From 045c8637f61c8e93a923dd6953daea707623a34f Mon Sep 17 00:00:00 2001
From: Sophie Bessac <sophie.bessac@ecl20.ec-lyon.fr>
Date: Tue, 7 Nov 2023 00:32:35 +0100
Subject: [PATCH] knn implementation

---
 .gitignore      |   4 ++
 README.md       |  95 +++++++++------------------------------------
 knn.py          |  92 +++++++++++++++++++++++++++++++++++++++++++
 read_cifar.py   | 101 ++++++++++++++++++++++++++++++++++++++++++++++++
 results/knn.png | Bin 0 -> 28568 bytes
 5 files changed, 216 insertions(+), 76 deletions(-)
 create mode 100644 .gitignore
 create mode 100644 knn.py
 create mode 100644 read_cifar.py
 create mode 100644 results/knn.png

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..f81f24f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+/data
+/.vscode
+/__pycache__
+MOD_4_6_TD_1.ipynb
\ No newline at end of file
diff --git a/README.md b/README.md
index 28e49af..7b0b361 100644
--- a/README.md
+++ b/README.md
@@ -1,92 +1,35 @@
 # Image classification
+This project is an image classification program. It is trained and tested using the CIFAR dataset. Two methods are used to perform image classification: k-nearest neighbours and neural networks.
 
+## Description
+First, the CIFAR dataset is loaded and prepared to be used. Then, the k-nearest neighbours method is used to perform image classification. Finally, neural networks are used to perform image classification.
 
+### Prepare the CIFAR dataset
+Each image is made 32x32 pixels. Each pixel is in color, and therefore has 3 numbers representing it.
+For each batch we have the following parameters:
+- batch_size = 10000,
+- data_size = 32x32x3 = 3072.
+For the entire datasets (5 train batches and 1 test batch), we have the following parameters:
+- batch_size = 60000,
+- data_size = 32x32x3 = 3072.
+Each batch is unpickled. All batches are concatenated into a matrix data and a list labels. They are then suffled and split to create training and test datasets.
 
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.ec-lyon.fr/sbessac/image-classification.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.ec-lyon.fr/sbessac/image-classification/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
+### k-nearest neighbours
 
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
 
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
+### Features
 
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
+### Background
 
 ## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
+
+## Requirements
 
 ## Usage
 Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
 
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
+## Author
+Sophie BESSAC
 
 ## License
 For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
diff --git a/knn.py b/knn.py
new file mode 100644
index 0000000..20fe832
--- /dev/null
+++ b/knn.py
@@ -0,0 +1,92 @@
+# imports
+import numpy as np
+
+## QUESTION 1
+def distance_matrix(m1, m2):
+    """Compute the L2 Euclidean distance matrix between 2 matrixes.
+    Args:
+        m1: A np.float32 array of shape n x m.
+        m2: A np.float32 array of shape p x m.
+    Returns:
+        dists: A np.float32 array of shape p x n, the L2 Euclidean distance matrix between m1 and m2.
+    """
+    # Check shapes
+    m1 = m1.reshape(m1.shape[0], -1)
+    m2 = m2.reshape(m2.shape[0], -1)
+    # Compute distance matrix
+    dists = np.sqrt(np.sum(m1**2, axis=1) + np.sum(m2**2, axis=1, keepdims=True) - 2 * np.dot(m2, m1.T))
+    return dists
+
+## QUESTION 2
+def knn_predict(dists, labels_train, k):
+    """Predict the label of data_test using k-nearest neighbors.
+    Args:
+        dists: A np.float32 array of shape (batch_size x (1-split)) x (batch_size x split), the L2 Euclidean distance matrix between data_train and data_test, where each row is an example we wish to predict label for.
+        labels_train: A np.int64 array of shape batch_size x split, the labels of the training set.
+        k: The number of neighbours to use for classification.
+    """
+    num_test = dists.shape[0]
+    # lets make sure that the output type matches the input type
+    Ypred = np.zeros(num_test, dtype=np.int64)
+    # loop over all test rows
+    for i in range(num_test):
+        nearest_y = []
+        nearest_y = labels_train[np.argsort(dists[i])[:k]]
+        Ypred[i] = np.argmax(np.bincount(nearest_y))
+    return Ypred
+
+## QUESTION 3
+def evaluate_knn(data_train, labels_train, data_test, labels_test, k):
+    """Evaluate the performance of k-nearest neighbors on the given dataset.
+    Args:
+        data_train: A np.float32 array of shape split x batch_size x data_size, the training set.
+        labels_train: A np.int64 array of shape split x batch_size, the labels of the training set.
+        data_test: A np.float32 array of shape (1-split) x batch_size x data_size, the test set.
+        labels_test: A np.int64 array of shape (1-split) x batch_size, the labels of the test set.
+        k: The number of neighbours to use for classification.
+    Returns:
+        accuracy: The accuracy of k-nearest neighbors on the given dataset.
+    """
+    dists = distance_matrix(data_train, data_test)
+    y_test_pred = knn_predict(dists, labels_train, k)
+    accuracy = np.mean(y_test_pred == labels_test)
+    return accuracy
+
+if __name__ == "__main__":
+    # test distance_matrix
+    m1 = np.array([[1, 1], [1, 0], [0, 1]])
+    m2 = np.array([[0, 2], [1, 1]])
+    print(distance_matrix(m1, m2))
+    print(distance_matrix(m1, m2).shape)
+
+    # test knn_predict
+    dists = np.array([[1, 2, 3], [4, 5, 6]])
+    labels_train = np.array([1, 2, 3])
+    k = 2
+    print(knn_predict(dists, labels_train, k))
+
+    ## QUESTION 4
+    import read_cifar as rc
+    import matplotlib.pyplot as plt
+    data, labels = rc.read_cifar(r"data\cifar-10-batches-py")
+
+    def plot_accuracy(data, labels, split_factor=0.9, n=[1,20]):
+        """Plot the variation of the accuracy as a function of k from n[0] to n[1].
+        Save the plot as an image named knn.png in the directory results.
+        
+        Args:
+            split_factor: The ratio of the size of the validation set over the size of the whole dataset. Must be a float between 0 and 1.
+            n: A list of two integers, the first and the last value of k.
+        """
+        data_train, labels_train, data_test, labels_test = rc.split_dataset(data, labels, split_factor)
+        accuracies = []
+        for k in range(n[0], n[1] + 1):
+            accuracy = evaluate_knn(data_train, labels_train, data_test, labels_test, k)
+            accuracies.append(accuracy)
+        plt.plot(range(n[0], n[1] + 1), accuracies)
+        plt.xlabel("k")
+        plt.ylabel("accuracy")
+        plt.savefig(r"results\knn.png")
+        plt.show()
+    
+    plot_accuracy(data, labels)
diff --git a/read_cifar.py b/read_cifar.py
new file mode 100644
index 0000000..0dee379
--- /dev/null
+++ b/read_cifar.py
@@ -0,0 +1,101 @@
+# imports
+import numpy as np
+import pickle
+
+## QUESTION 2
+def unpickle(file):
+    # Source: https://www.cs.toronto.edu/~kriz/cifar.html
+    with open(file, 'rb') as fo:
+        dict = pickle.load(fo, encoding='bytes')
+    return dict
+
+def read_cifar_batch(file):
+    """Read a batch of the CIFAR dataset.
+    Args:
+        path: The path to the batch file.
+    Returns:
+        data: A np.float32 array of size batch_size x data_size, where batch_size is the number of available images in the batch, and data_size is the dimension of these images (number of numerical values describing one image).
+        labels: A list of labels of size batch_size whose values correspond to the class code of the data of the same index in the matrix data. labels must be a np.int64 array.
+    """
+    dict = unpickle(file)
+    data = np.array(dict[b"data"], dtype=np.float32)
+    labels = np.array(dict[b"labels"], dtype=np.int64)
+    return data, labels
+
+## QUESTION 3
+def read_cifar(path):
+    """Read the whole CIFAR dataset.
+    Args:
+        path: The directory containing the CIFAR dataset.
+    Returns:
+        data: A np.float32 array of shape batch_size x data_size where batch_size is the number of available images in all_batches (including test_batch), and data_size is the the dimension of these data (number of numerical values describing the data).
+        labels: A np.int64 array of size batch_size whose values correspond to the class code of the data of the same index in the matrix data.
+    """
+    data_batches = []
+    label_batches = []
+    for i in range(1, 6):
+        data_batch, label_batch = read_cifar_batch(path + r"\data_batch_" + str(i))
+        data_batches.append(data_batch)
+        label_batches.append(label_batch)
+    test_batch, test_label = read_cifar_batch(path + r"\test_batch")
+    data_batches.append(test_batch)
+    label_batches.append(test_label)
+    data = np.concatenate(data_batches, axis=0)
+    labels = np.concatenate(label_batches, axis=0)
+    return data, labels
+
+## QUESTION 4
+def split_dataset(data, labels, split):
+    """Split the dataset into a training set and a validation set. Data are shuffled before splitting.
+    Args:
+        data: A np.float32 array of shape batch_size x data_size where batch_size is the number of available images in all_batches (including test_batch), and data_size is the the dimension of these images (number of numerical values describing each image).
+        labels: A np.int64 array of size batch_size whose values correspond to the class code of the data of the same index in the matrix data.
+        split: A float between 0 and 1 which determines the split factor of the training set with respect to the test set. For example, if split = 0.8, then 80% of the data will be used for training and 20% for validation.
+    Returns:
+        data_train: A np.float32 array of shape split x batch_size x data_size, the training set.
+        labels_train: A np.int64 array of shape split x batch_size, the labels of the training set.
+        data_test: A np.float32 array of shape (1 - split) x batch_size x data_size, the validation set.
+        labels_test: A np.int64 array of shape (1 - split) x batch_size, the labels of the validation set.
+    """
+    assert 0 <= split <= 1 # split must be between 0 and 1
+    data_size = data.shape[0]
+    # shuffle data and labels
+    indices = np.arange(data_size)
+    np.random.shuffle(indices)
+    data = data[indices]
+    labels = labels[indices]
+    # split data and labels
+    split_index = int(data_size * split)
+    data_train = data[:split_index]
+    labels_train = labels[:split_index]
+    data_test = data[split_index:]
+    labels_test = labels[split_index:]
+    return data_train, labels_train, data_test, labels_test
+
+if __name__ == "__main__":
+    dict = unpickle(r"data\cifar-10-batches-py\data_batch_1")
+    print(dict.keys())
+    print(dict[b"data"].shape)
+    print(dict[b"labels"][:10])
+
+    data, labels = read_cifar_batch(r"data\cifar-10-batches-py\data_batch_1")
+    print(data.dtype)
+    print(labels.dtype)
+    print(data.shape)
+    print(labels.shape)
+
+    data, labels = read_cifar(r"data\cifar-10-batches-py")
+    print(data.dtype)
+    print(labels.dtype)
+    print(data.shape)
+    print(labels.shape)
+
+    data_train, labels_train, data_test, labels_test = split_dataset(data, labels, 0.8)
+    print(data_train.dtype)
+    print(labels_train.dtype)
+    print(data_train.shape)
+    print(labels_train.shape)
+    print(data_test.dtype)
+    print(labels_test.dtype)
+    print(data_test.shape)
+    print(labels_test.shape)
diff --git a/results/knn.png b/results/knn.png
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-- 
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