demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing\n",
    "- Remove URLs and Mentions\n",
    "- Separate composite camel case words (e.g. BlackLives --> black lives)\n",
    "- Remove non-alphanumeric characters\n",
    "- Replace numbers with the token \"_number_\"\n",
    "- Lowercase everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:45:10.994786Z",
     "start_time": "2020-06-07T23:45:10.990361Z"
    }
   },
   "outputs": [],
   "source": [
    "from src.preprocessing import clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:46:12.708646Z",
     "start_time": "2020-06-07T23:46:12.680332Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'black lives matter'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean(\"#BlackLivesMatter https://www.google.com/ @realDonaldTrump\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding\n",
    "## Universal Sentence Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:55:34.962132Z",
     "start_time": "2020-06-07T23:55:33.625304Z"
    }
   },
   "outputs": [],
   "source": [
    "from src.encoder import Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:55:40.034473Z",
     "start_time": "2020-06-07T23:55:37.473873Z"
    }
   },
   "outputs": [],
   "source": [
    "# default encoder is USE for English only.\n",
    "# But you can use multilingual as well, like ...\n",
    "encoder = Encoder(model_url=\"https://tfhub.dev/google/universal-sentence-encoder-multilingual/3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:55:57.481777Z",
     "start_time": "2020-06-07T23:55:57.448134Z"
    }
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.encode(\"hello world\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:58:33.082587Z",
     "start_time": "2020-06-07T23:58:33.053011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>username</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>user1</td>\n",
       "      <td>hello world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user1</td>\n",
       "      <td>merhaba dunya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>user2</td>\n",
       "      <td>Bonjour le monde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>user2</td>\n",
       "      <td>مرحبا بالعالم</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  username              text\n",
       "0    user1       hello world\n",
       "1    user1     merhaba dunya\n",
       "2    user2  Bonjour le monde\n",
       "3    user2     مرحبا بالعالم"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    \"username\":[\"user1\", \"user1\", \"user2\", \"user2\"],\n",
    "    \"text\": [\"hello world\", \"merhaba dunya\", \"Bonjour le monde\", \"مرحبا بالعالم\"]\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:59:26.926654Z",
     "start_time": "2020-06-07T23:59:26.886003Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:00<00:00, 95.93it/s]\n"
     ]
    }
   ],
   "source": [
    "encoder.encode_df(df, user_col=\"username\", text_col=\"text\", out_path=\"demo.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:59:38.423912Z",
     "start_time": "2020-06-07T23:59:38.418479Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['user1', 'user2'], dtype='<U5')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = np.load(\"demo.npz\")\n",
    "f[\"users\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:59:42.624482Z",
     "start_time": "2020-06-07T23:59:42.610824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.1271717 ,  0.00337878, -0.00207015, ..., -0.03947673,\n",
       "         0.02093398, -0.00205181],\n",
       "       [ 0.07556362, -0.00347732, -0.01797179, ..., -0.0588544 ,\n",
       "         0.02632636, -0.00710142]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f[\"vectors\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-07T23:59:52.972054Z",
     "start_time": "2020-06-07T23:59:52.959046Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 512)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f[\"vectors\"].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T00:00:16.428320Z",
     "start_time": "2020-06-08T00:00:16.423172Z"
    }
   },
   "outputs": [],
   "source": [
    "from src.encoder import EncoderBERT\n",
    "\n",
    "encoder = EncoderBERT(\"roberta-base-nli-stsb-mean-tokens\")\n",
    "\n",
    "# same API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T00:07:17.361732Z",
     "start_time": "2020-06-08T00:07:17.338281Z"
    }
   },
   "outputs": [],
   "source": [
    "from src.projection import Projector, os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T00:14:15.163004Z",
     "start_time": "2020-06-08T00:14:15.159439Z"
    }
   },
   "outputs": [],
   "source": [
    "projector = Projector(\"election_vectors.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T00:16:09.544929Z",
     "start_time": "2020-06-08T00:16:09.537875Z"
    }
   },
   "outputs": [],
   "source": [
    "projector.project(\"projections.npz\", min_counts=3, min_dist=0.0, n_neighbors=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T00:16:24.565870Z",
     "start_time": "2020-06-08T00:16:24.558409Z"
    }
   },
   "outputs": [],
   "source": [
    "os.makedirs(\"trials\")\n",
    "\n",
    "projector.grid_search(\n",
    "    trials_dir=\"trials\",\n",
    "    min_counts=3, # minimum 3 tweets per user\n",
    "    min_dists_range = [0.0, 0.1, 0.25, 0.5, 0.75, 0.8, 0.9, 0.99],\n",
    "    n_neighbors_range=[20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T00:19:11.426399Z",
     "start_time": "2020-06-08T00:19:11.420912Z"
    }
   },
   "outputs": [],
   "source": [
    "from src.clustering import Clusterer, pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T00:17:49.673292Z",
     "start_time": "2020-06-08T00:17:49.667639Z"
    }
   },
   "outputs": [],
   "source": [
    "clusterer = Clusterer(\"projections.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T00:21:46.942189Z",
     "start_time": "2020-06-08T00:21:46.174795Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1bdfd9908>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterer.cluster(tree_path=\"tree.pkl\")\n",
    "\n",
    "clusterer.plot_tree(path=\"tree.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Injecting labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T01:01:34.529613Z",
     "start_time": "2020-06-08T01:01:34.509232Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>username</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>user1</td>\n",
       "      <td>pro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user2</td>\n",
       "      <td>anti</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  username label\n",
       "0    user1   pro\n",
       "1    user2  anti"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is just an example. We are hiding the actual labels of real users here\n",
    "labels = pd.DataFrame({\"username\":[\"user1\", \"user2\"], \"label\":[\"pro\", \"anti\"]})\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T01:21:09.279291Z",
     "start_time": "2020-06-08T01:21:09.274152Z"
    }
   },
   "outputs": [],
   "source": [
    "clusterer.inject_labels(users=labels.username, labels=labels.label)\n",
    "\n",
    "clusterer.align_clusters_with_labels(\n",
    "    # this means multiple clusters can be assigned the same label\n",
    "    allow_multiple_clusters=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example on Turkish Election dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T01:23:28.426960Z",
     "start_time": "2020-06-08T01:23:28.419726Z"
    }
   },
   "outputs": [],
   "source": [
    "clusterer.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"ed.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example on Trump dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T01:05:41.558112Z",
     "start_time": "2020-06-08T01:05:41.554222Z"
    }
   },
   "outputs": [],
   "source": [
    "# this calculates the micro f1 score for all umap configurations in the grid search\n",
    "# and plots the result of each configuration\n",
    "# then returns the results matrix and a heatmap plot of it\n",
    "results, hm = cluster_projection_grid_search(\n",
    "    \"trials\", users=labels.username, labels=labels.label,\n",
    "    # this means multiple clusters can be assigned the same label\n",
    "    allow_multiple_clusters=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of plotted projections and grid search heatmap\n",
    "<img src=\"trials/hm.png?\">\n",
    "<img src=\"trials/0.0_30.png?\">\n",
    "<img src=\"trials/0.1_60.png?\">"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
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