{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bf6a40d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-04T17:30:02.674284Z",
     "start_time": "2023-09-04T17:29:59.371085Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stefankrawczyk/.pyenv/versions/knowledge_retrieval-py39/lib/python3.9/site-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "\n",
    "import data_loaders\n",
    "import model_training\n",
    "\n",
    "from hamilton import base, driver\n",
    "from hamilton.io.materialization import to\n",
    "import pandas as pd\n",
    "\n",
    "import custom_materializers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a449245",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-04T17:30:02.682841Z",
     "start_time": "2023-09-04T17:30:02.679637Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: Hamilton collects completely anonymous data about usage. This will help us improve Hamilton over time. See https://github.com/dagworks-inc/hamilton#usage-analytics--data-privacy for details.\n"
     ]
    }
   ],
   "source": [
    "dag_config = {\n",
    "    \"test_size_fraction\": 0.5,\n",
    "    \"shuffle_train_test_split\": True,\n",
    "    \"data_loader\" : \"iris\",\n",
    "    \"clf\" : \"logistic\",\n",
    "    \"penalty\" : \"l2\"\n",
    "}\n",
    "dr = (\n",
    "        driver.Builder()\n",
    "        .with_adapter(base.DefaultAdapter())\n",
    "        .with_config(dag_config)\n",
    "        .with_modules(data_loaders, model_training)\n",
    "        .build()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "397b09bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-04T17:30:03.208108Z",
     "start_time": "2023-09-04T17:30:02.696138Z"
    }
   },
   "outputs": [
    {
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class=\"edge\">\n<title>X_test&#45;&gt;predicted_output</title>\n<path fill=\"none\" stroke=\"black\" d=\"M495.19,-287.7C495.51,-280.24 495.89,-271.32 496.25,-262.97\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"499.78,-263.25 496.71,-253.1 492.79,-262.95 499.78,-263.25\"/>\n</g>\n<!-- predicted_output&#45;&gt;predicted_output_with_labels -->\n<g id=\"edge23\" class=\"edge\">\n<title>predicted_output&#45;&gt;predicted_output_with_labels</title>\n<path fill=\"none\" stroke=\"black\" d=\"M502.14,-215.7C504.19,-208.15 506.64,-199.12 508.93,-190.68\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"512.56,-191.67 511.8,-181.1 505.81,-189.84 512.56,-191.67\"/>\n</g>\n</g>\n</svg>\n",
      "text/plain": "<graphviz.graphs.Digraph at 0x140ea8f10>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "materializers = [\n",
    "        to.json(\n",
    "            dependencies=[\"model_parameters\"],\n",
    "            id=\"model_params_to_json\",\n",
    "            path=\"./data/params.json\"\n",
    "        ),\n",
    "        # classification report to .txt file\n",
    "        to.file(\n",
    "            dependencies=[\"classification_report\"],\n",
    "            id=\"classification_report_to_txt\",\n",
    "            path=\"./data/classification_report.txt\",\n",
    "        ),\n",
    "        # materialize the model to a pickle file\n",
    "        to.pickle(\n",
    "            dependencies=[\"fit_clf\"], id=\"clf_to_pickle\", path=\"./data/clf.pkl\"\n",
    "        ),\n",
    "        # materialize the predictions we made to a csv file\n",
    "        to.csv(\n",
    "            dependencies=[\"predicted_output_with_labels\"],\n",
    "            id=\"predicted_output_with_labels_to_csv\",\n",
    "            path=\"./data/predicted_output_with_labels.csv\",\n",
    "        ),\n",
    "    ]\n",
    "\n",
    "dr.visualize_materialization(\n",
    "    *materializers,\n",
    "    additional_vars=[\"classification_report\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5727b54",
   "metadata": {},
   "outputs": [],
   "source": [
    "materialization_results, additional_vars = dr.materialize(\n",
    "        # materialize model parameters to json\n",
    "        *materializers,\n",
    "        additional_vars=[\"classification_report\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8bdfde70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        94\n",
      "           1       0.91      0.93      0.92        85\n",
      "           2       0.97      0.99      0.98        96\n",
      "           3       0.99      0.97      0.98        93\n",
      "           4       0.99      0.92      0.95        88\n",
      "           5       0.95      0.95      0.95        85\n",
      "           6       0.99      0.97      0.98        97\n",
      "           7       0.97      0.97      0.97        89\n",
      "           8       0.88      0.88      0.88        82\n",
      "           9       0.91      0.97      0.94        90\n",
      "\n",
      "    accuracy                           0.96       899\n",
      "   macro avg       0.95      0.95      0.95       899\n",
      "weighted avg       0.96      0.96      0.96       899\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(additional_vars['classification_report'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a6f5fe83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        94\n",
      "           1       0.91      0.93      0.92        85\n",
      "           2       0.97      0.99      0.98        96\n",
      "           3       0.99      0.97      0.98        93\n",
      "           4       0.99      0.92      0.95        88\n",
      "           5       0.95      0.95      0.95        85\n",
      "           6       0.99      0.97      0.98        97\n",
      "           7       0.97      0.97      0.97        89\n",
      "           8       0.88      0.88      0.88        82\n",
      "           9       0.91      0.97      0.94        90\n",
      "\n",
      "    accuracy                           0.96       899\n",
      "   macro avg       0.95      0.95      0.95       899\n",
      "weighted avg       0.96      0.96      0.96       899\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(open((materialization_results['classification_report_to_txt']['path'])).read())"
   ]
  }
 ],
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