snapshot...

modules/machine_learning/classification.py

@@ -15,10 +15,10 @@ import sklearn.tree as tree
 from sklearn.tree import export_graphviz
 
 
-# def analyze(dataFrame):
-#     no_icing_df = dataFrame[dataFrame['icing_intensity'] == -1]
-#     icing_df = dataFrame[dataFrame['icing_intensity'] >= 1]
-#     return no_icing_df, icing_df
+def analyze(dataFrame):
+    no_icing_df = dataFrame[dataFrame['icing_intensity'] == -1]
+    icing_df = dataFrame[dataFrame['icing_intensity'] >= 1]
+    return no_icing_df, icing_df
 
 
 def plot_confusion_matrix(cm, classes,
@@ -82,7 +82,7 @@ def get_feature_target_data(data_frame, standardize=True):
     icing_df = data_frame
 
     # The independent variables (features) we want to use:
-    params = ['cld_geo_thick', 'cld_temp_acha', 'conv_cloud_fraction', 'supercooled_cloud_fraction', 'cld_reff_dcomp',
+    params = ['cld_temp_acha', 'conv_cloud_fraction', 'supercooled_cloud_fraction', 'cld_reff_dcomp',
               'cld_opd_dcomp', 'iwc_dcomp']
     # Remove this column
     icing_df = icing_df.drop('lwc_dcomp', axis=1)