{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "490"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "data_folder = os.path.join(os.path.expanduser(\"~\"), \"Data\", \"websites\", \"textonly\")\n",
    "\n",
    "documents = [open(os.path.join(data_folder, filename)).read() for filename in os.listdir(data_folder)]\n",
    "len(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretty printing has been turned OFF\n"
     ]
    }
   ],
   "source": [
    "pprint([document[:100] for document in documents[:5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "n_clusters = 10\n",
    "\n",
    "pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\n",
    "                     ('clusterer', KMeans(n_clusters=n_clusters))\n",
    "                     ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('feature_extraction', TfidfVectorizer(analyzer='word', binary=False, charset=None,\n",
       "        charset_error=None, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.4, max_features=None, min_df=1,\n",
       "        ngram_range=(... n_init=10,\n",
       "    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n",
       "    verbose=0))])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pipeline.predict(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0 contains 1 samples\n",
      "Cluster 1 contains 2 samples\n",
      "Cluster 2 contains 439 samples\n",
      "Cluster 3 contains 1 samples\n",
      "Cluster 4 contains 2 samples\n",
      "Cluster 5 contains 3 samples\n",
      "Cluster 6 contains 27 samples\n",
      "Cluster 7 contains 2 samples\n",
      "Cluster 8 contains 12 samples\n",
      "Cluster 9 contains 1 samples\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "c = Counter(labels)\n",
    "for cluster_number in range(n_clusters):\n",
    "    print(\"Cluster {} contains {} samples\".format(cluster_number, c[cluster_number]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "381.97317261989065"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.named_steps['clusterer'].inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia_scores = []\n",
    "n_cluster_values = list(range(2, 20))\n",
    "for n_clusters in n_cluster_values:\n",
    "    cur_inertia_scores = []\n",
    "    X = TfidfVectorizer(max_df=0.4).fit_transform(documents)\n",
    "    for i in range(30):\n",
    "        km = KMeans(n_clusters=n_clusters).fit(X)\n",
    "        cur_inertia_scores.append(km.inertia_)\n",
    "    inertia_scores.append(cur_inertia_scores)\n",
    "inertia_scores = np.array(inertia_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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p3xpTAQAAAFiHCAcAAADgYE3vqp67uWq6cXVc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\nQHOoTa+rHtD0iOqHWp6CdF71hOr5TR9bdR8AAACHjAgHAAA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      "text/plain": [
       "<matplotlib.figure.Figure object at 0x7f0bc5ae9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "inertia_means = np.mean(inertia_scores, axis=1)\n",
    "inertia_stderr = np.std(inertia_scores, axis=1)\n",
    "\n",
    "fig = plt.figure(figsize=(40,20))\n",
    "plt.errorbar(n_cluster_values, inertia_means, inertia_stderr, color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('feature_extraction', TfidfVectorizer(analyzer='word', binary=False, charset=None,\n",
       "        charset_error=None, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.4, max_features=None, min_df=1,\n",
       "        ngram_range=(... n_init=10,\n",
       "    n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n",
       "    verbose=0))])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_clusters = 6\n",
    "\n",
    "pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\n",
    "                     ('clusterer', KMeans(n_clusters=n_clusters))\n",
    "                     ])\n",
    "pipeline.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pipeline.predict(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0 contains 21 samples\n",
      "  Most important terms\n",
      "  1) korea (score: 0.1591)\n",
      "  2) korean (score: 0.1573)\n",
      "  3) south (score: 0.1336)\n",
      "  4) kim (score: 0.1088)\n",
      "  5) north (score: 0.1077)\n",
      "\n",
      "Cluster 1 contains 33 samples\n",
      "  Most important terms\n",
      "  1) iran (score: 0.1047)\n",
      "  2) netanyahu (score: 0.0869)\n",
      "  3) nuclear (score: 0.0660)\n",
      "  4) he (score: 0.0615)\n",
      "  5) his (score: 0.0550)\n",
      "\n",
      "Cluster 2 contains 156 samples\n",
      "  Most important terms\n",
      "  1) palestinian (score: 0.0196)\n",
      "  2) israel (score: 0.0138)\n",
      "  3) security (score: 0.0118)\n",
      "  4) bank (score: 0.0115)\n",
      "  5) its (score: 0.0103)\n",
      "\n",
      "Cluster 3 contains 31 samples\n",
      "  Most important terms\n",
      "  1) browser (score: 0.2786)\n",
      "  2) able (score: 0.2429)\n",
      "  3) you (score: 0.2018)\n",
      "  4) css (score: 0.1925)\n",
      "  5) sheets (score: 0.1925)\n",
      "\n",
      "Cluster 4 contains 48 samples\n",
      "  Most important terms\n",
      "  1) al (score: 0.0862)\n",
      "  2) isis (score: 0.0723)\n",
      "  3) islamic (score: 0.0664)\n",
      "  4) iraq (score: 0.0657)\n",
      "  5) state (score: 0.0580)\n",
      "\n",
      "Cluster 5 contains 201 samples\n",
      "  Most important terms\n",
      "  1) he (score: 0.0448)\n",
      "  2) we (score: 0.0306)\n",
      "  3) they (score: 0.0294)\n",
      "  4) his (score: 0.0254)\n",
      "  5) who (score: 0.0253)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "c = Counter(labels)\n",
    "\n",
    "terms = pipeline.named_steps['feature_extraction'].get_feature_names()\n",
    "\n",
    "for cluster_number in range(n_clusters):\n",
    "    print(\"Cluster {} contains {} samples\".format(cluster_number, c[cluster_number]))\n",
    "    print(\"  Most important terms\")\n",
    "    centroid = pipeline.named_steps['clusterer'].cluster_centers_[cluster_number]\n",
    "    most_important = centroid.argsort()\n",
    "    for i in range(5):\n",
    "        term_index = most_important[-(i+1)]\n",
    "        print(\"  {0}) {1} (score: {2:.4f})\".format(i+1, terms[term_index], centroid[term_index]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.031820420989154712"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "X = pipeline.named_steps['feature_extraction'].transform(documents)\n",
    "silhouette_score(X, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14327"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = pipeline.transform(documents) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=n_clusters)\n",
    "labels = km.fit_predict(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0 contains 89 samples\n",
      "Cluster 1 contains 65 samples\n",
      "Cluster 2 contains 7 samples\n",
      "Cluster 3 contains 24 samples\n",
      "Cluster 4 contains 290 samples\n",
      "Cluster 5 contains 15 samples\n"
     ]
    }
   ],
   "source": [
    "c = Counter(labels)\n",
    "for cluster_number in range(n_clusters):\n",
    "    print(\"Cluster {} contains {} samples\".format(cluster_number, c[cluster_number]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56564461613141714"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(Y, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(490, 6)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evidence Accumulation Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "\n",
    "def create_coassociation_matrix(labels):\n",
    "    rows = []\n",
    "    cols = []\n",
    "    unique_labels = set(labels)\n",
    "    for label in unique_labels:\n",
    "        indices = np.where(labels == label)[0]\n",
    "        for index1 in indices:\n",
    "            for index2 in indices:\n",
    "                rows.append(index1)\n",
    "                cols.append(index2)\n",
    "    data = np.ones((len(rows),))\n",
    "    return csr_matrix((data, (rows, cols)), dtype='float')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = create_coassociation_matrix(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<490x490 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 97096 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((490, 490), 240100)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C.shape, C.shape[0] * C.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4043981674302374"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(C.nonzero()[0]) / (C.shape[0] * C.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse.csgraph import minimum_spanning_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mst = minimum_spanning_tree(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<490x490 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 484 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\n",
    "                     ('clusterer', KMeans(n_clusters=3))\n",
    "                     ])\n",
    "pipeline.fit(documents)\n",
    "labels2 = pipeline.predict(documents)\n",
    "C2 = create_coassociation_matrix(labels2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1. ,  0.5,  0.5, ...,  0.5,  0.5,  0.5],\n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\n",
       "        ..., \n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ],\n",
       "        [ 0.5,  1. ,  1. , ...,  1. ,  1. ,  1. ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C_sum = (C + C2) / 2\n",
    "#C_sum.data = C_sum.data\n",
    "C_sum.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<490x490 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 489 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mst = minimum_spanning_tree(-C_sum)\n",
    "mst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<490x490 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 481 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mst.data[mst.data < 1] = 0\n",
    "mst.data[mst.data > -1] = 0\n",
    "mst.eliminate_zeros()\n",
    "mst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse.csgraph import connected_components\n",
    "number_of_clusters, labels = connected_components(mst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.base import BaseEstimator, ClusterMixin\n",
    "\n",
    "class EAC(BaseEstimator, ClusterMixin):\n",
    "    def __init__(self, n_clusterings=10, cut_threshold=0.5, n_clusters_range=(3, 10)):\n",
    "        self.n_clusterings = n_clusterings\n",
    "        self.cut_threshold = cut_threshold\n",
    "        self.n_clusters_range = n_clusters_range\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        C = sum((create_coassociation_matrix(self._single_clustering(X))\n",
    "                 for i in range(self.n_clusterings)))\n",
    "        mst = minimum_spanning_tree(-C)\n",
    "        mst.data[mst.data > -self.cut_threshold] = 0\n",
    "        mst.eliminate_zeros()\n",
    "        self.n_components, self.labels_ = connected_components(mst)\n",
    "        return self\n",
    "    \n",
    "    def _single_clustering(self, X):\n",
    "        n_clusters = np.random.randint(*self.n_clusters_range)\n",
    "        km = KMeans(n_clusters=n_clusters)\n",
    "        return km.fit_predict(X)\n",
    "    \n",
    "    def fit_predict(self, X):\n",
    "        self.fit(X)\n",
    "        return self.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([('feature_extraction', TfidfVectorizer(max_df=0.4)),\n",
    "                     ('clusterer', EAC())\n",
    "                     ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('feature_extraction', TfidfVectorizer(analyzer='word', binary=False, charset=None,\n",
       "        charset_error=None, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.4, max_features=None, min_df=1,\n",
       "        ngram_range=(...ocabulary=None)), ('clusterer', EAC(cut_threshold=0.5, n_clusterings=10, n_clusters_range=(3, 10)))])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pipeline.named_steps['clusterer'].labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Counter(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 490})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Online Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import MiniBatchKMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec = TfidfVectorizer(max_df=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vec.fit_transform(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "mbkm = MiniBatchKMeans(random_state=14, n_clusters=3)\n",
    "batch_size = 500\n",
    "\n",
    "indices = np.arange(0, X.shape[0])\n",
    "for iteration in range(100):\n",
    "    sample = np.random.choice(indices, size=batch_size, replace=True)\n",
    "    mbkm.partial_fit(X[sample[:batch_size]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "mbkm = MiniBatchKMeans(random_state=14, n_clusters=3)\n",
    "batch_size = 10\n",
    "\n",
    "for iteration in range(int(X.shape[0] / batch_size)):\n",
    "    start = batch_size * iteration\n",
    "    end = batch_size * (iteration + 1)\n",
    "    mbkm.partial_fit(X[start:end])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "401.6913550000382"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_mbkm = mbkm.predict(X)\n",
    "mbkm.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "392.6561949236311"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(random_state=14, n_clusters=3)\n",
    "labels_km = km.fit_predict(X)\n",
    "km.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import adjusted_mutual_info_score, homogeneity_score\n",
    "from sklearn.metrics import mutual_info_score, v_measure_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39531764243316064"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_measure_score(labels_mbkm, labels_km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(490, 14327)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 2, 1, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 1, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       2, 2, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 1, 0, 0, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 0, 2, 1, 0, 0, 2, 2, 1,\n",
       "       2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 0, 0, 2, 0, 2, 2, 0, 2,\n",
       "       2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2,\n",
       "       2, 1, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2,\n",
       "       2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 1, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 0, 0, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 0, 2, 1, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2,\n",
       "       0, 2, 0, 2, 2, 0, 2, 2, 2, 1, 2, 2, 0, 2, 2, 1, 2, 2, 0, 0, 2, 2, 0,\n",
       "       2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       0, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 0, 0, 1, 2, 0, 1, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 0, 0, 1, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2,\n",
       "       2, 0, 0, 0, 2, 2, 0], dtype=int32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_mbkm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import HashingVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PartialFitPipeline(Pipeline):\n",
    "    def partial_fit(self, X, y=None):\n",
    "        Xt = X\n",
    "        for name, transform in self.steps[:-1]:\n",
    "            Xt = transform.transform(Xt)\n",
    "        return self.steps[-1][1].partial_fit(Xt, y=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = PartialFitPipeline([('feature_extraction', HashingVectorizer()),\n",
    "                             ('clusterer', MiniBatchKMeans(random_state=14, n_clusters=3))\n",
    "                             ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 10\n",
    "\n",
    "for iteration in range(int(len(documents) / batch_size)):\n",
    "    start = batch_size * iteration\n",
    "    end = batch_size * (iteration + 1)\n",
    "    pipeline.partial_fit(documents[start:end])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 2, 0, 1, 0, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 0, 2, 0, 2, 2, 1, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 0, 2, 1, 2, 2, 2, 1, 2,\n",
       "       2, 2, 2, 1, 1, 1, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 1, 0, 0, 2,\n",
       "       1, 2, 2, 2, 1, 0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 2, 2, 0, 2, 2, 0,\n",
       "       0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 1, 0, 0, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1,\n",
       "       2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 0, 0, 0, 1, 0, 2,\n",
       "       1, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 0, 2, 0, 2, 2, 2,\n",
       "       0, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 0,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 2, 1, 0, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\n",
       "       2, 2, 2, 1, 2, 2, 2, 0, 0, 0, 2, 0, 2, 2, 0, 0, 2, 1, 2, 2, 2, 2, 2,\n",
       "       0, 2, 0, 2, 0, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 0, 2, 2, 2, 2, 1, 0, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2,\n",
       "       2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 2, 2, 2, 2, 2], dtype=int32)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = pipeline.predict(documents)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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