clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "89bc28b0-58ae-4689-ae23-675906d7c051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56.7, 107.6)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('diagnoses_icd_combined.csv',low_memory=False)   \n",
    "\n",
    "# Function to ensure temperatures are between 56.7°F and 107.6°F\n",
    "def convert_temperature(x):\n",
    "    if x < 56.7:  # Likely in Celsius, convert to Fahrenheit\n",
    "        x = x * 1.8 + 32\n",
    "    elif x > 107.6:  # Likely in Fahrenheit, convert to Celsius and then back to Fahrenheit within range\n",
    "        x = (x - 32) / 1.8\n",
    "        x = x * 1.8 + 32\n",
    "    \n",
    "    # Ensure temperature is within 56.7°F and 107.6°F\n",
    "    if x < 56.7:\n",
    "        return 56.7\n",
    "    elif x > 107.6:\n",
    "        return 107.6\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "# Apply the conversion function\n",
    "df['temperature'] = df['temperature'].apply(convert_temperature)\n",
    "\n",
    "# Check the new min and max values\n",
    "min_temp, max_temp = df['temperature'].min(), df['temperature'].max()\n",
    "min_temp, max_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65acd8e9-cd09-4903-b513-7666ae9bd1ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_patients= pd.read_csv('patients.csv',low_memory=False)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "03fff3e8-1b4a-496c-9f9c-a078f92fdd48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 88)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Consider the 'subject_id' where 'anchor_age' is between 18 and 88\n",
    "df_patients = df_patients[(df_patients['anchor_age'] >= 18) & (df_patients['anchor_age'] <= 88)]\n",
    "\n",
    "# Check the new min and max values\n",
    "min_age, max_age = df_patients['anchor_age'].min(), df_patients['anchor_age'].max()\n",
    "min_age, max_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "845ab714-b1e5-4ef2-9d98-8164fbe7d249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_common = df[df['subject_id'].isin(df_patients['subject_id'])]\n",
    "df_common.columns==df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7d8a2aa-5427-44a5-9451-b2ceb4e136f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to diagnoses_icd_combined.csv\n",
    "df_common.to_csv('diagnoses_icd_combined_age_restricted.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0500a7ec-4c22-421c-bf68-95a85b29c82d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df_common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d070050-b485-43b1-856d-6a01e1b0235d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11801/2710633593.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pneumonia_detected.rename(columns={'temperature': 'Temperature VAP', 'heartrate': 'Heart Rate VAP', 'resprate': 'Respiratory Rate VAP', 'o2sat': 'SpO2 VAP'}, inplace=True)\n",
      "/tmp/ipykernel_11801/2710633593.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pneumonia_not_detected.rename(columns={'temperature': 'Temperature No VAP', 'heartrate': 'Heart Rate No VAP', 'resprate': 'Respiratory Rate No VAP', 'o2sat': 'SpO2 No VAP'}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Create separate dataframes based on the 'Pneumonia Detected' column\n",
    "df_pneumonia_detected = df[df['Pneumonia Detected'] == 1]\n",
    "df_pneumonia_not_detected = df[df['Pneumonia Detected'] == 0]\n",
    "\n",
    "# Rename the columns in the df_pneumonia_detected dataframes 'temperature', 'heartrate', 'resprate', 'o2sat' to 'Temperature VAP', 'Heart Rate VAP', 'Respiratory Rate VAP', 'SpO2 VAP' \n",
    "df_pneumonia_detected.rename(columns={'temperature': 'Temperature VAP', 'heartrate': 'Heart Rate VAP', 'resprate': 'Respiratory Rate VAP', 'o2sat': 'SpO2 VAP'}, inplace=True)\n",
    "\n",
    "# Rename the columns in the df_pneumonia_not_detected dataframes 'temperature', 'heartrate', 'resprate', 'o2sat' to 'Temperature No VAP', 'Heart Rate No VAP', 'Respiratory Rate No VAP', 'SpO2 No VAP'\n",
    "df_pneumonia_not_detected.rename(columns={'temperature': 'Temperature No VAP', 'heartrate': 'Heart Rate No VAP', 'resprate': 'Respiratory Rate No VAP', 'o2sat': 'SpO2 No VAP'}, inplace=True)\n",
    "\n",
    "# Save these dataframes to new CSV files if needed\n",
    "df_pneumonia_detected.to_csv('VAP detected.csv', index=False)\n",
    "df_pneumonia_not_detected.to_csv('No VAP detected.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7cd80e32-a4b9-4a37-97b8-3f9c331983e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11801/2710633593.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pneumonia_detected.rename(columns={'temperature': 'Temperature VAP', 'heartrate': 'Heart Rate VAP', 'resprate': 'Respiratory Rate VAP', 'o2sat': 'SpO2 VAP'}, inplace=True)\n",
      "/tmp/ipykernel_11801/2710633593.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pneumonia_not_detected.rename(columns={'temperature': 'Temperature No VAP', 'heartrate': 'Heart Rate No VAP', 'resprate': 'Respiratory Rate No VAP', 'o2sat': 'SpO2 No VAP'}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Create separate dataframes based on the 'Pneumonia Detected' column\n",
    "df_pneumonia_detected = df[df['Pneumonia Detected'] == 1]\n",
    "df_pneumonia_not_detected = df[df['Pneumonia Detected'] == 0]\n",
    "\n",
    "# Rename the columns in the df_pneumonia_detected dataframes 'temperature', 'heartrate', 'resprate', 'o2sat' to 'Temperature VAP', 'Heart Rate VAP', 'Respiratory Rate VAP', 'SpO2 VAP' \n",
    "df_pneumonia_detected.rename(columns={'temperature': 'Temperature VAP', 'heartrate': 'Heart Rate VAP', 'resprate': 'Respiratory Rate VAP', 'o2sat': 'SpO2 VAP'}, inplace=True)\n",
    "\n",
    "# Rename the columns in the df_pneumonia_not_detected dataframes 'temperature', 'heartrate', 'resprate', 'o2sat' to 'Temperature No VAP', 'Heart Rate No VAP', 'Respiratory Rate No VAP', 'SpO2 No VAP'\n",
    "df_pneumonia_not_detected.rename(columns={'temperature': 'Temperature No VAP', 'heartrate': 'Heart Rate No VAP', 'resprate': 'Respiratory Rate No VAP', 'o2sat': 'SpO2 No VAP'}, inplace=True)\n",
    "\n",
    "# Save these dataframes to new CSV files if needed\n",
    "df_pneumonia_detected.to_csv('VAP detected.csv', index=False)\n",
    "df_pneumonia_not_detected.to_csv('No VAP detected.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e638968e-b6d1-4547-8acd-15d7764c5a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List the unique subject_id values in the df_pneumonia_detected dataframe and df_pneumonia_not_detected dataframe\n",
    "unique_subjects_detected = df_pneumonia_detected['subject_id'].unique()\n",
    "unique_subjects_not_detected = df_pneumonia_not_detected['subject_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0314c7c1-1259-4891-9e0f-7adc54da70d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an empty dataframe df_pneumonia_detected_unique with columns 'subject_id', 'Count'\n",
    "df_pneumonia_detected_unique = pd.DataFrame(columns=['subject_id', 'Count'])\n",
    "\n",
    "# Create an empty dataframe df_pneumonia_not_detected_unique with columns 'subject_id', 'Count'\n",
    "df_pneumonia_not_detected_unique = pd.DataFrame(columns=['subject_id', 'Count'])\n",
    "\n",
    "# Ensure unique_subjects_detected is a list or array of unique subject IDs\n",
    "unique_subjects_detected = df_pneumonia_detected['subject_id'].unique()\n",
    "\n",
    "# Add the unique subject_id values and the count of each subject_id to the df_pneumonia_detected_unique dataframe\n",
    "df_pneumonia_detected_unique['subject_id'] = unique_subjects_detected\n",
    "df_pneumonia_detected_unique['Count'] = df_pneumonia_detected['subject_id'].value_counts().reindex(unique_subjects_detected).values\n",
    "\n",
    "# Ensure unique_subjects_not_detected is a list or array of unique subject IDs\n",
    "unique_subjects_not_detected = df_pneumonia_not_detected['subject_id'].unique()\n",
    "\n",
    "# Add the unique subject_id values and the count of each subject_id to the df_pneumonia_not_detected_unique dataframe\n",
    "df_pneumonia_not_detected_unique['subject_id'] = unique_subjects_not_detected\n",
    "df_pneumonia_not_detected_unique['Count'] = df_pneumonia_not_detected['subject_id'].value_counts().reindex(unique_subjects_not_detected).values\n",
    "\n",
    "# Save these dataframes to new CSV files\n",
    "df_pneumonia_detected_unique.to_csv('VAP detected unique.csv', index=False)\n",
    "df_pneumonia_not_detected_unique.to_csv('No VAP detected unique.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6df34a84-52ef-4f3e-9bab-7390c02b1fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop NaN values\n",
    "df_pneumonia_detected=df_pneumonia_detected.dropna()\n",
    "df_pneumonia_not_detected=df_pneumonia_not_detected.dropna()\n",
    "data=df_common.dropna()\n",
    "data=data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2ac0624-72ea-4131-8772-640d4b21d60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d19ad9e5-8a94-4eef-835a-49b647c00abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import t-SNE from sklearn\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# Drop NaN values\n",
    "df_pneumonia_detected = df_pneumonia_detected.dropna()\n",
    "df_pneumonia_not_detected = df_pneumonia_not_detected.dropna()\n",
    "data = df_common.dropna()\n",
    "\n",
    "# Features (vital parameters) and target (VAP)\n",
    "X = data[['temperature', 'heartrate', 'resprate', 'o2sat', 'sbp']]\n",
    "y = data['Pneumonia Detected']\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Perform t-SNE, reducing to 2 components\n",
    "tsne = TSNE(n_components=2, perplexity=30, random_state=42)\n",
    "X_tsne = tsne.fit_transform(X_scaled)\n",
    "# Import necessary libraries\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "### Apply K-Means Clustering (using t-SNE components)\n",
    "kmeans_tsne = KMeans(n_clusters=2, random_state=42)\n",
    "tsne_clusters = kmeans_tsne.fit_predict(X_tsne)\n",
    "\n",
    "# Define colors for clusters\n",
    "colors = {0: 'blue', 1: 'red'}\n",
    "\n",
    "### Plot t-SNE + K-Means\n",
    "plt.figure(figsize=(8, 6))\n",
    "scatter = plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=[colors[label] for label in tsne_clusters], alpha=0.6)\n",
    "plt.title('t-SNE + K-Means Clustering')\n",
    "plt.xlabel('t-SNE Component 1')\n",
    "plt.ylabel('t-SNE Component 2')\n",
    "# Removed plt.colorbar(label='Cluster')\n",
    "plt.legend(handles=[plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='blue', markersize=10, label='No VAP'),\n",
    "                    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='red', markersize=10, label='VAP')],\n",
    "           loc='best')\n",
    "plt.savefig('t-SNE + K-Means VAP No VAP.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7f9908a4-ed80-4584-88bf-fb539f3246d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best t-SNE parameters found: {'perplexity': 50, 'learning_rate': 100, 'n_iter': 500}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# Drop NaN values\n",
    "df_pneumonia_detected = df_pneumonia_detected.dropna()\n",
    "df_pneumonia_not_detected = df_pneumonia_not_detected.dropna()\n",
    "data = df_common.dropna()\n",
    "\n",
    "# Features (vital parameters) and target (VAP)\n",
    "X = data[['temperature', 'heartrate', 'resprate', 'o2sat', 'sbp']]\n",
    "y = data['Pneumonia Detected']\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Hyperparameter ranges for tuning\n",
    "perplexities = [10, 30, 50]\n",
    "learning_rates = [100, 200, 500]\n",
    "iterations = [500, 1000]\n",
    "\n",
    "# Best score and settings\n",
    "best_score = -np.inf\n",
    "best_tsne_params = {}\n",
    "\n",
    "# Loop over hyperparameter combinations\n",
    "for perplexity in perplexities:\n",
    "    for learning_rate in learning_rates:\n",
    "        for n_iter in iterations:\n",
    "            # Perform t-SNE with hyperparameters\n",
    "            tsne = TSNE(n_components=2, perplexity=perplexity, learning_rate=learning_rate, n_iter=n_iter, random_state=42)\n",
    "            X_tsne = tsne.fit_transform(X_scaled)\n",
    "            \n",
    "            # Apply K-Means Clustering (using t-SNE components)\n",
    "            kmeans_tsne = KMeans(n_clusters=2, random_state=42)\n",
    "            tsne_clusters = kmeans_tsne.fit_predict(X_tsne)\n",
    "            \n",
    "            # Calculate separation score (e.g., inertia from KMeans)\n",
    "            score = kmeans_tsne.inertia_  # Lower inertia indicates better cluster separation\n",
    "            \n",
    "            if score < best_score or best_score == -np.inf:\n",
    "                best_score = score\n",
    "                best_tsne_params = {'perplexity': perplexity, 'learning_rate': learning_rate, 'n_iter': n_iter}\n",
    "                best_X_tsne = X_tsne\n",
    "                best_tsne_clusters = tsne_clusters\n",
    "\n",
    "# Define colors for clusters\n",
    "colors = {0: 'blue', 1: 'red'}\n",
    "\n",
    "### Plot the best t-SNE + K-Means\n",
    "plt.figure(figsize=(8, 6))\n",
    "scatter = plt.scatter(best_X_tsne[:, 0], best_X_tsne[:, 1], c=[colors[label] for label in best_tsne_clusters], alpha=0.6)\n",
    "plt.title(f\"t-SNE + K-Means Clustering (Best Params: Perplexity={best_tsne_params['perplexity']}, \"\n",
    "          f\"Learning Rate={best_tsne_params['learning_rate']}, Iterations={best_tsne_params['n_iter']})\")\n",
    "plt.xlabel('t-SNE Component 1')\n",
    "plt.ylabel('t-SNE Component 2')\n",
    "plt.legend(handles=[plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='blue', markersize=10, label='No VAP'),\n",
    "                    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='red', markersize=10, label='VAP')],\n",
    "           loc='best')\n",
    "plt.savefig('t-SNE + K-Means VAP No VAP 1.png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best t-SNE parameters found:\", best_tsne_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4c0aade8-dbb8-4219-85da-bd0a4cfaa059",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n",
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/sklearn/manifold/_t_sne.py:1162: FutureWarning: 'n_iter' was renamed to 'max_iter' in version 1.5 and will be removed in 1.7.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best t-SNE parameters found: {'perplexity': 50, 'learning_rate': 100, 'n_iter': 500, 'early_exaggeration': 24.0, 'angle': 0.5, 'init': 'random', 'metric': 'euclidean'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# Drop NaN values\n",
    "df_pneumonia_detected = df_pneumonia_detected.dropna()\n",
    "df_pneumonia_not_detected = df_pneumonia_not_detected.dropna()\n",
    "data = df_common.dropna()\n",
    "\n",
    "# Features (vital parameters) and target (VAP)\n",
    "X = data[['temperature', 'heartrate', 'resprate', 'o2sat', 'sbp']]\n",
    "y = data['Pneumonia Detected']\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Hyperparameter ranges for tuning\n",
    "perplexities = [10, 30, 50]  # Tuning perplexity\n",
    "learning_rates = [100, 200, 500]  # Tuning learning rate\n",
    "iterations = [500, 1000]  # Tuning number of iterations\n",
    "early_exaggerations = [12.0, 24.0]  # Tuning early exaggeration\n",
    "angles = [0.2, 0.5]  # Tuning angle for approximation vs accuracy\n",
    "inits = ['random', 'pca']  # Tuning initialization method\n",
    "metrics = ['euclidean', 'cosine']  # Tuning distance metric\n",
    "\n",
    "# Best score and settings\n",
    "best_score = -np.inf\n",
    "best_tsne_params = {}\n",
    "\n",
    "# Loop over all hyperparameter combinations\n",
    "for perplexity in perplexities:\n",
    "    for learning_rate in learning_rates:\n",
    "        for n_iter in iterations:\n",
    "            for early_exaggeration in early_exaggerations:\n",
    "                for angle in angles:\n",
    "                    for init in inits:\n",
    "                        for metric in metrics:\n",
    "                            # Perform t-SNE with hyperparameters\n",
    "                            tsne = TSNE(n_components=2,\n",
    "                                        perplexity=perplexity,\n",
    "                                        learning_rate=learning_rate,\n",
    "                                        n_iter=n_iter,\n",
    "                                        early_exaggeration=early_exaggeration,\n",
    "                                        angle=angle,\n",
    "                                        init=init,\n",
    "                                        metric=metric,\n",
    "                                        random_state=42)\n",
    "                            X_tsne = tsne.fit_transform(X_scaled)\n",
    "                            \n",
    "                            # Apply K-Means Clustering (using t-SNE components)\n",
    "                            kmeans_tsne = KMeans(n_clusters=2, random_state=42)\n",
    "                            tsne_clusters = kmeans_tsne.fit_predict(X_tsne)\n",
    "                            \n",
    "                            # Calculate separation score (e.g., inertia from KMeans)\n",
    "                            score = kmeans_tsne.inertia_  # Lower inertia indicates better cluster separation\n",
    "                            \n",
    "                            if score < best_score or best_score == -np.inf:\n",
    "                                best_score = score\n",
    "                                best_tsne_params = {\n",
    "                                    'perplexity': perplexity,\n",
    "                                    'learning_rate': learning_rate,\n",
    "                                    'n_iter': n_iter,\n",
    "                                    'early_exaggeration': early_exaggeration,\n",
    "                                    'angle': angle,\n",
    "                                    'init': init,\n",
    "                                    'metric': metric\n",
    "                                }\n",
    "                                best_X_tsne = X_tsne\n",
    "                                best_tsne_clusters = tsne_clusters\n",
    "\n",
    "# Define colors for clusters\n",
    "colors = {0: 'blue', 1: 'red'}\n",
    "\n",
    "### Plot the best t-SNE + K-Means\n",
    "plt.figure(figsize=(8, 6))\n",
    "scatter = plt.scatter(best_X_tsne[:, 0], best_X_tsne[:, 1], c=[colors[label] for label in best_tsne_clusters], alpha=0.6)\n",
    "plt.title(f\"t-SNE + K-Means Clustering (Best Params: {best_tsne_params})\")\n",
    "plt.xlabel('t-SNE Component 1')\n",
    "plt.ylabel('t-SNE Component 2')\n",
    "plt.legend(handles=[plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='blue', markersize=10, label='No VAP'),\n",
    "                    plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='red', markersize=10, label='VAP')],\n",
    "           loc='best')\n",
    "plt.savefig('t-SNE + K-Means VAP No VAP 2.png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best t-SNE parameters found:\", best_tsne_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d155b537-2d49-45cd-bb78-35983b87306a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/soumyabrata/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/cuml/internals/api_decorators.py:344: UserWarning: Starting from version 22.04, the default method of TSNE is 'fft'.\n",
      "  return func(**kwargs)\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "exception occurred! file=/home/soumyabrata/miniconda3/envs/rapids-24.08/include/raft/linalg/detail/eig.cuh line=151: ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[18], line 59\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m metric \u001b[38;5;129;01min\u001b[39;00m metrics:\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;66;03m# Perform t-SNE with hyperparameters\u001b[39;00m\n\u001b[1;32m     50\u001b[0m     tsne \u001b[38;5;241m=\u001b[39m cumlTSNE(n_components\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m     51\u001b[0m                     perplexity\u001b[38;5;241m=\u001b[39mperplexity,\n\u001b[1;32m     52\u001b[0m                     learning_rate\u001b[38;5;241m=\u001b[39mlearning_rate,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     57\u001b[0m                     metric\u001b[38;5;241m=\u001b[39mmetric,\n\u001b[1;32m     58\u001b[0m                     random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[0;32m---> 59\u001b[0m     X_tsne \u001b[38;5;241m=\u001b[39m \u001b[43mtsne\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_scaled\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;66;03m# Apply K-Means Clustering (using t-SNE components)\u001b[39;00m\n\u001b[1;32m     62\u001b[0m     kmeans_tsne \u001b[38;5;241m=\u001b[39m cumlKMeans(n_clusters\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/cuml/internals/api_decorators.py:188\u001b[0m, in \u001b[0;36m_make_decorator_function.<locals>.decorator_function.<locals>.decorator_closure.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    185\u001b[0m     set_api_output_dtype(output_dtype)\n\u001b[1;32m    187\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m process_return:\n\u001b[0;32m--> 188\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32mt_sne.pyx:569\u001b[0m, in \u001b[0;36mcuml.manifold.t_sne.TSNE.fit_transform\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/miniconda3/envs/rapids-24.08/lib/python3.11/site-packages/cuml/internals/api_decorators.py:188\u001b[0m, in \u001b[0;36m_make_decorator_function.<locals>.decorator_function.<locals>.decorator_closure.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    185\u001b[0m     set_api_output_dtype(output_dtype)\n\u001b[1;32m    187\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m process_return:\n\u001b[0;32m--> 188\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    190\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32mt_sne.pyx:538\u001b[0m, in \u001b[0;36mcuml.manifold.t_sne.TSNE.fit\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: exception occurred! file=/home/soumyabrata/miniconda3/envs/rapids-24.08/include/raft/linalg/detail/eig.cuh line=151: "
     ]
    }
   ],
   "source": [
    "import cudf\n",
    "import cuml\n",
    "from cuml.manifold import TSNE as cumlTSNE\n",
    "from cuml.cluster import KMeans as cumlKMeans\n",
    "from cuml.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd  # Don't forget to import pandas\n",
    "\n",
    "# Ensure the data is loaded as pandas DataFrames\n",
    "df_pneumonia_detected = pd.read_csv('VAP detected.csv')\n",
    "df_pneumonia_not_detected = pd.read_csv('No VAP detected.csv')\n",
    "df_common = pd.read_csv('diagnoses_icd_combined_age_restricted.csv')\n",
    "\n",
    "# Convert pandas DataFrames to cuDF DataFrames\n",
    "df_pneumonia_detected = cudf.DataFrame.from_pandas(df_pneumonia_detected)\n",
    "df_pneumonia_not_detected = cudf.DataFrame.from_pandas(df_pneumonia_not_detected)\n",
    "data = cudf.DataFrame.from_pandas(df_common)\n",
    "\n",
    "# Features (vital parameters) and target (VAP)\n",
    "X = data[['temperature', 'heartrate', 'resprate', 'o2sat', 'sbp']]\n",
    "y = data['Pneumonia Detected']\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Hyperparameter ranges for tuning\n",
    "perplexities = [10, 30, 50]  # Tuning perplexity\n",
    "learning_rates = [100, 200, 500]  # Tuning learning rate\n",
    "iterations = [500, 1000]  # Tuning number of iterations\n",
    "early_exaggerations = [12.0, 24.0]  # Tuning early exaggeration\n",
    "angles = [0.2, 0.5]  # Tuning angle for approximation vs accuracy\n",
    "inits = ['random', 'pca']  # Tuning initialization method\n",
    "metrics = ['euclidean', 'cosine']  # Tuning distance metric\n",
    "\n",
    "# Best score and settings\n",
    "best_score = np.inf\n",
    "best_tsne_params = {}\n",
    "\n",
    "# Loop over all hyperparameter combinations\n",
    "for perplexity in perplexities:\n",
    "    for learning_rate in learning_rates:\n",
    "        for n_iter in iterations:\n",
    "            for early_exaggeration in early_exaggerations:\n",
    "                for angle in angles:\n",
    "                    for init in inits:\n",
    "                        for metric in metrics:\n",
    "                            # Perform t-SNE with hyperparameters\n",
    "                            tsne = cumlTSNE(n_components=2,\n",
    "                                            perplexity=perplexity,\n",
    "                                            learning_rate=learning_rate,\n",
    "                                            n_iter=n_iter,\n",
    "                                            early_exaggeration=early_exaggeration,\n",
    "                                            angle=angle,\n",
    "                                            init=init,\n",
    "                                            metric=metric,\n",
    "                                            random_state=42)\n",
    "                            X_tsne = tsne.fit_transform(X_scaled)\n",
    "\n",
    "                            # Apply K-Means Clustering (using t-SNE components)\n",
    "                            kmeans_tsne = cumlKMeans(n_clusters=2, random_state=42)\n",
    "                            tsne_clusters = kmeans_tsne.fit_predict(X_tsne)\n",
    "\n",
    "                            # Calculate separation score (e.g., inertia from KMeans)\n",
    "                            score = kmeans_tsne.inertia_  # Lower inertia indicates better cluster separation\n",
    "\n",
    "                            if score < best_score or best_score == np.inf:\n",
    "                                best_score = score\n",
    "                                best_tsne_params = {\n",
    "                                    'perplexity': perplexity,\n",
    "                                    'learning_rate': learning_rate,\n",
    "                                    'n_iter': n_iter,\n",
    "                                    'early_exaggeration': early_exaggeration,\n",
    "                                    'angle': angle,\n",
    "                                    'init': init,\n",
    "                                    'metric': metric\n",
    "                                }\n",
    "                                best_X_tsne = X_tsne\n",
    "                                best_tsne_clusters = tsne_clusters\n",
    "\n",
    "# Define colors for clusters\n",
    "colors = {0: 'blue', 1: 'red'}\n",
    "\n",
    "### Plot the best t-SNE + K-Means\n",
    "plt.figure(figsize=(8, 6))\n",
    "scatter = plt.scatter(best_X_tsne[:, 0].to_array(), best_X_tsne[:, 1].to_array(), \n",
    "                     c=[colors[label] for label in best_tsne_clusters.to_array()], alpha=0.6)\n",
    "plt.title(f\"t-SNE + K-Means Clustering (Best Params: {best_tsne_params})\")\n",
    "plt.xlabel('t-SNE Component 1')\n",
    "plt.ylabel('t-SNE Component 2')\n",
    "plt.legend(handles=[plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='blue', markersize=10, label='No VAP'),\n",
    "                   plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='red', markersize=10, label='VAP')],\n",
    "          loc='best')\n",
    "plt.savefig('t-SNE + K-Means VAP No VAP.png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best t-SNE parameters found:\", best_tsne_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1634c668-2917-446c-8482-9ca35b57810a",
   "metadata": {},
   "outputs": [],
   "source": []
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