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cachedir/joblib/run/randomSearch/05609dc6df4ae4898dbab381be14f176/metadata.json

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cachedir/joblib/run/randomSearch/0bdb0a3994557b46f8e06fc3ddb42976/metadata.json

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cachedir/joblib/run/randomSearch/1a2646f540c258852978a1d94b17742b/metadata.json

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cachedir/joblib/run/randomSearch/20516fbabb68f3b95016dc6e7620c891/metadata.json

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cachedir/joblib/run/randomSearch/27a2cfe3ccd1c8af73f4e7bc6238df2a/metadata.json

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cachedir/joblib/run/randomSearch/4c7c0701fcdc8bd6356e4638f53e5d81/metadata.json

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cachedir/joblib/run/randomSearch/52226770407f8f36dacd42bf3d36b36d/metadata.json

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cachedir/joblib/run/randomSearch/7d87fc60b78f9a8c0f2e5253807d1620/metadata.json

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cachedir/joblib/run/randomSearch/857a7d5d95dd356021a96950625e2685/metadata.json

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cachedir/joblib/run/randomSearch/97b072a4213180c78544732e6bf6bbb5/metadata.json

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cachedir/joblib/run/randomSearch/aba5eafe0e7bbd08a063cfe1b27cd2cb/metadata.json

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cachedir/joblib/run/randomSearch/be482d0bfafd528fb56c897a3ad6b5ef/metadata.json

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cachedir/joblib/run/randomSearch/c2a6a25b366a9dc4607b8475c742ec6f/metadata.json

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cachedir/joblib/run/randomSearch/d3471d9335c2698fe25ebe6d74c09a92/metadata.json

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cachedir/joblib/run/randomSearch/d7cd2236f54baeeddec6d3d27670d641/metadata.json

@@ -1 +0,0 @@
-{"duration": 129.17617201805115, "input_args": {"XData": "     Fbs  Slope  Trestbps  Exang  Thalach  Age  Chol  Sex  Oldpeak  Restecg  Cp  Ca  Thal\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]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "GradientBoostingClassifier(criterion='mae', learning_rate=0.12,\n                           loss='exponential', n_estimators=28, random_state=42,\n                           subsample=0.9)", "params": "{'n_estimators': [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'loss': ['deviance', 'exponential'], 'learning_rate': [0.01, 0.12, 0.23, 0.34, 0.45], 'subsample': [0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6, 0.7000000000000001, 0.8, 0.9], 'criterion': ['friedman_mse', 'mse', 'mae']}", "eachAlgor": "'GradB'", "AlgorithmsIDsEnd": "400"}}

cachedir/joblib/run/randomSearch/func_code.py

@@ -1,12 +1,12 @@
-# first line: 584
+# first line: 718
 @memory.cache
-def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
+def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation):
     print(clf)
     search = RandomizedSearchCV(    
         estimator=clf, param_distributions=params, n_iter=100,
         cv=crossValidation, refit='accuracy', scoring=scoring,
         verbose=0, n_jobs=-1)
-
+    
     # fit and extract the probabilities
     search.fit(XData, yData)
 

extra_data_sets/diabetes.csv

@@ -1,769 +0,0 @@
-Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DPF,Age,Outcome*
-6,148,72,35,0,33.6,0.627,50,Positive
-1,85,66,29,0,26.6,0.351,31,Negative
-8,183,64,0,0,23.3,0.672,32,Positive
-1,89,66,23,94,28.1,0.167,21,Negative
-0,137,40,35,168,43.1,2.288,33,Positive
-5,116,74,0,0,25.6,0.201,30,Negative
-3,78,50,32,88,31,0.248,26,Positive
-10,115,0,0,0,35.3,0.134,29,Negative
-2,197,70,45,543,30.5,0.158,53,Positive
-8,125,96,0,0,0,0.232,54,Positive
-4,110,92,0,0,37.6,0.191,30,Negative
-10,168,74,0,0,38,0.537,34,Positive
-10,139,80,0,0,27.1,1.441,57,Negative
-1,189,60,23,846,30.1,0.398,59,Positive
-5,166,72,19,175,25.8,0.587,51,Positive
-7,100,0,0,0,30,0.484,32,Positive
-0,118,84,47,230,45.8,0.551,31,Positive
-7,107,74,0,0,29.6,0.254,31,Positive
-1,103,30,38,83,43.3,0.183,33,Negative
-1,115,70,30,96,34.6,0.529,32,Positive
-3,126,88,41,235,39.3,0.704,27,Negative
-8,99,84,0,0,35.4,0.388,50,Negative
-7,196,90,0,0,39.8,0.451,41,Positive
-9,119,80,35,0,29,0.263,29,Positive
-11,143,94,33,146,36.6,0.254,51,Positive
-10,125,70,26,115,31.1,0.205,41,Positive
-7,147,76,0,0,39.4,0.257,43,Positive
-1,97,66,15,140,23.2,0.487,22,Negative
-13,145,82,19,110,22.2,0.245,57,Negative
-5,117,92,0,0,34.1,0.337,38,Negative
-5,109,75,26,0,36,0.546,60,Negative
-3,158,76,36,245,31.6,0.851,28,Positive
-3,88,58,11,54,24.8,0.267,22,Negative
-6,92,92,0,0,19.9,0.188,28,Negative
-10,122,78,31,0,27.6,0.512,45,Negative
-4,103,60,33,192,24,0.966,33,Negative
-11,138,76,0,0,33.2,0.42,35,Negative
-9,102,76,37,0,32.9,0.665,46,Positive
-2,90,68,42,0,38.2,0.503,27,Positive
-4,111,72,47,207,37.1,1.39,56,Positive
-3,180,64,25,70,34,0.271,26,Negative
-7,133,84,0,0,40.2,0.696,37,Negative
-7,106,92,18,0,22.7,0.235,48,Negative
-9,171,110,24,240,45.4,0.721,54,Positive
-7,159,64,0,0,27.4,0.294,40,Negative
-0,180,66,39,0,42,1.893,25,Positive
-1,146,56,0,0,29.7,0.564,29,Negative
-2,71,70,27,0,28,0.586,22,Negative
-7,103,66,32,0,39.1,0.344,31,Positive
-7,105,0,0,0,0,0.305,24,Negative
-1,103,80,11,82,19.4,0.491,22,Negative
-1,101,50,15,36,24.2,0.526,26,Negative
-5,88,66,21,23,24.4,0.342,30,Negative
-8,176,90,34,300,33.7,0.467,58,Positive
-7,150,66,42,342,34.7,0.718,42,Negative
-1,73,50,10,0,23,0.248,21,Negative
-7,187,68,39,304,37.7,0.254,41,Positive
-0,100,88,60,110,46.8,0.962,31,Negative
-0,146,82,0,0,40.5,1.781,44,Negative
-0,105,64,41,142,41.5,0.173,22,Negative
-2,84,0,0,0,0,0.304,21,Negative
-8,133,72,0,0,32.9,0.27,39,Positive
-5,44,62,0,0,25,0.587,36,Negative
-2,141,58,34,128,25.4,0.699,24,Negative
-7,114,66,0,0,32.8,0.258,42,Positive
-5,99,74,27,0,29,0.203,32,Negative
-0,109,88,30,0,32.5,0.855,38,Positive
-2,109,92,0,0,42.7,0.845,54,Negative
-1,95,66,13,38,19.6,0.334,25,Negative
-4,146,85,27,100,28.9,0.189,27,Negative
-2,100,66,20,90,32.9,0.867,28,Positive
-5,139,64,35,140,28.6,0.411,26,Negative
-13,126,90,0,0,43.4,0.583,42,Positive
-4,129,86,20,270,35.1,0.231,23,Negative
-1,79,75,30,0,32,0.396,22,Negative
-1,0,48,20,0,24.7,0.14,22,Negative
-7,62,78,0,0,32.6,0.391,41,Negative
-5,95,72,33,0,37.7,0.37,27,Negative
-0,131,0,0,0,43.2,0.27,26,Positive
-2,112,66,22,0,25,0.307,24,Negative
-3,113,44,13,0,22.4,0.14,22,Negative
-2,74,0,0,0,0,0.102,22,Negative
-7,83,78,26,71,29.3,0.767,36,Negative
-0,101,65,28,0,24.6,0.237,22,Negative
-5,137,108,0,0,48.8,0.227,37,Positive
-2,110,74,29,125,32.4,0.698,27,Negative
-13,106,72,54,0,36.6,0.178,45,Negative
-2,100,68,25,71,38.5,0.324,26,Negative
-15,136,70,32,110,37.1,0.153,43,Positive
-1,107,68,19,0,26.5,0.165,24,Negative
-1,80,55,0,0,19.1,0.258,21,Negative
-4,123,80,15,176,32,0.443,34,Negative
-7,81,78,40,48,46.7,0.261,42,Negative
-4,134,72,0,0,23.8,0.277,60,Positive
-2,142,82,18,64,24.7,0.761,21,Negative
-6,144,72,27,228,33.9,0.255,40,Negative
-2,92,62,28,0,31.6,0.13,24,Negative
-1,71,48,18,76,20.4,0.323,22,Negative
-6,93,50,30,64,28.7,0.356,23,Negative
-1,122,90,51,220,49.7,0.325,31,Positive
-1,163,72,0,0,39,1.222,33,Positive
-1,151,60,0,0,26.1,0.179,22,Negative
-0,125,96,0,0,22.5,0.262,21,Negative
-1,81,72,18,40,26.6,0.283,24,Negative
-2,85,65,0,0,39.6,0.93,27,Negative
-1,126,56,29,152,28.7,0.801,21,Negative
-1,96,122,0,0,22.4,0.207,27,Negative
-4,144,58,28,140,29.5,0.287,37,Negative
-3,83,58,31,18,34.3,0.336,25,Negative
-0,95,85,25,36,37.4,0.247,24,Positive
-3,171,72,33,135,33.3,0.199,24,Positive
-8,155,62,26,495,34,0.543,46,Positive
-1,89,76,34,37,31.2,0.192,23,Negative
-4,76,62,0,0,34,0.391,25,Negative
-7,160,54,32,175,30.5,0.588,39,Positive
-4,146,92,0,0,31.2,0.539,61,Positive
-5,124,74,0,0,34,0.22,38,Positive
-5,78,48,0,0,33.7,0.654,25,Negative
-4,97,60,23,0,28.2,0.443,22,Negative
-4,99,76,15,51,23.2,0.223,21,Negative
-0,162,76,56,100,53.2,0.759,25,Positive
-6,111,64,39,0,34.2,0.26,24,Negative
-2,107,74,30,100,33.6,0.404,23,Negative
-5,132,80,0,0,26.8,0.186,69,Negative
-0,113,76,0,0,33.3,0.278,23,Positive
-1,88,30,42,99,55,0.496,26,Positive
-3,120,70,30,135,42.9,0.452,30,Negative
-1,118,58,36,94,33.3,0.261,23,Negative
-1,117,88,24,145,34.5,0.403,40,Positive
-0,105,84,0,0,27.9,0.741,62,Positive
-4,173,70,14,168,29.7,0.361,33,Positive
-9,122,56,0,0,33.3,1.114,33,Positive
-3,170,64,37,225,34.5,0.356,30,Positive
-8,84,74,31,0,38.3,0.457,39,Negative
-2,96,68,13,49,21.1,0.647,26,Negative
-2,125,60,20,140,33.8,0.088,31,Negative
-0,100,70,26,50,30.8,0.597,21,Negative
-0,93,60,25,92,28.7,0.532,22,Negative
-0,129,80,0,0,31.2,0.703,29,Negative
-5,105,72,29,325,36.9,0.159,28,Negative
-3,128,78,0,0,21.1,0.268,55,Negative
-5,106,82,30,0,39.5,0.286,38,Negative
-2,108,52,26,63,32.5,0.318,22,Negative
-10,108,66,0,0,32.4,0.272,42,Positive
-4,154,62,31,284,32.8,0.237,23,Negative
-0,102,75,23,0,0,0.572,21,Negative
-9,57,80,37,0,32.8,0.096,41,Negative
-2,106,64,35,119,30.5,1.4,34,Negative
-5,147,78,0,0,33.7,0.218,65,Negative
-2,90,70,17,0,27.3,0.085,22,Negative
-1,136,74,50,204,37.4,0.399,24,Negative
-4,114,65,0,0,21.9,0.432,37,Negative
-9,156,86,28,155,34.3,1.189,42,Positive
-1,153,82,42,485,40.6,0.687,23,Negative
-8,188,78,0,0,47.9,0.137,43,Positive
-7,152,88,44,0,50,0.337,36,Positive
-2,99,52,15,94,24.6,0.637,21,Negative
-1,109,56,21,135,25.2,0.833,23,Negative
-2,88,74,19,53,29,0.229,22,Negative
-17,163,72,41,114,40.9,0.817,47,Positive
-4,151,90,38,0,29.7,0.294,36,Negative
-7,102,74,40,105,37.2,0.204,45,Negative
-0,114,80,34,285,44.2,0.167,27,Negative
-2,100,64,23,0,29.7,0.368,21,Negative
-0,131,88,0,0,31.6,0.743,32,Positive
-6,104,74,18,156,29.9,0.722,41,Positive
-3,148,66,25,0,32.5,0.256,22,Negative
-4,120,68,0,0,29.6,0.709,34,Negative
-4,110,66,0,0,31.9,0.471,29,Negative
-3,111,90,12,78,28.4,0.495,29,Negative
-6,102,82,0,0,30.8,0.18,36,Positive
-6,134,70,23,130,35.4,0.542,29,Positive
-2,87,0,23,0,28.9,0.773,25,Negative
-1,79,60,42,48,43.5,0.678,23,Negative
-2,75,64,24,55,29.7,0.37,33,Negative
-8,179,72,42,130,32.7,0.719,36,Positive
-6,85,78,0,0,31.2,0.382,42,Negative
-0,129,110,46,130,67.1,0.319,26,Positive
-5,143,78,0,0,45,0.19,47,Negative
-5,130,82,0,0,39.1,0.956,37,Positive
-6,87,80,0,0,23.2,0.084,32,Negative
-0,119,64,18,92,34.9,0.725,23,Negative
-1,0,74,20,23,27.7,0.299,21,Negative
-5,73,60,0,0,26.8,0.268,27,Negative
-4,141,74,0,0,27.6,0.244,40,Negative
-7,194,68,28,0,35.9,0.745,41,Positive
-8,181,68,36,495,30.1,0.615,60,Positive
-1,128,98,41,58,32,1.321,33,Positive
-8,109,76,39,114,27.9,0.64,31,Positive
-5,139,80,35,160,31.6,0.361,25,Positive
-3,111,62,0,0,22.6,0.142,21,Negative
-9,123,70,44,94,33.1,0.374,40,Negative
-7,159,66,0,0,30.4,0.383,36,Positive
-11,135,0,0,0,52.3,0.578,40,Positive
-8,85,55,20,0,24.4,0.136,42,Negative
-5,158,84,41,210,39.4,0.395,29,Positive
-1,105,58,0,0,24.3,0.187,21,Negative
-3,107,62,13,48,22.9,0.678,23,Positive
-4,109,64,44,99,34.8,0.905,26,Positive
-4,148,60,27,318,30.9,0.15,29,Positive
-0,113,80,16,0,31,0.874,21,Negative
-1,138,82,0,0,40.1,0.236,28,Negative
-0,108,68,20,0,27.3,0.787,32,Negative
-2,99,70,16,44,20.4,0.235,27,Negative
-6,103,72,32,190,37.7,0.324,55,Negative
-5,111,72,28,0,23.9,0.407,27,Negative
-8,196,76,29,280,37.5,0.605,57,Positive
-5,162,104,0,0,37.7,0.151,52,Positive
-1,96,64,27,87,33.2,0.289,21,Negative
-7,184,84,33,0,35.5,0.355,41,Positive
-2,81,60,22,0,27.7,0.29,25,Negative
-0,147,85,54,0,42.8,0.375,24,Negative
-7,179,95,31,0,34.2,0.164,60,Negative
-0,140,65,26,130,42.6,0.431,24,Positive
-9,112,82,32,175,34.2,0.26,36,Positive
-12,151,70,40,271,41.8,0.742,38,Positive
-5,109,62,41,129,35.8,0.514,25,Positive
-6,125,68,30,120,30,0.464,32,Negative
-5,85,74,22,0,29,1.224,32,Positive
-5,112,66,0,0,37.8,0.261,41,Positive
-0,177,60,29,478,34.6,1.072,21,Positive
-2,158,90,0,0,31.6,0.805,66,Positive
-7,119,0,0,0,25.2,0.209,37,Negative
-7,142,60,33,190,28.8,0.687,61,Negative
-1,100,66,15,56,23.6,0.666,26,Negative
-1,87,78,27,32,34.6,0.101,22,Negative
-0,101,76,0,0,35.7,0.198,26,Negative
-3,162,52,38,0,37.2,0.652,24,Positive
-4,197,70,39,744,36.7,2.329,31,Negative
-0,117,80,31,53,45.2,0.089,24,Negative
-4,142,86,0,0,44,0.645,22,Positive
-6,134,80,37,370,46.2,0.238,46,Positive
-1,79,80,25,37,25.4,0.583,22,Negative
-4,122,68,0,0,35,0.394,29,Negative
-3,74,68,28,45,29.7,0.293,23,Negative
-4,171,72,0,0,43.6,0.479,26,Positive
-7,181,84,21,192,35.9,0.586,51,Positive
-0,179,90,27,0,44.1,0.686,23,Positive
-9,164,84,21,0,30.8,0.831,32,Positive
-0,104,76,0,0,18.4,0.582,27,Negative
-1,91,64,24,0,29.2,0.192,21,Negative
-4,91,70,32,88,33.1,0.446,22,Negative
-3,139,54,0,0,25.6,0.402,22,Positive
-6,119,50,22,176,27.1,1.318,33,Positive
-2,146,76,35,194,38.2,0.329,29,Negative
-9,184,85,15,0,30,1.213,49,Positive
-10,122,68,0,0,31.2,0.258,41,Negative
-0,165,90,33,680,52.3,0.427,23,Negative
-9,124,70,33,402,35.4,0.282,34,Negative
-1,111,86,19,0,30.1,0.143,23,Negative
-9,106,52,0,0,31.2,0.38,42,Negative
-2,129,84,0,0,28,0.284,27,Negative
-2,90,80,14,55,24.4,0.249,24,Negative
-0,86,68,32,0,35.8,0.238,25,Negative
-12,92,62,7,258,27.6,0.926,44,Positive
-1,113,64,35,0,33.6,0.543,21,Positive
-3,111,56,39,0,30.1,0.557,30,Negative
-2,114,68,22,0,28.7,0.092,25,Negative
-1,193,50,16,375,25.9,0.655,24,Negative
-11,155,76,28,150,33.3,1.353,51,Positive
-3,191,68,15,130,30.9,0.299,34,Negative
-3,141,0,0,0,30,0.761,27,Positive
-4,95,70,32,0,32.1,0.612,24,Negative
-3,142,80,15,0,32.4,0.2,63,Negative
-4,123,62,0,0,32,0.226,35,Positive
-5,96,74,18,67,33.6,0.997,43,Negative
-0,138,0,0,0,36.3,0.933,25,Positive
-2,128,64,42,0,40,1.101,24,Negative
-0,102,52,0,0,25.1,0.078,21,Negative
-2,146,0,0,0,27.5,0.24,28,Positive
-10,101,86,37,0,45.6,1.136,38,Positive
-2,108,62,32,56,25.2,0.128,21,Negative
-3,122,78,0,0,23,0.254,40,Negative
-1,71,78,50,45,33.2,0.422,21,Negative
-13,106,70,0,0,34.2,0.251,52,Negative
-2,100,70,52,57,40.5,0.677,25,Negative
-7,106,60,24,0,26.5,0.296,29,Positive
-0,104,64,23,116,27.8,0.454,23,Negative
-5,114,74,0,0,24.9,0.744,57,Negative
-2,108,62,10,278,25.3,0.881,22,Negative
-0,146,70,0,0,37.9,0.334,28,Positive
-10,129,76,28,122,35.9,0.28,39,Negative
-7,133,88,15,155,32.4,0.262,37,Negative
-7,161,86,0,0,30.4,0.165,47,Positive
-2,108,80,0,0,27,0.259,52,Positive
-7,136,74,26,135,26,0.647,51,Negative
-5,155,84,44,545,38.7,0.619,34,Negative
-1,119,86,39,220,45.6,0.808,29,Positive
-4,96,56,17,49,20.8,0.34,26,Negative
-5,108,72,43,75,36.1,0.263,33,Negative
-0,78,88,29,40,36.9,0.434,21,Negative
-0,107,62,30,74,36.6,0.757,25,Positive
-2,128,78,37,182,43.3,1.224,31,Positive
-1,128,48,45,194,40.5,0.613,24,Positive
-0,161,50,0,0,21.9,0.254,65,Negative
-6,151,62,31,120,35.5,0.692,28,Negative
-2,146,70,38,360,28,0.337,29,Positive
-0,126,84,29,215,30.7,0.52,24,Negative
-14,100,78,25,184,36.6,0.412,46,Positive
-8,112,72,0,0,23.6,0.84,58,Negative
-0,167,0,0,0,32.3,0.839,30,Positive
-2,144,58,33,135,31.6,0.422,25,Positive
-5,77,82,41,42,35.8,0.156,35,Negative
-5,115,98,0,0,52.9,0.209,28,Positive
-3,150,76,0,0,21,0.207,37,Negative
-2,120,76,37,105,39.7,0.215,29,Negative
-10,161,68,23,132,25.5,0.326,47,Positive
-0,137,68,14,148,24.8,0.143,21,Negative
-0,128,68,19,180,30.5,1.391,25,Positive
-2,124,68,28,205,32.9,0.875,30,Positive
-6,80,66,30,0,26.2,0.313,41,Negative
-0,106,70,37,148,39.4,0.605,22,Negative
-2,155,74,17,96,26.6,0.433,27,Positive
-3,113,50,10,85,29.5,0.626,25,Negative
-7,109,80,31,0,35.9,1.127,43,Positive
-2,112,68,22,94,34.1,0.315,26,Negative
-3,99,80,11,64,19.3,0.284,30,Negative
-3,182,74,0,0,30.5,0.345,29,Positive
-3,115,66,39,140,38.1,0.15,28,Negative
-6,194,78,0,0,23.5,0.129,59,Positive
-4,129,60,12,231,27.5,0.527,31,Negative
-3,112,74,30,0,31.6,0.197,25,Positive
-0,124,70,20,0,27.4,0.254,36,Positive
-13,152,90,33,29,26.8,0.731,43,Positive
-2,112,75,32,0,35.7,0.148,21,Negative
-1,157,72,21,168,25.6,0.123,24,Negative
-1,122,64,32,156,35.1,0.692,30,Positive
-10,179,70,0,0,35.1,0.2,37,Negative
-2,102,86,36,120,45.5,0.127,23,Positive
-6,105,70,32,68,30.8,0.122,37,Negative
-8,118,72,19,0,23.1,1.476,46,Negative
-2,87,58,16,52,32.7,0.166,25,Negative
-1,180,0,0,0,43.3,0.282,41,Positive
-12,106,80,0,0,23.6,0.137,44,Negative
-1,95,60,18,58,23.9,0.26,22,Negative
-0,165,76,43,255,47.9,0.259,26,Negative
-0,117,0,0,0,33.8,0.932,44,Negative
-5,115,76,0,0,31.2,0.343,44,Positive
-9,152,78,34,171,34.2,0.893,33,Positive
-7,178,84,0,0,39.9,0.331,41,Positive
-1,130,70,13,105,25.9,0.472,22,Negative
-1,95,74,21,73,25.9,0.673,36,Negative
-1,0,68,35,0,32,0.389,22,Negative
-5,122,86,0,0,34.7,0.29,33,Negative
-8,95,72,0,0,36.8,0.485,57,Negative
-8,126,88,36,108,38.5,0.349,49,Negative
-1,139,46,19,83,28.7,0.654,22,Negative
-3,116,0,0,0,23.5,0.187,23,Negative
-3,99,62,19,74,21.8,0.279,26,Negative
-5,0,80,32,0,41,0.346,37,Positive
-4,92,80,0,0,42.2,0.237,29,Negative
-4,137,84,0,0,31.2,0.252,30,Negative
-3,61,82,28,0,34.4,0.243,46,Negative
-1,90,62,12,43,27.2,0.58,24,Negative
-3,90,78,0,0,42.7,0.559,21,Negative
-9,165,88,0,0,30.4,0.302,49,Positive
-1,125,50,40,167,33.3,0.962,28,Positive
-13,129,0,30,0,39.9,0.569,44,Positive
-12,88,74,40,54,35.3,0.378,48,Negative
-1,196,76,36,249,36.5,0.875,29,Positive
-5,189,64,33,325,31.2,0.583,29,Positive
-5,158,70,0,0,29.8,0.207,63,Negative
-5,103,108,37,0,39.2,0.305,65,Negative
-4,146,78,0,0,38.5,0.52,67,Positive
-4,147,74,25,293,34.9,0.385,30,Negative
-5,99,54,28,83,34,0.499,30,Negative
-6,124,72,0,0,27.6,0.368,29,Positive
-0,101,64,17,0,21,0.252,21,Negative
-3,81,86,16,66,27.5,0.306,22,Negative
-1,133,102,28,140,32.8,0.234,45,Positive
-3,173,82,48,465,38.4,2.137,25,Positive
-0,118,64,23,89,0,1.731,21,Negative
-0,84,64,22,66,35.8,0.545,21,Negative
-2,105,58,40,94,34.9,0.225,25,Negative
-2,122,52,43,158,36.2,0.816,28,Negative
-12,140,82,43,325,39.2,0.528,58,Positive
-0,98,82,15,84,25.2,0.299,22,Negative
-1,87,60,37,75,37.2,0.509,22,Negative
-4,156,75,0,0,48.3,0.238,32,Positive
-0,93,100,39,72,43.4,1.021,35,Negative
-1,107,72,30,82,30.8,0.821,24,Negative
-0,105,68,22,0,20,0.236,22,Negative
-1,109,60,8,182,25.4,0.947,21,Negative
-1,90,62,18,59,25.1,1.268,25,Negative
-1,125,70,24,110,24.3,0.221,25,Negative
-1,119,54,13,50,22.3,0.205,24,Negative
-5,116,74,29,0,32.3,0.66,35,Positive
-8,105,100,36,0,43.3,0.239,45,Positive
-5,144,82,26,285,32,0.452,58,Positive
-3,100,68,23,81,31.6,0.949,28,Negative
-1,100,66,29,196,32,0.444,42,Negative
-5,166,76,0,0,45.7,0.34,27,Positive
-1,131,64,14,415,23.7,0.389,21,Negative
-4,116,72,12,87,22.1,0.463,37,Negative
-4,158,78,0,0,32.9,0.803,31,Positive
-2,127,58,24,275,27.7,1.6,25,Negative
-3,96,56,34,115,24.7,0.944,39,Negative
-0,131,66,40,0,34.3,0.196,22,Positive
-3,82,70,0,0,21.1,0.389,25,Negative
-3,193,70,31,0,34.9,0.241,25,Positive
-4,95,64,0,0,32,0.161,31,Positive
-6,137,61,0,0,24.2,0.151,55,Negative
-5,136,84,41,88,35,0.286,35,Positive
-9,72,78,25,0,31.6,0.28,38,Negative
-5,168,64,0,0,32.9,0.135,41,Positive
-2,123,48,32,165,42.1,0.52,26,Negative
-4,115,72,0,0,28.9,0.376,46,Positive
-0,101,62,0,0,21.9,0.336,25,Negative
-8,197,74,0,0,25.9,1.191,39,Positive
-1,172,68,49,579,42.4,0.702,28,Positive
-6,102,90,39,0,35.7,0.674,28,Negative
-1,112,72,30,176,34.4,0.528,25,Negative
-1,143,84,23,310,42.4,1.076,22,Negative
-1,143,74,22,61,26.2,0.256,21,Negative
-0,138,60,35,167,34.6,0.534,21,Positive
-3,173,84,33,474,35.7,0.258,22,Positive
-1,97,68,21,0,27.2,1.095,22,Negative
-4,144,82,32,0,38.5,0.554,37,Positive
-1,83,68,0,0,18.2,0.624,27,Negative
-3,129,64,29,115,26.4,0.219,28,Positive
-1,119,88,41,170,45.3,0.507,26,Negative
-2,94,68,18,76,26,0.561,21,Negative
-0,102,64,46,78,40.6,0.496,21,Negative
-2,115,64,22,0,30.8,0.421,21,Negative
-8,151,78,32,210,42.9,0.516,36,Positive
-4,184,78,39,277,37,0.264,31,Positive
-0,94,0,0,0,0,0.256,25,Negative
-1,181,64,30,180,34.1,0.328,38,Positive
-0,135,94,46,145,40.6,0.284,26,Negative
-1,95,82,25,180,35,0.233,43,Positive
-2,99,0,0,0,22.2,0.108,23,Negative
-3,89,74,16,85,30.4,0.551,38,Negative
-1,80,74,11,60,30,0.527,22,Negative
-2,139,75,0,0,25.6,0.167,29,Negative
-1,90,68,8,0,24.5,1.138,36,Negative
-0,141,0,0,0,42.4,0.205,29,Positive
-12,140,85,33,0,37.4,0.244,41,Negative
-5,147,75,0,0,29.9,0.434,28,Negative
-1,97,70,15,0,18.2,0.147,21,Negative
-6,107,88,0,0,36.8,0.727,31,Negative
-0,189,104,25,0,34.3,0.435,41,Positive
-2,83,66,23,50,32.2,0.497,22,Negative
-4,117,64,27,120,33.2,0.23,24,Negative
-8,108,70,0,0,30.5,0.955,33,Positive
-4,117,62,12,0,29.7,0.38,30,Positive
-0,180,78,63,14,59.4,2.42,25,Positive
-1,100,72,12,70,25.3,0.658,28,Negative
-0,95,80,45,92,36.5,0.33,26,Negative
-0,104,64,37,64,33.6,0.51,22,Positive
-0,120,74,18,63,30.5,0.285,26,Negative
-1,82,64,13,95,21.2,0.415,23,Negative
-2,134,70,0,0,28.9,0.542,23,Positive
-0,91,68,32,210,39.9,0.381,25,Negative
-2,119,0,0,0,19.6,0.832,72,Negative
-2,100,54,28,105,37.8,0.498,24,Negative
-14,175,62,30,0,33.6,0.212,38,Positive
-1,135,54,0,0,26.7,0.687,62,Negative
-5,86,68,28,71,30.2,0.364,24,Negative
-10,148,84,48,237,37.6,1.001,51,Positive
-9,134,74,33,60,25.9,0.46,81,Negative
-9,120,72,22,56,20.8,0.733,48,Negative
-1,71,62,0,0,21.8,0.416,26,Negative
-8,74,70,40,49,35.3,0.705,39,Negative
-5,88,78,30,0,27.6,0.258,37,Negative
-10,115,98,0,0,24,1.022,34,Negative
-0,124,56,13,105,21.8,0.452,21,Negative
-0,74,52,10,36,27.8,0.269,22,Negative
-0,97,64,36,100,36.8,0.6,25,Negative
-8,120,0,0,0,30,0.183,38,Positive
-6,154,78,41,140,46.1,0.571,27,Negative
-1,144,82,40,0,41.3,0.607,28,Negative
-0,137,70,38,0,33.2,0.17,22,Negative
-0,119,66,27,0,38.8,0.259,22,Negative
-7,136,90,0,0,29.9,0.21,50,Negative
-4,114,64,0,0,28.9,0.126,24,Negative
-0,137,84,27,0,27.3,0.231,59,Negative
-2,105,80,45,191,33.7,0.711,29,Positive
-7,114,76,17,110,23.8,0.466,31,Negative
-8,126,74,38,75,25.9,0.162,39,Negative
-4,132,86,31,0,28,0.419,63,Negative
-3,158,70,30,328,35.5,0.344,35,Positive
-0,123,88,37,0,35.2,0.197,29,Negative
-4,85,58,22,49,27.8,0.306,28,Negative
-0,84,82,31,125,38.2,0.233,23,Negative
-0,145,0,0,0,44.2,0.63,31,Positive
-0,135,68,42,250,42.3,0.365,24,Positive
-1,139,62,41,480,40.7,0.536,21,Negative
-0,173,78,32,265,46.5,1.159,58,Negative
-4,99,72,17,0,25.6,0.294,28,Negative
-8,194,80,0,0,26.1,0.551,67,Negative
-2,83,65,28,66,36.8,0.629,24,Negative
-2,89,90,30,0,33.5,0.292,42,Negative
-4,99,68,38,0,32.8,0.145,33,Negative
-4,125,70,18,122,28.9,1.144,45,Positive
-3,80,0,0,0,0,0.174,22,Negative
-6,166,74,0,0,26.6,0.304,66,Negative
-5,110,68,0,0,26,0.292,30,Negative
-2,81,72,15,76,30.1,0.547,25,Negative
-7,195,70,33,145,25.1,0.163,55,Positive
-6,154,74,32,193,29.3,0.839,39,Negative
-2,117,90,19,71,25.2,0.313,21,Negative
-3,84,72,32,0,37.2,0.267,28,Negative
-6,0,68,41,0,39,0.727,41,Positive
-7,94,64,25,79,33.3,0.738,41,Negative
-3,96,78,39,0,37.3,0.238,40,Negative
-10,75,82,0,0,33.3,0.263,38,Negative
-0,180,90,26,90,36.5,0.314,35,Positive
-1,130,60,23,170,28.6,0.692,21,Negative
-2,84,50,23,76,30.4,0.968,21,Negative
-8,120,78,0,0,25,0.409,64,Negative
-12,84,72,31,0,29.7,0.297,46,Positive
-0,139,62,17,210,22.1,0.207,21,Negative
-9,91,68,0,0,24.2,0.2,58,Negative
-2,91,62,0,0,27.3,0.525,22,Negative
-3,99,54,19,86,25.6,0.154,24,Negative
-3,163,70,18,105,31.6,0.268,28,Positive
-9,145,88,34,165,30.3,0.771,53,Positive
-7,125,86,0,0,37.6,0.304,51,Negative
-13,76,60,0,0,32.8,0.18,41,Negative
-6,129,90,7,326,19.6,0.582,60,Negative
-2,68,70,32,66,25,0.187,25,Negative
-3,124,80,33,130,33.2,0.305,26,Negative
-6,114,0,0,0,0,0.189,26,Negative
-9,130,70,0,0,34.2,0.652,45,Positive
-3,125,58,0,0,31.6,0.151,24,Negative
-3,87,60,18,0,21.8,0.444,21,Negative
-1,97,64,19,82,18.2,0.299,21,Negative
-3,116,74,15,105,26.3,0.107,24,Negative
-0,117,66,31,188,30.8,0.493,22,Negative
-0,111,65,0,0,24.6,0.66,31,Negative
-2,122,60,18,106,29.8,0.717,22,Negative
-0,107,76,0,0,45.3,0.686,24,Negative
-1,86,66,52,65,41.3,0.917,29,Negative
-6,91,0,0,0,29.8,0.501,31,Negative
-1,77,56,30,56,33.3,1.251,24,Negative
-4,132,0,0,0,32.9,0.302,23,Positive
-0,105,90,0,0,29.6,0.197,46,Negative
-0,57,60,0,0,21.7,0.735,67,Negative
-0,127,80,37,210,36.3,0.804,23,Negative
-3,129,92,49,155,36.4,0.968,32,Positive
-8,100,74,40,215,39.4,0.661,43,Positive
-3,128,72,25,190,32.4,0.549,27,Positive
-10,90,85,32,0,34.9,0.825,56,Positive
-4,84,90,23,56,39.5,0.159,25,Negative
-1,88,78,29,76,32,0.365,29,Negative
-8,186,90,35,225,34.5,0.423,37,Positive
-5,187,76,27,207,43.6,1.034,53,Positive
-4,131,68,21,166,33.1,0.16,28,Negative
-1,164,82,43,67,32.8,0.341,50,Negative
-4,189,110,31,0,28.5,0.68,37,Negative
-1,116,70,28,0,27.4,0.204,21,Negative
-3,84,68,30,106,31.9,0.591,25,Negative
-6,114,88,0,0,27.8,0.247,66,Negative
-1,88,62,24,44,29.9,0.422,23,Negative
-1,84,64,23,115,36.9,0.471,28,Negative
-7,124,70,33,215,25.5,0.161,37,Negative
-1,97,70,40,0,38.1,0.218,30,Negative
-8,110,76,0,0,27.8,0.237,58,Negative
-11,103,68,40,0,46.2,0.126,42,Negative
-11,85,74,0,0,30.1,0.3,35,Negative
-6,125,76,0,0,33.8,0.121,54,Positive
-0,198,66,32,274,41.3,0.502,28,Positive
-1,87,68,34,77,37.6,0.401,24,Negative
-6,99,60,19,54,26.9,0.497,32,Negative
-0,91,80,0,0,32.4,0.601,27,Negative
-2,95,54,14,88,26.1,0.748,22,Negative
-1,99,72,30,18,38.6,0.412,21,Negative
-6,92,62,32,126,32,0.085,46,Negative
-4,154,72,29,126,31.3,0.338,37,Negative
-0,121,66,30,165,34.3,0.203,33,Positive
-3,78,70,0,0,32.5,0.27,39,Negative
-2,130,96,0,0,22.6,0.268,21,Negative
-3,111,58,31,44,29.5,0.43,22,Negative
-2,98,60,17,120,34.7,0.198,22,Negative
-1,143,86,30,330,30.1,0.892,23,Negative
-1,119,44,47,63,35.5,0.28,25,Negative
-6,108,44,20,130,24,0.813,35,Negative
-2,118,80,0,0,42.9,0.693,21,Positive
-10,133,68,0,0,27,0.245,36,Negative
-2,197,70,99,0,34.7,0.575,62,Positive
-0,151,90,46,0,42.1,0.371,21,Positive
-6,109,60,27,0,25,0.206,27,Negative
-12,121,78,17,0,26.5,0.259,62,Negative
-8,100,76,0,0,38.7,0.19,42,Negative
-8,124,76,24,600,28.7,0.687,52,Positive
-1,93,56,11,0,22.5,0.417,22,Negative
-8,143,66,0,0,34.9,0.129,41,Positive
-6,103,66,0,0,24.3,0.249,29,Negative
-3,176,86,27,156,33.3,1.154,52,Positive
-0,73,0,0,0,21.1,0.342,25,Negative
-11,111,84,40,0,46.8,0.925,45,Positive
-2,112,78,50,140,39.4,0.175,24,Negative
-3,132,80,0,0,34.4,0.402,44,Positive
-2,82,52,22,115,28.5,1.699,25,Negative
-6,123,72,45,230,33.6,0.733,34,Negative
-0,188,82,14,185,32,0.682,22,Positive
-0,67,76,0,0,45.3,0.194,46,Negative
-1,89,24,19,25,27.8,0.559,21,Negative
-1,173,74,0,0,36.8,0.088,38,Positive
-1,109,38,18,120,23.1,0.407,26,Negative
-1,108,88,19,0,27.1,0.4,24,Negative
-6,96,0,0,0,23.7,0.19,28,Negative
-1,124,74,36,0,27.8,0.1,30,Negative
-7,150,78,29,126,35.2,0.692,54,Positive
-4,183,0,0,0,28.4,0.212,36,Positive
-1,124,60,32,0,35.8,0.514,21,Negative
-1,181,78,42,293,40,1.258,22,Positive
-1,92,62,25,41,19.5,0.482,25,Negative
-0,152,82,39,272,41.5,0.27,27,Negative
-1,111,62,13,182,24,0.138,23,Negative
-3,106,54,21,158,30.9,0.292,24,Negative
-3,174,58,22,194,32.9,0.593,36,Positive
-7,168,88,42,321,38.2,0.787,40,Positive
-6,105,80,28,0,32.5,0.878,26,Negative
-11,138,74,26,144,36.1,0.557,50,Positive
-3,106,72,0,0,25.8,0.207,27,Negative
-6,117,96,0,0,28.7,0.157,30,Negative
-2,68,62,13,15,20.1,0.257,23,Negative
-9,112,82,24,0,28.2,1.282,50,Positive
-0,119,0,0,0,32.4,0.141,24,Positive
-2,112,86,42,160,38.4,0.246,28,Negative
-2,92,76,20,0,24.2,1.698,28,Negative
-6,183,94,0,0,40.8,1.461,45,Negative
-0,94,70,27,115,43.5,0.347,21,Negative
-2,108,64,0,0,30.8,0.158,21,Negative
-4,90,88,47,54,37.7,0.362,29,Negative
-0,125,68,0,0,24.7,0.206,21,Negative
-0,132,78,0,0,32.4,0.393,21,Negative
-5,128,80,0,0,34.6,0.144,45,Negative
-4,94,65,22,0,24.7,0.148,21,Negative
-7,114,64,0,0,27.4,0.732,34,Positive
-0,102,78,40,90,34.5,0.238,24,Negative
-2,111,60,0,0,26.2,0.343,23,Negative
-1,128,82,17,183,27.5,0.115,22,Negative
-10,92,62,0,0,25.9,0.167,31,Negative
-13,104,72,0,0,31.2,0.465,38,Positive
-5,104,74,0,0,28.8,0.153,48,Negative
-2,94,76,18,66,31.6,0.649,23,Negative
-7,97,76,32,91,40.9,0.871,32,Positive
-1,100,74,12,46,19.5,0.149,28,Negative
-0,102,86,17,105,29.3,0.695,27,Negative
-4,128,70,0,0,34.3,0.303,24,Negative
-6,147,80,0,0,29.5,0.178,50,Positive
-4,90,0,0,0,28,0.61,31,Negative
-3,103,72,30,152,27.6,0.73,27,Negative
-2,157,74,35,440,39.4,0.134,30,Negative
-1,167,74,17,144,23.4,0.447,33,Positive
-0,179,50,36,159,37.8,0.455,22,Positive
-11,136,84,35,130,28.3,0.26,42,Positive
-0,107,60,25,0,26.4,0.133,23,Negative
-1,91,54,25,100,25.2,0.234,23,Negative
-1,117,60,23,106,33.8,0.466,27,Negative
-5,123,74,40,77,34.1,0.269,28,Negative
-2,120,54,0,0,26.8,0.455,27,Negative
-1,106,70,28,135,34.2,0.142,22,Negative
-2,155,52,27,540,38.7,0.24,25,Positive
-2,101,58,35,90,21.8,0.155,22,Negative
-1,120,80,48,200,38.9,1.162,41,Negative
-11,127,106,0,0,39,0.19,51,Negative
-3,80,82,31,70,34.2,1.292,27,Positive
-10,162,84,0,0,27.7,0.182,54,Negative
-1,199,76,43,0,42.9,1.394,22,Positive
-8,167,106,46,231,37.6,0.165,43,Positive
-9,145,80,46,130,37.9,0.637,40,Positive
-6,115,60,39,0,33.7,0.245,40,Positive
-1,112,80,45,132,34.8,0.217,24,Negative
-4,145,82,18,0,32.5,0.235,70,Positive
-10,111,70,27,0,27.5,0.141,40,Positive
-6,98,58,33,190,34,0.43,43,Negative
-9,154,78,30,100,30.9,0.164,45,Negative
-6,165,68,26,168,33.6,0.631,49,Negative
-1,99,58,10,0,25.4,0.551,21,Negative
-10,68,106,23,49,35.5,0.285,47,Negative
-3,123,100,35,240,57.3,0.88,22,Negative
-8,91,82,0,0,35.6,0.587,68,Negative
-6,195,70,0,0,30.9,0.328,31,Positive
-9,156,86,0,0,24.8,0.23,53,Positive
-0,93,60,0,0,35.3,0.263,25,Negative
-3,121,52,0,0,36,0.127,25,Positive
-2,101,58,17,265,24.2,0.614,23,Negative
-2,56,56,28,45,24.2,0.332,22,Negative
-0,162,76,36,0,49.6,0.364,26,Positive
-0,95,64,39,105,44.6,0.366,22,Negative
-4,125,80,0,0,32.3,0.536,27,Positive
-5,136,82,0,0,0,0.64,69,Negative
-2,129,74,26,205,33.2,0.591,25,Negative
-3,130,64,0,0,23.1,0.314,22,Negative
-1,107,50,19,0,28.3,0.181,29,Negative
-1,140,74,26,180,24.1,0.828,23,Negative
-1,144,82,46,180,46.1,0.335,46,Positive
-8,107,80,0,0,24.6,0.856,34,Negative
-13,158,114,0,0,42.3,0.257,44,Positive
-2,121,70,32,95,39.1,0.886,23,Negative
-7,129,68,49,125,38.5,0.439,43,Positive
-2,90,60,0,0,23.5,0.191,25,Negative
-7,142,90,24,480,30.4,0.128,43,Positive
-3,169,74,19,125,29.9,0.268,31,Positive
-0,99,0,0,0,25,0.253,22,Negative
-4,127,88,11,155,34.5,0.598,28,Negative
-4,118,70,0,0,44.5,0.904,26,Negative
-2,122,76,27,200,35.9,0.483,26,Negative
-6,125,78,31,0,27.6,0.565,49,Positive
-1,168,88,29,0,35,0.905,52,Positive
-2,129,0,0,0,38.5,0.304,41,Negative
-4,110,76,20,100,28.4,0.118,27,Negative
-6,80,80,36,0,39.8,0.177,28,Negative
-10,115,0,0,0,0,0.261,30,Positive
-2,127,46,21,335,34.4,0.176,22,Negative
-9,164,78,0,0,32.8,0.148,45,Positive
-2,93,64,32,160,38,0.674,23,Positive
-3,158,64,13,387,31.2,0.295,24,Negative
-5,126,78,27,22,29.6,0.439,40,Negative
-10,129,62,36,0,41.2,0.441,38,Positive
-0,134,58,20,291,26.4,0.352,21,Negative
-3,102,74,0,0,29.5,0.121,32,Negative
-7,187,50,33,392,33.9,0.826,34,Positive
-3,173,78,39,185,33.8,0.97,31,Positive
-10,94,72,18,0,23.1,0.595,56,Negative
-1,108,60,46,178,35.5,0.415,24,Negative
-5,97,76,27,0,35.6,0.378,52,Positive
-4,83,86,19,0,29.3,0.317,34,Negative
-1,114,66,36,200,38.1,0.289,21,Negative
-1,149,68,29,127,29.3,0.349,42,Positive
-5,117,86,30,105,39.1,0.251,42,Negative
-1,111,94,0,0,32.8,0.265,45,Negative
-4,112,78,40,0,39.4,0.236,38,Negative
-1,116,78,29,180,36.1,0.496,25,Negative
-0,141,84,26,0,32.4,0.433,22,Negative
-2,175,88,0,0,22.9,0.326,22,Negative
-2,92,52,0,0,30.1,0.141,22,Negative
-3,130,78,23,79,28.4,0.323,34,Positive
-8,120,86,0,0,28.4,0.259,22,Positive
-2,174,88,37,120,44.5,0.646,24,Positive
-2,106,56,27,165,29,0.426,22,Negative
-2,105,75,0,0,23.3,0.56,53,Negative
-4,95,60,32,0,35.4,0.284,28,Negative
-0,126,86,27,120,27.4,0.515,21,Negative
-8,65,72,23,0,32,0.6,42,Negative
-2,99,60,17,160,36.6,0.453,21,Negative
-1,102,74,0,0,39.5,0.293,42,Positive
-11,120,80,37,150,42.3,0.785,48,Positive
-3,102,44,20,94,30.8,0.4,26,Negative
-1,109,58,18,116,28.5,0.219,22,Negative
-9,140,94,0,0,32.7,0.734,45,Positive
-13,153,88,37,140,40.6,1.174,39,Negative
-12,100,84,33,105,30,0.488,46,Negative
-1,147,94,41,0,49.3,0.358,27,Positive
-1,81,74,41,57,46.3,1.096,32,Negative
-3,187,70,22,200,36.4,0.408,36,Positive
-6,162,62,0,0,24.3,0.178,50,Positive
-4,136,70,0,0,31.2,1.182,22,Positive
-1,121,78,39,74,39,0.261,28,Negative
-3,108,62,24,0,26,0.223,25,Negative
-0,181,88,44,510,43.3,0.222,26,Positive
-8,154,78,32,0,32.4,0.443,45,Positive
-1,128,88,39,110,36.5,1.057,37,Positive
-7,137,90,41,0,32,0.391,39,Negative
-0,123,72,0,0,36.3,0.258,52,Positive
-1,106,76,0,0,37.5,0.197,26,Negative
-6,190,92,0,0,35.5,0.278,66,Positive
-2,88,58,26,16,28.4,0.766,22,Negative
-9,170,74,31,0,44,0.403,43,Positive
-9,89,62,0,0,22.5,0.142,33,Negative
-10,101,76,48,180,32.9,0.171,63,Negative
-2,122,70,27,0,36.8,0.34,27,Negative
-5,121,72,23,112,26.2,0.245,30,Negative
-1,126,60,0,0,30.1,0.349,47,Positive
-1,93,70,31,0,30.4,0.315,23,Negative

frontend/src/components/DataSetExecController.vue

@@ -2,12 +2,10 @@
   <div class="text-center">
     <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="HeartC.csv" selected>Heart Disease</option>
-        <option value="RiskC.csv">Risk Disease</option>
-        <option value="SeismicC.csv">Seismic Disease</option>
-        <option value="biodegC.csv">Biodeg Disease</option>
-        <option value="ImportsC.csv">Imports Disease</option>
-        <option value="local">Upload File</option>
+        <option value="biodegC.csv" selected>Biodegradation</option>
+        <option value="seismicC.csv">Seismic bumps</option>
+        <option value="heartC.csv">Heart disease</option>
+        <option value="local">Upload file</option>
     </select>
     <button class="btn-outline-success"
     id="initializeID"
@@ -49,7 +47,7 @@ export default {
       this.defaultDataSet = fileName.options[fileName.selectedIndex].value
       this.defaultDataSet = this.defaultDataSet.split('.')[0]
 
-      if (this.defaultDataSet == "HeartC" || this.defaultDataSet == "RiskC" || this.defaultDataSet == "SeismicC" || this.defaultDataSet == "biodegC" || this.defaultDataSet == "ImportsC") { // This is a function that handles a new file, which users can upload.
+      if (this.defaultDataSet == "heartC" || this.defaultDataSet == "seismicC" || this.defaultDataSet == "biodegC") { // 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

@@ -172,7 +172,7 @@ export default Vue.extend({
   data () {
     return {
       Status: " (S) Stage 0",
-      MeanShift: "Mean Shift Clustering",
+      MeanShift: "K-means Clustering",
       mode: 0,
       valuePickled: 'Close',
       sankeyCallS: 1,
@@ -195,7 +195,7 @@ export default Vue.extend({
       DataResults: '',
       keyNow: 1,
       instancesImportance: '',
-      RetrieveValueFile: 'HeartC', // this is for the default data set
+      RetrieveValueFile: 'biodegC', // this is for the default data set
       ClassifierIDsList: [],
       ClassifierIDsListCM: [],
       SelectedFeaturesPerClassifier: '',
@@ -911,6 +911,7 @@ export default Vue.extend({
     swapSymbol () {
       var off = document.getElementById('mode0');
       var on = document.getElementById('mode1');
+      EventBus.$emit('ON', true)
       on.style.display = 'inline'
       off.style.display = 'none'
     },

frontend/src/components/Predictions.vue

@@ -25,7 +25,8 @@ export default {
       predictSelection: [],
       StoreIndices: [],
       classesNumber: 9,
-      InfoPred: []
+      InfoPred: [],
+      flag: false
     }
   },
   methods: {
@@ -51,18 +52,33 @@ export default {
       var yValues = JSON.parse(this.GetResultsAll[14])
 
       var targetNames = JSON.parse(this.GetResultsAll[7])
-
       var getIndices = []
-      for (let i = 0; i < targetNames.length; i++) {
-        let clTemp = []
-        let j = -1
-        while((j = yValues.indexOf(targetNames[i], j + 1)) !== -1) {
-          clTemp.push(j);
+      if (!this.flag) {
+
+        for (let i = 0; i < targetNames.length; i++) {
+          let clTemp = []
+          let j = -1
+          while((j = yValues.indexOf(targetNames[i], j + 1)) !== -1) {
+            clTemp.push(j);
+          }
+          getIndices.push(clTemp)
+        }
+
+      }
+      else {
+        var tempFirst = []
+        for (let i = 0; i < 169; i++) {
+          tempFirst.push(i)
+        }
+        var tempLast = []
+        for (let i = 169; i < 338; i++) {
+          tempLast.push(i)
         }
-        getIndices.push(clTemp)
+        getIndices.push(tempFirst)
+        getIndices.push(tempLast)
       }
       getIndices.reverse()
-
+      console.log(getIndices)
       var predictions = JSON.parse(this.GetResultsAll[12])
       var KNNPred = predictions[0]
       var LRPred = predictions[1]
@@ -83,15 +99,8 @@ export default {
       var dataGradB = []
       var dataGradBResults = []
 
-      var max = 0
-      for (let i = 0; i < targetNames.length; i++) {
-        if (getIndices[targetNames[i]].length > max) {
-          max = getIndices[targetNames[i]].length
-        } 
-      }
-
-      var sqrtSize = Math.ceil(Math.sqrt(max))
-      var size = sqrtSize * sqrtSize
+      var sqrtSize = 13
+      var size = 169
 
       for (let i = 0; i < targetNames.length; i++) {
         dataAver = []
@@ -101,20 +110,44 @@ export default {
         dataRF = []
         dataGradB = []
         getIndices[targetNames[i]].forEach(element => {
-          dataAver.push({ id: element, value: PredAver[element] })
-          dataKNN.push({ id: element, value: KNNPred[element] })
-          dataLR.push({ id: element, value: LRPred[element] })
-          dataMLP.push({ id: element, value: MLPPred[element] })
-          dataRF.push({ id: element, value: RFPred[element] })
-          dataGradB.push({ id: element, value: GradBPred[element] })
+          if (PredAver.length == 0) {
+            dataAver.push({ id: -1, value: 0 })
+          } else {
+            dataAver.push({ id: element, value: PredAver[element] })
+          }
+          if (KNNPred.length == 0) {
+            dataKNN.push({ id: -1, value: 0 })
+          } else {
+            dataKNN.push({ id: element, value: KNNPred[element] })
+          }
+          if (LRPred.length == 0) {
+            dataLR.push({ id: -1, value: 0 })
+          } else {
+            dataLR.push({ id: element, value: LRPred[element] })
+          }
+          if (MLPPred.length == 0) {
+            dataMLP.push({ id: -1, value: 0 })
+          } else {
+            dataMLP.push({ id: element, value: MLPPred[element] })
+          }
+          if (RFPred.length == 0) {
+            dataRF.push({ id: -1, value: 0 })
+          } else {
+            dataRF.push({ id: element, value: RFPred[element] })
+          }
+          if (GradBPred.length == 0) {
+            dataGradB.push({ id: -1, value: 0 })
+          } else {
+            dataGradB.push({ id: element, value: GradBPred[element] })
+          } 
         });
         for (let j = 0; j < size - getIndices[targetNames[i]].length; j++) {
-          dataAver.push({ id: -1, value: 100 })
-          dataKNN.push({ id: -1, value: 100 })
-          dataLR.push({ id: -1, value: 100 })
-          dataMLP.push({ id: -1, value: 100 })
-          dataRF.push({ id: -1, value: 100 })
-          dataGradB.push({ id: -1, value: 100 })
+          dataAver.push({ id: -1, value: 0 })
+          dataKNN.push({ id: -1, value: 0 })
+          dataLR.push({ id: -1, value: 0 })
+          dataMLP.push({ id: -1, value: 0 })
+          dataRF.push({ id: -1, value: 0 })
+          dataGradB.push({ id: -1, value: 0 })
         }
         dataAverGetResults.push(dataAver)
         dataKNNResults.push(dataKNN)
@@ -129,7 +162,8 @@ export default {
     dataMLPResults.reverse()
     dataRFResults.reverse()
     dataGradBResults.reverse()
-
+    console.log(dataAverGetResults)
+    console.log(dataKNNResults)
     var classArray = []
     this.StoreIndices = []
     for (let i = 0; i < dataAverGetResults.length; i++) {
@@ -140,7 +174,7 @@ export default {
         indices[j] = dataAverGetResults[i][j].id;
       }
       this.StoreIndices.push(indices)
-      
+      console.log(indices)
       dataKNNResults[i].sort(function(a, b){
         return indices.indexOf(a.id) - indices.indexOf(b.id)
       });
@@ -163,7 +197,7 @@ export default {
 
       classArray.push(dataAverGetResults[i].concat(dataKNNResults[i], dataLRResults[i],dataMLPResults[i],dataRFResults[i],dataGradBResults[i]));
     }
-
+    console.log(classArray)
     var classStore = [].concat.apply([], classArray);
 		// === Set up canvas === //
 
@@ -187,7 +221,7 @@ export default {
     // settings for a grid with 40 cells in a row and 2x5 cells in a group
 		var groupSpacing = 42;
 		var cellSpacing = 2;
-    var cellSize = Math.floor((width - 1 * groupSpacing) / (13 * sqrtSize)) - cellSpacing;
+    var cellSize = 4
 
 		// === First call === //
 		databind(classStore, size, sqrtSize); // ...then update the databind function
@@ -215,7 +249,7 @@ export default {
 	        return groupSpacing * x0 + (cellSpacing + cellSize) * (x1 + x0 * 10);
 	      })
 	      .attr('y', function(d, i) {
-	        var y0 = Math.floor(i / data.length), y1 = Math.floor(i % size / sqrtSize);
+	        var y0 = Math.floor(i / 2028), y1 = Math.floor(i % size / sqrtSize);
 	        return groupSpacing * y0 + (cellSpacing + cellSize) * (y1 + y0 * 10);
 	      })
 				.attr('width', 0)
@@ -304,13 +338,29 @@ export default {
       var targetNames = JSON.parse(this.GetResultsAll[7])
 
       var getIndices = []
-      for (let i = 0; i < targetNames.length; i++) {
-        let clTemp = []
-        let j = -1
-        while((j = yValues.indexOf(targetNames[i], j + 1)) !== -1) {
-          clTemp.push(j);
+      if (!this.flag) {
+
+        for (let i = 0; i < targetNames.length; i++) {
+          let clTemp = []
+          let j = -1
+          while((j = yValues.indexOf(targetNames[i], j + 1)) !== -1) {
+            clTemp.push(j);
+          }
+          getIndices.push(clTemp)
         }
-        getIndices.push(clTemp)
+
+      }
+      else {
+        var tempFirst = []
+        for (let i = 0; i < 169; i++) {
+          tempFirst.push(i)
+        }
+        var tempLast = []
+        for (let i = 169; i < 338; i++) {
+          tempLast.push(i)
+        }
+        getIndices.push(tempFirst)
+        getIndices.push(tempLast)
       }
       getIndices.reverse()
 
@@ -327,15 +377,8 @@ export default {
       var dataGradB = []
       var dataGradBResults = []
 
-      var max = 0
-      for (let i = 0; i < targetNames.length; i++) {
-        if (getIndices[targetNames[i]].length > max) {
-          max = getIndices[targetNames[i]].length
-        } 
-      }
-
-      var sqrtSize = Math.ceil(Math.sqrt(max))
-      var size = sqrtSize * sqrtSize
+      var sqrtSize = 13
+      var size = 169
 
       for (let i = 0; i < targetNames.length; i++) {
         dataAver = []
@@ -345,13 +388,37 @@ export default {
         dataRF = []
         dataGradB = []
         getIndices[targetNames[i]].forEach(element => {
-
-          dataAver.push({ id: element, value: PredAver[element] - PredAverAll[element] })
-          dataKNN.push({ id: element, value: KNNPred[element] - KNNPredAll[element] })
-          dataLR.push({ id: element, value: LRPred[element] - LRPredAll[element] })
-          dataMLP.push({ id: element, value: MLPPred[element] - MLPPredAll[element] })
-          dataRF.push({ id: element, value: RFPred[element] - RFPredAll[element] })
-          dataGradB.push({ id: element, value: GradBPred[element] - GradBPredAll[element] })
+          if (PredAver.length == 0) {
+            dataAver.push({ id: -1, value: 0 })
+          } else {
+            dataAver.push({ id: element, value: PredAver[element] - PredAverAll[element] })
+          }
+          if (KNNPred.length == 0) {
+            dataKNN.push({ id: -1, value: 0 })
+          } else {
+            dataKNN.push({ id: element, value: KNNPred[element] - KNNPredAll[element] })
+          }
+          if (LRPred.length == 0) {
+            dataLR.push({ id: -1, value: 0 })
+          } else {
+            dataLR.push({ id: element, value: LRPred[element] - LRPredAll[element] })
+          }
+          if (MLPPred.length == 0) {
+            dataMLP.push({ id: -1, value: 0 })
+          } else {
+            dataMLP.push({ id: element, value: MLPPred[element] - MLPPredAll[element] })
+          }
+          if (RFPred.length == 0) {
+            dataRF.push({ id: -1, value: 0 })
+          } else {
+            dataRF.push({ id: element, value: RFPred[element] - RFPredAll[element] })
+          }
+          if (GradBPred.length == 0) {
+            dataGradB.push({ id: -1, value: 0 })
+          } else {
+            dataGradB.push({ id: element, value: GradBPred[element] - GradBPredAll[element] })
+          }       
+          
         });
         for (let j = 0; j < size - getIndices[targetNames[i]].length; j++) {
           dataAver.push({ id: -1, value: 0 })
@@ -431,7 +498,7 @@ export default {
     // settings for a grid with 40 cells in a row and 2x5 cells in a group
 		var groupSpacing = 42;
 		var cellSpacing = 2;
-    var cellSize = Math.floor((width - 1 * groupSpacing) / (13 * sqrtSize)) - cellSpacing;
+    var cellSize = 4
 
 		// === First call === //
 		databind(classStore, size, sqrtSize); // ...then update the databind function
@@ -459,7 +526,7 @@ export default {
 	        return groupSpacing * x0 + (cellSpacing + cellSize) * (x1 + x0 * 10);
 	      })
 	      .attr('y', function(d, i) {
-	        var y0 = Math.floor(i / data.length), y1 = Math.floor(i % size / sqrtSize);
+	        var y0 = Math.floor(i / 2028), y1 = Math.floor(i % size / sqrtSize);
 	        return groupSpacing * y0 + (cellSpacing + cellSize) * (y1 + y0 * 10);
 	      })
 				.attr('width', 0)
@@ -626,6 +693,8 @@ export default {
   },
   },
   mounted () {
+      EventBus.$on('ON', data => {this.flag = data})
+
       EventBus.$on('emittedEventCallingInfo', data => { this.InfoPred = data })
       EventBus.$on('LegendPredict', this.legendCol)
 

frontend/src/components/ValidationController.vue

@@ -45,7 +45,6 @@ export default {
       var colorsGlobalBins = []
       var countFactors = 0
       var activeLines = []
-
       for (let j=0; j<this.factorsValid.length; j++) {
         if (this.factorsValid[j] == 1) {
             countFactors = countFactors + 1
@@ -56,13 +55,17 @@ export default {
       var sumGlobalSel = new Array(countFactors).fill(0)
       var countValuesSel = new Array(countFactors).fill(0)
       var globalActive = this.activeCurr
+      var measure = 0
 
       if (globalActive == 1) {
         var IDs = JSON.parse(this.ResultsValid[0])
         var valid = JSON.parse(this.ResultsValid[3])
 
         for (let j=0; j<this.factorsValid.length; j++) {
-          if (this.factorsValid[j] == 1) {
+            if (this.factorsValid[j] == 0) {
+                measure = measure + 1
+            }
+          else {
             for (let i=0; i<IDs.length; i++){
 
                 let tempValid = JSON.parse(valid[j])
@@ -84,12 +87,11 @@ export default {
                 else {
                   data.push({Algorithm: this.Metrics[j], value: tempValid[i], category: "#a6cee3"})
                 }
-                
                 if (this.selectedSimple.length != 0) {
-
+  
                   if (this.selectedSimple.includes(IDs[i])) {
-                    sumGlobalSel[j] = sumGlobalSel[j] + tempValid[i]
-                    countValuesSel[j] = countValuesSel[j] + 1
+                    sumGlobalSel[j-measure] = sumGlobalSel[j-measure] + tempValid[i]
+                    countValuesSel[j-measure] = countValuesSel[j-measure] + 1
                   }
                 }
               }
@@ -110,7 +112,10 @@ export default {
                 mergedStoreEnsembleLocFormatted.push(parseInt(mergedStoreEnsembleLoc[i].replace(/\D/g,'')))
             }
             for (let j=0; j<this.factorsValid.length; j++) {
-                if (this.factorsValid[j] == 1) {
+                    if (this.factorsValid[j] == 0) {
+                        measure = measure + 1
+                    }
+                    else {
                     for (let i=0; i<mergedStoreEnsembleLoc.length; i++){
 
                       let tempValid = JSON.parse(valid[j])

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 = 'biodegC' 
+    collection_name = 'biodegCExt' 
     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/HyperSearVis_code/new_data_sets/biodeg.csv'
+  filepath = '/Users/anchaa/Documents/Research/HyperSearVis_code/new_data_sets/biodegext.csv'
   import_content(filepath)

new_data_sets/biodegext.csv

@@ -0,0 +1,671 @@
+SpMax_L,F04[C-N],C%,nO,HyWi_B(m),LOC,SM6_L,F03[C-O],Me,Mi,nN-N,nArNO2,nCRX3,SpPosA_B(p),nCIR,B01[C-Br],B03[C-Cl],N-073,SpMax_A,Psi_i_1d,B04[C-Br],SdO,TI2_L,class*
+5.146,15,39.4,8,4.696,1.471,11.447,12,1.036,1.133,0,2,0,1.234,3,0,1,0,2.439,0,0,68.836,4.958,NRB
+5.127,15,39.4,8,4.63,1.474,11.395,12,1.031,1.134,0,2,0,1.226,3,0,0,0,2.431,0,0,68.414,4.899,NRB
+4.958,13,41.3,2,4.33,1.476,11.064,4,1.014,1.127,0,1,0,1.296,4,0,0,0,2.422,0,0,21.767,5.289,NRB
+5.014,10,43.9,0,4.364,1.423,10.971,0,1.013,1.117,0,0,0,1.335,4,0,1,0,2.439,0,0,0,5.224,NRB
+4.97,10,43.9,0,4.364,1.423,10.971,0,1.013,1.117,0,0,0,1.334,4,0,1,0,2.428,0,0,0,5.251,NRB
+4.704,12,47.4,2,4.12,0.49,10.767,4,1.013,1.118,0,0,0,1.269,3,0,0,0,2.277,0,0,0,5.547,NRB
+5.216,5,51.4,2,4.211,0.566,11.057,6,1.013,1.104,0,0,0,1.322,7,0,1,1,2.53,0,0,12.687,3.608,NRB
+5.058,8,38.1,4,4.401,1.487,11.015,4,1.045,1.133,0,1,0,1.255,2,0,1,0,2.385,0,0,21.604,4.881,NRB
+4.871,9,42.6,2,4.357,1.107,10.946,2,1.014,1.124,0,1,0,1.296,3,0,0,0,2.327,0,0,21.375,5.5,NRB
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new_data_sets/biodegtest.csv

@@ -0,0 +1,219 @@
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new_data_sets/cancer.csv

@@ -0,0 +1,836 @@
+Age,Number of sexual partners,First sexual intercourse,Num of pregnancies,Smokes,Smokes (years),Smokes (packs/year),Hormonal Contraceptives,Hormonal Contraceptives (years),IUD,IUD (years),STDs,STDs (number),STDs:condylomatosis,STDs:cervical condylomatosis,STDs:vaginal condylomatosis,STDs:vulvo-perineal condylomatosis,STDs:syphilis,STDs:pelvic inflammatory disease,STDs:genital herpes,STDs:molluscum contagiosum,STDs:AIDS,STDs:HIV,STDs:Hepatitis B,STDs:HPV,STDs: Number of diagnosis,STDs: Time since first diagnosis,STDs: Time since last diagnosis,Dx:Cancer,Dx:CIN,Dx:HPV,Dx,Hinselmann,Schiller,Citology,Biopsy*
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new_data_sets/risk.csv

@@ -1,859 +0,0 @@
-Age,Number of sexual partners,First sexual intercourse,Num of pregnancies,Smokes,Smokes (years),Smokes (packs/year),Hormonal Contraceptives,Hormonal Contraceptives (years),IUD,IUD (years),STDs,STDs (number),STDs:condylomatosis,STDs:cervical condylomatosis,STDs:vaginal condylomatosis,STDs:vulvo-perineal condylomatosis,STDs:syphilis,STDs:pelvic inflammatory disease,STDs:genital herpes,STDs:molluscum contagiosum,STDs:AIDS,STDs:HIV,STDs:Hepatitis B,STDs:HPV,STDs: Number of diagnosis,STDs: Time since first diagnosis,STDs: Time since last diagnosis,Dx:Cancer,Dx:CIN,Dx:HPV,Dx,Hinselmann,Schiller,Citology,Biopsy*
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-37,3.0,?,0.0,0.0,0.0,0.0,1.0,0.25,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-31,9.0,?,1.0,1.0,11.0,5.5,1.0,0.25,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-35,3.0,18.0,3.0,0.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-31,3.0,19.0,1.0,0.0,0.0,0.0,1.0,0.08,1.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,1,0,0,1,0,0,0,0
-24,2.0,16.0,3.0,0.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-23,2.0,15.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-36,3.0,16.0,3.0,1.0,6.0,0.3,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-30,3.0,14.0,3.0,0.0,0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-26,8.0,15.0,1.0,1.0,9.0,1.35,1.0,5.0,1.0,0.17,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-19,2.0,15.0,2.0,0.0,0.0,0.0,1.0,0.75,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-35,2.0,17.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-30,3.0,22.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-31,3.0,18.0,1.0,0.0,0.0,0.0,1.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-32,3.0,18.0,1.0,1.0,11.0,0.16,1.0,6.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,?,?,1,0,1,0,0,0,0,0
-19,1.0,14.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-23,2.0,15.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-43,3.0,17.0,3.0,0.0,0.0,0.0,1.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-34,3.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-32,2.0,19.0,1.0,0.0,0.0,0.0,1.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-25,2.0,17.0,0.0,0.0,0.0,0.0,1.0,0.08,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,1,0
-33,2.0,24.0,2.0,0.0,0.0,0.0,1.0,0.08,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0
-29,2.0,20.0,1.0,0.0,0.0,0.0,1.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,?,?,0,0,0,0,0,0,0,0

run.py

@@ -33,6 +33,7 @@ from sklearn.metrics import classification_report, accuracy_score, make_scorer,
 from sklearn.manifold import MDS
 from sklearn.manifold import TSNE
 from sklearn.cluster import MeanShift, estimate_bandwidth
+from sklearn import cluster
 import umap
 
 
@@ -121,6 +122,9 @@ def reset():
     global storeClass0
     storeClass0 = 0
 
+    global StanceTest
+    StanceTest = False
+
     global storeClass1
     storeClass1 = 0
 
@@ -224,6 +228,8 @@ def retrieveFileName():
     global DataResultsRaw
     global DataResultsRawTest
     global DataRawLengthTest
+    global DataResultsRawExternal
+    global DataRawLengthExternal
 
     global yDataSorted
     yDataSorted = []
@@ -248,7 +254,7 @@ def retrieveFileName():
 
     global crossValidation
     crossValidation = int(data['CrossValidation'])
-
+    print(crossValidation)
     global randomSearchVar
     randomSearchVar = int(data['RandomSearch'])
 
@@ -381,8 +387,8 @@ def retrieveFileName():
 
     DataRawLength = -1
     DataRawLengthTest = -1
-
-    if data['fileName'] == 'HeartC':
+    print(data['fileName'])
+    if data['fileName'] == 'heartC':
         CollectionDB = mongo.db.HeartC.find()
         names_labels.append('Healthy')
         names_labels.append('Diseased')
@@ -390,8 +396,21 @@ def retrieveFileName():
         StanceTest = True
         CollectionDB = mongo.db.StanceC.find()
         CollectionDBTest = mongo.db.StanceCTest.find()
-    elif data['fileName'] == 'DiabetesC':
-        CollectionDB = mongo.db.DiabetesC.find()
+    elif data['fileName'] == 'biodegC':
+        StanceTest = True
+        CollectionDB = mongo.db.biodegC.find()
+        CollectionDBTest = mongo.db.biodegCTest.find()
+        CollectionDBExternal = mongo.db.biodegCExt.find()
+        names_labels.append('Biodegradable')
+        names_labels.append('Non-biodegradable')
+    elif data['fileName'] == 'seismicC':
+        CollectionDB = mongo.db.seismicC.find()
+        names_labels.append('Hazardous')
+        names_labels.append('Non-hazardous')
+    elif data['fileName'] == 'breastC':
+        CollectionDB = mongo.db.diabetesC.find()
+        names_labels.append('Malignant')
+        names_labels.append('Benign')
     else:
         CollectionDB = mongo.db.IrisC.find()
     DataResultsRaw = []
@@ -402,12 +421,18 @@ def retrieveFileName():
     DataRawLength = len(DataResultsRaw)
 
     DataResultsRawTest = []
+    DataResultsRawExternal = []
     if (StanceTest):
         for index, item in enumerate(CollectionDBTest):
             item['_id'] = str(item['_id'])
             item['InstanceID'] = index
             DataResultsRawTest.append(item)
         DataRawLengthTest = len(DataResultsRawTest)
+        for index, item in enumerate(CollectionDBExternal):
+            item['_id'] = str(item['_id'])
+            item['InstanceID'] = index
+            DataResultsRawExternal.append(item)
+        DataRawLengthExternal = len(DataResultsRawExternal)
 
     dataSetSelection()
     return 'Everything is okay'
@@ -478,6 +503,8 @@ def sendToServerData():
 def dataSetSelection():
     global XDataTest, yDataTest
     XDataTest = pd.DataFrame()
+    global XDataExternal, yDataExternal
+    XDataExternal = pd.DataFrame()
     global StanceTest
     global AllTargets
     global target_names
@@ -521,6 +548,44 @@ def dataSetSelection():
 
         XDataTest, yDataTest = ArrayDataResultsTest, AllTargetsFloatValuesTest
 
+        DataResultsExternal = copy.deepcopy(DataResultsRawExternal)
+
+        for dictionary in DataResultsRawExternal:
+            for key in dictionary.keys():
+                if (key.find('*') != -1):
+                    target = key
+                    continue
+            continue
+
+        DataResultsRawExternal.sort(key=lambda x: x[target], reverse=True)
+        DataResultsExternal.sort(key=lambda x: x[target], reverse=True)
+
+        for dictionary in DataResultsExternal:
+            del dictionary['_id']
+            del dictionary['InstanceID']
+            del dictionary[target]
+
+        AllTargetsExternal = [o[target] for o in DataResultsRawExternal]
+        AllTargetsFloatValuesExternal = []
+
+        previous = None
+        Class = 0
+        for i, value in enumerate(AllTargetsExternal):
+            if (i == 0):
+                previous = value
+                target_namesLoc.append(value)
+            if (value == previous):
+                AllTargetsFloatValuesExternal.append(Class)
+            else:
+                Class = Class + 1
+                target_namesLoc.append(value)
+                AllTargetsFloatValuesExternal.append(Class)
+                previous = value
+
+        ArrayDataResultsExternal = pd.DataFrame.from_dict(DataResultsExternal)
+
+        XDataExternal, yDataExternal = ArrayDataResultsExternal, AllTargetsFloatValuesExternal
+
     DataResults = copy.deepcopy(DataResultsRaw)
 
     for dictionary in DataResultsRaw:
@@ -559,7 +624,7 @@ def dataSetSelection():
 
     global XData, yData, RANDOM_SEED
     XData, yData = ArrayDataResults, AllTargetsFloatValues
-    
+
     global storeClass0
     global storeClass1
 
@@ -607,6 +672,7 @@ def retrieveModel():
     # loop through the algorithms
     global allParametersPerformancePerModel
     global HistoryPreservation
+    global crossValidation
 
     for eachAlgor in algorithms:
         if (eachAlgor) == 'KNN':
@@ -635,11 +701,12 @@ def retrieveModel():
             AlgorithmsIDsEnd = countAllModels
         else: 
             clf = GradientBoostingClassifier(random_state=RANDOM_SEED)
-            params = {'n_estimators': list(range(20, 100)), 'loss': ['deviance', 'exponential'], 'learning_rate': list(np.arange(0.01,0.56,0.11)), 'subsample': list(np.arange(0.1,1,0.1)), 'criterion': ['friedman_mse', 'mse', 'mae']}
+            # add exponential in loss
+            params = {'n_estimators': list(range(20, 100)), 'loss': ['deviance','exponential'], 'learning_rate': list(np.arange(0.01,0.56,0.11)), 'subsample': list(np.arange(0.1,1,0.1)), 'criterion': ['friedman_mse', 'mse', 'mae']}
             countAllModels = countAllModels + randomSearchVar
             AlgorithmsIDsEnd = countAllModels
             countAllModels = countAllModels + randomSearchVar
-        allParametersPerformancePerModel = randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd)
+        allParametersPerformancePerModel = randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation)
     HistoryPreservation = allParametersPerformancePerModel.copy()
     # call the function that sends the results to the frontend
 
@@ -649,13 +716,13 @@ location = './cachedir'
 memory = Memory(location, verbose=0)
 
 @memory.cache
-def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
+def randomSearch(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd,crossValidation):
     print(clf)
     search = RandomizedSearchCV(    
         estimator=clf, param_distributions=params, n_iter=100,
         cv=crossValidation, refit='accuracy', scoring=scoring,
         verbose=0, n_jobs=-1)
-
+    
     # fit and extract the probabilities
     search.fit(XData, yData)
 
@@ -883,71 +950,85 @@ def PreprocessingPred():
                 lastElPredAv.append(predictions[index][item]*100)
             yDataSortedLast.append(item)
 
-    if (storeClass0 > 169 & storeClass1 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-        lastElKNN = computeClusters(lastElKNN)
-        firstElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass0 < 169 & storeClass1 > 169):
-                
-        lastElKNN = computeClusters(lastElKNN)
-        lastElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass1 < 169 & storeClass0 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-
-    else:
-        pass
-
-    predictionsKNN = firstElKNN + lastElKNN
-    predictionsLR = firstElLR + lastElLR        
-    predictionsMLP = firstElMLP + lastElMLP
-    predictionsRF = firstElRF + lastElRF
-    predictionsGradB = firstElGradB + lastElGradB
-    predictions = firstElPredAv + lastElPredAv
+    if (storeClass0 > 169 and storeClass1 > 169):
+        yDataSortedFirst = []
+        yDataSortedLast = []    
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        for item in lastElPredAv:
+            yDataSortedFirst.append(0)
+            yDataSortedLast.append(0)
+
+    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
     yDataSorted = yDataSortedFirst + yDataSortedLast
-
+    
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
-def computeClusters(dataLocal):
-    if (dataLocal.length != 0):
+def computeClusters(dataLocal,one,two,three,four,five):
+    if (len(dataLocal) != 0):
+        XKNN = np.array(list(zip(one,np.zeros(len(one)))), dtype=np.int)
+        XLR = np.array(list(zip(two,np.zeros(len(two)))), dtype=np.int)
+        XMLP = np.array(list(zip(three,np.zeros(len(three)))), dtype=np.int)
+        XRF = np.array(list(zip(four,np.zeros(len(four)))), dtype=np.int)
+        XGradB = np.array(list(zip(five,np.zeros(len(five)))), dtype=np.int)
         X = np.array(list(zip(dataLocal,np.zeros(len(dataLocal)))), dtype=np.int)
-        bandwidth = estimate_bandwidth(X, quantile=0.015, random_state=RANDOM_SEED, n_jobs=-1)
-        ms = MeanShift(bandwidth=bandwidth, bin_seeding=True, n_jobs=-1)
+        ms = cluster.KMeans(n_clusters=169,random_state=RANDOM_SEED, n_jobs=-1)
         ms.fit(X)
         labels = ms.labels_
-        cluster_centers = ms.cluster_centers_
-
-        labels_unique = np.unique(labels)
-        n_clusters_ = len(labels_unique)
-
-        gatherPoints = []
-        for k in range(n_clusters_):
+        #labels_unique = np.unique(labels)
+        #n_clusters_ = len(labels)
+
+        gatherPointsAv = []
+        gatherPointsKNN = []
+        gatherPointsLR = []
+        gatherPointsMLP = []
+        gatherPointsRF = []
+        gatherPointsGradB = []
+        for k in range(169):
             my_members = labels == k
-            gatherPoints.append(sum(X[my_members, 0])/len(X[my_members, 0]))
+            if (len(X[my_members, 0]) == 0):
+                gatherPointsAv.append(0)
+            else:
+                gatherPointsAv.append(sum(X[my_members, 0])/len(X[my_members, 0]))
+            if (len(one) == 0):
+                gatherPointsKNN = []
+            elif (len(XKNN[my_members, 0]) == 0):
+                gatherPointsKNN.append(0)
+            else:
+                gatherPointsKNN.append(sum(XKNN[my_members, 0])/len(XKNN[my_members, 0]))
+            if (len(two) == 0):
+                gatherPointsLR = []
+            elif (len(XLR[my_members, 0]) == 0):
+                gatherPointsLR.append(0)
+            else:
+                gatherPointsLR.append(sum(XLR[my_members, 0])/len(XLR[my_members, 0]))
+            if (len(three) == 0):
+                gatherPointsMLP = []
+            elif (len(XMLP[my_members, 0]) == 0):
+                gatherPointsMLP.append(0)
+            else:
+                gatherPointsMLP.append(sum(XMLP[my_members, 0])/len(XMLP[my_members, 0]))
+            if (len(four) == 0):
+                gatherPointsRF = []
+            elif (len(XRF[my_members, 0]) == 0):
+                gatherPointsRF.append(0)
+            else:
+                gatherPointsRF.append(sum(XRF[my_members, 0])/len(XRF[my_members, 0]))
+            if (len(five) == 0):
+                gatherPointsGradB = []
+            elif (len(XGradB[my_members, 0]) == 0):
+                gatherPointsGradB.append(0)
+            else:
+                gatherPointsGradB.append(sum(XGradB[my_members, 0])/len(XGradB[my_members, 0]))
     else:
-        gatherPoints = []
-    return gatherPoints
+        gatherPointsAv = []
+        
+    return [gatherPointsAv,gatherPointsKNN,gatherPointsLR,gatherPointsMLP,gatherPointsRF,gatherPointsGradB]
 
 def EnsembleIDs():
     global EnsembleActive
@@ -1110,48 +1191,21 @@ def PreprocessingPredEnsemble():
                 lastElPredAv.append(predictions[index][item]*100)
             yDataSortedLast.append(item)
 
-    if (storeClass0 > 169 & storeClass1 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-        lastElKNN = computeClusters(lastElKNN)
-        firstElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass0 < 169 & storeClass1 > 169):
-                
-        lastElKNN = computeClusters(lastElKNN)
-        lastElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass1 < 169 & storeClass0 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-
-    else:
-        pass
-
-    predictionsKNN = firstElKNN + lastElKNN
-    predictionsLR = firstElLR + lastElLR        
-    predictionsMLP = firstElMLP + lastElMLP
-    predictionsRF = firstElRF + lastElRF
-    predictionsGradB = firstElGradB + lastElGradB
-    predictions = firstElPredAv + lastElPredAv
+    if (storeClass0 > 169 and storeClass1 > 169):
+        yDataSortedFirst = []
+        yDataSortedLast = []    
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        for item in lastElPredAv:
+            yDataSortedFirst.append(0)
+            yDataSortedLast.append(0)
+
+    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
     yDataSorted = yDataSortedFirst + yDataSortedLast
 
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
@@ -1295,6 +1349,7 @@ def preProcMetricsAllAndSel():
     loopThroughMetrics = PreprocessingMetrics()
     loopThroughMetrics = loopThroughMetrics.fillna(0)
     global factors
+
     metricsPerModelColl = []
     metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'])
     metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_macro'])
@@ -1320,6 +1375,7 @@ def preProcMetricsAllAndSelEnsem():
     loopThroughMetrics = PreprocessingMetricsEnsem()
     loopThroughMetrics = loopThroughMetrics.fillna(0)
     global factors
+
     metricsPerModelColl = []
     metricsPerModelColl.append(loopThroughMetrics['mean_test_accuracy'])
     metricsPerModelColl.append(loopThroughMetrics['geometric_mean_score_macro'])
@@ -1416,6 +1472,7 @@ def InitializeEnsemble():
 def EnsembleModel (Models, keyRetrieved):
 
     global XDataTest, yDataTest
+    global XDataExternal, yDataExternal
     global scores
     global previousState
     global crossValidation
@@ -1474,24 +1531,18 @@ def EnsembleModel (Models, keyRetrieved):
     tempDic = {    
         'params': temp
     }
-    print(numberIDLRGlob)
+
     dfParamLR = pd.DataFrame.from_dict(tempDic)
     dfParamLRFilt = dfParamLR.iloc[:,0]
-    print(dfParamLRFilt)
-    print(addLR)
-    print(stage1addLR)
+
     for eachelem in numberIDLRGlob:
         if (eachelem >= stageTotalReached):
-            print('mpike1')
-            print(eachelem-addLR)
             arg = dfParamLRFilt[eachelem-addLR]
         elif (eachelem >= greater):
-            print('mpike2')
             arg = dfParamLRFilt[eachelem-stage1addLR]
         else:
             arg = dfParamLRFilt[eachelem-LRModelsCount]
-            print('mpike3')
-        print(arg)
+
         all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), LogisticRegression(random_state=RANDOM_SEED).set_params(**arg)))
 
     temp = allParametersPerformancePerModel[9]
@@ -1548,7 +1599,7 @@ def EnsembleModel (Models, keyRetrieved):
 
     global sclf 
     sclf = 0
-    print(all_classifiers)
+
     sclf = EnsembleVoteClassifier(clfs=all_classifiers,
                         voting='soft')
 
@@ -1642,6 +1693,19 @@ def EnsembleModel (Models, keyRetrieved):
         else:
             scores.append(previousState[7])
 
+    global StanceTest
+    if (StanceTest):
+        sclf.fit(XData, yData)
+        y_pred = sclf.predict(XDataTest)
+        print('Test data set')
+        print(accuracy_score(yDataTest, y_pred))
+        print(classification_report(yDataTest, y_pred))
+
+        y_pred = sclf.predict(XDataExternal)
+        print('External data set')
+        print(accuracy_score(yDataExternal, y_pred))
+        print(classification_report(yDataExternal, y_pred))
+
     return 'Okay'
 
 # Sending the final results to be visualized as a line plot
@@ -1677,7 +1741,7 @@ def returnResults(ModelSpaceMDS,ModelSpaceTSNE,ModelSpaceUMAP,parametersGen,sumP
     global storeClass0
     global storeClass1
 
-    if(storeClass0 > 169 | storeClass1 > 169):
+    if(storeClass0 > 169 or storeClass1 > 169):
         mode = 1
     else:
         mode = 0
@@ -3592,8 +3656,6 @@ def InitializeFirstStageCM (RemainingIds, setMaxLoopValue):
     stage1addGradB = addGradB
     stageTotalReached = stageTotalReached + addAllNew
 
-    print(stageTotalReached)
-
     return 'Everything Okay'
 
 def crossoverMutation(XData, yData, clf, params, eachAlgor, AlgorithmsIDsEnd):
@@ -3914,50 +3976,23 @@ def PreprocessingPredCM():
                 lastElPredAv.append(predictions[index][item]*100)
             yDataSortedLast.append(item)
 
-    if (storeClass0 > 169 & storeClass1 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-        lastElKNN = computeClusters(lastElKNN)
-        firstElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass0 < 169 & storeClass1 > 169):
-                
-        lastElKNN = computeClusters(lastElKNN)
-        lastElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass1 < 169 & storeClass0 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-
-    else:
-        pass
-
-    predictionsKNN = firstElKNN + lastElKNN
-    predictionsLR = firstElLR + lastElLR        
-    predictionsMLP = firstElMLP + lastElMLP
-    predictionsRF = firstElRF + lastElRF
-    predictionsGradB = firstElGradB + lastElGradB
-    predictions = firstElPredAv + lastElPredAv
+    if (storeClass0 > 169 and storeClass1 > 169):
+        yDataSortedFirst = []
+        yDataSortedLast = []    
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        for item in lastElPredAv:
+            yDataSortedFirst.append(0)
+            yDataSortedLast.append(0)
+
+    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
     yDataSorted = yDataSortedFirst + yDataSortedLast
-
+    
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
 def PreprocessingPredCMSecond():
@@ -4094,50 +4129,23 @@ def PreprocessingPredCMSecond():
                 lastElPredAv.append(predictions[index][item]*100)
             yDataSortedLast.append(item)
 
-    if (storeClass0 > 169 & storeClass1 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-        lastElKNN = computeClusters(lastElKNN)
-        firstElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass0 < 169 & storeClass1 > 169):
-                
-        lastElKNN = computeClusters(lastElKNN)
-        lastElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass1 < 169 & storeClass0 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-
-    else:
-        pass
-
-    predictionsKNN = firstElKNN + lastElKNN
-    predictionsLR = firstElLR + lastElLR        
-    predictionsMLP = firstElMLP + lastElMLP
-    predictionsRF = firstElRF + lastElRF
-    predictionsGradB = firstElGradB + lastElGradB
-    predictions = firstElPredAv + lastElPredAv
+    if (storeClass0 > 169 and storeClass1 > 169):
+        yDataSortedFirst = []
+        yDataSortedLast = []    
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        for item in lastElPredAv:
+            yDataSortedFirst.append(0)
+            yDataSortedLast.append(0)
+
+    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
     yDataSorted = yDataSortedFirst + yDataSortedLast
-
+    
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
 def PreprocessingParamCM():
@@ -4420,6 +4428,7 @@ def PreprocessingParamSepCM():
     return [dfKNNCC, dfKNNCM, dfLRCC, dfLRCM, dfMLPCC, dfMLPCM, dfRFCC, dfRFCM, dfGradBCC, dfGradBCM, dfKNNMC, dfKNNMM, dfLRMC, dfLRMM, dfMLPMC, dfMLPMM, dfRFMC, dfRFMM, dfGradBMC, dfGradBMM]
 
 def preProcsumPerMetricCM(factors):
+
     sumPerClassifier = []
     loopThroughMetrics = PreprocessingMetricsCM()
     loopThroughMetrics = loopThroughMetrics.fillna(0)
@@ -4570,7 +4579,7 @@ def CrossMutateResults(ModelSpaceMDSCM,ModelSpaceTSNECM,ModelSpaceUMAPCM,Predict
     global names_labels
     global yDataSorted
     ResultsCM = []
-
+    global factors
     parametersGenCM = PreprocessingParamCM()
     metricsPerModelCM = preProcMetricsAllAndSelCM()
     sumPerClassifierCM = preProcsumPerMetricCM(factors)
@@ -4777,50 +4786,23 @@ def PreprocessingPredSel(SelectedIDs):
                 lastElPredAv.append(predictions[index][item]*100)
             yDataSortedLast.append(item)
 
-    if (storeClass0 > 169 & storeClass1 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-        lastElKNN = computeClusters(lastElKNN)
-        firstElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass0 < 169 & storeClass1 > 169):
-                
-        lastElKNN = computeClusters(lastElKNN)
-        lastElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass1 < 169 & storeClass0 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-
-    else:
-        pass
-
-    predictionsKNN = firstElKNN + lastElKNN
-    predictionsLR = firstElLR + lastElLR        
-    predictionsMLP = firstElMLP + lastElMLP
-    predictionsRF = firstElRF + lastElRF
-    predictionsGradB = firstElGradB + lastElGradB
-    predictions = firstElPredAv + lastElPredAv
+    if (storeClass0 > 169 and storeClass1 > 169):
+        yDataSortedFirst = []
+        yDataSortedLast = []    
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        for item in lastElPredAv:
+            yDataSortedFirst.append(0)
+            yDataSortedLast.append(0)
+
+    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
     yDataSorted = yDataSortedFirst + yDataSortedLast
- 
+    
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
 @cross_origin(origin='localhost',headers=['Content-Type','Authorization'])
@@ -4971,50 +4953,23 @@ def PreprocessingPredSelEnsem(SelectedIDsEnsem):
                 lastElPredAv.append(predictions[index][item]*100)
             yDataSortedLast.append(item)
 
-    if (storeClass0 > 169 & storeClass1 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-        lastElKNN = computeClusters(lastElKNN)
-        firstElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass0 < 169 & storeClass1 > 169):
-                
-        lastElKNN = computeClusters(lastElKNN)
-        lastElLR = computeClusters(lastElLR)
-        lastElMLP = computeClusters(lastElMLP)
-        lastElRF = computeClusters(lastElRF)
-        lastElGradB = computeClusters(lastElGradB)
-        lastElPredAv = computeClusters(lastElPredAv)
-
-    elif (storeClass1 < 169 & storeClass0 > 169):
-                
-        firstElKNN = computeClusters(firstElKNN)
-        firstElLR = computeClusters(firstElLR)
-        firstElMLP = computeClusters(firstElMLP)
-        firstElRF = computeClusters(firstElRF)
-        firstElGradB = computeClusters(firstElGradB)
-        firstElPredAv = computeClusters(firstElPredAv)
-
-    else:
-        pass
-
-    predictionsKNN = firstElKNN + lastElKNN
-    predictionsLR = firstElLR + lastElLR        
-    predictionsMLP = firstElMLP + lastElMLP
-    predictionsRF = firstElRF + lastElRF
-    predictionsGradB = firstElGradB + lastElGradB
-    predictions = firstElPredAv + lastElPredAv
+    if (storeClass0 > 169 and storeClass1 > 169):
+        yDataSortedFirst = []
+        yDataSortedLast = []    
+        ResultsGatheredFirst = computeClusters(firstElPredAv,firstElKNN,firstElLR,firstElMLP,firstElRF,firstElGradB)
+        ResultsGatheredLast = computeClusters(lastElPredAv,lastElKNN,lastElLR,lastElMLP,lastElRF,lastElGradB)
+        for item in lastElPredAv:
+            yDataSortedFirst.append(0)
+            yDataSortedLast.append(0)
+
+    predictions = ResultsGatheredFirst[0] + ResultsGatheredLast[0]
+    predictionsKNN = ResultsGatheredFirst[1] + ResultsGatheredLast[1]
+    predictionsLR = ResultsGatheredFirst[2] + ResultsGatheredLast[2]        
+    predictionsMLP = ResultsGatheredFirst[3] + ResultsGatheredLast[3]
+    predictionsRF = ResultsGatheredFirst[4] + ResultsGatheredLast[4]
+    predictionsGradB = ResultsGatheredFirst[5] + ResultsGatheredLast[5]
     yDataSorted = yDataSortedFirst + yDataSortedLast
-
+    
     return [predictionsKNN, predictionsLR, predictionsMLP, predictionsRF, predictionsGradB, predictions]
 
 @cross_origin(origin='localhost',headers=['Content-Type','Authorization'])