NE_Clusers_ClusteringAndViz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "from collections import defaultdict\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read vectors and names\n",
    "\n",
    "frame = pd.read_csv(\"/home/rin/CrossPlatform/Code/entity_vecs_onlyPROPN.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectors = frame.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "        1         2         3         4         5         6         7    \\\n",
       "0  0.001111  0.007694 -0.013193  0.002277  0.001425  0.008855  0.001344   \n",
       "1  0.002282  0.013210 -0.013372  0.007318  0.002672  0.015021  0.005350   \n",
       "2 -0.006058  0.035130 -0.019281  0.000566  0.006083  0.019783  0.015781   \n",
       "3 -0.008021  0.041882 -0.023921  0.000074  0.012999  0.030620  0.011261   \n",
       "4  0.013215  1.012015 -0.852180  0.490863  0.338856  0.683575  0.696290   \n",
       "\n",
       "        8         9         10     ...          91        92        93   \\\n",
       "0 -0.003634  0.002648 -0.001622    ...     0.000936  0.005777  0.006525   \n",
       "1  0.001469  0.000112 -0.003898    ...     0.003916  0.006667  0.007948   \n",
       "2 -0.008688  0.004104 -0.013092    ...     0.001131  0.011191  0.012964   \n",
       "3 -0.007864  0.011873 -0.016689    ...    -0.004126  0.012571  0.018760   \n",
       "4 -0.108314  0.155370 -0.419443    ...    -0.025576  0.228806  0.409325   \n",
       "\n",
       "        94        95        96        97        98        99        100  \n",
       "0  0.000239  0.003887  0.002487 -0.001279 -0.005166  0.005642  0.000028  \n",
       "1 -0.003036 -0.000457  0.007797 -0.005251 -0.005143  0.003674 -0.002303  \n",
       "2 -0.009249  0.003564  0.008661 -0.001564 -0.012390  0.010775 -0.009490  \n",
       "3 -0.000521  0.009847  0.016102 -0.008885 -0.014268  0.010243 -0.005245  \n",
       "4 -0.104014  0.052956  0.272815 -0.410445 -0.741947  0.190902 -0.510724  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "#tsne = TSNE(n_components=2, random_state=0)\n",
    "#tsne_2d = tsne.fit_transform(vectors.values) \n",
    "\n",
    "pca = PCA( random_state=0)\n",
    "pca_2d= pca.fit_transform(vectors.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "ax=plot(pca.explained_variance_ratio_)\n",
    "gca().set_xlim((0, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9826435111168063"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_[:5].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bokeh\n",
    "import bokeh.models as bmo\n",
    "from bokeh.palettes import d3, viridis\n",
    "from bokeh.models import HoverTool, LabelSet\n",
    "from bokeh.plotting import figure, show, ColumnDataSource\n",
    "# from bokeh.models import ColumnDataSource, Range1d, LabelSet, Label\n",
    "\n",
    "x = pca_2d[:, 0]\n",
    "y = pca_2d[:, 1]\n",
    "names = frame.iloc[:, 0]\n",
    "\n",
    "source = ColumnDataSource(\n",
    "        data=dict(\n",
    "            x=x,\n",
    "            y=y,\n",
    "            names=names,\n",
    "            \n",
    "        )\n",
    "    )\n",
    "\n",
    "# Add author names and sizes to mouse-over info.\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "        (\"entity\", \"@names\"),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "p = figure(tools=[hover, 'crosshair,pan,wheel_zoom,box_zoom,reset,save,lasso_select'], plot_width=1000, plot_height=1000)\n",
    "p.scatter('x', 'y', source=source, \n",
    "          fill_alpha=0.6, line_color=None)\n",
    "labels = LabelSet(x='x', y='y', text='names', level='glyph',\n",
    "              x_offset=0, y_offset=0, source=source, render_mode='canvas', text_font_size=\"6pt\")\n",
    "p.add_layout(labels)\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "tsne_2d = tsne.fit_transform(vectors.values) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bokeh\n",
    "import bokeh.models as bmo\n",
    "from bokeh.palettes import d3, viridis\n",
    "from bokeh.models import HoverTool, LabelSet\n",
    "from bokeh.plotting import figure, show, ColumnDataSource\n",
    "# from bokeh.models import ColumnDataSource, Range1d, LabelSet, Label\n",
    "\n",
    "x = tsne_2d[:, 0]\n",
    "y = tsne_2d[:, 1]\n",
    "names = frame.iloc[:, 0]\n",
    "\n",
    "source = ColumnDataSource(\n",
    "        data=dict(\n",
    "            x=x,\n",
    "            y=y,\n",
    "            names=names,\n",
    "            \n",
    "        )\n",
    "    )\n",
    "\n",
    "# Add author names and sizes to mouse-over info.\n",
    "hover = HoverTool(\n",
    "        tooltips=[\n",
    "        (\"entity\", \"@names\"),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "p = figure(tools=[hover, 'crosshair,pan,wheel_zoom,box_zoom,reset,save,lasso_select'], plot_width=1000, plot_height=1000)\n",
    "p.scatter('x', 'y', source=source, \n",
    "          fill_alpha=0.6, line_color=None)\n",
    "labels = LabelSet(x='x', y='y', text='names', level='glyph',\n",
    "              x_offset=0, y_offset=0, source=source, render_mode='canvas', text_font_size=\"6pt\")\n",
    "p.add_layout(labels)\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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