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csinva
/
imodels
mirror of https://github.com/csinva/imodels

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discretizer_test.py 5.6 KB
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  1. import os
    2. 

  2. import random
    3. 

  3. import unittest
    4. 

  4. 

  5. import numpy as np
    6. 

  6. import pandas as pd
    7. 

  7. 

  8. from imodels.discretization.discretizer import BasicDiscretizer, RFDiscretizer
    9. 

  9. 

  10. path_to_tests = os.path.dirname(os.path.realpath(__file__))
    11. 

  11. 

  12. 

  13. # class TestBasicDiscretizer(unittest.TestCase):
    14. 

  14. #     def setup(self):
    15. 

  15. #         np.random.seed(13)
    16. 

  16. #         random.seed(13)
    17. 

  17. 

  18. #     def test_discretizer_simple(self):
    19. 

  19. #         """ test discretizer on small synthetic dataset
    20. 

  20. #         """
    21. 

  21. 

  22. #         X = np.array([[1, 99, 43, 34],
    23. 

  23. #                       [1, 76, 22, 10],
    24. 

  24. #                       [0, 83, 11, 0],
    25. 

  25. #                       [0, 99, 74, 33],
    26. 

  26. #                       [0, 53, 40, 34]])
    27. 

  27. #         X = pd.DataFrame(X)
    28. 

  28. 

  29. #         X_test = np.array([[1, 50, 10, 20],
    30. 

  30. #                            [0, 29, 70, 10],
    31. 

  31. #                            [3, 80, 60, 50],
    32. 

  32. #                            [0, 100, 30, 10]])
    33. 

  33. #         X_test = pd.DataFrame(X_test)
    34. 

  34. 

  35. #         discretizer = BasicDiscretizer(n_bins=2, dcols=[],
    36. 

  36. #                                        encode="onehot", strategy="quantile",
    37. 

  37. #                                        onehot_drop="if_binary")
    38. 

  38. #         discretizer.fit(X)
    39. 

  39. #         Xd = discretizer.transform(X)
    40. 

  40. #         Xd_test = discretizer.transform(X_test)
    41. 

  41. #         Xd2 = discretizer.fit_transform(X)
    42. 

  42. 

  43. #         Xd_expected = pd.DataFrame({"0_1": [1, 1, 0, 0, 0],
    44. 

  44. #                                     "1_1": [1, 0, 1, 1, 0],
    45. 

  45. #                                     "2_1": [1, 0, 0, 1, 1],
    46. 

  46. #                                     "3_1": [1, 0, 0, 1, 1]})
    47. 

  47. #         Xd_test_expected = pd.DataFrame({"0_1": [1, 0, 1, 0],
    48. 

  48. #                                          "1_1": [0, 0, 0, 1],
    49. 

  49. #                                          "2_1": [0, 1, 1, 0],
    50. 

  50. #                                          "3_1": [0, 0, 1, 0]})
    51. 

  51. #         assert Xd.equals(Xd_expected)
    52. 

  52. #         assert Xd.equals(Xd2)
    53. 

  53. #         assert Xd_test.equals(Xd_test_expected)
    54. 

  54. 

  55. # def test_discretizer(self):
    56. 

  56. 

  57. #     dir_data = oj("../../Subgroups/supervised-subgroups/data/enhancer_small")
    58. 

  58. #     X_train = pd.read_csv(oj(dir_data, "X_train.csv"), index_col = 0)
    59. 

  59. #     X_test = pd.read_csv(oj(dir_data, "X_test.csv"), index_col = 0)
    60. 

  60. #     Y_train = pd.read_csv(oj(dir_data, "Y_train.csv"))['y']
    61. 

  61. #     Y_test = pd.read_csv(oj(dir_data, "Y_test.csv"))['y']
    62. 

  62. #     X_train.columns = X_train.columns.str.replace("_", "")
    63. 

  63. #     X_test.columns = X_test.columns.str.replace("_", "")
    64. 

  64. 

  65. #     discretizer = BasicDiscretizer(n_bins = 4, dcols = list(X_train.columns)[:40],
    66. 

  66. #                                    encode = "onehot", strategy = "quantile",
    67. 

  67. #                                    onehot_drop = "if_binary")
    68. 

  68. #     discretizer.fit(X_train)
    69. 

  69. #     Xd = discretizer.transform(X_train)
    70. 

  70. #     Xd_test = discretizer.transform(X_test)
    71. 

  71. 

  72. #     Xd.head()
    73. 

  73. #     Xd_test.head()
    74. 

  74. 

  75. #     assert Xd.shape[1] == Xd_test.shape[1]
    76. 

  76. 

  77. 

  78. # class TestRFDiscretizer(unittest.TestCase):
    79. 

  79. 

  80. #     def setup(self):
    81. 

  81. #         np.random.seed(13)
    82. 

  82. #         random.seed(13)
    83. 

  83. 

  84. #     def test_discretizer_simple(self):
    85. 

  85. #         """ test discretizer on small synthetic dataset
    86. 

  86. #         """
    87. 

  87. 

  88. #         X = np.array([[1, 99, 43, 34],
    89. 

  89. #                       [1, 76, 22, 10],
    90. 

  90. #                       [0, 83, 11, 0],
    91. 

  91. #                       [0, 99, 74, 33],
    92. 

  92. #                       [0, 53, 40, 34]])
    93. 

  93. #         X = pd.DataFrame(X)
    94. 

  94. #         y = pd.Series([1, 0, 1, 0, 0])
    95. 

  95. 

  96. #         X_test = np.array([[1, 50, 10, 20],
    97. 

  97. #                            [0, 29, 70, 10],
    98. 

  98. #                            [3, 80, 60, 50],
    99. 

  99. #                            [0, 100, 30, 10]])
    100. 

  100. #         X_test = pd.DataFrame(X_test)
    101. 

  101. #         y_test = pd.Series([1, 0, 1, 0])
    102. 

  102. 

  103. #         random.seed(12345)
    104. 

  104. #         discretizer = RFDiscretizer(rf_model=None, classification=True,
    105. 

  105. #                                     n_bins=2, dcols=[],
    106. 

  106. #                                     encode="onehot", strategy="quantile",
    107. 

  107. #                                     onehot_drop="if_binary")
    108. 

  108. #         discretizer.fit(X, y)
    109. 

  109. #         Xd = discretizer.transform(X)
    110. 

  110. #         Xd_test = discretizer.transform(X_test)
    111. 

  111. #         Xd2 = discretizer.fit_transform(X, y)
    112. 

  112. 

  113. #         Xd_expected = pd.DataFrame({"0_1": [1, 1, 0, 0, 0],
    114. 

  114. #                                     "1_1": [1, 0, 1, 1, 0],
    115. 

  115. #                                     "2_1": [1, 0, 0, 1, 0],
    116. 

  116. #                                     "3_1": [1, 0, 0, 1, 1]})
    117. 

  117. #         Xd_test_expected = pd.DataFrame({"0_1": [1, 0, 1, 0],
    118. 

  118. #                                          "1_1": [0, 0, 1, 1],
    119. 

  119. #                                          "2_1": [0, 1, 1, 0],
    120. 

  120. #                                          "3_1": [1, 0, 1, 0]})
    121. 

  121. # assert Xd.equals(Xd_expected)
    122. 

  122. # assert Xd_test.equals(Xd_test_expected)
    123. 

  123. # assert Xd.equals(Xd2)
    124. 

  124. 

  125. # def test_discretizer(self):
    126. 

  126. 

  127. #     dir_data = oj("../../Subgroups/supervised-subgroups/data/enhancer_small")
    128. 

  128. #     X_train = pd.read_csv(oj(dir_data, "X_train.csv"), index_col = 0)
    129. 

  129. #     X_test = pd.read_csv(oj(dir_data, "X_test.csv"), index_col = 0)
    130. 

  130. #     Y_train = pd.read_csv(oj(dir_data, "Y_train.csv"))['y']
    131. 

  131. #     Y_test = pd.read_csv(oj(dir_data, "Y_test.csv"))['y']
    132. 

  132. #     X_train.columns = X_train.columns.str.replace("_", "")
    133. 

  133. #     X_test.columns = X_test.columns.str.replace("_", "")
    134. 

  134. 

  135. #     discretizer = RFDiscretizer(rf_model = None, classification = True,
    136. 

  136. #                                 n_bins = 4, dcols = [],
    137. 

  137. #                                 encode = "onehot", strategy = "quantile",
    138. 

  138. #                                 onehot_drop = "if_binary")
    139. 

  139. #     discretizer.reweight_n_bins(X = X_train, y = Y_train)
    140. 

  140. #     discretizer.fit(X_train, Y_train)
    141. 

  141. #     Xd = discretizer.transform(X_train)
    142. 

  142. #     Xd_test = discretizer.transform(X_test)
    143. 

  143. 

  144. #     Xd.head()
    145. 

  145. #     Xd_test.head()
    146. 

  146. 

  147. #     assert discretizer.n_bins.sum() == (len(discretizer.dcols) * 4)
    148.
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