sitc/ml1/2_7_Model_Persistence.ipynb

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    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
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   "source": [
    "# Course Notes for Learning Intelligent Systems"
   ]
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   "metadata": {},
   "source": [
    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "* [Model Persistence](#Model-Persistence)\n",
    "* [References](#References)\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Persistence"
   ]
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   "source": [
    "The goal of this notebook is to learn how to save a model in the the scikit by using Python’s built-in persistence model, namely pickle\n",
    "\n",
    "First we recap the previous tasks: load data, preprocess and train the model."
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       "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('KNN', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'))])"
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     "execution_count": 1,
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   "source": [
    "# load iris\n",
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# Training and test spliting\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_iris, y_iris = iris.data, iris.target\n",
    "# Test set will be the 25% taken randomly\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n",
    "\n",
    "# Create the model using the pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# create a composite estimator made by a pipeline of preprocessing and the KNN model\n",
    "model = Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('KNN', KNeighborsClassifier())\n",
    "])\n",
    "\n",
    "# Train the model\n",
    "model.fit(x_train, y_train) \n"
   ]
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   "metadata": {},
   "source": [
    "Now we are going to save the model to a data structure called *pickle*. A pickle is a dictionary and can be used as a file or a string."
   ]
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   "source": [
    "import pickle\n",
    "s = pickle.dumps(model)\n",
    "model2 = pickle.loads(s)\n",
    "model2.predict(x_iris[0:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more efficient alternative to pickle is joblib, especially for big data problems. In this case the model can only be saved to a file and not to a string."
   ]
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   "execution_count": 3,
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   "source": [
    "# save model\n",
    "from sklearn.externals import joblib\n",
    "joblib.dump(model, 'filename.pkl') \n",
    "\n",
    "#load model\n",
    "model2 = joblib.load('filename.pkl') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Tutorial scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html)\n",
    "* [Model persistence in scikit-learn](http://scikit-learn.org/stable/modules/model_persistence.html#model-persistence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Licence\n",
    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
    "\n",
    "© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
   ]
  }
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