05_02_feature_extraction.ipynb

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   "source": [
    "import urllib.request\n",
    "# データのダウンロード\n",
    "urllib.request.urlretrieve(\"http://www.gutenberg.org/files/11/11-0.txt\", \"allice.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "with open('allice.txt', 'r', encoding='UTF-8') as f:\n",
    "    print(f.read()[660:1400])"
   ]
  },
  {
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   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "end_time": "2017-06-22T08:23:34.045111Z",
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   "source": [
    "txt_vec = CountVectorizer(input='filename')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": [
    "txt_vec.fit(['allice.txt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "scrolled": true
   },
   "outputs": [],
   "source": [
    "txt_vec.get_feature_names_out()[100:120]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "scrolled": false
   },
   "outputs": [],
   "source": [
    "len(txt_vec.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "allice_vec = txt_vec.transform(['allice.txt'])"
   ]
  },
  {
   "cell_type": "code",
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     "start_time": "2017-06-22T08:23:34.150929Z"
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   },
   "outputs": [],
   "source": [
    "allice_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "end_time": "2017-06-22T08:23:34.159154Z",
     "start_time": "2017-06-22T08:23:34.155883Z"
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   },
   "outputs": [],
   "source": [
    "allice_vec.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "end_time": "2017-06-22T08:23:34.163863Z",
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   },
   "outputs": [],
   "source": [
    "allice_vec = allice_vec.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "end_time": "2017-06-22T08:23:34.168945Z",
     "start_time": "2017-06-22T08:23:34.164975Z"
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   },
   "outputs": [],
   "source": [
    "allice_vec[0, 100:120]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "end_time": "2017-06-22T08:23:34.180421Z",
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    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for word,count in zip(txt_vec.get_feature_names_out()[100:120], allice_vec[0, 100:120]):\n",
    "    print(word, count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "end_time": "2017-06-22T08:23:34.212589Z",
     "start_time": "2017-06-22T08:23:34.181860Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_sample_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-22T08:23:34.352055Z",
     "start_time": "2017-06-22T08:23:34.213911Z"
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   },
   "outputs": [],
   "source": [
    "china = load_sample_image('china.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     "end_time": "2017-06-22T08:23:34.711763Z",
     "start_time": "2017-06-22T08:23:34.353006Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.imshow(china);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-22T08:23:34.716119Z",
     "start_time": "2017-06-22T08:23:34.713111Z"
    }
   },
   "outputs": [],
   "source": [
    "china.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-22T08:23:35.107165Z",
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   },
   "outputs": [],
   "source": [
    "histR = plt.hist(china[:,:,0].ravel(), bins=10)\n",
    "plt.show()\n",
    "histG = plt.hist(china[:,:,1].ravel(), bins=10)\n",
    "plt.show()\n",
    "histB = plt.hist(china[:,:,2].ravel(), bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-22T08:23:35.111297Z",
     "start_time": "2017-06-22T08:23:35.108356Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "histRGBcat = np.hstack((histR[0], histG[0], histB[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-22T08:23:35.260958Z",
     "start_time": "2017-06-22T08:23:35.113045Z"
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   },
   "outputs": [],
   "source": [
    "plt.bar(range(len(histRGBcat)), histRGBcat);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-06-22T08:23:35.416171Z",
     "start_time": "2017-06-22T08:23:35.262669Z"
    }
   },
   "outputs": [],
   "source": [
    "histRGBcat_l1 = histRGBcat / (china.shape[0]*china.shape[1])\n",
    "plt.bar(range(len(histRGBcat_l1)), histRGBcat_l1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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