small adjustments

README.rst

@@ -12,8 +12,8 @@ a machine-learning tool for conflict risk projections based on climate, environm
 .. image:: https://img.shields.io/badge/License-MIT-blue.svg
     :target: https://github.com/JannisHoch/copro/blob/dev/LICENSE
 
-.. image:: https://readthedocs.org/projects/copro/badge/?version=dev
-    :target: https://copro.readthedocs.io/en/dev/?badge=dev
+.. image:: https://readthedocs.org/projects/copro/badge/?version=latest
+    :target: https://copro.readthedocs.io/en/latest/?badge=latest
 
 .. image:: https://img.shields.io/github/v/release/JannisHoch/copro
     :target: https://github.com/JannisHoch/copro/releases/tag/v0.0.5-pre
@@ -76,7 +76,7 @@ By default, output is stored to the output directory specified in the settings-f
 Documentation
 ---------------
 
-Model documentation including model API can be found at http://copro.rtfd.io/
+Model documentation including model API can be found at http://conflict-model.rtfd.io/
 
 Code of conduct and Contributing
 ---------------------------------

copro/plots.py

@@ -55,9 +55,9 @@ def metrics_distribution(out_dict, **kwargs):
 
     fig, ax = plt.subplots(1, 1, **kwargs)
 
-    sbs.distplot(out_dict['Accuracy'], ax=ax, color="k", label='Accuracy')
-    sbs.distplot(out_dict['Precision'], ax=ax, color="r", label='Precision')
-    sbs.distplot(out_dict['Recall'], ax=ax, color="b", label='Recall')
+    sbs.displot(out_dict['Accuracy'], ax=ax, color="k", label='Accuracy')
+    sbs.displot(out_dict['Precision'], ax=ax, color="r", label='Precision')
+    sbs.displot(out_dict['Recall'], ax=ax, color="b", label='Recall')
     plt.legend()
 
     return ax

environment.yml

@@ -14,15 +14,15 @@ dependencies:
   - pytest>=5.4.2
   - pytest-runner
   - descartes
-  - rioxarray
+  - rioxarray==0.1.0
   - ipython
   - notebook
   - nbconvert
-  - scikit-learn
+  - scikit-learn==0.23.2
   - netcdf4
   - ConfigParser
   - click
-  - seaborn
+  - seaborn==0.11.0
   - pip
   - pip:
     - rasterstats==0.14.0

example/example_settings.cfg

@@ -17,8 +17,7 @@ n_runs=50
 [pre_calc]
 # if nothing is specified, the XY array will be stored in input_dir
 # if XY already pre-calculated, then provide path to npy-file
-# XY=
-XY=XY.npy
+XY=
 
 [extent]
 shp=waterProvinces/waterProvinces_Africa.shp
@@ -47,5 +46,5 @@ irr_water_demand=irrWaterDemand.nc
 # choose from: MinMaxScaler, StandardScaler, RobustScaler, QuantileTransformer
 scaler=QuantileTransformer
 # choose from: NuSVC, KNeighborsClassifier, RFClassifier
-model=KNeighborsClassifier
+model=RFClassifier
 train_fraction=0.7