EBEST-2024-priorizacao-de-chamadas.ipynb

{
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   "execution_count": 1,
   "id": "44ddf012",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:39.810658Z",
     "start_time": "2024-10-08T14:20:38.890305Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from scipy.special import expit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70262b23",
   "metadata": {},
   "source": [
    "# Processo Gerador de Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb8c812f",
   "metadata": {},
   "source": [
    "$$\n",
    "Y_i^* = \\alpha + \\beta_1  call_i + \\beta_2 call_i X_i + \\theta X_i + e_i\n",
    "$$\n",
    "\n",
    "$$\n",
    "Y_i = \\sigma(Y_i^*) + \\dfrac{1}{1+e^{-Y_i^*}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e38d51b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:39.870718Z",
     "start_time": "2024-10-08T14:20:39.812271Z"
    }
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   "outputs": [
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       "      <th>agreement</th>\n",
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       "      <td>0.846815</td>\n",
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       "<p>100000 rows × 12 columns</p>\n",
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       "       agreement  call        x0        x1        x2        x3        x4  \\\n",
       "0              0     1  1.564163  0.091429  1.388867  0.240799  0.154027   \n",
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       "2              0     1 -0.216074 -0.404754  0.179214 -0.342899 -0.524485   \n",
       "3              1     1  0.002662 -0.354355 -1.624813  0.849674 -0.167226   \n",
       "4              0     0 -0.148254 -0.579394 -0.736772 -1.012188 -0.126697   \n",
       "...          ...   ...       ...       ...       ...       ...       ...   \n",
       "99995          0     1  0.746418  0.523360  1.744976 -0.159363 -1.361788   \n",
       "99996          0     1  0.548423  1.992458 -0.149136  0.393278  0.877541   \n",
       "99997          0     1 -0.734112 -0.430433  0.103465  0.578520  1.374382   \n",
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       "99999          0     0 -0.729641 -1.065881 -0.045931 -0.135761  0.019943   \n",
       "\n",
       "             x5        x6        x7        x8        x9  \n",
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       "4      1.160276  0.846815  2.236606 -0.106393  0.547150  \n",
       "...         ...       ...       ...       ...       ...  \n",
       "99995 -0.317357  0.700818  0.474245  1.420319 -1.049133  \n",
       "99996  1.400714  0.263317 -0.364506 -0.000415  1.163906  \n",
       "99997 -0.380007 -1.023640  0.652050  1.027991 -0.488377  \n",
       "99998  0.266058 -0.549152  1.055482 -0.791112 -0.347339  \n",
       "99999 -0.661033 -0.800669  0.230657  1.801893 -0.313673  \n",
       "\n",
       "[100000 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 100000\n",
    "n_x = 10\n",
    "\n",
    "np.random.seed(123)\n",
    "\n",
    "# true parameters\n",
    "alpha = -2\n",
    "beta1 = np.random.uniform(0,1)\n",
    "beta2 = np.random.uniform(-1,1, n_x)\n",
    "theta = np.random.uniform(-1,1, n_x)\n",
    "\n",
    "\n",
    "# data\n",
    "call = np.random.binomial(1, 0.5, n)\n",
    "X = np.random.normal(0, 1, (n, n_x))\n",
    "\n",
    "Y_star = alpha + beta1*call + call*X.dot(beta2) + X.dot(theta)\n",
    "Y = np.random.binomial(1, expit(Y_star))\n",
    "\n",
    "df = pd.DataFrame(dict(agreement=Y, call=call)).join(pd.DataFrame(X, columns=[f\"x{i}\" for i in range(n_x)]))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "55db5e5a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:39.877306Z",
     "start_time": "2024-10-08T14:20:39.871865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agreement    0.199530\n",
       "call         0.500960\n",
       "x0          -0.002751\n",
       "x1           0.001546\n",
       "x2          -0.003561\n",
       "x3           0.003214\n",
       "x4           0.001600\n",
       "x5          -0.002346\n",
       "x6           0.005613\n",
       "x7          -0.002390\n",
       "x8           0.004779\n",
       "x9           0.001502\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a04148",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-07T12:54:17.468269Z",
     "start_time": "2024-10-07T12:54:17.463123Z"
    }
   },
   "source": [
    "# Criando um Modelo Preditivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37cc2908",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:40.232550Z",
     "start_time": "2024-10-08T14:20:39.879889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((80000, 12), (20000, 12))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(df, test_size=0.2, random_state=1)\n",
    "\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5965c47b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.097656Z",
     "start_time": "2024-10-08T14:20:40.233714Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "clf = LGBMClassifier()\n",
    "\n",
    "\n",
    "X = [f\"x{i}\" for i in range(10)]\n",
    "\n",
    "clf.fit(train[X], train[\"agreement\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24dfbc70",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.209663Z",
     "start_time": "2024-10-08T14:20:41.098914Z"
    }
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       agreement  call        x0        x1        x2        x3        x4  \\\n",
       "78689          1     1 -1.202416 -0.280416 -1.810579 -0.882309  0.622271   \n",
       "76423          1     1 -0.805696 -1.232675 -0.760623  0.221013  0.366721   \n",
       "86945          1     1 -0.850826 -0.982348 -1.233703 -2.310498  1.078682   \n",
       "57427          0     0 -0.238013  0.522953  1.027834 -1.299981 -0.458112   \n",
       "34616          0     0 -0.854292  0.989608 -0.277672  0.676710  0.239035   \n",
       "\n",
       "             x5        x6        x7        x8        x9  prediction  \n",
       "78689  0.911877  0.445060 -0.191113 -0.577777  0.766118    0.442860  \n",
       "76423  0.143864 -2.010781  0.693280  0.571393 -0.752304    0.483840  \n",
       "86945  0.586545  0.100963 -0.744785 -1.819578 -0.355326    0.400985  \n",
       "57427  1.211409 -1.098646  0.289592 -0.975876  1.028182    0.070045  \n",
       "34616  0.961658 -1.529399 -0.285434  1.462993 -0.722879    0.137348  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred = train.assign(prediction = clf.predict_proba(train[X])[:, 1])\n",
    "test_pred = test.assign(prediction = clf.predict_proba(test[X])[:, 1])\n",
    "\n",
    "train_pred.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b76df3ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.237971Z",
     "start_time": "2024-10-08T14:20:41.211528Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train AUC: 0.7944200704193806\n",
      "Test AUC: 0.744037781244937\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "print(\"Train AUC:\", roc_auc_score(train_pred[\"agreement\"], train_pred[\"prediction\"]))\n",
    "print(\"Test AUC:\", roc_auc_score(test_pred[\"agreement\"], test_pred[\"prediction\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ddc5a5",
   "metadata": {},
   "source": [
    "# Priorizando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "67c804ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.248208Z",
     "start_time": "2024-10-08T14:20:41.239911Z"
    }
   },
   "outputs": [],
   "source": [
    "test_ranked = test_pred.assign(\n",
    "    lost_cause_1st = np.argsort(test_pred[\"prediction\"]).argsort(),\n",
    "    sure_thing_1st = np.argsort(-1*test_pred[\"prediction\"]).argsort(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb134537",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.262594Z",
     "start_time": "2024-10-08T14:20:41.249467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>agreement</th>\n",
       "      <th>call</th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>x6</th>\n",
       "      <th>x7</th>\n",
       "      <th>x8</th>\n",
       "      <th>x9</th>\n",
       "      <th>prediction</th>\n",
       "      <th>lost_cause_1st</th>\n",
       "      <th>sure_thing_1st</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16728</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.945492</td>\n",
       "      <td>-1.086310</td>\n",
       "      <td>-2.824423</td>\n",
       "      <td>3.129536</td>\n",
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       "      <td>0.938343</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>83602</th>\n",
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       "      <td>0.396695</td>\n",
       "      <td>0.763306</td>\n",
       "      <td>-2.222330</td>\n",
       "      <td>1.006972</td>\n",
       "      <td>1.208722</td>\n",
       "      <td>-2.681995</td>\n",
       "      <td>-2.063088</td>\n",
       "      <td>-2.461327</td>\n",
       "      <td>1.827371</td>\n",
       "      <td>1.495011</td>\n",
       "      <td>0.909190</td>\n",
       "      <td>19998</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>30040</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.366963</td>\n",
       "      <td>-0.363662</td>\n",
       "      <td>-2.933559</td>\n",
       "      <td>1.628908</td>\n",
       "      <td>1.208574</td>\n",
       "      <td>-0.107877</td>\n",
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       "      <td>-1.809503</td>\n",
       "      <td>-1.789250</td>\n",
       "      <td>0.144943</td>\n",
       "      <td>0.887803</td>\n",
       "      <td>19997</td>\n",
       "      <td>2</td>\n",
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       "      <th>45111</th>\n",
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       "      <td>-0.949689</td>\n",
       "      <td>-1.404334</td>\n",
       "      <td>-1.337377</td>\n",
       "      <td>-0.154336</td>\n",
       "      <td>-0.945339</td>\n",
       "      <td>-1.523429</td>\n",
       "      <td>0.082615</td>\n",
       "      <td>1.999488</td>\n",
       "      <td>3.023697</td>\n",
       "      <td>0.881731</td>\n",
       "      <td>19996</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69081</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.317000</td>\n",
       "      <td>-0.786124</td>\n",
       "      <td>-3.111926</td>\n",
       "      <td>0.140252</td>\n",
       "      <td>-0.002939</td>\n",
       "      <td>-0.573890</td>\n",
       "      <td>-1.536846</td>\n",
       "      <td>-0.033591</td>\n",
       "      <td>-0.339201</td>\n",
       "      <td>3.004093</td>\n",
       "      <td>0.877442</td>\n",
       "      <td>19995</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23290</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.327306</td>\n",
       "      <td>2.265318</td>\n",
       "      <td>3.128859</td>\n",
       "      <td>1.704484</td>\n",
       "      <td>0.605941</td>\n",
       "      <td>-0.737590</td>\n",
       "      <td>-0.512640</td>\n",
       "      <td>0.161190</td>\n",
       "      <td>0.069481</td>\n",
       "      <td>-0.663878</td>\n",
       "      <td>0.008257</td>\n",
       "      <td>4</td>\n",
       "      <td>19995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50003</th>\n",
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       "      <td>0</td>\n",
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       "      <td>-0.031479</td>\n",
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       "      <td>0.090610</td>\n",
       "      <td>-2.431704</td>\n",
       "      <td>0.206866</td>\n",
       "      <td>2.548993</td>\n",
       "      <td>-0.450988</td>\n",
       "      <td>-0.508580</td>\n",
       "      <td>-1.575627</td>\n",
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       "      <td>3</td>\n",
       "      <td>19996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33631</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.235599</td>\n",
       "      <td>0.833632</td>\n",
       "      <td>3.398472</td>\n",
       "      <td>-0.106204</td>\n",
       "      <td>-1.968615</td>\n",
       "      <td>-0.192665</td>\n",
       "      <td>0.262232</td>\n",
       "      <td>-1.117790</td>\n",
       "      <td>-0.420411</td>\n",
       "      <td>-0.225667</td>\n",
       "      <td>0.007683</td>\n",
       "      <td>2</td>\n",
       "      <td>19997</td>\n",
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       "    <tr>\n",
       "      <th>3637</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.739348</td>\n",
       "      <td>0.953969</td>\n",
       "      <td>1.420068</td>\n",
       "      <td>0.040782</td>\n",
       "      <td>-1.136918</td>\n",
       "      <td>0.237545</td>\n",
       "      <td>3.221730</td>\n",
       "      <td>0.750923</td>\n",
       "      <td>0.679389</td>\n",
       "      <td>-0.126095</td>\n",
       "      <td>0.007620</td>\n",
       "      <td>1</td>\n",
       "      <td>19998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98621</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.160329</td>\n",
       "      <td>1.906405</td>\n",
       "      <td>2.749332</td>\n",
       "      <td>3.145082</td>\n",
       "      <td>0.426580</td>\n",
       "      <td>-0.220984</td>\n",
       "      <td>0.113411</td>\n",
       "      <td>-1.455558</td>\n",
       "      <td>-0.999710</td>\n",
       "      <td>-0.139740</td>\n",
       "      <td>0.006562</td>\n",
       "      <td>0</td>\n",
       "      <td>19999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       agreement  call        x0        x1        x2        x3        x4  \\\n",
       "16728          0     0  0.945492 -1.086310 -2.824423  3.129536 -0.022026   \n",
       "83602          1     0  0.396695  0.763306 -2.222330  1.006972  1.208722   \n",
       "30040          1     1 -2.366963 -0.363662 -2.933559  1.628908  1.208574   \n",
       "45111          1     1  2.853939 -0.949689 -1.404334 -1.337377 -0.154336   \n",
       "69081          1     1 -0.317000 -0.786124 -3.111926  0.140252 -0.002939   \n",
       "...          ...   ...       ...       ...       ...       ...       ...   \n",
       "23290          0     0  0.327306  2.265318  3.128859  1.704484  0.605941   \n",
       "50003          0     0  0.326796 -0.031479  2.710118  0.090610 -2.431704   \n",
       "33631          0     1 -0.235599  0.833632  3.398472 -0.106204 -1.968615   \n",
       "3637           0     0 -0.739348  0.953969  1.420068  0.040782 -1.136918   \n",
       "98621          0     0 -0.160329  1.906405  2.749332  3.145082  0.426580   \n",
       "\n",
       "             x5        x6        x7        x8        x9  prediction  \\\n",
       "16728  0.149056 -0.821917  1.940036 -1.191617 -0.562290    0.938343   \n",
       "83602 -2.681995 -2.063088 -2.461327  1.827371  1.495011    0.909190   \n",
       "30040 -0.107877 -1.718892 -1.809503 -1.789250  0.144943    0.887803   \n",
       "45111 -0.945339 -1.523429  0.082615  1.999488  3.023697    0.881731   \n",
       "69081 -0.573890 -1.536846 -0.033591 -0.339201  3.004093    0.877442   \n",
       "...         ...       ...       ...       ...       ...         ...   \n",
       "23290 -0.737590 -0.512640  0.161190  0.069481 -0.663878    0.008257   \n",
       "50003  0.206866  2.548993 -0.450988 -0.508580 -1.575627    0.008114   \n",
       "33631 -0.192665  0.262232 -1.117790 -0.420411 -0.225667    0.007683   \n",
       "3637   0.237545  3.221730  0.750923  0.679389 -0.126095    0.007620   \n",
       "98621 -0.220984  0.113411 -1.455558 -0.999710 -0.139740    0.006562   \n",
       "\n",
       "       lost_cause_1st  sure_thing_1st  \n",
       "16728           19999               0  \n",
       "83602           19998               1  \n",
       "30040           19997               2  \n",
       "45111           19996               3  \n",
       "69081           19995               4  \n",
       "...               ...             ...  \n",
       "23290               4           19995  \n",
       "50003               3           19996  \n",
       "33631               2           19997  \n",
       "3637                1           19998  \n",
       "98621               0           19999  \n",
       "\n",
       "[20000 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ranked.sort_values([\"sure_thing_1st\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90fa6f3b",
   "metadata": {},
   "source": [
    "# Qual Ordenamento é Melhor?\n",
    "\n",
    "## Intuição Rápida"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e6cff59",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.274033Z",
     "start_time": "2024-10-08T14:20:41.267100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "call\n",
       "0    0.300340\n",
       "1    0.303861\n",
       "Name: agreement, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mais provaveis\n",
    "test_ranked.query(\"sure_thing_1st<10000\").groupby([\"call\"])[\"agreement\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "811e916c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.281709Z",
     "start_time": "2024-10-08T14:20:41.275268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "call\n",
       "0         NaN\n",
       "1    0.003521\n",
       "Name: agreement, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ranked.query(\"sure_thing_1st<10000\").groupby([\"call\"])[\"agreement\"].mean().diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a33445b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.288792Z",
     "start_time": "2024-10-08T14:20:41.283120Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "call\n",
       "0    0.071064\n",
       "1    0.102705\n",
       "Name: agreement, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# menos provaveis\n",
    "test_ranked.query(\"sure_thing_1st>10000\").groupby([\"call\"])[\"agreement\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19e40244",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.296623Z",
     "start_time": "2024-10-08T14:20:41.290368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "call\n",
       "0         NaN\n",
       "1    0.031641\n",
       "Name: agreement, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ranked.query(\"sure_thing_1st>10000\").groupby([\"call\"])[\"agreement\"].mean().diff()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e530e2",
   "metadata": {},
   "source": [
    "## Efeito Cumulativo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19b49d97",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.325065Z",
     "start_time": "2024-10-08T14:20:41.297877Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agreement_test</th>\n",
       "      <th>agreement_control</th>\n",
       "      <th>size_test</th>\n",
       "      <th>size_control</th>\n",
       "      <th>cumulative_effect</th>\n",
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       "      <th>16728</th>\n",
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       "    <tr>\n",
       "      <th>30040</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45111</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69081</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23290</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10101</td>\n",
       "      <td>9895</td>\n",
       "      <td>0.015294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50003</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10101</td>\n",
       "      <td>9896</td>\n",
       "      <td>0.015313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33631</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10102</td>\n",
       "      <td>9896</td>\n",
       "      <td>0.015293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3637</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10102</td>\n",
       "      <td>9897</td>\n",
       "      <td>0.015312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98621</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10102</td>\n",
       "      <td>9898</td>\n",
       "      <td>0.015331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       agreement_test  agreement_control  size_test  size_control  \\\n",
       "16728               0                  0          0             1   \n",
       "83602               0                  1          0             2   \n",
       "30040               1                  1          1             2   \n",
       "45111               2                  1          2             2   \n",
       "69081               3                  1          3             2   \n",
       "...               ...                ...        ...           ...   \n",
       "23290            2043               1850      10101          9895   \n",
       "50003            2043               1850      10101          9896   \n",
       "33631            2043               1850      10102          9896   \n",
       "3637             2043               1850      10102          9897   \n",
       "98621            2043               1850      10102          9898   \n",
       "\n",
       "       cumulative_effect  \n",
       "16728                NaN  \n",
       "83602                NaN  \n",
       "30040           0.500000  \n",
       "45111           0.500000  \n",
       "69081           0.500000  \n",
       "...                  ...  \n",
       "23290           0.015294  \n",
       "50003           0.015313  \n",
       "33631           0.015293  \n",
       "3637            0.015312  \n",
       "98621           0.015331  \n",
       "\n",
       "[20000 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_effect_df_sure_thing_1st = (\n",
    "    test_ranked\n",
    "    .sort_values(\"sure_thing_1st\")\n",
    "    .assign(\n",
    "     agreement_test = lambda d: d[\"call\"] * d[\"agreement\"],\n",
    "     agreement_control = lambda d: (1-d[\"call\"]) * d[\"agreement\"],\n",
    "     size_test =  lambda d: d[\"call\"],\n",
    "     size_control =  lambda d: (1-d[\"call\"]),\n",
    " )\n",
    " [[\"agreement_test\", \"agreement_control\", \"size_test\", \"size_control\"]]\n",
    " .cumsum()\n",
    " .assign(cumulative_effect = lambda d: d[\"agreement_test\"]/d[\"size_test\"] - d[\"agreement_control\"]/d[\"size_control\"])\n",
    "\n",
    ")\n",
    "\n",
    "cumulative_effect_df_sure_thing_1st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea337ff7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.331562Z",
     "start_time": "2024-10-08T14:20:41.327160Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.015330735009670415"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check\n",
    "ate = test_ranked.groupby(\"call\")[\"agreement\"].mean().diff()[1]\n",
    "ate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9c88a4dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.457704Z",
     "start_time": "2024-10-08T14:20:41.332911Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.05, 0.15)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.plot(cumulative_effect_df_sure_thing_1st[\"size_test\"]+cumulative_effect_df_sure_thing_1st[\"size_control\"],\n",
    "         cumulative_effect_df_sure_thing_1st[\"cumulative_effect\"])\n",
    "\n",
    "plt.hlines(ate, 0, len(cumulative_effect_df_sure_thing_1st), ls='dashed', color=\"black\", label=\"ATE\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Cummulative Effect\")\n",
    "plt.ylim([-0.05, 0.15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0e87cba0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.471553Z",
     "start_time": "2024-10-08T14:20:41.459508Z"
    }
   },
   "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>agreement_test</th>\n",
       "      <th>agreement_control</th>\n",
       "      <th>size_test</th>\n",
       "      <th>size_control</th>\n",
       "      <th>cumulative_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98621</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3637</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33631</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50003</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23290</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69081</th>\n",
       "      <td>2041</td>\n",
       "      <td>1849</td>\n",
       "      <td>10100</td>\n",
       "      <td>9896</td>\n",
       "      <td>0.015236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45111</th>\n",
       "      <td>2042</td>\n",
       "      <td>1849</td>\n",
       "      <td>10101</td>\n",
       "      <td>9896</td>\n",
       "      <td>0.015315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30040</th>\n",
       "      <td>2043</td>\n",
       "      <td>1849</td>\n",
       "      <td>10102</td>\n",
       "      <td>9896</td>\n",
       "      <td>0.015394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83602</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10102</td>\n",
       "      <td>9897</td>\n",
       "      <td>0.015312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16728</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10102</td>\n",
       "      <td>9898</td>\n",
       "      <td>0.015331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       agreement_test  agreement_control  size_test  size_control  \\\n",
       "98621               0                  0          0             1   \n",
       "3637                0                  0          0             2   \n",
       "33631               0                  0          1             2   \n",
       "50003               0                  0          1             3   \n",
       "23290               0                  0          1             4   \n",
       "...               ...                ...        ...           ...   \n",
       "69081            2041               1849      10100          9896   \n",
       "45111            2042               1849      10101          9896   \n",
       "30040            2043               1849      10102          9896   \n",
       "83602            2043               1850      10102          9897   \n",
       "16728            2043               1850      10102          9898   \n",
       "\n",
       "       cumulative_effect  \n",
       "98621                NaN  \n",
       "3637                 NaN  \n",
       "33631           0.000000  \n",
       "50003           0.000000  \n",
       "23290           0.000000  \n",
       "...                  ...  \n",
       "69081           0.015236  \n",
       "45111           0.015315  \n",
       "30040           0.015394  \n",
       "83602           0.015312  \n",
       "16728           0.015331  \n",
       "\n",
       "[20000 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_effect_df_lost_cause_1st = (\n",
    "    test_ranked\n",
    "    .sort_values(\"lost_cause_1st\")\n",
    "    .assign(\n",
    "     agreement_test = lambda d: d[\"call\"] * d[\"agreement\"],\n",
    "     agreement_control = lambda d: (1-d[\"call\"]) * d[\"agreement\"],\n",
    "     size_test =  lambda d: d[\"call\"],\n",
    "     size_control =  lambda d: (1-d[\"call\"]),\n",
    " )\n",
    " [[\"agreement_test\", \"agreement_control\", \"size_test\", \"size_control\"]]\n",
    " .cumsum()\n",
    " .assign(cumulative_effect = lambda d: d[\"agreement_test\"]/d[\"size_test\"] - d[\"agreement_control\"]/d[\"size_control\"])\n",
    "\n",
    ")\n",
    "\n",
    "cumulative_effect_df_lost_cause_1st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a9ffddb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.576894Z",
     "start_time": "2024-10-08T14:20:41.472827Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.05, 0.15)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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30MzekfSrpEJJ1zvnisL3tAAAAAAAAFCRiARSzrlsScNDrN8gaXTA8kRJE0Psd0k5535I0kNV09IaKeaGIaLSuPaHL6794Ytrf3jiuh++uPaHL6794Ytrf/iK+WtvzoWs+Q0AAAAAAABUi0jVkAIAAAAAAMBhikAqRpjZSDNbYmbLzWxcpNuDQ2dmrc3sazNbZGYLzewm3/r7zGy9mc3x3UYHHHOH7zWwxMxODlh/lJnN9237u5lZJJ4TKs/MVvuu2Rwzm+Fb19DMvjSzZb6fDQL259rHADPrEvDenmNmu8zsZt73scnMXjKzLWa2IGBdlb3PfZO8vO1b/7OZtQvrE0RIZVz3v5rZYjObZ2Yfmll93/p2ZrYv4L0/PuAYrnsNU8a1r7Lf71z76FXGtX874LqvNrM5vvW872OElf15jr/1xZxz3Gr4Td6i7ysktZeUJGmupG6Rbhe3Q76uzSX19d2vK2mppG6S7pN0e4j9u/mufbKkDN9rIt637RdJx0gySZ9JGhXp58etwuu/WlJ6iXWPSRrnuz9O0l+49rF78/1u3ySpLe/72LxJGiKpr6QFAeuq7H0u6TpJ4333L5D0dqSfM7cyr/tJkhJ89/8ScN3bBe5X4jxc9xp2K+PaV9nvd6599N5CXfsS25+QdI/vPu/7GLmp7M9z/K333eghFRv6S1runFvpnMuX9JakMRFuEw6Rc26jc26W7/5uSYsktSznkDGS3nLO5TnnVklaLqm/mTWXlOac+9F5f1O9KumM6m09qskYSa/47r+i/deRax+bhkta4ZxbU84+XPsazDk3VdK2Equr8n0eeK73JA2np1zkhbruzrlJzrlC3+JPklqVdw6ue81Uxnu+LLznY0h51953jc6T9GZ55+Da1zzlfJ7jb70PgVRsaClpXcBypsoPLlDD+LpeHinpZ9+qG3zd+l8K6OJZ1uugpe9+yfWIbk7SJDObaWZjfeuaOuc2St4/cJKa+NZz7WPTBQr+zynv+8NDVb7P/cf4wo6dkhpVW8tRVa6U99vvYhlmNtvMvjWzwb51XPfYUlW/37n2NdNgSZudc8sC1vG+jzElPs/xt96HQCo2hEpAmT4xRphZHUnvS7rZObdL0rOSOkjqI2mjvF18pbJfB7w+aqZjnXN9JY2SdL2ZDSlnX659jDGzJEmnS3rXt4r3PQ7mWvM6qGHM7M+SCiW97lu1UVIb59yRkm6V9IaZpYnrHkuq8vc7175mulDBX0Dxvo8xIT7PlblriHUx/b4nkIoNmZJaByy3krQhQm1BFTKzRHl/eb3unPtAkpxzm51zRc45j6Tn5R2yKZX9OshUcNd/Xh81gHNug+/nFkkfynudN/u67BZ3297i251rH3tGSZrlnNss8b4/zFTl+9x/jJklSKqnyg8XQpiZ2WWSTpX0G9+QDPmGbWT77s+Ut55IZ3HdY0YV/37n2tcwvut0lqS3i9fxvo8toT7Pib/1fgRSsWG6pE5mluH7Vv0CSRMi3CYcIt/Y3xclLXLOPRmwvnnAbmdKKp6tY4KkC3wzLWRI6iTpF1830N1mNtB3zkslfRyWJ4GDYmapZla3+L68xW4XyHuNL/Ptdpn2X0eufewJ+raU9/1hpSrf54HnOkfSV8VBB6KLmY2U9CdJpzvncgLWNzazeN/99vJe95Vc99hRxb/fufY1zwhJi51z/uFYvO9jR1mf58Tf+v0OtSo6t+i4SRotb9X+FZL+HOn2cKuSa3qcvN0t50ma47uNlvSapPm+9RMkNQ845s++18ASBcyoJamfvP/BWSHpn5Is0s+PW7nXvr28M2zMlbSw+D0t73jwKZKW+X425NrH3k1SbUnZkuoFrON9H4M3eUPHjZIK5P2G86qqfJ9LSpF32OdyeWfnaR/p58ytzOu+XN4aIMV/74tnTDrb93dgrqRZkk7jutfcWxnXvsp+v3Pto/cW6tr71r8s6ZoS+/K+j5Gbyv48x9963634SQAAAAAAAABhwZA9AAAAAAAAhBWBFAAAAAAAAMKKQAoAAAAAAABhRSAFAAAAAACAsCKQAgAAAAAAQFgRSAEAAAAAACCsCKQAAAAAAAAQVgRSAAAAAAAACKv/B8/kvDAC8ulRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.plot(cumulative_effect_df_lost_cause_1st[\"size_test\"]+cumulative_effect_df_lost_cause_1st[\"size_control\"],\n",
    "         cumulative_effect_df_lost_cause_1st[\"cumulative_effect\"])\n",
    "\n",
    "plt.hlines(ate, 0, len(cumulative_effect_df_lost_cause_1st), ls='dashed', color=\"black\", label=\"ATE\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Cummulative Effect\")\n",
    "plt.ylim([-0.05, 0.15])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14fe1f3",
   "metadata": {},
   "source": [
    "# Ordenamento Baseado em Derivadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "17d6401c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.590537Z",
     "start_time": "2024-10-08T14:20:41.578242Z"
    }
   },
   "outputs": [],
   "source": [
    "test_ranked = test_pred.assign(\n",
    "    lost_cause_1st = np.argsort(test_pred[\"prediction\"]).argsort(),\n",
    "    sure_thing_1st = np.argsort(-1*test_pred[\"prediction\"]).argsort(),\n",
    "    close_to_50_1st = np.argsort(np.abs(test_pred[\"prediction\"]-0.5)).argsort(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5a52db",
   "metadata": {},
   "source": [
    "## Mais próximo de 50%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1965dd33",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.605060Z",
     "start_time": "2024-10-08T14:20:41.591743Z"
    }
   },
   "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>agreement_test</th>\n",
       "      <th>agreement_control</th>\n",
       "      <th>size_test</th>\n",
       "      <th>size_control</th>\n",
       "      <th>cumulative_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65995</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57952</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46185</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69469</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53340</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23290</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10101</td>\n",
       "      <td>9895</td>\n",
       "      <td>0.015294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50003</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10101</td>\n",
       "      <td>9896</td>\n",
       "      <td>0.015313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33631</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10102</td>\n",
       "      <td>9896</td>\n",
       "      <td>0.015293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3637</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10102</td>\n",
       "      <td>9897</td>\n",
       "      <td>0.015312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98621</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10102</td>\n",
       "      <td>9898</td>\n",
       "      <td>0.015331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       agreement_test  agreement_control  size_test  size_control  \\\n",
       "65995               0                  0          0             1   \n",
       "57952               0                  0          0             2   \n",
       "46185               1                  0          1             2   \n",
       "69469               1                  1          1             3   \n",
       "53340               1                  1          1             4   \n",
       "...               ...                ...        ...           ...   \n",
       "23290            2043               1850      10101          9895   \n",
       "50003            2043               1850      10101          9896   \n",
       "33631            2043               1850      10102          9896   \n",
       "3637             2043               1850      10102          9897   \n",
       "98621            2043               1850      10102          9898   \n",
       "\n",
       "       cumulative_effect  \n",
       "65995                NaN  \n",
       "57952                NaN  \n",
       "46185           1.000000  \n",
       "69469           0.666667  \n",
       "53340           0.750000  \n",
       "...                  ...  \n",
       "23290           0.015294  \n",
       "50003           0.015313  \n",
       "33631           0.015293  \n",
       "3637            0.015312  \n",
       "98621           0.015331  \n",
       "\n",
       "[20000 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_effect_df_close_to_50_1st = (\n",
    "    test_ranked\n",
    "    .sort_values(\"close_to_50_1st\")\n",
    "    .assign(\n",
    "     agreement_test = lambda d: d[\"call\"] * d[\"agreement\"],\n",
    "     agreement_control = lambda d: (1-d[\"call\"]) * d[\"agreement\"],\n",
    "     size_test =  lambda d: d[\"call\"],\n",
    "     size_control =  lambda d: (1-d[\"call\"]),\n",
    " )\n",
    " [[\"agreement_test\", \"agreement_control\", \"size_test\", \"size_control\"]]\n",
    " .cumsum()\n",
    " .assign(cumulative_effect = lambda d: d[\"agreement_test\"]/d[\"size_test\"] - d[\"agreement_control\"]/d[\"size_control\"])\n",
    "\n",
    ")\n",
    "\n",
    "cumulative_effect_df_close_to_50_1st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a964657",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:41.711530Z",
     "start_time": "2024-10-08T14:20:41.606685Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.05, 0.15)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.plot(cumulative_effect_df_close_to_50_1st[\"size_test\"]+cumulative_effect_df_close_to_50_1st[\"size_control\"],\n",
    "         cumulative_effect_df_close_to_50_1st[\"cumulative_effect\"])\n",
    "\n",
    "plt.hlines(ate, 0, len(cumulative_effect_df_close_to_50_1st), ls='dashed', color=\"black\", label=\"ATE\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Cummulative Effect\")\n",
    "plt.ylim([-0.05, 0.15])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9374fd8c",
   "metadata": {},
   "source": [
    "## Modelo Causal Simples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0047d4c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:42.612055Z",
     "start_time": "2024-10-08T14:20:41.713230Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "causal_clf = LGBMClassifier()\n",
    "\n",
    "\n",
    "X = [f\"x{i}\" for i in range(10)]\n",
    "\n",
    "causal_clf.fit(train[X+[\"call\"]], train[\"agreement\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0914b26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:42.668210Z",
     "start_time": "2024-10-08T14:20:42.613396Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agreement</th>\n",
       "      <th>call</th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>x6</th>\n",
       "      <th>x7</th>\n",
       "      <th>x8</th>\n",
       "      <th>x9</th>\n",
       "      <th>prediction</th>\n",
       "      <th>deriv_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43660</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.159491</td>\n",
       "      <td>-0.090660</td>\n",
       "      <td>2.430949</td>\n",
       "      <td>-1.127080</td>\n",
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       "      <td>-2.398350</td>\n",
       "      <td>-1.393830</td>\n",
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       "      <td>-1.804984</td>\n",
       "      <td>-0.617416</td>\n",
       "      <td>0.055956</td>\n",
       "      <td>-0.033081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87278</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.161243</td>\n",
       "      <td>-0.496317</td>\n",
       "      <td>0.863277</td>\n",
       "      <td>0.320810</td>\n",
       "      <td>1.949677</td>\n",
       "      <td>1.021588</td>\n",
       "      <td>-1.482750</td>\n",
       "      <td>0.582966</td>\n",
       "      <td>-0.037880</td>\n",
       "      <td>-0.214991</td>\n",
       "      <td>0.293817</td>\n",
       "      <td>0.088442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14317</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.393299</td>\n",
       "      <td>2.086833</td>\n",
       "      <td>0.139414</td>\n",
       "      <td>0.826503</td>\n",
       "      <td>0.097820</td>\n",
       "      <td>1.507955</td>\n",
       "      <td>-1.105425</td>\n",
       "      <td>0.341311</td>\n",
       "      <td>-0.145129</td>\n",
       "      <td>1.254981</td>\n",
       "      <td>0.116230</td>\n",
       "      <td>-0.018872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81932</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.666458</td>\n",
       "      <td>-2.761416</td>\n",
       "      <td>0.583532</td>\n",
       "      <td>0.160105</td>\n",
       "      <td>1.717938</td>\n",
       "      <td>0.305316</td>\n",
       "      <td>1.956600</td>\n",
       "      <td>-0.137366</td>\n",
       "      <td>0.707691</td>\n",
       "      <td>1.274815</td>\n",
       "      <td>0.169452</td>\n",
       "      <td>0.347485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95321</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.697644</td>\n",
       "      <td>1.080617</td>\n",
       "      <td>0.649876</td>\n",
       "      <td>1.316316</td>\n",
       "      <td>0.319485</td>\n",
       "      <td>-0.298049</td>\n",
       "      <td>0.243747</td>\n",
       "      <td>-0.444826</td>\n",
       "      <td>0.921988</td>\n",
       "      <td>0.535202</td>\n",
       "      <td>0.087090</td>\n",
       "      <td>-0.036346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       agreement  call        x0        x1        x2        x3        x4  \\\n",
       "43660          0     0 -1.159491 -0.090660  2.430949 -1.127080  0.761413   \n",
       "87278          0     1  0.161243 -0.496317  0.863277  0.320810  1.949677   \n",
       "14317          0     0  1.393299  2.086833  0.139414  0.826503  0.097820   \n",
       "81932          0     1 -1.666458 -2.761416  0.583532  0.160105  1.717938   \n",
       "95321          0     0  1.697644  1.080617  0.649876  1.316316  0.319485   \n",
       "\n",
       "             x5        x6        x7        x8        x9  prediction  \\\n",
       "43660 -2.398350 -1.393830  0.008505 -1.804984 -0.617416    0.055956   \n",
       "87278  1.021588 -1.482750  0.582966 -0.037880 -0.214991    0.293817   \n",
       "14317  1.507955 -1.105425  0.341311 -0.145129  1.254981    0.116230   \n",
       "81932  0.305316  1.956600 -0.137366  0.707691  1.274815    0.169452   \n",
       "95321 -0.298049  0.243747 -0.444826  0.921988  0.535202    0.087090   \n",
       "\n",
       "       deriv_prediction  \n",
       "43660         -0.033081  \n",
       "87278          0.088442  \n",
       "14317         -0.018872  \n",
       "81932          0.347485  \n",
       "95321         -0.036346  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred = test_pred.assign(\n",
    "    deriv_prediction = causal_clf.predict_proba(test_pred[X].assign(call=1))[:, 1] - causal_clf.predict_proba(test_pred[X].assign(call=0))[:, 1]\n",
    ")\n",
    "\n",
    "test_pred.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7e567a36",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:42.698029Z",
     "start_time": "2024-10-08T14:20:42.669466Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agreement</th>\n",
       "      <th>call</th>\n",
       "      <th>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>x6</th>\n",
       "      <th>x7</th>\n",
       "      <th>x8</th>\n",
       "      <th>x9</th>\n",
       "      <th>prediction</th>\n",
       "      <th>deriv_prediction</th>\n",
       "      <th>lost_cause_1st</th>\n",
       "      <th>sure_thing_1st</th>\n",
       "      <th>close_to_50_1st</th>\n",
       "      <th>high_deriv_1st</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24181</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.336192</td>\n",
       "      <td>-2.521239</td>\n",
       "      <td>-0.513799</td>\n",
       "      <td>0.150143</td>\n",
       "      <td>1.298106</td>\n",
       "      <td>1.792662</td>\n",
       "      <td>0.114836</td>\n",
       "      <td>0.857446</td>\n",
       "      <td>-1.959142</td>\n",
       "      <td>-0.063700</td>\n",
       "      <td>0.400271</td>\n",
       "      <td>0.782869</td>\n",
       "      <td>17638</td>\n",
       "      <td>2361</td>\n",
       "      <td>1859</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96619</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.426374</td>\n",
       "      <td>-2.266634</td>\n",
       "      <td>-1.239955</td>\n",
       "      <td>2.685777</td>\n",
       "      <td>1.332310</td>\n",
       "      <td>1.866959</td>\n",
       "      <td>0.750656</td>\n",
       "      <td>0.080033</td>\n",
       "      <td>0.021534</td>\n",
       "      <td>-1.568641</td>\n",
       "      <td>0.673524</td>\n",
       "      <td>0.733359</td>\n",
       "      <td>19787</td>\n",
       "      <td>212</td>\n",
       "      <td>3465</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85424</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.097317</td>\n",
       "      <td>-2.381872</td>\n",
       "      <td>-0.649158</td>\n",
       "      <td>0.072231</td>\n",
       "      <td>-0.209131</td>\n",
       "      <td>0.646079</td>\n",
       "      <td>-0.033616</td>\n",
       "      <td>-1.341663</td>\n",
       "      <td>-2.376823</td>\n",
       "      <td>-1.339155</td>\n",
       "      <td>0.374447</td>\n",
       "      <td>0.732442</td>\n",
       "      <td>17256</td>\n",
       "      <td>2743</td>\n",
       "      <td>2356</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38282</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.159534</td>\n",
       "      <td>-2.302738</td>\n",
       "      <td>0.106635</td>\n",
       "      <td>0.450348</td>\n",
       "      <td>0.961823</td>\n",
       "      <td>1.397829</td>\n",
       "      <td>-0.188850</td>\n",
       "      <td>-0.946370</td>\n",
       "      <td>-1.405352</td>\n",
       "      <td>-1.166850</td>\n",
       "      <td>0.298610</td>\n",
       "      <td>0.720374</td>\n",
       "      <td>15658</td>\n",
       "      <td>4341</td>\n",
       "      <td>4186</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81971</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.633168</td>\n",
       "      <td>-2.422002</td>\n",
       "      <td>-0.149869</td>\n",
       "      <td>0.999430</td>\n",
       "      <td>0.990098</td>\n",
       "      <td>0.849382</td>\n",
       "      <td>-0.240825</td>\n",
       "      <td>0.759178</td>\n",
       "      <td>-1.017341</td>\n",
       "      <td>-1.840667</td>\n",
       "      <td>0.572426</td>\n",
       "      <td>0.719771</td>\n",
       "      <td>19338</td>\n",
       "      <td>661</td>\n",
       "      <td>1309</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19365</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.260678</td>\n",
       "      <td>1.634242</td>\n",
       "      <td>0.018614</td>\n",
       "      <td>-0.851287</td>\n",
       "      <td>0.405899</td>\n",
       "      <td>-2.422536</td>\n",
       "      <td>-0.676943</td>\n",
       "      <td>-1.478162</td>\n",
       "      <td>0.434040</td>\n",
       "      <td>1.631969</td>\n",
       "      <td>0.362696</td>\n",
       "      <td>-0.699826</td>\n",
       "      <td>17055</td>\n",
       "      <td>2944</td>\n",
       "      <td>2606</td>\n",
       "      <td>19995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11287</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.692935</td>\n",
       "      <td>1.657792</td>\n",
       "      <td>-0.266750</td>\n",
       "      <td>-1.225804</td>\n",
       "      <td>0.802654</td>\n",
       "      <td>-2.105247</td>\n",
       "      <td>0.178585</td>\n",
       "      <td>-0.359876</td>\n",
       "      <td>-0.646146</td>\n",
       "      <td>2.243397</td>\n",
       "      <td>0.551654</td>\n",
       "      <td>-0.702480</td>\n",
       "      <td>19193</td>\n",
       "      <td>806</td>\n",
       "      <td>903</td>\n",
       "      <td>19996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16371</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.961369</td>\n",
       "      <td>1.034285</td>\n",
       "      <td>0.307684</td>\n",
       "      <td>0.228382</td>\n",
       "      <td>1.081897</td>\n",
       "      <td>-2.802280</td>\n",
       "      <td>-1.179708</td>\n",
       "      <td>0.635849</td>\n",
       "      <td>0.149375</td>\n",
       "      <td>0.072811</td>\n",
       "      <td>0.459828</td>\n",
       "      <td>-0.708978</td>\n",
       "      <td>18418</td>\n",
       "      <td>1581</td>\n",
       "      <td>696</td>\n",
       "      <td>19997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888609</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>-0.332905</td>\n",
       "      <td>0.497530</td>\n",
       "      <td>1.310246</td>\n",
       "      <td>-2.113098</td>\n",
       "      <td>-0.342935</td>\n",
       "      <td>0.260192</td>\n",
       "      <td>0.159337</td>\n",
       "      <td>1.460655</td>\n",
       "      <td>0.458079</td>\n",
       "      <td>-0.715905</td>\n",
       "      <td>18400</td>\n",
       "      <td>1599</td>\n",
       "      <td>726</td>\n",
       "      <td>19998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73663</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.347957</td>\n",
       "      <td>1.298293</td>\n",
       "      <td>-0.228386</td>\n",
       "      <td>-0.355525</td>\n",
       "      <td>-0.147524</td>\n",
       "      <td>-2.078341</td>\n",
       "      <td>-1.393268</td>\n",
       "      <td>0.019436</td>\n",
       "      <td>0.572210</td>\n",
       "      <td>0.558052</td>\n",
       "      <td>0.402934</td>\n",
       "      <td>-0.725231</td>\n",
       "      <td>17684</td>\n",
       "      <td>2315</td>\n",
       "      <td>1797</td>\n",
       "      <td>19999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       agreement  call        x0        x1        x2        x3        x4  \\\n",
       "24181          1     1 -1.336192 -2.521239 -0.513799  0.150143  1.298106   \n",
       "96619          0     0  0.426374 -2.266634 -1.239955  2.685777  1.332310   \n",
       "85424          1     1 -1.097317 -2.381872 -0.649158  0.072231 -0.209131   \n",
       "38282          0     0 -1.159534 -2.302738  0.106635  0.450348  0.961823   \n",
       "81971          1     1 -0.633168 -2.422002 -0.149869  0.999430  0.990098   \n",
       "...          ...   ...       ...       ...       ...       ...       ...   \n",
       "19365          1     0  0.260678  1.634242  0.018614 -0.851287  0.405899   \n",
       "11287          0     1  2.692935  1.657792 -0.266750 -1.225804  0.802654   \n",
       "16371          1     0  1.961369  1.034285  0.307684  0.228382  1.081897   \n",
       "2284           0     1  0.888609  0.899017 -0.332905  0.497530  1.310246   \n",
       "73663          0     1  1.347957  1.298293 -0.228386 -0.355525 -0.147524   \n",
       "\n",
       "             x5        x6        x7        x8        x9  prediction  \\\n",
       "24181  1.792662  0.114836  0.857446 -1.959142 -0.063700    0.400271   \n",
       "96619  1.866959  0.750656  0.080033  0.021534 -1.568641    0.673524   \n",
       "85424  0.646079 -0.033616 -1.341663 -2.376823 -1.339155    0.374447   \n",
       "38282  1.397829 -0.188850 -0.946370 -1.405352 -1.166850    0.298610   \n",
       "81971  0.849382 -0.240825  0.759178 -1.017341 -1.840667    0.572426   \n",
       "...         ...       ...       ...       ...       ...         ...   \n",
       "19365 -2.422536 -0.676943 -1.478162  0.434040  1.631969    0.362696   \n",
       "11287 -2.105247  0.178585 -0.359876 -0.646146  2.243397    0.551654   \n",
       "16371 -2.802280 -1.179708  0.635849  0.149375  0.072811    0.459828   \n",
       "2284  -2.113098 -0.342935  0.260192  0.159337  1.460655    0.458079   \n",
       "73663 -2.078341 -1.393268  0.019436  0.572210  0.558052    0.402934   \n",
       "\n",
       "       deriv_prediction  lost_cause_1st  sure_thing_1st  close_to_50_1st  \\\n",
       "24181          0.782869           17638            2361             1859   \n",
       "96619          0.733359           19787             212             3465   \n",
       "85424          0.732442           17256            2743             2356   \n",
       "38282          0.720374           15658            4341             4186   \n",
       "81971          0.719771           19338             661             1309   \n",
       "...                 ...             ...             ...              ...   \n",
       "19365         -0.699826           17055            2944             2606   \n",
       "11287         -0.702480           19193             806              903   \n",
       "16371         -0.708978           18418            1581              696   \n",
       "2284          -0.715905           18400            1599              726   \n",
       "73663         -0.725231           17684            2315             1797   \n",
       "\n",
       "       high_deriv_1st  \n",
       "24181               0  \n",
       "96619               1  \n",
       "85424               2  \n",
       "38282               3  \n",
       "81971               4  \n",
       "...               ...  \n",
       "19365           19995  \n",
       "11287           19996  \n",
       "16371           19997  \n",
       "2284            19998  \n",
       "73663           19999  \n",
       "\n",
       "[20000 rows x 18 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ranked = test_pred.assign(\n",
    "    lost_cause_1st = np.argsort(test_pred[\"prediction\"]).argsort(),\n",
    "    sure_thing_1st = np.argsort(-1*test_pred[\"prediction\"]).argsort(),\n",
    "    close_to_50_1st = np.argsort(np.abs(test_pred[\"prediction\"]-0.5)).argsort(),\n",
    "    high_deriv_1st = np.argsort(-1*test_pred[\"deriv_prediction\"]).argsort(),\n",
    ")\n",
    "\n",
    "test_ranked.sort_values([\"high_deriv_1st\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7160f31e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:42.713592Z",
     "start_time": "2024-10-08T14:20:42.700130Z"
    }
   },
   "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>agreement_test</th>\n",
       "      <th>agreement_control</th>\n",
       "      <th>size_test</th>\n",
       "      <th>size_control</th>\n",
       "      <th>cumulative_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24181</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96619</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85424</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38282</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81971</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19365</th>\n",
       "      <td>2043</td>\n",
       "      <td>1849</td>\n",
       "      <td>10099</td>\n",
       "      <td>9897</td>\n",
       "      <td>0.015473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11287</th>\n",
       "      <td>2043</td>\n",
       "      <td>1849</td>\n",
       "      <td>10100</td>\n",
       "      <td>9897</td>\n",
       "      <td>0.015453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16371</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10100</td>\n",
       "      <td>9898</td>\n",
       "      <td>0.015371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10101</td>\n",
       "      <td>9898</td>\n",
       "      <td>0.015351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73663</th>\n",
       "      <td>2043</td>\n",
       "      <td>1850</td>\n",
       "      <td>10102</td>\n",
       "      <td>9898</td>\n",
       "      <td>0.015331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       agreement_test  agreement_control  size_test  size_control  \\\n",
       "24181               1                  0          1             0   \n",
       "96619               1                  0          1             1   \n",
       "85424               2                  0          2             1   \n",
       "38282               2                  0          2             2   \n",
       "81971               3                  0          3             2   \n",
       "...               ...                ...        ...           ...   \n",
       "19365            2043               1849      10099          9897   \n",
       "11287            2043               1849      10100          9897   \n",
       "16371            2043               1850      10100          9898   \n",
       "2284             2043               1850      10101          9898   \n",
       "73663            2043               1850      10102          9898   \n",
       "\n",
       "       cumulative_effect  \n",
       "24181                NaN  \n",
       "96619           1.000000  \n",
       "85424           1.000000  \n",
       "38282           1.000000  \n",
       "81971           1.000000  \n",
       "...                  ...  \n",
       "19365           0.015473  \n",
       "11287           0.015453  \n",
       "16371           0.015371  \n",
       "2284            0.015351  \n",
       "73663           0.015331  \n",
       "\n",
       "[20000 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_effect_df_high_deriv_1st = (\n",
    "    test_ranked\n",
    "    .sort_values(\"high_deriv_1st\")\n",
    "    .assign(\n",
    "     agreement_test = lambda d: d[\"call\"] * d[\"agreement\"],\n",
    "     agreement_control = lambda d: (1-d[\"call\"]) * d[\"agreement\"],\n",
    "     size_test =  lambda d: d[\"call\"],\n",
    "     size_control =  lambda d: (1-d[\"call\"]),\n",
    " )\n",
    " [[\"agreement_test\", \"agreement_control\", \"size_test\", \"size_control\"]]\n",
    " .cumsum()\n",
    " .assign(cumulative_effect = lambda d: d[\"agreement_test\"]/d[\"size_test\"] - d[\"agreement_control\"]/d[\"size_control\"])\n",
    "\n",
    ")\n",
    "\n",
    "cumulative_effect_df_high_deriv_1st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0fba4d11",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-08T14:20:42.822519Z",
     "start_time": "2024-10-08T14:20:42.715313Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.05, 0.8)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.plot(cumulative_effect_df_high_deriv_1st[\"size_test\"]+cumulative_effect_df_high_deriv_1st[\"size_control\"],\n",
    "         cumulative_effect_df_high_deriv_1st[\"cumulative_effect\"])\n",
    "\n",
    "plt.hlines(ate, 0, len(cumulative_effect_df_high_deriv_1st), ls='dashed', color=\"black\", label=\"ATE\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"Cummulative Effect\")\n",
    "plt.ylim([-0.05, 0.8])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
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}