tsne_dim_reduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Load the data\n",
    "df = pd.read_json(\"https://github.com/do-me/SDG-Analyzer/raw/main/assets/SDG_Target_2023_jina_base.json.gz\")\n",
    "\n",
    "# Assuming 'jina-embeddings-v2-base-en' holds your embeddings\n",
    "embeddings = np.stack(df['jina-embeddings-v2-base-en'].values)\n",
    "\n",
    "# Initialize t-SNE with two components (2D)\n",
    "tsne = TSNE(n_components=2, perplexity=10, n_iter=10000, random_state=42)\n",
    "\n",
    "# Fit and transform your embeddings\n",
    "tsne_results = tsne.fit_transform(embeddings)\n",
    "\n",
    "# Initialize MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Normalize the t-SNE results\n",
    "tsne_normalized = scaler.fit_transform(tsne_results)\n",
    "\n",
    "# Add the normalized 2D coordinates back to your DataFrame\n",
    "df['x'] = tsne_normalized[:,0]\n",
    "df['y'] = tsne_normalized[:,1]\n",
    "\n",
    "# Now you can plot the normalized results using matplotlib\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(df['x'], df['y'], s=1)  # You can also color the points by some label using the `c` argument\n",
    "plt.xlabel('Normalized t-SNE 2D One')\n",
    "plt.ylabel('Normalized t-SNE 2D Two')\n",
    "plt.title('2D t-SNE Visualization of Normalized Embeddings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SDGNumber</th>\n",
       "      <th>SDGTitle</th>\n",
       "      <th>TargetsNumber</th>\n",
       "      <th>Targets</th>\n",
       "      <th>jina-embeddings-v2-base-en</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>No Poverty</td>\n",
       "      <td>1.1</td>\n",
       "      <td>By 2030, eradicate extreme poverty for all pe...</td>\n",
       "      <td>[0.013646188200000001, -0.8851571083, 0.273902...</td>\n",
       "      <td>0.474702</td>\n",
       "      <td>0.237202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>No Poverty</td>\n",
       "      <td>1.2</td>\n",
       "      <td>By 2030, reduce at least by half the proporti...</td>\n",
       "      <td>[-0.5175303221, -0.1017927676, 0.1907873154000...</td>\n",
       "      <td>0.508123</td>\n",
       "      <td>0.238149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>No Poverty</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Implement nationally appropriate social prote...</td>\n",
       "      <td>[-0.0685161054, -0.188393563, 0.4002646804, 0....</td>\n",
       "      <td>0.702829</td>\n",
       "      <td>0.147774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>No Poverty</td>\n",
       "      <td>1.4</td>\n",
       "      <td>By 2030, ensure that all men and women, in pa...</td>\n",
       "      <td>[-4.54066e-05, -0.1530471444, 0.4292953908, 0....</td>\n",
       "      <td>0.849568</td>\n",
       "      <td>0.211390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>No Poverty</td>\n",
       "      <td>1.5</td>\n",
       "      <td>By 2030, build the resilience of the poor and...</td>\n",
       "      <td>[-0.32836982610000004, -0.5124217868000001, 0....</td>\n",
       "      <td>0.280105</td>\n",
       "      <td>0.292581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>17</td>\n",
       "      <td>Partnerships for the Goals</td>\n",
       "      <td>17.15</td>\n",
       "      <td>Respect each country’s policy space and leade...</td>\n",
       "      <td>[-0.0687223226, -0.2726017833, 0.9761043191000...</td>\n",
       "      <td>0.764401</td>\n",
       "      <td>0.534448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>17</td>\n",
       "      <td>Partnerships for the Goals</td>\n",
       "      <td>17.16</td>\n",
       "      <td>Enhance the Global Partnership for Sustainabl...</td>\n",
       "      <td>[-0.3509787023, -0.0232130904, 0.8275802135, 0...</td>\n",
       "      <td>0.540611</td>\n",
       "      <td>0.603604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>17</td>\n",
       "      <td>Partnerships for the Goals</td>\n",
       "      <td>17.17</td>\n",
       "      <td>Encourage and promote effective public, publi...</td>\n",
       "      <td>[-0.2692470551, 0.1061384082, 0.93294286730000...</td>\n",
       "      <td>0.825518</td>\n",
       "      <td>0.620819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>17</td>\n",
       "      <td>Partnerships for the Goals</td>\n",
       "      <td>17.18</td>\n",
       "      <td>By 2020, enhance capacity-building support to...</td>\n",
       "      <td>[-0.28500363230000003, -0.2784230411, 0.434557...</td>\n",
       "      <td>0.606899</td>\n",
       "      <td>0.750743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>17</td>\n",
       "      <td>Partnerships for the Goals</td>\n",
       "      <td>17.19</td>\n",
       "      <td>By 2030, build on existing initiatives to dev...</td>\n",
       "      <td>[-0.2612594366, -0.2701257765, 0.741984725, 0....</td>\n",
       "      <td>0.406619</td>\n",
       "      <td>0.661063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SDGNumber                    SDGTitle TargetsNumber   \n",
       "0            1                  No Poverty           1.1  \\\n",
       "1            1                  No Poverty           1.2   \n",
       "2            1                  No Poverty           1.3   \n",
       "3            1                  No Poverty           1.4   \n",
       "4            1                  No Poverty           1.5   \n",
       "..         ...                         ...           ...   \n",
       "164         17  Partnerships for the Goals         17.15   \n",
       "165         17  Partnerships for the Goals         17.16   \n",
       "166         17  Partnerships for the Goals         17.17   \n",
       "167         17  Partnerships for the Goals         17.18   \n",
       "168         17  Partnerships for the Goals         17.19   \n",
       "\n",
       "                                               Targets   \n",
       "0     By 2030, eradicate extreme poverty for all pe...  \\\n",
       "1     By 2030, reduce at least by half the proporti...   \n",
       "2     Implement nationally appropriate social prote...   \n",
       "3     By 2030, ensure that all men and women, in pa...   \n",
       "4     By 2030, build the resilience of the poor and...   \n",
       "..                                                 ...   \n",
       "164   Respect each country’s policy space and leade...   \n",
       "165   Enhance the Global Partnership for Sustainabl...   \n",
       "166   Encourage and promote effective public, publi...   \n",
       "167   By 2020, enhance capacity-building support to...   \n",
       "168   By 2030, build on existing initiatives to dev...   \n",
       "\n",
       "                            jina-embeddings-v2-base-en    x    y  \n",
       "0    [0.013646188200000001, -0.8851571083, 0.273902...  0.474702  0.237202  \n",
       "1    [-0.5175303221, -0.1017927676, 0.1907873154000...  0.508123  0.238149  \n",
       "2    [-0.0685161054, -0.188393563, 0.4002646804, 0....  0.702829  0.147774  \n",
       "3    [-4.54066e-05, -0.1530471444, 0.4292953908, 0....  0.849568  0.211390  \n",
       "4    [-0.32836982610000004, -0.5124217868000001, 0....  0.280105  0.292581  \n",
       "..                                                 ...       ...       ...  \n",
       "164  [-0.0687223226, -0.2726017833, 0.9761043191000...  0.764401  0.534448  \n",
       "165  [-0.3509787023, -0.0232130904, 0.8275802135, 0...  0.540611  0.603604  \n",
       "166  [-0.2692470551, 0.1061384082, 0.93294286730000...  0.825518  0.620819  \n",
       "167  [-0.28500363230000003, -0.2784230411, 0.434557...  0.606899  0.750743  \n",
       "168  [-0.2612594366, -0.2701257765, 0.741984725, 0....  0.406619  0.661063  \n",
       "\n",
       "[169 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_parquet(\"SDGs.parquet\")"
   ]
  }
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