data-cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we only need positive and negatice reviews \n",
    "root = \"./data/amazon-review/\"\n",
    "files = [\"positive.review\", \"negative.review\"]\n",
    "             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iterate over subdirs (domains)\n",
    "for domain in os.listdir(root):\n",
    "    # iterate through both classes\n",
    "    data = {\n",
    "            \"product_type\":[],\n",
    "            \"helpful\":[],\n",
    "            \"rating\":[],\n",
    "            \"title\":[],\n",
    "            \"review_text\":[]\n",
    "    }\n",
    "    for file in files:\n",
    "        text = open(os.path.join(root, domain, file)).read().splitlines()\n",
    "        # each line is a tag or value\n",
    "        for line in range(len(text)-1):\n",
    "            if text[line][1:-1] in data.keys(): # [1:-1] to skim the \"<\" and \">\" \n",
    "                data[text[line][1:-1]].append(text[line+1])\n",
    "                    \n",
    "    df = pd.DataFrame(data=data, index=None)\n",
    "    df['sentiment'] = np.where(df.rating.astype(float) > 3, 1, 0)\n",
    "    df.to_csv(os.path.join(root, domain, domain+\".csv\"), index=False)\n",
    "#     df.to_csv(domain+\".csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm as tqdm\n",
    "import glob\n",
    "import os\n",
    "from sklearn.decomposition import PCA\n",
    "import time\n",
    "import plotly.express as px\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47567033767700195\n"
     ]
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        "title": {
         "text": "Distributional Shift"
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   "source": [
    "df = pd.read_pickle(\"./amazon-product.pkl\")\n",
    "mat = np.matrix([x for x in df.embeddings])\n",
    "start = time.time()\n",
    "pca = PCA(n_components=3, random_state=42)\n",
    "components = pca.fit_transform(mat)\n",
    "print(time.time()-start)\n",
    "fig = px.scatter_3d(\n",
    "    components, x=0, y=1, z=2, color=df['dataset'],\n",
    "    title=f'Distributional Shift',\n",
    "    labels={'color': 'dataset'},\n",
    ")\n",
    "fig.update_traces(\n",
    "    marker_size=3\n",
    "  )\n",
    "fig.update_layout(\n",
    "    # autosize=False,\n",
    "   )\n",
    "fig.write_html(\"3dtsne.html\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffle the dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path = \"./amazon-review/\"\n",
    "# for file in os.listdir(path):\n",
    "#     df = pd.read_csv(os.path.join(path, file))\n",
    "#     df = df.sample(frac=1).reset_index(drop=True)\n",
    "#     df.to_csv(os.path.join(path, file), index=False)\n",
    "# #     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv(os.path.join(path, 'kitchen_&_housewares.csv'))\n",
    "# df = df.sample(frac=1).reset_index(drop=True)\n",
    "# df.to_csv(os.path.join(path, 'kitchen_&_housewares.csv'))\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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