diff --git a/nlp/NLP_project.ipynb b/nlp/NLP_project.ipynb index 5024558..b2d1ba9 100644 --- a/nlp/NLP_project.ipynb +++ b/nlp/NLP_project.ipynb @@ -22,7 +22,7 @@ "base_uri": "https://localhost:8080/" }, "id": "SsVTf5y5eeO3", - "outputId": "ae7de3db-c654-4bc5-8acc-28c05ba7833e" + "outputId": "01f591f7-8e50-4030-9611-d7f435358aad" }, "outputs": [ { @@ -75,12 +75,19 @@ "cell_type": "code", "source": [ "import itertools\n", + "from sklearn.manifold import TSNE\n", + "from sklearn.decomposition import PCA\n", + "import pandas as pd\n", + "import matplotlib.cm as cm\n", + "import numpy as np\n", "class Agent:\n", " def __init__(self, board):\n", " self.model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True, limit=500000)\n", "\n", " nltk.download('wordnet')\n", " self.past_clues = []\n", + " self.embedding_clusters = []\n", + " self.word_clusters = []\n", "\n", " def generate_clue(self, board):\n", " new_cands = self.get_neighbors(board)\n", @@ -96,7 +103,6 @@ "\n", " return clue\n", "\n", - "\n", " def get_neighbors(self, board, n_words=15):\n", " candidate_words = []\n", " new_cands = []\n", @@ -113,6 +119,14 @@ "\n", " try:\n", " neighbors = self.model.most_similar([synonyms], topn=n_words)\n", + " for word in board[\"Red\"]:\n", + " embeddings = []\n", + " words = []\n", + " for similar_word, _ in neighbors:\n", + " words.append(similar_word)\n", + " embeddings.append(self.model[similar_word])\n", + " self.embedding_clusters.append(embeddings)\n", + " self.word_clusters.append(words)\n", " candidate_words.extend(neighbors)\n", " except KeyError:\n", " pass\n", @@ -126,7 +140,6 @@ "\n", " return sorted(list(set((new_cands))), key=lambda x: x[1], reverse = True)\n", "\n", - "\n", " def generate_synonyms(self, word):\n", " synonyms = []\n", " altered_syn = []\n", @@ -152,21 +165,41 @@ " final_clues.append((word,1))\n", "\n", " return final_clues\n", + "\n", + " def create_embeddings(self):\n", + " embedding_clusters = np.array(self.embedding_clusters)\n", + " n, m, k = embedding_clusters.shape\n", + " tsne_model_en_2d = TSNE(perplexity=45, n_components=2, init='pca', n_iter=3500, random_state=32)\n", + " embeddings_en_2d = np.array(tsne_model_en_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2)\n", + " return embeddings_en_2d\n", + "\n", + " def tsne_plot_similar_words(self, title, labels, a, filename=None):\n", + " embedding_clusters = self.create_embeddings()\n", + " word_clusters = self.word_clusters\n", + " plt.figure(figsize=(16, 9))\n", + " colors = cm.rainbow(np.linspace(0, 1, len(labels)))\n", + " for label, embeddings, words, color in zip(labels, embedding_clusters, word_clusters, colors):\n", + " x = embeddings[:, 0]\n", + " y = embeddings[:, 1]\n", + "\n", + " plt.scatter(x, y, c=color, alpha=a, label=label)\n", + " for i, word in enumerate(words):\n", + " plt.annotate(word, alpha=0.5, xy=(x[i], y[i]), xytext=(5, 2),\n", + " textcoords='offset points', ha='right', va='bottom', size=8)\n", + "\n", + " plt.legend(loc=4)\n", + " plt.title(title)\n", + " plt.grid(True)\n", + "\n", + " plt.savefig(\"plot.pdf\")\n", + " plt.show()\n", + "\n", "\n" ], "metadata": { "id": "0z8nn5_6ATQw" }, - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "7sEkF8jpfHbW" - }, - "execution_count": null, + "execution_count": 11, "outputs": [] }, { @@ -305,34 +338,38 @@ " elif word in self.assassin:\n", " self.board[\"Assasin\"] = [i if i != word else 'ASSASSIN' for i in self.board[\"Assasin\"]]\n", " self.assassin.remove(word)\n", - " return" + " return\n", + " def create_plot(self):\n", + " self.spymaster.generate_clue(self.board)\n", + " self.spymaster.tsne_plot_similar_words('Similar words from Google News', self.red, 0.7)" ], "metadata": { "id": "cXU-q1-YWd2f" }, - "execution_count": 9, + "execution_count": 14, "outputs": [] }, { "cell_type": "code", "source": [ "game = Game(\"game_wordpool.txt\")\n", - "game.execute()" + "game.create_plot()\n" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 885 }, "id": "r78c0rTKf04R", - "outputId": "a4fc67a0-0b69-45af-cee6-682a8db2da75" + "outputId": "cc7ce8a8-9365-4961-bdc7-534a007b0cd4" }, - "execution_count": 10, + "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "{'Red': ['COLD', 'POUND', 'BOW', 'CELL', 'QUEEN', 'FIELD', 'PISTOL', 'GIANT'], 'Blue': ['MODEL', 'NOVEL', 'FIRE', 'BOX', 'CROSS', 'LITTER', 'SOUND'], 'Civillian': ['PLATE', 'BERMUDA', 'TRACK', 'ROBIN', 'PARACHUTE', 'PRESS', 'FACE', 'GAS', 'ANGEL'], 'Assasin': ['NINJA']}\n" + "{'Red': ['SINK', 'HOOD', 'KETCHUP', 'ROBIN', 'TELESCOPE', 'SUB', 'CRASH', 'SPOT'], 'Blue': ['CROSS', 'NOVEL', 'CRICKET', 'POISON', 'GERMANY', 'CHARGE', 'PIT'], 'Civillian': ['YARD', 'SCREEN', 'PITCH', 'CLUB', 'BACK', 'CENTER', 'HEAD', 'NET', 'APPLE'], 'Assasin': ['NAIL']}\n" ] }, { @@ -340,54 +377,29 @@ "name": "stderr", "text": [ "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", - "[nltk_data] Package wordnet is already up-to-date!\n" + "[nltk_data] Package wordnet is already up-to-date!\n", + ":109: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " plt.scatter(x, y, c=color, alpha=a, label=label)\n" ] }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Let's play Codenames\n", - "Your color is: red\n", - "the board is:\n", - "['TRACK', 'PARACHUTE', 'ANGEL', 'FACE', 'QUEEN', 'BOW', 'COLD', 'FIRE', 'GIANT', 'MODEL', 'ROBIN', 'BERMUDA', 'PRESS', 'POUND', 'GAS', 'BOX', 'LITTER', 'FIELD', 'PLATE', 'SOUND', 'PISTOL', 'NOVEL', 'CROSS', 'CELL', 'NINJA']\n", - "It's the computer's turn:\n", - "the computer has gone.\n", - "It's your turn; type \"PASS\" to end your turn.\n", - "['ANGEL', 'PRESS', 'MODEL', 'FACE', 'FIELD', 'NOVEL', 'BERMUDA', 'BOW', 'CROSS', 'COLD', 'ROBIN', 'PISTOL', 'QUEEN', 'TRACK', 'NINJA', 'GAS', 'POUND', 'CELL', 'SOUND', 'LITTER', 'GIANT', 'FIRE', 'PLATE', 'PARACHUTE', 'BOX']\n", - "Spymaster is generating a clue...\n", - "{'Red': ['COLD', 'POUND', 'BOW', 'CELL', 'QUEEN', 'FIELD', 'PISTOL', 'GIANT'], 'Blue': ['MODEL', 'NOVEL', 'FIRE', 'BOX', 'CROSS', 'LITTER', 'SOUND'], 'Civillian': ['PLATE', 'BERMUDA', 'TRACK', 'ROBIN', 'PARACHUTE', 'PRESS', 'FACE', 'GAS', 'ANGEL'], 'Assasin': ['NINJA']}\n", - "Your clue is: whales, 2\n", - "Guess a word: GIANT\n", - "q: GIANT\n", - "Correct. You have 1 more guess(es).\n", - "Guess a word: BERMUDA\n", - "q: BERMUDA\n", - "Your guess was incorrect. Your turn is over.\n", - "\n", - "It's the computer's turn:\n", - "the computer has gone.\n", - "It's your turn; type \"PASS\" to end your turn.\n", - "['POUND', 'MODEL', 'SOUND', 'LITTER', 'COLD', 'PARACHUTE', 'FIRE', 'ROBIN', 'NINJA', 'CELL', 'PRESS', 'TRACK', 'GAS', 'FIELD', 'FACE', 'CROSS', 'QUEEN', 'PISTOL', 'BOX', 'NOVEL', 'PLATE', 'BOW', 'ANGEL']\n", - "Spymaster is generating a clue...\n", - "{'Red': ['COLD', 'POUND', 'BOW', 'CELL', 'QUEEN', 'FIELD', 'PISTOL', 'RED'], 'Blue': ['MODEL', 'NOVEL', 'FIRE', 'BOX', 'CROSS', 'LITTER', 'SOUND'], 'Civillian': ['PLATE', 'NEUTRAL', 'TRACK', 'ROBIN', 'PARACHUTE', 'PRESS', 'FACE', 'GAS', 'ANGEL'], 'Assasin': ['NINJA']}\n", - "Your clue is: punts, 2\n", - "Guess a word: FIELD\n", - "q: FIELD\n", - "Correct. You have 1 more guess(es).\n", - "Guess a word: NINJA\n", - "q: NINJA\n", - "Your guess was incorrect. Your turn is over.\n", - "\n", - "Assassin!! Game over! Computer wins!\n", - "['QUEEN', 'MODEL', 'ANGEL', 'LITTER', 'SOUND', 'PRESS', 'GAS', 'FACE', 'COLD', 'POUND', 'BOW', 'NOVEL', 'TRACK', 'FIRE', 'PISTOL', 'PARACHUTE', 'CROSS', 'PLATE', 'ROBIN', 'BOX', 'CELL']\n" - ] + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} } ] }, { "cell_type": "code", - "source": [], + "source": [ + "game2 = Game(\"game_wordpool.txt\")\n", + "game2.execute()" + ], "metadata": { "id": "MRcwSEbtglxR" },