removed test stuff

.gitignore

@@ -1,2 +1,3 @@
 **/data/*
 /env
+**/*.zip

dqn_wordle.ipynb

@@ -0,0 +1,114 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gym\n",
+    "import gym_wordle\n",
+    "from stable_baselines3 import DQN\n",
+    "import numpy as np\n",
+    "import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "env = gym.make(\"Wordle-v0\")\n",
+    "\n",
+    "print(env)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_timesteps = 100000\n",
+    "model = DQN(\"MlpPolicy\", env, verbose=0)\n",
+    "model.learn(total_timesteps=total_timesteps, progress_bar=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test(model):\n",
+    "\n",
+    "    end_rewards = []\n",
+    "\n",
+    "    for i in range(1000):\n",
+    "        \n",
+    "        state = env.reset()\n",
+    "\n",
+    "        done = False\n",
+    "\n",
+    "        while not done:\n",
+    "\n",
+    "            action, _states = model.predict(state, deterministic=True)\n",
+    "\n",
+    "            state, reward, done, info = env.step(action)\n",
+    "            \n",
+    "        end_rewards.append(reward == 0)\n",
+    "        \n",
+    "    return np.sum(end_rewards) / len(end_rewards)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save(\"dqn_wordle\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = DQN.load(\"dqn_wordle\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(test(model))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

test.py

@@ -1,61 +0,0 @@
-
-from torch.utils.data import Dataset
-from transformers import BertGenerationEncoder, BertGenerationDecoder, EncoderDecoderModel, BertTokenizer
-from tqdm import tqdm as progress_bar
-import torch
-import matplotlib
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(device)
-
-encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-base-uncased", bos_token_id=101, eos_token_id=102)
-# add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
-decoder = BertGenerationDecoder.from_pretrained("google-bert/bert-base-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
-model = EncoderDecoderModel(encoder=encoder, decoder=decoder)
-
-# create tokenizer...
-tokenizer = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")
-
-import json
-
-class CodeDataset(Dataset):
-    def __init__(self):
-        with open("data/conala-train.json") as f:
-            self.data = json.load(f)
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        intent = self.data[idx]["rewritten_intent"] if self.data[idx]["rewritten_intent"] else self.data[idx]["intent"]
-        return intent, self.data[idx]["snippet"]
-
-
-optimizer = torch.optim.AdamW(params=model.parameters(), lr=1e-3)
-dataloader = CodeDataset()
-model = model.to(device)
-
-losses = []
-epochs = 10
-for i in range(epochs):
-
-    epoch_loss = 0
-
-    for idx, (question, answer) in progress_bar(enumerate(dataloader), total=len(dataloader)):
-
-        input_ids = tokenizer(question, add_special_tokens=False, return_tensors="pt").input_ids.to(device)
-        label_ids = tokenizer(answer, return_tensors="pt").input_ids.to(device)
-
-        loss = model(input_ids=input_ids, decoder_input_ids=label_ids, labels=label_ids).loss
-
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
-        epoch_loss += loss.item()
-
-    losses.append(epoch_loss)
-
-plt.plot(losses, color="green", label="Training Loss")
-plt.legend(loc = 'upper left')
-plt.savefig("plot.png")