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baseline3.ipyn
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"baseline3.ipyn","provenance":[],"mount_file_id":"1LiZTucociDPo_b9m6RXkEI55_N6_vERP","authorship_tag":"ABX9TyNHyCbHOBGglxWRSvRVwjfF"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":13,"metadata":{"id":"t8jWpPkkwcmC","executionInfo":{"status":"ok","timestamp":1661956930703,"user_tz":-540,"elapsed":269,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}}},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import os\n","import pickle\n","import gc\n","import re\n","\n","import pandas_profiling as pdp\n","\n","import matplotlib.pyplot as plt\n","\n","from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder, OneHotEncoder\n","\n","from sklearn.feature_extraction.text import CountVectorizer\n","\n","from sklearn.model_selection import train_test_split, StratifiedKFold\n","from sklearn.metrics import accuracy_score, f1_score\n","import lightgbm as lgb\n","\n","import warnings\n","warnings.filterwarnings('ignore')"]},{"cell_type":"code","source":["train = pd.read_csv('/content/drive/MyDrive/signate/MUFG Data Science Champion Ship/Input/train.csv')\n","test = pd.read_csv('/content/drive/MyDrive/signate/MUFG Data Science Champion Ship/Input/test.csv')\n","sub = pd.read_csv('/content/drive/MyDrive/signate/MUFG Data Science Champion Ship/Input/sample_submit.csv')"],"metadata":{"id":"II86qtDxzcCs","executionInfo":{"status":"ok","timestamp":1661956932243,"user_tz":-540,"elapsed":1283,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["class cfg:\n"," n_splits = 5\n","\n"," seed = 123\n","\n"," num_round = 100"],"metadata":{"id":"ran-jxiw4x38","executionInfo":{"status":"ok","timestamp":1661956932565,"user_tz":-540,"elapsed":10,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["params = {\n"," 'boosting_type': 'gbdt',\n"," 'objective': 'binary',\n"," 'learning_rate': 0.1,\n"," 'num_leaves': 16,\n"," 'm_estimators': 100000,\n"," 'random_state': cfg.seed,\n"," 'importance_type': 'gain',\n","}"],"metadata":{"id":"WW-y_ILi_AI2","executionInfo":{"status":"ok","timestamp":1661956932566,"user_tz":-540,"elapsed":10,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["def cleaning(texts):\n"," clean_texts = []\n"," for text in texts:\n"," text = remove_tag(text)\n"," clean_texts.append(text)\n"," return clean_texts\n","\n","def remove_tag(x):\n"," p = re.compile(r\"<[^>]*?>\")\n"," return p.sub('',x)"],"metadata":{"id":"RsJUdSW51Nui","executionInfo":{"status":"ok","timestamp":1661956932568,"user_tz":-540,"elapsed":11,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["df_text = train[['html_content']]\n","df_text['html_content'] = cleaning(df_text['html_content'])\n","print(df_text.shape)\n","\n","vec = CountVectorizer(min_df=200)\n","\n","vec.fit(df_text['html_content'])\n","\n","html_content = pd.DataFrame(vec.transform(df_text['html_content']).toarray(), columns=vec.get_feature_names())\n","print(html_content.shape)\n","html_content"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":521},"id":"zos69xUtIr3z","executionInfo":{"status":"ok","timestamp":1661956938210,"user_tz":-540,"elapsed":5652,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}},"outputId":"bdbb255b-f992-4faa-adf3-42907efdc95c"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["(9791, 1)\n","(9791, 1435)\n"]},{"output_type":"execute_result","data":{"text/plain":[" 00 000 10 100 1000 11 12 13 14 15 ... young your yourself \\\n","0 0 1 0 1 0 0 0 0 0 0 ... 0 2 0 \n","1 0 0 0 0 0 0 0 0 0 0 ... 0 2 0 \n","2 0 0 0 0 0 0 0 0 0 0 ... 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 1 0 \n","... .. ... .. ... ... .. .. .. .. .. ... ... ... ... \n","9786 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 \n","9787 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 \n","9788 0 0 1 0 0 0 0 0 0 0 ... 0 5 0 \n","9789 0 0 1 0 0 0 0 0 0 0 ... 0 19 0 \n","9790 0 0 1 0 0 0 0 0 0 0 ... 0 0 0 \n","\n"," youth youtube このコンテンツを表示するにはhtml5対応のブラウザが必要です 再生 動画を再生 音ありでリプレイ \\\n","0 0 0 0 0 0 0 \n","1 0 0 0 0 0 0 \n","2 0 0 0 0 0 0 \n","3 0 0 0 0 0 0 \n","4 0 0 0 0 0 0 \n","... ... ... ... .. ... ... \n","9786 0 0 0 0 0 0 \n","9787 0 0 0 0 0 0 \n","9788 0 0 0 0 0 0 \n","9789 0 0 0 0 0 0 \n","9790 0 0 0 0 0 0 \n","\n"," 音声ありで \n","0 0 \n","1 0 \n","2 0 \n","3 0 \n","4 0 \n","... ... \n","9786 0 \n","9787 0 \n","9788 0 \n","9789 0 \n","9790 0 \n","\n","[9791 rows x 1435 columns]"],"text/html":["\n"," <div id=\"df-2dc051a2-d3cc-465f-b50e-63c0a8da2a73\">\n"," <div class=\"colab-df-container\">\n"," <div>\n","<style scoped>\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","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>00</th>\n"," <th>000</th>\n"," <th>10</th>\n"," <th>100</th>\n"," <th>1000</th>\n"," <th>11</th>\n"," <th>12</th>\n"," <th>13</th>\n"," <th>14</th>\n"," <th>15</th>\n"," <th>...</th>\n"," <th>young</th>\n"," <th>your</th>\n"," <th>yourself</th>\n"," <th>youth</th>\n"," <th>youtube</th>\n"," <th>このコンテンツを表示するにはhtml5対応のブラウザが必要です</th>\n"," <th>再生</th>\n"," <th>動画を再生</th>\n"," <th>音ありでリプレイ</th>\n"," <th>音声ありで</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>0</td>\n"," <td>1</td>\n"," <td>0</td>\n"," 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<td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>9786</th>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>...</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>9787</th>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>...</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>9788</th>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>1</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>...</td>\n"," <td>0</td>\n"," <td>5</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>9789</th>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>1</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>...</td>\n"," <td>0</td>\n"," <td>19</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>9790</th>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>1</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>...</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>9791 rows × 1435 columns</p>\n","</div>\n"," <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2dc051a2-d3cc-465f-b50e-63c0a8da2a73')\"\n"," title=\"Convert this dataframe to an interactive table.\"\n"," style=\"display:none;\">\n"," \n"," <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n"," width=\"24px\">\n"," <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n"," <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 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const buttonEl =\n"," document.querySelector('#df-2dc051a2-d3cc-465f-b50e-63c0a8da2a73 button.colab-df-convert');\n"," buttonEl.style.display =\n"," google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n"," async function convertToInteractive(key) {\n"," const element = document.querySelector('#df-2dc051a2-d3cc-465f-b50e-63c0a8da2a73');\n"," const dataTable =\n"," await google.colab.kernel.invokeFunction('convertToInteractive',\n"," [key], {});\n"," if (!dataTable) return;\n","\n"," const docLinkHtml = 'Like what you see? Visit the ' +\n"," '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n"," + ' to learn more about interactive tables.';\n"," element.innerHTML = '';\n"," dataTable['output_type'] = 'display_data';\n"," await google.colab.output.renderOutput(dataTable, element);\n"," const docLink = document.createElement('div');\n"," docLink.innerHTML = docLinkHtml;\n"," element.appendChild(docLink);\n"," }\n"," </script>\n"," </div>\n"," </div>\n"," "]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["x_train = train[['goal', 'country', 'duration', 'category1', 'category2']]\n","x_train.columns = ['goal1', 'country1', 'duration1', 'category1', 'category2']\n","# x_train = train[['duration']]\n","y_train = train['state']\n","id_train = train['id']\n","categorical_features = ['goal1', 'country1', 'category1', 'category2']\n","\n","cv = list(StratifiedKFold(n_splits=cfg.n_splits, shuffle=True, random_state=cfg.seed).split(x_train, y_train))\n","\n","\n","metrics = []\n","imp = pd.DataFrame()\n","\n","for column in categorical_features:\n"," target_column = x_train[column]\n"," le = LabelEncoder()\n"," le.fit(target_column)\n"," label_encoded_column = le.transform(target_column)\n"," x_train[column] = pd.Series(label_encoded_column).astype('category')\n","\n","print(x_train.shape)\n","x_train = pd.concat([x_train, html_content], axis=1)\n","print(x_train.shape)\n","\n","for nfold in range(cfg.n_splits):\n"," print('-'*20, nfold, '-'*20)\n"," idx_tr, idx_va = cv[nfold][0], cv[nfold][1]\n"," x_tr, y_tr = x_train.loc[idx_tr], y_train.loc[idx_tr]\n"," x_va, y_va = x_train.loc[idx_va], y_train.loc[idx_va]\n"," print(x_tr.shape, y_tr.shape)\n"," print(x_va.shape, y_va.shape)\n","\n"," lgb_tr = lgb.Dataset(x_tr, y_tr)\n"," lgb_va = lgb.Dataset(x_va, y_va)\n","\n"," lgb_results = {}\n"," model = lgb.train(params, lgb_tr, num_boost_round=cfg.num_round,\n"," valid_names=['train', 'valid'],\n"," valid_sets=[lgb_tr, lgb_va])\n","\n"," y_tr_pred = model.predict(x_tr)\n"," y_va_pred = model.predict(x_va)\n"," y_tr_pred = np.where(y_tr_pred > 0.5, 1, 0)\n"," y_va_pred = np.where(y_va_pred > 0.5, 1, 0)\n","\n"," print(y_tr)\n"," print(y_tr_pred)\n"," f1_tr = f1_score(y_tr, y_tr_pred)\n"," f1_va = f1_score(y_va, y_va_pred)\n"," print('[f1 score] tr:{:.2f}, va:{:.2f}'.format(f1_tr, f1_va))\n"," metrics.append([nfold, f1_tr, f1_va])\n","\n","print('='*20, 'result', '='*20)\n","metrics = np.array(metrics)\n","print(metrics)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IeQum-Sk45Kn","executionInfo":{"status":"ok","timestamp":1661956958055,"user_tz":-540,"elapsed":19887,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}},"outputId":"e3ec8c6a-b13d-4ae3-e3cd-a456fd8e1f2a"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["(9791, 5)\n","(9791, 1440)\n","-------------------- 0 --------------------\n","(7832, 1440) (7832,)\n","(1959, 1440) (1959,)\n","[1]\ttrain's binary_logloss: 0.659589\tvalid's binary_logloss: 0.662501\n","[2]\ttrain's binary_logloss: 0.631108\tvalid's binary_logloss: 0.636316\n","[3]\ttrain's binary_logloss: 0.606502\tvalid's binary_logloss: 0.613826\n","[4]\ttrain's binary_logloss: 0.58523\tvalid's binary_logloss: 0.594114\n","[5]\ttrain's binary_logloss: 0.566715\tvalid's binary_logloss: 0.577176\n","[6]\ttrain's binary_logloss: 0.550397\tvalid's binary_logloss: 0.562884\n","[7]\ttrain's binary_logloss: 0.535887\tvalid's binary_logloss: 0.550022\n","[8]\ttrain's binary_logloss: 0.52287\tvalid's binary_logloss: 0.538522\n","[9]\ttrain's binary_logloss: 0.51066\tvalid's binary_logloss: 0.528246\n","[10]\ttrain's binary_logloss: 0.500269\tvalid's binary_logloss: 0.520154\n","[11]\ttrain's binary_logloss: 0.490685\tvalid's binary_logloss: 0.512664\n","[12]\ttrain's binary_logloss: 0.482132\tvalid's binary_logloss: 0.50615\n","[13]\ttrain's 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binary_logloss: 0.413902\n","[65]\ttrain's binary_logloss: 0.318362\tvalid's binary_logloss: 0.413678\n","[66]\ttrain's binary_logloss: 0.316907\tvalid's binary_logloss: 0.413378\n","[67]\ttrain's binary_logloss: 0.315373\tvalid's binary_logloss: 0.413417\n","[68]\ttrain's binary_logloss: 0.313857\tvalid's binary_logloss: 0.413363\n","[69]\ttrain's binary_logloss: 0.312292\tvalid's binary_logloss: 0.413307\n","[70]\ttrain's binary_logloss: 0.310981\tvalid's binary_logloss: 0.413406\n","[71]\ttrain's binary_logloss: 0.30963\tvalid's binary_logloss: 0.412956\n","[72]\ttrain's binary_logloss: 0.308192\tvalid's binary_logloss: 0.412483\n","[73]\ttrain's binary_logloss: 0.306763\tvalid's binary_logloss: 0.412605\n","[74]\ttrain's binary_logloss: 0.305346\tvalid's binary_logloss: 0.412277\n","[75]\ttrain's binary_logloss: 0.303809\tvalid's binary_logloss: 0.412284\n","[76]\ttrain's binary_logloss: 0.302644\tvalid's binary_logloss: 0.411837\n","[77]\ttrain's binary_logloss: 0.301256\tvalid's binary_logloss: 0.411541\n","[78]\ttrain's binary_logloss: 0.299483\tvalid's binary_logloss: 0.411244\n","[79]\ttrain's binary_logloss: 0.298002\tvalid's binary_logloss: 0.410854\n","[80]\ttrain's binary_logloss: 0.29642\tvalid's binary_logloss: 0.411078\n","[81]\ttrain's binary_logloss: 0.294874\tvalid's binary_logloss: 0.411104\n","[82]\ttrain's binary_logloss: 0.293223\tvalid's binary_logloss: 0.410612\n","[83]\ttrain's binary_logloss: 0.292089\tvalid's binary_logloss: 0.410412\n","[84]\ttrain's binary_logloss: 0.29087\tvalid's binary_logloss: 0.410207\n","[85]\ttrain's binary_logloss: 0.289568\tvalid's binary_logloss: 0.409997\n","[86]\ttrain's binary_logloss: 0.288262\tvalid's binary_logloss: 0.409828\n","[87]\ttrain's binary_logloss: 0.287038\tvalid's binary_logloss: 0.409646\n","[88]\ttrain's binary_logloss: 0.285744\tvalid's binary_logloss: 0.409062\n","[89]\ttrain's binary_logloss: 0.284655\tvalid's binary_logloss: 0.40868\n","[90]\ttrain's binary_logloss: 0.283068\tvalid's binary_logloss: 0.408478\n","[91]\ttrain's binary_logloss: 0.281916\tvalid's binary_logloss: 0.40795\n","[92]\ttrain's binary_logloss: 0.280758\tvalid's binary_logloss: 0.407709\n","[93]\ttrain's binary_logloss: 0.279742\tvalid's binary_logloss: 0.407631\n","[94]\ttrain's binary_logloss: 0.27866\tvalid's binary_logloss: 0.407792\n","[95]\ttrain's binary_logloss: 0.277668\tvalid's binary_logloss: 0.407478\n","[96]\ttrain's binary_logloss: 0.276418\tvalid's binary_logloss: 0.407236\n","[97]\ttrain's binary_logloss: 0.275087\tvalid's binary_logloss: 0.406941\n","[98]\ttrain's binary_logloss: 0.27402\tvalid's binary_logloss: 0.406745\n","[99]\ttrain's binary_logloss: 0.272784\tvalid's binary_logloss: 0.406838\n","[100]\ttrain's binary_logloss: 0.271687\tvalid's binary_logloss: 0.407094\n","0 1\n","1 0\n","2 0\n","3 1\n","5 0\n"," ..\n","9785 0\n","9786 0\n","9787 0\n","9789 1\n","9790 0\n","Name: state, Length: 7833, dtype: int64\n","[1 0 1 ... 0 1 0]\n","[f1 score] tr:0.90, va:0.79\n","==================== result ====================\n","[[0. 0.90311419 0.78247261]\n"," [1. 0.89978735 0.77998924]\n"," [2. 0.90035919 0.7613941 ]\n"," [3. 0.90147195 0.77443609]\n"," [4. 0.89859304 0.79397526]]\n"]}]},{"cell_type":"code","source":["sub = pd.read_csv('/content/drive/MyDrive/signate/MUFG Data Science Champion Ship/Input/sample_submit.csv', header=None)\n","sub"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"DH_es7BImRH3","executionInfo":{"status":"ok","timestamp":1661956958056,"user_tz":-540,"elapsed":10,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}},"outputId":"8f9e2fdd-fe10-42a2-d96a-9e674efd479e"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" 0 1\n","0 test_00000 1\n","1 test_00001 0\n","2 test_00002 0\n","3 test_00003 0\n","4 test_00004 1\n","... ... ..\n","9795 test_09795 0\n","9796 test_09796 0\n","9797 test_09797 1\n","9798 test_09798 0\n","9799 test_09799 0\n","\n","[9800 rows x 2 columns]"],"text/html":["\n"," <div id=\"df-bf04adef-95d9-4202-bbd9-8f865bc51760\">\n"," <div class=\"colab-df-container\">\n"," <div>\n","<style scoped>\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","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>0</th>\n"," <th>1</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>test_00000</td>\n"," <td>1</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>test_00001</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>test_00002</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>test_00003</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>test_00004</td>\n"," <td>1</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>9795</th>\n"," <td>test_09795</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>9796</th>\n"," <td>test_09796</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>9797</th>\n"," <td>test_09797</td>\n"," <td>1</td>\n"," </tr>\n"," <tr>\n"," <th>9798</th>\n"," <td>test_09798</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>9799</th>\n"," <td>test_09799</td>\n"," <td>0</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>9800 rows × 2 columns</p>\n","</div>\n"," <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf04adef-95d9-4202-bbd9-8f865bc51760')\"\n"," title=\"Convert this dataframe to an interactive table.\"\n"," style=\"display:none;\">\n"," \n"," <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n"," width=\"24px\">\n"," <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n"," <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 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{\n"," background-color: #3B4455;\n"," fill: #D2E3FC;\n"," }\n","\n"," [theme=dark] .colab-df-convert:hover {\n"," background-color: #434B5C;\n"," box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n"," filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n"," fill: #FFFFFF;\n"," }\n"," </style>\n","\n"," <script>\n"," const buttonEl =\n"," document.querySelector('#df-bf04adef-95d9-4202-bbd9-8f865bc51760 button.colab-df-convert');\n"," buttonEl.style.display =\n"," google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n"," async function convertToInteractive(key) {\n"," const element = document.querySelector('#df-bf04adef-95d9-4202-bbd9-8f865bc51760');\n"," const dataTable =\n"," await google.colab.kernel.invokeFunction('convertToInteractive',\n"," [key], {});\n"," if (!dataTable) return;\n","\n"," const docLinkHtml = 'Like what you see? Visit the ' +\n"," '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n"," + ' to learn more about interactive tables.';\n"," element.innerHTML = '';\n"," dataTable['output_type'] = 'display_data';\n"," await google.colab.output.renderOutput(dataTable, element);\n"," const docLink = document.createElement('div');\n"," docLink.innerHTML = docLinkHtml;\n"," element.appendChild(docLink);\n"," }\n"," </script>\n"," </div>\n"," </div>\n"," "]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["df_text = test[['html_content']]\n","df_text['html_content'] = cleaning(df_text['html_content'])\n","print(df_text.shape)\n","\n","vec = CountVectorizer(min_df=200)\n","\n","vec.fit(df_text['html_content'])\n","\n","html_content_test = pd.DataFrame(vec.transform(df_text['html_content']).toarray(), columns=vec.get_feature_names())\n","print(html_content_test.shape)\n","html_content_test"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":521},"id":"8Z7ttkZ9Jij9","executionInfo":{"status":"ok","timestamp":1661956963926,"user_tz":-540,"elapsed":5878,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}},"outputId":"74455a14-3121-4250-b829-d43a0224d10f"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["(9800, 1)\n","(9800, 1453)\n"]},{"output_type":"execute_result","data":{"text/plain":[" 00 000 10 100 1000 11 12 13 14 15 ... young your yourself \\\n","0 0 0 0 0 0 0 0 0 0 2 ... 0 0 0 \n","1 0 0 1 0 0 0 0 0 0 0 ... 2 2 0 \n","2 0 0 0 0 0 0 0 0 0 1 ... 3 2 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","9795 8 0 0 0 2 0 0 0 0 0 ... 0 1 0 \n","9796 12 0 1 0 0 0 1 0 1 0 ... 4 2 0 \n","9797 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 \n","9798 0 0 0 0 0 0 0 0 0 0 ... 0 3 1 \n","9799 0 2 0 0 0 0 1 0 0 0 ... 0 3 0 \n","\n"," youth youtube このコンテンツを表示するにはhtml5対応のブラウザが必要です 再生 動画を再生 音ありでリプレイ \\\n","0 0 0 0 0 0 0 \n","1 0 0 0 0 0 0 \n","2 0 0 0 0 0 0 \n","3 0 0 0 0 0 0 \n","4 0 0 0 0 0 0 \n","... ... ... ... .. ... ... \n","9795 0 0 2 2 2 2 \n","9796 0 0 3 3 3 3 \n","9797 0 0 0 0 0 0 \n","9798 0 0 0 0 0 0 \n","9799 0 0 0 0 0 0 \n","\n"," 音声ありで \n","0 0 \n","1 0 \n","2 0 \n","3 0 \n","4 0 \n","... ... \n","9795 2 \n","9796 3 \n","9797 0 \n","9798 0 \n","9799 0 \n","\n","[9800 rows x 1453 columns]"],"text/html":["\n"," <div id=\"df-a6b2412e-e161-4283-940f-96994f30974c\">\n"," <div class=\"colab-df-container\">\n"," <div>\n","<style scoped>\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","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>00</th>\n"," <th>000</th>\n"," <th>10</th>\n"," <th>100</th>\n"," <th>1000</th>\n"," <th>11</th>\n"," <th>12</th>\n"," <th>13</th>\n"," <th>14</th>\n"," <th>15</th>\n"," <th>...</th>\n"," <th>young</th>\n"," <th>your</th>\n"," <th>yourself</th>\n"," <th>youth</th>\n"," <th>youtube</th>\n"," <th>このコンテンツを表示するにはhtml5対応のブラウザが必要です</th>\n"," <th>再生</th>\n"," <th>動画を再生</th>\n"," <th>音ありでリプレイ</th>\n"," <th>音声ありで</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>2</td>\n"," <td>...</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>1</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>...</td>\n"," <td>2</td>\n"," <td>2</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>1</td>\n"," <td>...</td>\n"," <td>3</td>\n"," <td>2</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>...</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," <td>0</td>\n"," 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Visit the ' +\n"," '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n"," + ' to learn more about interactive tables.';\n"," element.innerHTML = '';\n"," dataTable['output_type'] = 'display_data';\n"," await google.colab.output.renderOutput(dataTable, element);\n"," const docLink = document.createElement('div');\n"," docLink.innerHTML = docLinkHtml;\n"," element.appendChild(docLink);\n"," }\n"," </script>\n"," </div>\n"," </div>\n"," "]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["x_test = test[['goal', 'country', 'duration', 'category1', 'category2']]\n","x_test.columns = ['goal1', 'country1', 'duration1', 'category1', 'category2']\n","id_test = test['id']\n","\n","for column in categorical_features:\n"," target_column = x_test[column]\n"," le = LabelEncoder()\n"," le.fit(target_column)\n"," label_encoded_column = le.transform(target_column)\n"," x_test[column] = pd.Series(label_encoded_column).astype('category')\n","\n","x_test = pd.concat([x_test, html_content_test], axis=1)\n","\n","y_test_pred = model.predict(x_test)\n","y_test_pred = np.where(y_test_pred > 0.5, 1, 0)\n","print(id_test)\n","print(y_test_pred)\n","df_submit = pd.DataFrame({'id':id_test, 'pred':y_test_pred})\n","print(df_submit)\n","df_submit.to_csv('/content/drive/MyDrive/signate/MUFG Data Science Champion Ship/Output/submit_3.csv', index=None, header=None)"],"metadata":{"id":"jQgEYKDW_rRw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661956997664,"user_tz":-540,"elapsed":849,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}},"outputId":"e71cbd05-d6fc-463c-ef06-6b078a249ce8"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["0 test_00000\n","1 test_00001\n","2 test_00002\n","3 test_00003\n","4 test_00004\n"," ... \n","9795 test_09795\n","9796 test_09796\n","9797 test_09797\n","9798 test_09798\n","9799 test_09799\n","Name: id, Length: 9800, dtype: object\n","[1 1 1 ... 0 0 0]\n"," id pred\n","0 test_00000 1\n","1 test_00001 1\n","2 test_00002 1\n","3 test_00003 0\n","4 test_00004 0\n","... ... ...\n","9795 test_09795 0\n","9796 test_09796 1\n","9797 test_09797 0\n","9798 test_09798 0\n","9799 test_09799 0\n","\n","[9800 rows x 2 columns]\n"]}]}]}