diff --git "a/src/test.ipynb" "b/src/test.ipynb"
--- "a/src/test.ipynb"
+++ "b/src/test.ipynb"
@@ -2,13 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "execution_count": 2,
    "outputs": [],
    "source": [
     "from typing import Any\n",
@@ -21,11 +15,17 @@
     "from catboost import CatBoostClassifier\n",
     "from category_encoders import CatBoostEncoder\n",
     "import pickle"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "def get_data() -> tuple[Any, Any, Any]:\n",
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "dataset, target, treatment = get_data()\n",
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "models_results = {\n",
@@ -109,19 +109,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 1440x504 with 3 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "cbr = CatBoostClassifier(iterations=500, task_type=\"GPU\", random_state=42, silent=True)\n",
     "\n",
@@ -152,35 +141,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "P:\\uplift_lab\\venv\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3156: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
-      "  return asarray(a).ndim\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<AxesSubplot:title={'center':'Uplift by percentile\\nweighted average uplift = 0.0452'}, xlabel='Percentile', ylabel='Uplift = treatment response rate - control response rate'>"
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 576x432 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "from sklift.viz import plot_uplift_by_percentile\n",
     "\n",
@@ -195,18 +157,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "      feature_name  feature_score\n0        treatment      23.090004\n1          channel      19.284459\n2         zip_code      15.911562\n3  history_segment      13.699184\n4          history      13.131164\n5          recency       7.301571\n6             mens       3.429339\n7           womens       3.394266\n8           newbie       0.758451",
-      "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>feature_name</th>\n      <th>feature_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>treatment</td>\n      <td>23.090004</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>channel</td>\n      <td>19.284459</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>zip_code</td>\n      <td>15.911562</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>history_segment</td>\n      <td>13.699184</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>history</td>\n      <td>13.131164</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>recency</td>\n      <td>7.301571</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>mens</td>\n      <td>3.429339</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>womens</td>\n      <td>3.394266</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>newbie</td>\n      <td>0.758451</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "sm_fi = pd.DataFrame({\n",
     "    'feature_name': sm.estimator.feature_names_,\n",
@@ -236,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "from sklift.models import ClassTransformation\n",
@@ -261,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "ct.estimator.save_model('models/ct_cbc.cbm')"
@@ -287,19 +239,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 1440x504 with 3 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "from sklift.models import TwoModels\n",
     "\n",
@@ -333,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "tm.estimator_ctrl.save_model('models/tm_ctrl_cbc.cbm')\n",
@@ -360,19 +301,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 1440x504 with 3 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "tm_ctrl = TwoModels(\n",
     "    estimator_trmnt=CatBoostClassifier(iterations=500, task_type='GPU', random_state=42, silent=True),\n",
@@ -405,18 +335,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "       Unnamed: 0         0\n0           44164  0.034348\n1           56555  0.044075\n2             434  0.047825\n3           31278  0.064600\n4           17464  0.032285\n...           ...       ...\n21342       16804  0.076078\n21343       55206  0.009654\n21344        1288  0.068835\n21345       42903  0.018540\n21346       39709  0.028321\n\n[21347 rows x 2 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>44164</td>\n      <td>0.034348</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>56555</td>\n      <td>0.044075</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>434</td>\n      <td>0.047825</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>31278</td>\n      <td>0.064600</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17464</td>\n      <td>0.032285</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>21342</th>\n      <td>16804</td>\n      <td>0.076078</td>\n    </tr>\n    <tr>\n      <th>21343</th>\n      <td>55206</td>\n      <td>0.009654</td>\n    </tr>\n    <tr>\n      <th>21344</th>\n      <td>1288</td>\n      <td>0.068835</td>\n    </tr>\n    <tr>\n      <th>21345</th>\n      <td>42903</td>\n      <td>0.018540</td>\n    </tr>\n    <tr>\n      <th>21346</th>\n      <td>39709</td>\n      <td>0.028321</td>\n    </tr>\n  </tbody>\n</table>\n<p>21347 rows × 2 columns</p>\n</div>"
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "pd.read_csv('model_predictions/ct_cbc.csv')"
    ],
@@ -429,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "pd.Series(uplift_tm_ctrl, index=data_test.index).to_csv('tm_dependend_cbc.csv')"
@@ -443,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "outputs": [],
    "source": [
     "tm.estimator_ctrl.save_model('models/tm_dependend_ctrl_cbc.cbm')\n",
@@ -458,28 +378,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Shape mismatch: if categories is an array, it has to be of shape (n_features,).",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
-      "Input \u001B[1;32mIn [52]\u001B[0m, in \u001B[0;36m<cell line: 21>\u001B[1;34m()\u001B[0m\n\u001B[0;32m     10\u001B[0m pipeline_trtmnt \u001B[38;5;241m=\u001B[39m make_pipeline(\n\u001B[0;32m     11\u001B[0m     OrdinalEncoder(categories\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mchannel\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mzip_code\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mhistory_segment\u001B[39m\u001B[38;5;124m'\u001B[39m]),\n\u001B[0;32m     12\u001B[0m     RandomForestClassifier(n_estimators\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m, max_depth\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m5\u001B[39m, random_state\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m42\u001B[39m)\n\u001B[0;32m     13\u001B[0m )\n\u001B[0;32m     15\u001B[0m tm_ctrl \u001B[38;5;241m=\u001B[39m TwoModels(\n\u001B[0;32m     16\u001B[0m     estimator_trmnt\u001B[38;5;241m=\u001B[39mpipeline_ctrl,\n\u001B[0;32m     17\u001B[0m     estimator_ctrl\u001B[38;5;241m=\u001B[39mpipeline_trtmnt,\n\u001B[0;32m     18\u001B[0m     method\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mddr_control\u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m     19\u001B[0m )\n\u001B[1;32m---> 21\u001B[0m tm_ctrl \u001B[38;5;241m=\u001B[39m \u001B[43mtm_ctrl\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m     22\u001B[0m \u001B[43m    \u001B[49m\u001B[43mdata_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtarget_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtreatment_train\u001B[49m\n\u001B[0;32m     23\u001B[0m \u001B[43m)\u001B[49m\n\u001B[0;32m     25\u001B[0m uplift_tm_ctrl \u001B[38;5;241m=\u001B[39m tm_ctrl\u001B[38;5;241m.\u001B[39mpredict(data_test)\n\u001B[0;32m     27\u001B[0m tm_ctrl_score \u001B[38;5;241m=\u001B[39m uplift_at_k(y_true\u001B[38;5;241m=\u001B[39mtarget_test, uplift\u001B[38;5;241m=\u001B[39muplift_tm_ctrl, treatment\u001B[38;5;241m=\u001B[39mtreatment_test, strategy\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mby_group\u001B[39m\u001B[38;5;124m'\u001B[39m, k\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.3\u001B[39m)\n",
-      "File \u001B[1;32mP:\\uplift_lab\\venv\\lib\\site-packages\\sklift\\models\\models.py:401\u001B[0m, in \u001B[0;36mTwoModels.fit\u001B[1;34m(self, X, y, treatment, estimator_trmnt_fit_params, estimator_ctrl_fit_params)\u001B[0m\n\u001B[0;32m    396\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mestimator_trmnt\u001B[38;5;241m.\u001B[39mfit(\n\u001B[0;32m    397\u001B[0m         X_trmnt, y_trmnt, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mestimator_trmnt_fit_params\n\u001B[0;32m    398\u001B[0m     )\n\u001B[0;32m    400\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmethod \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mddr_control\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[1;32m--> 401\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mestimator_ctrl\u001B[38;5;241m.\u001B[39mfit(\n\u001B[0;32m    402\u001B[0m         X_ctrl, y_ctrl, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mestimator_ctrl_fit_params\n\u001B[0;32m    403\u001B[0m     )\n\u001B[0;32m    404\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_type_of_target \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbinary\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m    405\u001B[0m         ddr_control \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mestimator_ctrl\u001B[38;5;241m.\u001B[39mpredict_proba(X_trmnt)[:, \u001B[38;5;241m1\u001B[39m]\n",
-      "File \u001B[1;32mP:\\uplift_lab\\venv\\lib\\site-packages\\sklearn\\pipeline.py:378\u001B[0m, in \u001B[0;36mPipeline.fit\u001B[1;34m(self, X, y, **fit_params)\u001B[0m\n\u001B[0;32m    352\u001B[0m \u001B[38;5;124;03m\"\"\"Fit the model.\u001B[39;00m\n\u001B[0;32m    353\u001B[0m \n\u001B[0;32m    354\u001B[0m \u001B[38;5;124;03mFit all the transformers one after the other and transform the\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    375\u001B[0m \u001B[38;5;124;03m    Pipeline with fitted steps.\u001B[39;00m\n\u001B[0;32m    376\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    377\u001B[0m fit_params_steps \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_fit_params(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params)\n\u001B[1;32m--> 378\u001B[0m Xt \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_fit(X, y, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params_steps)\n\u001B[0;32m    379\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m _print_elapsed_time(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPipeline\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_log_message(\u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msteps) \u001B[38;5;241m-\u001B[39m \u001B[38;5;241m1\u001B[39m)):\n\u001B[0;32m    380\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_final_estimator \u001B[38;5;241m!=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpassthrough\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n",
-      "File \u001B[1;32mP:\\uplift_lab\\venv\\lib\\site-packages\\sklearn\\pipeline.py:336\u001B[0m, in \u001B[0;36mPipeline._fit\u001B[1;34m(self, X, y, **fit_params_steps)\u001B[0m\n\u001B[0;32m    334\u001B[0m     cloned_transformer \u001B[38;5;241m=\u001B[39m clone(transformer)\n\u001B[0;32m    335\u001B[0m \u001B[38;5;66;03m# Fit or load from cache the current transformer\u001B[39;00m\n\u001B[1;32m--> 336\u001B[0m X, fitted_transformer \u001B[38;5;241m=\u001B[39m fit_transform_one_cached(\n\u001B[0;32m    337\u001B[0m     cloned_transformer,\n\u001B[0;32m    338\u001B[0m     X,\n\u001B[0;32m    339\u001B[0m     y,\n\u001B[0;32m    340\u001B[0m     \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m    341\u001B[0m     message_clsname\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPipeline\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    342\u001B[0m     message\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_log_message(step_idx),\n\u001B[0;32m    343\u001B[0m     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params_steps[name],\n\u001B[0;32m    344\u001B[0m )\n\u001B[0;32m    345\u001B[0m \u001B[38;5;66;03m# Replace the transformer of the step with the fitted\u001B[39;00m\n\u001B[0;32m    346\u001B[0m \u001B[38;5;66;03m# transformer. This is necessary when loading the transformer\u001B[39;00m\n\u001B[0;32m    347\u001B[0m \u001B[38;5;66;03m# from the cache.\u001B[39;00m\n\u001B[0;32m    348\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msteps[step_idx] \u001B[38;5;241m=\u001B[39m (name, fitted_transformer)\n",
-      "File \u001B[1;32mP:\\uplift_lab\\venv\\lib\\site-packages\\joblib\\memory.py:349\u001B[0m, in \u001B[0;36mNotMemorizedFunc.__call__\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m    348\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__call__\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m--> 349\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunc(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
-      "File \u001B[1;32mP:\\uplift_lab\\venv\\lib\\site-packages\\sklearn\\pipeline.py:870\u001B[0m, in \u001B[0;36m_fit_transform_one\u001B[1;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001B[0m\n\u001B[0;32m    868\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m _print_elapsed_time(message_clsname, message):\n\u001B[0;32m    869\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(transformer, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfit_transform\u001B[39m\u001B[38;5;124m\"\u001B[39m):\n\u001B[1;32m--> 870\u001B[0m         res \u001B[38;5;241m=\u001B[39m transformer\u001B[38;5;241m.\u001B[39mfit_transform(X, y, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params)\n\u001B[0;32m    871\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    872\u001B[0m         res \u001B[38;5;241m=\u001B[39m transformer\u001B[38;5;241m.\u001B[39mfit(X, y, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params)\u001B[38;5;241m.\u001B[39mtransform(X)\n",
-      "File \u001B[1;32mP:\\uplift_lab\\venv\\lib\\site-packages\\sklearn\\base.py:870\u001B[0m, in \u001B[0;36mTransformerMixin.fit_transform\u001B[1;34m(self, X, y, **fit_params)\u001B[0m\n\u001B[0;32m    867\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfit(X, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params)\u001B[38;5;241m.\u001B[39mtransform(X)\n\u001B[0;32m    868\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    869\u001B[0m     \u001B[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001B[39;00m\n\u001B[1;32m--> 870\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfit(X, y, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_params)\u001B[38;5;241m.\u001B[39mtransform(X)\n",
-      "File \u001B[1;32mP:\\uplift_lab\\venv\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:1294\u001B[0m, in \u001B[0;36mOrdinalEncoder.fit\u001B[1;34m(self, X, y)\u001B[0m\n\u001B[0;32m   1287\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[0;32m   1288\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124munknown_value should only be set when \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   1289\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhandle_unknown is \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124muse_encoded_value\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   1290\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgot \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39munknown_value\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   1291\u001B[0m     )\n\u001B[0;32m   1293\u001B[0m \u001B[38;5;66;03m# `_fit` will only raise an error when `self.handle_unknown=\"error\"`\u001B[39;00m\n\u001B[1;32m-> 1294\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_fit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mhandle_unknown\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mhandle_unknown\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mforce_all_finite\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mallow-nan\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1296\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandle_unknown \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124muse_encoded_value\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m   1297\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m feature_cats \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcategories_:\n",
-      "File \u001B[1;32mP:\\uplift_lab\\venv\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:87\u001B[0m, in \u001B[0;36m_BaseEncoder._fit\u001B[1;34m(self, X, handle_unknown, force_all_finite, return_counts)\u001B[0m\n\u001B[0;32m     85\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcategories \u001B[38;5;241m!=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mauto\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m     86\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcategories) \u001B[38;5;241m!=\u001B[39m n_features:\n\u001B[1;32m---> 87\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m     88\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mShape mismatch: if categories is an array,\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m     89\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m it has to be of shape (n_features,).\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m     90\u001B[0m         )\n\u001B[0;32m     92\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcategories_ \u001B[38;5;241m=\u001B[39m []\n\u001B[0;32m     93\u001B[0m category_counts \u001B[38;5;241m=\u001B[39m []\n",
-      "\u001B[1;31mValueError\u001B[0m: Shape mismatch: if categories is an array, it has to be of shape (n_features,)."
-     ]
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.preprocessing import OrdinalEncoder\n",
@@ -523,18 +423,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "      0         1         2         3         4         5         6      \\\n0  0.187557  0.105281  0.161889  0.140160  0.468606  0.172560  0.130849   \n1  0.036023  0.028304  0.045443  0.069228  0.181322  0.113093  0.053770   \n\n      7         8         9      ...     21337     21338     21339     21340  \\\n0  0.079300  0.039731  0.114872  ...  0.091512  0.254877  0.163009  0.089335   \n1  0.000879  0.005583  0.026389  ...  0.045846  0.089228  0.013675 -0.014415   \n\n      21341     21342     21343     21344     21345     21346  \n0  0.200761  0.215388  0.142818  0.231624  0.232737  0.143152  \n1  0.111710  0.087185  0.035382 -0.003007 -0.011135  0.048037  \n\n[2 rows x 21347 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>21337</th>\n      <th>21338</th>\n      <th>21339</th>\n      <th>21340</th>\n      <th>21341</th>\n      <th>21342</th>\n      <th>21343</th>\n      <th>21344</th>\n      <th>21345</th>\n      <th>21346</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.187557</td>\n      <td>0.105281</td>\n      <td>0.161889</td>\n      <td>0.140160</td>\n      <td>0.468606</td>\n      <td>0.172560</td>\n      <td>0.130849</td>\n      <td>0.079300</td>\n      <td>0.039731</td>\n      <td>0.114872</td>\n      <td>...</td>\n      <td>0.091512</td>\n      <td>0.254877</td>\n      <td>0.163009</td>\n      <td>0.089335</td>\n      <td>0.200761</td>\n      <td>0.215388</td>\n      <td>0.142818</td>\n      <td>0.231624</td>\n      <td>0.232737</td>\n      <td>0.143152</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.036023</td>\n      <td>0.028304</td>\n      <td>0.045443</td>\n      <td>0.069228</td>\n      <td>0.181322</td>\n      <td>0.113093</td>\n      <td>0.053770</td>\n      <td>0.000879</td>\n      <td>0.005583</td>\n      <td>0.026389</td>\n      <td>...</td>\n      <td>0.045846</td>\n      <td>0.089228</td>\n      <td>0.013675</td>\n      <td>-0.014415</td>\n      <td>0.111710</td>\n      <td>0.087185</td>\n      <td>0.035382</td>\n      <td>-0.003007</td>\n      <td>-0.011135</td>\n      <td>0.048037</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 21347 columns</p>\n</div>"
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
     "pd.DataFrame([tm_ctrl.trmnt_preds_, uplift_tm_ctrl])"
    ],
@@ -557,6 +447,37 @@
     }
    }
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "pd.DataFrame(data=models_results).sort_values('uplift@30%', ascending=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from sklift.viz import plot_uplift_by_percentile\n",
+    "\n",
+    "# line plot\n",
+    "plot_uplift_by_percentile(target_test, uplift_ct, treatment_test, strategy='overall', kind='bar');"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": 40,