{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pronunciation-based location prediction confusion\n",
"\n",
"Confusion of pronunciation-based predictions of someone's location"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"import folium\n",
"import pandas\n",
"import MySQLdb\n",
"import numpy\n",
"from sklearn.metrics import confusion_matrix\n",
"from folium.plugins import HeatMap\n",
"from IPython.display import display\n",
"import itertools\n",
"import requests\n",
"import json\n",
"import random\n",
"from vincenty import vincenty\n",
"\n",
"db = MySQLdb.connect(user='root', passwd='Nmmxhjgt1@', db='stimmenfryslan')\n",
"\n",
"\n",
"%matplotlib inline\n",
"from matplotlib import pyplot, rcParams\n",
"from jupyter_progressbar import ProgressBar\n",
"\n",
"# rcParams['font.family'] = 'Lucinda Console'\n",
"rcParams['font.size'] = '24'\n",
"rcParams['figure.figsize'] = (20, 10)\n",
"rcParams['figure.dpi'] = 100\n",
"\n",
"from jupyter_progressbar import ProgressBar"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"with open('simplified_predictions.p3', 'rb') as f:\n",
" simplified_predictions = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"simplified_predictions['actual_latlon_rounded'] = [\n",
" (\n",
" (x[0] // 0.1) * 0.1,\n",
" (x[1] // 0.1) * 0.1,\n",
" ) if x == x else numpy.nan\n",
" for x in simplified_predictions['actual_latlon']\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"actual_latlon = [tuple(x) for x in simplified_predictions['actual_latlon'] if x == x]\n",
"\n",
"m = folium.Map((52.16370310266682, 5.601701825033824), tiles='stamentoner', zoom_start=8)\n",
"\n",
"for p in set(actual_latlon):\n",
" if p == p:\n",
" pp = ((p[0] // 0.1) * 0.1, (p[1] // 0.1) * 0.1)\n",
" folium.Circle(pp, radius=10).add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from collections import Counter"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9b3470db2073443a9ee9ed47076116b9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='0s passed', placeholder='0…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"confusions = Counter([\n",
" (tuple(actual), tuple(predicted))\n",
" for actual, predicted in zip(\n",
" ProgressBar(simplified_predictions['actual_latlon_rounded']),\n",
" simplified_predictions['prediction_1_latlon']\n",
" )\n",
" if actual == actual and predicted == predicted\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m = folium.Map((52.16370310266682, 5.601701825033824), tiles='stamentoner', zoom_start=8)\n",
"\n",
"for (actual, predicted), count in confusions.items():\n",
" \n",
" folium.PolyLine([actual, predicted], color=\"red\", weight=numpy.log(count), opacity=1).add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map((52.16370310266682, 5.601701825033824), tiles='stamentoner', zoom_start=8)\n",
"\n",
"for (actual, predicted), count in confusions.items():\n",
" \n",
" folium.PolyLine([actual, predicted], color=\"red\", weight=numpy.log(count), opacity=1).add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}