:recycle: removed false positives from prescore matrix by swapping in euclidean distance for cosine similarity

backend/db/data-generator/config.json

@@ -1,10 +1,10 @@
 {
     "mockOutputPath": "./db/generated",
-    "magic": 10000,
+    "magic": 1000,
     "total": 100,
     "batchSize": 10,
     "percentageOfSeekers": 90,
-    "scoreVals": [107, 414, 721, 1028, 1336, 1648, 2056],
+    "scoreVals": [1, 2, 3, 4, 5, 6, 7],
     "header": "/**\n* GENERATED MOCK SIIMEE DATA\n*/",
     "possibleZipcodes": [
         "90012",

backend/db/data-generator/generate.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -10,23 +10,26 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Scoring: ((107, 107), (1336, 1336))\n",
-      "10000.0\n",
+      "Scoring: ((1, 1), (7, 7))\n",
+      "1515\n",
       "\n",
-      "Scoring: ((414, 107), (414, 414))\n",
-      "8616.0\n",
+      "Scoring: ((2, 1), (2, 2))\n",
+      "9000\n",
       "\n",
-      "Scoring: ((414, 2056), (1648, 721))\n",
-      "5738.0\n",
+      "Scoring: ((2, 7), (6, 3))\n",
+      "4343\n",
       "\n",
-      "Scoring: ((1648, 721), (107, 414))\n",
-      "6173.0\n",
+      "Scoring: ((6, 3), (1, 2))\n",
+      "4901\n",
       "\n",
-      "Scoring: ((1336, 1648), (1028, 1648))\n",
-      "9924.0\n",
+      "Scoring: ((5, 6), (4, 6))\n",
+      "9000\n",
       "\n",
-      "Scoring: ((107, 414), (721, 721))\n",
-      "8616.0\n"
+      "Scoring: ((1, 2), (3, 3))\n",
+      "7764\n",
+      "\n",
+      "Scoring: ((1, 1), (1, 1))\n",
+      "10000\n"
      ]
     }
    ],
@@ -35,6 +38,8 @@
     "import copy\n",
     "from itertools import product\n",
     "from scipy import spatial\n",
+    "import math\n",
+    "import numpy\n",
     "\n",
     " \n",
     "f = open('./config.json')\n",
@@ -47,6 +52,11 @@
     "config[\"file\"] = \"../generated/prescore_matrix.json\"\n",
     "config[\"delimiter\"] = \"-\"\n",
     "config[\"version\"] = \"0.1.0\"\n",
+    "# max distance = distance between top left and bottom right indices in matrix\n",
+    "config[\"max_euclidean_distance\"] =  math.sqrt(\n",
+    "    math.pow(len(config[\"all_possible_responses\"])-1, 2) +\n",
+    "    math.pow(len(config[\"all_possible_responses\"])-1, 2)\n",
+    "    ) \n",
     "f.close()\n",
     "\n",
     "def createPermutations(possibilities, size):\n",
@@ -79,13 +89,19 @@
     "    b = aspect_ab[1]\n",
     "    return (1 - spatial.distance.cosine(a,b)) * config[\"magic\"]\n",
     "\n",
+    "def euclideanDistance(aspect_ab):\n",
+    "    a = numpy.array(aspect_ab[0])\n",
+    "    b = numpy.array(aspect_ab[1])\n",
+    "    return numpy.linalg.norm(a - b)\n",
     "\n",
     "def prescore_matrix_from(vals):\n",
     "    m = {}\n",
     "    for val in vals:\n",
     "        m[val] = []\n",
     "        for other_val in vals:\n",
-    "            score = scoreAspect((val, other_val))\n",
+    "            distance = euclideanDistance((val,other_val))\n",
+    "            score = (config[\"max_euclidean_distance\"] - distance) * config[\"magic\"] + 1515\n",
+    "            # score = scoreAspect((val, other_val))\n",
     "            adjusted_score = round(score)\n",
     "            m[val].append(adjusted_score)\n",
     "    return m\n",
@@ -120,14 +136,16 @@
     "        res[5], res[2],\n",
     "        res[4], res[5],\n",
     "        res[0], res[1],\n",
+    "        res[0], res[0],\n",
     "    ]\n",
     "    input_b = [\n",
-    "        res[4], res[4], # One aspect\n",
+    "        res[6], res[6], # One aspect\n",
     "        res[1], res[1],\n",
     "        res[5], res[2],\n",
     "        res[0], res[1],\n",
     "        res[3], res[5],\n",
     "        res[2], res[2],\n",
+    "        res[0], res[0],\n",
     "    ]\n",
     "    for i in range(round(input_a.__len__() / 2)):\n",
     "        print(score_aspect(input_a, input_b, m, xy_axis_vals))\n",
@@ -150,11 +168,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "a4118c1262ac97709ca0d199809af279fe9249120a4ac5f6c92359d01f3f0cd0"
-  },
   "kernelspec": {
-   "display_name": "Python 3.7.10 64-bit ('base': conda)",
+   "display_name": "Python 3.10.6 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -168,9 +183,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.10.6"
   },
-  "orig_nbformat": 4
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+   }
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2