text: import unittest
  import numpy as np
  import pandas as pd
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import roc_auc_score
  
  from bcselector.variable_selection import DiffVariableSelector, FractionVariableSelector, NoCostVariableSelector
  from bcselector.data_generation import MatrixGenerator
  
  class TestDiffVariableSelector(unittest.TestCase):
      def test_numpy_input(self):
          integer_matrix = np.random.randint(0,10,(100,10))
          diverse_target = np.random.randint(0,10,(100))
          costs = [1.76,  0.19, 0.36,  0.96,  0.41,  0.17, 0.36,  0.75,  0.79, 1.38]
          lamb = 1
  
          dvs = DiffVariableSelector()
          dvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  lamb=lamb,
                  j_criterion_func='mim')
  
          self.assertIsInstance(dvs.variables_selected_order, list)
          self.assertEqual(len(dvs.variables_selected_order), len(costs))
          self.assertAlmostEqual(sum(costs), sum(dvs.cost_variables_selected_order))
  
      def test_pandas_input(self):
          integer_matrix = pd.DataFrame(np.random.randint(0,10,(100,3)), columns=['AA','BB','CC'])
          diverse_target = pd.Series(np.random.randint(0,2,(100)))
          costs = {'AA': 10, 'BB': 1, 'CC': 1.5}
          lamb = 1
  
          dvs = DiffVariableSelector()
          dvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  lamb=lamb,
                  j_criterion_func='mim')
  
          self.assertIsInstance(dvs.variables_selected_order, list)
          self.assertEqual(len(dvs.variables_selected_order), len(costs))
      
      def test_stop_budget(self):
          integer_matrix = pd.DataFrame(np.random.randint(0,10,(100,3)), columns=['AA','BB','CC'])
          diverse_target = pd.Series(np.random.randint(0,2,(100)))
          costs = {'AA': 2, 'BB': 1.1, 'CC': 1.5}
          lamb = 1
  
          dvs = DiffVariableSelector()
          dvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  lamb=lamb,
                  j_criterion_func='mim',
                  budget=2,
                  stop_budget=True)
          self.assertGreater(2, sum(dvs.cost_variables_selected_order))
          self.assertGreaterEqual(2, len(dvs.variables_selected_order))
  
  #     def test_real_dataset(self):
  #         colnames = ['Class','age','sex','steroid','antviral','fatigue','malaise','anorexia','liver_big','liver_firm',
  #             'spleen_palpable','spiders','ascites','varices','bilirubin','alk_phosphate','sgot','albumin',
  #             'protime','histology']
  #         hepatitis = pd.read_csv('./bcselector/tests/data/hepatitis.data', header = None, names = colnames)
  #         hepatitis = hepatitis.fillna(-1)
  #         costs = {'age':1.00,'sex':1.00,'steroid':1.00,'antviral':1.00,'fatigue':1.00,'malaise':1.00,'anorexia':1.00,'liver_big':1.00,'liver_firm':1.00,'spleen_palpable':1.00,'spiders':1.00,'ascites':1.00,'varices':1.00,'bilirubin':7.27,'alk_phosphate':7.27,'sgot':7.27,'albumin':7.27,'protime':8.30,'histology': 1.00}
  #         y_target = hepatitis['Class']
  #         hepatitis.drop(['Class'], axis = 1, inplace = True)
  
  #         # Results are calculated iteratively on paper and checked
  #         # MIM
  #         lamb = 0.1
  #         mim_order = [0, 11, 10, 4, 18, 5, 12, 1, 9, 7, 8, 2, 3, 6, 14, 15, 16, 13, 17]
  #         mim_costs = [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,1., 1., 1., 7.27, 7.27, 7.27, 7.27, 8.3 ]
  #         dvs = DiffVariableSelector()
  #         dvs.fit(data=hepatitis,
  #                 target_variable=y_target,
  #                 costs=costs,
  #                 lamb=lamb,
  #                 j_criterion_func='mim')
  #         model = LogisticRegression()
  #         dvs.scoreCV(model)
  #         self.assertListEqual(mim_costs, dvs.cost_variables_selected_order)
  #         self.assertListEqual(mim_order, dvs.variables_selected_order)
  
  #         # MIFS
  #         lamb = 1
  #         beta = 10
  #         mifs_order = [0, 3, 1, 6, 2, 18, 7, 4, 9, 12, 5, 8, 11, 10, 16, 13, 17, 14, 15]
  #         mifs_costs = [1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,7.27,7.27,8.3,7.27,7.27]
  #         dvs = DiffVariableSelector()
  #         dvs.fit(data=hepatitis,
  #                 target_variable=y_target,
  #                 costs=costs,
  #                 lamb=lamb,
  #                 j_criterion_func='mifs',
  #                 beta=beta)
  #         model = LogisticRegression()
  #         dvs.scoreCV(model,seed=42)
  
  #         self.assertListEqual(mifs_costs, dvs.cost_variables_selected_order)
  #         self.assertListEqual(mifs_order, dvs.variables_selected_order)
  
  #         # MRMR
  #         lamb = 0.001
  #         mrmr_order = [14, 1, 11, 4, 18, 3, 2, 10, 5, 12, 7, 9, 6, 16, 8, 13, 17, 0, 15]
  #         mrmr_costs = [7.27,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,7.27,1.0,7.27,8.3,1.0,7.27]
  #         dvs = DiffVariableSelector()
  #         dvs.fit(data=hepatitis,
  #                 target_variable=y_target,
  #                 costs=costs,
  #                 lamb=lamb,
  #                 j_criterion_func='mrmr')
  #         model = LogisticRegression()
  #         dvs.scoreCV(model)
  #         self.assertListEqual(mrmr_costs, dvs.cost_variables_selected_order)
  #         self.assertListEqual(mrmr_order, dvs.variables_selected_order)
  
  #         # JMI
  #         lamb = 0.1
  #         jmi_order = [0, 5, 10, 11, 18, 12, 4, 7, 9, 8, 2, 1, 6, 3, 15, 14, 13, 16, 17]
  #         jmi_costs = [1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,7.27,7.27,7.27,7.27,8.3]
  #         dvs = DiffVariableSelector()
  #         dvs.fit(data=hepatitis,
  #                 target_variable=y_target,
  #                 costs=costs,
  #                 lamb=lamb,
  #                 j_criterion_func='jmi')
  #         model = LogisticRegression()
  #         dvs.scoreCV(model)
  #         self.assertListEqual(jmi_costs, dvs.cost_variables_selected_order)
  #         self.assertListEqual(jmi_order, dvs.variables_selected_order)
  
  #         # CIFE
  #         lamb = 0.1
  #         beta = 1
  #         cife_order = [0, 5, 9, 8, 10, 2, 7, 12, 6, 15, 18, 3, 11, 13, 1, 4, 16, 17, 14]
  #         cife_costs = [1.,1.,1.,1.,1.,1.,1.,1.,1.,7.27,1.,1.,1.,7.27,1.,1.,7.27,8.3,7.27]
  #         dvs = DiffVariableSelector()
  #         dvs.fit(data=hepatitis,
  #                 target_variable=y_target,
  #                 costs=costs,
  #                 lamb=lamb,
  #                 j_criterion_func='cife',
  #                 beta=beta)
  #         model = LogisticRegression()
  #         dvs.scoreCV(model)
  
  #         self.assertListEqual(cife_order, dvs.variables_selected_order)
  #         self.assertListEqual(cife_costs, dvs.cost_variables_selected_order)
          
      def test_theoretical_output(self):
          integer_matrix = np.array([[0,1,0],[0,1,0],[0,1,2],[0,1,3],[1,1,5]])
          diverse_target = np.array([0,0,0,0,1])
          costs = [1,1,1]
          lamb = 1
  
          dvs = DiffVariableSelector()
          dvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  lamb=lamb,
                  j_criterion_func='mim')
          
          self.assertEqual(dvs.variables_selected_order[0], 2)
  
      def test_score(self):
          integer_matrix = np.random.randint(0,10,(100,10))
          diverse_target = np.random.randint(0,2,(100))
          costs = [ 1.76,  0.19, -0.36,  0.96,  0.41,  0.17, -0.36,  0.75,  0.79, -1.38]
          lamb = 1
  
          dvs = DiffVariableSelector()
          dvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  lamb=lamb,
                  j_criterion_func='mim')
  
          model = LogisticRegression()
          dvs.score(model, scoring_function=roc_auc_score)
          
          self.assertEqual(len(dvs.total_scores), len(costs))
  
      def test_run_score_before_fit(self):
          dvs = DiffVariableSelector()
          model = LogisticRegression()
          with self.assertRaises(AssertionError): dvs.score(model, scoring_function=roc_auc_score)
  
      def test_plot_without_comparision(self):
          integer_matrix = np.random.randint(0,10,(100,10))
          diverse_target = np.random.randint(0,2,(100))
          costs = [1.76,  0.19, 0.36,  0.96,  0.41,  0.17, 0.36,  0.75,  0.79, 1.38]
          lamb = 1
  
          dvs = DiffVariableSelector()
          dvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  lamb=lamb,
                  j_criterion_func='mim')
  
          model = LogisticRegression()
          dvs.score(model, scoring_function=roc_auc_score)
          dvs.plot_scores(budget=1)
  
      def test_plot_comparision(self):
          mg = MatrixGenerator()
          X,y,costs = mg.generate(n_basic_cols=10, noise_sigmas=[0.1,1])
          lamb = 1
  
          dvs = DiffVariableSelector()
          dvs.fit(data=X,
                  target_variable=y,
                  costs=costs,
                  lamb=lamb,
                  j_criterion_func='mim')
  
          model = LogisticRegression()
          dvs.score(model, scoring_function=roc_auc_score)
          dvs.plot_scores(compare_no_cost_method=True, budget=1, model = model)
  
  class TestFractionVariableSelector(unittest.TestCase):
      def test_numpy_input(self):
          integer_matrix = np.random.randint(0,10,(100,10))
          diverse_target = np.random.randint(0,10,(100))
          costs = [ 1.76,  0.19, -0.36,  0.96,  0.41,  0.17, -0.36,  0.75,  0.79, -1.38]
          r = 1
  
          fvs = FractionVariableSelector()
          fvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  r=r,
                  j_criterion_func='mim')
  
          self.assertIsInstance(fvs.variables_selected_order, list)
          self.assertEqual(len(fvs.variables_selected_order), len(costs))
  
      def test_pandas_input(self):
          integer_matrix = pd.DataFrame(np.random.randint(0,10,(100,3)), columns=['AA','BB','CC'])
          diverse_target = np.random.randint(0,2,(100))
          costs = {'AA': 10, 'BB': 1, 'CC': 1.5}
          r = 1
          fvs = FractionVariableSelector()
          fvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  r=r,
                  j_criterion_func='mim')
  
          self.assertIsInstance(fvs.variables_selected_order, list)
          self.assertEqual(len(fvs.variables_selected_order), len(costs))
  
      def test_theoretical_output(self):
          integer_matrix = np.array([[0,1,0],[0,1,0],[0,1,2],[0,1,3],[1,1,5]])
          diverse_target = np.array([0,0,0,0,1])
          costs = [1,1,1]
          r = 1
  
          fvs = FractionVariableSelector()
          fvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  r=r,
                  j_criterion_func='mim')
          
          self.assertEqual(fvs.variables_selected_order[0], 2)
  
      def test_score(self):
          integer_matrix = np.random.randint(0,10,(100,10))
          diverse_target = np.random.randint(0,2,(100))
          costs = [ 1.76,  0.19, -0.36,  0.96,  0.41,  0.17, -0.36,  0.75,  0.79, -1.38]
          r = 1
  
          fvs = FractionVariableSelector()
          fvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  r=r,
                  j_criterion_func='mim')
  
          model = LogisticRegression()
          fvs.score(model, scoring_function=roc_auc_score)
          
          self.assertEqual(len(fvs.total_scores), len(costs))
  
      def test_run_score_before_fit(self):
          fvs = FractionVariableSelector()
          model = LogisticRegression()
          with self.assertRaises(AssertionError): fvs.score(model, scoring_function=roc_auc_score)
  
      def test_stop_budget(self):
          integer_matrix = pd.DataFrame(np.random.randint(0,10,(100,3)), columns=['AA','BB','CC'])
          diverse_target = pd.Series(np.random.randint(0,2,(100)))
          costs = {'AA': 2, 'BB': 1.1, 'CC': 1.5}
          r = 1
  
          fvs = FractionVariableSelector()
          fvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  r=r,
                  j_criterion_func='mim',
                  budget=2,
                  stop_budget=True)
          self.assertGreater(2, sum(fvs.cost_variables_selected_order))
          self.assertGreaterEqual(2, len(fvs.variables_selected_order))
  
  class TestNoCostVariableSelector(unittest.TestCase):
      def test_numpy_input(self):
          integer_matrix = np.random.randint(0,10,(100,10))
          diverse_target = np.random.randint(0,10,(100))
          costs = [ 1.76,  0.19, -0.36,  0.96,  0.41,  0.17, -0.36,  0.75,  0.79, -1.38]
  
          ncvs = NoCostVariableSelector()
          ncvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  j_criterion_func='mim')
  
          self.assertIsInstance(ncvs.variables_selected_order, list)
          self.assertEqual(len(ncvs.variables_selected_order), len(costs))
  
      def test_pandas_input(self):
          integer_matrix = pd.DataFrame(np.random.randint(0,10,(100,3)), columns=['AA','BB','CC'])
          diverse_target = np.random.randint(0,2,(100))
          costs = {'AA': 10, 'BB': 1, 'CC': 1.5}
  
          ncvs = NoCostVariableSelector()
          ncvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  j_criterion_func='mim')
  
          self.assertIsInstance(ncvs.variables_selected_order, list)
          self.assertEqual(len(ncvs.variables_selected_order), len(costs))
  
      def test_theoretical_output(self):
          integer_matrix = np.array([[0,1,0],[0,1,0],[0,1,2],[0,1,3],[1,1,5]])
          diverse_target = np.array([0,0,0,0,1])
          costs = [1,1,1]
  
          ncvs = NoCostVariableSelector()
          ncvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  j_criterion_func='mim')
          
          self.assertEqual(ncvs.variables_selected_order[0], 2)
  
      def test_score(self):
          integer_matrix = np.random.randint(0,10,(100,10))
          diverse_target = np.random.randint(0,2,(100))
          costs = [ 1.76,  0.19, -0.36,  0.96,  0.41,  0.17, -0.36,  0.75,  0.79, -1.38]
  
          ncvs = NoCostVariableSelector()
          ncvs.fit(data=integer_matrix,
                  target_variable=diverse_target,
                  costs=costs,
                  j_criterion_func='mim')
  
          model = LogisticRegression()
          ncvs.score(model, scoring_function=roc_auc_score)
          
          self.assertEqual(len(ncvs.total_scores), len(costs))
  
      def test_run_score_before_fit(self):
          ncvs = NoCostVariableSelector()
          model = LogisticRegression()
          with self.assertRaises(AssertionError): ncvs.score(model, scoring_function=roc_auc_score)
  
  if __name__ == '__main__':
      unittest.main()