Merge pull request #99 from nyanp/feature/drop-old-python

drop supporting python 3.6/3.7

.github/workflows/pythonpackage.yml

@@ -9,7 +9,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.8]
 
     steps:
       - uses: actions/checkout@v1

.github/workflows/pythonpublish.yml

@@ -12,7 +12,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v1
       with:
-        python-version: '3.6'
+        python-version: '3.8'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/weekly_test.yml

@@ -11,7 +11,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.8]
 
     steps:
       - uses: actions/checkout@v1

nyaggle/experiment/auto_prep.py

@@ -2,7 +2,7 @@ from typing import List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
-from pandas.api.types import is_integer_dtype, is_categorical
+from pandas.api.types import is_integer_dtype, is_categorical_dtype
 from sklearn.preprocessing import LabelEncoder
 
 
@@ -24,7 +24,7 @@ def autoprep_gbdt(algorithm_type: str, X_train: pd.DataFrame, X_test: Optional[p
 
     if algorithm_type == 'lgbm':
         # LightGBM can handle categorical dtype natively
-        categorical_feature_to_treat = [c for c in categorical_feature_to_treat if not is_categorical(X_train[c])]
+        categorical_feature_to_treat = [c for c in categorical_feature_to_treat if not is_categorical_dtype(X_train[c])]
 
     if algorithm_type == 'cat' and len(categorical_feature_to_treat) > 0:
         X_train = X_train.copy()
@@ -49,7 +49,7 @@ def autoprep_gbdt(algorithm_type: str, X_train: pd.DataFrame, X_test: Optional[p
 
 
 def _fill_na_by_unique_value(strain: pd.Series, stest: Optional[pd.Series]) -> Tuple[pd.Series, pd.Series]:
-    if is_categorical(strain):
+    if is_categorical_dtype(strain):
         return strain.cat.codes, stest.cat.codes
     elif is_integer_dtype(strain.dtype):
         fillval = min(strain.min(), stest.min()) - 1

setup.py

@@ -55,8 +55,6 @@ setup(
     keywords='nyaggle kaggle',
     classifiers=[
         'License :: OSI Approved :: BSD License',
-        'Programming Language :: Python :: 3.6',
-        'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8'
     ]
 )