new

cachedir/joblib/run/estimator/07007f7dca9bdfd648d3542e509afb89/metadata.json

@@ -1 +1 @@
-{"duration": 1.3943150043487549, "input_args": {"n_estimators": "74.90081706613316", "eta": "0.24281758667148645", "max_depth": "6.444267910404542", "subsample": "0.823173811905026", "colsample_bytree": "0.9458014336081975"}}
+{"duration": 0.445972204208374, "input_args": {"n_estimators": "74.90081706613316", "eta": "0.24281758667148645", "max_depth": "6.444267910404542", "subsample": "0.823173811905026", "colsample_bytree": "0.9458014336081975"}}

cachedir/joblib/run/estimator/25b4ca0245787f8f861a32778e868901/metadata.json

@@ -1 +0,0 @@
-{"duration": 5.069570064544678, "input_args": {"n_estimators": "166.5474027620548", "eta": "0.050400330952768344", "max_depth": "11.980222535313743", "subsample": "0.9982340891007953", "colsample_bytree": "0.8542949699823068"}}

cachedir/joblib/run/estimator/2d3aa4b9a7a5e05fecd214f13f35c2f6/metadata.json

@@ -1 +1 @@
-{"duration": 0.3925189971923828, "input_args": {"n_estimators": "13.819321337554923", "eta": "0.07212312551297988", "max_depth": "7.175897174514871", "subsample": "0.8650660661526529", "colsample_bytree": "0.9843748470046234"}}
+{"duration": 0.24505186080932617, "input_args": {"n_estimators": "13.819321337554923", "eta": "0.07212312551297988", "max_depth": "7.175897174514871", "subsample": "0.8650660661526529", "colsample_bytree": "0.9843748470046234"}}

cachedir/joblib/run/estimator/3e39bc61e5d32214dbfb09418b17d489/metadata.json

@@ -1 +0,0 @@
-{"duration": 2.5982677936553955, "input_args": {"n_estimators": "172.49319703423618", "eta": "0.3", "max_depth": "11.956219561804252", "subsample": "0.9002699436157899", "colsample_bytree": "0.8"}}

cachedir/joblib/run/estimator/49b09e262e319b7bfb24ff4a0ac5c3f5/metadata.json

@@ -0,0 +1 @@
+{"duration": 0.840461015701294, "input_args": {"n_estimators": "89.20416101025876", "eta": "0.298254354073293", "max_depth": "11.531170050483237", "subsample": "0.8631047353903125", "colsample_bytree": "0.8937180539904231"}}

cachedir/joblib/run/estimator/501c8577828b16d7edbbaa0c4c50298a/metadata.json

@@ -1 +1 @@
-{"duration": 2.650658130645752, "input_args": {"n_estimators": "121.73840441842214", "eta": "0.28767857660247903", "max_depth": "10.39196365086843", "subsample": "0.8312037280884873", "colsample_bytree": "0.8749080237694725"}}
+{"duration": 0.9199130535125732, "input_args": {"n_estimators": "121.73840441842214", "eta": "0.28767857660247903", "max_depth": "10.39196365086843", "subsample": "0.8312037280884873", "colsample_bytree": "0.8749080237694725"}}

cachedir/joblib/run/estimator/53c192c9764e2921d7ee2543dca13d78/metadata.json

@@ -1 +1 @@
-{"duration": 2.5463922023773193, "input_args": {"n_estimators": "182.3174784053625", "eta": "0.17379422752781754", "max_depth": "6.20633112669131", "subsample": "0.8517559963200034", "colsample_bytree": "0.8244076469689559"}}
+{"duration": 2.4921109676361084, "input_args": {"n_estimators": "182.3174784053625", "eta": "0.17379422752781754", "max_depth": "6.20633112669131", "subsample": "0.8517559963200034", "colsample_bytree": "0.8244076469689559"}}

cachedir/joblib/run/estimator/6090fba618ef6aae9c62af2152c10831/metadata.json

@@ -1 +1 @@
-{"duration": 3.295583963394165, "input_args": {"n_estimators": "114.44905352605177", "eta": "0.22831119680574874", "max_depth": "10.564710291701385", "subsample": "0.9541934359909122", "colsample_bytree": "0.8239188491876603"}}
+{"duration": 0.7512750625610352, "input_args": {"n_estimators": "114.44905352605177", "eta": "0.22831119680574874", "max_depth": "10.564710291701385", "subsample": "0.9541934359909122", "colsample_bytree": "0.8239188491876603"}}

cachedir/joblib/run/estimator/61cc762b5a9bd4586cee079d6c1a7776/metadata.json

@@ -0,0 +1 @@
+{"duration": 1.6831128597259521, "input_args": {"n_estimators": "185.43130710367382", "eta": "0.07633917613590695", "max_depth": "6.883647273396065", "subsample": "0.9541408808296614", "colsample_bytree": "0.9812765080381792"}}

cachedir/joblib/run/estimator/65ad99c302a3e6128ffd21fb73ff7858/metadata.json

@@ -1 +1 @@
-{"duration": 1.2244741916656494, "input_args": {"n_estimators": "46.40612658226385", "eta": "0.2924774630404986", "max_depth": "10.99465584480253", "subsample": "0.8363649934414201", "colsample_bytree": "0.8041168988591605"}}
+{"duration": 0.8074049949645996, "input_args": {"n_estimators": "46.40612658226385", "eta": "0.2924774630404986", "max_depth": "10.99465584480253", "subsample": "0.8363649934414201", "colsample_bytree": "0.8041168988591605"}}

cachedir/joblib/run/estimator/68528debde2113dac0b069dc3ab767b4/metadata.json

@@ -0,0 +1 @@
+{"duration": 0.9398980140686035, "input_args": {"n_estimators": "57.427799444575626", "eta": "0.0939475479410324", "max_depth": "7.900373700039049", "subsample": "0.9451773493842789", "colsample_bytree": "0.9615101551425086"}}

cachedir/joblib/run/estimator/7b6a78563e5e8041baad07d84d667dd1/metadata.json

@@ -0,0 +1 @@
+{"duration": 1.448106050491333, "input_args": {"n_estimators": "167.24441970961547", "eta": "0.22277938316283252", "max_depth": "7.548261010130628", "subsample": "0.9219628477814731", "colsample_bytree": "0.8774212362335956"}}

cachedir/joblib/run/estimator/7bb9a6ed4f9c4665154bf105b6bc49d2/metadata.json

@@ -0,0 +1 @@
+{"duration": 0.3969099521636963, "input_args": {"n_estimators": "25.38275875171714", "eta": "0.06194579800544693", "max_depth": "6.214846370130013", "subsample": "0.846639951990341", "colsample_bytree": "0.9168007862570122"}}

cachedir/joblib/run/estimator/878e230e8d668ff8bf9ae658973cf276/metadata.json

@@ -1 +1 @@
-{"duration": 2.3453609943389893, "input_args": {"n_estimators": "89.22927863521258", "eta": "0.12606056073988442", "max_depth": "9.148538589793427", "subsample": "0.8582458280396084", "colsample_bytree": "0.8366809019706868"}}
+{"duration": 1.7156198024749756, "input_args": {"n_estimators": "89.22927863521258", "eta": "0.12606056073988442", "max_depth": "9.148538589793427", "subsample": "0.8582458280396084", "colsample_bytree": "0.8366809019706868"}}

cachedir/joblib/run/estimator/8dbc0d7097e6aca21bb30a8dc860f045/metadata.json

@@ -1 +1 @@
-{"duration": 2.071702003479004, "input_args": {"n_estimators": "74.56689870524991", "eta": "0.11783725794347398", "max_depth": "10.972425054911575", "subsample": "0.8561869019374762", "colsample_bytree": "0.8777354579378964"}}
+{"duration": 0.6619899272918701, "input_args": {"n_estimators": "74.56689870524991", "eta": "0.11783725794347398", "max_depth": "10.972425054911575", "subsample": "0.8561869019374762", "colsample_bytree": "0.8777354579378964"}}

cachedir/joblib/run/estimator/96ba12b58138896ed090735e777c34ba/metadata.json

@@ -1 +1 @@
-{"duration": 0.6976380348205566, "input_args": {"n_estimators": "17.393878305774606", "eta": "0.20582453170688947", "max_depth": "7.985388149115895", "subsample": "0.8621964643431325", "colsample_bytree": "0.9726206851751187"}}
+{"duration": 0.19493722915649414, "input_args": {"n_estimators": "17.393878305774606", "eta": "0.20582453170688947", "max_depth": "7.985388149115895", "subsample": "0.8621964643431325", "colsample_bytree": "0.9726206851751187"}}

cachedir/joblib/run/estimator/9d6d5c57facf6580e27ecc5ac3a9da74/metadata.json

@@ -1 +1 @@
-{"duration": 3.0700769424438477, "input_args": {"n_estimators": "179.4913333333915", "eta": "0.24378320584027863", "max_depth": "11.636993649385134", "subsample": "0.919579995762217", "colsample_bytree": "0.9939169255529117"}}
+{"duration": 1.4896700382232666, "input_args": {"n_estimators": "179.4913333333915", "eta": "0.24378320584027863", "max_depth": "11.636993649385134", "subsample": "0.919579995762217", "colsample_bytree": "0.9939169255529117"}}

cachedir/joblib/run/estimator/b8a9b059b71e375385bcb3affc80aaae/metadata.json

@@ -1 +1 @@
-{"duration": 2.545977830886841, "input_args": {"n_estimators": "138.4254401698706", "eta": "0.1261534422933427", "max_depth": "6.586032684038303", "subsample": "0.8880304987479203", "colsample_bytree": "0.9616794696232922"}}
+{"duration": 2.3435099124908447, "input_args": {"n_estimators": "138.4254401698706", "eta": "0.1261534422933427", "max_depth": "6.586032684038303", "subsample": "0.8880304987479203", "colsample_bytree": "0.9616794696232922"}}

cachedir/joblib/run/estimator/bd6da16bc4f265fe52537fa5b2e84845/metadata.json

@@ -1 +1 @@
-{"duration": 2.421952962875366, "input_args": {"n_estimators": "120.52084092809828", "eta": "0.09991844553958994", "max_depth": "9.08540663048167", "subsample": "0.8092900825439996", "colsample_bytree": "0.9570351922786027"}}
+{"duration": 2.5639889240264893, "input_args": {"n_estimators": "120.52084092809828", "eta": "0.09991844553958994", "max_depth": "9.08540663048167", "subsample": "0.8092900825439996", "colsample_bytree": "0.9570351922786027"}}

cachedir/joblib/run/estimator/c5ae132fa788a41769fd65f6ea6fc878/metadata.json

@@ -1 +1 @@
-{"duration": 5.754271984100342, "input_args": {"n_estimators": "164.01497854869265", "eta": "0.0996789203835431", "max_depth": "6.033132702741614", "subsample": "0.9413714687695234", "colsample_bytree": "0.9544489538593315"}}
+{"duration": 1.0486700534820557, "input_args": {"n_estimators": "164.01497854869265", "eta": "0.0996789203835431", "max_depth": "6.033132702741614", "subsample": "0.9413714687695234", "colsample_bytree": "0.9544489538593315"}}

cachedir/joblib/run/estimator/c78d75f852ea2ac732cd2c4d8cf5e3e3/metadata.json

@@ -1 +1 @@
-{"duration": 3.1356942653656006, "input_args": {"n_estimators": "178.00648480238368", "eta": "0.232401544584516", "max_depth": "9.825344828131279", "subsample": "0.8944429850323898", "colsample_bytree": "0.8650366644053494"}}
+{"duration": 0.867225170135498, "input_args": {"n_estimators": "178.00648480238368", "eta": "0.232401544584516", "max_depth": "9.825344828131279", "subsample": "0.8944429850323898", "colsample_bytree": "0.8650366644053494"}}

cachedir/joblib/run/estimator/d7a4fed50ef206c00c730c9b2e013bcb/metadata.json

@@ -1 +1 @@
-{"duration": 2.6464250087738037, "input_args": {"n_estimators": "111.60850447193953", "eta": "0.12792776902235276", "max_depth": "9.120408127066865", "subsample": "0.8369708911051055", "colsample_bytree": "0.9325044568707964"}}
+{"duration": 0.880241870880127, "input_args": {"n_estimators": "111.60850447193953", "eta": "0.12792776902235276", "max_depth": "9.120408127066865", "subsample": "0.8369708911051055", "colsample_bytree": "0.9325044568707964"}}

cachedir/joblib/run/estimator/e4ed322c7348967a87d404da78936ef6/metadata.json

@@ -1 +0,0 @@
-{"duration": 2.8645551204681396, "input_args": {"n_estimators": "104.7411734315532", "eta": "0.05", "max_depth": "6.001102182596818", "subsample": "0.8", "colsample_bytree": "1.0"}}

cachedir/joblib/run/estimator/e5b137b59fcad7738516be11e783d76d/metadata.json

@@ -1 +1 @@
-{"duration": 3.4205880165100098, "input_args": {"n_estimators": "190.03267976439997", "eta": "0.09263103092182289", "max_depth": "6.390309557911677", "subsample": "0.9931264066149119", "colsample_bytree": "0.9215089703802877"}}
+{"duration": 3.4960217475891113, "input_args": {"n_estimators": "190.03267976439997", "eta": "0.09263103092182289", "max_depth": "6.390309557911677", "subsample": "0.9931264066149119", "colsample_bytree": "0.9215089703802877"}}

cachedir/joblib/run/estimator/e907a73e538f95a05e43901c7239d26f/metadata.json

@@ -1 +1 @@
-{"duration": 1.844560146331787, "input_args": {"n_estimators": "76.44055944226989", "eta": "0.08487346516301046", "max_depth": "7.752867891211309", "subsample": "0.8912139968434072", "colsample_bytree": "0.9223705789444759"}}
+{"duration": 1.6984941959381104, "input_args": {"n_estimators": "76.44055944226989", "eta": "0.08487346516301046", "max_depth": "7.752867891211309", "subsample": "0.8912139968434072", "colsample_bytree": "0.9223705789444759"}}

cachedir/joblib/run/estimator/ecc4f427737ec1d8fd0403c51e99091c/metadata.json

@@ -1 +1 @@
-{"duration": 0.32785892486572266, "input_args": {"n_estimators": "9.956729715098561", "eta": "0.18068320734549853", "max_depth": "8.565246110151298", "subsample": "0.821578285398661", "colsample_bytree": "0.8987591192728782"}}
+{"duration": 0.17142605781555176, "input_args": {"n_estimators": "9.956729715098561", "eta": "0.18068320734549853", "max_depth": "8.565246110151298", "subsample": "0.821578285398661", "colsample_bytree": "0.8987591192728782"}}

cachedir/joblib/run/estimator/f1d92e5f2a3bd05ecb2416c00ab93942/metadata.json

@@ -1 +1 @@
-{"duration": 0.9457027912139893, "input_args": {"n_estimators": "19.53737551755531", "eta": "0.08523105624369066", "max_depth": "10.813181884524237", "subsample": "0.9973773873201035", "colsample_bytree": "0.9085392166316497"}}
+{"duration": 0.35938477516174316, "input_args": {"n_estimators": "19.53737551755531", "eta": "0.08523105624369066", "max_depth": "10.813181884524237", "subsample": "0.9973773873201035", "colsample_bytree": "0.9085392166316497"}}

cachedir/joblib/run/estimator/f3a05fd0f610f6b4476dedeade21eb2d/metadata.json

@@ -1 +0,0 @@
-{"duration": 1.8966717720031738, "input_args": {"n_estimators": "69.63591316205535", "eta": "0.3", "max_depth": "6.0", "subsample": "0.8", "colsample_bytree": "0.9778170465164595"}}

cachedir/joblib/run/estimator/f636511a768ea642182097d47d727cb3/metadata.json

@@ -1 +1 @@
-{"duration": 4.356304883956909, "input_args": {"n_estimators": "122.21742728992572", "eta": "0.06452090304204987", "max_depth": "11.197056874649611", "subsample": "0.941614515559209", "colsample_bytree": "0.8311989040672406"}}
+{"duration": 1.8177359104156494, "input_args": {"n_estimators": "122.21742728992572", "eta": "0.06452090304204987", "max_depth": "11.197056874649611", "subsample": "0.941614515559209", "colsample_bytree": "0.8311989040672406"}}

cachedir/joblib/run/estimator/f711c74235d9de3e98bcceeac842befd/metadata.json

@@ -1 +0,0 @@
-{"duration": 4.243398904800415, "input_args": {"n_estimators": "174.53471828158587", "eta": "0.09632267985087588", "max_depth": "6.020831446310865", "subsample": "0.845639293415064", "colsample_bytree": "0.9738813732950363"}}

cachedir/joblib/run/estimator/func_code.py

@@ -2,7 +2,7 @@
     @memory.cache
     def estimator(n_estimators, eta, max_depth, subsample, colsample_bytree):
         # initialize model
-        print('inside')
+        print('loopingIris')
         n_estimators = int(n_estimators)
         max_depth = int(max_depth)
         model = XGBClassifier(n_estimators=n_estimators, eta=eta, max_depth=max_depth, subsample=subsample, colsample_bytree=colsample_bytree, n_jobs=-1, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)

cachedir/joblib/run/estimatorFeatureSelection/1edf0aab1322bad6d8bc1faf9d99ec87/metadata.json

@@ -0,0 +1 @@
+{"duration": 21.04797101020813, "input_args": {"Data": "         F1_l2  F6_b  F1_l2+F6_b  |F1_l2-F6_b|  F1_l2xF6_b  F1_l2/F6_b  F6_b/F1_l2\n0    -0.621488     1    0.378512      1.621488   -0.621488   -0.621488   -1.609041\n1    -0.785875     1    0.214125      1.785875   -0.785875   -0.785875   -1.272467\n2    -1.836501    11    9.163499     12.836501  -20.201514   -0.166955   -5.989650\n3    -1.395929     6    4.604071      7.395929   -8.375572   -0.232655   -4.298214\n4    -0.943416     3    2.056584      3.943416   -2.830249   -0.314472   -3.179932\n...        ...   ...         ...           ...         ...         ...         ...\n1594 -0.736966     2    1.263034      2.736966   -1.473931   -0.368483   -2.713831\n1595 -0.862496     2    1.137504      2.862496   -1.724993   -0.431248   -2.318850\n1596 -0.971431     3    2.028569      3.971431   -2.914293   -0.323810   -3.088228\n1597 -0.632629     3    2.367371      3.632629   -1.897887   -0.210876   -4.742116\n1598 -1.689660     9    7.310340     10.689660  -15.206939   -0.187740   -5.326516\n\n[1599 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9223705789444759,\n              eta=0.08487346516301046, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.0848734677, max_delta_step=0, max_depth=7,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=76, 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=0.8912139968434072, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/5c6d9ec9ebd0c96ad37bcece2a7b1ab1/metadata.json

@@ -0,0 +1 @@
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cachedir/joblib/run/estimatorFeatureSelection/5fa740243b273457bc3baef8701c08b7/metadata.json

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+{"duration": 13.591701030731201, "input_args": {"Data": "         F1_l2     F2       F3   F4     F5  F6_b   F7    F8    F9_l10   F10   F11\n0    -0.621488  0.065  0.99460  7.3   21.0     1  1.2  3.39 -0.327902  15.0  10.0\n1    -0.785875  0.073  0.99680  7.8   18.0     1  2.0  3.36 -0.244125   9.0   9.5\n2    -1.836501  0.092  0.99690  8.5  103.0    11  1.8  3.30 -0.124939  35.0  10.5\n3    -1.395929  0.066  0.99680  8.1   30.0     6  2.1  3.23 -0.136677  13.0   9.7\n4    -0.943416  0.085  0.99680  7.5   35.0     3  1.9  3.38 -0.207608  12.0   9.5\n...        ...    ...      ...  ...    ...   ...  ...   ...       ...   ...   ...\n1594 -0.736966  0.090  0.99490  6.2   44.0     2  2.0  3.45 -0.236572  32.0  10.5\n1595 -0.862496  0.062  0.99512  5.9   51.0     2  2.2  3.52 -0.119186  39.0  11.2\n1596 -0.971431  0.076  0.99574  6.3   40.0     3  2.3  3.42 -0.124939  29.0  11.0\n1597 -0.632629  0.075  0.99547  5.9   44.0     3  2.0  3.57 -0.148742  32.0  10.2\n1598 -1.689660  0.067  0.99549  6.0   42.0     9  3.6  3.39 -0.180456  18.0  11.0\n\n[1599 rows x 11 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9223705789444759,\n              eta=0.08487346516301046, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.0848734677, max_delta_step=0, max_depth=7,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=76, 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=0.8912139968434072, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/9bb8074ec6b631e352538287bdfa0b0a/metadata.json

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cachedir/joblib/run/estimatorFeatureSelection/9cfb8bb7fc51dc943ddd76f4f567f229/metadata.json

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+{"duration": 16.84209895133972, "input_args": {"Data": "         F1_l2    F9_l10  F1_l2+F9_l10  |F1_l2-F9_l10|  F1_l2xF9_l10  F1_l2/F9_l10  F9_l10/F1_l2\n0    -0.621488 -0.327902     -0.949391        0.293586      0.203787      1.895347      0.527608\n1    -0.785875 -0.244125     -1.030000        0.541750      0.191852      3.219149      0.310641\n2    -1.836501 -0.124939     -1.961440        1.711563      0.229450     14.699214      0.068031\n3    -1.395929 -0.136677     -1.532606        1.259252      0.190792     10.213330      0.097911\n4    -0.943416 -0.207608     -1.151025        0.735808      0.195861      4.544213      0.220060\n...        ...       ...           ...             ...           ...           ...           ...\n1594 -0.736966 -0.236572     -0.973538        0.500394      0.174345      3.115185      0.321008\n1595 -0.862496 -0.119186     -0.981683        0.743310      0.102798      7.236534      0.138188\n1596 -0.971431 -0.124939     -1.096370        0.846492      0.121369      7.775257      0.128613\n1597 -0.632629 -0.148742     -0.781371        0.483887      0.094098      4.253206      0.235117\n1598 -1.689660 -0.180456     -1.870116        1.509204      0.304909      9.363276      0.106800\n\n[1599 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9223705789444759,\n              eta=0.08487346516301046, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.0848734677, max_delta_step=0, max_depth=7,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=76, 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=0.8912139968434072, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/bdd807c5f0ea9d506784044cc715fea4/metadata.json

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+{"duration": 9.041555166244507, "input_args": {"Data": "      F6_b    F9_l10  F6_b+F9_l10  |F6_b-F9_l10|  F6_bxF9_l10  F6_b/F9_l10  F9_l10/F6_b\n0        1 -0.327902     0.672098       1.327902    -0.327902    -3.049690    -0.327902\n1        1 -0.244125     0.755875       1.244125    -0.244125    -4.096260    -0.244125\n2       11 -0.124939    10.875061      11.124939    -1.374326   -88.043151    -0.011358\n3        6 -0.136677     5.863323       6.136677    -0.820063   -43.899075    -0.022780\n4        3 -0.207608     2.792392       3.207608    -0.622825   -14.450289    -0.069203\n...    ...       ...          ...            ...          ...          ...          ...\n1594     2 -0.236572     1.763428       2.236572    -0.473144    -8.454086    -0.118286\n1595     2 -0.119186     1.880814       2.119186    -0.238373   -16.780437    -0.059593\n1596     3 -0.124939     2.875061       3.124939    -0.374816   -24.011768    -0.041646\n1597     3 -0.148742     2.851258       3.148742    -0.446225   -20.169199    -0.049581\n1598     9 -0.180456     8.819544       9.180456    -1.624105   -49.873636    -0.020051\n\n[1599 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9223705789444759,\n              eta=0.08487346516301046, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.0848734677, max_delta_step=0, max_depth=7,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=76, 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=0.8912139968434072, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/c1b8be511e9a8599714fe6a780347fd0/metadata.json

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+{"duration": 24.650119066238403, "input_args": {"Data": "         F1_l2     F2       F3   F4     F5    F6   F7    F8    F9   F10   F11\n0    -0.621488  0.065  0.99460  7.3   21.0  0.00  1.2  3.39  0.47  15.0  10.0\n1    -0.785875  0.073  0.99680  7.8   18.0  0.02  2.0  3.36  0.57   9.0   9.5\n2    -1.836501  0.092  0.99690  8.5  103.0  0.56  1.8  3.30  0.75  35.0  10.5\n3    -1.395929  0.066  0.99680  8.1   30.0  0.28  2.1  3.23  0.73  13.0   9.7\n4    -0.943416  0.085  0.99680  7.5   35.0  0.16  1.9  3.38  0.62  12.0   9.5\n...        ...    ...      ...  ...    ...   ...  ...   ...   ...   ...   ...\n1594 -0.736966  0.090  0.99490  6.2   44.0  0.08  2.0  3.45  0.58  32.0  10.5\n1595 -0.862496  0.062  0.99512  5.9   51.0  0.10  2.2  3.52  0.76  39.0  11.2\n1596 -0.971431  0.076  0.99574  6.3   40.0  0.13  2.3  3.42  0.75  29.0  11.0\n1597 -0.632629  0.075  0.99547  5.9   44.0  0.12  2.0  3.57  0.71  32.0  10.2\n1598 -1.689660  0.067  0.99549  6.0   42.0  0.47  3.6  3.39  0.66  18.0  11.0\n\n[1599 rows x 11 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9223705789444759,\n              eta=0.08487346516301046, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.0848734677, max_delta_step=0, max_depth=7,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=76, 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=0.8912139968434072, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/d7ba7e4f73aab0f3f46d8930988f7af2/metadata.json

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cachedir/joblib/run/estimatorFeatureSelection/e8dffeaa3cab0e986513f7194a282bce/metadata.json

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+{"duration": 25.00281596183777, "input_args": {"Data": "         F1     F2       F3   F4     F5    F6   F7    F8    F9   F10   F11\n0     0.650  0.065  0.99460  7.3   21.0  0.00  1.2  3.39  0.47  15.0  10.0\n1     0.580  0.073  0.99680  7.8   18.0  0.02  2.0  3.36  0.57   9.0   9.5\n2     0.280  0.092  0.99690  8.5  103.0  0.56  1.8  3.30  0.75  35.0  10.5\n3     0.380  0.066  0.99680  8.1   30.0  0.28  2.1  3.23  0.73  13.0   9.7\n4     0.520  0.085  0.99680  7.5   35.0  0.16  1.9  3.38  0.62  12.0   9.5\n...     ...    ...      ...  ...    ...   ...  ...   ...   ...   ...   ...\n1594  0.600  0.090  0.99490  6.2   44.0  0.08  2.0  3.45  0.58  32.0  10.5\n1595  0.550  0.062  0.99512  5.9   51.0  0.10  2.2  3.52  0.76  39.0  11.2\n1596  0.510  0.076  0.99574  6.3   40.0  0.13  2.3  3.42  0.75  29.0  11.0\n1597  0.645  0.075  0.99547  5.9   44.0  0.12  2.0  3.57  0.71  32.0  10.2\n1598  0.310  0.067  0.99549  6.0   42.0  0.47  3.6  3.39  0.66  18.0  11.0\n\n[1599 rows x 11 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9223705789444759,\n              eta=0.08487346516301046, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.0848734677, max_delta_step=0, max_depth=7,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=76, 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=0.8912139968434072, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/ee3aaf15c216be57aa4f78a295113ee9/metadata.json

@@ -0,0 +1 @@
+{"duration": 34.51085186004639, "input_args": {"Data": "         F1_l2  F6_b    F9_l10  F1_l2+F6_b  F6_b+F9_l10  F1_l2+F9_l10  F1_l2+F6_b+F9_l10  |F1_l2-F6_b|  |F6_b-F9_l10|  ...  F9_l10/F6_b  F1_l2/F9_l10  F9_l10/F1_l2  F1_l2/F6_b/F9_l10  F1_l2/F9_l10/F6_b  F6_b/F9_l10/F1_l2  F6_b/F1_l2/F9_l10  F9_l10/F1_l2/F6_b  F9_l10/F6_b/F1_l2\n0    -0.621488     1 -0.327902    0.378512     0.672098     -0.949391           0.050609      1.621488       1.327902  ...    -0.327902      1.895347      0.527608           1.895347           1.895347           4.907075           4.907075           0.527608           0.527608\n1    -0.785875     1 -0.244125    0.214125     0.755875     -1.030000          -0.030000      1.785875       1.244125  ...    -0.244125      3.219149      0.310641           3.219149           3.219149           5.212354           5.212354           0.310641           0.310641\n2    -1.836501    11 -0.124939    9.163499    10.875061     -1.961440           9.038560     12.836501      11.124939  ...    -0.011358     14.699214      0.068031           1.336292           1.336292          47.940697          47.940697           0.006185           0.006185\n3    -1.395929     6 -0.136677    4.604071     5.863323     -1.532606           4.467394      7.395929       6.136677  ...    -0.022780     10.213330      0.097911           1.702222           1.702222          31.447935          31.447935           0.016319           0.016319\n4    -0.943416     3 -0.207608    2.056584     2.792392     -1.151025           1.848975      3.943416       3.207608  ...    -0.069203      4.544213      0.220060           1.514738           1.514738          15.316977          15.316977           0.073353           0.073353\n...        ...   ...       ...         ...          ...           ...                ...           ...            ...  ...          ...           ...           ...                ...                ...                ...                ...                ...                ...\n1594 -0.736966     2 -0.236572    1.263034     1.763428     -0.973538           1.026462      2.736966       2.236572  ...    -0.118286      3.115185      0.321008           1.557593           1.557593          11.471479          11.471479           0.160504           0.160504\n1595 -0.862496     2 -0.119186    1.137504     1.880814     -0.981683           1.018317      2.862496       2.119186  ...    -0.059593      7.236534      0.138188           3.618267           3.618267          19.455659          19.455659           0.069094           0.069094\n1596 -0.971431     3 -0.124939    2.028569     2.875061     -1.096370           1.903630      3.971431       3.124939  ...    -0.041646      7.775257      0.128613           2.591752           2.591752          24.717939          24.717939           0.042871           0.042871\n1597 -0.632629     3 -0.148742    2.367371     2.851258     -0.781371           2.218629      3.632629       3.148742  ...    -0.049581      4.253206      0.235117           1.417735           1.417735          31.881563          31.881563           0.078372           0.078372\n1598 -1.689660     9 -0.180456    7.310340     8.819544     -1.870116           7.129884     10.689660       9.180456  ...    -0.020051      9.363276      0.106800           1.040364           1.040364          29.516967          29.516967           0.011867           0.011867\n\n[1599 rows x 27 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9223705789444759,\n              eta=0.08487346516301046, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.0848734677, max_delta_step=0, max_depth=7,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=76, 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=0.8912139968434072, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

cachedir/joblib/run/estimatorFeatureSelection/ffcfeece26d8684e0ea24c2611359d2b/metadata.json

@@ -0,0 +1 @@
+{"duration": 12.692944049835205, "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=0.8311989040672406,\n              eta=0.06452090304204987, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.0645209029, max_delta_step=0, max_depth=11,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=122, 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=0.941614515559209, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}}

frontend/src/components/Main.vue

@@ -51,7 +51,7 @@
           </b-col>
           <b-col cols="2">
             <mdb-card style="margin-top: 15px;">
-              <mdb-card-header color="primary-color" tag="h5" class="text-center">Process Tracker and Predictive Results 
+              <mdb-card-header color="primary-color" tag="h5" class="text-left">Process Tracker and Predictive Results 
               </mdb-card-header>
               <mdb-card-body>
                 <mdb-card-text class="text-center"  style="min-height: 920px">
@@ -149,6 +149,7 @@ export default Vue.extend({
       DataResults: '',
       keyNow: 1,
       instancesImportance: '',
+      //RetrieveValueFile: 'VehicleC', // this is for the default data set
       RetrieveValueFile: 'VehicleC', // this is for the default data set
       SelectedFeaturesPerClassifier: '',
       FinalResults: 0,
@@ -476,7 +477,7 @@ 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)
@@ -862,7 +863,7 @@ body {
   top: 0px;
   bottom: 0px;
   margin-top: -4px !important;
-  overflow: hidden !important; // remove scrolling
+  //overflow: hidden !important; // remove scrolling
 }
 
 .modal-backdrop {

run.py

@@ -525,7 +525,7 @@ def create_global_function():
     @memory.cache
     def estimator(n_estimators, eta, max_depth, subsample, colsample_bytree):
         # initialize model
-        print('inside')
+        print('loopingVehicle')
         n_estimators = int(n_estimators)
         max_depth = int(max_depth)
         model = XGBClassifier(n_estimators=n_estimators, eta=eta, max_depth=max_depth, subsample=subsample, colsample_bytree=colsample_bytree, n_jobs=-1, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)