xgb_trainer/xgb_trainer.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost trainer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook function handles training and logging of xgboost models **only**, exposing both the sklearn and low level api\"s.<br>\n",
    "More information about XGBoost - [here](https://en.wikipedia.org/wiki/XGBoost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple model types that can be estimated using the XGBoost Scikit-Learn API. <br>\n",
    "Input can either be a predefined json model configuration or one\n",
    "of the five xgboost model types. <br>\n",
    "In either case one can pass in a params dict to modify defaults values.<br>\n",
    "Based on `mlutils.models.gen_sklearn_model`, see the function\n",
    "`sklearn_classifier` in the function-marketplace repository. <br>\n",
    "> **param model_type:**\n",
    "  one of \"classifier\", \"regressor\",\n",
    "  \"ranker\", \"rf_classifier\", or\n",
    "  \"rf_regressor\"<br>\n",
    "> **param xgb_params:** class init parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps\n",
    "1. [Data Exploration](#Data-Exploration)\n",
    "2. [Importing the function](#Importing-the-function)\n",
    "3. [Setup XGBoost parameters](#Setup-XGBoost-parameters)\n",
    "4. [Running the function locally](#Running-the-function-locally)\n",
    "5. [Getting the model](#Getting-the-model)\n",
    "6. [Some plotting](#Some-plotting)\n",
    "7. [Running the function remotely](#Running-the-function-remotely)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data Exploration**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate the dataset we used the \"gen_class_data\" function from the hub, \n",
    "which wraps scikit-learn's [make_classification](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html#sklearn-datasets-make-classification).<br> \n",
    "See the link for a description of all parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure proper xgboost version installed, uncomment to install\n",
    "# !pip install xgboost==1.3.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data set containing 10000 instances, with 2 labels.\n",
      "Number of instances labeled 1 : 5008\n",
      "Number of instances labeled 0 : 4992\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feat_0</th>\n",
       "      <th>feat_1</th>\n",
       "      <th>feat_2</th>\n",
       "      <th>feat_3</th>\n",
       "      <th>feat_4</th>\n",
       "      <th>labels</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.265115</td>\n",
       "      <td>-1.932260</td>\n",
       "      <td>0.303992</td>\n",
       "      <td>-1.863833</td>\n",
       "      <td>-1.045635</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.135479</td>\n",
       "      <td>-2.835548</td>\n",
       "      <td>1.338381</td>\n",
       "      <td>-1.385303</td>\n",
       "      <td>-2.276456</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.519005</td>\n",
       "      <td>-1.807549</td>\n",
       "      <td>0.697304</td>\n",
       "      <td>-1.118860</td>\n",
       "      <td>1.104900</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.632087</td>\n",
       "      <td>-0.345659</td>\n",
       "      <td>0.244329</td>\n",
       "      <td>-0.046066</td>\n",
       "      <td>0.447280</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.405883</td>\n",
       "      <td>-1.746045</td>\n",
       "      <td>0.653617</td>\n",
       "      <td>-1.110985</td>\n",
       "      <td>-1.675466</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "     feat_0    feat_1    feat_2    feat_3    feat_4  labels\n",
       "0 -0.265115 -1.932260  0.303992 -1.863833 -1.045635       1\n",
       "1 -3.135479 -2.835548  1.338381 -1.385303 -2.276456       0\n",
       "2 -1.519005 -1.807549  0.697304 -1.118860  1.104900       0\n",
       "3 -0.632087 -0.345659  0.244329 -0.046066  0.447280       0\n",
       "4 -1.405883 -1.746045  0.653617 -1.110985 -1.675466       0"
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     "execution_count": 3,
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   ],
   "source": [
    "# Getting the data from wasabi\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv')\n",
    "print(f'Data set containing {df.shape[0]} instances, with {len(df[\"labels\"].unique())} labels.')\n",
    "\n",
    "print(f\"Number of instances labeled {df['labels'].unique()[0]} : {df.groupby('labels').count()[df.columns[0]][df['labels'].unique()[0]]}\")\n",
    "print(f\"Number of instances labeled {df['labels'].unique()[1]} : {df.groupby('labels').count()[df.columns[0]][df['labels'].unique()[1]]}\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Importing the function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-13 10:10:21,588 [info] loaded project function-marketplace from MLRun DB\n"
     ]
    },
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     "execution_count": 4,
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    }
   ],
   "source": [
    "import mlrun\n",
    "mlrun.set_environment(project='function-marketplace')\n",
    "# If GPU is available - set to True\n",
    "GPU = False\n",
    "\n",
    "\n",
    "fn = mlrun.import_function(\"hub://xgb_trainer\")\n",
    "fn.image = \"mlrun/ml-models\" if not GPU else \"mlrun/ml-models-gpu\"\n",
    "fn.apply(mlrun.auto_mount())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Setup XGBoost parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_params = {\"model_type\": \"classifier\",\n",
    "               \"CLASS_tree_method\": \"hist\",\n",
    "               \"CLASS_objective\": \"binary:logistic\",\n",
    "               \"CLASS_booster\": \"gbtree\",\n",
    "               \"FIT_verbose\": 0,\n",
    "               \"label_column\": \"labels\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Running the function locally**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-13 10:10:21,807 [info] starting run xgb-trainer-train_model uid=5ec8a83eb65b46dc9b7f9dd654cd1b31 DB=http://mlrun-api:8080\n"
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       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>function-marketplace</td>\n",
       "      <td><div title=\"5ec8a83eb65b46dc9b7f9dd654cd1b31\"><a href=\"https://dashboard.default-tenant.app.app-lab-eks-testing.iguazio-cd1.com/mlprojects/function-marketplace/jobs/monitor/5ec8a83eb65b46dc9b7f9dd654cd1b31/overview\" target=\"_blank\" >...54cd1b31</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Oct 13 10:10:22</td>\n",
       "      <td>completed</td>\n",
       "      <td>xgb-trainer-train_model</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=dani</div><div class=\"dictlist\">kind=</div><div class=\"dictlist\">owner=dani</div><div class=\"dictlist\">host=jupyter-dani-5bbd9959b7-tsgh8</div></td>\n",
       "      <td><div title=\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv\">dataset</div></td>\n",
       "      <td><div class=\"dictlist\">model_type=classifier</div><div class=\"dictlist\">CLASS_tree_method=hist</div><div class=\"dictlist\">CLASS_objective=binary:logistic</div><div class=\"dictlist\">CLASS_booster=gbtree</div><div class=\"dictlist\">FIT_verbose=0</div><div class=\"dictlist\">label_column=labels</div></td>\n",
       "      <td><div class=\"dictlist\">accuracy=0.9552</div><div class=\"dictlist\">test-error=0.0448</div><div class=\"dictlist\">rocauc=0.9799618829687036</div><div class=\"dictlist\">brier_score=0.038984999293145965</div><div class=\"dictlist\">f1-score=0.954983922829582</div><div class=\"dictlist\">precision_score=0.965679190751445</div><div class=\"dictlist\">recall_score=0.9445229681978798</div></td>\n",
       "      <td><div title=\"v3io:///projects/function-marketplace/artifacts/test_set.parquet\">test_set</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result91b6cb8c\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/probability-calibration.html\">probability-calibration</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result91b6cb8c\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/confusion-matrix.html\">confusion-matrix</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result91b6cb8c\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/feature-importances.html\">feature-importances</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result91b6cb8c\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/precision-recall-binary.html\">precision-recall-binary</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result91b6cb8c\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/roc-binary.html\">roc-binary</div><div title=\"v3io:///projects/function-marketplace/artifacts/models/\">model</div></td>\n",
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       "<b> > to track results use the .show() or .logs() methods  or <a href=\"https://dashboard.default-tenant.app.app-lab-eks-testing.iguazio-cd1.com/mlprojects/function-marketplace/jobs/monitor/5ec8a83eb65b46dc9b7f9dd654cd1b31/overview\" target=\"_blank\">click here</a> to open in UI</b>"
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      "> 2021-10-13 10:10:24,178 [info] run executed, status=completed\n"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_run = fn.run(params = task_params,    \n",
    "                   inputs={\"dataset\"  : 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv'},\n",
    "                   local=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Getting the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlrun.artifacts import get_model\n",
    "import pickle\n",
    "\n",
    "model_file, model_obj, _ = get_model(train_run.artifact('model'))\n",
    "model = pickle.load(open(model_file,'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model score : 0.9632\n"
     ]
    }
   ],
   "source": [
    "print(f\"model score : {model.score(train_run.artifact('test_set').as_df().drop(['labels'],axis=1),train_run.artifact('test_set').as_df()['labels'])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Some plotting**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display the probability calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<h3 style=\"text-align:center\">probability calibration plot</h3>\n",
       "<img title=\"probability-calibration\" src=\"data:image/png;base64,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\">\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_run.artifact('probability-calibration').show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<h3 style=\"text-align:center\">Feature Importances</h3>\n",
       "<img title=\"feature-importances\" src=\"data:image/png;base64,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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_run.artifact('feature-importances').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Running the function remotely**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-13 10:10:24,882 [info] Started building image: .mlrun/func-function-marketplace-xgb-trainer:latest\n",
      "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1  \n",
      "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1  \n",
      "\u001b[36mINFO\u001b[0m[0000] Built cross stage deps: map[]                \n",
      "\u001b[36mINFO\u001b[0m[0000] Retrieving image manifest mlrun/mlrun:0.7.1  \n",
      "\u001b[36mINFO\u001b[0m[0001] Retrieving image manifest mlrun/mlrun:0.7.1  \n",
      "\u001b[36mINFO\u001b[0m[0002] Executing 0 build triggers                   \n",
      "\u001b[36mINFO\u001b[0m[0002] Unpacking rootfs as cmd RUN pip install xgboost==1.3.1 requires it. \n",
      "\u001b[36mINFO\u001b[0m[0024] RUN pip install xgboost==1.3.1               \n",
      "\u001b[36mINFO\u001b[0m[0024] Taking snapshot of full filesystem...        \n",
      "\u001b[36mINFO\u001b[0m[0035] cmd: /bin/sh                                 \n",
      "\u001b[36mINFO\u001b[0m[0035] args: [-c pip install xgboost==1.3.1]        \n",
      "\u001b[36mINFO\u001b[0m[0035] Running: [/bin/sh -c pip install xgboost==1.3.1] \n",
      "Collecting xgboost==1.3.1\n",
      "  Downloading xgboost-1.3.1-py3-none-manylinux2010_x86_64.whl (157.5 MB)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.7/site-packages (from xgboost==1.3.1) (1.7.1)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/site-packages (from xgboost==1.3.1) (1.19.5)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.3.1\n",
      "WARNING: You are using pip version 20.2.4; however, version 21.3 is available.\n",
      "You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.\n",
      "\u001b[36mINFO\u001b[0m[0042] Taking snapshot of full filesystem...        \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn.spec.build.commands=['pip install xgboost==1.3.1']\n",
    "fn.deploy(with_mlrun=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-13 10:11:39,577 [info] starting run xgb-trainer-train_model uid=7332ff5d727948c89221d4645b84d028 DB=http://mlrun-api:8080\n",
      "> 2021-10-13 10:11:39,764 [info] Job is running in the background, pod: xgb-trainer-train-model-4scfq\n",
      "> 2021-10-13 10:11:55,207 [info] run executed, status=completed\n",
      "The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "final state: completed\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>function-marketplace</td>\n",
       "      <td><div title=\"7332ff5d727948c89221d4645b84d028\"><a href=\"https://dashboard.default-tenant.app.app-lab-eks-testing.iguazio-cd1.com/mlprojects/function-marketplace/jobs/monitor/7332ff5d727948c89221d4645b84d028/overview\" target=\"_blank\" >...5b84d028</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Oct 13 10:11:51</td>\n",
       "      <td>completed</td>\n",
       "      <td>xgb-trainer-train_model</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=dani</div><div class=\"dictlist\">kind=job</div><div class=\"dictlist\">owner=dani</div><div class=\"dictlist\">host=xgb-trainer-train-model-4scfq</div></td>\n",
       "      <td><div title=\"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv\">dataset</div></td>\n",
       "      <td><div class=\"dictlist\">model_type=classifier</div><div class=\"dictlist\">CLASS_tree_method=hist</div><div class=\"dictlist\">CLASS_objective=binary:logistic</div><div class=\"dictlist\">CLASS_booster=gbtree</div><div class=\"dictlist\">FIT_verbose=0</div><div class=\"dictlist\">label_column=labels</div></td>\n",
       "      <td><div class=\"dictlist\">accuracy=0.9552</div><div class=\"dictlist\">test-error=0.0448</div><div class=\"dictlist\">rocauc=0.9799618829687036</div><div class=\"dictlist\">brier_score=0.038984999293145965</div><div class=\"dictlist\">f1-score=0.954983922829582</div><div class=\"dictlist\">precision_score=0.965679190751445</div><div class=\"dictlist\">recall_score=0.9445229681978798</div></td>\n",
       "      <td><div title=\"v3io:///projects/function-marketplace/artifacts/test_set.parquet\">test_set</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result0d1658f0\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/probability-calibration.html\">probability-calibration</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result0d1658f0\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/confusion-matrix.html\">confusion-matrix</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result0d1658f0\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/feature-importances.html\">feature-importances</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result0d1658f0\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/precision-recall-binary.html\">precision-recall-binary</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result0d1658f0\" title=\"files/v3io/projects/function-marketplace/artifacts/plots/roc-binary.html\">roc-binary</div><div title=\"v3io:///projects/function-marketplace/artifacts/models/\">model</div></td>\n",
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      "\n"
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       "<b> > to track results use the .show() or .logs() methods  or <a href=\"https://dashboard.default-tenant.app.app-lab-eks-testing.iguazio-cd1.com/mlprojects/function-marketplace/jobs/monitor/7332ff5d727948c89221d4645b84d028/overview\" target=\"_blank\">click here</a> to open in UI</b>"
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     "text": [
      "> 2021-10-13 10:11:58,969 [info] run executed, status=completed\n"
     ]
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    "fn.run(inputs={\"dataset\"  : 'https://s3.wasabisys.com/iguazio/data/function-marketplace-data/xgb_trainer/classifier-data.csv'},\n",
    "       params=task_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to the top](#XGBoost-trainer)"
   ]
  }
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