Merge 083adb2 into b1cb06d

.appveyor.yml

@@ -18,7 +18,7 @@ install:
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q conda
   - conda info -a
-  - conda create -q -n test-environment python=%PYTHON_VERSION% numpy scipy scikit-learn nose cython pandas pywin32 joblib
+  - conda create -q -n test-environment python=%PYTHON_VERSION% numpy scipy scikit-learn nose cython pandas joblib
   - activate test-environment
   - pip install deap tqdm update_checker stopit xgboost dask[delayed] dask[dataframe] cloudpickle==0.5.6 fsspec>=0.3.3 dask_ml==%DASK_ML_VERSION%
 

README.md

@@ -6,8 +6,7 @@ Development status: [![Development Build Status - Mac/Linux](https://travis-ci.o
 [![Development Build Status - Windows](https://ci.appveyor.com/api/projects/status/b7bmpwpkjhifrm7v/branch/development?svg=true)](https://ci.appveyor.com/project/weixuanfu/tpot?branch=development)
 [![Development Coverage Status](https://coveralls.io/repos/github/EpistasisLab/tpot/badge.svg?branch=development)](https://coveralls.io/github/EpistasisLab/tpot?branch=development)
 
-Package information: [![Python 2.7](https://img.shields.io/badge/python-2.7-blue.svg)](https://www.python.org/download/releases/2.7/)
-[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)
+Package information: [![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)
 [![License: LGPL v3](https://img.shields.io/badge/license-LGPL%20v3-blue.svg)](http://www.gnu.org/licenses/lgpl-3.0)
 [![PyPI version](https://badge.fury.io/py/TPOT.svg)](https://badge.fury.io/py/TPOT)
 

docs_sources/installing.md

@@ -32,12 +32,6 @@ DEAP, update_checker, tqdm and stopit can be installed with `pip` via the comman
 pip install deap update_checker tqdm stopit
 ```
 
-**For the Windows users**, the pywin32 module is required if Python is NOT installed via the [Anaconda Python distribution](https://www.continuum.io/downloads) and can be installed with `pip` for Python version <=3.3 or `conda` (e.g. miniconda) for any Python version:
-
-```Shell
-conda install pywin32
-```
-
 **Optionally**, you can install [XGBoost](https://github.com/dmlc/xgboost) if you would like TPOT to use the eXtreme Gradient Boosting models. XGBoost is entirely optional, and TPOT will still function normally without XGBoost if you do not have it installed. **Windows users: pip installation may not work on some Windows environments, and it may cause unexpected errors.**
 
 ```Shell

requirements.txt

@@ -1,10 +1,10 @@
-deap==1.0.2.post2
+deap>=1.2
 nose==1.3.7
-numpy==1.12.1
-scikit-learn==0.18.1
-scipy==0.19.0
-tqdm==4.26.0
-update-checker==0.16
-stopit==1.1.1
-pandas==0.20.2
-joblib==0.10.3
+numpy>=1.16.3
+scikit-learn>=0.21.0
+scipy>=1.3.1
+tqdm>=4.36.1
+update-checker>=0.16
+stopit>=1.1.1
+pandas>=0.24.2
+joblib>=0.13.2

setup.py

@@ -35,30 +35,26 @@ def calculate_version():
 This project is hosted at https://github.com/EpistasisLab/tpot
 ''',
     zip_safe=True,
-    install_requires=['numpy>=1.12.1',
-                    'scipy>=0.19.0',
-                    'scikit-learn>=0.18.1',
-                    'deap>=1.0',
+    install_requires=['numpy>=1.16.3',
+                    'scipy>=1.3.1',
+                    'scikit-learn>=0.21.0',
+                    'deap>=1.2',
                     'update_checker>=0.16',
-                    'tqdm>=4.26.0',
+                    'tqdm>=4.36.1',
                     'stopit>=1.1.1',
-                    'pandas>=0.20.2',
-                    'joblib>=0.10.3'],
+                    'pandas>=0.24.2',
+                    'joblib>=0.13.2'],
     extras_require={
-        'xgboost': ['xgboost==0.6a2'],
+        'xgboost': ['xgboost==0.90'],
         'skrebate': ['skrebate>=0.3.4'],
         'mdr': ['scikit-mdr>=0.4.4'],
         'dask': ['dask>=0.18.2',
                  'distributed>=1.22.1',
-                 'dask-ml>=0.9.0'],
+                 'dask-ml>=1.0.0'],
     },
     classifiers=[
         'Intended Audience :: Science/Research',
         'License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)',
-        'Programming Language :: Python :: 2',
-        'Programming Language :: Python :: 2.7',
-        'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.4',
         'Programming Language :: Python :: 3.5',
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',

tests/export_tests.py

@@ -645,18 +645,15 @@ def test_imputer_in_export():
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsClassifier
-try:
-    from sklearn.impute import SimpleImputer as Imputer
-except ImportError:
-    from sklearn.preprocessing import Imputer
+from sklearn.impute import SimpleImputer
 
 # NOTE: Make sure that the outcome column is labeled 'target' in the data file
 tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
 features = tpot_data.drop('target', axis=1)
 training_features, testing_features, training_target, testing_target = \\
             train_test_split(features, tpot_data['target'], random_state=None)
 
-imputer = Imputer(strategy="median")
+imputer = SimpleImputer(strategy="median")
 imputer.fit(training_features)
 training_features = imputer.transform(training_features)
 testing_features = imputer.transform(testing_features)

tpot/_version.py

@@ -23,4 +23,4 @@
 
 """
 
-__version__ = '0.10.2'
+__version__ = '0.11.0'

tpot/base.py

@@ -50,10 +50,7 @@
 from sklearn.utils import check_X_y, check_consistent_length, check_array
 from sklearn.pipeline import make_pipeline, make_union
 from sklearn.preprocessing import FunctionTransformer
-try:
-    from sklearn.impute import SimpleImputer as Imputer
-except ImportError:
-    from sklearn.preprocessing import Imputer
+from sklearn.impute import SimpleImputer
 from sklearn.model_selection import train_test_split
 from sklearn.metrics.scorer import make_scorer, _BaseScorer
 
@@ -81,28 +78,6 @@
     warnings.simplefilter('ignore')
     from tqdm.autonotebook import tqdm
 
-# hot patch for Windows: solve the problem of crashing python after Ctrl + C in Windows OS
-# https://github.com/ContinuumIO/anaconda-issues/issues/905
-if sys.platform.startswith('win'):
-    import win32api
-
-    try:
-        import _thread
-    except ImportError:
-        import thread as _thread
-
-
-    def handler(dwCtrlType, hook_sigint=_thread.interrupt_main):
-        """SIGINT handler function."""
-        if dwCtrlType == 0:  # CTRL_C_EVENT
-            hook_sigint()
-            return 1  # don't chain to the next handler
-        return 0
-
-
-    win32api.SetConsoleCtrlHandler(handler, 1)
-
-
 
 class TPOTBase(BaseEstimator):
     """Automatically creates and optimizes machine learning pipelines using GP."""
@@ -1147,7 +1122,7 @@ def _impute_values(self, features):
             print('Imputing missing values in feature set')
 
         if self._fitted_imputer is None:
-            self._fitted_imputer = Imputer(strategy="median")
+            self._fitted_imputer = SimpleImputer(strategy="median")
             self._fitted_imputer.fit(features)
 
         return self._fitted_imputer.transform(features)

tpot/export_utils.py

@@ -106,7 +106,7 @@ def export_pipeline(exported_pipeline,
     # Add the imputation step if it was used by TPOT
     if impute:
         pipeline_text += """
-imputer = Imputer(strategy="median")
+imputer = SimpleImputer(strategy="median")
 imputer.fit(training_features)
 training_features = imputer.transform(training_features)
 testing_features = imputer.transform(testing_features)
@@ -217,10 +217,7 @@ def merge_imports(old_dict, new_dict):
 
     # Add the imputer if necessary
     if impute:
-        pipeline_text += """try:
-    from sklearn.impute import SimpleImputer as Imputer
-except ImportError:
-    from sklearn.preprocessing import Imputer
+        pipeline_text += """from sklearn.impute import SimpleImputer
 """
     if random_state is not None:
         pipeline_text += """from tpot.export_utils import set_param_recursive