use_case_preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db89d2ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import all functions to load, preprocess the data\n",
    "import re \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from model_process import pca\n",
    "from model_process import outlier_detection, apply_birch, apply_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "051b5b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading data with \"re\" module\n",
    "data = []\n",
    "with open('data/n2v_embeddings/test_embeds.txt') as f:\n",
    "    for row in f:\n",
    "        data.append(re.split(',', re.split(' ',row.strip('\\n'))[1]))\n",
    "data = np.array(data,dtype = float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d9b8ccf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Detect outlier with z-score\n",
    "z_data = outlier_detection(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46cf636d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Detect outlier with quantile range application\n",
    "q_data = outlier_detection(data, function='quantile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85acbd04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Applying clustering and PCA to data if needed\n",
    "pred_k = apply_kmeans(data, n = 2)\n",
    "pca(data, pred_k, plot = True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('anaconda3')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "vscode": {
   "interpreter": {
    "hash": "26b669ad8bf268938f44ac29f5c4b2ad9da9f9cba6d60b038579058dbb91077c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}