new

cachedir/joblib/run/estimatorFeatureSelection/06dd5713420a922c24ec4885229fa897/metadata.json

@@ -0,0 +1 @@
+{"duration": 1.665640115737915, "input_args": {"Data": "     F1_p2    F2_l1p     F1+F2   |F1-F2|     F1xF2     F1/F2      F2/F1\n0     6.25  1.458615  7.708615  4.791385  9.116344  4.284887   0.233378\n1     3.61  1.308333  4.918333  2.301667  4.723081  2.759237   0.362419\n2     4.41  1.386294  5.796294  3.023706  6.113558  3.181143   0.314352\n3     3.24  1.360977  4.600977  1.879023  4.409564  2.380644   0.420054\n4     4.84  1.386294  6.226294  3.453706  6.709665  3.491322   0.286424\n..     ...       ...       ...       ...       ...       ...        ...\n145   0.09  1.386294  1.476294  1.296294  0.124766  0.064921  15.403271\n146   0.04  1.568616  1.608616  1.528616  0.062745  0.025500  39.215398\n147   0.04  1.435085  1.475085  1.395085  0.057403  0.027873  35.877113\n148   0.04  1.547563  1.587563  1.507563  0.061903  0.025847  38.689063\n149   0.04  1.458615  1.498615  1.418615  0.058345  0.027423  36.465376\n\n[150 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/0b5341d3e1474cd2b4d28e1033a5e75b/metadata.json

@@ -0,0 +1 @@
+{"duration": 15.683412075042725, "input_args": {"Data": "     F1  F2   F3  F4   F5  F6   F7  F8   F9  F10  F11  F12  F13\n0     1   0  145   0  150  63  233   1  2.3    0    3    0    1\n1     0   0  130   0  187  37  250   1  3.5    1    2    0    2\n2     0   2  130   0  172  41  204   0  1.4    0    1    0    2\n3     0   2  120   0  178  56  236   1  0.8    1    1    0    2\n4     0   2  120   1  163  57  354   0  0.6    1    0    0    2\n..   ..  ..  ...  ..  ...  ..  ...  ..  ...  ...  ...  ...  ...\n298   0   1  140   1  123  57  241   0  0.2    1    0    0    3\n299   0   1  110   0  132  45  264   1  1.2    1    3    0    3\n300   1   1  144   0  141  68  193   1  3.4    1    0    2    3\n301   0   1  130   1  115  57  131   1  1.2    1    0    1    3\n302   0   1  130   0  174  57  236   0  0.0    0    1    1    2\n\n[303 rows x 13 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.1436350297118406,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.143635035,\n              max_delta_step=0, max_depth=11, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=196, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/1276da95d3d21f94d4712976522769ea/metadata.json

@@ -0,0 +1 @@
+{"duration": 2.59732985496521, "input_args": {"Data": "       F1_l10   F3   F4\n0    0.397940  6.3  6.0\n1    0.278754  5.8  5.1\n2    0.322219  7.1  5.9\n3    0.255273  6.3  5.6\n4    0.342423  6.5  5.8\n..        ...  ...  ...\n145 -0.522879  4.8  1.4\n146 -0.698970  5.1  1.6\n147 -0.698970  4.6  1.4\n148 -0.698970  5.3  1.5\n149 -0.698970  5.0  1.4\n\n[150 rows x 3 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/155fb13ac5302f6e83e8dee4e6e50e10/metadata.json

@@ -0,0 +1 @@
+{"duration": 1.2182409763336182, "input_args": {"Data": "      F1   F2   F3     F4_l2\n0    2.5  3.3  6.3  2.584963\n1    1.9  2.7  5.8  2.350497\n2    2.1  3.0  7.1  2.560715\n3    1.8  2.9  6.3  2.485427\n4    2.2  3.0  6.5  2.536053\n..   ...  ...  ...       ...\n145  0.3  3.0  4.8  0.485427\n146  0.2  3.8  5.1  0.678072\n147  0.2  3.2  4.6  0.485427\n148  0.2  3.7  5.3  0.584963\n149  0.2  3.3  5.0  0.485427\n\n[150 rows x 4 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/15b0b603c741872ef31ad44a4ac4bc66/metadata.json

@@ -0,0 +1 @@
+{"duration": 1.2176530361175537, "input_args": {"Data": "     F1_p2    F2_l1p       F3_em1     F4_l2\n0     6.25  1.458615   543.571910  2.584963\n1     3.61  1.308333   329.299560  2.350497\n2     4.41  1.386294  1210.967074  2.560715\n3     3.24  1.360977   543.571910  2.485427\n4     4.84  1.386294   664.141633  2.536053\n..     ...       ...          ...       ...\n145   0.09  1.386294   120.510418  0.485427\n146   0.04  1.568616   163.021907  0.678072\n147   0.04  1.435085    98.484316  0.485427\n148   0.04  1.547563   199.336810  0.584963\n149   0.04  1.458615   147.413159  0.485427\n\n[150 rows x 4 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/1d757a543a7ff69019ec13d167f09684/metadata.json

@@ -0,0 +1 @@
+{"duration": 7.925899028778076, "input_args": {"Data": "     F1_p2    F2_l1p       F3_em1     F1+F2        F2+F3        F1+F3     F1+F2+F3   |F1-F2|  ...     F1/F3        F3/F1  F1/F2/F3  F1/F3/F2  F2/F3/F1  F2/F1/F3     F3/F1/F2     F3/F2/F1\n0     6.25  1.458615   543.571910  7.708615   545.030525   549.821910   551.280525  4.791385  ...  0.011498    86.971506  0.007883  0.007883  0.000429  0.000429    59.626087    59.626087\n1     3.61  1.308333   329.299560  4.918333   330.607893   332.909560   334.217893  2.301667  ...  0.010963    91.218715  0.008379  0.008379  0.001101  0.001101    69.721338    69.721338\n2     4.41  1.386294  1210.967074  5.796294  1212.353369  1215.377074  1216.763369  3.023706  ...  0.003642   274.595709  0.002627  0.002627  0.000260  0.000260   198.078933   198.078933\n3     3.24  1.360977   543.571910  4.600977   544.932887   546.811910   548.172887  1.879023  ...  0.005961   167.769108  0.004380  0.004380  0.000773  0.000773   123.271123   123.271123\n4     4.84  1.386294   664.141633  6.226294   665.527927   668.981633   670.367927  3.453706  ...  0.007288   137.219346  0.005257  0.005257  0.000431  0.000431    98.982835    98.982835\n..     ...       ...          ...       ...          ...          ...          ...       ...  ...       ...          ...       ...       ...       ...       ...          ...          ...\n145   0.09  1.386294   120.510418  1.476294   121.896712   120.600418   121.986712  1.296294  ...  0.000747  1339.004639  0.000539  0.000539  0.127817  0.127817   965.887676   965.887676\n146   0.04  1.568616   163.021907  1.608616   164.590523   163.061907   164.630523  1.528616  ...  0.000245  4075.547682  0.000156  0.000156  0.240553  0.240553  2598.180750  2598.180750\n147   0.04  1.435085    98.484316  1.475085    99.919400    98.524316    99.959400  1.395085  ...  0.000406  2462.107891  0.000283  0.000283  0.364293  0.364293  1715.653571  1715.653571\n148   0.04  1.547563   199.336810  1.587563   200.884372   199.376810   200.924372  1.507563  ...  0.000201  4983.420249  0.000130  0.000130  0.194089  0.194089  3220.173803  3220.173803\n149   0.04  1.458615   147.413159  1.498615   148.871774   147.453159   148.911774  1.418615  ...  0.000271  3685.328978  0.000186  0.000186  0.247369  0.247369  2526.594694  2526.594694\n\n[150 rows x 27 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/1ed1e3543e5b8c0b8ed8273e2b6d737b/metadata.json

@@ -1 +0,0 @@
-{"duration": 7.470085859298706, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=9.044474534604408, gamma=0.1, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/2ea39cd41bf1eea844679842787364a7/metadata.json

@@ -1 +0,0 @@
-{"duration": 2.0144307613372803, "input_args": {"Data": "      F1    F2_mms   F3   F4  F1xF3\n0    2.5  0.541667  6.3  6.0  15.75\n1    1.9  0.291667  5.8  5.1  11.02\n2    2.1  0.416667  7.1  5.9  14.91\n3    1.8  0.375000  6.3  5.6  11.34\n4    2.2  0.416667  6.5  5.8  14.30\n..   ...       ...  ...  ...    ...\n145  0.3  0.416667  4.8  1.4   1.44\n146  0.2  0.750000  5.1  1.6   1.02\n147  0.2  0.500000  4.6  1.4   0.92\n148  0.2  0.708333  5.3  1.5   1.06\n149  0.2  0.541667  5.0  1.4   1.00\n\n[150 rows x 5 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/3ce1789aa381a937c9967ca360bdcf7f/metadata.json

@@ -0,0 +1 @@
+{"duration": 5.265573978424072, "input_args": {"Data": "     F1  F2    F3  F4  F5     F6   F7   F8\n0    72  50  33.6   6  35  0.627    0  148\n1    64  32  23.3   8   0  0.672    0  183\n2    40  33  43.1   0  35  2.288  168  137\n3    50  26  31.0   3  32  0.248   88   78\n4    70  53  30.5   2  45  0.158  543  197\n..   ..  ..   ...  ..  ..    ...  ...  ...\n763  62  33  22.5   9   0  0.142    0   89\n764  76  63  32.9  10  48  0.171  180  101\n765  70  27  36.8   2  27  0.340    0  122\n766  72  30  26.2   5  23  0.245  112  121\n767  70  23  30.4   1  31  0.315    0   93\n\n[768 rows x 8 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9728230822718118,\n              eta=0.15918739742144755, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.11327384412356725, max_delta_step=0, max_depth=6,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=130, n_jobs=12, num_parallel_tree=1,\n              probability=True, random_state=42, reg_alpha=0, reg_lambda=1,\n              scale_pos_weight=1, silent=True, subsample=0.9666339466107166,\n              tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/3cfbe165b148a5cf6d67371edc55c4ad/metadata.json

@@ -0,0 +1 @@
+{"duration": 16.90331506729126, "input_args": {"Data": "     F1  F2   F3  F4   F5  F6   F7  F8   F9  F10  F11  F12  F13\n0     1   0  145   0  150  63  233   1  2.3    0    3    0    1\n1     0   0  130   0  187  37  250   1  3.5    1    2    0    2\n2     0   2  130   0  172  41  204   0  1.4    0    1    0    2\n3     0   2  120   0  178  56  236   1  0.8    1    1    0    2\n4     0   2  120   1  163  57  354   0  0.6    1    0    0    2\n..   ..  ..  ...  ..  ...  ..  ...  ..  ...  ...  ...  ...  ...\n298   0   1  140   1  123  57  241   0  0.2    1    0    0    3\n299   0   1  110   0  132  45  264   1  1.2    1    3    0    3\n300   1   1  144   0  141  68  193   1  3.4    1    0    2    3\n301   0   1  130   1  115  57  131   1  1.2    1    0    1    3\n302   0   1  130   0  174  57  236   0  0.0    0    1    1    2\n\n[303 rows x 13 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.1436350297118406,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.143635035,\n              max_delta_step=0, max_depth=11, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=147, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/6c2a1b83e6bcde1b3562dd60983bd528/metadata.json

@@ -0,0 +1 @@
+{"duration": 35.23522090911865, "input_args": {"Data": "        F1     F2  F3  F4     F5  F6  F7     F8      F9  F10  F11     F12   F13  F14     F15  ...  F27  F28  F29  F30    F31  F32  F33  F34    F35  F36    F37  F38  F39  F40  F41\n0    1.185  9.085   0   0  2.263   3   0  3.642   0.000    0    1   9.902  46.7    1  3.1934  ...    0    0    2    0  0.000    1    0    0  0.014    0  4.054    0    0    0    0\n1    0.000  8.179   2   0  2.194   0   6  3.526   0.000    6    0  10.054  35.1    0  1.8929  ...    0    0    0    0  0.000    2    0    0  0.000    0  3.489    0    4    0    0\n2    0.762  8.297   0   3  2.424   0   0  3.339  21.884    0    0  10.226  42.1    0  2.3934  ...    0    0    0    8 -0.686    3    0    0  0.004    0  3.693    0    0    0    0\n3    1.747  9.673   0   2  2.690  23   0  4.645   9.855    0    1  12.353  31.6    0  7.7233  ...    1    0    0    2 -4.617    0    0   11  0.000    0  3.993    0    0    0    1\n4    1.824  9.825   0   2  2.700  27   0  4.795   9.894    0    1  12.519  31.8    0  7.9184  ...    1    0    0    2 -4.724    0    0   13  0.000    0  4.005    0    0    0    1\n..     ...    ...  ..  ..    ...  ..  ..    ...     ...  ...  ...     ...   ...  ...     ...  ...  ...  ...  ...  ...    ...  ...  ...  ...    ...  ...    ...  ...  ...  ...  ...\n832  1.125  7.878   0   1  2.136   0   0  2.990   0.000    0    0   9.311  43.8    0  3.0778  ...    0    0    1    2  0.000    1    0    0  0.045    0  3.573    0    0    0    0\n833  1.187  8.046   1   1  2.222   0   2  3.105   0.000    2    0   9.668  38.9    0  3.2726  ...    2    0    2    2  0.000    1    0    0 -0.025    0  3.666    0    2    0    0\n834  0.625  8.901   0   2  2.499   0   0  3.745  24.203    0    0  10.681  58.3    0  2.3715  ...    0    0    0    8 -0.128    6    0    0  0.000    0  3.942    0    0    0    0\n835  3.866  8.778   0   6  2.361   0   0  4.201  11.747    0    1  10.735  32.4    0  1.9452  ...    3    0    0    9 -0.347    1    0    0  0.000    0  3.497    0    0    0    0\n836  3.706  8.680   0   6  2.361   0   0  4.127  11.724    0    1  10.694  31.4    0  1.9472  ...    3    0    0    9 -0.338    1    0    0  0.000    0  3.497    0    0    0    0\n\n[837 rows x 41 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.90580836121682,\n              eta=0.2665440364437338, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.14016725176148134, max_delta_step=0, max_depth=10,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=108, n_jobs=12, num_parallel_tree=1,\n              probability=True, random_state=42, reg_alpha=0, reg_lambda=1,\n              scale_pos_weight=1, silent=True, subsample=0.9969909852161994,\n              tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/6c4a3c6ccc38028fd8eb8e3cafea6fb8/metadata.json

@@ -1 +0,0 @@
-{"duration": 2.988314151763916, "input_args": {"Data": "      F1   F2    F3_l10   F4\n0    2.5  3.3  0.298389  6.0\n1    1.9  2.7  0.282605  5.1\n2    2.1  3.0  0.320533  5.9\n3    1.8  2.9  0.298389  5.6\n4    2.2  3.0  0.304254  5.8\n..   ...  ...       ...  ...\n145  0.3  3.0  0.244984  1.4\n146  0.2  3.8  0.257268  1.6\n147  0.2  3.2  0.236226  1.4\n148  0.2  3.7  0.264948  1.5\n149  0.2  3.3  0.253280  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/750508bdebd79d7c12403e0a9209597e/metadata.json

@@ -0,0 +1 @@
+{"duration": 12.906700134277344, "input_args": {"Data": "     F1  F2  F3  F4  F5  F6  F7  F8  F9\n0     7   8   7   8   9  10  10   1  10\n1     4   5   2   3   4   3   3   1   3\n2     5   8   7  10   5   7   5   4   9\n3     3   7   6   4   4   4   6   1   1\n4     1  10   4   6   4   7   7   2  10\n..   ..  ..  ..  ..  ..  ..  ..  ..  ..\n694   1   1   2   3   1   1   1   1   1\n695   1   3   2   1   1   1   1   1   1\n696   1   3   2   1   2   1   1   2   1\n697   1   3   3   1   1   1   1   1   2\n698   1   2   2   1   1   1   1   1   1\n\n[699 rows x 9 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.0989957156047863,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.0989957154,\n              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=68, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/768047f135420d9d2955707ad2357419/metadata.json

@@ -0,0 +1 @@
+{"duration": 17.325084924697876, "input_args": {"Data": "     F1  F2   F3  F4   F5  F6   F7  F8   F9  F10  F11  F12  F13\n0     1   0  145   0  150  63  233   1  2.3    0    3    0    1\n1     0   0  130   0  187  37  250   1  3.5    1    2    0    2\n2     0   2  130   0  172  41  204   0  1.4    0    1    0    2\n3     0   2  120   0  178  56  236   1  0.8    1    1    0    2\n4     0   2  120   1  163  57  354   0  0.6    1    0    0    2\n..   ..  ..  ...  ..  ...  ..  ...  ..  ...  ...  ...  ...  ...\n298   0   1  140   1  123  57  241   0  0.2    1    0    0    3\n299   0   1  110   0  132  45  264   1  1.2    1    3    0    3\n300   1   1  144   0  141  68  193   1  3.4    1    0    2    3\n301   0   1  130   1  115  57  131   1  1.2    1    0    1    3\n302   0   1  130   0  174  57  236   0  0.0    0    1    1    2\n\n[303 rows x 13 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17578338868707993,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.175783396,\n              max_delta_step=0, max_depth=7, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=183, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/77fea9bd3b261749a4919132a55d4c8b/metadata.json

@@ -1 +0,0 @@
-{"duration": 7.335001707077026, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=46.46493418419044, gamma=0.1, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/8169bbe5eb5987ead864ab325567de1e/metadata.json

@@ -0,0 +1 @@
+{"duration": 10.679436922073364, "input_args": {"Data": "     F1  F2    F3  F4  F5     F6   F7   F8\n0    72  50  33.6   6  35  0.627    0  148\n1    64  32  23.3   8   0  0.672    0  183\n2    40  33  43.1   0  35  2.288  168  137\n3    50  26  31.0   3  32  0.248   88   78\n4    70  53  30.5   2  45  0.158  543  197\n..   ..  ..   ...  ..  ..    ...  ...  ...\n763  62  33  22.5   9   0  0.142    0   89\n764  76  63  32.9  10  48  0.171  180  101\n765  70  27  36.8   2  27  0.340    0  122\n766  72  30  26.2   5  23  0.245  112  121\n767  70  23  30.4   1  31  0.315    0   93\n\n[768 rows x 8 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.2389507198189842,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.238950714,\n              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=17, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/85e0be8fbd7841f797f3bc77720c7db8/metadata.json

@@ -0,0 +1 @@
+{"duration": 16.14510416984558, "input_args": {"Data": "      F1  F2    F3  F4  F5     F6   F7   F8\n0     72  50  33.6   6  35  0.627    0  148\n1     64  32  23.3   8   0  0.672    0  183\n2     40  33  43.1   0  35  2.288  168  137\n3     50  26  31.0   3  32  0.248   88   78\n4     70  53  30.5   2  45  0.158  543  197\n...   ..  ..   ...  ..  ..    ...  ...  ...\n1531  62  33  22.5   9   0  0.142    0   89\n1532  76  63  32.9  10  48  0.171  180  101\n1533  70  27  36.8   2  27  0.340    0  122\n1534  72  30  26.2   5  23  0.245  112  121\n1535  70  23  30.4   1  31  0.315    0   93\n\n[1536 rows x 8 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.1261534422933427,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.126153439,\n              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=138, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/8acd1de1317c1ff3977e18a58b7d3733/metadata.json

@@ -1 +0,0 @@
-{"duration": 3.0411131381988525, "input_args": {"Data": "      F1   F2    F3_l1p   F4\n0    2.5  3.3  1.987874  6.0\n1    1.9  2.7  1.916923  5.1\n2    2.1  3.0  2.091864  5.9\n3    1.8  2.9  1.987874  5.6\n4    2.2  3.0  2.014903  5.8\n..   ...  ...       ...  ...\n145  0.3  3.0  1.757858  1.4\n146  0.2  3.8  1.808289  1.6\n147  0.2  3.2  1.722767  1.4\n148  0.2  3.7  1.840550  1.5\n149  0.2  3.3  1.791759  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/94da8e9a31bea28d8ff8ea53a406d37b/metadata.json

@@ -1 +0,0 @@
-{"duration": 8.3280029296875, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=4.407539756085735, gamma=0.1935853267468035, probability=True,\n    random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/9902cbfccf20447a3c3f958bc3ba28f0/metadata.json

@@ -1 +0,0 @@
-{"duration": 5.4526307582855225, "input_args": {"Data": "      F1    F2_mms   F3   F4\n0    2.5  0.541667  6.3  6.0\n1    1.9  0.291667  5.8  5.1\n2    2.1  0.416667  7.1  5.9\n3    1.8  0.375000  6.3  5.6\n4    2.2  0.416667  6.5  5.8\n..   ...       ...  ...  ...\n145  0.3  0.416667  4.8  1.4\n146  0.2  0.750000  5.1  1.6\n147  0.2  0.500000  4.6  1.4\n148  0.2  0.708333  5.3  1.5\n149  0.2  0.541667  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/9a08c17b423b35a0c58bb1d4053f6ab9/metadata.json

@@ -1 +0,0 @@
-{"duration": 8.652288913726807, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/a42f327147499bdd5ab86868e8668849/metadata.json

@@ -1 +0,0 @@
-{"duration": 3.5112879276275635, "input_args": {"Data": "      F1   F3  F1+F3  |F1-F3|  F1xF3     F1/F3      F3/F1\n0    2.5  6.3    8.8      3.8  15.75  0.396825   2.520000\n1    1.9  5.8    7.7      3.9  11.02  0.327586   3.052632\n2    2.1  7.1    9.2      5.0  14.91  0.295775   3.380952\n3    1.8  6.3    8.1      4.5  11.34  0.285714   3.500000\n4    2.2  6.5    8.7      4.3  14.30  0.338462   2.954545\n..   ...  ...    ...      ...    ...       ...        ...\n145  0.3  4.8    5.1      4.5   1.44  0.062500  16.000000\n146  0.2  5.1    5.3      4.9   1.02  0.039216  25.500000\n147  0.2  4.6    4.8      4.4   0.92  0.043478  23.000000\n148  0.2  5.3    5.5      5.1   1.06  0.037736  26.500000\n149  0.2  5.0    5.2      4.8   1.00  0.040000  25.000000\n\n[150 rows x 7 columns]", "clf": "SVC(C=44.03816499590499, gamma=0.01, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/a6cd214b11321a47cd883aa6152a0c8c/metadata.json

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+{"duration": 2.294894218444824, "input_args": {"Data": "     F1_p2       F3_em1        F1+F3      |F1-F3|        F1xF3     F1/F3        F3/F1\n0     6.25   543.571910   549.821910   537.321910  3397.324438  0.011498    86.971506\n1     3.61   329.299560   332.909560   325.689560  1188.771411  0.010963    91.218715\n2     4.41  1210.967074  1215.377074  1206.557074  5340.364799  0.003642   274.595709\n3     3.24   543.571910   546.811910   540.331910  1761.172989  0.005961   167.769108\n4     4.84   664.141633   668.981633   659.301633  3214.445504  0.007288   137.219346\n..     ...          ...          ...          ...          ...       ...          ...\n145   0.09   120.510418   120.600418   120.420418    10.845938  0.000747  1339.004639\n146   0.04   163.021907   163.061907   162.981907     6.520876  0.000245  4075.547682\n147   0.04    98.484316    98.524316    98.444316     3.939373  0.000406  2462.107891\n148   0.04   199.336810   199.376810   199.296810     7.973472  0.000201  4983.420249\n149   0.04   147.413159   147.453159   147.373159     5.896526  0.000271  3685.328978\n\n[150 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/ac707cf3d91bd14a68c24c11e4b4686b/metadata.json

@@ -0,0 +1 @@
+{"duration": 19.514513969421387, "input_args": {"Data": "     F1  F2   F3  F4   F5  F6   F7  F8   F9  F10  F11  F12  F13\n0     1   0  145   0  150  63  233   1  2.3    0    3    0    1\n1     0   0  130   0  187  37  250   1  3.5    1    2    0    2\n2     0   2  130   0  172  41  204   0  1.4    0    1    0    2\n3     0   2  120   0  178  56  236   1  0.8    1    1    0    2\n4     0   2  120   1  163  57  354   0  0.6    1    0    0    2\n..   ..  ..  ...  ..  ...  ..  ...  ..  ...  ...  ...  ...  ...\n298   0   1  140   1  123  57  241   0  0.2    1    0    0    3\n299   0   1  110   0  132  45  264   1  1.2    1    3    0    3\n300   1   1  144   0  141  68  193   1  3.4    1    0    2    3\n301   0   1  130   1  115  57  131   1  1.2    1    0    1    3\n302   0   1  130   0  174  57  236   0  0.0    0    1    1    2\n\n[303 rows x 13 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.13526658776256462,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.135266587,\n              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=231, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/bc39a1e21fff991e52cf92f23a887f48/metadata.json

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+{"duration": 1.298875093460083, "input_args": {"Data": "      F1    F2_l1p   F3     F4_l2\n0    2.5  1.458615  6.3  2.584963\n1    1.9  1.308333  5.8  2.350497\n2    2.1  1.386294  7.1  2.560715\n3    1.8  1.360977  6.3  2.485427\n4    2.2  1.386294  6.5  2.536053\n..   ...       ...  ...       ...\n145  0.3  1.386294  4.8  0.485427\n146  0.2  1.568616  5.1  0.678072\n147  0.2  1.435085  4.6  0.485427\n148  0.2  1.547563  5.3  0.584963\n149  0.2  1.458615  5.0  0.485427\n\n[150 rows x 4 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/c5a7d28fb08d1dd352f1a14ab72c2f82/metadata.json

@@ -1 +0,0 @@
-{"duration": 2.5880067348480225, "input_args": {"Data": "      F1   F2    F3_l1p   F4\n0    2.5  3.3  1.987874  6.0\n1    1.9  2.7  1.916923  5.1\n2    2.1  3.0  2.091864  5.9\n3    1.8  2.9  1.987874  5.6\n4    2.2  3.0  2.014903  5.8\n..   ...  ...       ...  ...\n145  0.3  3.0  1.757858  1.4\n146  0.2  3.8  1.808289  1.6\n147  0.2  3.2  1.722767  1.4\n148  0.2  3.7  1.840550  1.5\n149  0.2  3.3  1.791759  1.4\n\n[150 rows x 4 columns]", "clf": "SVC(C=46.46493418419044, gamma=0.1, probability=True, random_state=42)"}}

cachedir/joblib/run/estimatorFeatureSelection/cb379999e8f23ffa41cf2d9e4bf80e02/metadata.json

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+{"duration": 9.43068790435791, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/ce4afe135d18dbc976623139201713b9/metadata.json

@@ -0,0 +1 @@
+{"duration": 14.877521991729736, "input_args": {"Data": "     F1  F2  F3  F4  F5  F6  F7  F8  F9\n0     7   8   7   8   9  10  10   1  10\n1     4   5   2   3   4   3   3   1   3\n2     5   8   7  10   5   7   5   4   9\n3     3   7   6   4   4   4   6   1   1\n4     1  10   4   6   4   7   7   2  10\n..   ..  ..  ..  ..  ..  ..  ..  ..  ..\n694   1   1   2   3   1   1   1   1   1\n695   1   3   2   1   1   1   1   1   1\n696   1   3   2   1   2   1   1   2   1\n697   1   3   3   1   1   1   1   1   2\n698   1   2   2   1   1   1   1   1   1\n\n[699 rows x 9 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9597899978811085,\n              eta=0.2804685587557792, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.06327387530778793, max_delta_step=0, max_depth=7,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=118, n_jobs=12, num_parallel_tree=1,\n              probability=True, random_state=42, reg_alpha=0, reg_lambda=1,\n              scale_pos_weight=1, silent=True, subsample=0.9325330330763264,\n              tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/cf56b660dce5c9c3345c3e0a4d30aee3/metadata.json

@@ -0,0 +1 @@
+{"duration": 4.280111074447632, "input_args": {"Data": "      F1   F3   F4\n0    2.5  6.3  6.0\n1    1.9  5.8  5.1\n2    2.1  7.1  5.9\n3    1.8  6.3  5.6\n4    2.2  6.5  5.8\n..   ...  ...  ...\n145  0.3  4.8  1.4\n146  0.2  5.1  1.6\n147  0.2  4.6  1.4\n148  0.2  5.3  1.5\n149  0.2  5.0  1.4\n\n[150 rows x 3 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/df2fd4f312f161f0999ac44199d8b992/metadata.json

@@ -0,0 +1 @@
+{"duration": 17.733853340148926, "input_args": {"Data": "     F1  F2   F3  F4   F5  F6   F7  F8   F9  F10  F11  F12  F13\n0     1   0  145   0  150  63  233   1  2.3    0    3    0    1\n1     0   0  130   0  187  37  250   1  3.5    1    2    0    2\n2     0   2  130   0  172  41  204   0  1.4    0    1    0    2\n3     0   2  120   0  178  56  236   1  0.8    1    1    0    2\n4     0   2  120   1  163  57  354   0  0.6    1    0    0    2\n..   ..  ..  ...  ..  ...  ..  ...  ..  ...  ...  ...  ...  ...\n298   0   1  140   1  123  57  241   0  0.2    1    0    0    3\n299   0   1  110   0  132  45  264   1  1.2    1    3    0    3\n300   1   1  144   0  141  68  193   1  3.4    1    0    2    3\n301   0   1  130   1  115  57  131   1  1.2    1    0    1    3\n302   0   1  130   0  174  57  236   0  0.0    0    1    1    2\n\n[303 rows x 13 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.29618953754932364,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.296189547,\n              max_delta_step=0, max_depth=11, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=147, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/f0cb389e3296abf9505fe1d126fe551a/metadata.json

@@ -0,0 +1 @@
+{"duration": 11.408310174942017, "input_args": {"Data": "        F1     F2  F3  F4     F5  F6  F7     F8      F9  F10  F11     F12   F13  F14     F15  ...  F27  F28  F29  F30    F31  F32  F33  F34    F35  F36    F37  F38  F39  F40  F41\n0    1.185  9.085   0   0  2.263   3   0  3.642   0.000    0    1   9.902  46.7    1  3.1934  ...    0    0    2    0  0.000    1    0    0  0.014    0  4.054    0    0    0    0\n1    0.000  8.179   2   0  2.194   0   6  3.526   0.000    6    0  10.054  35.1    0  1.8929  ...    0    0    0    0  0.000    2    0    0  0.000    0  3.489    0    4    0    0\n2    0.762  8.297   0   3  2.424   0   0  3.339  21.884    0    0  10.226  42.1    0  2.3934  ...    0    0    0    8 -0.686    3    0    0  0.004    0  3.693    0    0    0    0\n3    1.747  9.673   0   2  2.690  23   0  4.645   9.855    0    1  12.353  31.6    0  7.7233  ...    1    0    0    2 -4.617    0    0   11  0.000    0  3.993    0    0    0    1\n4    1.824  9.825   0   2  2.700  27   0  4.795   9.894    0    1  12.519  31.8    0  7.9184  ...    1    0    0    2 -4.724    0    0   13  0.000    0  4.005    0    0    0    1\n..     ...    ...  ..  ..    ...  ..  ..    ...     ...  ...  ...     ...   ...  ...     ...  ...  ...  ...  ...  ...    ...  ...  ...  ...    ...  ...    ...  ...  ...  ...  ...\n832  1.125  7.878   0   1  2.136   0   0  2.990   0.000    0    0   9.311  43.8    0  3.0778  ...    0    0    1    2  0.000    1    0    0  0.045    0  3.573    0    0    0    0\n833  1.187  8.046   1   1  2.222   0   2  3.105   0.000    2    0   9.668  38.9    0  3.2726  ...    2    0    2    2  0.000    1    0    0 -0.025    0  3.666    0    2    0    0\n834  0.625  8.901   0   2  2.499   0   0  3.745  24.203    0    0  10.681  58.3    0  2.3715  ...    0    0    0    8 -0.128    6    0    0  0.000    0  3.942    0    0    0    0\n835  3.866  8.778   0   6  2.361   0   0  4.201  11.747    0    1  10.735  32.4    0  1.9452  ...    3    0    0    9 -0.347    1    0    0  0.000    0  3.497    0    0    0    0\n836  3.706  8.680   0   6  2.361   0   0  4.127  11.724    0    1  10.694  31.4    0  1.9472  ...    3    0    0    9 -0.338    1    0    0  0.000    0  3.497    0    0    0    0\n\n[837 rows x 41 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.2581106602001054,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.258110672,\n              max_delta_step=0, max_depth=7, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=40, n_jobs=12,\n              num_parallel_tree=1, probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, silent=True,\n              subsample=1, tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/f101c13f7a3889fbd799ae832bdaaa8a/metadata.json

@@ -0,0 +1 @@
+{"duration": 11.91932487487793, "input_args": {"Data": "      F1   F2   F3   F4\n0    2.5  3.3  6.3  6.0\n1    1.9  2.7  5.8  5.1\n2    2.1  3.0  7.1  5.9\n3    1.8  2.9  6.3  5.6\n4    2.2  3.0  6.5  5.8\n..   ...  ...  ...  ...\n145  0.3  3.0  4.8  1.4\n146  0.2  3.8  5.1  1.6\n147  0.2  3.2  4.6  1.4\n148  0.2  3.7  5.3  1.5\n149  0.2  3.3  5.0  1.4\n\n[150 rows x 4 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9374540118847363,\n              eta=0.28767857660247903, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.15979909127171077, max_delta_step=0, max_depth=9,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=162, n_jobs=12, num_parallel_tree=1,\n              objective='multi:softprob', probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=None, silent=True,\n              subsample=0.9155994520336203, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/f9af228a2a31e2aa3608a5268c7e2c6b/metadata.json

@@ -0,0 +1 @@
+{"duration": 10.098360776901245, "input_args": {"Data": "     F1  F2    F3  F4  F5     F6   F7   F8\n0    72  50  33.6   6  35  0.627    0  148\n1    64  32  23.3   8   0  0.672    0  183\n2    40  33  43.1   0  35  2.288  168  137\n3    50  26  31.0   3  32  0.248   88   78\n4    70  53  30.5   2  45  0.158  543  197\n..   ..  ..   ...  ..  ..    ...  ...  ...\n763  62  33  22.5   9   0  0.142    0   89\n764  76  63  32.9  10  48  0.171  180  101\n765  70  27  36.8   2  27  0.340    0  122\n766  72  30  26.2   5  23  0.245  112  121\n767  70  23  30.4   1  31  0.315    0   93\n\n[768 rows x 8 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.3, gamma=0,\n              gpu_id=-1, importance_type='gain', interaction_constraints='',\n              learning_rate=0.300000012, max_delta_step=0, max_depth=10,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=15, n_jobs=12, num_parallel_tree=1, probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, verbosity=0)"}}

cachedir/joblib/run/estimatorFeatureSelection/fd4be3fcb80f1a53fcae34da198197cc/metadata.json

@@ -0,0 +1 @@
+{"duration": 1.2616550922393799, "input_args": {"Data": "     F1_p2    F2_l1p   F3     F4_l2\n0     6.25  1.458615  6.3  2.584963\n1     3.61  1.308333  5.8  2.350497\n2     4.41  1.386294  7.1  2.560715\n3     3.24  1.360977  6.3  2.485427\n4     4.84  1.386294  6.5  2.536053\n..     ...       ...  ...       ...\n145   0.09  1.386294  4.8  0.485427\n146   0.04  1.568616  5.1  0.678072\n147   0.04  1.435085  4.6  0.485427\n148   0.04  1.547563  5.3  0.584963\n149   0.04  1.458615  5.0  0.485427\n\n[150 rows x 4 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n              gamma=0, gpu_id=-1, importance_type='gain',\n              interaction_constraints='', learning_rate=0.178558603,\n              max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n              monotone_constraints='()', n_estimators=14, n_jobs=12,\n              num_parallel_tree=1, objective='multi:softprob', probability=True,\n              random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n              silent=True, subsample=1, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/func_code.py

@@ -1,4 +1,4 @@
-# first line: 687
+# first line: 693
 @memory.cache
 def estimatorFeatureSelection(Data, clf):
 
@@ -9,15 +9,11 @@ def estimatorFeatureSelection(Data, clf):
     ImpurityFS = []
     RankingFS = []
     
-    rf = RandomForestClassifier(n_estimators = 100,
+    estim = clf.fit(Data, yData)
-                            n_jobs = -1,
-                            random_state = RANDOM_SEED)
-    rf.fit(Data, yData)
 
-    importances = rf.feature_importances_
+    importances = clf.feature_importances_
-
+    # std = np.std([tree.feature_importances_ for tree in estim.feature_importances_],
-    std = np.std([tree.feature_importances_ for tree in rf.estimators_],
+    #             axis=0)
-                axis=0)
 
     maxList = max(importances)
     minList = min(importances)

frontend/src/components/DataSetSlider.vue

@@ -3,8 +3,10 @@
       <b-col cols="2" style="margin-left: -35px">
         <label id="data" for="param-dataset" data-toggle="tooltip" data-placement="right" title="Tip: use one of the data sets already provided or upload a new file.">{{ dataset }}</label>
         <select id="selectFile" @change="selectDataSet()">
+            <option value="BreastC.csv" selected>Breast cancer</option>
+            <option value="DiabetesC.csv">Indian diabetes</option>
             <option value="HeartC.csv">Heart disease</option>
-            <option value="IrisC.csv" selected>Iris flower</option>
+            <option value="IrisC.csv">Iris flower</option>
             <option value="local">Upload file</option>
         </select>
       </b-col>
@@ -54,7 +56,7 @@ export default {
       this.defaultDataSet = fileName.options[fileName.selectedIndex].value
       this.defaultDataSet = this.defaultDataSet.split('.')[0]
 
-      if (this.defaultDataSet == "BiodegC" || this.defaultDataSet == "HeartC" || this.defaultDataSet == "IrisC") { // This is a function that handles a new file, which users can upload.
+      if (this.defaultDataSet == "BiodegC" || this.defaultDataSet == "HeartC" || this.defaultDataSet == "BreastC" || this.defaultDataSet == "DiabetesC" || this.defaultDataSet == "IrisC") { // This is a function that handles a new file, which users can upload.
         this.dataset = "Data set"
         d3.select("#data").select("input").remove(); // Remove the selection field.
         EventBus.$emit('SendToServerDataSetConfirmation', this.defaultDataSet)

frontend/src/components/Main.vue

@@ -149,7 +149,7 @@ export default Vue.extend({
       DataResults: '',
       keyNow: 1,
       instancesImportance: '',
-      RetrieveValueFile: 'IrisC', // this is for the default data set
+      RetrieveValueFile: 'BreastC', // this is for the default data set
       SelectedFeaturesPerClassifier: '',
       FinalResults: 0,
       selectedAlgorithm: '',
@@ -476,7 +476,7 @@ export default Vue.extend({
             EventBus.$emit('SlidersCall')
             this.keySlider = false
           }
-          EventBus.$emit('ConfirmDataSet') // REMOVE THAT!
+          // EventBus.$emit('ConfirmDataSet') // REMOVE THAT!
         } else {
           EventBus.$emit('dataSpace', this.correlResul)
           EventBus.$emit('quad', this.correlResul)

insertMongo.py

@@ -10,7 +10,7 @@ def import_content(filepath):
     mng_client = pymongo.MongoClient('localhost', 27017)
     mng_db = mng_client['mydb']
     #collection_name = 'StanceCTest' 
-    collection_name = 'StanceC' 
+    collection_name = 'HeartC' 
     db_cm = mng_db[collection_name]
     cdir = os.path.dirname(__file__)
     file_res = os.path.join(cdir, filepath)
@@ -21,5 +21,5 @@ def import_content(filepath):
     db_cm.insert(data_json)
 
 if __name__ == "__main__":
-  filepath = '/Users/anchaa/Documents/Research/StackVis_code/StackVis/stance.csv'
+  filepath = '/Users/anchaa/Documents/Research/FeatureEnVi_code/extra_data_sets/heart.csv'
   import_content(filepath)

run.py

@@ -14,7 +14,7 @@ import multiprocessing
 
 from joblib import Memory
 
-from sklearn.svm import SVC
+from xgboost import XGBClassifier
 from sklearn import model_selection
 from bayes_opt import BayesianOptimization
 from sklearn.model_selection import cross_validate
@@ -257,7 +257,7 @@ def retrieveFileName():
 
     global listofTransformations
     listofTransformations = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
-    print('data set:',fileName)
+
     DataRawLength = -1
     DataRawLengthTest = -1
     data = json.loads(fileName)  
@@ -274,8 +274,10 @@ def retrieveFileName():
         target_names.append('Biodegradable')
     elif data['fileName'] == 'BreastC':
         CollectionDB = mongo.db.breastC.find()
-        target_names.append('Malignant')
+    elif data['fileName'] == 'DiabetesC':
-        target_names.append('Benign')
+        CollectionDB = mongo.db.diabetesC.find()
+        target_names.append('Negative')
+        target_names.append('Positive')
     else:
         CollectionDB = mongo.db.IrisC.find()
     DataResultsRaw = []
@@ -333,7 +335,7 @@ def sendToServerData():
     for i, value in enumerate(AllTargets):
         if (i == 0):
             previous = value
-            if (data['fileName'] == 'IrisC'):
+            if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
                 target_names.append(value)
             else:
                 pass
@@ -341,7 +343,7 @@ def sendToServerData():
             AllTargetsFloatValues.append(Class)
         else:
             Class = Class + 1
-            if (data['fileName'] == 'IrisC'):
+            if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
                 target_names.append(value)
             else:
                 pass
@@ -439,7 +441,7 @@ def dataSetSelection():
     for i, value in enumerate(AllTargets):
         if (i == 0):
             previous = value
-            if (data['fileName'] == 'IrisC'):
+            if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
                 target_names.append(value)
             else:
                 pass
@@ -447,7 +449,7 @@ def dataSetSelection():
             AllTargetsFloatValues.append(Class)
         else:
             Class = Class + 1
-            if (data['fileName'] == 'IrisC'):
+            if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
                 target_names.append(value)
             else:
                 pass
@@ -484,9 +486,11 @@ def dataSetSelection():
 
 def create_global_function():
     global estimator
-    def estimator(C, gamma):
+    def estimator(n_estimators, eta, max_depth):
         # initialize model
-        model = SVC(C=C, gamma=gamma, degree=1, random_state=RANDOM_SEED)
+        n_estimators = int(n_estimators)
+        max_depth = int(max_depth)
+        model = XGBClassifier(n_estimators=n_estimators, eta=eta, max_depth=max_depth, n_jobs=-1, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
         # set in cross-validation
         result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy')
         # result is mean of test_score
@@ -524,14 +528,14 @@ def executeModel(exeCall, flagEx, nodeTransfName):
             XData = XDataStored.copy()
             XDataStoredOriginal = XDataStored.copy()
     columnsNewGen = keepOriginalFeatures.columns.values.tolist()
-    # Bayesian Optimization for 50 iterations
+    # Bayesian Optimization CHANGE INIT_POINTS!
     if (keyFirstTime):
         create_global_function()
-        params = {"C": (0.01, 100), "gamma": (0.01, 100)}
+        params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "max_depth": (6,12)}
-        svc_bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
+        bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
-        svc_bayesopt.maximize(init_points=30, n_iter=20, acq='ucb')
+        bayesopt.maximize(init_points=35, n_iter=15, acq='ucb')
-        bestParams = svc_bayesopt.max['params']
+        bestParams = bayesopt.max['params']
-        estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True, random_state=RANDOM_SEED)
+        estimator = XGBClassifier(n_estimators=int(bestParams.get('n_estimators')), eta=bestParams.get('eta'), max_depth=int(bestParams.get('max_depth')), probability=True, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
 
     if (len(exeCall) != 0):
         if (flagEx == 1):
@@ -580,21 +584,35 @@ def executeModel(exeCall, flagEx, nodeTransfName):
                 elif (splittedCol[1] == 'mms'):
                     XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].min())/(XData[nodeTransfName].max()-XData[nodeTransfName].min())
                 elif (splittedCol[1] == 'l2'):
+                    dfTemp = []
                     dfTemp = np.log2(XData[nodeTransfName])
+                    dfTemp = dfTemp.replace(np.nan, 0)
                     dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
-                    dfTemp = dfTemp.replace(-np.inf, 2.2250738585072014e-308)
+                    dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
                     XData[nodeTransfName] = dfTemp
                 elif (splittedCol[1] == 'l1p'):
                     XData[nodeTransfName] = np.log1p(XData[nodeTransfName])
                 elif (splittedCol[1] == 'l10'):
+                    dfTemp = []
                     dfTemp = np.log10(XData[nodeTransfName])
+                    dfTemp = dfTemp.replace(np.nan, 0)
                     dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
-                    dfTemp = dfTemp.replace(-np.inf, 2.2250738585072014e-308)
+                    dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
                     XData[nodeTransfName] = dfTemp
                 elif (splittedCol[1] == 'e2'):
-                    XData[nodeTransfName] = np.exp2(XData[nodeTransfName])
+                    dfTemp = []
+                    dfTemp = np.exp2(XData[nodeTransfName])
+                    dfTemp = dfTemp.replace(np.nan, 0)
+                    dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
+                    dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
+                    XData[nodeTransfName] = dfTemp
                 elif (splittedCol[1] == 'em1'):
-                    XData[nodeTransfName] = np.expm1(XData[nodeTransfName])
+                    dfTemp = []
+                    dfTemp = np.expm1(XData[nodeTransfName])
+                    dfTemp = dfTemp.replace(np.nan, 0)
+                    dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
+                    dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
+                    XData[nodeTransfName] = dfTemp
                 elif (splittedCol[1] == 'p2'):
                     XData[nodeTransfName] = np.power(XData[nodeTransfName], 2)
                 elif (splittedCol[1] == 'p3'):
@@ -620,7 +638,7 @@ def executeModel(exeCall, flagEx, nodeTransfName):
     estimator.fit(XData, yData)
     yPredict = estimator.predict(XData)
     yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
-    print(XData)
+    print('Data set:',XData)
     num_cores = multiprocessing.cpu_count()
     inputsSc = ['accuracy','precision_macro','recall_macro']
 
@@ -636,7 +654,7 @@ def executeModel(exeCall, flagEx, nodeTransfName):
     
     if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
         finalResultsData = XData.copy()
-        print('improved')
+        print('Improved!')
 
     if (keyFirstTime == False):
         if ((scoresAct[0]-scoresAct[1]) > (previousState[0]-previousState[1])):
@@ -694,15 +712,11 @@ def estimatorFeatureSelection(Data, clf):
     ImpurityFS = []
     RankingFS = []
     
-    rf = RandomForestClassifier(n_estimators = 100,
+    estim = clf.fit(Data, yData)
-                            n_jobs = -1,
-                            random_state = RANDOM_SEED)
-    rf.fit(Data, yData)
-
-    importances = rf.feature_importances_
 
-    std = np.std([tree.feature_importances_ for tree in rf.estimators_],
+    importances = clf.feature_importances_
-                axis=0)
+    # std = np.std([tree.feature_importances_ for tree in estim.feature_importances_],
+    #             axis=0)
 
     maxList = max(importances)
     minList = min(importances)
@@ -837,32 +851,36 @@ def Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
         splittedCol = columnsNames[(count)*len(listofTransformations)+0].split('_')
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             XDataNumericCopy[i] = XDataNumericCopy[i].round()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)          
+            dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)          
         splittedCol = columnsNames[(count)*len(listofTransformations)+1].split('_')
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             number_of_bins = np.histogram_bin_edges(XDataNumericCopy[i], bins='auto')
             emptyLabels = []
@@ -877,204 +895,236 @@ def Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)          
+            dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)          
         splittedCol = columnsNames[(count)*len(listofTransformations)+2].split('_')        
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].mean())/XDataNumericCopy[i].std()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)          
+            dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)          
         splittedCol = columnsNames[(count)*len(listofTransformations)+3].split('_')        
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].min())/(XDataNumericCopy[i].max()-XDataNumericCopy[i].min())
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)          
+            dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)          
         splittedCol = columnsNames[(count)*len(listofTransformations)+4].split('_')
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
+            dfTemp = []
             dfTemp = np.log2(XDataNumericCopy[i])
+            dfTemp = dfTemp.replace(np.nan, 0)
             dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
-            dfTemp = dfTemp.replace(-np.inf, 2.2250738585072014e-308)
+            dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
             XDataNumericCopy[i] = dfTemp
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         splittedCol = columnsNames[(count)*len(listofTransformations)+5].split('_')
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             XDataNumericCopy[i] = np.log1p(XDataNumericCopy[i])
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         splittedCol = columnsNames[(count)*len(listofTransformations)+6].split('_')
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
+            dfTemp = []
             dfTemp = np.log10(XDataNumericCopy[i])
+            dfTemp = dfTemp.replace(np.nan, 0)
             dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
-            dfTemp = dfTemp.replace(-np.inf, 2.2250738585072014e-308)
+            dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
             XDataNumericCopy[i] = dfTemp
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         splittedCol = columnsNames[(count)*len(listofTransformations)+7].split('_')   
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
-            XDataNumericCopy[i] = np.exp2(XDataNumericCopy[i])
+            dfTemp = []
+            dfTemp = np.exp2(XDataNumericCopy[i])
+            dfTemp = dfTemp.replace(np.nan, 0)
+            dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
+            dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
+            XDataNumericCopy[i] = dfTemp
+            if (np.isinf(dfTemp.var())):
+                flagInf = True
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         splittedCol = columnsNames[(count)*len(listofTransformations)+8].split('_')   
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
-            XDataNumericCopy[i] = np.expm1(XDataNumericCopy[i])
+            dfTemp = []
+            dfTemp = np.expm1(XDataNumericCopy[i])
+            dfTemp = dfTemp.replace(np.nan, 0)
+            dfTemp = dfTemp.replace(np.inf, 1.7976931348623157e+308)
+            dfTemp = dfTemp.replace(-np.inf, 1.7976931348623157e-308)
+            XDataNumericCopy[i] = dfTemp
+            if (np.isinf(dfTemp.var())):
+                flagInf = True
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         splittedCol = columnsNames[(count)*len(listofTransformations)+9].split('_')   
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 2)
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         splittedCol = columnsNames[(count)*len(listofTransformations)+10].split('_')   
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 3)
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         splittedCol = columnsNames[(count)*len(listofTransformations)+11].split('_')   
         if(len(splittedCol) == 1):
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         else:
             d={}
+            flagInf = False
             XDataNumericCopy = XDataNumeric.copy()
             XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 4)
             for number in range(1,6):
                 quadrantVariable = str('quadrant%s' % number)
                 illusion = locals()[quadrantVariable]
                 d["DataRows{0}".format(number)] = XDataNumericCopy.iloc[illusion, :]
-            dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count)
+            dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
         packCorrTransformed.append(dicTransf)
 
     return 'Everything Okay'
 
-def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, feature, count):
+def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, feature, count, flagInf):
-    
-    print(DataRows1)
-    print(DataRows2)
-    print(DataRows3)
-    print(DataRows4)
-    print(DataRows5)
 
     corrMatrix1 = DataRows1.corr()
     corrMatrix1 = corrMatrix1.abs()
@@ -1129,9 +1179,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5,
         VIF1 = pd.Series([variance_inflation_factor(X1.values, i) 
             for i in range(X1.shape[1])], 
             index=X1.columns)
+        VIF1 = VIF1.replace(np.nan, 0)
+        VIF1 = VIF1.replace(-np.inf, 1.7976931348623157e-308)
+        VIF1 = VIF1.replace(np.inf, 1.7976931348623157e+308)
         VIF1 = VIF1.loc[[feature]]
-        if (len(targetRows1Arr) > 2):
+        if ((len(targetRows1Arr) > 2) and (flagInf == False)):
-            MI1 = mutual_info_classif(DataRows1, targetRows1Arr)
+            MI1 = mutual_info_classif(DataRows1, targetRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI1List = MI1.tolist()
             MI1List = MI1List[count]
         else:
@@ -1154,9 +1207,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5,
         VIF2 = pd.Series([variance_inflation_factor(X2.values, i) 
                 for i in range(X2.shape[1])], 
                 index=X2.columns)
+        VIF2 = VIF2.replace(np.nan, 0)
+        VIF2 = VIF2.replace(-np.inf, 1.7976931348623157e-308)
+        VIF2 = VIF2.replace(np.inf, 1.7976931348623157e+308)
         VIF2 = VIF2.loc[[feature]]
-        if (len(targetRows2Arr) > 2):
+        if ((len(targetRows2Arr) > 2) and (flagInf == False)):
-            MI2 = mutual_info_classif(DataRows2, targetRows2Arr)
+            MI2 = mutual_info_classif(DataRows2, targetRows2Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI2List = MI2.tolist()
             MI2List = MI2List[count]
         else:
@@ -1179,9 +1235,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5,
         VIF3 = pd.Series([variance_inflation_factor(X3.values, i) 
                 for i in range(X3.shape[1])], 
                 index=X3.columns)
+        VIF3 = VIF3.replace(np.nan, 0)
+        VIF3 = VIF3.replace(-np.inf, 1.7976931348623157e-308)
+        VIF3 = VIF3.replace(np.inf, 1.7976931348623157e+308)
         VIF3 = VIF3.loc[[feature]]
-        if (len(targetRows3Arr) > 2):
+        if ((len(targetRows3Arr) > 2) and (flagInf == False)):
-            MI3 = mutual_info_classif(DataRows3, targetRows3Arr)
+            MI3 = mutual_info_classif(DataRows3, targetRows3Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI3List = MI3.tolist()
             MI3List = MI3List[count]
         else:
@@ -1204,9 +1263,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5,
         VIF4 = pd.Series([variance_inflation_factor(X4.values, i) 
                 for i in range(X4.shape[1])], 
                 index=X4.columns)
+        VIF4 = VIF4.replace(np.nan, 0)
+        VIF4 = VIF4.replace(-np.inf, 1.7976931348623157e-308)
+        VIF4 = VIF4.replace(np.inf, 1.7976931348623157e+308)
         VIF4 = VIF4.loc[[feature]]
-        if (len(targetRows4Arr) > 2):
+        if ((len(targetRows4Arr) > 2) and (flagInf == False)):
-            MI4 = mutual_info_classif(DataRows4, targetRows4Arr)
+            MI4 = mutual_info_classif(DataRows4, targetRows4Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI4List = MI4.tolist()
             MI4List = MI4List[count]
         else:
@@ -1229,9 +1291,12 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5,
         VIF5 = pd.Series([variance_inflation_factor(X5.values, i) 
                 for i in range(X5.shape[1])], 
                 index=X5.columns)
+        VIF5 = VIF5.replace(np.nan, 0)
+        VIF5 = VIF5.replace(-np.inf, 1.7976931348623157e-308)
+        VIF5 = VIF5.replace(np.inf, 1.7976931348623157e+308)
         VIF5 = VIF5.loc[[feature]]
-        if (len(targetRows5Arr) > 2):
+        if ((len(targetRows5Arr) > 2) and (flagInf == False)):
-            MI5 = mutual_info_classif(DataRows5, targetRows5Arr)
+            MI5 = mutual_info_classif(DataRows5, targetRows5Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI5List = MI5.tolist()
             MI5List = MI5List[count]
         else:
@@ -1241,11 +1306,26 @@ def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5,
         VIF5 = pd.Series()
         MI5List = []
 
-    corrMatrixComb1 = corrMatrixComb1.loc[[feature]]
+    if(corrMatrixComb1.empty):
-    corrMatrixComb2 = corrMatrixComb2.loc[[feature]]
+        corrMatrixComb1 = pd.DataFrame()
-    corrMatrixComb3 = corrMatrixComb3.loc[[feature]]
+    else:
-    corrMatrixComb4 = corrMatrixComb4.loc[[feature]]
+        corrMatrixComb1 = corrMatrixComb1.loc[[feature]]
-    corrMatrixComb5 = corrMatrixComb5.loc[[feature]]
+    if(corrMatrixComb2.empty):
+        corrMatrixComb2 = pd.DataFrame()
+    else:
+        corrMatrixComb2 = corrMatrixComb2.loc[[feature]]
+    if(corrMatrixComb3.empty):
+        corrMatrixComb3 = pd.DataFrame()
+    else:
+        corrMatrixComb3 = corrMatrixComb3.loc[[feature]]
+    if(corrMatrixComb4.empty):
+        corrMatrixComb4 = pd.DataFrame()
+    else:
+        corrMatrixComb4 = corrMatrixComb4.loc[[feature]]
+    if(corrMatrixComb5.empty):
+        corrMatrixComb5 = pd.DataFrame()
+    else:
+        corrMatrixComb5 = corrMatrixComb5.loc[[feature]]
 
     targetRows1ArrDF = pd.DataFrame(targetRows1Arr)
     targetRows2ArrDF = pd.DataFrame(targetRows2Arr)
@@ -1362,6 +1442,7 @@ def Seperation():
     DataRows4 = XData.iloc[quadrant4, :]
     DataRows5 = XData.iloc[quadrant5, :]
 
+
     Transformation(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5)
     
     corrMatrix1 = DataRows1.corr()
@@ -1412,8 +1493,11 @@ def Seperation():
         VIF1 = pd.Series([variance_inflation_factor(X1.values, i) 
             for i in range(X1.shape[1])], 
             index=X1.columns)
+        VIF1 = VIF1.replace(np.nan, 0)
+        VIF1 = VIF1.replace(-np.inf, 1.7976931348623157e-308)
+        VIF1 = VIF1.replace(np.inf, 1.7976931348623157e+308)
         if (len(targetRows1Arr) > 2):
-            MI1 = mutual_info_classif(DataRows1, targetRows1Arr)
+            MI1 = mutual_info_classif(DataRows1, targetRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI1List = MI1.tolist()
         else:
             MI1List = []
@@ -1435,8 +1519,11 @@ def Seperation():
         VIF2 = pd.Series([variance_inflation_factor(X2.values, i) 
                 for i in range(X2.shape[1])], 
                 index=X2.columns)
+        VIF2 = VIF2.replace(np.nan, 0)
+        VIF2 = VIF2.replace(-np.inf, 1.7976931348623157e-308)
+        VIF2 = VIF2.replace(np.inf, 1.7976931348623157e+308)
         if (len(targetRows2Arr) > 2):
-            MI2 = mutual_info_classif(DataRows2, targetRows2Arr)
+            MI2 = mutual_info_classif(DataRows2, targetRows2Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI2List = MI2.tolist()
         else:
             MI2List = []
@@ -1458,8 +1545,11 @@ def Seperation():
         VIF3 = pd.Series([variance_inflation_factor(X3.values, i) 
                 for i in range(X3.shape[1])], 
                 index=X3.columns)
+        VIF3 = VIF3.replace(np.nan, 0)
+        VIF3 = VIF3.replace(-np.inf, 1.7976931348623157e-308)
+        VIF3 = VIF3.replace(np.inf, 1.7976931348623157e+308)
         if (len(targetRows3Arr) > 2):
-            MI3 = mutual_info_classif(DataRows3, targetRows3Arr)
+            MI3 = mutual_info_classif(DataRows3, targetRows3Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI3List = MI3.tolist()
         else:
             MI3List = []
@@ -1481,8 +1571,11 @@ def Seperation():
         VIF4 = pd.Series([variance_inflation_factor(X4.values, i) 
                 for i in range(X4.shape[1])], 
                 index=X4.columns)
+        VIF4 = VIF4.replace(np.nan, 0)
+        VIF4 = VIF4.replace(-np.inf, 1.7976931348623157e-308)
+        VIF4 = VIF4.replace(np.inf, 1.7976931348623157e+308)
         if (len(targetRows4Arr) > 2):
-            MI4 = mutual_info_classif(DataRows4, targetRows4Arr)
+            MI4 = mutual_info_classif(DataRows4, targetRows4Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI4List = MI4.tolist()
         else:
             MI4List = []
@@ -1504,8 +1597,11 @@ def Seperation():
         VIF5 = pd.Series([variance_inflation_factor(X5.values, i) 
                 for i in range(X5.shape[1])], 
                 index=X5.columns)
+        VIF5 = VIF5.replace(np.nan, 0)
+        VIF5 = VIF5.replace(-np.inf, 1.7976931348623157e-308)
+        VIF5 = VIF5.replace(np.inf, 1.7976931348623157e+308)
         if (len(targetRows5Arr) > 2):
-            MI5 = mutual_info_classif(DataRows5, targetRows5Arr)
+            MI5 = mutual_info_classif(DataRows5, targetRows5Arr, n_neighbors=3, random_state=RANDOM_SEED)
             MI5List = MI5.tolist()
         else:
             MI5List = []