{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dario/.local/lib/python3.10/site-packages/transformers/utils/hub.py:124: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import Tuple, Any, Callable\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import umap\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the embedding model\n",
    "model = SentenceTransformer(\"jinaai/jina-embeddings-v2-base-es\", trust_remote_code=True, device ='cpu')\n",
    "model.max_seq_length = 2048"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "data = pd.read_csv('./es_intent_data.csv', sep=';')\n",
    "for col in data.select_dtypes(include=['object']).columns:\n",
    "    data[col] = data[col].map(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "censura_categories = ['violencia', 'pornografía', 'controversia', 'religión', 'seguridad', 'insultos']\n",
    "data['category'] = data['category'].replace(censura_categories, 'censura')\n",
    "data = data.loc[data['text'].str.lower().drop_duplicates().index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [],
   "source": [
    "# Generate embeddings for the sentences\n",
    "def get_embeddings(sentences):\n",
    "    embeddings = model.encode(sentences, batch_size=12, normalize_embeddings= False)\n",
    "    return embeddings\n",
    "\n",
    "# Function to predict the intent of a single text input for all the available classes probas\n",
    "def predict_intent_classes(label_encoder, model, text: str, threshold: float) -> Tuple[str, float, Any]:\n",
    "    embedding = get_embeddings([text]).tolist()\n",
    "    embedding = np.array(embedding).reshape(1, -1)\n",
    "    \n",
    "    # Use svm.predict_proba to get the probabilities for each class\n",
    "    probabilities = model.predict_proba(embedding)[0]\n",
    "    best_class_index = np.argmax(probabilities)\n",
    "    best_class_prob = probabilities[best_class_index]\n",
    "    \n",
    "    all_class_labels = label_encoder.inverse_transform(np.arange(len(probabilities)))\n",
    "    class_prob_pairs = list(zip(all_class_labels, probabilities))\n",
    "    sorted_class_prob_pairs = sorted(class_prob_pairs, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    if best_class_prob < threshold:\n",
    "        return \"Desconocido\", best_class_prob, sorted_class_prob_pairs\n",
    "    else:\n",
    "        return label_encoder.inverse_transform([best_class_index])[0], best_class_prob, sorted_class_prob_pairs\n",
    "    \n",
    "# Make prediction function\n",
    "def predict_intent(label_encoder: LabelEncoder, model: Any, text: str, threshold: float) -> Tuple[str, float]:\n",
    "    embedding = get_embeddings([text]).tolist()\n",
    "    embedding = np.array(embedding).reshape(1, -1)\n",
    "    \n",
    "    # Use svm.predict_proba to get the probabilities for each class\n",
    "    probabilities = model.predict_proba(embedding)[0]\n",
    "    best_class_index = np.argmax(probabilities)\n",
    "    best_class_prob = probabilities[best_class_index]\n",
    "    \n",
    "    if best_class_prob < threshold:\n",
    "        return \"Desconocido\", best_class_prob\n",
    "    else:\n",
    "        return label_encoder.inverse_transform([best_class_index])[0], best_class_prob\n",
    "    \n",
    "# model evaluations\n",
    "def evaluate_model(model: Any, label_encoder: LabelEncoder, X_test: pd.Series, y_test: pd.Series, threshold: float) -> None:\n",
    "    y_pred = [predict_intent(label_encoder, model, text, threshold)[0] for text in X_test['text']]\n",
    "    y_test_labels = label_encoder.inverse_transform(y_test)\n",
    "\n",
    "    # Add \"Desconocido\" category\n",
    "    all_labels = list(label_encoder.classes_) + [\"Desconocido\"]\n",
    "\n",
    "    # Deal with \"Desconocido\" category\n",
    "    y_test_labels_extended = np.append(y_test_labels, \"Desconocido\")\n",
    "    y_pred_extended = np.append(y_pred, \"Desconocido\")\n",
    "\n",
    "    # Confusion Matrix\n",
    "    conf_matrix = confusion_matrix(y_test_labels_extended[:-1], y_pred_extended[:-1], labels=all_labels)\n",
    "    conf_matrix_percentage = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis] * 100\n",
    "\n",
    "    misclassified_df = pd.DataFrame({\n",
    "        'text': X_test['text'],\n",
    "        'label': y_test_labels,\n",
    "        'predicted': y_pred\n",
    "    })\n",
    "    misclassified_df = misclassified_df[misclassified_df['label'] != misclassified_df['predicted']]\n",
    "\n",
    "    # Classification Report\n",
    "    report = classification_report(y_test_labels, y_pred, labels=label_encoder.classes_)\n",
    "\n",
    "    # Display the confusion matrix in percentages\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    sns.heatmap(conf_matrix_percentage, annot=True, fmt='.2f', cmap='Blues', xticklabels=all_labels, yticklabels=label_encoder.classes_)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.title('Confusion Matrix (Percentage)')\n",
    "    plt.show()\n",
    "\n",
    "    print(\"Classification Report:\\n\", report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the data\n",
    "data['embeddings'] = get_embeddings(data['text'].tolist()).tolist()\n",
    "\n",
    "# Encode the labels\n",
    "label_encoder = LabelEncoder()\n",
    "data['category_encoded'] = label_encoder.fit_transform(data['category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data[['text', 'embeddings']], data['category_encoded'], test_size=0.3, \n",
    "    random_state=42, stratify= data['category_encoded']\n",
    ")\n",
    "\n",
    "train_labels = pd.Series(label_encoder.inverse_transform(y_train))\n",
    "test_labels = pd.Series(label_encoder.inverse_transform(y_test))\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('Train categories')\n",
    "train_labels.value_counts().plot(kind='bar')\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('Test categories')\n",
    "test_labels.value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM fit\n",
      "k-NN fit\n",
      "LR fit\n",
      "SVM CVCC\n",
      "k-NN CVCC\n",
      "LR CVCC\n",
      "Ensemble fit\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\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-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;svm&#x27;,\n",
       "                              CalibratedClassifierCV(cv=5,\n",
       "                                                     estimator=SVC(C=0.01,\n",
       "                                                                   kernel=&#x27;linear&#x27;,\n",
       "                                                                   probability=True),\n",
       "                                                     method=&#x27;isotonic&#x27;,\n",
       "                                                     n_jobs=1)),\n",
       "                             (&#x27;knn&#x27;,\n",
       "                              CalibratedClassifierCV(cv=5,\n",
       "                                                     estimator=KNeighborsClassifier(metric=&#x27;cosine&#x27;,\n",
       "                                                                                    n_jobs=6,\n",
       "                                                                                    n_neighbors=3),\n",
       "                                                     method=&#x27;isotonic&#x27;,\n",
       "                                                     n_jobs=1)),\n",
       "                             (&#x27;lr&#x27;,\n",
       "                              CalibratedClassifierCV(cv=5,\n",
       "                                                     estimator=LogisticRegression(C=0.01,\n",
       "                                                                                  max_iter=2000,\n",
       "                                                                                  n_jobs=6),\n",
       "                                                     method=&#x27;isotonic&#x27;,\n",
       "                                                     n_jobs=1))],\n",
       "                 n_jobs=6, voting=&#x27;soft&#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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;VotingClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.VotingClassifier.html\">?<span>Documentation for VotingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>VotingClassifier(estimators=[(&#x27;svm&#x27;,\n",
       "                              CalibratedClassifierCV(cv=5,\n",
       "                                                     estimator=SVC(C=0.01,\n",
       "                                                                   kernel=&#x27;linear&#x27;,\n",
       "                                                                   probability=True),\n",
       "                                                     method=&#x27;isotonic&#x27;,\n",
       "                                                     n_jobs=1)),\n",
       "                             (&#x27;knn&#x27;,\n",
       "                              CalibratedClassifierCV(cv=5,\n",
       "                                                     estimator=KNeighborsClassifier(metric=&#x27;cosine&#x27;,\n",
       "                                                                                    n_jobs=6,\n",
       "                                                                                    n_neighbors=3),\n",
       "                                                     method=&#x27;isotonic&#x27;,\n",
       "                                                     n_jobs=1)),\n",
       "                             (&#x27;lr&#x27;,\n",
       "                              CalibratedClassifierCV(cv=5,\n",
       "                                                     estimator=LogisticRegression(C=0.01,\n",
       "                                                                                  max_iter=2000,\n",
       "                                                                                  n_jobs=6),\n",
       "                                                     method=&#x27;isotonic&#x27;,\n",
       "                                                     n_jobs=1))],\n",
       "                 n_jobs=6, voting=&#x27;soft&#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\"><label>svm</label></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=0.01, kernel=&#x27;linear&#x27;, probability=True)</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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=0.01, kernel=&#x27;linear&#x27;, probability=True)</pre></div> </div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>knn</label></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(metric=&#x27;cosine&#x27;, n_jobs=6, n_neighbors=3)</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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(metric=&#x27;cosine&#x27;, n_jobs=6, n_neighbors=3)</pre></div> </div></div></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.01, max_iter=2000, n_jobs=6)</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-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.01, max_iter=2000, n_jobs=6)</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('svm',\n",
       "                              CalibratedClassifierCV(cv=5,\n",
       "                                                     estimator=SVC(C=0.01,\n",
       "                                                                   kernel='linear',\n",
       "                                                                   probability=True),\n",
       "                                                     method='isotonic',\n",
       "                                                     n_jobs=1)),\n",
       "                             ('knn',\n",
       "                              CalibratedClassifierCV(cv=5,\n",
       "                                                     estimator=KNeighborsClassifier(metric='cosine',\n",
       "                                                                                    n_jobs=6,\n",
       "                                                                                    n_neighbors=3),\n",
       "                                                     method='isotonic',\n",
       "                                                     n_jobs=1)),\n",
       "                             ('lr',\n",
       "                              CalibratedClassifierCV(cv=5,\n",
       "                                                     estimator=LogisticRegression(C=0.01,\n",
       "                                                                                  max_iter=2000,\n",
       "                                                                                  n_jobs=6),\n",
       "                                                     method='isotonic',\n",
       "                                                     n_jobs=1))],\n",
       "                 n_jobs=6, voting='soft')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the SVM\n",
    "print('SVM fit')\n",
    "svm_classifier = svm.SVC(\n",
    "        kernel='linear', C=.01, probability=True, \n",
    "    )\n",
    "svm_classifier.fit(np.vstack(X_train['embeddings']), y_train)\n",
    "\n",
    "# Train the k-NN\n",
    "print('k-NN fit')\n",
    "knn = KNeighborsClassifier(n_neighbors=3, metric='cosine', weights='uniform', n_jobs=6)\n",
    "knn.fit(np.vstack(X_train['embeddings']), y_train)\n",
    "\n",
    "# Train LR\n",
    "print('LR fit')\n",
    "log_reg = LogisticRegression(max_iter= 2000, solver= 'lbfgs', penalty= 'l2', C= 0.01, n_jobs=6)\n",
    "log_reg.fit(np.vstack(X_train['embeddings']), y_train)\n",
    "\n",
    "# Prob calibration\n",
    "print('SVM CVCC')\n",
    "calibrated_svm = CalibratedClassifierCV(svm_classifier, method='isotonic', cv= 5, n_jobs=1)\n",
    "calibrated_svm.fit(np.vstack(X_train['embeddings']), y_train)\n",
    "print('k-NN CVCC')\n",
    "calibrated_knn = CalibratedClassifierCV(knn, method='isotonic', cv= 5, n_jobs=1)\n",
    "calibrated_knn.fit(np.vstack(X_train['embeddings']), y_train)\n",
    "print('LR CVCC')\n",
    "calibrated_log_reg = CalibratedClassifierCV(log_reg, method='isotonic', cv= 5, n_jobs=1)\n",
    "calibrated_log_reg.fit(np.vstack(X_train['embeddings']), y_train)\n",
    "\n",
    "# Create an ensemble of the calibrated classifiers\n",
    "print('Ensemble fit')\n",
    "ensemble = VotingClassifier(estimators= [\n",
    "    ('svm', calibrated_svm),\n",
    "    ('knn', calibrated_knn),\n",
    "    ('lr', calibrated_log_reg)\n",
    "], voting= 'soft', n_jobs= 6)\n",
    "\n",
    "# Train the ensemble\n",
    "ensemble.fit(np.vstack(X_train['embeddings']), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM metrics\n",
      "k-NN metrics\n",
      "LR metrics\n",
      "Ensemble metrics\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_212073/4012238302.py:54: RuntimeWarning: invalid value encountered in divide\n",
      "  conf_matrix_percentage = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis] * 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "  afirmación       1.00      1.00      1.00        14\n",
      "     censura       0.99      1.00      0.99       539\n",
      "      charla       1.00      0.67      0.80        15\n",
      "    contacto       0.97      1.00      0.99        38\n",
      " direcciones       1.00      1.00      1.00        71\n",
      "        lead       0.99      0.99      0.99       140\n",
      "        meet       0.97      1.00      0.98        29\n",
      "    negación       1.00      0.94      0.97        18\n",
      "       otros       0.98      0.97      0.98       171\n",
      "\n",
      "   micro avg       0.99      0.99      0.99      1035\n",
      "   macro avg       0.99      0.95      0.97      1035\n",
      "weighted avg       0.99      0.99      0.99      1035\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('SVM metrics')\n",
    "#evaluate_model(calibrated_svm, label_encoder, X_test, y_test, threshold=0.5)\n",
    "\n",
    "print('k-NN metrics')\n",
    "#evaluate_model(calibrated_knn, label_encoder, X_test, y_test, threshold=0.5)\n",
    "\n",
    "print('LR metrics')\n",
    "#evaluate_model(calibrated_log_reg, label_encoder, X_test, y_test, threshold=0.5)\n",
    "\n",
    "print('Ensemble metrics')\n",
    "evaluate_model(ensemble, label_encoder, X_test, y_test, threshold=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6 otros: cómo seguimos?\n",
      "0.9 censura: donde puedo comprar marihuana?\n",
      "0.8 censura: tomemos MDMA?\n",
      "0.7 censura: vendamos whisky en la calle?\n",
      "1.0 lead: lo quiero ya\n",
      "0.6 charla: hablemos de dios?\n",
      "1.0 censura: por qué las religiones son tan castigadoras?\n",
      "0.9 lead: me interesa una demo\n",
      "1.0 lead: quiero hacer un piloto del proyecto\n",
      "0.5 afirmación: está fantástico\n",
      "1.0 negación: ni lo pienses\n",
      "1.0 afirmación: claro que sí men!\n",
      "0.5 contacto: quiero ayuda para pedir una pizza por delivery\n",
      "0.7 censura: andate a la chucha\n",
      "1.0 censura: patético de mierda\n",
      "0.8 charla: hueón tonto\n",
      "0.8 censura: sólo gastas oxigeno con tu miserable existencia\n",
      "0.6 otros: computador de la puta madre\n",
      "0.7 meet: quiero una hora para atenderme con mi medico de cabecera\n",
      "0.7 contacto: con quien me puedo atender\n",
      "1.0 censura: quiero verte desnuda\n",
      "0.6 censura: y si comemos pizza, para terminar viendo una pelicula de mujeres en trapos menores\n",
      "0.7 otros: y si jugamos futbol? y después comemos pizza, para terminar viendo anime\n",
      "0.3 charla: y si nos vamos a lo oscurito tu y yo?\n",
      "0.4 lead: dónde firmo?\n",
      "0.6 charla: holi, cómo tay?\n",
      "0.7 censura: uds contratan menores de edad?\n",
      "0.8 censura: puedo trabajar sin documentos con uds?\n",
      "0.5 censura: me reciben si soy indocumentado?\n",
      "0.8 otros: quiero saber más del caec\n",
      "0.6 censura: que es la ley ricarte soto?\n",
      "0.4 otros: como me atiendo por fonasa?\n",
      "0.9 censura: quiero sacrificar a mi perrito por que está enfermo y sufre mucho\n"
     ]
    }
   ],
   "source": [
    "for t in [\n",
    "    'cómo seguimos?',\n",
    "    'donde puedo comprar marihuana?',\n",
    "    'tomemos MDMA?',\n",
    "    'vendamos whisky en la calle?',\n",
    "    'lo quiero ya',\n",
    "    'hablemos de dios?',\n",
    "    'por qué las religiones son tan castigadoras?',\n",
    "    'me interesa una demo',\n",
    "    'quiero hacer un piloto del proyecto',\n",
    "    'está fantástico',\n",
    "    'ni lo pienses',\n",
    "    'claro que sí men!',\n",
    "    'quiero ayuda para pedir una pizza por delivery',\n",
    "    'andate a la chucha',\n",
    "    'patético de mierda',\n",
    "    'hueón tonto',\n",
    "    'sólo gastas oxigeno con tu miserable existencia',\n",
    "    'computador de la puta madre',\n",
    "    'quiero una hora para atenderme con mi medico de cabecera',\n",
    "    'con quien me puedo atender',\n",
    "    'quiero verte desnuda',\n",
    "    'y si comemos pizza, para terminar viendo una pelicula de mujeres en trapos menores',\n",
    "    'y si jugamos futbol? y después comemos pizza, para terminar viendo anime',\n",
    "    'y si nos vamos a lo oscurito tu y yo?',\n",
    "    'dónde firmo?',\n",
    "    'holi, cómo tay?',\n",
    "    'uds contratan menores de edad?',\n",
    "    'puedo trabajar sin documentos con uds?',\n",
    "    'me reciben si soy indocumentado?',\n",
    "    'quiero saber más del caec',\n",
    "    'que es la ley ricarte soto?',\n",
    "    'como me atiendo por fonasa?',\n",
    "    'quiero sacrificar a mi perrito por que está enfermo y sufre mucho']:\n",
    "    predicted_category, prob  = predict_intent(label_encoder, ensemble, t, threshold=.1)\n",
    "    print(f\"{prob:.1f} {predicted_category}: {t}\")\n",
    "    \n",
    "#print('\\n'.join([f\"{k} {v:.2f}\" for k,v in probs]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dario/.local/lib/python3.10/site-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
      "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
     ]
    }
   ],
   "source": [
    "# Reduce embeddings to 2D using UMAP\n",
    "embedding_array = np.array(data['embeddings'].tolist())\n",
    "umap_reducer = umap.UMAP(n_epochs= 600, n_components= 2, random_state= 42, n_jobs= 20, metric=\"correlation\")\n",
    "embedding_2d = umap_reducer.fit_transform(embedding_array)#, y= data['category_encoded'])\n",
    "\n",
    "# Add the 2D embeddings to the DataFrame\n",
    "data['embedding_2d_x'] = embedding_2d[:, 0]\n",
    "data['embedding_2d_y'] = embedding_2d[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 2D embeddings with categories\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.scatterplot(\n",
    "    x='embedding_2d_x', y='embedding_2d_y', size = 2,\n",
    "    hue='category', palette=sns.color_palette(\"bright\", len(data['category'].unique())),\n",
    "    data=data,\n",
    "    alpha=1\n",
    ")\n",
    "plt.legend(loc='best')\n",
    "plt.title('UMAP projection of the sentence embeddings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def add_point_to_umap(data, umap_reducer, text):\n",
    "    # Generate embedding for the new text\n",
    "    new_embedding = get_embeddings([text]).tolist()\n",
    "    new_embedding_2d = umap_reducer.transform(np.array(new_embedding))\n",
    "    \n",
    "    # Plot the existing points\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    scatter = sns.scatterplot(\n",
    "        x= 'embedding_2d_x', y= 'embedding_2d_y', size= 2,\n",
    "        hue= 'category', palette=sns.color_palette(\"bright\", len(data['category'].unique())),\n",
    "        data= data,\n",
    "        legend= 'full',\n",
    "        alpha= 1\n",
    "    )\n",
    "\n",
    "    # Plot the new point as a red star\n",
    "    plt.scatter(new_embedding_2d[:, 0], new_embedding_2d[:, 1], color= '#00ffff', edgecolors= 'red', marker='X', s= 300, label= 'New Point', alpha= 1)\n",
    "\n",
    "    # Mostrar leyenda completa\n",
    "    handles, labels = scatter.get_legend_handles_labels()\n",
    "    plt.legend(loc='best')\n",
    "\n",
    "    plt.title('UMAP projection with new point')\n",
    "    plt.show()\n",
    "\n",
    "# Example usage\n",
    "add_point_to_umap(data, umap_reducer, \"wikipedia es una enciclopedia muy buena\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}