{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scoring: ((107, 107), (1336, 1336))\n", "10000.0\n", "\n", "Scoring: ((414, 107), (414, 414))\n", "8616.0\n", "\n", "Scoring: ((414, 2056), (1648, 721))\n", "5738.0\n", "\n", "Scoring: ((1648, 721), (107, 414))\n", "6173.0\n", "\n", "Scoring: ((1336, 1648), (1028, 1648))\n", "9924.0\n", "\n", "Scoring: ((107, 414), (721, 721))\n", "8616.0\n" ] } ], "source": [ "import json\n", "import copy\n", "from itertools import product\n", "from scipy import spatial\n", "\n", " \n", "f = open('./config.json')\n", "from_json_config = json.load(f)\n", "config = {}\n", "config[\"all_possible_responses\"] = from_json_config[\"scoreVals\"]\n", "config[\"magic\"] = from_json_config[\"magic\"]\n", "config[\"aspect_size\"] = 2\n", "config[\"write\"]= True\n", "config[\"file\"] = \"../generated/prescore_matrix.json\"\n", "config[\"delimiter\"] = \"-\"\n", "config[\"version\"] = \"0.1.0\"\n", "f.close()\n", "\n", "def createPermutations(possibilities, size):\n", " return tuple(product(possibilities, repeat=size))\n", "\n", "\n", "def getAspectFromSurveys(survey_a, survey_b, size):\n", " if (survey_a.__len__() < size | survey_b.__len__() < size):\n", " raise Exception(\"Surveys must both contain more items than size\")\n", "\n", " def store(survey, length):\n", " col = []\n", " for i in range(size):\n", " val = survey.pop(0)\n", " col.append(val)\n", " return col\n", "\n", " # Take the first elements from the list\n", " col_a = tuple(store(survey_a, size))\n", " col_b = tuple(store(survey_b, size))\n", "\n", " if (col_a.__len__() != size | col_b.__len__() != size ):\n", " raise Exception(\"No aspect values found in survey\")\n", "\n", " return (col_a, col_b)\n", "\n", "\n", "def scoreAspect(aspect_ab):\n", " a = aspect_ab[0]\n", " b = aspect_ab[1]\n", " return (1 - spatial.distance.cosine(a,b)) * config[\"magic\"]\n", "\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", " adjusted_score = round(score)\n", " m[val].append(adjusted_score)\n", " return m\n", "\n", "\n", "def lookup_prescore_in(score_matrix, vals, aspect_ab):\n", " print(\"\\nScoring:\", aspect_ab)\n", " aspect_a, aspect_b = aspect_ab\n", " # Look-up using the index because\n", " # \n", " pos_b = vals.index(aspect_b)\n", " return score_matrix[aspect_a][pos_b]\n", "\n", "\n", "# !: Mutates your input\n", "def score_aspect(input_a, input_b, score_matrix, vals):\n", " aspect_ab = getAspectFromSurveys(input_a, input_b, config[\"aspect_size\"])\n", " return lookup_prescore_in(score_matrix, vals, aspect_ab)\n", "\n", "\n", "def run():\n", " # Set the keys for the look-up\n", " xy_axis_vals = createPermutations(config[\"all_possible_responses\"], config[\"aspect_size\"])\n", " m = prescore_matrix_from(xy_axis_vals)\n", "\n", " # Example:\n", " res = config[\"all_possible_responses\"]\n", " input_a = [\n", " res[0], res[0], # One aspect\n", " res[1], res[0],\n", " res[1], res[6],\n", " res[5], res[2],\n", " res[4], res[5],\n", " res[0], res[1],\n", " ]\n", " input_b = [\n", " res[4], res[4], # 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", " ]\n", " for i in range(round(input_a.__len__() / 2)):\n", " print(score_aspect(input_a, input_b, m, xy_axis_vals))\n", "\n", "\n", " if(config[\"write\"] == True):\n", " # Serializing json\n", " str_m = {}\n", " for key, values in m.items():\n", " delimiter = config[\"delimiter\"]\n", " str_key = delimiter.join([str(v) for v in key])\n", " str_m[str_key] = [int(val) for val in values]\n", " str_m[\"_config\"] = config\n", " json_object = json.dumps(str_m, indent = 4)\n", " with open(config[\"file\"], \"w\") as file:\n", " # write to file\n", " file.write(json_object)\n", "run()\n" ] } ], "metadata": { "interpreter": { "hash": "a4118c1262ac97709ca0d199809af279fe9249120a4ac5f6c92359d01f3f0cd0" }, "kernelspec": { "display_name": "Python 3.7.10 64-bit ('base': conda)", "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": "3.6.9" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }