new data set

cachedir/joblib/run/estimator/01638357491a1f7a73e0ab8e8f582409/metadata.json

@@ -0,0 +1 @@
+{"duration": 2.468634843826294, "input_args": {"n_estimators": "123.22873559815328", "eta": "0.18061861147148012", "max_depth": "9.30263441184733", "subsample": "0.8914267677503952", "colsample_bytree": "0.8510959039929198"}}

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

@@ -1 +1 @@
-{"duration": 12.095735788345337, "input_args": {"n_estimators": "74.90081706613316", "eta": "0.24281758667148645", "max_depth": "6.444267910404542", "subsample": "0.823173811905026", "colsample_bytree": "0.9458014336081975"}}
+{"duration": 1.096883773803711, "input_args": {"n_estimators": "74.90081706613316", "eta": "0.24281758667148645", "max_depth": "6.444267910404542", "subsample": "0.823173811905026", "colsample_bytree": "0.9458014336081975"}}

cachedir/joblib/run/estimator/24436eb7944e5f756192f4144586be52/metadata.json

@@ -1 +0,0 @@
-{"duration": 18.44561195373535, "input_args": {"n_estimators": "75.86346086389126", "eta": "0.06372449073826002", "max_depth": "9.472648321000811", "subsample": "0.8945798865585665", "colsample_bytree": "0.9196463484970115"}}

cachedir/joblib/run/estimator/26eaea169055571f02d589ab35824eec/metadata.json

@@ -1 +0,0 @@
-{"duration": 15.748680114746094, "input_args": {"n_estimators": "91.10980257621819", "eta": "0.1812911976270734", "max_depth": "10.851008286060418", "subsample": "0.9410392159460682", "colsample_bytree": "0.9881736755727641"}}

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

@@ -1 +1 @@
-{"duration": 2.4347519874572754, "input_args": {"n_estimators": "13.819321337554923", "eta": "0.07212312551297988", "max_depth": "7.175897174514871", "subsample": "0.8650660661526529", "colsample_bytree": "0.9843748470046234"}}
+{"duration": 0.2637169361114502, "input_args": {"n_estimators": "13.819321337554923", "eta": "0.07212312551297988", "max_depth": "7.175897174514871", "subsample": "0.8650660661526529", "colsample_bytree": "0.9843748470046234"}}

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

@@ -1 +1 @@
-{"duration": 9.82509994506836, "input_args": {"n_estimators": "121.73840441842214", "eta": "0.28767857660247903", "max_depth": "10.39196365086843", "subsample": "0.8312037280884873", "colsample_bytree": "0.8749080237694725"}}
+{"duration": 1.1226909160614014, "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": 18.10935401916504, "input_args": {"n_estimators": "182.3174784053625", "eta": "0.17379422752781754", "max_depth": "6.20633112669131", "subsample": "0.8517559963200034", "colsample_bytree": "0.8244076469689559"}}
+{"duration": 2.356210947036743, "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": 17.332634925842285, "input_args": {"n_estimators": "114.44905352605177", "eta": "0.22831119680574874", "max_depth": "10.564710291701385", "subsample": "0.9541934359909122", "colsample_bytree": "0.8239188491876603"}}
+{"duration": 2.3413259983062744, "input_args": {"n_estimators": "114.44905352605177", "eta": "0.22831119680574874", "max_depth": "10.564710291701385", "subsample": "0.9541934359909122", "colsample_bytree": "0.8239188491876603"}}

cachedir/joblib/run/estimator/61afb17f88fffa151324e4d016d0ccee/metadata.json

@@ -1 +0,0 @@
-{"duration": 17.338062047958374, "input_args": {"n_estimators": "72.11194788979516", "eta": "0.06999909260172242", "max_depth": "11.660767639606618", "subsample": "0.8937069405852791", "colsample_bytree": "0.9970550492754673"}}

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

@@ -1 +1 @@
-{"duration": 8.648480892181396, "input_args": {"n_estimators": "46.40612658226385", "eta": "0.2924774630404986", "max_depth": "10.99465584480253", "subsample": "0.8363649934414201", "colsample_bytree": "0.8041168988591605"}}
+{"duration": 1.0125761032104492, "input_args": {"n_estimators": "46.40612658226385", "eta": "0.2924774630404986", "max_depth": "10.99465584480253", "subsample": "0.8363649934414201", "colsample_bytree": "0.8041168988591605"}}

cachedir/joblib/run/estimator/76ed4fb5a8425d0cdcb38d7aab16b0b2/metadata.json

@@ -0,0 +1 @@
+{"duration": 3.263929843902588, "input_args": {"n_estimators": "186.59460399385202", "eta": "0.13823719698301656", "max_depth": "6.001743521016889", "subsample": "0.8394748246777188", "colsample_bytree": "0.9130349754235753"}}

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

@@ -1 +1 @@
-{"duration": 13.893840074539185, "input_args": {"n_estimators": "89.22927863521258", "eta": "0.12606056073988442", "max_depth": "9.148538589793427", "subsample": "0.8582458280396084", "colsample_bytree": "0.8366809019706868"}}
+{"duration": 2.778671979904175, "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": 16.08168911933899, "input_args": {"n_estimators": "74.56689870524991", "eta": "0.11783725794347398", "max_depth": "10.972425054911575", "subsample": "0.8561869019374762", "colsample_bytree": "0.8777354579378964"}}
+{"duration": 2.2322471141815186, "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": 4.351968050003052, "input_args": {"n_estimators": "17.393878305774606", "eta": "0.20582453170688947", "max_depth": "7.985388149115895", "subsample": "0.8621964643431325", "colsample_bytree": "0.9726206851751187"}}
+{"duration": 0.447145938873291, "input_args": {"n_estimators": "17.393878305774606", "eta": "0.20582453170688947", "max_depth": "7.985388149115895", "subsample": "0.8621964643431325", "colsample_bytree": "0.9726206851751187"}}

cachedir/joblib/run/estimator/9814e674b9997336c137ce7a498b89d2/metadata.json

@@ -0,0 +1 @@
+{"duration": 8.4358651638031, "input_args": {"n_estimators": "189.22741347546307", "eta": "0.18638712930060214", "max_depth": "8.451877954263406", "subsample": "0.9977836095949623", "colsample_bytree": "0.8977236474707452"}}

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

@@ -1 +1 @@
-{"duration": 17.240567922592163, "input_args": {"n_estimators": "179.4913333333915", "eta": "0.24378320584027863", "max_depth": "11.636993649385134", "subsample": "0.919579995762217", "colsample_bytree": "0.9939169255529117"}}
+{"duration": 2.5005016326904297, "input_args": {"n_estimators": "179.4913333333915", "eta": "0.24378320584027863", "max_depth": "11.636993649385134", "subsample": "0.919579995762217", "colsample_bytree": "0.9939169255529117"}}

cachedir/joblib/run/estimator/a0b1a379a10f9b720cb2a91b1b4f2fa1/metadata.json

@@ -0,0 +1 @@
+{"duration": 3.0930938720703125, "input_args": {"n_estimators": "89.69435178841537", "eta": "0.07551767978969", "max_depth": "11.392717959532579", "subsample": "0.8693834288925464", "colsample_bytree": "0.8955099076945017"}}

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

@@ -1 +1 @@
-{"duration": 18.743260145187378, "input_args": {"n_estimators": "138.4254401698706", "eta": "0.1261534422933427", "max_depth": "6.586032684038303", "subsample": "0.8880304987479203", "colsample_bytree": "0.9616794696232922"}}
+{"duration": 2.0926430225372314, "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": 20.84002995491028, "input_args": {"n_estimators": "120.52084092809828", "eta": "0.09991844553958994", "max_depth": "9.08540663048167", "subsample": "0.8092900825439996", "colsample_bytree": "0.9570351922786027"}}
+{"duration": 2.1937928199768066, "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": 21.34256410598755, "input_args": {"n_estimators": "164.01497854869265", "eta": "0.0996789203835431", "max_depth": "6.033132702741614", "subsample": "0.9413714687695234", "colsample_bytree": "0.9544489538593315"}}
+{"duration": 3.594921827316284, "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": 19.493457794189453, "input_args": {"n_estimators": "178.00648480238368", "eta": "0.232401544584516", "max_depth": "9.825344828131279", "subsample": "0.8944429850323898", "colsample_bytree": "0.8650366644053494"}}
+{"duration": 3.4963488578796387, "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": 16.748509883880615, "input_args": {"n_estimators": "111.60850447193953", "eta": "0.12792776902235276", "max_depth": "9.120408127066865", "subsample": "0.8369708911051055", "colsample_bytree": "0.9325044568707964"}}
+{"duration": 3.7734007835388184, "input_args": {"n_estimators": "111.60850447193953", "eta": "0.12792776902235276", "max_depth": "9.120408127066865", "subsample": "0.8369708911051055", "colsample_bytree": "0.9325044568707964"}}

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

@@ -1 +1 @@
-{"duration": 24.637609004974365, "input_args": {"n_estimators": "190.03267976439997", "eta": "0.09263103092182289", "max_depth": "6.390309557911677", "subsample": "0.9931264066149119", "colsample_bytree": "0.9215089703802877"}}
+{"duration": 2.851027011871338, "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": 13.197858095169067, "input_args": {"n_estimators": "76.44055944226989", "eta": "0.08487346516301046", "max_depth": "7.752867891211309", "subsample": "0.8912139968434072", "colsample_bytree": "0.9223705789444759"}}
+{"duration": 1.353074073791504, "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": 3.0423359870910645, "input_args": {"n_estimators": "9.956729715098561", "eta": "0.18068320734549853", "max_depth": "8.565246110151298", "subsample": "0.821578285398661", "colsample_bytree": "0.8987591192728782"}}
+{"duration": 1.3453011512756348, "input_args": {"n_estimators": "9.956729715098561", "eta": "0.18068320734549853", "max_depth": "8.565246110151298", "subsample": "0.821578285398661", "colsample_bytree": "0.8987591192728782"}}

cachedir/joblib/run/estimator/eefbc848db1507e48416e389ebd02837/metadata.json

@@ -0,0 +1 @@
+{"duration": 5.720117807388306, "input_args": {"n_estimators": "188.7569839959147", "eta": "0.2593099998788648", "max_depth": "6.400782463573032", "subsample": "0.9972633684544026", "colsample_bytree": "0.8702434039276976"}}

cachedir/joblib/run/estimator/ef6bb9df399c46bfde63ecf0478c6582/metadata.json

@@ -1 +0,0 @@
-{"duration": 17.00960898399353, "input_args": {"n_estimators": "73.50613754427229", "eta": "0.05", "max_depth": "9.336611726304309", "subsample": "1.0", "colsample_bytree": "0.8894830486960281"}}

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

@@ -1 +1 @@
-{"duration": 5.744019031524658, "input_args": {"n_estimators": "19.53737551755531", "eta": "0.08523105624369066", "max_depth": "10.813181884524237", "subsample": "0.9973773873201035", "colsample_bytree": "0.9085392166316497"}}
+{"duration": 0.5371649265289307, "input_args": {"n_estimators": "19.53737551755531", "eta": "0.08523105624369066", "max_depth": "10.813181884524237", "subsample": "0.9973773873201035", "colsample_bytree": "0.9085392166316497"}}

cachedir/joblib/run/estimator/f4424e7e031a773cea1548224ffc9bc5/metadata.json

@@ -1 +0,0 @@
-{"duration": 12.92500615119934, "input_args": {"n_estimators": "78.05929830372413", "eta": "0.26819361491123006", "max_depth": "8.416776430272158", "subsample": "0.9282741702875538", "colsample_bytree": "0.8650884194092069"}}

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

@@ -1 +1 @@
-{"duration": 14.425058126449585, "input_args": {"n_estimators": "122.21742728992572", "eta": "0.06452090304204987", "max_depth": "11.197056874649611", "subsample": "0.941614515559209", "colsample_bytree": "0.8311989040672406"}}
+{"duration": 4.091923952102661, "input_args": {"n_estimators": "122.21742728992572", "eta": "0.06452090304204987", "max_depth": "11.197056874649611", "subsample": "0.941614515559209", "colsample_bytree": "0.8311989040672406"}}

cachedir/joblib/run/estimator/func_code.py

@@ -1,8 +1,8 @@
-# first line: 541
+# first line: 586
     @memory.cache
     def estimator(n_estimators, eta, max_depth, subsample, colsample_bytree):
         # initialize model
-        print('loopingVehicle')
+        print('loopingNew')
         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/b9f18063a956e60963835da31891e04a/metadata.json

@@ -0,0 +1 @@
+{"duration": 48.241634130477905, "input_args": {"Data": "        F1     F2  F3  F4     F5  F6  F7     F8      F9  F10  F11     F12   F13  F14  ...  F28  F29  F30    F31  F32  F33  F34    F35  F36    F37  F38  F39  F40  F41\n0    1.185  9.085   0   0  2.263   3   0  3.642   0.000    0    1   9.902  46.7    1  ...    0    2    0  0.000    1    0    0  0.014    0  4.054    0    0    0    0\n1    0.000  8.179   2   0  2.194   0   6  3.526   0.000    6    0  10.054  35.1    0  ...    0    0    0  0.000    2    0    0  0.000    0  3.489    0    4    0    0\n2    0.762  8.297   0   3  2.424   0   0  3.339  21.884    0    0  10.226  42.1    0  ...    0    0    8 -0.686    3    0    0  0.004    0  3.693    0    0    0    0\n3    1.747  9.673   0   2  2.690  23   0  4.645   9.855    0    1  12.353  31.6    0  ...    0    0    2 -4.617    0    0   11  0.000    0  3.993    0    0    0    1\n4    1.824  9.825   0   2  2.700  27   0  4.795   9.894    0    1  12.519  31.8    0  ...    0    0    2 -4.724    0    0   13  0.000    0  4.005    0    0    0    1\n..     ...    ...  ..  ..    ...  ..  ..    ...     ...  ...  ...     ...   ...  ...  ...  ...  ...  ...    ...  ...  ...  ...    ...  ...    ...  ...  ...  ...  ...\n832  1.125  7.878   0   1  2.136   0   0  2.990   0.000    0    0   9.311  43.8    0  ...    0    1    2  0.000    1    0    0  0.045    0  3.573    0    0    0    0\n833  1.187  8.046   1   1  2.222   0   2  3.105   0.000    2    0   9.668  38.9    0  ...    0    2    2  0.000    1    0    0 -0.025    0  3.666    0    2    0    0\n834  0.625  8.901   0   2  2.499   0   0  3.745  24.203    0    0  10.681  58.3    0  ...    0    0    8 -0.128    6    0    0  0.000    0  3.942    0    0    0    0\n835  3.866  8.778   0   6  2.361   0   0  4.201  11.747    0    1  10.735  32.4    0  ...    0    0    9 -0.347    1    0    0  0.000    0  3.497    0    0    0    0\n836  3.706  8.680   0   6  2.361   0   0  4.127  11.724    0    1  10.694  31.4    0  ...    0    0    9 -0.338    1    0    0  0.000    0  3.497    0    0    0    0\n\n[837 rows x 41 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.8702434039276976,\n              eta=0.2593099998788648, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.259310007, max_delta_step=0, max_depth=6,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=188, n_jobs=12, num_parallel_tree=1,\n              probability=True, random_state=42, reg_alpha=0, reg_lambda=1,\n              scale_pos_weight=1, silent=True, subsample=0.9972633684544026,\n              tree_method='exact', use_label_encoder=False,\n              validate_parameters=1, verbosity=0)"}}

frontend/src/components/Main.vue

@@ -477,7 +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)

run.py

@@ -312,14 +312,6 @@ def retrieveFileName():
         DataResultsRaw.append(item)
     DataRawLength = len(DataResultsRaw)
 
-    DataResultsRawTest = []
-    if (StanceTest):
-        for index, item in enumerate(CollectionDBTest):
-            item['_id'] = str(item['_id'])
-            item['InstanceID'] = index
-            DataResultsRawTest.append(item)
-        DataRawLengthTest = len(DataResultsRawTest)
-
     DataResultsRawTest = []
     DataResultsRawExternal = []
     if (StanceTest):
@@ -554,6 +546,8 @@ def dataSetSelection():
     OrignList = keepOriginalFeatures.columns.values.tolist()
 
     XData.columns = ['F'+str(idx+1) for idx, col in enumerate(XData.columns)]
+    XDataTest.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataTest.columns)]
+    XDataExternal.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataExternal.columns)]
 
     global XDataStored, yDataStored
     XDataStored = XData.copy()
@@ -586,7 +580,7 @@ def create_global_function():
     @memory.cache
     def estimator(n_estimators, eta, max_depth, subsample, colsample_bytree):
         # initialize model
-        print('loopingVehicle')
+        print('loopingNew')
         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)
@@ -793,7 +787,7 @@ def executeModel(exeCall, flagEx, nodeTransfName):
     num_cores = multiprocessing.cpu_count()
     inputsSc = ['accuracy','precision_weighted','recall_weighted']
 
-    print(XData)
+    #print(XData)
 
     flat_results = Parallel(n_jobs=num_cores)(delayed(solve)(estimator,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
     scoresAct = [item for sublist in flat_results for item in sublist]
@@ -831,19 +825,18 @@ def executeModel(exeCall, flagEx, nodeTransfName):
     else:
        scores.append(0)
 
+    #print(scores)
+
     global StanceTest
     if (StanceTest):
-        sclf.fit(XData, yData)
-        y_pred = sclf.predict(XDataTest)
+        y_pred = estimator.predict(XDataTest)
         print('Test data set')
         print(classification_report(yDataTest, y_pred))
 
-        y_pred = sclf.predict(XDataExternal)
+        y_pred = estimator.predict(XDataExternal)
         print('External data set')
         print(classification_report(yDataExternal, y_pred))
 
-    print(scores)
-
     return 'Everything Okay'
 
 @app.route('/data/RequestBestFeatures', methods=["GET", "POST"])
@@ -2044,7 +2037,7 @@ def CompareFunPy():
         columnsKeep.append(columnsKeepID[2]+'/'+columnsKeepID[1]+'/'+columnsKeepID[0])
     else:
         pass
-    print(XDataGen)
+    #print(XDataGen)
     XDataGen = XDataGen.replace([np.inf, -np.inf], np.nan)
     XDataGen = XDataGen.fillna(0)
     featureCompareData = estimatorFeatureSelection(XDataGen, estimator)