Decorator Function for fitted parameter

OvO_with_is_fitted_prop.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:ee93ac627aa6027de5b43cee374610a87019d885d5c329c87c258c7f9d29ce3a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin\n",
      "from sklearn.svm import LinearSVC\n",
      "from sklearn.utils import check_consistent_length\n",
      "from sklearn.externals.joblib import Parallel, delayed\n",
      "from sklearn.multiclass import _fit_binary, _fit_ovo_binary, OneVsOneClassifier\n",
      "from sklearn import datasets\n",
      "import numpy as np\n",
      "import functools"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def set_is_fitted(fit_method):\n",
      "    @functools.wraps(fit_method)\n",
      "    def wrapped_fit_method(self, *args, **kwargs):\n",
      "        self.is_fitted_ = False\n",
      "        return_val = fit_method(self, *args, **kwargs)\n",
      "        # If the fit was successful\n",
      "        self.is_fitted_ = True\n",
      "        return return_val\n",
      "    return wrapped_fit_method\n",
      "\n",
      "@property\n",
      "def _is_fitted(self):\n",
      "    if hasattr(self, 'is_fitted_'):\n",
      "        return self.is_fitted_\n",
      "    return None # May be True or False\n",
      "\n",
      "BaseEstimator.is_fitted = _is_fitted"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# @set_is_fitted to be used over all the desired fit methods\n",
      "\n",
      "@set_is_fitted\n",
      "def _fit(self, X, y):\n",
      "    \"\"\"Fit underlying estimators.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    X : (sparse) array-like, shape = [n_samples, n_features]\n",
      "        Data.\n",
      "\n",
      "    y : array-like, shape = [n_samples]\n",
      "        Multi-class targets.\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    self\n",
      "    \"\"\"\n",
      "    y = np.asarray(y)\n",
      "    check_consistent_length(X, y)\n",
      "\n",
      "    self.classes_ = np.unique(y)\n",
      "    n_classes = self.classes_.shape[0]\n",
      "    self.estimators_ = Parallel(n_jobs=self.n_jobs)(\n",
      "        delayed(_fit_ovo_binary)(\n",
      "            self.estimator, X, y, self.classes_[i], self.classes_[j])\n",
      "        for i in range(n_classes) for j in range(i + 1, n_classes))\n",
      "\n",
      "    return self\n",
      "\n",
      "OneVsOneClassifier.fit = _fit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iris = datasets.load_iris()\n",
      "rng = np.random.RandomState(0)\n",
      "perm = rng.permutation(iris.target.size)\n",
      "iris.data = iris.data[perm]\n",
      "iris.target = iris.target[perm]\n",
      "n_classes = 3\n",
      "\n",
      "ovo = OneVsOneClassifier(LinearSVC())\n",
      "ovo.fit(iris.data, iris.target)\n",
      "ovo.is_fitted"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
}