Code to reproduce grid search freeze

gridsearch_debug_snippet.py

import sys
import timeit
from os.path import join as pjoin
import logging
import traceback

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import mutual_info_classif, SelectKBest
from sklearn.model_selection import GridSearchCV, ShuffleSplit
from sklearn.pipeline import Pipeline

rf = RandomForestClassifier(max_features=8, n_estimators=100, oob_score=True)
feat_selector = SelectKBest(score_func=mutual_info_classif, k=10)

fs_name = 'MI_top_K'
clf_name = 'random_forest_clf'

steps = [(fs_name , feat_selector),
         (clf_name, rf)]
pipeline = Pipeline(steps)

param_name = lambda string: '{}__{}'.format(clf_name, string)
param_grid = {param_name('min_samples_leaf'): range(1, 5, 2),
              param_name('max_features'): range(1, 6, 2),
              param_name('n_estimators'): range(50, 250, 50)}

inner_cv = ShuffleSplit(n_splits=25, train_size=0.8)
gs = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=inner_cv,
                  verbose=2)

print(gs)

cur_dir =  '.'
# train_data_mat = np.genfromtxt(pjoin(cur_dir, 'test_data20.csv'), delimiter=',', dtype='float')
train_data_full = np.genfromtxt(pjoin(cur_dir, 'JS_sklearn_test.txt'))
train_labels = np.genfromtxt(pjoin(cur_dir, 'labels_sklearn_test.txt'), dtype='int')


log_file = pjoin(cur_dir,'logfile_cv.txt')
logging.basicConfig(filename=log_file,level=logging.INFO)

def get_stop_time(start):
    return timeit.default_timer() - start

for data_dim in range(100, 5000, 62000):

    train_data_subset = train_data_full[:,:data_dim]
    print('\ndata size: {} \n'.format(train_data_subset.shape))

    start = timeit.default_timer()

    try:
        gs.fit(train_data_subset, train_labels)
    except:
        print('fit failed ')
        traceback.print_exc()

    log_msg = 'gridsearch at dimensionality {} just done after {} msecs.\n' \
              ' Best score: {}\nBest params: {}'.format(train_data_subset.shape,
                get_stop_time(start), gs.best_score_, gs.best_params_)
    print(log_msg)
    logging.info(log_msg)