Shakespeare/FedMed.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tushar-semwal/fedperf/blob/main/Santiago/Shakespeare/FedMed.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZXnVpPCFfQkm"
      },
      "source": [
        "# FedPerf - Shakespeare + FedMed algorithm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWDjzbCzfUFt"
      },
      "source": [
        "## Setup & Dependencies Installation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_f-rWudW0Fo"
      },
      "source": [
        "%%capture\n",
        "!pip install torchsummaryX unidecode"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TmMInb_fnw_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33105564-74ba-4760-9375-0f9de60c7d62"
      },
      "source": [
        "%load_ext tensorboard\n",
        "\n",
        "import copy\n",
        "from functools import reduce\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import random\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "import time\n",
        "import torch\n",
        "from torch.autograd import Variable\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset\n",
        "from torch.utils.data.dataloader import DataLoader\n",
        "from torch.utils.data.sampler import Sampler\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from torchsummary import summary\n",
        "from torchsummaryX import summary as summaryx\n",
        "from torchvision import transforms, utils, datasets\n",
        "from tqdm.notebook import tqdm\n",
        "from unidecode import unidecode\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "# Check assigned GPU\n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)\n",
        "\n",
        "# set manual seed for reproducibility\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "# general reproducibility\n",
        "random.seed(RANDOM_SEED)\n",
        "np.random.seed(RANDOM_SEED)\n",
        "torch.manual_seed(RANDOM_SEED)\n",
        "torch.cuda.manual_seed(RANDOM_SEED)\n",
        "\n",
        "# gpu training specific\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = False"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Apr 26 02:09:39 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   52C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Mfqv6uHfwh-"
      },
      "source": [
        "## Mount GDrive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75qbJwxsGj-k"
      },
      "source": [
        "BASE_DIR = '/content/drive/MyDrive/FedPerf/shakespeare/FedMed'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ost-6lXSfveC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82c4f239-0725-4f91-e19e-cdf9856bf6e3"
      },
      "source": [
        "try:\n",
        "    from google.colab import drive\n",
        "    drive.mount('/content/drive')\n",
        "    os.makedirs(BASE_DIR, exist_ok=True)\n",
        "except:\n",
        "    print(\"WARNING: Results won't be stored on GDrive\")\n",
        "    BASE_DIR = './'\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y-ZegBArf4xQ"
      },
      "source": [
        "## Loading Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hf03LRxof7Zj"
      },
      "source": [
        "!rm -Rf data\n",
        "!mkdir -p data scripts"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngygA4-Fgobx"
      },
      "source": [
        "GENERATE_DATASET = False  # If False, download the dataset provided by the q-FFL paper\n",
        "DATA_DIR = 'data/'\n",
        "# Dataset generation params\n",
        "SAMPLES_FRACTION = 1.  # If using an already generated dataset\n",
        "# SAMPLES_FRACTION = 0.2  # Fraction of total samples in the dataset - FedProx default script\n",
        "# SAMPLES_FRACTION = 0.05  # Fraction of total samples in the dataset - qFFL\n",
        "TRAIN_FRACTION = 0.8  # Train set size\n",
        "MIN_SAMPLES = 0  # Min samples per client (for filtering purposes) - FedProx\n",
        "# MIN_SAMPLES = 64  # Min samples per client (for filtering purposes) - qFFL"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUmwJgJygoYD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a017ba0-204f-4941-d3d4-4e91e6e7c2bb"
      },
      "source": [
        "# Download raw dataset\n",
        "# !wget https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt -O data/shakespeare.txt\n",
        "!wget --adjust-extension http://www.gutenberg.org/files/100/100-0.txt -O data/shakespeare.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-26 02:09:39--  http://www.gutenberg.org/files/100/100-0.txt\n",
            "Resolving www.gutenberg.org (www.gutenberg.org)... 152.19.134.47, 2610:28:3090:3000:0:bad:cafe:47\n",
            "Connecting to www.gutenberg.org (www.gutenberg.org)|152.19.134.47|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5757108 (5.5M) [text/plain]\n",
            "Saving to: ‘data/shakespeare.txt’\n",
            "\n",
            "\rdata/shakespeare.tx   0%[                    ]       0  --.-KB/s               \rdata/shakespeare.tx 100%[===================>]   5.49M  28.5MB/s    in 0.2s    \n",
            "\n",
            "2021-04-26 02:09:39 (28.5 MB/s) - ‘data/shakespeare.txt’ saved [5757108/5757108]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4dCvx80BgoVr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09e315ce-0f27-486d-84b6-8a659300bdce"
      },
      "source": [
        "if not GENERATE_DATASET:\n",
        "    !rm -Rf data/train data/test\n",
        "    !gdown --id 1n46Mftp3_ahRi1Z6jYhEriyLtdRDS1tD  # Download Shakespeare dataset used by the FedProx paper\n",
        "    !unzip shakespeare.zip\n",
        "    !mv -f shakespeare_paper/train data/\n",
        "    !mv -f shakespeare_paper/test data/\n",
        "    !rm -R shakespeare_paper/ shakespeare.zip\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1n46Mftp3_ahRi1Z6jYhEriyLtdRDS1tD\n",
            "To: /content/shakespeare.zip\n",
            "\r0.00B [00:00, ?B/s]\r2.96MB [00:00, 42.7MB/s]\n",
            "Archive:  shakespeare.zip\n",
            "   creating: shakespeare_paper/\n",
            "   creating: shakespeare_paper/test/\n",
            "  inflating: shakespeare_paper/test/all_data_niid_2_keep_0_test_8.json  \n",
            "   creating: shakespeare_paper/train/\n",
            "  inflating: shakespeare_paper/train/all_data_niid_2_keep_0_train_8.json  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4pzFvPvhQhq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dddead7-80fc-4e71-d35f-2958165bd99b"
      },
      "source": [
        "corpus = []\n",
        "with open('data/shakespeare.txt', 'r') as f:\n",
        "    data = list(unidecode(f.read()))\n",
        "    corpus = list(set(list(data)))\n",
        "print('Corpus Length:', len(corpus))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Corpus Length: 90\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cce_-qnxhD4n"
      },
      "source": [
        "#### Dataset Preprocessing script"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rt13M4IcgoTV"
      },
      "source": [
        "%%capture\n",
        "if GENERATE_DATASET:\n",
        "    # Download dataset generation scripts\n",
        "    !wget https://raw.githubusercontent.com/ml-lab/FedProx/master/data/shakespeare/preprocess/preprocess_shakespeare.py -O scripts/preprocess_shakespeare.py\n",
        "    !wget https://raw.githubusercontent.com/ml-lab/FedProx/master/data/shakespeare/preprocess/shake_utils.py -O scripts/shake_utils.py\n",
        "    !wget https://raw.githubusercontent.com/ml-lab/FedProx/master/data/shakespeare/preprocess/gen_all_data.py -O scripts/gen_all_data.py\n",
        "\n",
        "    # Download data preprocessing scripts\n",
        "    !wget https://raw.githubusercontent.com/ml-lab/FedProx/master/utils/sample.py -O scripts/sample.py\n",
        "    !wget https://raw.githubusercontent.com/ml-lab/FedProx/master/utils/remove_users.py -O scripts/remove_users.py"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIEyRW27goPo"
      },
      "source": [
        "# Running scripts\n",
        "if GENERATE_DATASET:\n",
        "    !mkdir -p data/raw_data data/all_data data/train data/test\n",
        "    !python scripts/preprocess_shakespeare.py data/shakespeare.txt data/raw_data\n",
        "    !python scripts/gen_all_data.py"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mq8V6v_4hhhD"
      },
      "source": [
        "#### Dataset class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2SjEBKoWDxv"
      },
      "source": [
        "class ShakespeareDataset(Dataset):\n",
        "    def __init__(self, x, y, corpus, seq_length):\n",
        "        self.x = x\n",
        "        self.y = y\n",
        "        self.corpus = corpus\n",
        "        self.corpus_size = len(self.corpus)\n",
        "        super(ShakespeareDataset, self).__init__()\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.x)\n",
        "\n",
        "    def __repr__(self):\n",
        "        return f'{self.__class__} - (length: {self.__len__()})'\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        input_seq = self.x[i]\n",
        "        next_char = self.y[i]\n",
        "        # print('\\tgetitem', i, input_seq, next_char)\n",
        "        input_value = self.text2charindxs(input_seq)\n",
        "        target_value = self.get_label_from_char(next_char)\n",
        "        return input_value, target_value\n",
        "\n",
        "    def text2charindxs(self, text):\n",
        "        tensor = torch.zeros(len(text), dtype=torch.int32)\n",
        "        for i, c in enumerate(text):\n",
        "            tensor[i] = self.get_label_from_char(c)\n",
        "        return tensor\n",
        "\n",
        "    def get_label_from_char(self, c):\n",
        "        return self.corpus.index(c)\n",
        "\n",
        "    def get_char_from_label(self, l):\n",
        "        return self.corpus[l]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fgJtS62lYAN"
      },
      "source": [
        "##### Federated Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DqL5pTmgn5X"
      },
      "source": [
        "class ShakespeareFedDataset(ShakespeareDataset):\n",
        "    def __init__(self, x, y, corpus, seq_length):\n",
        "        super(ShakespeareFedDataset, self).__init__(x, y, corpus, seq_length)\n",
        "\n",
        "    def dataloader(self, batch_size, shuffle=True):\n",
        "        return DataLoader(self,\n",
        "                          batch_size=batch_size,\n",
        "                          shuffle=shuffle,\n",
        "                          num_workers=0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XelbyPsDlfgb"
      },
      "source": [
        "## Partitioning & Data Loaders"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOBblyFGlwlU"
      },
      "source": [
        "### IID"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSZFWKmsgn1p"
      },
      "source": [
        "def iid_partition_(dataset, clients):\n",
        "  \"\"\"\n",
        "  I.I.D paritioning of data over clients\n",
        "  Shuffle the data\n",
        "  Split it between clients\n",
        "  \n",
        "  params:\n",
        "    - dataset (torch.utils.Dataset): Dataset\n",
        "    - clients (int): Number of Clients to split the data between\n",
        "\n",
        "  returns:\n",
        "    - Dictionary of image indexes for each client\n",
        "  \"\"\"\n",
        "\n",
        "  num_items_per_client = int(len(dataset)/clients)\n",
        "  client_dict = {}\n",
        "  image_idxs = [i for i in range(len(dataset))]\n",
        "\n",
        "  for i in range(clients):\n",
        "    client_dict[i] = set(np.random.choice(image_idxs, num_items_per_client, replace=False))\n",
        "    image_idxs = list(set(image_idxs) - client_dict[i])\n",
        "\n",
        "  return client_dict"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lGwDyhSll9h"
      },
      "source": [
        "def iid_partition(corpus, seq_length=80, val_split=False):\n",
        "\n",
        "    train_file = [os.path.join(DATA_DIR, 'train', f) for f in os.listdir(f'{DATA_DIR}/train') if f.endswith('.json')][0]\n",
        "    test_file = [os.path.join(DATA_DIR, 'test', f) for f in os.listdir(f'{DATA_DIR}/test') if f.endswith('.json')][0]\n",
        "\n",
        "    with open(train_file, 'r') as file:\n",
        "        data_train = json.loads(unidecode(file.read()))\n",
        "\n",
        "    with open(test_file, 'r') as file:\n",
        "        data_test = json.loads(unidecode(file.read()))\n",
        "\n",
        "    \n",
        "    total_samples_train = sum(data_train['num_samples'])\n",
        "\n",
        "    data_dict = {}\n",
        "\n",
        "    x_train, y_train = [], []\n",
        "    x_test, y_test = [], []\n",
        "    # x_val, y_val = [], []\n",
        "\n",
        "    users = list(zip(data_train['users'], data_train['num_samples']))\n",
        "    # random.shuffle(users)\n",
        "\n",
        "\n",
        "\n",
        "    total_samples = int(sum(data_train['num_samples']) * SAMPLES_FRACTION)\n",
        "    print('Objective', total_samples, '/', sum(data_train['num_samples']))\n",
        "    sample_count = 0\n",
        "    \n",
        "    for i, (author_id, samples) in enumerate(users):\n",
        "\n",
        "        if sample_count >= total_samples:\n",
        "            print('Max samples reached', sample_count, '/', total_samples)\n",
        "            break\n",
        "\n",
        "        if samples < MIN_SAMPLES: # or data_train['num_samples'][i] > 10000:\n",
        "            print('SKIP', author_id, samples)\n",
        "            continue\n",
        "        else:\n",
        "            udata_train = data_train['user_data'][author_id]\n",
        "            max_samples = samples if (sample_count + samples) <= total_samples else (sample_count + samples - total_samples) \n",
        "            \n",
        "            sample_count += max_samples\n",
        "            # print('sample_count', sample_count)\n",
        "\n",
        "            x_train.extend(data_train['user_data'][author_id]['x'][:max_samples])\n",
        "            y_train.extend(data_train['user_data'][author_id]['y'][:max_samples])\n",
        "\n",
        "            author_data = data_test['user_data'][author_id]\n",
        "            test_size = int(len(author_data['x']) * SAMPLES_FRACTION)\n",
        "\n",
        "            if val_split:\n",
        "                x_test.extend(author_data['x'][:int(test_size / 2)])\n",
        "                y_test.extend(author_data['y'][:int(test_size / 2)])\n",
        "                # x_val.extend(author_data['x'][int(test_size / 2):])\n",
        "                # y_val.extend(author_data['y'][int(test_size / 2):int(test_size)])\n",
        "\n",
        "            else:\n",
        "                x_test.extend(author_data['x'][:int(test_size)])\n",
        "                y_test.extend(author_data['y'][:int(test_size)])\n",
        "\n",
        "    train_ds = ShakespeareDataset(x_train, y_train, corpus, seq_length)\n",
        "    test_ds = ShakespeareDataset(x_test, y_test, corpus, seq_length)\n",
        "    # val_ds = ShakespeareDataset(x_val, y_val, corpus, seq_length)\n",
        "\n",
        "    data_dict = iid_partition_(train_ds, clients=len(users))\n",
        "\n",
        "    return train_ds, data_dict, test_ds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFvc8mLoouKa"
      },
      "source": [
        "### Non-IID"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZ76WsCZot9s"
      },
      "source": [
        "def noniid_partition(corpus, seq_length=80, val_split=False):\n",
        "\n",
        "    train_file = [os.path.join(DATA_DIR, 'train', f) for f in os.listdir(f'{DATA_DIR}/train') if f.endswith('.json')][0]\n",
        "    test_file = [os.path.join(DATA_DIR, 'test', f) for f in os.listdir(f'{DATA_DIR}/test') if f.endswith('.json')][0]\n",
        "\n",
        "    with open(train_file, 'r') as file:\n",
        "        data_train = json.loads(unidecode(file.read()))\n",
        "\n",
        "    with open(test_file, 'r') as file:\n",
        "        data_test = json.loads(unidecode(file.read()))\n",
        "\n",
        "    \n",
        "    total_samples_train = sum(data_train['num_samples'])\n",
        "\n",
        "    data_dict = {}\n",
        "\n",
        "    x_test, y_test = [], []\n",
        "\n",
        "    users = list(zip(data_train['users'], data_train['num_samples']))\n",
        "    # random.shuffle(users)\n",
        "\n",
        "    total_samples = int(sum(data_train['num_samples']) * SAMPLES_FRACTION)\n",
        "    print('Objective', total_samples, '/', sum(data_train['num_samples']))\n",
        "    sample_count = 0\n",
        "    \n",
        "    for i, (author_id, samples) in enumerate(users):\n",
        "\n",
        "        if sample_count >= total_samples:\n",
        "            print('Max samples reached', sample_count, '/', total_samples)\n",
        "            break\n",
        "\n",
        "        if samples < MIN_SAMPLES: # or data_train['num_samples'][i] > 10000:\n",
        "            print('SKIP', author_id, samples)\n",
        "            continue\n",
        "        else:\n",
        "            udata_train = data_train['user_data'][author_id]\n",
        "            max_samples = samples if (sample_count + samples) <= total_samples else (sample_count + samples - total_samples) \n",
        "            \n",
        "            sample_count += max_samples\n",
        "            # print('sample_count', sample_count)\n",
        "\n",
        "            x_train = data_train['user_data'][author_id]['x'][:max_samples]\n",
        "            y_train = data_train['user_data'][author_id]['y'][:max_samples]\n",
        "\n",
        "            train_ds = ShakespeareFedDataset(x_train, y_train, corpus, seq_length)\n",
        "\n",
        "            x_val, y_val = None, None\n",
        "            val_ds = None\n",
        "            author_data = data_test['user_data'][author_id]\n",
        "            test_size = int(len(author_data['x']) * SAMPLES_FRACTION)\n",
        "            if val_split:\n",
        "                x_test += author_data['x'][:int(test_size / 2)]\n",
        "                y_test += author_data['y'][:int(test_size / 2)]\n",
        "                x_val = author_data['x'][int(test_size / 2):]\n",
        "                y_val = author_data['y'][int(test_size / 2):int(test_size)]\n",
        "\n",
        "                val_ds = ShakespeareFedDataset(x_val, y_val, corpus, seq_length)\n",
        "\n",
        "            else:\n",
        "                x_test += author_data['x'][:int(test_size)]\n",
        "                y_test += author_data['y'][:int(test_size)]\n",
        "\n",
        "            data_dict[author_id] = {\n",
        "                'train_ds': train_ds,\n",
        "                'val_ds': val_ds\n",
        "            }\n",
        "\n",
        "    test_ds = ShakespeareFedDataset(x_test, y_test, corpus, seq_length)\n",
        "\n",
        "    return data_dict, test_ds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWVOxcAao2_t"
      },
      "source": [
        "## Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQQQ2mLeo6EA"
      },
      "source": [
        "### Shakespeare LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mGXTrXRot7R"
      },
      "source": [
        "class ShakespeareLSTM(nn.Module):\n",
        "    \"\"\"\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, input_dim, embedding_dim, hidden_dim, classes, lstm_layers=2, dropout=0.1, batch_first=True):\n",
        "        super(ShakespeareLSTM, self).__init__()\n",
        "        self.input_dim = input_dim\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.classes = classes\n",
        "        self.no_layers = lstm_layers\n",
        "        \n",
        "        self.embedding = nn.Embedding(num_embeddings=self.classes,\n",
        "                                      embedding_dim=self.embedding_dim)\n",
        "        self.lstm = nn.LSTM(input_size=self.embedding_dim, \n",
        "                            hidden_size=self.hidden_dim,\n",
        "                            num_layers=self.no_layers,\n",
        "                            batch_first=batch_first, \n",
        "                            dropout=dropout if self.no_layers > 1 else 0.)\n",
        "        self.fc = nn.Linear(hidden_dim, self.classes)\n",
        "\n",
        "    def forward(self, x, hc=None):\n",
        "        batch_size = x.size(0)\n",
        "        x_emb = self.embedding(x)\n",
        "        out, (ht, ct) = self.lstm(x_emb.view(batch_size, -1, self.embedding_dim), hc)\n",
        "        dense = self.fc(ht[-1])\n",
        "        return dense\n",
        "    \n",
        "    def init_hidden(self, batch_size):\n",
        "        return (Variable(torch.zeros(self.no_layers, batch_size, self.hidden_dim)),\n",
        "                Variable(torch.zeros(self.no_layers, batch_size, self.hidden_dim)))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5QsuJlVipMc8"
      },
      "source": [
        "#### Model Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_Vb0BYpot5I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0bd2316-6ea1-4edf-a628-a1a5ded46b74"
      },
      "source": [
        "batch_size = 10\n",
        "seq_length = 80 # mcmahan17a, fedprox, qFFL\n",
        "\n",
        "shakespeare_lstm = ShakespeareLSTM(input_dim=seq_length,  \n",
        "                                   embedding_dim=8,  # mcmahan17a, fedprox, qFFL\n",
        "                                   hidden_dim=256,  # mcmahan17a, fedprox impl\n",
        "                                #    hidden_dim=100,  # fedprox paper\n",
        "                                   classes=len(corpus),\n",
        "                                   lstm_layers=2,\n",
        "                                   dropout=0.1,  # TODO:\n",
        "                                   batch_first=True\n",
        "                                   )\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "  shakespeare_lstm.cuda()\n",
        "\n",
        "\n",
        "\n",
        "hc = shakespeare_lstm.init_hidden(batch_size)\n",
        "\n",
        "x_sample = torch.zeros((batch_size, seq_length),\n",
        "                       dtype=torch.long,\n",
        "                       device=(torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')))\n",
        "\n",
        "x_sample[0][0] = 1\n",
        "x_sample\n",
        "\n",
        "print(\"\\nShakespeare LSTM SUMMARY\")\n",
        "print(summaryx(shakespeare_lstm, x_sample))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Shakespeare LSTM SUMMARY\n",
            "==========================================================\n",
            "            Kernel Shape   Output Shape  Params  Mult-Adds\n",
            "Layer                                                     \n",
            "0_embedding      [8, 90]    [10, 80, 8]     720        720\n",
            "1_lstm                 -  [10, 80, 256]  798720     794624\n",
            "2_fc           [256, 90]       [10, 90]   23130      23040\n",
            "----------------------------------------------------------\n",
            "                      Totals\n",
            "Total params          822570\n",
            "Trainable params      822570\n",
            "Non-trainable params       0\n",
            "Mult-Adds             818384\n",
            "==========================================================\n",
            "            Kernel Shape   Output Shape  Params  Mult-Adds\n",
            "Layer                                                     \n",
            "0_embedding      [8, 90]    [10, 80, 8]     720        720\n",
            "1_lstm                 -  [10, 80, 256]  798720     794624\n",
            "2_fc           [256, 90]       [10, 90]   23130      23040\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qn7egnzTpeks"
      },
      "source": [
        "## FedMed Algorithm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vFFAfTOwpk4j"
      },
      "source": [
        "### Plot Utils"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyYjWa6IpnTY"
      },
      "source": [
        "from sklearn.metrics import f1_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "367THsiTpo-C"
      },
      "source": [
        "def plot_scores(history, exp_id, title, suffix):\n",
        "    accuracies = [x['accuracy'] for x in history]\n",
        "    f1_macro = [x['f1_macro'] for x in history]\n",
        "    f1_weighted = [x['f1_weighted'] for x in history]\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.plot(accuracies, 'tab:orange')\n",
        "    ax.set(xlabel='Rounds', ylabel='Test Accuracy', title=title)\n",
        "    ax.grid()\n",
        "    fig.savefig(f'{BASE_DIR}/{exp_id}/Test_Accuracy_{suffix}.jpg', format='jpg', dpi=300)\n",
        "    plt.show()\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.plot(f1_macro, 'tab:orange')\n",
        "    ax.set(xlabel='Rounds', ylabel='Test F1 (macro)', title=title)\n",
        "    ax.grid()\n",
        "    fig.savefig(f'{BASE_DIR}/{exp_id}/Test_F1_Macro_{suffix}.jpg', format='jpg')\n",
        "    plt.show()\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.plot(f1_weighted, 'tab:orange')\n",
        "    ax.set(xlabel='Rounds', ylabel='Test F1 (weighted)', title=title)\n",
        "    ax.grid()\n",
        "    fig.savefig(f'{BASE_DIR}/{exp_id}/Test_F1_Weighted_{suffix}.jpg', format='jpg')\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def plot_losses(history, exp_id, title, suffix):\n",
        "    val_losses = [x['loss'] for x in history]\n",
        "    train_losses = [x['train_loss'] for x in history]\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.plot(train_losses, 'tab:orange')\n",
        "    ax.set(xlabel='Rounds', ylabel='Train Loss', title=title)\n",
        "    ax.grid()\n",
        "    fig.savefig(f'{BASE_DIR}/{exp_id}/Train_Loss_{suffix}.jpg', format='jpg')\n",
        "    plt.show()\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.plot(val_losses, 'tab:orange')\n",
        "    ax.set(xlabel='Rounds', ylabel='Test Loss', title=title)\n",
        "    ax.grid()\n",
        "    fig.savefig(f'{BASE_DIR}/{exp_id}/Test_Loss_{suffix}.jpg', format='jpg')\n",
        "    plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQ9PZM0Gp9ve"
      },
      "source": [
        "### Local Training (Client Update)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDJFltwdotzZ"
      },
      "source": [
        "class CustomDataset(Dataset):\n",
        "  def __init__(self, dataset, idxs):\n",
        "      \n",
        "      self.dataset = dataset\n",
        "      self.idxs = list(idxs)\n",
        "\n",
        "  def __len__(self):\n",
        "      return len(self.idxs)\n",
        "\n",
        "  def __getitem__(self, item):\n",
        "      data, label = self.dataset[self.idxs[item]]\n",
        "      return data, label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HtRzU5Yepddq"
      },
      "source": [
        "class ClientUpdate(object):\n",
        "  def __init__(self, dataset, batchSize, learning_rate, epochs, idxs):\n",
        "    # self.train_loader = DataLoader(CustomDataset(dataset, idxs), batch_size=batchSize, shuffle=True)\n",
        "    if hasattr(dataset, 'dataloader'):\n",
        "        self.train_loader = dataset.dataloader(batch_size=batch_size, shuffle=True)\n",
        "    else:\n",
        "        self.train_loader = DataLoader(CustomDataset(dataset, idxs), batch_size=batch_size, shuffle=True)\n",
        "\n",
        "    self.learning_rate = learning_rate\n",
        "    self.epochs = epochs\n",
        "\n",
        "  def train(self, model):\n",
        "    # print(\"Client training for {} epochs.\".format(self.epochs))\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = torch.optim.SGD(model.parameters(), lr=self.learning_rate, momentum=0.5)\n",
        "\n",
        "    # use the weights of global model for proximal term calculation\n",
        "    global_model = copy.deepcopy(model)\n",
        "\n",
        "    # calculate local training time\n",
        "    start_time = time.time()\n",
        "\n",
        "\n",
        "    e_loss = []\n",
        "    for epoch in range(1, self.epochs+1):\n",
        "\n",
        "      train_loss = 0.0\n",
        "\n",
        "      model.train()\n",
        "      for data, labels in self.train_loader:\n",
        "\n",
        "        if torch.cuda.is_available():\n",
        "          data, labels = data.cuda(), labels.cuda()\n",
        "\n",
        "        # clear the gradients\n",
        "        optimizer.zero_grad()\n",
        "        # make a forward pass\n",
        "        output = model(data)\n",
        "\n",
        "        loss = criterion(output, labels)\n",
        "        # do a backwards pass\n",
        "        loss.backward()\n",
        "        # perform a single optimization step\n",
        "        optimizer.step()\n",
        "        # update training loss\n",
        "        train_loss += loss.item()*data.size(0)\n",
        "\n",
        "      # average losses\n",
        "      train_loss = train_loss/len(self.train_loader.dataset)\n",
        "      e_loss.append(train_loss)\n",
        "\n",
        "    total_loss = sum(e_loss)/len(e_loss)\n",
        "\n",
        "    return model.state_dict(), total_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3crFDN0xqGu6"
      },
      "source": [
        "### Server Side Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c085xSOoqEHk"
      },
      "source": [
        "def training(model, rounds, batch_size, lr, ds, data_dict, test_ds, C, K, E, M, plt_title, plt_color, classes, history=[], eval_every=1, tb_logger=None):\n",
        "  \"\"\"\n",
        "  Function implements the Federated Averaging Algorithm from the FedAvg paper.\n",
        "  Specifically, this function is used for the server side training and weight update\n",
        "\n",
        "  Params:\n",
        "    - model:           PyTorch model to train\n",
        "    - rounds:          Number of communication rounds for the client update\n",
        "    - batch_size:      Batch size for client update training\n",
        "    - lr:              Learning rate used for client update training\n",
        "    - ds:              Dataset used for training\n",
        "    - data_dict:       Type of data partition used for training (IID or non-IID)\n",
        "    - test_data_dict:  Data used for testing the model\n",
        "    - C:               Fraction of clients randomly chosen to perform computation on each round\n",
        "    - K:               Total number of clients\n",
        "    - E:               Number of training passes each client makes over its local dataset per round\n",
        "    - mu:              proximal term constant\n",
        "    - percentage:      percentage of selected client to have fewer than E epochs\n",
        "  Returns:\n",
        "    - model:           Trained model on the server\n",
        "  \"\"\"\n",
        "\n",
        "  start = time.time()\n",
        "\n",
        "  # global model weights\n",
        "  global_weights = model.state_dict()\n",
        "\n",
        "#   pk = np.ones(K) * 1.0 / K\n",
        "\n",
        "  # training loss\n",
        "  train_loss = []\n",
        "  # test accuracy\n",
        "  test_acc = []\n",
        "\n",
        "  users_id = list(data_dict.keys())\n",
        "\n",
        "  for curr_round in range(1, rounds+1):\n",
        "    w, local_loss = [], []\n",
        "\n",
        "    m = max(int(C*K), 1)\n",
        "    newM = max(int(M*m), 1)\n",
        "\n",
        "    c = 0\n",
        "    S_t = np.random.choice(range(K), m, replace=False)\n",
        "    print('Clients: {}/{} -> {}'.format(len(S_t), K, S_t))\n",
        "\n",
        "    # print('Robustness..')\n",
        "    # for i in range(len(S_t)):\n",
        "    #   if c == newM:\n",
        "    #     break\n",
        "    #   c += 1\n",
        "\n",
        "    #   k = S_t[i]\n",
        "    #   key = users_id[k]\n",
        "    #   ds_ = ds if ds else data_dict[key]['train_ds']\n",
        "    #   idxs = data_dict[key] if ds else None\n",
        "    #   print(f'Client {k}: {len(idxs) if idxs else len(ds_)} samples')\n",
        "    #   local_update = ClientUpdate(dataset=ds_, batchSize=batch_size, learning_rate=lr, epochs=E, idxs=idxs)\n",
        "    #   weights, loss = local_update.train(model=copy.deepcopy(model))\n",
        "\n",
        "    #   for k in weights.keys():\n",
        "    #     t = torch.Tensor(weights[k].shape).cuda()\n",
        "    #     t.fill_(0.1)\n",
        "    #     weights[k] = t\n",
        "\n",
        "    #   w.append(copy.deepcopy(weights))\n",
        "    #   local_loss.append(copy.deepcopy(loss))\n",
        "    # print('###############################')\n",
        "\n",
        "    # for i in tqdm(range(newM, len(S_t))): # if robustness is included\n",
        "    for i in tqdm(range(len(S_t))):\n",
        "      k = S_t[i]\n",
        "      key = users_id[k]\n",
        "      ds_ = ds if ds else data_dict[key]['train_ds']\n",
        "      idxs = data_dict[key] if ds else None\n",
        "      print(f'Client {k}: {len(idxs) if idxs else len(ds_)} samples')\n",
        "      local_update = ClientUpdate(dataset=ds_, batchSize=batch_size, learning_rate=lr, epochs=E, idxs=idxs)\n",
        "      weights, loss = local_update.train(model=copy.deepcopy(model))\n",
        "      \n",
        "      w.append(copy.deepcopy(weights))\n",
        "      local_loss.append(copy.deepcopy(loss))\n",
        "\n",
        "    print('Computing median...')\n",
        "\n",
        "    target = copy.deepcopy(w[0]);\n",
        "    weights_med = copy.deepcopy(w[0]);\n",
        "    for k in weights_med.keys():\n",
        "      tmp = copy.deepcopy(torch.median(torch.stack([w[i][k].data for i in range(0, len(w))]), dim=0))[0]\n",
        "      target[k].data = tmp\n",
        "\n",
        "\n",
        "    global_weights = target\n",
        "    # move the updated weights to our model state dict\n",
        "    model.load_state_dict(global_weights)\n",
        "\n",
        "    # loss\n",
        "    loss_avg = sum(local_loss) / len(local_loss)\n",
        "    print('Round: {}... \\tAverage Loss: {}'.format(curr_round, round(loss_avg, 3)))\n",
        "    train_loss.append(loss_avg)\n",
        "    if tb_logger:\n",
        "        tb_logger.add_scalar(f'Train/Loss', loss_avg, curr_round)\n",
        "\n",
        "    # testing\n",
        "    # if curr_round % eval_every == 0:\n",
        "    test_scores = testing(model, test_ds, batch_size * 2, nn.CrossEntropyLoss(), len(classes), classes)\n",
        "    test_scores['train_loss'] = loss_avg\n",
        "    test_loss, test_accuracy = test_scores['loss'], test_scores['accuracy']\n",
        "    history.append(test_scores)\n",
        "    \n",
        "    # print('Round: {}... \\tAverage Loss: {} \\tTest Loss: {} \\tTest Acc: {}'.format(curr_round, round(loss_avg, 3), round(test_loss, 3), round(test_accuracy, 3)))\n",
        "\n",
        "    if tb_logger:\n",
        "        tb_logger.add_scalar(f'Test/Loss', test_scores['loss'], curr_round)\n",
        "        tb_logger.add_scalars(f'Test/Scores', {\n",
        "            'accuracy': test_scores['accuracy'], 'f1_macro': test_scores['f1_macro'], 'f1_weighted': test_scores['f1_weighted']\n",
        "        }, curr_round)\n",
        "\n",
        "    test_acc.append(test_accuracy)\n",
        "\n",
        "\n",
        "  end = time.time()\n",
        "  \n",
        "  # plot train loss\n",
        "  fig, ax = plt.subplots()\n",
        "  x_axis = np.arange(1, rounds+1)\n",
        "  y_axis = np.array(train_loss)\n",
        "  ax.plot(x_axis, y_axis)\n",
        "\n",
        "  ax.set(xlabel='Number of Rounds', ylabel='Train Loss', title=plt_title)\n",
        "  ax.grid()\n",
        "  # fig.savefig(plt_title+'.jpg', format='jpg')\n",
        "\n",
        "  # plot test accuracy\n",
        "  fig1, ax1 = plt.subplots()\n",
        "  x_axis1 = np.arange(1, rounds+1)\n",
        "  y_axis1 = np.array(test_acc)\n",
        "  ax1.plot(x_axis1, y_axis1)\n",
        "\n",
        "  ax1.set(xlabel='Number of Rounds', ylabel='Test Accuracy', title=plt_title)\n",
        "  ax1.grid()\n",
        "  # fig1.savefig(plt_title+'-test.jpg', format='jpg')\n",
        "  \n",
        "  print(\"Training Done! Total time taken to Train: {}\".format(end-start))\n",
        "\n",
        "  return model, history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXtGLkoAqLIW"
      },
      "source": [
        "### Testing Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQJIJno4qKvc"
      },
      "source": [
        "def testing(model, dataset, bs, criterion, num_classes, classes, print_all=False):\n",
        "  #test loss \n",
        "  test_loss = 0.0\n",
        "  correct_class = list(0. for i in range(num_classes))\n",
        "  total_class = list(0. for i in range(num_classes))\n",
        "\n",
        "  test_loader = DataLoader(dataset, batch_size=bs)\n",
        "  l = len(test_loader)\n",
        "  model.eval()\n",
        "  print('running validation...')\n",
        "  for i, (data, labels) in enumerate(tqdm(test_loader)):\n",
        "\n",
        "    if torch.cuda.is_available():\n",
        "      data, labels = data.cuda(), labels.cuda()\n",
        "\n",
        "    output = model(data)\n",
        "    loss = criterion(output, labels)\n",
        "    test_loss += loss.item()*data.size(0)\n",
        "\n",
        "    _, pred = torch.max(output, 1)\n",
        "\n",
        "    # For F1Score\n",
        "    y_true = np.append(y_true, labels.data.view_as(pred).cpu().numpy()) if i != 0 else labels.data.view_as(pred).cpu().numpy()\n",
        "    y_hat = np.append(y_hat, pred.cpu().numpy()) if i != 0 else pred.cpu().numpy()\n",
        "\n",
        "    correct_tensor = pred.eq(labels.data.view_as(pred))\n",
        "    correct = np.squeeze(correct_tensor.numpy()) if not torch.cuda.is_available() else np.squeeze(correct_tensor.cpu().numpy())\n",
        "\n",
        "    for i, lbl in enumerate(labels.data):\n",
        "    #   print('lbl', i, lbl)\n",
        "      correct_class[lbl] += correct.data[i]\n",
        "      total_class[lbl] += 1\n",
        "    \n",
        "  # avg test loss\n",
        "  test_loss = test_loss/len(test_loader.dataset)\n",
        "  print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
        "\n",
        "  # Avg F1 Score\n",
        "  f1_macro = f1_score(y_true, y_hat, average='macro')\n",
        "  # F1-Score -> weigthed to consider class imbalance\n",
        "  f1_weighted =  f1_score(y_true, y_hat, average='weighted')\n",
        "  print(\"F1 Score: {:.6f} (macro) {:.6f} (weighted) %\\n\".format(f1_macro, f1_weighted))\n",
        "\n",
        "  # print test accuracy\n",
        "  if print_all:\n",
        "    for i in range(num_classes):\n",
        "        if total_class[i]>0:\n",
        "            print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % \n",
        "                    (classes[i], 100 * correct_class[i] / total_class[i],\n",
        "                    np.sum(correct_class[i]), np.sum(total_class[i])))\n",
        "        else:\n",
        "            print('Test Accuracy of %5s: N/A (no training examples)' % (classes[i]))\n",
        "\n",
        "  overall_accuracy = np.sum(correct_class) / np.sum(total_class)\n",
        "\n",
        "  print('\\nFinal Test  Accuracy: {:.3f} ({}/{})'.format(overall_accuracy, np.sum(correct_class), np.sum(total_class)))\n",
        "\n",
        "  return {'loss': test_loss, 'accuracy': overall_accuracy, 'f1_macro': f1_macro, 'f1_weighted': f1_weighted}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uxqXLBd8qbC2"
      },
      "source": [
        "## Experiments"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRKlrkVHO8Na",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "outputId": "3cd2eb73-240d-4112-ab67-dc3f6bef4f86"
      },
      "source": [
        "FAIL-ON-PURPOSE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-cee3c52d72bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFAIL\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mON\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mPURPOSE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'FAIL' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2CfSkNVqKtL"
      },
      "source": [
        "seq_length = 80  # mcmahan17a, fedprox, qFFL\n",
        "embedding_dim = 8  # mcmahan17a, fedprox, qFFL\n",
        "# hidden_dim = 100  # fedprox paper\n",
        "hidden_dim = 256  # mcmahan17a, fedprox impl\n",
        "num_classes = len(corpus)\n",
        "classes = list(range(num_classes))\n",
        "lstm_layers = 2  # mcmahan17a, fedprox, qFFL\n",
        "dropout = 0.1  # TODO\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OvCgT_qbmFAO"
      },
      "source": [
        "class Hyperparameters():\n",
        "\n",
        "    def __init__(self, total_clients):\n",
        "        # number of training rounds\n",
        "        self.rounds = 50\n",
        "        # client fraction\n",
        "        self.C = 0.5\n",
        "        # number of clients\n",
        "        self.K = total_clients\n",
        "        # number of training passes on local dataset for each roung\n",
        "        # self.E = 20\n",
        "        self.E = 1\n",
        "        self.M = 0.01  # FedMed\n",
        "        # batch size\n",
        "        self.batch_size = 10\n",
        "        # learning Rate\n",
        "        self.lr = 0.8"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_JVF83mfM3f"
      },
      "source": [
        "exp_log = dict()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYOPtnYoqhWd"
      },
      "source": [
        "### IID"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRKc7NrzqKpU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efbe2916-dfa3-4c25-b663-7b9835227395"
      },
      "source": [
        "train_ds, data_dict, test_ds = iid_partition(corpus, seq_length, val_split=True)  # Not using val_ds but makes train eval periods faster\n",
        "\n",
        "total_clients = len(data_dict.keys())\n",
        "'Total users:', total_clients"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Objective 413629 / 413629\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Total users:', 143)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaKtpKT5q1q_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17855f09-452a-4a22-a6ef-d3c7016d06e9"
      },
      "source": [
        "hparams = Hyperparameters(total_clients)\n",
        "hparams.__dict__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.5,\n",
              " 'E': 1,\n",
              " 'K': 143,\n",
              " 'M': 0.01,\n",
              " 'batch_size': 10,\n",
              " 'lr': 0.8,\n",
              " 'rounds': 50}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ilYMClTV_WR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c59bea16-990f-439f-fda9-5c8d283ac3ad"
      },
      "source": [
        "# Sweeping parameter\n",
        "PARAM_NAME = 'clients_fraction'\n",
        "PARAM_VALUE = hparams.C\n",
        "exp_id = f'{PARAM_NAME}/{PARAM_VALUE}'\n",
        "exp_id"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'clients_fraction/0.5'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAhy4CWVZy3F"
      },
      "source": [
        "EXP_DIR = f'{BASE_DIR}/{exp_id}'\n",
        "os.makedirs(EXP_DIR, exist_ok=True)\n",
        "\n",
        "# tb_logger = SummaryWriter(log_dir)\n",
        "# print(f'TBoard logger created at: {log_dir}')\n",
        "\n",
        "title = 'LSTM FedMed on IID'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwTdeiv8q8_L"
      },
      "source": [
        "def run_experiment(run_id):\n",
        "\n",
        "    shakespeare_lstm = ShakespeareLSTM(input_dim=seq_length,  \n",
        "                                   embedding_dim=embedding_dim,  \n",
        "                                   hidden_dim=hidden_dim,\n",
        "                                   classes=num_classes,\n",
        "                                   lstm_layers=lstm_layers,\n",
        "                                   dropout=dropout,\n",
        "                                   batch_first=True\n",
        "                                   )\n",
        "\n",
        "    if torch.cuda.is_available():\n",
        "        shakespeare_lstm.cuda()\n",
        "    \n",
        "    test_history = []\n",
        "\n",
        "    lstm_iid_trained, test_history = training(shakespeare_lstm,\n",
        "                                            hparams.rounds, hparams.batch_size, hparams.lr,\n",
        "                                            train_ds,\n",
        "                                            data_dict,\n",
        "                                            test_ds,\n",
        "                                            hparams.C, hparams.K, hparams.E, hparams.M,\n",
        "                                            title, \"green\",\n",
        "                                            corpus, # classes\n",
        "                                            history=test_history,\n",
        "                                            # tb_logger=tb_writer\n",
        "                                            )\n",
        "    \n",
        "\n",
        "    final_scores = testing(lstm_iid_trained, test_ds, batch_size * 2, nn.CrossEntropyLoss(), len(corpus), corpus)\n",
        "    print(f'\\n\\n========================================================\\n\\n')\n",
        "    print(f'Final scores for Exp {run_id} \\n {final_scores}')\n",
        "\n",
        "    log = {\n",
        "        'history': test_history,\n",
        "        'hyperparams': hparams.__dict__\n",
        "    }\n",
        "\n",
        "    with open(f'{EXP_DIR}/results_iid_{run_id}.pkl', 'wb') as file:\n",
        "        pickle.dump(log, file)\n",
        "\n",
        "    return test_history\n"
      ],
      "execution_count": null,
      "outputs": []
    },
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            "504a485c38044d8caf5abfe547e5d489",
            "ae83585f0cb4415fa36fa243ec1e7c9c"
          ]
        },
        "outputId": "093100b1-968d-4e8b-973b-44e81506b73e"
      },
      "source": [
        "exp_history = list()\n",
        "for run_id in range(2):  # TOTAL RUNS\n",
        "    print(f'============== RUNNING EXPERIMENT #{run_id} ==============')\n",
        "    exp_history.append(run_experiment(run_id))\n",
        "    print(f'\\n\\n========================================================\\n\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "============== RUNNING EXPERIMENT #0 ==============\n",
            "Clients: 71/143 -> [  4  12 100  50  46  39 127 112   8  93  58 125  66  78  79  27 142 115\n",
            " 124  52  49  20  86  14  57  22  17 120  83 116  28  77  40  54  26  59\n",
            " 134  23  98 139  72  32  64  70 107 122 135   9 141  84  44 138  92 130\n",
            " 128 114  67 102 108 129 104  81 103  42   7   0  43 101  18  36  37]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "35d1363939674dde9e033319ea0ff89e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 4: 2892 samples\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py:662: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at  /pytorch/aten/src/ATen/native/cudnn/RNN.cpp:915.)\n",
            "  self.dropout, self.training, self.bidirectional, self.batch_first)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Client 12: 2892 samples\n",
            "Client 100: 2892 samples\n",
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            "Client 0: 2892 samples\n",
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            "Client 18: 2892 samples\n",
            "Client 36: 2892 samples\n",
            "Client 37: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 1... \tAverage Loss: 3.204\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dff1f80f3ebe4018903c5b8ab7116fa4",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.928030\n",
            "\n",
            "F1 Score: 0.015211 (macro) 0.119871 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.230 (11898.0/51704.0)\n",
            "Clients: 71/143 -> [ 37  96 117  38  66  56  67  31  17  43  25  72  49  77  60  14  22  97\n",
            " 109  98 140  87  13 129 119 112  51 138  23  89  24  11 134 105   9  71\n",
            "  58  91  85  42  55  29 128  39 120  36  46  70  64  93 113 141  65   2\n",
            "  50  33 123   3  62  95 107  30  76 118 137  27  28  54 126 101 125]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0889efa4a7974efe89b024af6626756a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 37: 2892 samples\n",
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            "Client 2: 2892 samples\n",
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            "Client 3: 2892 samples\n",
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            "Client 126: 2892 samples\n",
            "Client 101: 2892 samples\n",
            "Client 125: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 2... \tAverage Loss: 2.869\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4944110aa3944deea10b9690a0cebfd7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.642471\n",
            "\n",
            "F1 Score: 0.032418 (macro) 0.185810 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.277 (14344.0/51704.0)\n",
            "Clients: 71/143 -> [ 49  28  12 133   4  90  27  89  77  65 136  81  39 103 121  67  32  13\n",
            "   0 116  47  58  37 139  24 130  19  25  76  66  96  61   2 126  53  71\n",
            "   5  42 101  46 129  91  93 107 132  56  80  29  55  92 102  85 111 138\n",
            "  30  26   1 104  44  88  31  94  33  22 100  34  69  14  79  84 106]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "35c406f1bcfb4e75b622aceb50517344",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 49: 2892 samples\n",
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            "\n",
            "Computing median...\n",
            "Round: 3... \tAverage Loss: 2.69\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9948ba4cba5a4dabb5bfad2fbb2bd795",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.487344\n",
            "\n",
            "F1 Score: 0.048434 (macro) 0.233902 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.320 (16528.0/51704.0)\n",
            "Clients: 71/143 -> [ 61  34  24  18 122  91  12  46  16  21   5  93  72 137  26 133   3  66\n",
            "  44 142  78  47  23  19   7  27 120 131 128  57  65 109  92  82 112  54\n",
            " 139 101 124  10  43  89  14 141 105  84 125  71  48   8  52  50  28  69\n",
            "  83  90  22  33  51  29 123   9  99 114  11  13 115  39  45  79  59]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ac89adc9e1184aa19c49625a4eeb09e7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
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            "Client 45: 2892 samples\n",
            "Client 79: 2892 samples\n",
            "Client 59: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 4... \tAverage Loss: 2.582\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "534e9e34930c4390b39fbfb8c1163b0f",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.399509\n",
            "\n",
            "F1 Score: 0.060108 (macro) 0.253967 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.334 (17284.0/51704.0)\n",
            "Clients: 71/143 -> [ 23  79  75   4 106 133 102  44 131  51  91  52  16   7 137  10  13  20\n",
            "  61  95  64  83  73  63   6  62  12  81  65  28 134 111 100  57   1  96\n",
            "  71  38 116 140   3  42   9  31  21 119  25  55  18 115  80  98  27  72\n",
            " 126  74 103 138  45  82 130  66   2  59   8 109  22  88  46  53  19]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e07d6f7ba7b447e8989c474c4afd7190",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 23: 2892 samples\n",
            "Client 79: 2892 samples\n",
            "Client 75: 2892 samples\n",
            "Client 4: 2892 samples\n",
            "Client 106: 2892 samples\n",
            "Client 133: 2892 samples\n",
            "Client 102: 2892 samples\n",
            "Client 44: 2892 samples\n",
            "Client 131: 2892 samples\n",
            "Client 51: 2892 samples\n",
            "Client 91: 2892 samples\n",
            "Client 52: 2892 samples\n",
            "Client 16: 2892 samples\n",
            "Client 7: 2892 samples\n",
            "Client 137: 2892 samples\n",
            "Client 10: 2892 samples\n",
            "Client 13: 2892 samples\n",
            "Client 20: 2892 samples\n",
            "Client 61: 2892 samples\n",
            "Client 95: 2892 samples\n",
            "Client 64: 2892 samples\n",
            "Client 83: 2892 samples\n",
            "Client 73: 2892 samples\n",
            "Client 63: 2892 samples\n",
            "Client 6: 2892 samples\n",
            "Client 62: 2892 samples\n",
            "Client 12: 2892 samples\n",
            "Client 81: 2892 samples\n",
            "Client 65: 2892 samples\n",
            "Client 28: 2892 samples\n",
            "Client 134: 2892 samples\n",
            "Client 111: 2892 samples\n",
            "Client 100: 2892 samples\n",
            "Client 57: 2892 samples\n",
            "Client 1: 2892 samples\n",
            "Client 96: 2892 samples\n",
            "Client 71: 2892 samples\n",
            "Client 38: 2892 samples\n",
            "Client 116: 2892 samples\n",
            "Client 140: 2892 samples\n",
            "Client 3: 2892 samples\n",
            "Client 42: 2892 samples\n",
            "Client 9: 2892 samples\n",
            "Client 31: 2892 samples\n",
            "Client 21: 2892 samples\n",
            "Client 119: 2892 samples\n",
            "Client 25: 2892 samples\n",
            "Client 55: 2892 samples\n",
            "Client 18: 2892 samples\n",
            "Client 115: 2892 samples\n",
            "Client 80: 2892 samples\n",
            "Client 98: 2892 samples\n",
            "Client 27: 2892 samples\n",
            "Client 72: 2892 samples\n",
            "Client 126: 2892 samples\n",
            "Client 74: 2892 samples\n",
            "Client 103: 2892 samples\n",
            "Client 138: 2892 samples\n",
            "Client 45: 2892 samples\n",
            "Client 82: 2892 samples\n",
            "Client 130: 2892 samples\n",
            "Client 66: 2892 samples\n",
            "Client 2: 2892 samples\n",
            "Client 59: 2892 samples\n",
            "Client 8: 2892 samples\n",
            "Client 109: 2892 samples\n",
            "Client 22: 2892 samples\n",
            "Client 88: 2892 samples\n",
            "Client 46: 2892 samples\n",
            "Client 53: 2892 samples\n",
            "Client 19: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 5... \tAverage Loss: 2.508\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c0d7e9e7a1134feda479f5b862a746c9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.332980\n",
            "\n",
            "F1 Score: 0.072108 (macro) 0.279542 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.349 (18059.0/51704.0)\n",
            "Clients: 71/143 -> [ 59  13  85 116  88 126  71 119 137  68  36 104  58 135  69  27  37   3\n",
            " 107  79 130  22  14 136  28  29  20  35  99  30 100  42  73  54  24 101\n",
            "  51 108  94 118   1   5  91  64  65  46  34  74 113 112  67  15  86  50\n",
            "  78  87  19  33  41  63   4  26  92 109 129  16 110 142 103  90  44]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f3fc2eada6545c1a47bdfc3b8f83c07",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 59: 2892 samples\n",
            "Client 13: 2892 samples\n",
            "Client 85: 2892 samples\n",
            "Client 116: 2892 samples\n",
            "Client 88: 2892 samples\n",
            "Client 126: 2892 samples\n",
            "Client 71: 2892 samples\n",
            "Client 119: 2892 samples\n",
            "Client 137: 2892 samples\n",
            "Client 68: 2892 samples\n",
            "Client 36: 2892 samples\n",
            "Client 104: 2892 samples\n",
            "Client 58: 2892 samples\n",
            "Client 135: 2892 samples\n",
            "Client 69: 2892 samples\n",
            "Client 27: 2892 samples\n",
            "Client 37: 2892 samples\n",
            "Client 3: 2892 samples\n",
            "Client 107: 2892 samples\n",
            "Client 79: 2892 samples\n",
            "Client 130: 2892 samples\n",
            "Client 22: 2892 samples\n",
            "Client 14: 2892 samples\n",
            "Client 136: 2892 samples\n",
            "Client 28: 2892 samples\n",
            "Client 29: 2892 samples\n",
            "Client 20: 2892 samples\n",
            "Client 35: 2892 samples\n",
            "Client 99: 2892 samples\n",
            "Client 30: 2892 samples\n",
            "Client 100: 2892 samples\n",
            "Client 42: 2892 samples\n",
            "Client 73: 2892 samples\n",
            "Client 54: 2892 samples\n",
            "Client 24: 2892 samples\n",
            "Client 101: 2892 samples\n",
            "Client 51: 2892 samples\n",
            "Client 108: 2892 samples\n",
            "Client 94: 2892 samples\n",
            "Client 118: 2892 samples\n",
            "Client 1: 2892 samples\n",
            "Client 5: 2892 samples\n",
            "Client 91: 2892 samples\n",
            "Client 64: 2892 samples\n",
            "Client 65: 2892 samples\n",
            "Client 46: 2892 samples\n",
            "Client 34: 2892 samples\n",
            "Client 74: 2892 samples\n",
            "Client 113: 2892 samples\n",
            "Client 112: 2892 samples\n",
            "Client 67: 2892 samples\n",
            "Client 15: 2892 samples\n",
            "Client 86: 2892 samples\n",
            "Client 50: 2892 samples\n",
            "Client 78: 2892 samples\n",
            "Client 87: 2892 samples\n",
            "Client 19: 2892 samples\n",
            "Client 33: 2892 samples\n",
            "Client 41: 2892 samples\n",
            "Client 63: 2892 samples\n",
            "Client 4: 2892 samples\n",
            "Client 26: 2892 samples\n",
            "Client 92: 2892 samples\n",
            "Client 109: 2892 samples\n",
            "Client 129: 2892 samples\n",
            "Client 16: 2892 samples\n",
            "Client 110: 2892 samples\n",
            "Client 142: 2892 samples\n",
            "Client 103: 2892 samples\n",
            "Client 90: 2892 samples\n",
            "Client 44: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 6... \tAverage Loss: 2.45\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "69775cd66434466aba6a23c2aa0455a1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.274479\n",
            "\n",
            "F1 Score: 0.078728 (macro) 0.298398 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.361 (18663.0/51704.0)\n",
            "Clients: 71/143 -> [ 85  65  34  71  31  28  88  13  48  21  53  87 114 100  35 130  24   5\n",
            "  92  15  49  19  16 111  57  33  91  98  42  14  26   8 101  10 131 117\n",
            "   1 133 105 137  58  32  41   0  12  73   9  82 103  76   7  44 140  90\n",
            " 132  56 102  81 125 122  55 108  59   3 126  43   4 115  54  95  30]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bd558272bddb4354b64f2ce50ba0282e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 85: 2892 samples\n",
            "Client 65: 2892 samples\n",
            "Client 34: 2892 samples\n",
            "Client 71: 2892 samples\n",
            "Client 31: 2892 samples\n",
            "Client 28: 2892 samples\n",
            "Client 88: 2892 samples\n",
            "Client 13: 2892 samples\n",
            "Client 48: 2892 samples\n",
            "Client 21: 2892 samples\n",
            "Client 53: 2892 samples\n",
            "Client 87: 2892 samples\n",
            "Client 114: 2892 samples\n",
            "Client 100: 2892 samples\n",
            "Client 35: 2892 samples\n",
            "Client 130: 2892 samples\n",
            "Client 24: 2892 samples\n",
            "Client 5: 2892 samples\n",
            "Client 92: 2892 samples\n",
            "Client 15: 2892 samples\n",
            "Client 49: 2892 samples\n",
            "Client 19: 2892 samples\n",
            "Client 16: 2892 samples\n",
            "Client 111: 2892 samples\n",
            "Client 57: 2892 samples\n",
            "Client 33: 2892 samples\n",
            "Client 91: 2892 samples\n",
            "Client 98: 2892 samples\n",
            "Client 42: 2892 samples\n",
            "Client 14: 2892 samples\n",
            "Client 26: 2892 samples\n",
            "Client 8: 2892 samples\n",
            "Client 101: 2892 samples\n",
            "Client 10: 2892 samples\n",
            "Client 131: 2892 samples\n",
            "Client 117: 2892 samples\n",
            "Client 1: 2892 samples\n",
            "Client 133: 2892 samples\n",
            "Client 105: 2892 samples\n",
            "Client 137: 2892 samples\n",
            "Client 58: 2892 samples\n",
            "Client 32: 2892 samples\n",
            "Client 41: 2892 samples\n",
            "Client 0: 2892 samples\n",
            "Client 12: 2892 samples\n",
            "Client 73: 2892 samples\n",
            "Client 9: 2892 samples\n",
            "Client 82: 2892 samples\n",
            "Client 103: 2892 samples\n",
            "Client 76: 2892 samples\n",
            "Client 7: 2892 samples\n",
            "Client 44: 2892 samples\n",
            "Client 140: 2892 samples\n",
            "Client 90: 2892 samples\n",
            "Client 132: 2892 samples\n",
            "Client 56: 2892 samples\n",
            "Client 102: 2892 samples\n",
            "Client 81: 2892 samples\n",
            "Client 125: 2892 samples\n",
            "Client 122: 2892 samples\n",
            "Client 55: 2892 samples\n",
            "Client 108: 2892 samples\n",
            "Client 59: 2892 samples\n",
            "Client 3: 2892 samples\n",
            "Client 126: 2892 samples\n",
            "Client 43: 2892 samples\n",
            "Client 4: 2892 samples\n",
            "Client 115: 2892 samples\n",
            "Client 54: 2892 samples\n",
            "Client 95: 2892 samples\n",
            "Client 30: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 7... \tAverage Loss: 2.394\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1f4637ad866a47ec80ba7a3689d210ab",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.221982\n",
            "\n",
            "F1 Score: 0.085083 (macro) 0.313551 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.374 (19360.0/51704.0)\n",
            "Clients: 71/143 -> [  8 101  75  73  29  62  94  26 140 108  43  83   7 112  63  33 132  23\n",
            "  25 124  22 135  52 118   9  11 115 138  35  77  64 114 105  21 128 126\n",
            "  51  91 123  95  28  70 127   4  60  39  16 122  46  90 141  81 133  88\n",
            "  31  74 129 100  41  67  40 107  36  66  38  12 116  30  61 131  24]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d94f7cccaa204ff78072a1671522b879",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 8: 2892 samples\n",
            "Client 101: 2892 samples\n",
            "Client 75: 2892 samples\n",
            "Client 73: 2892 samples\n",
            "Client 29: 2892 samples\n",
            "Client 62: 2892 samples\n",
            "Client 94: 2892 samples\n",
            "Client 26: 2892 samples\n",
            "Client 140: 2892 samples\n",
            "Client 108: 2892 samples\n",
            "Client 43: 2892 samples\n",
            "Client 83: 2892 samples\n",
            "Client 7: 2892 samples\n",
            "Client 112: 2892 samples\n",
            "Client 63: 2892 samples\n",
            "Client 33: 2892 samples\n",
            "Client 132: 2892 samples\n",
            "Client 23: 2892 samples\n",
            "Client 25: 2892 samples\n",
            "Client 124: 2892 samples\n",
            "Client 22: 2892 samples\n",
            "Client 135: 2892 samples\n",
            "Client 52: 2892 samples\n",
            "Client 118: 2892 samples\n",
            "Client 9: 2892 samples\n",
            "Client 11: 2892 samples\n",
            "Client 115: 2892 samples\n",
            "Client 138: 2892 samples\n",
            "Client 35: 2892 samples\n",
            "Client 77: 2892 samples\n",
            "Client 64: 2892 samples\n",
            "Client 114: 2892 samples\n",
            "Client 105: 2892 samples\n",
            "Client 21: 2892 samples\n",
            "Client 128: 2892 samples\n",
            "Client 126: 2892 samples\n",
            "Client 51: 2892 samples\n",
            "Client 91: 2892 samples\n",
            "Client 123: 2892 samples\n",
            "Client 95: 2892 samples\n",
            "Client 28: 2892 samples\n",
            "Client 70: 2892 samples\n",
            "Client 127: 2892 samples\n",
            "Client 4: 2892 samples\n",
            "Client 60: 2892 samples\n",
            "Client 39: 2892 samples\n",
            "Client 16: 2892 samples\n",
            "Client 122: 2892 samples\n",
            "Client 46: 2892 samples\n",
            "Client 90: 2892 samples\n",
            "Client 141: 2892 samples\n",
            "Client 81: 2892 samples\n",
            "Client 133: 2892 samples\n",
            "Client 88: 2892 samples\n",
            "Client 31: 2892 samples\n",
            "Client 74: 2892 samples\n",
            "Client 129: 2892 samples\n",
            "Client 100: 2892 samples\n",
            "Client 41: 2892 samples\n",
            "Client 67: 2892 samples\n",
            "Client 40: 2892 samples\n",
            "Client 107: 2892 samples\n",
            "Client 36: 2892 samples\n",
            "Client 66: 2892 samples\n",
            "Client 38: 2892 samples\n",
            "Client 12: 2892 samples\n",
            "Client 116: 2892 samples\n",
            "Client 30: 2892 samples\n",
            "Client 61: 2892 samples\n",
            "Client 131: 2892 samples\n",
            "Client 24: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 8... \tAverage Loss: 2.347\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ee1819f36b3439ebc725815b4c4b016",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.178158\n",
            "\n",
            "F1 Score: 0.088131 (macro) 0.324965 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.383 (19809.0/51704.0)\n",
            "Clients: 71/143 -> [ 39  10  19 113  77  50 100 117 142  96   0 141  61  84  43 122   9  98\n",
            "  80  73 112  71  32 106  24 128  35  78  33  63 108  66 111  53 125  46\n",
            " 126 138  85 123 127  74  89  95 109 132 139  21  51  48  58  62 129 136\n",
            "  49   5 103  94 124  93  99  69  25   3  34   4 104 102  72  56 119]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "99db018f07124388b8344e63cc733916",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 39: 2892 samples\n",
            "Client 10: 2892 samples\n",
            "Client 19: 2892 samples\n",
            "Client 113: 2892 samples\n",
            "Client 77: 2892 samples\n",
            "Client 50: 2892 samples\n",
            "Client 100: 2892 samples\n",
            "Client 117: 2892 samples\n",
            "Client 142: 2892 samples\n",
            "Client 96: 2892 samples\n",
            "Client 0: 2892 samples\n",
            "Client 141: 2892 samples\n",
            "Client 61: 2892 samples\n",
            "Client 84: 2892 samples\n",
            "Client 43: 2892 samples\n",
            "Client 122: 2892 samples\n",
            "Client 9: 2892 samples\n",
            "Client 98: 2892 samples\n",
            "Client 80: 2892 samples\n",
            "Client 73: 2892 samples\n",
            "Client 112: 2892 samples\n",
            "Client 71: 2892 samples\n",
            "Client 32: 2892 samples\n",
            "Client 106: 2892 samples\n",
            "Client 24: 2892 samples\n",
            "Client 128: 2892 samples\n",
            "Client 35: 2892 samples\n",
            "Client 78: 2892 samples\n",
            "Client 33: 2892 samples\n",
            "Client 63: 2892 samples\n",
            "Client 108: 2892 samples\n",
            "Client 66: 2892 samples\n",
            "Client 111: 2892 samples\n",
            "Client 53: 2892 samples\n",
            "Client 125: 2892 samples\n",
            "Client 46: 2892 samples\n",
            "Client 126: 2892 samples\n",
            "Client 138: 2892 samples\n",
            "Client 85: 2892 samples\n",
            "Client 123: 2892 samples\n",
            "Client 127: 2892 samples\n",
            "Client 74: 2892 samples\n",
            "Client 89: 2892 samples\n",
            "Client 95: 2892 samples\n",
            "Client 109: 2892 samples\n",
            "Client 132: 2892 samples\n",
            "Client 139: 2892 samples\n",
            "Client 21: 2892 samples\n",
            "Client 51: 2892 samples\n",
            "Client 48: 2892 samples\n",
            "Client 58: 2892 samples\n",
            "Client 62: 2892 samples\n",
            "Client 129: 2892 samples\n",
            "Client 136: 2892 samples\n",
            "Client 49: 2892 samples\n",
            "Client 5: 2892 samples\n",
            "Client 103: 2892 samples\n",
            "Client 94: 2892 samples\n",
            "Client 124: 2892 samples\n",
            "Client 93: 2892 samples\n",
            "Client 99: 2892 samples\n",
            "Client 69: 2892 samples\n",
            "Client 25: 2892 samples\n",
            "Client 3: 2892 samples\n",
            "Client 34: 2892 samples\n",
            "Client 4: 2892 samples\n",
            "Client 104: 2892 samples\n",
            "Client 102: 2892 samples\n",
            "Client 72: 2892 samples\n",
            "Client 56: 2892 samples\n",
            "Client 119: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 9... \tAverage Loss: 2.31\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf05ae88aaf7409fbaf4aeffe0bdfc4b",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.141748\n",
            "\n",
            "F1 Score: 0.090447 (macro) 0.330370 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.387 (20010.0/51704.0)\n",
            "Clients: 71/143 -> [ 80  93 129  65   1   7  74  35 130  53  17 104  95  58  96  75  28   0\n",
            " 128  40  89   6  48  18  87 131  14  70 114  79  47  27  26 123  77   8\n",
            "  67  29  73 108  92  59 122  52 142  33  15  45 126  36  88 115  25 109\n",
            "  44  78 133 141 135  54  71 121  11  12  23 111  85 118 119   4 125]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "66247c967e4f4c84b5351c8284086135",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 80: 2892 samples\n",
            "Client 93: 2892 samples\n",
            "Client 129: 2892 samples\n",
            "Client 65: 2892 samples\n",
            "Client 1: 2892 samples\n",
            "Client 7: 2892 samples\n",
            "Client 74: 2892 samples\n",
            "Client 35: 2892 samples\n",
            "Client 130: 2892 samples\n",
            "Client 53: 2892 samples\n",
            "Client 17: 2892 samples\n",
            "Client 104: 2892 samples\n",
            "Client 95: 2892 samples\n",
            "Client 58: 2892 samples\n",
            "Client 96: 2892 samples\n",
            "Client 75: 2892 samples\n",
            "Client 28: 2892 samples\n",
            "Client 0: 2892 samples\n",
            "Client 128: 2892 samples\n",
            "Client 40: 2892 samples\n",
            "Client 89: 2892 samples\n",
            "Client 6: 2892 samples\n",
            "Client 48: 2892 samples\n",
            "Client 18: 2892 samples\n",
            "Client 87: 2892 samples\n",
            "Client 131: 2892 samples\n",
            "Client 14: 2892 samples\n",
            "Client 70: 2892 samples\n",
            "Client 114: 2892 samples\n",
            "Client 79: 2892 samples\n",
            "Client 47: 2892 samples\n",
            "Client 27: 2892 samples\n",
            "Client 26: 2892 samples\n",
            "Client 123: 2892 samples\n",
            "Client 77: 2892 samples\n",
            "Client 8: 2892 samples\n",
            "Client 67: 2892 samples\n",
            "Client 29: 2892 samples\n",
            "Client 73: 2892 samples\n",
            "Client 108: 2892 samples\n",
            "Client 92: 2892 samples\n",
            "Client 59: 2892 samples\n",
            "Client 122: 2892 samples\n",
            "Client 52: 2892 samples\n",
            "Client 142: 2892 samples\n",
            "Client 33: 2892 samples\n",
            "Client 15: 2892 samples\n",
            "Client 45: 2892 samples\n",
            "Client 126: 2892 samples\n",
            "Client 36: 2892 samples\n",
            "Client 88: 2892 samples\n",
            "Client 115: 2892 samples\n",
            "Client 25: 2892 samples\n",
            "Client 109: 2892 samples\n",
            "Client 44: 2892 samples\n",
            "Client 78: 2892 samples\n",
            "Client 133: 2892 samples\n",
            "Client 141: 2892 samples\n",
            "Client 135: 2892 samples\n",
            "Client 54: 2892 samples\n",
            "Client 71: 2892 samples\n",
            "Client 121: 2892 samples\n",
            "Client 11: 2892 samples\n",
            "Client 12: 2892 samples\n",
            "Client 23: 2892 samples\n",
            "Client 111: 2892 samples\n",
            "Client 85: 2892 samples\n",
            "Client 118: 2892 samples\n",
            "Client 119: 2892 samples\n",
            "Client 4: 2892 samples\n",
            "Client 125: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 10... \tAverage Loss: 2.268\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6dc5840ae7a8477a9b2a6295419de753",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.108632\n",
            "\n",
            "F1 Score: 0.098451 (macro) 0.344847 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.397 (20541.0/51704.0)\n",
            "Clients: 71/143 -> [137  17  51  74  47 113 101   8  40 132  60 120 107  43  96  48 131   7\n",
            "  33 139  16  31 125  12   0  58   6  22 140  45  94  98  59  97   5 112\n",
            " 128  23 118  93  34  63  64  28 110  83  70 138  26  19  75 102  72 105\n",
            " 116  10  35  25  20  30  76 109  82 103  67  91 130  86   3  95  71]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "935c8ad8f57d4434a43e9afc070f9764",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 137: 2892 samples\n",
            "Client 17: 2892 samples\n",
            "Client 51: 2892 samples\n",
            "Client 74: 2892 samples\n",
            "Client 47: 2892 samples\n",
            "Client 113: 2892 samples\n",
            "Client 101: 2892 samples\n",
            "Client 8: 2892 samples\n",
            "Client 40: 2892 samples\n",
            "Client 132: 2892 samples\n",
            "Client 60: 2892 samples\n",
            "Client 120: 2892 samples\n",
            "Client 107: 2892 samples\n",
            "Client 43: 2892 samples\n",
            "Client 96: 2892 samples\n",
            "Client 48: 2892 samples\n",
            "Client 131: 2892 samples\n",
            "Client 7: 2892 samples\n",
            "Client 33: 2892 samples\n",
            "Client 139: 2892 samples\n",
            "Client 16: 2892 samples\n",
            "Client 31: 2892 samples\n",
            "Client 125: 2892 samples\n",
            "Client 12: 2892 samples\n",
            "Client 0: 2892 samples\n",
            "Client 58: 2892 samples\n",
            "Client 6: 2892 samples\n",
            "Client 22: 2892 samples\n",
            "Client 140: 2892 samples\n",
            "Client 45: 2892 samples\n",
            "Client 94: 2892 samples\n",
            "Client 98: 2892 samples\n",
            "Client 59: 2892 samples\n",
            "Client 97: 2892 samples\n",
            "Client 5: 2892 samples\n",
            "Client 112: 2892 samples\n",
            "Client 128: 2892 samples\n",
            "Client 23: 2892 samples\n",
            "Client 118: 2892 samples\n",
            "Client 93: 2892 samples\n",
            "Client 34: 2892 samples\n",
            "Client 63: 2892 samples\n",
            "Client 64: 2892 samples\n",
            "Client 28: 2892 samples\n",
            "Client 110: 2892 samples\n",
            "Client 83: 2892 samples\n",
            "Client 70: 2892 samples\n",
            "Client 138: 2892 samples\n",
            "Client 26: 2892 samples\n",
            "Client 19: 2892 samples\n",
            "Client 75: 2892 samples\n",
            "Client 102: 2892 samples\n",
            "Client 72: 2892 samples\n",
            "Client 105: 2892 samples\n",
            "Client 116: 2892 samples\n",
            "Client 10: 2892 samples\n",
            "Client 35: 2892 samples\n",
            "Client 25: 2892 samples\n",
            "Client 20: 2892 samples\n",
            "Client 30: 2892 samples\n",
            "Client 76: 2892 samples\n",
            "Client 109: 2892 samples\n",
            "Client 82: 2892 samples\n",
            "Client 103: 2892 samples\n",
            "Client 67: 2892 samples\n",
            "Client 91: 2892 samples\n",
            "Client 130: 2892 samples\n",
            "Client 86: 2892 samples\n",
            "Client 3: 2892 samples\n",
            "Client 95: 2892 samples\n",
            "Client 71: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 11... \tAverage Loss: 2.244\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "de6b610dde2a463eba211655587a5b46",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.081649\n",
            "\n",
            "F1 Score: 0.100926 (macro) 0.348966 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.403 (20846.0/51704.0)\n",
            "Clients: 71/143 -> [  8 141  73 116  15   0  18   3 122 100  50  13 102  53  91  23  42 111\n",
            "  36  98  72  88 103  30  75  48  85  31  52 120   5  41 101  84 128 131\n",
            " 105 104  82  34  60 117  19  24  61  58 109  86  62 133 132  96  35   2\n",
            "  43 118  38  57  76  11  20  68 115  74 108 142 124 130 134  55 123]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4a70953b246d43a9a7ff654665200f5a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 8: 2892 samples\n",
            "Client 141: 2892 samples\n",
            "Client 73: 2892 samples\n",
            "Client 116: 2892 samples\n",
            "Client 15: 2892 samples\n",
            "Client 0: 2892 samples\n",
            "Client 18: 2892 samples\n",
            "Client 3: 2892 samples\n",
            "Client 122: 2892 samples\n",
            "Client 100: 2892 samples\n",
            "Client 50: 2892 samples\n",
            "Client 13: 2892 samples\n",
            "Client 102: 2892 samples\n",
            "Client 53: 2892 samples\n",
            "Client 91: 2892 samples\n",
            "Client 23: 2892 samples\n",
            "Client 42: 2892 samples\n",
            "Client 111: 2892 samples\n",
            "Client 36: 2892 samples\n",
            "Client 98: 2892 samples\n",
            "Client 72: 2892 samples\n",
            "Client 88: 2892 samples\n",
            "Client 103: 2892 samples\n",
            "Client 30: 2892 samples\n",
            "Client 75: 2892 samples\n",
            "Client 48: 2892 samples\n",
            "Client 85: 2892 samples\n",
            "Client 31: 2892 samples\n",
            "Client 52: 2892 samples\n",
            "Client 120: 2892 samples\n",
            "Client 5: 2892 samples\n",
            "Client 41: 2892 samples\n",
            "Client 101: 2892 samples\n",
            "Client 84: 2892 samples\n",
            "Client 128: 2892 samples\n",
            "Client 131: 2892 samples\n",
            "Client 105: 2892 samples\n",
            "Client 104: 2892 samples\n",
            "Client 82: 2892 samples\n",
            "Client 34: 2892 samples\n",
            "Client 60: 2892 samples\n",
            "Client 117: 2892 samples\n",
            "Client 19: 2892 samples\n",
            "Client 24: 2892 samples\n",
            "Client 61: 2892 samples\n",
            "Client 58: 2892 samples\n",
            "Client 109: 2892 samples\n",
            "Client 86: 2892 samples\n",
            "Client 62: 2892 samples\n",
            "Client 133: 2892 samples\n",
            "Client 132: 2892 samples\n",
            "Client 96: 2892 samples\n",
            "Client 35: 2892 samples\n",
            "Client 2: 2892 samples\n",
            "Client 43: 2892 samples\n",
            "Client 118: 2892 samples\n",
            "Client 38: 2892 samples\n",
            "Client 57: 2892 samples\n",
            "Client 76: 2892 samples\n",
            "Client 11: 2892 samples\n",
            "Client 20: 2892 samples\n",
            "Client 68: 2892 samples\n",
            "Client 115: 2892 samples\n",
            "Client 74: 2892 samples\n",
            "Client 108: 2892 samples\n",
            "Client 142: 2892 samples\n",
            "Client 124: 2892 samples\n",
            "Client 130: 2892 samples\n",
            "Client 134: 2892 samples\n",
            "Client 55: 2892 samples\n",
            "Client 123: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 12... \tAverage Loss: 2.221\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "653372fff2e74c7ba1169a6055234948",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.052279\n",
            "\n",
            "F1 Score: 0.105824 (macro) 0.358565 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.407 (21037.0/51704.0)\n",
            "Clients: 71/143 -> [ 55 118  20  49 140  15 115  22  88 105   3  78  73  35 125  64 122 129\n",
            " 119  89 108 128  43  16  21  57  13 139  32  48 138 131  42 100  91  40\n",
            "   4  30  37   1  18   9  17  46  93 127  79  70  74  92  59  72 112  87\n",
            "  58  50  80 130  27  14 113  60   2  63  54 123  76 103  69  77 114]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2fd382edfd6e48d787fbcba33296da99",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 55: 2892 samples\n",
            "Client 118: 2892 samples\n",
            "Client 20: 2892 samples\n",
            "Client 49: 2892 samples\n",
            "Client 140: 2892 samples\n",
            "Client 15: 2892 samples\n",
            "Client 115: 2892 samples\n",
            "Client 22: 2892 samples\n",
            "Client 88: 2892 samples\n",
            "Client 105: 2892 samples\n",
            "Client 3: 2892 samples\n",
            "Client 78: 2892 samples\n",
            "Client 73: 2892 samples\n",
            "Client 35: 2892 samples\n",
            "Client 125: 2892 samples\n",
            "Client 64: 2892 samples\n",
            "Client 122: 2892 samples\n",
            "Client 129: 2892 samples\n",
            "Client 119: 2892 samples\n",
            "Client 89: 2892 samples\n",
            "Client 108: 2892 samples\n",
            "Client 128: 2892 samples\n",
            "Client 43: 2892 samples\n",
            "Client 16: 2892 samples\n",
            "Client 21: 2892 samples\n",
            "Client 57: 2892 samples\n",
            "Client 13: 2892 samples\n",
            "Client 139: 2892 samples\n",
            "Client 32: 2892 samples\n",
            "Client 48: 2892 samples\n",
            "Client 138: 2892 samples\n",
            "Client 131: 2892 samples\n",
            "Client 42: 2892 samples\n",
            "Client 100: 2892 samples\n",
            "Client 91: 2892 samples\n",
            "Client 40: 2892 samples\n",
            "Client 4: 2892 samples\n",
            "Client 30: 2892 samples\n",
            "Client 37: 2892 samples\n",
            "Client 1: 2892 samples\n",
            "Client 18: 2892 samples\n",
            "Client 9: 2892 samples\n",
            "Client 17: 2892 samples\n",
            "Client 46: 2892 samples\n",
            "Client 93: 2892 samples\n",
            "Client 127: 2892 samples\n",
            "Client 79: 2892 samples\n",
            "Client 70: 2892 samples\n",
            "Client 74: 2892 samples\n",
            "Client 92: 2892 samples\n",
            "Client 59: 2892 samples\n",
            "Client 72: 2892 samples\n",
            "Client 112: 2892 samples\n",
            "Client 87: 2892 samples\n",
            "Client 58: 2892 samples\n",
            "Client 50: 2892 samples\n",
            "Client 80: 2892 samples\n",
            "Client 130: 2892 samples\n",
            "Client 27: 2892 samples\n",
            "Client 14: 2892 samples\n",
            "Client 113: 2892 samples\n",
            "Client 60: 2892 samples\n",
            "Client 2: 2892 samples\n",
            "Client 63: 2892 samples\n",
            "Client 54: 2892 samples\n",
            "Client 123: 2892 samples\n",
            "Client 76: 2892 samples\n",
            "Client 103: 2892 samples\n",
            "Client 69: 2892 samples\n",
            "Client 77: 2892 samples\n",
            "Client 114: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 13... \tAverage Loss: 2.189\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1ed02bb7b9934278a6553fab554c024f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.028951\n",
            "\n",
            "F1 Score: 0.110174 (macro) 0.363117 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.413 (21360.0/51704.0)\n",
            "Clients: 71/143 -> [  6  68  65  63  18  98  72  27  20  45 137  31  51  71  32   8  50 105\n",
            "  66 142 141 112 118 109 110 130 101  29  56  28 127  62  23  58 116  78\n",
            "  52  12  69  30  49 123 131  25  53 124   9  59  38  24  61 122  84  33\n",
            "  85  60  14 102  95 115 100 132  54  90  94  42   5   4  48  81 121]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9eea886b790f414c9fef022cb337e991",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Client 6: 2892 samples\n",
            "Client 68: 2892 samples\n",
            "Client 65: 2892 samples\n",
            "Client 63: 2892 samples\n",
            "Client 18: 2892 samples\n",
            "Client 98: 2892 samples\n",
            "Client 72: 2892 samples\n",
            "Client 27: 2892 samples\n",
            "Client 20: 2892 samples\n",
            "Client 45: 2892 samples\n",
            "Client 137: 2892 samples\n",
            "Client 31: 2892 samples\n",
            "Client 51: 2892 samples\n",
            "Client 71: 2892 samples\n",
            "Client 32: 2892 samples\n",
            "Client 8: 2892 samples\n",
            "Client 50: 2892 samples\n",
            "Client 105: 2892 samples\n",
            "Client 66: 2892 samples\n",
            "Client 142: 2892 samples\n",
            "Client 141: 2892 samples\n",
            "Client 112: 2892 samples\n",
            "Client 118: 2892 samples\n",
            "Client 109: 2892 samples\n",
            "Client 110: 2892 samples\n",
            "Client 130: 2892 samples\n",
            "Client 101: 2892 samples\n",
            "Client 29: 2892 samples\n",
            "Client 56: 2892 samples\n",
            "Client 28: 2892 samples\n",
            "Client 127: 2892 samples\n",
            "Client 62: 2892 samples\n",
            "Client 23: 2892 samples\n",
            "Client 58: 2892 samples\n",
            "Client 116: 2892 samples\n",
            "Client 78: 2892 samples\n",
            "Client 52: 2892 samples\n",
            "Client 12: 2892 samples\n",
            "Client 69: 2892 samples\n",
            "Client 30: 2892 samples\n",
            "Client 49: 2892 samples\n",
            "Client 123: 2892 samples\n",
            "Client 131: 2892 samples\n",
            "Client 25: 2892 samples\n",
            "Client 53: 2892 samples\n",
            "Client 124: 2892 samples\n",
            "Client 9: 2892 samples\n",
            "Client 59: 2892 samples\n",
            "Client 38: 2892 samples\n",
            "Client 24: 2892 samples\n",
            "Client 61: 2892 samples\n",
            "Client 122: 2892 samples\n",
            "Client 84: 2892 samples\n",
            "Client 33: 2892 samples\n",
            "Client 85: 2892 samples\n",
            "Client 60: 2892 samples\n",
            "Client 14: 2892 samples\n",
            "Client 102: 2892 samples\n",
            "Client 95: 2892 samples\n",
            "Client 115: 2892 samples\n",
            "Client 100: 2892 samples\n",
            "Client 132: 2892 samples\n",
            "Client 54: 2892 samples\n",
            "Client 90: 2892 samples\n",
            "Client 94: 2892 samples\n",
            "Client 42: 2892 samples\n",
            "Client 5: 2892 samples\n",
            "Client 4: 2892 samples\n",
            "Client 48: 2892 samples\n",
            "Client 81: 2892 samples\n",
            "Client 121: 2892 samples\n",
            "\n",
            "Computing median...\n",
            "Round: 14... \tAverage Loss: 2.17\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c3724038cedc457e8e0a2c68b422747d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 2.007242\n",
            "\n",
            "F1 Score: 0.117510 (macro) 0.366815 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.419 (21644.0/51704.0)\n",
            "Clients: 71/143 -> [114  87 138  14  57  22  68  18  51  72  40  79  65  39  46  67  83  16\n",
            " 139 104  52  80  20 106   1 105  26  33  53  94 102  44 133  43  37  12\n",
            "  69   4  30 135 100  27   9 132  59 125  99  34 103  36 107  54  58 101\n",
            "  56  63 115  32 112  60 109  88  45  89  82   3  13   8  70  74 131]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "beb52f0e650546129bc9378a62e7d3c9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
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            "\n",
            "Computing median...\n",
            "Round: 15... \tAverage Loss: 2.145\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32a376e5546e438cb55e24694a3c5ed1",
              "version_minor": 0,
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            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 1.983722\n",
            "\n",
            "F1 Score: 0.122407 (macro) 0.375355 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.423 (21859.0/51704.0)\n",
            "Clients: 71/143 -> [105 107  65   1  95  34  10 120  76  22  59  75  30  98  17  91 117  33\n",
            "  81  58  20 123   4  71  35   2  92  41  77  12 140  63 103  94  21  83\n",
            "  73 126 138 113 101  99  32  18 121  25 104   8 137  43  78  60  42  28\n",
            "  15  55  46  38 131  57   9 111 116 132 112 135  64  39   6  88  40]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "26ea04b5d36049748583ad336116d2f5",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
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            "\n",
            "Computing median...\n",
            "Round: 16... \tAverage Loss: 2.125\n",
            "running validation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cf16db815f4645129a5fe0cb0ab4ecde",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=2586.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test Loss: 1.964554\n",
            "\n",
            "F1 Score: 0.130020 (macro) 0.382373 (weighted) %\n",
            "\n",
            "\n",
            "Final Test  Accuracy: 0.428 (22134.0/51704.0)\n",
            "Clients: 71/143 -> [ 12  21  76   0  65 135   8  61  78  11  74  50 101 122  90 107  91 130\n",
            "  42 133  17  66  46  60  57 141 102  34  85 103  56 124  49 105  98  82\n",
            "  47   4 138   3 109 112 113  97  86  95  87  58  13  38  72  80 119 139\n",
            "  89  24  25 114 132 123  37  41 140 127 128  68   1 129   6  77 110]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9c95a22eb6846f1b48eabbb0b7fcb58",
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              "HBox(children=(FloatProgress(value=0.0, max=71.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "us-HifGq3Uhf"
      },
      "source": [
        "exp_log[title] = {\n",
        "    'history': exp_history,\n",
        "    'hyperparams': hparams.__dict__\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDGpo4ug33dN"
      },
      "source": [
        "df = None\n",
        "for i, e in enumerate(exp_history):\n",
        "    if i == 0:\n",
        "        df = pd.json_normalize(e)\n",
        "        continue\n",
        "    df = df + pd.json_normalize(e)\n",
        "    \n",
        "df_avg = df / len(exp_history)\n",
        "avg_history = df_avg.to_dict(orient='records')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hf77BQAD36Eq"
      },
      "source": [
        "plot_scores(history=avg_history, exp_id=exp_id, title=title, suffix='IID')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJClynRJ38Dh"
      },
      "source": [
        "plot_losses(history=avg_history, exp_id=exp_id, title=title, suffix='IID')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qCp5Sjd7FBF"
      },
      "source": [
        "with open(f'{EXP_DIR}/results_iid.pkl', 'wb') as file:\n",
        "    pickle.dump(exp_log, file)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaoYWkWgqvUQ"
      },
      "source": [
        "### Non-IID"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eL9vsQDb7Oqr"
      },
      "source": [
        "exp_log = dict()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pILgaho8qKgF"
      },
      "source": [
        "data_dict, test_ds = noniid_partition(corpus, seq_length=seq_length, val_split=True)\n",
        "\n",
        "total_clients = len(data_dict.keys())\n",
        "'Total users:', total_clients"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3o7qgBcqKX_"
      },
      "source": [
        "hparams = Hyperparameters(total_clients)\n",
        "hparams.__dict__"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VANr1h0Pq51N"
      },
      "source": [
        "# Sweeping parameter\n",
        "PARAM_NAME = 'clients_fraction'\n",
        "PARAM_VALUE = hparams.C\n",
        "exp_id = f'{PARAM_NAME}/{PARAM_VALUE}'\n",
        "exp_id"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXgYFIyZ4ipm"
      },
      "source": [
        "EXP_DIR = f'{BASE_DIR}/{exp_id}'\n",
        "os.makedirs(EXP_DIR, exist_ok=True)\n",
        "\n",
        "# tb_logger = SummaryWriter(log_dir)\n",
        "# print(f'TBoard logger created at: {log_dir}')\n",
        "\n",
        "title = 'LSTM FedMed on Non-IID'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vnv7UaE0q6dG"
      },
      "source": [
        "def run_experiment(run_id):\n",
        "\n",
        "    shakespeare_lstm = ShakespeareLSTM(input_dim=seq_length,  \n",
        "                                   embedding_dim=embedding_dim,  \n",
        "                                   hidden_dim=hidden_dim,\n",
        "                                   classes=num_classes,\n",
        "                                   lstm_layers=lstm_layers,\n",
        "                                   dropout=dropout,\n",
        "                                   batch_first=True\n",
        "                                   )\n",
        "\n",
        "    if torch.cuda.is_available():\n",
        "        shakespeare_lstm.cuda()\n",
        "    \n",
        "    test_history = []\n",
        "\n",
        "    lstm_iid_trained, test_history = training(shakespeare_lstm,\n",
        "                                            hparams.rounds, hparams.batch_size, hparams.lr,\n",
        "                                            None,\n",
        "                                            data_dict,\n",
        "                                            test_ds,\n",
        "                                            hparams.C, hparams.K, hparams.E, hparams.M,\n",
        "                                            title, \"green\",\n",
        "                                            corpus, # classes\n",
        "                                            history=test_history,\n",
        "                                            # tb_logger=tb_writer\n",
        "                                            )\n",
        "    \n",
        "\n",
        "    final_scores = testing(lstm_iid_trained, test_ds, batch_size * 2, nn.CrossEntropyLoss(), len(corpus), corpus)\n",
        "    print(f'\\n\\n========================================================\\n\\n')\n",
        "    print(f'Final scores for Exp {run_id} \\n {final_scores}')\n",
        "\n",
        "    log = {\n",
        "        'history': test_history,\n",
        "        'hyperparams': hparams.__dict__\n",
        "    }\n",
        "\n",
        "    with open(f'{EXP_DIR}/results_niid_{run_id}.pkl', 'wb') as file:\n",
        "        pickle.dump(log, file)\n",
        "\n",
        "    return test_history\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pLbVBwVq6Uw"
      },
      "source": [
        "exp_history = list()\n",
        "for run_id in range(2):  # TOTAL RUNS\n",
        "    print(f'============== RUNNING EXPERIMENT #{run_id} ==============')\n",
        "    exp_history.append(run_experiment(run_id))\n",
        "    print(f'\\n\\n========================================================\\n\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5F38z5C4qw9"
      },
      "source": [
        "exp_log[title] = {\n",
        "    'history': exp_history,\n",
        "    'hyperparams': hparams.__dict__\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inIGn3Mh4qpO"
      },
      "source": [
        "df = None\n",
        "for i, e in enumerate(exp_history):\n",
        "    if i == 0:\n",
        "        df = pd.json_normalize(e)\n",
        "        continue\n",
        "    df = df + pd.json_normalize(e)\n",
        "    \n",
        "df_avg = df / len(exp_history)\n",
        "avg_history = df_avg.to_dict(orient='records')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z8ngcls64qjc"
      },
      "source": [
        "plot_scores(history=avg_history, exp_id=exp_id, title=title, suffix='nonIID')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GR9vjtYs4qBX"
      },
      "source": [
        "plot_losses(history=avg_history, exp_id=exp_id, title=title, suffix='nonIID')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "adK1OTS-40Z8"
      },
      "source": [
        "### Pickle Experiment Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5nl-hsa4zqw"
      },
      "source": [
        "with open(f'{EXP_DIR}/results_niid.pkl', 'wb') as file:\n",
        "    pickle.dump(exp_log, file)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}