plot_grad_old.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3488677/477056521.py:7: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use('seaborn-whitegrid')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>task_id</th>\n",
       "      <th>communication_round</th>\n",
       "      <th>time</th>\n",
       "      <th>algo</th>\n",
       "      <th>dataset</th>\n",
       "      <th>use_contrastive</th>\n",
       "      <th>avg_params</th>\n",
       "      <th>avg_params_stderr</th>\n",
       "      <th>avg_params_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>modular</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>5.351603e-09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>1.148144e-02</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>modular</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>1.834918e-09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>9.175830e-03</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.001452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>modular</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   task_id  communication_round  time        algo dataset  use_contrastive  \\\n",
       "0        0                    0     0     modular   mnist             True   \n",
       "1        0                    0     0  monolithic   mnist             True   \n",
       "2        0                    1     1     modular   mnist             True   \n",
       "3        0                    1     1  monolithic   mnist             True   \n",
       "4        0                    2     2     modular   mnist             True   \n",
       "\n",
       "     avg_params  avg_params_stderr  avg_params_std  \n",
       "0  5.351603e-09           0.000000        0.000000  \n",
       "1  1.148144e-02           0.000268        0.000759  \n",
       "2  1.834918e-09           0.000000        0.000000  \n",
       "3  9.175830e-03           0.000363        0.001452  \n",
       "4  0.000000e+00           0.000000        0.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path = os.path.join(\"grad_results/mnist_monolithic_numtrain_64_contrastive/mnist/monolithic/seed_0/agent_0\",\n",
    "#                     \"sharing_record.csv\")\n",
    "# df = pd.read_csv(path)\n",
    "# df.head()\n",
    "\n",
    "# path = \"grad_new_results_divergence.csv\"\n",
    "path = \"experiment_results/fl_divergence.csv\"\n",
    "df = pd.read_csv(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_magma_colors(total_elements):\n",
    "    start = 0.2\n",
    "    stop = 0.8\n",
    "    cm_subsection = np.linspace(start, stop, total_elements)\n",
    "    return [matplotlib.cm.plasma(x) for x in cm_subsection]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['modular', 'monolithic'], dtype=object),\n",
       " array(['mnist'], dtype=object),\n",
       " array([ True]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get unique algo, dataset, and use_contrastive\n",
    "algo_list = df[\"algo\"].unique()\n",
    "dataset_list = df[\"dataset\"].unique()\n",
    "use_contrastive_list = df[\"use_contrastive\"].unique()\n",
    "\n",
    "algo_list, dataset_list, use_contrastive_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(algo_list) * len(dataset_list) * len(use_contrastive_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# take one combination of algo, dataset, and use_contrastive,\n",
    "# and make df_one\n",
    "\n",
    "\n",
    "\n",
    "# Create a plot with colored and labeled data points\n",
    "# var = \"avg_params_stderr\"\n",
    "var = \"avg_params_std\"\n",
    "fig, axs = plt.subplots(figsize=(20, 10), nrows=2, ncols=3, sharex=True, sharey=True)\n",
    "axs = axs.ravel()\n",
    "plot_count = 0\n",
    "for algo in algo_list:\n",
    "        for dataset in dataset_list:\n",
    "                for use_contrastive in use_contrastive_list: \n",
    "                        ax = axs[plot_count]\n",
    "                        plot_count +=1\n",
    "                        df_one = df[(df[\"algo\"] == algo) & (df[\"dataset\"] == dataset) & (df[\"use_contrastive\"] == use_contrastive)]\n",
    "                        num_colors = len(df_one['task_id'].unique())\n",
    "\n",
    "                        colors = get_magma_colors(num_colors)\n",
    "\n",
    "                        for i, (task_id, group) in enumerate(df_one.groupby('task_id')):\n",
    "                                line = group.plot(x='time', y='avg_params', ax=ax,\n",
    "                                label=i,\n",
    "                                color=colors[i],\n",
    "                        )\n",
    "\n",
    "                        ax.fill_between(df_one['time'], df_one['avg_params'] - df_one[var], df_one['avg_params'] + df_one[var], \n",
    "                                        alpha=0.15, color='grey')\n",
    "                        ax.get_legend().remove()\n",
    "                        ax.set_xlabel(\"Time\", fontsize=14)\n",
    "                        ax.set_ylabel(\"Divergence\", fontsize=14)\n",
    "                        ax.set_title(f\"{algo} {dataset}\", fontsize=16)\n",
    "                        ax.grid(True, which='major', linestyle='--', alpha=0.5)\n",
    "                        ax.spines['top'].set_visible(False)\n",
    "                        ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.savefig(\"Figures/grad/divergence.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>task_id</th>\n",
       "      <th>communication_round</th>\n",
       "      <th>time</th>\n",
       "      <th>algo</th>\n",
       "      <th>dataset</th>\n",
       "      <th>use_contrastive</th>\n",
       "      <th>avg_params</th>\n",
       "      <th>avg_params_stderr</th>\n",
       "      <th>avg_params_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>1.148144e-02</td>\n",
       "      <td>2.683541e-04</td>\n",
       "      <td>7.590201e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>9.175830e-03</td>\n",
       "      <td>3.630477e-04</td>\n",
       "      <td>1.452191e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>9.020006e-03</td>\n",
       "      <td>5.015222e-04</td>\n",
       "      <td>2.006089e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>9.254460e-03</td>\n",
       "      <td>4.830253e-04</td>\n",
       "      <td>1.932101e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>9.302197e-03</td>\n",
       "      <td>4.650774e-04</td>\n",
       "      <td>1.860310e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>105</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>2.649964e-10</td>\n",
       "      <td>2.448519e-11</td>\n",
       "      <td>9.794076e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>106</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>2.649964e-10</td>\n",
       "      <td>2.444829e-11</td>\n",
       "      <td>9.779316e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>107</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>2.649964e-10</td>\n",
       "      <td>2.448519e-11</td>\n",
       "      <td>9.794076e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>108</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>2.649964e-10</td>\n",
       "      <td>2.444829e-11</td>\n",
       "      <td>9.779316e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>109</td>\n",
       "      <td>monolithic</td>\n",
       "      <td>mnist</td>\n",
       "      <td>True</td>\n",
       "      <td>2.864528e-10</td>\n",
       "      <td>2.538831e-11</td>\n",
       "      <td>7.180900e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     task_id  communication_round  time        algo dataset  use_contrastive  \\\n",
       "1          0                    0     0  monolithic   mnist             True   \n",
       "3          0                    1     1  monolithic   mnist             True   \n",
       "5          0                    2     2  monolithic   mnist             True   \n",
       "7          0                    3     3  monolithic   mnist             True   \n",
       "9          0                    4     4  monolithic   mnist             True   \n",
       "..       ...                  ...   ...         ...     ...              ...   \n",
       "202        9                    6   105  monolithic   mnist             True   \n",
       "204        9                    7   106  monolithic   mnist             True   \n",
       "206        9                    8   107  monolithic   mnist             True   \n",
       "208        9                    9   108  monolithic   mnist             True   \n",
       "209        9                   10   109  monolithic   mnist             True   \n",
       "\n",
       "       avg_params  avg_params_stderr  avg_params_std  \n",
       "1    1.148144e-02       2.683541e-04    7.590201e-04  \n",
       "3    9.175830e-03       3.630477e-04    1.452191e-03  \n",
       "5    9.020006e-03       5.015222e-04    2.006089e-03  \n",
       "7    9.254460e-03       4.830253e-04    1.932101e-03  \n",
       "9    9.302197e-03       4.650774e-04    1.860310e-03  \n",
       "..            ...                ...             ...  \n",
       "202  2.649964e-10       2.448519e-11    9.794076e-11  \n",
       "204  2.649964e-10       2.444829e-11    9.779316e-11  \n",
       "206  2.649964e-10       2.448519e-11    9.794076e-11  \n",
       "208  2.649964e-10       2.444829e-11    9.779316e-11  \n",
       "209  2.864528e-10       2.538831e-11    7.180900e-11  \n",
       "\n",
       "[110 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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