bilstm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "import torch\n",
    "import tensorboardX\n",
    "import dill as pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"sopa_master/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data import read_embeddings, read_docs, read_labels\n",
    "from soft_patterns import ProbSemiring, MaxPlusSemiring, LogSpaceMaxTimesSemiring, SoftPatternClassifier, train, Batch, evaluate_model\n",
    "from util import to_cuda\n",
    "from interpret_classification_results import interpret_documents\n",
    "from visualize import visualize_patterns\n",
    "from baseline.lstm import AveragingRnnClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_file = \"data/time_data_clean/train.data\"\n",
    "train_label_file =\"data/time_data_clean/train.labels\"\n",
    "dev_data_file = \"data/time_data_clean/dev.data\"\n",
    "dev_label_file = \"data/time_data_clean/dev.labels\"\n",
    "test_file = \"data/time_data_clean/test.data\"\n",
    "test_label=\"data/time_data_clean/test.labels\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab = pickle.load(open(\"vocab.p\",\"rb\"))\n",
    "embeddings = pickle.load(open(\"embeddings.p\",\"rb\"))\n",
    "word_dim = pickle.load(open(\"word_dim.p\",\"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bilstm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# params: 324032\n"
     ]
    }
   ],
   "source": [
    "model=AveragingRnnClassifier(\n",
    "    hidden_dim=100,\n",
    "    mlp_hidden_dim=10,\n",
    "    num_mlp_layers=2,\n",
    "    num_classes=2,\n",
    "    embeddings=embeddings\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_input, train_text = read_docs(train_data_file, vocab, num_padding_tokens=1)\n",
    "train_labels = read_labels(train_label_file)\n",
    "dev_input, dev_text = read_docs(dev_data_file, vocab, num_padding_tokens=1)\n",
    "dev_labels = read_labels(dev_label_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = list(zip(train_input, train_labels))\n",
    "dev_data = list(zip(dev_input, dev_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sopa_master/soft_patterns.py:502: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  log_softmax(output).view(batch.size(), num_classes),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....................\n",
      "\n",
      "num predicted 1s: 0\n",
      "num gold 1s:      343\n",
      "num predicted 1s: 0\n",
      "num gold 1s:      207\n",
      "iteration:       0 train time:     0.080m, eval time:     0.025m train loss:        0.648 train_acc:   65.700% dev loss:        0.641 dev_acc:   64.433%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_0.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 0\n",
      "num gold 1s:      320\n",
      "num predicted 1s: 0\n",
      "num gold 1s:      207\n",
      "iteration:       1 train time:     0.172m, eval time:     0.024m train loss:        0.615 train_acc:   68.000% dev loss:        0.633 dev_acc:   64.433%\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_1.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 0\n",
      "num gold 1s:      314\n",
      "num predicted 1s: 0\n",
      "num gold 1s:      207\n",
      "iteration:       2 train time:     0.262m, eval time:     0.026m train loss:        0.603 train_acc:   68.600% dev loss:        0.626 dev_acc:   64.433%\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_2.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 0\n",
      "num gold 1s:      320\n",
      "num predicted 1s: 0\n",
      "num gold 1s:      207\n",
      "iteration:       3 train time:     0.356m, eval time:     0.026m train loss:        0.583 train_acc:   68.000% dev loss:        0.589 dev_acc:   64.433%\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_3.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 161\n",
      "num gold 1s:      313\n",
      "num predicted 1s: 110\n",
      "num gold 1s:      207\n",
      "iteration:       4 train time:     0.453m, eval time:     0.026m train loss:        0.551 train_acc:   74.800% dev loss:        0.555 dev_acc:   72.680%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_4.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 288\n",
      "num gold 1s:      346\n",
      "num predicted 1s: 177\n",
      "num gold 1s:      207\n",
      "iteration:       5 train time:     0.549m, eval time:     0.027m train loss:        0.506 train_acc:   77.000% dev loss:        0.493 dev_acc:   76.976%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_5.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 255\n",
      "num gold 1s:      332\n",
      "num predicted 1s: 149\n",
      "num gold 1s:      207\n",
      "iteration:       6 train time:     0.646m, eval time:     0.027m train loss:        0.469 train_acc:   79.100% dev loss:        0.487 dev_acc:   77.663%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_6.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 257\n",
      "num gold 1s:      321\n",
      "num predicted 1s: 158\n",
      "num gold 1s:      207\n",
      "iteration:       7 train time:     0.744m, eval time:     0.025m train loss:        0.465 train_acc:   81.600% dev loss:        0.460 dev_acc:   78.522%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_7.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 305\n",
      "num gold 1s:      306\n",
      "num predicted 1s: 188\n",
      "num gold 1s:      207\n",
      "iteration:       8 train time:     0.836m, eval time:     0.026m train loss:        0.439 train_acc:   82.300% dev loss:        0.446 dev_acc:   79.553%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_8.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 419\n",
      "num gold 1s:      314\n",
      "num predicted 1s: 250\n",
      "num gold 1s:      207\n",
      "iteration:       9 train time:     0.930m, eval time:     0.025m train loss:        0.410 train_acc:   78.500% dev loss:        0.467 dev_acc:   78.179%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 347\n",
      "num gold 1s:      336\n",
      "num predicted 1s: 188\n",
      "num gold 1s:      207\n",
      "iteration:      10 train time:     1.024m, eval time:     0.026m train loss:        0.428 train_acc:   83.300% dev loss:        0.441 dev_acc:   79.897%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_10.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 319\n",
      "num gold 1s:      329\n",
      "num predicted 1s: 182\n",
      "num gold 1s:      207\n",
      "iteration:      11 train time:     1.118m, eval time:     0.026m train loss:        0.389 train_acc:   84.400% dev loss:        0.430 dev_acc:   79.897%\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_11.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 278\n",
      "num gold 1s:      327\n",
      "num predicted 1s: 166\n",
      "num gold 1s:      207\n",
      "iteration:      12 train time:     1.212m, eval time:     0.026m train loss:        0.371 train_acc:   87.100% dev loss:        0.433 dev_acc:   81.271%\n",
      "New best acc!\n",
      "saving model to modeltimebilstm/traintimebilstm_12.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 328\n",
      "num gold 1s:      314\n",
      "num predicted 1s: 203\n",
      "num gold 1s:      207\n",
      "iteration:      13 train time:     1.305m, eval time:     0.026m train loss:        0.374 train_acc:   86.400% dev loss:        0.422 dev_acc:   81.787%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_13.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 274\n",
      "num gold 1s:      327\n",
      "num predicted 1s: 158\n",
      "num gold 1s:      207\n",
      "iteration:      14 train time:     1.400m, eval time:     0.026m train loss:        0.357 train_acc:   85.500% dev loss:        0.428 dev_acc:   80.241%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 305\n",
      "num gold 1s:      340\n",
      "num predicted 1s: 172\n",
      "num gold 1s:      207\n",
      "iteration:      15 train time:     1.493m, eval time:     0.026m train loss:        0.350 train_acc:   85.500% dev loss:        0.408 dev_acc:   82.302%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_15.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 340\n",
      "num gold 1s:      338\n",
      "num predicted 1s: 197\n",
      "num gold 1s:      207\n",
      "iteration:      16 train time:     1.587m, eval time:     0.026m train loss:        0.343 train_acc:   86.200% dev loss:        0.415 dev_acc:   81.100%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 315\n",
      "num gold 1s:      339\n",
      "num predicted 1s: 197\n",
      "num gold 1s:      207\n",
      "iteration:      17 train time:     1.680m, eval time:     0.026m train loss:        0.352 train_acc:   85.400% dev loss:        0.412 dev_acc:   82.131%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 269\n",
      "num gold 1s:      333\n",
      "num predicted 1s: 169\n",
      "num gold 1s:      207\n",
      "iteration:      18 train time:     1.773m, eval time:     0.025m train loss:        0.354 train_acc:   84.200% dev loss:        0.420 dev_acc:   80.756%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 358\n",
      "num gold 1s:      326\n",
      "num predicted 1s: 209\n",
      "num gold 1s:      207\n",
      "iteration:      19 train time:     1.866m, eval time:     0.024m train loss:        0.376 train_acc:   87.000% dev loss:        0.407 dev_acc:   80.412%\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_19.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 337\n",
      "num gold 1s:      325\n",
      "num predicted 1s: 205\n",
      "num gold 1s:      207\n",
      "iteration:      20 train time:     1.958m, eval time:     0.025m train loss:        0.337 train_acc:   88.200% dev loss:        0.396 dev_acc:   82.818%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_20.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 325\n",
      "num gold 1s:      328\n",
      "num predicted 1s: 184\n",
      "num gold 1s:      207\n",
      "iteration:      21 train time:     2.052m, eval time:     0.026m train loss:        0.327 train_acc:   88.300% dev loss:        0.390 dev_acc:   83.677%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_21.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 327\n",
      "num gold 1s:      329\n",
      "num predicted 1s: 190\n",
      "num gold 1s:      207\n",
      "iteration:      22 train time:     2.147m, eval time:     0.026m train loss:        0.327 train_acc:   87.800% dev loss:        0.400 dev_acc:   81.615%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 249\n",
      "num gold 1s:      301\n",
      "num predicted 1s: 167\n",
      "num gold 1s:      207\n",
      "iteration:      23 train time:     2.240m, eval time:     0.026m train loss:        0.324 train_acc:   88.000% dev loss:        0.387 dev_acc:   84.536%\n",
      "New best acc!\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_23.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 254\n",
      "num gold 1s:      318\n",
      "num predicted 1s: 152\n",
      "num gold 1s:      207\n",
      "iteration:      24 train time:     2.332m, eval time:     0.026m train loss:        0.308 train_acc:   88.600% dev loss:        0.406 dev_acc:   84.021%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 320\n",
      "num gold 1s:      333\n",
      "num predicted 1s: 178\n",
      "num gold 1s:      207\n",
      "iteration:      25 train time:     2.427m, eval time:     0.026m train loss:        0.302 train_acc:   88.700% dev loss:        0.382 dev_acc:   84.364%\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_25.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 261\n",
      "num gold 1s:      304\n",
      "num predicted 1s: 175\n",
      "num gold 1s:      207\n",
      "iteration:      26 train time:     2.522m, eval time:     0.025m train loss:        0.295 train_acc:   90.900% dev loss:        0.378 dev_acc:   84.536%\n",
      "New best dev!\n",
      "saving model to modeltimebilstm/traintimebilstm_26.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 274\n",
      "num gold 1s:      301\n",
      "num predicted 1s: 178\n",
      "num gold 1s:      207\n",
      "iteration:      27 train time:     2.614m, eval time:     0.025m train loss:        0.289 train_acc:   88.900% dev loss:        0.388 dev_acc:   84.708%\n",
      "New best acc!\n",
      "saving model to modeltimebilstm/traintimebilstm_27.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 284\n",
      "num gold 1s:      308\n",
      "num predicted 1s: 181\n",
      "num gold 1s:      207\n",
      "iteration:      28 train time:     2.705m, eval time:     0.025m train loss:        0.278 train_acc:   90.200% dev loss:        0.398 dev_acc:   85.567%\n",
      "New best acc!\n",
      "saving model to modeltimebilstm/traintimebilstm_28.pth\n",
      "....................\n",
      "\n",
      "num predicted 1s: 281\n",
      "num gold 1s:      325\n",
      "num predicted 1s: 173\n",
      "num gold 1s:      207\n",
      "iteration:      29 train time:     2.798m, eval time:     0.025m train loss:        0.266 train_acc:   89.800% dev loss:        0.381 dev_acc:   85.223%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 302\n",
      "num gold 1s:      318\n",
      "num predicted 1s: 186\n",
      "num gold 1s:      207\n",
      "iteration:      30 train time:     2.890m, eval time:     0.026m train loss:        0.261 train_acc:   90.200% dev loss:        0.391 dev_acc:   85.052%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 293\n",
      "num gold 1s:      320\n",
      "num predicted 1s: 177\n",
      "num gold 1s:      207\n",
      "iteration:      31 train time:     2.982m, eval time:     0.026m train loss:        0.258 train_acc:   92.700% dev loss:        0.388 dev_acc:   84.880%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 287\n",
      "num gold 1s:      323\n",
      "num predicted 1s: 179\n",
      "num gold 1s:      207\n",
      "iteration:      32 train time:     3.076m, eval time:     0.025m train loss:        0.247 train_acc:   91.200% dev loss:        0.408 dev_acc:   84.192%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 431\n",
      "num gold 1s:      313\n",
      "num predicted 1s: 266\n",
      "num gold 1s:      207\n",
      "iteration:      33 train time:     3.168m, eval time:     0.025m train loss:        0.249 train_acc:   82.200% dev loss:        0.520 dev_acc:   79.553%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 333\n",
      "num gold 1s:      320\n",
      "num predicted 1s: 205\n",
      "num gold 1s:      207\n",
      "iteration:      34 train time:     3.260m, eval time:     0.025m train loss:        0.275 train_acc:   89.900% dev loss:        0.398 dev_acc:   83.849%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 277\n",
      "num gold 1s:      326\n",
      "num predicted 1s: 168\n",
      "num gold 1s:      207\n",
      "iteration:      35 train time:     3.352m, eval time:     0.025m train loss:        0.246 train_acc:   92.700% dev loss:        0.401 dev_acc:   85.052%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 317\n",
      "num gold 1s:      313\n",
      "num predicted 1s: 209\n",
      "num gold 1s:      207\n",
      "iteration:      36 train time:     3.446m, eval time:     0.025m train loss:        0.248 train_acc:   91.000% dev loss:        0.391 dev_acc:   82.818%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 317\n",
      "num gold 1s:      311\n",
      "num predicted 1s: 202\n",
      "num gold 1s:      207\n",
      "iteration:      37 train time:     3.539m, eval time:     0.027m train loss:        0.257 train_acc:   91.800% dev loss:        0.396 dev_acc:   84.021%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 300\n",
      "num gold 1s:      327\n",
      "num predicted 1s: 188\n",
      "num gold 1s:      207\n",
      "iteration:      38 train time:     3.633m, eval time:     0.026m train loss:        0.226 train_acc:   93.300% dev loss:        0.422 dev_acc:   84.708%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 290\n",
      "num gold 1s:      313\n",
      "num predicted 1s: 178\n",
      "num gold 1s:      207\n",
      "iteration:      39 train time:     3.727m, eval time:     0.026m train loss:        0.215 train_acc:   93.100% dev loss:        0.412 dev_acc:   84.708%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 279\n",
      "num gold 1s:      317\n",
      "num predicted 1s: 171\n",
      "num gold 1s:      207\n",
      "iteration:      40 train time:     3.822m, eval time:     0.025m train loss:        0.218 train_acc:   93.000% dev loss:        0.411 dev_acc:   84.536%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 292\n",
      "num gold 1s:      323\n",
      "num predicted 1s: 178\n",
      "num gold 1s:      207\n",
      "iteration:      41 train time:     3.915m, eval time:     0.026m train loss:        0.214 train_acc:   93.300% dev loss:        0.419 dev_acc:   85.052%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 282\n",
      "num gold 1s:      321\n",
      "num predicted 1s: 166\n",
      "num gold 1s:      207\n",
      "iteration:      42 train time:     4.007m, eval time:     0.026m train loss:        0.203 train_acc:   92.300% dev loss:        0.453 dev_acc:   84.708%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 296\n",
      "num gold 1s:      335\n",
      "num predicted 1s: 164\n",
      "num gold 1s:      207\n",
      "iteration:      43 train time:     4.100m, eval time:     0.025m train loss:        0.201 train_acc:   93.700% dev loss:        0.435 dev_acc:   85.052%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 293\n",
      "num gold 1s:      330\n",
      "num predicted 1s: 183\n",
      "num gold 1s:      207\n",
      "iteration:      44 train time:     4.193m, eval time:     0.025m train loss:        0.218 train_acc:   91.300% dev loss:        0.430 dev_acc:   84.192%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 292\n",
      "num gold 1s:      310\n",
      "num predicted 1s: 186\n",
      "num gold 1s:      207\n",
      "iteration:      45 train time:     4.289m, eval time:     0.025m train loss:        0.203 train_acc:   94.600% dev loss:        0.418 dev_acc:   84.021%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 296\n",
      "num gold 1s:      326\n",
      "num predicted 1s: 181\n",
      "num gold 1s:      207\n",
      "iteration:      46 train time:     4.381m, eval time:     0.026m train loss:        0.198 train_acc:   93.800% dev loss:        0.469 dev_acc:   84.536%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 289\n",
      "num gold 1s:      319\n",
      "num predicted 1s: 170\n",
      "num gold 1s:      207\n",
      "iteration:      47 train time:     4.475m, eval time:     0.026m train loss:        0.193 train_acc:   94.800% dev loss:        0.427 dev_acc:   85.395%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 281\n",
      "num gold 1s:      311\n",
      "num predicted 1s: 175\n",
      "num gold 1s:      207\n",
      "iteration:      48 train time:     4.571m, eval time:     0.026m train loss:        0.182 train_acc:   93.800% dev loss:        0.469 dev_acc:   84.880%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 286\n",
      "num gold 1s:      310\n",
      "num predicted 1s: 168\n",
      "num gold 1s:      207\n",
      "iteration:      49 train time:     4.665m, eval time:     0.025m train loss:        0.169 train_acc:   95.800% dev loss:        0.457 dev_acc:   85.395%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 343\n",
      "num gold 1s:      319\n",
      "num predicted 1s: 209\n",
      "num gold 1s:      207\n",
      "iteration:      50 train time:     4.759m, eval time:     0.025m train loss:        0.164 train_acc:   94.400% dev loss:        0.527 dev_acc:   83.162%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 269\n",
      "num gold 1s:      327\n",
      "num predicted 1s: 159\n",
      "num gold 1s:      207\n",
      "iteration:      51 train time:     4.853m, eval time:     0.026m train loss:        0.157 train_acc:   93.400% dev loss:        0.516 dev_acc:   84.192%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 316\n",
      "num gold 1s:      318\n",
      "num predicted 1s: 194\n",
      "num gold 1s:      207\n",
      "iteration:      52 train time:     4.947m, eval time:     0.025m train loss:        0.153 train_acc:   96.000% dev loss:        0.482 dev_acc:   84.364%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 340\n",
      "num gold 1s:      346\n",
      "num predicted 1s: 196\n",
      "num gold 1s:      207\n",
      "iteration:      53 train time:     5.040m, eval time:     0.025m train loss:        0.155 train_acc:   94.400% dev loss:        0.581 dev_acc:   82.990%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 315\n",
      "num gold 1s:      326\n",
      "num predicted 1s: 186\n",
      "num gold 1s:      207\n",
      "iteration:      54 train time:     5.133m, eval time:     0.025m train loss:        0.156 train_acc:   96.300% dev loss:        0.502 dev_acc:   84.364%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 336\n",
      "num gold 1s:      321\n",
      "num predicted 1s: 200\n",
      "num gold 1s:      207\n",
      "iteration:      55 train time:     5.226m, eval time:     0.025m train loss:        0.179 train_acc:   93.500% dev loss:        0.444 dev_acc:   83.677%\n",
      "....................\n",
      "\n",
      "num predicted 1s: 323\n",
      "num gold 1s:      338\n",
      "num predicted 1s: 178\n",
      "num gold 1s:      207\n",
      "iteration:      56 train time:     5.317m, eval time:     0.025m train loss:        0.165 train_acc:   96.300% dev loss:        0.475 dev_acc:   83.677%\n",
      "Reached 30 iterations without improving dev loss. Breaking\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AveragingRnnClassifier(\n",
       "  (rnn): Rnn(\n",
       "    (rnn): LSTM(300, 100, bidirectional=True)\n",
       "  )\n",
       "  (mlp): MLP(\n",
       "    (layers): ModuleList(\n",
       "      (0): Linear(in_features=200, out_features=10, bias=True)\n",
       "      (1): Linear(in_features=10, out_features=2, bias=True)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train(\n",
    "    train_data=train_data,\n",
    "    dev_data=dev_data,\n",
    "    model=model,\n",
    "    model_save_dir=\"data/models/modeltimebilstm/\",\n",
    "    num_iterations=250,\n",
    "    model_file_prefix=\"traintimebilstm\",\n",
    "    learning_rate=0.001,\n",
    "    batch_size=150,\n",
    "    num_classes=2,\n",
    "    patience=30,\n",
    "    gpu=False,\n",
    "    dropout=0.1,\n",
    "    word_dropout=0,\n",
    "    clip=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), \"data/models/modeltimebilstm.pth\")"
   ]
  }
 ],
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