plot feature correlation matrix

configs/ref_rf.yaml

@@ -83,6 +83,7 @@ prediction:
 validation:
   #- feature_rank
   - permutation_importance
+  # TODO: - feature_importance  # only works for tree based models
   - parallel
   - k-fold:
       folds: 5

uncoverml/geoio.py

@@ -12,6 +12,7 @@
 from typing import Union
 import pickle
 import matplotlib.pyplot as plt
+import seaborn as sns
 import rasterio
 from rasterio.warp import reproject
 from rasterio.windows import Window
@@ -736,6 +737,23 @@ def export_validation_scatter_plot_and_validation_csv(outfile_results, config: C
         plt.savefig(true_vs_pred_plot)
 
 
+def plot_feature_correlation_matrix(config: Config, x_all):
+    fig, corr_ax = plt.subplots()
+    features = [Path(f).stem for f in feature_names(config)]
+    corr_df = pd.DataFrame(x_all)
+    corr_df.columns = features
+    sns.heatmap(corr_df.corr(),
+                vmin=-1, vmax=1, annot=True,
+                square=True, linewidths=.5, cbar_kws={"shrink": .5},
+                cmap='BrBG',
+                )
+    fig.suptitle('Feature Correlations')
+    fig.tight_layout()
+    save_path = Path(config.output_dir).joinpath(config.name + "_feature_correlation.png") \
+        .as_posix()
+    fig.savefig(save_path)
+
+
 def resample(input_tif, output_tif, ratio, resampling="average"):
     """
     Parameters

uncoverml/scripts/uncoverml.py

@@ -55,6 +55,7 @@ def cli(verbosity):
 def run_crossval(x_all, targets_all, config):
     crossval_results = ls.validate.local_crossval(x_all, targets_all, config)
     ls.mpiops.run_once(ls.geoio.export_crossval, crossval_results, config)
+    ls.mpiops.run_once(ls.geoio.plot_feature_correlation_matrix, config, x_all)
 
 
 @cli.command()