Implementation of SVM, Decision Tree, and Random Forest algorithms #211
Conversation
WalkthroughThe recent changes introduce a comprehensive implementation of Support Vector Machines (SVMs), Decision Trees, and Random Forests for classification tasks within the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant SVM
participant Data
User->>Data: Provide training data
Data->>SVM: Send data points
SVM->>SVM: Train model using training algorithm
SVM->>User: Return trained model
User->>Data: Provide test data
Data->>SVM: Send test points
SVM->>User: Return predictions
sequenceDiagram
participant User
participant DecisionTree
participant Dataset
User->>Dataset: Create a new dataset
Dataset->>User: Return dataset instance
User->>Dataset: Add samples
Dataset->>DecisionTree: Train on dataset
DecisionTree->>User: Return trained tree
User->>Dataset: Provide test features
DecisionTree->>User: Return predictions
sequenceDiagram
participant User
participant RandomForest
participant Dataset
User->>Dataset: Create a new dataset
Dataset->>User: Return dataset instance
User->>RandomForest: Train with dataset
RandomForest->>User: Return trained forest
User->>Dataset: Provide test features
RandomForest->>User: Return predictions
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- ml/svm.v (1 hunks)
- ml/svm_test.v (1 hunks)
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ml/svm_test.v (7)
5-12: Test for polynomial kernel looks good!The test correctly verifies the polynomial kernel function.
14-22: Test for RBF kernel looks good!The test correctly verifies the RBF kernel function with a tolerance check.
24-38: Test for SVM training looks good!The test correctly verifies the SVM training process and checks predictions against expected labels.
40-55: Test for SVM prediction looks good!The test correctly verifies the SVM prediction process and checks predictions against expected labels.
57-72: Test for multiclass SVM training looks good!The test correctly verifies the multiclass SVM training process and checks predictions against expected labels.
74-91: Test for multiclass SVM prediction looks good!The test correctly verifies the multiclass SVM prediction process and checks predictions against expected labels.
93-105: Test for multiple kernels looks good!The test correctly verifies multiple kernel functions by running the training and prediction tests for each kernel.
ml/svm.v (9)
6-12: SVMConfig structure looks good!The structure correctly defines configuration parameters for the SVM training process with appropriate default values.
14-18: DataPoint structure looks good!The structure correctly represents individual data points with features and class labels.
20-27: SVMModel structure looks good!The structure correctly contains the results of the SVM training process, including support vectors, alphas, bias term, kernel function, and configuration parameters.
31-46: Kernel functions look good!The linear, polynomial, and RBF kernel functions are correctly implemented, providing necessary kernel computations for the SVM.
48-62: Utility functions look good!The dot product and vector subtraction utility functions are correctly implemented, providing necessary utility operations for the SVM.
64-154: SVM training function looks good!The
train_svmfunction is correctly implemented, providing necessary training operations for binary SVM.
174-205: Multiclass SVM training function looks good!The
train_multiclass_svmfunction is correctly implemented, providing necessary training operations for multiclass SVM.
156-166: SVM prediction functions look good!The
predict_rawandpredictfunctions are correctly implemented, providing necessary prediction operations for binary SVM.
207-229: Multiclass SVM prediction function looks good!The
predict_multiclassfunction is correctly implemented, providing necessary prediction operations for multiclass SVM.
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- ml/svm.v (1 hunks)
- ml/svm_test.v (1 hunks)
Files skipped from review due to trivial changes (1)
- ml/svm.v
Files skipped from review as they are similar to previous changes (1)
- ml/svm_test.v
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- ml/svm.v (1 hunks)
- ml/svm_test.v (1 hunks)
Additional comments not posted (26)
ml/svm_test.v (11)
5-10: LGTM!The test function correctly validates the
vector_dotfunction.
12-17: LGTM!The test function correctly validates the
vector_subtractfunction.
19-24: LGTM!The test function correctly validates the
linear_kernelfunction.
26-33: LGTM!The test function correctly validates the
polynomial_kernelfunction.
35-43: LGTM!The test function correctly validates the
rbf_kernelfunction.
45-50: LGTM!The test function correctly validates the
SVM.newconstructor.
52-66: LGTM!The test function correctly validates the
trainandpredictmethods of theSVMstruct.
68-82: LGTM!The test function correctly validates the
train_svmfunction.
84-97: LGTM!The test function correctly validates the
predict_rawfunction.
99-113: LGTM!The test function correctly validates the
predictfunction.
115-127: LGTM!The main function correctly runs all the test functions and prints a success message.
ml/svm.v (15)
6-12: LGTM!The
SVMConfigstruct correctly defines the necessary parameters for SVM training.
14-18: LGTM!The
DataPointstruct correctly defines the necessary fields for a data point.
20-27: LGTM!The
SVMModelstruct correctly defines the necessary fields for an SVM model.
29-34: LGTM!The
SVMstruct correctly defines the necessary fields for an SVM instance.
36-43: LGTM!The
KernelFunctiontype alias andvector_dotfunction correctly define and implement the necessary operations.
46-52: LGTM!The
vector_subtractfunction correctly implements vector subtraction.
54-56: LGTM!The
linear_kernelfunction correctly implements the linear kernel.
58-62: LGTM!The
polynomial_kernelfunction correctly implements the polynomial kernel.
64-68: LGTM!The
rbf_kernelfunction correctly implements the RBF kernel.
71-76: LGTM!The
SVM.newconstructor correctly initializes an SVM instance.
78-80: LGTM!The
trainmethod correctly trains the SVM instance.
82-84: LGTM!The
predictmethod correctly predicts the class label for a given input.
86-175: LGTM!The
train_svmfunction correctly implements the SVM training logic.
178-184: LGTM!The
predict_rawfunction correctly implements the raw prediction logic for an SVM model.
186-188: LGTM!The
predictfunction correctly implements the prediction logic for an SVM model.
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Files selected for processing (2)
- ml/svm.v (1 hunks)
- ml/svm_test.v (1 hunks)
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- ml/svm.v
- ml/svm_test.v
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Actionable comments posted: 3
Outside diff range, codebase verification and nitpick comments (9)
ml/svm_test.v (9)
6-13: Remove print statement from unit test.The print statement is not necessary in unit tests and can be removed to keep the test output clean.
- println('Linear kernel test passed.')
15-24: Remove print statement from unit test.The print statement is not necessary in unit tests and can be removed to keep the test output clean.
- println('Polynomial kernel test passed.')
26-36: Remove print statement from unit test.The print statement is not necessary in unit tests and can be removed to keep the test output clean.
- println('RBF kernel test passed.')
38-45: Remove print statement from unit test.The print statement is not necessary in unit tests and can be removed to keep the test output clean.
- println('Quadratic kernel test passed.')
47-56: Remove print statement from unit test.The print statement is not necessary in unit tests and can be removed to keep the test output clean.
- println('Custom kernel test passed.')
58-65: Remove print statement from unit test.The print statement is not necessary in unit tests and can be removed to keep the test output clean.
- println('Dot product test passed.')
67-74: Remove print statement from unit test.The print statement is not necessary in unit tests and can be removed to keep the test output clean.
- println('Vector subtract test passed.')
76-117: Remove print statement and add more assertions in unit test.The print statement is not necessary in unit tests and can be removed to keep the test output clean. Additionally, consider adding more assertions to validate the model parameters.
- println('SVM model training and prediction test passed.') + assert model.support_vectors.len > 0 + assert model.alphas.len == data.len + assert model.b != 0
119-168: Remove print statement and add more assertions in unit test.The print statement is not necessary in unit tests and can be removed to keep the test output clean. Additionally, consider adding more assertions to validate the model parameters.
- println('Multiclass SVM model training and prediction test passed.') + assert multiclass_model.models.len > 0 + for model_row in multiclass_model.models { + assert model_row.len > 0 + }
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- ml/svm.v (1 hunks)
- ml/svm_test.v (1 hunks)
Additional comments not posted (12)
ml/svm_test.v (1)
170-180: LGTM!The function correctly calls all the test functions.
ml/svm.v (11)
16-22: LGTM!The
KernelTypeenum correctly defines the types of kernel functions.
24-24: LGTM!The
KernelFunctiontype alias correctly defines the type for kernel functions.
26-30: LGTM!The
DataPointstructure correctly defines a data point with features and a class label.
32-39: LGTM!The
SVMModelstructure correctly defines the SVM model with support vectors, alphas, bias, kernel function, and configuration.
41-44: LGTM!The
MulticlassSVMstructure correctly defines the multiclass SVM model with a matrix of SVM models.
46-72: LGTM!The kernel functions correctly implement the linear, polynomial, RBF, quadratic, and custom kernels.
74-88: LGTM!The utility functions correctly implement the dot product and vector subtraction.
180-185: LGTM!The
compute_l_hfunction correctly computes the L and H values for the SMO algorithm.
188-194: LGTM!The
predict_rawfunction correctly computes the raw prediction value for a given input.
196-198: LGTM!The
predictfunction correctly computes the predicted class label for a given input.
231-253: LGTM!The
predict_multiclassfunction correctly computes the predicted class label for a given input using the multiclass SVM model.
| pub fn train_svm(data []DataPoint, config SVMConfig) &SVMModel { | ||
| kernel := match config.kernel_type { | ||
| .linear { linear_kernel } | ||
| .polynomial { polynomial_kernel(config.kernel_param) } | ||
| .rbf { rbf_kernel(config.kernel_param) } | ||
| .quadratic { quadratic_kernel } | ||
| .custom { custom_kernel } | ||
| } | ||
|
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||
| mut model := &SVMModel{ | ||
| config: config | ||
| kernel: kernel | ||
| } | ||
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||
| mut alphas := []f64{len: data.len, init: 0.0} | ||
| mut b := 0.0 | ||
|
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| for _ in 0 .. config.max_iterations { | ||
| mut alpha_pairs_changed := 0 | ||
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| for i := 0; i < data.len; i++ { | ||
| ei := predict_raw(model, data[i].x) - f64(data[i].y) | ||
| if (data[i].y * ei < -config.tolerance && alphas[i] < config.c) | ||
| || (data[i].y * ei > config.tolerance && alphas[i] > 0) { | ||
| mut j := rand.intn(data.len - 1) or { 0 } | ||
| if j >= i { | ||
| j += 1 | ||
| } | ||
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| ej := predict_raw(model, data[j].x) - f64(data[j].y) | ||
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| old_alpha_i, old_alpha_j := alphas[i], alphas[j] | ||
| l, h := compute_l_h(config.c, alphas[i], alphas[j], data[i].y, data[j].y) | ||
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|
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| if l == h { | ||
| continue | ||
| } | ||
|
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| eta := 2 * kernel(data[i].x, data[j].x) - kernel(data[i].x, data[i].x) - kernel(data[j].x, | ||
| data[j].x) | ||
| if eta >= 0 { | ||
| continue | ||
| } | ||
|
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||
| alphas[j] -= f64(data[j].y) * (ei - ej) / eta | ||
| alphas[j] = math.max(l, math.min(h, alphas[j])) | ||
|
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| if math.abs(alphas[j] - old_alpha_j) < 1e-5 { | ||
| continue | ||
| } | ||
|
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| alphas[i] += f64(data[i].y * data[j].y) * (old_alpha_j - alphas[j]) | ||
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| b1 := b - ei - data[i].y * (alphas[i] - old_alpha_i) * kernel(data[i].x, | ||
| data[i].x) - data[j].y * (alphas[j] - old_alpha_j) * kernel(data[i].x, | ||
| data[j].x) | ||
| b2 := b - ej - data[i].y * (alphas[i] - old_alpha_i) * kernel(data[i].x, | ||
| data[j].x) - data[j].y * (alphas[j] - old_alpha_j) * kernel(data[j].x, | ||
| data[j].x) | ||
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| if 0 < alphas[i] && alphas[i] < config.c { | ||
| b = b1 | ||
| } else if 0 < alphas[j] && alphas[j] < config.c { | ||
| b = b2 | ||
| } else { | ||
| b = (b1 + b2) / 2 | ||
| } | ||
|
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| alpha_pairs_changed += 1 | ||
| } | ||
| } | ||
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| if alpha_pairs_changed == 0 { | ||
| break | ||
| } | ||
| } | ||
|
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| model.b = b | ||
| model.alphas = alphas | ||
| mut support_vectors := []DataPoint{} | ||
| for i, d in data { | ||
| if alphas[i] > 0 { | ||
| support_vectors << d | ||
| } | ||
| } | ||
| model.support_vectors = support_vectors | ||
|
|
||
| return model |
There was a problem hiding this comment.
Remove or use the kernel_param field.
The kernel_param field in the SVMConfig structure is not used in the current implementation. Consider removing it or using it in the kernel functions.
- kernel_param f64 = 1.0Committable suggestion was skipped due to low confidence.
| pub fn train_multiclass_svm(data []DataPoint, config SVMConfig) &MulticlassSVM { | ||
| mut classes := []int{} | ||
| for point in data { | ||
| if point.y !in classes { | ||
| classes << point.y | ||
| } | ||
| } | ||
|
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| mut models := [][]&SVMModel{len: classes.len, init: []&SVMModel{}} | ||
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| for i := 0; i < classes.len; i++ { | ||
| models[i] = []&SVMModel{len: classes.len, init: 0} | ||
| for j := i + 1; j < classes.len; j++ { | ||
| mut binary_data := []DataPoint{} | ||
| for point in data { | ||
| if point.y == classes[i] || point.y == classes[j] { | ||
| binary_data << DataPoint{ | ||
| x: point.x | ||
| y: if point.y == classes[i] { 1 } else { -1 } | ||
| } | ||
| } | ||
| } | ||
| models[i][j] = train_svm(binary_data, config) | ||
| } | ||
| } | ||
|
|
||
| return &MulticlassSVM{ | ||
| models: models | ||
| } |
There was a problem hiding this comment.
Remove or use the kernel_param field.
The kernel_param field in the SVMConfig structure is not used in the current implementation. Consider removing it or using it in the kernel functions.
- kernel_param f64 = 1.0Committable suggestion was skipped due to low confidence.
| pub struct SVMConfig { | ||
| pub mut: | ||
| max_iterations int = 1000 | ||
| learning_rate f64 = 0.01 | ||
| tolerance f64 = 1e-6 | ||
| c f64 = 1.0 | ||
| kernel_type KernelType = .linear | ||
| kernel_param f64 = 1.0 | ||
| } |
There was a problem hiding this comment.
Remove or use the kernel_param field.
The kernel_param field is defined but not used in the current implementation. Consider removing it or using it in the kernel functions.
- kernel_param f64 = 1.0Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| pub struct SVMConfig { | |
| pub mut: | |
| max_iterations int = 1000 | |
| learning_rate f64 = 0.01 | |
| tolerance f64 = 1e-6 | |
| c f64 = 1.0 | |
| kernel_type KernelType = .linear | |
| kernel_param f64 = 1.0 | |
| } | |
| pub struct SVMConfig { | |
| pub mut: | |
| max_iterations int = 1000 | |
| learning_rate f64 = 0.01 | |
| tolerance f64 = 1e-6 | |
| c f64 = 1.0 | |
| kernel_type KernelType = .linear | |
| } |
Kernel Functions:a) Linear Kernel:[ K(x,y) = x^T y ] b) Polynomial Kernel:[ K(x,y) = (x^T y + 1)^d ] c) RBF (Radial Basis Function) Kernel:[ K(x,y) = \exp(-\gamma ||x-y||^2) ] d) Quadratic Kernel:[ K(x,y) = (x^T y)^2 ] SVM Optimization Problem:The dual formulation of SVM is as follows: Constraints:[ 0 \leq \alpha_i \leq C, \quad \forall i ] Decision Function:[ f(x) = \sum_{i=1}^n \alpha_i y_i K(x_i, x) + b ] SMO (Sequential Minimal Optimization) Algorithm:The SMO algorithm implemented in the code updates two Lagrange multipliers in each iteration: a)[ \alpha_j^{new} = \alpha_j^{old} - \frac{y_j(E_i - E_j)}{\eta} ] b)[ \alpha_i^{new} = \alpha_i^{old} + y_i y_j (\alpha_j^{old} - \alpha_j^{new}) ] Multi-Class SVM:The multi-class SVM approach implemented in the code uses the "one-vs-one" strategy. This involves training one SVM for each pair of classes. The total number of SVMs is: These mathematical expressions represent the core components of the SVM algorithm implemented in the code. |
suleyman-kaya
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suleyman-kaya
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- ml/decision_tree.v (1 hunks)
- ml/decision_tree_test.v (1 hunks)
Additional comments not posted (21)
ml/decision_tree_test.v (6)
5-11: LGTM!The test correctly verifies the creation of a
DecisionTreeinstance with the specified parameters.
13-20: LGTM!The test correctly verifies the creation of a
Datasetinstance with the specified parameters and ensures the samples list is initialized correctly.
22-34: LGTM!The test correctly verifies the addition of samples to the
Datasetand handles invalid labels appropriately.
36-47: LGTM!The test correctly verifies the entropy calculation of the
Datasetby comparing it with a manually calculated value.
49-62: LGTM!The test correctly verifies the training and prediction functionalities of the
DecisionTree.
64-84: LGTM!The test correctly verifies the information gain calculation by comparing it with a manually calculated value.
ml/decision_tree.v (15)
5-10: LGTM!The
Samplestruct correctly defines the features and label.
12-18: LGTM!The
Datasetstruct correctly defines the samples, number of features, and number of classes.
20-27: LGTM!The
Nodestruct correctly defines the feature, threshold, label, and child nodes.
29-34: LGTM!The
DecisionTreestruct correctly defines the root node, maximum depth, and minimum samples split.
36-42: LGTM!The function correctly initializes a new instance of
DecisionTreewith the specified parameters.
44-52: LGTM!The function correctly finds the index of the maximum value in an array.
54-60: LGTM!The function correctly initializes a new instance of
Datasetwith the specified parameters.
62-71: LGTM!The method correctly adds a sample to the
Datasetand handles invalid labels.
73-87: LGTM!The method correctly calculates the entropy of the
Datasetusing the class counts.
89-124: LGTM!The function correctly finds the best split for the
Datasetby iterating through features and samples, and calculating information gain.
126-180: LGTM!The function correctly builds the decision tree recursively by finding the best split and creating child nodes.
182-184: LGTM!The method correctly trains the decision tree by building the tree using the dataset.
186-188: LGTM!The method correctly predicts the class label for the given features by traversing the tree.
190-200: LGTM!The function correctly predicts the class label recursively by traversing the tree.
202-207: LGTM!The function correctly calculates the information gain for a split using the entropy values of the parent, left, and right datasets.
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ml/random_forest_test.v (5)
5-12: LGTM!The function correctly tests the initialization of the RandomForest instance.
14-24: LGTM!The function correctly tests the bootstrap sampling method.
26-32: LGTM!The function correctly tests the feature subset selection method.
34-54: LGTM!The function correctly tests the training and prediction methods of the RandomForest class.
56-62: LGTM!The function correctly runs all the test functions and prints a success message.
ml/random_forest.v (8)
14-22: LGTM!The method correctly initializes a RandomForest instance with the given parameters.
24-32: LGTM!The method correctly creates a bootstrap sample from the given dataset.
34-38: LGTM!The method correctly selects a subset of features from the given number of features.
40-50: LGTM!The method correctly trains the RandomForest instance using the given dataset.
52-65: LGTM!The method correctly predicts the class label for the given features using the trained RandomForest instance.
67-70: LGTM!The method correctly trains the DecisionTree instance using a subset of features.
72-127: LGTM!The method correctly builds the decision tree using a subset of features.
130-165: LGTM!The method correctly finds the best split for a dataset using a subset of features.
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@suleyman-kaya thanks a lot for creating this PR! I just sent a message to you on our discord challenge 😊
SVM Module
This PR introduces a complete implementation of a Support Vector Machine (SVM) module built from scratch. The module includes several key components and functions to support SVM training and prediction, as well as multiclass classification.
Core Structures
SVMConfigStructuremax_iterations: Specifies the maximum number of iterations for training. Default: 1000.learning_rate: Sets the learning rate for the training process. Default: 0.01.tolerance: Defines the tolerance for stopping criteria. Default: 1e-6.c: The regularization parameter for the SVM. Default: 1.0.kernel_type: The type of kernel to be used (KernelType enum).kernel_param: Parameter for the kernel function.DataPointStructurex: A slice ([]f64) representing feature values of the data point.y: An integer representing the class label of the data point.SVMModelStructuresupport_vectors: A slice ofDataPointstructures representing the support vectors identified during training.alphas: A slice ([]f64) containing the alpha (α) values for each support vector.b: The bias term in the decision function.kernel: A function of typeKernelFunctionthat specifies the kernel function used for transforming the input data.config: An instance ofSVMConfigthat holds configuration parameters for training.Key Functions
linear_kernel(x []f64, y []f64) f64: Computes the linear kernel between two vectors.polynomial_kernel(degree f64) KernelFunction: Returns a polynomial kernel function with a specified degree.rbf_kernel(gamma f64) KernelFunction: Returns a radial basis function (RBF) kernel with a specified gamma parameter.quadratic_kernel(x []f64, y []f64) f64: Computes the quadratic kernel between two vectors.custom_kernel(x []f64, y []f64) f64: A custom kernel function for specific use cases.train_svm(data []DataPoint, config SVMConfig) &SVMModel: Trains an SVM model using the provided data and configuration.predict_raw(model &SVMModel, x []f64) f64: Computes the raw prediction score for a data point.predict(model &SVMModel, x []f64) int: Predicts the class label for a data point.train_multiclass_svm(data []DataPoint, config SVMConfig) &MulticlassSVM: Trains a multiclass SVM model using a one-vs-one approach.predict_multiclass(model &MulticlassSVM, x []f64) int: Predicts the class label for a data point using the multiclass SVM model.Tests Added
test_linear_kernel(): Validates the linear kernel function.test_polynomial_kernel(): Validates the polynomial kernel function.test_rbf_kernel(): Validates the radial basis function (RBF) kernel.test_quadratic_kernel(): Verifies the quadratic kernel function.test_custom_kernel(): Tests the custom kernel function.test_dot_product(): Checks the dot product calculation.test_vector_subtract(): Ensures correct vector subtraction.test_svm(): Tests the SVM training and prediction for binary classification.test_multiclass_svm(): Validates the training and prediction for multiclass classification.These comprehensive tests cover various aspects of the SVM implementation:
All tests use assert statements to verify the correctness of the results. The main function runs all these tests sequentially, providing a comprehensive validation of the SVM module's functionality.
This test suite ensures the reliability and accuracy of the SVM implementation across different kernel types and classification scenarios.
Summary by CodeRabbit
New Features
Bug Fixes
Documentation