,7 @@ ], "text/html": [ "\n", - "
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