fix inference and results

README.md

@@ -1 +1,2 @@
 # Rozwiązanie dla wariantu kontekstu dwóch następnych słów (reszta z dzielenia przez 3 = 2)
+# Bugfixed inference and uploaded correctly generated results on 24.05.23.

gonito.yaml

@@ -4,8 +4,10 @@ tags:
   - right-context
   - trigrams
 params:
-  vocab_size: 5000
+  vocab_size: 20000
-  embed_size: 50
+  embed_size: 150
-  batch_size: 5000
+  batch_size: 512, 1024, 4096
+  hidden_size: 256, 1024
+  learning_rate: 0.0001, 0.001
 param-files:
   - "*.yaml"

solution.ipynb

@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 3,
    "outputs": [],
    "source": [
     "def simple_preprocess(line):\n",
@@ -63,14 +63,14 @@
     "\n",
     "def build_vocab(file, vocab_size):\n",
     "    try:\n",
-    "        with open(f'vocab_{vocab_size}.pickle', 'rb') as handle:\n",
+    "        with open(f'trigram_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'vocab_{vocab_size}.pickle', 'wb') as handle:\n",
+    "        with open(f'trigram_nn_vocab_{vocab_size}.pickle', 'wb') as handle:\n",
     "            pickle.dump(vocab, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
     "    return vocab\n",
     "\n",
@@ -85,18 +85,18 @@
     "            (self.vocab[t] for t in chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
     "\n",
     "class TrigramNeuralLanguageModel(nn.Module):\n",
-    "    def __init__(self, vocab_size, embed_size):\n",
+    "    def __init__(self, vocab_size, embed_size, hidden_size):\n",
     "        super(TrigramNeuralLanguageModel, self).__init__()\n",
     "        self.embeddings = nn.Embedding(vocab_size, embed_size)\n",
-    "        self.hidden_layer = nn.Linear(2*embed_size, 64)\n",
+    "        self.hidden_layer = nn.Linear(2*embed_size, hidden_size)\n",
-    "        self.output_layer = nn.Linear(64, vocab_size)\n",
+    "        self.output_layer = nn.Linear(hidden_size, vocab_size)\n",
+    "        self.softmax = nn.Softmax(dim=1)\n",
     "\n",
     "    def forward(self, x):\n",
     "        embeds = self.embeddings(x[0]), self.embeddings(x[1])\n",
     "        concat_embed = torch.concat(embeds, dim=1)\n",
     "        z = F.relu(self.hidden_layer(concat_embed))\n",
-    "        softmax = nn.Softmax(dim=1)\n",
+    "        y = self.softmax(self.output_layer(z))\n",
-    "        y = softmax(self.output_layer(z))\n",
     "        return y"
    ],
    "metadata": {
@@ -109,18 +109,20 @@
    "outputs": [],
    "source": [
     "max_steps = -1\n",
-    "vocab_size = 5000\n",
+    "vocab_size = 20000\n",
-    "embed_size = 50\n",
+    "embed_size = 150\n",
-    "batch_size = 5000\n",
+    "batch_size = 1024\n",
+    "hidden_size = 1024\n",
+    "learning_rate = 0.001\n",
     "vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
     "train_dataset = Trigrams('challenging-america-word-gap-prediction/train/in.tsv.xz')\n",
     "if torch.cuda.is_available():\n",
     "  device = 'cuda'\n",
     "else:\n",
     "  raise Exception()\n",
-    "model = TrigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
+    "model = TrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
     "data = DataLoader(train_dataset, batch_size=batch_size)\n",
-    "optimizer = torch.optim.Adam(model.parameters())\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
     "criterion = torch.nn.NLLLoss()\n",
     "\n",
     "model.train()\n",
@@ -132,9 +134,9 @@
     "    ypredicted = model(x)\n",
     "    loss = criterion(torch.log(ypredicted), y)\n",
     "    if step % 1000 == 0:\n",
-    "        print(step, loss)\n",
+    "        print(f'steps: {step}, loss: {loss.item()}')\n",
-    "    if step % 1000 == 0:\n",
+    "        if step != 0:\n",
-    "        torch.save(model.state_dict(), f'model_steps-{step}_vocab-{vocab_size}_embed-{embed_size}_batch-{batch_size}.bin')\n",
+    "            torch.save(model.state_dict(), f'trigram_nn_model_steps-{step}_vocab-{vocab_size}_embed-{embed_size}_batch-{batch_size}_hidden-{hidden_size}_lr-{learning_rate}.bin')\n",
     "    loss.backward()\n",
     "    optimizer.step()\n",
     "    if step == max_steps:\n",
@@ -150,12 +152,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "outputs": [],
    "source": [
-    "vocab_size = 5000\n",
+    "vocab_size = 20000\n",
-    "embed_size = 50\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>'])"
    ],
@@ -165,44 +166,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trigram_nn_model_steps-13000_vocab-20000_embed-150_batch-512_hidden-256_lr-0.0001.bin\n",
+      "512\n",
+      "256\n",
+      "trigram_nn_model_steps-7000_vocab-20000_embed-150_batch-1024_hidden-1024_lr-0.001.bin\n",
+      "1024\n",
+      "1024\n",
+      "trigram_nn_model_steps-6000_vocab-20000_embed-150_batch-4096_hidden-256_lr-0.001.bin\n",
+      "4096\n",
+      "256\n"
+     ]
+    }
+   ],
    "source": [
-    "for model_name in ['model_steps-1000_vocab-5000_embed-50_batch-5000.bin',\n",
+    "for model_name in ['trigram_nn_model_steps-13000_vocab-20000_embed-150_batch-512_hidden-256_lr-0.0001.bin', 'trigram_nn_model_steps-7000_vocab-20000_embed-150_batch-1024_hidden-1024_lr-0.001.bin', 'trigram_nn_model_steps-6000_vocab-20000_embed-150_batch-4096_hidden-256_lr-0.001.bin']:\n",
-    "                   'model_steps-1000_vocab-5000_embed-50_batch-5000.bin', 'model_steps-27000_vocab-5000_embed-50_batch-5000.bin']:\n",
+    "    print(model_name)\n",
+    "    batch_size = int(model_name.split('_')[-3].split('-')[1])\n",
+    "    print(batch_size)\n",
+    "    hidden_size = int(model_name.split('_')[-2].split('-')[1])\n",
+    "    print(hidden_size)\n",
+    "    topk = 10\n",
     "    preds = []\n",
     "    device = 'cuda'\n",
-    "    model = TrigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
+    "    model = TrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
     "    model.load_state_dict(torch.load(model_name))\n",
     "    model.eval()\n",
-    "    j = 0\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",
-    "          right_context = simple_preprocess(line.decode('utf-8').split('\\t')[-1]).split()[:2]\n",
+    "                right_context = simple_preprocess(line.decode('utf-8').split('\\t')[-1].strip()).split()[:2]\n",
     "                x = torch.tensor(vocab.forward([right_context[0]])).to(device), \\\n",
     "                torch.tensor(vocab.forward([right_context[1]])).to(device)\n",
     "                out = model(x)\n",
-    "          top = torch.topk(out[0], 5)\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 = list(zip(top_words, top_probs))\n",
+    "                top_zipped = zip(top_words, top_probs)\n",
     "                pred = ''\n",
-    "          unk = None\n",
+    "                total_prob = 0\n",
-    "          for i, tup in enumerate(top_zipped):\n",
+    "                for word, prob in top_zipped:\n",
-    "            if tup[0] == '<unk>':\n",
+    "                    if word != '<unk>':\n",
-    "              unk = top_zipped.pop(i)\n",
+    "                        pred += f'{word}:{prob} '\n",
-    "          for tup in top_zipped:\n",
+    "                        total_prob += prob\n",
-    "            pred += f'{tup[0]}:{tup[1]}\\t'\n",
+    "                unk_prob = 1 - total_prob\n",
-    "          if unk:\n",
+    "                pred += f':{unk_prob}'\n",
-    "            pred += f':{unk[1]}'\n",
-    "          else:\n",
-    "            pred = pred.rstrip()\n",
     "                f_out.write(pred + '\\n')\n",
-    "          if j % 1000 == 0:\n",
-    "            print(pred)\n",
-    "          j += 1\n",
     "        src=f'{path}/out.tsv'\n",
     "        dst=f\"{path}/{model_name.split('.')[0]}_out.tsv\"\n",
     "        shutil.copy(src, dst)"
@@ -210,6 +225,38 @@
    "metadata": {
     "collapsed": false
    }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ked/PycharmProjects/mj9/challenging-america-word-gap-prediction\n",
+      "300.66\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
+   }
   }
  ],
  "metadata": {