├── .ipynb_checkpoints ├── BERT Word Embeddings-checkpoint.ipynb ├── BanditPAM-checkpoint.ipynb ├── Image Clustering-checkpoint.ipynb ├── NLTK Tokenizers-checkpoint.ipynb └── plot_kmedoids_digits-checkpoint.py ├── BERT Word Embeddings.ipynb ├── BanditPAM.ipynb ├── Image Clustering.ipynb ├── LICENSE ├── NLTK Tokenizers.ipynb ├── README.md └── plot_kmedoids_digits.py /.ipynb_checkpoints/BERT Word Embeddings-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# BERT Word Embeddings\n", 8 | "\n", 9 | "Three ways to obtain word embeddings from BERT:\n", 10 | "- context-free\n", 11 | "- context-averaged\n", 12 | "- " 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 1, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "#!pip install torch\n", 22 | "#!pip install transformers" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 7, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "from transformers import BertTokenizer, BertModel\n", 32 | "import pandas as pd\n", 33 | "import numpy as np\n", 34 | "import nltk\n", 35 | "import torch" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 3, 41 | "metadata": {}, 42 | "outputs": [ 43 | { 44 | "data": { 45 | "application/vnd.jupyter.widget-view+json": { 46 | "model_id": "642b7dcad437414ebdaff58a032d2e35", 47 | "version_major": 2, 48 | "version_minor": 0 49 | }, 50 | "text/plain": [ 51 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…" 52 | ] 53 | }, 54 | "metadata": {}, 55 | "output_type": "display_data" 56 | }, 57 | { 58 | "name": "stdout", 59 | "output_type": "stream", 60 | "text": [ 61 | "\n" 62 | ] 63 | }, 64 | { 65 | "data": { 66 | "application/vnd.jupyter.widget-view+json": { 67 | "model_id": "292a2daf07e44455b863113d890f9109", 68 | "version_major": 2, 69 | "version_minor": 0 70 | }, 71 | "text/plain": [ 72 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=440473133.0, style=ProgressStyle(descri…" 73 | ] 74 | }, 75 | "metadata": {}, 76 | "output_type": "display_data" 77 | }, 78 | { 79 | "name": "stdout", 80 | "output_type": "stream", 81 | "text": [ 82 | "\n" 83 | ] 84 | }, 85 | { 86 | "data": { 87 | "application/vnd.jupyter.widget-view+json": { 88 | "model_id": "799d26a232d54abbbe603d1cb61d7795", 89 | "version_major": 2, 90 | "version_minor": 0 91 | }, 92 | "text/plain": [ 93 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…" 94 | ] 95 | }, 96 | "metadata": {}, 97 | "output_type": "display_data" 98 | }, 99 | { 100 | "name": "stdout", 101 | "output_type": "stream", 102 | "text": [ 103 | "\n" 104 | ] 105 | } 106 | ], 107 | "source": [ 108 | "# Loading the pre-trained BERT model\n", 109 | "###################################\n", 110 | "# Embeddings will be derived from\n", 111 | "# the outputs of this model\n", 112 | "model = BertModel.from_pretrained('bert-base-uncased',\n", 113 | " output_hidden_states = True,\n", 114 | " )\n", 115 | "\n", 116 | "# Setting up the tokenizer\n", 117 | "###################################\n", 118 | "# This is the same tokenizer that\n", 119 | "# was used in the model to generate \n", 120 | "# embeddings to ensure consistency\n", 121 | "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 29, 127 | "metadata": {}, 128 | "outputs": [], 129 | "source": [ 130 | "# Text corpus\n", 131 | "##############\n", 132 | "# These sentences show the different\n", 133 | "# forms of the word 'bank' to show the\n", 134 | "# value of contextualized embeddings\n", 135 | "\n", 136 | "texts = [\"bank\",\n", 137 | " \"The river bank was flooded.\",\n", 138 | " \"The bank vault was robust.\",\n", 139 | " \"He had to bank on her for support.\",\n", 140 | " \"The bank was out of money.\",\n", 141 | " \"The bank teller was a man.\"]" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": 30, 147 | "metadata": {}, 148 | "outputs": [], 149 | "source": [ 150 | "def bert_text_preparation(text, tokenizer):\n", 151 | " \"\"\"Preparing the input for BERT\n", 152 | " \n", 153 | " Takes a string argument and performs\n", 154 | " pre-processing like adding special tokens,\n", 155 | " tokenization, tokens to ids, and tokens to\n", 156 | " segment ids. All tokens are mapped to seg-\n", 157 | " ment id = 1.\n", 158 | " \n", 159 | " Args:\n", 160 | " text (str): Text to be converted\n", 161 | " tokenizer (obj): Tokenizer object\n", 162 | " to convert text into BERT-re-\n", 163 | " adable tokens and ids\n", 164 | " \n", 165 | " Returns:\n", 166 | " list: List of BERT-readable tokens\n", 167 | " obj: Torch tensor with token ids\n", 168 | " obj: Torch tensor segment ids\n", 169 | " \n", 170 | " \n", 171 | " \"\"\"\n", 172 | " marked_text = \"[CLS] \" + text + \" [SEP]\"\n", 173 | " tokenized_text = tokenizer.tokenize(marked_text)\n", 174 | " indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)\n", 175 | " segments_ids = [1]*len(indexed_tokens)\n", 176 | "\n", 177 | " # Convert inputs to PyTorch tensors\n", 178 | " tokens_tensor = torch.tensor([indexed_tokens])\n", 179 | " segments_tensors = torch.tensor([segments_ids])\n", 180 | "\n", 181 | " return tokenized_text, tokens_tensor, segments_tensors\n", 182 | " \n", 183 | "def get_bert_embeddings(tokens_tensor, segments_tensors, model):\n", 184 | " \"\"\"Get embeddings from an embedding model\n", 185 | " \n", 186 | " Args:\n", 187 | " tokens_tensor (obj): Torch tensor size [n_tokens]\n", 188 | " with token ids for each token in text\n", 189 | " segments_tensors (obj): Torch tensor size [n_tokens]\n", 190 | " with segment ids for each token in text\n", 191 | " model (obj): Embedding model to generate embeddings\n", 192 | " from token and segment ids\n", 193 | " \n", 194 | " Returns:\n", 195 | " list: List of list of floats of size\n", 196 | " [n_tokens, n_embedding_dimensions]\n", 197 | " containing embeddings for each token\n", 198 | " \n", 199 | " \"\"\"\n", 200 | " \n", 201 | " # Gradient calculation id disabled\n", 202 | " # Model is in inference mode\n", 203 | " with torch.no_grad():\n", 204 | " outputs = model(tokens_tensor, segments_tensors)\n", 205 | " # Removing the first hidden state\n", 206 | " # The first state is the input state\n", 207 | " hidden_states = outputs[2][1:]\n", 208 | "\n", 209 | " # Getting embeddings from the final BERT layer\n", 210 | " token_embeddings = hidden_states[-1]\n", 211 | " # Collapsing the tensor into 1-dimension\n", 212 | " token_embeddings = torch.squeeze(token_embeddings, dim=0)\n", 213 | " # Converting torchtensors to lists\n", 214 | " list_token_embeddings = [token_embed.tolist() for token_embed in token_embeddings]\n", 215 | "\n", 216 | " return list_token_embeddings" 217 | ] 218 | }, 219 | { 220 | "cell_type": "code", 221 | "execution_count": 31, 222 | "metadata": {}, 223 | "outputs": [], 224 | "source": [ 225 | "# Getting embeddings for the target\n", 226 | "# word in all given contexts\n", 227 | "target_word_embeddings = []\n", 228 | "\n", 229 | "for text in texts:\n", 230 | " tokenized_text, tokens_tensor, segments_tensors = bert_text_preparation(text, tokenizer)\n", 231 | " list_token_embeddings = get_bert_embeddings(tokens_tensor, segments_tensors, model)\n", 232 | " \n", 233 | " # Find the position 'bank' in list of tokens\n", 234 | " word_index = tokenized_text.index('bank')\n", 235 | " # Get the embedding for bank\n", 236 | " word_embedding = list_token_embeddings[word_index]\n", 237 | "\n", 238 | " target_word_embeddings.append(word_embedding)" 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 42, 244 | "metadata": {}, 245 | "outputs": [], 246 | "source": [ 247 | "from scipy.spatial.distance import cosine\n", 248 | "\n", 249 | "# Calculating the distance between the\n", 250 | "# embeddings of 'bank' in all the\n", 251 | "# given contexts of the word\n", 252 | "\n", 253 | "list_of_distances = []\n", 254 | "for text1, embed1 in zip(texts, target_word_embeddings):\n", 255 | " for text2, embed2 in zip(texts, target_word_embeddings):\n", 256 | " cos_dist = 1 - cosine(embed1, embed2)\n", 257 | " list_of_distances.append([text1, text2, cos_dist])\n", 258 | "\n", 259 | "distances_df = pd.DataFrame(list_of_distances, columns=['text1', 'text2', 'distance'])" 260 | ] 261 | }, 262 | { 263 | "cell_type": "code", 264 | "execution_count": 43, 265 | "metadata": {}, 266 | "outputs": [ 267 | { 268 | "data": { 269 | "text/html": [ 270 | "
\n", 271 | "\n", 284 | "\n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | "
text1text2distance
0bankbank1.000000
1bankThe river bank was flooded.0.338063
2bankThe bank vault was robust.0.494098
3bankHe had to bank on her for support.0.256140
4bankThe bank was out of money.0.469942
5bankThe bank teller was a man.0.466020
\n", 332 | "
" 333 | ], 334 | "text/plain": [ 335 | " text1 text2 distance\n", 336 | "0 bank bank 1.000000\n", 337 | "1 bank The river bank was flooded. 0.338063\n", 338 | "2 bank The bank vault was robust. 0.494098\n", 339 | "3 bank He had to bank on her for support. 0.256140\n", 340 | "4 bank The bank was out of money. 0.469942\n", 341 | "5 bank The bank teller was a man. 0.466020" 342 | ] 343 | }, 344 | "execution_count": 43, 345 | "metadata": {}, 346 | "output_type": "execute_result" 347 | } 348 | ], 349 | "source": [ 350 | "distances_df[distances_df.text1 == 'bank']" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": 44, 356 | "metadata": {}, 357 | "outputs": [ 358 | { 359 | "data": { 360 | "text/html": [ 361 | "
\n", 362 | "\n", 375 | "\n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | "
text1text2distance
12The bank vault was robust.bank0.494098
13The bank vault was robust.The river bank was flooded.0.523325
14The bank vault was robust.The bank vault was robust.1.000000
15The bank vault was robust.He had to bank on her for support.0.416074
16The bank vault was robust.The bank was out of money.0.759213
17The bank vault was robust.The bank teller was a man.0.867661
\n", 423 | "
" 424 | ], 425 | "text/plain": [ 426 | " text1 text2 distance\n", 427 | "12 The bank vault was robust. bank 0.494098\n", 428 | "13 The bank vault was robust. The river bank was flooded. 0.523325\n", 429 | "14 The bank vault was robust. The bank vault was robust. 1.000000\n", 430 | "15 The bank vault was robust. He had to bank on her for support. 0.416074\n", 431 | "16 The bank vault was robust. The bank was out of money. 0.759213\n", 432 | "17 The bank vault was robust. The bank teller was a man. 0.867661" 433 | ] 434 | }, 435 | "execution_count": 44, 436 | "metadata": {}, 437 | "output_type": "execute_result" 438 | } 439 | ], 440 | "source": [ 441 | "distances_df[distances_df.text1 == 'The bank vault was robust.']" 442 | ] 443 | }, 444 | { 445 | "cell_type": "code", 446 | "execution_count": 46, 447 | "metadata": {}, 448 | "outputs": [ 449 | { 450 | "name": "stdout", 451 | "output_type": "stream", 452 | "text": [ 453 | "Distance between context-free and context-averaged = 0.6590343155941127\n" 454 | ] 455 | } 456 | ], 457 | "source": [ 458 | "cos_dist = 1 - cosine(target_word_embeddings[0], np.sum(target_word_embeddings, axis=0))\n", 459 | "print(f'Distance between context-free and context-averaged = {cos_dist}')" 460 | ] 461 | }, 462 | { 463 | "cell_type": "code", 464 | "execution_count": null, 465 | "metadata": {}, 466 | "outputs": [], 467 | "source": [] 468 | } 469 | ], 470 | "metadata": { 471 | "kernelspec": { 472 | "display_name": "Python 3", 473 | "language": "python", 474 | "name": "python3" 475 | }, 476 | "language_info": { 477 | "codemirror_mode": { 478 | "name": "ipython", 479 | "version": 3 480 | }, 481 | "file_extension": ".py", 482 | "mimetype": "text/x-python", 483 | "name": "python", 484 | "nbconvert_exporter": "python", 485 | "pygments_lexer": "ipython3", 486 | "version": "3.8.3" 487 | } 488 | }, 489 | "nbformat": 4, 490 | "nbformat_minor": 4 491 | } 492 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/BanditPAM-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "%matplotlib inline" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 2, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "import numpy as np\n", 19 | "import matplotlib.pyplot as plt\n", 20 | "\n", 21 | "from sklearn.cluster import KMeans\n", 22 | "from sklearn_extra.cluster import KMedoids\n", 23 | "from sklearn.datasets import load_digits\n", 24 | "from sklearn.decomposition import PCA\n", 25 | "from sklearn.preprocessing import scale\n", 26 | "np.random.seed(42)" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 3, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "digits = load_digits()\n", 36 | "data = scale(digits.data)\n", 37 | "n_digits = len(np.unique(digits.target))" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": null, 43 | "metadata": {}, 44 | "outputs": [], 45 | "source": [ 46 | "reduced_data = PCA(n_components=2).fit_transform(data)\n", 47 | "\n", 48 | "# Step size of the mesh. Decrease to increase the quality of the VQ.\n", 49 | "h = 0.02 # point in the mesh [x_min, m_max]x[y_min, y_max].\n", 50 | "\n", 51 | "# Plot the decision boundary. For that, we will assign a color to each\n", 52 | "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n", 53 | "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n", 54 | "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", 55 | "\n", 56 | "plt.figure()\n", 57 | "plt.clf()\n", 58 | "\n", 59 | "plt.suptitle(\n", 60 | " \"Comparing multiple K-Medoids metrics to K-Means and each other\",\n", 61 | " fontsize=14,\n", 62 | ")\n", 63 | "\n", 64 | "\n", 65 | "selected_models = [\n", 66 | " (\n", 67 | " KMedoids(metric=\"manhattan\", n_clusters=n_digits),\n", 68 | " \"KMedoids (manhattan)\",\n", 69 | " ),\n", 70 | " (\n", 71 | " KMedoids(metric=\"euclidean\", n_clusters=n_digits),\n", 72 | " \"KMedoids (euclidean)\",\n", 73 | " ),\n", 74 | " (KMedoids(metric=\"cosine\", n_clusters=n_digits), \"KMedoids (cosine)\"),\n", 75 | " (KMeans(n_clusters=n_digits), \"KMeans\"),\n", 76 | "]\n", 77 | "\n", 78 | "plot_rows = int(np.ceil(len(selected_models) / 2.0))\n", 79 | "plot_cols = 2\n", 80 | "\n", 81 | "for i, (model, description) in enumerate(selected_models):\n", 82 | " print(description)\n", 83 | " # Obtain labels for each point in mesh. Use last trained model.\n", 84 | " model.fit(reduced_data)\n", 85 | " Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n", 86 | "\n", 87 | " # Put the result into a color plot\n", 88 | " Z = Z.reshape(xx.shape)\n", 89 | " plt.subplot(plot_cols, plot_rows, i + 1)\n", 90 | " plt.imshow(\n", 91 | " Z,\n", 92 | " interpolation=\"nearest\",\n", 93 | " extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n", 94 | " cmap=plt.cm.Paired,\n", 95 | " aspect=\"auto\",\n", 96 | " origin=\"lower\",\n", 97 | " )\n", 98 | "\n", 99 | " plt.plot(\n", 100 | " reduced_data[:, 0], reduced_data[:, 1], \"k.\", markersize=2, alpha=0.3\n", 101 | " )\n", 102 | " # Plot the centroids as a white X\n", 103 | " centroids = model.cluster_centers_\n", 104 | " plt.scatter(\n", 105 | " centroids[:, 0],\n", 106 | " centroids[:, 1],\n", 107 | " marker=\"x\",\n", 108 | " s=169,\n", 109 | " linewidths=3,\n", 110 | " color=\"w\",\n", 111 | " zorder=10,\n", 112 | " )\n", 113 | " plt.title(description)\n", 114 | " plt.xlim(x_min, x_max)\n", 115 | " plt.ylim(y_min, y_max)\n", 116 | " plt.xticks(())\n", 117 | " plt.yticks(())\n", 118 | "\n", 119 | "\n", 120 | "plt.show()" 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": null, 126 | "metadata": {}, 127 | "outputs": [], 128 | "source": [] 129 | } 130 | ], 131 | "metadata": { 132 | "kernelspec": { 133 | "display_name": "Python 3", 134 | "language": "python", 135 | "name": "python3" 136 | }, 137 | "language_info": { 138 | "codemirror_mode": { 139 | "name": "ipython", 140 | "version": 3 141 | }, 142 | "file_extension": ".py", 143 | "mimetype": "text/x-python", 144 | "name": "python", 145 | "nbconvert_exporter": "python", 146 | "pygments_lexer": "ipython3", 147 | "version": "3.8.3" 148 | } 149 | }, 150 | "nbformat": 4, 151 | "nbformat_minor": 4 152 | } 153 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Image Clustering-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Image Clustering with K-Means\n", 8 | "\n", 9 | "References:\n", 10 | "- Tutorial on PCA vs ZCA: http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/\n", 11 | "- Functions for image featurization: https://machinelearningmastery.com/image-augmentation-deep-learning-keras/ \n", 12 | "- Kmeans with VCG16 https://towardsdatascience.com/how-to-cluster-images-based-on-visual-similarity-cd6e7209fe34 \n", 13 | "- Kmeans with Resnet50 https://towardsdatascience.com/image-clustering-using-transfer-learning-df5862779571\n", 14 | " \n", 15 | "Things we want to do in this tutorial:\n", 16 | "- K-means clustering on MNIST data (standardization + whitening) -- decent quality\n", 17 | "- K-means clustering on MNIST data (standardization + whitening + rotations) -- worse quality\n", 18 | "- K-means clustering on MNIST data (standardization + whitening + rotations + transfer learning - VCG16 and Resnet50 with PCA) -- significant improvement in clustering quality" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 1, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "from keras.datasets import mnist\n", 28 | "from keras.preprocessing.image import ImageDataGenerator\n" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 20, 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | "(x_train, y_train), (_, _) = mnist.load_data()" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 21, 43 | "metadata": {}, 44 | "outputs": [ 45 | { 46 | "data": { 47 | "text/plain": [ 48 | "(60000, 28, 28)" 49 | ] 50 | }, 51 | "execution_count": 21, 52 | "metadata": {}, 53 | "output_type": "execute_result" 54 | } 55 | ], 56 | "source": [ 57 | "x_train.shape" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 28, 63 | "metadata": {}, 64 | "outputs": [], 65 | "source": [ 66 | "datagen = ImageDataGenerator(featurewise_center=True,\n", 67 | " featurewise_std_normalization=True,\n", 68 | " #zca_whitening=True\n", 69 | " )" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 29, 75 | "metadata": {}, 76 | "outputs": [], 77 | "source": [ 78 | "datagen.fit(x_train.reshape(x_train.shape[0], 28, 28, 1).astype('float32'))" 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": 31, 84 | "metadata": {}, 85 | "outputs": [ 86 | { 87 | "name": "stdout", 88 | "output_type": "stream", 89 | "text": [ 90 | "(10, 28, 28)\n" 91 | ] 92 | }, 93 | { 94 | "data": { 95 | "image/png": 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\n", 96 | "text/plain": [ 97 | "
" 98 | ] 99 | }, 100 | "metadata": { 101 | "needs_background": "light" 102 | }, 103 | "output_type": "display_data" 104 | } 105 | ], 106 | "source": [ 107 | "import matplotlib.pyplot as pyplot\n", 108 | "for X_batch, y_batch in datagen.flow(x_train.reshape(x_train.shape[0], 28, 28, 1).astype('float32'),\n", 109 | " y_train,\n", 110 | " batch_size=10):\n", 111 | " print(X_batch.reshape(10, 28, 28).shape)\n", 112 | " # create a grid of 3x3 images\n", 113 | " for i in range(0, 9):\n", 114 | " pyplot.subplot(330 + 1 + i)\n", 115 | " pyplot.imshow(X_batch[i].reshape(28, 28), cmap=pyplot.get_cmap('gray'))\n", 116 | " # show the plot\n", 117 | " pyplot.show()\n", 118 | " break" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 11, 124 | "metadata": {}, 125 | "outputs": [ 126 | { 127 | "data": { 128 | "text/plain": [ 129 | "
" 130 | ] 131 | }, 132 | "metadata": {}, 133 | "output_type": "display_data" 134 | }, 135 | { 136 | "data": { 137 | "image/png": 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\n", 138 | "text/plain": [ 139 | "
" 140 | ] 141 | }, 142 | "metadata": { 143 | "needs_background": "light" 144 | }, 145 | "output_type": "display_data" 146 | } 147 | ], 148 | "source": [ 149 | "import matplotlib.pyplot as plt\n", 150 | "plt.gray();\n", 151 | "plt.figure(figsize = (5,5));\n", 152 | "for i in range(9):\n", 153 | " plt.subplot(3,3,i+1);\n", 154 | " plt.imshow(x_train[i]);" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 32, 160 | "metadata": {}, 161 | "outputs": [], 162 | "source": [ 163 | "class MnistDataset:\n", 164 | " \n", 165 | " def __init__(self, zca_whitening=False, rotate=False):\n", 166 | " (self.x_train, self.y_train), (_, _) = mnist.load_data()\n", 167 | " self.width = 28 # image width in pixels\n", 168 | " self.height = 28 # image height in pixels\n", 169 | " self.channels = 1\n", 170 | " if zca_whitening:\n", 171 | " _zca_whitening()\n", 172 | " if rotate:\n", 173 | " _random_rotations()\n", 174 | " \n", 175 | " def _process_for_imagedatagenerator(self, image_matrix):\n", 176 | " \"\"\"Convert image matrix for processing with ImageDataGenerator\"\"\"\n", 177 | " reshaped_matrix = image_matrix \\\n", 178 | " .reshape((image_matrix.shape[0], self.width, self.height, self.channels))\n", 179 | " return reshaped_matrix.astype('float32')\n", 180 | " \n", 181 | " def _zca_whitening(self):\n", 182 | " \"\"\"ZCA whitening of dataset images\"\"\"\n", 183 | " x_processed = _process_for_imagedatagenerator(self.x_train)\n", 184 | " datagen = ImageDataGenerator(zca_whitening=True)\n", 185 | " datagen.fit(x_processed)\n", 186 | " for x_batch, y_batch in datagen.flow(x_processed,\n", 187 | " self.y_train,\n", 188 | " batch_size=x_processed.shape[0]):\n", 189 | " self.x_train = x_batch.reshape(x_processed.shape[0], self.width, self.height)\n", 190 | " break\n", 191 | " \n", 192 | " def _random_rotations(self):\n", 193 | " \"\"\"Rotate images by 90 degrees\"\"\"\n", 194 | " x_processed = _process_for_imagedatagenerator(self.x_train)\n", 195 | " datagen = ImageDataGenerator(rotation_range=90)\n", 196 | " datagen.fit(x_processed)\n", 197 | " for x_batch, y_batch in datagen.flow(x_processed,\n", 198 | " self.y_train,\n", 199 | " batch_size=x_processed.shape[0]):\n", 200 | " self.x_train = x_batch.reshape(x_processed.shape[0], self.width, self.height)\n", 201 | " break" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": null, 207 | "metadata": {}, 208 | "outputs": [], 209 | "source": [ 210 | "baseline_images = MnistDataset()\n", 211 | "whitened" 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": null, 217 | "metadata": {}, 218 | "outputs": [], 219 | "source": [ 220 | "X = x_train.reshape(len(x_train),-1)\n", 221 | "Y = y_train" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": null, 227 | "metadata": {}, 228 | "outputs": [], 229 | "source": [] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": null, 234 | "metadata": {}, 235 | "outputs": [], 236 | "source": [] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": null, 241 | "metadata": {}, 242 | "outputs": [], 243 | "source": [] 244 | } 245 | ], 246 | "metadata": { 247 | "kernelspec": { 248 | "display_name": "Python 3", 249 | "language": "python", 250 | "name": "python3" 251 | }, 252 | "language_info": { 253 | "codemirror_mode": { 254 | "name": "ipython", 255 | "version": 3 256 | }, 257 | "file_extension": ".py", 258 | "mimetype": "text/x-python", 259 | "name": "python", 260 | "nbconvert_exporter": "python", 261 | "pygments_lexer": "ipython3", 262 | "version": "3.8.3" 263 | } 264 | }, 265 | "nbformat": 4, 266 | "nbformat_minor": 4 267 | } 268 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/NLTK Tokenizers-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 8, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import nltk\n", 10 | "from nltk.tokenize import (TreebankWordTokenizer,\n", 11 | " word_tokenize,\n", 12 | " wordpunct_tokenize,\n", 13 | " TweetTokenizer,\n", 14 | " MWETokenizer)\n", 15 | "sentence = \"It's true, Ms. Martha Jones! #Truth\"" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 2, 21 | "metadata": {}, 22 | "outputs": [ 23 | { 24 | "name": "stdout", 25 | "output_type": "stream", 26 | "text": [ 27 | "Whitespace tokenization = [\"It's\", 'true,', 'Ms.', 'Martha', 'Jones!', '#Truth']\n" 28 | ] 29 | } 30 | ], 31 | "source": [ 32 | "print(f'Whitespace tokenization = {sentence.split()}')" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 3, 38 | "metadata": {}, 39 | "outputs": [ 40 | { 41 | "name": "stdout", 42 | "output_type": "stream", 43 | "text": [ 44 | "Punctuation-based tokenization = ['It', \"'\", 's', 'true', ',', 'Ms', '.', 'Martha', 'Jones', '!', '#', 'Truth']\n" 45 | ] 46 | } 47 | ], 48 | "source": [ 49 | "print(f'Punctuation-based tokenization = {wordpunct_tokenize(sentence)}')" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 4, 55 | "metadata": {}, 56 | "outputs": [ 57 | { 58 | "name": "stdout", 59 | "output_type": "stream", 60 | "text": [ 61 | "Multi-word expression (MWE) tokenization = ['It', \"'s\", 'true', ',', 'Ms.', 'Martha_Jones', '!', '#', 'Truth']\n" 62 | ] 63 | } 64 | ], 65 | "source": [ 66 | "tokenizer = MWETokenizer()\n", 67 | "tokenizer.add_mwe(('Martha', 'Jones'))\n", 68 | "print(f'Multi-word expression (MWE) tokenization = {tokenizer.tokenize(word_tokenize(sentence))}')" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 5, 74 | "metadata": {}, 75 | "outputs": [ 76 | { 77 | "name": "stdout", 78 | "output_type": "stream", 79 | "text": [ 80 | "Tweet-rules based tokenization = [\"It's\", 'true', ',', 'Ms', '.', 'Martha', 'Jones', '!', '#Truth']\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "tokenizer = TweetTokenizer()\n", 86 | "print(f'Tweet-rules based tokenization = {tokenizer.tokenize(sentence)}')" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 6, 92 | "metadata": {}, 93 | "outputs": [ 94 | { 95 | "name": "stdout", 96 | "output_type": "stream", 97 | "text": [ 98 | "Default/Treebank tokenization = ['It', \"'s\", 'true', ',', 'Ms.', 'Martha', 'Jones', '!', '#', 'Truth']\n" 99 | ] 100 | } 101 | ], 102 | "source": [ 103 | "tokenizer = TreebankWordTokenizer()\n", 104 | "print(f'Default/Treebank tokenization = {tokenizer.tokenize(sentence)}')" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": null, 110 | "metadata": {}, 111 | "outputs": [], 112 | "source": [] 113 | } 114 | ], 115 | "metadata": { 116 | "kernelspec": { 117 | "display_name": "Python 3", 118 | "language": "python", 119 | "name": "python3" 120 | }, 121 | "language_info": { 122 | "codemirror_mode": { 123 | "name": "ipython", 124 | "version": 3 125 | }, 126 | "file_extension": ".py", 127 | "mimetype": "text/x-python", 128 | "name": "python", 129 | "nbconvert_exporter": "python", 130 | "pygments_lexer": "ipython3", 131 | "version": "3.8.3" 132 | } 133 | }, 134 | "nbformat": 4, 135 | "nbformat_minor": 4 136 | } 137 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/plot_kmedoids_digits-checkpoint.py: -------------------------------------------------------------------------------- 1 | """ 2 | ============================================================= 3 | A demo of K-Medoids clustering on the handwritten digits data 4 | ============================================================= 5 | In this example we compare different pairwise distance 6 | metrics for K-Medoids. 7 | """ 8 | import numpy as np 9 | import matplotlib.pyplot as plt 10 | 11 | from sklearn.cluster import KMeans 12 | from sklearn_extra.cluster import KMedoids 13 | from sklearn.datasets import load_digits 14 | from sklearn.decomposition import PCA 15 | from sklearn.preprocessing import scale 16 | 17 | print(__doc__) 18 | 19 | # Authors: Timo Erkkilä 20 | # Antti Lehmussola 21 | # Kornel Kiełczewski 22 | # License: BSD 3 clause 23 | 24 | np.random.seed(42) 25 | 26 | digits = load_digits() 27 | data = scale(digits.data) 28 | data = data[0:10] 29 | n_digits = len(np.unique(digits.target)) 30 | 31 | reduced_data = PCA(n_components=2).fit_transform(data) 32 | 33 | # Step size of the mesh. Decrease to increase the quality of the VQ. 34 | h = 0.02 # point in the mesh [x_min, m_max]x[y_min, y_max]. 35 | 36 | # Plot the decision boundary. For that, we will assign a color to each 37 | x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1 38 | y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1 39 | xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) 40 | 41 | plt.figure() 42 | plt.clf() 43 | 44 | plt.suptitle( 45 | "Comparing multiple K-Medoids metrics to K-Means and each other", 46 | fontsize=14, 47 | ) 48 | 49 | 50 | selected_models = [ 51 | #( 52 | # KMedoids(metric="manhattan", n_clusters=n_digits), 53 | # "KMedoids (manhattan)", 54 | #), 55 | #( 56 | # KMedoids(metric="euclidean", n_clusters=n_digits), 57 | # "KMedoids (euclidean)", 58 | #), 59 | (KMedoids(metric="cosine", n_clusters=n_digits), "KMedoids (cosine)"), 60 | (KMeans(n_clusters=n_digits), "KMeans"), 61 | ] 62 | 63 | plot_rows = int(np.ceil(len(selected_models) / 2.0)) 64 | plot_cols = 2 65 | 66 | for i, (model, description) in enumerate(selected_models): 67 | 68 | # Obtain labels for each point in mesh. Use last trained model. 69 | model.fit(reduced_data) 70 | Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) 71 | 72 | # Put the result into a color plot 73 | Z = Z.reshape(xx.shape) 74 | plt.subplot(plot_cols, plot_rows, i + 1) 75 | plt.imshow( 76 | Z, 77 | interpolation="nearest", 78 | extent=(xx.min(), xx.max(), yy.min(), yy.max()), 79 | cmap=plt.cm.Paired, 80 | aspect="auto", 81 | origin="lower", 82 | ) 83 | 84 | plt.plot( 85 | reduced_data[:, 0], reduced_data[:, 1], "k.", markersize=2, alpha=0.3 86 | ) 87 | # Plot the centroids as a white X 88 | centroids = model.cluster_centers_ 89 | plt.scatter( 90 | centroids[:, 0], 91 | centroids[:, 1], 92 | marker="x", 93 | s=169, 94 | linewidths=3, 95 | color="w", 96 | zorder=10, 97 | ) 98 | plt.title(description) 99 | plt.xlim(x_min, x_max) 100 | plt.ylim(y_min, y_max) 101 | plt.xticks(()) 102 | plt.yticks(()) 103 | 104 | plt.show() 105 | -------------------------------------------------------------------------------- /BERT Word Embeddings.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# BERT Word Embeddings\n", 8 | "\n", 9 | "Three ways to obtain word embeddings from BERT:\n", 10 | "- context-free\n", 11 | "- context-averaged\n", 12 | "- " 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 1, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "#!pip install torch\n", 22 | "#!pip install transformers" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 7, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "from transformers import BertTokenizer, BertModel\n", 32 | "import pandas as pd\n", 33 | "import numpy as np\n", 34 | "import nltk\n", 35 | "import torch" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 3, 41 | "metadata": {}, 42 | "outputs": [ 43 | { 44 | "data": { 45 | "application/vnd.jupyter.widget-view+json": { 46 | "model_id": "642b7dcad437414ebdaff58a032d2e35", 47 | "version_major": 2, 48 | "version_minor": 0 49 | }, 50 | "text/plain": [ 51 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…" 52 | ] 53 | }, 54 | "metadata": {}, 55 | "output_type": "display_data" 56 | }, 57 | { 58 | "name": "stdout", 59 | "output_type": "stream", 60 | "text": [ 61 | "\n" 62 | ] 63 | }, 64 | { 65 | "data": { 66 | "application/vnd.jupyter.widget-view+json": { 67 | "model_id": "292a2daf07e44455b863113d890f9109", 68 | "version_major": 2, 69 | "version_minor": 0 70 | }, 71 | "text/plain": [ 72 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=440473133.0, style=ProgressStyle(descri…" 73 | ] 74 | }, 75 | "metadata": {}, 76 | "output_type": "display_data" 77 | }, 78 | { 79 | "name": "stdout", 80 | "output_type": "stream", 81 | "text": [ 82 | "\n" 83 | ] 84 | }, 85 | { 86 | "data": { 87 | "application/vnd.jupyter.widget-view+json": { 88 | "model_id": "799d26a232d54abbbe603d1cb61d7795", 89 | "version_major": 2, 90 | "version_minor": 0 91 | }, 92 | "text/plain": [ 93 | "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…" 94 | ] 95 | }, 96 | "metadata": {}, 97 | "output_type": "display_data" 98 | }, 99 | { 100 | "name": "stdout", 101 | "output_type": "stream", 102 | "text": [ 103 | "\n" 104 | ] 105 | } 106 | ], 107 | "source": [ 108 | "# Loading the pre-trained BERT model\n", 109 | "###################################\n", 110 | "# Embeddings will be derived from\n", 111 | "# the outputs of this model\n", 112 | "model = BertModel.from_pretrained('bert-base-uncased',\n", 113 | " output_hidden_states = True,\n", 114 | " )\n", 115 | "\n", 116 | "# Setting up the tokenizer\n", 117 | "###################################\n", 118 | "# This is the same tokenizer that\n", 119 | "# was used in the model to generate \n", 120 | "# embeddings to ensure consistency\n", 121 | "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 29, 127 | "metadata": {}, 128 | "outputs": [], 129 | "source": [ 130 | "# Text corpus\n", 131 | "##############\n", 132 | "# These sentences show the different\n", 133 | "# forms of the word 'bank' to show the\n", 134 | "# value of contextualized embeddings\n", 135 | "\n", 136 | "texts = [\"bank\",\n", 137 | " \"The river bank was flooded.\",\n", 138 | " \"The bank vault was robust.\",\n", 139 | " \"He had to bank on her for support.\",\n", 140 | " \"The bank was out of money.\",\n", 141 | " \"The bank teller was a man.\"]" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": 30, 147 | "metadata": {}, 148 | "outputs": [], 149 | "source": [ 150 | "def bert_text_preparation(text, tokenizer):\n", 151 | " \"\"\"Preparing the input for BERT\n", 152 | " \n", 153 | " Takes a string argument and performs\n", 154 | " pre-processing like adding special tokens,\n", 155 | " tokenization, tokens to ids, and tokens to\n", 156 | " segment ids. All tokens are mapped to seg-\n", 157 | " ment id = 1.\n", 158 | " \n", 159 | " Args:\n", 160 | " text (str): Text to be converted\n", 161 | " tokenizer (obj): Tokenizer object\n", 162 | " to convert text into BERT-re-\n", 163 | " adable tokens and ids\n", 164 | " \n", 165 | " Returns:\n", 166 | " list: List of BERT-readable tokens\n", 167 | " obj: Torch tensor with token ids\n", 168 | " obj: Torch tensor segment ids\n", 169 | " \n", 170 | " \n", 171 | " \"\"\"\n", 172 | " marked_text = \"[CLS] \" + text + \" [SEP]\"\n", 173 | " tokenized_text = tokenizer.tokenize(marked_text)\n", 174 | " indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)\n", 175 | " segments_ids = [1]*len(indexed_tokens)\n", 176 | "\n", 177 | " # Convert inputs to PyTorch tensors\n", 178 | " tokens_tensor = torch.tensor([indexed_tokens])\n", 179 | " segments_tensors = torch.tensor([segments_ids])\n", 180 | "\n", 181 | " return tokenized_text, tokens_tensor, segments_tensors\n", 182 | " \n", 183 | "def get_bert_embeddings(tokens_tensor, segments_tensors, model):\n", 184 | " \"\"\"Get embeddings from an embedding model\n", 185 | " \n", 186 | " Args:\n", 187 | " tokens_tensor (obj): Torch tensor size [n_tokens]\n", 188 | " with token ids for each token in text\n", 189 | " segments_tensors (obj): Torch tensor size [n_tokens]\n", 190 | " with segment ids for each token in text\n", 191 | " model (obj): Embedding model to generate embeddings\n", 192 | " from token and segment ids\n", 193 | " \n", 194 | " Returns:\n", 195 | " list: List of list of floats of size\n", 196 | " [n_tokens, n_embedding_dimensions]\n", 197 | " containing embeddings for each token\n", 198 | " \n", 199 | " \"\"\"\n", 200 | " \n", 201 | " # Gradient calculation id disabled\n", 202 | " # Model is in inference mode\n", 203 | " with torch.no_grad():\n", 204 | " outputs = model(tokens_tensor, segments_tensors)\n", 205 | " # Removing the first hidden state\n", 206 | " # The first state is the input state\n", 207 | " hidden_states = outputs[2][1:]\n", 208 | "\n", 209 | " # Getting embeddings from the final BERT layer\n", 210 | " token_embeddings = hidden_states[-1]\n", 211 | " # Collapsing the tensor into 1-dimension\n", 212 | " token_embeddings = torch.squeeze(token_embeddings, dim=0)\n", 213 | " # Converting torchtensors to lists\n", 214 | " list_token_embeddings = [token_embed.tolist() for token_embed in token_embeddings]\n", 215 | "\n", 216 | " return list_token_embeddings" 217 | ] 218 | }, 219 | { 220 | "cell_type": "code", 221 | "execution_count": 31, 222 | "metadata": {}, 223 | "outputs": [], 224 | "source": [ 225 | "# Getting embeddings for the target\n", 226 | "# word in all given contexts\n", 227 | "target_word_embeddings = []\n", 228 | "\n", 229 | "for text in texts:\n", 230 | " tokenized_text, tokens_tensor, segments_tensors = bert_text_preparation(text, tokenizer)\n", 231 | " list_token_embeddings = get_bert_embeddings(tokens_tensor, segments_tensors, model)\n", 232 | " \n", 233 | " # Find the position 'bank' in list of tokens\n", 234 | " word_index = tokenized_text.index('bank')\n", 235 | " # Get the embedding for bank\n", 236 | " word_embedding = list_token_embeddings[word_index]\n", 237 | "\n", 238 | " target_word_embeddings.append(word_embedding)" 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 42, 244 | "metadata": {}, 245 | "outputs": [], 246 | "source": [ 247 | "from scipy.spatial.distance import cosine\n", 248 | "\n", 249 | "# Calculating the distance between the\n", 250 | "# embeddings of 'bank' in all the\n", 251 | "# given contexts of the word\n", 252 | "\n", 253 | "list_of_distances = []\n", 254 | "for text1, embed1 in zip(texts, target_word_embeddings):\n", 255 | " for text2, embed2 in zip(texts, target_word_embeddings):\n", 256 | " cos_dist = 1 - cosine(embed1, embed2)\n", 257 | " list_of_distances.append([text1, text2, cos_dist])\n", 258 | "\n", 259 | "distances_df = pd.DataFrame(list_of_distances, columns=['text1', 'text2', 'distance'])" 260 | ] 261 | }, 262 | { 263 | "cell_type": "code", 264 | "execution_count": 43, 265 | "metadata": {}, 266 | "outputs": [ 267 | { 268 | "data": { 269 | "text/html": [ 270 | "
\n", 271 | "\n", 284 | "\n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | "
text1text2distance
0bankbank1.000000
1bankThe river bank was flooded.0.338063
2bankThe bank vault was robust.0.494098
3bankHe had to bank on her for support.0.256140
4bankThe bank was out of money.0.469942
5bankThe bank teller was a man.0.466020
\n", 332 | "
" 333 | ], 334 | "text/plain": [ 335 | " text1 text2 distance\n", 336 | "0 bank bank 1.000000\n", 337 | "1 bank The river bank was flooded. 0.338063\n", 338 | "2 bank The bank vault was robust. 0.494098\n", 339 | "3 bank He had to bank on her for support. 0.256140\n", 340 | "4 bank The bank was out of money. 0.469942\n", 341 | "5 bank The bank teller was a man. 0.466020" 342 | ] 343 | }, 344 | "execution_count": 43, 345 | "metadata": {}, 346 | "output_type": "execute_result" 347 | } 348 | ], 349 | "source": [ 350 | "distances_df[distances_df.text1 == 'bank']" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": 44, 356 | "metadata": {}, 357 | "outputs": [ 358 | { 359 | "data": { 360 | "text/html": [ 361 | "
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text1text2distance
12The bank vault was robust.bank0.494098
13The bank vault was robust.The river bank was flooded.0.523325
14The bank vault was robust.The bank vault was robust.1.000000
15The bank vault was robust.He had to bank on her for support.0.416074
16The bank vault was robust.The bank was out of money.0.759213
17The bank vault was robust.The bank teller was a man.0.867661
\n", 423 | "
" 424 | ], 425 | "text/plain": [ 426 | " text1 text2 distance\n", 427 | "12 The bank vault was robust. bank 0.494098\n", 428 | "13 The bank vault was robust. The river bank was flooded. 0.523325\n", 429 | "14 The bank vault was robust. The bank vault was robust. 1.000000\n", 430 | "15 The bank vault was robust. He had to bank on her for support. 0.416074\n", 431 | "16 The bank vault was robust. The bank was out of money. 0.759213\n", 432 | "17 The bank vault was robust. The bank teller was a man. 0.867661" 433 | ] 434 | }, 435 | "execution_count": 44, 436 | "metadata": {}, 437 | "output_type": "execute_result" 438 | } 439 | ], 440 | "source": [ 441 | "distances_df[distances_df.text1 == 'The bank vault was robust.']" 442 | ] 443 | }, 444 | { 445 | "cell_type": "code", 446 | "execution_count": 46, 447 | "metadata": {}, 448 | "outputs": [ 449 | { 450 | "name": "stdout", 451 | "output_type": "stream", 452 | "text": [ 453 | "Distance between context-free and context-averaged = 0.6590343155941127\n" 454 | ] 455 | } 456 | ], 457 | "source": [ 458 | "cos_dist = 1 - cosine(target_word_embeddings[0], np.sum(target_word_embeddings, axis=0))\n", 459 | "print(f'Distance between context-free and context-averaged = {cos_dist}')" 460 | ] 461 | }, 462 | { 463 | "cell_type": "code", 464 | "execution_count": null, 465 | "metadata": {}, 466 | "outputs": [], 467 | "source": [] 468 | } 469 | ], 470 | "metadata": { 471 | "kernelspec": { 472 | "display_name": "Python 3", 473 | "language": "python", 474 | "name": "python3" 475 | }, 476 | "language_info": { 477 | "codemirror_mode": { 478 | "name": "ipython", 479 | "version": 3 480 | }, 481 | "file_extension": ".py", 482 | "mimetype": "text/x-python", 483 | "name": "python", 484 | "nbconvert_exporter": "python", 485 | "pygments_lexer": "ipython3", 486 | "version": "3.8.3" 487 | } 488 | }, 489 | "nbformat": 4, 490 | "nbformat_minor": 4 491 | } 492 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /NLTK Tokenizers.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 8, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import nltk\n", 10 | "from nltk.tokenize import (TreebankWordTokenizer,\n", 11 | " word_tokenize,\n", 12 | " wordpunct_tokenize,\n", 13 | " TweetTokenizer,\n", 14 | " MWETokenizer)\n", 15 | "sentence = \"It's true, Ms. Martha Jones! #Truth\"" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 2, 21 | "metadata": {}, 22 | "outputs": [ 23 | { 24 | "name": "stdout", 25 | "output_type": "stream", 26 | "text": [ 27 | "Whitespace tokenization = [\"It's\", 'true,', 'Ms.', 'Martha', 'Jones!', '#Truth']\n" 28 | ] 29 | } 30 | ], 31 | "source": [ 32 | "print(f'Whitespace tokenization = {sentence.split()}')" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 3, 38 | "metadata": {}, 39 | "outputs": [ 40 | { 41 | "name": "stdout", 42 | "output_type": "stream", 43 | "text": [ 44 | "Punctuation-based tokenization = ['It', \"'\", 's', 'true', ',', 'Ms', '.', 'Martha', 'Jones', '!', '#', 'Truth']\n" 45 | ] 46 | } 47 | ], 48 | "source": [ 49 | "print(f'Punctuation-based tokenization = {wordpunct_tokenize(sentence)}')" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 4, 55 | "metadata": {}, 56 | "outputs": [ 57 | { 58 | "name": "stdout", 59 | "output_type": "stream", 60 | "text": [ 61 | "Multi-word expression (MWE) tokenization = ['It', \"'s\", 'true', ',', 'Ms.', 'Martha_Jones', '!', '#', 'Truth']\n" 62 | ] 63 | } 64 | ], 65 | "source": [ 66 | "tokenizer = MWETokenizer()\n", 67 | "tokenizer.add_mwe(('Martha', 'Jones'))\n", 68 | "print(f'Multi-word expression (MWE) tokenization = {tokenizer.tokenize(word_tokenize(sentence))}')" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 5, 74 | "metadata": {}, 75 | "outputs": [ 76 | { 77 | "name": "stdout", 78 | "output_type": "stream", 79 | "text": [ 80 | "Tweet-rules based tokenization = [\"It's\", 'true', ',', 'Ms', '.', 'Martha', 'Jones', '!', '#Truth']\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "tokenizer = TweetTokenizer()\n", 86 | "print(f'Tweet-rules based tokenization = {tokenizer.tokenize(sentence)}')" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 6, 92 | "metadata": {}, 93 | "outputs": [ 94 | { 95 | "name": "stdout", 96 | "output_type": "stream", 97 | "text": [ 98 | "Default/Treebank tokenization = ['It', \"'s\", 'true', ',', 'Ms.', 'Martha', 'Jones', '!', '#', 'Truth']\n" 99 | ] 100 | } 101 | ], 102 | "source": [ 103 | "tokenizer = TreebankWordTokenizer()\n", 104 | "print(f'Default/Treebank tokenization = {tokenizer.tokenize(sentence)}')" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": null, 110 | "metadata": {}, 111 | "outputs": [], 112 | "source": [] 113 | } 114 | ], 115 | "metadata": { 116 | "kernelspec": { 117 | "display_name": "Python 3", 118 | "language": "python", 119 | "name": "python3" 120 | }, 121 | "language_info": { 122 | "codemirror_mode": { 123 | "name": "ipython", 124 | "version": 3 125 | }, 126 | "file_extension": ".py", 127 | "mimetype": "text/x-python", 128 | "name": "python", 129 | "nbconvert_exporter": "python", 130 | "pygments_lexer": "ipython3", 131 | "version": "3.8.3" 132 | } 133 | }, 134 | "nbformat": 4, 135 | "nbformat_minor": 4 136 | } 137 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MachineLearning -------------------------------------------------------------------------------- /plot_kmedoids_digits.py: -------------------------------------------------------------------------------- 1 | """ 2 | ============================================================= 3 | A demo of K-Medoids clustering on the handwritten digits data 4 | ============================================================= 5 | In this example we compare different pairwise distance 6 | metrics for K-Medoids. 7 | """ 8 | import numpy as np 9 | import matplotlib.pyplot as plt 10 | 11 | from sklearn.cluster import KMeans 12 | from sklearn_extra.cluster import KMedoids 13 | from sklearn.datasets import load_digits 14 | from sklearn.decomposition import PCA 15 | from sklearn.preprocessing import scale 16 | 17 | print(__doc__) 18 | 19 | # Authors: Timo Erkkilä 20 | # Antti Lehmussola 21 | # Kornel Kiełczewski 22 | # License: BSD 3 clause 23 | 24 | np.random.seed(42) 25 | 26 | digits = load_digits() 27 | data = scale(digits.data) 28 | n_digits = len(np.unique(digits.target)) 29 | 30 | reduced_data = PCA(n_components=2).fit_transform(data) 31 | 32 | # Step size of the mesh. Decrease to increase the quality of the VQ. 33 | h = 0.02 # point in the mesh [x_min, m_max]x[y_min, y_max]. 34 | 35 | # Plot the decision boundary. For that, we will assign a color to each 36 | x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1 37 | y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1 38 | xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) 39 | 40 | plt.figure() 41 | plt.clf() 42 | 43 | plt.suptitle( 44 | "Comparing multiple K-Medoids metrics to K-Means and each other", 45 | fontsize=14, 46 | ) 47 | 48 | 49 | selected_models = [ 50 | #( 51 | # KMedoids(metric="manhattan", n_clusters=n_digits), 52 | # "KMedoids (manhattan)", 53 | #), 54 | #( 55 | # KMedoids(metric="euclidean", n_clusters=n_digits), 56 | # "KMedoids (euclidean)", 57 | #), 58 | (KMedoids(metric="cosine", n_clusters=n_digits), "KMedoids (cosine)"), 59 | (KMeans(n_clusters=n_digits), "KMeans"), 60 | ] 61 | 62 | plot_rows = int(np.ceil(len(selected_models) / 2.0)) 63 | plot_cols = 2 64 | 65 | for i, (model, description) in enumerate(selected_models): 66 | 67 | # Obtain labels for each point in mesh. Use last trained model. 68 | model.fit(reduced_data) 69 | Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) 70 | 71 | # Put the result into a color plot 72 | Z = Z.reshape(xx.shape) 73 | plt.subplot(plot_cols, plot_rows, i + 1) 74 | plt.imshow( 75 | Z, 76 | interpolation="nearest", 77 | extent=(xx.min(), xx.max(), yy.min(), yy.max()), 78 | cmap=plt.cm.Paired, 79 | aspect="auto", 80 | origin="lower", 81 | ) 82 | 83 | plt.plot( 84 | reduced_data[:, 0], reduced_data[:, 1], "k.", markersize=2, alpha=0.3 85 | ) 86 | # Plot the centroids as a white X 87 | centroids = model.cluster_centers_ 88 | plt.scatter( 89 | centroids[:, 0], 90 | centroids[:, 1], 91 | marker="x", 92 | s=169, 93 | linewidths=3, 94 | color="w", 95 | zorder=10, 96 | ) 97 | plt.title(description) 98 | plt.xlim(x_min, x_max) 99 | plt.ylim(y_min, y_max) 100 | plt.xticks(()) 101 | plt.yticks(()) 102 | 103 | plt.show() 104 | --------------------------------------------------------------------------------