{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8e44e75",
   "metadata": {},
   "source": [
    "# Support Vector Machine (SVM) Predictive Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c467f8df",
   "metadata": {},
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "030f5163",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import (\n",
    "    roc_auc_score, recall_score, precision_score, accuracy_score, \n",
    "    confusion_matrix, fbeta_score, precision_recall_curve, roc_curve,\n",
    "    balanced_accuracy_score, average_precision_score\n",
    ")\n",
    "from sklearn.utils import resample\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ba3077",
   "metadata": {},
   "source": [
    "Load tiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "babee804",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tier1: 6 features\n",
      "tier2: 10 features\n",
      "tier3: 24 features\n"
     ]
    }
   ],
   "source": [
    "tier1 = pd.read_csv(\"./tiers/tier1_basic.csv\")[\"feature\"].tolist()\n",
    "tier2 = pd.read_csv(\"./tiers/tier2_clinical.csv\")[\"feature\"].tolist()\n",
    "tier3 = pd.read_csv(\"./tiers/tier3_personalized.csv\")[\"feature\"].tolist()\n",
    "\n",
    "tiers = {\n",
    "    \"Tier_1_Basic\": tier1,\n",
    "    \"Tier_2_Clinical\": tier2,\n",
    "    \"Tier_3_Personalized\": tier3,\n",
    "}\n",
    "\n",
    "print(f'tier1: {len(tier1)} features')\n",
    "print(f'tier2: {len(tier2)} features')\n",
    "print(f'tier3: {len(tier3)} features')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa8e944",
   "metadata": {},
   "source": [
    "Load dataset, drop weight from X, but SAVE w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d728bf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total samples: 45133\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle(\"./data/cleaned/NSDUH_2023_clean.pkl\")\n",
    "\n",
    "TARGET = \"IRAMDEYR\"\n",
    "WEIGHT = \"ANALWT2_C\"\n",
    "\n",
    "y = df[TARGET]\n",
    "w = df[WEIGHT]\n",
    "X = df.drop(columns=[TARGET, WEIGHT])\n",
    "\n",
    "print(f'Total samples: {X.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a84949a",
   "metadata": {},
   "source": [
    "Split train/test (consistent with feature selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "739bca76",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(\n",
    "    X, y, w,\n",
    "    test_size=0.2,\n",
    "    stratify=y,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d4bb0e",
   "metadata": {},
   "source": [
    "Define metric helper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75f72c77",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(y_true, preds, probs):\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, preds).ravel()\n",
    "\n",
    "    specificity = tn / (tn + fp)\n",
    "    sensitivity = recall_score(y_true, preds)  # same as recall\n",
    "    ppv = precision_score(y_true, preds)\n",
    "    npv = tn / (tn + fn)\n",
    "\n",
    "    return {\n",
    "        \"Accuracy\": accuracy_score(y_true, preds),\n",
    "        # new (sensisty + specificity) / 2 -> it averages performance on both classes qually\n",
    "        \"Balanced_Accuracy\": balanced_accuracy_score(y_true, preds),  # <-- NEW\n",
    "        \"Sensitivity\": sensitivity,\n",
    "        \"Specificity\": specificity,\n",
    "        \"PPV\": ppv,\n",
    "        \"NPV\": npv,\n",
    "        \"ROC-AUC\": roc_auc_score(y_true, probs),\n",
    "        # new -> with an imblanced dataset, ROC-AUC can be misleading when model has a lot of false positives. More\n",
    "        # Applicable for imbalanced datasets where positive class is rare.\n",
    "        \"PR_AUC\": average_precision_score(y_true, probs),  # <-- NEW\n",
    "        \"F1\": fbeta_score(y_true, preds, beta=1),\n",
    "        \"F2\": fbeta_score(y_true, preds, beta=2),\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "391c4136",
   "metadata": {},
   "source": [
    "SVM Pipeline w/ Weighted Fitting + GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "966b088c",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    \"clf__C\": [0.01, 0.1, 1],              # Regularization (smaller = stronger)\n",
    "    \"clf__kernel\": [\"rbf\"],          # Kernel type\n",
    "    \"clf__gamma\": [\"scale\", \"auto\", 0.1], # RBF kernel coefficient\n",
    "    \"clf__class_weight\": [None, \"balanced\"],   # Class imbalance handling\n",
    "}\n",
    "\n",
    "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# RandomizedSearchCV\n",
    "def build_model_random():\n",
    "    pipe = Pipeline([\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "        (\"scaler\", StandardScaler()),  # CRITICAL for SVM\n",
    "        (\"clf\", SVC(\n",
    "            random_state=42,\n",
    "            probability=True  # Required for predict_proba\n",
    "        ))\n",
    "    ])\n",
    "\n",
    "    model = RandomizedSearchCV(\n",
    "        estimator=pipe,\n",
    "        param_distributions=param_grid,\n",
    "        n_iter=20, \n",
    "        scoring=\"recall\",\n",
    "        cv=cv,\n",
    "        n_jobs=-1,\n",
    "        random_state=42,\n",
    "        verbose=2\n",
    "    )\n",
    "\n",
    "    return model\n",
    "\n",
    "# GridSearchCV \n",
    "def build_model_grid():\n",
    "    pipe = Pipeline([\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "        (\"scaler\", StandardScaler()),  # CRITICAL for SVM\n",
    "        (\"clf\", SVC(\n",
    "            random_state=42,\n",
    "            probability=True  # Required for predict_proba\n",
    "        ))\n",
    "    ])\n",
    "    \n",
    "    model = GridSearchCV(\n",
    "        estimator=pipe,\n",
    "        param_grid=param_grid,\n",
    "        scoring=\"recall\",\n",
    "        cv=cv,\n",
    "        n_jobs=-1,\n",
    "        verbose=2\n",
    "    )\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff87b9f7",
   "metadata": {},
   "source": [
    "Bootstrapped Confidence Intervals (ROC + PR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0517b4e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_auc_ci(model, X, y, n_boot=1000):\n",
    "    aucs = []\n",
    "\n",
    "    for _ in range(n_boot):\n",
    "        X_bs, y_bs = resample(X, y, replace=True)\n",
    "        probs = model.predict_proba(X_bs)[:, 1]\n",
    "        aucs.append(roc_auc_score(y_bs, probs))\n",
    "\n",
    "    return np.percentile(aucs, [2.5, 97.5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84eeafd",
   "metadata": {},
   "source": [
    "Baseline Model (Dummy Classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a8839f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\agila\\Documents\\STM\\Predicting-Past-Year-Major-Depressive-Episode\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n"
     ]
    }
   ],
   "source": [
    "# dummy = DummyClassifier(strategy=\"most_frequent\")\n",
    "# dummy.fit(X_train, y_train)\n",
    "# dummy_preds = dummy.predict(X_test)\n",
    "# dummy_probs = np.zeros_like(dummy_preds)\n",
    "\n",
    "# dummy_metrics = evaluate_model(y_test, dummy_preds, dummy_probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7515b795",
   "metadata": {},
   "source": [
    "Train + Evaluate XGBoost on Each tier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1cfd0e26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----- Training SVM (RandomizedSearchCV) on Tier_1_Basic (6 features) -----\n",
      "Fitting 5 folds for each of 18 candidates, totalling 90 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/manav/miniconda3/envs/env-nsduh/lib/python3.13/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 18 is smaller than n_iter=20. Running 18 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 16\u001b[39m\n\u001b[32m     13\u001b[39m model = build_model_random()\n\u001b[32m     15\u001b[39m \u001b[38;5;66;03m# Fit WITH survey weights\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m model.fit(Xtr, y_train, clf__sample_weight=w_train)\n\u001b[32m     18\u001b[39m preds = model.predict(Xte)\n\u001b[32m     19\u001b[39m probs = model.predict_proba(Xte)[:, \u001b[32m1\u001b[39m]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/env-nsduh/lib/python3.13/site-packages/sklearn/base.py:1336\u001b[39m, in \u001b[36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[39m\u001b[34m(estimator, *args, **kwargs)\u001b[39m\n\u001b[32m   1329\u001b[39m     estimator._validate_params()\n\u001b[32m   1331\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[32m   1332\u001b[39m     skip_parameter_validation=(\n\u001b[32m   1333\u001b[39m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[32m   1334\u001b[39m     )\n\u001b[32m   1335\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1336\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, *args, **kwargs)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/env-nsduh/lib/python3.13/site-packages/sklearn/model_selection/_search.py:1053\u001b[39m, in \u001b[36mBaseSearchCV.fit\u001b[39m\u001b[34m(self, X, y, **params)\u001b[39m\n\u001b[32m   1047\u001b[39m     results = \u001b[38;5;28mself\u001b[39m._format_results(\n\u001b[32m   1048\u001b[39m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[32m   1049\u001b[39m     )\n\u001b[32m   1051\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[32m-> \u001b[39m\u001b[32m1053\u001b[39m \u001b[38;5;28mself\u001b[39m._run_search(evaluate_candidates)\n\u001b[32m   1055\u001b[39m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[32m   1056\u001b[39m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[32m   1057\u001b[39m first_test_score = all_out[\u001b[32m0\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mtest_scores\u001b[39m\u001b[33m\"\u001b[39m]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/env-nsduh/lib/python3.13/site-packages/sklearn/model_selection/_search.py:2002\u001b[39m, in \u001b[36mRandomizedSearchCV._run_search\u001b[39m\u001b[34m(self, evaluate_candidates)\u001b[39m\n\u001b[32m   2000\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[32m   2001\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Search n_iter candidates from param_distributions\"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2002\u001b[39m     evaluate_candidates(\n\u001b[32m   2003\u001b[39m         ParameterSampler(\n\u001b[32m   2004\u001b[39m             \u001b[38;5;28mself\u001b[39m.param_distributions, \u001b[38;5;28mself\u001b[39m.n_iter, random_state=\u001b[38;5;28mself\u001b[39m.random_state\n\u001b[32m   2005\u001b[39m         )\n\u001b[32m   2006\u001b[39m     )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/env-nsduh/lib/python3.13/site-packages/sklearn/model_selection/_search.py:999\u001b[39m, in \u001b[36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[39m\u001b[34m(candidate_params, cv, more_results)\u001b[39m\n\u001b[32m    991\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.verbose > \u001b[32m0\u001b[39m:\n\u001b[32m    992\u001b[39m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[32m    993\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mFitting \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[33m folds for each of \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[33m candidates,\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    994\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33m totalling \u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[33m fits\u001b[39m\u001b[33m\"\u001b[39m.format(\n\u001b[32m    995\u001b[39m             n_splits, n_candidates, n_candidates * n_splits\n\u001b[32m    996\u001b[39m         )\n\u001b[32m    997\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m999\u001b[39m out = parallel(\n\u001b[32m   1000\u001b[39m     delayed(_fit_and_score)(\n\u001b[32m   1001\u001b[39m         clone(base_estimator),\n\u001b[32m   1002\u001b[39m         X,\n\u001b[32m   1003\u001b[39m         y,\n\u001b[32m   1004\u001b[39m         train=train,\n\u001b[32m   1005\u001b[39m         test=test,\n\u001b[32m   1006\u001b[39m         parameters=parameters,\n\u001b[32m   1007\u001b[39m         split_progress=(split_idx, n_splits),\n\u001b[32m   1008\u001b[39m         candidate_progress=(cand_idx, n_candidates),\n\u001b[32m   1009\u001b[39m         **fit_and_score_kwargs,\n\u001b[32m   1010\u001b[39m     )\n\u001b[32m   1011\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m (cand_idx, parameters), (split_idx, (train, test)) \u001b[38;5;129;01min\u001b[39;00m product(\n\u001b[32m   1012\u001b[39m         \u001b[38;5;28menumerate\u001b[39m(candidate_params),\n\u001b[32m   1013\u001b[39m         \u001b[38;5;28menumerate\u001b[39m(cv.split(X, y, **routed_params.splitter.split)),\n\u001b[32m   1014\u001b[39m     )\n\u001b[32m   1015\u001b[39m )\n\u001b[32m   1017\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) < \u001b[32m1\u001b[39m:\n\u001b[32m   1018\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m   1019\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mNo fits were performed. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1020\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mWas the CV iterator empty? \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1021\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mWere there no candidates?\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1022\u001b[39m     )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/env-nsduh/lib/python3.13/site-packages/sklearn/utils/parallel.py:91\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m     79\u001b[39m warning_filters = (\n\u001b[32m     80\u001b[39m     filters_func() \u001b[38;5;28;01mif\u001b[39;00m filters_func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m warnings.filters\n\u001b[32m     81\u001b[39m )\n\u001b[32m     83\u001b[39m iterable_with_config_and_warning_filters = (\n\u001b[32m     84\u001b[39m     (\n\u001b[32m     85\u001b[39m         _with_config_and_warning_filters(delayed_func, config, warning_filters),\n\u001b[32m   (...)\u001b[39m\u001b[32m     89\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[32m     90\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m91\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__call__\u001b[39m(iterable_with_config_and_warning_filters)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/env-nsduh/lib/python3.13/site-packages/joblib/parallel.py:2072\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m   2066\u001b[39m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[32m   2067\u001b[39m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[32m   2068\u001b[39m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[32m   2069\u001b[39m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[32m   2070\u001b[39m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[32m-> \u001b[39m\u001b[32m2072\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(output)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/env-nsduh/lib/python3.13/site-packages/joblib/parallel.py:1682\u001b[39m, in \u001b[36mParallel._get_outputs\u001b[39m\u001b[34m(self, iterator, pre_dispatch)\u001b[39m\n\u001b[32m   1679\u001b[39m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m   1681\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.retrieval_context():\n\u001b[32m-> \u001b[39m\u001b[32m1682\u001b[39m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._retrieve()\n\u001b[32m   1684\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[32m   1685\u001b[39m     \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[32m   1686\u001b[39m     \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[32m   1687\u001b[39m     \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[32m   1688\u001b[39m     \u001b[38;5;28mself\u001b[39m._exception = \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/env-nsduh/lib/python3.13/site-packages/joblib/parallel.py:1800\u001b[39m, in \u001b[36mParallel._retrieve\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1789\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_ordered:\n\u001b[32m   1790\u001b[39m     \u001b[38;5;66;03m# Case ordered: wait for completion (or error) of the next job\u001b[39;00m\n\u001b[32m   1791\u001b[39m     \u001b[38;5;66;03m# that have been dispatched and not retrieved yet. If no job\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1795\u001b[39m     \u001b[38;5;66;03m# control only have to be done on the amount of time the next\u001b[39;00m\n\u001b[32m   1796\u001b[39m     \u001b[38;5;66;03m# dispatched job is pending.\u001b[39;00m\n\u001b[32m   1797\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m (nb_jobs == \u001b[32m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   1798\u001b[39m         \u001b[38;5;28mself\u001b[39m._jobs[\u001b[32m0\u001b[39m].get_status(timeout=\u001b[38;5;28mself\u001b[39m.timeout) == TASK_PENDING\n\u001b[32m   1799\u001b[39m     ):\n\u001b[32m-> \u001b[39m\u001b[32m1800\u001b[39m         time.sleep(\u001b[32m0.01\u001b[39m)\n\u001b[32m   1801\u001b[39m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m   1803\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m nb_jobs == \u001b[32m0\u001b[39m:\n\u001b[32m   1804\u001b[39m     \u001b[38;5;66;03m# Case unordered: jobs are added to the list of jobs to\u001b[39;00m\n\u001b[32m   1805\u001b[39m     \u001b[38;5;66;03m# retrieve `self._jobs` only once completed or in error, which\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1811\u001b[39m     \u001b[38;5;66;03m# timeouts before any other dispatched job has completed and\u001b[39;00m\n\u001b[32m   1812\u001b[39m     \u001b[38;5;66;03m# been added to `self._jobs` to be retrieved.\u001b[39;00m\n",
      "\u001b[31mKeyboardInterrupt\u001b[39m: "
     ]
    }
   ],
   "source": [
    "results_random = []\n",
    "curves_random = {}\n",
    "\n",
    "for tier_name, features in tiers.items():\n",
    "\n",
    "    print(f\"\\n----- Training SVM (RandomizedSearchCV) on {tier_name} ({len(features)} features) -----\")\n",
    "    \n",
    "    start_time = time.time()\n",
    "\n",
    "    Xtr = X_train[features]\n",
    "    Xte = X_test[features]\n",
    "\n",
    "    model = build_model_random()\n",
    "\n",
    "    # Fit WITH survey weights\n",
    "    model.fit(Xtr, y_train, clf__sample_weight=w_train)\n",
    "\n",
    "    preds = model.predict(Xte)\n",
    "    probs = model.predict_proba(Xte)[:, 1]\n",
    "\n",
    "    metrics = evaluate_model(y_test, preds, probs)\n",
    "\n",
    "    # Bootstrapped CI\n",
    "    ci_low, ci_high = bootstrap_auc_ci(model, Xte, y_test)\n",
    "\n",
    "    metrics[\"Tier\"] = tier_name\n",
    "    metrics[\"AUC_CI_Low\"] = ci_low\n",
    "    metrics[\"AUC_CI_High\"] = ci_high\n",
    "    metrics[\"Best_Params\"] = model.best_params_\n",
    "\n",
    "    results_random.append(metrics)\n",
    "    \n",
    "    # Store curve data for plotting later\n",
    "    curves_random[tier_name] = {\"y_true\": y_test, \"y_prob\": probs}\n",
    "    \n",
    "    print(f\"Best params: {model.best_params_}\")\n",
    "    print(f\"Completed in {time.time() - start_time:.1f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83b9aad4",
   "metadata": {},
   "source": [
    "Convert to DataFrame + Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46eefea0",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'results_random' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m results_random_df = pd.DataFrame(results_random)\n\u001b[32m      2\u001b[39m results_random_df.to_csv(\u001b[33m\"\u001b[39m\u001b[33m./results/svm_random_tier_performance.csv\u001b[39m\u001b[33m\"\u001b[39m, index=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m      4\u001b[39m \u001b[38;5;28mprint\u001b[39m(results_random_df)\n",
      "\u001b[31mNameError\u001b[39m: name 'results_random' is not defined"
     ]
    }
   ],
   "source": [
    "results_random_df = pd.DataFrame(results_random)\n",
    "results_random_df.to_csv(\"./results/svm_random_tier_performance.csv\", index=False)\n",
    "\n",
    "print(results_random_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41710c6e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'curves_random' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# Plot and save ROC Curve\u001b[39;00m\n\u001b[32m      2\u001b[39m plt.figure(figsize=(\u001b[32m8\u001b[39m, \u001b[32m6\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m tier_name, curve_data \u001b[38;5;129;01min\u001b[39;00m curves_random.items():\n\u001b[32m      4\u001b[39m     fpr, tpr, _ = roc_curve(curve_data[\u001b[33m\"\u001b[39m\u001b[33my_true\u001b[39m\u001b[33m\"\u001b[39m], curve_data[\u001b[33m\"\u001b[39m\u001b[33my_prob\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m      5\u001b[39m     auc_score = results_random_df[results_random_df[\u001b[33m\"\u001b[39m\u001b[33mTier\u001b[39m\u001b[33m\"\u001b[39m] == tier_name][\u001b[33m\"\u001b[39m\u001b[33mROC-AUC\u001b[39m\u001b[33m\"\u001b[39m].values[\u001b[32m0\u001b[39m]\n",
      "\u001b[31mNameError\u001b[39m: name 'curves_random' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot and save ROC Curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "for tier_name, curve_data in curves_random.items():\n",
    "    fpr, tpr, _ = roc_curve(curve_data[\"y_true\"], curve_data[\"y_prob\"])\n",
    "    auc_score = results_random_df[results_random_df[\"Tier\"] == tier_name][\"ROC-AUC\"].values[0]\n",
    "    plt.plot(fpr, tpr, label=f\"{tier_name} (AUC={auc_score:.3f})\")\n",
    "plt.plot([0, 1], [0, 1], 'k--', label=\"Random\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC Curve - SVM (RandomizedSearchCV)\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./results/figures/svm_random_roc_curve.png\", dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# Plot and save Precision-Recall Curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "for tier_name, curve_data in curves_random.items():\n",
    "    precision, recall, _ = precision_recall_curve(curve_data[\"y_true\"], curve_data[\"y_prob\"])\n",
    "    pr_auc = results_random_df[results_random_df[\"Tier\"] == tier_name][\"PR_AUC\"].values[0]\n",
    "    plt.plot(recall, precision, label=f\"{tier_name} (PR-AUC={pr_auc:.3f})\")\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.title(\"Precision-Recall Curve - SVM (RandomizedSearchCV)\")\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./results/figures/svm_random_pr_curve.png\", dpi=300)\n",
    "plt.show()\n",
    "\n",
    "print(\"Curves saved to ./results/figures/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1d088b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----- Training XGBoost (GridSearchCV) on Tier_1_Basic (6 features) -----\n",
      "Fitting 5 folds for each of 144 candidates, totalling 720 fits\n",
      "Best params: {'clf__learning_rate': 0.05, 'clf__max_depth': 3, 'clf__n_estimators': 50, 'clf__scale_pos_weight': 9, 'clf__subsample': 0.8}\n",
      "Completed in 19.4 seconds\n",
      "\n",
      "----- Training XGBoost (GridSearchCV) on Tier_2_Clinical (10 features) -----\n",
      "Fitting 5 folds for each of 144 candidates, totalling 720 fits\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.7s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.8s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.8s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.7s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.8s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.8s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.7s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3sBest params: {'clf__learning_rate': 0.05, 'clf__max_depth': 3, 'clf__n_estimators': 200, 'clf__scale_pos_weight': 9, 'clf__subsample': 1.0}\n",
      "Completed in 25.1 seconds\n",
      "\n",
      "----- Training XGBoost (GridSearchCV) on Tier_3_Personalized (24 features) -----\n",
      "Fitting 5 folds for each of 144 candidates, totalling 720 fits\n",
      "\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.8s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.7s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.7s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.8s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.7s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.8s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.7s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   1.0s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.9s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.5s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.7s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.7s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.1, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.1s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=5, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.4s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.6s\n",
      "[CV] END clf__learning_rate=0.01, clf__max_depth=7, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.9s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=0.8; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=150, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=3, clf__n_estimators=200, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=50, clf__scale_pos_weight=9, clf__subsample=0.8; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=1, clf__subsample=1.0; total time=   0.2s\n",
      "[CV] END clf__learning_rate=0.05, clf__max_depth=5, clf__n_estimators=100, clf__scale_pos_weight=9, clf__subsample=1.0; total time=   0.3sBest params: {'clf__learning_rate': 0.01, 'clf__max_depth': 3, 'clf__n_estimators': 200, 'clf__scale_pos_weight': 9, 'clf__subsample': 1.0}\n",
      "Completed in 36.8 seconds\n"
     ]
    }
   ],
   "source": [
    "results_grid = []\n",
    "curves_grid = {}\n",
    "\n",
    "for tier_name, features in tiers.items():\n",
    "\n",
    "    print(f\"\\n----- Training SVM (GridSearchCV) on {tier_name} ({len(features)} features) -----\")\n",
    "    \n",
    "    start_time = time.time()\n",
    "\n",
    "    Xtr = X_train[features]\n",
    "    Xte = X_test[features]\n",
    "\n",
    "    model = build_model_grid()\n",
    "\n",
    "    # Fit WITH survey weights\n",
    "    model.fit(Xtr, y_train, clf__sample_weight=w_train)\n",
    "\n",
    "    preds = model.predict(Xte)\n",
    "    probs = model.predict_proba(Xte)[:, 1]\n",
    "\n",
    "    metrics = evaluate_model(y_test, preds, probs)\n",
    "\n",
    "    # Bootstrapped CI\n",
    "    ci_low, ci_high = bootstrap_auc_ci(model, Xte, y_test)\n",
    "\n",
    "    metrics[\"Tier\"] = tier_name\n",
    "    metrics[\"AUC_CI_Low\"] = ci_low\n",
    "    metrics[\"AUC_CI_High\"] = ci_high\n",
    "    metrics[\"Best_Params\"] = model.best_params_\n",
    "\n",
    "    results_grid.append(metrics)\n",
    "    \n",
    "    # Store curve data for plotting later\n",
    "    curves_grid[tier_name] = {\"y_true\": y_test, \"y_prob\": probs}\n",
    "    \n",
    "    print(f\"Best params: {model.best_params_}\")\n",
    "    print(f\"Completed in {time.time() - start_time:.1f} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f43d963a",
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Accuracy  Balanced_Accuracy  Sensitivity  Specificity       PPV       NPV  \\\n",
      "0  0.565526           0.644218     0.746384     0.542053  0.174600  0.942751   \n",
      "1  0.872272           0.829246     0.773385     0.885106  0.466279  0.967839   \n",
      "2  0.930209           0.933722     0.938284     0.929161  0.632229  0.991453   \n",
      "\n",
      "    ROC-AUC    PR_AUC        F1        F2                 Tier  AUC_CI_Low  \\\n",
      "0  0.708552  0.250706  0.282998  0.450996         Tier_1_Basic    0.693359   \n",
      "1  0.887454  0.652660  0.581792  0.683367      Tier_2_Clinical    0.875411   \n",
      "2  0.985319  0.935596  0.755435  0.855460  Tier_3_Personalized    0.981958   \n",
      "\n",
      "   AUC_CI_High                                        Best_Params  \n",
      "0     0.724156  {'clf__learning_rate': 0.05, 'clf__max_depth':...  \n",
      "1     0.898878  {'clf__learning_rate': 0.05, 'clf__max_depth':...  \n",
      "2     0.988611  {'clf__learning_rate': 0.01, 'clf__max_depth':...  \n"
     ]
    }
   ],
   "source": [
    "results_grid_df = pd.DataFrame(results_grid)\n",
    "results_grid_df.to_csv(\"./results/svm_grid_tier_performance.csv\", index=False)\n",
    "\n",
    "print(results_grid_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e5f1f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Curves saved to ./results/figures/\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "for tier_name, curve_data in curves_grid.items():\n",
    "    fpr, tpr, _ = roc_curve(curve_data[\"y_true\"], curve_data[\"y_prob\"])\n",
    "    auc_score = results_grid_df[results_grid_df[\"Tier\"] == tier_name][\"ROC-AUC\"].values[0]\n",
    "    plt.plot(fpr, tpr, label=f\"{tier_name} (AUC={auc_score:.3f})\")\n",
    "plt.plot([0, 1], [0, 1], 'k--', label=\"Random\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.title(\"ROC Curve - SVM (GridSearchCV)\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./results/figures/svm_grid_roc_curve.png\", dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# Plot and save Precision-Recall Curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "for tier_name, curve_data in curves_grid.items():\n",
    "    precision, recall, _ = precision_recall_curve(curve_data[\"y_true\"], curve_data[\"y_prob\"])\n",
    "    pr_auc = results_grid_df[results_grid_df[\"Tier\"] == tier_name][\"PR_AUC\"].values[0]\n",
    "    plt.plot(recall, precision, label=f\"{tier_name} (PR-AUC={pr_auc:.3f})\")\n",
    "plt.xlabel(\"Recall\")\n",
    "plt.ylabel(\"Precision\")\n",
    "plt.title(\"Precision-Recall Curve - SVM (GridSearchCV)\")\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"./results/figures/svm_grid_pr_curve.png\", dpi=300)\n",
    "plt.show()\n",
    "\n",
    "print(\"Curves saved to ./results/figures/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d37c15a2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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