Creación y análisis de filtros de correo electrónico (con mejoras).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "15a3f7b5",
   "metadata": {},
   "source": [
    "### Lectura de los correos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d705e175",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to ....\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "from nltk import download\n",
    "download('punkt', download_dir='.')\n",
    "\n",
    "from nltk.corpus.reader.plaintext import PlaintextCorpusReader\n",
    "from nltk.data import load\n",
    "\n",
    "root1 = './Enron-Spam/subconjuntos/train/legítimo'\n",
    "corpus_entrenamiento_legitimo = PlaintextCorpusReader(root1, '.*', encoding='latin-1',\n",
    "                                             sent_tokenizer=load('tokenizers/punkt/spanish.pickle'))\n",
    "\n",
    "root2 = './Enron-Spam/subconjuntos/train/no_deseado'\n",
    "corpus_entrenamiento_no_deseado = PlaintextCorpusReader(root2, '.*', encoding='latin-1',\n",
    "                                             sent_tokenizer=load('tokenizers/punkt/spanish.pickle'))\n",
    "\n",
    "root3 = './Enron-Spam/subconjuntos/val/legítimo'\n",
    "corpus_prueba_legitimo = PlaintextCorpusReader(root3, '.*', encoding='latin-1',\n",
    "                                             sent_tokenizer=load('tokenizers/punkt/spanish.pickle'))\n",
    "\n",
    "root4 = './Enron-Spam/subconjuntos/val/no_deseado'\n",
    "corpus_prueba_no_deseado = PlaintextCorpusReader(root4, '.*', encoding='latin-1',\n",
    "                                             sent_tokenizer=load('tokenizers/punkt/spanish.pickle'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2dcb434",
   "metadata": {},
   "source": [
    "### Eliminación de ruido y Tokenización"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "18366f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "def strip_html(text):\n",
    "    soup = BeautifulSoup(text, \"html.parser\")\n",
    "    return soup.get_text()\n",
    "\n",
    "def replace_contractions(text):\n",
    "    \"\"\"Replace contractions in string of text\"\"\"\n",
    "    return contractions.fix(text)\n",
    "\n",
    "def denoise_text(text):\n",
    "    text = strip_html(text)\n",
    "    text = replace_contractions(text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73cb703c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import nltk\n",
    "from bs4 import BeautifulSoup\n",
    "import contractions # puede que sea necesario hacer un pip install contractions en la consola de comandos de anaconda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25c21e7f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiempo primer corpus: 71.92181587219238\n",
      "Tiempo segundo corpus: 111.9568145275116\n",
      "Tiempo total: 183.87863039970398\n"
     ]
    }
   ],
   "source": [
    "#Eliminación de ruido y tokenización de los correos de entrenamiento, y creación de la lista de las clases de cada correo\n",
    "inicio = time.time()\n",
    "\n",
    "tokenized_words_legitimo = []\n",
    "tokenized_words_no_deseado = []\n",
    "dataset = []\n",
    "clases_entrenamiento = []\n",
    "\n",
    "for filename in corpus_entrenamiento_legitimo.fileids():\n",
    "    denoise_email = denoise_text(corpus_entrenamiento_legitimo.raw(filename))\n",
    "    dataset.append(denoise_email)\n",
    "    clases_entrenamiento.append('legitimo')\n",
    "    tokenized_words_legitimo += nltk.word_tokenize(denoise_email)\n",
    "    \n",
    "tiempo_legitimo = time.time()\n",
    "print('Tiempo primer corpus: ' + str(tiempo_legitimo - inicio))\n",
    "    \n",
    "for filename in corpus_entrenamiento_no_deseado.fileids():\n",
    "    denoise_email = denoise_text(corpus_entrenamiento_no_deseado.raw(filename))\n",
    "    dataset.append(denoise_email)\n",
    "    clases_entrenamiento.append('spam')\n",
    "    tokenized_words_no_deseado += nltk.word_tokenize(denoise_email)\n",
    "    \n",
    "tiempo_no_deseados = time.time()\n",
    "print('Tiempo segundo corpus: ' + str(tiempo_no_deseados - tiempo_legitimo))   \n",
    "    \n",
    "final = time.time()\n",
    "print('Tiempo total: ' + str(final-inicio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4db6cc35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiempo total: 37.953675746917725\n"
     ]
    }
   ],
   "source": [
    "# Se tokenizan los correos de validación para poder aplicar la normalización más adelante\n",
    "\n",
    "inicio_val = time.time()\n",
    "\n",
    "clases_prueba = []\n",
    "tokenized_words_validation = []\n",
    "\n",
    "for filename in corpus_prueba_legitimo.fileids():\n",
    "    email = corpus_prueba_legitimo.raw(filename)\n",
    "    clases_prueba.append('legitimo')\n",
    "    tokenized_words_validation.append(nltk.word_tokenize(email))\n",
    "    \n",
    "for filename in corpus_prueba_no_deseado.fileids():\n",
    "    email = corpus_prueba_no_deseado.raw(filename)\n",
    "    clases_prueba.append('spam')\n",
    "    tokenized_words_validation.append(nltk.word_tokenize(email))\n",
    "    \n",
    "final_val = time.time()\n",
    "print('Tiempo total: ' + str(final_val-inicio_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a271833",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "codificador_clases = preprocessing.LabelEncoder()\n",
    "clases_codificadas = codificador_clases.fit_transform(clases_entrenamiento)\n",
    "clases_prueba_codificadas = codificador_clases.fit_transform(clases_prueba)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67df258b",
   "metadata": {},
   "source": [
    "### Normalización"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd4d0c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_non_ascii(words):\n",
    "    \"\"\"Remove non-ASCII characters from list of tokenized words\"\"\"\n",
    "    new_words = []\n",
    "    for word in words:\n",
    "        new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
    "        new_words.append(new_word)\n",
    "    return new_words\n",
    "\n",
    "def to_lowercase(words):\n",
    "    \"\"\"Convert all characters to lowercase from list of tokenized words\"\"\"\n",
    "    new_words = []\n",
    "    for word in words:\n",
    "        new_word = word.lower()\n",
    "        new_words.append(new_word)\n",
    "    return new_words\n",
    "\n",
    "def remove_punctuation(words):\n",
    "    \"\"\"Remove punctuation from list of tokenized words\"\"\"\n",
    "    new_words = []\n",
    "    for word in words:\n",
    "        new_word = re.sub(r'[^\\w\\s]', '', word)\n",
    "        if new_word != '':\n",
    "            new_words.append(new_word)\n",
    "    return new_words\n",
    "\n",
    "def normalize(words):\n",
    "    words = remove_non_ascii(words)\n",
    "    words = to_lowercase(words)\n",
    "    words = remove_punctuation(words)\n",
    "\n",
    "    return words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e0da24ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tiempo de ejecución: 11.83104133605957\n"
     ]
    }
   ],
   "source": [
    "#Normalizamos las palabras que se elegirán para el vocabulario\n",
    "import re, string, unicodedata\n",
    "\n",
    "inicio_normalizacion = time.time()\n",
    "normalized_words_legitimo = normalize(tokenized_words_legitimo)\n",
    "normalized_words_no_deseado = normalize(tokenized_words_no_deseado)\n",
    "final_normalizacion = time.time()\n",
    "print('Tiempo de ejecución: ' + str(final_normalizacion-inicio_normalizacion))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6afd6c09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# esta lista contiene string correspondientes a los emails tras aplicarles la normalización\n",
    "normalized_emails_validation = []\n",
    "\n",
    "for tokenized_email in tokenized_words_validation:\n",
    "    normalized_emails_validation.append(\" \".join(normalize(tokenized_email)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d2015c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculamos las 500 palabras más comunes de cada tipo de correo\n",
    "from nltk.probability import FreqDist\n",
    "\n",
    "fdist_legitimo = FreqDist(normalized_words_legitimo)\n",
    "most_common_legitimo = fdist_legitimo.most_common(500)\n",
    "\n",
    "fdist_legitimo = FreqDist(normalized_words_no_deseado)\n",
    "most_common_no_deseado = fdist_legitimo.most_common(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1a1830d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creación del vocabulario\n",
    "from nltk.lm import Vocabulary\n",
    "\n",
    "vocabularyTermsRepeated = [word[0] for word in most_common_no_deseado] + [word[0] for word in most_common_legitimo]\n",
    "vocabularyTerms = [word[0] for word in most_common_no_deseado] + [word[0] for word in most_common_legitimo]\n",
    "\n",
    "# eliminamos aquellas palabras que aparezcan tanto en los no deseados como en los legitimos, ya que no nos serviran para\n",
    "# diferenciar los correos\n",
    "for word in vocabularyTermsRepeated:\n",
    "    if vocabularyTerms.count(word) > 1:\n",
    "        vocabularyTerms.remove(word)\n",
    "        \n",
    "vocab = Vocabulary(vocabularyTerms)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6be28f5",
   "metadata": {},
   "source": [
    "### Bag of Words + Naive Bayes Multinomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "206698cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:1322: UserWarning: Upper case characters found in vocabulary while 'lowercase' is True. These entries will not be matched with any documents\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Count Vectorizer\n",
      "\n",
      "       2005  charset  0000  texthtml  microsoft  smtp  id  normal  \\\n",
      "0         0        1     0         0          0     0   1       0   \n",
      "10002     0        1     0         0          0     0   1       0   \n",
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      "10004     0        1     0         0          0     0   1       0   \n",
      "10005     0        1     0         0          0     0   1       0   \n",
      "...     ...      ...   ...       ...        ...   ...  ..     ...   \n",
      "9995      3        1     3         0          0     1   2       2   \n",
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      "9997      2        1     1         0          0     1   0       0   \n",
      "9998      2        1     1         0          0     1   0       0   \n",
      "9999      2        2     1         0          1     1   0       1   \n",
      "\n",
      "       quotedprintable  jun  ...  companies  america  operation  part  her  \\\n",
      "0                    0    0  ...          0        0          0     0    0   \n",
      "10002                0    0  ...          0        0          0     1    0   \n",
      "10003                0    0  ...          0        0          0     0    0   \n",
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      "...                ...  ...  ...        ...      ...        ...   ...  ...   \n",
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      "9998                 0    0  ...          0        0          1     0    0   \n",
      "9999                 0    0  ...          0        0          0     1    1   \n",
      "\n",
      "       option  global  riceedu  note  <UNK>  \n",
      "0           0       0        0     0      0  \n",
      "10002       0       0        0     1      0  \n",
      "10003       0       0        0     0      0  \n",
      "10004       1       0        0     0      0  \n",
      "10005       0       0        0     1      0  \n",
      "...       ...     ...      ...   ...    ...  \n",
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      "\n",
      "[20322 rows x 754 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#Entrenamiento del modelo Bag of Words\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import pandas as pd\n",
    "\n",
    "countvectorizer = CountVectorizer(vocabulary=vocab) #Creación del vectorizador con el vocabulario\n",
    "count_entrenamiento = countvectorizer.fit_transform(dataset)\n",
    "count_prueba = countvectorizer.transform(normalized_emails_validation)\n",
    "count_tokens = countvectorizer.get_feature_names()\n",
    "df_countvect = pd.DataFrame(data = count_entrenamiento.toarray(),index = corpus_entrenamiento_legitimo.fileids() + corpus_entrenamiento_no_deseado.fileids(),columns = count_tokens)\n",
    "print(\"\\nCount Vectorizer\\n\")\n",
    "print(df_countvect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67ba49dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Entrenamiento del modelo Naive-Bayes multinomial\n",
    "from sklearn import naive_bayes\n",
    "\n",
    "predicciones_NB = []\n",
    "\n",
    "for k in range(1,20):\n",
    "    multinomial_NB = naive_bayes.MultinomialNB(alpha=k)\n",
    "    multinomial_NB.fit(count_entrenamiento.toarray(), clases_codificadas)\n",
    "    prediccion_NB = multinomial_NB.predict(count_prueba.toarray())\n",
    "    prediccion_NB = codificador_clases.inverse_transform(prediccion_NB)\n",
    "    predicciones_NB.append(prediccion_NB)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4c65be",
   "metadata": {},
   "source": [
    "### TF - IDF + kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c36c752",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:1322: UserWarning: Upper case characters found in vocabulary while 'lowercase' is True. These entries will not be matched with any documents\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "TD-IDF Vectorizer\n",
      "\n",
      "           2005   charset      0000  texthtml  microsoft      smtp        id  \\\n",
      "0      0.000000  0.041569  0.000000       0.0   0.000000  0.000000  0.052257   \n",
      "10002  0.000000  0.009651  0.000000       0.0   0.000000  0.000000  0.012132   \n",
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      "9999   0.117178  0.052093  0.061184       0.0   0.058584  0.057604  0.000000   \n",
      "\n",
      "         normal  quotedprintable       jun  ...  companies  america  \\\n",
      "0      0.000000              0.0  0.000000  ...        0.0      0.0   \n",
      "10002  0.000000              0.0  0.000000  ...        0.0      0.0   \n",
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      "...         ...              ...       ...  ...        ...      ...   \n",
      "9995   0.139593              0.0  0.259704  ...        0.0      0.0   \n",
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      "9999   0.058833              0.0  0.000000  ...        0.0      0.0   \n",
      "\n",
      "       operation      part       her    option  global  riceedu      note  \\\n",
      "0       0.000000  0.000000  0.000000  0.000000     0.0      0.0  0.000000   \n",
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      "10005   0.000000  0.000000  0.000000  0.000000     0.0      0.0  0.063307   \n",
      "...          ...       ...       ...       ...     ...      ...       ...   \n",
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      "9998    0.201303  0.000000  0.000000  0.000000     0.0      0.0  0.000000   \n",
      "9999    0.000000  0.069899  0.101428  0.000000     0.0      0.0  0.000000   \n",
      "\n",
      "       <UNK>  \n",
      "0        0.0  \n",
      "10002    0.0  \n",
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      "\n",
      "[20322 rows x 754 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#Entrenamiento del modelo Tf-Idf\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidfvectorizer = TfidfVectorizer(vocabulary=vocab) #Creamos el vectorizador y le pasamos el vocabulario\n",
    "tfidf_entrenamiento = tfidfvectorizer.fit_transform(dataset)\n",
    "tfidf_prueba = tfidfvectorizer.transform(normalized_emails_validation)\n",
    "tfidf_tokens = tfidfvectorizer.get_feature_names()\n",
    "df_tfidfvect = pd.DataFrame(data = tfidf_entrenamiento.toarray(),index = corpus_entrenamiento_legitimo.fileids() + corpus_entrenamiento_no_deseado.fileids(),columns = tfidf_tokens)\n",
    "print(\"\\nTD-IDF Vectorizer\\n\")\n",
    "print(df_tfidfvect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc8c9f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Entrenamiento del modelo Knn\n",
    "from sklearn import neighbors\n",
    "\n",
    "predicciones_kNN = []\n",
    "\n",
    "for n_neighbors in range(1,20):\n",
    "    clasif_kNN = neighbors.KNeighborsClassifier(n_neighbors)\n",
    "    clasif_kNN.fit(tfidf_entrenamiento.toarray(), clases_codificadas)\n",
    "    prediccion_kNN = clasif_kNN.predict(tfidf_prueba.toarray())\n",
    "    prediccion_kNN = codificador_clases.inverse_transform(prediccion_kNN)\n",
    "    predicciones_kNN.append(prediccion_kNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59886ae9",
   "metadata": {},
   "source": [
    "## Comparación algoritmos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4eef9db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(tp, fp, fn, tn):\n",
    "    # porcentaje total de elementos clasificados correctamente\n",
    "    return (tp+tn)/(tp+fp+tn+fn)\n",
    "\n",
    "def recall(tp, fp, fn, tn):\n",
    "    # número de elementos identificados como positivos del total de verdaderos positivos\n",
    "    return (tp)/(tp+fn)\n",
    "\n",
    "def precision(tp, fp, fn, tn):\n",
    "    # número de elementos identificados correctamente como positivos del total de elementos identificados como positivos\n",
    "    return (tp)/(tp+fp)\n",
    "\n",
    "def especificidad(tp, fp, fn, tn):\n",
    "    # número de elementos correctamente identificados como negativos del total de negativos\n",
    "    return (tn)/(tn+fp)\n",
    "\n",
    "def f1_score(recall, precision):\n",
    "    return (2*recall*precision)/(recall+precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50e4f87c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def get_metrics(predicciones, objetivo_prueba):\n",
    "    accuracy_list = []\n",
    "    recall_list = []\n",
    "    precision_list = []\n",
    "    especificidad_list = []\n",
    "    \n",
    "    for prediccion in predicciones:\n",
    "        confusion_matrix_algorithm = confusion_matrix(objetivo_prueba, prediccion)\n",
    "        tp, fp, fn, tn = confusion_matrix_algorithm.ravel()\n",
    "        accuracy_algorithm = accuracy(tp, fp, fn, tn)\n",
    "        recall_algorithm = recall(tp, fp, fn, tn)\n",
    "        precision_algorithm = precision(tp, fp, fn, tn)\n",
    "        especificidad_algorithm = especificidad(tp, fp, fn, tn)\n",
    "        \n",
    "        accuracy_list.append(accuracy_algorithm)\n",
    "        recall_list.append(recall_algorithm)\n",
    "        precision_list.append(precision_algorithm)\n",
    "        especificidad_list.append(especificidad_algorithm)\n",
    "        \n",
    "    return (accuracy_list, recall_list, precision_list, especificidad_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b263b34f",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_NB_list, recall_NB_list, precision_NB_list, especificidad_NB_list = get_metrics(predicciones_NB, clases_prueba)\n",
    "\n",
    "accuracy_kNN_list, recall_kNN_list, precision_kNN_list, especificidad_kNN_list = get_metrics(predicciones_kNN, clases_prueba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ee5f73f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "hiperparametro = []\n",
    "h_string = []\n",
    "\n",
    "for n in range(1,20):\n",
    "    hiperparametro.append(n)\n",
    "    h_string.append(str(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bdd9dd5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c71da5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QAEwys1fdfU7MZrcA09z9LDPbL9q+n5m1A64Durt7kZm9CAwEngKGAGPdfVgUVIYAv0riuYmIVKiqK/Ujh73D8nVFOyxv16Ip/7r88Fqnf8MNN3DQQQcxaNCguOvz8/M55ZRTmDBhAgMHDqx1evEkUgPoCyx090XuvgV4ARhQbpvuwFgAd58HdDKzttG6PKCpmeUBzYAV0fIBwNPR56eBM2t6EiIiyXbzyV1pmt+wzLKm+Q25+eSuSTl+q1atOO+883j88cfjrnd3PvjgA/bee++kpBdPIgGgHbAs5ntBtCzWdOBsADPrC3QE2rv7cuBeYCmwEvja3d+K9mnr7isBovfd4yVuZpeZ2WQzm1xYWJjYWYmI1NKZvdvx+7MPoF2Lphjhyv/3Zx/Amb3LF381d9NNN+3QG2j48OH06tWLHj16UFJSwlVXXZW09Mozd698A7NzgZPdfXD0/adAX3e/NmabXYH7gN7ATGA/YDCh4P8PcD6wDvg38JK7P2tm69y9RcwxvnL3Su8D9OnTxydPnlzdcxQRAWDu3Ll069Yt09lIqXjnaGZT3L1P+W0T6QVUAHSI+d6e75pxAHD39cCgKCEDFkevk4HF7l4YrRsBHAE8C6wysz3cfaWZ7QGsTuz0REQkGRJpApoEdDGzzmbWiHAT99XYDcysRbQOwpX/hCgoLAUOM7NmUWDoB8yNtnsVuDj6fDHwSu1ORUREqqPKGoC7l5jZNcAYoCHwhLvPNrMrovUPA92AZ8xsKzAHuDRa97GZvQR8ApQAU4FHo0MPA140s0sJgeLcpJ6ZiIhUKqEHwdz9TeDNcssejvn8IdClgn3vAO6Is3wtoUYgIiIZoKEgRERylAKAiEiOUgAQEUmjzz//nB49epRZ9u6772JmvPbaa9uXnXbaabz77rsAHHfccfTp810vzsmTJ3PcccfVOi8KACIiFZnxIgzvAUNbhPcZL6Ysqfbt23P33XdXuH716tWMGjUqqWkqAIiIxDPjRXjtOvh6GeDh/bXrkhoEFi1aRO/evZk0aRIHHnggzZs35+2334677c0338xdd92VtLRBw0GLSK4aNQS+mFnx+oJJsHVz2WXFRfDKNTDl6fj7fO8A6D8soeTnz5/PwIEDefLJJ1m3bh3jx4/ntttu47bbbuPEE0/cYfvDDz+cl19+mXHjxrHLLrsklEZVVAMQEYmnfOFf1fJqKCwsZMCAATz77LNlxvs/+uijAXjvvffi7nfbbbcltRagGoCI5KaqrtSH94iaf8pp3gEGvVGrpJs3b06HDh14//332X//ssNS33rrrdx9993k5e1YPJ9wwgn85je/4aOPPqpV+qVUAxARiaff7ZDftOyy/KZheS01atSIkSNH8swzz/Dcc8+VWXfSSSfx1VdfMX369Lj73nrrrdxzzz21zgMoANRfaey9IFIv9TwPTr8/XPFj4f30+8PyJNhpp514/fXXGT58OF9//XWZdbfeeisFBQVx9zv11FNp06ZNUvJQ5XDQ2UTDQSeotPdCccxsRvlNk/rjFamLNBx0WaoB1Edj7yxb+EP4PvbOzORHRLKSAkB99HX8qmOFy0UkJykA1EfN21dvuYjkJAWA+qjf7dCwUdlleU2S0ntBpK6rS/c9q6u656YAUB/1PA/2PAisAWBhWedjdQNYcl6TJk1Yu3ZtvQwC7s7atWtp0qRJwvvoQbD6aNtWWLMADjgXzn4UXrwIFo2HLRuhUbNM504kY9q3b09BQQGFhYWZzkpKNGnShPbtE2/qVQCojwomQdGXsO8p4Xvfy2HOKzDzRTj4koxmTSST8vPz6dy5c6azkTXUBFQfzR8FDfJgn2jGzY5HQNsD4ONHoR5WfescPaQnWUIBoD5aMDoU+k2ah+9mcOjlsHo2fP6/zOYt16VhiGGRRCkA1DdfLobCebBv/7LLD/gRNG0FHz+cmXxJoIf0sluO1c4UAOqbBWPCe9dTyi7PbwoHXwzz34R1S9OfLwn0kF72ysHamQJAfbNgFOy2L7T6/o7r+lwa3if9Pb15kmDLxh2fzyiV1wS2bEhvfpKlvlw152DtTAGgPtm0Hj5//7veP+W16AD7nQafPBMKo7qkrhcyWzbC8wPDZCLlg0CDfCgpgidOga+XZyZ/NVWfrppzsHamAFCffPYObCuGrv0r3ubQK6DoK5j57/Tlq7bqeiGzZSO8cAEsngBnPgwDHiw7xPCZD8GP/x3u3zx2PBRMyXSOE1cfrpo3rIG3K3lKvmE+LPxvvexBp+cA6pMFo6FJC2jft+JtOh4BbXvAxEfhoItCD6FsV1khk+1PN5cW/ovGw5l/g14XhOXx8j34bXjufHjq1BAkDvhRevNaXVs2xp8xC8JVs3t2/742rIEP7oeJj0HJpvD/5ovp4XOphvnQaGd49pyw/rghsPcJ2X1e1aAaQH2xbSt8+hZ0OQkaVhLXS7uErpoFS95PX/5qo65WzYuLYgr/h74r/Cuyezf4+bgwjMd/LoV37oZt29KT1+pa+hE8fFQlGzg8dBhMeWrH4J1ppVf8fzkAPnggNIte9TEMfgvOeKBs7WzAQ3DTfDjtL/DNSnj2bHj8JFg4tl7UCDQhTH2x9GN44iT40RPQ45zKty0ugj93g05HwfnPpid/tTF8//iFffP2cOPs9OcnEcVFoc1/e+H/48T3LdkCb9wIU5+FbmfAWQ9Do51Sl9fqKC6Cd+6CD6NmrAN+BB//bcfJhw78cXgi/YsZ0Kw1HDI4vHbePXN5j73iLy4KQ6UcczO02Tex/Uu2wLRnYcKfYH1BnaoR1GpCGDM7xczmm9lCMxsSZ31LM3vZzGaY2UQz6xEt72pm02Je683shmjdUDNbHrPu1FqeY25bED39u3e/qrfNbwoHXQzz3oB1FVThs0lF59SkRbifkW1qU/gD5DWCM/4KJ90Nc1+DJ/tnx83hpR+Hq/4P/wp9BsFVH8AP7og/beJpf4bLJ8Alb0CHQ2H8PSGQv3I1rJqT3nxvWANv3wF/6Qnv3w/7/RCungjnPJZ44Q/h36XPz+C6qXDacFi/os7XCKqsAZhZQ2ABcCJQAEwCLnD3OTHb/BH41t1/a2b7AQ+6e784x1kOHOruS8xsaLTPvYlmVjWASjx4GOy0G1zyemLbr1sG9/WEI66DE3+b2rzVxuZv4YGDQ9DaVhJqAs3bh9rLzJdg1z3h/H/AHgdmOqdBbOE/4EHo/ZPaHW/BGHjpZ6EdeuBz0P7g5OSzOspf9Q94AL5/XPWOsWZhqClM/Wfo8bT3CXD41SG4p+rqecOa0MQz8TEo3hhqK8f8snqFfmVKNsO0f35XI+hwaKgRfP/4rKsR1KYG0BdY6O6L3H0L8AIwoNw23YGxAO4+D+hkZm3LbdMP+Mzdl1Q791K5rz6HwrmV9/4pr0WHcCX0ydPZ10Yb64P74dsv4OzH4MZZMHRdeD/rYRg0KgSFx08KzSWZVlwEz1+QvMIfYN+T4dK3w9XnU6eGoJdO8a76q1v4A+y2D/zwT/B/c8K8FKvmhBurDx0WuiUXb6r6GIkqc8V/H+x3Klz9MZzz9+QV/gB5jaMawSfwwz+HWto/zoInTg498upAjSCRANAOiG0nKIiWxZoOnA1gZn2BjkD5MUkHAs+XW3ZN1Gz0hJm1jJe4mV1mZpPNbHJ9HcK11kqf/q2o/39Fsr1L6NfLQ5W9xznQ4ZAd13c4JDQzdOgbmhZeuz5clWXC9sL/3eQV/qXado9uDvcON4fH/b/U3xwuLoIxt4bCrGQzXPRKaPZovEvtjtusFRx9E9wwE856JPSyefXa0Dw07vfwbS3+j29YW0nB37V2+a5MXmM45NI6GQgSaQI6FzjZ3QdH338K9HX3a2O22RW4D+gNzAT2Awa7+/RofSNgBbC/u6+KlrUF1gAO/A7Yw91/Vlle1ARUgWfODE0j11bzb+MOfzsyTBxzxXtZV23l5Stg1gi4ZhK07FjxdltLYNxd8L/hoQfNec+EGk66pLLwj1WyGV7/v3AjsvuZoVtpKuZ3WDYRRl4Faz+FgwfBSb+rfcFfEXf4/D348KFwH6th49BF9vCrQ6+oRGxYCx8+EEa7Ld4YLhiO/WVqC/3KlGwONdL3/pw1TUMVNQEl8hxAARD7v6k9oTDfzt3XA4OihAxYHL1K9Qc+KS38o322fzazx4AEG6+ljE3rwwifh11R/X1Lu4S+dh0s+QA6HZn8/NXUiqkw/Xk46sbKC38I3V5/MBTa9YGRV8Ijx4Srvn0SuCFeW8VF8MKPo8L/r6kr/CFcaQ74ayjY3r49NP1d8Hy4D5IMxUUw7u7Q1r9rO/jpSNj7+OQcuyJm0PmY8FrzKXz0N5j2HEz9R7g/cPjV4X7BzH+H5z5K7wH1uz2sz6aCv1RpjaD3hVEg+FOoEXQ4LAoEx8U/nww805JIDSCPcBO4H+Em7iTgx+4+O2abFsBGd99iZj8Hjnb3i2LWvwCMcfcnY5bt4e4ro883Em4OD6wsL6oBxDHnlTDj1yVv1qwA37IRhneHTkeHm6nZwB2eOi2ManrdVGiya+L7rv0M/nUhrJ4Lx98amhsapOhxl9LC/7NxUeF/YWrSiWf+6NAc1GjnEATaHVS74y2bFILn2k/DpEEn/q56f/dk2vglTH4iPKz47SrYZU/YWAhbi7/bpkEe0CA8+Z4tBX9FSjaHgPben2H9cmi1dyj4t8Y0V+Y3Db2nUhQEanwT2N1LgGuAMcBc4EV3n21mV5hZ6WVnN2C2mc0jXO1fH5NwM0IPohHlDn2Pmc00sxnA8cCNNTgvmR89/dvh0Jrt36hZeCI4m7qEznsDlvwPjr+l+oVQ671h8H9DH+9xd4UHsYrWJT+PmSz8IYz2eulbYVyhJ/uHprKaKC6Ct24Lz5AUF8FPX4bT78tc4Q/hPsExvwj3Cc58GDaUK/wh3PxvmBfa+H/0ePYW/hDVCAaHi5kf/inU3LaWu1eVoeEz9CBYXbZtK9zbJVSRz6nFCJ/rlsJ9B8KR14emlEwq2QIPHRoKtiver/yp5sq4h1FPRw8JVezzn4XvHZCcPBZvigr/dzJT+Mf6tjDUeJZ9BMcOCU0MibYzL5sEr1wV5o/O9FV/ZYa2INwqLM9Cr7C6JgPnU6sHwSRLFUyGjWur3/unvBZ7QddTYUoWdAmd9Hf4clF4CKqmhT+EQrDvz0NX0ZLN8PcfwLTyndBqILbwP+OBzBb+ADu3gYtfDU/ejh8GLw2qeqTX4k3w1m/CVf+Wjdlx1V+Z5hVMcl7R8myXReejAFCXLRgN1hD2+UHtj3XoFWEi+XT3M4+18UsY/4dwc69LEs4JQhfRyydA+0Ng5BXw+o017ypavvA/6KfJyWNt5TUOTxyfeCfMHhmeF1i/Iv62BZPhkaPD8xW9fwpXfRhqkNms3+2hjTxWftOwvC7KovPRaKCxZryYFXfmE1Y692/TFrU/VqejYPfuMPGRcFWbie5q4++BzevhpLuSe9yddw89Wt65M/QPXzk9dBWtzhVXthb+pcxCE17rLjDi5/DYCeEhpU+eCb/nXdtB2/1h4dvhpuqFI9LTSyoZSv8P1qX/m5XJovPRPYBSpWPOlx/UKoV35mvlqyVhKIeT7oYjrknOMac8FR6mGjQqBJZ0WrMwtP33vjA0R6TKnFdDH/e8RnDO44l1c8z2wr+8L2bB06eHGl15HY+CC56DJs3Tny/JGN0DqEpdm9hi+9y/1Rj+oSoHnBd6FGVi4vj/3hGmRTz+1tSm0/0MuGwc7LR7GMhrwr2VP1Vb1wp/gO/1gPwm8detW6LCX7ZTAChV18acXzAqVPdb7528Y5Z2CZ37enrPe/F7MO91OPr/0jNc8G5dQlfR/c+Gd34H//pJ/K6idbHwL7V+Zfzl2fp7loxQACiVRXfmq7T5m/D0774nJ//YhwwGHCY9nvxjx7NtG4y5JYwyedhV6UkToPHOoevsKX8IE+k8dnx4UKd03uE/7x/a0T8bW/cKf6hbv2fJGAWAUll0Z75Kn70DW7ckt/mnVMuOUZfQp9LTJXTGC2HSkB8M3fHvn2pmYQiNS94II0iO/e138w6vL4DVs0ONqK4V/lC3fs+SMQoApXqeFzOxReSE32TnDeAFY0I7bofDUnP8Qy8PNxBn/Sc1xy+1ZUO4x9Lu4KpnMUulvQ4LQyrE89m49OYlWcr8nmMmasnG37NkjLqBxup5XnitXxnmC/1ycdX7pNu2rSEA7HNi7R6Uqkyno0OX0I8fgV4/SV2X0A8eCPOsnvtU5kci/aYetpmX/p5FKqAaQDy77gEHnh9G8tuwJtO5KWv5FNi4JjXNP6XMoO9loWlm6UepSWP9ytAnv/uZ4Qo809RmLjlIAaAiR1wXpq6b+Fimc1LW9qd/U/wQT88Udwl9564woFemxx4qpTZzyUEKABVp0zXcDJ34aGirzhbzR8Neh0PTuBOoJU+jncLNz7mvJX9C8pXTw1yqh14BrTon99g1pTZzyUEKAJU58vpwM3TqPzOdk2Dd0tAzpWstB39L1CE/BxwmJ7FLqHuYarBpyzBWfzbpeV7ZeYdV+Es9pwBQmb0OC+Psf/hAmHYw0+aPDu/7prD9P1aZLqFJmrR7/qgwBeDxtyRnDCMRqTEFgKoceX248p4zMtM5Ce3/rfeB3fZJX5p9LwtDTiejS2jJljD5yG77hvHnRSSjFACqsm//MOTC+/eF5otM2fxNuHKu7dj/1dX5GGjTLdwMru35T34CvvwsjPbZMD85+RORGlMAqEqDBnDkdaFL5KJ3M5ePz8aFp3/THQDM4NCoS+iyj2t+nKKvwoQl3z8OupyUtOyJSM0pACSi5/mwc9tQC8iU0qd/M9Fnvuf5Ie3adAmdcG8YcO2kuzP/0JeIAAoAiclrDIddCYvGhS6M6bZtG3w6Jsz8lYmmk0Y7hTFx5rxasy6haz8LTxX3vjAMVSwiWUEBIFEHD4JGu8D796c/7eVTYENh+nr/xHPIYPBtoR2/uv57R5jk/YTbkp8vEakxBYBENW0BfS6B2S/DV5+nN+3Sp3+TNU9uTbTsVLMuoZ+/Hx4mO+pG2OV7qcqdiNSAAkB1HHolWAP48KH0prsgTU//VuXQy8I4RLNHJLZ96Vj/u7aDw69Obd5EpNoUAKqjebvwdOgnz8CGtelJc91SWDUrNZO/VFfnY6HNfqE9P5EuoTNfhJXToN8dYbYxEckqCgDVdcS1YZC4SX9PT3qpmPu3pkpHCV05DZZNrHzbLRvDWP979oYDzk1L9kSkehQAqmv3bqEv/sRHQiGXagtGQ6u9wzy22eDAgdA4gS6hHz4I65eHbp8N9DMTyUb6n1kTR14fhkeYluJB4jZ/C4snpP/hr8psHyX0VVi/Iv4233wB/xsO3U6HTkemN38ikrCEAoCZnWJm881soZkNibO+pZm9bGYzzGyimfWIlnc1s2kxr/VmdkO0rpWZvW1mn0bvGb7DWQ17HQ7tD4EP/5raQeIWRU//pmv0z0QdMjjMTFZRl9B37gr5/sFv05svEamWKgOAmTUEHgT6A92BC8yse7nNbgGmuXtP4CLgPgB3n+/uvdy9F3AwsBF4OdpnCDDW3bsAY6PvdYNZqAV89Xm4Ek6VBaNDc8teh6cujZpo1Tnck5j8JJRsLrvui5lhJrVDL4fWe2cmfyKSkERqAH2Bhe6+yN23AC8AA8pt051QiOPu84BOZta23Db9gM/cfUn0fQDwdPT5aeDM6mc/g7qeGkbmTNUgcdu2wYK3Qt//bBw4rW/UJXRWTJfQ7WP9t4BjfpGxrIlIYhIJAO2AZTHfC6JlsaYDZwOYWV+gI1B+MtWBwPMx39u6+0qA6H33eImb2WVmNtnMJhcWFiaQ3TRp0DD0CFo5LbTTJ9uKT2DD6uxq/4/1/eNgt65lRwn99C1YPB6OHZL5ZxZEpEqJBIB4I3eVv+QdBrQ0s2nAtcBUYHvjuJk1As4A/l3dDLr7o+7ex937tGnTprq7p1bPgakbJG7+qGju3ww+/VsZs9DMs3IaFEyCrcVhrP/W+8Ahl2Y6dyKSgEQCQAHQIeZ7e6BM9w93X+/ug6K2/ouANsDimE36A5+4+6qYZavMbA+A6H119bOfYflNwry2n40Nbd/JtGBMGPmzWavkHjeZep4PDZvCM2fA73aDNQugy8nZ2WQlIjtIJABMArqYWefoSn4gUObOp5m1iNYBDAYmuPv6mE0uoGzzD9ExLo4+Xwy8Ut3MZ4U+P4NGOyd3kLh1y2DVzOx4+rcy898EL4biou+WTXkCZryYuTyJSMKqDADuXgJcA4wB5gIvuvtsM7vCzK6INusGzDazeYSr/etL9zezZsCJQPkBZIYBJ5rZp9H6YbU9mYxo2iJMbzjrP2HYhmRYkOa5f2tq7J2wrVw32OKisFxEsl5eIhu5+5vAm+WWPRzz+UMg7qOq7r4RaB1n+VpCz6C677Crws3QDx+C/kmIYwvGQKvvZ8/TvxX5uqB6y0Ukq+hJ4GRo3g4OOA8+eRo2flm7Y23ZED392z/7Z85qXr6jVxXLRSSrKAAkyxHXQvFGmPR47Y7z2TjYujn72/8B+t0O+U3LLstvGpaLSNZTAEiWtt1DD5iPHy57U7S6FowKT/92PCJ5eUuVnufB6fdD8w6AhffT7w/LRSTrJXQPQBJ05PXw1KlhkLhDBld//9Knf/fpV3e6UvY8TwW+SB2lGkAydTwC2vWBDx4Ig6VV14qp2f30r4jUKwoAyVTbQeIWjApTTnY5MelZExEpTwEg2fb7YZjA5X9/qf4gcQtGQ4csf/pXROoNBYBkix0k7vP3Et/v64IwnES2jf0vIvWWAkAqHHgB7NSmeoPEbX/6VwFARNJDASAVSgeJW/hf+GJWYvvMHw0tO8Nu+6Y2byIiEQWAVDnkUsjfCT5IYJC40qd/u9aBp39FpN5QAEiVpi3DIHEzX6p6kLhF79adp39FpN5QAEilw64MV/Qf/a3y7eaPgsa7wl514OlfEak3FABSqUUH6PEjmFLJIHHbtoWpFPfpB3mN4m8jIpICCgCpduR1ULwBJlcwSNzKqfDtKvX+EZG0UwBItbb7wz4nwsePxB8kbv7o6Onfk9KfNxHJaQoA6XDk9bChEKaXnxWTMPxDh0P19K+IpJ0CQDp0Ogr2PGjHQeK+Xh6e/lXzj4hkgAJAOpQOEvflIpj3+nfL9fSviGSQAkC6dDs9POkbO0jcgtHQshO06ZrJnIlIjlIASJfSQeJWfAJL3g9P/y4aXzfm/hWRekkBIJ16/Ria7RYGiVs0Xk//ikhGaUrIdMpvCh2Pgrkjw8NfGHyzMtO5EpEcpRpAOs14ET4dE7PA4Y3/C8tFRNJMASCdxt4JJeUeBisuCstFRNJMASCdvi6o3nIRkRRSAEin5u2rt1xEJIUSCgBmdoqZzTezhWY2JM76lmb2spnNMLOJZtYjZl0LM3vJzOaZ2VwzOzxaPtTMlpvZtOh1avJOK0v1uz3cCI6V3zQsFxFJsyp7AZlZQ+BB4ESgAJhkZq+6+5yYzW4Bprn7WWa2X7R9v2jdfcBod/+RmTUCmsXsN9zd703GidQJPc8L72PvDM0+zduHwr90uYhIGiXSDbQvsNDdFwGY2QvAACA2AHQHfg/g7vPMrJOZtQWKgGOAS6J1W4AtSct9XdTzPBX4IpIVEmkCagcsi/leEC2LNR04G8DM+gIdgfbA94FC4Ekzm2pmfzeznWL2uyZqNnrCzFrGS9zMLjOzyWY2ubCwMLGzEhGRKiUSAOKNU+Dlvg8DWprZNOBaYCpQQqhhHAT8zd17AxuA0nsIfwP2BnoBK4E/xUvc3R919z7u3qdNmzYJZFdERBKRSBNQAdAh5nt7YEXsBu6+HhgEYGYGLI5ezYACd/842vQlogDg7qtK9zezx4DXERGRtEmkBjAJ6GJmnaObuAOBV2M3iHr6lE5oOxiY4O7r3f0LYJmZlQ532Y/o3oGZ7RFziLOAWbU4DxERqaYqawDuXmJm1wBjgIbAE+4+28yuiNY/DHQDnjGzrYQC/tKYQ1wL/DMKEIuIagrAPWbWi9Cc9DlweVLOSEREEmLu5Zvzs5eZFQJL0pDUbsCaepCG0sneNJRO9qZRH9Pp6O473EStUwEgXcxssrv3qetpKJ3sTUPpZG8a9TGdimgoCBGRHKUAICKSoxQA4nu0nqShdLI3DaWTvWnUx3Ti0j0AEZEcpRqAiEiOUgAQEclRCgAxzOxGM5ttZrPM7Hkza5KidK6P0phtZjck8bhPmNlqM5sVs6yVmb1tZp9G73EH3UtCOudG57PNzGrdra2CNP4YzSsxI5p/okWK0vldlMY0M3vLzPZMRTox635hZm5mu6UinWTPvVHRuZjZtdG8IbPN7J7apFFROmb2r5jz+DwafywV6fQys4+idCZHg1ymIp0DzexDM5tpZq+Z2a61Tada3F2vcB+kHWH8oqbR9xeBS1KQTg/CsBfNCE9i/xfokqRjH0MYfG9WzLJ7gCHR5yHAH1KUTjegK/Au0CdFaZwE5EWf/5DCc9k15vN1wMOpSCda3oHwlP0SYLcUnc9Q4BfJ+I1Vksbx0W+5cfR991T9zWLW/wm4PUXn8xbQP/p8KvBuitKZBBwbff4Z8Ltk/Tsl8lINoKw8oKmZ5REK6BVVbF8T3YCP3H2ju5cA4wljIdWau08Aviy3eADwdPT5aeDMVKTj7nPdfX5tj11FGm9FfzOAjwgDE6YinfUxX3dix9Fvk5JOZDjwy2SkUUU6SVNBGlcCw9x9c7TN6hSlA2wfdPI84PkUpeNA6dV4c5JQFlSQTldgQvT5beCc2qZTHQoAEXdfDtwLLCUMT/21u7+VgqRmAceYWWsza0a4uuhQxT610dbdVwJE77unMK10+hkwKlUHN7O7zWwZ8BMgJXN2mtkZwHJ3n56K45dT5dwbtbQvcLSZfWxm483skBSkEetoYJW7f5qi498A/DH6DdwL/DpF6cwCzog+n0tqy4IdKABEov8UA4DOwJ7ATmZ2YbLTcfe5hOaLt4HRhMl0SirdScows1sJf7N/pioNd7/V3TtEaVyT7ONHwf9WUhRcyklo7o1aygNaAocBNwMvRlfpqXIBSbj6r8SVwI3Rb+BG4PEUpfMz4GozmwLsQppnTFQA+M4PgMXuXujuxcAI4IhUJOTuj7v7Qe5+DKFKmKqrGIBVpUNvR++1rppnkpldDJwG/MSjhtMUe47UVMv3JlxsTDezzwnNWZ+Y2feSnZC7r3L3re6+DXiMMM1rshUAIzyYCGwjDHSWdFET7dnAv1Jx/MjFhDIA4N+k5m+Gu89z95Pc/WBCQPssFelURAHgO0uBw8ysWXTl0g+Ym4qEzGz36H0vwg85lVcyrxJ+zETvr6QwrZQys1OAXwFnuPvGFKbTJebrGcC8ZKfh7jPdfXd37+TunQgF6EEe5tBIKkvP3BsjgROi9PYFGpG6US5/AMxz94IUHR9Cm/+x0ecTSNFFWkxZ0AC4DXg4FelUKJ13nLP9BfyW8J99FvAPoh4NKUjnPcK8CdOBfkk87vOEKn4xoUC5FGgNjCX8gMcCrVKUzlnR583AKmBMCtJYSJifelr0SkbvnHjp/Cf6DcwAXgPapSKdcus/Jzm9gOKdzz+AmdH5vArskYI0GgHPRn+3T4ATUvU3A54Crqjt8as4n6OAKdH/0Y+Bg1OUzvXAgug1jGh0hnS9NBSEiEiOUhOQiEiOUgAQEclRCgAiIjlKAUBEJEcpAIiI5CgFABGRHKUAICKSo/4/WOt3LZsrYNMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(hiperparametro[7:], accuracy_NB_list[7:], marker='o', label= 'NB')\n",
    "ax.plot(hiperparametro[7:], accuracy_kNN_list[7:], marker='o', label= 'kNN')\n",
    "\n",
    "ax.legend(loc = 'center right')\n",
    "ax.set_xlim([7, 20])\n",
    "\n",
    "plt.title(\"Accuracy\")\n",
    "plt.xticks(hiperparametro[7:], h_string[7:])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df283d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(hiperparametro[7:], recall_NB_list[7:], marker='o', label= 'NB')\n",
    "ax.plot(hiperparametro[7:], recall_kNN_list[7:], marker='o', label= 'kNN')\n",
    "\n",
    "ax.legend(loc = 'center right')\n",
    "ax.set_xlim([7, 20])\n",
    "\n",
    "plt.title(\"Recall\")\n",
    "plt.xticks(hiperparametro[7:], h_string[7:])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2f4a8236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(hiperparametro[7:], precision_NB_list[7:], marker='o', label= 'NB')\n",
    "ax.plot(hiperparametro[7:], precision_kNN_list[7:], marker='o', label= 'kNN')\n",
    "\n",
    "ax.legend(loc = 'center right')\n",
    "ax.set_xlim([7, 20])\n",
    "\n",
    "plt.title(\"Precision\")\n",
    "plt.xticks(hiperparametro[7:], h_string[7:])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "29d6b323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(hiperparametro, especificidad_NB_list, marker='o', label= 'NB')\n",
    "ax.plot(hiperparametro, especificidad_kNN_list, marker='o', label= 'kNN')\n",
    "\n",
    "ax.legend(loc = 'center right')\n",
    "ax.set_xlim([0, 20])\n",
    "\n",
    "plt.title(\"Especificidad\")\n",
    "plt.xticks(hiperparametro, h_string)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bb47fbe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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