result_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"/Users/k.abhishek/Documents/customer_clusters/result_match_tax_bvd_name_country-1-2-3-4-5.json\"\n",
    "with open(file_path, \"r\") as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "scores = []\n",
    "predicted_labels = []\n",
    "false_postives = {}\n",
    "#matched_label \n",
    "for key, value in data.items():\n",
    "    input_customer = eval(key)\n",
    "    top_match = value['1'][0]\n",
    "    if type(top_match) == dict:\n",
    "        matched_customer_info = top_match['customer_info']\n",
    "        #check if they belong to the same cluster_legal_id\n",
    "        scores.append(top_match['score'])\n",
    "        predicted_labels.append(input_customer[1] == matched_customer_info[1])\n",
    "        if input_customer[1] != matched_customer_info[1]:\n",
    "            false_postives[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_file_path = \"/Users/k.abhishek/Documents/customer_clusters/result_match_tax_bvd_name_country-1-2-3-4-5_false_positives.json\"\n",
    "with open(save_file_path, \"w\") as f:\n",
    "    json.dump(false_postives, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "scores = [score for score, label in zip(scores, predicted_labels) if label]\n",
    "plt.hist(scores, bins=10, edgecolor='black')\n",
    "# Add titles and labels\n",
    "plt.title('Distribution of Scores')\n",
    "plt.xlabel('Scores')\n",
    "plt.ylabel('Frequency')\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = [score for score, label in zip(scores, predicted_labels) if not label]\n",
    "plt.hist(scores, bins=10, edgecolor='black')\n",
    "# Add titles and labels\n",
    "plt.title('Distribution of Scores')\n",
    "plt.xlabel('Scores')\n",
    "plt.ylabel('Frequency')\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_json_file(file_path):\n",
    "    with open(file_path, \"r\") as f:\n",
    "        return json.load(f) \n",
    "\n",
    "file_path = \"/Users/k.abhishek/Documents/customer_clusters/ind_threshold_95_qualified_5554_correctly_qualified_5326.json\"\n",
    "file_name = file_path.split(\"/\")[-1].split(\".\")[0]\n",
    "data = read_json_file(file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "incorrect_ind_threshold_95_qualified_5554_correctly_qualified_5326.json\n"
     ]
    }
   ],
   "source": [
    "incorrect_predictions = {}\n",
    "for key, value in data.items():\n",
    "    # print(key)\n",
    "    info = eval(key)\n",
    "    base_cluster_id = info[1]\n",
    "    top_match = value['1'][0]['customer_info'][1]\n",
    "    if base_cluster_id != top_match:\n",
    "        # print(base_cluster_id, top_match)\n",
    "        incorrect_predictions[key] = value\n",
    "\n",
    "save_file_path = \"incorrect_\" + file_name + \".json\"\n",
    "print(save_file_path)\n",
    "def save_json_file(data, file_path):\n",
    "    with open(file_path, \"w\") as f:\n",
    "        json.dump(data, f)\n",
    "\n",
    "save_json_file(incorrect_predictions, save_file_path)"
   ]
  },
  {
   "cell_type": "code",
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