from joblib import Parallel, delayed
from sklearn.base import BaseEstimator
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)
__all__ = ["fitModels"]
def fitOne(model, X, y, params):
"""
Makes one model fit using provided data and parameters
:param model: The instantiated model you wish to pass, e.g. LogisticRegression()
:param X: The independent variable data
:param y: The response variable data
:param params: The parameters passed through to the model from the parameter grid
:return: Returns the fitted model
"""
if isinstance(model, BaseEstimator):
model.set_params(**params)
else:
model = model(**params)
return model.fit(X, y)
def fitModels(model, paramGrid, X, y, n_jobs=-1, verbose=10):
"""
Parallelizes fitting all models using all combinations of parameters in paramGrid on provided data.
:param model: The instantiated model you wish to pass, e.g. LogisticRegression()
:param paramGrid: The ParameterGrid object created from sklearn.model_selection
:param X: The independent variable data
:param y: The response variable data
:param n_jobs: Number of cores to use in parallelization (defaults to -1: all cores)
:param verbose: The level of verbosity of reporting updates on parallel process
Default is 10 (send an update at the completion of each job)
:return: Returns a list of fitted models
Example usage:
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import ParameterGrid
model = LogisticRegression()
grid = {
'C': [1e-4, 1e-3], # regularization
'penalty': ['l1','l2'], # penalty type
'n_jobs': [-1] # parallelize within each fit over all cores
}
paramGrid = ParameterGrid(grid)
myModels = fitModels(model, paramGrid, X_train, y_train)
"""
return Parallel(n_jobs=n_jobs, verbose=verbose)(delayed(fitOne)(model, X, y, params) for params in paramGrid)
