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  1. ERROR:root:Input X contains NaN.
  2. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  3. Traceback (most recent call last):
  4. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 383, in <module>
  5. dt_multi.fit(X_train_fold, y_train_fold)
  6. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  7. super().fit(
  8. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  9. X, y = self._validate_data(
  10. ^^^^^^^^^^^^^^^^^^^^
  11. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  12. X = check_array(X, input_name="X", **check_X_params)
  13. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  14. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  15. _assert_all_finite(
  16. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  17. raise ValueError(msg_err)
  18. ValueError: Input X contains NaN.
  19. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  20. 28 Jun 2024 13:26
  21.  
  22. *********
  23.  
  24. ERROR:root:Input X contains NaN.
  25. LinearRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  26. Traceback (most recent call last):
  27. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 436, in <module>
  28. lr_multi.fit(X_train_fold, y_train_fold)
  29. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_base.py", line 648, in fit
  30. X, y = self._validate_data(
  31. ^^^^^^^^^^^^^^^^^^^^
  32. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  33. X, y = check_X_y(X, y, **check_params)
  34. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  35. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  36. X = check_array(
  37. ^^^^^^^^^^^^
  38. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  39. _assert_all_finite(
  40. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  41. raise ValueError(msg_err)
  42. ValueError: Input X contains NaN.
  43. LinearRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  44. 28 Jun 2024 13:26
  45.  
  46. *********
  47.  
  48. ERROR:root:Input X contains NaN.
  49. LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  50. Traceback (most recent call last):
  51. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 488, in <module>
  52. logreg_multi.fit(X_train_fold, y_train_fold)
  53. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py", line 1196, in fit
  54. X, y = self._validate_data(
  55. ^^^^^^^^^^^^^^^^^^^^
  56. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  57. X, y = check_X_y(X, y, **check_params)
  58. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  59. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  60. X = check_array(
  61. ^^^^^^^^^^^^
  62. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  63. _assert_all_finite(
  64. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  65. raise ValueError(msg_err)
  66. ValueError: Input X contains NaN.
  67. LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  68. 28 Jun 2024 13:26
  69.  
  70. *********
  71.  
  72. ERROR:root:Input X contains NaN.
  73. KNeighborsClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  74. Traceback (most recent call last):
  75. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 541, in <module>
  76. knn.fit(X_train_fold, y_train_fold)
  77. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/neighbors/_classification.py", line 215, in fit
  78. return self._fit(X, y)
  79. ^^^^^^^^^^^^^^^
  80. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/neighbors/_base.py", line 454, in _fit
  81. X, y = self._validate_data(
  82. ^^^^^^^^^^^^^^^^^^^^
  83. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  84. X, y = check_X_y(X, y, **check_params)
  85. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  86. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  87. X = check_array(
  88. ^^^^^^^^^^^^
  89. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  90. _assert_all_finite(
  91. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  92. raise ValueError(msg_err)
  93. ValueError: Input X contains NaN.
  94. KNeighborsClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  95. 28 Jun 2024 13:26
  96.  
  97. *********
  98.  
  99. ERROR:root:Input X contains NaN.
  100. RandomForestClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  101. Traceback (most recent call last):
  102. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 592, in <module>
  103. rf.fit(X_train_fold, y_train_fold)
  104. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_forest.py", line 345, in fit
  105. X, y = self._validate_data(
  106. ^^^^^^^^^^^^^^^^^^^^
  107. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  108. X, y = check_X_y(X, y, **check_params)
  109. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  110. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  111. X = check_array(
  112. ^^^^^^^^^^^^
  113. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  114. _assert_all_finite(
  115. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  116. raise ValueError(msg_err)
  117. ValueError: Input X contains NaN.
  118. RandomForestClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  119. 28 Jun 2024 13:26
  120.  
  121. *********
  122.  
  123. ERROR:root:Input X contains NaN.
  124. MLPClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  125. Traceback (most recent call last):
  126. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 642, in <module>
  127. mlp.fit(X_train_fold, y_train_fold)
  128. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py", line 749, in fit
  129. return self._fit(X, y, incremental=False)
  130. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  131. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py", line 437, in _fit
  132. X, y = self._validate_input(X, y, incremental, reset=first_pass)
  133. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  134. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_multilayer_perceptron.py", line 1089, in _validate_input
  135. X, y = self._validate_data(
  136. ^^^^^^^^^^^^^^^^^^^^
  137. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  138. X, y = check_X_y(X, y, **check_params)
  139. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  140. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  141. X = check_array(
  142. ^^^^^^^^^^^^
  143. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  144. _assert_all_finite(
  145. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  146. raise ValueError(msg_err)
  147. ValueError: Input X contains NaN.
  148. MLPClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  149. 28 Jun 2024 13:26
  150.  
  151. *********
  152.  
  153. ERROR:root:Input X contains NaN.
  154. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  155. Traceback (most recent call last):
  156. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 696, in <module>
  157. bagging_classifier.fit(X_train_fold, y_train_fold)
  158. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py", line 337, in fit
  159. return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
  160. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  161. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py", line 472, in _fit
  162. all_results = Parallel(
  163. ^^^^^^^^^
  164. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 63, in __call__
  165. return super().__call__(iterable_with_config)
  166. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  167. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1863, in __call__
  168. return output if self.return_generator else list(output)
  169. ^^^^^^^^^^^^
  170. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
  171. res = func(*args, **kwargs)
  172. ^^^^^^^^^^^^^^^^^^^^^
  173. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 123, in __call__
  174. return self.function(*args, **kwargs)
  175. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  176. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py", line 141, in _parallel_build_estimators
  177. estimator_fit(X_, y, sample_weight=curr_sample_weight)
  178. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  179. super().fit(
  180. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  181. X, y = self._validate_data(
  182. ^^^^^^^^^^^^^^^^^^^^
  183. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  184. X = check_array(X, input_name="X", **check_X_params)
  185. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  186. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  187. _assert_all_finite(
  188. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  189. raise ValueError(msg_err)
  190. ValueError: Input X contains NaN.
  191. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  192. 28 Jun 2024 13:26
  193.  
  194. *********
  195.  
  196. ERROR:root:Input X contains NaN.
  197. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  198. Traceback (most recent call last):
  199. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 747, in <module>
  200. classifier_j48.fit(X_train_fold, y_train_fold)
  201. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  202. super().fit(
  203. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  204. X, y = self._validate_data(
  205. ^^^^^^^^^^^^^^^^^^^^
  206. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  207. X = check_array(X, input_name="X", **check_X_params)
  208. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  209. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  210. _assert_all_finite(
  211. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  212. raise ValueError(msg_err)
  213. ValueError: Input X contains NaN.
  214. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  215. 28 Jun 2024 13:26
  216.  
  217. *********
  218.  
  219. ERROR:root:Input X contains NaN.
  220. GradientBoostingClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  221. Traceback (most recent call last):
  222. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 923, in <module>
  223. multi_gb.fit(X_train_fold, y_train_fold)
  224. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_gb.py", line 429, in fit
  225. X, y = self._validate_data(
  226. ^^^^^^^^^^^^^^^^^^^^
  227. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  228. X, y = check_X_y(X, y, **check_params)
  229. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  230. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  231. X = check_array(
  232. ^^^^^^^^^^^^
  233. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  234. _assert_all_finite(
  235. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  236. raise ValueError(msg_err)
  237. ValueError: Input X contains NaN.
  238. GradientBoostingClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  239. 28 Jun 2024 13:27
  240.  
  241. *********
  242.  
  243. ERROR:root:Input X contains NaN.
  244. GaussianNB does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  245. Traceback (most recent call last):
  246. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 1027, in <module>
  247. NB_model.fit(X_train_fold, y_train_fold)
  248. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/naive_bayes.py", line 267, in fit
  249. return self._partial_fit(
  250. ^^^^^^^^^^^^^^^^^^
  251. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/naive_bayes.py", line 428, in _partial_fit
  252. X, y = self._validate_data(X, y, reset=first_call)
  253. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  254. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  255. X, y = check_X_y(X, y, **check_params)
  256. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  257. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  258. X = check_array(
  259. ^^^^^^^^^^^^
  260. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  261. _assert_all_finite(
  262. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  263. raise ValueError(msg_err)
  264. ValueError: Input X contains NaN.
  265. GaussianNB does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  266. 28 Jun 2024 13:27
  267.  
  268. *********
  269.  
  270. ERROR:root:Input X contains NaN.
  271. AdaBoostClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  272. Traceback (most recent call last):
  273. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 1086, in <module>
  274. AB_model.fit(X_train_fold, y_train_fold)
  275. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py", line 126, in fit
  276. X, y = self._validate_data(
  277. ^^^^^^^^^^^^^^^^^^^^
  278. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  279. X, y = check_X_y(X, y, **check_params)
  280. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  281. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  282. X = check_array(
  283. ^^^^^^^^^^^^
  284. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  285. _assert_all_finite(
  286. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  287. raise ValueError(msg_err)
  288. ValueError: Input X contains NaN.
  289. AdaBoostClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  290. 28 Jun 2024 13:27
  291.  
  292. *********
  293.  
  294. ERROR:root:Input X contains NaN.
  295. QuadraticDiscriminantAnalysis does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  296. Traceback (most recent call last):
  297. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 1139, in <module>
  298. qda_multi.fit(X_train_fold, y_train_fold)
  299. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/discriminant_analysis.py", line 890, in fit
  300. X, y = self._validate_data(X, y)
  301. ^^^^^^^^^^^^^^^^^^^^^^^^^
  302. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  303. X, y = check_X_y(X, y, **check_params)
  304. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  305. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  306. X = check_array(
  307. ^^^^^^^^^^^^
  308. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  309. _assert_all_finite(
  310. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  311. raise ValueError(msg_err)
  312. ValueError: Input X contains NaN.
  313. QuadraticDiscriminantAnalysis does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  314. 28 Jun 2024 13:27
  315.  
  316. *********
  317.  
  318. ERROR:root:Input X contains NaN.
  319. BernoulliRBM does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  320. Traceback (most recent call last):
  321. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 1594, in <module>
  322. dbn_model.fit(X_train.iloc[train_index], y_train.iloc[train_index])
  323. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/pipeline.py", line 401, in fit
  324. Xt = self._fit(X, y, **fit_params_steps)
  325. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  326. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/pipeline.py", line 359, in _fit
  327. X, fitted_transformer = fit_transform_one_cached(
  328. ^^^^^^^^^^^^^^^^^^^^^^^^^
  329. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/memory.py", line 353, in __call__
  330. return self.func(*args, **kwargs)
  331. ^^^^^^^^^^^^^^^^^^^^^^^^^^
  332. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/pipeline.py", line 893, in _fit_transform_one
  333. res = transformer.fit_transform(X, y, **fit_params)
  334. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  335. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 140, in wrapped
  336. data_to_wrap = f(self, X, *args, **kwargs)
  337. ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  338. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 881, in fit_transform
  339. return self.fit(X, y, **fit_params).transform(X)
  340. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  341. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/neural_network/_rbm.py", line 402, in fit
  342. X = self._validate_data(X, accept_sparse="csr", dtype=(np.float64, np.float32))
  343. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  344. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 565, in _validate_data
  345. X = check_array(X, input_name="X", **check_params)
  346. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  347. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  348. _assert_all_finite(
  349. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  350. raise ValueError(msg_err)
  351. ValueError: Input X contains NaN.
  352. BernoulliRBM does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  353. 28 Jun 2024 13:29
  354.  
  355. *********
  356.  
  357. ERROR:root:Input X contains NaN.
  358. PassiveAggressiveClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  359. Traceback (most recent call last):
  360. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 1872, in <module>
  361. model_passive.fit(X_train_fold_passive, y_train_fold_passive)
  362. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_passive_aggressive.py", line 305, in fit
  363. return self._fit(
  364. ^^^^^^^^^^
  365. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 683, in _fit
  366. self._partial_fit(
  367. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 579, in _partial_fit
  368. X, y = self._validate_data(
  369. ^^^^^^^^^^^^^^^^^^^^
  370. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  371. X, y = check_X_y(X, y, **check_params)
  372. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  373. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  374. X = check_array(
  375. ^^^^^^^^^^^^
  376. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  377. _assert_all_finite(
  378. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  379. raise ValueError(msg_err)
  380. ValueError: Input X contains NaN.
  381. PassiveAggressiveClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  382. 28 Jun 2024 13:31
  383.  
  384. *********
  385.  
  386. ERROR:root:Input X contains NaN.
  387. RidgeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  388. Traceback (most recent call last):
  389. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 1925, in <module>
  390. model_ridge.fit(X_train_fold_ridge, y_train_fold_ridge)
  391. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_ridge.py", line 1422, in fit
  392. X, y, sample_weight, Y = self._prepare_data(X, y, sample_weight, self.solver)
  393. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  394. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_ridge.py", line 1171, in _prepare_data
  395. X, y = self._validate_data(
  396. ^^^^^^^^^^^^^^^^^^^^
  397. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  398. X, y = check_X_y(X, y, **check_params)
  399. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  400. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  401. X = check_array(
  402. ^^^^^^^^^^^^
  403. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  404. _assert_all_finite(
  405. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  406. raise ValueError(msg_err)
  407. ValueError: Input X contains NaN.
  408. RidgeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  409. 28 Jun 2024 13:31
  410.  
  411. *********
  412.  
  413. ERROR:root:Input X contains NaN.
  414. NearestCentroid does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  415. Traceback (most recent call last):
  416. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 1981, in <module>
  417. model_nc.fit(X_train_fold_nc, y_train_fold_nc)
  418. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/neighbors/_nearest_centroid.py", line 142, in fit
  419. X, y = self._validate_data(X, y, accept_sparse=["csr", "csc"])
  420. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  421. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  422. X, y = check_X_y(X, y, **check_params)
  423. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  424. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  425. X = check_array(
  426. ^^^^^^^^^^^^
  427. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  428. _assert_all_finite(
  429. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  430. raise ValueError(msg_err)
  431. ValueError: Input X contains NaN.
  432. NearestCentroid does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  433. 28 Jun 2024 13:31
  434.  
  435. *********
  436.  
  437. ERROR:root:Input X contains NaN.
  438. LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  439. Traceback (most recent call last):
  440. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 2072, in <module>
  441. model_cslr.fit(X_train_fold_cslr, y_train_fold_cslr, sample_weight=sample_weights_fold_cslr)
  442. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py", line 1196, in fit
  443. X, y = self._validate_data(
  444. ^^^^^^^^^^^^^^^^^^^^
  445. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  446. X, y = check_X_y(X, y, **check_params)
  447. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  448. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  449. X = check_array(
  450. ^^^^^^^^^^^^
  451. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  452. _assert_all_finite(
  453. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  454. raise ValueError(msg_err)
  455. ValueError: Input X contains NaN.
  456. LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  457. 28 Jun 2024 13:31
  458.  
  459. *********
  460.  
  461. ERROR:root:Input X contains NaN.
  462. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  463. Traceback (most recent call last):
  464. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 2139, in <module>
  465. bagging_model.fit(X_train_fold_csbc, y_train_fold_csbc)
  466. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py", line 337, in fit
  467. return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
  468. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  469. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py", line 472, in _fit
  470. all_results = Parallel(
  471. ^^^^^^^^^
  472. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 63, in __call__
  473. return super().__call__(iterable_with_config)
  474. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  475. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1863, in __call__
  476. return output if self.return_generator else list(output)
  477. ^^^^^^^^^^^^
  478. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
  479. res = func(*args, **kwargs)
  480. ^^^^^^^^^^^^^^^^^^^^^
  481. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 123, in __call__
  482. return self.function(*args, **kwargs)
  483. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  484. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py", line 141, in _parallel_build_estimators
  485. estimator_fit(X_, y, sample_weight=curr_sample_weight)
  486. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  487. super().fit(
  488. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  489. X, y = self._validate_data(
  490. ^^^^^^^^^^^^^^^^^^^^
  491. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  492. X = check_array(X, input_name="X", **check_X_params)
  493. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  494. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  495. _assert_all_finite(
  496. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  497. raise ValueError(msg_err)
  498. ValueError: Input X contains NaN.
  499. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  500. 28 Jun 2024 13:31
  501.  
  502. *********
  503.  
  504. ERROR:root:name 'LGBMClassifier' is not defined
  505. Traceback (most recent call last):
  506. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 2191, in <module>
  507. lgbm = LGBMClassifier()
  508. ^^^^^^^^^^^^^^
  509. NameError: name 'LGBMClassifier' is not defined
  510. 28 Jun 2024 13:31
  511.  
  512. *********
  513.  
  514. ERROR:root:Input X contains NaN.
  515. LinearDiscriminantAnalysis does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  516. Traceback (most recent call last):
  517. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 2246, in <module>
  518. X_train_fold_lda = lda.fit_transform(X_train_fold_lda, y_train_fold_lda)
  519. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  520. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 140, in wrapped
  521. data_to_wrap = f(self, X, *args, **kwargs)
  522. ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  523. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 881, in fit_transform
  524. return self.fit(X, y, **fit_params).transform(X)
  525. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  526. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/discriminant_analysis.py", line 575, in fit
  527. X, y = self._validate_data(
  528. ^^^^^^^^^^^^^^^^^^^^
  529. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  530. X, y = check_X_y(X, y, **check_params)
  531. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  532. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  533. X = check_array(
  534. ^^^^^^^^^^^^
  535. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  536. _assert_all_finite(
  537. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  538. raise ValueError(msg_err)
  539. ValueError: Input X contains NaN.
  540. LinearDiscriminantAnalysis does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  541. 28 Jun 2024 13:31
  542.  
  543. *********
  544.  
  545. ERROR:root:Input X contains NaN.
  546. SGDClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  547. Traceback (most recent call last):
  548. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 2391, in <module>
  549. sgd_fold.fit(X_train_fold, y_train_fold)
  550. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/pipeline.py", line 405, in fit
  551. self._final_estimator.fit(Xt, y, **fit_params_last_step)
  552. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 894, in fit
  553. return self._fit(
  554. ^^^^^^^^^^
  555. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 683, in _fit
  556. self._partial_fit(
  557. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_stochastic_gradient.py", line 579, in _partial_fit
  558. X, y = self._validate_data(
  559. ^^^^^^^^^^^^^^^^^^^^
  560. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  561. X, y = check_X_y(X, y, **check_params)
  562. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  563. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  564. X = check_array(
  565. ^^^^^^^^^^^^
  566. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  567. _assert_all_finite(
  568. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  569. raise ValueError(msg_err)
  570. ValueError: Input X contains NaN.
  571. SGDClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  572. 28 Jun 2024 13:32
  573.  
  574. *********
  575.  
  576. ERROR:root:Input X contains NaN.
  577. ExtraTreesClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  578. Traceback (most recent call last):
  579. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 2442, in <module>
  580. extra_trees_fold.fit(X_train_fold, y_train_fold)
  581. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_forest.py", line 345, in fit
  582. X, y = self._validate_data(
  583. ^^^^^^^^^^^^^^^^^^^^
  584. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  585. X, y = check_X_y(X, y, **check_params)
  586. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  587. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  588. X = check_array(
  589. ^^^^^^^^^^^^
  590. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  591. _assert_all_finite(
  592. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  593. raise ValueError(msg_err)
  594. ValueError: Input X contains NaN.
  595. ExtraTreesClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  596. 28 Jun 2024 13:32
  597.  
  598. *********
  599.  
  600. ERROR:root:Input X contains NaN.
  601. GaussianMixture does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  602. Traceback (most recent call last):
  603. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 2707, in <module>
  604. gmm_fold_gmm.fit(X_class_gmm)
  605. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/mixture/_base.py", line 186, in fit
  606. self.fit_predict(X, y)
  607. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/mixture/_base.py", line 218, in fit_predict
  608. X = self._validate_data(X, dtype=[np.float64, np.float32], ensure_min_samples=2)
  609. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  610. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 565, in _validate_data
  611. X = check_array(X, input_name="X", **check_params)
  612. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  613. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  614. _assert_all_finite(
  615. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  616. raise ValueError(msg_err)
  617. ValueError: Input X contains NaN.
  618. GaussianMixture does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  619. 28 Jun 2024 13:33
  620.  
  621. *********
  622.  
  623. ERROR:root:Input X contains NaN.
  624. BernoulliNB does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  625. Traceback (most recent call last):
  626. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 2782, in <module>
  627. bnb_fold_bnb.fit(X_train_fold_bnb, y_train_fold_bnb)
  628. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/naive_bayes.py", line 749, in fit
  629. X, y = self._check_X_y(X, y)
  630. ^^^^^^^^^^^^^^^^^^^^^
  631. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/naive_bayes.py", line 1201, in _check_X_y
  632. X, y = super()._check_X_y(X, y, reset=reset)
  633. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  634. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/naive_bayes.py", line 583, in _check_X_y
  635. return self._validate_data(X, y, accept_sparse="csr", reset=reset)
  636. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  637. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  638. X, y = check_X_y(X, y, **check_params)
  639. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  640. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  641. X = check_array(
  642. ^^^^^^^^^^^^
  643. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  644. _assert_all_finite(
  645. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  646. raise ValueError(msg_err)
  647. ValueError: Input X contains NaN.
  648. BernoulliNB does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  649. 28 Jun 2024 13:33
  650.  
  651. *********
  652.  
  653. ERROR:root:Input X contains NaN.
  654. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  655. Traceback (most recent call last):
  656. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 2904, in <module>
  657. base_model1.fit(X_train_fold_blend, y_train_fold_blend)
  658. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  659. super().fit(
  660. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  661. X, y = self._validate_data(
  662. ^^^^^^^^^^^^^^^^^^^^
  663. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  664. X = check_array(X, input_name="X", **check_X_params)
  665. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  666. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  667. _assert_all_finite(
  668. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  669. raise ValueError(msg_err)
  670. ValueError: Input X contains NaN.
  671. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  672. 28 Jun 2024 13:34
  673.  
  674. *********
  675.  
  676. ERROR:root:Input X contains NaN.
  677. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  678. Traceback (most recent call last):
  679. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3044, in <module>
  680. base_model_fold_constructive_learning.fit(X_train_fold_constructive_learning, y_train_fold_constructive_learning)
  681. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  682. super().fit(
  683. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  684. X, y = self._validate_data(
  685. ^^^^^^^^^^^^^^^^^^^^
  686. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  687. X = check_array(X, input_name="X", **check_X_params)
  688. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  689. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  690. _assert_all_finite(
  691. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  692. raise ValueError(msg_err)
  693. ValueError: Input X contains NaN.
  694. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  695. 28 Jun 2024 13:34
  696.  
  697. *********
  698.  
  699. ERROR:root:Input X contains NaN.
  700. RandomForestClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  701. Traceback (most recent call last):
  702. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3120, in <module>
  703. ais_model_fold_ais.fit(X_train_fold_ais, y_train_fold_ais)
  704. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3093, in fit
  705. self.base_model.fit(X_train, y_train)
  706. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_forest.py", line 345, in fit
  707. X, y = self._validate_data(
  708. ^^^^^^^^^^^^^^^^^^^^
  709. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  710. X, y = check_X_y(X, y, **check_params)
  711. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  712. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  713. X = check_array(
  714. ^^^^^^^^^^^^
  715. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  716. _assert_all_finite(
  717. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  718. raise ValueError(msg_err)
  719. ValueError: Input X contains NaN.
  720. RandomForestClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  721. 28 Jun 2024 13:34
  722.  
  723. *********
  724.  
  725. ERROR:root:Input X contains NaN.
  726. GaussianNB does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  727. Traceback (most recent call last):
  728. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3182, in <module>
  729. gbbk_model_fold_gbkk.fit(X_train_fold_gbkk, y_train_fold_gbkk)
  730. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/naive_bayes.py", line 267, in fit
  731. return self._partial_fit(
  732. ^^^^^^^^^^^^^^^^^^
  733. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/naive_bayes.py", line 428, in _partial_fit
  734. X, y = self._validate_data(X, y, reset=first_call)
  735. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  736. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  737. X, y = check_X_y(X, y, **check_params)
  738. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  739. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  740. X = check_array(
  741. ^^^^^^^^^^^^
  742. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  743. _assert_all_finite(
  744. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  745. raise ValueError(msg_err)
  746. ValueError: Input X contains NaN.
  747. GaussianNB does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  748. 28 Jun 2024 13:34
  749.  
  750. *********
  751.  
  752. ERROR:root:Input X contains NaN.
  753. SVC does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  754. Traceback (most recent call last):
  755. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3245, in <module>
  756. gbbk_model_fold_gbkk.fit(X_train_fold_gbkk, y_train_fold_gbkk)
  757. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/svm/_base.py", line 192, in fit
  758. X, y = self._validate_data(
  759. ^^^^^^^^^^^^^^^^^^^^
  760. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  761. X, y = check_X_y(X, y, **check_params)
  762. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  763. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  764. X = check_array(
  765. ^^^^^^^^^^^^
  766. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  767. _assert_all_finite(
  768. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  769. raise ValueError(msg_err)
  770. ValueError: Input X contains NaN.
  771. SVC does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  772. 28 Jun 2024 13:34
  773.  
  774. *********
  775.  
  776. ERROR:root:Input X contains NaN.
  777. GaussianNB does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  778. Traceback (most recent call last):
  779. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3306, in <module>
  780. hnb_model_fold.fit(pd.DataFrame(X_train_fold_hnb), pd.DataFrame(y_train_fold_hnb))
  781. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/naive_bayes.py", line 267, in fit
  782. return self._partial_fit(
  783. ^^^^^^^^^^^^^^^^^^
  784. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/naive_bayes.py", line 428, in _partial_fit
  785. X, y = self._validate_data(X, y, reset=first_call)
  786. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  787. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  788. X, y = check_X_y(X, y, **check_params)
  789. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  790. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  791. X = check_array(
  792. ^^^^^^^^^^^^
  793. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  794. _assert_all_finite(
  795. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  796. raise ValueError(msg_err)
  797. ValueError: Input X contains NaN.
  798. GaussianNB does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  799. 28 Jun 2024 13:34
  800.  
  801. *********
  802.  
  803. ERROR:root:Input X contains NaN.
  804. RFE does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  805. Traceback (most recent call last):
  806. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3436, in <module>
  807. rfe.fit(X_train_fold_igrf_rfe, y_train_fold_igrf_rfe)
  808. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/feature_selection/_rfe.py", line 251, in fit
  809. return self._fit(X, y, **fit_params)
  810. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  811. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/feature_selection/_rfe.py", line 260, in _fit
  812. X, y = self._validate_data(
  813. ^^^^^^^^^^^^^^^^^^^^
  814. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  815. X, y = check_X_y(X, y, **check_params)
  816. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  817. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  818. X = check_array(
  819. ^^^^^^^^^^^^
  820. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  821. _assert_all_finite(
  822. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  823. raise ValueError(msg_err)
  824. ValueError: Input X contains NaN.
  825. RFE does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  826. 28 Jun 2024 13:34
  827.  
  828. *********
  829.  
  830. ERROR:root:Input X contains NaN.
  831. FastICA does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  832. Traceback (most recent call last):
  833. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3502, in <module>
  834. X_train_ica_fold = ica_fold.fit_transform(X_train_fold_ica)
  835. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  836. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 140, in wrapped
  837. data_to_wrap = f(self, X, *args, **kwargs)
  838. ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  839. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py", line 708, in fit_transform
  840. return self._fit_transform(X, compute_sources=True)
  841. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  842. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/decomposition/_fastica.py", line 558, in _fit_transform
  843. XT = self._validate_data(
  844. ^^^^^^^^^^^^^^^^^^^^
  845. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 565, in _validate_data
  846. X = check_array(X, input_name="X", **check_params)
  847. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  848. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  849. _assert_all_finite(
  850. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  851. raise ValueError(msg_err)
  852. ValueError: Input X contains NaN.
  853. FastICA does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  854. 28 Jun 2024 13:34
  855.  
  856. *********
  857.  
  858. ERROR:root:Input X contains NaN.
  859. LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  860. Traceback (most recent call last):
  861. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3575, in <module>
  862. lasso_model_fold.fit(X_train_fold_lasso, y_train_fold_lasso)
  863. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py", line 1196, in fit
  864. X, y = self._validate_data(
  865. ^^^^^^^^^^^^^^^^^^^^
  866. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  867. X, y = check_X_y(X, y, **check_params)
  868. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  869. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  870. X = check_array(
  871. ^^^^^^^^^^^^
  872. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  873. _assert_all_finite(
  874. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  875. raise ValueError(msg_err)
  876. ValueError: Input X contains NaN.
  877. LogisticRegression does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  878. 28 Jun 2024 13:34
  879.  
  880. *********
  881.  
  882. ERROR:root:Input X contains NaN.
  883. KNeighborsClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  884. Traceback (most recent call last):
  885. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3639, in <module>
  886. meta_knn_model_fold.fit(X_train_fold_meta_knn, y_train_fold_meta_knn)
  887. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py", line 337, in fit
  888. return self._fit(X, y, self.max_samples, sample_weight=sample_weight)
  889. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  890. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py", line 472, in _fit
  891. all_results = Parallel(
  892. ^^^^^^^^^
  893. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 63, in __call__
  894. return super().__call__(iterable_with_config)
  895. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  896. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1863, in __call__
  897. return output if self.return_generator else list(output)
  898. ^^^^^^^^^^^^
  899. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
  900. res = func(*args, **kwargs)
  901. ^^^^^^^^^^^^^^^^^^^^^
  902. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 123, in __call__
  903. return self.function(*args, **kwargs)
  904. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  905. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_bagging.py", line 144, in _parallel_build_estimators
  906. estimator_fit(X_, y[indices])
  907. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/neighbors/_classification.py", line 215, in fit
  908. return self._fit(X, y)
  909. ^^^^^^^^^^^^^^^
  910. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/neighbors/_base.py", line 454, in _fit
  911. X, y = self._validate_data(
  912. ^^^^^^^^^^^^^^^^^^^^
  913. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  914. X, y = check_X_y(X, y, **check_params)
  915. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  916. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  917. X = check_array(
  918. ^^^^^^^^^^^^
  919. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  920. _assert_all_finite(
  921. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  922. raise ValueError(msg_err)
  923. ValueError: Input X contains NaN.
  924. KNeighborsClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  925. 28 Jun 2024 13:34
  926.  
  927. *********
  928.  
  929. ERROR:root:Input X contains NaN.
  930. TSNE does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  931. Traceback (most recent call last):
  932. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3706, in <module>
  933. X_train_tsne_fold_tsnerf = tsne.fit_transform(X_train_fold_tsnerf)
  934. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  935. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py", line 1119, in fit_transform
  936. embedding = self._fit(X)
  937. ^^^^^^^^^^^^
  938. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py", line 854, in _fit
  939. X = self._validate_data(
  940. ^^^^^^^^^^^^^^^^^^^^
  941. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 565, in _validate_data
  942. X = check_array(X, input_name="X", **check_params)
  943. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  944. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  945. _assert_all_finite(
  946. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  947. raise ValueError(msg_err)
  948. ValueError: Input X contains NaN.
  949. TSNE does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  950. 28 Jun 2024 13:34
  951.  
  952. *********
  953.  
  954. ERROR:root:Input X contains NaN.
  955. PCA does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  956. Traceback (most recent call last):
  957. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3871, in <module>
  958. X_train_fold_pca = pca_fold_pca.fit_transform(X_train_fold_pca)
  959. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  960. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/_set_output.py", line 140, in wrapped
  961. data_to_wrap = f(self, X, *args, **kwargs)
  962. ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  963. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/decomposition/_pca.py", line 462, in fit_transform
  964. U, S, Vt = self._fit(X)
  965. ^^^^^^^^^^^^
  966. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/decomposition/_pca.py", line 485, in _fit
  967. X = self._validate_data(
  968. ^^^^^^^^^^^^^^^^^^^^
  969. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 565, in _validate_data
  970. X = check_array(X, input_name="X", **check_params)
  971. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  972. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  973. _assert_all_finite(
  974. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  975. raise ValueError(msg_err)
  976. ValueError: Input X contains NaN.
  977. PCA does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  978. 28 Jun 2024 13:34
  979.  
  980. *********
  981.  
  982. ERROR:root:Input X contains NaN.
  983. SVC does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  984. Traceback (most recent call last):
  985. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 3939, in <module>
  986. gbbk_model_fold_gbkk.fit(X_train_fold_gbkk, y_train_fold_gbkk)
  987. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/svm/_base.py", line 192, in fit
  988. X, y = self._validate_data(
  989. ^^^^^^^^^^^^^^^^^^^^
  990. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  991. X, y = check_X_y(X, y, **check_params)
  992. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  993. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  994. X = check_array(
  995. ^^^^^^^^^^^^
  996. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  997. _assert_all_finite(
  998. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  999. raise ValueError(msg_err)
  1000. ValueError: Input X contains NaN.
  1001. SVC does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1002. 28 Jun 2024 13:34
  1003.  
  1004. *********
  1005.  
  1006. ERROR:root:Input X contains NaN.
  1007. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1008. Traceback (most recent call last):
  1009. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 4142, in <module>
  1010. best_params = simulated_annealing(objective_function, space)
  1011. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1012. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 4108, in simulated_annealing
  1013. current_score = objective_function(current_params)
  1014. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1015. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 4087, in objective_function
  1016. dt_model.fit(X_train, y_train)
  1017. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  1018. super().fit(
  1019. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  1020. X, y = self._validate_data(
  1021. ^^^^^^^^^^^^^^^^^^^^
  1022. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  1023. X = check_array(X, input_name="X", **check_X_params)
  1024. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1025. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  1026. _assert_all_finite(
  1027. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  1028. raise ValueError(msg_err)
  1029. ValueError: Input X contains NaN.
  1030. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1031. 28 Jun 2024 13:35
  1032.  
  1033. *********
  1034.  
  1035. ERROR:root:Input X contains NaN.
  1036. SVC does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1037. Traceback (most recent call last):
  1038. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 4217, in <module>
  1039. svm_model_fold.fit(X_train_fold_svm, y_train_fold_svm)
  1040. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/svm/_base.py", line 192, in fit
  1041. X, y = self._validate_data(
  1042. ^^^^^^^^^^^^^^^^^^^^
  1043. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  1044. X, y = check_X_y(X, y, **check_params)
  1045. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1046. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  1047. X = check_array(
  1048. ^^^^^^^^^^^^
  1049. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  1050. _assert_all_finite(
  1051. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  1052. raise ValueError(msg_err)
  1053. ValueError: Input X contains NaN.
  1054. SVC does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1055. 28 Jun 2024 13:35
  1056.  
  1057. *********
  1058.  
  1059. ERROR:root:Input X contains NaN.
  1060. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1061. Traceback (most recent call last):
  1062. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 4286, in <module>
  1063. stacking_classifier.fit(X_train_fold_stacking, y_train_fold_stacking)
  1064. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_stacking.py", line 660, in fit
  1065. return super().fit(X, y_encoded, sample_weight)
  1066. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1067. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_stacking.py", line 209, in fit
  1068. self.estimators_ = Parallel(n_jobs=self.n_jobs)(
  1069. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1070. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 63, in __call__
  1071. return super().__call__(iterable_with_config)
  1072. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1073. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1863, in __call__
  1074. return output if self.return_generator else list(output)
  1075. ^^^^^^^^^^^^
  1076. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
  1077. res = func(*args, **kwargs)
  1078. ^^^^^^^^^^^^^^^^^^^^^
  1079. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 123, in __call__
  1080. return self.function(*args, **kwargs)
  1081. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1082. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_base.py", line 46, in _fit_single_estimator
  1083. estimator.fit(X, y)
  1084. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  1085. super().fit(
  1086. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  1087. X, y = self._validate_data(
  1088. ^^^^^^^^^^^^^^^^^^^^
  1089. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  1090. X = check_array(X, input_name="X", **check_X_params)
  1091. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1092. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  1093. _assert_all_finite(
  1094. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  1095. raise ValueError(msg_err)
  1096. ValueError: Input X contains NaN.
  1097. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1098. 28 Jun 2024 13:35
  1099.  
  1100. *********
  1101.  
  1102. ERROR:root:Input X contains NaN.
  1103. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1104. Traceback (most recent call last):
  1105. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 4352, in <module>
  1106. stacking_model.fit(X_train_fold_stacking_dcae, y_train_fold_stacking_dcae)
  1107. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_stacking.py", line 660, in fit
  1108. return super().fit(X, y_encoded, sample_weight)
  1109. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1110. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_stacking.py", line 209, in fit
  1111. self.estimators_ = Parallel(n_jobs=self.n_jobs)(
  1112. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1113. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 63, in __call__
  1114. return super().__call__(iterable_with_config)
  1115. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1116. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1863, in __call__
  1117. return output if self.return_generator else list(output)
  1118. ^^^^^^^^^^^^
  1119. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
  1120. res = func(*args, **kwargs)
  1121. ^^^^^^^^^^^^^^^^^^^^^
  1122. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 123, in __call__
  1123. return self.function(*args, **kwargs)
  1124. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1125. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_base.py", line 46, in _fit_single_estimator
  1126. estimator.fit(X, y)
  1127. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/pipeline.py", line 405, in fit
  1128. self._final_estimator.fit(Xt, y, **fit_params_last_step)
  1129. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  1130. super().fit(
  1131. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  1132. X, y = self._validate_data(
  1133. ^^^^^^^^^^^^^^^^^^^^
  1134. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  1135. X = check_array(X, input_name="X", **check_X_params)
  1136. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1137. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  1138. _assert_all_finite(
  1139. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  1140. raise ValueError(msg_err)
  1141. ValueError: Input X contains NaN.
  1142. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1143. 28 Jun 2024 13:35
  1144.  
  1145. *********
  1146.  
  1147. ERROR:root:Input X contains NaN.
  1148. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1149. Traceback (most recent call last):
  1150. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 4512, in <module>
  1151. voting_clf.fit(X_train_fold_voting, y_train_fold_voting)
  1152. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_voting.py", line 346, in fit
  1153. return super().fit(X, transformed_y, sample_weight)
  1154. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1155. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_voting.py", line 81, in fit
  1156. self.estimators_ = Parallel(n_jobs=self.n_jobs)(
  1157. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1158. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 63, in __call__
  1159. return super().__call__(iterable_with_config)
  1160. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1161. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1863, in __call__
  1162. return output if self.return_generator else list(output)
  1163. ^^^^^^^^^^^^
  1164. File "/home/u/anaconda3/lib/python3.11/site-packages/joblib/parallel.py", line 1792, in _get_sequential_output
  1165. res = func(*args, **kwargs)
  1166. ^^^^^^^^^^^^^^^^^^^^^
  1167. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/parallel.py", line 123, in __call__
  1168. return self.function(*args, **kwargs)
  1169. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1170. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_base.py", line 46, in _fit_single_estimator
  1171. estimator.fit(X, y)
  1172. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 889, in fit
  1173. super().fit(
  1174. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py", line 186, in fit
  1175. X, y = self._validate_data(
  1176. ^^^^^^^^^^^^^^^^^^^^
  1177. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 579, in _validate_data
  1178. X = check_array(X, input_name="X", **check_X_params)
  1179. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1180. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  1181. _assert_all_finite(
  1182. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  1183. raise ValueError(msg_err)
  1184. ValueError: Input X contains NaN.
  1185. DecisionTreeClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1186. 28 Jun 2024 13:35
  1187.  
  1188. *********
  1189.  
  1190. ERROR:root:Input X contains NaN.
  1191. GradientBoostingClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1192. Traceback (most recent call last):
  1193. File "/home/u/Music/All_65_ML_Code/ML_65_Algo_k_fold_Stratified_1pc.py", line 4575, in <module>
  1194. gbt_model.fit(X_train_fold_gbt, y_train_fold_gbt)
  1195. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/ensemble/_gb.py", line 429, in fit
  1196. X, y = self._validate_data(
  1197. ^^^^^^^^^^^^^^^^^^^^
  1198. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/base.py", line 584, in _validate_data
  1199. X, y = check_X_y(X, y, **check_params)
  1200. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  1201. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 1106, in check_X_y
  1202. X = check_array(
  1203. ^^^^^^^^^^^^
  1204. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 921, in check_array
  1205. _assert_all_finite(
  1206. File "/home/u/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py", line 161, in _assert_all_finite
  1207. raise ValueError(msg_err)
  1208. ValueError: Input X contains NaN.
  1209. GradientBoostingClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
  1210. 28 Jun 2024 13:35
  1211.  
  1212. *********
  1213.  
  1214.  
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