sklearn_classifier_dask/sklearn_classifier_dask.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generic Scikit-Learn Classifier With Dask\n",
    "\n",
    "Run any scikit-learn compatible classifier or list of classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-01-26 15:20:40,329 [warning] Failed resolving version info. Ignoring and using defaults\n",
      "> 2021-01-26 15:20:42,044 [warning] Unable to parse server or client version. Assuming compatible: {'server_version': 'unstable', 'client_version': 'unstable'}\n"
     ]
    }
   ],
   "source": [
    "import mlrun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%nuclio: setting kind to 'job'\n",
      "%nuclio: setting spec.image to 'mlrun/ml-models'\n"
     ]
    }
   ],
   "source": [
    "%nuclio config kind = \"job\"\n",
    "%nuclio config spec.image = \"mlrun/ml-models\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "import joblib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from cloudpickle import dumps, load, dump\n",
    "\n",
    "from dask import dataframe as dd\n",
    "from dask import array as da\n",
    "from dask.delayed import delayed\n",
    "from dask_ml import model_selection\n",
    "from dask_ml import metrics\n",
    "from dask_ml.preprocessing import StandardScaler, LabelEncoder\n",
    "\n",
    "from mlrun.artifacts import PlotArtifact\n",
    "from mlrun.mlutils.models import gen_sklearn_model\n",
    "from mlrun.utils.helpers import create_class\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from yellowbrick.classifier import ROCAUC, ClassificationReport, ConfusionMatrix\n",
    "from yellowbrick.model_selection import FeatureImportances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(context,\n",
    "                dataset: mlrun.DataItem,\n",
    "                model_pkg_class: str,\n",
    "                label_column: str = \"label\",\n",
    "                train_validation_size: float = 0.75,\n",
    "                sample: float = 1.0,\n",
    "                models_dest: str = \"models\",\n",
    "                test_set_key: str = \"test_set\",\n",
    "                plots_dest: str = \"plots\",\n",
    "                dask_function: str = None,\n",
    "                dask_client=None,\n",
    "                file_ext: str = \"parquet\",\n",
    "                random_state: int = 42) -> None:\n",
    "    \n",
    "    \"\"\"\n",
    "    Train a sklearn classifier with Dask\n",
    "    \n",
    "    :param context:                 Function context.\n",
    "    :param dataset:                 Raw data file.\n",
    "    :param model_pkg_class:         Model to train, e.g, \"sklearn.ensemble.RandomForestClassifier\", \n",
    "                                    or json model config.\n",
    "    :param label_column:            (label) Ground-truth y labels.\n",
    "    :param train_validation_size:   (0.75) Train validation set proportion out of the full dataset.\n",
    "    :param sample:                  (1.0) Select sample from dataset (n-rows/% of total), randomzie rows as default.\n",
    "    :param models_dest:             (models) Models subfolder on artifact path.\n",
    "    :param test_set_key:            (test_set) Mlrun db key of held out data in artifact store.\n",
    "    :param plots_dest:              (plots) Plot subfolder on artifact path.\n",
    "    :param dask_function:           dask function url (db://..)\n",
    "    :param dask_client:             dask client object\n",
    "    :param file_ext:                (parquet) format for test_set_key hold out data\n",
    "    :param random_state:            (42) sklearn seed\n",
    "    \"\"\"\n",
    "    \n",
    "    # set up dask client \n",
    "    if dask_function:\n",
    "        client = mlrun.import_function(dask_function).client\n",
    "    elif dask_client:\n",
    "        client = dask_client\n",
    "    else:\n",
    "        raise ValueError('dask client was not provided')\n",
    "\n",
    "    context.logger.info(\"Read Data\")\n",
    "    # read data with dask and mlrun\n",
    "    df = dataset.as_df(df_module=dd) \n",
    "\n",
    "    # take only numrical cols\n",
    "    context.logger.info(\"Prep Data\")\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "    df = df.select_dtypes(include=numerics)\n",
    "    \n",
    "    # dropna\n",
    "    if df.isna().any().any().compute() == True:\n",
    "        raise Exception('NAs valus found')\n",
    "    \n",
    "    # save cols names\n",
    "    df_header = df.columns\n",
    "    \n",
    "    df = df.sample(frac=sample).reset_index(drop=True)\n",
    "    encoder = LabelEncoder()\n",
    "    encoder = encoder.fit(df[label_column])\n",
    "    X = df.drop(label_column, axis=1).to_dask_array(lengths=True)\n",
    "    y = encoder.transform(df[label_column])\n",
    "\n",
    "    classes = df[label_column].drop_duplicates() # no unique values in dask\n",
    "    classes = [str(i) for i in classes]\n",
    "\n",
    "    context.logger.info(\"Split and Train\")\n",
    "    X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, train_size=train_validation_size,\n",
    "                                                                        random_state=random_state)\n",
    "    \n",
    "    scaler = StandardScaler()\n",
    "    scaler = scaler.fit(X_train)\n",
    "    X_train_transformed = scaler.transform(X_train)\n",
    "    X_test_transformed = scaler.transform(X_test)\n",
    "    \n",
    "    model_config = gen_sklearn_model(model_pkg_class,\n",
    "                                     context.parameters.items())\n",
    "\n",
    "    model_config[\"FIT\"].update({\"X\": X_train_transformed,\n",
    "                                \"y\": y_train})\n",
    "    \n",
    "    ClassifierClass = create_class(model_config[\"META\"][\"class\"])\n",
    "    \n",
    "    model = ClassifierClass(**model_config[\"CLASS\"])\n",
    "    \n",
    "    # load and fit model\n",
    "    with joblib.parallel_backend(\"dask\"):\n",
    "        \n",
    "        # initialize classifier from sklearn\n",
    "        model = model.fit(**model_config[\"FIT\"])\n",
    "\n",
    "    # create reports\n",
    "    context.logger.info(\"Evaluate\")\n",
    "    extra_data_dict = {}\n",
    "    for report in (ROCAUC, ClassificationReport, ConfusionMatrix):\n",
    "        \n",
    "        report_name = str(report.__name__)\n",
    "        # clear output\n",
    "        plt.cla()\n",
    "        plt.clf()\n",
    "        plt.close()\n",
    "        \n",
    "        # genrate report\n",
    "        viz = report(model, classes=classes, per_class=True, is_fitted=True)\n",
    "        viz.fit(X_train_transformed, y_train)               # Fit the training data to the visualizer\n",
    "        viz.score(X_test_transformed, y_test.compute())     # Evaluate the model on the test data\n",
    "        \n",
    "        # log reports\n",
    "        plot = context.log_artifact(PlotArtifact(report_name, \n",
    "                                                 body=viz.fig,\n",
    "                                                 title=report_name), \n",
    "                                                 db_key=False)\n",
    "        extra_data_dict[str(report)] = plot\n",
    "        \n",
    "        # log results\n",
    "        if report_name == 'ROCAUC':\n",
    "            context.log_results({\"micro\": viz.roc_auc.get(\"micro\"),\n",
    "                                \"macro\": viz.roc_auc.get(\"macro\")})\n",
    "            \n",
    "        elif report_name == 'ClassificationReport':\n",
    "            for score_name in viz.scores_:\n",
    "                for score_class in viz.scores_[score_name]:\n",
    "                    \n",
    "                    context.log_results({score_name + \"-\" + score_class : \n",
    "                                         viz.scores_[score_name].get(score_class)})\n",
    "        \n",
    "        #viz.show()\n",
    "    \n",
    "    # get feature importance\n",
    "    viz = FeatureImportances(model, classes=classes, per_class=True, \n",
    "                             is_fitted=True, labels=df_header.delete(df_header.get_loc(label_column)))\n",
    "    viz.fit(X_train_transformed, y_train) \n",
    "    viz.score(X_test_transformed, y_test)\n",
    "    #viz.show()\n",
    "    \n",
    "    plot = context.log_artifact(PlotArtifact(\"FeatureImportances\", body=viz.fig, \n",
    "                                             title=\"FeatureImportances\"), db_key=False)\n",
    "    extra_data_dict[str(\"FeatureImportances\")] = plot\n",
    "    \n",
    "    # clear final output\n",
    "    plt.cla()\n",
    "    plt.clf()\n",
    "    plt.close()\n",
    "\n",
    "    # log artifacts\n",
    "    context.logger.info(\"Log artifacts\")\n",
    "    artifact_path = context.artifact_subpath(models_dest)\n",
    "    \n",
    "    # set label\n",
    "    context.set_label('class', model_pkg_class)\n",
    "    \n",
    "    # log models\n",
    "    context.log_model(\"model\", body=dumps(model),\n",
    "                      artifact_path=artifact_path,\n",
    "                      model_file=\"model.pkl\",\n",
    "                      extra_data=extra_data_dict,\n",
    "                      metrics=context.results,\n",
    "                      labels={\"class\": model_pkg_class})\n",
    "    \n",
    "    # log scalers\n",
    "    context.log_artifact(\"standard_scaler\", body=dumps(scaler),\n",
    "                         artifact_path=artifact_path,\n",
    "                         model_file=\"scaler.gz\",\n",
    "                         label=\"standard_scaler\")\n",
    "    \n",
    "    # log encoder\n",
    "    context.log_artifact(\"label_encoder\", body=dumps(encoder),\n",
    "                         artifact_path=artifact_path,\n",
    "                         model_file=\"encoder.gz\",\n",
    "                         label=\"label_encoder\")\n",
    "    \n",
    "    # set aside some test data\n",
    "    df_to_save = delayed(np.column_stack)((X_test, y_test)).compute()\n",
    "    context.log_dataset(test_set_key, \n",
    "                        df=pd.DataFrame(df_to_save, \n",
    "                                        columns=df_header), # improve log dataset ability\n",
    "                        format=file_ext, index=False, \n",
    "                        labels={\"data-type\": \"held-out\"},\n",
    "                        artifact_path=context.artifact_subpath('data'))\n",
    "    \n",
    "    context.logger.info(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nuclio: end-code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save and Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-01-26 15:31:31,836 [info] function spec saved to path: function.yaml\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.kubejob.KubejobRuntime at 0x7f25d2bc3a50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mlrun\n",
    "_, artifact_path = mlrun.set_environment(artifact_path='./')\n",
    "fn = mlrun.code_to_function('sklearn-classifier-dask', kind='job', handler='train_model', \n",
    "                            description=\"train any classifier using scikit-learn's API over Dask\", \n",
    "                            categories=[\"ml\", \"training\", \"dask\"], \n",
    "                            labels={'author': 'yjb', \"framework\": \"sklearn\"},\n",
    "                            code_output='.')\n",
    "\n",
    "fn.export()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Init Dask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### init a dask cluster and set dask specs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn.apply(mlrun.platforms.auto_mount())\n",
    "DATA_URL = \"/User/iris.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  2776  100  2776    0     0  16722      0 --:--:-- --:--:-- --:--:-- 16622\n"
     ]
    }
   ],
   "source": [
    "!curl -L \"https://s3.wasabisys.com/iguazio/data/iris/iris_dataset.csv\" > {DATA_URL}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-01-26 15:22:37,907 [info] using in-cluster config.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'0f3172545726617a0f38a9661e5d731315f0b4ca'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dask test cluster (dask function)\n",
    "dask_cluster = mlrun.new_function('dask_tests', kind='dask', image='mlrun/ml-models')\n",
    "dask_cluster.apply(mlrun.mount_v3io())\n",
    "dask_cluster.spec.remote = True\n",
    "dask_cluster.with_requests(mem='8G')\n",
    "dask_cluster.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### init dask client \n",
    "copy the scheduler address to **DASK_CLIENT** param in the following cell, this will make the function use the dask cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-01-26 15:22:45,162 [info] to get a dashboard link, use NodePort service_type\n",
      "> 2021-01-26 15:22:45,163 [info] trying dask client at: tcp://mlrun-dask-tests-58d71599-7.default-tenant:8786\n",
      "> 2021-01-26 15:22:45,197 [info] using remote dask scheduler (mlrun-dask-tests-58d71599-7) at: tcp://mlrun-dask-tests-58d71599-7.default-tenant:8786\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://mlrun-dask-tests-58d71599-7.default-tenant:8786</li>\n",
       "  <li><b>Dashboard: </b><a href='http://mlrun-dask-tests-58d71599-7.default-tenant:8787/status' target='_blank'>http://mlrun-dask-tests-58d71599-7.default-tenant:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>0</li>\n",
       "  <li><b>Cores: </b>0</li>\n",
       "  <li><b>Memory: </b>0 B</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
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      "text/plain": [
       "<Client: 'tcp://10.200.0.54:8786' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask_cluster.client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_params = {\n",
    "    \"params\" : {\n",
    "        \"sample\"             : 1,\n",
    "        \"train_val_split\"    : 0.75,\n",
    "        \"random_state\"       : 42,\n",
    "        \"n_jobs\"             : -1,\n",
    "        \"plots_dest\"         : \"plots-p\",\n",
    "        \"models_dest\"        : 'sklearn-clfmodel'}}\n",
    "\n",
    "\n",
    "models = [\n",
    "    \"sklearn.ensemble.RandomForestClassifier\",\n",
    "    \"sklearn.ensemble.AdaBoostClassifier\",\n",
    "    \"sklearn.linear_model.LogisticRegression\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test and Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-01-26 15:22:47,942 [info] starting run sklearn_ensemble_RandomForestClassifier uid=c536e15d7f804262a3b14305fbb8d47b DB=http://mlrun-api:8080\n",
      "> 2021-01-26 15:22:48,160 [info] Job is running in the background, pod: sklearn-ensemble-randomforestclassifier-r48fw\n",
      "> 2021-01-26 15:22:51,784 [warning] Unable to parse server or client version. Assuming compatible: {'server_version': 'unstable', 'client_version': 'unstable'}\n",
      "> 2021-01-26 15:22:53,372 [info] using in-cluster config.\n",
      "> 2021-01-26 15:22:53,372 [info] to get a dashboard link, use NodePort service_type\n",
      "> 2021-01-26 15:22:53,372 [info] trying dask client at: tcp://mlrun-dask-tests-58d71599-7.default-tenant:8786\n",
      "> 2021-01-26 15:22:53,404 [info] using remote dask scheduler (mlrun-dask-tests-58d71599-7) at: tcp://mlrun-dask-tests-58d71599-7.default-tenant:8786\n",
      "> 2021-01-26 15:22:53,404 [info] Read Data\n",
      "> 2021-01-26 15:22:53,426 [info] Prep Data\n",
      "> 2021-01-26 15:22:57,916 [info] Split and Train\n",
      "> 2021-01-26 15:22:59,187 [info] Evaluate\n",
      "> 2021-01-26 15:23:00,997 [info] Log artifacts\n",
      "> 2021-01-26 15:23:01,557 [info] Done!\n",
      "> 2021-01-26 15:23:01,601 [info] run executed, status=completed\n",
      "final state: completed\n"
     ]
    },
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       "      <td>default</td>\n",
       "      <td><div title=\"c536e15d7f804262a3b14305fbb8d47b\"><a href=\"https://mlrun-ui.default-tenant.app.yh57.iguazio-cd0.com/projects/default/jobs/monitor/c536e15d7f804262a3b14305fbb8d47b/info\" target=\"_blank\" >...fbb8d47b</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Jan 26 15:22:53</td>\n",
       "      <td>completed</td>\n",
       "      <td>sklearn_ensemble_RandomForestClassifier</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=job</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=sklearn-ensemble-randomforestclassifier-r48fw</div><div class=\"dictlist\">class=sklearn.ensemble.RandomForestClassifier</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultec8bfe34\" title=\"/files/iris.csv\">dataset</div></td>\n",
       "      <td><div class=\"dictlist\">model_pkg_class=sklearn.ensemble.RandomForestClassifier</div><div class=\"dictlist\">label_column=label</div><div class=\"dictlist\">dask_function=db://default/dask_tests</div><div class=\"dictlist\">CLASS_max_depth=5</div></td>\n",
       "      <td><div class=\"dictlist\">micro=1.0</div><div class=\"dictlist\">macro=1.0</div><div class=\"dictlist\">precision-2=1.0</div><div class=\"dictlist\">precision-1=1.0</div><div class=\"dictlist\">precision-0=1.0</div><div class=\"dictlist\">recall-2=1.0</div><div class=\"dictlist\">recall-1=1.0</div><div class=\"dictlist\">recall-0=1.0</div><div class=\"dictlist\">f1-2=1.0</div><div class=\"dictlist\">f1-1=1.0</div><div class=\"dictlist\">f1-0=1.0</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultec8bfe34\" title=\"/files/functions/sklearn_classifier_dask/sklearn.ensemble.RandomForestClassifier/ROCAUC.html\">ROCAUC</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultec8bfe34\" title=\"/files/functions/sklearn_classifier_dask/sklearn.ensemble.RandomForestClassifier/ClassificationReport.html\">ClassificationReport</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultec8bfe34\" title=\"/files/functions/sklearn_classifier_dask/sklearn.ensemble.RandomForestClassifier/ConfusionMatrix.html\">ConfusionMatrix</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultec8bfe34\" title=\"/files/functions/sklearn_classifier_dask/sklearn.ensemble.RandomForestClassifier/FeatureImportances.html\">FeatureImportances</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.ensemble.RandomForestClassifier/models\">model</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.ensemble.RandomForestClassifier/models/standard_scaler\">standard_scaler</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.ensemble.RandomForestClassifier/models/label_encoder\">label_encoder</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.ensemble.RandomForestClassifier/data/test_set.parquet\">test_set</div></td>\n",
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     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run c536e15d7f804262a3b14305fbb8d47b --project default , !mlrun logs c536e15d7f804262a3b14305fbb8d47b --project default\n",
      "> 2021-01-26 15:23:03,504 [info] run executed, status=completed\n",
      "> 2021-01-26 15:23:03,505 [info] starting run sklearn_ensemble_AdaBoostClassifier uid=3ebb600c11d542cda56fceaad8a5f32d DB=http://mlrun-api:8080\n",
      "> 2021-01-26 15:23:03,704 [info] Job is running in the background, pod: sklearn-ensemble-adaboostclassifier-vhxtc\n",
      "> 2021-01-26 15:23:07,391 [warning] Unable to parse server or client version. Assuming compatible: {'server_version': 'unstable', 'client_version': 'unstable'}\n",
      "> 2021-01-26 15:23:08,963 [info] using in-cluster config.\n",
      "> 2021-01-26 15:23:08,963 [info] to get a dashboard link, use NodePort service_type\n",
      "> 2021-01-26 15:23:08,963 [info] trying dask client at: tcp://mlrun-dask-tests-58d71599-7.default-tenant:8786\n",
      "> 2021-01-26 15:23:08,993 [info] using remote dask scheduler (mlrun-dask-tests-58d71599-7) at: tcp://mlrun-dask-tests-58d71599-7.default-tenant:8786\n",
      "> 2021-01-26 15:23:08,993 [info] Read Data\n",
      "> 2021-01-26 15:23:09,017 [info] Prep Data\n",
      "> 2021-01-26 15:23:13,294 [info] Split and Train\n",
      "> 2021-01-26 15:23:14,196 [info] Evaluate\n",
      "> 2021-01-26 15:23:16,017 [info] Log artifacts\n",
      "> 2021-01-26 15:23:23,885 [info] Done!\n",
      "> 2021-01-26 15:23:23,928 [info] run executed, status=completed\n",
      "final state: completed\n"
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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",
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       "      <td>default</td>\n",
       "      <td><div title=\"3ebb600c11d542cda56fceaad8a5f32d\"><a href=\"https://mlrun-ui.default-tenant.app.yh57.iguazio-cd0.com/projects/default/jobs/monitor/3ebb600c11d542cda56fceaad8a5f32d/info\" target=\"_blank\" >...d8a5f32d</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Jan 26 15:23:08</td>\n",
       "      <td>completed</td>\n",
       "      <td>sklearn_ensemble_AdaBoostClassifier</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=job</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=sklearn-ensemble-adaboostclassifier-vhxtc</div><div class=\"dictlist\">class=sklearn.ensemble.AdaBoostClassifier</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultb55dd1e1\" title=\"/files/iris.csv\">dataset</div></td>\n",
       "      <td><div class=\"dictlist\">model_pkg_class=sklearn.ensemble.AdaBoostClassifier</div><div class=\"dictlist\">label_column=label</div><div class=\"dictlist\">dask_function=db://default/dask_tests</div><div class=\"dictlist\">CLASS_n_estimators=200</div><div class=\"dictlist\">CLASS_learning_rate=0.01</div></td>\n",
       "      <td><div class=\"dictlist\">micro=0.9761080332409972</div><div class=\"dictlist\">macro=0.9848504273504274</div><div class=\"dictlist\">precision-2=1.0</div><div class=\"dictlist\">precision-0=0.8571428571428571</div><div class=\"dictlist\">precision-1=0.9230769230769231</div><div class=\"dictlist\">recall-2=1.0</div><div class=\"dictlist\">recall-0=0.9230769230769231</div><div class=\"dictlist\">recall-1=0.8571428571428571</div><div class=\"dictlist\">f1-2=1.0</div><div class=\"dictlist\">f1-0=0.888888888888889</div><div class=\"dictlist\">f1-1=0.888888888888889</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultb55dd1e1\" title=\"/files/functions/sklearn_classifier_dask/sklearn.ensemble.AdaBoostClassifier/ROCAUC.html\">ROCAUC</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultb55dd1e1\" title=\"/files/functions/sklearn_classifier_dask/sklearn.ensemble.AdaBoostClassifier/ClassificationReport.html\">ClassificationReport</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultb55dd1e1\" title=\"/files/functions/sklearn_classifier_dask/sklearn.ensemble.AdaBoostClassifier/ConfusionMatrix.html\">ConfusionMatrix</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultb55dd1e1\" title=\"/files/functions/sklearn_classifier_dask/sklearn.ensemble.AdaBoostClassifier/FeatureImportances.html\">FeatureImportances</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.ensemble.AdaBoostClassifier/models\">model</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.ensemble.AdaBoostClassifier/models/standard_scaler\">standard_scaler</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.ensemble.AdaBoostClassifier/models/label_encoder\">label_encoder</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.ensemble.AdaBoostClassifier/data/test_set.parquet\">test_set</div></td>\n",
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     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 3ebb600c11d542cda56fceaad8a5f32d --project default , !mlrun logs 3ebb600c11d542cda56fceaad8a5f32d --project default\n",
      "> 2021-01-26 15:23:29,053 [info] run executed, status=completed\n",
      "> 2021-01-26 15:23:29,054 [info] starting run sklearn_linear_model_LogisticRegression uid=9c623457a4de47b3881cc1ef3dacbd24 DB=http://mlrun-api:8080\n",
      "> 2021-01-26 15:23:29,247 [info] Job is running in the background, pod: sklearn-linear-model-logisticregression-pr8j9\n",
      "> 2021-01-26 15:23:32,848 [warning] Unable to parse server or client version. Assuming compatible: {'server_version': 'unstable', 'client_version': 'unstable'}\n",
      "> 2021-01-26 15:23:34,379 [info] using in-cluster config.\n",
      "> 2021-01-26 15:23:34,380 [info] to get a dashboard link, use NodePort service_type\n",
      "> 2021-01-26 15:23:34,380 [info] trying dask client at: tcp://mlrun-dask-tests-58d71599-7.default-tenant:8786\n",
      "> 2021-01-26 15:23:34,405 [info] using remote dask scheduler (mlrun-dask-tests-58d71599-7) at: tcp://mlrun-dask-tests-58d71599-7.default-tenant:8786\n",
      "> 2021-01-26 15:23:34,405 [info] Read Data\n",
      "> 2021-01-26 15:23:34,424 [info] Prep Data\n",
      "> 2021-01-26 15:23:38,842 [info] Split and Train\n",
      "> 2021-01-26 15:23:39,510 [info] Evaluate\n",
      "> 2021-01-26 15:23:41,290 [info] Log artifacts\n",
      "> 2021-01-26 15:23:41,871 [info] Done!\n",
      "> 2021-01-26 15:23:41,920 [info] run executed, status=completed\n",
      "final state: completed\n"
     ]
    },
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       "  flex: 3\n",
       "}\n",
       ".master-wrapper > div {\n",
       "  margin: 4px;\n",
       "  padding: 10px;\n",
       "}\n",
       "iframe.fileview {\n",
       "  border: 0 none;\n",
       "  height: 100%;\n",
       "  width: 100%;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       ".pane-header-title {\n",
       "  width: 80%;\n",
       "  font-weight: 500;\n",
       "}\n",
       ".pane-header {\n",
       "  line-height: 1;\n",
       "  background-color: #4EC64B;\n",
       "  padding: 3px;\n",
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       ".pane-header .close {\n",
       "  font-size: 20px;\n",
       "  font-weight: 700;\n",
       "  float: right;\n",
       "  margin-top: -5px;\n",
       "}\n",
       ".master-wrapper .right-pane {\n",
       "  border: 1px inset silver;\n",
       "  width: 40%;\n",
       "  min-height: 300px;\n",
       "  flex: 3\n",
       "  min-width: 500px;\n",
       "}\n",
       ".master-wrapper * {\n",
       "  box-sizing: border-box;\n",
       "}\n",
       "</style><script>\n",
       "function copyToClipboard(fld) {\n",
       "    if (document.queryCommandSupported && document.queryCommandSupported('copy')) {\n",
       "        var textarea = document.createElement('textarea');\n",
       "        textarea.textContent = fld.innerHTML;\n",
       "        textarea.style.position = 'fixed';\n",
       "        document.body.appendChild(textarea);\n",
       "        textarea.select();\n",
       "\n",
       "        try {\n",
       "            return document.execCommand('copy'); // Security exception may be thrown by some browsers.\n",
       "        } catch (ex) {\n",
       "\n",
       "        } finally {\n",
       "            document.body.removeChild(textarea);\n",
       "        }\n",
       "    }\n",
       "}\n",
       "function expandPanel(el) {\n",
       "  const panelName = \"#\" + el.getAttribute('paneName');\n",
       "  console.log(el.title);\n",
       "\n",
       "  document.querySelector(panelName + \"-title\").innerHTML = el.title\n",
       "  iframe = document.querySelector(panelName + \"-body\");\n",
       "\n",
       "  const tblcss = `<style> body { font-family: Arial, Helvetica, sans-serif;}\n",
       "    #csv { margin-bottom: 15px; }\n",
       "    #csv table { border-collapse: collapse;}\n",
       "    #csv table td { padding: 4px 8px; border: 1px solid silver;} </style>`;\n",
       "\n",
       "  function csvToHtmlTable(str) {\n",
       "    return '<div id=\"csv\"><table><tr><td>' +  str.replace(/[\\n\\r]+$/g, '').replace(/[\\n\\r]+/g, '</td></tr><tr><td>')\n",
       "      .replace(/,/g, '</td><td>') + '</td></tr></table></div>';\n",
       "  }\n",
       "\n",
       "  function reqListener () {\n",
       "    if (el.title.endsWith(\".csv\")) {\n",
       "      iframe.setAttribute(\"srcdoc\", tblcss + csvToHtmlTable(this.responseText));\n",
       "    } else {\n",
       "      iframe.setAttribute(\"srcdoc\", this.responseText);\n",
       "    }\n",
       "    console.log(this.responseText);\n",
       "  }\n",
       "\n",
       "  const oReq = new XMLHttpRequest();\n",
       "  oReq.addEventListener(\"load\", reqListener);\n",
       "  oReq.open(\"GET\", el.title);\n",
       "  oReq.send();\n",
       "\n",
       "\n",
       "  //iframe.src = el.title;\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.remove(\"hidden\");\n",
       "  }\n",
       "}\n",
       "function closePanel(el) {\n",
       "  const panelName = \"#\" + el.getAttribute('paneName')\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (!resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.add(\"hidden\");\n",
       "  }\n",
       "}\n",
       "\n",
       "</script>\n",
       "<div class=\"master-wrapper\">\n",
       "  <div class=\"block master-tbl\"><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>default</td>\n",
       "      <td><div title=\"9c623457a4de47b3881cc1ef3dacbd24\"><a href=\"https://mlrun-ui.default-tenant.app.yh57.iguazio-cd0.com/projects/default/jobs/monitor/9c623457a4de47b3881cc1ef3dacbd24/info\" target=\"_blank\" >...3dacbd24</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Jan 26 15:23:34</td>\n",
       "      <td>completed</td>\n",
       "      <td>sklearn_linear_model_LogisticRegression</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=job</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=sklearn-linear-model-logisticregression-pr8j9</div><div class=\"dictlist\">class=sklearn.linear_model.LogisticRegression</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result8120860f\" title=\"/files/iris.csv\">dataset</div></td>\n",
       "      <td><div class=\"dictlist\">model_pkg_class=sklearn.linear_model.LogisticRegression</div><div class=\"dictlist\">label_column=label</div><div class=\"dictlist\">dask_function=db://default/dask_tests</div><div class=\"dictlist\">CLASS_solver=liblinear</div></td>\n",
       "      <td><div class=\"dictlist\">micro=0.9916897506925207</div><div class=\"dictlist\">macro=0.9985119047619048</div><div class=\"dictlist\">precision-2=1.0</div><div class=\"dictlist\">precision-1=1.0</div><div class=\"dictlist\">precision-0=0.7857142857142857</div><div class=\"dictlist\">recall-2=1.0</div><div class=\"dictlist\">recall-1=0.7857142857142857</div><div class=\"dictlist\">recall-0=1.0</div><div class=\"dictlist\">f1-2=1.0</div><div class=\"dictlist\">f1-1=0.88</div><div class=\"dictlist\">f1-0=0.88</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result8120860f\" title=\"/files/functions/sklearn_classifier_dask/sklearn.linear_model.LogisticRegression/ROCAUC.html\">ROCAUC</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result8120860f\" title=\"/files/functions/sklearn_classifier_dask/sklearn.linear_model.LogisticRegression/ClassificationReport.html\">ClassificationReport</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result8120860f\" title=\"/files/functions/sklearn_classifier_dask/sklearn.linear_model.LogisticRegression/ConfusionMatrix.html\">ConfusionMatrix</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result8120860f\" title=\"/files/functions/sklearn_classifier_dask/sklearn.linear_model.LogisticRegression/FeatureImportances.html\">FeatureImportances</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.linear_model.LogisticRegression/models\">model</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.linear_model.LogisticRegression/models/standard_scaler\">standard_scaler</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.linear_model.LogisticRegression/models/label_encoder\">label_encoder</div><div title=\"/User/functions/sklearn_classifier_dask/sklearn.linear_model.LogisticRegression/data/test_set.parquet\">test_set</div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"result8120860f-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"result8120860f-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"result8120860f\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"result8120860f-body\"></iframe>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 9c623457a4de47b3881cc1ef3dacbd24 --project default , !mlrun logs 9c623457a4de47b3881cc1ef3dacbd24 --project default\n",
      "> 2021-01-26 15:23:44,614 [info] run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "outputs = []\n",
    "for model in models:\n",
    "    task_copy = task_params.copy()\n",
    "    task_copy.update(\n",
    "        {\n",
    "            \"params\":{ \"model_pkg_class\" : model,\n",
    "                       \"label_column\"    : \"label\",\n",
    "                       \"dask_function\": 'db://default/dask_tests'}\n",
    "        }\n",
    "    )\n",
    "    \n",
    "    # customize specific model parameters\n",
    "    if \"RandomForestClassifier\" in model:\n",
    "        task_copy[\"params\"].update({\"CLASS_max_depth\" : 5})\n",
    "\n",
    "    if \"LogisticRegression\" in model:\n",
    "        task_copy[\"params\"].update({\"CLASS_solver\" : \"liblinear\"})\n",
    "    \n",
    "    if \"AdaBoostClassifier\" in model:\n",
    "        task_copy[\"params\"].update({\"CLASS_n_estimators\"  : 200,\n",
    "                                    \"CLASS_learning_rate\" : 0.01\n",
    "                                   })\n",
    "    \n",
    "    name = model.replace('.', '_')\n",
    "    output = fn.run(mlrun.NewTask(**task_copy),\n",
    "                             handler=train_model,\n",
    "                             name=name,\n",
    "                             inputs={\"dataset\"      : DATA_URL},\n",
    "                             artifact_path=os.path.join(artifact_path, model),\n",
    "                             local=False)\n",
    "    \n",
    "    outputs.append({name: output.outputs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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