615 lines
54 KiB
Plaintext
615 lines
54 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"outputs": [],
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"source": [
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"from catboost import CatBoostClassifier\n",
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"import pandas as pd\n",
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"import pickle\n",
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"from sklearn.model_selection import train_test_split"
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],
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"metadata": {
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"collapsed": false,
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"outputs": [],
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"source": [
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"with open('actors_usa_embeddings.pkl', 'rb') as f:\n",
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" dict_usa = pickle.load(f)"
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],
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"metadata": {
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"collapsed": false,
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"outputs": [
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{
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"data": {
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},
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}
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],
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"source": [
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"data_usa = pd.DataFrame.from_dict(dict_usa.items())\n",
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"data_usa"
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],
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"metadata": {
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{
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"cell_type": "code",
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"execution_count": 29,
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"outputs": [],
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"source": [
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"data_usa.rename(columns={0: \"id\", 1: \"embd\"}, inplace=True, errors=\"ignore\")"
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],
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"metadata": {
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"cell_type": "code",
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"outputs": [
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{
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"data": {
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"text/plain": " id embd\n0 1_1 [-1.5706723928451538, 0.6335924863815308, 1.53...\n1 10009_1 [0.5788402557373047, -0.5220502614974976, -1.7...\n2 10013_1 [-0.5845983028411865, 1.5863220691680908, -1.1...\n3 10013_2 [-0.41548410058021545, 1.5801037549972534, -0....\n4 1003_1 [-0.052914053201675415, -0.35606303811073303, ...\n... ... ...\n1051 13616_1 [0.6910120248794556, 1.2189278602600098, -1.64...\n1052 13616_2 [0.2329411804676056, 1.3537031412124634, -1.17...\n1053 13616_3 [0.8600691556930542, 1.441170573234558, -1.856...\n1054 13616_4 [1.2998018264770508, 2.4858486652374268, -1.51...\n1055 13631_2 [0.08967465162277222, 1.597952127456665, -1.96...\n\n[1056 rows x 2 columns]",
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"cell_type": "code",
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"execution_count": 31,
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"outputs": [
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{
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"data": {
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<td>0.615498</td>\n <td>-1.899799</td>\n <td>-1.392390</td>\n <td>-1.029162</td>\n <td>1.198919</td>\n </tr>\n <tr>\n <th>1055</th>\n <td>13631_2</td>\n <td>0.089675</td>\n <td>1.597952</td>\n <td>-1.962265</td>\n <td>-2.278883</td>\n <td>0.774878</td>\n <td>-1.617782</td>\n <td>-0.833502</td>\n <td>1.292249</td>\n <td>-0.641687</td>\n <td>...</td>\n <td>0.419192</td>\n <td>1.590851</td>\n <td>-1.200108</td>\n <td>1.717416</td>\n <td>-1.234612</td>\n <td>0.177451</td>\n <td>-0.677651</td>\n <td>0.334710</td>\n <td>0.589466</td>\n <td>0.305988</td>\n </tr>\n </tbody>\n</table>\n<p>1056 rows × 129 columns</p>\n</div>"
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},
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||
"execution_count": 31,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"new_cols = pd.DataFrame(data_usa['embd'].apply(pd.Series))\n",
|
||
"df_usa = pd.concat([data_usa, new_cols], axis=1)\n",
|
||
"df_usa.drop([\"embd\"], axis=1, inplace=True, errors=\"ignore\")\n",
|
||
"df_usa"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-29T18:30:49.465033800Z",
|
||
"start_time": "2024-03-29T18:30:49.271020400Z"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"outputs": [],
|
||
"source": [
|
||
"df_usa[\"id\"] = df_usa[\"id\"].apply(lambda x: str(x)[:str(x).find(\"_\")])"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-29T18:30:52.716203400Z",
|
||
"start_time": "2024-03-29T18:30:52.692504100Z"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"outputs": [],
|
||
"source": [
|
||
"df_usa[\"id\"] = df_usa[\"id\"].astype(int)\n",
|
||
"y, X = df_usa[\"id\"], df_usa.drop([\"id\"], axis=1)\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=1)"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-29T18:30:57.870505500Z",
|
||
"start_time": "2024-03-29T18:30:57.845736100Z"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "id\n13078 4\n10593 4\n10766 4\n13209 4\n1320 4\n ..\n12114 1\n12143 1\n12151 1\n12163 1\n13631 1\nName: count, Length: 457, dtype: int64"
|
||
},
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y.value_counts()"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-29T18:40:33.247010800Z",
|
||
"start_time": "2024-03-29T18:40:33.123293900Z"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "[]"
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"cat_features = [col for col in df_usa.columns if df_usa[col].dtype.name == 'category' or df_usa[col].dtype.name == 'object']\n",
|
||
"cat_features"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-29T17:08:02.745660400Z",
|
||
"start_time": "2024-03-29T17:08:02.735557900Z"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))",
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "2f8ef01d3ad3469ba13c90ed7dcc9b43"
|
||
}
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Learning rate set to 0.185024\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
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|
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|
||
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|
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|
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|
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|
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"241:\tlearn: 0.1307784\ttest: 2.5946309\tbest: 2.5946309 (241)\ttotal: 4m 46s\tremaining: 1m 8s\n",
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"242:\tlearn: 0.1296369\ttest: 2.5891581\tbest: 2.5891581 (242)\ttotal: 4m 47s\tremaining: 1m 7s\n",
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"243:\tlearn: 0.1287263\ttest: 2.5854040\tbest: 2.5854040 (243)\ttotal: 4m 48s\tremaining: 1m 6s\n",
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"244:\tlearn: 0.1276074\ttest: 2.5824097\tbest: 2.5824097 (244)\ttotal: 4m 49s\tremaining: 1m 5s\n",
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"245:\tlearn: 0.1265279\ttest: 2.5803019\tbest: 2.5803019 (245)\ttotal: 4m 50s\tremaining: 1m 3s\n",
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"246:\tlearn: 0.1257017\ttest: 2.5781031\tbest: 2.5781031 (246)\ttotal: 4m 51s\tremaining: 1m 2s\n",
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"247:\tlearn: 0.1246384\ttest: 2.5743144\tbest: 2.5743144 (247)\ttotal: 4m 53s\tremaining: 1m 1s\n",
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"248:\tlearn: 0.1235830\ttest: 2.5710983\tbest: 2.5710983 (248)\ttotal: 4m 54s\tremaining: 1m\n",
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"249:\tlearn: 0.1226734\ttest: 2.5685760\tbest: 2.5685760 (249)\ttotal: 4m 55s\tremaining: 59.1s\n",
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"250:\tlearn: 0.1216873\ttest: 2.5627749\tbest: 2.5627749 (250)\ttotal: 4m 56s\tremaining: 57.9s\n",
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"251:\tlearn: 0.1207624\ttest: 2.5604650\tbest: 2.5604650 (251)\ttotal: 4m 57s\tremaining: 56.7s\n",
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"252:\tlearn: 0.1197748\ttest: 2.5567001\tbest: 2.5567001 (252)\ttotal: 4m 58s\tremaining: 55.5s\n",
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"253:\tlearn: 0.1188709\ttest: 2.5522751\tbest: 2.5522751 (253)\ttotal: 4m 59s\tremaining: 54.3s\n",
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"254:\tlearn: 0.1182093\ttest: 2.5495618\tbest: 2.5495618 (254)\ttotal: 5m\tremaining: 53.1s\n",
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"255:\tlearn: 0.1174326\ttest: 2.5474543\tbest: 2.5474543 (255)\ttotal: 5m 2s\tremaining: 51.9s\n",
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"256:\tlearn: 0.1164967\ttest: 2.5437579\tbest: 2.5437579 (256)\ttotal: 5m 3s\tremaining: 50.7s\n",
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"257:\tlearn: 0.1159389\ttest: 2.5436483\tbest: 2.5436483 (257)\ttotal: 5m 4s\tremaining: 49.6s\n",
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"258:\tlearn: 0.1151440\ttest: 2.5400177\tbest: 2.5400177 (258)\ttotal: 5m 5s\tremaining: 48.4s\n",
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"259:\tlearn: 0.1143520\ttest: 2.5357119\tbest: 2.5357119 (259)\ttotal: 5m 6s\tremaining: 47.2s\n",
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"260:\tlearn: 0.1135108\ttest: 2.5356219\tbest: 2.5356219 (260)\ttotal: 5m 7s\tremaining: 46s\n",
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"261:\tlearn: 0.1129622\ttest: 2.5327922\tbest: 2.5327922 (261)\ttotal: 5m 8s\tremaining: 44.8s\n",
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"262:\tlearn: 0.1122684\ttest: 2.5336555\tbest: 2.5327922 (261)\ttotal: 5m 9s\tremaining: 43.6s\n",
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"263:\tlearn: 0.1114187\ttest: 2.5309291\tbest: 2.5309291 (263)\ttotal: 5m 10s\tremaining: 42.4s\n",
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"264:\tlearn: 0.1107954\ttest: 2.5280614\tbest: 2.5280614 (264)\ttotal: 5m 12s\tremaining: 41.3s\n",
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"265:\tlearn: 0.1100099\ttest: 2.5250799\tbest: 2.5250799 (265)\ttotal: 5m 15s\tremaining: 40.3s\n",
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"266:\tlearn: 0.1091868\ttest: 2.5226593\tbest: 2.5226593 (266)\ttotal: 5m 17s\tremaining: 39.3s\n",
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"267:\tlearn: 0.1083813\ttest: 2.5218791\tbest: 2.5218791 (267)\ttotal: 5m 20s\tremaining: 38.3s\n",
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"268:\tlearn: 0.1077584\ttest: 2.5200068\tbest: 2.5200068 (268)\ttotal: 5m 23s\tremaining: 37.2s\n",
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"269:\tlearn: 0.1069557\ttest: 2.5163676\tbest: 2.5163676 (269)\ttotal: 5m 25s\tremaining: 36.2s\n",
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"270:\tlearn: 0.1062898\ttest: 2.5125971\tbest: 2.5125971 (270)\ttotal: 5m 26s\tremaining: 34.9s\n",
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"271:\tlearn: 0.1055449\ttest: 2.5100169\tbest: 2.5100169 (271)\ttotal: 5m 27s\tremaining: 33.7s\n",
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"272:\tlearn: 0.1048197\ttest: 2.5061801\tbest: 2.5061801 (272)\ttotal: 5m 28s\tremaining: 32.5s\n",
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"273:\tlearn: 0.1042048\ttest: 2.5042010\tbest: 2.5042010 (273)\ttotal: 5m 29s\tremaining: 31.3s\n",
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"274:\tlearn: 0.1035214\ttest: 2.5016566\tbest: 2.5016566 (274)\ttotal: 5m 30s\tremaining: 30.1s\n",
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"275:\tlearn: 0.1029245\ttest: 2.4995392\tbest: 2.4995392 (275)\ttotal: 5m 32s\tremaining: 28.9s\n",
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"276:\tlearn: 0.1021868\ttest: 2.4970133\tbest: 2.4970133 (276)\ttotal: 5m 34s\tremaining: 27.8s\n",
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"277:\tlearn: 0.1014778\ttest: 2.4964457\tbest: 2.4964457 (277)\ttotal: 5m 37s\tremaining: 26.7s\n",
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"278:\tlearn: 0.1010127\ttest: 2.4948021\tbest: 2.4948021 (278)\ttotal: 5m 38s\tremaining: 25.5s\n",
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"279:\tlearn: 0.1005303\ttest: 2.4927968\tbest: 2.4927968 (279)\ttotal: 5m 40s\tremaining: 24.3s\n",
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"280:\tlearn: 0.0998295\ttest: 2.4913981\tbest: 2.4913981 (280)\ttotal: 5m 42s\tremaining: 23.2s\n",
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"281:\tlearn: 0.0992066\ttest: 2.4884183\tbest: 2.4884183 (281)\ttotal: 5m 44s\tremaining: 22s\n",
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"282:\tlearn: 0.0986053\ttest: 2.4854838\tbest: 2.4854838 (282)\ttotal: 5m 46s\tremaining: 20.8s\n",
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"283:\tlearn: 0.0979615\ttest: 2.4831693\tbest: 2.4831693 (283)\ttotal: 5m 47s\tremaining: 19.6s\n",
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"284:\tlearn: 0.0973415\ttest: 2.4804797\tbest: 2.4804797 (284)\ttotal: 5m 50s\tremaining: 18.4s\n",
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"285:\tlearn: 0.0967829\ttest: 2.4795830\tbest: 2.4795830 (285)\ttotal: 5m 52s\tremaining: 17.3s\n",
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"286:\tlearn: 0.0961815\ttest: 2.4767011\tbest: 2.4767011 (286)\ttotal: 5m 55s\tremaining: 16.1s\n",
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"287:\tlearn: 0.0956358\ttest: 2.4739070\tbest: 2.4739070 (287)\ttotal: 5m 57s\tremaining: 14.9s\n",
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"288:\tlearn: 0.0950924\ttest: 2.4721359\tbest: 2.4721359 (288)\ttotal: 6m\tremaining: 13.7s\n",
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"289:\tlearn: 0.0946056\ttest: 2.4681132\tbest: 2.4681132 (289)\ttotal: 6m 2s\tremaining: 12.5s\n",
|
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"290:\tlearn: 0.0939822\ttest: 2.4648636\tbest: 2.4648636 (290)\ttotal: 6m 5s\tremaining: 11.3s\n",
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"291:\tlearn: 0.0933801\ttest: 2.4642611\tbest: 2.4642611 (291)\ttotal: 6m 7s\tremaining: 10.1s\n",
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"292:\tlearn: 0.0927768\ttest: 2.4625615\tbest: 2.4625615 (292)\ttotal: 6m 10s\tremaining: 8.85s\n",
|
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"293:\tlearn: 0.0923576\ttest: 2.4605372\tbest: 2.4605372 (293)\ttotal: 6m 12s\tremaining: 7.6s\n",
|
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"294:\tlearn: 0.0917621\ttest: 2.4595634\tbest: 2.4595634 (294)\ttotal: 6m 13s\tremaining: 6.33s\n",
|
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"295:\tlearn: 0.0911821\ttest: 2.4556208\tbest: 2.4556208 (295)\ttotal: 6m 14s\tremaining: 5.06s\n",
|
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"296:\tlearn: 0.0906390\ttest: 2.4531431\tbest: 2.4531431 (296)\ttotal: 6m 15s\tremaining: 3.79s\n",
|
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"297:\tlearn: 0.0900808\ttest: 2.4503985\tbest: 2.4503985 (297)\ttotal: 6m 16s\tremaining: 2.53s\n",
|
||
"298:\tlearn: 0.0895346\ttest: 2.4491837\tbest: 2.4491837 (298)\ttotal: 6m 18s\tremaining: 1.26s\n",
|
||
"299:\tlearn: 0.0889600\ttest: 2.4474767\tbest: 2.4474767 (299)\ttotal: 6m 19s\tremaining: 0us\n",
|
||
"bestTest = 2.447476705\n",
|
||
"bestIteration = 299\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": "<catboost.core.CatBoostClassifier at 0x1c3b6fb2ce0>"
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"params_cat = {\n",
|
||
" \"iterations\":300,\n",
|
||
" \"depth\":7,\n",
|
||
" #\"learning_rate\":0.1,\n",
|
||
" #\"eval_metric\":'F1',\n",
|
||
" \"loss_function\": \"MultiClass\",\n",
|
||
" \"random_seed\":1,\n",
|
||
" \"bootstrap_type\":'Bayesian',\n",
|
||
" \"bagging_temperature\":1,\n",
|
||
" \"od_type\":'Iter',\n",
|
||
" \"od_wait\":20,\n",
|
||
" \"task_type\":'GPU',\n",
|
||
"}\n",
|
||
"cat_model = CatBoostClassifier(**params_cat)\n",
|
||
"cat_model.fit(\n",
|
||
" X_train, y_train,\n",
|
||
" eval_set=(X_test, y_test),\n",
|
||
" use_best_model=True,\n",
|
||
" verbose=True,\n",
|
||
" plot=True\n",
|
||
" )"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-29T17:14:29.140678900Z",
|
||
"start_time": "2024-03-29T17:08:08.540374300Z"
|
||
}
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"outputs": [],
|
||
"source": [
|
||
"cat_model.save_model(\"catboost_blogger.cbm\")"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-29T17:26:38.910177200Z",
|
||
"start_time": "2024-03-29T17:26:38.604629500Z"
|
||
}
|
||
}
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|