v0.1.2 (#162)

* updated dependencies

* correct dtype of list with selection conflict type

* got rid of fiona requirement

* improved downloading of example data

* deprecated plotting for now

as sklearn API has changed

* v0.1.2

copro/__init__.py

@@ -13,4 +13,4 @@
 
 __author__ = """Jannis M. Hoch, Sophie de Bruin, Niko Wanders"""
 __email__ = 'j.m.hoch@uu.nl'
-__version__ = '0.1.1'
+__version__ = '0.1.2'

copro/plots.py

@@ -101,8 +101,13 @@ def plot_ROC_curve_n_times(ax, clf, X_test, y_test, tprs, aucs, mean_fpr, **kwar
         list: lists with true positive rates and area-under-curve values per plot.
     """    
 
+    raise DeprecationWarning('Plotting API in sklearn is changed, function needs updating.')
+
     viz = metrics.plot_roc_curve(clf, X_test, y_test, ax=ax,
                             	 alpha=0.15, color='b', lw=1, label=None, **kwargs)
+
+    # rfc_disp = metrics.RocCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax,
+    #                                                   alpha=0.15, color='b', lw=1, label=None, **kwargs)
 
     interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
     interp_tpr[0] = 0.0
@@ -121,6 +126,8 @@ def plot_ROC_curve_n_mean(ax, tprs, aucs, mean_fpr, **kwargs):
         mean_fpr (array): array with mean false positive rate.
     """    
 
+    raise DeprecationWarning('Plotting API in sklearn is changed, function needs updating.')
+
     mean_tpr = np.mean(tprs, axis=0)
     mean_tpr[-1] = 1.0
     mean_auc = metrics.auc(mean_fpr, mean_tpr)

copro/scripts/copro_runner.py

@@ -90,16 +90,18 @@ def cli(cfg, make_plots=True, verbose=False):
         out_y_df = copro.evaluation.fill_out_df(out_y_df, y_df)
         out_dict = copro.evaluation.fill_out_dict(out_dict, eval_dict)
 
+        ## NOTE 15-Mar-2023: ROC plotting has been changed in sklearn, needs updating
         #- plot ROC curve per model execution
-        tprs, aucs = copro.plots.plot_ROC_curve_n_times(ax1, clf, X_df.to_numpy(), y_df.y_test.to_list(),
-                                                                    trps, aucs, mean_fpr)
-
-    #- plot mean ROC curve
-    copro.plots.plot_ROC_curve_n_mean(ax1, tprs, aucs, mean_fpr)
-    #- save plot
-    plt.savefig(os.path.join(out_dir_REF, 'ROC_curve_per_run.png'), dpi=300, bbox_inches='tight')
-    #- save data for plot
-    copro.evaluation.save_out_ROC_curve(tprs, aucs, out_dir_REF)
+        # tprs, aucs = copro.plots.plot_ROC_curve_n_times(ax1, clf, X_df.to_numpy(), y_df.y_test.to_list(),
+        #                                                             trps, aucs, mean_fpr)
+
+    ## NOTE 15-Mar-2023: ROC plotting has been changed in sklearn, needs updating
+    # #- plot mean ROC curve
+    # copro.plots.plot_ROC_curve_n_mean(ax1, tprs, aucs, mean_fpr)
+    # #- save plot
+    # plt.savefig(os.path.join(out_dir_REF, 'ROC_curve_per_run.png'), dpi=300, bbox_inches='tight')
+    # #- save data for plot
+    # copro.evaluation.save_out_ROC_curve(tprs, aucs, out_dir_REF)
 
     #- save output dictionary to csv-file
     copro.utils.save_to_csv(out_dict, out_dir_REF, 'evaluation_metrics')

copro/selection.py

@@ -37,6 +37,7 @@ def filter_conflict_properties(gdf, config):
                 pass
             else:
                 if config.getboolean('general', 'verbose'): print('DEBUG: filtering key', key, 'with value(s)', selection_criteria[key])
+                selection_criteria[key] = [eval(i) for i in selection_criteria[key]]
                 gdf = gdf[gdf[key].isin(selection_criteria[key])]
 
     return gdf

environment.yml

@@ -29,6 +29,5 @@ dependencies:
   - pillow==8.1.0
   - geopandas>=0.8.0
   - rasterio>=1.1.0
-  - fiona
   - rasterstats>=0.14.0
 

example/_scripts/download_example_data.sh

@@ -5,15 +5,15 @@
 
 echo download zip-file
 # for WIN
-curl https://zenodo.org/record/4617719/files/example_data.zip -o example_data.zip
+#curl https://zenodo.org/record/4617719/files/example_data.zip -o example_data.zip
 # for UNIX
-wget curl https://zenodo.org/record/4617719/files/example_data.zip
+wget https://zenodo.org/record/4617719/files/example_data.zip
 
 echo unzip data
 unzip example_data.zip -d example_data
 
 echo copy data
-cp example_data ../example_data
+mv example_data/example_data ../example_data
 
 echo remove zip-file
-rm example_data.zip
+rm -r example_data*

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.1
+current_version = 0.1.2
 commit = True
 tag = True
 

setup.py

@@ -17,8 +17,7 @@
                 'numpy>=1.21.0',
                 'scikit-learn>=0.22.1',
                 'seaborn>=0.11',
-                'numpy>=1.21.0']
-
+                ]
 
 setup_requirements = ['pytest-runner', ]
 
@@ -27,16 +26,15 @@
 setup(
     author="Jannis M. Hoch",
     author_email='j.m.hoch@uu.nl',
-    python_requires='>=3.6',
+    python_requires='>=3.9',
     classifiers=[
         'Development Status :: 4 - Beta',
         'Intended Audience :: Developers',
         'License :: OSI Approved :: MIT License',
         'Natural Language :: English',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.6',
-        'Programming Language :: Python :: 3.7',
-        'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
     ],
     description="Python-model build on scikit-learn functions, designed to facilitate the set-up, execution, and evaluation of machine-learning models for the study of the climate-conflict nexus.",
     entry_points={
@@ -55,6 +53,6 @@
     test_suite='tests',
     tests_require=test_requirements,
     url='https://copro.readthedocs.io/',
-    version='0.1.1',
+    version='0.1.2',
     zip_safe=False,
 )