6_decision_trees_excercises.ipynb

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  {
   "source": [
    "### Decision tree classifier for moons dataset"
   ],
   "cell_type": "markdown",
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  },
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      "\u001b[0;31mSignature:\u001b[0m \u001b[0mmake_moons\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoise\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Make two interleaving half circles\n",
      "\n",
      "A simple toy dataset to visualize clustering and classification\n",
      "algorithms. Read more in the :ref:`User Guide <sample_generators>`.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "n_samples : int, optional (default=100)\n",
      "    The total number of points generated.\n",
      "\n",
      "shuffle : bool, optional (default=True)\n",
      "    Whether to shuffle the samples.\n",
      "\n",
      "noise : double or None (default=None)\n",
      "    Standard deviation of Gaussian noise added to the data.\n",
      "\n",
      "random_state : int, RandomState instance or None (default)\n",
      "    Determines random number generation for dataset shuffling and noise.\n",
      "    Pass an int for reproducible output across multiple function calls.\n",
      "    See :term:`Glossary <random_state>`.\n",
      "\n",
      "Returns\n",
      "-------\n",
      "X : array of shape [n_samples, 2]\n",
      "    The generated samples.\n",
      "\n",
      "y : array of shape [n_samples]\n",
      "    The integer labels (0 or 1) for class membership of each sample.\n",
      "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/envs/ml/lib/python3.7/site-packages/sklearn/datasets/samples_generator.py\n",
      "\u001b[0;31mType:\u001b[0m      function\n"
     ],
     "name": "stdout"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "?make_moons\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_moons(n_samples=10000, noise=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(X[(y == 0), 0], X[(y == 0), 1], 'b.')\n",
    "plt.plot(X[(y == 1), 0], X[(y == 1), 1], 'r.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
       "            splitter='best'),\n",
       "       fit_params=None, iid='warn', n_jobs=3,\n",
       "       param_grid=[{'max_leaf_nodes': [10, 100, 1000, 10000]}, {'criterion': ['gini', 'entropy']}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "param_grid = [\n",
    "    {'max_leaf_nodes': [10, 100, 1000, 10000]},\n",
    "    {'criterion': ['gini', 'entropy']}\n",
    "]\n",
    "\n",
    "grid_search = GridSearchCV(tree_clf, param_grid, cv=3, n_jobs=3)\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=10,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
       "            splitter='best')"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=10,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
       "            splitter='best')"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "tree_clf = DecisionTreeClassifier(max_leaf_nodes=10, random_state=42)\n",
    "\n",
    "tree_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.872"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "tree_clf.score(X_test, y_test)"
   ]
  },
  {
   "source": [
    "### Grow a forest"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "n_trees = 1000\n",
    "n_instances = 100\n",
    "\n",
    "mini_sets = []\n",
    "\n",
    "rs = ShuffleSplit(n_splits=n_trees, test_size=len(X_train) - n_instances, random_state=42)\n",
    "for mini_train_index, mini_test_index in rs.split(X_train):\n",
    "    X_mini_train = X_train[mini_train_index]\n",
    "    y_mini_train = y_train[mini_train_index]\n",
    "    mini_sets.append((X_mini_train, y_mini_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}