{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "data = pd.read_csv('./供热测试结果.csv')"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "            0         1\n0    0.072858  0.072700\n1    0.073347  0.075045\n2    0.082159  0.080671\n3    0.084120  0.081944\n4    0.065845  0.066739\n..        ...       ...\n408  0.066066  0.066927\n409  0.084331  0.082709\n410  0.069216  0.069256\n411  0.065259  0.066203\n412  0.069608  0.071754\n\n[413 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.072858</td>\n      <td>0.072700</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.073347</td>\n      <td>0.075045</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.082159</td>\n      <td>0.080671</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.084120</td>\n      <td>0.081944</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.065845</td>\n      <td>0.066739</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>408</th>\n      <td>0.066066</td>\n      <td>0.066927</td>\n    </tr>\n    <tr>\n      <th>409</th>\n      <td>0.084331</td>\n      <td>0.082709</td>\n    </tr>\n    <tr>\n      <th>410</th>\n      <td>0.069216</td>\n      <td>0.069256</td>\n    </tr>\n    <tr>\n      <th>411</th>\n      <td>0.065259</td>\n      <td>0.066203</td>\n    </tr>\n    <tr>\n      <th>412</th>\n      <td>0.069608</td>\n      <td>0.071754</td>\n    </tr>\n  </tbody>\n</table>\n<p>413 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "0.8483477508497194"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(data.values[:,1], data.values[:,0])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function r2_score in module sklearn.metrics._regression:\n",
      "\n",
      "r2_score(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average')\n",
      "    :math:`R^2` (coefficient of determination) regression score function.\n",
      "    \n",
      "    Best possible score is 1.0 and it can be negative (because the\n",
      "    model can be arbitrarily worse). A constant model that always\n",
      "    predicts the expected value of y, disregarding the input features,\n",
      "    would get a :math:`R^2` score of 0.0.\n",
      "    \n",
      "    Read more in the :ref:`User Guide <r2_score>`.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      "        Ground truth (correct) target values.\n",
      "    \n",
      "    y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      "        Estimated target values.\n",
      "    \n",
      "    sample_weight : array-like of shape (n_samples,), default=None\n",
      "        Sample weights.\n",
      "    \n",
      "    multioutput : {'raw_values', 'uniform_average', 'variance_weighted'},             array-like of shape (n_outputs,) or None, default='uniform_average'\n",
      "    \n",
      "        Defines aggregating of multiple output scores.\n",
      "        Array-like value defines weights used to average scores.\n",
      "        Default is \"uniform_average\".\n",
      "    \n",
      "        'raw_values' :\n",
      "            Returns a full set of scores in case of multioutput input.\n",
      "    \n",
      "        'uniform_average' :\n",
      "            Scores of all outputs are averaged with uniform weight.\n",
      "    \n",
      "        'variance_weighted' :\n",
      "            Scores of all outputs are averaged, weighted by the variances\n",
      "            of each individual output.\n",
      "    \n",
      "        .. versionchanged:: 0.19\n",
      "            Default value of multioutput is 'uniform_average'.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    z : float or ndarray of floats\n",
      "        The :math:`R^2` score or ndarray of scores if 'multioutput' is\n",
      "        'raw_values'.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    This is not a symmetric function.\n",
      "    \n",
      "    Unlike most other scores, :math:`R^2` score may be negative (it need not\n",
      "    actually be the square of a quantity R).\n",
      "    \n",
      "    This metric is not well-defined for single samples and will return a NaN\n",
      "    value if n_samples is less than two.\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] `Wikipedia entry on the Coefficient of determination\n",
      "            <https://en.wikipedia.org/wiki/Coefficient_of_determination>`_\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> from sklearn.metrics import r2_score\n",
      "    >>> y_true = [3, -0.5, 2, 7]\n",
      "    >>> y_pred = [2.5, 0.0, 2, 8]\n",
      "    >>> r2_score(y_true, y_pred)\n",
      "    0.948...\n",
      "    >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]\n",
      "    >>> y_pred = [[0, 2], [-1, 2], [8, -5]]\n",
      "    >>> r2_score(y_true, y_pred,\n",
      "    ...          multioutput='variance_weighted')\n",
      "    0.938...\n",
      "    >>> y_true = [1, 2, 3]\n",
      "    >>> y_pred = [1, 2, 3]\n",
      "    >>> r2_score(y_true, y_pred)\n",
      "    1.0\n",
      "    >>> y_true = [1, 2, 3]\n",
      "    >>> y_pred = [2, 2, 2]\n",
      "    >>> r2_score(y_true, y_pred)\n",
      "    0.0\n",
      "    >>> y_true = [1, 2, 3]\n",
      "    >>> y_pred = [3, 2, 1]\n",
      "    >>> r2_score(y_true, y_pred)\n",
      "    -3.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(r2_score)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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