notebooks/foretold-submission.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import seaborn\n",
    "import ergo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the internal method used to build a CDF from a bag of samples:\n",
    "\n",
    "Generate some samples, and convert to Foretold CDF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ForetoldCdf(xs=[32.96928993558896, 36.938179897263346, 40.907069858937724, 44.87595982061211, 48.844849782286495, 52.81373974396088, 56.78262970563526, 60.751519667309644, 64.72040962898403, 68.68929959065841], ys=[0.0, 0.002, 0.034500000000000065, 0.16050000000000006, 0.4175000000000001, 0.7265000000000001, 0.9165000000000004, 0.9830000000000004, 0.9965000000000004, 1.0])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc = 50\n",
    "scale = 5\n",
    "samples = np.random.normal(loc, scale, size=2000)\n",
    "cdf = ergo.foretold.ForetoldCdf.from_samples(samples, length=10)\n",
    "cdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the CDF derived from samples with the true CDF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.linspace(loc - scale * 4, loc + scale * 4, 100)\n",
    "ys = scipy.stats.norm.cdf(xs, loc=loc, scale=scale)\n",
    "seaborn.lineplot(xs, ys);\n",
    "seaborn.lineplot(cdf.xs, cdf.ys);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing submission:\n",
    "\n",
    "Submit samples as a prediction (measurement) of a [question](https://www.foretold.io/c/f45577e4-f1b0-4bba-8cf6-63944e63d70c/m/cf86da3f-c257-4787-b526-3ef3cb670cb4) outcome (measureable):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token = \"YOUR-TOKEN\"\n",
    "foretold = ergo.Foretold(token)\n",
    "question = foretold.get_question(\"cf86da3f-c257-4787-b526-3ef3cb670cb4\")\n",
    "response = question.submit_from_samples(samples, length=20)\n",
    "response"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}