plot_grad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19775/477056521.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": 4,
   "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>epoch</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>modular</td>\n",
       "      <td>cifar100</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>modular</td>\n",
       "      <td>combined</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>modular</td>\n",
       "      <td>fashionmnist</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>modular</td>\n",
       "      <td>kmnist</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>modular</td>\n",
       "      <td>mnist</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   task_id  communication_round  epoch     algo       dataset  \\\n",
       "0        4                    0    400  modular      cifar100   \n",
       "1        4                    0    400  modular      combined   \n",
       "2        4                    0    400  modular  fashionmnist   \n",
       "3        4                    0    400  modular        kmnist   \n",
       "4        4                    0    400  modular         mnist   \n",
       "\n",
       "   use_contrastive  avg_params  avg_params_stderr  avg_params_std  \n",
       "0            False         0.0                0.0             0.0  \n",
       "1            False         0.0                0.0             0.0  \n",
       "2            False         0.0                0.0             0.0  \n",
       "3            False         0.0                0.0             0.0  \n",
       "4            False         0.0                0.0             0.0  "
      ]
     },
     "execution_count": 4,
     "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 = \"experiment_results/jorge_setting_fedavg_divergence.csv\"\n",
    "# path = \"budget_experiment_results/jorge_setting_fedavg_divergence.csv\"\n",
    "df = pd.read_csv(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['modular', 'monolithic'], dtype=object),\n",
       " array(['cifar100', 'combined', 'fashionmnist', 'kmnist', 'mnist'],\n",
       "       dtype=object),\n",
       " array([False]))"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(algo_list) * len(dataset_list) * len(use_contrastive_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df[df['dataset'] == 'cifar100']['algo'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 10 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",
    "\n",
    "fig, axs = plt.subplots(figsize=(15, 5), nrows=len(algo_list), ncols=len(df['dataset'].unique()), 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='epoch', y='avg_params', ax=ax,\n",
    "                                label=i,\n",
    "                                color=colors[i],\n",
    "                                marker='o',\n",
    "                                markersize=3,\n",
    "                        )\n",
    "\n",
    "                        ax.fill_between(df_one['epoch'], 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(\"Epoch\", fontsize=14)\n",
    "                        ax.set_ylabel(\"Divergence\", fontsize=14)\n",
    "                        ax.set_title(f\"{algo} {dataset}\", fontsize=12)\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.tight_layout()\n",
    "plt.savefig(\"Figures/grad/divergence.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming df, algo_list, dataset_list, and use_contrastive_list are defined\n",
    "fig, axs = plt.subplots(figsize=(15, 5), nrows=len(algo_list), ncols=len(df['dataset'].unique()), sharex=True, sharey=True)\n",
    "\n",
    "# If axs is not an array of arrays (e.g., when len(algo_list) or len(dataset_list) == 1), normalize it:\n",
    "if len(algo_list) == 1 or len(df['dataset'].unique()) == 1:\n",
    "    axs = np.atleast_2d(axs)  # Ensure axs is always a 2D array for consistency\n",
    "\n",
    "plot_count = 0\n",
    "for algo_index, algo in enumerate(algo_list):\n",
    "    for dataset_index, dataset in enumerate(dataset_list):\n",
    "        for use_contrastive in use_contrastive_list:\n",
    "            ax = axs[algo_index, dataset_index]  # Access using 2D indexing\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",
    "            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='epoch', y='avg_params', ax=ax,\n",
    "                                  label=i,\n",
    "                                  color=colors[i],\n",
    "                                  marker='o',\n",
    "                                  markersize=3)\n",
    "\n",
    "            ax.fill_between(df_one['epoch'], 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_title(f\"{algo} {dataset}\", fontsize=12)\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",
    "            ax.spines['bottom'].set_visible(False)\n",
    "            ax.set_xlabel('')\n",
    "\n",
    "fig.text(-0.02, 0.5, \"Divergence\", ha='center', va='center', rotation='vertical', fontsize=20, weight='bold')\n",
    "fig.text(0.5, -0.05, 'Epoch', ha='center', va='center', fontsize=20, weight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Figures/grad/divergence.pdf\", bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_agg_learning_curves(fleet, ax=None, name=None, tasks=None, agent_ids=None, viz=True, mode=\"current\",\n",
    "                             metric=\"test_acc\"):\n",
    "    if ax is None and viz:\n",
    "        fig, ax = plt.subplots()\n",
    "\n",
    "    if tasks is None:\n",
    "        tasks = range(0, fleet.num_tasks)\n",
    "\n",
    "    dfs = []\n",
    "    for agent in fleet.agents:\n",
    "        if agent_ids is not None and agent.node_id not in agent_ids:\n",
    "            continue\n",
    "        df = agent.get_record().df\n",
    "        for task in tasks:\n",
    "            task_df = df[df[\"train_task\"] == task]\n",
    "            if mode == \"current\":\n",
    "                task_df = task_df[task_df[\"test_task\"] == str(task)]\n",
    "            elif mode == \"avg\":\n",
    "                task_df = task_df[task_df[\"test_task\"] == 'avg']\n",
    "            else:\n",
    "                raise ValueError(\"mode must be current or avg\")\n",
    "            task_df['agent_id'] = agent.node_id\n",
    "            dfs.append(task_df)\n",
    "\n",
    "    combined_df = pd.concat(dfs)\n",
    "\n",
    "    agg_df = combined_df.groupby(['epoch']).agg({metric: ['mean', 'std']}).reset_index()\n",
    "\n",
    "    # Extract mean and standard deviation values\n",
    "    mean_test_acc = agg_df[metric]['mean']\n",
    "    std_test_acc = agg_df[metric]['std']\n",
    "\n",
    "    if viz:\n",
    "        # Plot the mean test_acc with shaded areas for standard deviation\n",
    "        ax.plot(agg_df['epoch'], mean_test_acc, \n",
    "                label=name)\n",
    "        plt.fill_between(agg_df['epoch'], mean_test_acc - 0.1 * std_test_acc, mean_test_acc + 0.1 * std_test_acc, alpha=0.3)\n",
    "\n",
    "        ax.set_xlabel('Epoch')\n",
    "        ax.set_ylabel('Test Accuracy')\n",
    "        if name is not None:\n",
    "            ax.legend();\n",
    "\n",
    "    # compute area under the curve\n",
    "    auc = np.trapz(mean_test_acc, agg_df['epoch'])\n",
    "    return auc, agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FakeRecord:\n",
    "    def __init__(self, df):\n",
    "        self.df = df\n",
    "\n",
    "class FakeAgent:\n",
    "    def __init__(self, save_dir, node_id):\n",
    "        self.node_id = node_id\n",
    "        self.save_dir = save_dir\n",
    "\n",
    "\n",
    "    def get_record(self):\n",
    "        return FakeRecord(pd.read_csv(os.path.join(self.save_dir, \"record.csv\")))\n",
    "\n",
    "class FakeFleet:\n",
    "    def __init__(self, save_dir, num_init_tasks=4):\n",
    "        self.save_dir = save_dir\n",
    "        self.num_init_tasks = num_init_tasks\n",
    "        self.num_tasks = 20 if \"cifar100\" in save_dir else 10\n",
    "        self.agents = [FakeAgent(os.path.join(save_dir, agent_id), agent_id) for agent_id in os.listdir(save_dir) if agent_id != \"hydra_out\" and agent_id != \"agent_69420\"]\n",
    "    \n",
    "    def load_records(self):\n",
    "        pass\n",
    "\n",
    "def setup_fake_fleet(result_dir, modify_cfg=None, parallel=False):\n",
    "    return FakeFleet(result_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "comm_freqs = [10, 20, 50, 100]\n",
    "algos = [\"monolithic\", \"modular\"]\n",
    "dataset=\"mnist\"\n",
    "metric='test_acc'\n",
    "tasks=None\n",
    "tasks=[4]\n",
    "agent_ids=None\n",
    "viz=False\n",
    "mode=\"current\"\n",
    "# mode=\"avg\"\n",
    "\n",
    "fleets = {}\n",
    "agg_dfs=[]\n",
    "for seed in range(8):\n",
    "    for comm_freq in comm_freqs:\n",
    "        for algo in algos:\n",
    "            name = f'{algo}_comm_freq_{comm_freq}'\n",
    "            fleets[name] = FakeFleet(f'budget_experiment_results/jorge_setting_fedavg/comm_freq_{comm_freq}/{dataset}_{algo}_numtrain_64/{dataset}/{algo}/seed_{seed}')\n",
    "\n",
    "            for name, fleet in fleets.items():\n",
    "                _, agg_df = plot_agg_learning_curves(fleet, ax=None, name=seed, tasks=tasks, agent_ids=agent_ids, viz=viz, mode=mode,\n",
    "                                                                    metric=metric)\n",
    "                agg_df['seed'] = seed\n",
    "                agg_df['name'] = name\n",
    "                agg_dfs.append(agg_df)\n",
    "\n",
    "combined_agg_df = pd.concat(agg_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_agg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_agg_over_seeds(combined_agg_df, title_name=None, ax=None, std_scale=1.0, metric='test_acc',\n",
    "                        remap_name=None, colormap=None):\n",
    "    if title_name is None:\n",
    "        title_name ='Learning curve'\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    agg_over_seed_name = combined_agg_df.groupby(['name', 'epoch']).agg({(metric, 'mean'): 'mean', (metric, 'std'): 'std'}).reset_index()\n",
    "\n",
    "    for name, group in agg_over_seed_name.groupby('name'):\n",
    "        if remap_name is not None and name not in remap_name:\n",
    "            continue\n",
    "\n",
    "        name = name if remap_name is None else remap_name[name]\n",
    "        mean_test_acc = group[metric]['mean']\n",
    "        std_test_acc = group[metric]['std']\n",
    "        ax.plot(group['epoch'], mean_test_acc, label=name, marker='o',\n",
    "                color=colormap[name] if colormap is not None else None)\n",
    "        ax.fill_between(group['epoch'], mean_test_acc - std_scale * std_test_acc, mean_test_acc + std_scale * std_test_acc, alpha=0.3,\n",
    "                        color=colormap[name] if colormap is not None else None)\n",
    "\n",
    "    # ax.set_xlabel('Epoch', fontsize=14)\n",
    "    # ax.set_ylabel('Test Accuracy', fontsize=14)\n",
    "    ax.set_title(title_name, fontsize=20)\n",
    "    # Setting legend font size\n",
    "    ax.legend(frameon=True, loc='lower right', fontsize=12)\n",
    "    # Setting x and y ticks font size\n",
    "    ax.tick_params(axis='x', labelsize=16)\n",
    "    ax.tick_params(axis='y', labelsize=16)\n",
    "\n",
    "    ax.grid(True, which='major', linestyle='--', alpha=0.5)\n",
    "    # ax.set_ylim(0.4, 1);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remap_name = {\n",
    "    'modular_comm_freq_10': 'Modular (10)',\n",
    "    'modular_comm_freq_20': 'Modular (5)',\n",
    "    'modular_comm_freq_50': 'Modular (2)',\n",
    "    'modular_comm_freq_100': 'Modular (1)',\n",
    "\n",
    "    # 'monolithic_comm_freq_10': 'Monolithic (10)',\n",
    "    # 'monolithic_comm_freq_20': 'Monolithic (20)',\n",
    "    # 'monolithic_comm_freq_50': 'Monolithic (50)',\n",
    "    # 'monolithic_comm_freq_100': 'Monolithic (100)',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_map = {\n",
    "    'Modular (1)': 'C1',\n",
    "    'Modular (5)': 'C2',\n",
    "    'Modular (2)': 'C3',\n",
    "    'Modular (10)': 'C4',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_agg_over_seeds(combined_agg_df, remap_name=remap_name, colormap=color_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import omegaconf\n",
    "from shell.utils.experiment_utils import *\n",
    "from shell.fleet.utils.fleet_utils import *\n",
    "from shell.utils.metric import *\n",
    "import matplotlib.pyplot as plt\n",
    "from shell.fleet.network import TopologyGenerator\n",
    "from sklearn.manifold import TSNE\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from shell.fleet.fleet import Agent, Fleet\n",
    "from shell.fleet.data.data_utilize import *\n",
    "from shell.fleet.data.recv import *\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from torchvision.utils import make_grid\n",
    "from shell.fleet.data.data_utilize import *\n",
    "import logging\n",
    "from sklearn.metrics import f1_score\n",
    "import os\n",
    "from shell.fleet.data.recv_utils import *\n",
    "from pythresh.thresholds.aucp import AUCP\n",
    "from pythresh.thresholds.boot import BOOT\n",
    "from pythresh.thresholds.zscore import ZSCORE\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from collections import defaultdict\n",
    "\n",
    "logging.basicConfig(level=logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from shell.utils.record import Record\n",
    "def l1_distance(module1, module2):\n",
    "    \"\"\"\n",
    "    Compute the L1 distance between two nn.Linear modules, considering both weights and biases.\n",
    "    \"\"\"\n",
    "    weight_diff_avg = torch.mean(torch.abs(module1.weight - module2.weight)).item()\n",
    "    bias_diff_avg = torch.mean(torch.abs(module1.bias - module2.bias)).item()\n",
    "    return (weight_diff_avg + bias_diff_avg) / 2\n",
    "\n",
    "def diff_between(agent, neighbor):\n",
    "    \"\"\"\n",
    "    For each module in agent.net.components, find the closest module in neighbor.net.components based\n",
    "    on L1 distance. Returns a list of distances where each distance corresponds to the closest module\n",
    "    in neighbor for each module in agent.\n",
    "    \"\"\"\n",
    "    diffs = defaultdict(lambda: float('inf'))\n",
    "\n",
    "    # Iterate through each module in the agent\n",
    "    for i, agent_module in enumerate(agent.net.components):\n",
    "        # Compare with each module in the neighbor to find the closest one\n",
    "        for j, neighbor_module in enumerate(neighbor.net.components):\n",
    "            distance = l1_distance(agent_module, neighbor_module)\n",
    "            # print(i, j, distance)\n",
    "            diffs[f'comp.{i}'] = min(diffs[f'comp.{i}'], distance)\n",
    "\n",
    "    return diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_fleet_diffs(fleet):\n",
    "    # Initialize a dictionary to store the summed distances for each component of each agent\n",
    "    summed_diffs = defaultdict(lambda: defaultdict(list))\n",
    "    \n",
    "    # Initialize a dictionary to store the final averaged results for each agent\n",
    "    final_diffs = {}\n",
    "    \n",
    "    n_agents = len(fleet.agents)\n",
    "    \n",
    "    # Calculate differences for each pair of agents\n",
    "    for i, agent in enumerate(fleet.agents):\n",
    "        for j, neighbor in enumerate(fleet.agents):\n",
    "            if i == j:\n",
    "                continue  # Skip comparing the agent with itself\n",
    "            diffs = diff_between(agent, neighbor)\n",
    "            for comp, diff in diffs.items():\n",
    "                summed_diffs[i][comp].append(diff)\n",
    "    \n",
    "    # Calculate mean differences for each component of each agent\n",
    "    for agent_idx, comps_diffs in summed_diffs.items():\n",
    "        final_diffs[agent_idx] = {}\n",
    "        for comp, diffs in comps_diffs.items():\n",
    "            final_diffs[agent_idx][comp] = np.mean(diffs)\n",
    "    \n",
    "    # Calculate `avg_params` for each agent\n",
    "    for agent_idx, comps_diffs in final_diffs.items():\n",
    "        avg_params = np.mean(list(comps_diffs.values()))\n",
    "        final_diffs[agent_idx]['avg_params'] = avg_params\n",
    "    \n",
    "    return final_diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_fleet_div(save_dir, start_task_id, end_task_id,\n",
    "                     modify_cfg, parallel=False):\n",
    "    fleet = setup_fleet(save_dir, modify_cfg=modify_cfg, parallel=parallel,\n",
    "                    task_id=start_task_id)\n",
    "    \n",
    "    for agent in fleet.agents:\n",
    "        agent.div_record = Record(os.path.join(agent.save_dir, \"div_record.csv\"))\n",
    "\n",
    "    for t in range(start_task_id, end_task_id+1):\n",
    "        fleet.load_model_from_ckpoint(task_ids=t)\n",
    "        diffs = compute_fleet_diffs(fleet)\n",
    "        for agent_idx, diff in diffs.items():\n",
    "            diff['task_id'] = t\n",
    "            diff['communication_round'] = 0\n",
    "            agent = fleet.agents[agent_idx]\n",
    "            agent.div_record.write(diff)\n",
    "            agent.div_record.save()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [\"mnist\", \"kmnist\", \"fashionmnist\"]\n",
    "\n",
    "experiment_folder = \"experiment_results\"\n",
    "experiment_name = \"vanilla_jorge_setting_basis_no_sparse\"\n",
    "# experiment_name = \"jorge_setting_fedavg\"\n",
    "\n",
    "use_contrastive_opt = False\n",
    "num_trains_per_class = 64\n",
    "seeds = [0, 1, 2, 3, 4, 5, 6, 7]\n",
    "\n",
    "algo = \"monolithic\"\n",
    "\n",
    "\n",
    "def modify_cfg(net_cfg, agent_cfg, train_cfg, fleet_additional_cfg, cfg):\n",
    "    return net_cfg, agent_cfg, train_cfg, fleet_additional_cfg, cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for dataset in datasets:\n",
    "#     for seed in seeds:\n",
    "#         save_dir = get_save_dir(experiment_folder, experiment_name,\n",
    "#                                                 dataset, algo, num_trains_per_class, use_contrastive_opt, seed)\n",
    "#         record_fleet_div(save_dir, 4, 9, modify_cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from shell.utils.metric import DivergenceMetric\n",
    "from shell.utils.record import Record\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "result_dir = \"experiment_results/jorge_setting_fedavg\"\n",
    "\n",
    "pattern = r\".*\"\n",
    "\n",
    "def get_div_df(result_dir):\n",
    "    concat_df = pd.DataFrame()\n",
    "    for job_name in os.listdir(result_dir):\n",
    "        use_contrastive = \"contrastive\" in job_name\n",
    "        if use_contrastive:\n",
    "            continue\n",
    "        for dataset_name in os.listdir(os.path.join(result_dir, job_name)):\n",
    "            for algo in os.listdir(os.path.join(result_dir, job_name, dataset_name)):\n",
    "                if algo == \"modular\":\n",
    "                    continue\n",
    "                for seed in os.listdir(os.path.join(result_dir, job_name, dataset_name, algo)):\n",
    "                    for agent_id in os.listdir(os.path.join(result_dir, job_name, dataset_name, algo, seed)):\n",
    "                        if agent_id == \"hydra_out\" or agent_id == \"agent_69420\":\n",
    "                            continue\n",
    "                        save_dir = os.path.join(\n",
    "                            result_dir, job_name, dataset_name, algo, seed, agent_id)\n",
    "                        # if the pattern doesn't match, continue\n",
    "                        if not re.search(pattern, save_dir):\n",
    "                            continue\n",
    "                        df = pd.read_csv(os.path.join(save_dir, \"div_record.csv\"))\n",
    "                        df = df[[\"task_id\", \"communication_round\",\n",
    "                                \"avg_params\"]]\n",
    "                        # add seed and agent_id columns\n",
    "                        df[\"seed\"] = seed\n",
    "                        df[\"agent_id\"] = agent_id\n",
    "                        df['algo'] = algo\n",
    "                        df['dataset'] = dataset_name\n",
    "                        df['use_contrastive'] = use_contrastive\n",
    "                        concat_df = pd.concat([concat_df, df])\n",
    "    return concat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fedavg_concat_df = get_div_df(\"experiment_results/jorge_setting_fedavg\")\n",
    "mono_concat_df = get_div_df(\"experiment_results/vanilla_jorge_setting_basis_no_sparse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fedavg_concat_df[\"algo\"] = \"fedavg_\" + fedavg_concat_df[\"algo\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concat_df = pd.concat([fedavg_concat_df, mono_concat_df])\n",
    "# concat_df = pd.concat([mono_concat_df])\n",
    "concat_df = pd.concat([fedavg_concat_df])\n",
    "concat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_df = concat_df.groupby(\n",
    "    [\"task_id\", \"communication_round\", \"algo\", \"dataset\", \"use_contrastive\"]).agg(\n",
    "        avg_params=(\"avg_params\", \"mean\"),\n",
    "        avg_params_stderr=(\"avg_params\", \"sem\"),\n",
    "        avg_params_std=(\"avg_params\", \"std\")\n",
    ").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_df =  concat_df[[\"task_id\", \"algo\", \"dataset\", \"avg_params\", \"avg_params_stderr\", \"avg_params_std\"]]\n",
    "concat_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_datasets(dataframe):\n",
    "    datasets = dataframe['dataset'].unique()\n",
    "    algos = dataframe['algo'].unique()\n",
    "    colors = plt.cm.tab10(np.linspace(0, 1, len(algos)))  # Generate colors for each algorithm\n",
    "\n",
    "    algo_color_map = {algo: color for algo, color in zip(algos, colors)}  # Map algos to colors\n",
    "\n",
    "    fig, axs = plt.subplots(1, len(datasets), figsize=(20, 6), sharey=True)\n",
    "    \n",
    "    if len(datasets) == 1:  # If there's only one dataset, axs is not an array\n",
    "        axs = [axs]\n",
    "    \n",
    "    for ax, dataset in zip(axs, datasets):\n",
    "        df_filtered = dataframe[dataframe['dataset'] == dataset]\n",
    "        task_ids = df_filtered['task_id'].unique()\n",
    "        \n",
    "        for algo in algos:\n",
    "            # Filter by algorithm\n",
    "            df_algo = df_filtered[df_filtered['algo'] == algo]\n",
    "            if df_algo.empty:\n",
    "                continue\n",
    "            \n",
    "            # Calculate bar positions\n",
    "            positions = [i + 0.5 * (np.where(algos == algo)[0][0] - len(algos) / 2) for i in task_ids]\n",
    "            \n",
    "            ax.bar(positions, df_algo['avg_params'], yerr=df_algo['avg_params_stderr'], capsize=5,\n",
    "                   color=algo_color_map[algo], label=algo, width=0.5)\n",
    "        \n",
    "        ax.set_title(f'{dataset}')\n",
    "        ax.set_xlabel('Task ID')\n",
    "        ax.set_xticks(task_ids)  # Set x-ticks to match the task IDs\n",
    "        ax.set_ylabel('Average Parameters' if ax is axs[0] else \"\")\n",
    "    \n",
    "    # Add legend outside of the last subplot\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    fig.legend(handles, labels, loc='upper right', bbox_to_anchor=(1.1, 1), title='Algorithm')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Assuming `dataframe` is your DataFrame object, call the function with:\n",
    "# plot_datasets(dataframe)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_datasets(concat_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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