4.2a-nclasses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.2-a How does the number of classes affect accuracy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/atila.alr/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import datetime\n",
    "import random\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import OrderedDict\n",
    "\n",
    "import models\n",
    "import report\n",
    "from dataset import Dataset\n",
    "from report import Reporter\n",
    "from trainer import Trainer\n",
    "from batch_encoder import Dataset\n",
    "\n",
    "from tensorflow.keras.layers import Input, Conv1D, MaxPooling1D, LSTM, Dense, Activation, TimeDistributed, Flatten, Dot, Softmax, Lambda, RepeatVector, Multiply, Permute, Reshape, BatchNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1.14.0', '2.2.4-tf')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__, tf.keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.is_gpu_available(), tf.test.is_built_with_cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_dataset_folder='govdocs1/sample200'\n",
    "minimum=200\n",
    "maximum=200\n",
    "result_dir = 'results/4.2a-nclasses'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawset = Dataset.new_from_folders(raw_dataset_folder)\n",
    "rawset = rawset.filter(lambda x: rawset.category_from(x) not in ['text', 'unk'])\n",
    "rawset = rawset.filter_min_max(200, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CLD(classes, len_byte_vector):\n",
    "    last = l0 = Input(shape=(512,len_byte_vector))\n",
    "    last = Conv1D(256, (16,), strides=16)(last)\n",
    "    last = LSTM(128)(last)\n",
    "    last = Dense(classes)(last)\n",
    "    last = Activation('softmax')(last)\n",
    "    name = sys._getframe().f_code.co_name\n",
    "    model = tf.keras.Model([l0], last, name=name)\n",
    "    model.compile(loss=tf.keras.losses.categorical_crossentropy,\n",
    "        optimizer=tf.keras.optimizers.Adam(),\n",
    "        metrics=['binary_accuracy', 'categorical_accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_nclasses(rawset, reporter, iterations=1):\n",
    "    for it in range(iterations):\n",
    "        for n in range(2, len(rawset.categories)+1):\n",
    "            cats = random.sample(rawset.by_category().keys(), n)\n",
    "            print(n, cats)\n",
    "            myset = rawset.filter(lambda x: rawset.category_from(x) in cats)\n",
    "            myset.rebuild_categories()\n",
    "            tset, vset = myset.rnd_split_fraction_by_category(0.5)\n",
    "\n",
    "            model = CLD(n, 256)\n",
    "            result = Trainer(\n",
    "                model,\n",
    "                xs_encoder='one_hot',\n",
    "                batch_size=100,\n",
    "                steps_per_epoch=28,\n",
    "                validation_steps=28,\n",
    "                patience=10,\n",
    "            ).train(tset, vset)\n",
    "            r.line(n=n,\n",
    "                cats=','.join(cats),\n",
    "                val_categorical_accuracy=result.history.history['val_categorical_accuracy'][-1],\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 ['f', 'wp']\n",
      "WARNING:tensorflow:From /home/atila.alr/.local/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From /home/atila.alr/.local/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "3 ['rtf', 'pps', 'csv']\n",
      "4 ['pptx', 'csv', 'dwf', 'sql']\n",
      "5 ['log', 'kmz', 'wp', 'dbase3', 'hlp']\n",
      "6 ['jpg', 'swf', 'f', 'xls', 'dbase3', 'wp']\n",
      "7 ['sql', 'ps', 'f', 'txt', 'dbase3', 'gif', 'wp']\n",
      "8 ['dwf', 'doc', 'swf', 'dbase3', 'sql', 'gif', 'csv', 'hlp']\n",
      "9 ['pdf', 'eps', 'java', 'html', 'ppt', 'csv', 'xml', 'swf', 'wp']\n",
      "10 ['gz', 'xls', 'kmz', 'sql', 'doc', 'pps', 'pptx', 'hlp', 'rtf', 'f']\n",
      "11 ['ps', 'wp', 'jpg', 'ppt', 'rtf', 'dwf', 'hlp', 'csv', 'kmz', 'pps', 'java']\n",
      "12 ['sql', 'xls', 'f', 'xml', 'pptx', 'log', 'png', 'kmz', 'gz', 'swf', 'pps', 'gif']\n",
      "13 ['png', 'pptx', 'csv', 'dwf', 'pdf', 'swf', 'txt', 'xml', 'pps', 'doc', 'wp', 'ps', 'ppt']\n",
      "14 ['dwf', 'csv', 'dbase3', 'jpg', 'png', 'f', 'wp', 'xls', 'gz', 'sql', 'eps', 'pps', 'html', 'ps']\n",
      "15 ['f', 'dbase3', 'sql', 'doc', 'kmz', 'wp', 'html', 'ppt', 'ps', 'txt', 'java', 'jpg', 'xls', 'gif', 'pps']\n",
      "16 ['dbase3', 'png', 'pdf', 'html', 'java', 'jpg', 'sql', 'doc', 'rtf', 'gz', 'swf', 'dwf', 'hlp', 'gif', 'pptx', 'xls']\n",
      "17 ['dwf', 'wp', 'java', 'gif', 'kmz', 'pps', 'gz', 'kml', 'eps', 'png', 'csv', 'rtf', 'f', 'html', 'ppt', 'log', 'xml']\n",
      "18 ['jpg', 'java', 'sql', 'png', 'kmz', 'xml', 'dbase3', 'log', 'txt', 'doc', 'gif', 'dwf', 'f', 'pptx', 'wp', 'kml', 'rtf', 'html']\n",
      "19 ['csv', 'java', 'xml', 'log', 'gz', 'ppt', 'png', 'swf', 'sql', 'doc', 'gif', 'eps', 'hlp', 'kml', 'html', 'dbase3', 'pdf', 'pptx', 'kmz']\n",
      "20 ['gif', 'dwf', 'xls', 'ps', 'png', 'java', 'wp', 'pptx', 'ppt', 'swf', 'pps', 'html', 'sql', 'jpg', 'log', 'rtf', 'csv', 'doc', 'xml', 'eps']\n",
      "21 ['ppt', 'hlp', 'jpg', 'eps', 'sql', 'swf', 'html', 'f', 'dbase3', 'log', 'gz', 'png', 'pps', 'wp', 'doc', 'java', 'xml', 'rtf', 'pptx', 'ps', 'txt']\n",
      "22 ['pdf', 'gz', 'kml', 'kmz', 'pps', 'html', 'dwf', 'xls', 'pptx', 'xml', 'rtf', 'dbase3', 'eps', 'sql', 'java', 'png', 'csv', 'gif', 'log', 'doc', 'f', 'ppt']\n",
      "23 ['f', 'csv', 'png', 'dbase3', 'sql', 'gif', 'rtf', 'html', 'eps', 'xls', 'wp', 'txt', 'doc', 'kml', 'java', 'swf', 'kmz', 'log', 'pps', 'dwf', 'jpg', 'ppt', 'gz']\n",
      "24 ['swf', 'html', 'sql', 'xml', 'hlp', 'kml', 'pdf', 'java', 'pptx', 'gif', 'f', 'pps', 'wp', 'doc', 'dwf', 'rtf', 'txt', 'eps', 'ppt', 'dbase3', 'gz', 'png', 'jpg', 'xls']\n",
      "25 ['eps', 'rtf', 'jpg', 'java', 'xls', 'swf', 'ppt', 'log', 'f', 'html', 'pptx', 'hlp', 'dwf', 'kml', 'kmz', 'ps', 'sql', 'gif', 'xml', 'pps', 'txt', 'gz', 'pdf', 'wp', 'dbase3']\n",
      "26 ['log', 'swf', 'f', 'gz', 'rtf', 'wp', 'kmz', 'jpg', 'ps', 'java', 'dwf', 'csv', 'gif', 'pdf', 'html', 'dbase3', 'png', 'pps', 'sql', 'pptx', 'eps', 'doc', 'xml', 'hlp', 'txt', 'xls']\n",
      "27 ['doc', 'xml', 'eps', 'ps', 'txt', 'pps', 'java', 'html', 'log', 'pptx', 'rtf', 'gif', 'ppt', 'kmz', 'sql', 'pdf', 'dwf', 'csv', 'wp', 'dbase3', 'f', 'kml', 'jpg', 'hlp', 'swf', 'xls', 'gz']\n",
      "28 ['eps', 'csv', 'png', 'xls', 'dbase3', 'html', 'pdf', 'pps', 'log', 'rtf', 'pptx', 'txt', 'doc', 'kml', 'jpg', 'f', 'ppt', 'wp', 'xml', 'java', 'swf', 'kmz', 'dwf', 'gz', 'hlp', 'ps', 'gif', 'sql']\n",
      "2 ['kml', 'dbase3']\n",
      "3 ['gif', 'kml', 'jpg']\n",
      "4 ['xls', 'dbase3', 'xml', 'txt']\n",
      "5 ['dwf', 'pdf', 'gif', 'kml', 'sql']\n",
      "6 ['txt', 'ppt', 'png', 'html', 'dbase3', 'xml']\n",
      "7 ['doc', 'swf', 'hlp', 'log', 'pdf', 'jpg', 'rtf']\n",
      "8 ['hlp', 'sql', 'html', 'xls', 'pdf', 'txt', 'ppt', 'xml']\n",
      "9 ['swf', 'dbase3', 'ps', 'html', 'f', 'rtf', 'png', 'eps', 'hlp']\n",
      "10 ['swf', 'eps', 'sql', 'rtf', 'dbase3', 'ppt', 'pdf', 'pptx', 'dwf', 'doc']\n",
      "11 ['html', 'wp', 'rtf', 'ps', 'kml', 'gif', 'hlp', 'dwf', 'dbase3', 'sql', 'ppt']\n",
      "12 ['f', 'sql', 'dwf', 'pptx', 'gz', 'dbase3', 'xml', 'xls', 'rtf', 'swf', 'pdf', 'hlp']\n",
      "13 ['gif', 'ppt', 'dwf', 'xls', 'kmz', 'java', 'xml', 'swf', 'dbase3', 'gz', 'png', 'pptx', 'pdf']\n",
      "14 ['png', 'doc', 'kmz', 'dwf', 'gif', 'java', 'pps', 'txt', 'rtf', 'ppt', 'ps', 'kml', 'html', 'wp']\n",
      "15 ['ppt', 'pptx', 'sql', 'xls', 'java', 'eps', 'txt', 'csv', 'wp', 'jpg', 'ps', 'doc', 'gif', 'html', 'hlp']\n",
      "16 ['html', 'gz', 'xml', 'ps', 'dwf', 'jpg', 'png', 'wp', 'log', 'kmz', 'pps', 'ppt', 'dbase3', 'hlp', 'kml', 'txt']\n",
      "17 ['pps', 'dwf', 'txt', 'ppt', 'html', 'gz', 'sql', 'f', 'ps', 'png', 'kmz', 'swf', 'log', 'gif', 'xls', 'doc', 'jpg']\n",
      "18 ['dwf', 'pdf', 'pps', 'log', 'swf', 'pptx', 'rtf', 'gz', 'xls', 'txt', 'sql', 'png', 'gif', 'kml', 'hlp', 'html', 'eps', 'csv']\n",
      "19 ['jpg', 'log', 'xml', 'kml', 'eps', 'ppt', 'swf', 'gz', 'txt', 'kmz', 'rtf', 'java', 'sql', 'pps', 'dbase3', 'pdf', 'f', 'hlp', 'png']\n",
      "20 ['eps', 'dwf', 'kml', 'rtf', 'kmz', 'csv', 'ppt', 'txt', 'doc', 'sql', 'jpg', 'html', 'java', 'pptx', 'swf', 'log', 'gif', 'f', 'png', 'gz']\n",
      "21 ['html', 'png', 'java', 'pps', 'doc', 'ppt', 'eps', 'dbase3', 'pptx', 'gz', 'txt', 'swf', 'xls', 'hlp', 'csv', 'log', 'pdf', 'kml', 'gif', 'dwf', 'wp']\n",
      "22 ['pps', 'dwf', 'log', 'java', 'sql', 'ps', 'gz', 'hlp', 'ppt', 'kml', 'kmz', 'csv', 'pptx', 'f', 'xls', 'dbase3', 'wp', 'eps', 'txt', 'png', 'doc', 'jpg']\n",
      "23 ['f', 'png', 'doc', 'jpg', 'swf', 'gif', 'ppt', 'xml', 'txt', 'dwf', 'kml', 'java', 'dbase3', 'html', 'xls', 'csv', 'hlp', 'pdf', 'pptx', 'log', 'pps', 'rtf', 'wp']\n",
      "24 ['dbase3', 'gz', 'java', 'pptx', 'f', 'hlp', 'swf', 'png', 'txt', 'eps', 'ppt', 'gif', 'rtf', 'xml', 'wp', 'kml', 'dwf', 'pps', 'html', 'ps', 'sql', 'jpg', 'log', 'pdf']\n",
      "25 ['eps', 'gz', 'wp', 'kmz', 'rtf', 'csv', 'swf', 'xml', 'txt', 'html', 'ppt', 'xls', 'jpg', 'f', 'png', 'pps', 'gif', 'pdf', 'dwf', 'sql', 'doc', 'pptx', 'log', 'kml', 'ps']\n",
      "26 ['wp', 'gz', 'dwf', 'swf', 'xml', 'jpg', 'txt', 'sql', 'eps', 'pptx', 'ppt', 'f', 'hlp', 'pdf', 'gif', 'dbase3', 'kmz', 'ps', 'rtf', 'pps', 'csv', 'png', 'xls', 'html', 'java', 'kml']\n",
      "27 ['pps', 'ps', 'dwf', 'pptx', 'gz', 'hlp', 'jpg', 'doc', 'xml', 'csv', 'xls', 'html', 'java', 'sql', 'txt', 'kml', 'dbase3', 'gif', 'png', 'eps', 'kmz', 'log', 'pdf', 'f', 'wp', 'swf', 'ppt']\n",
      "28 ['pdf', 'dwf', 'html', 'log', 'jpg', 'pptx', 'gz', 'png', 'sql', 'java', 'kmz', 'rtf', 'xls', 'kml', 'doc', 'f', 'xml', 'gif', 'eps', 'ps', 'swf', 'hlp', 'ppt', 'pps', 'wp', 'dbase3', 'csv', 'txt']\n",
      "2 ['pdf', 'hlp']\n",
      "3 ['pdf', 'jpg', 'xls']\n",
      "4 ['html', 'gz', 'kmz', 'xls']\n",
      "5 ['eps', 'html', 'pptx', 'hlp', 'gif']\n",
      "6 ['pptx', 'xls', 'ps', 'rtf', 'gz', 'gif']\n",
      "7 ['doc', 'jpg', 'hlp', 'gif', 'pps', 'pptx', 'f']\n",
      "8 ['dwf', 'f', 'pptx', 'png', 'dbase3', 'eps', 'wp', 'kml']\n",
      "9 ['swf', 'kml', 'java', 'gz', 'pdf', 'f', 'wp', 'pptx', 'xml']\n",
      "10 ['swf', 'ps', 'java', 'xls', 'pdf', 'wp', 'jpg', 'pptx', 'ppt', 'pps']\n",
      "11 ['f', 'pps', 'doc', 'kml', 'hlp', 'kmz', 'txt', 'xml', 'gz', 'dbase3', 'eps']\n",
      "12 ['swf', 'wp', 'png', 'pps', 'rtf', 'xls', 'dbase3', 'kmz', 'sql', 'eps', 'f', 'hlp']\n",
      "13 ['jpg', 'rtf', 'ps', 'swf', 'dbase3', 'dwf', 'gif', 'doc', 'xls', 'gz', 'ppt', 'java', 'f']\n",
      "14 ['gif', 'kmz', 'txt', 'dbase3', 'jpg', 'pdf', 'rtf', 'swf', 'pptx', 'gz', 'xls', 'png', 'wp', 'eps']\n",
      "15 ['doc', 'dwf', 'png', 'hlp', 'csv', 'kml', 'dbase3', 'ps', 'txt', 'jpg', 'java', 'xls', 'html', 'ppt', 'pps']\n",
      "16 ['kmz', 'eps', 'dwf', 'wp', 'ppt', 'java', 'doc', 'png', 'dbase3', 'gif', 'kml', 'jpg', 'html', 'pptx', 'log', 'f']\n",
      "17 ['eps', 'jpg', 'wp', 'ps', 'gz', 'log', 'pps', 'dbase3', 'pdf', 'gif', 'rtf', 'hlp', 'dwf', 'xls', 'sql', 'kmz', 'swf']\n",
      "18 ['ps', 'html', 'ppt', 'dbase3', 'log', 'kmz', 'gz', 'pdf', 'doc', 'jpg', 'hlp', 'png', 'swf', 'txt', 'gif', 'f', 'pps', 'csv']\n",
      "19 ['doc', 'dbase3', 'xml', 'xls', 'hlp', 'sql', 'jpg', 'html', 'gz', 'pps', 'pptx', 'pdf', 'wp', 'png', 'ppt', 'ps', 'rtf', 'kml', 'txt']\n",
      "20 ['hlp', 'log', 'dwf', 'csv', 'pps', 'gz', 'rtf', 'dbase3', 'jpg', 'txt', 'eps', 'pptx', 'swf', 'html', 'xml', 'doc', 'ps', 'ppt', 'png', 'kmz']\n",
      "21 ['kmz', 'txt', 'hlp', 'gz', 'pptx', 'csv', 'png', 'doc', 'ppt', 'html', 'wp', 'kml', 'eps', 'dbase3', 'jpg', 'swf', 'log', 'f', 'ps', 'pdf', 'xml']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 ['png', 'doc', 'java', 'xml', 'eps', 'hlp', 'rtf', 'f', 'csv', 'pdf', 'gz', 'html', 'ppt', 'dbase3', 'pps', 'xls', 'gif', 'log', 'jpg', 'swf', 'sql', 'kmz']\n",
      "23 ['dwf', 'kmz', 'rtf', 'f', 'pdf', 'xml', 'html', 'ps', 'swf', 'csv', 'pps', 'pptx', 'xls', 'jpg', 'sql', 'ppt', 'log', 'eps', 'gif', 'wp', 'doc', 'hlp', 'dbase3']\n",
      "24 ['gif', 'xls', 'gz', 'dwf', 'dbase3', 'xml', 'log', 'pptx', 'kml', 'csv', 'kmz', 'txt', 'hlp', 'rtf', 'java', 'jpg', 'html', 'ppt', 'f', 'eps', 'png', 'pps', 'pdf', 'ps']\n",
      "25 ['jpg', 'xls', 'gif', 'txt', 'png', 'hlp', 'doc', 'dwf', 'sql', 'dbase3', 'pdf', 'java', 'html', 'eps', 'pptx', 'f', 'kml', 'xml', 'csv', 'wp', 'swf', 'gz', 'rtf', 'pps', 'ps']\n",
      "26 ['hlp', 'pptx', 'jpg', 'ppt', 'png', 'pdf', 'doc', 'swf', 'wp', 'xls', 'rtf', 'java', 'log', 'kml', 'eps', 'html', 'txt', 'dbase3', 'csv', 'sql', 'f', 'kmz', 'gif', 'dwf', 'ps', 'pps']\n",
      "27 ['txt', 'rtf', 'kml', 'html', 'ps', 'doc', 'wp', 'dwf', 'dbase3', 'ppt', 'jpg', 'eps', 'pdf', 'hlp', 'xls', 'log', 'gif', 'java', 'pptx', 'f', 'xml', 'pps', 'png', 'csv', 'swf', 'sql', 'kmz']\n",
      "28 ['gz', 'kmz', 'sql', 'wp', 'kml', 'java', 'dwf', 'log', 'xml', 'rtf', 'swf', 'html', 'xls', 'eps', 'csv', 'png', 'pptx', 'doc', 'pdf', 'pps', 'hlp', 'jpg', 'ppt', 'ps', 'dbase3', 'txt', 'f', 'gif']\n",
      "2 ['ppt', 'pdf']\n",
      "3 ['java', 'swf', 'gif']\n",
      "4 ['ppt', 'html', 'dbase3', 'hlp']\n",
      "5 ['dbase3', 'xls', 'dwf', 'pps', 'gz']\n",
      "6 ['java', 'eps', 'log', 'dbase3', 'txt', 'gif']\n",
      "7 ['html', 'ppt', 'png', 'kmz', 'log', 'pptx', 'txt']\n",
      "8 ['wp', 'eps', 'doc', 'txt', 'ppt', 'jpg', 'java', 'pptx']\n",
      "9 ['java', 'dbase3', 'f', 'pps', 'pdf', 'doc', 'dwf', 'gif', 'xls']\n",
      "10 ['csv', 'eps', 'txt', 'sql', 'rtf', 'log', 'wp', 'kmz', 'pdf', 'kml']\n",
      "11 ['csv', 'pps', 'kml', 'sql', 'pdf', 'png', 'pptx', 'jpg', 'xls', 'wp', 'f']\n",
      "12 ['rtf', 'java', 'xls', 'log', 'sql', 'dwf', 'png', 'kml', 'csv', 'pps', 'hlp', 'jpg']\n",
      "13 ['png', 'html', 'doc', 'pdf', 'dwf', 'kml', 'xls', 'ppt', 'xml', 'txt', 'csv', 'gif', 'wp']\n",
      "14 ['txt', 'doc', 'gz', 'png', 'kmz', 'eps', 'xls', 'dbase3', 'log', 'wp', 'sql', 'pptx', 'gif', 'pdf']\n",
      "15 ['pptx', 'ppt', 'kml', 'xml', 'wp', 'sql', 'java', 'dbase3', 'xls', 'pps', 'swf', 'html', 'pdf', 'jpg', 'gz']\n",
      "16 ['pdf', 'wp', 'eps', 'ps', 'rtf', 'gif', 'dbase3', 'pptx', 'doc', 'txt', 'f', 'xls', 'xml', 'hlp', 'csv', 'java']\n",
      "17 ['java', 'hlp', 'csv', 'log', 'pdf', 'dbase3', 'f', 'kmz', 'gif', 'swf', 'kml', 'wp', 'xml', 'ppt', 'sql', 'dwf', 'pps']\n",
      "18 ['java', 'rtf', 'jpg', 'pps', 'xml', 'pptx', 'txt', 'kmz', 'pdf', 'swf', 'csv', 'xls', 'log', 'wp', 'html', 'gz', 'ps', 'ppt']\n",
      "19 ['xls', 'jpg', 'pps', 'kml', 'ppt', 'ps', 'swf', 'log', 'png', 'hlp', 'sql', 'dwf', 'gz', 'dbase3', 'java', 'doc', 'gif', 'txt', 'csv']\n",
      "20 ['sql', 'png', 'kml', 'xml', 'csv', 'log', 'wp', 'dwf', 'html', 'kmz', 'hlp', 'dbase3', 'doc', 'ps', 'ppt', 'gz', 'txt', 'pptx', 'f', 'pps']\n",
      "21 ['kmz', 'hlp', 'pps', 'xml', 'png', 'txt', 'log', 'dbase3', 'html', 'jpg', 'sql', 'rtf', 'xls', 'doc', 'csv', 'f', 'swf', 'eps', 'pptx', 'gz', 'ps']\n",
      "22 ['rtf', 'java', 'sql', 'xls', 'kmz', 'txt', 'pps', 'dwf', 'doc', 'log', 'html', 'hlp', 'pptx', 'gz', 'jpg', 'f', 'swf', 'xml', 'dbase3', 'pdf', 'ppt', 'eps']\n",
      "23 ['dwf', 'f', 'ppt', 'java', 'xls', 'pptx', 'hlp', 'wp', 'png', 'html', 'jpg', 'eps', 'doc', 'swf', 'xml', 'gif', 'txt', 'ps', 'rtf', 'kml', 'csv', 'pdf', 'pps']\n",
      "24 ['doc', 'swf', 'jpg', 'sql', 'f', 'kml', 'txt', 'dbase3', 'dwf', 'gif', 'ps', 'pdf', 'xls', 'xml', 'eps', 'html', 'rtf', 'hlp', 'java', 'csv', 'kmz', 'pptx', 'log', 'ppt']\n",
      "25 ['gz', 'csv', 'xml', 'java', 'ppt', 'eps', 'xls', 'hlp', 'pps', 'sql', 'gif', 'log', 'ps', 'wp', 'f', 'swf', 'html', 'jpg', 'png', 'dwf', 'pptx', 'rtf', 'kml', 'txt', 'doc']\n",
      "26 ['dwf', 'doc', 'wp', 'hlp', 'csv', 'html', 'ppt', 'kml', 'sql', 'log', 'kmz', 'xml', 'gz', 'pps', 'eps', 'f', 'ps', 'gif', 'pdf', 'dbase3', 'txt', 'pptx', 'rtf', 'java', 'jpg', 'swf']\n",
      "27 ['kmz', 'jpg', 'swf', 'java', 'csv', 'wp', 'pdf', 'xls', 'hlp', 'kml', 'sql', 'pptx', 'dbase3', 'rtf', 'ppt', 'dwf', 'log', 'html', 'pps', 'txt', 'xml', 'gz', 'f', 'eps', 'png', 'gif', 'ps']\n",
      "28 ['dbase3', 'xml', 'html', 'java', 'jpg', 'kml', 'csv', 'doc', 'sql', 'kmz', 'gif', 'rtf', 'xls', 'log', 'gz', 'hlp', 'eps', 'wp', 'png', 'f', 'swf', 'pps', 'pdf', 'dwf', 'txt', 'ppt', 'ps', 'pptx']\n",
      "2 ['pptx', 'f']\n",
      "3 ['rtf', 'swf', 'kmz']\n",
      "4 ['xls', 'pptx', 'dwf', 'sql']\n",
      "5 ['pptx', 'kml', 'csv', 'xml', 'jpg']\n",
      "6 ['xml', 'pps', 'wp', 'log', 'pdf', 'gif']\n",
      "7 ['gif', 'dwf', 'ps', 'eps', 'kmz', 'xls', 'dbase3']\n",
      "8 ['doc', 'gz', 'f', 'eps', 'pptx', 'kml', 'pps', 'rtf']\n",
      "9 ['dwf', 'ppt', 'pdf', 'kml', 'kmz', 'dbase3', 'csv', 'gz', 'txt']\n",
      "10 ['csv', 'dwf', 'doc', 'dbase3', 'hlp', 'pptx', 'ppt', 'txt', 'eps', 'sql']\n",
      "11 ['rtf', 'sql', 'pptx', 'ppt', 'xml', 'gz', 'java', 'ps', 'kmz', 'xls', 'hlp']\n",
      "12 ['doc', 'dwf', 'ps', 'gz', 'xls', 'hlp', 'gif', 'pps', 'html', 'xml', 'log', 'swf']\n",
      "13 ['html', 'jpg', 'doc', 'f', 'dbase3', 'xml', 'pps', 'java', 'ppt', 'txt', 'pdf', 'pptx', 'swf']\n",
      "14 ['csv', 'pps', 'rtf', 'pptx', 'ps', 'kml', 'jpg', 'pdf', 'dbase3', 'txt', 'xls', 'doc', 'png', 'java']\n",
      "15 ['sql', 'jpg', 'dbase3', 'hlp', 'kml', 'rtf', 'java', 'gz', 'log', 'gif', 'pptx', 'eps', 'wp', 'txt', 'csv']\n",
      "16 ['png', 'jpg', 'gif', 'gz', 'hlp', 'java', 'ppt', 'eps', 'xls', 'doc', 'wp', 'pptx', 'rtf', 'txt', 'kmz', 'pdf']\n",
      "17 ['f', 'gz', 'csv', 'pdf', 'gif', 'dwf', 'sql', 'ps', 'png', 'kml', 'txt', 'kmz', 'hlp', 'pps', 'xls', 'ppt', 'eps']\n",
      "18 ['swf', 'pps', 'kml', 'gif', 'wp', 'ps', 'java', 'jpg', 'html', 'doc', 'rtf', 'txt', 'xls', 'pptx', 'log', 'dwf', 'f', 'eps']\n",
      "19 ['ppt', 'pptx', 'dbase3', 'wp', 'pps', 'doc', 'eps', 'txt', 'gif', 'jpg', 'kml', 'xml', 'f', 'png', 'kmz', 'html', 'log', 'rtf', 'csv']\n",
      "20 ['eps', 'pps', 'xls', 'gif', 'rtf', 'csv', 'xml', 'f', 'html', 'gz', 'png', 'wp', 'kml', 'jpg', 'kmz', 'ps', 'java', 'pptx', 'txt', 'doc']\n",
      "21 ['csv', 'doc', 'log', 'rtf', 'png', 'f', 'gz', 'kml', 'pps', 'pdf', 'ps', 'kmz', 'jpg', 'sql', 'dwf', 'ppt', 'eps', 'swf', 'xml', 'html', 'hlp']\n",
      "22 ['kmz', 'dbase3', 'gz', 'log', 'java', 'hlp', 'pps', 'csv', 'doc', 'f', 'png', 'pdf', 'ppt', 'sql', 'jpg', 'ps', 'xml', 'txt', 'rtf', 'kml', 'pptx', 'xls']\n",
      "23 ['kmz', 'csv', 'gif', 'txt', 'xml', 'dbase3', 'swf', 'pdf', 'gz', 'wp', 'ps', 'f', 'log', 'html', 'jpg', 'dwf', 'kml', 'rtf', 'hlp', 'java', 'pptx', 'doc', 'pps']\n",
      "24 ['eps', 'png', 'hlp', 'gz', 'log', 'dwf', 'rtf', 'pps', 'jpg', 'doc', 'sql', 'csv', 'swf', 'pdf', 'xls', 'dbase3', 'f', 'ps', 'txt', 'java', 'ppt', 'kml', 'gif', 'pptx']\n",
      "25 ['csv', 'pptx', 'dbase3', 'ps', 'wp', 'txt', 'sql', 'ppt', 'log', 'jpg', 'pdf', 'xml', 'swf', 'pps', 'hlp', 'png', 'kmz', 'dwf', 'eps', 'kml', 'xls', 'gz', 'doc', 'rtf', 'gif']\n",
      "26 ['rtf', 'ps', 'dwf', 'gz', 'ppt', 'xls', 'png', 'pdf', 'f', 'swf', 'gif', 'pptx', 'pps', 'hlp', 'kmz', 'java', 'log', 'jpg', 'dbase3', 'eps', 'txt', 'html', 'wp', 'csv', 'sql', 'doc']\n",
      "27 ['dwf', 'wp', 'kmz', 'sql', 'ps', 'ppt', 'csv', 'rtf', 'f', 'pps', 'dbase3', 'eps', 'html', 'xls', 'txt', 'swf', 'xml', 'log', 'doc', 'pptx', 'java', 'hlp', 'gif', 'kml', 'gz', 'pdf', 'jpg']\n",
      "28 ['pps', 'csv', 'hlp', 'kml', 'ppt', 'swf', 'pptx', 'xml', 'java', 'png', 'gif', 'gz', 'log', 'txt', 'eps', 'jpg', 'dbase3', 'doc', 'dwf', 'ps', 'sql', 'pdf', 'rtf', 'wp', 'html', 'kmz', 'xls', 'f']\n"
     ]
    }
   ],
   "source": [
    "if os.path.exists(result_dir + \"/nclasses.tsv\"):\n",
    "    os.remove(result_dir + \"/nclasses.tsv\")\n",
    "r = report.Reporter2(result_dir + '/nclasses.tsv')\n",
    "train_nclasses(rawset, r, 5)\n",
    "r.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>cats</th>\n",
       "      <th>val_categorical_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>f,wp</td>\n",
       "      <td>0.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>rtf,pps,csv</td>\n",
       "      <td>0.978929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>pptx,csv,dwf,sql</td>\n",
       "      <td>0.795714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>log,kmz,wp,dbase3,hlp</td>\n",
       "      <td>0.951071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>jpg,swf,f,xls,dbase3,wp</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>24</td>\n",
       "      <td>eps,png,hlp,gz,log,dwf,rtf,pps,jpg,doc,sql,csv...</td>\n",
       "      <td>0.569286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>25</td>\n",
       "      <td>csv,pptx,dbase3,ps,wp,txt,sql,ppt,log,jpg,pdf,...</td>\n",
       "      <td>0.567143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>26</td>\n",
       "      <td>rtf,ps,dwf,gz,ppt,xls,png,pdf,f,swf,gif,pptx,p...</td>\n",
       "      <td>0.537143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>27</td>\n",
       "      <td>dwf,wp,kmz,sql,ps,ppt,csv,rtf,f,pps,dbase3,eps...</td>\n",
       "      <td>0.580357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>28</td>\n",
       "      <td>pps,csv,hlp,kml,ppt,swf,pptx,xml,java,png,gif,...</td>\n",
       "      <td>0.586071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      n                                               cats  \\\n",
       "0     2                                               f,wp   \n",
       "1     3                                        rtf,pps,csv   \n",
       "2     4                                   pptx,csv,dwf,sql   \n",
       "3     5                              log,kmz,wp,dbase3,hlp   \n",
       "4     6                            jpg,swf,f,xls,dbase3,wp   \n",
       "..   ..                                                ...   \n",
       "130  24  eps,png,hlp,gz,log,dwf,rtf,pps,jpg,doc,sql,csv...   \n",
       "131  25  csv,pptx,dbase3,ps,wp,txt,sql,ppt,log,jpg,pdf,...   \n",
       "132  26  rtf,ps,dwf,gz,ppt,xls,png,pdf,f,swf,gif,pptx,p...   \n",
       "133  27  dwf,wp,kmz,sql,ps,ppt,csv,rtf,f,pps,dbase3,eps...   \n",
       "134  28  pps,csv,hlp,kml,ppt,swf,pptx,xml,java,png,gif,...   \n",
       "\n",
       "     val_categorical_accuracy  \n",
       "0                    0.964286  \n",
       "1                    0.978929  \n",
       "2                    0.795714  \n",
       "3                    0.951071  \n",
       "4                    0.857143  \n",
       "..                        ...  \n",
       "130                  0.569286  \n",
       "131                  0.567143  \n",
       "132                  0.537143  \n",
       "133                  0.580357  \n",
       "134                  0.586071  \n",
       "\n",
       "[135 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(result_dir + '/nclasses.tsv', sep='\\t')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "def plot_acc(xs, ys, labels=None, loc='upper right', alpha_on=None):\n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "#     fig.yaxis.set_minor_locator()\n",
    "    if labels is None:\n",
    "        labels = [None for x in xs]\n",
    "    for x, y, label, mark in zip(xs, ys, labels, ['bx', 'g-', 'r*', '.mD']):\n",
    "        plt.plot(x[x==alpha_on], y[x==alpha_on], mark, alpha=0.1, label='')\n",
    "        plt.plot(x[x!=alpha_on], y[x!=alpha_on], mark, label=label)\n",
    "    ax1 = plt.gca()\n",
    "    plt.ylabel('accuracy', fontsize=16)\n",
    "    plt.xlabel('number of classes', fontsize=16)\n",
    "    plt.xlim((-0, 31))\n",
    "    plt.ylim((-0, 1))\n",
    "    ax1.xaxis.set_major_locator(MultipleLocator(5))\n",
    "    ax1.xaxis.set_minor_locator(MultipleLocator(1))\n",
    "    ax1.yaxis.set_major_locator(MultipleLocator(0.1))\n",
    "    ax1.yaxis.set_minor_locator(MultipleLocator(0.05))\n",
    "    plt.grid(linestyle='-', color='k', which='major')\n",
    "    plt.grid(linestyle='--', which='minor')\n",
    "    for tick in ax1.xaxis.get_major_ticks():\n",
    "        tick.label.set_fontsize(16) \n",
    "    for tick in ax1.yaxis.get_major_ticks():\n",
    "        tick.label.set_fontsize(16) \n",
    "    plt.legend(loc=loc, prop={'size': 16})\n",
    "#     plt.savefig('acc.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(2,30,0.01)\n",
    "y = 1/x\n",
    "plot_acc([data['n'], x], [data['val_categorical_accuracy'], y], ['models trained with many classes', 'random chance'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}