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- sega_learn.svm.baseSVM.BaseSVM(builtins.object)
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- LinearSVC
- LinearSVR
class LinearSVC(sega_learn.svm.baseSVM.BaseSVM) |
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LinearSVC(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, numba=False)
LinearSVC is a linear Support Vector Classifier (SVC) implementation that uses gradient descent for optimization.
It supports binary and multi-class classification using a one-vs-rest strategy.
Attributes:
C (float): Regularization parameter. Default is 1.0.
tol (float): Tolerance for stopping criteria. Default is 1e-4.
max_iter (int): Maximum number of iterations for gradient descent. Default is 1000.
learning_rate (float): Learning rate for gradient descent. Default is 0.01.
numba (bool): Whether to use Numba-accelerated computations. Default is False.
w (ndarray): Weight vector for the linear model.
b (float): Bias term for the linear model.
numba_available (bool): Indicates if Numba is available for use.
Methods:
__init__(self, C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, numba=False):
Initializes the LinearSVC instance with hyperparameters and checks for Numba availability.
_fit(self, X, y):
Fits the LinearSVC model to the training data using gradient descent.
_predict_binary(self, X):
Predicts class labels {-1, 1} for binary classification.
_predict_multiclass(self, X):
Predicts class labels for multi-class classification using one-vs-rest strategy.
decision_function(self, X):
Computes raw decision function values before thresholding.
_score_binary(self, X, y):
Computes the mean accuracy of predictions for binary classification.
_score_multiclass(self, X, y):
Computes the mean accuracy of predictions for multi-class classification.
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- Method resolution order:
- LinearSVC
- sega_learn.svm.baseSVM.BaseSVM
- builtins.object
Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, numba=False)
- Initializes the LinearSVC instance with hyperparameters and checks for Numba availability.
Args:
C: (float) - Regularization parameter. Default is 1.0.
tol: (float) - Tolerance for stopping criteria. Default is 1e-4.
max_iter: (int) - Maximum number of iterations for gradient descent. Default is 1000.
learning_rate: (float) - Learning rate for gradient descent. Default is 0.01.
numba: (bool) - Whether to use Numba-accelerated computations. Default is False.
- decision_function(self, X)
- Compute raw decision function values before thresholding.
Args:
X (array-like of shape (n_samples, n_features)): Input samples.
Returns:
scores (array of shape (n_samples,)): Decision function values.
Methods inherited from sega_learn.svm.baseSVM.BaseSVM:
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted (for sklearn compatibility).
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- predict(self, X)
- Predicts class labels for input samples.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
predicted_labels: (ndarray of shape (n_samples,)) - Predicted class labels.
- score(self, X, y)
- Computes the mean accuracy of the model on the given test data.
Args:
X: (array-like of shape (n_samples, n_features)) - Test samples.
y: (array-like of shape (n_samples,)) - True class labels.
Returns:
score: (float) - Mean accuracy of predictions.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors inherited from sega_learn.svm.baseSVM.BaseSVM:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class LinearSVR(sega_learn.svm.baseSVM.BaseSVM) |
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LinearSVR(C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, epsilon=0.1, numba=False)
LinearSVR: A linear Support Vector Regression (SVR) model using epsilon-insensitive loss.
This class implements a linear SVR model with support for mini-batch gradient descent
and optional acceleration using Numba. It is designed for regression tasks and uses
epsilon-insensitive loss to handle errors within a specified margin.
Attributes:
C (float): Regularization parameter. Default is 1.0.
tol (float): Tolerance for stopping criteria. Default is 1e-4.
max_iter (int): Maximum number of iterations for gradient descent. Default is 1000.
learning_rate (float): Learning rate for gradient descent. Default is 0.01.
epsilon (float): Epsilon parameter for epsilon-insensitive loss. Default is 0.1.
numba (bool): Whether to use Numba for acceleration. Default is False.
w (ndarray): Weight vector of the model.
b (float): Bias term of the model.
numba_available (bool): Indicates if Numba is available for acceleration.
X_train (ndarray): Training data used for fitting.
y_train (ndarray): Target values used for fitting.
Methods:
__init__(self, C=1.0, tol=1e-4, max_iter=1000, learning_rate=0.01, epsilon=0.1, numba=False):
Initialize the LinearSVR model with specified hyperparameters.
_fit(self, X, y):
Fit the LinearSVR model to the training data using mini-batch gradient descent.
predict(self, X):
Predict continuous target values for input samples.
decision_function(self, X):
Compute raw decision function values for input samples.
score(self, X, y):
Compute the coefficient of determination (R² score) for the model's predictions.
Raises:
ValueError: If a non-linear kernel is specified, as LinearSVR only supports linear kernels.
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- Method resolution order:
- LinearSVR
- sega_learn.svm.baseSVM.BaseSVM
- builtins.object
Methods defined here:
- __init__(self, C=1.0, tol=0.0001, max_iter=1000, learning_rate=0.01, epsilon=0.1, numba=False)
- Initializes the LinearSVR instance with hyperparameters and checks for Numba availability.
Args:
C: (float) - Regularization parameter. Default is 1.0.
tol: (float) - Tolerance for stopping criteria. Default is 1e-4.
max_iter: (int) - Maximum number of iterations for gradient descent. Default is 1000.
learning_rate: (float) - Learning rate for gradient descent. Default is 0.01.
epsilon: (float) - Epsilon parameter for epsilon-insensitive loss. Default is 0.1.
numba: (bool) - Whether to use Numba-accelerated computations. Default is False.
Returns:
None
- decision_function(self, X)
- Compute raw decision function values.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
scores: (array of shape (n_samples,)) - Predicted values.
- predict(self, X)
- Predict continuous target values for input samples.
Args:
X: (array-like of shape (n_samples, n_features)) - Input samples.
Returns:
y_pred: (array of shape (n_samples,)) - Predicted values.
- score(self, X, y)
- Compute the coefficient of determination (R² score).
Args:
X: (array-like of shape (n_samples, n_features)) - Test samples.
y: (array-like of shape (n_samples,)) - True target values.
Returns:
score: (float) - R² score of predictions.
Methods inherited from sega_learn.svm.baseSVM.BaseSVM:
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted (for sklearn compatibility).
Returns:
fitted: (bool) - True if the model has been fitted, otherwise False.
- fit(self, X, y=None)
- Fits the SVM model to the training data.
Args:
X: (array-like of shape (n_samples, n_features)) - Training vectors.
y: (array-like of shape (n_samples,)) - Target values. Default is None.
Returns:
self: (BaseSVM) - The fitted instance.
- get_params(self, deep=True)
- Retrieves the hyperparameters of the model.
Args:
deep: (bool) - If True, returns parameters of subobjects as well. Default is True.
Returns:
params: (dict) - Dictionary of hyperparameter names and values.
- set_params(self, **parameters)
- Sets the hyperparameters of the model.
Args:
**parameters: (dict) - Hyperparameter names and values.
Returns:
self: (BaseSVM) - The updated estimator instance.
Data descriptors inherited from sega_learn.svm.baseSVM.BaseSVM:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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