old_contents: import unittest
  import analyze
  import pprint
  from sklearn.linear_model import LogisticRegression
  from sklearn import datasets
  import pandas as pd
  
  #TODO: Separate explanation mechanism tests from visualization tests
  #TODO: Add assertions to explanation mechanism tests
  #TODO: Add test for Titanic data set
  
  class AnalyzerTest(unittest.TestCase):
      @unittest.skip('Temp')
      def test_approximate_contribution(self):
          feature_names = ['feature', 'useless_1', 'useless_2']
          training_inputs = [[0,10,10],[10,10,10]]
          training_outputs = [0,1]
          model = LogisticRegression()
          explainable_model = analyze.ExplainableClassifier(feature_names, model)
          explainable_model.fit(training_inputs, training_outputs)
          explanations = [explainable_model.explain_classification(inp, 1000) for inp in training_inputs]
          pprint.pprint(list(zip(training_inputs,explanations)))
          analyze.BarPlot(explanations[0])
  
      def test_iris(self):
          data = datasets.load_iris()
          model = LogisticRegression()
          target = [data.target_names[t] for t in data.target]
          explainable_model = analyze.ExplainableClassifier(data.feature_names, model)
          explainable_model.fit(data.data, target)
          explanation = explainable_model.explain_classification(data.data[0])
          analyze.BarPlot(explanation)
new_contents: import unittest
  import analyze
  import pprint
  from sklearn.linear_model import LogisticRegression
  from sklearn import datasets
  import pandas as pd
  
  #TODO: Separate explanation mechanism tests from visualization tests
  #TODO: Add assertions to explanation mechanism tests
  #TODO: Add test for Titanic data set
  
  class AnalyzerTest(unittest.TestCase):
      def test_approximate_contribution(self):
          feature_names = ['feature', 'useless_1', 'useless_2']
          training_inputs = [[0,10,10],[10,10,10]]
          training_outputs = [0,1]
          model = LogisticRegression()
          explainable_model = analyze.ExplainableClassifier(feature_names, model)
          explainable_model.fit(training_inputs, training_outputs)
          explanations = [explainable_model.explain_classification(inp, 1000) for inp in training_inputs]
          pprint.pprint(list(zip(training_inputs,explanations)))
          analyze.BarPlot(explanations[0])
  
      def test_iris(self):
          data = datasets.load_iris()
          model = LogisticRegression()
          target = [data.target_names[t] for t in data.target]
          explainable_model = analyze.ExplainableClassifier(data.feature_names, model)
          explainable_model.fit(data.data, target)
          explanation = explainable_model.explain_classification(data.data[0])
          analyze.BarPlot(explanation)
subject: Test no longer temp