new

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

@@ -0,0 +1 @@
+{"duration": 9.244415044784546, "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

@@ -0,0 +1 @@
+{"duration": 6.655983924865723, "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

@@ -0,0 +1 @@
+{"duration": 6.171159029006958, "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

@@ -0,0 +1 @@
+{"duration": 1.049651861190796, "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

@@ -0,0 +1 @@
+{"duration": 4.3242270946502686, "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

@@ -0,0 +1 @@
+{"duration": 7.772159814834595, "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

@@ -0,0 +1 @@
+{"duration": 6.074524879455566, "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

@@ -0,0 +1 @@
+{"duration": 5.8916871547698975, "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

@@ -0,0 +1 @@
+{"duration": 2.6692090034484863, "input_args": {"n_estimators": "46.40612658226385", "eta": "0.2924774630404986", "max_depth": "10.99465584480253", "subsample": "0.8363649934414201", "colsample_bytree": "0.8041168988591605"}}

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

@@ -0,0 +1 @@
+{"duration": 6.29893684387207, "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

@@ -0,0 +1 @@
+{"duration": 8.566855907440186, "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

@@ -0,0 +1 @@
+{"duration": 3.1254148483276367, "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

@@ -0,0 +1 @@
+{"duration": 8.37943410873413, "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

@@ -0,0 +1 @@
+{"duration": 7.252876043319702, "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

@@ -0,0 +1 @@
+{"duration": 7.435333251953125, "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

@@ -0,0 +1 @@
+{"duration": 8.807473182678223, "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

@@ -0,0 +1 @@
+{"duration": 11.164775848388672, "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

@@ -0,0 +1 @@
+{"duration": 6.8578290939331055, "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

@@ -0,0 +1 @@
+{"duration": 8.713475227355957, "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

@@ -0,0 +1 @@
+{"duration": 4.456164121627808, "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

@@ -0,0 +1 @@
+{"duration": 0.946181058883667, "input_args": {"n_estimators": "9.956729715098561", "eta": "0.18068320734549853", "max_depth": "8.565246110151298", "subsample": "0.821578285398661", "colsample_bytree": "0.8987591192728782"}}

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

@@ -0,0 +1 @@
+{"duration": 7.827345132827759, "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

@@ -0,0 +1 @@
+{"duration": 2.4281439781188965, "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

@@ -0,0 +1 @@
+{"duration": 4.726137161254883, "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

@@ -0,0 +1 @@
+{"duration": 6.652954816818237, "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

@@ -0,0 +1,11 @@
+# first line: 524
+    @memory.cache
+    def estimator(n_estimators, eta, max_depth, subsample, colsample_bytree):
+        # initialize model
+        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)
+        # set in cross-validation
+        result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy')
+        # result is mean of test_score
+        return np.mean(result['test_score'])

cachedir/joblib/run/estimatorFeatureSelection/061f896909a89910a77d5c028ec2dbc5/metadata.json

@@ -0,0 +1 @@
+{"duration": 14.65022587776184, "input_args": {"Data": "           F3  F6_b   F3+F6_b  |F3-F6_b|   F3xF6_b   F3/F6_b    F6_b/F3\n0     0.99460     1   1.99460    0.00540   0.99460  0.994600   1.005429\n1     0.99680     1   1.99680    0.00320   0.99680  0.996800   1.003210\n2     0.99690    11  11.99690   10.00310  10.96590  0.090627  11.034206\n3     0.99680     6   6.99680    5.00320   5.98080  0.166133   6.019262\n4     0.99680     3   3.99680    2.00320   2.99040  0.332267   3.009631\n...       ...   ...       ...        ...       ...       ...        ...\n1594  0.99490     2   2.99490    1.00510   1.98980  0.497450   2.010252\n1595  0.99512     2   2.99512    1.00488   1.99024  0.497560   2.009808\n1596  0.99574     3   3.99574    2.00426   2.98722  0.331913   3.012835\n1597  0.99547     3   3.99547    2.00453   2.98641  0.331823   3.013652\n1598  0.99549     9   9.99549    8.00451   8.95941  0.110610   9.040774\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/07f66c2e206e1b52a9124c8774227ae2/metadata.json

@@ -0,0 +1 @@
+{"duration": 3.801353931427002, "input_args": {"Data": "      F1   F2       F3_em1   F4\n0    2.5  3.3   543.571910  6.0\n1    1.9  2.7   329.299560  5.1\n2    2.1  3.0  1210.967074  5.9\n3    1.8  2.9   543.571910  5.6\n4    2.2  3.0   664.141633  5.8\n..   ...  ...          ...  ...\n145  0.3  3.0   120.510418  1.4\n146  0.2  3.8   163.021907  1.6\n147  0.2  3.2    98.484316  1.4\n148  0.2  3.7   199.336810  1.5\n149  0.2  3.3   147.413159  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=0.9223705789444759,\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/083200d84a0a4d1fbb7b178419d0dc79/metadata.json

@@ -0,0 +1 @@
+{"duration": 18.034281015396118, "input_args": {"Data": "         F1_l2     F2       F3   F4     F5  F6_b   F7    F8     F9_l2   F10   F11   F3xF6_b\n0    -0.621488  0.065  0.99460  7.3   21.0     1  1.2  3.39 -1.089267  15.0  10.0   0.99460\n1    -0.785875  0.073  0.99680  7.8   18.0     1  2.0  3.36 -0.810966   9.0   9.5   0.99680\n2    -1.836501  0.092  0.99690  8.5  103.0    11  1.8  3.30 -0.415037  35.0  10.5  10.96590\n3    -1.395929  0.066  0.99680  8.1   30.0     6  2.1  3.23 -0.454032  13.0   9.7   5.98080\n4    -0.943416  0.085  0.99680  7.5   35.0     3  1.9  3.38 -0.689660  12.0   9.5   2.99040\n...        ...    ...      ...  ...    ...   ...  ...   ...       ...   ...   ...       ...\n1594 -0.736966  0.090  0.99490  6.2   44.0     2  2.0  3.45 -0.785875  32.0  10.5   1.98980\n1595 -0.862496  0.062  0.99512  5.9   51.0     2  2.2  3.52 -0.395929  39.0  11.2   1.99024\n1596 -0.971431  0.076  0.99574  6.3   40.0     3  2.3  3.42 -0.415037  29.0  11.0   2.98722\n1597 -0.632629  0.075  0.99547  5.9   44.0     3  2.0  3.57 -0.494109  32.0  10.2   2.98641\n1598 -1.689660  0.067  0.99549  6.0   42.0     9  3.6  3.39 -0.599462  18.0  11.0   8.95941\n\n[1599 rows x 12 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/10a4c33e4cbdc2169a509ee22bd63ae5/metadata.json

@@ -0,0 +1 @@
+{"duration": 15.355908155441284, "input_args": {"Data": "         F1_l2     F2       F3   F4     F5  F6_b   F7    F8     F9_l2   F10   F11\n0    -0.621488  0.065  0.99460  7.3   21.0     1  1.2  3.39 -1.089267  15.0  10.0\n1    -0.785875  0.073  0.99680  7.8   18.0     1  2.0  3.36 -0.810966   9.0   9.5\n2    -1.836501  0.092  0.99690  8.5  103.0    11  1.8  3.30 -0.415037  35.0  10.5\n3    -1.395929  0.066  0.99680  8.1   30.0     6  2.1  3.23 -0.454032  13.0   9.7\n4    -0.943416  0.085  0.99680  7.5   35.0     3  1.9  3.38 -0.689660  12.0   9.5\n...        ...    ...      ...  ...    ...   ...  ...   ...       ...   ...   ...\n1594 -0.736966  0.090  0.99490  6.2   44.0     2  2.0  3.45 -0.785875  32.0  10.5\n1595 -0.862496  0.062  0.99512  5.9   51.0     2  2.2  3.52 -0.395929  39.0  11.2\n1596 -0.971431  0.076  0.99574  6.3   40.0     3  2.3  3.42 -0.415037  29.0  11.0\n1597 -0.632629  0.075  0.99547  5.9   44.0     3  2.0  3.57 -0.494109  32.0  10.2\n1598 -1.689660  0.067  0.99549  6.0   42.0     9  3.6  3.39 -0.599462  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/2bc932fe8a1e9f104d58df4b3b8a39ce/metadata.json

@@ -0,0 +1 @@
+{"duration": 21.434664964675903, "input_args": {"Data": "         F1     F2       F3   F4     F5  F6_b   F7    F8    F9   F10   F11   F3/F6_b\n0     0.650  0.065  0.99460  7.3   21.0     1  1.2  3.39  0.47  15.0  10.0  0.994600\n1     0.580  0.073  0.99680  7.8   18.0     1  2.0  3.36  0.57   9.0   9.5  0.996800\n2     0.280  0.092  0.99690  8.5  103.0    11  1.8  3.30  0.75  35.0  10.5  0.090627\n3     0.380  0.066  0.99680  8.1   30.0     6  2.1  3.23  0.73  13.0   9.7  0.166133\n4     0.520  0.085  0.99680  7.5   35.0     3  1.9  3.38  0.62  12.0   9.5  0.332267\n...     ...    ...      ...  ...    ...   ...  ...   ...   ...   ...   ...       ...\n1594  0.600  0.090  0.99490  6.2   44.0     2  2.0  3.45  0.58  32.0  10.5  0.497450\n1595  0.550  0.062  0.99512  5.9   51.0     2  2.2  3.52  0.76  39.0  11.2  0.497560\n1596  0.510  0.076  0.99574  6.3   40.0     3  2.3  3.42  0.75  29.0  11.0  0.331913\n1597  0.645  0.075  0.99547  5.9   44.0     3  2.0  3.57  0.71  32.0  10.2  0.331823\n1598  0.310  0.067  0.99549  6.0   42.0     9  3.6  3.39  0.66  18.0  11.0  0.110610\n\n[1599 rows x 12 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/323bcced5d24b6fd133968cfa915f141/metadata.json

@@ -0,0 +1 @@
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cachedir/joblib/run/estimatorFeatureSelection/336149011b41011f5eace5e3c35a4d11/metadata.json

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cachedir/joblib/run/estimatorFeatureSelection/38078c3a7f5180a8959621fb9dfbf197/metadata.json

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

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cachedir/joblib/run/estimatorFeatureSelection/5fa740243b273457bc3baef8701c08b7/metadata.json

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cachedir/joblib/run/estimatorFeatureSelection/a9b24b0486a77e3af10405b43d597233/metadata.json

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cachedir/joblib/run/estimatorFeatureSelection/accf67d0f037a80983901bb4a984b31a/metadata.json

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cachedir/joblib/run/estimatorFeatureSelection/bc724c850afc489e801bdeb1e2860f13/metadata.json

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