Visualize the results of the division of the data set

README.md

@@ -622,6 +622,61 @@ Client 2         Samples of labels:  [(0, 75), (1, 107), (3, 130), (7, 291), (8,
     Finish generating dataset.
 </details>
 
+## Visualize data set partitioning results 
+Visualize the types of data sets assigned to each client graphically with the help of visualization tools
+
+### Example ``MNIST``
+- MNIST
+    ```
+    cd ./dataset
+    python python show_data_distribution.py -dsname=MNIST  # dsname represents the dataset name, e.g. [MNIST,FashionMNIST,Cifar10,Cifar100,Flowers102]
+    ```
+
+The output of `show_data_distribution.py -dsname=MNISt`
+```
+The client owns the data classification
+
+Client Id:   0 | Dataset Classes: {0, 1, 4, 5, 7, 8, 9}
+Client Id:   1 | Dataset Classes: {0, 2, 5, 6, 8, 9}
+Client Id:   2 | Dataset Classes: {0, 9, 3, 6}
+Client Id:   3 | Dataset Classes: {0, 8, 4, 7}
+Client Id:   4 | Dataset Classes: {0, 1, 3, 5, 6, 8, 9}
+Client Id:   5 | Dataset Classes: {1, 3, 4, 8, 9}
+Client Id:   6 | Dataset Classes: {1, 2, 3, 6, 8, 9}
+Client Id:   7 | Dataset Classes: {1, 2, 3, 5, 7, 8}
+Client Id:   8 | Dataset Classes: {0, 1}
+Client Id:   9 | Dataset Classes: {0, 1, 2, 4, 6}
+Client Id:  10 | Dataset Classes: {0, 1, 2, 3, 4, 5}
+Client Id:  11 | Dataset Classes: {2, 3, 5}
+Client Id:  12 | Dataset Classes: {0, 1, 2, 5}
+Client Id:  13 | Dataset Classes: {1, 2, 4, 5, 7}
+Client Id:  14 | Dataset Classes: {5, 7}
+Client Id:  15 | Dataset Classes: {0, 3, 5, 6, 7, 8}
+Client Id:  16 | Dataset Classes: {0}
+Client Id:  17 | Dataset Classes: {1, 2, 3, 4, 5, 7, 8}
+Client Id:  18 | Dataset Classes: {0, 5, 6}
+Client Id:  19 | Dataset Classes: {0, 1, 2, 3, 4, 9}
+```
+```
+Each type of label is distributed across that client
+
+Label ID:   0 - zero | Client ID: [0, 1, 2, 3, 4, 8, 9, 10, 12, 15, 16, 18, 19]
+Label ID:    1 - one | Client ID: [0, 4, 5, 6, 7, 8, 9, 10, 12, 13, 17, 19]
+Label ID:    2 - two | Client ID: [1, 6, 7, 9, 10, 11, 12, 13, 17, 19]
+Label ID:  3 - three | Client ID: [2, 4, 5, 6, 7, 10, 11, 15, 17, 19]
+Label ID:   4 - four | Client ID: [0, 3, 5, 9, 10, 13, 17, 19]
+Label ID:   5 - five | Client ID: [0, 1, 4, 7, 10, 11, 12, 13, 14, 15, 17, 18]
+Label ID:    6 - six | Client ID: [1, 2, 4, 6, 9, 15, 18]
+Label ID:  7 - seven | Client ID: [0, 3, 7, 13, 14, 15, 17]
+Label ID:  8 - eight | Client ID: [0, 1, 3, 4, 5, 6, 7, 15, 17]
+Label ID:   9 - nine | Client ID: [0, 1, 2, 4, 5, 6, 19]
+```
+Display Partition Results  
+
+![](./dataset/images/mnist_partition.png)
+
+
+
 ## Models
 - for MNIST and Fashion-MNIST
 

dataset/dataset_label/cat_to_name.json

@@ -0,0 +1,104 @@
+{
+  "21": "fire lily",
+  "3": "canterbury bells",
+  "45": "bolero deep blue",
+  "1": "pink primrose",
+  "34": "mexican aster",
+  "27": "prince of wales feathers",
+  "7": "moon orchid",
+  "16": "globe-flower",
+  "25": "grape hyacinth",
+  "26": "corn poppy",
+  "79": "toad lily",
+  "39": "siam tulip",
+  "24": "red ginger",
+  "67": "spring crocus",
+  "35": "alpine sea holly",
+  "32": "garden phlox",
+  "10": "globe thistle",
+  "6": "tiger lily",
+  "93": "ball moss",
+  "33": "love in the mist",
+  "9": "monkshood",
+  "102": "blackberry lily",
+  "14": "spear thistle",
+  "19": "balloon flower",
+  "100": "blanket flower",
+  "13": "king protea",
+  "49": "oxeye daisy",
+  "15": "yellow iris",
+  "61": "cautleya spicata",
+  "31": "carnation",
+  "64": "silverbush",
+  "68": "bearded iris",
+  "63": "black-eyed susan",
+  "69": "windflower",
+  "62": "japanese anemone",
+  "20": "giant white arum lily",
+  "38": "great masterwort",
+  "4": "sweet pea",
+  "86": "tree mallow",
+  "101": "trumpet creeper",
+  "42": "daffodil",
+  "22": "pincushion flower",
+  "2": "hard-leaved pocket orchid",
+  "54": "sunflower",
+  "66": "osteospermum",
+  "70": "tree poppy",
+  "85": "desert-rose",
+  "99": "bromelia",
+  "87": "magnolia",
+  "5": "english marigold",
+  "92": "bee balm",
+  "28": "stemless gentian",
+  "97": "mallow",
+  "57": "gaura",
+  "40": "lenten rose",
+  "47": "marigold",
+  "59": "orange dahlia",
+  "48": "buttercup",
+  "55": "pelargonium",
+  "36": "ruby-lipped cattleya",
+  "91": "hippeastrum",
+  "29": "artichoke",
+  "71": "gazania",
+  "90": "canna lily",
+  "18": "peruvian lily",
+  "98": "mexican petunia",
+  "8": "bird of paradise",
+  "30": "sweet william",
+  "17": "purple coneflower",
+  "52": "wild pansy",
+  "84": "columbine",
+  "12": "colt's foot",
+  "11": "snapdragon",
+  "96": "camellia",
+  "23": "fritillary",
+  "50": "common dandelion",
+  "44": "poinsettia",
+  "53": "primula",
+  "72": "azalea",
+  "65": "californian poppy",
+  "80": "anthurium",
+  "76": "morning glory",
+  "37": "cape flower",
+  "56": "bishop of llandaff",
+  "60": "pink-yellow dahlia",
+  "82": "clematis",
+  "58": "geranium",
+  "75": "thorn apple",
+  "41": "barbeton daisy",
+  "95": "bougainvillea",
+  "43": "sword lily",
+  "83": "hibiscus",
+  "78": "lotus lotus",
+  "88": "cyclamen",
+  "94": "foxglove",
+  "81": "frangipani",
+  "74": "rose",
+  "89": "watercress",
+  "73": "water lily",
+  "46": "wallflower",
+  "77": "passion flower",
+  "51": "petunia"
+}

dataset/show_data_distribution.py

@@ -0,0 +1,126 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2024/4/11
+# By Qiantao Yang
+
+import argparse
+import numpy as np
+import os
+import sys
+import random
+import json
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from matplotlib import pyplot as plt
+
+
+def get_data(dataset_name):
+    client_dict = []
+    if dataset_name == 'Cifar10':
+        dir_path = "Cifar10/"
+        if not os.path.exists(dir_path):
+            os.makedirs(dir_path)
+        with open(dir_path + 'config.json') as f:
+            client_dict = json.load(f)
+
+        transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+        dataset = torchvision.datasets.CIFAR10(root=dir_path + "rawdata", train=False, download=True,
+                                               transform=transform)
+        dataset_label = np.array(dataset.classes)
+    elif dataset_name == 'Cifar100':
+        dir_path = "Cifar100/"
+        if not os.path.exists(dir_path):
+            os.makedirs(dir_path)
+        with open(dir_path + 'config.json') as f:
+            client_dict = json.load(f)
+
+        transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+        dataset = torchvision.datasets.CIFAR100(root=dir_path + "rawdata", train=False, download=True,
+                                                transform=transform)
+        dataset_label = np.array(dataset.classes)
+    elif dataset_name == 'FashionMNIST':
+        dir_path = "FashionMNIST/"
+        if not os.path.exists(dir_path):
+            os.makedirs(dir_path)
+        with open(dir_path + 'config.json') as f:
+            client_dict = json.load(f)
+
+        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
+        dataset = torchvision.datasets.FashionMNIST(root=dir_path + "rawdata", train=False, download=True,
+                                                    transform=transform)
+        dataset_label = np.array(dataset.classes)
+        print(dataset_label)
+
+    elif dataset_name == 'Flowers102':
+        dir_path = "Flowers102/"
+        if not os.path.exists(dir_path):
+            os.makedirs(dir_path)
+        with open(dir_path + 'config.json') as f:
+            client_dict = json.load(f)
+        with open('dataset_label/cat_to_name.json') as f:
+            labels = json.load(f)
+        dataset_label = []
+        for i in range(1, len(labels.keys()) + 1):
+            dataset_label.append(labels[str(i)])
+    elif dataset_name == 'MNIST':
+        dir_path = "MNIST/"
+        if not os.path.exists(dir_path):
+            os.makedirs(dir_path)
+        with open(dir_path + 'config.json') as f:
+            client_dict = json.load(f)
+
+        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
+        dataset = torchvision.datasets.MNIST(root=dir_path + "rawdata", train=False, download=True, transform=transform)
+        dataset_label = np.array(dataset.classes)
+    else:
+        print('There are only a few data sets, e.g. [MNIST, FashionMNIST, Cifar10, Cifar100, Flowers102]')
+
+    return dataset_label, client_dict
+
+
+def show_data_distribution(args):
+    dataset_label, client_dict = get_data(args.datasetname)
+
+    label_distribution = [[] for _ in range(len(dataset_label))]
+
+    client_num = client_dict['num_clients']
+    client_data = client_dict['Size of samples for labels in clients']
+
+    client_labels = {clientid: [] for clientid in range(client_num)}
+
+    for c_id, c_data in enumerate(client_data):
+        for data in c_data:
+            label_distribution[data[0]].append(c_id)
+            client_labels[c_id].append(data[0])
+    print('The client owns the data classification')
+    for key, value in client_labels.items():
+        print('Client Id: {:>3} | Dataset Classes: {}'.format(key, set(value)))
+
+    print('\n Each type of label is distributed across that client ')
+    for label_id, data in enumerate(label_distribution):
+        print('Label ID: {:>10} | Client ID: {}'.format(dataset_label[label_id], data))
+
+    plt.figure(figsize=(20, 6))
+    plt.hist(label_distribution, stacked=True, bins=np.arange(-0.5, client_num + 2, 1),
+             label=dataset_label,
+             rwidth=0.5)
+    plt.xticks(np.arange(client_num), ["%d" % c_id for c_id in range(client_num)])
+    plt.ylabel("Number of samples")
+    plt.xlabel("Client ID")
+    plt.legend()
+    plt.title("Dataset {} Distribution: {} Non-IID: {}".format(args.datasetname, client_dict['partition'],
+                                                               client_dict['partition']))
+    plt.show()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # general
+    parser.add_argument("-dsname", "--datasetname", type=str, default="MNIST",
+                        help="input dataset name, e.g. [MNIST,FashionMNIST,Cifar10,Cifar100,Flowers102]")
+
+    args = parser.parse_args()
+
+    show_data_distribution(args)