{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Set a schema on a project\n",
    "\n",
    "\"Log a ``rubicon_ml`` experiment with a ``rubicon_schema``\" showed how ``rubicon_schema`` can\n",
    "infer schema from the object to log - sometimes, this may not be possible and a schema may need to be set manually\n",
    "\n",
    "## Select a schema\n",
    "\n",
    "View all available schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sklearn__RandomForestClassifier',\n",
       " 'xgboost__XGBClassifier',\n",
       " 'xgboost__DaskXGBClassifier']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rubicon_ml.schema import registry\n",
    "\n",
    "available_schema = registry.available_schema()\n",
    "available_schema"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'artifacts': ['self'],\n",
      " 'compatibility': {'scikit-learn': {'max_version': None,\n",
      "                                    'min_version': '1.0.2'}},\n",
      " 'docs_url': 'https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html',\n",
      " 'features': [{'importances_attr': 'feature_importances_',\n",
      "               'names_attr': 'feature_names_in_',\n",
      "               'optional': True}],\n",
      " 'metrics': [{'name': 'classes', 'value_attr': 'classes_'},\n",
      "             {'name': 'n_classes', 'value_attr': 'n_classes_'},\n",
      "             {'name': 'n_features_in', 'value_attr': 'n_features_in_'},\n",
      "             {'name': 'n_outputs', 'value_attr': 'n_outputs_'},\n",
      "             {'name': 'oob_decision_function',\n",
      "              'optional': True,\n",
      "              'value_attr': 'oob_decision_function_'},\n",
      "             {'name': 'oob_score',\n",
      "              'optional': True,\n",
      "              'value_attr': 'oob_score_'}],\n",
      " 'name': 'sklearn__RandomForestClassifier',\n",
      " 'parameters': [{'name': 'bootstrap', 'value_attr': 'bootstrap'},\n",
      "                {'name': 'ccp_alpha', 'value_attr': 'ccp_alpha'},\n",
      "                {'name': 'class_weight', 'value_attr': 'class_weight'},\n",
      "                {'name': 'criterion', 'value_attr': 'criterion'},\n",
      "                {'name': 'max_depth', 'value_attr': 'max_depth'},\n",
      "                {'name': 'max_features', 'value_attr': 'max_features'},\n",
      "                {'name': 'min_impurity_decrease',\n",
      "                 'value_attr': 'min_impurity_decrease'},\n",
      "                {'name': 'max_leaf_nodes', 'value_attr': 'max_leaf_nodes'},\n",
      "                {'name': 'max_samples', 'value_attr': 'max_samples'},\n",
      "                {'name': 'min_samples_split',\n",
      "                 'value_attr': 'min_samples_split'},\n",
      "                {'name': 'min_samples_leaf', 'value_attr': 'min_samples_leaf'},\n",
      "                {'name': 'min_weight_fraction_leaf',\n",
      "                 'value_attr': 'min_weight_fraction_leaf'},\n",
      "                {'name': 'n_estimators', 'value_attr': 'n_estimators'},\n",
      "                {'name': 'oob_score', 'value_attr': 'oob_score'},\n",
      "                {'name': 'random_state', 'value_attr': 'random_state'}],\n",
      " 'verison': '1.0.0'}\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "rfc_schema = registry.get_schema(\"sklearn__RandomForestClassifier\")\n",
    "pprint.pprint(rfc_schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply the schema to a project\n",
    "\n",
    "Create a ``rubicon_ml`` project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<rubicon_ml.client.project.Project at 0x134d4fd50>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rubicon_ml import Rubicon\n",
    "\n",
    "rubicon = Rubicon(persistence=\"memory\")\n",
    "project = rubicon.create_project(name=\"apply schema\")\n",
    "project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the schema on the project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "project.set_schema(rfc_schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, ``log_with_schema`` will leverage the schema ``rfc_schema`` instead of trying to infer one"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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