{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Positivity filtering\n",
    "\n",
    "This Notebooks presents several models that perform overlap exclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trimming Positivity Checker\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from causallib.positivity import Trimming\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import make_classification\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_trimmed_data(X, a, threshold=[0.01,0.05,'crump']):\n",
    "    filtered_series = dict()\n",
    "    tr = Trimming()\n",
    "    tr.fit(X, a)\n",
    "    for th in threshold:\n",
    "        filtered_series['filtered_' + str(th)] = tr.predict(X, a, threshold=th)\n",
    "    df_filtered = pd.DataFrame.from_dict(filtered_series)\n",
    "\n",
    "    df = pd.concat([X, a, df_filtered], axis=1)\n",
    "    df.columns = ['X_1','X_2','a', *list(filtered_series.keys())]\n",
    "    return df\n",
    "\n",
    "def plot_trimmed_obs_2d_spcae(data, number_of_methods=3):\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=number_of_methods, figsize=(15,6),sharey=True)\n",
    "    for ind, fliter_type in enumerate(data.columns[3:]):\n",
    "        sns.scatterplot(data=data, x=\"X_1\", y=\"X_2\", hue=\"a\", style=fliter_type, ax=ax[ind], style_order=[True, False])\n",
    "        ax[ind].set_title('2d space with treatment assignment\\nx - are observations that filtered out ({})'\n",
    "                          .format(fliter_type), fontsize=11)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create 2d overlapping data \n",
    "X, a = make_classification(n_samples=120, n_features=2, n_redundant=0, n_informative=1,\n",
    "                           random_state=1, n_clusters_per_class=1, class_sep=0)\n",
    "X, a = pd.DataFrame(X), pd.Series(a)\n",
    "\n",
    "df = create_trimmed_data(X, a)\n",
    "plot_trimmed_obs_2d_spcae(data, number_of_methods=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create parialy seperated dataset \n",
    "X, a = make_classification(n_samples=120, n_features=2, n_redundant=0, n_informative=1,\n",
    "                           random_state=1, n_clusters_per_class=1, class_sep=1)\n",
    "X, a = pd.DataFrame(X), pd.Series(a)\n",
    "\n",
    "df = create_trimmed_data(X, a)\n",
    "plot_trimmed_obs_2d_spcae(df, number_of_methods=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matching Positivity Checker\n",
    "\n",
    "One way to filter samples as violating positivity assumption or not is to match them and only return samples that successfully matched. Here we show how a positivity checker can be used that is based on the matching logic in causallib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "\n",
    "from causallib.positivity import Matching\n",
    "from causallib.datasets import load_nhefs\n",
    "data = load_nhefs(augment=False,onehot=False)\n",
    "X, a, y = data.X, data.a, data.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "m=Matching(with_replacement=False)\n",
    "m.fit(X, a)\n",
    "overlap=m.predict(X, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert(min(len(a) - a.sum(), a.sum()) * 2 == overlap.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from causallib.evaluation.weight_evaluator import calculate_covariate_balance\n",
    "covbalance = calculate_covariate_balance(X, a, overlap)\n",
    "sb.heatmap(covbalance, annot=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "pc1,pc2 = TSNE(n_components=2).fit_transform(X).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pc1', ylabel='pc2'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xaug = X.join(a).assign(pc1=pc1,pc2=pc2,overlap=overlap)\n",
    "sb.set(\"notebook\")\n",
    "sb.scatterplot(x=\"pc1\", y=\"pc2\", hue=\"overlap\", markers=\"qsmk\", data=Xaug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Bounding Box Positivity Checker\n",
    "\n",
    "One way to filter samples as violating the positivity assumption or not is to check for overlap on each variable. As a univariate problem, this is straightforward and we can either use raw min/max to estimate support, quantiles or other methods. Here we have implemented the raw and quantile versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from causallib.positivity import UnivariateBoundingBox\n",
    "from causallib.datasets import load_nhefs\n",
    "data = load_nhefs(augment=False, onehot=False)\n",
    "X, a, y = data.X, data.a, data.y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic usage is like the other modules, supporting `fit` and `predict`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "uvbb = UnivariateBoundingBox(quantile_alpha=0.05, continuous_columns=[\"age\", \"smokeintensity\", \"smokeyrs\", \"wt71\"])\n",
    "uvbb.fit(X, a)\n",
    "overlap = uvbb.predict(X, a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the results in a table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>treatment</th>\n",
       "      <th>control</th>\n",
       "      <th>joint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>active</th>\n",
       "      <td>support: {0, 1, 2}</td>\n",
       "      <td>support: {0, 1, 2}</td>\n",
       "      <td>support: {0, 1, 2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>support: [26.0, 69.0]</td>\n",
       "      <td>support: [25.0, 67.0]</td>\n",
       "      <td>support: [26.0, 67.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>support: {1, 2, 3, 4, 5}</td>\n",
       "      <td>support: {1, 2, 3, 4, 5}</td>\n",
       "      <td>support: {1, 2, 3, 4, 5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exercise</th>\n",
       "      <td>support: {0, 1, 2}</td>\n",
       "      <td>support: {0, 1, 2}</td>\n",
       "      <td>support: {0, 1, 2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>support: {0, 1}</td>\n",
       "      <td>support: {0, 1}</td>\n",
       "      <td>support: {0, 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>support: {0, 1}</td>\n",
       "      <td>support: {0, 1}</td>\n",
       "      <td>support: {0, 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smokeintensity</th>\n",
       "      <td>support: [1.0, 40.0]</td>\n",
       "      <td>support: [3.0, 45.0]</td>\n",
       "      <td>support: [3.0, 40.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smokeyrs</th>\n",
       "      <td>support: [6.0, 52.0]</td>\n",
       "      <td>support: [5.0, 48.0]</td>\n",
       "      <td>support: [6.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt71</th>\n",
       "      <td>support: [47.0655, 106.79149999999998]</td>\n",
       "      <td>support: [46.72, 104.52200000000002]</td>\n",
       "      <td>support: [47.0655, 104.52200000000002]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             treatment  \\\n",
       "active                              support: {0, 1, 2}   \n",
       "age                              support: [26.0, 69.0]   \n",
       "education                     support: {1, 2, 3, 4, 5}   \n",
       "exercise                            support: {0, 1, 2}   \n",
       "race                                   support: {0, 1}   \n",
       "sex                                    support: {0, 1}   \n",
       "smokeintensity                    support: [1.0, 40.0]   \n",
       "smokeyrs                          support: [6.0, 52.0]   \n",
       "wt71            support: [47.0655, 106.79149999999998]   \n",
       "\n",
       "                                             control  \\\n",
       "active                            support: {0, 1, 2}   \n",
       "age                            support: [25.0, 67.0]   \n",
       "education                   support: {1, 2, 3, 4, 5}   \n",
       "exercise                          support: {0, 1, 2}   \n",
       "race                                 support: {0, 1}   \n",
       "sex                                  support: {0, 1}   \n",
       "smokeintensity                  support: [3.0, 45.0]   \n",
       "smokeyrs                        support: [5.0, 48.0]   \n",
       "wt71            support: [46.72, 104.52200000000002]   \n",
       "\n",
       "                                                 joint  \n",
       "active                              support: {0, 1, 2}  \n",
       "age                              support: [26.0, 67.0]  \n",
       "education                     support: {1, 2, 3, 4, 5}  \n",
       "exercise                            support: {0, 1, 2}  \n",
       "race                                   support: {0, 1}  \n",
       "sex                                    support: {0, 1}  \n",
       "smokeintensity                    support: [3.0, 40.0]  \n",
       "smokeyrs                          support: [6.0, 48.0]  \n",
       "wt71            support: [47.0655, 104.52200000000002]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uvbb.supports_table_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the subtract operation on the support objects to check the regions of lack of overlap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "active                                                  {}\n",
       "age                                             [1.0, 2.0]\n",
       "education                                               {}\n",
       "exercise                                                {}\n",
       "race                                                    {}\n",
       "sex                                                     {}\n",
       "smokeintensity                                [-2.0, -5.0]\n",
       "smokeyrs                                        [1.0, 4.0]\n",
       "wt71              [0.34550000000000125, 2.269499999999965]\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uvbb.supports_table_[\"treatment\"] - uvbb.supports_table_[\"control\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This, in turn, defines a kind of score: how much of a support mismatch is there, in standardized units:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll want to have a nice way to visualize the results so we'll make some plotting helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_support(supports, ax):\n",
    "    heights = [2, 1, 0]\n",
    "    xmin = [s.support[0] for s in supports]\n",
    "    xmax = [s.support[1] for s in supports]\n",
    "    ax.hlines(y=heights, xmin=min(xmin), xmax=max(xmax), ls=\":\")\n",
    "    ax.hlines(y=heights, xmin=xmin, xmax=xmax,\n",
    "              colors=[\"white\"], lw=10, alpha=1)\n",
    "    ax.hlines(y=heights, xmin=xmin, xmax=xmax, colors=[\n",
    "              \"blue\", \"black\", \"orange\"], lw=10, alpha=0.5)\n",
    "    for h, label in zip(heights, [\"treatment\", \"joint\", \"control\"]):\n",
    "        midpoint = (min(xmin) + max(xmax))/2\n",
    "        ax.text(midpoint, h, label, horizontalalignment='center',\n",
    "                verticalalignment='center', color=\"white\", fontweight=\"bold\", fontsize=\"large\")\n",
    "    ax.set_yticks([])\n",
    "    ax.set_ylim(-0.5, 2.5)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "\n",
    "\n",
    "def plot_supports(uvbb, columns):\n",
    "    fig, axes = plt.subplots(len(columns), 1, figsize=(5, 5))\n",
    "    for ax, c in zip(axes, columns):\n",
    "        support_list = [uvbb.treatment_support_[c],\n",
    "                        uvbb.joint_support_[c], uvbb.control_support_[c]]\n",
    "        plot_support(support_list, ax)\n",
    "        ax.set_ylabel(c)\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_supports(\n",
    "    UnivariateBoundingBox(quantile_alpha=None,\n",
    "                          continuous_columns=[\n",
    "                              \"age\", \"smokeintensity\", \"smokeyrs\", \"wt71\"]\n",
    "                          ).fit(X, a),\n",
    "    [\"wt71\", \"age\", \"smokeintensity\", \"smokeyrs\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_supports(\n",
    "    UnivariateBoundingBox(quantile_alpha=0.05,\n",
    "                          continuous_columns=[\n",
    "                              \"age\", \"smokeintensity\", \"smokeyrs\", \"wt71\"]\n",
    "                          ).fit(X, a),\n",
    "    [\"wt71\", \"age\", \"smokeintensity\", \"smokeyrs\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "from causallib.evaluation.weight_evaluator import calculate_covariate_balance\n",
    "covbalance = calculate_covariate_balance(X, a, UnivariateBoundingBox(quantile_alpha=0.05).fit_predict(X, a))\n",
    "sb.heatmap(covbalance, annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am not happy with the results of this heatmap. It is showing that the trimming does not help the `abs_smd` even for variables like `smokeintensity`. I don't really see how that makes sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lalonde\n",
    "\n",
    "This is a great positivity dataset because we know it has problems. Let's see how our simple system works for that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22106, 12)\n"
     ]
    }
   ],
   "source": [
    "columns = [\"training\",   # Treatment assignment indicator\n",
    "           \"age\",        # Age of participant\n",
    "           \"education\",  # Years of education\n",
    "           \"black\",      # Indicate whether individual is black\n",
    "           \"hispanic\",   # Indicate whether individual is hispanic\n",
    "           \"married\",    # Indicate whether individual is married\n",
    "           \"no_degree\",  # Indicate if individual has no high-school diploma\n",
    "           \"re74\",       # Real earnings in 1974, prior to study participation\n",
    "           \"re75\",       # Real earnings in 1975, prior to study participation\n",
    "           \"re78\"]       # Real earnings in 1978, after study end\n",
    "file_names = [\"http://www.nber.org/~rdehejia/data/nswre74_treated.txt\",\n",
    "              \"http://www.nber.org/~rdehejia/data/nswre74_control.txt\",\n",
    "              \"http://www.nber.org/~rdehejia/data/psid_controls.txt\",\n",
    "              \"http://www.nber.org/~rdehejia/data/psid2_controls.txt\",\n",
    "              \"http://www.nber.org/~rdehejia/data/psid3_controls.txt\",\n",
    "              \"http://www.nber.org/~rdehejia/data/cps_controls.txt\",\n",
    "              \"http://www.nber.org/~rdehejia/data/cps2_controls.txt\",\n",
    "              \"http://www.nber.org/~rdehejia/data/cps3_controls.txt\"]\n",
    "files = [pd.read_csv(file_name, delim_whitespace=True,\n",
    "                     header=None, names=columns) for file_name in file_names]\n",
    "lalonde = pd.concat(files, ignore_index=True)\n",
    "lalonde = lalonde.sample(frac=1.0, random_state=42)  # Shuffle\n",
    "lalonde = lalonde.sample(frac=1.0, random_state=42)  # Shuffle\n",
    "lalonde = lalonde.join(\n",
    "    (lalonde[[\"re74\", \"re75\"]] == 0).astype(int), rsuffix=(\"=0\"))\n",
    "\n",
    "print(lalonde.shape)\n",
    "\n",
    "a_ll = lalonde.pop(\"training\")\n",
    "y_ll = lalonde.pop(\"re78\")\n",
    "X_ll = lalonde"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want the categoricals to be correctly identified as such. For our purposes today we'll treat years of education as a continuous number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uvbbll = UnivariateBoundingBox(0.0, continuous_columns=[\"age\", \"re74\", \"re75\", \"education\"]).fit(X_ll, a_ll)\n",
    "\n",
    "plot_supports(uvbbll, [\"age\", \"re74\", \"re75\", \"education\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "uvbbll_q005 = UnivariateBoundingBox(0.05, continuous_columns=[\"age\", \"re74\", \"re75\", \"education\"]).fit(X_ll, a_ll)\n",
    "\n",
    "plot_supports(uvbbll, [\"age\", \"re74\", \"re75\", \"education\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>treatment</th>\n",
       "      <th>control</th>\n",
       "      <th>joint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>support: [17.0, 48.0]</td>\n",
       "      <td>support: [16.0, 55.0]</td>\n",
       "      <td>support: [17.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>support: [4.0, 16.0]</td>\n",
       "      <td>support: [0.0, 18.0]</td>\n",
       "      <td>support: [4.0, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black</th>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic</th>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_degree</th>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "      <td>support: [0.0, 1.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>re74</th>\n",
       "      <td>support: [0.0, 35040.07]</td>\n",
       "      <td>support: [0.0, 137148.68]</td>\n",
       "      <td>support: [0.0, 35040.07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>re75</th>\n",
       "      <td>support: [0.0, 25142.24]</td>\n",
       "      <td>support: [0.0, 156653.23]</td>\n",
       "      <td>support: [0.0, 25142.24]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>re74=0</th>\n",
       "      <td>support: {0, 1}</td>\n",
       "      <td>support: {0, 1}</td>\n",
       "      <td>support: {0, 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>re75=0</th>\n",
       "      <td>support: {0, 1}</td>\n",
       "      <td>support: {0, 1}</td>\n",
       "      <td>support: {0, 1}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          treatment                    control  \\\n",
       "age           support: [17.0, 48.0]      support: [16.0, 55.0]   \n",
       "education      support: [4.0, 16.0]       support: [0.0, 18.0]   \n",
       "black           support: [0.0, 1.0]        support: [0.0, 1.0]   \n",
       "hispanic        support: [0.0, 1.0]        support: [0.0, 1.0]   \n",
       "married         support: [0.0, 1.0]        support: [0.0, 1.0]   \n",
       "no_degree       support: [0.0, 1.0]        support: [0.0, 1.0]   \n",
       "re74       support: [0.0, 35040.07]  support: [0.0, 137148.68]   \n",
       "re75       support: [0.0, 25142.24]  support: [0.0, 156653.23]   \n",
       "re74=0              support: {0, 1}            support: {0, 1}   \n",
       "re75=0              support: {0, 1}            support: {0, 1}   \n",
       "\n",
       "                              joint  \n",
       "age           support: [17.0, 48.0]  \n",
       "education      support: [4.0, 16.0]  \n",
       "black           support: [0.0, 1.0]  \n",
       "hispanic        support: [0.0, 1.0]  \n",
       "married         support: [0.0, 1.0]  \n",
       "no_degree       support: [0.0, 1.0]  \n",
       "re74       support: [0.0, 35040.07]  \n",
       "re75       support: [0.0, 25142.24]  \n",
       "re74=0              support: {0, 1}  \n",
       "re75=0              support: {0, 1}  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uvbbll.supports_table_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                         [1.0, -7.0]\n",
       "education                   [4.0, -2.0]\n",
       "black                        [0.0, 0.0]\n",
       "hispanic                     [0.0, 0.0]\n",
       "married                      [0.0, 0.0]\n",
       "no_degree                    [0.0, 0.0]\n",
       "re74         [0.0, -102108.60999999999]\n",
       "re75         [0.0, -131510.99000000002]\n",
       "re74=0                               {}\n",
       "re75=0                               {}\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uvbbll.supports_table_[\"treatment\"] - uvbbll.supports_table_[\"control\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Possible scoring by measuring escape from bounding box\n",
    "This is an experimental idea by which we can tell the \"score\" of the fit by reporting the largest overlap violation in standardized units. It is not included in the main module because it is still experimental."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score(uvbb, full_output=False):\n",
    "    support_diff = (uvbb.scaled_supports_table_[\"treatment\"] - uvbb.scaled_supports_table_[\"control\"])\n",
    "    rescaled_delta_df = support_diff[support_diff.apply(bool)]\n",
    "    if not full_output:\n",
    "        return  abs(np.vstack(rescaled_delta_df)).max()\n",
    "    return rescaled_delta_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41880441363664245"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(uvbb,False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.54002226126555"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(uvbbll, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Multiple treatment positivity </H1>\n",
    "\n",
    "We assume that every patient received one treatment\n",
    "\n",
    "When considering multiple treatments we need to asses the positivity estimation scheme. In the binary case we will have an experimental group and control group in that case we would want to unconfound each treatment group against the control. We provide a scheme that enable positivity estimation even without a control group. In The notebook we present two meta-schemes for multiple treatment positivity that uses a basic positivity algorithm such as Trimming.\n",
    "For further theoritical discussion we reffer the reader to the paper: <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4219328/'> Theoritical basis of multiple treatments and multiple versions by Miguel A. Hernán </href>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> One versus another </h2>\n",
    "In this meta algorithm we asses the positivity of pairs of treatments. In the default mode this scheme will asses the pairwise positivity but if a control group is defined we can asses the positivity of each treatment against the control group. Or define which pairs specifically should be considered. <br> <br>\n",
    "Let's create some simulation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "import pandas as pd\n",
    "from causallib.positivity import Trimming\n",
    "from causallib.positivity.multiple_treatment_positivity import OneVersusRestPositivity, OneVersusAnotherPositivity\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "def create_simulated_data():\n",
    "    X, y = make_classification(\n",
    "            n_samples=500,\n",
    "            n_features=3,\n",
    "            n_informative=2,\n",
    "            n_redundant=0,\n",
    "            n_repeated=0,\n",
    "            n_classes=2,\n",
    "            n_clusters_per_class=1,\n",
    "            flip_y=0.01,\n",
    "            class_sep=1.0,\n",
    "            random_state=899,\n",
    "        )\n",
    "    X, a = X[:, :-1], pd.Series(\n",
    "        pd.qcut(X[:, -1], 5, labels=[1, 2, 3, 4, 5], retbins=True)[0].to_numpy()\n",
    "    )\n",
    "    index_ = ['patient_'+str(i) for i in range(500)]\n",
    "    return {\n",
    "        \"X\": pd.DataFrame(X,index=index_),\n",
    "        \"a\": pd.Series(data=a.values,index=index_),\n",
    "        \"y\": pd.Series(data = y,index=index_),\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> With a control group </h3>\n",
    "Let's assume that treatment five is the control group. so we will define the versus another positivity with trimming as the base algorithm. Here each treatment is measured individually versus treatment five only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVersusAnotherPositivity(base_positivity_estimator=Trimming(threshold='crump'),\n",
       "                           treatment_a_name='Treatment',\n",
       "                           treatment_b_name='Control',\n",
       "                           treatment_pairs_list=[(4, 5), (2, 5), (3, 5),\n",
       "                                                 (1, 5)])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = create_simulated_data()\n",
    "pos_one_vs_control = OneVersusAnotherPositivity(Trimming(),verbose=False, treatment_pairs_list=5, treatment_a_name='Treatment', treatment_b_name='Control')\n",
    "pos_one_vs_control.fit(data['X'],data['a'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the positivity profile of each patient on the tuple he was treated. Notice that the estimation population is both i.e the treated and control groups.<br>\n",
    "Note that the None values are for the patient that were not included in the single positivity estimations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Treatment</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient_0</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_1</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Treatment     4     2     3     1  a\n",
       "Control       5     5     5     5   \n",
       "patient_0  True   NaN   NaN   NaN  4\n",
       "patient_1  True   NaN   NaN   NaN  4\n",
       "patient_2   NaN  True   NaN   NaN  2\n",
       "patient_3   NaN   NaN  True   NaN  3\n",
       "patient_4  True  True  True  True  5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile = pos_one_vs_control.positivity_profile(data['X'],data['a'],estimation_population='Both').head()\n",
    "profile['a']  =data['a']\n",
    "profile.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can review the report for every patient - for those who are in the data but not necessarily were part of the control or treated groups for the single estimator. Treated and control modes are available as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Treatment</th>\n",
       "      <th>4</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <th>5</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient_0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Treatment     4      2     3      1  a\n",
       "Control       5      5     5      5   \n",
       "patient_0  True   True  True  False  4\n",
       "patient_1  True   True  True   True  4\n",
       "patient_2  True   True  True   True  2\n",
       "patient_3  True  False  True  False  3\n",
       "patient_4  True   True  True   True  5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile = pos_one_vs_control.positivity_profile(data['X'],data['a'],estimation_population='Every').head()\n",
    "profile['a']  =data['a']\n",
    "profile.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also view a report per treatment, same modes are available as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Control</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Control        5\n",
       "Treatment       \n",
       "1          0.760\n",
       "2          0.885\n",
       "3          1.000\n",
       "4          1.000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_one_vs_control.treatment_positivity_summary(data['X'],a=data['a'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the rate of the overlapping cases per treated group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H3> Without a control group </H3>\n",
    "In this case we will set a pairwise positivity. The default value (None) of the versus_list will be turned into a list containing a tuple per pair. <br>\n",
    "Because of the large number of estimators we would consider setting verbose to true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting treatment 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:06<00:00,  1.59it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OneVersusAnotherPositivity(base_positivity_estimator=Trimming(threshold='crump'),\n",
       "                           treatment_pairs_list=[(4, 2), (4, 3), (4, 5), (4, 1),\n",
       "                                                 (2, 3), (2, 5), (2, 1), (3, 5),\n",
       "                                                 (3, 1), (5, 1)],\n",
       "                           verbose=True)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_one_vs_another = OneVersusAnotherPositivity(Trimming(),verbose=True)\n",
    "pos_one_vs_another.fit(data['X'],data['a'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>treatmentB</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>treatmentA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.945</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "treatmentB     1      2    3      5\n",
       "treatmentA                         \n",
       "2           1.00    NaN  1.0  0.885\n",
       "3           1.00    NaN  NaN  1.000\n",
       "4           0.80  0.945  1.0  1.000\n",
       "5           0.76    NaN  NaN    NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_one_vs_another.treatment_positivity_summary(data['X'],a=data['a'],estimation_population='Both')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rows are for the first treatment in the tuple and the columns are for the second treatment in tuple. The report is not symmetric because treatment one for example, appears only as the first treatment.\n",
    "This report has the same modes as the previous. If we want a symmetric report we can use the contingency profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "con = pos_one_vs_another.treatment_positivity_summary(data['X'],a=data['a'],estimation_population='Both',as_contingency_table=True)\n",
    "sns.heatmap(con,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do a cool trick we will create a contingency table were i,j is the rate of i treated cases that overlap between i and j and cell j,i is the rate of j cases that overlap between i and j. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tr = pos_one_vs_another.treatment_positivity_summary(data['X'],a=data['a'],estimation_population='treated',as_contingency_table=True)\n",
    "cr = pos_one_vs_another.treatment_positivity_summary(data['X'],a=data['a'],estimation_population='control',as_contingency_table=True)\n",
    "con = tr.fillna(0)+cr.fillna(0).T\n",
    "sns.heatmap(con,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a excessive report the diagonal is non relevant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<H2> One versus the the rest </H2>\n",
    "In this scheme we require a similar case that was treated with any other treatment, we asses positivity when each treatment is the treatment group and the control is all of the other treatments combined. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 treatment\n"
     ]
    },
    {
     "data": {
      "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient_0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient_4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1      2     3     4     5\n",
       "patient_0  False  False  True  True  True\n",
       "patient_1   True   True  True  True  True\n",
       "patient_2   True   True  True  True  True\n",
       "patient_3  False  False  True  True  True\n",
       "patient_4   True   True  True  True  True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_one_vs_rest = OneVersusRestPositivity(Trimming(),verbose=True)\n",
    "pos_one_vs_rest.fit(data['X'],data['a'])\n",
    "pos_one_vs_rest.positivity_profile(data['X'],data['a']).head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that every treatment here is in the positivity population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.882\n",
       "2    0.880\n",
       "3    1.000\n",
       "4    0.942\n",
       "5    0.878\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_one_vs_rest.treatment_positivity_summary(data['X'],a=data['a'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here we can see the contingency table as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "con = pos_one_vs_rest.treatment_positivity_summary(data['X'],a=data['a'],as_contingency_table=True)\n",
    "sns.heatmap(con,annot=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}