notebooks/survey_calibration_simulated.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "survey_calibration_simulated.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhdK9tifRPYR"
      },
      "source": [
        "This is a toy example of using empirical calibration for survey calibration."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "NBeKkRlhq1-Q"
      },
      "source": [
        "#@title Copyright 2019 The Empirical Calibration Authors.\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "# ============================================================================"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rLVCPSCxCJez"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/empirical_calibration/blob/master/notebooks/survey_calibration_simulated.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/google/empirical_calibration/blob/master/notebooks/survey_calibration_simulated.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGDMBD7TRIf4"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzy_rdy7WXyS",
        "outputId": "7d80089c-1972-4dc4-c121-76462cb9216d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "sns.set_style('whitegrid')\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "# install and import ec\n",
        "!pip install -q git+https://github.com/google/empirical_calibration\n",
        "import empirical_calibration as ec"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "  Building wheel for empirical-calibration (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8orhXBdZq9l"
      },
      "source": [
        "## Selection Bias"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2BGbpoHcLOc"
      },
      "source": [
        "Generate a population of size 1000."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJ9oaJ4VeD2b",
        "outputId": "8ebf10de-c658-4724-ad46-c5f3cd051d05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.random.seed(123)\n",
        "N = 1000  # population size.\n",
        "df_population = pd.DataFrame({\n",
        "    'x1': np.repeat(['F', 'F', 'M', 'M'], repeats=N),\n",
        "    'x2': np.repeat(['H', 'L', 'H', 'L'], repeats=N),\n",
        "    'y': (np.repeat([1e1, 2e1, 3e1, 4e1], repeats=N) +\n",
        "          np.random.normal(size=4*N)),\n",
        "    'response_rate': np.repeat([1.0e-2, 1.1e-2, 1.2e-2, 1.3e-2],\n",
        "                               repeats=N)\n",
        "})\n",
        "true_mean = df_population['y'].mean()\n",
        "print('true population mean is {}.'.format(true_mean))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "true population mean is 25.011414202186163.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "utqFsTLJEUjP"
      },
      "source": [
        "Define a function that draws a sample and calculates the estimates of mean."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CkmBwMobdm2A"
      },
      "source": [
        "def simulate(seed):\n",
        "  \"\"\"Draws sample and calculates (un)weighted estimates.\"\"\"\n",
        "  np.random.seed(seed)\n",
        "  responded = (np.random.uniform(size=4*N) <=\n",
        "               df_population['response_rate'])\n",
        "  df_sample = df_population.loc[responded]\n",
        "  weights, _ = ec.from_formula(\n",
        "      formula='~x1+x2',\n",
        "      df=df_sample,\n",
        "      baseline_weights = np.repeat(1.0 / df_sample.shape[0], df_sample.shape[0]),\n",
        "      target_df=df_population,\n",
        "      objective=ec.Objective.QUADRATIC)\n",
        "  return pd.Series({\n",
        "      'n': df_sample.shape[0],\n",
        "      'unweighted': df_sample['y'].mean(),\n",
        "      'weighted': df_sample['y'].mul(weights).sum()})"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRB-gGkpWcNu",
        "outputId": "c1d9d36b-5c27-4336-ae07-3feaf8282e83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "# Try it out.\n",
        "simulate(123)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "n             58.000000\n",
              "unweighted    27.434867\n",
              "weighted      25.135381\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlwdzFnfetYi"
      },
      "source": [
        "# Do it 1000 times and collect results.\n",
        "result = pd.DataFrame(simulate(seed) for seed in range(1000))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaZYjzvn_w7s",
        "outputId": "2c37d85b-d983-4b81-9a04-90cb2c2c6914",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Calculate rmse.\n",
        "print('unweighted rmse is {}'.format(\n",
        "    np.sqrt(np.mean((result['unweighted']-true_mean)**2))))\n",
        "\n",
        "print('weighted rmse is {}'.format(\n",
        "    np.sqrt(np.mean((result['weighted']-true_mean)**2))))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "unweighted rmse is 1.9925982840693195\n",
            "weighted rmse is 0.15064039778853136\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyTeGKSF6dMT",
        "outputId": "00f93779-529b-489f-aba2-e138813a322c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        }
      },
      "source": [
        "# visually compare unweighted vs weighted estimates.\n",
        "def compare_estimates(unweighted, weighted, truth,\n",
        "                      figsize=(12, 8),\n",
        "                      fontsize=22,\n",
        "                      output_path=None):\n",
        "  f = plt.figure(figsize=figsize)\n",
        "  plt.rcParams.update({'font.size': fontsize})\n",
        "  plt.hist(unweighted,\n",
        "           alpha=0.4,\n",
        "           color='orange',\n",
        "           edgecolor='none',\n",
        "           label='unweighted')\n",
        "  plt.hist(weighted,\n",
        "           alpha=0.4,\n",
        "           color='blue',\n",
        "           edgecolor='none',\n",
        "           label='weighted')\n",
        "  plt.axvline(truth, linestyle='dashed', color='red')\n",
        "  plt.legend(loc='upper left')\n",
        "  plt.show()\n",
        "  if output_path is not None:\n",
        "     f.savefig(output_path, bbox_inches='tight')\n",
        "\n",
        "compare_estimates(unweighted=result['unweighted'],\n",
        "                  weighted=result['weighted'],\n",
        "                  truth=true_mean)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 738,
              "height": 477
            },
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}