Regressors

In alphabetical order

[source]

LassoRegressor

mlsauce.LassoRegressor(reg_lambda=0.1, max_iter=10, tol=0.001, backend="cpu")

Lasso.

Attributes:

reg_lambda: float
    L1 regularization parameter.

max_iter: int
    number of iterations of lasso shooting algorithm.

tol: float          
    tolerance for convergence of lasso shooting algorithm.

backend: str    
    type of backend; must be in ('cpu', 'gpu', 'tpu').

[source]

fit

LassoRegressor.fit(X, y, **kwargs)

Fit matrixops (classifier) to training data (X, y)

Args:

X: {array-like}, shape = [n_samples, n_features]
    Training vectors, where n_samples is the number 
    of samples and n_features is the number of features.

y: array-like, shape = [n_samples]
    Target values.

**kwargs: additional parameters to be passed to self.cook_training_set.

Returns:

self: object.

[source]

predict

LassoRegressor.predict(X, **kwargs)

Predict test data X.

Args:

X: {array-like}, shape = [n_samples, n_features]
    Training vectors, where n_samples is the number 
    of samples and n_features is the number of features.

**kwargs: additional parameters to be passed to `predict_proba`

Returns:

model predictions: {array-like}

[source]

LSBoostRegressor

mlsauce.LSBoostRegressor(
    n_estimators=100,
    learning_rate=0.1,
    n_hidden_features=5,
    reg_lambda=0.1,
    row_sample=1,
    col_sample=1,
    dropout=0,
    tolerance=0.0001,
    direct_link=1,
    verbose=1,
    seed=123,
    backend="cpu",
    solver="ridge",
)

LSBoost regressor.

Attributes:

n_estimators: int
    number of boosting iterations.

learning_rate: float
    controls the learning speed at training time.

n_hidden_features: int 
    number of nodes in successive hidden layers.

reg_lambda: float
    L2 regularization parameter for successive errors in the optimizer 
    (at training time).

row_sample: float
    percentage of rows chosen from the training set.

col_sample: float
    percentage of columns chosen from the training set.

dropout: float
    percentage of nodes dropped from the training set.

tolerance: float
    controls early stopping in gradient descent (at training time).

direct_link: bool
    indicates whether the original features are included (True) in model's 
    fitting or not (False).

verbose: int
    progress bar (yes = 1) or not (no = 0) (currently).

seed: int 
    reproducibility seed for nodes_sim=='uniform', clustering and dropout.

backend: str    
    type of backend; must be in ('cpu', 'gpu', 'tpu')

solver: str    
    type of 'weak' learner; currently in ('ridge', 'lasso')

[source]

fit

LSBoostRegressor.fit(X, y, **kwargs)

Fit Booster (regressor) to training data (X, y)

Args:

X: {array-like}, shape = [n_samples, n_features]
    Training vectors, where n_samples is the number 
    of samples and n_features is the number of features.

y: array-like, shape = [n_samples]
   Target values.

**kwargs: additional parameters to be passed to self.cook_training_set.

Returns:

self: object.

[source]

predict

LSBoostRegressor.predict(X, **kwargs)

Predict probabilities for test data X.

Args:

X: {array-like}, shape = [n_samples, n_features]
    Training vectors, where n_samples is the number 
    of samples and n_features is the number of features.

**kwargs: additional parameters to be passed to 
    self.cook_test_set

Returns:

probability estimates for test data: {array-like}

[source]

RidgeRegressor

mlsauce.RidgeRegressor(reg_lambda=0.1, backend="cpu")

Ridge.

Attributes:

reg_lambda: float
    regularization parameter.

backend: str    
    type of backend; must be in ('cpu', 'gpu', 'tpu')

[source]

fit

RidgeRegressor.fit(X, y, **kwargs)

Fit matrixops (classifier) to training data (X, y)

Args:

X: {array-like}, shape = [n_samples, n_features]
    Training vectors, where n_samples is the number 
    of samples and n_features is the number of features.

y: array-like, shape = [n_samples]
    Target values.

**kwargs: additional parameters to be passed to self.cook_training_set.

Returns:

self: object.

[source]

predict

RidgeRegressor.predict(X, **kwargs)

Predict test data X.

Args:

X: {array-like}, shape = [n_samples, n_features]
    Training vectors, where n_samples is the number 
    of samples and n_features is the number of features.

**kwargs: additional parameters to be passed to `predict_proba`

Returns:

model predictions: {array-like}