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- builtins.object
-
- sega_learn.linear_models.classifiers.LinearDiscriminantAnalysis
- sega_learn.linear_models.classifiers.LogisticRegression
- sega_learn.linear_models.classifiers.Perceptron
- sega_learn.linear_models.classifiers.QuadraticDiscriminantAnalysis
- sega_learn.linear_models.regressors.Bayesian
- sega_learn.linear_models.regressors.Lasso
- sega_learn.linear_models.regressors.OrdinaryLeastSquares
- sega_learn.linear_models.regressors.PassiveAggressiveRegressor
- sega_learn.linear_models.regressors.RANSAC
- sega_learn.linear_models.regressors.Ridge
class Bayesian(builtins.object) |
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Bayesian(max_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, fit_intercept=None)
Fits the Bayesian Regression model to the training data using Coordinate Descent.
Args:
X_train: (np.ndarray) - Training feature data.
y_train: (np.ndarray) - Training target data.
X_test: (np.ndarray), optional - Testing feature data (default is None).
y_test: (np.ndarray), optional - Testing target data (default is None).
max_iter: (int), optional - The maximum number of iterations to perform (default is 300).
tol: (float), optional - The convergence threshold. The algorithm stops when the coefficients change less than this threshold (default is 0.001).
alpha_1: (float), optional - The shape parameter for the prior on the weights (default is 1e-06).
alpha_2: (float), optional - The scale parameter for the prior on the weights (default is 1e-06).
lambda_1: (float), optional - The shape parameter for the prior on the noise (default is 1e-06).
lambda_2: (float), optional - The scale parameter for the prior on the noise (default is 1e-06).
fit_intercept: (bool), optional - Whether to calculate the intercept for this model (default is True).
Returns:
intercept_: (float) - The intercept of the model.
coef_: (np.ndarray) - Estimated coefficients for the linear regression problem. If `fit_intercept` is True, the first element is the intercept.
n_iter_: (int) - The number of iterations performed.
alpha_: (float) - The precision of the weights.
lambda_: (float) - The precision of the noise.
sigma_: (np.ndarray) - The posterior covariance of the weights.
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Methods defined here:
- __init__(self, max_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, fit_intercept=None)
- Implements Bayesian Regression using Coordinate Descent.
Bayesian regression applies both L1 and L2 regularization to prevent overfitting by adding penalty terms to the loss function.
Args:
max_iter: (int) - The maximum number of iterations to perform (default is 300).
tol: (float) - The convergence threshold. The algorithm stops when the coefficients change less than this threshold (default is 0.001).
alpha_1: (float) - The shape parameter for the prior on the weights (default is 1e-06).
alpha_2: (float) - The scale parameter for the prior on the weights (default is 1e-06).
lambda_1: (float) - The shape parameter for the prior on the noise (default is 1e-06).
lambda_2: (float) - The scale parameter for the prior on the noise (default is 1e-06).
fit_intercept: (bool), optional - Whether to calculate the intercept for this model (default is True).
Returns:
intercept_: (float) - The intercept of the model.
coef_: (np.ndarray) - Estimated coefficients for the linear regression problem. If `fit_intercept` is True, the first element is the intercept.
n_iter_: (int) - The number of iterations performed.
alpha_: (float) - The precision of the weights.
lambda_: (float) - The precision of the noise.
sigma_: (np.ndarray) - The posterior covariance of the weights.
- __str__(self)
- Returns the string representation of the model.
- fit(self, X, y)
- Fits the Bayesian Regression model to the training data.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
Returns:
self: (Bayesian) - The fitted Bayesian Regression model.
- get_formula(self)
- Computes the formula of the model.
Returns:
formula: (str) - The formula of the model as a string.
- predict(self, X)
- Predicts the target values using the Bayesian Regression model.
Args:
X: (np.ndarray) - Feature data of shape (n_samples, n_features).
Returns:
y_pred: (np.ndarray) - Predicted target values of shape (n_samples,).
- tune(self, X, y, beta1=0.9, beta2=0.999, iter=1000)
- Tunes the hyperparameters alpha_1, alpha_2, lambda_1, and lambda_2 using ADAM optimizer.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
beta1: (float), optional - The exponential decay rate for the first moment estimates (default is 0.9).
beta2: (float), optional - The exponential decay rate for the second moment estimates (default is 0.999).
iter: (int), optional - The maximum number of iterations to perform (default is 1000).
Returns:
best_alpha_1: (float) - The best value of alpha_1.
best_alpha_2: (float) - The best value of alpha_2.
best_lambda_1: (float) - The best value of lambda_1.
best_lambda_2: (float) - The best value of lambda_2.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class Lasso(builtins.object) |
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Lasso(alpha=1.0, fit_intercept=True, max_iter=10000, tol=0.0001, compile_numba=False)
Fits the Lasso Regression model to the training data.
Lasso regression implements L1 regularization, which helps to prevent overfitting by adding a penalty term to the loss function.
Args:
X_train: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y_train: (np.ndarray) - Training target data of shape (n_samples,).
X_test: (np.ndarray), optional - Testing feature data (default is None).
y_test: (np.ndarray), optional - Testing target data (default is None).
custom_metrics: (dict: str -> callable), optional - Custom metrics for evaluation (default is None).
verbose: (bool), optional - If True, prints progress (default is False).
Attributes:
coef_: (np.ndarray) - Estimated coefficients for the linear regression problem. If `fit_intercept` is True, the first element is the intercept.
intercept_: (float) - Independent term in the linear model. Set to 0.0 if `fit_intercept` is False.
Returns:
results: (list) - A list of dictionaries containing model performance metrics.
predictions: (dict) - A dictionary of predictions for each model.
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Methods defined here:
- __init__(self, alpha=1.0, fit_intercept=True, max_iter=10000, tol=0.0001, compile_numba=False)
- Initializes the Lasso Regression model.
Lasso regression implements L1 regularization, which helps to prevent overfitting by adding a penalty term to the loss function.
Args:
alpha: (float) - Regularization strength; must be a positive float (default is 1.0).
fit_intercept: (bool), optional - Whether to calculate the intercept for this model (default is True).
max_iter: (int), optional - Maximum number of iterations for the coordinate descent solver (default is 10000).
tol: (float), optional - Tolerance for the optimization. The optimization stops when the change in the coefficients is less than this tolerance (default is 1e-4).
compile_numba: (bool), optional - Whether to precompile the numba functions (default is False). If True, the numba fitting functions will be compiled before use.
- __str__(self)
- Returns the string representation of the model.
- fit(self, X, y, numba=False)
- Fits the Lasso Regression model to the training data using coordinate descent.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
numba: (bool), optional - Whether to use numba for faster computation (default is False).
Returns:
self: (Lasso) - The fitted Lasso Regression model.
- get_formula(self)
- Computes the formula of the model.
Returns:
- formula : str: The formula of the model.
- predict(self, X)
- Predicts the target values using the Lasso Regression model.
Args:
X: (np.ndarray) - Feature data of shape (n_samples, n_features).
Returns:
y_pred: (np.ndarray) - Predicted target values of shape (n_samples,).
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class LinearDiscriminantAnalysis(builtins.object) |
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LinearDiscriminantAnalysis(solver='svd', priors=None)
Implements Linear Discriminant Analysis.
A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes' rule.
Args:
solver: (str) - {'svd', 'lsqr', 'eigen'}, default='svd'. Solver to use for the LDA.
priors: (np.ndarray), optional - Prior probabilities of the classes (default is None).
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Methods defined here:
- __init__(self, solver='svd', priors=None)
- Initializes the Linear Discriminant Analysis model.
Args:
solver: (str) - {'svd', 'lsqr', 'eigen'}, default='svd'. Solver to use for the LDA.
priors: (np.ndarray), optional - Prior probabilities of the classes (default is None).
- decision_function(self, X)
- Computes the log-likelihood of each class for the input data. The decision function is the log-likelihood of each class.
Args:
X: (np.ndarray) - Test feature data.
Returns:
scores: (np.ndarray) - Log-likelihood of each class for the input samples.
- fit(self, X, y)
- Fits the LDA model to the training data.
Args:
X: (np.ndarray) - Training feature data.
y: (np.ndarray) - Training target data.
- predict(self, X)
- Predicts class labels for the input data.
Args:
X: (np.ndarray) - Test feature data.
Returns:
predictions: (np.ndarray) - Predicted class labels.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class LogisticRegression(builtins.object) |
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LogisticRegression(learning_rate=0.01, max_iter=1000)
Implements Logistic Regression using gradient descent. Supports binary and multiclass classification.
Args:
learning_rate: (float) - Learning rate for gradient updates (default is 0.01).
max_iter: (int) - Maximum number of iterations (default is 1000).
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Methods defined here:
- __init__(self, learning_rate=0.01, max_iter=1000)
- Initializes the classifier with specified hyperparameters.
Args:
learning_rate (float, optional): The step size for updating weights during training. Defaults to 0.01.
max_iter (int, optional): The maximum number of iterations for the training process. Defaults to 1000.
- fit(self, X, y)
- Fits the Logistic Regression model to the training data.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
- predict(self, X)
- Predicts class labels for the input data.
Args:
X: (np.ndarray) - Test feature data.
Returns:
predictions: (np.ndarray) - Predicted class labels.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class OrdinaryLeastSquares(builtins.object) |
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OrdinaryLeastSquares(fit_intercept=True) -> None
Ordinary Least Squares (OLS) linear regression model.
Attributes:
coef_ : ndarray of shape (n_features,) or (n_features + 1,) - Estimated coefficients for the linear regression problem. If `fit_intercept` is True, the first element is the intercept.
intercept_ : float - Independent term in the linear model. Set to 0.0 if `fit_intercept` is False.
Methods:
fit(X, y): Fit the linear model to the data.
predict(X): Predict using the linear model.
get_formula(): Returns the formula of the model as a string.
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Methods defined here:
- __init__(self, fit_intercept=True) -> None
- Initializes the OrdinaryLeastSquares object.
Args:
fit_intercept: (bool) - Whether to calculate the intercept for this model (default is True).
- __str__(self)
- Returns the string representation of the model.
- fit(self, X, y)
- Fits the linear regression model to the training data.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
Returns:
self: (OrdinaryLeastSquares) - The fitted linear regression model.
- get_formula(self)
- Returns the formula of the model as a string.
Returns:
formula: (str) - The formula of the model.
- predict(self, X)
- Predicts the target values using the linear model.
Args:
X: (np.ndarray) - Feature data of shape (n_samples, n_features).
Returns:
y_pred: (np.ndarray) - Predicted target values of shape (n_samples,).
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class PassiveAggressiveRegressor(builtins.object) |
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PassiveAggressiveRegressor(C=1.0, max_iter=1000, tol=0.001)
Fits the Passive Aggressive Regression model to the training data.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
save_steps: (bool), optional - Whether to save the weights and intercept at each iteration (default is False).
verbose: (bool), optional - If True, prints progress during training (default is False).
Attributes:
coef_: (np.ndarray) - Estimated coefficients for the regression problem.
intercept_: (float) - Independent term in the linear model.
steps_: (list of tuples), optional - The weights and intercept at each iteration if `save_steps` is True.
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Methods defined here:
- __init__(self, C=1.0, max_iter=1000, tol=0.001)
- Fits the Passive Aggressive Regression model to the training data.
Args:
C: (float) - Regularization parameter/step size (default is 1.0).
max_iter: (int) - The maximum number of passes over the training data (default is 1000).
tol: (float) - The stopping criterion (default is 1e-3).
Attributes:
coef_: (np.ndarray) - Estimated coefficients for the regression problem.
intercept_: (float) - Independent term in the linear model.
- __str__(self)
- Returns the string representation of the model.
- fit(self, X, y, save_steps=False, verbose=False)
- Fits the Passive Aggressive Regression model to the training data.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
save_steps: (bool), optional - Whether to save the weights and intercept at each iteration (default is False).
verbose: (bool), optional - If True, prints progress during training (default is False).
Returns:
None
- get_formula(self)
- Computes the formula of the model.
Returns:
formula : str: The formula of the model.
- predict(self, X)
- Predict using the linear model. Dot product of X and the coefficients.
- predict_all_steps(self, X)
- Predict using the linear model at each iteration. (save_steps=True).
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class Perceptron(builtins.object) |
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Perceptron(max_iter=1000, learning_rate=0.01)
Implements the Perceptron algorithm for binary and multiclass classification.
Args:
max_iter: (int) - Maximum number of iterations (default is 1000).
learning_rate: (float) - Learning rate for weight updates (default is 0.01).
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Methods defined here:
- __init__(self, max_iter=1000, learning_rate=0.01)
- Initializes the classifier with the specified maximum number of iterations and learning rate.
Args:
max_iter (int, optional): The maximum number of iterations for the training process. Defaults to 1000.
learning_rate (float, optional): The learning rate for the optimization algorithm. Defaults to 0.01.
- fit(self, X, y)
- Fits the Perceptron model to the training data.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
- predict(self, X)
- Predicts class labels for the input data.
Args:
X: (np.ndarray) - Test feature data.
Returns:
predictions: (np.ndarray) - Predicted class labels.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class QuadraticDiscriminantAnalysis(builtins.object) |
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QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0)
Implements Quadratic Discriminant Analysis.
The quadratic term allows for more flexibility in modeling the class conditional
A classifier with a quadratic decision boundary, generated by fitting class conditional densities to the data and using Bayes' rule.
Args:
priors: (np.ndarray), optional - Prior probabilities of the classes (default is None).
reg_param: (float), optional - Regularization parameter (default is 0.0).
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Methods defined here:
- __init__(self, priors=None, reg_param=0.0)
- Initialize the Quadratic Discriminant Analysis model with the specified prior probabilities and regularization parameter.
Args:
priors: (np.ndarray), optional - Prior probabilities of the classes (default is None).
reg_param: (float), optional - Regularization parameter (default is 0.0).
- decision_function(self, X)
- Apply decision function to an array of samples.
The decision function is the log-likelihood of each class.
Args:
X: (np.ndarray) - Test feature data.
Returns:
scores: (np.ndarray) - Log-likelihood of each class for the input samples.
- fit(self, X, y)
- Fit the model according to the given training data. Uses the means and covariance matrices of each class.
Args:
X: (np.ndarray) - Training feature data.
y: (np.ndarray) - Training target data.
- predict(self, X)
- Perform classification on an array of test vectors X.
Args:
X: (np.ndarray) - Test feature data.
Returns:
predictions: (np.ndarray) - Predicted class labels.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class RANSAC(builtins.object) |
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RANSAC(n=10, k=100, t=0.05, d=10, model=None, auto_scale_t=False, scale_t_factor=2, auto_scale_n=False, scale_n_factor=2)
Fits the RANSAC (RANdom SAmple Consensus) algorithm for robust linear regression.
Args:
X_train: (np.ndarray) - Training feature data.
y_train: (np.ndarray) - Training target data.
X_test: (np.ndarray), optional - Testing feature data (default is None).
y_test: (np.ndarray), optional - Testing target data (default is None).
n: (int), optional - Number of data points to estimate parameters (default is 10).
k: (int), optional - Maximum iterations allowed (default is 100).
t: (float), optional - Threshold value to determine if points are fit well, in terms of residuals (default is 0.05).
d: (int), optional - Number of close data points required to assert model fits well (default is 10).
model: (object), optional - The model to use for fitting. If None, uses Ordinary Least Squares (default is None).
auto_scale_t: (bool), optional - Whether to automatically scale the threshold until a model is fit (default is False).
scale_t_factor: (float), optional - Factor by which to scale the threshold until a model is fit (default is 2).
auto_scale_n: (bool), optional - Whether to automatically scale the number of data points until a model is fit (default is False).
scale_n_factor: (float), optional - Factor by which to scale the number of data points until a model is fit (default is 2).
Returns:
best_fit: (object) - The best model fit.
best_error: (float) - The best error achieved by the model.
best_n: (int) - The best number of data points used to fit the model.
best_t: (float) - The best threshold value used to determine if points are fit well, in terms of residuals.
best_model: (object) - The best model fit.
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Methods defined here:
- __init__(self, n=10, k=100, t=0.05, d=10, model=None, auto_scale_t=False, scale_t_factor=2, auto_scale_n=False, scale_n_factor=2)
- Fits the RANSAC (RANdom SAmple Consensus) algorithm for robust linear regression.
Args:
X_train: (np.ndarray) - Training feature data.
y_train: (np.ndarray) - Training target data.
X_test: (np.ndarray), optional - Testing feature data (default is None).
y_test: (np.ndarray), optional - Testing target data (default is None).
n: (int), optional - Number of data points to estimate parameters (default is 10).
k: (int), optional - Maximum iterations allowed (default is 100).
t: (float), optional - Threshold value to determine if points are fit well, in terms of residuals (default is 0.05).
d: (int), optional - Number of close data points required to assert model fits well (default is 10).
model: (object), optional - The model to use for fitting. If None, uses Ordinary Least Squares (default is None).
auto_scale_t: (bool), optional - Whether to automatically scale the threshold until a model is fit (default is False).
scale_t_factor: (float), optional - Factor by which to scale the threshold until a model is fit (default is 2).
auto_scale_n: (bool), optional - Whether to automatically scale the number of data points until a model is fit (default is False).
scale_n_factor: (float), optional - Factor by which to scale the number of data points until a model is fit (default is 2).
Returns:
best_fit: (object) - The best model fit.
best_error: (float) - The best error achieved by the model.
best_n: (int) - The best number of data points used to fit the model.
best_t: (float) - The best threshold value used to determine if points are fit well, in terms of residuals.
best_model: (object) - The best model fit.
- __str__(self)
- Returns the string representation of the model.
- fit(self, X, y)
- Fits the RANSAC model to the training data.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
Returns:
None
- get_formula(self)
- Computes the formula of the model if fit, else returns "No model fit available".
- predict(self, X)
- Predicts the target values using the best fit model.
Args:
X: (np.ndarray) - Feature data of shape (n_samples, n_features).
Returns:
y_pred: (np.ndarray) - Predicted target values of shape (n_samples,).
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class Ridge(builtins.object) |
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Ridge(alpha=1.0, fit_intercept=True, max_iter=10000, tol=0.0001, compile_numba=False)
Fits the Ridge Regression model to the training data.
Ridge regression implements L2 regularization, which helps to prevent overfitting by adding a penalty term to the loss function.
Args:
alpha: (float) - Regularization strength; must be a positive float (default is 1.0).
fit_intercept: (bool), optional - Whether to calculate the intercept for this model (default is True).
max_iter: (int), optional - Maximum number of iterations for the coordinate descent solver (default is 10000).
tol: (float), optional - Tolerance for the optimization. The optimization stops when the change in the coefficients is less than this tolerance (default is 1e-4).
Attributes:
coef_: (np.ndarray) - Estimated coefficients for the linear regression problem. If `fit_intercept` is True, the first element is the intercept.
intercept_: (float) - Independent term in the linear model. Set to 0.0 if `fit_intercept` is False.
Methods:
fit(X, y): Fits the Ridge Regression model to the training data.
predict(X): Predicts using the Ridge Regression model.
get_formula(): Returns the formula of the model as a string.
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Methods defined here:
- __init__(self, alpha=1.0, fit_intercept=True, max_iter=10000, tol=0.0001, compile_numba=False)
- Initializes the Ridge Regression model.
Ridge regression implements L2 regularization, which helps to prevent overfitting by adding a penalty term to the loss function.
Args:
alpha: (float) - Regularization strength; must be a positive float (default is 1.0).
fit_intercept: (bool), optional - Whether to calculate the intercept for this model (default is True).
max_iter: (int), optional - Maximum number of iterations for the coordinate descent solver (default is 10000).
tol: (float), optional - Tolerance for the optimization. The optimization stops when the change in the coefficients is less than this tolerance (default is 1e-4).
compile_numba: (bool), optional - Whether to precompile the numba functions (default is False). If True, the numba fitting functions will be compiled before use.
- __str__(self)
- Returns the string representation of the model.
- fit(self, X, y, numba=False)
- Fits the Ridge Regression model to the training data.
Args:
X: (np.ndarray) - Training feature data of shape (n_samples, n_features).
y: (np.ndarray) - Training target data of shape (n_samples,).
numba: (bool), optional - Whether to use numba for faster computation (default is False).
Returns:
self: (Ridge) - The fitted Ridge Regression model.
- get_formula(self)
- Computes the formula of the model.
Returns:
formula: (str) - The formula of the model as a string.
- predict(self, X)
- Predicts the target values using the Ridge Regression model.
Args:
X: (np.ndarray) - Feature data of shape (n_samples, n_features).
Returns:
y_pred: (np.ndarray) - Predicted target values of shape (n_samples,).
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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