pycasso package¶
pycasso¶
PICASSO: Penalized Generalized Linear Model Solver  Unleash the Power of Nonconvex Penalty
Author:  Jason Ge, Haoming Jiang 

Maintainer:  Haoming Jiang <jianghm@gatech.edu> 

pycasso.
test
()¶ Show welcome information.
pycasso.core¶
Main Interface of the package

class
pycasso.core.
Solver
(x, y, lambdas=(100, 0.05), family='gaussian', penalty='l1', gamma=3, useintercept=False, prec=0.0001, max_ite=1000, verbose=False)¶ Bases:
object
The PICASSO Solver For GLM.
Parameters:  x – An n*m design matrix where n is the sample size and d is the data dimension.
 y – The n dimensional response vector. y is numeric vector for gaussian and sqrtlasso, or a twolevel factor for binomial, or a nonnegative integer vector representing counts for gaussian.
 lambdas – The parameters of controling regularization. Can be one of the following two cases:
Case1 (default): A tuple of 2 variables (n, lambda_min_ratio), where the default values are (100,0.05). The program calculates lambdas as an array of n elements starting from lambda_max to lambda_min_ratio * lambda_max in log scale. lambda_max is the minimum regularization parameter which yields an allzero estimates. Caution: logistic and poisson regression can be illconditioned if lambda is too small for nonconvex penalty. We suggest the user to avoid using any lambda_min_raito smaller than 0.05 for logistic/poisson regression under nonconvex penalty.
Case2: A manually specified sequence (size > 2) of decreasing positive values to control the regularization.  family – Options for model. Sparse linear regression and sparse multivariate regression is applied if family = “gaussian”, sqrt lasso is applied if family = “sqrtlasso”, sparse logistic regression is applied if family = “binomial” and sparse poisson regression is applied if family = “poisson”. The default value is “gaussian”.
 penalty – Options for regularization. Lasso is applied if method = “l1”, MCP is applied if ` method = “mcp”` and SCAD Lasso is applied if method = “scad”. The default value is “l1”.
 gamma – The concavity parameter for MCP and SCAD. The default value is 3.
 useintercept – Whether or not to include intercept term. Default value is False.
 prec – Stopping precision. The default value is 1e7.
 max_ite – The iteration limit. The default value is 1000.
 verbose – Tracing information is disabled if verbose = False. The default value is False.

coef
()¶ Extract model coefficients.
Returns: a dictionary of the model coefficients. Return type: dict{name : value} The detail of returned list:
 beta  A matrix of regression estimates whose columns correspond to regularization parameters for sparse linear regression and sparse logistic regression. A list of matrices of regression estimation corresponding to regularization parameters for sparse column inverse operator.
 intercept  The value of intercepts corresponding to regularization parameters for sparse linear regression, and sparse logistic regression.
 ite_lamb  Number of iterations for each lambda.
 size_act  An array of solution sparsity (model degree of freedom).
 train_time  The training time on each lambda.
 total_train_time  The total training time.
 state  The training state.
 df  The number of nonzero coefficients

plot
()¶ Visualize the solution path of regression estimate corresponding to regularization parameters.

predict
(newdata=None, lambdidx=None)¶ Predicting responses of the new data.
Parameters:  newdata – An optional data frame in which to look for variables with which to predict. If omitted, the training data of the model are used.
 lambdidx – Use the model coefficient corresponding to the lambdidx th lambda.
Returns: The predicted response vectors based on the estimated models.
Return type: np.array

train
()¶ The trigger function for training the model
pycasso.libpath¶
Find the path to picasso dynamic library files.

exception
pycasso.libpath.
PicassoLibraryNotFound
¶ Bases:
Exception
Error thrown by when picasso is not found

pycasso.libpath.
find_lib_path
()¶ Find the path to picasso dynamic library files.
Returns: List of all found library path to picasso Return type: list(string)