Less freezing time in multiprocessing with large arrays

ci/.travis_install.sh

@@ -56,6 +56,8 @@ pip install tqdm
 pip install toolz
 pip install dask[complete]
 pip install stopit
+pip install scikit-mdr
+pip install skrebate
 
 if [[ "$COVERAGE" == "true" ]]; then
     pip install coverage coveralls

tests.py

@@ -41,7 +41,7 @@
 import subprocess
 import sys
 
-from sklearn.datasets import load_digits, load_boston
+from sklearn.datasets import load_digits, load_boston, make_classification
 from sklearn.model_selection import train_test_split, cross_val_score
 from sklearn.linear_model import LogisticRegression, Lasso
 from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
@@ -675,6 +675,7 @@ def test_fit3():
     assert not (tpot_obj._start_datetime is None)
 
 
+<<<<<<< HEAD
 def test_evaluated_individuals():
     """Assert that _evaluated_individuals stores corrent pipelines and their CV scores."""
     tpot_obj = TPOTClassifier(
@@ -775,8 +776,18 @@ def test_imputer3():
     assert_not_equal(imputed_features[0][0], float('nan'))
 
 
-def test_tpot_operator_factory_class():
-    """Assert that the TPOT operators class factory."""
+def test_fit3():
+    """Assert that the TPOT fit function provides an optimized pipeline when config_dict is \'TPOT MDR\'"""
+    X, y = make_classification(n_samples=50, n_features=10, random_state=42, n_classes=2) # binary classification problem
+    tpot_obj = TPOTClassifier(random_state=42, population_size=1, offspring_size=2, generations=1, verbosity=0, config_dict='TPOT MDR')
+    tpot_obj.fit(X, y)
+
+    assert isinstance(tpot_obj._optimized_pipeline, creator.Individual)
+    assert not (tpot_obj._start_datetime is None)
+
+
+def testTPOTOperatorClassFactory():
+    """Assert that the TPOT operators class factory"""
     test_config_dict = {
         'sklearn.svm.LinearSVC': {
             'penalty': ["l1", "l2"],

tpot/_version.py

@@ -19,4 +19,4 @@
 
 """
 
-__version__ = '0.7.3'
+__version__ = '0.7.4'

tpot/base.py

@@ -149,7 +149,7 @@ def __init__(self, generations=100, population_size=100, offspring_size=None,
             Random number generator seed for TPOT. Use this to make sure
             that TPOT will give you the same results each time you run it
             against the same data set with that seed.
-        config_dict: a Python dictionary or string (default: None)
+        config_dict: Python dictionary or string (default: None)
             Python dictionary:
                 A dictionary customizing the operators and parameters that
                 TPOT uses in the optimization process.