Merge pull request #294 from ndawe/master

update TMVA examples to use new DataLoader interface

Author: ndawe

examples/tmva/plot_multiclass.py

@@ -38,17 +38,19 @@
 factory = TMVA.Factory('classifier', output,
                        'AnalysisType=Multiclass:'
                        '!V:Silent:!DrawProgressBar')
+
+data = TMVA.DataLoader('.')
 for n in range(2):
-    factory.AddVariable('f{0}'.format(n), 'F')
+    data.AddVariable('f{0}'.format(n), 'F')
 
 # Call root_numpy's utility functions to add events from the arrays
-add_classification_events(factory, X_train, y_train, weights=w_train)
-add_classification_events(factory, X_test, y_test, weights=w_test, test=True)
+add_classification_events(data, X_train, y_train, weights=w_train)
+add_classification_events(data, X_test, y_test, weights=w_test, test=True)
 # The following line is necessary if events have been added individually:
-factory.PrepareTrainingAndTestTree(TCut('1'), 'NormMode=EqualNumEvents')
+data.PrepareTrainingAndTestTree(TCut('1'), 'NormMode=EqualNumEvents')
 
 # Train an MLP
-factory.BookMethod('MLP', 'MLP',
+factory.BookMethod(data, 'MLP', 'MLP',
                    'NeuronType=tanh:NCycles=200:HiddenLayers=N+2,2:'
                    'TestRate=5:EstimatorType=MSE')
 factory.TrainAllMethods()

examples/tmva/plot_regression.py

@@ -23,18 +23,20 @@
 factory = TMVA.Factory('regressor', output,
                        'AnalysisType=Regression:'
                        '!V:Silent:!DrawProgressBar')
-factory.AddVariable('x', 'F')
-factory.AddTarget('y', 'F')
 
-add_regression_events(factory, X, y)
-add_regression_events(factory, X, y, test=True)
+data = TMVA.DataLoader('.')
+data.AddVariable('x', 'F')
+data.AddTarget('y', 'F')
+
+add_regression_events(data, X, y)
+add_regression_events(data, X, y, test=True)
 # The following line is necessary if events have been added individually:
-factory.PrepareTrainingAndTestTree(TCut('1'), '')
+data.PrepareTrainingAndTestTree(TCut('1'), '')
 
-factory.BookMethod('BDT', 'BDT1',
+factory.BookMethod(data, 'BDT', 'BDT1',
                    'nCuts=20:NTrees=1:MaxDepth=4:BoostType=AdaBoostR2:'
                    'SeparationType=RegressionVariance')
-factory.BookMethod('BDT', 'BDT2',
+factory.BookMethod(data, 'BDT', 'BDT2',
                    'nCuts=20:NTrees=300:MaxDepth=4:BoostType=AdaBoostR2:'
                    'SeparationType=RegressionVariance')
 factory.TrainAllMethods()

examples/tmva/plot_twoclass.py

@@ -36,17 +36,18 @@
 factory = TMVA.Factory('classifier', output,
                        'AnalysisType=Classification:'
                        '!V:Silent:!DrawProgressBar')
+data = TMVA.DataLoader('.')
 for n in range(n_vars):
-    factory.AddVariable('f{0}'.format(n), 'F')
+    data.AddVariable('f{0}'.format(n), 'F')
 
 # Call root_numpy's utility functions to add events from the arrays
-add_classification_events(factory, X_train, y_train, weights=w_train)
-add_classification_events(factory, X_test, y_test, weights=w_test, test=True)
+add_classification_events(data, X_train, y_train, weights=w_train)
+add_classification_events(data, X_test, y_test, weights=w_test, test=True)
 # The following line is necessary if events have been added individually:
-factory.PrepareTrainingAndTestTree(TCut('1'), 'NormMode=EqualNumEvents')
+data.PrepareTrainingAndTestTree(TCut('1'), 'NormMode=EqualNumEvents')
 
 # Train a classifier
-factory.BookMethod('Fisher', 'Fisher',
+factory.BookMethod(data, 'Fisher', 'Fisher',
                    'Fisher:VarTransform=None:CreateMVAPdfs:'
                    'PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:'
                    'NsmoothMVAPdf=10')