Update 'README.md'

README.md

@@ -1,9 +1 @@
-Challenging America word-gap prediction
-===================================
-
-Guess a word in a gap.
-
-Evaluation metric
------------------
-
-LikelihoodHashed is the metric
+# Bugfixy inferencji i wstawienie lepszych wyników 24.05.23. 

generate_out.py

@@ -1,11 +0,0 @@
-import sys
-
-file = sys.argv[1]
-
-with open(file, encoding='utf-8') as f1, open('out.tsv', 'w', encoding='utf-8') as f2:
-    for line in f1:
-        line = line.split('\t')
-        if line[-1][0].isupper():
-            f2.write('the:0.9 :0.1\n')
-        else:
-            f2.write('the:0.4 a:0.4 :0.2\n')

generations.txt

@@ -0,0 +1,9 @@
+According to recent news and a half of the north side or the other must be a man of great force of character to the country. He was a member of a family in the United States to the full amount of the principal and interest of this section shall be subject to the
+
+Recent studies have shown that the present condition of things in which we have been in a very short time after the war and the war was over and that every man who has been in the hands of the United States of the North and the South American States, and those States who had
+
+Today I was taking a stroll in the park when suddenly and that the said estate has by no Mr. ii him, but he was too young to be a very good reason that the above named de- - . . . They are able six e of a good deal of time and money to the amount of the tax
+
+The most unbelievable story ever told goes like this to be the most important of these are the only two men who were at the time of the year when they tried an 1 the said sum of money to be paid in case of your South and the West may have to do the work of the committee
+
+he war between natural and the few who are not in the interest of the said William H. and acres of them, more o the State from the control of the state of New York and New York and New York are the looked for food in the greatest number of the most

gonito.yaml

@@ -0,0 +1,11 @@
+description: neural network with bigrams
+tags:
+  - neural-network
+  - left-context
+  - bigrams
+params:
+  vocab_size: 20000
+  embed_size: 150
+  batch_size: 5000
+param-files:
+  - "*.yaml"

solution.ipynb

@@ -0,0 +1,286 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "gpuClass": "standard"
+ },
+ "cells": [
+  {
+   "cell_type": "code",
+   "source": [
+    "from torchtext.vocab import build_vocab_from_iterator\n",
+    "import pickle\n",
+    "from torch.utils.data import IterableDataset\n",
+    "import itertools\n",
+    "from torch import nn\n",
+    "import torch\n",
+    "import lzma\n",
+    "from torch.utils.data import DataLoader\n",
+    "from tqdm import tqdm"
+   ],
+   "metadata": {
+    "id": "WnglOFA8gGJl"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "def simple_preprocess(line):\n",
+    "    return line.replace(r'\\n', ' ')\n",
+    "\n",
+    "def get_words_from_line(line):\n",
+    "    line = line.strip()\n",
+    "    line = simple_preprocess(line)\n",
+    "    yield '<s>'\n",
+    "    for t in line.split():\n",
+    "     yield t\n",
+    "    yield '</s>'\n",
+    "\n",
+    "def get_word_lines_from_file(file_name, n_size=-1):\n",
+    "    with lzma.open(file_name, 'r') as fh:\n",
+    "        n = 0\n",
+    "        for line in fh:\n",
+    "            n += 1\n",
+    "            yield get_words_from_line(line.decode('utf-8'))\n",
+    "            if n == n_size:\n",
+    "                break\n",
+    "\n",
+    "def look_ahead_iterator(gen):\n",
+    "    prev = None\n",
+    "    for item in gen:\n",
+    "        if prev is not None:\n",
+    "            yield prev, item\n",
+    "        prev = item\n",
+    "\n",
+    "def build_vocab(file, vocab_size):\n",
+    "    try:\n",
+    "        with open(f'bigram_nn_vocab_{vocab_size}.pickle', 'rb') as handle:\n",
+    "            vocab = pickle.load(handle)\n",
+    "    except:\n",
+    "        vocab = build_vocab_from_iterator(\n",
+    "            get_word_lines_from_file(file),\n",
+    "            max_tokens = vocab_size,\n",
+    "            specials = ['<unk>'])\n",
+    "        with open(f'bigram_nn_vocab_{vocab_size}.pickle', 'wb') as handle:\n",
+    "            pickle.dump(vocab, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
+    "    return vocab\n",
+    "\n",
+    "class Bigrams(IterableDataset):\n",
+    "    def __init__(self, text_file, vocabulary_size):\n",
+    "        self.vocab = vocab\n",
+    "        self.vocab.set_default_index(self.vocab['<unk>'])\n",
+    "        self.vocabulary_size = vocabulary_size\n",
+    "        self.text_file = text_file\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return look_ahead_iterator(\n",
+    "            (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
+    "\n",
+    "class SimpleBigramNeuralLanguageModel(nn.Module):\n",
+    "    def __init__(self, vocabulary_size, embedding_size):\n",
+    "        super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
+    "        self.model = nn.Sequential(\n",
+    "            nn.Embedding(vocabulary_size, embedding_size),\n",
+    "            nn.Linear(embedding_size, vocabulary_size),\n",
+    "            nn.Softmax(dim=1)\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.model(x)"
+   ],
+   "metadata": {
+    "id": "aW_3JqSNgLLr"
+   },
+   "execution_count": 3,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "max_steps= -1\n",
+    "vocab_size = 20000\n",
+    "embed_size = 150\n",
+    "batch_size = 5000\n",
+    "learning_rate = 0.001\n",
+    "vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
+    "train_dataset = Bigrams('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
+    "if torch.cuda.is_available():\n",
+    "  device = 'cuda'\n",
+    "else:\n",
+    "  raise Exception()\n",
+    "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
+    "data = DataLoader(train_dataset, batch_size=batch_size)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
+    "criterion = torch.nn.NLLLoss()\n",
+    "\n",
+    "model.train()\n",
+    "step = 0\n",
+    "for x, y in data:\n",
+    "    x = x.to(device)\n",
+    "    y = y.to(device)\n",
+    "    optimizer.zero_grad()\n",
+    "    y_predicted = model(x)\n",
+    "    loss = criterion(torch.log(y_predicted), y)\n",
+    "    if step % 1000 == 0:\n",
+    "        print(f'steps: {step}, loss: {loss.item()}')\n",
+    "        if step != 0:\n",
+    "            torch.save(model.state_dict(), f'bigram_nn_model_steps-{step}_vocab-{vocab_size}_embed-{embed_size}_batch-{batch_size}.bin')\n",
+    "    if step == max_steps:\n",
+    "      break\n",
+    "    step += 1\n",
+    "    loss.backward()\n",
+    "    optimizer.step()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QQw_E7Ku4h0a",
+    "outputId": "4a37d9ba-1abd-46ae-b157-cd6d52b951a2"
+   },
+   "execution_count": 4,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ked/PycharmProjects/mj9/venv/lib/python3.10/site-packages/torch/nn/modules/container.py:217: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
+      "  input = module(input)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "steps: 0, loss: 10.091094017028809\n",
+      "steps: 1000, loss: 5.73332405090332\n",
+      "steps: 2000, loss: 5.655370712280273\n",
+      "steps: 3000, loss: 5.457630634307861\n",
+      "steps: 4000, loss: 5.38517427444458\n",
+      "steps: 5000, loss: 5.467936992645264\n",
+      "steps: 6000, loss: 5.372152328491211\n",
+      "steps: 7000, loss: 5.272013187408447\n",
+      "steps: 8000, loss: 5.439966201782227\n",
+      "steps: 9000, loss: 5.268238544464111\n",
+      "steps: 10000, loss: 5.1395182609558105\n",
+      "steps: 11000, loss: 5.2558159828186035\n",
+      "steps: 12000, loss: 5.263617515563965\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[4], line 31\u001B[0m\n\u001B[1;32m     29\u001B[0m   \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[1;32m     30\u001B[0m step \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m---> 31\u001B[0m \u001B[43mloss\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     32\u001B[0m optimizer\u001B[38;5;241m.\u001B[39mstep()\n",
+      "File \u001B[0;32m~/PycharmProjects/mj9/venv/lib/python3.10/site-packages/torch/_tensor.py:487\u001B[0m, in \u001B[0;36mTensor.backward\u001B[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001B[0m\n\u001B[1;32m    477\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m has_torch_function_unary(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m    478\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m handle_torch_function(\n\u001B[1;32m    479\u001B[0m         Tensor\u001B[38;5;241m.\u001B[39mbackward,\n\u001B[1;32m    480\u001B[0m         (\u001B[38;5;28mself\u001B[39m,),\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    485\u001B[0m         inputs\u001B[38;5;241m=\u001B[39minputs,\n\u001B[1;32m    486\u001B[0m     )\n\u001B[0;32m--> 487\u001B[0m \u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mautograd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbackward\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    488\u001B[0m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgradient\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minputs\u001B[49m\n\u001B[1;32m    489\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[0;32m~/PycharmProjects/mj9/venv/lib/python3.10/site-packages/torch/autograd/__init__.py:200\u001B[0m, in \u001B[0;36mbackward\u001B[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001B[0m\n\u001B[1;32m    195\u001B[0m     retain_graph \u001B[38;5;241m=\u001B[39m create_graph\n\u001B[1;32m    197\u001B[0m \u001B[38;5;66;03m# The reason we repeat same the comment below is that\u001B[39;00m\n\u001B[1;32m    198\u001B[0m \u001B[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001B[39;00m\n\u001B[1;32m    199\u001B[0m \u001B[38;5;66;03m# calls in the traceback and some print out the last line\u001B[39;00m\n\u001B[0;32m--> 200\u001B[0m \u001B[43mVariable\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_execution_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun_backward\u001B[49m\u001B[43m(\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001B[39;49;00m\n\u001B[1;32m    201\u001B[0m \u001B[43m    \u001B[49m\u001B[43mtensors\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgrad_tensors_\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mretain_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcreate_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    202\u001B[0m \u001B[43m    \u001B[49m\u001B[43mallow_unreachable\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maccumulate_grad\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n",
+      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "vocab_size = 20000\n",
+    "embed_size = 150\n",
+    "batch_size = 5000\n",
+    "vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
+    "vocab.set_default_index(vocab['<unk>'])"
+   ],
+   "metadata": {
+    "id": "N9-wmLOEZ2aV"
+   },
+   "execution_count": 5,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "topk = 5\n",
+    "preds = []\n",
+    "device = 'cuda'\n",
+    "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
+    "model.load_state_dict(torch.load('bigram_nn_model_steps-10000_vocab-20000_embed-150_batch-5000.bin'))\n",
+    "model.eval()\n",
+    "for path in ['challenging-america-word-gap-prediction/dev-0', 'challenging-america-word-gap-prediction/test-A']:\n",
+    "    with lzma.open(f'{path}/in.tsv.xz', 'r') as fh, open(f'{path}/out.tsv', 'w', encoding='utf-8') as f_out:\n",
+    "        for line in fh:\n",
+    "            previous_word = simple_preprocess(line.decode('utf-8').split('\\t')[-2].strip()).split()[-1]\n",
+    "            ixs = torch.tensor(vocab.forward([previous_word])).to(device)\n",
+    "            out = model(ixs)\n",
+    "            top = torch.topk(out[0], topk)\n",
+    "            top_indices = top.indices.tolist()\n",
+    "            top_probs = top.values.tolist()\n",
+    "            top_words = vocab.lookup_tokens(top_indices)\n",
+    "            top_zipped = zip(top_words, top_probs)\n",
+    "            pred = ''\n",
+    "            total_prob = 0\n",
+    "            for word, prob in top_zipped:\n",
+    "                if word != '<unk>':\n",
+    "                    pred += f'{word}:{prob} '\n",
+    "                    total_prob += prob\n",
+    "            unk_prob = 1 - total_prob\n",
+    "            pred += f':{unk_prob}'\n",
+    "            f_out.write(pred + '\\n')"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "99uioFpVCJL8",
+    "outputId": "d4267cb1-e557-478a-8cf7-91a90db07698"
+   },
+   "execution_count": 24,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ked/PycharmProjects/mj9/challenging-america-word-gap-prediction\n",
+      "394.97\r\n",
+      "/home/ked/PycharmProjects/mj9\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd challenging-america-word-gap-prediction/\n",
+    "!./geval --test-name dev-0\n",
+    "%cd ../"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ]
+}