demo/zhidemai_autogluon.ipynb

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   "source": [
    "from autogluon.tabular import TabularDataset, TabularPredictor"
   ]
  },
  {
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   "source": [
    "train_data = TabularDataset('./data/zhidemai/train.csv')"
   ]
  },
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   "source": [
    "train_data.shape"
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   "source": [
    "train_data.head()"
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   "outputs": [],
   "source": [
    "label = 'orders_3h_15h'"
   ]
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   "id": "a225cdb6",
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   "outputs": [],
   "source": [
    "save_path = 'agModels-zhidemai'  # specifies folder to store trained models\n",
    "predictor = TabularPredictor(label=label, path=save_path).fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "test_data_nolab = TabularDataset('./data/zhidemai/test.csv')"
   ]
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   "source": [
    "y_pred = predictor.predict(test_data_nolab)"
   ]
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   "outputs": [],
   "source": [
    "y_pred"
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "240cd16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_ = ['article_id']\n",
    "target = 'orders_3h_15h'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4fd354e",
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   "outputs": [],
   "source": [
    "sub = test_data_nolab[id_].copy()\n",
    "sub[target] = list(y_pred.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c59b336a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv(\"./autogluon_sub.csv\", index = False)"
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