snapshot...

modules/machine_learning/classification.py

@@ -15,8 +15,7 @@ import sklearn.tree as tree
 from sklearn.tree import export_graphviz
 
 # The independent variables (features) we want to use:
-params = ['cld_temp_acha', 'conv_cloud_fraction', 'supercooled_cloud_fraction', 'cld_reff_dcomp',
-          'cld_opd_dcomp', 'cld_cwp_dcomp']
+params = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp', 'cld_cwp_dcomp']
 
 
 def metrics(y_true, y_pred, y_pred_prob=None):
@@ -89,6 +88,8 @@ def get_feature_target_data(csv_file, reduce_frac=1.0, random_state=42, standard
         icing_df = icing_df.dropna()
         print('NaN removed num obs, features: ', icing_df.shape)
 
+    # icing_df = icing_df[icing_df.cld_temp_acha < 273.5]
+
     x = np.asarray(icing_df[params])
     if standardize:
         x = preprocessing.StandardScaler().fit(x).transform(x)