amazon-ml-inference.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import gc\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5c7398",
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   "source": [
    "# KNN Predictions"
   ]
  },
  {
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   "execution_count": 2,
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   "outputs": [],
   "source": [
    "data = pd.read_csv('../input/amazon-clean-dataset2/data.csv')\n",
    "y = np.array(data['BROWSE_NODE_ID']).astype(np.int)\n",
    "del data\n",
    "\n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "del y\n",
    "\n",
    "d1 = np.load('../input/amazon-ml-challenge-2/proba_knn.npy')\n",
    "d1 *= 0.3\n",
    "gc.collect();\n",
    "d2 = np.load('../input/amazon-ml-challenge/proba_knn.npy')\n",
    "d2 *= 0.3\n",
    "d1 += d2\n",
    "del d2\n",
    "gc.collect();\n",
    "d3 = np.load('../input/amazon-ml-challenge-3/proba_knn.npy')\n",
    "d3 *= 0.2\n",
    "d1 += d3\n",
    "del d3\n",
    "gc.collect();\n",
    "d4 = np.load('../input/amazon-ml-challenge-4/proba_knn.npy')\n",
    "d4 *= 0.2\n",
    "d1 += d4\n",
    "del d4\n",
    "gc.collect();\n",
    "\n",
    "d1 = np.argmax(d1, axis = -1)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRODUCT_ID</th>\n",
       "      <th>BROWSE_NODE_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1140</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7644</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   PRODUCT_ID  BROWSE_NODE_ID\n",
       "0           1            1140\n",
       "1           2            2321\n",
       "2           3            3711\n",
       "3           4             125\n",
       "4           5            7644"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "test = pd.read_csv('../input/amazon-ml-challenge-dataset/dataset/test.csv', escapechar = \"\\\\\"  , quoting = csv.QUOTE_NONE, usecols = ['PRODUCT_ID'])\n",
    "test['BROWSE_NODE_ID'] = le.inverse_transform(d1)\n",
    "test.to_csv(\"submission_knn.csv\", index = False)\n",
    "test.head()"
   ]
  },
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