experiments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spectral relaxation for K-means clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nikita Petrashen, Daniil Svirskiy, Nikita Drobyshev, Dinar Sharafutdinov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:31:15.264477Z",
     "start_time": "2019-12-21T15:31:15.252486Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "YHF36exWngeW",
    "outputId": "f0c10896-2ae6-49f1-db3a-b64ad603fe7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.rc_context at 0x1520d2435c0>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics.cluster import v_measure_score, homogeneity_completeness_v_measure\n",
    "from sklearn.datasets import fetch_20newsgroups, make_blobs\n",
    "from sklearn.datasets.base import get_data_home\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.cluster import SpectralClustering, KMeans, MiniBatchKMeans\n",
    "import matplotlib.pyplot as plt\n",
    "plt.xkcd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rvyIOhhUw_ta"
   },
   "source": [
    "### 20newsgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:52:21.112825Z",
     "start_time": "2019-12-21T14:52:20.132388Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "nJO7AeFEnsqb",
    "outputId": "381e81fb-1bf3-4127-e523-09268717425c"
   },
   "outputs": [],
   "source": [
    "dataset = fetch_20newsgroups(subset='all', shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:52:28.523589Z",
     "start_time": "2019-12-21T14:52:28.520593Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "d12thDtWnsvn"
   },
   "outputs": [],
   "source": [
    "labels = dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:52:29.889809Z",
     "start_time": "2019-12-21T14:52:29.879815Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "yL5E35RUsnbS",
    "outputId": "ae93d100-2166-4ed7-a744-c69ac60fd6dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18846,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:52:31.149090Z",
     "start_time": "2019-12-21T14:52:31.098117Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "S5owDm7_s0tQ",
    "outputId": "f18dbbfd-5007-456c-c70a-6c0840c40c25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(labels).shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8aYpcQzRA3ws"
   },
   "outputs": [],
   "source": [
    "['alt.atheism',\n",
    " 'comp.graphics',\n",
    " 'comp.os.ms-windows.misc',\n",
    " 'comp.sys.ibm.pc.hardware',\n",
    " 'comp.sys.mac.hardware',\n",
    " 'comp.windows.x',\n",
    " 'misc.forsale',\n",
    " 'rec.autos',\n",
    " 'rec.motorcycles',\n",
    " 'rec.sport.baseball',\n",
    " 'rec.sport.hockey',\n",
    " 'sci.crypt',\n",
    " 'sci.electronics',\n",
    " 'sci.med',\n",
    " 'sci.space',\n",
    " 'soc.religion.christian',\n",
    " 'talk.politics.guns',\n",
    " 'talk.politics.mideast',\n",
    " 'talk.politics.misc',\n",
    " 'talk.religion.misc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:54:42.988753Z",
     "start_time": "2019-12-21T14:54:42.549005Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "IL-BO8vLYzdA"
   },
   "outputs": [],
   "source": [
    "categories = ['alt.atheism','comp.graphics']\n",
    "dataset2 = fetch_20newsgroups(subset='all',\n",
    "                             shuffle=True, random_state=42, categories = categories)\n",
    "labels = dataset2.target\n",
    "texts = dataset2.data\n",
    "target = dataset2.target\n",
    "number_of_clusters = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:54:43.099689Z",
     "start_time": "2019-12-21T14:54:43.090693Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(labels).shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:54:43.563423Z",
     "start_time": "2019-12-21T14:54:43.555428Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "LsOFblOcJnNr",
    "outputId": "daebf4d3-1e74-45cd-c7ed-92a4044e5267"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1772, 1772)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(texts), len(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:54:45.602258Z",
     "start_time": "2019-12-21T14:54:45.596263Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "CEjkUJpNtKVh",
    "outputId": "647432da-b74e-4ee9-e970-91efb01f17d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"From: jliddle@rs6000.cmp.ilstu.edu (Jean Liddle)\\nSubject: Re: HELP: Need 24 bits viewer\\nOrganization: Illinois State University\\nKeywords: 24 bit\\nLines: 23\\n\\nIn article <1993Apr29.041601.8884@labtam.labtam.oz.au> graeme@labtam.labtam.oz.a\\nu (Graeme Gill) writes:\\n>In article <5713@seti.inria.fr>, deniaud@cartoon.inria.fr (Gilles Deniaud) writ\\nes:\\n>> Hi,\\n>>\\n>> I'm looking for a program which is able to display 24 bits\\n>> images. We are using a Sun Sparc equipped with Parallax\\n>> graphics board running X11.\\n>\\n>    xli, xloadimage or ImageMagick - export.lcs.mit.edu [18.24.0.12] /contrib\\n>\\n\\nxv 3.0 (shareware) supports 24-bit displays, and has lots of other\\nimprovements over earlier versions.  Definitely worth checking out\\n(also at export)\\n\\nJean.\\n-- \\nJean Liddle                                 \\nComputer Science, Illinois State University  \\ne-mail:  jliddle@ilstu.edu                  \\n--------------------------------------------\\n\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:54:52.064568Z",
     "start_time": "2019-12-21T14:54:51.044149Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "GSQUwHUUtKgw"
   },
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(stop_words='english')\n",
    "X = vectorizer.fit_transform(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:54:52.132528Z",
     "start_time": "2019-12-21T14:54:52.125530Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "Zy0nDnnYt63k",
    "outputId": "32f6bc67-d061-4dc8-d0e4-94ace85b9945"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1772, 27756)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:55:21.145947Z",
     "start_time": "2019-12-21T14:55:16.540578Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "GceZY3aauz_B",
    "outputId": "3850140e-7c51-4db1-a0cb-952286a7550b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=100,\n",
       "    n_clusters=2, n_init=1, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KMeans(n_clusters=number_of_clusters, \n",
    "               init='k-means++', \n",
    "               max_iter=100, \n",
    "               n_init=1)\n",
    "\n",
    "model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:55:23.061855Z",
     "start_time": "2019-12-21T14:55:23.054857Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "Kr508fV4KL0a",
    "outputId": "69a93509-d2da-433a-e92c-08aab7c86dd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1772, 27756)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:55:42.609684Z",
     "start_time": "2019-12-21T14:55:42.604685Z"
    }
   },
   "outputs": [],
   "source": [
    "random_state = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:56:30.171056Z",
     "start_time": "2019-12-21T14:55:43.248318Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "29h4_4TBJ7_V"
   },
   "outputs": [],
   "source": [
    "y_pred_texts = KMeans(n_clusters=number_of_clusters, random_state=random_state).fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:56:36.268573Z",
     "start_time": "2019-12-21T14:56:36.055696Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "8SxUKTTCZlYZ",
    "outputId": "fefda6dd-ec4c-470a-d8d8-20f5c8199876"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7772500533604567"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_measure_score(target, y_pred_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:56:54.366233Z",
     "start_time": "2019-12-21T14:56:54.355238Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "MiSAQ19vPZ9b",
    "outputId": "18befbfc-5b96-4552-e7c8-601b529f6f93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7829561746289551"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.completeness_score(target, y_pred_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:56:56.638932Z",
     "start_time": "2019-12-21T14:56:56.368088Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "59rUtnfrPoyT",
    "outputId": "c392d089-2f90-4e7e-f60d-624a7c94fcda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8789917637221477"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.silhouette_score(target.reshape(-1,1), y_pred_texts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lEW3HKxJ5thv"
   },
   "source": [
    "### Test K-means for blobls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:57:22.438190Z",
     "start_time": "2019-12-21T14:57:21.370801Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 464
    },
    "colab_type": "code",
    "id": "l5ma53bA5qjK",
    "outputId": "b4131389-b785-47c9-fd38-2e8a10aa7bbb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1520d0dc9b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 15))\n",
    "\n",
    "n_clusters = 3\n",
    "n_samples = 1500\n",
    "X, y = make_blobs(n_samples=n_samples, random_state=random_state, n_features=6)\n",
    "y_pred = KMeans(n_clusters=n_clusters, random_state=random_state).fit_predict(X)\n",
    "\n",
    "plt.xkcd()\n",
    "plt.subplot(221)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:57:24.361092Z",
     "start_time": "2019-12-21T14:57:24.352096Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "gYTyGzrk5rC_",
    "outputId": "b17dd29a-ca24-411f-e13e-85cf3d0aa528"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 6)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:57:25.619373Z",
     "start_time": "2019-12-21T14:57:25.613376Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "aM2TypAy5q_2",
    "outputId": "7461c9f7-1a5f-421e-f09f-f1dbded07fe8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 1, 1, 2])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:57:28.091960Z",
     "start_time": "2019-12-21T14:57:28.081967Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "y5yc1lGZ71j0",
    "outputId": "740f054b-9e20-4e07-a593-d746d20cb521"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_measure_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:57:34.353383Z",
     "start_time": "2019-12-21T14:57:34.349385Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ibeZDx6nGRgS"
   },
   "outputs": [],
   "source": [
    "y_pr = y_pred\n",
    "y_pr[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:57:35.254866Z",
     "start_time": "2019-12-21T14:57:35.243873Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "RBRaC1OEGY-Z",
    "outputId": "9321f771-1d38-4ddb-9c90-9d0cf3c2947a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9956219848839059"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_measure_score(y, y_pr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qrcGCknQGvfL"
   },
   "source": [
    "### p-K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:58:45.946541Z",
     "start_time": "2019-12-21T14:58:45.927549Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "QQC-XC4OG3kx"
   },
   "outputs": [],
   "source": [
    "A = X.T\n",
    "AA = A.T @ A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:58:46.806049Z",
     "start_time": "2019-12-21T14:58:46.799052Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "pQMEn2AOG9oj",
    "outputId": "969b6a7e-acc7-405b-8c70-55f774d854c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 1500)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AA.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:58:51.456392Z",
     "start_time": "2019-12-21T14:58:51.451392Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "colab_type": "code",
    "id": "bJcxKQ2rb5pX",
    "outputId": "bec2d6a1-61b4-46d6-b945-0f834d8e4f82"
   },
   "outputs": [],
   "source": [
    "AA;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:58:59.901563Z",
     "start_time": "2019-12-21T14:58:53.924982Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "IwhdsEe8HDoX"
   },
   "outputs": [],
   "source": [
    "val, vect = np.linalg.eig(AA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:58:59.914559Z",
     "start_time": "2019-12-21T14:58:59.905563Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "_cpe3hshHU2W",
    "outputId": "1a86e2a7-2470-49e6-980a-a68bd28af0c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 1500)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:59:48.501793Z",
     "start_time": "2019-12-21T14:59:48.492797Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "njgi9KjI26xW",
    "outputId": "8e0dc0be-907d-4f33-b368-fe7965f29c0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.27763158e+05, 8.19542684e+04, 5.15776274e+04, 1.67292841e+03,\n",
       "       1.52508629e+03, 1.48771492e+03, 1.88979682e-11, 1.88979682e-11,\n",
       "       1.72362728e-11, 1.72362728e-11])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.flip(np.sort(np.abs(val)))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:59:52.977238Z",
     "start_time": "2019-12-21T14:59:52.973239Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ncvey83vHjog"
   },
   "outputs": [],
   "source": [
    "x_m_vect = np.argsort(val)[-n_clusters:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T14:59:53.856735Z",
     "start_time": "2019-12-21T14:59:53.849738Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "IBWBTaLUH0Qs",
    "outputId": "4e2c7b41-84cf-4036-f1bc-297793334a2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_X = vect[:, x_m_vect]\n",
    "new_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:00:01.901137Z",
     "start_time": "2019-12-21T15:00:01.895140Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "colab_type": "code",
    "id": "vOFIutFVKbHM",
    "outputId": "2e172c55-2b8e-4169-c894-0d0f5a65152c"
   },
   "outputs": [],
   "source": [
    "new_X = np.abs(new_X)\n",
    "new_X;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:00:05.622009Z",
     "start_time": "2019-12-21T15:00:05.539057Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "A3c9-F0AICUR"
   },
   "outputs": [],
   "source": [
    "y_pred2 = KMeans(n_clusters=n_clusters, random_state=random_state).fit_predict(new_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:00:06.358589Z",
     "start_time": "2019-12-21T15:00:06.348594Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "AROVRHytJTTa",
    "outputId": "a8edaa52-9e3d-4423-9169-8f590410eab9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8782105236855258"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_measure_score(y, y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:00:13.968241Z",
     "start_time": "2019-12-21T15:00:13.388572Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "colab_type": "code",
    "id": "EGO1eBRXeRJp",
    "outputId": "df61223b-4558-4e7f-fcc0-b294ff75e3fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1520d0e5da0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 9))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For different amount of n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:01:24.819753Z",
     "start_time": "2019-12-21T15:00:45.411277Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "9TDUjwTCNaLU"
   },
   "outputs": [],
   "source": [
    "scores = []\n",
    "n_f = []\n",
    "for i in range(1, 25, 4):\n",
    "  n_f.append(i)\n",
    "  X, y = make_blobs(n_samples=n_samples, random_state=random_state, n_features=i)\n",
    "  A = X.T\n",
    "  AA = A.T @ A\n",
    "  val, vect = np.linalg.eig(AA)\n",
    "  x_m_vect = np.argsort(val)[-n_clusters:]\n",
    "  new_X = vect[:, x_m_vect]\n",
    "  new_X = np.abs(new_X)\n",
    "  y_pred2 = KMeans(n_clusters=n_clusters, random_state=random_state).fit_predict(new_X)\n",
    "  scores.append(v_measure_score(y, y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:01:24.834745Z",
     "start_time": "2019-12-21T15:01:24.822752Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "id": "Zn7yrfd998Wx",
    "outputId": "d6b4524b-a94b-4633-e571-6ff153164804"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.30666099734759816,\n",
       " 0.8096210505828637,\n",
       " 0.8522458585682574,\n",
       " 0.8170002256131205,\n",
       " 1.0,\n",
       " 1.0]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:01:24.946682Z",
     "start_time": "2019-12-21T15:01:24.839742Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "JjimCfO3-91k",
    "outputId": "1cb24d1e-88ff-4ee6-ec6f-f4da0960b78b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 5, 9, 13, 17, 21]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:01:46.924123Z",
     "start_time": "2019-12-21T15:01:46.203534Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 641
    },
    "colab_type": "code",
    "id": "JwiAavtM9--d",
    "outputId": "c57c31e6-6c74-4301-8ae4-d1089d808c0a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'number of features')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(n_f, scores, linewidth=2.5)\n",
    "plt.ylabel(r\"accuracy\", fontsize=20)\n",
    "plt.xlabel(r\"number of features\", fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YLwJCextILRZ"
   },
   "source": [
    "We see that when the number of features becomes substantially bigger than the number of clusters, then the algorithm works well at least with synthetic data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e05agYUUINQv"
   },
   "source": [
    "### Let's use p-kmeans for 20newsgroups dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:29:49.435425Z",
     "start_time": "2019-12-21T15:29:47.612467Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ZIdvSMY7Z_nC"
   },
   "outputs": [],
   "source": [
    "categories =['rec.motorcycles', 'rec.sport.baseball']\n",
    "dataset2 = fetch_20newsgroups(subset='all',\n",
    "                             shuffle=True, random_state=random_state, categories = categories)\n",
    "texts = dataset2.data\n",
    "target = dataset2.target\n",
    "number_of_clusters = 2\n",
    "\n",
    "vectorizer = TfidfVectorizer(stop_words='english', max_features=10000, norm = 'l2', max_df = 0.1, min_df = 6)\n",
    "X = vectorizer.fit_transform(texts).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:29:49.447419Z",
     "start_time": "2019-12-21T15:29:49.439422Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "qMb0fTIm3HLL",
    "outputId": "a589d712-ce47-4037-d701-bef63641ff69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1990, 5610)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:30:04.599760Z",
     "start_time": "2019-12-21T15:29:49.451417Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "F5t2W1f1ILu9",
    "outputId": "4a2d8e29-fa18-47f9-9dfd-79abce9cdeb6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02819917858609398"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = X.T\n",
    "AA = A.T @ A\n",
    "val, vect = np.linalg.eig(AA)\n",
    "x_m_vect = np.argsort(val)[-number_of_clusters:]\n",
    "new_X = vect[:, x_m_vect]\n",
    "new_X = np.abs(new_X)\n",
    "y_pred2 = KMeans(n_clusters=number_of_clusters, random_state=42).fit_predict(new_X)\n",
    "v_measure_score(target, y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:30:04.618749Z",
     "start_time": "2019-12-21T15:30:04.603758Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "yLldsKL9v5De",
    "outputId": "680bc655-8728-48ff-d8bc-a48b4b12ea3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.015732793541401797, 0.13582252653917729, 0.02819917858609398)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "homogeneity_completeness_v_measure(target, y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By some reason p-k-means does not work with vectorised textual data, it seems that problem in vectorisation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:29:42.328487Z",
     "start_time": "2019-12-21T15:29:27.688853Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "colab_type": "code",
    "id": "PGDTkz0NsA-Z",
    "outputId": "37a7db89-e577-4f86-9cc8-51e391b11b18"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8728259418353201"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_texts = KMeans(n_clusters=number_of_clusters, random_state=42).fit_predict(X)\n",
    "v_measure_score(target, y_pred_texts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ms_9USMBigHO"
   },
   "source": [
    "### p-QR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:15:24.819525Z",
     "start_time": "2019-12-21T15:15:24.803535Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "GTeKFAfdihXs"
   },
   "outputs": [],
   "source": [
    "def modified_gram_schmidt_qr(A):\n",
    "    m = A.shape[0]\n",
    "    n = A.shape[1]\n",
    "    Q = A[:m, :min(m,n)].copy()\n",
    "    R = np.zeros((min(m,n), n))\n",
    "    Q[:, 0] = A[:, 0] / np.linalg.norm(A[:, 0])\n",
    "    for i in range(min(m, n)):\n",
    "        for j in range(i):\n",
    "            Q[:, i] -= proj(Q[:, i], Q[:, j])      \n",
    "        Q[:, i] = Q[:, i] / np.linalg.norm(Q[:, i])\n",
    "        for k in range(i, n):\n",
    "            R[i, k] = np.dot(Q[:, i], A[:, k])\n",
    "            \n",
    "    return Q, R\n",
    "\n",
    "def proj(a, e):\n",
    "    return e * np.dot(a, e)\n",
    "\n",
    "def pivoted_QR(A):\n",
    "    k = A.shape[0]\n",
    "    n = A.shape[1]\n",
    "    Q = np.zeros((k, k))\n",
    "    R = np.zeros((k, n))\n",
    "    B = A.copy()\n",
    "    P = np.arange(n)\n",
    "    for j in range(k):\n",
    "        norms = np.linalg.norm(B[:, j:], axis=0)\n",
    "        j_max = np.argmax(norms) + j\n",
    "        P[[j, j_max]] = P[[j_max, j]]\n",
    "        B[:, [j, j_max]] = B[:, [j_max, j]]\n",
    "        R[:, [j, j_max]] = R[:, [j_max, j]]\n",
    "        Q[:, j] = B[:, j] / np.linalg.norm(B[:, j])\n",
    "    \n",
    "        R[j, j] = np.linalg.norm(B[:, j])\n",
    "        \n",
    "        for i in range(j+1, n):\n",
    "            R[j, i] = np.dot(Q[:, j], B[:, i])\n",
    "        \n",
    "        for i in range(j+1, n):\n",
    "            B[:, i] -= proj(B[: , i], Q[:, j])\n",
    "            \n",
    "    return P, Q, R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:15:26.556533Z",
     "start_time": "2019-12-21T15:15:26.547538Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "-LUEa-AFimD_"
   },
   "outputs": [],
   "source": [
    "def PQR_clustering(A, k):\n",
    "    ATA = A.T@A\n",
    "    vals, vecs = np.linalg.eigh(ATA)\n",
    "    indices = np.argsort(vals)[-k: ]\n",
    "    X = vecs[:, indices]\n",
    "    P, Q, R = pivoted_QR(X.T)\n",
    "    PT = np.argsort(P)\n",
    "    R_hat = (np.linalg.inv(R[:, :k]) @ R)[:, PT]\n",
    "    labels = np.argmax(np.abs(R_hat), axis=0)\n",
    "    \n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:16:51.093262Z",
     "start_time": "2019-12-21T15:16:49.715049Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "a2GF-BpCinTk",
    "outputId": "da80c0b9-cba1-4e48-d477-0685efd23530"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = make_blobs(n_samples=n_samples, random_state=random_state, n_features=6)\n",
    "A = X.T\n",
    "number_of_clusters = 3\n",
    "y_pred_qr = PQR_clustering(A, number_of_clusters)\n",
    "v_measure_score(y, y_pred_qr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For different amount of n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:17:10.289291Z",
     "start_time": "2019-12-21T15:16:58.976759Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ze1RHkMRjHe9"
   },
   "outputs": [],
   "source": [
    "scores = []\n",
    "n_f = []\n",
    "for i in range(1, 25, 4):\n",
    "  n_f.append(i)\n",
    "  X, y = make_blobs(n_samples=n_samples, random_state=random_state, n_features=i)\n",
    "  A = X.T\n",
    "  number_of_clusters = 3\n",
    "  y_pred_qr = PQR_clustering(A, number_of_clusters)\n",
    "  scores.append(v_measure_score(y, y_pred_qr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:17:24.564135Z",
     "start_time": "2019-12-21T15:17:23.801570Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 641
    },
    "colab_type": "code",
    "id": "LMtMu8irjraw",
    "outputId": "ccc74c77-3dcb-413c-bab1-bb6a855b7c69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'number of features')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.plot(n_f, scores, linewidth=2.5)\n",
    "plt.ylabel(r\"accuracy\", fontsize=20)\n",
    "plt.xlabel(r\"number of features\", fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EO1-t1K5BHCc"
   },
   "source": [
    "### Timing check for three methods (blobs data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "eZHSvT5kBso7",
    "outputId": "9fa81e5d-1a67-46ca-fd31-b21cb82d82ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 14.5 ms per loop\n",
      "10 loops, best of 3: 25.5 ms per loop\n",
      "10 loops, best of 3: 46.9 ms per loop\n"
     ]
    }
   ],
   "source": [
    "number_of_clusters = 3\n",
    "\n",
    "for i in [100, 1000, 5000]:\n",
    "  X, y = make_blobs(n_samples=i, random_state=42, n_features=10)\n",
    "  %timeit y_pred = KMeans(n_clusters=number_of_clusters, random_state=42).fit_predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p-Kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "-YgGvwzwBpii",
    "outputId": "8223bf53-faa1-438b-f454-cf631a18f400"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 36.1 ms per loop\n",
      "1 loop, best of 3: 1.46 s per loop\n",
      "1 loop, best of 3: 1min 8s per loop\n"
     ]
    }
   ],
   "source": [
    "def p_kmeans(X, number_of_clusters):\n",
    "  A = X.T\n",
    "  AA = A.T @ A\n",
    "  val, vect = np.linalg.eig(AA)\n",
    "  x_m_vect = np.argsort(val)[-number_of_clusters:]\n",
    "  new_X = vect[:, x_m_vect]\n",
    "  new_X = np.abs(new_X)\n",
    "  y_pred2 = KMeans(n_clusters=number_of_clusters, random_state=42).fit_predict(new_X)\n",
    "  return(y_pred2)\n",
    "\n",
    "for i in [100, 1000, 5000]:\n",
    "  X, y = make_blobs(n_samples=i, random_state=42, n_features=10)\n",
    "  %timeit y_pred2 = p_kmeans(X, number_of_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p-QR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "E8RR6XPnBGan",
    "outputId": "7049a728-c0c0-45d5-95a8-c941d70eac1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 6.94 ms per loop\n",
      "1 loop, best of 3: 206 ms per loop\n",
      "1 loop, best of 3: 14.7 s per loop\n"
     ]
    }
   ],
   "source": [
    "for i in [100, 1000, 5000]:\n",
    "  X, y = make_blobs(n_samples=i, random_state=42, n_features=10)\n",
    "  A = X.T\n",
    "  %timeit y_pred_qr = PQR_clustering(A, number_of_clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Dqt9XLivj2RW"
   },
   "source": [
    "### p-QR for 20newsgroups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number_of_clusters = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:22:58.568787Z",
     "start_time": "2019-12-21T15:22:57.337495Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "CqrXEKKKuDJn"
   },
   "outputs": [],
   "source": [
    "categories =['rec.motorcycles','rec.sport.baseball']\n",
    "dataset2 = fetch_20newsgroups(subset='all',\n",
    "                             shuffle=True, random_state=random_state, categories = categories)\n",
    "texts = dataset2.data\n",
    "target = dataset2.target\n",
    "number_of_clusters = 2\n",
    "\n",
    "vectorizer = TfidfVectorizer(stop_words='english', max_features=1000, norm = 'l2', max_df = 0.3, min_df = 8) # k means лучше на 5% с 10000 чем с 1000\n",
    "X = vectorizer.fit_transform(texts).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:23:02.059794Z",
     "start_time": "2019-12-21T15:22:59.120475Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "vcUeEyqJkCBy",
    "outputId": "57053983-d5ad-4e46-d8b5-75dd53c02d90"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7708731846509155"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = X.T\n",
    "y_pred_qr = PQR_clustering(A, number_of_clusters)\n",
    "v_measure_score(target, y_pred_qr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:23:04.876185Z",
     "start_time": "2019-12-21T15:23:02.752403Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "7Fd4ZYXLkop0",
    "outputId": "52fc148a-f1b5-4d4b-fd35-2d8a865851e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.808339951305998"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_texts = KMeans(n_clusters=number_of_clusters, random_state=42).fit_predict(X)\n",
    "v_measure_score(target, y_pred_texts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number_of_clusters = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:24:22.460851Z",
     "start_time": "2019-12-21T15:23:53.283525Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "DSoO7ectve-W",
    "outputId": "cd99f1f7-0914-4e84-b0b0-12ec1d86c4fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7547051246420183"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories =['rec.motorcycles', 'rec.sport.baseball', 'alt.atheism', 'comp.graphics']\n",
    "dataset2 = fetch_20newsgroups(subset='all',\n",
    "                             shuffle=True, random_state=random_state, categories = categories)\n",
    "texts = dataset2.data\n",
    "target = dataset2.target\n",
    "number_of_clusters = 4\n",
    "\n",
    "vectorizer = TfidfVectorizer(stop_words='english', norm = 'l2', max_df = 0.3, min_df = 8)\n",
    "X = vectorizer.fit_transform(texts).toarray()\n",
    "A = X.T\n",
    "y_pred_qr = PQR_clustering(A, number_of_clusters)\n",
    "v_measure_score(target, y_pred_qr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:25:01.433582Z",
     "start_time": "2019-12-21T15:24:22.465848Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "dWK0HC4QvyA-",
    "outputId": "65388c84-99d5-4c2c-d721-cf55cf15632b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6734266363809611"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_texts = KMeans(n_clusters=number_of_clusters, random_state=42).fit_predict(X)\n",
    "v_measure_score(target, y_pred_texts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number_of_clusters = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "qb6_w7_Iwyth",
    "outputId": "0ef22150-9cc7-4745-933b-3c43bc5a7b19"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38067964973580287"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2 = fetch_20newsgroups(subset='all',\n",
    "                             shuffle=True, random_state=42)\n",
    "texts = dataset2.data\n",
    "target = dataset2.target\n",
    "number_of_clusters = 20\n",
    "\n",
    "vectorizer = TfidfVectorizer(stop_words='english', norm = 'l2', max_df = 0.3, min_df = 8)\n",
    "X = vectorizer.fit_transform(texts).toarray()\n",
    "\n",
    "A = X.T\n",
    "y_pred_qr = PQR_clustering(A, number_of_clusters)\n",
    "v_measure_score(target, y_pred_qr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "UJ2zY5qDyAmY",
    "outputId": "09b2109d-7b6d-49d1-b3d2-0cf1dd498b2b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3533243402768801"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_texts = KMeans(n_clusters=number_of_clusters, random_state=42).fit_predict(X)\n",
    "v_measure_score(target, y_pred_texts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-21T15:26:05.596333Z",
     "start_time": "2019-12-21T15:26:05.589338Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "7tR32qtNyIU7"
   },
   "source": [
    "We see that p-QR is more stable at shows better results for a bigger amount of clusters."
   ]
  }
 ],
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