{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Project Specific: Column Selection\n",
    "\n",
    "In order to reference specific columns in a project's dataset **as input to a task**,\n",
    "column selection (i.e. feature selection) is required.\n",
    "\n",
    "*Note: this technique is specifically for when preprocessing or estimation requires or should be\n",
    "applied to a specific column- not to be confused with feature lists, which allow an entire blueprint\n",
    "to make use of a subset of a project's features.*\n",
    "\n",
    "The following demonstrates how to select a specific\n",
    "column from a project's dataset and use it in a blueprint.\n",
    "\n",
    "*Selecting multiple columns or specifically excluding columns, to be used as input to a specific\n",
    " task is still being tested / validated, and will be added soon.*"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "287b9d50-22dc-4313-8729-eb6c4519f505",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datarobot as dr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de66c3b1-468e-41ea-8c91-18242717b89c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datarobot_bp_workshop import Workshop, Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9e62e4b-64db-4d6d-ba20-3f3fd483cbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../../../api.token', 'r') as f:\n",
    "    token = f.read()\n",
    "    dr.Client(token=token, endpoint='https://app.datarobot.com/api/v2')"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Initialize Workshop with a `project_id`"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6fde5672-f307-441e-bb1a-df1e16579d88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please upgrade to the latest version: pip install --upgrade datarobot_bp_workshop\n"
     ]
    }
   ],
   "source": [
    "w = Workshop(project_id='5eb9656901f6bb026828f14e')"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Select a specific feature"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f0ed7c63-f6cf-4e8a-8ae1-499492212213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Single Column Converter, Select Feature: 'Insurance_Type' (SCPICK)\n\nInput Summary: Categorical Data\nOutput Method: TaskOutputMethod.TRANSFORM\nSelect Feature: 'Insurance_Type'\n\nTask Parameters:\n  column_name (cn) = '496e737572616e63655f54797065'"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.Features.Insurance_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba8eaf3f-46fb-40e8-b602-d0b88848078b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Single Column Converter: 'Insurance_Duration' (SCPICK)\n",
       "\n",
       "Input Summary: Categorical Data\n",
       "Output Method: TaskOutputMethod.TRANSFORM\n",
       "\n",
       "Task Parameters:\n",
       "  column_name (cn) = '496e737572616e63655f4475726174696f6e'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.Feature('Insurance_Duration')"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Select multiple features\n",
    "This can be done either by using `w.Features.<feature name>` (to leverage autocomplete)\n",
    "or `'<feature name>'` as shown below.\n",
    "\n",
    "Note: all features must be the same input data type"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "Multiple Column Selector, Select Features: 'Insurance_Duration', 'Insurance_Type' (MCPICK)\n\nInput Summary: Categorical Data\nOutput Method: TaskOutputMethod.TRANSFORM\nSelect Features: 'Insurance_Duration', 'Insurance_Type'\n\nTask Parameters:\n  column_names (cns) = ['496e737572616e63655f4475726174696f6e', '496e737572616e63655f54797065']\n  method (method) = 'include'"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.FeatureSelection(w.Features.Insurance_Duration, w.Features.Insurance_Type)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "Multiple Column Selector, Select Features: 'Insurance_Duration', 'Insurance_Type' (MCPICK)\n\nInput Summary: Categorical Data\nOutput Method: TaskOutputMethod.TRANSFORM\nSelect Features: 'Insurance_Duration', 'Insurance_Type'\n\nTask Parameters:\n  column_names (cns) = ['496e737572616e63655f4475726174696f6e', '496e737572616e63655f54797065']\n  method (method) = 'include'"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.FeatureSelection('Insurance_Duration', 'Insurance_Type')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Features may also be excluded instead, which will select all other features of the same type."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "Multiple Column Selector, Exclude Features: 'Insurance_Duration', 'Insurance_Type' (MCPICK)\n\nInput Summary: Categorical Data\nOutput Method: TaskOutputMethod.TRANSFORM\nExclude Features: 'Insurance_Duration', 'Insurance_Type'\n\nTask Parameters:\n  column_names (cns) = ['496e737572616e63655f4475726174696f6e', '496e737572616e63655f54797065']\n  method (method) = 'exclude'"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.FeatureSelection('Insurance_Duration', 'Insurance_Type', exclude=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Build a blueprint with a specific feature"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5867a23b-b803-41f3-be48-b46794076908",
   "metadata": {},
   "outputs": [],
   "source": [
    "pni = w.Tasks.PNI2(w.Features.Age)\n",
    "rdt = w.Tasks.RDT5(pni)\n",
    "binning = w.Tasks.BINNING(pni)\n",
    "keras = w.Tasks.KERASC(rdt, binning)\n",
    "keras.set_task_parameters_by_name(learning_rate=0.123)\n",
    "keras_blueprint = w.BlueprintGraph(keras, name='A blueprint I made with the Python API')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf527467-e78d-42ce-a3a9-0042de7355a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = Workshop(project_id='5eb9656901f6bb026828f14e')\n",
      "\n",
      "age = w.Features.Age\n",
      "\n",
      "pni2 = w.Tasks.PNI2(age)\n",
      "\n",
      "binning = w.Tasks.BINNING(pni2)\n",
      "\n",
      "rdt5 = w.Tasks.RDT5(pni2)\n",
      "\n",
      "kerasc = w.Tasks.KERASC(binning, rdt5)\n",
      "kerasc.set_task_parameters(learning_rate=0.123)\n",
      "\n",
      "kerasc_blueprint = w.BlueprintGraph(kerasc, name='A blueprint I made with the Python API')\n"
     ]
    }
   ],
   "source": [
    "source_code = keras_blueprint.to_source_code(to_stdout=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2febc7f-fd52-4beb-a929-1b93874ae2e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "exec(compile(source_code, 'blueprint', 'exec'), locals())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c38c7a8-c9be-494b-a096-1df2fb2c227f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kerasc_blueprint.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "26b64378-11f3-4f20-add3-20e866d3840b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<workshop.workshop.Workshop at 0x7ffad3b7e650>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.set_project(project_id='605ab63ecd8a6669dfd64901')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b3231146-3ce9-4e9b-b4b3-f849e76b734b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name: 'A blueprint I made with the Python API'\n",
       "\n",
       "Input Data: Numeric\n",
       "Tasks: Single Column Converter: 'Age' | Missing Values Imputed | Binning of numerical variables | Smooth Ridit Transform | Keras Neural Network Classifier"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kerasc_blueprint.train(w.project.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "08debff0-07f3-46e2-8372-680f64bbc5fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "starred_models = w.project.get_models(search_params=dict(is_starred=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f20c3e7e-1383-478c-8304-1a5195af8b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_to_clone = starred_models[0].blueprint_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2875213c-5c65-4ce1-a9cb-070cef0bfac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bp = w.clone(blueprint_id=model_to_clone, name='Now featuring selected columns!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bf680cc5-582d-47a5-aaf9-518f8358bbd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6a71ce08-6686-4885-8c4b-cf67b27da6bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[32mBlueprint deleted.\u001B[0m\n"
     ]
    }
   ],
   "source": [
    "bp.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "191c5962-a3a8-4f40-ab09-6ea621e4ea63",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}