Release v0.7 (#115)

* test on py310

* test on py39

* fix deprecated base_estimator

* bump version

.github/workflows/docbuild.yml

@@ -9,7 +9,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.7]
+        python-version: [3.9]
 
     steps:
     - name: Checkout repo

.github/workflows/pythonpackage.yml

@@ -9,7 +9,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.7, 3.8]
+        python-version: [3.9]
 
     steps:
     - uses: actions/checkout@v1

README.md

@@ -15,8 +15,6 @@ implemented in scikit-learn random forest regression or classification objects.
 The core functions calculate an in-bag and error bars for random forest
 objects.
 
-Compatible with Python2.7 and Python3.6
-
 This module is based on R code from Stefan Wager (see important links below)
 and is licensed under the MIT open source license (see [LICENSE](LICENSE))
 

examples/plot_mpg_svr.py

@@ -39,7 +39,7 @@
 # Create RandomForestRegressor
 n_estimators = 1000
 mpg_bagger = BaggingRegressor(
-    base_estimator=SVR(), n_estimators=n_estimators, random_state=42
+    estimator=SVR(), n_estimators=n_estimators, random_state=42
 )
 mpg_bagger.fit(mpg_X_train, mpg_y_train)
 mpg_y_hat = mpg_bagger.predict(mpg_X_test)

forestci/tests/test_forestci.py

@@ -91,7 +91,7 @@ def test_bagging_svr_error():
     X_train = X[train_idx]
 
     n_trees = 4
-    bagger = BaggingRegressor(base_estimator=SVR(), n_estimators=n_trees)
+    bagger = BaggingRegressor(estimator=SVR(), n_estimators=n_trees)
     bagger.fit(X_train, y_train)
     inbag = fci.calc_inbag(X_train.shape[0], bagger)
     for ib in [inbag, None]:

forestci/version.py

@@ -2,7 +2,7 @@
 _version_major = 0
 _version_minor = 7
 _version_micro = ''  # use '' for first of series, number for 1 and above
-_version_extra = 'dev'
+_version_extra = ''
 #  _version_extra = ''  # Uncomment this for full releases
 
 # Construct full version string from these.