mroe logging of where we are in the feature transformation process

ClimbsRocks · Sep 17, 2017 · 9ef593c · 9ef593c
1 parent e839044
commit 9ef593c
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diff --git a/auto_ml/DataFrameVectorizer.py b/auto_ml/DataFrameVectorizer.py
@@ -42,6 +42,7 @@ def get(self, prop_name, default=None):
 
 
     def fit(self, X, y=None):
+        print('Fitting DataFrameVectorizer')
 
         feature_names = []
         vocab = {}

diff --git a/auto_ml/utils_data_cleaning.py b/auto_ml/utils_data_cleaning.py
@@ -116,6 +116,7 @@ def get(self, prop_name, default=None):
             return default
 
     def fit(self, X_df, y=None):
+        print('Running basic data cleaning')
 
         # See if we should fit TfidfVectorizer or not
         for key in X_df.columns:

diff --git a/auto_ml/utils_feature_selection.py b/auto_ml/utils_feature_selection.py
@@ -44,6 +44,7 @@ def get(self, prop_name, default=None):
 
 
     def fit(self, X, y=None):
+        print('Performing feature selection')
 
 
         self.selector = get_feature_selection_model_from_name(self.type_of_estimator, self.feature_selection_model)

diff --git a/auto_ml/utils_scaling.py b/auto_ml/utils_scaling.py
@@ -79,6 +79,7 @@ def get(self, prop_name, default=None):
 
 
     def fit(self, X, y=None):
+        print('Performing feature scaling')
         self.column_ranges = {}
         self.cols_to_ignore = []