90_persistence.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Model Persistence](https://scikit-learn.org/stable/modules/model_persistence.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Using Python's built-in persistence model ([pickle](https://docs.python.org/3/library/pickle.html))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn import datasets\n",
    "\n",
    "X,y = datasets.load_iris(return_X_y=True)\n",
    "clf = svm.SVC().fit(X, y)\n",
    "\n",
    "import pickle\n",
    "s = pickle.dumps(clf)\n",
    "clf2 = pickle.loads(s)\n",
    "clf2.predict(X[0:1])\n",
    "\n",
    "y[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- consider instead using joblib's `dump` & `load` methods.\n",
    "- More efficient on large numpy arrays, but can only pickle to disk - not a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['filename.joblib']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump,load\n",
    "dump(clf, 'filename.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = load('filename.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Pickle (and joblib by extension) have maintainability & security issues. **Never unpickle untrusted data**. \n",
    "\n",
    "- To rebuild a model with future versions of scikit-learn, save the following objects along with the pickled model:\n",
    "    - *training data* (an immutable snapshot)\n",
    "    - *python source code*\n",
    "    - *versions* of scikit-learn and dependencies\n",
    "    - *cross-validation score* obtained on the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:working] *",
   "language": "python",
   "name": "conda-env-working-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}