crossref_analysis_2_kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jXBBztbUKgSP"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Lf2PykHmMimP"
   },
   "outputs": [],
   "source": [
    "fields = pd.read_csv('fields.csv',\n",
    "                     names=['DOI', 'field'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "colab_type": "code",
    "id": "cdWbHdLNMl1X",
    "outputId": "16338d51-7374-4c09-fc00-4c04410db2bb"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOI</th>\n",
       "      <th>field</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.1163/1568525043083505</td>\n",
       "      <td>Humanities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.1163/1568525043083532</td>\n",
       "      <td>Humanities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.1163/1568525043083541</td>\n",
       "      <td>Humanities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1163/1568525043083514</td>\n",
       "      <td>Humanities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1163/1568527053083412</td>\n",
       "      <td>Humanities</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                        DOI       field\n",
       "0  10.1163/1568525043083505  Humanities\n",
       "1  10.1163/1568525043083532  Humanities\n",
       "2  10.1163/1568525043083541  Humanities\n",
       "3  10.1163/1568525043083514  Humanities\n",
       "4  10.1163/1568527053083412  Humanities"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fields.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9dqluk6tMo5Z"
   },
   "outputs": [],
   "source": [
    "texts = pd.read_csv('prepr_texts_v2.tsv', sep='\\t',\n",
    "                    names=['DOI', 'prepr_title', 'prepr_abstract'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOI</th>\n",
       "      <th>prepr_title</th>\n",
       "      <th>prepr_abstract</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.1163/1568525043083505</td>\n",
       "      <td>aristotle fr. 44 rose: midas and silenus</td>\n",
       "      <td>scholars have identified two supposedly separa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.1163/1568525043083532</td>\n",
       "      <td>loca loquuntur. lucretius' explanation of the ...</td>\n",
       "      <td>a discussion of the second part of lucretius' ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.1163/1568525043083541</td>\n",
       "      <td>poverty and demography: the case of the gracch...</td>\n",
       "      <td>according to many ancient historians the secon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1163/1568525043083514</td>\n",
       "      <td>old persian in athens revisited (ar. ach. 100)</td>\n",
       "      <td>the old persian line in aristophanes' acharnia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1163/1568527053083412</td>\n",
       "      <td>religion and violence: what can sociology offer?</td>\n",
       "      <td>this essay presents a sketch of a sociological...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                        DOI  \\\n",
       "0  10.1163/1568525043083505   \n",
       "1  10.1163/1568525043083532   \n",
       "2  10.1163/1568525043083541   \n",
       "3  10.1163/1568525043083514   \n",
       "4  10.1163/1568527053083412   \n",
       "\n",
       "                                         prepr_title  \\\n",
       "0           aristotle fr. 44 rose: midas and silenus   \n",
       "1  loca loquuntur. lucretius' explanation of the ...   \n",
       "2  poverty and demography: the case of the gracch...   \n",
       "3     old persian in athens revisited (ar. ach. 100)   \n",
       "4   religion and violence: what can sociology offer?   \n",
       "\n",
       "                                      prepr_abstract  \n",
       "0  scholars have identified two supposedly separa...  \n",
       "1  a discussion of the second part of lucretius' ...  \n",
       "2  according to many ancient historians the secon...  \n",
       "3  the old persian line in aristophanes' acharnia...  \n",
       "4  this essay presents a sketch of a sociological...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(fields, texts, on='DOI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOI</th>\n",
       "      <th>field</th>\n",
       "      <th>prepr_title</th>\n",
       "      <th>prepr_abstract</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.1163/1568525043083505</td>\n",
       "      <td>Humanities</td>\n",
       "      <td>aristotle fr. 44 rose: midas and silenus</td>\n",
       "      <td>scholars have identified two supposedly separa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.1163/1568525043083532</td>\n",
       "      <td>Humanities</td>\n",
       "      <td>loca loquuntur. lucretius' explanation of the ...</td>\n",
       "      <td>a discussion of the second part of lucretius' ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.1163/1568525043083541</td>\n",
       "      <td>Humanities</td>\n",
       "      <td>poverty and demography: the case of the gracch...</td>\n",
       "      <td>according to many ancient historians the secon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1163/1568525043083514</td>\n",
       "      <td>Humanities</td>\n",
       "      <td>old persian in athens revisited (ar. ach. 100)</td>\n",
       "      <td>the old persian line in aristophanes' acharnia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1163/1568527053083412</td>\n",
       "      <td>Humanities</td>\n",
       "      <td>religion and violence: what can sociology offer?</td>\n",
       "      <td>this essay presents a sketch of a sociological...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                        DOI       field  \\\n",
       "0  10.1163/1568525043083505  Humanities   \n",
       "1  10.1163/1568525043083532  Humanities   \n",
       "2  10.1163/1568525043083541  Humanities   \n",
       "3  10.1163/1568525043083514  Humanities   \n",
       "4  10.1163/1568527053083412  Humanities   \n",
       "\n",
       "                                         prepr_title  \\\n",
       "0           aristotle fr. 44 rose: midas and silenus   \n",
       "1  loca loquuntur. lucretius' explanation of the ...   \n",
       "2  poverty and demography: the case of the gracch...   \n",
       "3     old persian in athens revisited (ar. ach. 100)   \n",
       "4   religion and violence: what can sociology offer?   \n",
       "\n",
       "                                      prepr_abstract  \n",
       "0  scholars have identified two supposedly separa...  \n",
       "1  a discussion of the second part of lucretius' ...  \n",
       "2  according to many ancient historians the secon...  \n",
       "3  the old persian line in aristophanes' acharnia...  \n",
       "4  this essay presents a sketch of a sociological...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('prepr_abstract_field.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('prepr_abstract_field_GM_all_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9N4rdzIfM4JM",
    "outputId": "ae3e5323-e4b9-4eca-ee45-46665a71c1f6"
   },
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import HashingVectorizer\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['prepr_abstract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "labels = df['field']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, labels, train_size=.5, test_size=.15, random_state=1337, stratify=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = HashingVectorizer(ngram_range=(1, 2), stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = vectorizer.fit_transform(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1064356, 1048576)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.csr.csr_matrix"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import save_npz\n",
    "\n",
    "np.save('features_v4.npy', features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import load_npz\n",
    "\n",
    "features = load_npz('features_v4.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance of the SVD step: 3%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "\n",
    "svd = TruncatedSVD(13)\n",
    "normalizer = Normalizer(copy=False)\n",
    "lsa = make_pipeline(svd, normalizer)\n",
    "\n",
    "features = lsa.fit_transform(features)\n",
    "\n",
    "explained_variance = svd.explained_variance_ratio_.sum()\n",
    "print(\"Explained variance of the SVD step: {}%\".format(int(explained_variance * 100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import MiniBatchKMeans\n",
    "\n",
    "km = MiniBatchKMeans(n_clusters=np.unique(labels).shape[0], verbose=True, random_state=1337,\n",
    "                    batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init 1/3 with method: k-means++\n",
      "Inertia for init 1/3: 880.903900\n",
      "Init 2/3 with method: k-means++\n",
      "Inertia for init 2/3: 962.985541\n",
      "Init 3/3 with method: k-means++\n",
      "Inertia for init 3/3: 843.724448\n",
      "Minibatch iteration 1/106500: mean batch inertia: 0.275594, ewa inertia: 0.275594 \n",
      "Minibatch iteration 2/106500: mean batch inertia: 0.277221, ewa inertia: 0.275597 \n",
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      "Minibatch iteration 1396/106500: mean batch inertia: 0.257065, ewa inertia: 0.259364 \n",
      "Minibatch iteration 1397/106500: mean batch inertia: 0.256141, ewa inertia: 0.259357 \n",
      "Minibatch iteration 1398/106500: mean batch inertia: 0.256643, ewa inertia: 0.259352 \n",
      "Minibatch iteration 1399/106500: mean batch inertia: 0.259776, ewa inertia: 0.259353 \n",
      "Minibatch iteration 1400/106500: mean batch inertia: 0.254112, ewa inertia: 0.259343 \n",
      "Minibatch iteration 1401/106500: mean batch inertia: 0.259076, ewa inertia: 0.259343 \n",
      "Minibatch iteration 1402/106500: mean batch inertia: 0.258595, ewa inertia: 0.259341 \n",
      "Minibatch iteration 1403/106500: mean batch inertia: 0.253515, ewa inertia: 0.259330 \n",
      "Minibatch iteration 1404/106500: mean batch inertia: 0.259081, ewa inertia: 0.259330 \n",
      "Minibatch iteration 1405/106500: mean batch inertia: 0.255893, ewa inertia: 0.259324 \n",
      "Minibatch iteration 1406/106500: mean batch inertia: 0.257210, ewa inertia: 0.259320 \n",
      "Minibatch iteration 1407/106500: mean batch inertia: 0.256876, ewa inertia: 0.259315 \n",
      "Minibatch iteration 1408/106500: mean batch inertia: 0.253463, ewa inertia: 0.259304 \n",
      "Minibatch iteration 1409/106500: mean batch inertia: 0.262487, ewa inertia: 0.259310 \n",
      "Minibatch iteration 1410/106500: mean batch inertia: 0.258190, ewa inertia: 0.259308 \n",
      "Minibatch iteration 1411/106500: mean batch inertia: 0.256701, ewa inertia: 0.259303 \n",
      "Minibatch iteration 1412/106500: mean batch inertia: 0.257004, ewa inertia: 0.259299 \n",
      "Minibatch iteration 1413/106500: mean batch inertia: 0.254566, ewa inertia: 0.259290 \n",
      "Minibatch iteration 1414/106500: mean batch inertia: 0.254624, ewa inertia: 0.259281 \n",
      "Minibatch iteration 1415/106500: mean batch inertia: 0.254243, ewa inertia: 0.259272 \n",
      "Minibatch iteration 1416/106500: mean batch inertia: 0.254857, ewa inertia: 0.259263 \n",
      "Minibatch iteration 1417/106500: mean batch inertia: 0.258582, ewa inertia: 0.259262 \n",
      "Minibatch iteration 1418/106500: mean batch inertia: 0.259445, ewa inertia: 0.259262 \n",
      "Minibatch iteration 1419/106500: mean batch inertia: 0.264719, ewa inertia: 0.259273 \n",
      "Minibatch iteration 1420/106500: mean batch inertia: 0.253407, ewa inertia: 0.259262 \n",
      "Minibatch iteration 1421/106500: mean batch inertia: 0.257544, ewa inertia: 0.259258 \n",
      "Minibatch iteration 1422/106500: mean batch inertia: 0.266224, ewa inertia: 0.259271 \n",
      "Minibatch iteration 1423/106500: mean batch inertia: 0.259294, ewa inertia: 0.259271 \n",
      "Minibatch iteration 1424/106500: mean batch inertia: 0.255111, ewa inertia: 0.259264 \n",
      "Minibatch iteration 1425/106500: mean batch inertia: 0.260952, ewa inertia: 0.259267 \n",
      "Minibatch iteration 1426/106500: mean batch inertia: 0.260987, ewa inertia: 0.259270 \n",
      "Minibatch iteration 1427/106500: mean batch inertia: 0.266428, ewa inertia: 0.259283 \n",
      "Minibatch iteration 1428/106500: mean batch inertia: 0.262509, ewa inertia: 0.259290 \n",
      "Minibatch iteration 1429/106500: mean batch inertia: 0.259295, ewa inertia: 0.259290 \n",
      "Minibatch iteration 1430/106500: mean batch inertia: 0.260045, ewa inertia: 0.259291 \n",
      "Minibatch iteration 1431/106500: mean batch inertia: 0.251834, ewa inertia: 0.259277 \n",
      "Converged (lack of improvement in inertia) at iteration 1431/106500\n",
      "Computing label assignment and total inertia\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MiniBatchKMeans(batch_size=1000, compute_labels=True, init='k-means++',\n",
       "        init_size=None, max_iter=100, max_no_improvement=10, n_clusters=13,\n",
       "        n_init=3, random_state=1337, reassignment_ratio=0.01, tol=0.0,\n",
       "        verbose=True)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.fit(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Homogeneity: 0.260\n",
      "Completeness: 0.204\n",
      "V-measure: 0.228\n",
      "Adjusted Rand-Index: 0.121\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "labels = y_train\n",
    "\n",
    "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels, km.labels_))\n",
    "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels, km.labels_))\n",
    "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels, km.labels_))\n",
    "print(\"Adjusted Rand-Index: %.3f\"\n",
    "      % metrics.adjusted_rand_score(labels, km.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x47c837a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(15, 10))\n",
    "\n",
    "l, values = zip(*Counter(labels).items())\n",
    "\n",
    "indexes = np.arange(len(l))\n",
    "width = 1\n",
    "\n",
    "ax1.bar(indexes, values, width)\n",
    "plt.xticks(indexes + width * 0.5, l)\n",
    "\n",
    "ax2.hist(km.labels_)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "crossref_analysis_even_more_better.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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