plot_results_iccm2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Accuracy by carries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plot_results_iccm2019 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'iccm2019'\n",
    "operators_list = ['add', 'subtract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 288x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 288x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean_accuracy_by_carries(experiment_name, operators_list, mode='show')\n",
    "plot_mean_accuracy_by_carries(experiment_name, operators_list, mode='save')\n",
    "plot_mean_accuracy_by_carries(experiment_name, operators_list, mode='save', file_format='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Mean solving solving time by carries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plot_results_iccm2019 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'iccm2019'\n",
    "operators_list = ['add', 'subtract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x240 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 288x240 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x240 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 288x240 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x240 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean_mean_solving_time_by_carries(experiment_name, operators_list, ylim_range=(2,12), rm_carry_outlier=True, rm_carry_outlier_method='iqr', mode='show')\n",
    "plot_mean_mean_solving_time_by_carries(experiment_name, operators_list, ylim_range=(2,12), rm_carry_outlier=True, rm_carry_outlier_method='iqr', mode='save')\n",
    "plot_mean_mean_solving_time_by_carries(experiment_name, operators_list, ylim_range=(2,12), rm_carry_outlier=True, rm_carry_outlier_method='iqr', mode='save', file_format='png')"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}