Part 05 - Preprocessing and Pipelines.ipynb

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   "cell_type": "markdown",
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   "source": [
    "Preprocessing and Pipelines\n",
    "============================="
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.cross_validation import train_test_split\n",
    "digits = load_digits()\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data,\n",
    "                                                    digits.target)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Cross-validated pipelines including scaling, we need to estimate mean and standard deviation separately for each fold.\n",
    "To do that, we build a pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "source": [
    "standard_scaler = StandardScaler()\n",
    "standard_scaler.fit(X_train)\n",
    "X_train_scaled = standard_scaler.transform(X_train)\n",
    "svm = SVC().fit(X_train_scale, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "source": [
    "#pipeline = Pipeline([(\"scaler\", StandardScaler()),\n",
    "#                     (\"svm\", SVC())])\n",
    "# in new versions:\n",
    "pipeline = make_pipeline(StandardScaler(), SVC())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross-validation with a pipeline\n",
    "---------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "cross_val_score(pipeline, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid Search with a pipeline\n",
    "==========================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "param_grid = {'svc__C': 10. ** np.arange(-3, 3),\n",
    "              'svc__gamma' : 10. ** np.arange(-3, 3),\n",
    "              'standardscalar__with_mean' : [True, False],\n",
    "             }\n",
    "\n",
    "grid_pipeline = GridSearchCV(pipeline, param_grid=param_grid, n_jobs=-1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid_pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  }
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