cca_zoo: TerminatedWorkerError when using GridSearchCV

Hi James, with the latest version of cca_zoo I get this error:

TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.
The exit codes of the workers are {SIGSEGV(-11), SIGSEGV(-11), SIGSEGV(-11)}

Didn’t happen in older versions (although I am using the exact same script). Can you reproduce this? Here’s my full code + attached my X,y and groups as txt files.

import numpy as np
from sklearn.model_selection import GroupShuffleSplit
from cca_zoo.model_selection import GridSearchCV
from cca_zoo.linear import SCCA_PMD

###############################################################################
## Settings ###################################################################
###############################################################################

n_jobs = 8
pre_dispatch = 3
rng = np.random.RandomState(42)

###############################################################################
## Prepare Analysis ###########################################################
###############################################################################

X = np.loadtxt('X.txt')
y = np.loadtxt('y.txt')
groups = np.loadtxt('groups.txt')

###############################################################################
## Analysis settings ##########################################################
###############################################################################

# define latent dimensions
latent_dimensions = 3

# pretend that there are subject groups in the dataset
cv = GroupShuffleSplit(n_splits=10,train_size=0.7,random_state=rng)

# define a search space (optimize left and right penalty parameters)
param_grid = {'tau':[np.arange(0.1,1.1,0.1),0]}

# define an estimator
estimator = SCCA_PMD(latent_dimensions=latent_dimensions,random_state=rng)

##############################################################################
## Run GridSearch
##############################################################################

def scorer(estimator, views):
    scores = estimator.score(views)
    return np.mean(scores)

grid = GridSearchCV(estimator,param_grid,scoring=scorer,n_jobs=n_jobs,cv=cv)
grid.fit([X,y],groups=groups)

Data:

groups.txt X.txt y.txt

Note that X and y have been normalized prior to GridSearch, so each fold “sees” different batches of the normalized dataset. Not sure if this is related to https://github.com/jameschapman19/cca_zoo/issues/175