pipeline_for_essay.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%pip install ruts\n","%pip install shap\n","%pip install pymystem3\n","%pip install slovnet\n","%pip install evaluate\n","%pip install rouge_score\n","%pip install crosslingual-coreference\n","%pip install spacy\n","%pip install https://github.com/explosion/spacy-models/releases/download/ru_core_news_sm-3.7.0/ru_core_news_sm-3.7.0.tar.gz\n","%pip install seaborn\n","%pip install catboost==1.2\n","%pip install xgboost"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import pandas as pd \n","import numpy as np\n","import re\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import spacy\n","from pymystem3 import Mystem\n","from tqdm import tqdm\n","\n","from ruts import BasicStats, ReadabilityStats, DiversityStats\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.model_selection import train_test_split\n","from dataclasses import dataclass, field\n","import collections\n","from typing import List\n","\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.metrics import classification_report\n","from sklearn.ensemble import RandomForestClassifier\n","from catboost import CatBoostClassifier\n","from xgboost import XGBClassifier\n","\n","import nltk\n","nltk.download('stopwords')\n","nltk.download('punkt')\n","from nltk.corpus import stopwords\n","russian_stopwords = set(stopwords.words('russian'))\n","\n","from navec import Navec\n","from slovnet import NER, Morph\n","from razdel import sentenize, tokenize\n","\n","from evaluate import load\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","metadata":{},"source":["## **Preprocessing**"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["path_to_equal = \"\"\n","path_to_different = \"\""]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:11:13.905430Z","iopub.status.busy":"2024-04-07T13:11:13.904744Z","iopub.status.idle":"2024-04-07T13:11:14.665248Z","shell.execute_reply":"2024-04-07T13:11:14.663645Z","shell.execute_reply.started":"2024-04-07T13:11:13.905391Z"},"trusted":true},"outputs":[],"source":["equal_themes_df = pd.read_csv(path_to_equal, encoding='utf-8')\n","different_themes_df = pd.read_csv(path_to_different, encoding='utf-8')"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:26:57.368890Z","iopub.status.busy":"2024-04-07T13:26:57.368445Z","iopub.status.idle":"2024-04-07T13:26:57.381859Z","shell.execute_reply":"2024-04-07T13:26:57.380352Z","shell.execute_reply.started":"2024-04-07T13:26:57.368860Z"},"trusted":true},"outputs":[],"source":["class DatasetPreprocessor:\n","    def __init__(self, dataframe, source: str, target: str):\n","        self.df = dataframe\n","        self.source_column = source\n","        self.target_column = target\n","\n","    def analyse_dataset(self):\n","        print(self.df.head())\n","        print(self.df.info())\n","        print(f'Shape: {self.df.shape}')\n","        print(f'Number of NaN: {self.df.isnull().sum()}')\n","        print(f'Number of duplicates: {self.df.duplicated().sum()}')\n","\n","    def basic_preprocess(self):\n","        self.df.dropna(axis=0, inplace=True)\n","        self.df.drop_duplicates(f'{self.source_column}', inplace=True)\n","        self.df.drop_duplicates(f'{self.target_column}', inplace=True)\n","        self.df.reset_index(inplace=True, drop=True)\n","        return self.df\n","\n","    def remove_garbage(self, text: str):\n","        text = re.sub(r'\\s+', ' ', text)\n","        text = re.sub(r'\\n', '', text)\n","        return text"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:26:59.796642Z","iopub.status.busy":"2024-04-07T13:26:59.796246Z","iopub.status.idle":"2024-04-07T13:27:01.041836Z","shell.execute_reply":"2024-04-07T13:27:01.040632Z","shell.execute_reply.started":"2024-04-07T13:26:59.796603Z"},"trusted":true},"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>text</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Говоря о современной культуре, мы зачастую по...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Данное эссе посвящено известной модели Портер...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Жизнь имеет в точности ту ценность, которой м...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Влияние техники на человека и общество в цело...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Право человека на жизнь и достойное существов...</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text  class\n","0   Говоря о современной культуре, мы зачастую по...      0\n","1   Данное эссе посвящено известной модели Портер...      0\n","2   Жизнь имеет в точности ту ценность, которой м...      0\n","3   Влияние техники на человека и общество в цело...      0\n","4   Право человека на жизнь и достойное существов...      0"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["preprocessor_equal = DatasetPreprocessor(equal_themes_df, 'human-written text', 'neural text')\n","processed_equal_df = preprocessor_equal.basic_preprocess()\n","\n","cleaned_human_equal = [preprocessor_equal.remove_garbage(text) for text in processed_equal_df['human-written text']]\n","human_equal = {'text': cleaned_human_equal, 'class': 0}\n","human_equal_df = pd.DataFrame(human_equal)\n","\n","cleaned_neural_equal = [preprocessor_equal.remove_garbage(text) for text in processed_equal_df['neural text']]\n","neural_equal = {'text': cleaned_neural_equal, 'class': 1}\n","neural_equal_df = pd.DataFrame(neural_equal)\n","\n","equal_df = pd.concat([human_equal_df, neural_equal_df])\n","equal_df.head()"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:27:08.589752Z","iopub.status.busy":"2024-04-07T13:27:08.588729Z","iopub.status.idle":"2024-04-07T13:27:09.323950Z","shell.execute_reply":"2024-04-07T13:27:09.322808Z","shell.execute_reply.started":"2024-04-07T13:27:08.589704Z"},"trusted":true},"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>text</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Английский учёный-агностик XIX века Томас Генр...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Индийский писатель и поэт Рабиндранат Тагор го...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Он усвоил мотивы всех своих предшественников в...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Так, автор выделил ряд критериев, на которые о...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Почему же именно любовь? Адам Дж. Джексон сказ...</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text  class\n","0  Английский учёный-агностик XIX века Томас Генр...      0\n","1  Индийский писатель и поэт Рабиндранат Тагор го...      0\n","2  Он усвоил мотивы всех своих предшественников в...      0\n","3  Так, автор выделил ряд критериев, на которые о...      0\n","4  Почему же именно любовь? Адам Дж. Джексон сказ...      0"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["preprocessor_different = DatasetPreprocessor(different_themes_df, 'human-written text', 'neural text')\n","processed_different_df = preprocessor_different.basic_preprocess()\n","\n","cleaned_human_equal = [preprocessor_different.remove_garbage(text) for text in processed_different_df['human-written text']]\n","human_different = {'text': cleaned_human_equal, 'class': 0}\n","human_different_df = pd.DataFrame(human_different)\n","\n","cleaned_neural_different = [preprocessor_different.remove_garbage(text) for text in processed_different_df['neural text']]\n","neural_different = {'text': cleaned_neural_different, 'class': 1}\n","neural_different_df = pd.DataFrame(neural_different)\n","\n","different_df = pd.concat([human_different_df, neural_different_df])\n","different_df.head()"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:32:05.956976Z","iopub.status.busy":"2024-04-07T13:32:05.956505Z","iopub.status.idle":"2024-04-07T13:32:06.377282Z","shell.execute_reply":"2024-04-07T13:32:06.375992Z","shell.execute_reply.started":"2024-04-07T13:32:05.956946Z"},"trusted":true},"outputs":[],"source":["long_mask_different = different_df['text'].apply(lambda x: len(x.split(' ')) >= 2000)\n","different_df = different_df[~long_mask_different]\n","\n","small_mask_different = different_df['text'].apply(lambda x: len(x.split(' ')) <= 200)\n","different_df = different_df[~small_mask_different]\n","\n","different_df.reset_index(inplace=True, drop=True)\n","\n","long_mask_equal = equal_df['text'].apply(lambda x: len(x.split(' ')) >= 2000)\n","equal_df = equal_df[~long_mask_equal]\n","\n","small_mask_equal = equal_df['text'].apply(lambda x: len(x.split(' ')) <= 200)\n","equal_df = equal_df[~small_mask_equal]\n","\n","equal_df.reset_index(inplace=True, drop=True)"]},{"cell_type":"markdown","metadata":{},"source":["## **Basic statistics**"]},{"cell_type":"code","execution_count":18,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:32:40.364030Z","iopub.status.busy":"2024-04-07T13:32:40.363599Z","iopub.status.idle":"2024-04-07T13:32:40.383019Z","shell.execute_reply":"2024-04-07T13:32:40.381347Z","shell.execute_reply.started":"2024-04-07T13:32:40.363998Z"},"trusted":true},"outputs":[],"source":["@dataclass\n","class TextStatsCollection:\n","    n_letters: List[int] = field(default_factory=list)\n","    n_syllables: List[int] = field(default_factory=list)\n","    n_words: List[int] = field(default_factory=list)\n","    n_words_per_sentence: List[float] = field(default_factory=list)\n","    n_simple_words: List[int] = field(default_factory=list)\n","    n_simple_words_per_sentence: List[float] = field(default_factory=list)\n","    n_monosyllable_words: List[int] = field(default_factory=list)\n","    n_monosyllable_words_per_sentence: List[float] = field(default_factory=list)\n","    n_polysyllable_words: List[int] = field(default_factory=list)\n","    n_polysyllable_words_per_sentence: List[float] = field(default_factory=list)\n","    n_long_words: List[int] = field(default_factory=list)\n","    n_long_words_per_sentence: List[float] = field(default_factory=list)\n","    n_complex_words: List[int] = field(default_factory=list)\n","    n_complex_words_per_sentence: List[float] = field(default_factory=list)\n","    n_unique_words_per_sentence: List[float] = field(default_factory=list)\n","    n_sents: List[int] = field(default_factory=list)\n","    n_punctuations: List[int] = field(default_factory=list)\n","    n_punctuations_per_sentence: List[float] = field(default_factory=list)"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:32:43.775868Z","iopub.status.busy":"2024-04-07T13:32:43.775478Z","iopub.status.idle":"2024-04-07T13:32:43.788128Z","shell.execute_reply":"2024-04-07T13:32:43.786677Z","shell.execute_reply.started":"2024-04-07T13:32:43.775839Z"},"trusted":true},"outputs":[],"source":["def get_statistics(df):\n","    stats = TextStatsCollection()\n","    \n","    for text in df.text: \n","        bs = BasicStats(text)\n","        stats.n_letters.append(bs.n_letters / bs.n_words)\n","        stats.n_syllables.append(bs.n_syllables / bs.n_words)\n","        stats.n_words.append(bs.n_words)\n","        stats.n_words_per_sentence.append(bs.n_words / bs.n_sents)\n","        stats.n_simple_words.append(bs.n_simple_words / bs.n_words)\n","        stats.n_simple_words_per_sentence.append(bs.n_simple_words / bs.n_sents)\n","        stats.n_monosyllable_words.append(bs.n_monosyllable_words / bs.n_words)\n","        stats.n_monosyllable_words_per_sentence.append(bs.n_monosyllable_words / bs.n_sents)\n","        stats.n_polysyllable_words.append(bs.n_polysyllable_words / bs.n_words)\n","        stats.n_polysyllable_words_per_sentence.append(bs.n_polysyllable_words / bs.n_sents)\n","        stats.n_long_words.append(bs.n_long_words / bs.n_words)\n","        stats.n_long_words_per_sentence.append(bs.n_long_words / bs.n_sents)\n","        stats.n_complex_words.append(bs.n_complex_words / bs.n_words)\n","        stats.n_complex_words_per_sentence.append(bs.n_complex_words / bs.n_sents)\n","        stats.n_unique_words_per_sentence.append(bs.n_unique_words / bs.n_sents)\n","        stats.n_sents.append(bs.n_sents / bs.n_words)\n","        stats.n_punctuations.append(bs.n_punctuations / bs.n_words)\n","        stats.n_punctuations_per_sentence.append(bs.n_punctuations / bs.n_sents)\n","    \n","    return stats"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:32:46.693198Z","iopub.status.busy":"2024-04-07T13:32:46.692749Z","iopub.status.idle":"2024-04-07T13:33:54.747580Z","shell.execute_reply":"2024-04-07T13:33:54.746079Z","shell.execute_reply.started":"2024-04-07T13:32:46.693165Z"},"trusted":true},"outputs":[],"source":["statistics_equal = get_statistics(equal_df)\n","statistics_different = get_statistics(different_df)"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:34:43.143332Z","iopub.status.busy":"2024-04-07T13:34:43.142869Z","iopub.status.idle":"2024-04-07T13:34:43.185542Z","shell.execute_reply":"2024-04-07T13:34:43.184009Z","shell.execute_reply.started":"2024-04-07T13:34:43.143298Z"},"trusted":true},"outputs":[],"source":["# Basic statistics for equal thematics\n","equal_df['n_letters'] = statistics_equal.n_letters\n","equal_df['n_syllables'] = statistics_equal.n_syllables\n","equal_df['n_words'] = statistics_equal.n_words\n","equal_df['n_words_per_sentence'] = statistics_equal.n_words_per_sentence\n","equal_df['n_simple_words'] = statistics_equal.n_simple_words\n","equal_df['n_simple_words_per_sentence'] = statistics_equal.n_simple_words_per_sentence\n","equal_df['n_monosyllable_words'] = statistics_equal.n_monosyllable_words\n","equal_df['n_monosyllable_words_per_sentence'] = statistics_equal.n_monosyllable_words_per_sentence\n","equal_df['n_polysyllable_words'] = statistics_equal.n_polysyllable_words\n","equal_df['n_polysyllable_words_per_sentence'] = statistics_equal.n_polysyllable_words_per_sentence\n","equal_df['n_long_words'] = statistics_equal.n_long_words\n","equal_df['n_long_words_per_sentence'] = statistics_equal.n_long_words_per_sentence\n","equal_df['n_complex_words'] = statistics_equal.n_complex_words\n","equal_df['n_complex_words_per_sentence'] = statistics_equal.n_complex_words_per_sentence\n","equal_df['n_unique_words_per_sentence'] = statistics_equal.n_unique_words_per_sentence\n","equal_df['n_sents'] = statistics_equal.n_sents\n","equal_df['n_punctuations'] = statistics_equal.n_punctuations\n","equal_df['n_punctuations_per_sentence'] = statistics_equal.n_punctuations_per_sentence"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:34:45.580008Z","iopub.status.busy":"2024-04-07T13:34:45.579590Z","iopub.status.idle":"2024-04-07T13:34:45.611565Z","shell.execute_reply":"2024-04-07T13:34:45.610473Z","shell.execute_reply.started":"2024-04-07T13:34:45.579976Z"},"trusted":true},"outputs":[],"source":["# Basic statistics for different thematics\n","different_df['n_letters'] = statistics_different.n_letters\n","different_df['n_syllables'] = statistics_different.n_syllables\n","different_df['n_words'] = statistics_different.n_words\n","different_df['n_words_per_sentence'] = statistics_different.n_words_per_sentence\n","different_df['n_simple_words'] = statistics_different.n_simple_words\n","different_df['n_simple_words_per_sentence'] = statistics_different.n_simple_words_per_sentence\n","different_df['n_monosyllable_words'] = statistics_different.n_monosyllable_words\n","different_df['n_monosyllable_words_per_sentence'] = statistics_different.n_monosyllable_words_per_sentence\n","different_df['n_polysyllable_words'] = statistics_different.n_polysyllable_words\n","different_df['n_polysyllable_words_per_sentence'] = statistics_different.n_polysyllable_words_per_sentence\n","different_df['n_long_words'] = statistics_different.n_long_words\n","different_df['n_long_words_per_sentence'] = statistics_different.n_long_words_per_sentence\n","different_df['n_complex_words'] = statistics_different.n_complex_words\n","different_df['n_complex_words_per_sentence'] = statistics_different.n_complex_words_per_sentence\n","different_df['n_unique_words_per_sentence'] = statistics_different.n_unique_words_per_sentence\n","different_df['n_sents'] = statistics_different.n_sents\n","different_df['n_punctuations'] = statistics_different.n_punctuations\n","different_df['n_punctuations_per_sentence'] = statistics_different.n_punctuations_per_sentence"]},{"cell_type":"markdown","metadata":{},"source":["## **Readability scores**"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:34:53.080119Z","iopub.status.busy":"2024-04-07T13:34:53.079455Z","iopub.status.idle":"2024-04-07T13:34:53.088320Z","shell.execute_reply":"2024-04-07T13:34:53.086851Z","shell.execute_reply.started":"2024-04-07T13:34:53.080077Z"},"trusted":true},"outputs":[],"source":["def get_readability_scores(df):\n","    flesch_kincaid_grade = []\n","    flesch_reading_easy = []\n","\n","    for index in range(len(df.text)):\n","        rs = ReadabilityStats(df.text.iloc[index])\n","        flesch_kincaid_grade.append(rs.flesch_kincaid_grade)\n","        flesch_reading_easy.append(rs.flesch_reading_easy)\n","    return flesch_kincaid_grade, flesch_reading_easy"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:34:55.176102Z","iopub.status.busy":"2024-04-07T13:34:55.175671Z","iopub.status.idle":"2024-04-07T13:36:04.166251Z","shell.execute_reply":"2024-04-07T13:36:04.164982Z","shell.execute_reply.started":"2024-04-07T13:34:55.176071Z"},"trusted":true},"outputs":[],"source":["readability_scores_equal = get_readability_scores(equal_df)\n","readability_scores_different = get_readability_scores(different_df)"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:36:07.094548Z","iopub.status.busy":"2024-04-07T13:36:07.093864Z","iopub.status.idle":"2024-04-07T13:36:07.115547Z","shell.execute_reply":"2024-04-07T13:36:07.114225Z","shell.execute_reply.started":"2024-04-07T13:36:07.094513Z"},"trusted":true},"outputs":[],"source":["# Readability scores fot equal thematics\n","equal_df['flesch_kincaid_grade'] = readability_scores_equal[0]\n","equal_df['flesch_reading_easy'] = readability_scores_equal[1]"]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:36:08.879944Z","iopub.status.busy":"2024-04-07T13:36:08.879260Z","iopub.status.idle":"2024-04-07T13:36:08.892157Z","shell.execute_reply":"2024-04-07T13:36:08.890174Z","shell.execute_reply.started":"2024-04-07T13:36:08.879911Z"},"trusted":true},"outputs":[],"source":["# Readability scores fot different thematics\n","different_df['flesch_kincaid_grade'] = readability_scores_different[0]\n","different_df['flesch_reading_easy'] = readability_scores_different[1]"]},{"cell_type":"markdown","metadata":{},"source":["## **Lexical diversity scores**"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:36:21.380683Z","iopub.status.busy":"2024-04-07T13:36:21.380291Z","iopub.status.idle":"2024-04-07T13:36:21.389623Z","shell.execute_reply":"2024-04-07T13:36:21.388209Z","shell.execute_reply.started":"2024-04-07T13:36:21.380654Z"},"trusted":true},"outputs":[],"source":["def get_lexical_diversity_scores(df):\n","    mtld = []\n","    hdd = []\n","\n","    for index in range(len(df.text)):\n","        ds = DiversityStats(df.text.iloc[index])\n","        mtld.append(ds.mtld)\n","        hdd.append(ds.hdd)\n","    return mtld, hdd"]},{"cell_type":"code","execution_count":32,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:36:23.246453Z","iopub.status.busy":"2024-04-07T13:36:23.245342Z","iopub.status.idle":"2024-04-07T13:38:01.703685Z","shell.execute_reply":"2024-04-07T13:38:01.702358Z","shell.execute_reply.started":"2024-04-07T13:36:23.246393Z"},"trusted":true},"outputs":[],"source":["lexical_diversity_equal = get_lexical_diversity_scores(equal_df)\n","lexical_diversity_different = get_lexical_diversity_scores(different_df)"]},{"cell_type":"code","execution_count":33,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:38:10.911618Z","iopub.status.busy":"2024-04-07T13:38:10.911205Z","iopub.status.idle":"2024-04-07T13:38:10.920636Z","shell.execute_reply":"2024-04-07T13:38:10.919131Z","shell.execute_reply.started":"2024-04-07T13:38:10.911590Z"},"trusted":true},"outputs":[],"source":["# Lexical diversity scores fot equal thematics\n","equal_df['mtld'] = lexical_diversity_equal[0]\n","equal_df['hdd'] = lexical_diversity_equal[1]"]},{"cell_type":"code","execution_count":34,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:38:12.683502Z","iopub.status.busy":"2024-04-07T13:38:12.683114Z","iopub.status.idle":"2024-04-07T13:38:12.691409Z","shell.execute_reply":"2024-04-07T13:38:12.690063Z","shell.execute_reply.started":"2024-04-07T13:38:12.683474Z"},"trusted":true},"outputs":[],"source":["# Lexical diversity scores fot different thematics\n","different_df['mtld'] = lexical_diversity_different[0]\n","different_df['hdd'] = lexical_diversity_different[1]"]},{"cell_type":"markdown","metadata":{},"source":["## **Repetitiveness**"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:38:19.531487Z","iopub.status.busy":"2024-04-07T13:38:19.530796Z","iopub.status.idle":"2024-04-07T13:38:19.612896Z","shell.execute_reply":"2024-04-07T13:38:19.611632Z","shell.execute_reply.started":"2024-04-07T13:38:19.531454Z"},"trusted":true},"outputs":[],"source":["most_frequent_words_df = pd.read_csv('most_frequent_russian_words.csv', encoding='utf-8')\n","most_frequent_words = most_frequent_words_df['Лемма']"]},{"cell_type":"code","execution_count":42,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:38:21.379524Z","iopub.status.busy":"2024-04-07T13:38:21.379090Z","iopub.status.idle":"2024-04-07T13:38:21.403261Z","shell.execute_reply":"2024-04-07T13:38:21.401696Z","shell.execute_reply.started":"2024-04-07T13:38:21.379493Z"},"trusted":true},"outputs":[],"source":["punctuation_marks = ['.', ',', '?', '!', ';', ':', \"'\", '\"', '(', ')', '[', ']', '{', '}', '-', '–', '—', '/', '\\\\', '*', '&', '%', '$', '#', '@', '+', '=', '<', '>', '|', '~', '^', '`']\n","\n","def lemmatize(df):\n","    lemmatized_corpora = []\n","    mystem = Mystem()\n","    for text in df.text:\n","        lemmas = mystem.lemmatize(text)\n","        lemmatized_text = ''.join(lemmas)\n","        lemmatized_corpora.append(lemmatized_text)\n","    df['lemmatized_text'] = lemmatized_corpora\n","    return df\n","\n","\n","def get_unique_words(df):\n","    number_unique_words = {}\n","    for index in range(len(df.lemmatized_text)):\n","        text = df.lemmatized_text.iloc[index]\n","        freq_per_text = {}\n","        tokens = tokenize(text)\n","        for token in tokens:\n","            if token.text in punctuation_marks:\n","                continue\n","            elif token.text not in freq_per_text:\n","                freq_per_text[token.text] = 0\n","            freq_per_text[token.text] += 1\n","        number_unique_words[index] = len(freq_per_text.keys()) / df.n_words.iloc[index]\n","    return number_unique_words\n","\n","def get_top_list_words(df):\n","    counter_dict = {index: 0 for index in range(len(df.text))}\n","    for index in tqdm(range(len(df.lemmatized_text))):\n","        text = df.lemmatized_text.iloc[index]\n","        tokens = text.split(' ')\n","        for word in most_frequent_words:\n","            if word in tokens:\n","                counter_dict[index] += 1 / df.n_words.iloc[index]\n","    return counter_dict\n","\n","def get_number_of_stop_words(df): \n","    counter_dict = {index: 0 for index in range(len(df.text))}\n","    for index in tqdm(range(len(df.lemmatized_text))):\n","        text = df.lemmatized_text.iloc[index]\n","        tokens = tokenize(text)\n","        for token in tokens:\n","            if token.text in russian_stopwords:\n","                counter_dict[index] += 1 / df.n_words.iloc[index]\n","    return counter_dict\n","\n","def get_number_of_unique_stop_words(df): \n","    counter_dict = {index: 0 for index in range(len(df.text))}\n","    for index in tqdm(range(len(df.lemmatized_text))):\n","        text = df.lemmatized_text.iloc[index]\n","        tokens = tokenize(text)\n","        unique_stop_words = {}\n","        for token in tokens:\n","            if token.text in russian_stopwords: \n","                if token.text not in unique_stop_words:\n","                    unique_stop_words[token.text] = 0\n","                unique_stop_words[token.text] += 1\n","        counter_dict[index] = len(unique_stop_words.keys()) / df.n_words.iloc[index]\n","    return counter_dict"]},{"cell_type":"code","execution_count":38,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:38:24.677826Z","iopub.status.busy":"2024-04-07T13:38:24.677368Z","iopub.status.idle":"2024-04-07T13:40:49.809252Z","shell.execute_reply":"2024-04-07T13:40:49.807651Z","shell.execute_reply.started":"2024-04-07T13:38:24.677791Z"},"trusted":true},"outputs":[],"source":["equal_df = lemmatize(equal_df)\n","different_df = lemmatize(different_df)"]},{"cell_type":"code","execution_count":45,"metadata":{},"outputs":[],"source":["equal_df.drop(['number_of_most_frequent_words'], axis=1, inplace=True)\n","equal_df.reset_index(drop=True, inplace=True)"]},{"cell_type":"code","execution_count":43,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:40:55.036827Z","iopub.status.busy":"2024-04-07T13:40:55.035778Z","iopub.status.idle":"2024-04-07T13:47:37.676334Z","shell.execute_reply":"2024-04-07T13:47:37.675244Z","shell.execute_reply.started":"2024-04-07T13:40:55.036784Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 2709/2709 [01:53<00:00, 23.76it/s]\n","100%|██████████| 2709/2709 [00:04<00:00, 626.43it/s]\n","100%|██████████| 2709/2709 [00:02<00:00, 982.16it/s] \n"]}],"source":["equal_df['number_of_unique_words'] = get_unique_words(equal_df)\n","equal_df['number_of_top_list_words'] = get_top_list_words(equal_df)\n","equal_df['number_of_stop_words'] = get_number_of_stop_words(equal_df)\n","equal_df['number_of_unique_stop_words'] = get_number_of_unique_stop_words(equal_df)"]},{"cell_type":"code","execution_count":47,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:48:04.150259Z","iopub.status.busy":"2024-04-07T13:48:04.149813Z","iopub.status.idle":"2024-04-07T13:51:42.526246Z","shell.execute_reply":"2024-04-07T13:51:42.524623Z","shell.execute_reply.started":"2024-04-07T13:48:04.150223Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 1356/1356 [01:03<00:00, 21.42it/s]\n","100%|██████████| 1356/1356 [00:02<00:00, 554.02it/s]\n","100%|██████████| 1356/1356 [00:01<00:00, 870.67it/s] \n"]}],"source":["different_df['number_of_unique_words'] = get_unique_words(different_df)\n","different_df['number_of_top_list_words'] = get_top_list_words(different_df)\n","different_df['number_of_stop_words'] = get_number_of_stop_words(different_df)\n","different_df['number_of_unique_stop_words'] = get_number_of_unique_stop_words(different_df)"]},{"cell_type":"markdown","metadata":{},"source":["## **NER**"]},{"cell_type":"code","execution_count":50,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:51:52.982331Z","iopub.status.busy":"2024-04-07T13:51:52.981851Z","iopub.status.idle":"2024-04-07T13:51:52.997865Z","shell.execute_reply":"2024-04-07T13:51:52.996419Z","shell.execute_reply.started":"2024-04-07T13:51:52.982262Z"},"trusted":true},"outputs":[],"source":["class NERMarkuper:\n","    def __init__(self, df):\n","        self.df = df\n","        navec = Navec.load('vocab/navec_news_v1_1B_250K_300d_100q.tar')\n","        self.ner = NER.load('vocab/slovnet_ner_news_v1.tar')\n","        self.ner.navec(navec)\n","\n","    def get_ners(self):\n","        unique_ner = {}\n","        number_of_ner_per_text = {}\n","        for index in tqdm(range(len(self.df.text))):\n","            text = self.df.text[index]\n","            unique_ner_per_text = {}\n","            unique_ner_type_per_text = {}\n","            markup = self.ner(text)\n","            for span in markup.spans:\n","                if text[span.start:span.stop] not in unique_ner_per_text:\n","                    unique_ner_per_text[text[span.start:span.stop]] = 0\n","                unique_ner_per_text[text[span.start:span.stop]] += 1\n","\n","            for span in markup.spans:\n","                if span.type not in unique_ner_type_per_text:\n","                    unique_ner_type_per_text[span.type] = 0\n","                unique_ner_type_per_text[span.type] += 1\n","\n","            unique_ner[index] = len(unique_ner_per_text.keys()) / self.df.n_words.iloc[index] # количество уникальных текстовых неров в тексте: Миша, Маша, Саша\n","            number_of_ner_per_text[index] = sum(list(unique_ner_type_per_text.values())) / self.df.n_words.iloc[index]  # общее количество неров в тексте \n","\n","        return unique_ner, number_of_ner_per_text\n","\n","    def get_mean_ner_per_sentence(self):\n","        mean_ner_per_sentence = []\n","        for index in tqdm(range(len(self.df.text))):\n","            text = self.df.text[index]\n","            number_of_ner = []\n","            sentenized_text = list(sentenize(text))\n","            for sentence in sentenized_text:\n","                sentence = sentence.text\n","                markup = self.ner(sentence)\n","                number_of_ner.append(len(markup.spans))\n","            mean_ner_per_sentence.append(np.mean(number_of_ner))\n","        return mean_ner_per_sentence # среднее количество неров в тексте из расчета на предложение"]},{"cell_type":"code","execution_count":51,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T13:51:56.069873Z","iopub.status.busy":"2024-04-07T13:51:56.069423Z","iopub.status.idle":"2024-04-07T14:04:38.964186Z","shell.execute_reply":"2024-04-07T14:04:38.962474Z","shell.execute_reply.started":"2024-04-07T13:51:56.069839Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 2709/2709 [00:59<00:00, 45.80it/s]\n","100%|██████████| 2709/2709 [02:00<00:00, 22.50it/s]\n"]}],"source":["ner_markuper_equal = NERMarkuper(equal_df)\n","ners_equal = ner_markuper_equal.get_ners()\n","\n","equal_df['unique_ner'] = ners_equal[0]\n","equal_df['number_of_ner_per_text'] = ners_equal[1]\n","equal_df['mean_ner_per_sentence'] = ner_markuper_equal.get_mean_ner_per_sentence()"]},{"cell_type":"code","execution_count":52,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T14:04:42.145894Z","iopub.status.busy":"2024-04-07T14:04:42.145446Z","iopub.status.idle":"2024-04-07T14:11:39.577645Z","shell.execute_reply":"2024-04-07T14:11:39.576472Z","shell.execute_reply.started":"2024-04-07T14:04:42.145859Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 1356/1356 [00:31<00:00, 43.18it/s]\n","100%|██████████| 1356/1356 [01:01<00:00, 22.10it/s]\n"]}],"source":["ner_markuper_different = NERMarkuper(different_df)\n","ners_different = ner_markuper_different.get_ners()\n","\n","different_df['unique_ner'] = ners_different[0]\n","different_df['number_of_ner_per_text'] = ners_different[1]\n","different_df['mean_ner_per_sentence'] = ner_markuper_different.get_mean_ner_per_sentence()"]},{"cell_type":"markdown","metadata":{},"source":["## **POS**"]},{"cell_type":"code","execution_count":53,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T14:13:04.562178Z","iopub.status.busy":"2024-04-07T14:13:04.561703Z","iopub.status.idle":"2024-04-07T14:13:04.583500Z","shell.execute_reply":"2024-04-07T14:13:04.582294Z","shell.execute_reply.started":"2024-04-07T14:13:04.562145Z"},"trusted":true},"outputs":[],"source":["class POSMarkuper:\n","    def __init__(self, df):\n","        self.df = df\n","        navec = Navec.load('vocab/navec_news_v1_1B_250K_300d_100q.tar')\n","        self.morph = Morph.load('vocab/slovnet_morph_news_v1.tar', batch_size=4)\n","        self.morph.navec(navec)\n","        self.chunks = self.preprocess()\n","\n","    def preprocess(self):\n","        all_chunks = []\n","        for text in self.df.text:\n","            text_chunks = []\n","            for sent in sentenize(text):\n","                tokens = [_.text for _ in tokenize(sent.text)]\n","                text_chunks.append(tokens)\n","            all_chunks.append(text_chunks)\n","        return all_chunks\n","\n","    def get_morph_stats(self): \n","        number_nouns = {i: 0 for i in range(len(self.chunks))}\n","        number_verbs = {i: 0 for i in range(len(self.chunks))}\n","        number_adj = {i: 0 for i in range(len(self.chunks))}\n","        number_pronouns = {i: 0 for i in range(len(self.chunks))}\n","        number_nums = {i: 0 for i in range(len(self.chunks))}\n","        for i in range(len(self.chunks)): \n","            chunk = self.chunks[i]\n","            for markup in self.morph.map(chunk): \n","                for token in markup.tokens:\n","                    if token.pos == 'NOUN':\n","                        number_nouns[i] = number_nouns.get(i, 0) + 1 / self.df.n_words.iloc[i]\n","                    elif token.pos == 'VERB':\n","                        number_verbs[i] = number_verbs.get(i, 0) + 1 / self.df.n_words.iloc[i]\n","                    elif token.pos == 'ADJ':\n","                        number_adj[i] = number_adj.get(i, 0) + 1 / self.df.n_words.iloc[i]\n","                    elif token.pos == 'PROPN':\n","                        number_pronouns[i] = number_pronouns.get(i, 0) + 1 / self.df.n_words.iloc[i]\n","                    elif token.pos == 'NUM':\n","                        number_nums[i] = number_nums.get(i, 0) + 1 / self.df.n_words.iloc[i]\n","        return number_nouns, number_verbs, number_adj, number_pronouns, number_nums\n","\n","    def get_functional_words(self):\n","        functional_words_count = []\n","        for i in range(len(self.chunks)):\n","            chunk = self.chunks[i]\n","            functional_words_per_text = 0\n","            for markup in self.morph.map(chunk): \n","                for token in markup.tokens:\n","                    if token.pos == 'ADP' or token.pos == 'CCONJ' or token.pos == 'PART':\n","                        functional_words_per_text += 1 / self.df.n_words.iloc[i]\n","            functional_words_count.append(functional_words_per_text)\n","        return functional_words_count"]},{"cell_type":"code","execution_count":54,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T14:13:07.437142Z","iopub.status.busy":"2024-04-07T14:13:07.436481Z","iopub.status.idle":"2024-04-07T14:17:24.954907Z","shell.execute_reply":"2024-04-07T14:17:24.953186Z","shell.execute_reply.started":"2024-04-07T14:13:07.437106Z"},"trusted":true},"outputs":[],"source":["morph_markuper_equal = POSMarkuper(equal_df)\n","morph_markup_equal = morph_markuper_equal.get_morph_stats()\n","\n","equal_df['number_nouns'] = morph_markup_equal[0]\n","equal_df['number_verbs'] = morph_markup_equal[1]\n","equal_df['number_adj'] = morph_markup_equal[2]\n","equal_df['number_pronouns'] = morph_markup_equal[3]\n","equal_df['number_nums'] = morph_markup_equal[4]\n","equal_df['number_of_func_words'] = morph_markuper_equal.get_functional_words()"]},{"cell_type":"code","execution_count":55,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T14:19:50.303952Z","iopub.status.busy":"2024-04-07T14:19:50.303518Z","iopub.status.idle":"2024-04-07T14:22:12.977397Z","shell.execute_reply":"2024-04-07T14:22:12.975586Z","shell.execute_reply.started":"2024-04-07T14:19:50.303923Z"},"trusted":true},"outputs":[],"source":["morph_markuper_different = POSMarkuper(different_df)\n","morph_markup_different = morph_markuper_different.get_morph_stats()\n","\n","different_df['number_nouns'] = morph_markup_different[0]\n","different_df['number_verbs'] = morph_markup_different[1]\n","different_df['number_adj'] = morph_markup_different[2]\n","different_df['number_pronouns'] = morph_markup_different[3]\n","different_df['number_nums'] = morph_markup_different[4]\n","different_df['number_of_func_words'] = morph_markuper_different.get_functional_words()"]},{"cell_type":"markdown","metadata":{},"source":["## **ROUGE-1, ROUGE-2, ROUGE-L and BLEU**"]},{"cell_type":"code","execution_count":57,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T14:22:57.284979Z","iopub.status.busy":"2024-04-07T14:22:57.284147Z","iopub.status.idle":"2024-04-07T14:22:57.782546Z","shell.execute_reply":"2024-04-07T14:22:57.781241Z","shell.execute_reply.started":"2024-04-07T14:22:57.284939Z"},"trusted":true},"outputs":[],"source":["rouge = load('rouge')\n","\n","def get_metric(df):\n","    mystem = Mystem()\n","    rouge_metrics = []\n","    rouge_1_metrics = []\n","    rouge_2_metrics = []\n","    rouge_l_metrics = []\n","    for index in tqdm(range(len(df.text))):\n","        text = df.text.iloc[index]\n","        sentences = list(sentenize(text))\n","        predictions = []\n","        references = []\n","        for i in range(len(sentences) - 1):\n","            predictions.append(''.join(mystem.lemmatize(sentences[i].text)[:-2]))\n","            references.append(''.join(mystem.lemmatize(sentences[i + 1].text)[:-2]))\n","        rouge_metrics = get_rouge(predictions, references)\n","        rouge_1_metrics.append(rouge_metrics['rouge1'])\n","        rouge_2_metrics.append(rouge_metrics['rouge2'])\n","        rouge_l_metrics.append(rouge_metrics['rougeL'])\n","\n","    return rouge_1_metrics, rouge_2_metrics, rouge_l_metrics\n","\n","def get_rouge(predictions, references):\n","    result = rouge.compute(predictions=predictions, references=references, use_stemmer=False, tokenizer=lambda x: [\n","                                                token.text.lower() for token in tokenize(x)])\n","    return result"]},{"cell_type":"code","execution_count":58,"metadata":{"execution":{"iopub.execute_input":"2024-04-07T14:23:03.691208Z","iopub.status.busy":"2024-04-07T14:23:03.690737Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 2709/2709 [03:08<00:00, 14.37it/s]\n","100%|██████████| 1356/1356 [01:39<00:00, 13.70it/s]\n"]}],"source":["metrics_equal = get_metric(equal_df)\n","metrics_different = get_metric(different_df)"]},{"cell_type":"code","execution_count":59,"metadata":{},"outputs":[],"source":["equal_df['rouge_1'] = metrics_equal[0]\n","equal_df['rouge_2'] = metrics_equal[1]\n","equal_df['rouge_l'] = metrics_equal[2]"]},{"cell_type":"code","execution_count":60,"metadata":{},"outputs":[],"source":["different_df['rouge_1'] = metrics_different[0]\n","different_df['rouge_2'] = metrics_different[1]\n","different_df['rouge_l'] = metrics_different[2]"]},{"cell_type":"markdown","metadata":{},"source":["## **Parallel Structures** "]},{"cell_type":"code","execution_count":62,"metadata":{"execution":{"iopub.execute_input":"2024-04-05T20:41:55.243710Z","iopub.status.busy":"2024-04-05T20:41:55.243301Z","iopub.status.idle":"2024-04-05T20:41:57.138257Z","shell.execute_reply":"2024-04-05T20:41:57.137135Z","shell.execute_reply.started":"2024-04-05T20:41:55.243679Z"},"trusted":true},"outputs":[],"source":["model = spacy.load(\"ru_core_news_sm\")"]},{"cell_type":"code","execution_count":63,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T11:08:23.202381Z","iopub.status.busy":"2024-04-02T11:08:23.201959Z","iopub.status.idle":"2024-04-02T11:08:23.210860Z","shell.execute_reply":"2024-04-02T11:08:23.209516Z","shell.execute_reply.started":"2024-04-02T11:08:23.202350Z"},"trusted":true},"outputs":[],"source":["def make_syntax_markup(df): \n","    df_with_markup = {}\n","    for index in tqdm(range(len(df.text))):\n","        text_with_markup = []\n","        text = df.text.iloc[index]\n","        sentences = list(sentenize(text))\n","        for sentence in sentences: \n","            sentence_with_markup = []\n","            sentence = model(sentence.text)\n","            for token in sentence:\n","                if token.dep_ == 'punct':\n","                    continue\n","                else: \n","                    sentence_with_markup.append(f'{token.pos_}_{token.dep_}')\n","            text_with_markup.append(' '.join(sentence_with_markup))\n","        df_with_markup[index] = text_with_markup\n","    return df_with_markup"]},{"cell_type":"code","execution_count":64,"metadata":{},"outputs":[],"source":["class Score(\n","    collections.namedtuple(\"Score\", [\"precision\", \"recall\", \"fmeasure\"])):\n","\n","  def fmeasure(precision, recall):\n","\n","      if precision + recall > 0:\n","          return 2 * precision * recall / (precision + recall)\n","      else:\n","          return 0.0\n","\n","\n","class CustomRougeLScorer:\n","  def __init__(self, min_subseq_len=2):\n","    self.min_subseq_len = min_subseq_len\n","\n","  def calculate(self, predictions, targets, tokenization):\n","    if len(predictions) != len(targets):\n","      raise ValueError(\"The lengths of predictions and targets don't match.\")\n","\n","    tokenized_predictions = [tokenization(prediction) for prediction in predictions]\n","    tokenized_targets = [tokenization(target) for target in targets]\n","\n","    scores = [self.score(tokenized_predictions[i], tokenized_targets[i]) for i in range(len(tokenized_predictions))]\n","    fmeasure = np.mean([score[\"rougeL\"][2] for score in scores])\n","\n","    return fmeasure\n","\n","\n","  def score(self, target_tokens, prediction_tokens):\n","    result = {}\n","\n","    lcs_length = self._contiguous_lcs_length(target_tokens, prediction_tokens)\n","    if lcs_length < self.min_subseq_len:\n","      result[\"rougeL\"] = Score(precision=0, recall=0, fmeasure=0)\n","      return result\n","\n","    precision = lcs_length / len(prediction_tokens)\n","    recall = lcs_length / len(target_tokens)\n","    fmeasure_result = fmeasure(precision, recall)\n","\n","    result[\"rougeL\"] = Score(precision=precision, recall=recall, fmeasure=fmeasure_result)\n","\n","    return result\n","\n","\n","  def _contiguous_lcs_length(self, tokens1, tokens2):\n","    dp = [[0] * (len(tokens2) + 1) for _ in range(len(tokens1) + 1)]\n","\n","    longest = 0\n","    for i in range(1, len(tokens1) + 1):\n","      for j in range(1, len(tokens2) + 1):\n","        if tokens1[i - 1] == tokens2[j - 1]:\n","          dp[i][j] = dp[i - 1][j - 1] + 1\n","          if dp[i][j] >= self.min_subseq_len:\n","            longest = max(longest, dp[i][j])\n","        else:\n","          dp[i][j] = 0 \n","\n","    return longest"]},{"cell_type":"code","execution_count":65,"metadata":{},"outputs":[],"source":["def get_parallel_structures(df): \n","    rouge_scorer = CustomRougeLScorer()\n","    rouge = []\n","    for index in tqdm(range(len(df.syntax_markup))):\n","        predictions = []\n","        references = []\n","        text = df.syntax_markup.iloc[index]\n","        for i in range(len(text) - 1): \n","            predictions.append(text[i])\n","            references.append(text[i + 1])\n","        rouge.append(rouge_scorer.calculate(predictions, references, lambda x: x.split(' ')))\n","    return rouge"]},{"cell_type":"code","execution_count":66,"metadata":{"execution":{"iopub.execute_input":"2024-04-05T20:42:55.734168Z","iopub.status.busy":"2024-04-05T20:42:55.733760Z","iopub.status.idle":"2024-04-05T20:42:55.741173Z","shell.execute_reply":"2024-04-05T20:42:55.740025Z","shell.execute_reply.started":"2024-04-05T20:42:55.734138Z"},"trusted":true},"outputs":[],"source":["def make_morph_markup(df): \n","    df_with_markup = {}\n","    for index in tqdm(range(len(df.text))):\n","        text_with_markup = []\n","        text = df.text.iloc[index]\n","        sentences = list(sentenize(text))\n","        for sentence in sentences: \n","            sentence_with_markup = []\n","            sentence = model(sentence.text)\n","            for token in sentence:\n","                if token.pos_ == 'PUNCT':\n","                    continue \n","                else: \n","                    sentence_with_markup.append(token.pos_)\n","            text_with_markup.append(' '.join(sentence_with_markup))\n","        df_with_markup[index] = text_with_markup\n","    return df_with_markup"]},{"cell_type":"code","execution_count":67,"metadata":{"execution":{"iopub.execute_input":"2024-04-05T20:17:51.240881Z","iopub.status.busy":"2024-04-05T20:17:51.239894Z","iopub.status.idle":"2024-04-05T20:17:51.247880Z","shell.execute_reply":"2024-04-05T20:17:51.246660Z","shell.execute_reply.started":"2024-04-05T20:17:51.240839Z"},"trusted":true},"outputs":[],"source":["def get_pos_repetitiveness(df): \n","    rouge_1 = []\n","    rouge_2 = []\n","    rouge_l = []\n","    for index in tqdm(range(len(df.morph_markup))):\n","        predictions = []\n","        references = []\n","        text = df.morph_markup.iloc[index]\n","        for i in range(len(text) - 1): \n","            predictions.append(text[i])\n","            references.append(text[i + 1])\n","        rouge_metrics = get_rouge(predictions, references)\n","        rouge_1.append(rouge_metrics['rouge1'])\n","        rouge_2.append(rouge_metrics['rouge2'])\n","        rouge_l.append(rouge_metrics['rougeL'])\n","    return rouge_1, rouge_2, rouge_l"]},{"cell_type":"code","execution_count":68,"metadata":{},"outputs":[],"source":["def get_rouge_l(predictions, references):\n","    results = rouge.compute(predictions=predictions, references=references, use_stemmer=False, tokenizer=lambda x: [\n","                                                token.lower() for token in x.split(' ')])\n","    return np.mean(results[\"rougeL\"])"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["equal_df['syntax_markup'] = make_syntax_markup(equal_df).values()\n","equal_df['parallel_structures_level'] = get_parallel_structures(equal_df)\n","equal_df['morph_markup'] = make_morph_markup(equal_df).values()\n","morph_rouge_result = get_pos_repetitiveness(equal_df)"]},{"cell_type":"code","execution_count":73,"metadata":{},"outputs":[],"source":["equal_df['morph_rouge_1'] = morph_rouge_result[0]\n","equal_df['morph_rouge_2'] = morph_rouge_result[1]\n","equal_df['morph_rouge_l'] = morph_rouge_result[2]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["different_df['syntax_markup'] = make_syntax_markup(different_df).values()\n","different_df['parallel_structures_level'] = get_parallel_structures(different_df)\n","different_df['morph_markup'] = make_morph_markup(different_df).values()\n","morph_rouge_different_result = get_pos_repetitiveness(different_df)"]},{"cell_type":"code","execution_count":79,"metadata":{},"outputs":[],"source":["different_df['morph_rouge_1'] = morph_rouge_different_result[0]\n","different_df['morph_rouge_2'] = morph_rouge_different_result[1]\n","different_df['morph_rouge_l'] = morph_rouge_different_result[2]"]},{"cell_type":"markdown","metadata":{},"source":["## **Кореференция**"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2024-03-31T07:32:08.880047Z","iopub.status.busy":"2024-03-31T07:32:08.879281Z","iopub.status.idle":"2024-03-31T07:33:15.995770Z","shell.execute_reply":"2024-03-31T07:33:15.994794Z","shell.execute_reply.started":"2024-03-31T07:32:08.880009Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package omw-1.4 to /usr/share/nltk_data...\n","[nltk_data] Downloading package omw-1.4 to /usr/share/nltk_data...\n","[nltk_data]   Package omw-1.4 is already up-to-date!\n","models/crosslingual-coreference/minilm/model.tar.gz: 358490KB [00:14, 25106.54KB/s]                            \n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"071e422c4f744f7e9f583d4b5850b033","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/357 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2d8a8562991e4460a3ce8356292a1794","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/489 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a306653d4c8449478f1998dbc642adc0","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/4.83M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"970086c0ab744ac8b6febc735dbe5f3e","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/8.66M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4228d6cad74041fb80e0bbd41e7cab47","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/239 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"04709e4d2482440fadec63c5ec76e655","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/225M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["Some weights of the model checkpoint at nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large were not used when initializing XLMRobertaModel: ['lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.layer_norm.bias']\n","- This IS expected if you are initializing XLMRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing XLMRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of XLMRobertaModel were not initialized from the model checkpoint at nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large and are newly initialized: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"data":{"text/plain":["<crosslingual_coreference.CrossLingualPredictorSpacy.CrossLingualPredictorSpacy at 0x791861c19b10>"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["nlp = spacy.load(\"ru_core_news_sm\")\n","nlp.add_pipe(\"xx_coref\", config={\"chunk_size\": 2500, \"chunk_overlap\": 2, \"device\": 0})"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2024-03-31T07:33:21.321286Z","iopub.status.busy":"2024-03-31T07:33:21.320913Z","iopub.status.idle":"2024-03-31T07:33:21.328656Z","shell.execute_reply":"2024-03-31T07:33:21.327608Z","shell.execute_reply.started":"2024-03-31T07:33:21.321259Z"},"trusted":true},"outputs":[],"source":["def get_clusters(df):\n","    clusters = []\n","    mean_cluster_length = []\n","    for i in tqdm(range(len(df.text))):\n","        text = df.text[i]\n","        doc = nlp(text)\n","        clusters.append(len(doc._.coref_clusters) / df.n_words.iloc[i])\n","        cluster_lengths = []\n","        for cluster in doc._.coref_clusters:\n","            cluster_lengths.append(len(cluster))\n","        mean_cluster_length.append(np.mean(cluster_lengths))\n","    return clusters, mean_cluster_length"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["num_clusters_equal, mean_cluster_lengths_equal = get_clusters(equal_df)\n","equal_df['num_coref_clusters'] = num_clusters_equal\n","equal_df['mean_cluster_length'] = mean_cluster_lengths_equal"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["num_clusters_different, mean_cluster_lengths_different = get_clusters(different_df)\n","different_df['num_coref_clusters'] = num_clusters_different\n","different_df['mean_cluster_length'] = mean_cluster_lengths_different"]},{"cell_type":"markdown","metadata":{},"source":["## **Normalization**"]},{"cell_type":"code","execution_count":93,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:19:05.366629Z","iopub.status.busy":"2024-04-02T12:19:05.366175Z","iopub.status.idle":"2024-04-02T12:19:05.377412Z","shell.execute_reply":"2024-04-02T12:19:05.376199Z","shell.execute_reply.started":"2024-04-02T12:19:05.366596Z"},"trusted":true},"outputs":[],"source":["equal_df.drop(['text', 'lemmatized_text', 'syntax_markup', 'morph_markup', 'n_words'], axis=1, inplace=True)\n","equal_df.reset_index(drop=True, inplace=True)"]},{"cell_type":"code","execution_count":94,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:19:15.244919Z","iopub.status.busy":"2024-04-02T12:19:15.244457Z","iopub.status.idle":"2024-04-02T12:19:15.284761Z","shell.execute_reply":"2024-04-02T12:19:15.283434Z","shell.execute_reply.started":"2024-04-02T12:19:15.244881Z"},"trusted":true},"outputs":[],"source":["different_df.drop(['text', 'lemmatized_text', 'syntax_markup', 'morph_markup', 'n_words'], axis=1, inplace=True)\n","different_df.reset_index(drop=True, inplace=True)"]},{"cell_type":"code","execution_count":95,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:20:20.438596Z","iopub.status.busy":"2024-04-02T12:20:20.438149Z","iopub.status.idle":"2024-04-02T12:20:20.444911Z","shell.execute_reply":"2024-04-02T12:20:20.443598Z","shell.execute_reply.started":"2024-04-02T12:20:20.438563Z"},"trusted":true},"outputs":[],"source":["class_equal = equal_df['class']\n","class_different = different_df['class']"]},{"cell_type":"code","execution_count":96,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:20:50.153532Z","iopub.status.busy":"2024-04-02T12:20:50.153097Z","iopub.status.idle":"2024-04-02T12:20:50.163721Z","shell.execute_reply":"2024-04-02T12:20:50.162264Z","shell.execute_reply.started":"2024-04-02T12:20:50.153497Z"},"trusted":true},"outputs":[],"source":["equal_df.drop(['class'], axis=1, inplace=True)\n","equal_df.reset_index(drop=True, inplace=True)\n","\n","different_df.drop(['class'], axis=1, inplace=True)\n","different_df.reset_index(drop=True, inplace=True)"]},{"cell_type":"code","execution_count":98,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:28:43.986677Z","iopub.status.busy":"2024-04-02T12:28:43.986221Z","iopub.status.idle":"2024-04-02T12:28:44.002515Z","shell.execute_reply":"2024-04-02T12:28:44.000904Z","shell.execute_reply.started":"2024-04-02T12:28:43.986639Z"},"trusted":true},"outputs":[],"source":["X_train_equal, X_test_equal, y_train_equal, y_test_equal = train_test_split(equal_df, class_equal, test_size = 0.2, shuffle=True, stratify=class_equal)\n","X_train_equal, X_val_equal, y_train_equal, y_val_equal = train_test_split(X_train_equal, y_train_equal, test_size = 0.25, shuffle=True, stratify=y_train_equal)\n","\n","X_train_different, X_test_different, y_train_different, y_test_different = train_test_split(different_df, class_different, test_size = 0.2, shuffle=True, stratify=class_different)\n","X_train_different, X_val_different, y_train_different, y_val_different = train_test_split(X_train_different, y_train_different, test_size = 0.25, shuffle=True, stratify=y_train_different)"]},{"cell_type":"code","execution_count":101,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:28:50.175220Z","iopub.status.busy":"2024-04-02T12:28:50.174778Z","iopub.status.idle":"2024-04-02T12:28:50.183485Z","shell.execute_reply":"2024-04-02T12:28:50.182085Z","shell.execute_reply.started":"2024-04-02T12:28:50.175186Z"},"trusted":true},"outputs":[],"source":["X_train_equal.reset_index(inplace=True, drop=True)\n","X_test_equal.reset_index(inplace=True, drop=True)\n","X_val_equal.reset_index(inplace=True, drop=True)\n","\n","y_train_equal.reset_index(inplace=True, drop=True)\n","y_test_equal.reset_index(inplace=True, drop=True)\n","y_val_equal.reset_index(inplace=True, drop=True)"]},{"cell_type":"code","execution_count":102,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:28:52.255060Z","iopub.status.busy":"2024-04-02T12:28:52.254588Z","iopub.status.idle":"2024-04-02T12:28:52.262565Z","shell.execute_reply":"2024-04-02T12:28:52.261266Z","shell.execute_reply.started":"2024-04-02T12:28:52.255024Z"},"trusted":true},"outputs":[],"source":["X_train_different.reset_index(inplace=True, drop=True)\n","X_test_different.reset_index(inplace=True, drop=True)\n","X_val_different.reset_index(inplace=True, drop=True)\n","\n","y_train_different.reset_index(inplace=True, drop=True)\n","y_test_different.reset_index(inplace=True, drop=True)\n","y_val_different.reset_index(inplace=True, drop=True)"]},{"cell_type":"code","execution_count":103,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:30:44.175035Z","iopub.status.busy":"2024-04-02T12:30:44.174225Z","iopub.status.idle":"2024-04-02T12:30:44.182314Z","shell.execute_reply":"2024-04-02T12:30:44.180729Z","shell.execute_reply.started":"2024-04-02T12:30:44.174967Z"},"trusted":true},"outputs":[],"source":["scaler_train_equal = StandardScaler()\n","scaler_test_equal = StandardScaler()\n","scaler_val_equal = StandardScaler()\n","\n","scaler_train_different = StandardScaler()\n","scaler_test_different = StandardScaler()\n","scaler_val_different = StandardScaler()"]},{"cell_type":"code","execution_count":104,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:31:34.454267Z","iopub.status.busy":"2024-04-02T12:31:34.453794Z","iopub.status.idle":"2024-04-02T12:31:34.480616Z","shell.execute_reply":"2024-04-02T12:31:34.479335Z","shell.execute_reply.started":"2024-04-02T12:31:34.454222Z"},"trusted":true},"outputs":[],"source":["X_train_equal_scaled = pd.DataFrame(scaler_train_equal.fit_transform(X_train_equal), columns=X_train_equal.columns)\n","X_val_equal_scaled = pd.DataFrame(scaler_val_equal.fit_transform(X_val_equal), columns=X_val_equal.columns)\n","X_test_equal_scaled = pd.DataFrame(scaler_test_equal.fit_transform(X_test_equal), columns=X_test_equal.columns)"]},{"cell_type":"code","execution_count":105,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:31:36.452416Z","iopub.status.busy":"2024-04-02T12:31:36.451954Z","iopub.status.idle":"2024-04-02T12:31:36.480911Z","shell.execute_reply":"2024-04-02T12:31:36.479313Z","shell.execute_reply.started":"2024-04-02T12:31:36.452381Z"},"trusted":true},"outputs":[],"source":["X_train_different_scaled = pd.DataFrame(scaler_train_different.fit_transform(X_train_different), columns=X_train_different.columns)\n","X_val_different_scaled = pd.DataFrame(scaler_val_different.fit_transform(X_val_different), columns=X_val_different.columns)\n","X_test_different_scaled = pd.DataFrame(scaler_test_different.fit_transform(X_test_different), columns=X_test_different.columns)"]},{"cell_type":"markdown","metadata":{},"source":["## **Models**"]},{"cell_type":"markdown","metadata":{},"source":["### **RandomForestClassifier: equal themes sample**"]},{"cell_type":"code","execution_count":109,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:34:41.287743Z","iopub.status.busy":"2024-04-02T12:34:41.287283Z","iopub.status.idle":"2024-04-02T12:34:41.294692Z","shell.execute_reply":"2024-04-02T12:34:41.293287Z","shell.execute_reply.started":"2024-04-02T12:34:41.287706Z"},"trusted":true},"outputs":[],"source":["param_grid_rf = {\n","    'n_estimators': [10, 50, 100, 200],\n","    'max_depth': [5, 10, 20],\n","    'min_samples_split': [2, 5, 10],\n","    'min_samples_leaf': [1, 2, 4]\n","}"]},{"cell_type":"code","execution_count":110,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:34:43.517643Z","iopub.status.busy":"2024-04-02T12:34:43.517210Z","iopub.status.idle":"2024-04-02T12:38:48.129852Z","shell.execute_reply":"2024-04-02T12:38:48.128577Z","shell.execute_reply.started":"2024-04-02T12:34:43.517610Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-1 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-1 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-1 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-1 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-1 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-1 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-1 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-1 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-1 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-1 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-1 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-1 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-1 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-1 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-1 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-1 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n","             param_grid={&#x27;max_depth&#x27;: [5, 10, 20],\n","                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n","                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n","                         &#x27;n_estimators&#x27;: [10, 50, 100, 200]},\n","             scoring=&#x27;f1_macro&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n","             param_grid={&#x27;max_depth&#x27;: [5, 10, 20],\n","                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n","                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n","                         &#x27;n_estimators&#x27;: [10, 50, 100, 200]},\n","             scoring=&#x27;f1_macro&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n","             param_grid={'max_depth': [5, 10, 20],\n","                         'min_samples_leaf': [1, 2, 4],\n","                         'min_samples_split': [2, 5, 10],\n","                         'n_estimators': [10, 50, 100, 200]},\n","             scoring='f1_macro')"]},"execution_count":110,"metadata":{},"output_type":"execute_result"}],"source":["rf_equal = RandomForestClassifier(random_state=42)\n","\n","grid_search_equal = GridSearchCV(estimator=rf_equal, param_grid=param_grid_rf, cv=5, scoring='f1_macro')\n","grid_search_equal.fit(X_val_equal_scaled, y_val_equal)"]},{"cell_type":"code","execution_count":111,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:39:14.634451Z","iopub.status.busy":"2024-04-02T12:39:14.633720Z","iopub.status.idle":"2024-04-02T12:39:14.640940Z","shell.execute_reply":"2024-04-02T12:39:14.639697Z","shell.execute_reply.started":"2024-04-02T12:39:14.634417Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["These are the best parameters: {'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n","This is the best score: 0.9796003533138278\n"]}],"source":["best_params_equal = grid_search_equal.best_params_\n","best_score_equal = grid_search_equal.best_score_\n","\n","print(f'These are the best parameters: {best_params_equal}')\n","print(f'This is the best score: {best_score_equal}')"]},{"cell_type":"code","execution_count":114,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:40:05.267849Z","iopub.status.busy":"2024-04-02T12:40:05.267383Z","iopub.status.idle":"2024-04-02T12:40:06.284663Z","shell.execute_reply":"2024-04-02T12:40:06.283488Z","shell.execute_reply.started":"2024-04-02T12:40:05.267816Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-3 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-3 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-3 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-3 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-3 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-3 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-3 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-3 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-3 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-3 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-3 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-3 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-3 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-3 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-3 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-3 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-3 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-3 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-3 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-3 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-3 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-3 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-3 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-3 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-3 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-3 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-3 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-3 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-3 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, min_samples_leaf=4, min_samples_split=10,\n","                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=5, min_samples_leaf=4, min_samples_split=10,\n","                       random_state=42)</pre></div> </div></div></div></div>"],"text/plain":["RandomForestClassifier(max_depth=5, min_samples_leaf=4, min_samples_split=10,\n","                       random_state=42)"]},"execution_count":114,"metadata":{},"output_type":"execute_result"}],"source":["best_equal_rf = RandomForestClassifier(max_depth=5, min_samples_leaf=4, min_samples_split=10, n_estimators=100, random_state=42)\n","best_equal_rf.fit(X_train_equal_scaled, y_train_equal)"]},{"cell_type":"code","execution_count":115,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:40:30.136524Z","iopub.status.busy":"2024-04-02T12:40:30.136081Z","iopub.status.idle":"2024-04-02T12:40:30.181352Z","shell.execute_reply":"2024-04-02T12:40:30.179679Z","shell.execute_reply.started":"2024-04-02T12:40:30.136490Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.97      0.97      0.97       251\n","           1       0.97      0.98      0.97       291\n","\n","    accuracy                           0.97       542\n","   macro avg       0.97      0.97      0.97       542\n","weighted avg       0.97      0.97      0.97       542\n","\n"]}],"source":["y_pred_equal = best_equal_rf.predict(X_test_equal_scaled)\n","print(\"Classification Report:\")\n","print(classification_report(y_test_equal, y_pred_equal))"]},{"cell_type":"markdown","metadata":{},"source":["### **RandomForestClassifier: different themes sample**"]},{"cell_type":"code","execution_count":280,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:42:24.341935Z","iopub.status.busy":"2024-04-02T12:42:24.341411Z","iopub.status.idle":"2024-04-02T12:45:53.537661Z","shell.execute_reply":"2024-04-02T12:45:53.536434Z","shell.execute_reply.started":"2024-04-02T12:42:24.341901Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-12 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-12 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-12 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-12 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-12 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-12 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-12 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-12 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-12 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-12 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-12 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-12 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-12 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-12 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-12 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-12 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-12 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-12 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-12 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-12 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-12 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-12 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-12 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-12 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-12 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-12 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-12 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-12 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-12 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n","             param_grid={&#x27;max_depth&#x27;: [5, 10, 20],\n","                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n","                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n","                         &#x27;n_estimators&#x27;: [10, 50, 100, 200]},\n","             scoring=&#x27;f1_macro&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n","             param_grid={&#x27;max_depth&#x27;: [5, 10, 20],\n","                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n","                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n","                         &#x27;n_estimators&#x27;: [10, 50, 100, 200]},\n","             scoring=&#x27;f1_macro&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n","             param_grid={'max_depth': [5, 10, 20],\n","                         'min_samples_leaf': [1, 2, 4],\n","                         'min_samples_split': [2, 5, 10],\n","                         'n_estimators': [10, 50, 100, 200]},\n","             scoring='f1_macro')"]},"execution_count":280,"metadata":{},"output_type":"execute_result"}],"source":["rf_different = RandomForestClassifier(random_state=42)\n","\n","grid_search_different = GridSearchCV(estimator=rf_different, param_grid=param_grid_rf, cv=5, scoring='f1_macro')\n","grid_search_different.fit(X_val_different_scaled, y_val_different)"]},{"cell_type":"code","execution_count":281,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:48:03.893695Z","iopub.status.busy":"2024-04-02T12:48:03.893186Z","iopub.status.idle":"2024-04-02T12:48:03.900087Z","shell.execute_reply":"2024-04-02T12:48:03.899128Z","shell.execute_reply.started":"2024-04-02T12:48:03.893658Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["These are the best parameters: {'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 50}\n","This is the best score: 0.9814001003875695\n"]}],"source":["best_params_different = grid_search_different.best_params_\n","best_score_different = grid_search_different.best_score_\n","\n","print(f'These are the best parameters: {best_params_different}')\n","print(f'This is the best score: {best_score_different}')"]},{"cell_type":"code","execution_count":282,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:48:38.908324Z","iopub.status.busy":"2024-04-02T12:48:38.907832Z","iopub.status.idle":"2024-04-02T12:48:39.372828Z","shell.execute_reply":"2024-04-02T12:48:39.371555Z","shell.execute_reply.started":"2024-04-02T12:48:38.908289Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-13 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-13 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-13 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-13 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-13 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-13 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-13 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-13 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-13 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-13 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-13 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-13 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-13 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-13 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-13 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-13 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-13 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-13 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-13 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-13 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-13 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-13 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-13 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-13 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-13 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-13 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-13 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-13 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-13 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-13 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-13 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-13 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-13 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-13 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-13 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-13 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, min_samples_split=5, n_estimators=10,\n","                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" checked><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10, min_samples_split=5, n_estimators=10,\n","                       random_state=42)</pre></div> </div></div></div></div>"],"text/plain":["RandomForestClassifier(max_depth=10, min_samples_split=5, n_estimators=10,\n","                       random_state=42)"]},"execution_count":282,"metadata":{},"output_type":"execute_result"}],"source":["best_different_rf = RandomForestClassifier(max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=10, random_state=42)\n","best_different_rf.fit(X_train_different_scaled, y_train_different)"]},{"cell_type":"code","execution_count":283,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T12:49:12.236232Z","iopub.status.busy":"2024-04-02T12:49:12.235803Z","iopub.status.idle":"2024-04-02T12:49:12.264973Z","shell.execute_reply":"2024-04-02T12:49:12.264099Z","shell.execute_reply.started":"2024-04-02T12:49:12.236200Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.94      0.96      0.95       124\n","           1       0.97      0.95      0.96       148\n","\n","    accuracy                           0.96       272\n","   macro avg       0.96      0.96      0.96       272\n","weighted avg       0.96      0.96      0.96       272\n","\n"]}],"source":["y_pred_different = best_different_rf.predict(X_test_different_scaled)\n","print(\"Classification Report:\")\n","print(classification_report(y_test_different, y_pred_different))"]},{"cell_type":"markdown","metadata":{},"source":["### **CatBoostClassifier: equal themes sample**"]},{"cell_type":"code","execution_count":289,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:20:09.496522Z","iopub.status.busy":"2024-04-02T13:20:09.495637Z","iopub.status.idle":"2024-04-02T13:20:09.501671Z","shell.execute_reply":"2024-04-02T13:20:09.500797Z","shell.execute_reply.started":"2024-04-02T13:20:09.496485Z"},"trusted":true},"outputs":[],"source":["catboost_params = {'depth': [4, 6, 8],\n","                   'learning_rate': [0.01, 0.05, 0.1]}"]},{"cell_type":"code","execution_count":290,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:20:24.883108Z","iopub.status.busy":"2024-04-02T13:20:24.882575Z","iopub.status.idle":"2024-04-02T13:23:44.976278Z","shell.execute_reply":"2024-04-02T13:23:44.974990Z","shell.execute_reply.started":"2024-04-02T13:20:24.882955Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-14 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-14 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-14 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-14 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-14 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-14 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-14 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-14 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-14 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-14 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-14 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-14 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-14 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-14 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-14 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-14 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-14 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-14 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-14 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-14 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-14 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-14 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-14 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-14 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-14 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-14 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-14 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-14 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-14 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-14 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-14 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-14 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-14 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-14 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-14 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-14 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n","             estimator=&lt;catboost.core.CatBoostClassifier object at 0x30ebf13d0&gt;,\n","             param_grid={&#x27;depth&#x27;: [4, 6, 8],\n","                         &#x27;learning_rate&#x27;: [0.01, 0.05, 0.1]},\n","             scoring=&#x27;f1_macro&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3,\n","             estimator=&lt;catboost.core.CatBoostClassifier object at 0x30ebf13d0&gt;,\n","             param_grid={&#x27;depth&#x27;: [4, 6, 8],\n","                         &#x27;learning_rate&#x27;: [0.01, 0.05, 0.1]},\n","             scoring=&#x27;f1_macro&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: CatBoostClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x30ebf13d0&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">CatBoostClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x30ebf13d0&gt;</pre></div> </div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=3,\n","             estimator=<catboost.core.CatBoostClassifier object at 0x30ebf13d0>,\n","             param_grid={'depth': [4, 6, 8],\n","                         'learning_rate': [0.01, 0.05, 0.1]},\n","             scoring='f1_macro')"]},"execution_count":290,"metadata":{},"output_type":"execute_result"}],"source":["catboost_model_equal = CatBoostClassifier(verbose=False)\n","\n","catboost_grid_equal = GridSearchCV(catboost_model_equal, catboost_params, cv=3, scoring='f1_macro')\n","catboost_grid_equal.fit(X_val_equal_scaled, y_val_equal)"]},{"cell_type":"code","execution_count":291,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:25:57.409255Z","iopub.status.busy":"2024-04-02T13:25:57.408746Z","iopub.status.idle":"2024-04-02T13:25:57.417059Z","shell.execute_reply":"2024-04-02T13:25:57.415677Z","shell.execute_reply.started":"2024-04-02T13:25:57.409222Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["These are the best parameters: {'depth': 4, 'learning_rate': 0.05}\n","This is the best score: 0.9795335712672903\n"]}],"source":["best_catboost_params_equal = catboost_grid_equal.best_params_\n","best_catboost_score_equal = catboost_grid_equal.best_score_\n","\n","print(f'These are the best parameters: {best_catboost_params_equal}')\n","print(f'This is the best score: {best_catboost_score_equal}')"]},{"cell_type":"code","execution_count":292,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:26:44.445520Z","iopub.status.busy":"2024-04-02T13:26:44.445109Z","iopub.status.idle":"2024-04-02T13:26:47.678142Z","shell.execute_reply":"2024-04-02T13:26:47.676834Z","shell.execute_reply.started":"2024-04-02T13:26:44.445491Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.98      0.98      0.98       251\n","           1       0.98      0.98      0.98       291\n","\n","    accuracy                           0.98       542\n","   macro avg       0.98      0.98      0.98       542\n","weighted avg       0.98      0.98      0.98       542\n","\n"]}],"source":["catboost_model_equal.set_params(**best_catboost_params_equal)\n","catboost_model_equal.fit(X_train_equal_scaled, y_train_equal)\n","catboost_pred_equal = catboost_model_equal.predict(X_test_equal_scaled)\n","\n","print(classification_report(y_test_equal, catboost_pred_equal))"]},{"cell_type":"markdown","metadata":{},"source":["### **CatBoostClassifier: different themes sample**"]},{"cell_type":"code","execution_count":297,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:28:50.591664Z","iopub.status.busy":"2024-04-02T13:28:50.591116Z","iopub.status.idle":"2024-04-02T13:31:15.574952Z","shell.execute_reply":"2024-04-02T13:31:15.573628Z","shell.execute_reply.started":"2024-04-02T13:28:50.591626Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-15 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-15 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-15 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-15 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-15 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-15 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-15 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-15 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-15 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-15 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-15 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-15 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-15 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-15 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-15 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-15 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-15 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-15 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-15 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-15 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-15 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-15 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-15 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-15 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-15 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-15 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-15 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-15 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-15 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-15 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-15 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-15 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-15 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n","             estimator=&lt;catboost.core.CatBoostClassifier object at 0x323616e50&gt;,\n","             param_grid={&#x27;depth&#x27;: [4, 6, 8],\n","                         &#x27;learning_rate&#x27;: [0.01, 0.05, 0.1]},\n","             scoring=&#x27;f1_macro&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3,\n","             estimator=&lt;catboost.core.CatBoostClassifier object at 0x323616e50&gt;,\n","             param_grid={&#x27;depth&#x27;: [4, 6, 8],\n","                         &#x27;learning_rate&#x27;: [0.01, 0.05, 0.1]},\n","             scoring=&#x27;f1_macro&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: CatBoostClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x323616e50&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">CatBoostClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x323616e50&gt;</pre></div> </div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=3,\n","             estimator=<catboost.core.CatBoostClassifier object at 0x323616e50>,\n","             param_grid={'depth': [4, 6, 8],\n","                         'learning_rate': [0.01, 0.05, 0.1]},\n","             scoring='f1_macro')"]},"execution_count":297,"metadata":{},"output_type":"execute_result"}],"source":["catboost_model_different = CatBoostClassifier(verbose=False)\n","\n","catboost_grid_different = GridSearchCV(catboost_model_different, catboost_params, cv=3, scoring='f1_macro')\n","catboost_grid_different.fit(X_val_different_scaled, y_val_different)"]},{"cell_type":"code","execution_count":299,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:32:05.898522Z","iopub.status.busy":"2024-04-02T13:32:05.898024Z","iopub.status.idle":"2024-04-02T13:32:05.906013Z","shell.execute_reply":"2024-04-02T13:32:05.904646Z","shell.execute_reply.started":"2024-04-02T13:32:05.898486Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["These are the best parameters: {'depth': 4, 'learning_rate': 0.01}\n","This is the best score: 0.9663737119140886\n"]}],"source":["best_catboost_params_different = catboost_grid_different.best_params_\n","best_catboost_score_different = catboost_grid_different.best_score_\n","\n","print(f'These are the best parameters: {best_catboost_params_different}')\n","print(f'This is the best score: {best_catboost_score_different}')"]},{"cell_type":"code","execution_count":300,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:32:57.656916Z","iopub.status.busy":"2024-04-02T13:32:57.656463Z","iopub.status.idle":"2024-04-02T13:33:03.802355Z","shell.execute_reply":"2024-04-02T13:33:03.801041Z","shell.execute_reply.started":"2024-04-02T13:32:57.656880Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.99      0.96      0.98       124\n","           1       0.97      0.99      0.98       148\n","\n","    accuracy                           0.98       272\n","   macro avg       0.98      0.98      0.98       272\n","weighted avg       0.98      0.98      0.98       272\n","\n"]}],"source":["catboost_model_different.set_params(**best_catboost_params_different)\n","catboost_model_different.fit(X_train_different_scaled, y_train_different)\n","catboost_pred_different = catboost_model_different.predict(X_test_different_scaled)\n","\n","print(classification_report(y_test_different, catboost_pred_different))"]},{"cell_type":"markdown","metadata":{},"source":["### **XGBoostClassifier: equal themes sample**"]},{"cell_type":"code","execution_count":307,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:41:06.506554Z","iopub.status.busy":"2024-04-02T13:41:06.506148Z","iopub.status.idle":"2024-04-02T13:41:06.513017Z","shell.execute_reply":"2024-04-02T13:41:06.511445Z","shell.execute_reply.started":"2024-04-02T13:41:06.506525Z"},"trusted":true},"outputs":[],"source":["xgboost_params = {'max_depth': [4, 6, 8],\n","                  'learning_rate': [0.01, 0.05, 0.1]}"]},{"cell_type":"code","execution_count":308,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:42:00.448600Z","iopub.status.busy":"2024-04-02T13:42:00.447294Z","iopub.status.idle":"2024-04-02T13:42:05.425547Z","shell.execute_reply":"2024-04-02T13:42:05.424295Z","shell.execute_reply.started":"2024-04-02T13:42:00.448544Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-16 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-16 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-16 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-16 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-16 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-16 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-16 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-16 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-16 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-16 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-16 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-16 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-16 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-16 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-16 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-16 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-16 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-16 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-16 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-16 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-16 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-16 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-16 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-16 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-16 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-16 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-16 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-16 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-16 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-16 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-16 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-16 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-16 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-16 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-16 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-16 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n","             estimator=XGBClassifier(base_score=None, booster=None,\n","                                     callbacks=None, colsample_bylevel=None,\n","                                     colsample_bynode=None,\n","                                     colsample_bytree=None, device=None,\n","                                     early_stopping_rounds=None,\n","                                     enable_categorical=False, eval_metric=None,\n","                                     feature_types=None, gamma=None,\n","                                     grow_policy=None, importance_type=None,\n","                                     interaction_constraints=None,\n","                                     learning_rate=None, max_bin=None,\n","                                     max_cat_threshold=None,\n","                                     max_cat_to_onehot=None,\n","                                     max_delta_step=None, max_depth=None,\n","                                     max_leaves=None, min_child_weight=None,\n","                                     missing=nan, monotone_constraints=None,\n","                                     multi_strategy=None, n_estimators=None,\n","                                     n_jobs=None, num_parallel_tree=None,\n","                                     random_state=None, ...),\n","             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.05, 0.1],\n","                         &#x27;max_depth&#x27;: [4, 6, 8]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3,\n","             estimator=XGBClassifier(base_score=None, booster=None,\n","                                     callbacks=None, colsample_bylevel=None,\n","                                     colsample_bynode=None,\n","                                     colsample_bytree=None, device=None,\n","                                     early_stopping_rounds=None,\n","                                     enable_categorical=False, eval_metric=None,\n","                                     feature_types=None, gamma=None,\n","                                     grow_policy=None, importance_type=None,\n","                                     interaction_constraints=None,\n","                                     learning_rate=None, max_bin=None,\n","                                     max_cat_threshold=None,\n","                                     max_cat_to_onehot=None,\n","                                     max_delta_step=None, max_depth=None,\n","                                     max_leaves=None, min_child_weight=None,\n","                                     missing=nan, monotone_constraints=None,\n","                                     multi_strategy=None, n_estimators=None,\n","                                     n_jobs=None, num_parallel_tree=None,\n","                                     random_state=None, ...),\n","             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.05, 0.1],\n","                         &#x27;max_depth&#x27;: [4, 6, 8]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, device=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=None, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              multi_strategy=None, n_estimators=None, n_jobs=None,\n","              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, device=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=None, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              multi_strategy=None, n_estimators=None, n_jobs=None,\n","              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=3,\n","             estimator=XGBClassifier(base_score=None, booster=None,\n","                                     callbacks=None, colsample_bylevel=None,\n","                                     colsample_bynode=None,\n","                                     colsample_bytree=None, device=None,\n","                                     early_stopping_rounds=None,\n","                                     enable_categorical=False, eval_metric=None,\n","                                     feature_types=None, gamma=None,\n","                                     grow_policy=None, importance_type=None,\n","                                     interaction_constraints=None,\n","                                     learning_rate=None, max_bin=None,\n","                                     max_cat_threshold=None,\n","                                     max_cat_to_onehot=None,\n","                                     max_delta_step=None, max_depth=None,\n","                                     max_leaves=None, min_child_weight=None,\n","                                     missing=nan, monotone_constraints=None,\n","                                     multi_strategy=None, n_estimators=None,\n","                                     n_jobs=None, num_parallel_tree=None,\n","                                     random_state=None, ...),\n","             param_grid={'learning_rate': [0.01, 0.05, 0.1],\n","                         'max_depth': [4, 6, 8]})"]},"execution_count":308,"metadata":{},"output_type":"execute_result"}],"source":["xgboost_model_equal = XGBClassifier()\n","\n","xgboost_grid_equal = GridSearchCV(xgboost_model_equal, xgboost_params, cv=3)\n","xgboost_grid_equal.fit(X_val_equal_scaled, y_val_equal)"]},{"cell_type":"code","execution_count":309,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:42:58.056732Z","iopub.status.busy":"2024-04-02T13:42:58.056312Z","iopub.status.idle":"2024-04-02T13:42:58.064346Z","shell.execute_reply":"2024-04-02T13:42:58.063103Z","shell.execute_reply.started":"2024-04-02T13:42:58.056703Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["These are the best parameters: {'learning_rate': 0.1, 'max_depth': 4}\n","This is the best score: 0.9593922651933701\n"]}],"source":["best_xgboost_params_equal = xgboost_grid_equal.best_params_\n","best_xgboost_score_equal = xgboost_grid_equal.best_score_\n","\n","print(f'These are the best parameters: {best_xgboost_params_equal}')\n","print(f'This is the best score: {best_xgboost_score_equal}')"]},{"cell_type":"code","execution_count":310,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T13:44:05.281559Z","iopub.status.busy":"2024-04-02T13:44:05.281067Z","iopub.status.idle":"2024-04-02T13:44:05.544490Z","shell.execute_reply":"2024-04-02T13:44:05.543100Z","shell.execute_reply.started":"2024-04-02T13:44:05.281528Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.95      0.98      0.97       251\n","           1       0.99      0.96      0.97       291\n","\n","    accuracy                           0.97       542\n","   macro avg       0.97      0.97      0.97       542\n","weighted avg       0.97      0.97      0.97       542\n","\n"]}],"source":["xgboost_model_equal.set_params(**best_xgboost_params_equal)\n","xgboost_model_equal.fit(X_train_equal_scaled, y_train_equal)\n","xgboost_pred_equal = xgboost_model_equal.predict(X_test_equal_scaled)\n","\n","print(classification_report(y_test_equal, xgboost_pred_equal))"]},{"cell_type":"markdown","metadata":{},"source":["### **XGBoostClassifier: different themes sample**"]},{"cell_type":"code","execution_count":313,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T14:04:22.354889Z","iopub.status.busy":"2024-04-02T14:04:22.354258Z","iopub.status.idle":"2024-04-02T14:04:25.762512Z","shell.execute_reply":"2024-04-02T14:04:25.761422Z","shell.execute_reply.started":"2024-04-02T14:04:22.354846Z"},"trusted":true},"outputs":[{"data":{"text/html":["<style>#sk-container-id-17 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-17 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-17 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-17 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-17 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-17 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-17 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-17 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-17 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-17 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-17 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-17 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-17 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-17 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-17 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-17 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-17 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-17 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-17 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-17 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-17 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-17 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-17 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-17 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-17 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-17 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-17 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-17 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-17 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-17 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-17 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-17 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-17 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-17 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-17 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-17 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n","             estimator=XGBClassifier(base_score=None, booster=None,\n","                                     callbacks=None, colsample_bylevel=None,\n","                                     colsample_bynode=None,\n","                                     colsample_bytree=None, device=None,\n","                                     early_stopping_rounds=None,\n","                                     enable_categorical=False, eval_metric=None,\n","                                     feature_types=None, gamma=None,\n","                                     grow_policy=None, importance_type=None,\n","                                     interaction_constraints=None,\n","                                     learning_rate=None, max_bin=None,\n","                                     max_cat_threshold=None,\n","                                     max_cat_to_onehot=None,\n","                                     max_delta_step=None, max_depth=None,\n","                                     max_leaves=None, min_child_weight=None,\n","                                     missing=nan, monotone_constraints=None,\n","                                     multi_strategy=None, n_estimators=None,\n","                                     n_jobs=None, num_parallel_tree=None,\n","                                     random_state=None, ...),\n","             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.05, 0.1],\n","                         &#x27;max_depth&#x27;: [4, 6, 8]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=3,\n","             estimator=XGBClassifier(base_score=None, booster=None,\n","                                     callbacks=None, colsample_bylevel=None,\n","                                     colsample_bynode=None,\n","                                     colsample_bytree=None, device=None,\n","                                     early_stopping_rounds=None,\n","                                     enable_categorical=False, eval_metric=None,\n","                                     feature_types=None, gamma=None,\n","                                     grow_policy=None, importance_type=None,\n","                                     interaction_constraints=None,\n","                                     learning_rate=None, max_bin=None,\n","                                     max_cat_threshold=None,\n","                                     max_cat_to_onehot=None,\n","                                     max_delta_step=None, max_depth=None,\n","                                     max_leaves=None, min_child_weight=None,\n","                                     missing=nan, monotone_constraints=None,\n","                                     multi_strategy=None, n_estimators=None,\n","                                     n_jobs=None, num_parallel_tree=None,\n","                                     random_state=None, ...),\n","             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.05, 0.1],\n","                         &#x27;max_depth&#x27;: [4, 6, 8]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, device=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=None, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              multi_strategy=None, n_estimators=None, n_jobs=None,\n","              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, device=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=None, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              multi_strategy=None, n_estimators=None, n_jobs=None,\n","              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=3,\n","             estimator=XGBClassifier(base_score=None, booster=None,\n","                                     callbacks=None, colsample_bylevel=None,\n","                                     colsample_bynode=None,\n","                                     colsample_bytree=None, device=None,\n","                                     early_stopping_rounds=None,\n","                                     enable_categorical=False, eval_metric=None,\n","                                     feature_types=None, gamma=None,\n","                                     grow_policy=None, importance_type=None,\n","                                     interaction_constraints=None,\n","                                     learning_rate=None, max_bin=None,\n","                                     max_cat_threshold=None,\n","                                     max_cat_to_onehot=None,\n","                                     max_delta_step=None, max_depth=None,\n","                                     max_leaves=None, min_child_weight=None,\n","                                     missing=nan, monotone_constraints=None,\n","                                     multi_strategy=None, n_estimators=None,\n","                                     n_jobs=None, num_parallel_tree=None,\n","                                     random_state=None, ...),\n","             param_grid={'learning_rate': [0.01, 0.05, 0.1],\n","                         'max_depth': [4, 6, 8]})"]},"execution_count":313,"metadata":{},"output_type":"execute_result"}],"source":["xgboost_model_different = XGBClassifier()\n","\n","xgboost_grid_different = GridSearchCV(xgboost_model_different, xgboost_params, cv=3)\n","xgboost_grid_different.fit(X_val_different_scaled, y_val_different)"]},{"cell_type":"code","execution_count":314,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T14:04:59.409531Z","iopub.status.busy":"2024-04-02T14:04:59.409079Z","iopub.status.idle":"2024-04-02T14:04:59.416145Z","shell.execute_reply":"2024-04-02T14:04:59.414893Z","shell.execute_reply.started":"2024-04-02T14:04:59.409496Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["These are the best parameters: {'learning_rate': 0.1, 'max_depth': 4}\n","This is the best score: 0.9484330484330484\n"]}],"source":["best_xgboost_params_different = xgboost_grid_different.best_params_\n","best_xgboost_score_different = xgboost_grid_different.best_score_\n","\n","print(f'These are the best parameters: {best_xgboost_params_different}')\n","print(f'This is the best score: {best_xgboost_score_different}')"]},{"cell_type":"code","execution_count":315,"metadata":{"execution":{"iopub.execute_input":"2024-04-02T14:05:44.981757Z","iopub.status.busy":"2024-04-02T14:05:44.981296Z","iopub.status.idle":"2024-04-02T14:05:45.179848Z","shell.execute_reply":"2024-04-02T14:05:45.178618Z","shell.execute_reply.started":"2024-04-02T14:05:44.981722Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.98      0.96      0.97       124\n","           1       0.97      0.98      0.97       148\n","\n","    accuracy                           0.97       272\n","   macro avg       0.97      0.97      0.97       272\n","weighted avg       0.97      0.97      0.97       272\n","\n"]}],"source":["xgboost_model_different.set_params(**best_xgboost_params_different)\n","xgboost_model_different.fit(X_train_different_scaled, y_train_different)\n","xgboost_pred_different = xgboost_model_different.predict(X_test_different_scaled)\n","\n","print(classification_report(y_test_different, xgboost_pred_different))"]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":4336525,"sourceId":7450003,"sourceType":"datasetVersion"},{"datasetId":4395402,"sourceId":7547139,"sourceType":"datasetVersion"},{"datasetId":4693873,"sourceId":7975916,"sourceType":"datasetVersion"},{"datasetId":4715181,"sourceId":8006025,"sourceType":"datasetVersion"},{"datasetId":4715183,"sourceId":8006029,"sourceType":"datasetVersion"},{"datasetId":4749445,"sourceId":8053212,"sourceType":"datasetVersion"},{"datasetId":4749448,"sourceId":8053218,"sourceType":"datasetVersion"},{"datasetId":4749452,"sourceId":8053224,"sourceType":"datasetVersion"},{"datasetId":4749454,"sourceId":8053226,"sourceType":"datasetVersion"}],"dockerImageVersionId":30673,"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":4}