{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "98e6e358", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:12:53.676429Z", "iopub.status.busy": "2022-04-21T18:12:53.675850Z", "iopub.status.idle": "2022-04-21T18:12:54.842911Z", "shell.execute_reply": "2022-04-21T18:12:54.842105Z" }, "id": "QOMbo-YvzxW0", "papermill": { "duration": 1.211142, "end_time": "2022-04-21T18:12:54.845437", "exception": false, "start_time": "2022-04-21T18:12:53.634295", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.preprocessing import MultiLabelBinarizer\n" ] }, { "cell_type": "markdown", "id": "33e6de96", "metadata": { "id": "_V7ars6WxrbQ", "papermill": { "duration": 0.038668, "end_time": "2022-04-21T18:12:54.924313", "exception": false, "start_time": "2022-04-21T18:12:54.885645", "status": "completed" }, "tags": [] }, "source": [ "# Data preparation" ] }, { "cell_type": "code", "execution_count": 2, "id": "05157ec2", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:12:55.005798Z", "iopub.status.busy": "2022-04-21T18:12:55.004958Z", "iopub.status.idle": "2022-04-21T18:12:55.095581Z", "shell.execute_reply": "2022-04-21T18:12:55.094925Z" }, "id": "4WYl2oz8zYEM", "outputId": "d948152b-385f-4742-dbdd-f54a9115ead0", "papermill": { "duration": 0.134347, "end_time": "2022-04-21T18:12:55.097633", "exception": false, "start_time": "2022-04-21T18:12:54.963286", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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descripcion030914151618192224...73757677798085909298
0Servicio de consultoria implantación de una of...000000000...0000000000
1Reforma integral del sistema de ACS de vestuar...000000000...0000000000
2Sº. de vigilancia de la Salud, reconocimiento ...000000000...0000001000
3Acondicionamiento de local para oficinas comar...000000000...0000000000
4Soporte para la verificación del grado de adec...000000000...0000000000
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5 rows × 46 columns

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" ], "text/plain": [ " descripcion 03 09 14 15 16 18 \\\n", "0 Servicio de consultoria implantación de una of... 0 0 0 0 0 0 \n", "1 Reforma integral del sistema de ACS de vestuar... 0 0 0 0 0 0 \n", "2 Sº. de vigilancia de la Salud, reconocimiento ... 0 0 0 0 0 0 \n", "3 Acondicionamiento de local para oficinas comar... 0 0 0 0 0 0 \n", "4 Soporte para la verificación del grado de adec... 0 0 0 0 0 0 \n", "\n", " 19 22 24 ... 73 75 76 77 79 80 85 90 92 98 \n", "0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "2 0 0 0 ... 0 0 0 0 0 0 1 0 0 0 \n", "3 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "4 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "\n", "[5 rows x 46 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's us read the input data\n", "#dftest = pd.read_csv('../input/dataset/test.csv')\n", "dftest = pd.read_csv('../input/dataset10/test10.csv')\n", "\n", "dftest['descripcion'] = dftest['descripcion'].apply(lambda x: x.strip('\"'))\n", "\n", "dftest.pop('Unnamed: 0')\n", "\n", "dftest.head()\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "920a9106", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:12:55.183632Z", "iopub.status.busy": "2022-04-21T18:12:55.183058Z", "iopub.status.idle": "2022-04-21T18:12:55.192201Z", "shell.execute_reply": "2022-04-21T18:12:55.191388Z" }, "id": "Eh-ya32v0kaU", "outputId": "f0e23efa-e9ed-4342-b96a-97cc253e180b", "papermill": { "duration": 0.055581, "end_time": "2022-04-21T18:12:55.194612", "exception": false, "start_time": "2022-04-21T18:12:55.139031", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "(3104, 45)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test = dftest.drop('descripcion', axis=1)\n", "y_test.shape" ] }, { "cell_type": "code", "execution_count": 4, "id": "c8b499c5", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:12:55.276637Z", "iopub.status.busy": "2022-04-21T18:12:55.276061Z", "iopub.status.idle": "2022-04-21T18:12:55.283872Z", "shell.execute_reply": "2022-04-21T18:12:55.283240Z" }, "id": "JbbwCXtUNv0G", "papermill": { "duration": 0.050651, "end_time": "2022-04-21T18:12:55.285801", "exception": false, "start_time": "2022-04-21T18:12:55.235150", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0 Servicio de consultoria implantación de una of...\n", "1 Reforma integral del sistema de ACS de vestuar...\n", "2 Sº. de vigilancia de la Salud, reconocimiento ...\n", "3 Acondicionamiento de local para oficinas comar...\n", "4 Soporte para la verificación del grado de adec...\n", " ... \n", "3099 Servicio de instalación y mantenimiento de la ...\n", "3100 Campamentos urbanos y de Multiaventura, ofreci...\n", "3101 Servicio de mantenimiento de los equipos elect...\n", "3102 Servicio de mantenimiento de instalaciones y e...\n", "3103 Realización de reconocimientos médicos laboral...\n", "Name: descripcion, Length: 3104, dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test = dftest['descripcion']\n", "X_test" ] }, { "cell_type": "code", "execution_count": 5, "id": "e65ed304", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:12:55.370362Z", "iopub.status.busy": "2022-04-21T18:12:55.370026Z", "iopub.status.idle": "2022-04-21T18:12:55.391252Z", "shell.execute_reply": "2022-04-21T18:12:55.390437Z" }, "papermill": { "duration": 0.066798, "end_time": "2022-04-21T18:12:55.393331", "exception": false, "start_time": "2022-04-21T18:12:55.326533", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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descripcion030914151618192224...73757677798085909298
0\"Subministración de postos de traballo informa...000000000...0000000000
1\"Suministro e instalación de elementos de acce...000000000...0000000000
2\"Prórroga del contrato derivado del Acuerdo ma...000000000...0000000100
3\"Ejecución del Programa de Intervención Comuni...000000000...0000001000
4\"Lote 1 Guante blanco uniformidad, Lote 2 guan...000001000...0000000000
..................................................................
7238\"Suministro, montaje y retirada de neumáticos ...000000100...0000000000
7239\"Servicio Parametrización de los modulos de Fo...000000000...1000000001
7240\"Servicio de instalación y mantenimiento de ap...000000000...0000000000
7241\"Servicios de asistencia para el cumplimiento ...000000000...0000100000
7242\"Mantenimiento de Licencias y Productos Veeam ...000000000...0000000000
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7243 rows × 46 columns

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" ], "text/plain": [ " descripcion 03 09 14 15 16 \\\n", "0 \"Subministración de postos de traballo informa... 0 0 0 0 0 \n", "1 \"Suministro e instalación de elementos de acce... 0 0 0 0 0 \n", "2 \"Prórroga del contrato derivado del Acuerdo ma... 0 0 0 0 0 \n", "3 \"Ejecución del Programa de Intervención Comuni... 0 0 0 0 0 \n", "4 \"Lote 1 Guante blanco uniformidad, Lote 2 guan... 0 0 0 0 0 \n", "... ... .. .. .. .. .. \n", "7238 \"Suministro, montaje y retirada de neumáticos ... 0 0 0 0 0 \n", "7239 \"Servicio Parametrización de los modulos de Fo... 0 0 0 0 0 \n", "7240 \"Servicio de instalación y mantenimiento de ap... 0 0 0 0 0 \n", "7241 \"Servicios de asistencia para el cumplimiento ... 0 0 0 0 0 \n", "7242 \"Mantenimiento de Licencias y Productos Veeam ... 0 0 0 0 0 \n", "\n", " 18 19 22 24 ... 73 75 76 77 79 80 85 90 92 98 \n", "0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "2 0 0 0 0 ... 0 0 0 0 0 0 0 1 0 0 \n", "3 0 0 0 0 ... 0 0 0 0 0 0 1 0 0 0 \n", "4 1 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "... .. .. .. .. ... .. .. .. .. .. .. .. .. .. .. \n", "7238 0 1 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "7239 0 0 0 0 ... 1 0 0 0 0 0 0 0 0 1 \n", "7240 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "7241 0 0 0 0 ... 0 0 0 0 1 0 0 0 0 0 \n", "7242 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 \n", "\n", "[7243 rows x 46 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dftrain" ] }, { "cell_type": "code", "execution_count": 8, "id": "9e109059", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:12:55.797976Z", "iopub.status.busy": "2022-04-21T18:12:55.797671Z", "iopub.status.idle": "2022-04-21T18:12:55.835534Z", "shell.execute_reply": "2022-04-21T18:12:55.834767Z" }, "papermill": { "duration": 0.083483, "end_time": "2022-04-21T18:12:55.837747", "exception": false, "start_time": "2022-04-21T18:12:55.754264", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'45': 738.0, '76': nan, '79': 230.0, '50': 216.0, '85': 173.0, '71': 160.0, '72': 159.0, '34': 151.0, '90': 134.0, '92': 130.0, '44': 92.0, '55': 92.0, '30': 88.0, '33': 86.0, '60': 71.0, '39': 68.0, '09': 67.0, '42': 66.0, '80': 63.0, '48': 62.0, '31': 57.0, '35': 52.0, '77': 48.0, '98': 48.0, '32': 41.0, '66': 41.0, '64': 38.0, '15': 37.0, '38': 36.0, '18': 35.0, '24': 33.0, '63': 30.0, '03': 28.0, '70': 25.0, '22': 22.0, '37': 20.0, '73': 20.0, '51': 14.0, '75': 12.0, '14': 9.0, '43': 9.0, '65': 7.0, '16': 6.0, '19': 4.0, '41': 4.0}\n" ] } ], "source": [ "from collections import Counter\n", "\n", "#sorted_dicttest = dict( sorted(Counter([item for sublist in y_test for item in sublist]).items(),\n", "# key=lambda item: item[1],\n", "# reverse=True))\n", "\n", "sorted_dicttest = y_test.apply(pd.value_counts)\n", "sorted_dicttest = dict( sorted(sorted_dicttest.iloc[1].items(),\n", " key=lambda item: item[1],\n", " reverse=True))\n", "\n", "print(sorted_dicttest)" ] }, { "cell_type": "code", "execution_count": 9, "id": "5dc34ce8", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:12:55.926834Z", "iopub.status.busy": "2022-04-21T18:12:55.926253Z", "iopub.status.idle": "2022-04-21T18:12:56.944019Z", "shell.execute_reply": "2022-04-21T18:12:56.943202Z" }, "papermill": { "duration": 1.065775, "end_time": "2022-04-21T18:12:56.946159", "exception": false, "start_time": "2022-04-21T18:12:55.880384", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "x_pos=range(40,46*40,40)\n", "bars = list(sorted_dict.keys())\n", "height = sorted_dict.values()\n", "\n", "plt.bar(x_pos, height, align='edge', width=25)\n", "plt.bar(x_pos, sorted_dicttest.values(), color='r', align='edge', width=25)\n", "\n", "plt.xlim(0,1500)\n", "\n", "#plt.gca().axes.get_xaxis().set_visible(False)\n", "\n", "plt.xticks(rotation='vertical',fontsize=8)\n", "\n", "plt.legend(['train', 'test'],loc=1)\n", "\n", "plt.xticks(x_pos, bars)\n", "plt.savefig('plot.pdf')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f542179a", "metadata": { "id": "p7qJRlF4zt0z", "papermill": { "duration": 0.044067, "end_time": "2022-04-21T18:12:57.034753", "exception": false, "start_time": "2022-04-21T18:12:56.990686", "status": "completed" }, "tags": [] }, "source": [ "# Transformers\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "08da9e54", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:12:57.124915Z", "iopub.status.busy": "2022-04-21T18:12:57.124274Z", "iopub.status.idle": "2022-04-21T18:13:09.155939Z", "shell.execute_reply": "2022-04-21T18:13:09.155113Z" }, "id": "8l9LDfuNzTDH", "outputId": "34f1fcaf-5e15-4963-a2c3-4d807fc6e757", "papermill": { "duration": 12.079003, "end_time": "2022-04-21T18:13:09.158361", "exception": false, "start_time": "2022-04-21T18:12:57.079358", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: transformers in /opt/conda/lib/python3.7/site-packages (4.17.0)\r\n", "Requirement already satisfied: tokenizers!=0.11.3,>=0.11.1 in /opt/conda/lib/python3.7/site-packages (from transformers) (0.11.6)\r\n", "Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers) (3.6.0)\r\n", "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers) (2.27.1)\r\n", "Requirement already satisfied: sacremoses in /opt/conda/lib/python3.7/site-packages (from transformers) (0.0.49)\r\n", "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers) (4.11.3)\r\n", "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.7/site-packages (from transformers) (6.0)\r\n", "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from transformers) (21.3)\r\n", "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from transformers) (4.63.0)\r\n", "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers) (2021.11.10)\r\n", "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from transformers) (1.21.5)\r\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /opt/conda/lib/python3.7/site-packages (from transformers) (0.5.1)\r\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\r\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->transformers) (3.0.7)\r\n", "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->transformers) (3.7.0)\r\n", "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (1.26.8)\r\n", "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (2.0.12)\r\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (2021.10.8)\r\n", "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers) (3.3)\r\n", "Requirement already satisfied: click in /opt/conda/lib/python3.7/site-packages (from sacremoses->transformers) (8.0.4)\r\n", "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from sacremoses->transformers) (1.16.0)\r\n", "Requirement already satisfied: joblib in /opt/conda/lib/python3.7/site-packages (from sacremoses->transformers) (1.0.1)\r\n", "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", "\u001b[0m" ] } ], "source": [ "!pip install transformers" ] }, { "cell_type": "code", "execution_count": 11, "id": "1c51983a", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:13:09.254049Z", "iopub.status.busy": "2022-04-21T18:13:09.253763Z", "iopub.status.idle": "2022-04-21T18:13:49.928539Z", "shell.execute_reply": "2022-04-21T18:13:49.927688Z" }, "id": "JP8m_CGdzHn_", "outputId": "28649acc-95f2-4501-cd87-3ac49b330456", "papermill": { "duration": 40.726121, "end_time": "2022-04-21T18:13:49.931606", "exception": false, "start_time": "2022-04-21T18:13:09.205485", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d398a79bd3d54b039be145a5944a4a56", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/7.69k [00:00), hidden_states=None, attentions=None)\n" ] } ], "source": [ "outputs = model(**input)\n", "print(outputs)" ] }, { "cell_type": "code", "execution_count": 15, "id": "ee7b9aba", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:13:50.536309Z", "iopub.status.busy": "2022-04-21T18:13:50.536016Z", "iopub.status.idle": "2022-04-21T18:13:50.542178Z", "shell.execute_reply": "2022-04-21T18:13:50.541233Z" }, "id": "cpsogNH477Ke", "outputId": "d8659d8a-fbd9-45cc-cee9-7b8bca8964d1", "papermill": { "duration": 0.057963, "end_time": "2022-04-21T18:13:50.544427", "exception": false, "start_time": "2022-04-21T18:13:50.486464", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0017836058, 0.0026176595, 0.5258297, 0.00078376336, 0.0009816448, 0.0015207805, 7.7948316e-05, 8.828583e-05, 0.000119484794, 6.566386e-05, 0.0013172509, 0.00020751476, 4.2914915e-05, 0.00015681704, 0.000103631544, 0.00013502072, 0.0015253319, 0.000647885, 0.0003261591, 0.000104932646, 9.0391535e-05, 5.8603386e-05, 0.0003040775, 0.00016042733, 4.3636188e-05, 0.00011481965, 2.9098393e-05, 3.4694676e-05, 0.016151082, 9.792931e-05, 0.00017269181, 9.450511e-05, 0.0003689366, 3.5735153e-05, 0.0029282405, 0.00078291766, 0.011130989, 0.0002240465, 2.9648774e-05, 0.00032310697, 0.4264065, 4.6567624e-05, 0.0008410903, 0.00014100652, 0.0009532497]\n" ] } ], "source": [ "logits = outputs.logits\n", "\n", "import torch.nn.functional as F \n", "\n", "sm = F.softmax(logits, dim=1)\n", "sm = sm.detach().numpy()[0]\n", "\n", "print(list(sm))" ] }, { "cell_type": "code", "execution_count": 16, "id": "29640fa2", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:13:50.643194Z", "iopub.status.busy": "2022-04-21T18:13:50.642905Z", "iopub.status.idle": "2022-04-21T18:13:50.652364Z", "shell.execute_reply": "2022-04-21T18:13:50.651492Z" }, "papermill": { "duration": 0.061412, "end_time": "2022-04-21T18:13:50.654382", "exception": false, "start_time": "2022-04-21T18:13:50.592970", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "tensor([0.8198, 0.8697, 0.9993, 0.6665, 0.7146, 0.7950, 0.1658, 0.1838, 0.2335,\n", " 0.1434, 0.7706, 0.3461, 0.0986, 0.2857, 0.2090, 0.2561, 0.7955, 0.6230,\n", " 0.4541, 0.2111, 0.1873, 0.1300, 0.4368, 0.2903, 0.1001, 0.2265, 0.0691,\n", " 0.0813, 0.9763, 0.1998, 0.3058, 0.1942, 0.4848, 0.0835, 0.8819, 0.6663,\n", " 0.9660, 0.3636, 0.0703, 0.4518, 0.9991, 0.1062, 0.6820, 0.2645, 0.7085],\n", " grad_fn=)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "sigmoid = torch.nn.Sigmoid()\n", "probs = sigmoid(logits.squeeze().cpu())\n", "predictions = np.zeros(probs.shape)\n", "predictions[np.where(probs >= 0.5)] = 1\n", "predictions\n", "probs" ] }, { "cell_type": "code", "execution_count": 17, "id": "00f7858e", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:13:50.753517Z", "iopub.status.busy": "2022-04-21T18:13:50.753233Z", "iopub.status.idle": "2022-04-21T18:13:50.761292Z", "shell.execute_reply": "2022-04-21T18:13:50.760390Z" }, "papermill": { "duration": 0.060305, "end_time": "2022-04-21T18:13:50.763354", "exception": false, "start_time": "2022-04-21T18:13:50.703049", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "03 0\n", "09 0\n", "14 0\n", "15 0\n", "16 0\n", "18 0\n", "19 0\n", "22 0\n", "24 0\n", "30 0\n", "31 0\n", "32 0\n", "33 0\n", "34 0\n", "35 0\n", "37 0\n", "38 0\n", "39 0\n", "41 0\n", "42 0\n", "43 0\n", "44 0\n", "45 0\n", "48 0\n", "50 0\n", "51 0\n", "55 0\n", "60 0\n", "63 0\n", "64 0\n", "65 0\n", "66 0\n", "70 0\n", "71 0\n", "72 1\n", "73 0\n", "75 0\n", "76 0\n", "77 0\n", "79 0\n", "80 0\n", "85 0\n", "90 0\n", "92 0\n", "98 0\n", "Name: 0, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test.iloc[0]" ] }, { "cell_type": "code", "execution_count": 18, "id": "c41dbcf9", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:13:50.863953Z", "iopub.status.busy": "2022-04-21T18:13:50.863647Z", "iopub.status.idle": "2022-04-21T18:13:50.869453Z", "shell.execute_reply": "2022-04-21T18:13:50.868900Z" }, "id": "5TaWwSMA9B7i", "papermill": { "duration": 0.058621, "end_time": "2022-04-21T18:13:50.871189", "exception": false, "start_time": "2022-04-21T18:13:50.812568", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "clases = [\"75\",\"16\",\"3\",\"77\",\"71\",\"79\",\"24\",\"18\",\"41\",\"44\",\"45\",\"80\",\"31\",\"66\",\"15\",\"39\",\"85\",\"55\",\"42\",\"51\",\"72\",\"38\",\"19\",\"43\",\"33\",\"14\",\"37\",\"30\",\"98\",\"9\",\"64\",\"22\",\"65\",\"32\",\"70\",\"92\",\"50\",\"73\",\"35\",\"76\",\"90\",\"48\",\"63\",\"34\",\"60\"]" ] }, { "cell_type": "markdown", "id": "5cd68f1c", "metadata": { "id": "_2L937f8FLfd", "papermill": { "duration": 0.049903, "end_time": "2022-04-21T18:13:50.970713", "exception": false, "start_time": "2022-04-21T18:13:50.920810", "status": "completed" }, "tags": [] }, "source": [ "# Evaluation\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "1b607a8e", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:13:51.072896Z", "iopub.status.busy": "2022-04-21T18:13:51.072639Z", "iopub.status.idle": "2022-04-21T18:13:51.195616Z", "shell.execute_reply": "2022-04-21T18:13:51.194534Z" }, "id": "T85s31kGIrnS", "outputId": "bc7d074c-fb41-4d8a-a02b-6223048855c2", "papermill": { "duration": 0.180266, "end_time": "2022-04-21T18:13:51.200951", "exception": false, "start_time": "2022-04-21T18:13:51.020685", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([2.67060776e-03, 1.55167399e-05, 4.72867323e-05, 1.61917214e-05,\n", " 5.96244168e-03, 7.90072680e-02, 1.22768943e-05, 2.79920241e-05,\n", " 1.42296085e-05, 1.44713467e-05, 8.03357034e-05, 1.58141227e-03,\n", " 6.69759247e-05, 3.21837084e-04, 3.50497467e-05, 3.71090209e-05,\n", " 2.30490766e-03, 4.40965123e-05, 8.57902467e-01, 5.78225990e-05,\n", " 4.00173594e-04, 4.58681898e-04, 1.03664170e-05, 8.52038647e-06,\n", " 1.63705190e-04, 8.63014247e-06, 7.97538360e-06, 1.08217797e-03,\n", " 1.77102676e-03, 2.36717588e-05, 1.55866635e-03, 2.13309104e-04,\n", " 1.23500839e-04, 6.86979678e-04, 1.60013195e-04, 7.59537274e-04,\n", " 3.93792026e-04, 2.94404496e-02, 8.64315225e-05, 5.61640190e-05,\n", " 1.21170910e-04, 1.20277274e-02, 1.11463742e-04, 3.53206342e-05,\n", " 7.01516256e-05], dtype=float32)]\n" ] } ], "source": [ "ypred = []\n", "\n", "row = X_test.iloc[0]\n", "input = torch.tensor([tokenizer.encode(row)])\n", "outputs = model(input)\n", "logits = outputs.logits\n", "sm = F.softmax(logits, dim=1)\n", "sm = sm.detach().numpy()[0]\n", "ypred.append(sm)\n", "#list(sm)\n", "\n", "#row = X_test.iloc[1]\n", "#input = torch.tensor([tokenizer.encode(row)])\n", "#outputs = model(input)\n", "#logits = outputs.logits\n", "#sm = F.softmax(logits, dim=1)\n", "#sm = sm.detach().numpy()[0]\n", "#ypred.append(sm)\n", "\n", "print(ypred)" ] }, { "cell_type": "code", "execution_count": 20, "id": "bd0c7f9f", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:13:51.342225Z", "iopub.status.busy": "2022-04-21T18:13:51.341680Z", "iopub.status.idle": "2022-04-21T18:13:51.348543Z", "shell.execute_reply": "2022-04-21T18:13:51.347968Z" }, "papermill": { "duration": 0.070255, "end_time": "2022-04-21T18:13:51.350337", "exception": false, "start_time": "2022-04-21T18:13:51.280082", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "0 Servicio de consultoria implantación de una of...\n", "1 Reforma integral del sistema de ACS de vestuar...\n", "2 Sº. de vigilancia de la Salud, reconocimiento ...\n", "3 Acondicionamiento de local para oficinas comar...\n", "4 Soporte para la verificación del grado de adec...\n", " ... \n", "3099 Servicio de instalación y mantenimiento de la ...\n", "3100 Campamentos urbanos y de Multiaventura, ofreci...\n", "3101 Servicio de mantenimiento de los equipos elect...\n", "3102 Servicio de mantenimiento de instalaciones y e...\n", "3103 Realización de reconocimientos médicos laboral...\n", "Name: descripcion, Length: 3104, dtype: object" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test" ] }, { "cell_type": "code", "execution_count": 21, "id": "17a6a6ea", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:13:51.452957Z", "iopub.status.busy": "2022-04-21T18:13:51.452436Z", "iopub.status.idle": "2022-04-21T18:18:57.888469Z", "shell.execute_reply": "2022-04-21T18:18:57.887286Z" }, "id": "nvKKTeq5FNpq", "outputId": "e22b4900-8635-4ee3-af2a-67846391a332", "papermill": { "duration": 306.490338, "end_time": "2022-04-21T18:18:57.891280", "exception": false, "start_time": "2022-04-21T18:13:51.400942", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 /31042\n", "200 /31042\n", "300 /31042\n", "400 /31042\n", "500 /31042\n", "600 /31042\n", "700 /31042\n", "800 /31042\n", "900 /31042\n", "1000 /31042\n", "1100 /31042\n", "1200 /31042\n", "1300 /31042\n", "1400 /31042\n", "1500 /31042\n", "1600 /31042\n", "1700 /31042\n", "1800 /31042\n", "1900 /31042\n", "2000 /31042\n", "2100 /31042\n", "2200 /31042\n", "2300 /31042\n", "2400 /31042\n", "2500 /31042\n", "2600 /31042\n", "2700 /31042\n", "2800 /31042\n", "2900 /31042\n", "3000 /31042\n", "3100 /31042\n" ] } ], "source": [ "import numpy as np\n", "ypred_softmax5 = []\n", "ypred_softmax01 = []\n", "ypred_softmax3 = []\n", "ypred_softmax05 = []\n", "ypred_softmax1 = []\n", "\n", "ypred_sigmoid5 = []\n", "ypred_sigmoid7 = []\n", "ypred_sigmoid8 = []\n", "ypred_sigmoid9 = []\n", "ypred_sigmoid95 = []\n", "\n", "sigmoid = torch.nn.Sigmoid()\n", "i=0\n", "# softmax\n", "for row in X_test:\n", " i = i + 1\n", " input = torch.tensor([tokenizer.encode(row)])\n", " outputs = model(input)\n", " logits = outputs.logits\n", " \n", " sm = F.softmax(logits, dim=1)\n", " sm = sm.detach().numpy()[0]\n", " predictions = np.zeros(45)\n", " predictions[np.where(sm >= 0.5)] = 1\n", " ypred_softmax5.append(predictions)\n", " predictions = np.zeros(45)\n", " predictions[np.where(sm >= 0.1)] = 1\n", " ypred_softmax1.append(predictions)\n", " predictions = np.zeros(45)\n", " predictions[np.where(sm >= 0.05)] = 1\n", " ypred_softmax05.append(predictions)\n", " predictions = np.zeros(45)\n", " predictions[np.where(sm >= 0.01)] = 1\n", " ypred_softmax01.append(predictions)\n", " predictions = np.zeros(45)\n", " predictions[np.where(sm >= 0.3)] = 1\n", " ypred_softmax3.append(predictions)\n", "\n", " probs = sigmoid(logits.squeeze().cpu())\n", " predictions = np.zeros(probs.shape)\n", " predictions[np.where(probs >= 0.5)] = 1\n", " ypred_sigmoid5.append(predictions)\n", " predictions = np.zeros(probs.shape)\n", " predictions[np.where(probs >= 0.7)] = 1\n", " ypred_sigmoid7.append(predictions)\n", " predictions = np.zeros(probs.shape)\n", " predictions[np.where(probs >= 0.8)] = 1\n", " ypred_sigmoid8.append(predictions)\n", " predictions = np.zeros(probs.shape)\n", " predictions[np.where(probs >= 0.9)] = 1\n", " ypred_sigmoid9.append(predictions)\n", " predictions = np.zeros(probs.shape)\n", " predictions[np.where(probs >= 0.95)] = 1\n", " ypred_sigmoid95.append(predictions)\n", " if(i%100==0):\n", " print(i, '/31042')\n", "\n", "ypred_softmax5 = np.array(ypred_softmax5)\n", "ypred_softmax3 = np.array(ypred_softmax3)\n", "ypred_softmax1 = np.array(ypred_softmax1)\n", "ypred_softmax05 = np.array(ypred_softmax05)\n", "ypred_softmax01 = np.array(ypred_softmax01)\n", "ypred_sigmoid5 = np.array(ypred_sigmoid5)\n", "ypred_sigmoid7 = np.array(ypred_sigmoid7)\n", "ypred_sigmoid8 = np.array(ypred_sigmoid8)\n", "ypred_sigmoid9 = np.array(ypred_sigmoid9)\n", "ypred_sigmoid95 = np.array(ypred_sigmoid95)" ] }, { "cell_type": "markdown", "id": "b8e8f0c4", "metadata": { "id": "3yyKioMcahp7", "papermill": { "duration": 0.061514, "end_time": "2022-04-21T18:18:58.014023", "exception": false, "start_time": "2022-04-21T18:18:57.952509", "status": "completed" }, "tags": [] }, "source": [ "We reorder the columns so they correspond to the ones by the MKaan model (that were in alphabetical order in English)." ] }, { "cell_type": "code", "execution_count": 22, "id": "e409a1b0", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:18:58.141387Z", "iopub.status.busy": "2022-04-21T18:18:58.140799Z", "iopub.status.idle": "2022-04-21T18:18:58.171732Z", "shell.execute_reply": "2022-04-21T18:18:58.170928Z" }, "id": "rJWq3dz_uRVq", "papermill": { "duration": 0.097567, "end_time": "2022-04-21T18:18:58.173714", "exception": false, "start_time": "2022-04-21T18:18:58.076147", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "done!\n" ] } ], "source": [ "ypred_softmax5 = ypred_softmax5[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "ypred_softmax3 = ypred_softmax3[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "ypred_softmax1 = ypred_softmax1[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "ypred_softmax05 = ypred_softmax05[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "ypred_softmax01 = ypred_softmax01[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "ypred_sigmoid5 = ypred_sigmoid5[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "ypred_sigmoid7 = ypred_sigmoid7[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "ypred_sigmoid8 = ypred_sigmoid8[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "ypred_sigmoid9 = ypred_sigmoid9[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "ypred_sigmoid95 = ypred_sigmoid95[:, [2,29,25,14,1,7,22,31,6,27,12,33,24,43,38,26,21,15,8,18,23,9,10,41,36,19,17,44,42,30,32,13,34,4,20,37,0,39,3,5,11,16,40,35,28]]\n", "\n", "print('done!')" ] }, { "cell_type": "code", "execution_count": 23, "id": "5a426478", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:18:58.300758Z", "iopub.status.busy": "2022-04-21T18:18:58.300235Z", "iopub.status.idle": "2022-04-21T18:18:58.307335Z", "shell.execute_reply": "2022-04-21T18:18:58.306487Z" }, "id": "5DSV18vYLtmB", "outputId": "3992d2e6-5740-4001-9880-91aa5865a0cc", "papermill": { "duration": 0.072377, "end_time": "2022-04-21T18:18:58.309270", "exception": false, "start_time": "2022-04-21T18:18:58.236893", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ypred_softmax5" ] }, { "cell_type": "code", "execution_count": 24, "id": "b194ac10", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:18:58.435021Z", "iopub.status.busy": 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"execution_count": 33, "id": "73984f14", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:18:59.690678Z", "iopub.status.busy": "2022-04-21T18:18:59.690409Z", "iopub.status.idle": "2022-04-21T18:18:59.747980Z", "shell.execute_reply": "2022-04-21T18:18:59.747220Z" }, "id": "bGuMhAMOSX07", "papermill": { "duration": 0.126339, "end_time": "2022-04-21T18:18:59.750255", "exception": false, "start_time": "2022-04-21T18:18:59.623916", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score\n", "from sklearn.metrics import coverage_error\n", "from sklearn.metrics import label_ranking_average_precision_score\n", " \n", "# source: https://jesusleal.io/2021/04/21/Longformer-multilabel-classification/\n", "def multi_label_metrics(ytest,y_pred):\n", " # finally, compute metrics\n", " y_true = ytest\n", " f1_micro_average = f1_score(y_true=y_true, y_pred=y_pred, average='micro')\n", " roc_auc = roc_auc_score(y_true, y_pred, average = 'micro')\n", " accuracy = accuracy_score(y_true, y_pred)\n", " coverage_err = coverage_error(y_test, y_pred)\n", " label_ranking_average_precision = label_ranking_average_precision_score(y_test, y_pred)\n", " # return as dictionary\n", " metrics = {'f1': f1_micro_average,\n", " 'roc_auc': roc_auc,\n", " 'accuracy': accuracy,\n", " 'coverage_error': coverage_err,\n", " 'label_ranking_average_precision_score': label_ranking_average_precision}\n", " return metrics" ] }, { "cell_type": "code", "execution_count": 34, "id": "c409184d", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:18:59.884940Z", "iopub.status.busy": "2022-04-21T18:18:59.884512Z", "iopub.status.idle": "2022-04-21T18:19:02.136022Z", "shell.execute_reply": "2022-04-21T18:19:02.134869Z" }, "id": "B4M2ou8tM7fD", "outputId": "04e0c03d-ce5e-4d8c-eb90-31adbf738a56", "papermill": { "duration": 2.322297, "end_time": "2022-04-21T18:19:02.138406", "exception": false, "start_time": "2022-04-21T18:18:59.816109", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mkaan softmax01:\n", "{'f1': 0.3739384116693679, 'roc_auc': 0.8763068511972499, 'accuracy': 0.24677835051546393, 'coverage_error': 11.05090206185567, 'label_ranking_average_precision_score': 0.437372161008736}\n", "mkaan softmax05:\n", "{'f1': 0.5539080703240838, 'roc_auc': 0.8591012976131323, 'accuracy': 0.43073453608247425, 'coverage_error': 12.47583762886598, 'label_ranking_average_precision_score': 0.5887068263566251}\n", "mkaan softmax1:\n", "{'f1': 0.6095705521472392, 'roc_auc': 0.8447673390166294, 'accuracy': 0.5096649484536082, 'coverage_error': 13.639175257731958, 'label_ranking_average_precision_score': 0.634794306137723}\n", "mkaan softmax3:\n", "{'f1': 0.6535444723989936, 'roc_auc': 0.8096869084826369, 'accuracy': 0.6095360824742269, 'coverage_error': 16.629832474226806, 'label_ranking_average_precision_score': 0.6634970297099686}\n", "mkaan softmax5:\n", "{'f1': 0.6535293167501212, 'roc_auc': 0.7848225316849927, 'accuracy': 0.5976159793814433, 'coverage_error': 18.704896907216494, 'label_ranking_average_precision_score': 0.6323978847255124}\n" ] } ], "source": [ "# softmax\n", "from sklearn.metrics import coverage_error\n", "from sklearn.metrics import label_ranking_average_precision_score\n", "\n", "print(f'mkaan softmax01:')\t\n", "print(multi_label_metrics(y_test, ypred_softmax01))\n", "print(f'mkaan softmax05:')\t\n", "print(multi_label_metrics(y_test, ypred_softmax05))\n", "print(f'mkaan softmax1:')\t\n", "print(multi_label_metrics(y_test, ypred_softmax1))\n", "print(f'mkaan softmax3:')\t\n", "print(multi_label_metrics(y_test, ypred_softmax3))\n", "print(f'mkaan softmax5:')\t\n", "print(multi_label_metrics(y_test, ypred_softmax5))" ] }, { "cell_type": "code", "execution_count": 35, "id": "c582ea67", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:19:02.276509Z", "iopub.status.busy": "2022-04-21T18:19:02.275619Z", "iopub.status.idle": "2022-04-21T18:19:04.501564Z", "shell.execute_reply": "2022-04-21T18:19:04.500413Z" }, "id": "_poB-8-mlm6G", "papermill": { "duration": 2.297529, "end_time": "2022-04-21T18:19:04.504063", "exception": false, "start_time": "2022-04-21T18:19:02.206534", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mkaan sigmoid5:\n", "{'f1': 0.1275078536049492, 'roc_auc': 0.8041610229202355, 'accuracy': 0.0, 'coverage_error': 17.383698453608247, 'label_ranking_average_precision_score': 0.07050076964808946}\n", "mkaan sigmoid7:\n", "{'f1': 0.1871137526242645, 'roc_auc': 0.8495386835511494, 'accuracy': 0.0, 'coverage_error': 13.305090206185566, 'label_ranking_average_precision_score': 0.11076625959441773}\n", "mkaan sigmoid8:\n", "{'f1': 0.23450290150263245, 'roc_auc': 0.8617175524086119, 'accuracy': 0.0, 'coverage_error': 12.2097293814433, 'label_ranking_average_precision_score': 0.14719043316780178}\n", "mkaan sigmoid9:\n", "{'f1': 0.32355881372875717, 'roc_auc': 0.8710523646831483, 'accuracy': 0.006121134020618557, 'coverage_error': 11.493878865979381, 'label_ranking_average_precision_score': 0.22734927854934545}\n", "mkaan sigmoid95:\n", "{'f1': 0.4165602224058907, 'roc_auc': 0.8677658919303917, 'accuracy': 0.06443298969072164, 'coverage_error': 11.639819587628866, 'label_ranking_average_precision_score': 0.33622612675126184}\n" ] } ], "source": [ "# sigmoid\n", "from sklearn.metrics import coverage_error\n", "from sklearn.metrics import label_ranking_average_precision_score\n", "\n", "print(f'mkaan sigmoid5:')\t\n", "print(multi_label_metrics(y_test, ypred_sigmoid5))\n", "print(f'mkaan sigmoid7:')\t\n", "print(multi_label_metrics(y_test, ypred_sigmoid7))\n", "print(f'mkaan sigmoid8:')\t\n", "print(multi_label_metrics(y_test, ypred_sigmoid8))\n", "print(f'mkaan sigmoid9:')\t\n", "print(multi_label_metrics(y_test, ypred_sigmoid9))\n", "print(f'mkaan sigmoid95:')\t\n", "print(multi_label_metrics(y_test, ypred_sigmoid95))" ] }, { "cell_type": "code", "execution_count": 36, "id": "483eb183", "metadata": { "execution": { "iopub.execute_input": "2022-04-21T18:19:04.647693Z", "iopub.status.busy": "2022-04-21T18:19:04.647365Z", "iopub.status.idle": "2022-04-21T18:19:04.667568Z", "shell.execute_reply": "2022-04-21T18:19:04.666969Z" }, "papermill": { "duration": 0.094961, "end_time": "2022-04-21T18:19:04.669611", "exception": false, "start_time": "2022-04-21T18:19:04.574650", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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