Now feel good about confusion plots

setup.py

@@ -2,7 +2,7 @@
 
 # Package information
 name = 'madml'
-version = '2.1.0'  # Need to increment every time to push to PyPI
+version = '2.1.2'  # Need to increment every time to push to PyPI
 description = 'Application domain of machine learning in materials science.'
 url = 'https://github.com/leschultz/'\
       'materials_application_domain_machine_learning.git'

src/madml/calculators.py

@@ -172,7 +172,6 @@ def pr(d, labels, precs):
 
     # Maximum F1 score
     max_f1_index = np.argmax(f1_scores)
-    print(f1_scores, max_f1_index)
 
     data['Max F1'] = {
                       'Precision': precision[max_f1_index],

src/madml/models.py

@@ -406,7 +406,7 @@ def cv(self, split, gs_model, ds_model, X, y, g=None):
 
         return data
 
-    def fit(self, X, y, g=None):
+    def fit(self, X, y, g=None, d_input=None):
         '''
         Fit all models. Thresholds for domain classification are also set.
 
@@ -506,6 +506,12 @@ def fit(self, X, y, g=None):
                              bin_cv['domain_cdf_area'].values,
                              )
 
+        pred = self.combine_domains_preds(data_cv['d_pred'], d_input)
+        data_cv = pd.concat([
+                             data_cv.reset_index(drop=True),
+                             pred.reset_index(drop=True),
+                             ], axis=1)
+
         self.data_cv = data_cv
         self.bin_cv = bin_cv
 

src/madml/plots.py

@@ -5,7 +5,6 @@
                              )
 
 from matplotlib import pyplot as pl
-from madml.models import domain
 from madml import calculators
 from sklearn import metrics