{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as ss\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from collections.abc import Iterable, Callable\n",
    "from typing import Union\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from abc import ABC, abstractmethod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\"n_estimators\": {\"distribution\": \"randint\", \"args\": (10, 100)},\n",
    "            \"min_weight_fraction_leaf\": {\"distribution\": \"norm\", \"args\": (0.25, 0.01)},\n",
    "            \"criterion\": {\"distribution\": \"choice\", \"args\": [\"gini\", \"entropy\"]},\n",
    "            } "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_distributions(parameters: dict[str, dict[str, Union[str, tuple, Iterable]]]) -> dict[str, Union[Callable, Iterable]]:\n",
    "    params_dict = {}\n",
    "    for parameter, description in parameters.items():\n",
    "        try:\n",
    "            distr = getattr(ss, description['distribution'])\n",
    "            \n",
    "        except AttributeError:\n",
    "            distribution = description['args']\n",
    "            \n",
    "        else:\n",
    "            distribution = distr(*description['args'])\n",
    "            \n",
    "        params_dict[parameter] = distribution\n",
    "        \n",
    "        return params_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=11, estimator=RandomForestClassifier(),\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'min_weight_fraction_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f4ffa5190d0>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f4ffa58fd90>})"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = RandomizedSearchCV(RandomForestClassifier(), params_dist, cv=11)\n",
    "cv.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'min_weight_fraction_leaf': 0.23251444102460192,\n",
       " 'n_estimators': 79}"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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