Dacon_해석가능한_모델링.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"6fhHrnruFLrN"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting pycaret\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a2/7b/70e41d8aa900ed47e0e2ac6a8f5cbaf9e359efdf8ae10bf89502c14ce3ed/pycaret-2.2.3-py3-none-any.whl (249kB)\n","\u001b[K     |████████████████████████████████| 256kB 14.1MB/s \n","\u001b[?25hRequirement already satisfied: IPython in /usr/local/lib/python3.6/dist-packages (from pycaret) (5.5.0)\n","Requirement already satisfied: gensim in /usr/local/lib/python3.6/dist-packages (from pycaret) (3.6.0)\n","Collecting lightgbm\u003e=2.3.1\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/70/cd/2b7783e8c250f8191b72e9a0010e0429a799d3305c27764d7bf113dfd078/lightgbm-3.1.1-py2.py3-none-manylinux1_x86_64.whl (1.8MB)\n","\u001b[K     |████████████████████████████████| 1.8MB 28.1MB/s 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(20.8)\n","Building wheels for collected packages: pyLDAvis, pyod, htmlmin, phik, combo, suod, databricks-cli, alembic, prometheus-flask-exporter, Mako\n","  Building wheel for pyLDAvis (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pyLDAvis: filename=pyLDAvis-2.1.2-py2.py3-none-any.whl size=97712 sha256=67f401ef43a8212a7814cb6755264aa87f119a0d49a663f12570df2bee917a60\n","  Stored in directory: /root/.cache/pip/wheels/98/71/24/513a99e58bb6b8465bae4d2d5e9dba8f0bef8179e3051ac414\n","  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pyod: filename=pyod-0.8.6-cp36-none-any.whl size=112146 sha256=a78fd4df5fa0dfd668277ecbd753d5760b615f77ea28383a1ee4f871153e1699\n","  Stored in directory: /root/.cache/pip/wheels/48/95/81/2c6f52c3d366e7b5ef4770550f8bf0764257c66fbe64deecdb\n","  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for htmlmin: filename=htmlmin-0.1.12-cp36-none-any.whl size=27085 sha256=1332c0015248c8e08f29a29541de46fc5a07b90e445f4d42e09f67f9b65bbf09\n","  Stored in directory: /root/.cache/pip/wheels/43/07/ac/7c5a9d708d65247ac1f94066cf1db075540b85716c30255459\n","  Building wheel for phik (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for phik: filename=phik-0.11.0-cp36-none-any.whl size=599738 sha256=4482473ffdf4d46c23a8f42f481f8c5bbccf6297de0246d9d45d6c67fbfa4ba0\n","  Stored in directory: /root/.cache/pip/wheels/af/54/11/aba77f21075918de02f7964eabfe8c10d5542df9e6ad10b225\n","  Building wheel for combo (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for combo: filename=combo-0.1.2-cp36-none-any.whl size=42028 sha256=43d12442685130c58806ce8fe6658f2707c4f782fa7c7d30ab248fe4ee2038c3\n","  Stored in directory: /root/.cache/pip/wheels/01/d9/bf/d1a371a5f0844cd8a53c04c14daa89974c93f429dda9dceb86\n","  Building wheel for suod (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for suod: filename=suod-0.0.6-cp36-none-any.whl size=2154759 sha256=fc758814081ecf1a9630b0f04869e354d231c06fbbb033870cc9f7f2ca187d0e\n","  Stored in directory: /root/.cache/pip/wheels/c5/d7/c1/6c778aee7fccfe3c054ea9bab92c5994ae3a0f6bba7078541e\n","  Building wheel for databricks-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for databricks-cli: filename=databricks_cli-0.14.1-cp36-none-any.whl size=100579 sha256=e92878d62d9cb2ad257fa6202b4d6d64169474a27cb42c2cf0b0c43ec121e6e9\n","  Stored in directory: /root/.cache/pip/wheels/82/91/ac/5d417ee5ccbb76c8cca096cf4cfb9ed9d49d889d1d1ca0fc39\n","  Building wheel for alembic (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for alembic: filename=alembic-1.4.1-py2.py3-none-any.whl size=158155 sha256=2897b267425ea1625e037de0af6d60098209498aa0782120661aa9aa018f7fee\n","  Stored in directory: /root/.cache/pip/wheels/84/07/f7/12f7370ca47a66030c2edeedcc23dec26ea0ac22dcb4c4a0f3\n","  Building wheel for prometheus-flask-exporter (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for prometheus-flask-exporter: filename=prometheus_flask_exporter-0.18.1-cp36-none-any.whl size=17159 sha256=853385bf96895611ff414ab5668b6c3d96b9e92273cd2f7589f24d59209ad4ff\n","  Stored in directory: /root/.cache/pip/wheels/b4/1f/b8/66bd9bc3a9d6c6987ff6c4dfeb6f1fe97b5a0e5ed5849c0437\n","  Building wheel for Mako (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for Mako: filename=Mako-1.1.4-py2.py3-none-any.whl size=75675 sha256=242c737ad7b2f562a22bf07014257c54cf8d440f883426b6f18ace9c5ec72e81\n","  Stored in directory: /root/.cache/pip/wheels/ad/10/d3/aeb26e20d19045e2a68e5d3cbb57432e11b5d9c92c99f98d47\n","Successfully built pyLDAvis pyod htmlmin phik combo suod databricks-cli alembic prometheus-flask-exporter Mako\n","\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n","\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n","Installing collected packages: threadpoolctl, scikit-learn, lightgbm, htmlmin, phik, requests, tqdm, confuse, tangled-up-in-unicode, imagehash, visions, pandas-profiling, scikit-plot, funcy, pyLDAvis, catboost, imbalanced-learn, combo, suod, pyod, databricks-cli, Mako, python-editor, alembic, smmap, gitdb, gitpython, isodate, msrest, azure-core, cryptography, azure-storage-blob, websocket-client, docker, gunicorn, querystring-parser, prometheus-flask-exporter, mlflow, kmodes, yellowbrick, xgboost, pycaret\n","  Found existing installation: scikit-learn 0.22.2.post1\n","    Uninstalling scikit-learn-0.22.2.post1:\n","      Successfully uninstalled scikit-learn-0.22.2.post1\n","  Found existing installation: lightgbm 2.2.3\n","    Uninstalling lightgbm-2.2.3:\n","      Successfully uninstalled lightgbm-2.2.3\n","  Found existing installation: requests 2.23.0\n","    Uninstalling requests-2.23.0:\n","      Successfully uninstalled requests-2.23.0\n","  Found existing installation: tqdm 4.41.1\n","    Uninstalling tqdm-4.41.1:\n","      Successfully uninstalled tqdm-4.41.1\n","  Found existing installation: pandas-profiling 1.4.1\n","    Uninstalling pandas-profiling-1.4.1:\n","      Successfully uninstalled pandas-profiling-1.4.1\n","  Found existing installation: imbalanced-learn 0.4.3\n","    Uninstalling imbalanced-learn-0.4.3:\n","      Successfully uninstalled imbalanced-learn-0.4.3\n","  Found existing installation: yellowbrick 0.9.1\n","    Uninstalling yellowbrick-0.9.1:\n","      Successfully uninstalled yellowbrick-0.9.1\n","  Found existing installation: xgboost 0.90\n","    Uninstalling xgboost-0.90:\n","      Successfully uninstalled xgboost-0.90\n","Successfully installed Mako-1.1.4 alembic-1.4.1 azure-core-1.10.0 azure-storage-blob-12.7.1 catboost-0.24.4 combo-0.1.2 confuse-1.4.0 cryptography-3.3.1 databricks-cli-0.14.1 docker-4.4.1 funcy-1.15 gitdb-4.0.5 gitpython-3.1.12 gunicorn-20.0.4 htmlmin-0.1.12 imagehash-4.2.0 imbalanced-learn-0.7.0 isodate-0.6.0 kmodes-0.10.2 lightgbm-3.1.1 mlflow-1.13.1 msrest-0.6.21 pandas-profiling-2.10.0 phik-0.11.0 prometheus-flask-exporter-0.18.1 pyLDAvis-2.1.2 pycaret-2.2.3 pyod-0.8.6 python-editor-1.0.4 querystring-parser-1.2.4 requests-2.25.1 scikit-learn-0.23.2 scikit-plot-0.3.7 smmap-3.0.5 suod-0.0.6 tangled-up-in-unicode-0.0.6 threadpoolctl-2.1.0 tqdm-4.56.0 visions-0.6.0 websocket-client-0.57.0 xgboost-1.3.3 yellowbrick-1.2.1\n"]}],"source":["!pip install pycaret"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"p4RyBmZiFPvk"},"outputs":[],"source":["from pycaret.classification import *\n","import pandas as pd\n","import numpy as np\n","import datetime as dt\n","import matplotlib.pyplot as plt\n","from tqdm import tqdm\n","import gc\n","import random\n","import lightgbm as lgb\n","import re\n","from sklearn.metrics import *\n","from sklearn.model_selection import KFold\n","import warnings\n","warnings.filterwarnings(action='ignore')\n","\n","PATH = '/content/drive/MyDrive/Dacon_해석가능_모델/Dacon_해석가능모델_공유폴더/'\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PJC5fTogNJ6T"},"outputs":[],"source":["from google.colab import drive\r\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EVFnc94HQB7Q"},"outputs":[],"source":["#train_err  = pd.read_csv(PATH+'train_err_data.csv')\r\n","#train_quality = pd.read_csv(PATH + \"train_quality_data.csv\")\r\n","train_prob = pd.read_csv(\"/content/drive/MyDrive/Dacon_해석가능_모델/data/train_problem_data.csv\")\r\n","\r\n","train_errtype_errcode = pd.read_csv(PATH + 'train_errcode.csv')\r\n","train_model = pd.read_csv(PATH + 'train_model_nm_사본.csv')\r\n","train_quality = pd.read_csv(PATH + 'train_total_quality_4_[2:7].csv')\r\n","\r\n","test_errtype_errcode = pd.read_csv(PATH + 'test_errcode.csv')\r\n","test_model = pd.read_csv(PATH + 'test_model_nm_사본.csv')\r\n","test_quality = pd.read_csv(PATH + 'test_total_quality_4_[2:7].csv')\r\n","\r\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":169},"executionInfo":{"elapsed":4475,"status":"error","timestamp":1612180660682,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"5N46yDQ4-g-0","outputId":"ff172954-6670-420b-b372-ba27d18b31c0"},"outputs":[{"ename":"NameError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m\u003cipython-input-5-aa9aa9f4a77e\u003e\u001b[0m in \u001b[0;36m\u003cmodule\u003e\u001b[0;34m()\u001b[0m\n\u001b[0;32m----\u003e 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'train' is not defined"]}],"source":["train.dtypes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"executionInfo":{"elapsed":727,"status":"ok","timestamp":1611971089580,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"fKTi9RhexMuO","outputId":"c7347d3e-20ff-4298-c9be-d18de904ff9f"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  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    0.692386   \n","min    2.020103e+13   10000.000000      -1.000000      -1.000000   \n","25%    2.020111e+13   13685.000000       0.000000       0.000000   \n","50%    2.020112e+13   17597.000000       0.000000       0.000000   \n","75%    2.020112e+13   21423.000000       0.000000       0.000000   \n","max    2.020113e+13   24997.000000  157667.000000     171.000000   \n","\n","           quality_2  quality_3  quality_4      quality_6     quality_11  \\\n","count  788511.000000   828624.0   828624.0  828624.000000  828624.000000   \n","mean        4.751094        0.0        0.0       2.043391      -0.181638   \n","std       586.252469        0.0        0.0      32.695380       0.397767   \n","min        -1.000000        0.0        0.0      -1.000000      -1.000000   \n","25%         0.000000        0.0        0.0       0.000000       0.000000   \n","50%         0.000000        0.0        0.0       0.000000       0.000000   \n","75%         0.000000        0.0        0.0       0.000000       0.000000   \n","max    191859.000000        0.0        0.0     600.000000      14.000000   \n","\n","          quality_12  \n","count  828624.000000  \n","mean        0.045878  \n","std         0.302452  \n","min         0.000000  \n","25%         0.000000  \n","50%         0.000000  \n","75%         0.000000  \n","max        14.000000  "]},"execution_count":9,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["train_quality.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":284},"executionInfo":{"elapsed":956,"status":"ok","timestamp":1611727171384,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"L6JdP0fEbM_s","outputId":"6394de8c-fec3-4761-ecfa-55d4f2c84172"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    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   quality_12  \n","count   747972.0  747972.000000  747972.000000  747972.000000  \n","mean         0.0       2.118419      -0.186356       0.040558  \n","std          0.0      33.651757       0.401407       0.337775  \n","min          0.0      -1.000000      -1.000000       0.000000  \n","25%          0.0       0.000000       0.000000       0.000000  \n","50%          0.0       0.000000       0.000000       0.000000  \n","75%          0.0       0.000000       0.000000       0.000000  \n","max          0.0     600.000000      17.000000      19.000000  "]},"execution_count":10,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["test_quality.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZtfmLaqfVtU_"},"outputs":[],"source":["id_error = train_quality[['user_id','errtype']].values\r\n","error = np.zeros((15000,42))\r\n","for person_idx, err in tqdm(id_error):\r\n","    # person_idx - 10000 위치에 person_idx, errtype에 해당하는 error값을 +1\r\n","    error[person_idx - 10000,err - 1] += 1\r\n","\r\n","\r\n","problem = np.zeros(15000)\r\n","problem[train_prob.user_id.unique()-10000] = 1 "]},{"cell_type":"markdown","metadata":{"id":"yw43kouVQy46"},"source":["### 데이터 간단하게 살펴보기"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":284},"executionInfo":{"elapsed":7127,"status":"ok","timestamp":1611579515872,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"gb9ctrvyQM-G","outputId":"c5ea996a-a851-4fd2-fd8a-d55160e66a21"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  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columns]"]},"execution_count":9,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["train_prob"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":900,"status":"ok","timestamp":1611579541604,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"-6HoMEdlRb5L","outputId":"320cf5fe-cd71-49e0-bef9-8aa8a6f7a9aa"},"outputs":[{"data":{"text/plain":["20201101000000     1\n","20201101010000     3\n","20201101020000     3\n","20201101090000     1\n","20201101110000     3\n","20201101120000     3\n","20201101130000    11\n","20201101140000     3\n","20201101150000     2\n","20201101160000    10\n","20201101170000     3\n","20201101180000     4\n","20201101190000    11\n","20201101200000     3\n","20201101210000     3\n","20201101220000     2\n","20201101230000     4\n","20201102000000     3\n","20201102100000     2\n","20201102110000    36\n","20201102120000 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4\n","20201129120000     4\n","20201129130000     8\n","20201129140000     2\n","20201129150000     8\n","20201129160000     3\n","20201129170000    10\n","20201129180000     5\n","20201129190000     6\n","20201129200000     1\n","20201129210000     1\n","20201129220000     3\n","20201129230000     2\n","20201130000000     4\n","20201130010000     2\n","20201130110000    38\n","20201130120000    42\n","20201130130000    27\n","20201130140000    14\n","20201130150000    27\n","20201130160000    19\n","20201130170000    25\n","20201130180000    26\n","20201130190000    35\n","20201130200000    11\n","20201130210000     5\n","20201130220000    10\n","20201130230000     2\n","Name: time, dtype: int64"]},"execution_count":10,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["# Target Variable의 분포를 살펴보자\r\n","target_col = 'time'\r\n","\r\n","train_prob[target_col].value_counts().sort_index()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":308},"executionInfo":{"elapsed":1358,"status":"ok","timestamp":1611579546078,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"LLaYHnU5RqRd","outputId":"7132ab27-f574-4c03-9158-0355f2bedecb"},"outputs":[{"data":{"text/plain":["\u003cmatplotlib.axes._subplots.AxesSubplot at 0x7fd0f9566160\u003e"]},"execution_count":11,"metadata":{"tags":[]},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["\u003cFigure size 432x288 with 1 Axes\u003e"]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["# Dist-plot을 그려보도록 한다.\r\n","import seaborn as sns\r\n","sns.distplot( train_prob[target_col] )"]},{"cell_type":"markdown","metadata":{"id":"Sihmy58nvtCl"},"source":["### Precessing model_nm"]},{"cell_type":"markdown","metadata":{"id":"fqoOcY24Q2X4"},"source":[""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X6TJwBtuogaf"},"outputs":[],"source":["train_err['nm'] = train_err['model_nm'].str.split('_').str[1]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21361,"status":"ok","timestamp":1611579572034,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"rDyZtMqboh4M","outputId":"59202054-6cc7-49ae-94c0-4a4afedde8bf"},"outputs":[{"data":{"text/plain":["count     16554663\n","unique           9\n","top              1\n","freq       5384491\n","Name: nm, dtype: object"]},"execution_count":13,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["train_err['nm'].describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":996},"executionInfo":{"elapsed":3835,"status":"error","timestamp":1611580986237,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"2c-kNpzHgIY4","outputId":"1c33bd76-6b2a-4e44-fc2e-69163364252e"},"outputs":[{"name":"stderr","output_type":"stream","text":["\n","\n","  0%|          | 0/16554663 [00:00\u003c?, ?it/s]\u001b[A\u001b[A\n","\n","  0%|          | 48732/16554663 [00:00\u003c00:33, 487314.66it/s]\u001b[A\u001b[A\n","\n","  1%|          | 97470/16554663 [00:00\u003c00:33, 487332.31it/s]\u001b[A\u001b[A\n","\n","  1%|          | 146605/16554663 [00:00\u003c00:33, 488529.79it/s]\u001b[A\u001b[A\n","\n","  1%|          | 194981/16554663 [00:00\u003c00:33, 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[00:01\u003c00:31, 504864.64it/s]\u001b[A\u001b[A\n","\n","  5%|▍         | 795867/16554663 [00:01\u003c00:31, 503288.76it/s]\u001b[A\u001b[A\n","\n","  5%|▌         | 846998/16554663 [00:01\u003c00:31, 505665.86it/s]\u001b[A\u001b[A\n","\n","  5%|▌         | 897460/16554663 [00:01\u003c00:31, 495531.16it/s]\u001b[A\u001b[A\n","\n","  6%|▌         | 946985/16554663 [00:01\u003c00:32, 480761.05it/s]\u001b[A\u001b[A\n","\n","  6%|▌         | 995138/16554663 [00:02\u003c00:32, 473201.83it/s]\n"]},{"ename":"KeyboardInterrupt","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m\u003cipython-input-24-5b46c3328cfe\u003e\u001b[0m in \u001b[0;36m\u003cmodule\u003e\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mid_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_err\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'nm'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----\u003e 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mperson_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m   \u001b[0;31m#print(person_idx)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0;31m#print(model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["id_model = train_err[['user_id', 'nm']].values\r\n","model = np.zeros((15000,10))\r\n","for person_idx, mod in tqdm(id_model):\r\n","  #print(person_idx)\r\n","  #print(model)\r\n","  model[person_idx - 10000, int(mod) - 1] += 1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4YydiLaIvyFI"},"outputs":[],"source":[""]},{"cell_type":"markdown","metadata":{"id":"HgaVUeI6v0mf"},"source":["### Processing err_type"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":239},"executionInfo":{"elapsed":7304,"status":"error","timestamp":1612057430161,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"SFPDsCcRFmap","outputId":"60a36bc0-87fa-45f0-a637-3f563bb192eb"},"outputs":[{"ename":"NameError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m\u003cipython-input-5-6fa369e7e350\u003e\u001b[0m in \u001b[0;36m\u003cmodule\u003e\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----\u003e 2\u001b[0;31m \u001b[0mid_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_err\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'errtype'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mperson_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid_error\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# person_idx - 10000 위치에 person_idx, errtype에 해당하는 error값을 +1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'train_err' is not defined"]}],"source":["\n","id_error = train_err[['user_id','errtype']].values\n","error = np.zeros((15000,42))\n","for person_idx, err in tqdm(id_error):\n","    # person_idx - 10000 위치에 person_idx, errtype에 해당하는 error값을 +1\n","    error[person_idx - 10000,err - 1] += 1\n","\n","\n","problem = np.zeros(15000)\n","problem[train_prob.user_id.unique()-10000] = 1 \n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ac1x4cMLtM3n"},"outputs":[],"source":[""]},{"cell_type":"markdown","metadata":{"id":"PCi6qhqttNw0"},"source":["### Processing err_code"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":122},"executionInfo":{"elapsed":3832,"status":"ok","timestamp":1611580444563,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"fCCF-LK_oUFE","outputId":"170b021a-2c72-4f7e-da36-c42ba735f38d"},"outputs":[{"data":{"text/plain":["1                     8906967\n","0                     2599123\n","connection timeout    1835600\n","B-A8002                897863\n","80                     334018\n","Name: errcode, dtype: int64"]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["train_errcode_table = train_err['errcode'].value_counts()\r\n","train_errcode_rname = list(train_err['errcode'].value_counts().index)\r\n","display(train_errcode_table.head())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1028,"status":"ok","timestamp":1611580695312,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"xgQx5N-WsYQw","outputId":"2e4e4a21-cc10-4661-dc03-39de9595c722"},"outputs":[{"name":"stdout","output_type":"stream","text":["{'1': 0, '0': 1, 'connection timeout': 2, 'B-A8002': 3, '80': 4, '79': 5, '14': 6, 'active': 7, '2': 8, '84': 9, '85': 10, 'standby': 11, 'NFANDROID2': 12, 'connection fail to establish': 13, '3': 14, '90': 15, '89': 16, 'S-61001': 17, '95': 18, '94': 19, '4': 20, '13': 21, 'Q-64002': 22, '8.0': 23, '6': 24, '5': 25, '78': 26, '81': 27, '86': 28, 'connectionterminated by local host': 29, 'UNKNOWN': 30, '100': 31, 'terminate by peer user': 32, '99': 33, '83': 34, 'V-21008': 35, 'S-65002': 36, '87': 37, 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98}\n"]}],"source":["selected_errcode = list(train_errcode_rname[:100])\r\n","# Only elements which are in 'test_errcode_rname' as well. Preserve order\r\n","selected_errcode = [x for x in selected_errcode if x in test_errcode_rname]\r\n","other_errcode = list(set(selected_errcode) ^ set(test_errcode_rname))\r\n","\r\n","# Assign 0, 1, 2, ..., n to selected_errcode\r\n","selected_dictionary = {string : i for i,string in enumerate(selected_errcode)}\r\n","# Assign 98 to other_errcode (98 is 'Others')\r\n","other_dictionary = {string : len(selected_errcode) for string in other_errcode}\r\n","# Merge together (No duplicate, therefore it does not matter)\r\n","merged_dictionary = dict(selected_dictionary, **other_dictionary)\r\n","print(merged_dictionary)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QH-B7ualsp7G"},"outputs":[],"source":["train_err[\"errcode\"] = train_err[\"errcode\"].map(merged_dictionary)\r\n","train_err[\"errcode\"] = train_err[\"errcode\"].fillna(len(selected_errcode))\r\n","train_err[\"errcode\"] = pd.to_numeric(train_err[\"errcode\"], downcast=\"integer\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":44321,"status":"ok","timestamp":1611580967178,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"pc4qgsvrrhge","outputId":"7dbc160c-667f-4e1d-c560-2a42d0a6e2ed"},"outputs":[{"name":"stderr","output_type":"stream","text":["\n","\n","  0%|          | 0/16554663 [00:00\u003c?, ?it/s]\u001b[A\u001b[A\n","\n","  0%|          | 38581/16554663 [00:00\u003c00:42, 385803.01it/s]\u001b[A\u001b[A\n","\n","  0%|          | 77726/16554663 [00:00\u003c00:42, 387477.29it/s]\u001b[A\u001b[A\n","\n","  1%|          | 115572/16554663 [00:00\u003c00:42, 384725.46it/s]\u001b[A\u001b[A\n","\n","  1%|          | 153245/16554663 [00:00\u003c00:42, 382289.30it/s]\u001b[A\u001b[A\n","\n","  1%|          | 189802/16554663 [00:00\u003c00:43, 377114.94it/s]\u001b[A\u001b[A\n","\n","  1%|▏         | 228386/16554663 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len(selected_dictionary) + 1))\r\n","\r\n","for person_idx, errcode in tqdm(id_errorcode):\r\n","  errorcode[person_idx - train_user_id_min, errcode - 1] += 1\r\n","\r\n","#print(error.shape)\r\n","#print(errorcode.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"executionInfo":{"elapsed":997,"status":"ok","timestamp":1611374151725,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"cCsbgm6jTOkH","outputId":"1fbd7086-16af-410e-9af6-d34420c0c015"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  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0.0  0.0    0.0   0.0  0.0  0.0  0.0  0.0  0.0  15.0  15.0  0.0   \n","14999  0.0  0.0  4.0  192.0   7.0  5.0  4.0  0.0  0.0  0.0  15.0  19.0  4.0   \n","\n","         13     14     15   16   17   18   19   20     21     22   23   24  \\\n","0      10.0   59.0   61.0  0.0  0.0  0.0  0.0  0.0    0.0    0.0  0.0  0.0   \n","1      15.0  151.0  128.0  0.0  4.0  1.0  3.0  1.0  756.0  751.0  5.0  1.0   \n","2       4.0   52.0   52.0  0.0  0.0  0.0  0.0  0.0    0.0    0.0  2.0  0.0   \n","3       0.0   52.0   30.0  1.0  0.0  0.0  0.0  0.0   28.0   19.0  0.0  0.0   \n","4       5.0  143.0   91.0  0.0  0.0  0.0  0.0  0.0  140.0  119.0  0.0  0.0   \n","...     ...    ...    ...  ...  ...  ...  ...  ...    ...    ...  ...  ...   \n","14995   8.0   26.0   18.0  0.0  0.0  0.0  0.0  0.0   17.0   12.0  0.0  0.0   \n","14996   0.0    0.0    0.0  0.0  0.0  0.0  0.0  0.0    0.0    0.0  0.0  0.0   \n","14997   1.0  181.0  138.0  0.0  0.0  0.0  0.0  0.0   50.0   29.0  0.0  0.0   \n","14998   1.0   51.0   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pd.DataFrame(data=model)\r\n","train_model.columns = ['model'+'_'+str(i) for i in range(1,11)]\r\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QV-zUg68TkWH"},"outputs":[],"source":["#train_model.to_csv(PATH+'model_nm.csv', index = False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2dKkp1DkopgE"},"outputs":[],"source":["train_err = pd.DataFrame(data=error)\r\n","train_model = pd.DataFrame(data=model)\r\n","train_model.columns = [i for i in range(42,42+10)]\r\n","\r\n","train = pd.concat([train_err, train_model], axis = 1)\r\n","\r\n","train['problem'] = problem\r\n","del error, model, problem\r\n","\r\n"]},{"cell_type":"markdown","metadata":{"id":"LaDnX4J4xpeR"},"source":["### Concat data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_MI1OwUWxJ9v"},"outputs":[],"source":["train = pd.concat([train_errtype_errcode, train_model], axis = 1)\r\n","train = pd.concat([train, train_quality], axis = 1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ldBrTgbEfbEE"},"outputs":[],"source":["train_errtype_errcode = []\r\n","train_model = []\r\n","train_quality = []"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":579,"status":"ok","timestamp":1612182214653,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"Ow6Bx7R6yHny","outputId":"3b61738a-0df1-4b5d-bc19-bec8a3d4237f"},"outputs":[{"data":{"text/plain":["0               float64\n","1               float64\n","2               float64\n","3               float64\n","4               float64\n","5               float64\n","6               float64\n","7               float64\n","8               float64\n","9               float64\n","10              float64\n","11              float64\n","12              float64\n","13              float64\n","14              float64\n","15              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float64\n","quality_9_8     float64\n","quality_9_9     float64\n","quality_10_0    float64\n","quality_10_1    float64\n","quality_10_2    float64\n","quality_10_3    float64\n","quality_10_4    float64\n","quality_10_5    float64\n","quality_10_6    float64\n","quality_10_7    float64\n","quality_10_8    float64\n","quality_10_9    float64\n","quality_11_0    float64\n","quality_11_1    float64\n","quality_11_2    float64\n","quality_11_3    float64\n","quality_11_4    float64\n","quality_11_5    float64\n","quality_11_6    float64\n","quality_11_7    float64\n","quality_11_8    float64\n","quality_11_9    float64\n","quality_12_0    float64\n","quality_12_1    float64\n","quality_12_2    float64\n","quality_12_3    float64\n","quality_12_4    float64\n","quality_12_5    float64\n","quality_12_6    float64\n","quality_12_7    float64\n","quality_12_8    float64\n","quality_12_9    float64\n","dtype: object"]},"execution_count":10,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["train.dtypes"]},{"cell_type":"markdown","metadata":{"id":"2JXVZVR0w787"},"source":["### Precessing problem(숨김)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wQgxhetnxAA0"},"outputs":[],"source":["\r\n","problem = np.zeros(15000)\r\n","problem[train_prob.user_id.unique()-10000] = 1 \r\n","\r\n","train['problem'] = problem\r\n","del problem"]},{"cell_type":"markdown","metadata":{"id":"g-6aHlUYx6tm"},"source":["### Processing problem"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iDmN595Nx1in"},"outputs":[],"source":["problem = np.zeros(15000)\r\n","problem[train_prob.user_id.unique()-10000] = 1 \r\n","\r\n","train['problem'] = problem\r\n","del problem"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1285,"status":"ok","timestamp":1612215848456,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"Ez8HHs1D130l","outputId":"9454e247-cc4f-40e3-ea47-213324a901f5"},"outputs":[{"data":{"text/plain":["0        0.0\n","1        1.0\n","2        0.0\n","3        0.0\n","4        1.0\n","        ... \n","14995    0.0\n","14996    0.0\n","14997    1.0\n","14998    1.0\n","14999    0.0\n","Name: problem, Length: 15000, dtype: float64"]},"execution_count":9,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["train['problem']"]},{"cell_type":"markdown","metadata":{"id":"88wpb3S7w2p_"},"source":["### Modeling"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":608},"id":"rIE46Q4TwwM_","outputId":"c14e4e99-cb28-49f1-f00d-7e8b82be243d"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4eb01e132a42451698d810a13e8eee59","version_major":2,"version_minor":0},"text/plain":["IntProgress(value=0, description='Processing: ', max=3)"]},"metadata":{"tags":[]},"output_type":"display_data"},{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  \u003cthead\u003e\n","    \u003ctr style=\"text-align: right;\"\u003e\n","      \u003cth\u003e\u003c/th\u003e\n","      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. . . . . . . . . . . . . . .            06:44:11\n","Status     . . . . . . . . . . . . . . . . . .  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(num_leaves=31).\n"]}],"source":["clf = setup(data = train, target = 'problem', feature_selection = True, feature_interaction = True, use_gpu = True, fix_imbalance = True, feature_ratio = True) "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":491},"executionInfo":{"elapsed":5598072,"status":"ok","timestamp":1612187883152,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"7WQRI2o9Fmfk","outputId":"e690ef19-a518-4357-eb8c-8420c984173c"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  \u003cthead\u003e\n","    \u003ctr 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0.8154  0.5842  0.7159   \n","ada                  Ada Boost Classifier    0.7766  0.8054  0.5839  0.6970   \n","lda          Linear Discriminant Analysis    0.7566  0.7671  0.5707  0.6548   \n","ridge                    Ridge Classifier    0.7494  0.0000  0.5693  0.6391   \n","lr                    Logistic Regression    0.7106  0.7184  0.6299  0.5584   \n","dt               Decision Tree Classifier    0.7023  0.6742  0.5887  0.5500   \n","nb                            Naive Bayes    0.6858  0.6942  0.1055  0.6851   \n","knn                K Neighbors Classifier    0.6201  0.6432  0.5587  0.4443   \n","svm                   SVM - Linear Kernel    0.5491  0.0000  0.5216  0.3778   \n","qda       Quadratic Discriminant Analysis    0.3354  0.4990  0.9897  0.3328   \n","xgboost         Extreme Gradient Boosting    0.0000  0.0000  0.0000  0.0000   \n","\n","              F1   Kappa     MCC  TT (Sec)  \n","lightgbm  0.6562  0.5184  0.5290    16.023  \n","catboost  0.6496  0.5120  0.5242   167.459  \n","gbc       0.6465  0.5067  0.5180   138.309  \n","rf        0.6439  0.4948  0.5015    15.497  \n","et        0.6431  0.4907  0.4961    14.870  \n","ada       0.6350  0.4759  0.4801    32.437  \n","lda       0.6095  0.4341  0.4365    11.509  \n","ridge     0.6018  0.4200  0.4217     9.387  \n","lr        0.5918  0.3690  0.3708    20.856  \n","dt        0.5685  0.3417  0.3423    17.235  \n","nb        0.1825  0.1031  0.1734     7.919  \n","knn       0.4950  0.1968  0.2001    14.084  \n","svm       0.4185  0.0756  0.0840     8.454  \n","qda       0.4981 -0.0013 -0.0070    10.903  \n","xgboost   0.0000  0.0000  0.0000     7.849  "]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["best_5 = compare_models(sort = 'Accuracy', n_select = 5)"]},{"cell_type":"markdown","metadata":{"id":"9wmVsFL0I_l7"},"source":["CatBoost, Lightgbm, gbc, et,rf 가 골고루 AUC 0.8대를 보여주기 때문에 5개를 골라 블렌딩 해보겠습니다"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":254},"executionInfo":{"elapsed":5307353,"status":"ok","timestamp":1612189717127,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"SMX9R5YMGLBP","outputId":"ada898f0-18e9-44e6-ea22-74587ea4d26b"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  \u003cthead\u003e\n","    \u003ctr style=\"text-align: right;\"\u003e\n","      \u003cth\u003e\u003c/th\u003e\n","      \u003cth\u003eAccuracy\u003c/th\u003e\n","      \u003cth\u003eAUC\u003c/th\u003e\n","      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F1   Kappa     MCC\n","0       0.8095  0.8431  0.6243  0.7613  0.6860  0.5512  0.5568\n","1       0.8010  0.8328  0.5843  0.7631  0.6618  0.5243  0.5337\n","2       0.7938  0.8242  0.5429  0.7708  0.6370  0.4990  0.5140\n","3       0.8052  0.8243  0.5814  0.7782  0.6656  0.5322  0.5435\n","4       0.7842  0.8215  0.5279  0.7500  0.6196  0.4753  0.4896\n","Mean    0.7987  0.8292  0.5722  0.7647  0.6540  0.5164  0.5275\n","SD      0.0089  0.0079  0.0340  0.0095  0.0232  0.0265  0.0236"]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["blended = blend_models(estimator_list = best_5, fold = 5, method = 'soft')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":77},"executionInfo":{"elapsed":5307342,"status":"ok","timestamp":1612189718255,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"Sk8IgG6aGLYJ","outputId":"7ce8b444-7d57-4dab-b097-1678b5cce833"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  \u003cthead\u003e\n","    \u003ctr style=\"text-align: right;\"\u003e\n","      \u003cth\u003e\u003c/th\u003e\n","      \u003cth\u003eModel\u003c/th\u003e\n","      \u003cth\u003eAccuracy\u003c/th\u003e\n","      \u003cth\u003eAUC\u003c/th\u003e\n","      \u003cth\u003eRecall\u003c/th\u003e\n","      \u003cth\u003ePrec.\u003c/th\u003e\n","      \u003cth\u003eF1\u003c/th\u003e\n","      \u003cth\u003eKappa\u003c/th\u003e\n","      \u003cth\u003eMCC\u003c/th\u003e\n","    \u003c/tr\u003e\n","  \u003c/thead\u003e\n","  \u003ctbody\u003e\n","    \u003ctr\u003e\n","      \u003cth\u003e0\u003c/th\u003e\n","      \u003ctd\u003eVoting Classifier\u003c/td\u003e\n","      \u003ctd\u003e0.7845\u003c/td\u003e\n","      \u003ctd\u003e0.8208\u003c/td\u003e\n","      \u003ctd\u003e0.5423\u003c/td\u003e\n","      \u003ctd\u003e0.742\u003c/td\u003e\n","      \u003ctd\u003e0.6266\u003c/td\u003e\n","      \u003ctd\u003e0.4803\u003c/td\u003e\n","      \u003ctd\u003e0.4919\u003c/td\u003e\n","    \u003c/tr\u003e\n","  \u003c/tbody\u003e\n","\u003c/table\u003e\n","\u003c/div\u003e"],"text/plain":["               Model  Accuracy     AUC  Recall  Prec.      F1   Kappa     MCC\n","0  Voting Classifier    0.7845  0.8208  0.5423  0.742  0.6266  0.4803  0.4919"]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["pred_holdout = predict_model(blended)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TI8_0CoXGLa5"},"outputs":[],"source":["final_model = finalize_model(blended)"]},{"cell_type":"markdown","metadata":{"id":"HP8d-_aY7SKc"},"source":["### Processing test data(숨김)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":108088,"status":"ok","timestamp":1611580622065,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"OAUIDJmHK9ir","outputId":"d0282bbc-3cf3-49f4-a531-a5b2fb6acb99"},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 16532648/16532648 [00:40\u003c00:00, 408939.01it/s]\n","100%|██████████| 16532648/16532648 [00:33\u003c00:00, 489914.65it/s]\n"]}],"source":["test_err  = pd.read_csv(PATH+'test_err_data.csv')\n","test_err['nm'] = test_err['model_nm'].str.split('_').str[1]\n","\n","id_error = test_err[['user_id','errtype']].values\n","test_err_x = np.zeros((14999,42))\n","for person_idx, err in tqdm(id_error):\n","    test_err_x[person_idx - 30000,err - 1] += 1\n","\n","id_model = test_err[['user_id','nm']].values\n","test_model_x = np.zeros((14999,10))\n","for person_idx, mod in tqdm(id_model):\n","  test_model_x[person_idx - 30000, int(mod) - 1] += 1\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":122},"executionInfo":{"elapsed":109173,"status":"ok","timestamp":1611580624372,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"-0Qa1bVgrxNH","outputId":"74af9b04-75be-49ae-f20a-ffa945960c96"},"outputs":[{"data":{"text/plain":["1                     8750154\n","0                     2565532\n","connection timeout    1890632\n","B-A8002                855747\n","80                     326179\n","Name: errcode, dtype: int64"]},"metadata":{"tags":[]},"output_type":"display_data"}],"source":["test_errcode_table = test_err['errcode'].value_counts()\r\n","test_errcode_rname = list(test_err['errcode'].value_counts().index)\r\n","display(test_errcode_table.head())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7JQaO5aWvFv7"},"outputs":[],"source":["\r\n","#test_err = pd.DataFrame(data=test_err_x)\r\n","#test_model = pd.DataFrame(data=test_model_x)\r\n","\r\n","\r\n","test_err = test_err_x.reshape(test_err_x.shape[0],-1)\r\n","test_model = test_model_x.reshape(test_model_x.shape[0],-1)\r\n","test_err_df = pd.DataFrame(data=test_err)\r\n","test_model_df = pd.DataFrame(data=test_model)\r\n","test_model_df.columns = ['model'+'_'+str(i) for i in range(1,11)]\r\n","test_model_df.to_csv(PATH+'test_model_nm.csv', index = False)\r\n","\r\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":402},"executionInfo":{"elapsed":942,"status":"ok","timestamp":1611373118642,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"S74hXRavv1lp","outputId":"94762c6f-67c0-499a-daa6-53fd2b410f59"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  \u003cthead\u003e\n","    \u003ctr style=\"text-align: right;\"\u003e\n","      \u003cth\u003e\u003c/th\u003e\n","      \u003cth\u003emodel_1\u003c/th\u003e\n","      \u003cth\u003emodel_2\u003c/th\u003e\n","      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columns]"]},"execution_count":31,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["test_model_df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-KOd3LoQv1AI"},"outputs":[],"source":["\r\n","test = pd.concat([test_err_df, test_model_df], axis = 1)"]},{"cell_type":"markdown","metadata":{"id":"ay-SrBa-7pdw"},"source":["### Concat data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2pdZTL3W7orF"},"outputs":[],"source":["test = pd.concat([test_errtype_errcode, test_model], axis = 1)\r\n","#test = pd.concat([test, test_quality], axis = 1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":402},"executionInfo":{"elapsed":666,"status":"ok","timestamp":1611934157153,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"oJABKv6iCkyW","outputId":"6098e041-466c-4e41-f4d9-1e182d6ab2ed"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  \u003cthead\u003e\n","    \u003ctr style=\"text-align: right;\"\u003e\n","      \u003cth\u003e\u003c/th\u003e\n","      \u003cth\u003emodel_1\u003c/th\u003e\n","      \u003cth\u003emodel_2\u003c/th\u003e\n","      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test[0:len(test)-1]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"executionInfo":{"elapsed":811,"status":"ok","timestamp":1612014521524,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"E40bGU_pwO7J","outputId":"a275d9b4-0ad7-49f6-b94c-2248ab9876ba"},"outputs":[{"data":{"text/html":["\u003cdiv\u003e\n","\u003cstyle scoped\u003e\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","\u003c/style\u003e\n","\u003ctable border=\"1\" class=\"dataframe\"\u003e\n","  \u003cthead\u003e\n","    \u003ctr style=\"text-align: right;\"\u003e\n","      \u003cth\u003e\u003c/th\u003e\n","      \u003cth\u003e0\u003c/th\u003e\n","      \u003cth\u003e1\u003c/th\u003e\n","      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test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26962,"status":"ok","timestamp":1612194208143,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"BtVqy2kBQAai","outputId":"33c62d6e-da2d-4226-fff1-669794dda47b"},"outputs":[{"data":{"text/plain":["0        0.9410\n","1        0.6966\n","2        0.6083\n","3        0.8584\n","4        0.8622\n","          ...  \n","14994    0.7646\n","14995    0.5072\n","14996    0.6971\n","14997    0.8245\n","14998    0.5695\n","Name: Score, Length: 14999, dtype: float64"]},"execution_count":23,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["predictions['Score']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WBZ9WMf2Ljgp"},"outputs":[],"source":["#pycaret에서는 score이 label을 맞출 확률이기때문에 output을 제출 양식에 맞게 바꿔줍니다\n","x = []\n","for i in range(len(predictions['Score'])):\n","  if predictions['Label'][i] =='1.0':\n","    x.append(predictions['Score'][i])\n","  else:\n","    x.append(1-predictions['Score'][i])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CGTrMtTzJRCI"},"outputs":[],"source":["sample_submssion = pd.read_csv('/content/drive/MyDrive/Dacon_해석가능_모델/data/sample_submission.csv')\n","sample_submssion['problem'] = x\n","sample_submssion.to_csv(\"/content/drive/MyDrive/Dacon_해석가능_모델/data/AutoML_19.csv\", index = False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":899,"status":"ok","timestamp":1611153847442,"user":{"displayName":"홍유빈학부생","photoUrl":"","userId":"00679493735292500145"},"user_tz":-540},"id":"VAYSQNt1Zaob","outputId":"5c80b49d-0d70-425c-baf2-b9e9336d04f6"},"outputs":[{"name":"stdout","output_type":"stream","text":["AutoML.csv  catboost_info  drive  logs.log  sample_data\n"]}],"source":["!ls"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RjdB2WTTQLCw"},"outputs":[],"source":[""]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["nKKXt7YmWJeK","yw43kouVQy46","Sihmy58nvtCl","HgaVUeI6v0mf","PCi6qhqttNw0","5awp-SuJv-Wk","2JXVZVR0w787","HP8d-_aY7SKc"],"machine_shape":"hm","name":"Dacon_해석가능한_모델링.ipynb","version":""},"kernelspec":{"display_name":"Python 3","name":"python3"},"widgets":{"application/vnd.jupyter.widget-state+json":{"4b12d0de40ac4fabb3c694077b92d336":{"model_module":"@jupyter-widgets/controls","model_name":"IntProgressModel","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"IntProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"","description":"Processing: 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