great-ai/docs/tutorial/train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train your model\n",
    "\n",
    "The first step is, of course, to install `great-ai` in your Python environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install great-ai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "First, we have to get some data. After downloading it [from here](https://github.com/allenai/scibert/tree/master/data/text_classification/mag), we might notice that the dataset is in [JSON Lines](https://jsonlines.org/) format (each line is a seperate JSON). \n",
    "\n",
    "Let's write a function which takes a single line, and returns the sentence and the corresponding label from it. Before returning, the sentence is also cleaned to remove any LaTeX, XML, unicode, PDF-extraction artifacts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from great_ai.utilities import clean\n",
    "\n",
    "\n",
    "def preprocess(line):\n",
    "    data_point = json.loads(line)\n",
    "\n",
    "    sentence = data_point[\"text\"]\n",
    "    label = data_point[\"label\"]\n",
    "\n",
    "    return clean(sentence), label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can load the dataset and extract the *training* samples from it. Since we're impatient, we can do it in parallel using the `simple_parallel_map` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 84000/84000 [00:09<00:00, 8705.41it/s] \n"
     ]
    }
   ],
   "source": [
    "from great_ai.utilities import simple_parallel_map\n",
    "\n",
    "with open(\"data/train.txt\", encoding=\"utf-8\") as f:\n",
    "    training_data = simple_parallel_map(preprocess, f.readlines())\n",
    "\n",
    "X_train = [d[0] for d in training_data]\n",
    "y_train = [d[1] for d in training_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do the same for the *test* data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22399/22399 [00:02<00:00, 7963.06it/s] \n"
     ]
    }
   ],
   "source": [
    "with open(\"data/test.txt\", encoding=\"utf-8\") as f:\n",
    "    test_data = simple_parallel_map(preprocess, f.readlines())\n",
    "\n",
    "X_test = [d[0] for d in test_data]\n",
    "y_test = [d[1] for d in test_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After obtaining some clean data, it's time to create and train a model on it. We are going to use [scikit-learn](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) for this purpose.\n",
    "\n",
    "In this case, we opted for a [linear Support Vector Machine](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html) because it's known to be an adequate baseline for simple classification tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;linearsvc&#x27;, LinearSVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tfidfvectorizer&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;linearsvc&#x27;, LinearSVC())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
       "                ('linearsvc', LinearSVC())])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "model = make_pipeline(TfidfVectorizer(), LinearSVC())\n",
    "# todo: hyperparameter-optimisation\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's already finished. Let's see how well it performs. But first, we need to configure matplotlib to make the charts more legible. \n",
    "> `ConfusionMatrixDisplay` relies on `matplotlib` for rendering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (30, 15)\n",
    "plt.rcParams[\"font.size\"] = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we check the quality of the model on the `test` split. We can use the classification report to check the common metrics, such as the macro F1-score. We also draw the confusion matrix to get some insights into the types of mistakes being made by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    business       0.67      0.69      0.68      3198\n",
      "   economics       0.69      0.71      0.70      3189\n",
      "   geography       0.70      0.73      0.72      3207\n",
      "    medicine       0.90      0.91      0.90      3187\n",
      "    politics       0.63      0.57      0.60      3169\n",
      "  psychology       0.73      0.73      0.73      3252\n",
      "   sociology       0.51      0.51      0.51      3197\n",
      "\n",
      "    accuracy                           0.69     22399\n",
      "   macro avg       0.69      0.69      0.69     22399\n",
      "weighted avg       0.69      0.69      0.69     22399\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "y_predicted = model.predict(X_test)\n",
    "\n",
    "print(metrics.classification_report(y_test, y_predicted))\n",
    "metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_predicted)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great work, we can be rightfully satisfied with our model. Seeing the results, we achieved an F1-score of 0.69 which is about **5% better than SciBERT's** 0.6571!\n",
    "\n",
    "You might wonder that \"this is great, but besides some utility functions (`clean`, `simple_parallel_map`, ...) what more value does GreatAI add?\". This would be a valid argument because the scope of GreatAI actually only starts here.\n",
    "\n",
    "> Not coincidentally, this is the point where the scope of Data Science ends but it's still a grey-zone for software engineering.\n",
    "\n",
    "In order to use this model in production, we have to make it accessible on some possibly shared infrastructure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;226m2022-07-09 22:33:18 |  WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
      "\u001b[38;5;226m2022-07-09 22:33:18 |  WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n",
      "\u001b[38;5;226m2022-07-09 22:33:18 |  WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n",
      "\u001b[38;5;226m2022-07-09 22:33:18 |  WARNING | Cannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:18 |     INFO | GreatAI (v0.1.0): configured ✅\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:18 |     INFO |   🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:18 |     INFO |   🔩 large_file_implementation: LargeFileLocal\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:18 |     INFO |   🔩 is_production: False\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:18 |     INFO |   🔩 should_log_exception_stack: True\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:18 |     INFO |   🔩 prediction_cache_size: 512\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:18 |     INFO |   🔩 dashboard_table_size: 20\u001b[0m\n",
      "\u001b[38;5;226m2022-07-09 22:33:18 |  WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
      "\u001b[38;5;226m2022-07-09 22:33:18 |  WARNING | > Find out more at https://se-ml.github.io/practices\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:18 |     INFO | Fetching cached versions of my-domain-predictor\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:19 |     INFO | Copying file for my-domain-predictor-5\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:19 |     INFO | Compressing my-domain-predictor-5\u001b[0m\n",
      "\u001b[38;5;39m2022-07-09 22:33:20 |     INFO | Model my-domain-predictor uploaded with version 5\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'my-domain-predictor:5'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from great_ai import save_model\n",
    "\n",
    "save_model(model, key=\"my-domain-predictor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Let's continue by finishing the deployment in the next notebook.](/tutorial/deploy)"
   ]
  }
 ],
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