malss package

API

class malss.malss.MALSS(X, y, task, shuffle=True, n_jobs=1, random_state=0, verbose=True)[source]
__init__(X, y, task, shuffle=True, n_jobs=1, random_state=0, verbose=True)[source]

Set the given training data.

X : {numpy.ndarray, pandas.DataFrame}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and n_features is the number of features.
y : {numpy.ndarray, pandas.Series}, shape = [n_samples]
Target values (class labels in classification, real numbers in regression)
task : string
Specifies the task of the analysis. It must be one of ‘classification’, ‘regression’.
shuffle : boolean, optional (default=True)
Whether to shuffle the data.
n_jobs : integer, optional (default=1)
The number of jobs to run in parallel. If -1, then the number of jobs is set to the number of cores.
random_state : int seed, RandomState instance, or None (default=0)
The seed of the pseudo random number generator
verbos : bool, default: True
Enable verbose output.
__weakref__

list of weak references to the object (if defined)

execute()[source]

Tune parameters and search best algorithm

make_report(dname='report')[source]

Make the report

dname : string (default=”report”)
A string containing a path to a output directory.
make_sample_code(fname='sample_code.py')[source]

Make the sample code

fname : string (default=”sample_code.py”)
A string containing a path to a output file.

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