04_05_leave_one_out.ipynb

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   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "data = load_breast_cancer()\n",
    "\n",
    "X = data.data\n",
    "y = data.target\n",
    "\n",
    "from sklearn import linear_model\n",
    "clf = linear_model.LogisticRegression(solver='liblinear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "leave-one-out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "loocv = LeaveOneOut()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "train_index, test_index = next(loocv.split(X, y)) # １つだけ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "y.size, train_index.size, test_index.size # サイズを見てみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(clf, \n",
    "                         X, y, \n",
    "                         cv=loocv) # LeaveOneOut"
   ]
  },
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   },
   "outputs": [],
   "source": [
    "scores.mean()*100, scores.std()*100, scores.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "leave p out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import LeavePOut\n",
    "loocv = LeavePOut(2)\n",
    "# scores = cross_val_score(clf, X, y, cv=loocv) # LeavePOut 終わらない！ n_C_2オーダー\n",
    "# scores.mean(), scores.std(), scores.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "leave one-group out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "group = np.array(list(range(50))*12)\n",
    "group = np.sort(group[:y.size])\n",
    "group.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "group"
   ]
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   "cell_type": "code",
   "execution_count": null,
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     "start_time": "2017-06-22T08:22:49.563631Z"
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   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import LeaveOneGroupOut\n",
    "loocv = LeaveOneGroupOut()\n",
    "\n",
    "for train_index, test_index in loocv.split(X, y, group):\n",
    "\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "    clf.fit(X_train, y_train)\n",
    "    print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2017-06-22T08:22:49.831506Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(clf, \n",
    "                         X, y, \n",
    "                         groups=group, \n",
    "                         cv=loocv) # LeaveOneGroupOut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2017-06-22T08:22:50.102021Z"
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   },
   "outputs": [],
   "source": [
    "scores.mean(), scores.std(), scores.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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