From 11c887064b999ca9073db5903845359a4c508f8f Mon Sep 17 00:00:00 2001 From: stanton119 Date: Thu, 18 Jul 2024 09:58:48 +0100 Subject: [PATCH] various updates --- README.md | 2 + .../dirichlet_prior_networks.ipynb | 489 ++++++++++-------- .../text_embedding.ipynb | 2 +- paper_list.md | 8 +- .../gaussian_processes_from_scratch.ipynb | 1 + 5 files changed, 273 insertions(+), 229 deletions(-) create mode 100644 unfinished/GaussianProcesses/gaussian_processes_from_scratch.ipynb diff --git a/README.md b/README.md index f638523..16ebc68 100644 --- a/README.md +++ b/README.md @@ -194,6 +194,8 @@ Use the `settings.json` file in the repo * Beta Bernouli bandit vs logistic regression with no features * NN multi-row vs multi-column - do they perform similarly? * Multi horizon forecasting direct method - with shared NN architecture - compare separate models for each horizon with a NN that shares layers. Compare with sequence to sequence models. +* Gaussian process from scratch + * ref - https://www.youtube.com/watch?v=HA-VHNVbvwQ&list=WL&index=26 * Probabilistic neural networks * Normalizing flows - model complex distributions with transformations of gaussians * Can we train an output layer as a gaussian mixture to model complex distirbutions via gradient descent diff --git a/neural_networks/unfinished-classification_uncertainty/dirichlet_prior_networks.ipynb b/neural_networks/unfinished-classification_uncertainty/dirichlet_prior_networks.ipynb index f0b3560..76915f6 100644 --- a/neural_networks/unfinished-classification_uncertainty/dirichlet_prior_networks.ipynb +++ b/neural_networks/unfinished-classification_uncertainty/dirichlet_prior_networks.ipynb @@ -61,7 +61,16 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "import torch\n", "from torch.utils.data import DataLoader\n", @@ -229,12 +238,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/stantoon/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:478: LightningDeprecationWarning: Setting `Trainer(gpus=1)` is deprecated in v1.7 and will be removed in v2.0. Please use `Trainer(accelerator='gpu', devices=1)` instead.\n", - " rank_zero_deprecation(\n", "GPU available: True (mps), used: True\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", " | Name | Type | Params\n", "------------------------------------------\n", @@ -248,84 +261,41 @@ ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3b4ae0d2dd18457092e477fee4820291", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/stantoon/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n", - "/Users/stantoon/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + "/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:430: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", " rank_zero_warn(\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ffbd390c960647e5bf5e30178ee463ba", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a8ef8c351aaf48fb82d8cfcaff088281", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a68bed391434495594be20b7e9246c1e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:430: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + " rank_zero_warn(\n" + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "eba37752bc9244a193c9fff87b7aba58", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2: 100%|██████████| 54/54 [00:04<00:00, 11.76it/s, v_num=9]" + ] }, { "name": "stderr", @@ -333,12 +303,19 @@ "text": [ "`Trainer.fit` stopped: `max_epochs=3` reached.\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2: 100%|██████████| 54/54 [00:04<00:00, 11.72it/s, v_num=9]\n" + ] } ], "source": [ "model = MNISTModel()\n", "\n", - "trainer = pl.Trainer(max_epochs=3, gpus=1, accelerator=\"mps\")\n", + "trainer = pl.Trainer(max_epochs=3, accelerator=\"mps\")\n", "trainer.fit(model, data_module)" ] }, @@ -358,15 +335,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/_v/nlh4h1yx2n1gd6f3szjlgxt40000gr/T/ipykernel_73909/804253465.py:16: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_52426/804253465.py:16: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n", - "/var/folders/_v/nlh4h1yx2n1gd6f3szjlgxt40000gr/T/ipykernel_73909/804253465.py:25: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_52426/804253465.py:25: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n" ] }, { "data": { 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", 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cgwcPRmlpKS5cuIBWrVrh4sWLXm5xdexxcIGgNkBjyueVFUREvkyhsP/l78OnKgDg7rvvxqeffoq7774bW7duxW233QaNRoPPPvsM77//PhQKBaKiolBaWgq12vd+Tftei3yQoK4c18AeByIi36ZQAFp/b7eiTuPHj8crr7yC/fv3Iz09HbNnz8bOnTuxYcMGbNq0CR06dAAALFmyBOnp6V5ubXU8VeEC56kK3q+CiIiuUmhoKG677TY8+eSTGDZsGAICAlBSUgKlUgmdTgdRFLF371588sknsFgs3m5uNQwOLmCPAxERudOkSZNw/vx53HXXXQCAO++8E/369cOoUaPwl7/8BWvXrsX999+P33//HWaz2cutleKpChdc7nFgcCAioqvXv39/nDp1CoIg4Pjx49BqtXjxxRerrZeUlAQAmDBhAiZMmODpZtaIPQ4uEFS8qoKIiAjwcnDIz8/HsGHDcOhQ/dfcpqeno2fPni6t627OUxUc40BERDLnteBw9OhRTJ48GVlZWfWuW15ejsceewwVFd6Z8pnzOBAREdl5ZYzD1q1bsWrVKiQnJ2Pu3Ln1rr9o0SIMHTrU5ctSBEGAIAhX28zL+6oMDraKYohu2m9z5airu+rb3LEeUqxHdayJFOsh5Uv1cLUNXgkO/fv3x5gxY6BWq+sNDp988gnOnj2LpUuXYs2aNS7t393XvYZVnqoozj2PjOPH3brv5iotLc3bTfAprIcU61EdayLFekg1p3p4JTi0aePa/R4yMjKwcuVKbNq0CSqVyuX9R0VFwWAwNLZ5EoIg4I/z3wIAgnUqxMXFuWW/zZUgCEhLS0NsbGyDvictFeshxXpUx5pIsR5SvlQPo9Ho0h/ePns5pslkwty5c/HEE08gMjKyQduqVCq3fgMcpyoU5lKvf2N9hbtr3NyxHlKsR3WsiRTrIeUL9XD1+D57OWZaWhoyMzOxcOFCJCQkICEhAQDwwAMP4JlnnvFoWzg4koiIyM5nexwSEhLw008/SZZFR0fjjTfeQN++fT3aFoETQBEREQHwwR6H+Ph4bNu2zdvNkOCU00RERHZe73E4deqU5HVqaqrL63qK81SFpQyw2QClz+UtIiIij+BvQBc4T1UAPF1BRESyxuDgAlGphaioHG3K4EBERDLG4OAKhQLwC7A/5zgHIiKSMQYHV2krgwNvdEVERDLG4OAqLXsciIiIGBxc5ThVwTEOREQkYwwOrmKPAxEREYODy7SB9q8c40BERDLG4OAikVdVEBERMTi4TMsxDkRERAwOruIYByIiIgYHl/lxHgciIiIGB1exx4GIiIjBwWWcx4GIiIjBwVUiexyIiIgYHFzGqyqIiIgYHFzmnMeBgyOJiEi+GBxcxR4HIiIiBgeXOaac5hgHIiKSMQYHVzlOVQgmQLB4ty1ERERewuDgKsepCoDjHIiISLYYHFyl0gAqP/tzjnMgIiKZYnBoCN4hk4iIZI7BoSF4ZQUREckcg0ND+AXZv3KMAxERyRSDQ0PwfhVERCRzXg0O+fn5GDZsGA4dOlTrOps2bcLw4cMRHx+P4cOHY+PGjR5s4RV4vwoiIpI5tbcOfPToUSxYsABZWVm1rvP111/j5ZdfxltvvYWePXvi+PHjmDlzJsLCwjB8+HAPtrYSexyIiEjmvNLjsHXrVsybNw9z586tc72LFy9ixowZiIuLg0KhQHx8PPr27YvDhw97qKVXcPY4FHvn+ERERF7mlR6H/v37Y8yYMVCr1XWGh3vvvVfyOi8vD4cPH8a//vWvOvcvCAIEQXBLWx37EQQBCq0/lABsFSUQ3bT/5qZqPYj1uBLrUR1rIsV6SPlSPVxtg1eCQ5s2bRq8zaVLlzBr1izExMRg9OjRda6bnp7e2KbVKi0tDdfmlyESQG52Js4dP+72YzQnaWlp3m6CT2E9pFiP6lgTKdZDqjnVw2tjHBri+PHjeOSRR5CQkIDnn38eanXdzY6KioLBYHDLsQVBQFpaGmJjY6E2dgZOA2GBOrSOi3PL/pubqvVQqVTebo7XsR5SrEd1rIkU6yHlS/UwGo0u/eHt88Fh8+bNePbZZ5GUlIRp06a5tI1KpXL7N0ClUkGps8/joLSUATL/gW+KGjdnrIcU61EdayLFekj5Qj1cPb5PB4ddu3bhmWeewdq1azFgwABvNwfwc9xamxNAERGRPPncBFDx8fHYtm0bAOD111+HIAhISkpCfHy88/HUU095p3GccpqIiGTO6z0Op06dkrxOTU11Pt++fbunm1M33uSKiIhkzud6HHwaexyIiEjmGBwawjnGgcGBiIjkicGhIZw9DiWAKHq3LURERF7A4NAQjjEOog2wlHu3LURERF7A4NAQGv/LzznOgYiIZIjBoSGUyio3uuJcDkREJD8MDg3FKyuIiEjGGBwainM5EBGRjDE4NBR7HIiISMYYHBqK96sgIiIZY3BoKPY4EBGRjDE4NBTHOBARkYwxODQUexyIiEjGGBwayo/zOBARkXwxODSUtnJwJHsciIhIhhgcGopjHIiISMYYHBrKjz0OREQkXwwODaVljwMREckXg0NDOXscODiSiIjkh8GhodjjQEREMsbg0FB+nMeBiIjki8GhodjjQEREMsbg0FCOMQ6WMsBm825biIiIPIzBoaEcPQ4AT1cQEZHsMDg0lNoPUKrtzxkciIhIZhgcGkqh4DgHIiKSLQaHxuBcDkREJFNeDQ75+fkYNmwYDh06VOs6e/bswZgxYxAXF4c77rgDu3fv9mALa8EeByIikimvBYejR49i8uTJyMrKqnWdzMxMzJ49G4888giOHDmC2bNnY86cObh48aIHW1oD3lqbiIhkyivBYevWrZg3bx7mzp1b73oJCQkYOnQo1Go1Ro4ciT59+uCDDz7wUEtroeUkUEREJE9qbxy0f//+GDNmDNRqdZ3h4cyZM4iKipIs69KlC06ePFnn/gVBgCAIbmmrYz9V96fU+kMBwFZRDNFNx2kuaqqHnLEeUqxHdayJFOsh5Uv1cLUNXgkObdq0cWm9srIy6PV6yTKdTgej0Vjndunp6Y1uW23S0tKczzuUWhAGIDszHRfVx91+rOagaj2I9bgS61EdayLFekg1p3p4JTi4Sq/Xo6KiQrKsoqIC/v7+dW4XFRUFg8HgljYIgoC0tDTExsZCpVIBABQXrgP+ACJbB+HauDi3HKe5qKkecsZ6SLEe1bEmUqyHlC/Vw2g0uvSHt08Hh6ioKPzyyy+SZWfOnEFMTEyd26lUKrd/AyT7rLwcU2kpA2T6g98UNW7OWA8p1qM61kSK9ZDyhXq4enyfnsdh7NixSElJwY4dO2C1WrFjxw6kpKRg3Lhx3m2YHy/HJCIiefK54BAfH49t27YBADp37ozVq1dj3bp16NOnD9asWYPXXnsN119/vXcbqeUEUEREJE9eP1Vx6tQpyevU1FTJ6wEDBmDAgAGebFL92ONAREQy5XM9Ds0C53EgIiKZYnBoDPY4EBGRTDE4NAbHOBARkUwxODQGexyIiEimGBwag2MciIhIphgcGsPR4yCYAavZu20hIiLyIAaHxnCMcQDY60BERLLC4NAYKjWg1tmfmzhAkoiI5IPBobH8HFdWsMeBiIjkg8GhsbS8soKIiOSHwaGxHAMkOZcDERHJCINDYzkGSLLHgYiIZITBobH8OJcDERHJD4NDY3GMAxERyRCDQ2NxjAMREckQg0NjcYwDERHJEINDY3GMAxERyRCDQ2NxjAMREckQg0NjsceBiIhkiMGhsZxjHDg4koiI5IPBobEcPQ4MDkREJCMMDo3Fm1wREZEMuSU45OfnY8qUKe7YVfPBwZFERCRDbgkOFosFP/74ozt21Xz4cYwDERHJD09VNFbVUxU2m3fbQkRE5CEMDo3lCA4QAUuZV5tCRETkKV4JDnl5eUhMTERCQgL69u2LpUuXwmq11rjuu+++i8GDB6NXr14YM2YMdu3a5eHW1kKtAxQq+3OOcyAiIplQu7ri4MGDoVAoanxPEIQGHXTOnDkIDw/Hvn37kJubiwcffBDr16/H9OnTJevt2bMH69atw4YNG9CpUyfs2rULc+bMwVdffYV27do16Jhup1DYex0qCivHOVzr3fYQERF5gMvBYfbs2W454NmzZ5GSkoK9e/dCr9ejffv2SExMxPLly6sFh99++w2iKDofKpUKGo0GarXLzW5ajuDAO2QSEZFMuPwbePz48bW+JwgCsrKyXNrP6dOnERISgvDwcOeyzp07Izs7G8XFxQgKCnIuHzVqFLZs2YKRI0dCpVJBoVBg+fLliIiIqPMYgiA0uBekrn1V/VqVUhsABQChvAhw0/F8XV31kCPWQ4r1qI41kWI9pHypHq62wS1/uufm5mLkyJE4ceJEveuWlZVBr9dLljleG41GSXCwWCzo1q0bli5dim7dumH79u1YuHAhOnfujOjo6FqPkZ6e3shPUru0tLRqy6KtCgQA+P1kGoqKgt1+TF9WUz3kjPWQYj2qY02kWA+p5lQPt/X5i6Lo0noGgwHl5eWSZY7X/v7+kuVLlixBr1690KNHDwDAXXfdhc8++wxbt27FggULaj1GVFQUDAZDQ5pfK0EQkJaWhtjYWKhUKsl7yl/CgYJf0SkyDGLPOLccz9fVVQ85Yj2kWI/qWBMp1kPKl+phNBpd+sPbbcGhtoGTV+ratSsKCwuRm5uLsLAwAEBGRgYiIiIQGBgoWTc7OxsxMTGSZWq1GhqNps5jqFQqt38Datynzt47orQaAZn9A2iKGjdnrIcU61EdayLFekj5Qj1cPb7HL8fs2LEjevfujeeeew6lpaU4d+4c1qxZg4kTJ1Zbd/DgwdiwYQN++eUX2Gw27Ny5E4cOHcLIkSM93eyaOe+QWezddhAREXmIyz0Ohw8frvW9/Pz8Bh101apVWLx4MYYMGQKlUok777wTiYmJAID4+HgsWrQIY8eOxcMPPwyVSoXZs2ejqKgIHTp0wOrVq3HDDTc06HhNxjntNOdxICIieXA5ONx33311vu/qqQoACAsLw6pVq2p8LzU11flcrVZj9uzZbrsU1O14a20iIpIZl4PDyZMnm7IdzRNvrU1ERDLDe1VcDS17HIiISF4YHK6GX+WcEwwOREQkEwwOV4NjHIiISGYYHK4GxzgQEZHMMDhcDY5xICIimWFwuBqcx4GIiGSGweFqOE9VlAA2m3fbQkRE5AEMDlfDr8q9NSxl3msHERGRhzA4XA21DlBU3hSE4xyIiEgGGByuhkLBcQ5ERCQrDA5Xyxkc2ONAREQtH4PD1ao6QJKIiKiFY3C4Ws65HHiqgoiIWj4Gh6vFUxVERCQjDA5Xy3G/Ck47TUREMsDgcLWcPQ7F3m0HERGRBzA4XC0tL8ckIiL5YHC4WhzjQEREMsLgcLU4xoGIiGSEweFqsceBiIhkhMHhamkZHIiISD4YHK4WexyIiEhGGByuFsc4EBGRjDA4XC32OBARkYwwOFwt3quCiIhkhMHhavkF2b+aSwCbzbttISIiamJeCQ55eXlITExEQkIC+vbti6VLl8Jqtda4bkpKCu6++27Ex8dj4MCBWLdunYdbWw/HGAcAsJR5rx1EREQe4JXgMGfOHBgMBuzbtw+bN2/GgQMHsH79+mrrZWRkYObMmbjnnntw7NgxrFu3Du+88w527tzp+UbXRq0DlGr7c45zICKiFs7jweHs2bNISUlBcnIy9Ho92rdvj8TERGzcuLHauv/73/8wZMgQjB8/HgqFAt26dcP777+P3r17e7rZtVMoOM6BiIhkQ+3pA54+fRohISEIDw93LuvcuTOys7NRXFyMoKAg5/KffvoJ/fr1w6OPPooffvgBoaGhmDp1KiZPnlznMQRBgCAIbmmvYz917U/pFwBFRSGE8kLATcf1Va7UQ05YDynWozrWRIr1kPKlerjaBo8Hh7KyMuj1eskyx2uj0SgJDkVFRXjvvfewcuVKvPjii0hNTcWsWbMQHByMESNG1HqM9PR0t7c7LS2t1ve62zTQA8j49UeUXFK5/di+qK56yBHrIcV6VMeaSLEeUs2pHh4PDgaDAeXl5ZJljtf+/v6S5VqtFkOGDMGgQYMAAH369MG4cePwxRdf1BkcoqKiYDAY3NJeQRCQlpaG2NhYqFQ1hwJlahhQ8js6t78G6BbnluP6KlfqISeshxTrUR1rIsV6SPlSPYxGo0t/eHs8OHTt2hWFhYXIzc1FWFgYAPsgyIiICAQGBkrW7dy5M8xms2SZIAgQRbHOY6hUKrd/A+rcZ+UlmSqLEZDJP4SmqHFzxnpIsR7VsSZSrIeUL9TD1eN7fHBkx44d0bt3bzz33HMoLS3FuXPnsGbNGkycOLHaulOmTME333yDTz/9FKIo4vDhw9i+fTvGjRvn6WbXzXFJJq+qICKiFs4rl2OuWrUKVqsVQ4YMwaRJkzBgwAAkJiYCAOLj47Ft2zYAwM0334w1a9bgvffeQ+/evfGvf/0L8+fPx5AhQ7zR7No5pp02MzgQEVHL5vFTFQAQFhaGVatW1fheamqq5PXAgQMxcOBATzSr8XhrbSIikglOOe0OzhtdcR4HIiJq2Rgc3IFjHIiISCYYHNzBOcaBPQ5ERNSyMTi4g3OMQ7F320FERNTEGBzcgWMciIhIJhgc3IFjHIiISCYYHNyBYxyIiEgmGBzcQcseByIikgcGB3eovFcFzKWAzebdthARETUhBgd3cIxxAHi6goiIWjQGB3dQ6wBl5ezdDA5ERNSCMTi4g0LBcQ5ERCQLDA7u4hjnwLkciIioBWNwcBfnXA6cPZKIiFouBgd34VwOREQkAwwO7sIxDkREJAMMDu7C+1UQEZEMMDi4C8c4EBGRDDA4uEvV2SOJiIhaKAYHd+EYByIikgEGB3fhGAciIpIBBgd38WOPAxERtXwMDu7iHOPA4EBERC0Xg4O7cIwDERHJAIODu3CMAxERyQCDg7twjAMREcmAV4JDXl4eEhMTkZCQgL59+2Lp0qWwWq11bpOeno6ePXvi0KFDHmplA/FeFUREJANeCQ5z5syBwWDAvn37sHnzZhw4cADr16+vdf3y8nI89thjqKio8FwjG0pbJTjYbN5tCxERURPxeHA4e/YsUlJSkJycDL1ej/bt2yMxMREbN26sdZtFixZh6NChHmxlIzhOVQDsdSAiohZL7ekDnj59GiEhIQgPD3cu69y5M7Kzs1FcXIygoCDJ+p988gnOnj2LpUuXYs2aNS4dQxAECILglvY69lPv/hQaKJVqKGxWCOWFgMbfLcf3NS7XQyZYDynWozrWRIr1kPKlerjaBo8Hh7KyMuj1eskyx2uj0SgJDhkZGVi5ciU2bdoElUrl8jHS09Pd09gq0tLS6l2np0oPta0EJ388gorAS25vgy9xpR5ywnpIsR7VsSZSrIdUc6qHx4ODwWBAeXm5ZJnjtb//5b/STSYT5s6diyeeeAKRkZENOkZUVBQMBsPVNxb2BJaWlobY2Nh6w4tybwhQVIJundoCbePccnxf05B6yAHrIcV6VMeaSLEeUr5UD6PR6NIf3h4PDl27dkVhYSFyc3MRFhYGwN6zEBERgcDAQOd6aWlpyMzMxMKFC7Fw4ULn8gceeADjxo3DM888U+sxVCqV278BLu2z8soKlcUItPB/EE1R4+aM9ZBiPapjTaRYDylfqIerx/d4cOjYsSN69+6N5557DosXL0ZBQQHWrFmDiRMnStZLSEjATz/9JFkWHR2NN954A3379vVkk13nnASKczkQEVHL5JXLMVetWgWr1YohQ4Zg0qRJGDBgABITEwEA8fHx2LZtmzeadfUc007zqgoiImqhPN7jAABhYWFYtWpVje+lpqbWut2pU6eaqknuwR4HIiJq4TjltDtx2mkiImrhGBzcyXFrbQYHIiJqoRgc3IljHIiIqIVjcHAnjnEgIqIWjsHBnZxjHNjjQERELRODQx1EUcSTn/yCN48V4VKJqf4NnGMcipu2YURERF7C4FAHk9WG7T9lY1dGOYau3Id3vv8dVqGOW2ZzjAMREbVwDA510GlU2PDPm9C5lRqlJisWf/YrRr/2PQ5n5te8Acc4EBFRC8fgUI/YtsF4fkhrPDvuRoQYNDh5oQR3v3EAj354vPrpC45xICKiFo7BwQUqhQJ/vak9dj82CH+96TooFMCWY+cxeMV3eHvfbzBbK09fsMeBiIhaOAaHBmjlr8XzE2KxNfEW9GgXjBKTFc9+fgLDVu7BF2l/QnSMcbCUATbBu40lIiJqAgwOjRDXPgRbE2/BsgmxaBPoh7N5Rjy48Rju/b9fL6/EAZJERNQCMTg0kkqpwJSbrsN38wYhaXAX6DRK7D9bCotov5/5n5dyvdxCIiIi92NwuEr+fmo8ens0ds8bhAm92qEUegDAtHXfYunnvyKv1IX5H4iIiJoJBgc3uTZYj5cnxcE/MAQA4CcY8da+3zHgxd1YvuskCo1m7zaQiIjIDRgc3ExrCAYAPDO8PWLaBsFoFrB6dwYGvLAbr3ydjpIKi5dbSERE1HgMDu5WOZdD3DVqbH+4P9bd1xvR4YEoMVnxytenMeDF3Vjz3RmUmqxebigREVHDMTi4W5W5HBQKBYbfGIEvHhmA1/4aj05t/FFotODFnafQ7/lv8NKXpzgGgoiImhUGB3er4X4VSqUCY3pG4ss5t+Klu3uiU5g/iiuseO3bM7jlhW/xzLZfcL6w3EsNJiIich2Dg7s5exyq3yFTrVLirt7t8NWjA7H23l6IbRuMCosN6/dnYuCLu/HYhz/i9EXOOklERL5L7e0GtDjO4FD7BFAqpQJ3xF6LETER+P5MLtZ+l4H9GXn4+Ngf+PjYH+jfJQx/v7kDhtwQDpVS4aGGExER1Y/Bwd0acL8KhUKBAV3bYEDXNkjNKsDa7zLw1YmL+P5MLr4/k4u2IXrc+5frMDmhPVoH+DVxw4mIiOrH4OBuNYxxcEX8da3w5t8TcC7fiA2HzuKDw+dwvrAcL+48hVe+Po3RPa7FvX07oNd1IVAo2AtBRETeweDgbld5h8z2oQb8644bMHdoFLb/mI33DpxF2vkibDl2HluOnUf7UD3G9ozE2J5tER0R6MaGExER1Y/Bwd3cdGttnUaFuxPaY2Lvdjh+rhD/d/Asdv58Aefyy7F6dwZW785AdHggxsZFYmzPSLQPNbih8URERHVjcHA3NwUHB4VCgfjrWiH+ulZ49k4rvj6Rg23Hs7EnPQenLpZg+a5TWL7rFGLbBuO2btdgcLdr0KNtMJQcVElERE2AwcHdGjnGwRUGrbryNEUkiowW7PzlT2z7MRv7M/KQdr4IaeeLsOqb0wgL0GJQtD1E9O8ahiCdxu1tISIiefJKcMjLy8O///1vpKSkQKVSYezYsZg/fz7U6urN2bRpE9avX4+cnBxcc801+Pvf/457773XC612UeWU0+7qcahNsEGDyX2uw+Q+1+FSiQm7T+Vg98kc7Dudi9xSMzYf/QObj/4BtVKBuPYh+Eun1vhLp9bo3aEV9FpVk7aNiIhaLq8Ehzlz5iA8PBz79u1Dbm4uHnzwQaxfvx7Tp0+XrPf111/j5ZdfxltvvYWePXvi+PHjmDlzJsLCwjB8+HBvNL1+Lszj4G5tAv0wKaE9JiW0h9lqw5HMfHx7MgffnszBb7llOHK2AEfOFuD13WegUV0OEn2vb40e7YPZI0FERC7zeHA4e/YsUlJSsHfvXuj1erRv3x6JiYlYvnx5teBw8eJFzJgxA3FxcQCA+Ph49O3bF4cPH/bd4KCtDA6WMsAmAErP/nWvVSvRr0sY+nUJw5OjuyMrz4iDv+Xh4G95OPBbHv4sqsDhzAIczizAazgDAOjY2oCYtsGIaRuM2LbBiIkMRrCBYYKIiKrzeHA4ffo0QkJCEB4e7lzWuXNnZGdno7i4GEFBQc7lV56SyMvLw+HDh/Gvf/2rzmMIggBBENzSXsd+XN6fxgBHVBDKiwFdUJ2rN7W2IX64q1ck7uoVCVEUkZVfjkO/5+PQ7/k4nJmP84UVyMwzIjPPiM9++tO5XftWenS9JgCdrwlAl2v80aVNADq3CYBBYx906a76NncN/vlo4ViP6lgTKdZDypfq4WobPB4cysrKoNfrJcscr41GoyQ4VHXp0iXMmjULMTExGD16dJ3HSE9Pd09jq0hLS3NtRVFEL4UKClHAL6kHYdFf4/a2XK0oNRDVFbivawiKTTb8VmCxPwotyCiwIqdMwLmCcpwrKMe3py5Jtg3VK9EuUI12qT+gXZDa+Qj2k/dtT1z++ZAJ1qM61kSK9ZBqTvXweHAwGAwoL5feCdLx2t/fv8Ztjh8/jkceeQQJCQl4/vnnaxxEWVVUVBQMBvfMayAIAtLS0hAbGwuVysXTDt8EAeUFuLHLdUCbbm5pR1O69YrXhUYzTlwoQUZOGc5cKsWZnFJkXCpDTokJ+eU25Jeb8VOOWbJNK4MGndsEoFOYP9qH6nFdqAHXhRrQobUBwfqWe9qjUT8fLRjrUR1rIsV6SPlSPYxGo0t/eHs8OHTt2hWFhYXIzc1FWFgYACAjIwMREREIDKw+E+LmzZvx7LPPIikpCdOmTXPpGCqVyu3fgAbtUxsIlBdAZTECzfAfRutAPfoH6tG/q3R5UbkFp/4swu5jJ2DWheK3S2U4nVOKPwrKUWC0OAdhXilIp0aH1vZA0a6VAe1a6dE2xP68bSs9Avya/1XBTfEz15yxHtWxJlKsh5Qv1MPV43v8f+yOHTuid+/eeO6557B48WIUFBRgzZo1mDhxYrV1d+3ahWeeeQZr167FgAEDPN3UxnNcWWFuWbfIDtZr0LtDK6gKDIiL6+b8ISs3C8io7JnIzCtDVr4RWXlGZOUbkVNiQnGF1TnPRE1CDBpcG6xHZLAO14bo7M9DdIgIqvwarIOfmv/BEBH5Aq/8qbdq1SosXrwYQ4YMgVKpxJ133onExEQA9isnFi1ahLFjx+L111+HIAhISkqSbD9mzBgsXrzYG013jYfmcvAVeq3KeVXGlcrNAs4VGHE2z4g/Coz4o6Ac5wvK8Ueh/Xmh0eJ8nPizuNZjtPbXIiLYHiquDbaHCUe4cLzWaRguiIiamleCQ1hYGFatWlXje6mpqc7n27dv91ST3MsLczn4Kr1WhajwQESF13xDrlKTFX8UGPFnUQX+LKzAn0XlyK78+mdRBbILy2Gy2pBXZkZemRm/ZNceLloZNIioDBbhQTp7oAjSITxY51wWpFPz7qJERFeh+Z9c9kVaefU4XI0APzW6RQShW0TNV9OIoohCowV/FlXgQrE9VFwoqrAHjaJyXCi2B45yi4ACowUF9fRcGLQqtAn0Q2t/LVoH+CEsQIvW/n5oHWB/3dpfi1B/LVr7axFi0EKrlvfVIkREV2JwaAotdIyDNygUCrTy16KVvxbdI2sPF8UV1spAYe+puFBUgYvF9oDh+FpUboHRLOBsnv3UiSsCderLYSLAD2GVYSMswB42Qg0a5BRbEVlcgVYBPF1CRC0fg0NTaOwdMm2Vk294eLbJ5k6hUCBYr0GwXoPoiJpPiQD28RYXiyuQW2pCbqkZeWUm5JWakVdqQm6ZGbklJhQYzcgvsz9sIlBSYUVJhRWZ9QWNXd8BsM/cGaTTIFivRpBegyCdpvKrGoE6DYL0aueyYL0GoQYtQgwahPprYdCqeBqFiHweg0NTcASHg2uBM98AbaLt8zk4HkHXAvm/A7np9selU0DuaSDvDKD1B8a9DnQb5d3P0ALptSp0DPNHx7Ca5wupymYTUVRuQV6ZGQXGynBRakZeqbkyeNhDx6XSClwqKofRKsImAmarzfl+Q2lVSrTy16CVQYsgnQaBOnXlQyP5GmKwrxNi0CDEoEUrgwZ6DUMHEXkGg0NT6DIUOLQOqCgELv5sf7iq3AS8fy9w+xLg5ocB/jLwCqXy8imSugiCgOPHj6NHj56oEOxho7jciuIKC4rK7Q97r8Xl5SXO96woKDMj32iG2WqDWbDhYrEJF4sbETrUSoTor+zdkPZ0BPqpEaBTI8BPgwA/eyhxfA3Sa6BRcTwHEdWPwaEptL8JePx3oCgLyDkJXDpp71VwfLWUAfpWQFg0ENbV3iMRFg207gwceB048g7w5ZP2XohRLwGqljvzYkuhVCoQqLH/kkarhm0riiLKLQLyy8woKLOgwGiuDBj2wOE4XVJSURlIjPZ1CsstKDSaYRFEmK025JSYkFPS8NDh4K9VIVh/+TRKsDOIaJzhIlCnRpDOfrrF3xlE1PD3U8PA8R1EssDg0FSUSqBVR/sjesTl5TabfdCkX1DNvQmjXgZadwV2PQEcexcoyAQmvWsPGtQiKRQKGLRqGLRqtGtE6CgzCygoM9t7Oyp7NkoqLCiu0tNRUmFBqcmKUpPVGUgcz41m+9iaMrOAMrOA7KKKRn4OwKBRwU8pIvS7fZVBQ9rz4ejlMGhV9q9+avhrVfYQ4qdGsEGDQD9eMkvkyxgcPE2pBHTVJ0pyUiiAmxOB0E7A5mnA73uAt4cB935oX0ZUhUKhQEDlL932jdyHVbChuMLqPLVS9VFc9VRLlR6Q4nILyiqDSJlZgGATIYqV4QNAfkVZoz+TUgFJr0fwFadgAv2kYz8cPR72hz2E+GvVUCkZPoiaAoODr4oeAfxzF/C/yUDeaeCtIcCUjUCHft5uGbUwapUSoZWXnDaGKIowWW2VgcKEYz/9ims7dEKZ2eYMHlVPvZSZBJSZrTCaBJSarDCarSg1CSipsMBktcEmwjmb6NXQa1TQa1XSrxoVdFoV9BqlM2DYeztUMGgre0P8VDBoVdBpVJU9QZf3YdCqoFOroGQoIRljcPBlEbHAjG+BTVOA7FRg/Wig32xg0AJAo69/eyIPUCgU0Gnsv2hDDWoUttIgrlPrRt2wp8IioLhKj0eh0eI8BXPlmA/HsjJT5cMsoMxkhdUmAgDKLQLKLYK7Py4AQKdRwqBV1xpODFpHQFHBT61A/qVSHCr+DVq1ChqVsvKhgFathJ9aCT+1Cn4aJXQaFfzUSmc9DZX79VMrefqGfAaDg68LjACm7gC2PwKkfQj88ApwYjsw9jWg4y3ebh2RWzl+YV4TpGvU9qIowizY7L0aJisqLAKMZsEZIioqnxsrQ0bVwFFa5bVzO+f6VlRYbM7jVFhsqLCY62hJDX6p/3bFtVEqqvSgVAYSncbe++EIHPbXjueXlzmCyJUhx6Ct3rPCgEKuYHBoDrQG4K63gBvHA58/CuRnAOtHAn2mA0OeBnQ1z6hIJDcKhcL+17ta1ehTL7Wx2URUWK8MFI7nVpSbbc6AUm6+/LrMZMGFnEsIDgmF1Sbar4IRbLAK9ktwTRYbKqyC82uFxQaTxf7VLNjDik28PHi1KSkVsPekOIPF5UDhCCBVe0gczx09J9oqr3Ua5eWAUyW4aJVAscmGUpMVBj8F1EoFw0ozw+DQnHQbae9l+PLf9isuDr8NnNoJjHkF6DrM260jatGUystXvzSEY66PuLjYBp++sQqVYaRKUDFW9ohUVIaLCovgDBwVFgEmq63K+1XXsaHCLMBosVYLP2br5YDiuPqmyW37GoB9PLhWpZSetlEr4ae5HFgczzUqBdRKJdQqe+BQq5TQKO2nfC6PX5H2pDgCjVathFalrNyP/bVGZV+mUSugUSkZYlzE4NDc6IKBsauAmLuA7Un2yzU3TgQ6Dwa63wlEjwQC2ni7lUTkBmqVEoEqpX1+kCZUNaAYzfbBq47nRrMAU2WPiMl6OZg4vjomLzNZ7Q9z5VdTlbBy5akixzgUABBFOLf1hbv7aFQK5ziUqmGjavhwhA6NM4hUbqO2BxmNSmkPNSrHcwW0lcFEulwJFUScy65AiX8u/DRqZ4DSSMKUPTzp1PZtvI3BobnqNBB48ACweylwcA2Q8a398dkcoP1fgBtGA91GA606eLulROTjPBVQAHsPzNHUVHS/sQcEUQGTUBk+rJdP3VQNJybH6RurAKsgwmoTYRVslad9bBBs9gnQqvbMVO1NqbAIsFSeFnIcxzFpmuNUUFUWQYRFEAA07Wmhan444tJqKqUCfmolAvzUeHJ0d4ztGdnEDauOwaE50xqA4UuB3v8Afv0EOPmZ/eqLrP32x64n7FdmtP8LcM0NwDXd7V/1Id5uORHJmEqhgF6rqjx1472ZcUVRdAYQe2Cw2R9WsbIXRRpqqj53rG8PIpfftwoiLLbKr5XrWR37dYQe5zgXEWZBQHFJGdRaHSyVIci5T0cvTpWAI9hEZ0/QqQvFAIMDNUpYF+DWefZH4Tng5Of2EHH2B+BCmv1RVVBbe4AI7QxYy4HyQvt9NcoLgPIi+3MAuP5WIGoE0PV2IDDcwx+KiKhpKRQK52kDb7k8Biau1jEwNps9aDh7YSw22EQRHVobPNxaOwaHliakPfCXB+yPsjwg4xt7cMg5YX8U/wEUn7c/8HXd+zr5mf0BAJHx9hDRZRggVu/eIyKipqFUKqBT2gd8+gIGh5bMvzXQY5L94VBRZL/xVs6vQMHvgDYA0IXY74Whr/yqCwFMxcDpr4D0nUD2MfspkOxUqL57Hj20IVCm32y/mVf7m+yhQlvDraoFq/1GX3m/2Y+l0gCGMMA/DDC0tj90IfZpuEURMJcB5fmAMQ8w5tt7QJRq+y3GeaMvIiKfwOAgN7pg4Lq+9kd92vYCBs0HSi44Q4SYsRsacyGQ/oX9AQAKFRB+oz1EqLRAXoZ9romCs4CtnmmDFSp7m8ylgFDLhDqTN9oHexIRkdcxOFD9AiOAXvcBve6DzWTE6b0fIUpfCGX2UeDcYaAkG7jwk/1xJbXOfnOuVtcDogCU5QLGXPtpFHOJfVl5/uX1VVp7T4Q+FDCEAqHXA9fd7LnPSkREdWJwoIZR+6EsNAZiXBzgGMhT9AdwLgU4f9T+OrQT0LqzffBlUFv7qYiaWE320xLlBYBfoD0saP1rvt04ERH5BAYHunrB7eyPmAkN207tBwRF2h9ERNQseH8KKiIiImo2GByIiIjIZQwORERE5DIGByIiInKZV4JDXl4eEhMTkZCQgL59+2Lp0qWwWmu+jeuePXswZswYxMXF4Y477sDu3bs93FoiIiJy8EpwmDNnDgwGA/bt24fNmzfjwIEDWL9+fbX1MjMzMXv2bDzyyCM4cuQIZs+ejTlz5uDixYuebzQRERF5/nLMs2fPIiUlBXv37oVer0f79u2RmJiI5cuXY/r06ZJ1t27dioSEBAwdOhQAMHLkSGzZsgUffPABkpKSaj2GIAgQBPfcEtWxH3ftr7ljPaRYDynWozrWRIr1kPKlerjaBo8Hh9OnTyMkJATh4Zfvtti5c2dkZ2ejuLgYQUFBzuVnzpxBVFSUZPsuXbrg5MmTdR4jPT3dvY0GkJaWVv9KMsJ6SLEeUqxHdayJFOsh1Zzq4fHgUFZWBr1eL1nmeG00GiXBoaZ1dTodjEZjnceIioqCweCe240KgoC0tDTExsbWestTOWE9pFgPKdajOtZEivWQ8qV6GI1Gl/7w9nhwMBgMKC8vlyxzvPb3l95hUa/Xo6KiQrKsoqKi2npXUqlUbv8GNMU+mzPWQ4r1kGI9qmNNpFgPKV+oh6vH9/jgyK5du6KwsBC5ubnOZRkZGYiIiEBgYKBk3aioKJw+fVqy7MyZM+jatatH2kpERERSHu9x6NixI3r37o3nnnsOixcvRkFBAdasWYOJEydWW3fs2LH473//ix07duD222/Hl19+iZSUFCxcuLDGfdtsNgCo1qNxNRyDRYxGo9fToC9gPaRYDynWozrWRIr1kPKlejh+dzp+l9ZGIYqi6IkGVZWbm4vFixfj0KFDUCqVuPPOOzFv3jyoVCrEx8dj0aJFGDt2LABg3759WLFiBbKystC2bVskJydj4MCBNe43Ly8PmZmZHvwkRERELUvHjh3RunXrWt/3SnBoKlarFUVFRfDz84Oytls5ExERUTU2mw0mkwnBwcFQq2s/IdGiggMRERE1Lf5ZTkRERC5jcCAiIiKXMTjUoSE342rJ8vPzMWzYMBw6dMi57Mcff8Tdd9+N+Ph4DB48GB999JEXW+gZJ0+exD/+8Q/cdNNNuOWWW/D4448jPz8fgDzrceDAAdx9993o1asXbrnlFixZssQ574oc6+EgCALuu+8+LFiwwLlMrvXYsWMHunfvjvj4eOcjOTkZgDxrUlhYiMcffxx9+/ZFnz59kJiYiJycHADNrB4i1epvf/ub+Nhjj4lGo1HMysoSR40aJb711lvebpZHHTlyRBw6dKgYFRUlHjx4UBRFUSwsLBRvuukmccOGDaLFYhH3798vxsfHiz/++KOXW9t0ysvLxVtuuUV89dVXRZPJJObn54szZswQZ82aJct65OXlibGxseLHH38sCoIgXrx4URw9erT46quvyrIeVb3yyitit27dxPnz54uiKM9/Lw7Lli0TFyxYUG25XGvyt7/9TXzooYfEoqIisaSkRHz44YfFmTNnNrt6sMehFo6bcSUnJ0tuxrVx40ZvN81jtm7dinnz5mHu3LmS5V9++SVCQkJw7733Qq1W4+abb8aYMWNadG2ys7PRrVs3PPTQQ9BqtWjVqhUmT56Mw4cPy7IeoaGh2L9/PyZMmACFQoHCwkKYTCaEhobKsh4OBw4cwJdffonbb7/duUzO9UhLS0NMTEy15XKsyc8//4wff/wRy5YtQ1BQEAICArBkyRLMmzev2dWDwaEW9d2MSw769++Pr776CiNHjpQsP336dKNuPtacderUCW+//bZkgpZdu3bhxhtvlGU9ACAgIAAAMHDgQIwZMwZt2rTBhAkTZFuPvLw8LFy4EC+99JLkHjtyrYfNZsMvv/yC7777DrfddhtuvfVW/Pvf/0ZRUZEsa/LTTz+hS5cu+PDDDzFs2DD0798fL7zwAtq0adPs6sHgUIv6bsYlB23atKnxWt7G3nyspRBFEStXrsTu3buxcOFC2dfjyy+/xN69e6FUKpGUlCTLethsNiQnJ+Mf//gHunXrJnlPjvUA7GOjunfvjuHDh2PHjh14//33kZmZieTkZFnWpKioCKdOnUJmZia2bt2KTz75BBcvXsT8+fObXT0YHGrRkJtxyU1jbz7WEpSWliIpKQnbt2/Hhg0bEB0dLet6APb/4MLDw5GcnIx9+/bJsh7r1q2DVqvFfffdV+09OdYDAMLCwrBx40ZMnDgRer0ekZGRSE5Oxt69eyGKouxqotVqAQALFy5EQEAAwsLCMGfOHOzZs6fZ1YPBoRYNuRmX3Mj15mNZWVm46667UFpais2bNyM6OhqAPOtx7NgxjBgxAmaz2bnMbDZDo9GgS5cusqvHp59+ipSUFCQkJCAhIQGfffYZPvvsMyQkJMjy5wOwX4W0YsUKiFXmGDSbzVAqlejRo4fsatKlSxfYbDZYLBbnMsc9IW644YZmVQ8Gh1pUvRlXaWkpzp07V+vNuORm2LBhyM3Nxfr162GxWHDw4EFs374dd911l7eb1mSKiopw//33o1evXvjPf/6D0NBQ53tyrEd0dDQqKirw0ksvwWw24/z583jhhRcwceJEDB8+XHb12LlzJ44dO4YjR47gyJEjGD16NEaPHo0jR47I8ucDAEJCQrBx40a8/fbbsFqtyM7OxvLlyzF+/HhZ/oz069cP7du3xxNPPIGysjLk5+dj5cqVGDp0KEaPHt2s6sEpp+tQ18245CY6Ohrvvfce+vbtC8A+Wnrp0qVIT09HaGgoEhMTMWHCBC+3sun897//xbJly6DX66FQKCTvpaamyq4egP0voueeew5paWkIDAzEmDFjnFedyLEeVTnmcFi2bBkA+f17cUhJScHLL7+M9PR0+Pn5YdSoUUhOToafn58sa3Lx4kUsW7YMhw8fhslkwuDBg7Fw4UIEBQU1q3owOBAREZHLeKqCiIiIXMbgQERERC5jcCAiIiKXMTgQERGRyxgciIiIyGUMDkREROQyBgciIiJyGYMDEbmVyWTChQsXvN0MImoiDA5E5BQdHY0ePXogPj4ecXFx6NOnDx588EH8+eefLu/jnnvuwf79++tdb8uWLRg8ePDVNJeIvIDBgYgk3nrrLaSmpuL48ePYvXs3RFFEcnKyy9sXFBQ0YeuIyNsYHIioVgEBAZg0aRJ+/vln57KMjAzMmjULgwYNQo8ePTBy5Ejs3r0bADBt2jRkZ2fj6aefxuLFiwEAP/zwAyZOnIj4+HgMHjwYGzZscO7LarVixYoVGDRoEHr16oUnn3wSVqsVACCKIt577z0MHz4cCQkJuOeeeyTt2LVrF0aNGoXevXvjjjvuwJo1azxREiISiYgqRUVFiQcPHnS+LiwsFB999FExOTnZueyOO+4QV6xYIZrNZtFkMolLly4Vb731Vuf7t912m/jxxx+LoiiKv/32mxgTEyN+9NFHosViEdPS0sT4+Hhx79694scffyxGRUWJ69atEy0Wi3j69GmxZ8+e4vbt20VRFMUNGzaIgwYNEk+cOCGazWbxo48+EhMSEsRLly6J5eXlYmxsrLOtv/zyixgXFyf++OOPnigTkaypvR1ciMi3PPDAA1CpVLDZbCgrK0NgYCDWrVvnfH/dunUIDw+HKIo4f/48goKCcPHixRr39fnnn+PGG2903o4+JiYG//vf/3DNNdfgu+++Q0BAAGbMmAGFQoEuXbqgW7duyMrKAgBs3LgRs2bNQrdu3QAAEydOxObNm7Ft2zbcc8890Ol02Lx5M2w2G3r16oWjR49CqWQnKlFTY3AgIok33njDefv0iooKbNy4Effffz8++OAD3HjjjTh58iQSExNx6dIldO7cGaGhoRBrucluTk4OIiMjJcscQQAAgoODJbcp12g0EAQBAHD+/Hm88MILWLFihfN9q9WKmJgY6HQ6bNq0CWvWrMFjjz2G0tJSDB8+HE8++SSCg4PdVgsiqo7BgYhqpdPp8M9//hNvvvkm9u/fj7CwMDzyyCN4/fXXnVdE7Nq1C19++WWN21977bXYs2ePZNnHH3+M1q1b13vsiIgIJCUlYdSoUc5lWVlZCAkJQWlpKXJycvDSSy8BAE6cOIFHH30Ub7zxBubPn9/Yj0tELmC/HhHVymq14uOPP0ZxcTF69+6NsrIyCIIAvV4PADhz5gxWr14NADCbzQAArVaLkpISAMCoUaPw66+/4pNPPoEgCPj555+xbNkyqNX1/80yadIkrF27FhkZGQCAffv2YdSoUTh8+DDKysowY8YMbN++HaIo4pprroFSqUSrVq2aogxEVAV7HIhIYsaMGVCpVAAAhUKBjh074uWXX0avXr0AAI8//jiSk5NRXl6OiIgITJo0CcuXL0d6ejpiYmIwceJErFy5EmlpaVixYgXefPNNvPTSS1iyZAlat26NBQsWoH///tiyZUud7Zg6dSpEUURiYiJycnIQHh6Op556CkOGDAEArFq1Cq+88gqeeuop6HQ6jBw5ElOnTm3S2hARoBBrOzlJREREdAWeqiAiIiKXMTgQERGRyxgciIiIyGUMDkREROQyBgciIiJyGYMDERERuYzBgYiIiFzG4EBEREQuY3AgIiIilzE4EBERkcsYHIiIiMhl/w95a1uhs0gxjwAAAABJRU5ErkJggg==", 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", + "image/png": 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", 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", 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", 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" ] @@ -477,7 +454,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['0.00', '0.00', '0.00', '1.00', '0.00', '0.00', '0.00', '0.00', '0.00', '0.00']\n" + "['0.00', '0.00', '0.02', '0.97', '0.00', '0.00', '0.00', '0.00', '0.00', '0.00']\n" ] } ], @@ -567,7 +544,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -601,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -616,14 +593,14 @@ { "data": { "text/plain": [ - "tensor([[1.0740e-04, 4.6795e-06, 1.4407e-03, 1.0600e-02, 8.6712e-02, 1.2271e-03,\n", - " 2.7817e-04, 2.9068e-01, 2.2229e-03, 6.0672e-01],\n", - " [5.3206e-08, 3.4645e-10, 2.6333e-04, 9.9962e-01, 1.6306e-08, 1.9286e-05,\n", - " 5.3828e-11, 3.9853e-09, 4.4153e-05, 5.7737e-05]],\n", + "tensor([[1.6849e-08, 2.4025e-06, 6.8811e-06, 9.9919e-01, 2.8559e-07, 7.7187e-04,\n", + " 1.6810e-06, 2.3518e-08, 2.7687e-05, 2.3870e-06],\n", + " [2.5023e-12, 2.4782e-10, 4.4331e-07, 1.0000e+00, 2.3995e-12, 3.9583e-09,\n", + " 1.6467e-13, 3.9655e-06, 1.7548e-08, 2.0199e-07]],\n", " grad_fn=)" ] }, - "execution_count": 39, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -648,16 +625,16 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([8, 3, 7, ..., 0, 9, 8])" + "tensor([3, 3, 7, ..., 5, 6, 4])" ] }, - "execution_count": 96, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -668,55 +645,45 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 15, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(1705.9429, grad_fn=)" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "K = 10 # self.n_classes\n", + "if 0:\n", + " K = 10 # self.n_classes\n", "\n", - "# formulation 1\n", - "evidence = nn.ReLU()(logits)\n", - "S = torch.sum(evidence + 1, dim=1)\n", - "b_k = torch.divide(evidence, S.view(-1, 1))\n", - "u = K / S\n", + " # formulation 1\n", + " evidence = nn.ReLU()(logits)\n", + " S = torch.sum(evidence + 1, dim=1)\n", + " b_k = torch.divide(evidence, S.view(-1, 1))\n", + " u = K / S\n", "\n", - "# formulation 2\n", - "evidence = nn.ReLU()(logits)\n", - "alpha = evidence + 1 # concentration parameters of the dirichlet distribution\n", - "S = torch.sum(alpha, dim=1)\n", - "b_k = torch.divide(alpha - 1, S.view(-1, 1))\n", - "u = K / S\n", + " # formulation 2\n", + " evidence = nn.ReLU()(logits)\n", + " alpha = evidence + 1 # concentration parameters of the dirichlet distribution\n", + " S = torch.sum(alpha, dim=1)\n", + " b_k = torch.divide(alpha - 1, S.view(-1, 1))\n", + " u = K / S\n", "\n", - "torch.sum(b_k, dim=1) + u # all sum correctly to 1\n", + " torch.sum(b_k, dim=1) + u # all sum correctly to 1\n", "\n", - "alpha/S.view(-1, 1) # class probabilities\n", + " alpha/S.view(-1, 1) # class probabilities\n", "\n", - "# loss function - pdf -> likelihood, given the correct class = 1, all others = 0\n", - "# 1/B(alpha) Prod_i x_i^(alpha_i-1)\n", + " # loss function - pdf -> likelihood, given the correct class = 1, all others = 0\n", + " # 1/B(alpha) Prod_i x_i^(alpha_i-1)\n", "\n", - "# sum over k outcome_k * (log(S) - log(alpha_k))\n", - "(torch.log(S).view(-1, 1) - torch.log(alpha))\n", - "alpha[batch_rotated_labels].shape\n", + " # sum over k outcome_k * (log(S) - log(alpha_k))\n", + " (torch.log(S).view(-1, 1) - torch.log(alpha))\n", + " alpha[batch_rotated_labels].shape\n", "\n", - "# select alpha values from only the positive class, all others are multiplied by 0 so dont calculate\n", - "alpha_positive = alpha[torch.arange(alpha.size(0)), batch_rotated_labels]\n", - "(torch.log(S) - torch.log(alpha_positive)).sum()\n" + " # select alpha values from only the positive class, all others are multiplied by 0 so dont calculate\n", + " alpha_positive = alpha[torch.arange(alpha.size(0)), batch_rotated_labels]\n", + " (torch.log(S) - torch.log(alpha_positive)).sum()\n" ] }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -742,6 +709,7 @@ " nn.Linear(128, n_classes),\n", " )\n", "\n", + " self.n_classes = n_classes\n", " self.train_log_error = []\n", " self.train_log_acc = []\n", " self.val_log_error = []\n", @@ -762,8 +730,8 @@ " \n", " # b_k = torch.divide(alpha - 1, S.view(-1, 1))\n", " # K = self.n_classes\n", - " # u = K / S\n", - "\n", + " u = K / S # uncertainty\n", + " \n", " return alpha/S.view(-1, 1) # class probabilities\n", "\n", " def loss_function(self, labels, alpha):\n", @@ -775,7 +743,10 @@ "\n", " def concentration_to_probs(self, alpha):\n", " S = torch.sum(alpha, dim=1)\n", - " return alpha/S.view(-1, 1) # class probabilities\n", + " K = self.n_classes\n", + " u = K / S # uncertainty\n", + " \n", + " return alpha/S.view(-1, 1), u # class probabilities\n", "\n", " def training_step(self, batch, batch_idx):\n", " data, labels = batch\n", @@ -787,7 +758,7 @@ " self.train_log_error.append(loss.item())\n", " \n", " # Calculate training accuracy\n", - " probs = self.concentration_to_probs(alpha)\n", + " probs = self.concentration_to_probs(alpha)[0]\n", " _, predicted = torch.max(probs, 1)\n", " correct = (predicted == labels).sum().item()\n", " accuracy = correct / labels.size(0)\n", @@ -808,7 +779,7 @@ " # loss.detach().numpy()\n", "\n", " # Calculate training accuracy\n", - " probs = self.concentration_to_probs(alpha)\n", + " probs = self.concentration_to_probs(alpha)[0]\n", " _, predicted = torch.max(probs, 1)\n", " correct = (predicted == labels).sum().item()\n", " accuracy = correct / labels.size(0)\n", @@ -835,19 +806,23 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/stantoon/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:478: LightningDeprecationWarning: Setting `Trainer(gpus=1)` is deprecated in v1.7 and will be removed in v2.0. Please use `Trainer(accelerator='gpu', devices=1)` instead.\n", - " rank_zero_deprecation(\n", "GPU available: True (mps), used: True\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", " | Name | Type | Params\n", "------------------------------------------\n", @@ -861,97 +836,61 @@ ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f60dc0c717674ac0af53746e845c6e36", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/stantoon/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n", - "/Users/stantoon/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + "/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:430: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", " rank_zero_warn(\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cc037361026443ba85762a49b5d13102", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + " " + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0c205b380bd245d38c7945bc206f3205", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:430: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + " rank_zero_warn(\n" + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b9854448bb31440e8235d8e14376a219", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 9: 100%|██████████| 54/54 [00:04<00:00, 11.33it/s, v_num=14]" + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "aa88333f3cd94151923bfda7584a2457", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=10` reached.\n" + ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "`Trainer.fit` stopped: `max_epochs=3` reached.\n" + "Epoch 9: 100%|██████████| 54/54 [00:04<00:00, 11.30it/s, v_num=14]\n" ] } ], "source": [ - "model_d = DirichletModel()\n", + "model_d = DirichletModel(learning_rate=1e-4)\n", "\n", - "trainer = pl.Trainer(max_epochs=3, gpus=1, accelerator=\"mps\")\n", + "trainer = pl.Trainer(max_epochs=10, accelerator=\"mps\")\n", "trainer.fit(model_d, data_module)" ] }, @@ -964,22 +903,22 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/_v/nlh4h1yx2n1gd6f3szjlgxt40000gr/T/ipykernel_73909/1358147501.py:16: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_52426/2349103316.py:8: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n", - "/var/folders/_v/nlh4h1yx2n1gd6f3szjlgxt40000gr/T/ipykernel_73909/1358147501.py:25: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_52426/2349103316.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n" ] }, { "data": { - "image/png": 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", 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", 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", 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B/eth58fQ8bfQ6uKmOw8REflVU7A4m7hcYLG4vwDK8uCD+6DbSKgohK+e89x+xaQT35fnwbp/wO+erf3YmxbBvq/c3//bBlsWu78//B3c+LZ3z0NERM5bChZni6yP3Zc4ErufaOg/nQmZb7u/ergfvMbF4yGmLax64MS+XYbD1mWQuRS6joCfPofCAxAQDEOfgqoKWPv8ie2rQwXAb2c39pmJnBNcLoMjxRUczC8nyN9K+/gwbIG//idWinibgsXZICcLFo10f198yB0KKgph479ObPPd/7n/je8CYQme+w94AA5thvw98K9rPdcl9XXvW7AXAsPAcdKgz9SbILadt89GxKcqKp2s3ZXLup/yyCmxc+EFEbSOCaHS6WL9njy2Hy4GICI4gLwyB4VllVRUOTlcWIG9ymUeJ8Bq4bL2cSREBDOgczwtomyEBflT5XLRIirkjENHcUUloYH++PlZKHNUAeDv50dOiZ0Ne/Kw+lloHx9Gu2ZhBFjdw+DsVU6qnAahQeffn2yny2D3sRJaxYRQ5TLIPFBIVEgAAVYLG/cV8O2efHJL7XRKDKdtXBjBAVaC/P1IjgslOTYEAH+rhhM2pfPvt9QXDnzrHhvR73/BWsuTV1c/4fl6/Uvw9TxwOmpuG5UE4Yk/W9YaLkhxB4ufWzUDynLA4ge3rIA3R7vDC0BU8hmcjEjTcroMckvtHCqoYNO+fFpGh/Cb9rHYAqzklTr477ajbNybj8Xi3vaz7cc4Wmw/o/fy97OQGBlMRaWTnBIHn24/BsCbG/Z7bBdo9ePalOa0jgmh2F5FfHgQLaNtJEba2JdXxtGiCjbtL+BgfjnBAX44XQYFZZXklTrILXUQEexPoL+VnJJTlzPAaqF9fDhOl4usoyUYBkTaAmjbLJTQQH8MDJpHBhPsKKEo9BhWPytVLhd7csrYcaSYY8V29ueXkRARTL8OzeiYGE6H+DCKK6rYm1tKi2gbXS6IwFJ96RXYl1vGntxSggOs7DhSzP78MiKCA6h0uogOCaRnUjT788v48PvD2AKstGkWSuuYEAL93edoAJe0jSXSVvPvXJXTRW6pA6fLoMRexdEiO0eLK/ghu4jvDhQQFxZEnzYxNI+ycbTYzuodx9hxpJii8kryyyoJtPrhMgyqXEat9fXxj0drXW71s5AYEcxFrSJpExdKpdOgXbNQujaPpHmUjeiQAI86OJlhGFQ6DSwWzJBXG3uVi8+2HsXpMgj09yMsyB8/i4WOCWHEhAZysKCcI0UV5Ja4f/7bDhWx+UAhsaGBtIq20bV5JAH+FhIigunbNvaU5amtTg8WlBPo70eZw0nWkWK+2pVLoNWPEnsVU4d0IjYsqE7H8iYFi8bmrIR/DnR/H54IaX90f//V8+6eiSumwfdL3Ms6/hZ2fAhfPnN8ZwvcuOREbwYcDxYXnHgdFOG+5BGVdGKZXwD87w54urM7VACEN4cWPSGm3YlgEd3a22crPlTpdLE3twyrnwULEBceRNgpPuFWOV3syyujVUwITpfBxr35VFQ5qXQaFJQ52JtbRlFFJUXlVfhbLfRoEUmX5pHEhwcR4O9HTEggwQF+7DhSzJYjdprll5MUF+bxHkUVlQT7W9mdU8J3Bwr5Zk8eO46U0CLaxsBO8bSItmEB2jQLJdIWQFF5FQcLyvH3sxDo74dhwLLNB1n09V6KKqo8jl19jqdqZBIigujfsRmJkTa2ZhdytNhOgNWPFlE2+nVsRoDVQmF5JeHB/sSHBxPk70d8eDDNo4Lxt/phGAbbDhfz8dYj/JRTyuodx/Dzs1Bqr8JqsVBsr2LJtwfO+GflPp+qGst7tIwkwOrHjsPFFNur+PFQkcf6wvJKNu0rqLHfwsxvT/leO46UsCYrp9Z1nRPDCQvyZ19eGVUug7zSWj7M1FOg1Y/uLSMZ0jWBAKsfh4sqOFRQwWfbj9b4Of7cB98fPuU6h9PdmxQXFoTLMCi1V9G9RSRpyTFcEBnM1uwiDhaU46hyUVZZxdbsIlyGO2weLCjnYEF5rccNsFro0jyS9s3C6NcxjtBAf/bklrLqh8Ns3l9ApdMgwGqhXbMwYsMCCQvyp7zSRctoG/5+8NnWHLKX/qfW30WLBaJsAeSXVda5/qx+FqJsAfRuE0PfdrF0axFJRHAAH35/iN3HSs3/l/nH/59W10ttftM+jmsval7n9/YWBYvG9sO7J77fv84dLMry4KMZ7mXhCWC4wBYDl05yB4tq3UdBuwGex4tsBYEhJy04nmxPDhaRLSEkxj0W49iPJ5YBxLaFvV8c30fBojaGYZBdWMGXx/8Yt4yx0TIqBD8/uCDSRkWlky37C/h2bz67c0ppFW2jWXgQRRVVJMWEEGC10Do2lDZxoQQHNM41+k+3HWVNVg4RNn/32IAiOx9tPezxBywk0MoVnZoRHGAlOMDKT8dK2Z9fRmxYELuPlVBcUUVYkD9Ol0F5pfO07/fOxoMery0WCPa3mvs98vlqWseGEGD1o1NCOPllDr7alVvrsTbvL+D97w7VOJ5Re0Yw18eEBNIuPozsgnIO5J9oJLo2j3Cfp78VA2jbLJTBXRII8j/zurdYLFx4QQQXXhBR6/oNe/L4IPMweaV2YkKDOFZiZ09OKXmlDuLCAmkZHUKrmBBSk6KoqHQSYPUjyhZAVEggLaJsHCwox2UYtIoJIdDqh8Ppwt/PYl7qMAyDA/nl/HioiPJKJ33bxmILtJJdUMGPh4qwV7mPuS+3lPXbD3C4wkqA1Y8AfwsXRNq4MDGc+IhgWkTZ2JNbypqsHPbmlrI7p5TQQH9ax4aQdaSEbccvC50sISKIkEB/WkbbaNcsjLxSB1Y/C0eLK/h2bz7x4cFceWECYcH+7Mlx/065XAYWizus/ZRTyrd78/l2b36NY/tZ3Jd9bIFW4sODaBYeRNtmoXRrHsmB/HKyjhaTXVBBpC2APm1i6JUcjQULPVpGkne8t6N1bEidPtEfK7ZTaq8iOMDK3txSvtiZQ3FFFRYLfH+wkJ9yysgpsVPpNNiyv4At+wtYurH2sFjpNGqtq5PFhQXRIioYLBZKKiqxV7k4kF9OflklAVb3zyUmNJC4sEDiI4K5ODkae6WLHUdK2HWsBJdhsP6nPOxV7p6dD74/fNqgVS3I348ql0FIgJXYsEB6t4khwOpHalI013S/4Bf3bwwKFo1t/Usnvj+0xf3vT5+fWPb1fPe/ES0g/kLPfSNbgJ8V/PzBdTzpe4SKk5wcLKq/j2lzIlhEtaq5XXRynU/j16ii0sm/Nx9k8/4Cckoc5JbYyS11kFNsp9RRe0MbEmilotLJKT4oe7BYIDY0iFYxNkb3TuKKTvE0C69ft+ShwnL25rr/cG89VMS3e/Mprqjii521fwINCbTidBnYq1yUOZyszKz5h+nkRrnE7v69ahYeRPPIYKzHG7e2caFEhQQSYQuguKKSLfsL2HGkhIIyB5VOA4fTRXmlk+AAP+KCLRwqdfeWAOw86jl5W2iglW4tIklpFUXXFpFsP1zE2l255JdVUlHp5FBhhRkq4o/XT5XLoLLKxYUXRDChX1sGdGpmXic3DIPDRRVYsBAW7H/KXpnGdHFyDBcnx5zx/pEhnpcKbHiGIIvFQqsYdzg5WafEADolhpuvnU4nm2NKSElJwWo9dZD646VtAHfdVTfKR4sq+PqnPAzDoG1cGPYqJ6FB/qcMU3VhGAb78sr4z9YjfL07D6sfND8+NqV/x2akJkVj9atbN//P1Xd8SbPjwQUgMTKYPm1ja2xjPz62ZsuBQr4/WMjnO44R6O9HTGgg/To0Y0DneGJCAskvc7Ant5TsggqKKyqJtAVwsKCcyiongRV5pPdLoUVMaI1zO1ZsZ19eKR0SwokIruUy+M8cLa5gf14ZThes/ymXtbtz2XM8AHVICOOaHs2JtAUQERxAhM2f1jGhtIy24XeGddpYFCwaU+4uOLD+xOujP0J5Aez65MSykuN/+COau+eisMW4bx0Fd9gACAqH8prpHzgxb4VHsDgeImLanlhW3WPhH3xiWVh8vU7nXOB0GWzcl8+hwgqOFFawZmcOlVUuqlwuBndJYETPlsSEBLL5QAFT397CrmOltR7H6mehW/MIImwBHMx3d6NWOt2NNUCLKBs9W0fTMT6M/fllHCmyExbsz5HCCipdBj8dK6GoooqcEjs5JXaz+/rCCyJITYrCMAwibYEkRrgvLQRa/Vi7K5eth4qw+lkoKKvEYvEMAT93RadmtIiyYfWzEBEcQK/W0fTr2IzqvzHf7M1n4958SuxVfLUrlx4tI7mqSyIH8stoHx9GtxaR7DpWQl6Jg17J0fX6hJ9bYqewvJJmYQHs2Po9bTt1ZdOBQsodLr7Zm0d8eDBDuycSHGAlLizI8w/uz7pmDcNgf145Fgs1GtLaWCzuT39Sfyd/0o+PCPZ6N7nF4u6tG395W8Zf3vaXd/CxIH8rrWNDaR0byrUXNeeBoRfWul1kSADJcaE1ljudTjZv3kyLaFutgenkcFMX8eHBxIe7/0b3bhPDnwZ2qPO+ZxMFi8aUefy20XaDIG+Xe3DlwW8g6z81t404/h88pi0cPB4sqsNAyo3u20WbnfRLP/Z9WPM0XP3k8W1bnVgXHOX+9+QeiepjdR8Fn8x0T65VxwFCZzuH0+D/rd/HrmNlfLb9KHuOf3L+uQ178nn8w+0EHR/oBO7/+DekteKCqGBiQ4OICwskJjSQ5lG2GpcxHFUuMg8W0iLKRmJkcG1vYTIMg5wSB4cL3deWP/zhMD9kF/HjoaIa181Px+pnoVW0jQCrH6FB/gzoFE+kzZ/ebWLp0vz0nyxP/lQ9xWPNiU9unRPP7NNpbFgQsWFBOJ3ueoywBTCws/tupWE96tf9arFYSIr95UAhIucGBYvGtO9r978X/g5+XOEOFusWQHE2hMS6xzhkb3RvU907EdPWHT5OXjZghjs4dB524tjJl7m/qgWdNHCuOjDEtDmxLPJ4j0Z4Ikz5EQJPdKc2VPUo77Ag/xqpvaDMwYffH+ajrUfonBhOalI0Px4qIiLYn5iwIH44WMjm/QUkRATTMtpmdncmxYTgMgwMA/q2iyWv1MGhwnLKHE6OFNmpdLr4IbuIsEAr63fmsiv/iPme4cH+dEoIp9ThZPCF8bRtFkZBmYN3N2ezZX8BZQ4nIYFW+raN5bER3UmIOH1IqBbo70ev1tF12tZisZifVrq3jOTuQR3IKbHz5c4cdh0twWKxkFNip6C8EkeVi5wSO92aR3JFp2ZYLBAWFEBBmYNeraN9MqpbRORMKVg0hoJ98P07cOQH9+vYdiduEa2ekvvi8e7LG2awaO75L5zoZQgMgUvu+OX3TewOhzPdM3XCzy6FtDjxva1ujePJ12NPtje3lHW789iXV8a6n3LZcqAQR5WL2NBAftM+Dnulk7Bgfw7kl/Pt3nycxwckfLKt9tvBvCEk0Ep6r5b0ah3NoAsTar3uPvbSNmw7XMShwgr6to1ttIGVpxIXFsR1KS1+eUMRkXOYgoW3FR2CZ7p7LotsWXNSq27pJ6bYhhOB4uQxEHUMAKaxK6Eo+8S4i5Mvjxwfg1FQ5uCz7cf48XARB/LKaRUTwsDO7lv/bAFW/r35IF9k5bAvr4xdx0q4INJG69gQyiudHMwvJyTQespLDbmlDlZsya6xvFNCOD1bR3OosJydR0vockEEfhYLeaUOWkTbuDg5hhJ7JQfz3T0STsPgp5xSqpwGZY4qdueUYguw0rZZKCGB/sSFBWIY7ssYjionFUX5/GloT9on/HK3fufEiDPu/hcRkV+mYOFtnz9Rc1lEC8+5J7C455CwF3luA9B+EKyeDdbA+o+BCI5wf1WzBuD600b++8MBvly1j69359Z6y9T81btOechT3f/du00MHeLD6JwYzmUdmpEYEcyarGPszS0jKMCPw4UVxIcHMejChDoNyDudKqcLi8VS6+Co6sFTbWoZWCUiIk1PwcKbDAO2f+C5LCwR/IPc81VUi2juXtass+cygFa94eZ/Q3QbzkRRRSU/HCxib24pP+WW8tm2Y2w/UgKcuAWwU0I4l7SNoWV0CD9kF/LFzlwKy923EXZKCGdUWkvaNQujbbNQc/Y+w4DWsaE4qly0iQutdfDiVV0TayzzBk3HKyJy7lCw8KZDm0/MalmtOlCc3GNRfbdGUBj88UMwnJ6DL9teUevh7VVO1v+UR4DVz5xmt22zULZmF7HupzwyDxby6bajNWaACw20Mjy1BRe1jOLKLgnEhAbWOLbLZZBX5iA2NNBjXEXr2FDSGnC/voiInF8ULLxpxyr3v52vgW3vub93Hp8J8eQxFifPeNm6b62Hqp6zf39+GV/tyuVwYQV7c0vJKfnlKXerZ8xrExfKhReEc1WXRKJrCRMn8/OzEKe7D0REpIEULLxp92fufzsMPhEsHMcnYDo5WARH1tg180AhP2QXciC/nP9uO3rKuQ5iQgOJsgVQVFGJYbgHTAb6+zGwUzwto22MSmvlMTOfiIhIU1Kw8BZHCRzY4P6+TX/och1s/Tf85m73soATYxL2FTtZ/kkWOSUOyh1Ovs8u5IdszyDh7+d+6E18RBA9k6LpnBhOUIAfvdvEmrdSGoZBqcNJkL/faZ+8JyIi0lQULLxl31r38zwik9xjKIb/A3rfDkmXAO4xDE5rCAHOMv53UwLrjR0euwdYLfRtF0eLqGB6tIzi6m6JRIWc/vKFxWLxyXMSRERETkWtkpdYDh5/bHHyZe7bRANDIflSwD0V9F3/byPflz5OG79D7I9I5epWUSTFhBDk70enxAjSkqPrPAOkiIjI2UrBwlsK9rv/jWsPQLnDyZc7c/h0+1GWbTp4/JJFM/541aW8fmkbXboQEZFfJQULL7EU7nN/E5nEt3vzmLh4s8fEUuHB/jw/uif9OzbzUQlFREQan4KFtxS4g0W2pRnjXvuGwvJK4sOD6NsullG9WpGWHN3kz6YQERFpagoW3uByup/RATz5dRmF5QapSVEsGt+HkEBVsYiInD90od8LAiuOYTGcuPwCWbbLidXPwpzrUxQqRETkvKNg4QWBZUcAyLE2w8CP61Kak6yHYomIyHlIwcILAsvdwSLL7n6mxrhLz+wBYiIiIuc6BQsv8HcUAnDMCKdHy0i6tag5ZbeIiMj5QMHCCyy4AHBiZXhKCx+XRkRExHcULLzAUeUOFi7DwqAL431cGhEREd9RsPCCQ0XuR5mH2QJpHatBmyIicv5SsPCCfYWVAFwQpVAhIiLnNwWLBiqxV3G42B0sktRbISIi5zkFiwb6ZNtRDMMAIDpUTycVEZHzm4JFA63JysHP4h68afFTdYqIyPlNLWEDGIbBlztz8Tt+uykWVaeIiJzffNIS5ubmkpGRQVpaGn369GHmzJlUVVWddp8dO3Zw0UUXsW7dOnOZy+UiNTWVlJQUUlNTza+ysrLGPgUAdh0r4UixHX+L+1KIgoWIiJzvfPKUrMmTJ5OQkMCaNWvIycnhzjvv5LXXXmP8+PG1bl9eXs6UKVOoqKjwWL5z504qKyvZuHEjgYGBTVF0D9kF7vIkhPiBHQULERE57zV5sNi7dy/r16/n888/x2az0apVKzIyMnjyySdPGSwefvhhrrzySnbs2OGxPDMzk06dOtU7VDidTpxO5xmfQ7W+baJ57oYedNu8DPaCCzC8cFypu+qfozd+nlJ/qn/fUv371vlW/3U9zyYPFllZWURFRZGQkGAua9euHdnZ2RQVFREREeGx/bJly9i7dy8zZ85k3rx5HusyMzOx2+2MHDmSgwcP0q5dO6ZMmULPnj1PW4afB5SGaA7Yjtfi0WO5HNy82WvHlrrLzMz0dRHOa6p/31L9+5bq31OTB4vS0lJsNpvHsurXZWVlHsFi165dzJkzh8WLF2O1WmscKzg4mB49ejBp0iQiIyNZtGgRt956K8uXL6dVq1anLEPHjh0JCQnxyvk4nU7yf3CPsYhPSKRZSopXjit143Q6yczMpHv37rX+jkjjUv37lurft863+i8rK6vTB/MmDxYhISGUl5d7LKt+HRp6YoIpu93OPffcwwMPPEDz5s1rPdb06dM9Xt9666288847rF69mptuuumUZbBard79JTDcd4X4+VnhPPjlOht5/Wcq9aL69y3Vv2+dL/Vf13Ns8tGGHTp0oKCggJycHHPZrl27SExMJDw83FyWmZnJnj17mDFjBmlpaaSlpQFwxx138Ne//hWAOXPmsHXrVo/jOxwOgoKCGv9EPOiuEBEREfBBj0VycjK9evXiscce45FHHiE/P5958+aRnp7usV1aWhrfffedx7JOnToxf/58+vTpA7jHSnzzzTc888wzREZG8uKLL1JSUsLgwYOb7HwALIbmsRAREQEfzWMxd+5cqqqqGDRoENdffz2XX345GRkZAKSmprJ8+fI6HWfWrFkkJSVx3XXX0adPH9avX8+rr75KVFRUI5a+NuqxEBERAR/NYxEXF8fcuXNrXbdp06ZT7rd9+3aP11FRUcyaNcurZTsT6rEQERFxU0voDYZ6LEREREDBwkuqeywsvi2GiIiIjylYeIFFPRYiIiKAgoWXaIyFiIgIKFh4hXosRERE3NQSeoPuChEREQEULLxEPRYiIiKgYOEVmsdCRETETS2hV6jHQkREBBQsvOJEj4XmsRARkfObgoU36K4QERERQMHCSzTGQkREBBQsvELzWIiIiLipJfQK9ViIiIiAgoVX6HZTERERN7WE3qBLISIiIoCChVeox0JERMRNLaFXqMdCREQEFCy8o7rHwk/VKSIi5ze1hF5gUY+FiIgIoGDhHRpjISIiAihYeIUmyBIREXFTS+gV6rEQEREBBQvvUI+FiIgIoGDhFRb1WIiIiAAKFt6hHgsRERFAwcIrTsy8afFtQURERHxMwcIr1GMhIiICChbeoUshIiIigIKFV2jwpoiIiJtaQm9Qj4WIiAigYOEVemy6iIiIm1pCr1CPhYiICChYeId6LERERAAFC6/QY9NFRETc1BJ6gybIEhERARQsvEKPTRcREXFTS+gVGmMhIiICChbeoR4LERERQMHCKzSPhYiIiJtaQq9QsBAREQEFC6/Q4E0RERE3tYReUd1jYfVtMURERHxMwcILTvRYaB4LERE5v/kkWOTm5pKRkUFaWhp9+vRh5syZVFVVnXafHTt2cNFFF7Fu3TqP5S+99BL9+vUjJSWFMWPGsHv37sYseu00eFNERATwUbCYPHkyISEhrFmzhiVLlrB27Vpee+21U25fXl7OlClTqKio8Fj+7rvvsnDhQl5++WXWrVtH165dmThxIkZ1D0KT0RgLERER8EGw2Lt3L+vXr2fq1KnYbDZatWpFRkYGixYtOuU+Dz/8MFdeeWWN5W+99RajR4+mQ4cOBAUFMWXKFLKzs2v0ajQ23W4qIiLi5t/Ub5iVlUVUVBQJCQnmsnbt2pGdnU1RUREREREe2y9btoy9e/cyc+ZM5s2b57Fu586dTJgwwXwdEBBAcnIy27Zt45JLLjllGZxOJ06n0yvn43Q68T/eY+E0DPDScaVuqn+O3vp5Sv2o/n1L9e9b51v91/U8mzxYlJaWYrPZPJZVvy4rK/MIFrt27WLOnDksXrwYq7XmHRe1HSs4OJiysrLTlmHHjh1nWvxa9TzeY/HD1h+pDD7m1WNL3WRmZvq6COc11b9vqf59S/XvqcmDRUhICOXl5R7Lql+Hhoaay+x2O/fccw8PPPAAzZs3r/VYNputxriLiooKj+PUpmPHjoSEhJxJ8WtwOp2wwt1j0bVbdwhL+IU9xJucTieZmZl079691vApjUv171uqf9863+q/rKysTh/MmzxYdOjQgYKCAnJycoiLiwPcPROJiYmEh4eb22VmZrJnzx5mzJjBjBkzzOV33HEH1113HX/961/p0KEDWVlZDBgwAIDKykr27NlDx44dT1sGq9Xq1V8Cy/F5LKzWADgPfrnORt7+mUr9qP59S/XvW+dL/df1HJs8WCQnJ9OrVy8ee+wxHnnkEfLz85k3bx7p6eke26WlpfHdd995LOvUqRPz58+nT58+AIwcOZLnnnuOfv360aZNG+bMmUNcXBxpaWlNdj6cfAeKBm+KiMh5zict4dy5c6mqqmLQoEFcf/31XH755WRkZACQmprK8uXL63Sc9PR0xo4dy1133cUll1zC1q1bWbBgAQEBAY1ZfE/Vd4SAJsgSEZHzXpP3WADExcUxd+7cWtdt2rTplPtt377d47XFYmHcuHGMGzfOq+WrF49goR4LERE5v6klbCgFCxEREZNawoZSsBARETGpJWwoBQsRERGTWsKGUrAQERExqSVsKN1uKiIiYlJL2FDqsRARETGpJWwoBQsRERGTWsKG0gRZIiIiJgWLhjoeLAwsChYiInLeU7BoqOoeC10GERERUbBoMMPp/lfBQkRERMGiwapvN1WwEBERqV+wmD59Ohs2bGisspybdClERETEVK/WMCQkhLvvvpvBgwczb948Dh8+3FjlOndUBws/q2/LISIichaoV7B46KGHWLNmDVOnTiUzM5OrrrqKW2+9lZUrV+JwOBqrjGc3s8dCd4SIiIjUu/8+ICCAq666in/84x/861//Ij8/n3vvvZfLL7+cxx9/nOLi4sYo59lLl0JERERM9W4Njx07xquvvsrw4cMZM2YMzZs3Z968ebz++uv89NNP3HnnnY1RzrOXgoWIiIjJvz4b33rrrXz99de0bduWESNGcN111xETE2Ouv/fee7nhhhu8XsizmoKFiIiIqV7BomXLlixevJgePXrUur5FixYsWbLEKwU7ZyhYiIiImOrVGs6YMYP//ve/7N+/H4DXX3+dOXPm4HK5G9fQ0FDatWvn/VKezTSPhYiIiKlereHs2bNZs2YNVqv71squXbvy5Zdf8tRTTzVK4c4J6rEQEREx1as1XLVqFf/85z9p3rw5AGlpacyfP5/ly5c3SuHOCbrdVERExFSvYGG32wkJCfFYFhYWRlVVlVcLdU5Rj4WIiIipXq1hWloas2bNMifDstvtPPHEE/Ts2bNRCndOULAQEREx1euukBkzZjB+/Hh69uxJdHQ0+fn5tGnThvnz5zdW+c5+ChYiIiKmegWLVq1asXLlSr799ltycnJITEykR48e+PvX6zC/LgoWIiIipnonAofDQVJSEi1btgTg4MGD7Nixg8GDB3u9cOeE6ttN9QR6ERGR+gWLpUuX8uijj2K32z2Wx8bGnsfBQneFiIiIVKtXsJg/fz6TJ08mNDSUDRs2cMstt/Dkk09y6aWXNlb5zn66FCIiImKqV2t47NgxbrnlFvr27cu+ffvo2rUrjz32GG+//XZjle/sp2AhIiJiqldrGBsbS2VlJRdccAE//fQTAM2bNyc3N7dRCndOULAQEREx1as17NGjBw899BAVFRUkJyezePFi3n33XaKiohqpeOcABQsRERFTvcZY3H///Tz44IOUlpYydepU7rjjDioqKpg1a1Zjle/sp2AhIiJiqlew2LBhA8899xxBQUHEx8fz9ddfU1lZic1ma6zynf30dFMRERFTvVrDhx9+GD+/E7v4+/uf36ECdLupiIjISeoVLLp3787KlSsbqyznJsPp/lc9FiIiIvW7FFJQUMC0adP485//TFxcHJaTPqX/97//9XrhzgkaYyEiImKqV7C46aabGqsc567qYOFn9W05REREzgL1Cha///3vG6sc5y71WIiIiJjqFSzGjBnjcfnjZP/617+8UqBzjUXBQkRExFSvYNGnTx+P1/n5+Xz44YfccMMNXi3UOUW3m4qIiJjqFSz+9Kc/1Vg2YsQInnjiCa8V6Jyj201FRERMDf6Y3bVrV77//ntvlOXcpEshIiIipnr1WGRnZ3u8rqys5P333+eCCy7waqHOKQoWIiIipnoFi4EDB3oM3jQMg8jISP72t7/V601zc3P585//zPr167FarVx77bVMmzYNf3/P4rhcLl544QWWLFlCUVERLVu25M4772To0KHm+l69emEYhke5vvzyS0JCQupVpjOmYCEiImKqV7D4+SRYVquV2NhYAgIC6vWmkydPJiEhgTVr1pCTk8Odd97Ja6+9xvjx4z22W7RoEcuWLWPhwoUkJSXx6aefkpGRQbdu3UhKSmLnzp1UVlayceNGAgMD61UGr6kOFg2/qiQiInLOq1drGB8fz1tvvYXL5aJFixasWrWKF154AZfL9cs7H7d3717Wr1/P1KlTsdlstGrVioyMDBYtWlRj2xtvvJEVK1aQlJSEw+EgLy8Pm81GcHAwAJmZmXTq1Ml3oQLUYyEiInKSevVYPPbYY2zevNm8vbRr167Mnj0bh8PBfffdV6djZGVlERUVRUJCgrmsXbt2ZGdnU1RUREREhLncz8+PkJAQvvjiCyZMmIBhGNx///3Ex8cD7mBht9sZOXIkBw8epF27dkyZMoWePXuetgxOpxOn01mfUz8lw+nEDzAAl5eOKXVX/XP01s9T6kf171uqf9863+q/rudZr2Dx0UcfsWLFCmJiYgBIS0tj/vz5DB8+vM7BorS0tMYTUatfl5WVeQSLar179yYzM5MNGzaQkZFBs2bNGDp0KMHBwfTo0YNJkyYRGRnJokWLuPXWW1m+fDmtWrU6ZRl27NhR11P+RXEH99MaKCwuYffmzV47rtRPZmamr4twXlP9+5bq37dU/57qFSzsdnuNQZFhYWFUVVXV+RghISGUl5d7LKt+HRoaWus+1Zc6+vbty3XXXceKFSsYOnQo06dP99ju1ltv5Z133mH16tWnfa5Jx44dvTa407Cvh0yIiIwiJSXFK8eUunM6nWRmZtK9e3esVj2vpamp/n1L9e9b51v9l5WV1emDeb2CRVpaGrNmzWLGjBkEBgZit9t54oknfvHSw8k6dOhAQUEBOTk5xMXFAbBr1y4SExMJDw/32Hb27NkAHgHC4XAQFRUFwJw5cxgyZAhdunTxWB8UFHTaMlitVq/9EriO34xi8bPidx78Yp2tvPkzlfpT/fuW6t+3zpf6r+s51mvE4YwZM1i7di09e/bk8ssvp1evXmzYsIEZM2bU+RjJycn06tWLxx57jJKSEvbv38+8efNIT0+vsW1aWhpvvvkmGzZswOVy8cknn7By5UpGjRoFuC9pzJw5k2PHjuFwOHj++ecpKSlh8ODB9TmthtHMmyIiIqZ69Vi0atWKDz74gI0bN3Ls2DESExPp0aNHjfknfsncuXN55JFHGDRoEH5+fgwfPpyMjAwAUlNTefjhh7n22mu58sorefDBB3nwwQfJyckhOTmZ5557zuwhmTVrFo8//jjXXXcd5eXldO/enVdffdXs0WgaelaIiIhItXolgqKiIh5++GEyMjK4+OKLefbZZ1m8eDF//etfTzk+ojZxcXHMnTu31nWbNm3yeJ2enl5rbwZAVFQUs2bNqvsJNAbdbioiImKqV2v417/+lcLCQrNH4JprrqG4uJjHHnusMcp2blCwEBERMdWrx+Krr77iv//9r9k70a5dO5566qmmHdNwtjEfm64xFiIiIvX6mO1yuWpMkGEYxnkxGvaU1GMhIiJiqldr2K9fP6ZNm8a+ffuorKxk37593H///Vx66aWNVb6zn4KFiIiIqV6t4QMPPEBJSQlXXXUVPXr0YMiQIZSXlzNt2rTGKt/ZT8FCRETEVK8xFjExMSxcuJDs7GyOHTuG0+lk2bJlDBw4kM3n63TW5tNNNcZCRESkfhNQHJednc3LL7/M6tWr6dChA1OnTvV2uc4d6rEQEREx1TlYuFwuPvzwQ1599VWysrKoqqpiwYIFXH755Y1ZvrOfoQmyREREqtWpNXz99dcZPHgwTz75JIMHD+azzz4jLCyMjh07Nnb5zn7qsRARETHVqcdi1qxZjB49munTp5tPGpXjNI+FiIiIqU4fs//85z+zbt06+vfvz5w5czhy5AgWNaRu6rEQEREx1ak1vPHGG3n//ff5+9//zs6dOxk8eDBFRUWsXbu2xoRZ5x0FCxEREVO9WsO+ffvywgsv8MEHHzB27Fhmz57N5ZdfzuzZsxurfOcABQsREZFqZ9QatmjRgqlTp/L5559z7733sn79em+X69yhHgsRERFTg1rDwMBA0tPTeeedd7xVnnOPJsgSEREx6WN2Q2keCxEREZNaw4bS7aYiIiImBYuG0hgLERERk1rDhlKwEBERMak1bCgFCxEREZNaw4ZSsBARETGpNWwoBQsRERGTWsOGMoOF7goRERFRsGgwzWMhIiJSTa1hQ2keCxEREZOCRUNpjIWIiIhJrWFDKViIiIiY1Bo2lIKFiIiISa1hQylYiIiImNQaNpBFwUJERMSk1rChFCxERERMag0bqvp2U3S7qYiIiIJFQ2keCxEREZOCRUPpUoiIiIhJrWGDKViIiIhUU2vYUOqxEBERMak1bCgFCxEREZNaw4ZSsBARETGpNWwoQ49NFxERqabWsKGqeyxEREREwaLB1GMhIiJiUmvYUMd7LAwFCxEREQWLBtPgTREREZNaw4ZSsBARETH5pDXMzc0lIyODtLQ0+vTpw8yZM6mqqqqxncvl4rnnnqN///6kpqbyu9/9jpUrV3ps89JLL9GvXz9SUlIYM2YMu3fvbqrTcFOwEBERMfmkNZw8eTIhISGsWbOGJUuWsHbtWl577bUa2y1atIhly5axcOFCNm3axL333suUKVPYt28fAO+++y4LFy7k5ZdfZt26dXTt2pWJEydimE8cbQIKFiIiIqYmbw337t3L+vXrmTp1KjabjVatWpGRkcGiRYtqbHvjjTeyYsUKkpKScDgc5OXlYbPZCA4OBuCtt95i9OjRdOjQgaCgIKZMmUJ2djbr1q1ruhPSXSEiIiIm/6Z+w6ysLKKiokhISDCXtWvXjuzsbIqKioiIiDCX+/n5ERISwhdffMGECRMwDIP777+f+Ph4AHbu3MmECRPM7QMCAkhOTmbbtm1ccsklpyyD0+nE6XR65XwshhML4DJc4KVjSt1V/xy99fOU+lH9+5bq37fOt/qv63k2ebAoLS3FZrN5LKt+XVZW5hEsqvXu3ZvMzEw2bNhARkYGzZo1Y+jQobUeKzg4mLKystOWYceOHQ08ixMuLCsjBNizZx9FZZu9dlypn8zMTF8X4bym+vct1b9vqf49NXmwCAkJoby83GNZ9evQ0NBa9wkMDASgb9++XHfddaxYsYKhQ4dis9moqKjw2LaiouKUx6nWsWNHQkJCzvQUPFjWBUERJLdti1/7FK8cU+rO6XSSmZlJ9+7dsVqtvi7OeUf171uqf9863+q/rKysTh/MmzxYdOjQgYKCAnJycoiLiwNg165dJCYmEh4e7rHt7NmzAZg+fbq5zOFwEBUVZR4rKyuLAQMGAFBZWcmePXvo2LHjactgtVq99ktg4B5j4Wf1Py9+sc5W3vyZSv2p/n1L9e9b50v91/Ucm3zEYXJyMr169eKxxx6jpKSE/fv3M2/ePNLT02tsm5aWxptvvsmGDRtwuVx88sknrFy5klGjRgEwcuRI3njjDbZt24bdbufpp58mLi6OtLS0pjsh3RUiIiJiavIeC4C5c+fyyCOPMGjQIPz8/Bg+fDgZGRkApKam8vDDD3Pttddy5ZVX8uCDD/Lggw+Sk5NDcnIyzz33HD179gQgPT2d4uJi7rrrLvLy8ujevTsLFiwgICCg6U5GwUJERMTkk2ARFxfH3Llza123adMmj9fp6em19mYAWCwWxo0bx7hx47xexjrT7aYiIiImtYYNZfZYWHxbDhERkbOAgkVD6VKIiIiISa1hQ1UHC9RjISIiomDRYBpjISIiUk2tYUPpUoiIiIhJrWFDKViIiIiY1Bo2lG43FRERMak1bCj1WIiIiJjUGjaU5rEQERExKVg0lHosRERETGoNG0rzWIiIiJgULBpKgzdFRERMag0bSpdCRERETGoNG0rBQkRExKTWsKF0KURERMSk1rCh1GMhIiJiUmvYUJrHQkRExKRg0VDqsRARETGpNWwo9ViIiIiYFCwaSj0WIiIiJrWGDWRBd4WIiIhUU2vYENW3moKChYiICAoWDWM+JwQFCxERERQsGkbBQkRExINaw4bwCBa6K0RERETBoiHUYyEiIuJBrWFDKFiIiIh4UGvYECcHC3QpRERERMGiIXS7qYiIiAe1hg2hSyEiIiIe1Bo2hIKFiIiIB7WGDaFLISIiIh7UGjaE5rEQERHxoGDREMeDhaFqFBERARQsGsZ8ZLp6K0REREDBomHMHgsFCxEREVCwaKDjgzfVYyEiIgIoWDSMOXhT1SgiIgJqERum+lKIeixEREQABYuGMQdvqhpFRERAwaJhjk+QpcGbIiLiLXa7ncOHD/u6GGdMwaIh1GMhIiJeNnr0aL766qsz2nfYsGEsX77cyyWqH3+fvvu5zhy8qR4LERHxjvz8/DPe9/333/diSc6MgkVDnDR4U9FCROTsZxgG5ZVOrxzL6XRSUeWizFGF1WqccjtbgBVLHQf5jxs3juzsbP7yl7/wyiuvUFxcTFpaGqtXr+a2225j9OjRzJ49m/Xr13P06FHCw8O58cYbueOOOwAYOHAgf/rTnxgxYgRjxowhJSWFjRs3snXrVhITE7n77rsZOnSoV87/VHwSLHJzc/nzn//M+vXrsVqtXHvttUybNg1//5rFWbx4Ma+99hpHjx4lPj6em2++mRtvvBEAl8tFr169MAzD44f25ZdfEhIS0vgnottNRUTOGYZhkD5/Ld/uPfMegVq9+/FpV6e1jubtO/rWKVy88sorZjho0aIFN998M23btmX27NnY7XaeeuopDhw4wJIlSwgPD+ejjz5i4sSJXH311bRu3brG8d566y1effVV2rdvzwsvvMBDDz3EoEGDCAoKOuPT/SU+CRaTJ08mISGBNWvWkJOTw5133slrr73G+PHjPbb7+OOP+fvf/85LL73ERRddxObNm7ntttuIi4tjyJAh7Ny5k8rKSjZu3EhgYGDTn4ihCbJERM4l5+Jf6/T0dAICAggICODuu+/GarUSFhbG4cOHzYBw9OjRWoPFkCFD6NKlCwC///3vmT9/Prm5uTRv3rzRytvkwWLv3r2sX7+ezz//HJvNRqtWrcjIyODJJ5+sESyOHDnChAkTSElJASA1NZU+ffqwYcMGhgwZQmZmJp06dfJNqADNYyEicg6xWCy8fUdfr14K+e677+jRowdWq/WU29XnUkht4uPjze9zc3OZOXMmW7dupWXLlnTr1g1w9+DXplmzZub31VcFTrWttzR5sMjKyiIqKoqEhARzWbt27cjOzqaoqIiIiAhzefUlj2q5ubls2LCB+++/H4DMzEzsdjsjR47k4MGDtGvXjilTptCzZ8/TlsHpdOJ0euEXy1mF+1fJzzvHk3qrrnfVv2+o/n1L9X9mgqze+TDoxEKwvx9BVgvW0xyzvg25YRi4XC5zv5P3nzRpEgMGDODFF1/E39+fgoIC3nrrLVwuF06n09z359+ffJyTl9VHXfdp8mBRWlqKzWbzWFb9uqyszCNYnOzYsWPcfvvtdOvWjWuuuQaA4OBgevTowaRJk4iMjGTRokXceuutLF++nFatWp2yDDt27PDKuYQUbOdC3D0W32dmeuWYcmYyVf8+pfr3LdW/b3m7/g3DYNu2bZSXlwOwefNmc11eXh75+flkZmZSXFzMv/71LwC2bdtGYGAgDoeDffv2sXnzZkpKSjh8+LC5/7FjxwDYunWr+X1jaPJgERISYlZWterXoaGhte6zefNmJk2aRFpaGrNmzTK7c6ZPn+6x3a233so777zD6tWruemmm05Zho4dO3pncOdBJ6wB8KN79+6n7QqTxuF0OsnMzFT9+4jq37dU/77VWPU/evRo5s2bZ37Qrh4OAPDEE08we/ZsPvzwQyIiIhg6dCj5+fk4nU5SUlIIDAwkKSmJlJQUwsLCSExMNPc/ePAgAF26dKFFixb1LldZWVmdPpg3ebDo0KEDBQUF5OTkEBcXB8CuXbtITEwkPDy8xvZLlizhb3/7GxMnTmTcuHEe6+bMmeMxMAXA4XD84mhXq9XqnV+C6mtmFov3jilnRPXvW6p/31L9+5a36/+2227jtttuq3Vd//796d+//yn3/fTTT83v33jjDY91SUlJbN++/YzLVddzbPL7JJOTk+nVqxePPfYYJSUl7N+/n3nz5pGenl5j21WrVvHXv/6V5557rkaoAPcljZkzZ3Ls2DEcDgfPP/88JSUlDB48uClO5aTBm7rdVEREBHw0AcPcuXOpqqpi0KBBXH/99Vx++eVkZGQA7js/qqcjff7553E6nUycOJHU1FTz66GHHgJg1qxZJCUlcd1119GnTx/Wr1/Pq6++SlRUVNOciGbeFBER8eCTeSzi4uKYO3dures2bdpkfr9ixYrTHicqKopZs2Z5tWz1Uz3TmoKFiIgIaMrIhtGlEBEREQ9qERvCfLqpeixERERAwaJhqnssVI0iIiKAgkXDqMdCRETEg4JFQ+iuEBEREQ8KFg1x/OmmGrwpIiLiphaxIdRjISIi4kHBoiGO91hojIWIiPjSO++8w8CBA31dDMBHE2T9amgeCxGRc4thQGWZd47ldOJXVQ6OUjjdczQCQs6rD6AKFg2hSyEiIucOw4BXhsD+dV45nBVIBfjgFzZsdQmM+7BO4eK+++7D6XTy9NNPm8smT55MdHQ0l19+OS+++CJ79+6lrKyM7t2787e//Y3k5OQGnIX36aN2Q6jHQkTkHHN2fxC8/vrr+fjjjykpKQGgqKiITz75hKFDhzJp0iRuu+021q5dy2effYZhGLzwwgs+LnFN6rFoCPVYiIicOywWd8+Bly6FOJ1OvvvuO3r06HH6R4rX41JIWloaF1xwAR988AGjRo3ivffeo23btlx00UW8//77JCUlUVJSwuHDh4mOjubIkSNeORdvUrBoCHOCLPVYiIicEywWCAz1zrGcTlz+NvfxThcs6mnUqFH8+9//ZtSoUbz77ruMGjWKgIAA3nvvPd58800sFgsdO3akpKQEf/+zrxlXi9gQ5qUQ9ViIiIh3/P73v2fLli189dVXbN++nWuuuYYPPviAN954g4ULF7J69WpeeuklunTp4uui1krBoiGqbzdVNYqIiJfExMQwYMAAHnzwQa666ioiIyMpLi7Gz8+P4OBgDMPg888/Z9myZVRWVvq6uDWoRWwIc4yFiIiI91x//fUcPHiQ9PR0wN2L8Zvf/IZhw4ZxySWX8I9//INbbrmFn376CYfD4ePSejr7Ls6cS/yDAHBZg31cEBER+TW57LLL2L59u/k6MDCQJ554osZ2EydOBGDEiBGMGDGiycp3OgoWDdFhMK7L/5dsSwc6+bosIiIiZwFdCmmIwFCMKx6gPLKDr0siIiJyVlCwEBEREa9RsBARERGvUbAQERERr1GwEBEREa9RsBARERGvUbAQERERr1GwEBEREa9RsBARERGvUbAQERERr1GwEBEREa9RsBARERGvOa8eQuZyuR9zXl5e7rVjOp1OAMrKyrBarV47rtSN6t+3VP++pfr3rfOt/qvbzuq29FQshmEYTVGgs0Fubi579uzxdTFERETOWcnJycTGxp5y/XkVLKqqqigsLCQoKAg/P10FEhERqSuXy4XdbicyMhJ//1Nf8DivgoWIiIg0Ln1sFxEREa9RsBARERGvUbA4Q7m5uWRkZJCWlkafPn2YOXMmVVVVvi7Wr1JeXh6DBw9m3bp15rItW7YwatQoUlNTGThwIG+//bbHPu+++y6DBw8mJSWFESNGsGnTpqYu9jlv27Zt/PGPf6R3795ceuml3HfffeTl5QGq/6awdu1aRo0aRc+ePbn00kt59NFHqaioAFT/TcXpdDJmzBimT59uLlPd14EhZ+Smm24ypkyZYpSVlRn79u0zhg0bZrz00ku+LtavzjfffGNceeWVRseOHY2vv/7aMAzDKCgoMHr37m288cYbRmVlpfHVV18ZqampxpYtWwzDMIyvv/7aSE1NNb755hvD4XAYr776qtGnTx+jrKzMl6dyTikvLzcuvfRS49lnnzXsdruRl5dnTJgwwbj99ttV/00gNzfX6N69u7F06VLD6XQaR44cMa655hrj2WefVf03oWeeecbo3LmzMW3aNMMw9LenrtRjcQb27t3L+vXrmTp1KjabjVatWpGRkcGiRYt8XbRflXfffZf//d//5Z577vFY/tFHHxEVFcWNN96Iv78/ffv25Xe/+51Z/2+//TbDhg2jV69eBAQEMHbsWKKjo1m5cqUvTuOclJ2dTefOnbnrrrsIDAwkOjqaG264gQ0bNqj+m0BMTAxfffUVI0aMwGKxUFBQgN1uJyYmRvXfRNauXctHH33EVVddZS5T3deNgsUZyMrKIioqioSEBHNZu3btyM7OpqioyIcl+3W57LLL+M9//sPQoUM9lmdlZdGxY0ePZe3bt2fbtm0A7Ny587Tr5Ze1bduWf/7znx6T/qxatYquXbuq/ptIWFgYAP379+d3v/sdzZo1Y8SIEar/JpCbm8uMGTN4+umnsdls5nLVfd0oWJyB0tJSj182wHxdVlbmiyL9KjVr1qzWe6Vrq//g4GCz7n9pvdSPYRjMmTOHTz/9lBkzZqj+m9hHH33E559/jp+fHxMnTlT9NzKXy8XUqVP54x//SOfOnT3Wqe7rRsHiDISEhNSYFrz6dWhoqC+KdF6x2WzmILZqFRUVZt3/0nqpu5KSEiZOnMiKFSt444036NSpk+q/iQUHB5OQkMDUqVNZs2aN6r+RLViwgMDAQMaMGVNjneq+bhQszkCHDh0oKCggJyfHXLZr1y4SExMJDw/3YcnODx07diQrK8tj2c6dO+nQoQPg/vmcbr3Uzb59+xg5ciQlJSUsWbKETp06Aar/prBx40Z++9vf4nA4zGUOh4OAgADat2+v+m9E//73v1m/fj1paWmkpaXx3nvv8d5775GWlqbf/bry9ejRc9X//M//GPfcc49RXFxs3hUyd+5cXxfrV+vku0Ly8vKMtLQ049VXXzUcDoexdu1aIzU11Vi7dq1hGIY5Unvt2rXmyOyLL77YyM/P9+EZnFsKCgqMK664wpg+fbrhdDo91qn+G19JSYnRv39/47HHHjPsdrtx4MABIz093fjLX/6i+m9i06ZNM+8KUd3XjYLFGTp27Jhx9913G7179zYuueQSY/bs2UZVVZWvi/WrdXKwMAzD+O6774wbbrjBSE1NNQYNGmQsXbrUY/tly5YZQ4YMMVJSUoz09HRj8+bNTV3kc9orr7xidOzY0bjooouMlJQUjy/DUP03haysLOOPf/yjkZaWZgwYMMD4+9//btjtdsMwVP9N6eRgYRiq+7rQs0JERETEazTGQkRERLxGwUJERES8RsFCREREvEbBQkRERLxGwUJERES8RsFCREREvEbBQkRERLxGwUJEmpTdbufw4cO+LoaINBIFCxGps06dOtGjRw9SU1NJSUnh4osv5s477+TQoUN1Psbo0aP56quvfnG7d955h4EDBzakuCLiAwoWIlIvL730Eps2bWLz5s18+umnGIbB1KlT67x/fn5+I5ZORHxNwUJEzlhYWBjXX38933//vbls165d3H777VxxxRX06NGDoUOH8umnnwIwbtw4srOz+ctf/sIjjzwCwJdffkl6ejqpqakMHDiQN954wzxWVVUVTz31FFdccQU9e/bkwQcfpKqqCgDDMPjXv/7FkCFDSEtLY/To0R7lWLVqFcOGDaNXr15cffXVzJs3rymqRER8+6gSETmX/PxhcAUFBca9995rTJ061Vx29dVXG0899ZThcDgMu91uzJw50+jXr5+5fsCAAeaDm3bv3m1069bNePvtt43KykojMzPTSE1NNT7//HNj6dKlRseOHY0FCxYYlZWVRlZWlnHRRRcZK1asMAzDMN544w3jiiuuMH788UfD4XAYb7/9tpGWlmYcO3bMKC8vN7p3726W9YcffjBSUlKMLVu2NEU1iZzX/H0dbETk3HLHHXdgtVpxuVyUlpYSHh7OggULzPULFiwgISEBwzA4ePAgERERHDlypNZjvf/++3Tt2pX09HQAunXrxv/7f/+P+Ph4PvvsM8LCwpgwYQIWi4X27dvTuXNn9u3bB8CiRYu4/fbb6dy5MwDp6eksWbKE5cuXM3r0aIKDg1myZAkul4uePXvy7bff4uenTlqRxqZgISL1Mn/+fPr06QNARUUFixYt4pZbbuH//u//6Nq1K9u2bSMjI4Njx47Rrl07YmJiME7xEOWjR4/SvHlzj2XVQQEgMjISi8Vivg4ICMDpdAJw8OBBHn/8cZ566ilzfVVVFd26dSM4OJjFixczb948pkyZQklJCUOGDOHBBx8kMjLSa3UhIjUpWIjIGQsODubWW2/lxRdf5KuvviIuLo5Jkybx/PPPm3d0rFq1io8++qjW/S+44AJWr17tsWzp0qXExsb+4nsnJiYyceJEhg0bZi7bt28fUVFRlJSUcPToUZ5++mkAfvzxR+69917mz5/PtGnTzvR0RaQO1C8oImesqqqKpUuXUlRURK9evSgtLcXpdGKz2QDYuXMnL7zwAgAOhwOAwMBAiouLARg2bBhbt25l2bJlOJ1Ovv/+e2bPno2//y9/5rn++uv5xz/+wa5duwBYs2YNw4YNY8OGDZSWljJhwgRWrFiBYRjEx8fj5+dHdHR0Y1SDiJxEPRYiUi8TJkzAarUCYLFYSE5O5u9//zs9e/YE4L777mPq1KmUl5eTmJjI9ddfz5NPPsmOHTvo1q0b6enpzJkzh8zMTJ566ilefPFFnn76aR599FFiY2OZPn06l112Ge+8885pyzF27FgMwyAjI4OjR4+SkJDAQw89xKBBgwCYO3cuzzzzDA899BDBwcEMHTqUsWPHNmrdiAhYjFNd/BQRERGpJ10KEREREa9RsBARERGvUbAQERERr1GwEBEREa9RsBARERGvUbAQERERr1GwEBEREa9RsBARERGvUbAQERERr1GwEBEREa9RsBARERGv+f9yp5oRiX5mRAAAAABJRU5ErkJggg==", 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" ] @@ -999,14 +938,6 @@ } ], "source": [ - "import numpy as np\n", - "\n", - "\n", - "def moving_average(x, window_size):\n", - " \"\"\"Calculate the moving average of a list using numpy.\"\"\"\n", - " return np.convolve(x, np.ones(window_size) / window_size, mode=\"valid\")\n", - "\n", - "\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", "ax.plot(moving_average(model_d.train_log_error, 100), label=\"train\")\n", "ax.plot(moving_average(model_d.val_log_error, 3), label=\"val\")\n", @@ -1035,12 +966,66 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn.functional as F\n", + "\n", + "\n", + "def plot_multiple_digits(model_callable, batch_data, batch_labels, n_images: int = 10):\n", + " \"plot a grid of 5 images with their predictions\"\n", + " fig, ax = plt.subplots(figsize=(16, 4), ncols=n_images)\n", + " for i in range(n_images):\n", + " output_probs, output_uncertainty = model_callable(batch_data[[i], :])\n", + " output_probs = output_probs.squeeze()\n", + " ax[i].imshow(batch_data[i].squeeze().numpy(), cmap=\"gray\")\n", + " ax[i].set_title(\n", + " f\"\"\"\n", + " Pred: {torch.argsort(output_probs)[-1]}\n", + " Pred%: {output_probs[torch.argsort(output_probs)[-1]]:0.2f}\n", + " Uncertainty: {output_uncertainty[0]:0.2f}\n", + " Label: {batch_labels[i]}\n", + " Label%: {output_probs[batch_labels[i]]:0.2f}\n", + " \"\"\"\n", + " )\n", + " ax[i].axis(\"off\")" + ] + }, + { + "cell_type": "code", + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def probs_from_batch_model_d(batch_data):\n", + " return model_d.concentration_to_probs(model_d(batch_data))\n", + "\n", + "def probs_from_batch_model(batch_data):\n", + " return F.softmax(model(batch_data), dim=1)[0], torch.zeros(batch_data.shape[0])\n", + " \n", + "output_probs = plot_multiple_digits(probs_from_batch_model_d, batch_data, batch_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" ] @@ -1055,12 +1040,42 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "(tensor([[0.1000, 0.1000, 0.1000, ..., 0.1000, 0.1000, 0.1000],\n", + " [0.0061, 0.0061, 0.9455, ..., 0.0061, 0.0061, 0.0061],\n", + " [0.9809, 0.0021, 0.0021, ..., 0.0021, 0.0021, 0.0021],\n", + " ...,\n", + " [0.1000, 0.1000, 0.1000, ..., 0.1000, 0.1000, 0.1000],\n", + " [0.1000, 0.1000, 0.1000, ..., 0.1000, 0.1000, 0.1000],\n", + " [0.1000, 0.1000, 0.1000, ..., 0.1000, 0.1000, 0.1000]],\n", + " grad_fn=),\n", + " tensor([1.0000, 0.0606, 0.0212, ..., 1.0000, 1.0000, 1.0000],\n", + " grad_fn=))" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "model_d.concentration_to_probs(model_d(batch_data))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" ] @@ -1073,6 +1088,26 @@ "output_probs = plot_multiple_digits(model, batch_data, batch_labels)" ] }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "output_probs = plot_multiple_digits(probs_from_batch_model_d, batch_rotated_data, batch_labels)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1114,7 +1149,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1205,7 +1240,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1256,20 +1291,20 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/_v/nlh4h1yx2n1gd6f3szjlgxt40000gr/T/ipykernel_18424/90226784.py:9: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_52426/90226784.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", " fig.show()\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1299,7 +1334,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1308,7 +1343,7 @@ "tensor(-0.4328)" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1331,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1409,7 +1444,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1427,7 +1462,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1437,7 +1472,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[24], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1000\u001b[39m)\n\u001b[1;32m 2\u001b[0m output_dist \u001b[38;5;241m=\u001b[39m model()\n\u001b[0;32m----> 3\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241m.\u001b[39mstats\u001b[38;5;241m.\u001b[39mbeta(output_dist\u001b[38;5;241m.\u001b[39mconcentration0\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy(), output_dist\u001b[38;5;241m.\u001b[39mconcentration1\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy())\u001b[38;5;241m.\u001b[39mpdf(x)\n\u001b[1;32m 5\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 6\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(x, y, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrior distribution\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn[27], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1000\u001b[39m)\n\u001b[1;32m 2\u001b[0m output_dist \u001b[38;5;241m=\u001b[39m model()\n\u001b[0;32m----> 3\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mscipy\u001b[49m\u001b[38;5;241m.\u001b[39mstats\u001b[38;5;241m.\u001b[39mbeta(output_dist\u001b[38;5;241m.\u001b[39mconcentration0\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy(), output_dist\u001b[38;5;241m.\u001b[39mconcentration1\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy())\u001b[38;5;241m.\u001b[39mpdf(x)\n\u001b[1;32m 5\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 6\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(x, y, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrior distribution\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mNameError\u001b[0m: name 'scipy' is not defined" ] } @@ -4551,7 +4586,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.10" }, "orig_nbformat": 4, "vscode": { diff --git a/nlp/unfinished-text_embeddings/text_embedding.ipynb b/nlp/unfinished-text_embeddings/text_embedding.ipynb index 1ad472b..696b1f2 100644 --- a/nlp/unfinished-text_embeddings/text_embedding.ipynb +++ b/nlp/unfinished-text_embeddings/text_embedding.ipynb @@ -1 +1 @@ -{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# Finding similar words\n","\n","Can we find an embedding space built on the movielens dataset to compare distance between titles?\n","\n","Consider each person's viewing history as a series of titles.\n","Build a model to predict the next title watched based on the ones before it.\n","Or build a model to predict the middle title based on the ones either side.\n","\n","Represent the titles as an embedding vector.\n","Build a dense layer on top of the embedding vector to predict the next title.\n","\n","Follow an approach similar to Word2Vec.\n","Instead of treating each word as an entity or token, we use each title.\n","We treat the vocabulary as the set of titles.\n","\n","1. Load the movielens dataset\n","2. Convert each movie title to an integer token\n","3. Create an embedding layer on the tokens\n","4. \n","\n","References:\n","* https://en.wikipedia.org/wiki/Word2vec\n","\n","Start by importing stuff:"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"ename":"ModuleNotFoundError","evalue":"No module named 'polars'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpolars\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpl\u001b[39;00m\n\u001b[1;32m 7\u001b[0m plt\u001b[38;5;241m.\u001b[39mstyle\u001b[38;5;241m.\u001b[39muse(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseaborn-v0_8-whitegrid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'polars'"]}],"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","import numpy as np\n","import pandas as pd\n","import polars as pl\n","\n","plt.style.use(\"seaborn-v0_8-whitegrid\")"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Load data\n","\n","Load the tiny Shakespeare dataset."]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["downloading data\n"]},{"data":{"text/plain":["[(['i'], 'F'),\n"," (['F', 'r'], 'i'),\n"," (['i', 's'], 'r'),\n"," (['r', 't'], 's'),\n"," (['s', ' '], 't'),\n"," (['t', 'C'], ' '),\n"," ([' ', 'i'], 'C'),\n"," (['C', 't'], 'i'),\n"," (['i', 'i'], 't'),\n"," (['t', 'z'], 'i'),\n"," (['i', 'e'], 'z'),\n"," (['z', 'n'], 'e'),\n"," (['e', ':'], 'n'),\n"," (['n'], ':')]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["import torch\n","import requests\n","from torch.utils.data.dataset import Dataset\n","from pathlib import Path\n","import random\n","\n","\n","class ShakespeareDataCBOW(Dataset):\n"," def __init__(\n"," self,\n"," filepath: Path,\n"," window_size: int\n"," ):\n"," self.filepath = filepath\n"," self.window_size = window_size\n","\n"," self._download_data()\n"," self.data = self._load_data()\n","\n"," def __len__(self):\n"," return len(self.data)\n","\n"," def __getitem__(self, idx):\n"," sequence = self.data[idx]\n"," \n"," # need to convert to tokens first\n"," \n"," data = []\n"," for i in range(len(sequence)):\n"," target_token = sequence[i]\n"," context = []\n"," for j in range(\n"," max(0, i - self.window_size),\n"," min(len(sequence), i + self.window_size + 1),\n"," ):\n"," if j != i:\n"," context.append(sequence[j])\n"," data.append((context, target_token))\n"," return data\n","\n"," def _download_data(self):\n"," \"download to disk if not present already\"\n","\n"," self.filepath.parent.mkdir(parents=True, exist_ok=True)\n","\n"," if not self.filepath.exists():\n"," print(\"downloading data\")\n"," data_url = \"https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\"\n"," with open(self.filepath, \"wb\") as f:\n"," f.write(requests.get(data_url).content)\n","\n"," def _load_data(self) -> str:\n"," with open(self.filepath, \"r\") as f:\n"," data = f.read()\n"," data = data.splitlines()\n"," data = [sentence for sentence in data if sentence!='']\n"," return data\n","\n","\n","class CBOWDataset(Dataset):\n"," def __init__(self, sequences: list[int], window_size: int):\n"," self.sequences = sequences\n"," self.window_size = window_size\n","\n"," def __len__(self):\n"," return len(self.sequences)\n","\n"," def __getitem__(self, idx):\n"," sequence = self.sequences[idx]\n"," data = []\n"," for i in range(len(sequence)):\n"," target_word = sequence[i]\n"," context = []\n"," for j in range(\n"," max(0, i - self.window_size),\n"," min(len(sequence), i + self.window_size + 1),\n"," ):\n"," if j != i:\n"," context.append(sequence[j])\n"," data.append((context, target_word))\n"," return data\n","\n","\n","\n","filepath = Path().absolute() / \"data\" / \"shakespeare.txt\"\n","dataset = ShakespeareDataCBOW(\n"," filepath=filepath, window_size=1\n",")\n","dataset.__getitem__(0)\n"]},{"cell_type":"markdown","metadata":{},"source":["Create tokens\n","\n","1. Split by spaces - x\n","2. Lower case - x\n","3. Stem\n","4. Counter\n","5. Make vocab of top N words\n","6. Make word to token lookup table"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["from pathlib import Path\n","\n","def _load_data(filepath) -> str:\n"," with open(filepath, \"r\") as f:\n"," data = f.read()\n"," data = data.splitlines()\n"," data = [sentence for sentence in data if sentence!='']\n"," return data\n","\n","filepath = Path().absolute() / \"data\" / \"shakespeare.txt\"\n","data = _load_data(filepath=filepath)"]},{"cell_type":"markdown","metadata":{},"source":["Convert tokenizer to class\n","stateful vocab"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["No more talking on't; let it be done: away, away!\n","[35, 53, 2750, 46, 61, 15, 16, 158, 144, 144]\n","no more talking on let it be done away away\n"]}],"source":["import re\n","from typing import List, Dict\n","\n","REG = re.compile(\"\\w*\")\n","\n","\n","class Tokenizer:\n"," def __init__(self, sentences: List[str]) -> None:\n"," word_counts, word_tokens = self._get_vocab(sentences)\n"," self.vocab = word_tokens\n"," self.reverse_vocab = {val: key for key, val in self.vocab.items()}\n","\n"," def _get_vocab(self, sentences: List[str]) -> Dict[str, int]:\n","\n"," words = \" \".join(sentences).lower().split(\" \")\n"," word_counts = {}\n"," for word in words:\n"," _word = REG.search(word)[0]\n"," word_counts[_word] = word_counts.get(_word, 0) + 1\n","\n"," word_counts = dict(\n"," sorted(word_counts.items(), key=lambda x: x[1], reverse=True)\n"," )\n"," # convert to tokens\n"," word_tokens = {key: idx for idx, (key, val) in enumerate(word_counts.items())}\n"," return word_counts, word_tokens\n","\n"," def encode(self, sentence: str) -> List[int]:\n"," words = sentence.lower().split(\" \")\n"," tokens = []\n"," for word in words:\n"," _word = REG.search(word)[0]\n"," tokens.append(self.vocab[_word])\n"," return tokens\n","\n"," def decode(self, tokens: List[int]) -> str:\n"," words = []\n"," for token in tokens:\n"," words.append(self.reverse_vocab[token])\n"," return \" \".join(words)\n","\n","\n","tokenizer = Tokenizer(sentences=data)\n","\n","sentence = data[16]\n","print(sentence)\n","tokens = tokenizer.encode(sentence)\n","print(tokens)\n","words = tokenizer.decode(tokens)\n","print(words)\n"]},{"cell_type":"markdown","metadata":{},"source":["We will make a small subset of data for initial building and testing our models"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":["32777"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["data_small = data[:int(1e5)]\n","len(data_small)\n"]},{"cell_type":"markdown","metadata":{},"source":["## Build model architecture\n","\n","We will build the embedding using word2vec. There are two forms:\n","\n","1. Continuous Bag of Words (CBOW):\n","In CBOW, the model predicts the current word (target word) based on the context words within a fixed window size.\n","\n","1. Skip-gram:\n","In Skip-gram, the model predicts context words (surrounding words) given the current word (target word).\n","\n","CBOW is generally faster to train compared to Skip-gram, especially when using small training datasets.\n","In our case the words are actually movies.\n","\n","We will use CBOW for this test as hopefully its faster and more appropriate on a smallish training dataset.\n","Therefore we need to define a window of entities to train over. We will start with 3, so predict the current entity given the one before and the one after.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Data preparation"]},{"cell_type":"markdown","metadata":{},"source":["We need to get sequenences of tokens. So we next create tokens from the movieIds as they do not start from 0 and are not sequential.\n","Then for each userId we convert the tokens in to a list."]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["def create_vocab():\n"," pass\n","\n","def convert_to_tokens():\n"," pass\n","\n"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["import tqdm\n","\n","\n","def convert_movies_to_tokens(ratings_df: pl.DataFrame):\n"," if \"token\" in ratings_df:\n"," ratings_df = ratings_df.drop(\"token\")\n"," mapping = (\n"," ratings_df.group_by(\"movieId\")\n"," .len()\n"," .sort(\"movieId\")\n"," .with_row_index(name=\"token\")\n"," .drop(\"len\")\n"," )\n"," ratings_df = ratings_df.join(mapping, on=\"movieId\")\n"," return ratings_df, mapping\n","\n","\n","def get_sequences_from_df(ratings_df: pl.DataFrame):\n"," sequences = []\n"," for _user_id in tqdm.tqdm(ratings_df[\"userId\"].unique()):\n"," sequences.append(\n"," ratings_df.filter(pl.col(\"userId\") == _user_id)[\"token\"].to_list()\n"," )\n"," return sequences"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"ename":"NameError","evalue":"name 'ratings_small_df' is not defined","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ratings_small_df, token_mapping \u001b[38;5;241m=\u001b[39m convert_movies_to_tokens(\u001b[43mratings_small_df\u001b[49m)\n\u001b[1;32m 2\u001b[0m display(ratings_small_df\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 4\u001b[0m sequences \u001b[38;5;241m=\u001b[39m get_sequences_from_df(ratings_small_df)\n","\u001b[0;31mNameError\u001b[0m: name 'ratings_small_df' is not defined"]}],"source":["ratings_small_df, token_mapping = convert_movies_to_tokens(ratings_small_df)\n","display(ratings_small_df.head(4))\n","\n","sequences = get_sequences_from_df(ratings_small_df)\n","print(sequences[0][:10])"]},{"cell_type":"markdown","metadata":{},"source":["We can now create the CBOW dataset from the sequences.\n","We will use a small embedding size and a window of only 1.\n","\n","\n","We create a `collate_fn` to collect all the sequences and combine into tensors for training.\n","\n","```\n","Sequence Data (e.g., Text, Time Series):\n","For sequence data, such as text or time series, the shape of a batch is often: (batch_size, sequence_length, input_dim).\n","batch_size is the number of sequences in the batch.\n","sequence_length is the length of each sequence.\n","input_dim represents the dimensionality of each element in the sequence (e.g., word embeddings for text).\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from torch.utils.data import Dataset, DataLoader\n","import torch\n","\n","\n","class CBOWDataset(Dataset):\n"," def __init__(self, sequences: list[int], window_size: int):\n"," self.sequences = sequences\n"," self.window_size = window_size\n","\n"," def __len__(self):\n"," return len(self.sequences)\n","\n"," def __getitem__(self, idx):\n"," sequence = self.sequences[idx]\n"," data = []\n"," for i in range(len(sequence)):\n"," target_word = sequence[i]\n"," context = []\n"," for j in range(\n"," max(0, i - self.window_size),\n"," min(len(sequence), i + self.window_size + 1),\n"," ):\n"," if j != i:\n"," context.append(sequence[j])\n"," data.append((context, target_word))\n"," return data\n","\n","\n","def collate_fn(batch):\n"," # join all training examples into single tensor\n"," data = []\n"," targets = []\n"," for batch_item in batch:\n"," for _item in batch_item[1:-1]:\n"," data.append(_item[0])\n"," targets.append(_item[1])\n","\n"," return torch.tensor(data), torch.tensor(targets).view(-1, 1)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["vocab_size = ratings_small_df[\"movieId\"].unique().count()\n","window_size = 1\n","\n","# Create dataset and dataloader\n","dataset = CBOWDataset(sequences, window_size)\n","dataloader = DataLoader(dataset, batch_size=64, shuffle=False, collate_fn=collate_fn)"]},{"cell_type":"markdown","metadata":{},"source":["The training data is made up of pairs of tokens and the token from the middle. Here is a preview:"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["[([5], 46), ([46, 42], 5), ([5, 13], 42), ([42, 16], 13), ([13, 0], 16)]"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["dataset.__getitem__(0)[:5]"]},{"cell_type":"markdown","metadata":{},"source":["Following the dataloader we get."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["(tensor([[46, 42],\n"," [ 5, 13],\n"," [42, 16],\n"," ...,\n"," [29, 30],\n"," [26, 38],\n"," [30, 9]]),\n"," tensor([[ 5],\n"," [42],\n"," [13],\n"," ...,\n"," [26],\n"," [30],\n"," [38]]))"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["batch = next(iter(dataloader))\n","batch"]},{"cell_type":"markdown","metadata":{},"source":["### Model architecture\n","\n","The input to the model is a one-hot encoded vector representing the context words.\n","The hidden layer is a projection layer (embedding layer) that converts the one-hot encoded vectors into dense embedding vectors.\n","The output layer predicts the probability distribution of the target word given the context.\n","The model is trained to minimize the difference between the predicted probabilities and the actual word (softmax output).\n","\n","\n","The input to the model is a series of tokens representing the movies.\n","(These are converted to one-hot encoded vectors. Not needed?)\n","The tokens are converted into embedding vectors.\n","\n","The embedding vectors of the context words are averaged to obtain a single context vector.\n","This context vector represents the overall context of the surrounding words.\n","The context vector is then passed through a linear transformation followed by a softmax activation function to produce a probability distribution over the entire vocabulary.\n","\n","We then use a dense layer(s) to find the probability of each element in the vocabulary.\n","We compare against the true target word and use cross entropy loss to train the model weights, including the embedding layer.\n","\n","\n","With large vocabularies cross entropy loss can be expensive. There are approximations which are faster. We will stick with the full computation as we have a small vocab."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n"," from .autonotebook import tqdm as notebook_tqdm\n"]}],"source":["import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import pytorch_lightning as pytl\n","\n","\n","class CBOWModel(pytl.LightningModule):\n"," def __init__(\n"," self, vocab_size: int, embedding_dim: int, learning_rate: float = 1e-2\n"," ):\n"," super(CBOWModel, self).__init__()\n"," self.embeddings = nn.Embedding(vocab_size, embedding_dim)\n"," self.linear = nn.Linear(embedding_dim, vocab_size)\n","\n"," self.learning_rate = learning_rate\n"," self.train_log_error = []\n"," self.val_log_error = []\n","\n"," def forward(self, context):\n"," embedded_context = self.embeddings(context)\n"," # sum over context to get single embedding vector\n"," sum_embedded_context = torch.sum(embedded_context, dim=1)\n"," output = self.linear(sum_embedded_context)\n"," return output\n","\n"," def training_step(self, batch, batch_idx):\n"," context, target = batch\n"," context = torch.tensor(context).squeeze(1)\n"," target = torch.tensor(target).squeeze(1)\n"," output = self(context)\n"," loss = nn.CrossEntropyLoss()(output, target)\n","\n"," self.train_log_error.append(loss.item())\n"," return loss\n","\n"," def configure_optimizers(self):\n"," return optim.Adam(self.parameters(), lr=self.learning_rate)"]},{"cell_type":"markdown","metadata":{},"source":["Test with a single training example\n","\n","We have 28 sequences in the training example and they return a value for each title in the vocabulary (50)."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["torch.Size([1770, 50])"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["embedding_dim = 20\n","model = CBOWModel(vocab_size, embedding_dim)\n","batch = next(iter(dataloader))\n","model(batch[0]).shape"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["GPU available: True (mps), used: True\n","TPU available: False, using: 0 TPU cores\n","IPU available: False, using: 0 IPUs\n","HPU available: False, using: 0 HPUs\n"]},{"name":"stderr","output_type":"stream","text":["\n"," | Name | Type | Params\n","-----------------------------------------\n","0 | embeddings | Embedding | 1.0 K \n","1 | linear | Linear | 1.1 K \n","-----------------------------------------\n","2.0 K Trainable params\n","0 Non-trainable params\n","2.0 K Total params\n","0.008 Total estimated model params size (MB)\n","/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:430: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n"," rank_zero_warn(\n"]},{"name":"stdout","output_type":"stream","text":["Epoch 0: 0%| | 0/770 [00:00"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","\n","\n","def moving_average(x, window_size):\n"," \"\"\"Calculate the moving average of a list using numpy.\"\"\"\n"," return np.convolve(x, np.ones(window_size) / window_size, mode=\"valid\")\n","\n","\n","fig, ax = plt.subplots(figsize=(6, 4))\n","ax.plot(moving_average(model.train_log_error, 100), label=\"train\")\n","ax.set_title(f\"Training error\")\n","ax.set_xlabel(\"Batches\")\n","ax.set_ylabel(\"LL\")\n","ax.legend()\n","fig.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["tensor([[ 0.1166, -1.3725, 0.0836, ..., 2.5572, 0.0068, 1.3334],\n"," [ 1.4388, 0.8244, 0.1035, ..., -2.7618, -1.5350, -1.8414],\n"," [-0.3982, -0.0392, 0.1584, ..., 0.4545, -0.1839, 0.6076],\n"," ...,\n"," [ 0.1540, 0.3423, -0.2818, ..., -0.5759, -0.4444, -2.2692],\n"," [ 0.0410, 0.4492, 0.1212, ..., 0.9471, -0.5792, 0.4750],\n"," [ 0.3203, 1.1729, 0.1938, ..., -1.3214, -1.2435, -1.9945]],\n"," grad_fn=)"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["batch = next(iter(dataloader))\n","model(batch[0])"]},{"cell_type":"markdown","metadata":{},"source":["## Find embedding vectors"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import torch\n","import torch.nn.functional as F\n","\n","\n","def calculate_cosine_similarity(embedding_matrix):\n"," normalized_embeddings = F.normalize(embedding_matrix, p=2, dim=1)\n"," return torch.matmul(normalized_embeddings, normalized_embeddings.t())\n","\n","\n","embedding_matrix = model.embeddings.weight\n","similarities = calculate_cosine_similarity(embedding_matrix.cpu()).detach().numpy()\n","similarities[np.triu_indices(similarities.shape[0], k=1)] = np.nan"]},{"cell_type":"markdown","metadata":{},"source":["Plot results"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_13627/3547380529.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"," fig.show()\n"]},{"data":{"image/png":"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"]},"metadata":{},"output_type":"display_data"}],"source":["labels = token_mapping.join(names_df, on=\"movieId\").sort(\"token\")[\"title\"].to_list()\n","\n","fig, ax = plt.subplots(figsize=(10, 10))\n","sns.heatmap(\n"," data=similarities,\n"," ax=ax,\n"," xticklabels=labels,\n"," yticklabels=labels,\n",")\n","fig.show()"]},{"cell_type":"markdown","metadata":{},"source":["Strong correlations"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["-0.70105886\n","American Beauty (1999) Dances with Wolves (1990)\n","-0.6309868\n","Terminator, The (1984) Terminator 2: Judgment Day (1991)\n","-0.6087099\n","Lord of the Rings: The Return of the King, The (2003) Terminator 2: Judgment Day (1991)\n"]},{"data":{"text/plain":["[(40, 19),\n"," (30, 18),\n"," (47, 14),\n"," (37, 2),\n"," (43, 41),\n"," (38, 37),\n"," (25, 23),\n"," (25, 16),\n"," (39, 35),\n"," (35, 7)]"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["def top_n_indices(arr, n: int = 10):\n"," arr = np.abs(np.nan_to_num(arr, 0))\n"," np.fill_diagonal(arr, 0)\n"," sorted_indices = np.argsort(arr.flatten())\n"," # return sorted_indices\n"," top_n_indices = np.flip(sorted_indices[-n:])\n"," row_indices, col_indices = np.unravel_index(top_n_indices, arr.shape)\n","\n"," return list(zip(row_indices, col_indices))\n","\n","\n","indices = top_n_indices(similarities)\n","print(similarities[indices[0][0], indices[0][1]])\n","print(labels[indices[0][0]], labels[indices[0][1]])\n","print(similarities[indices[1][0], indices[1][1]])\n","print(labels[indices[1][0]], labels[indices[1][1]])\n","print(similarities[indices[2][0], indices[2][1]])\n","print(labels[indices[2][0]], labels[indices[1][1]])\n","indices"]},{"cell_type":"markdown","metadata":{},"source":["The model trains quickly but doesn't give much useful results.\n","Let's now try with a larger dataset."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["# TODO\n","* Larger dataset\n","* Using\n","\n","### Ratings based\n","The above Word2Vec approach will be assuming that people watched films that are similar in succession.\n","\n","We have explicit ratings in the dataset we can use for a better indication.\n","We are assuming that people rate highly movies that are similar.\n","\n","We also have genre information, how can we exploit this?\n","\n","### Text dataset\n","Can we confirm the above approach with text data."]},{"cell_type":"markdown","metadata":{},"source":["## Larger dataset"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_14579/3647007902.py:3: DeprecationWarning: `count` is deprecated. It has been renamed to `len`.\n"," .count()\n","/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_14579/3647007902.py:11: DeprecationWarning: `count` is deprecated. It has been renamed to `len`.\n"," .count()\n"]},{"data":{"text/plain":["(5954554, 4)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["top_movie_ids = (\n"," ratings_df.group_by(\"movieId\")\n"," .count()\n"," .sort(\"count\", descending=True)\n"," .head(200)[[\"movieId\"]]\n",")\n","ratings_med_df = ratings_df.join(top_movie_ids, on=\"movieId\", how=\"inner\")\n","\n","user_id_counts = (\n"," ratings_med_df.group_by(\"userId\")\n"," .count()\n"," .filter(pl.col(\"count\") >= 20)[[\"userId\"]]\n",")\n","ratings_med_df = ratings_med_df.join(user_id_counts, on=\"userId\", how=\"inner\")\n","ratings_med_df.shape"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["
\n","shape: (4, 5)
userIdmovieIdratingtimestamptoken
i64i64f64i64u32
159524.011478680535840
120122.511478680681923
120112.511478680791922
116534.011478680971591
"],"text/plain":["shape: (4, 5)\n","┌────────┬─────────┬────────┬────────────┬───────┐\n","│ userId ┆ movieId ┆ rating ┆ timestamp ┆ token │\n","│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n","│ i64 ┆ i64 ┆ f64 ┆ i64 ┆ u32 │\n","╞════════╪═════════╪════════╪════════════╪═══════╡\n","│ 1 ┆ 5952 ┆ 4.0 ┆ 1147868053 ┆ 5840 │\n","│ 1 ┆ 2012 ┆ 2.5 ┆ 1147868068 ┆ 1923 │\n","│ 1 ┆ 2011 ┆ 2.5 ┆ 1147868079 ┆ 1922 │\n","│ 1 ┆ 1653 ┆ 4.0 ┆ 1147868097 ┆ 1591 │\n","└────────┴─────────┴────────┴────────────┴───────┘"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["100%|██████████| 162541/162541 [14:16<00:00, 189.87it/s]\n"]},{"name":"stdout","output_type":"stream","text":["[5840, 1923, 1922, 1591, 1217, 6416, 6258, 3352, 1061, 878]\n"]},{"name":"stderr","output_type":"stream","text":["GPU available: True (mps), used: True\n","TPU available: False, using: 0 TPU cores\n","IPU available: False, using: 0 IPUs\n","HPU available: False, using: 0 HPUs\n","\n"," | Name | Type | Params\n","-----------------------------------------\n","0 | embeddings | Embedding | 1.2 M \n","1 | linear | Linear | 1.2 M \n","-----------------------------------------\n","2.4 M Trainable params\n","0 Non-trainable params\n","2.4 M Total params\n","9.684 Total estimated model params size (MB)\n","/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:430: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n"," rank_zero_warn(\n"]},{"name":"stdout","output_type":"stream","text":["Epoch 0: 0%| | 0/2540 [00:00 17\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 20\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(moving_average(model\u001b[38;5;241m.\u001b[39mtrain_log_error, \u001b[38;5;241m100\u001b[39m), label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:520\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 518\u001b[0m model \u001b[38;5;241m=\u001b[39m _maybe_unwrap_optimized(model)\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m_lightning_module \u001b[38;5;241m=\u001b[39m model\n\u001b[0;32m--> 520\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 521\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 522\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:44\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 47\u001b[0m _call_teardown_hook(trainer)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:559\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_connector\u001b[38;5;241m.\u001b[39mattach_data(\n\u001b[1;32m 550\u001b[0m model, train_dataloaders\u001b[38;5;241m=\u001b[39mtrain_dataloaders, val_dataloaders\u001b[38;5;241m=\u001b[39mval_dataloaders, datamodule\u001b[38;5;241m=\u001b[39mdatamodule\n\u001b[1;32m 551\u001b[0m )\n\u001b[1;32m 553\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 554\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 555\u001b[0m ckpt_path,\n\u001b[1;32m 556\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 557\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 558\u001b[0m )\n\u001b[0;32m--> 559\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 561\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m 562\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:935\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[1;32m 932\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 933\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 934\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 935\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 937\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 938\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 939\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 940\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:978\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 976\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[1;32m 977\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m--> 978\u001b[0m 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\u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 133\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py:218\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_batch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 217\u001b[0m \u001b[38;5;66;03m# in automatic optimization, there can only be one optimizer\u001b[39;00m\n\u001b[0;32m--> 218\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautomatic_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 220\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_optimization\u001b[38;5;241m.\u001b[39mrun(kwargs)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:185\u001b[0m, in \u001b[0;36m_AutomaticOptimization.run\u001b[0;34m(self, optimizer, kwargs)\u001b[0m\n\u001b[1;32m 178\u001b[0m closure()\n\u001b[1;32m 180\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 182\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_idx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 187\u001b[0m result \u001b[38;5;241m=\u001b[39m closure\u001b[38;5;241m.\u001b[39mconsume_result()\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:261\u001b[0m, in \u001b[0;36m_AutomaticOptimization._optimizer_step\u001b[0;34m(self, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_ready()\n\u001b[1;32m 260\u001b[0m \u001b[38;5;66;03m# model hook\u001b[39;00m\n\u001b[0;32m--> 261\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 262\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 263\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptimizer_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 264\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 265\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_step_and_backward_closure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m should_accumulate:\n\u001b[1;32m 271\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_completed()\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:142\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 139\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 142\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 145\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/core/module.py:1265\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_closure)\u001b[0m\n\u001b[1;32m 1226\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimizer_step\u001b[39m(\n\u001b[1;32m 1227\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1228\u001b[0m epoch: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1231\u001b[0m optimizer_closure: Optional[Callable[[], Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1232\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;124;03m Override this method to adjust the default way the :class:`~pytorch_lightning.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1235\u001b[0m \u001b[38;5;124;03m the optimizer.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1263\u001b[0m \u001b[38;5;124;03m pg[\"lr\"] = lr_scale * self.learning_rate\u001b[39;00m\n\u001b[1;32m 1264\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1265\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer_closure\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py:158\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen `optimizer.step(closure)` is called, the closure should be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 158\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on_after_step()\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m step_output\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py:224\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;66;03m# TODO(fabric): remove assertion once strategy's optimizer_step typing is fixed\u001b[39;00m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, pl\u001b[38;5;241m.\u001b[39mLightningModule)\n\u001b[0;32m--> 224\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:114\u001b[0m, in \u001b[0;36mPrecisionPlugin.optimizer_step\u001b[0;34m(self, optimizer, model, closure, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Hook to run the optimizer step.\"\"\"\u001b[39;00m\n\u001b[1;32m 113\u001b[0m closure \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_closure, model, optimizer, closure)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/optim/optimizer.py:280\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs),\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 280\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 283\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/optim/optimizer.py:33\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 32\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m---> 33\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 35\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(prev_grad)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/optim/adam.py:121\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m closure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39menable_grad():\n\u001b[0;32m--> 121\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups:\n\u001b[1;32m 124\u001b[0m params_with_grad \u001b[38;5;241m=\u001b[39m []\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:101\u001b[0m, in \u001b[0;36mPrecisionPlugin._wrap_closure\u001b[0;34m(self, model, optimizer, closure)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_wrap_closure\u001b[39m(\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 91\u001b[0m model: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpl.LightningModule\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 92\u001b[0m optimizer: Optimizer,\n\u001b[1;32m 93\u001b[0m closure: Callable[[], Any],\n\u001b[1;32m 94\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 95\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"This double-closure allows makes sure the ``closure`` is executed before the\u001b[39;00m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;124;03m ``on_before_optimizer_step`` hook is called.\u001b[39;00m\n\u001b[1;32m 97\u001b[0m \n\u001b[1;32m 98\u001b[0m \u001b[38;5;124;03m The closure (generally) runs ``backward`` so this allows inspecting gradients in this hook. This structure is\u001b[39;00m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;124;03m consistent with the ``PrecisionPlugin`` subclasses that cannot pass ``optimizer.step(closure)`` directly.\u001b[39;00m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 101\u001b[0m closure_result \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_closure(model, optimizer)\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m closure_result\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:140\u001b[0m, in \u001b[0;36mClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Tensor]:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\u001b[38;5;241m.\u001b[39mloss\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:126\u001b[0m, in \u001b[0;36mClosure.closure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclosure\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ClosureResult:\n\u001b[0;32m--> 126\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_output\u001b[38;5;241m.\u001b[39mclosure_loss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarning_cache\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`training_step` returned `None`. If this was on purpose, ignore this warning...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:308\u001b[0m, in \u001b[0;36m_AutomaticOptimization._training_step\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\n\u001b[1;32m 307\u001b[0m \u001b[38;5;66;03m# manually capture logged metrics\u001b[39;00m\n\u001b[0;32m--> 308\u001b[0m training_step_output \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtraining_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mpost_training_step()\n\u001b[1;32m 311\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_result_cls\u001b[38;5;241m.\u001b[39mfrom_training_step_output(training_step_output, trainer\u001b[38;5;241m.\u001b[39maccumulate_grad_batches)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:288\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 288\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 290\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 291\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py:366\u001b[0m, in \u001b[0;36mStrategy.training_step\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprecision_plugin\u001b[38;5;241m.\u001b[39mtrain_step_context():\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, TrainingStep)\n\u001b[0;32m--> 366\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","Cell \u001b[0;32mIn[12], line 31\u001b[0m, in \u001b[0;36mCBOWModel.training_step\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m 29\u001b[0m target \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(target)\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 30\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m(context)\n\u001b[0;32m---> 31\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCrossEntropyLoss\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_log_error\u001b[38;5;241m.\u001b[39mappend(loss\u001b[38;5;241m.\u001b[39mitem())\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/nn/modules/loss.py:1174\u001b[0m, in \u001b[0;36mCrossEntropyLoss.forward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m 1173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor, target: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m-> 1174\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcross_entropy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1175\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1176\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabel_smoothing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel_smoothing\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/nn/functional.py:3029\u001b[0m, in \u001b[0;36mcross_entropy\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)\u001b[0m\n\u001b[1;32m 3027\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size_average \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m reduce \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3028\u001b[0m reduction \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[0;32m-> 3029\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcross_entropy_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_Reduction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_enum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreduction\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_smoothing\u001b[49m\u001b[43m)\u001b[49m\n","\u001b[0;31mRuntimeError\u001b[0m: MPS backend out of memory (MPS allocated: 7.13 GB, other allocations: 1.86 GB, max allowed: 9.07 GB). Tried to allocate 1.73 GB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure)."]}],"source":["ratings_med_df, token_mapping = convert_movies_to_tokens(ratings_med_df)\n","display(ratings_med_df.head(4))\n","\n","sequences = get_sequences_from_df(ratings_med_df)\n","print(sequences[0][:10])\n","\n","vocab_size = ratings_med_df[\"movieId\"].unique().count()\n","window_size = 1\n","\n","# Create dataset and dataloader\n","dataset = CBOWDataset(sequences, window_size)\n","dataloader = DataLoader(dataset, batch_size=64, shuffle=False, collate_fn=collate_fn)\n","\n","embedding_dim = 20\n","model = CBOWModel(vocab_size, embedding_dim)\n","trainer = pytl.Trainer(max_epochs=1)\n","trainer.fit(model, dataloader)\n","\n","fig, ax = plt.subplots(figsize=(6, 4))\n","ax.plot(moving_average(model.train_log_error, 100), label=\"train\")\n","ax.set_title(f\"Training error\")\n","ax.set_xlabel(\"Batches\")\n","ax.set_ylabel(\"LL\")\n","ax.legend()\n","fig.show()\n","\n","embedding_matrix = model.embeddings.weight\n","similarities = calculate_cosine_similarity(embedding_matrix.cpu()).detach().numpy()\n","similarities[np.triu_indices(similarities.shape[0], k=1)] = np.nan\n","\n","labels = token_mapping.join(names_df, on=\"movieId\").sort(\"token\")[\"title\"].to_list()\n","\n","fig, ax = plt.subplots(figsize=(10, 10))\n","sns.heatmap(\n"," data=similarities,\n"," ax=ax,\n"," xticklabels=labels,\n"," yticklabels=labels,\n",")\n","fig.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"ename":"NotImplementedError","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n","\u001b[0;31mNotImplementedError\u001b[0m: "]}],"source":["raise NotImplementedError"]}],"metadata":{"kernelspec":{"display_name":"pytorch_env","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":"3.10.13"},"orig_nbformat":2},"nbformat":4,"nbformat_minor":2} +{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# Finding similar words\n","\n","Can we find an embedding space built on the movielens dataset to compare distance between titles?\n","\n","Consider each person's viewing history as a series of titles.\n","Build a model to predict the next title watched based on the ones before it.\n","Or build a model to predict the middle title based on the ones either side.\n","\n","Represent the titles as an embedding vector.\n","Build a dense layer on top of the embedding vector to predict the next title.\n","\n","Follow an approach similar to Word2Vec.\n","Instead of treating each word as an entity or token, we use each title.\n","We treat the vocabulary as the set of titles.\n","\n","1. Load the movielens dataset\n","2. Convert each movie title to an integer token\n","3. Create an embedding layer on the tokens\n","4. \n","\n","References:\n","* https://en.wikipedia.org/wiki/Word2vec\n","* https://github.com/karpathy/nanoGPT/blob/master/data/shakespeare/prepare.py\n","* https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html\n","* https://medium.com/@bijil.subhash/code-walkthrough-of-word2vec-pytorch-implementation-3a9ca0ad55a7\n","\n","Start by importing stuff:"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"ename":"ModuleNotFoundError","evalue":"No module named 'polars'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpolars\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpl\u001b[39;00m\n\u001b[1;32m 7\u001b[0m plt\u001b[38;5;241m.\u001b[39mstyle\u001b[38;5;241m.\u001b[39muse(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseaborn-v0_8-whitegrid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'polars'"]}],"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","import numpy as np\n","import pandas as pd\n","import polars as pl\n","\n","plt.style.use(\"seaborn-v0_8-whitegrid\")"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["## Load data\n","\n","Load the tiny Shakespeare dataset."]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["downloading data\n"]},{"data":{"text/plain":["[(['i'], 'F'),\n"," (['F', 'r'], 'i'),\n"," (['i', 's'], 'r'),\n"," (['r', 't'], 's'),\n"," (['s', ' '], 't'),\n"," (['t', 'C'], ' '),\n"," ([' ', 'i'], 'C'),\n"," (['C', 't'], 'i'),\n"," (['i', 'i'], 't'),\n"," (['t', 'z'], 'i'),\n"," (['i', 'e'], 'z'),\n"," (['z', 'n'], 'e'),\n"," (['e', ':'], 'n'),\n"," (['n'], ':')]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["import torch\n","import requests\n","from torch.utils.data.dataset import Dataset\n","from pathlib import Path\n","import random\n","\n","\n","class ShakespeareDataCBOW(Dataset):\n"," def __init__(\n"," self,\n"," filepath: Path,\n"," window_size: int\n"," ):\n"," self.filepath = filepath\n"," self.window_size = window_size\n","\n"," self._download_data()\n"," self.data = self._load_data()\n","\n"," def __len__(self):\n"," return len(self.data)\n","\n"," def __getitem__(self, idx):\n"," sequence = self.data[idx]\n"," \n"," # need to convert to tokens first\n"," \n"," data = []\n"," for i in range(len(sequence)):\n"," target_token = sequence[i]\n"," context = []\n"," for j in range(\n"," max(0, i - self.window_size),\n"," min(len(sequence), i + self.window_size + 1),\n"," ):\n"," if j != i:\n"," context.append(sequence[j])\n"," data.append((context, target_token))\n"," return data\n","\n"," def _download_data(self):\n"," \"download to disk if not present already\"\n","\n"," self.filepath.parent.mkdir(parents=True, exist_ok=True)\n","\n"," if not self.filepath.exists():\n"," print(\"downloading data\")\n"," data_url = \"https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\"\n"," with open(self.filepath, \"wb\") as f:\n"," f.write(requests.get(data_url).content)\n","\n"," def _load_data(self) -> str:\n"," with open(self.filepath, \"r\") as f:\n"," data = f.read()\n"," data = data.splitlines()\n"," data = [sentence for sentence in data if sentence!='']\n"," return data\n","\n","\n","class CBOWDataset(Dataset):\n"," def __init__(self, sequences: list[int], window_size: int):\n"," self.sequences = sequences\n"," self.window_size = window_size\n","\n"," def __len__(self):\n"," return len(self.sequences)\n","\n"," def __getitem__(self, idx):\n"," sequence = self.sequences[idx]\n"," data = []\n"," for i in range(len(sequence)):\n"," target_word = sequence[i]\n"," context = []\n"," for j in range(\n"," max(0, i - self.window_size),\n"," min(len(sequence), i + self.window_size + 1),\n"," ):\n"," if j != i:\n"," context.append(sequence[j])\n"," data.append((context, target_word))\n"," return data\n","\n","\n","\n","filepath = Path().absolute() / \"data\" / \"shakespeare.txt\"\n","dataset = ShakespeareDataCBOW(\n"," filepath=filepath, window_size=1\n",")\n","dataset.__getitem__(0)\n"]},{"cell_type":"markdown","metadata":{},"source":["Create tokens\n","\n","1. Split by spaces - x\n","2. Lower case - x\n","3. Stem\n","4. Counter\n","5. Make vocab of top N words\n","6. Make word to token lookup table"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["from pathlib import Path\n","\n","def _load_data(filepath) -> str:\n"," with open(filepath, \"r\") as f:\n"," data = f.read()\n"," data = data.splitlines()\n"," data = [sentence for sentence in data if sentence!='']\n"," return data\n","\n","filepath = Path().absolute() / \"data\" / \"shakespeare.txt\"\n","data = _load_data(filepath=filepath)"]},{"cell_type":"markdown","metadata":{},"source":["Convert tokenizer to class\n","stateful vocab"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["No more talking on't; let it be done: away, away!\n","[35, 53, 2750, 46, 61, 15, 16, 158, 144, 144]\n","no more talking on let it be done away away\n"]}],"source":["import re\n","from typing import List, Dict\n","\n","REG = re.compile(\"\\w*\")\n","\n","\n","class Tokenizer:\n"," def __init__(self, sentences: List[str]) -> None:\n"," word_counts, word_tokens = self._get_vocab(sentences)\n"," self.vocab = word_tokens\n"," self.reverse_vocab = {val: key for key, val in self.vocab.items()}\n","\n"," def _get_vocab(self, sentences: List[str]) -> Dict[str, int]:\n","\n"," words = \" \".join(sentences).lower().split(\" \")\n"," word_counts = {}\n"," for word in words:\n"," _word = REG.search(word)[0]\n"," word_counts[_word] = word_counts.get(_word, 0) + 1\n","\n"," word_counts = dict(\n"," sorted(word_counts.items(), key=lambda x: x[1], reverse=True)\n"," )\n"," # convert to tokens\n"," word_tokens = {key: idx for idx, (key, val) in enumerate(word_counts.items())}\n"," return word_counts, word_tokens\n","\n"," def encode(self, sentence: str) -> List[int]:\n"," words = sentence.lower().split(\" \")\n"," tokens = []\n"," for word in words:\n"," _word = REG.search(word)[0]\n"," tokens.append(self.vocab[_word])\n"," return tokens\n","\n"," def decode(self, tokens: List[int]) -> str:\n"," words = []\n"," for token in tokens:\n"," words.append(self.reverse_vocab[token])\n"," return \" \".join(words)\n","\n","\n","tokenizer = Tokenizer(sentences=data)\n","\n","sentence = data[16]\n","print(sentence)\n","tokens = tokenizer.encode(sentence)\n","print(tokens)\n","words = tokenizer.decode(tokens)\n","print(words)\n"]},{"cell_type":"markdown","metadata":{},"source":["We will make a small subset of data for initial building and testing our models"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":["32777"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["data_small = data[:int(1e5)]\n","len(data_small)\n"]},{"cell_type":"markdown","metadata":{},"source":["## Build model architecture\n","\n","We will build the embedding using word2vec. There are two forms:\n","\n","1. Continuous Bag of Words (CBOW):\n","In CBOW, the model predicts the current word (target word) based on the context words within a fixed window size.\n","\n","1. Skip-gram:\n","In Skip-gram, the model predicts context words (surrounding words) given the current word (target word).\n","\n","CBOW is generally faster to train compared to Skip-gram, especially when using small training datasets.\n","In our case the words are actually movies.\n","\n","We will use CBOW for this test as hopefully its faster and more appropriate on a smallish training dataset.\n","Therefore we need to define a window of entities to train over. We will start with 3, so predict the current entity given the one before and the one after.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Data preparation"]},{"cell_type":"markdown","metadata":{},"source":["We need to get sequenences of tokens. So we next create tokens from the movieIds as they do not start from 0 and are not sequential.\n","Then for each userId we convert the tokens in to a list."]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["def create_vocab():\n"," pass\n","\n","def convert_to_tokens():\n"," pass\n","\n"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["import tqdm\n","\n","\n","def convert_movies_to_tokens(ratings_df: pl.DataFrame):\n"," if \"token\" in ratings_df:\n"," ratings_df = ratings_df.drop(\"token\")\n"," mapping = (\n"," ratings_df.group_by(\"movieId\")\n"," .len()\n"," .sort(\"movieId\")\n"," .with_row_index(name=\"token\")\n"," .drop(\"len\")\n"," )\n"," ratings_df = ratings_df.join(mapping, on=\"movieId\")\n"," return ratings_df, mapping\n","\n","\n","def get_sequences_from_df(ratings_df: pl.DataFrame):\n"," sequences = []\n"," for _user_id in tqdm.tqdm(ratings_df[\"userId\"].unique()):\n"," sequences.append(\n"," ratings_df.filter(pl.col(\"userId\") == _user_id)[\"token\"].to_list()\n"," )\n"," return sequences"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"ename":"NameError","evalue":"name 'ratings_small_df' is not defined","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ratings_small_df, token_mapping \u001b[38;5;241m=\u001b[39m convert_movies_to_tokens(\u001b[43mratings_small_df\u001b[49m)\n\u001b[1;32m 2\u001b[0m display(ratings_small_df\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 4\u001b[0m sequences \u001b[38;5;241m=\u001b[39m get_sequences_from_df(ratings_small_df)\n","\u001b[0;31mNameError\u001b[0m: name 'ratings_small_df' is not defined"]}],"source":["ratings_small_df, token_mapping = convert_movies_to_tokens(ratings_small_df)\n","display(ratings_small_df.head(4))\n","\n","sequences = get_sequences_from_df(ratings_small_df)\n","print(sequences[0][:10])"]},{"cell_type":"markdown","metadata":{},"source":["We can now create the CBOW dataset from the sequences.\n","We will use a small embedding size and a window of only 1.\n","\n","\n","We create a `collate_fn` to collect all the sequences and combine into tensors for training.\n","\n","```\n","Sequence Data (e.g., Text, Time Series):\n","For sequence data, such as text or time series, the shape of a batch is often: (batch_size, sequence_length, input_dim).\n","batch_size is the number of sequences in the batch.\n","sequence_length is the length of each sequence.\n","input_dim represents the dimensionality of each element in the sequence (e.g., word embeddings for text).\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from torch.utils.data import Dataset, DataLoader\n","import torch\n","\n","\n","class CBOWDataset(Dataset):\n"," def __init__(self, sequences: list[int], window_size: int):\n"," self.sequences = sequences\n"," self.window_size = window_size\n","\n"," def __len__(self):\n"," return len(self.sequences)\n","\n"," def __getitem__(self, idx):\n"," sequence = self.sequences[idx]\n"," data = []\n"," for i in range(len(sequence)):\n"," target_word = sequence[i]\n"," context = []\n"," for j in range(\n"," max(0, i - self.window_size),\n"," min(len(sequence), i + self.window_size + 1),\n"," ):\n"," if j != i:\n"," context.append(sequence[j])\n"," data.append((context, target_word))\n"," return data\n","\n","\n","def collate_fn(batch):\n"," # join all training examples into single tensor\n"," data = []\n"," targets = []\n"," for batch_item in batch:\n"," for _item in batch_item[1:-1]:\n"," data.append(_item[0])\n"," targets.append(_item[1])\n","\n"," return torch.tensor(data), torch.tensor(targets).view(-1, 1)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["vocab_size = ratings_small_df[\"movieId\"].unique().count()\n","window_size = 1\n","\n","# Create dataset and dataloader\n","dataset = CBOWDataset(sequences, window_size)\n","dataloader = DataLoader(dataset, batch_size=64, shuffle=False, collate_fn=collate_fn)"]},{"cell_type":"markdown","metadata":{},"source":["The training data is made up of pairs of tokens and the token from the middle. Here is a preview:"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["[([5], 46), ([46, 42], 5), ([5, 13], 42), ([42, 16], 13), ([13, 0], 16)]"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["dataset.__getitem__(0)[:5]"]},{"cell_type":"markdown","metadata":{},"source":["Following the dataloader we get."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["(tensor([[46, 42],\n"," [ 5, 13],\n"," [42, 16],\n"," ...,\n"," [29, 30],\n"," [26, 38],\n"," [30, 9]]),\n"," tensor([[ 5],\n"," [42],\n"," [13],\n"," ...,\n"," [26],\n"," [30],\n"," [38]]))"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["batch = next(iter(dataloader))\n","batch"]},{"cell_type":"markdown","metadata":{},"source":["### Model architecture\n","\n","The input to the model is a one-hot encoded vector representing the context words.\n","The hidden layer is a projection layer (embedding layer) that converts the one-hot encoded vectors into dense embedding vectors.\n","The output layer predicts the probability distribution of the target word given the context.\n","The model is trained to minimize the difference between the predicted probabilities and the actual word (softmax output).\n","\n","\n","The input to the model is a series of tokens representing the movies.\n","(These are converted to one-hot encoded vectors. Not needed?)\n","The tokens are converted into embedding vectors.\n","\n","The embedding vectors of the context words are averaged to obtain a single context vector.\n","This context vector represents the overall context of the surrounding words.\n","The context vector is then passed through a linear transformation followed by a softmax activation function to produce a probability distribution over the entire vocabulary.\n","\n","We then use a dense layer(s) to find the probability of each element in the vocabulary.\n","We compare against the true target word and use cross entropy loss to train the model weights, including the embedding layer.\n","\n","\n","With large vocabularies cross entropy loss can be expensive. There are approximations which are faster. We will stick with the full computation as we have a small vocab."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n"," from .autonotebook import tqdm as notebook_tqdm\n"]}],"source":["import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import pytorch_lightning as pytl\n","\n","\n","class CBOWModel(pytl.LightningModule):\n"," def __init__(\n"," self, vocab_size: int, embedding_dim: int, learning_rate: float = 1e-2\n"," ):\n"," super(CBOWModel, self).__init__()\n"," self.embeddings = nn.Embedding(vocab_size, embedding_dim)\n"," self.linear = nn.Linear(embedding_dim, vocab_size)\n","\n"," self.learning_rate = learning_rate\n"," self.train_log_error = []\n"," self.val_log_error = []\n","\n"," def forward(self, context):\n"," embedded_context = self.embeddings(context)\n"," # sum over context to get single embedding vector\n"," sum_embedded_context = torch.sum(embedded_context, dim=1)\n"," output = self.linear(sum_embedded_context)\n"," return output\n","\n"," def training_step(self, batch, batch_idx):\n"," context, target = batch\n"," context = torch.tensor(context).squeeze(1)\n"," target = torch.tensor(target).squeeze(1)\n"," output = self(context)\n"," loss = nn.CrossEntropyLoss()(output, target)\n","\n"," self.train_log_error.append(loss.item())\n"," return loss\n","\n"," def configure_optimizers(self):\n"," return optim.Adam(self.parameters(), lr=self.learning_rate)"]},{"cell_type":"markdown","metadata":{},"source":["Test with a single training example\n","\n","We have 28 sequences in the training example and they return a value for each title in the vocabulary (50)."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["torch.Size([1770, 50])"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["embedding_dim = 20\n","model = CBOWModel(vocab_size, embedding_dim)\n","batch = next(iter(dataloader))\n","model(batch[0]).shape"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["GPU available: True (mps), used: True\n","TPU available: False, using: 0 TPU cores\n","IPU available: False, using: 0 IPUs\n","HPU available: False, using: 0 HPUs\n"]},{"name":"stderr","output_type":"stream","text":["\n"," | Name | Type | Params\n","-----------------------------------------\n","0 | embeddings | Embedding | 1.0 K \n","1 | linear | Linear | 1.1 K \n","-----------------------------------------\n","2.0 K Trainable params\n","0 Non-trainable params\n","2.0 K Total params\n","0.008 Total estimated model params size (MB)\n","/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:430: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n"," rank_zero_warn(\n"]},{"name":"stdout","output_type":"stream","text":["Epoch 0: 0%| | 0/770 [00:00"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","\n","\n","def moving_average(x, window_size):\n"," \"\"\"Calculate the moving average of a list using numpy.\"\"\"\n"," return np.convolve(x, np.ones(window_size) / window_size, mode=\"valid\")\n","\n","\n","fig, ax = plt.subplots(figsize=(6, 4))\n","ax.plot(moving_average(model.train_log_error, 100), label=\"train\")\n","ax.set_title(f\"Training error\")\n","ax.set_xlabel(\"Batches\")\n","ax.set_ylabel(\"LL\")\n","ax.legend()\n","fig.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["tensor([[ 0.1166, -1.3725, 0.0836, ..., 2.5572, 0.0068, 1.3334],\n"," [ 1.4388, 0.8244, 0.1035, ..., -2.7618, -1.5350, -1.8414],\n"," [-0.3982, -0.0392, 0.1584, ..., 0.4545, -0.1839, 0.6076],\n"," ...,\n"," [ 0.1540, 0.3423, -0.2818, ..., -0.5759, -0.4444, -2.2692],\n"," [ 0.0410, 0.4492, 0.1212, ..., 0.9471, -0.5792, 0.4750],\n"," [ 0.3203, 1.1729, 0.1938, ..., -1.3214, -1.2435, -1.9945]],\n"," grad_fn=)"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["batch = next(iter(dataloader))\n","model(batch[0])"]},{"cell_type":"markdown","metadata":{},"source":["## Find embedding vectors"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import torch\n","import torch.nn.functional as F\n","\n","\n","def calculate_cosine_similarity(embedding_matrix):\n"," normalized_embeddings = F.normalize(embedding_matrix, p=2, dim=1)\n"," return torch.matmul(normalized_embeddings, normalized_embeddings.t())\n","\n","\n","embedding_matrix = model.embeddings.weight\n","similarities = calculate_cosine_similarity(embedding_matrix.cpu()).detach().numpy()\n","similarities[np.triu_indices(similarities.shape[0], k=1)] = np.nan"]},{"cell_type":"markdown","metadata":{},"source":["Plot results"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_13627/3547380529.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"," fig.show()\n"]},{"data":{"image/png":"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"]},"metadata":{},"output_type":"display_data"}],"source":["labels = token_mapping.join(names_df, on=\"movieId\").sort(\"token\")[\"title\"].to_list()\n","\n","fig, ax = plt.subplots(figsize=(10, 10))\n","sns.heatmap(\n"," data=similarities,\n"," ax=ax,\n"," xticklabels=labels,\n"," yticklabels=labels,\n",")\n","fig.show()"]},{"cell_type":"markdown","metadata":{},"source":["Strong correlations"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["-0.70105886\n","American Beauty (1999) Dances with Wolves (1990)\n","-0.6309868\n","Terminator, The (1984) Terminator 2: Judgment Day (1991)\n","-0.6087099\n","Lord of the Rings: The Return of the King, The (2003) Terminator 2: Judgment Day (1991)\n"]},{"data":{"text/plain":["[(40, 19),\n"," (30, 18),\n"," (47, 14),\n"," (37, 2),\n"," (43, 41),\n"," (38, 37),\n"," (25, 23),\n"," (25, 16),\n"," (39, 35),\n"," (35, 7)]"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["def top_n_indices(arr, n: int = 10):\n"," arr = np.abs(np.nan_to_num(arr, 0))\n"," np.fill_diagonal(arr, 0)\n"," sorted_indices = np.argsort(arr.flatten())\n"," # return sorted_indices\n"," top_n_indices = np.flip(sorted_indices[-n:])\n"," row_indices, col_indices = np.unravel_index(top_n_indices, arr.shape)\n","\n"," return list(zip(row_indices, col_indices))\n","\n","\n","indices = top_n_indices(similarities)\n","print(similarities[indices[0][0], indices[0][1]])\n","print(labels[indices[0][0]], labels[indices[0][1]])\n","print(similarities[indices[1][0], indices[1][1]])\n","print(labels[indices[1][0]], labels[indices[1][1]])\n","print(similarities[indices[2][0], indices[2][1]])\n","print(labels[indices[2][0]], labels[indices[1][1]])\n","indices"]},{"cell_type":"markdown","metadata":{},"source":["The model trains quickly but doesn't give much useful results.\n","Let's now try with a larger dataset."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["# TODO\n","* Larger dataset\n","* Using\n","\n","### Ratings based\n","The above Word2Vec approach will be assuming that people watched films that are similar in succession.\n","\n","We have explicit ratings in the dataset we can use for a better indication.\n","We are assuming that people rate highly movies that are similar.\n","\n","We also have genre information, how can we exploit this?\n","\n","### Text dataset\n","Can we confirm the above approach with text data."]},{"cell_type":"markdown","metadata":{},"source":["## Larger dataset"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_14579/3647007902.py:3: DeprecationWarning: `count` is deprecated. It has been renamed to `len`.\n"," .count()\n","/var/folders/ky/4qby95090jbbq38_mh94x72r0000gn/T/ipykernel_14579/3647007902.py:11: DeprecationWarning: `count` is deprecated. It has been renamed to `len`.\n"," .count()\n"]},{"data":{"text/plain":["(5954554, 4)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["top_movie_ids = (\n"," ratings_df.group_by(\"movieId\")\n"," .count()\n"," .sort(\"count\", descending=True)\n"," .head(200)[[\"movieId\"]]\n",")\n","ratings_med_df = ratings_df.join(top_movie_ids, on=\"movieId\", how=\"inner\")\n","\n","user_id_counts = (\n"," ratings_med_df.group_by(\"userId\")\n"," .count()\n"," .filter(pl.col(\"count\") >= 20)[[\"userId\"]]\n",")\n","ratings_med_df = ratings_med_df.join(user_id_counts, on=\"userId\", how=\"inner\")\n","ratings_med_df.shape"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["
\n","shape: (4, 5)
userIdmovieIdratingtimestamptoken
i64i64f64i64u32
159524.011478680535840
120122.511478680681923
120112.511478680791922
116534.011478680971591
"],"text/plain":["shape: (4, 5)\n","┌────────┬─────────┬────────┬────────────┬───────┐\n","│ userId ┆ movieId ┆ rating ┆ timestamp ┆ token │\n","│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n","│ i64 ┆ i64 ┆ f64 ┆ i64 ┆ u32 │\n","╞════════╪═════════╪════════╪════════════╪═══════╡\n","│ 1 ┆ 5952 ┆ 4.0 ┆ 1147868053 ┆ 5840 │\n","│ 1 ┆ 2012 ┆ 2.5 ┆ 1147868068 ┆ 1923 │\n","│ 1 ┆ 2011 ┆ 2.5 ┆ 1147868079 ┆ 1922 │\n","│ 1 ┆ 1653 ┆ 4.0 ┆ 1147868097 ┆ 1591 │\n","└────────┴─────────┴────────┴────────────┴───────┘"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["100%|██████████| 162541/162541 [14:16<00:00, 189.87it/s]\n"]},{"name":"stdout","output_type":"stream","text":["[5840, 1923, 1922, 1591, 1217, 6416, 6258, 3352, 1061, 878]\n"]},{"name":"stderr","output_type":"stream","text":["GPU available: True (mps), used: True\n","TPU available: False, using: 0 TPU cores\n","IPU available: False, using: 0 IPUs\n","HPU available: False, using: 0 HPUs\n","\n"," | Name | Type | Params\n","-----------------------------------------\n","0 | embeddings | Embedding | 1.2 M \n","1 | linear | Linear | 1.2 M \n","-----------------------------------------\n","2.4 M Trainable params\n","0 Non-trainable params\n","2.4 M Total params\n","9.684 Total estimated model params size (MB)\n","/Users/rich/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:430: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n"," rank_zero_warn(\n"]},{"name":"stdout","output_type":"stream","text":["Epoch 0: 0%| | 0/2540 [00:00 17\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataloader\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 20\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(moving_average(model\u001b[38;5;241m.\u001b[39mtrain_log_error, \u001b[38;5;241m100\u001b[39m), label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:520\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 518\u001b[0m model \u001b[38;5;241m=\u001b[39m _maybe_unwrap_optimized(model)\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m_lightning_module \u001b[38;5;241m=\u001b[39m model\n\u001b[0;32m--> 520\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 521\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[1;32m 522\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:44\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[0;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[1;32m 47\u001b[0m _call_teardown_hook(trainer)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:559\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_connector\u001b[38;5;241m.\u001b[39mattach_data(\n\u001b[1;32m 550\u001b[0m model, train_dataloaders\u001b[38;5;241m=\u001b[39mtrain_dataloaders, val_dataloaders\u001b[38;5;241m=\u001b[39mval_dataloaders, datamodule\u001b[38;5;241m=\u001b[39mdatamodule\n\u001b[1;32m 551\u001b[0m )\n\u001b[1;32m 553\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[1;32m 554\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[1;32m 555\u001b[0m ckpt_path,\n\u001b[1;32m 556\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 557\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 558\u001b[0m )\n\u001b[0;32m--> 559\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 561\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[1;32m 562\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:935\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[1;32m 932\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 933\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[1;32m 934\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m--> 935\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 937\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 938\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[1;32m 939\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m 940\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:978\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 976\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[1;32m 977\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[0;32m--> 978\u001b[0m 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\u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 133\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py:218\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_batch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mlightning_module\u001b[38;5;241m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 217\u001b[0m \u001b[38;5;66;03m# in automatic optimization, there can only be one optimizer\u001b[39;00m\n\u001b[0;32m--> 218\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautomatic_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 219\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 220\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmanual_optimization\u001b[38;5;241m.\u001b[39mrun(kwargs)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:185\u001b[0m, in \u001b[0;36m_AutomaticOptimization.run\u001b[0;34m(self, optimizer, kwargs)\u001b[0m\n\u001b[1;32m 178\u001b[0m closure()\n\u001b[1;32m 180\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;66;03m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 182\u001b[0m \u001b[38;5;66;03m# ------------------------------\u001b[39;00m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;66;03m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 185\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbatch_idx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 187\u001b[0m result \u001b[38;5;241m=\u001b[39m closure\u001b[38;5;241m.\u001b[39mconsume_result()\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:261\u001b[0m, in \u001b[0;36m_AutomaticOptimization._optimizer_step\u001b[0;34m(self, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_ready()\n\u001b[1;32m 260\u001b[0m \u001b[38;5;66;03m# model hook\u001b[39;00m\n\u001b[0;32m--> 261\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_lightning_module_hook\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 262\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 263\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moptimizer_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 264\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcurrent_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 265\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_idx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_step_and_backward_closure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m should_accumulate:\n\u001b[1;32m 271\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_progress\u001b[38;5;241m.\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mstep\u001b[38;5;241m.\u001b[39mincrement_completed()\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:142\u001b[0m, in \u001b[0;36m_call_lightning_module_hook\u001b[0;34m(trainer, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 139\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m hook_name\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LightningModule]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpl_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 142\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 145\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/core/module.py:1265\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_closure)\u001b[0m\n\u001b[1;32m 1226\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimizer_step\u001b[39m(\n\u001b[1;32m 1227\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1228\u001b[0m epoch: \u001b[38;5;28mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1231\u001b[0m optimizer_closure: Optional[Callable[[], Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 1232\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1233\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;124;03m Override this method to adjust the default way the :class:`~pytorch_lightning.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1235\u001b[0m \u001b[38;5;124;03m the optimizer.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1263\u001b[0m \u001b[38;5;124;03m pg[\"lr\"] = lr_scale * self.learning_rate\u001b[39;00m\n\u001b[1;32m 1264\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1265\u001b[0m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimizer_closure\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py:158\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MisconfigurationException(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen `optimizer.step(closure)` is called, the closure should be callable\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 158\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_strategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_on_after_step()\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m step_output\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py:224\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;66;03m# TODO(fabric): remove assertion once strategy's optimizer_step typing is fixed\u001b[39;00m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, pl\u001b[38;5;241m.\u001b[39mLightningModule)\n\u001b[0;32m--> 224\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprecision_plugin\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimizer_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:114\u001b[0m, in \u001b[0;36mPrecisionPlugin.optimizer_step\u001b[0;34m(self, optimizer, model, closure, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Hook to run the optimizer step.\"\"\"\u001b[39;00m\n\u001b[1;32m 113\u001b[0m closure \u001b[38;5;241m=\u001b[39m partial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_closure, model, optimizer, closure)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moptimizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclosure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/optim/optimizer.py:280\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs),\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 280\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 283\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/optim/optimizer.py:33\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 32\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefaults[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdifferentiable\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m---> 33\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 35\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(prev_grad)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/optim/adam.py:121\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m closure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39menable_grad():\n\u001b[0;32m--> 121\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_groups:\n\u001b[1;32m 124\u001b[0m params_with_grad \u001b[38;5;241m=\u001b[39m []\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:101\u001b[0m, in \u001b[0;36mPrecisionPlugin._wrap_closure\u001b[0;34m(self, model, optimizer, closure)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_wrap_closure\u001b[39m(\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 91\u001b[0m model: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpl.LightningModule\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 92\u001b[0m optimizer: Optimizer,\n\u001b[1;32m 93\u001b[0m closure: Callable[[], Any],\n\u001b[1;32m 94\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 95\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"This double-closure allows makes sure the ``closure`` is executed before the\u001b[39;00m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;124;03m ``on_before_optimizer_step`` hook is called.\u001b[39;00m\n\u001b[1;32m 97\u001b[0m \n\u001b[1;32m 98\u001b[0m \u001b[38;5;124;03m The closure (generally) runs ``backward`` so this allows inspecting gradients in this hook. This structure is\u001b[39;00m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;124;03m consistent with the ``PrecisionPlugin`` subclasses that cannot pass ``optimizer.step(closure)`` directly.\u001b[39;00m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 101\u001b[0m closure_result \u001b[38;5;241m=\u001b[39m \u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_after_closure(model, optimizer)\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m closure_result\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:140\u001b[0m, in \u001b[0;36mClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Tensor]:\n\u001b[0;32m--> 140\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclosure\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\u001b[38;5;241m.\u001b[39mloss\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:126\u001b[0m, in \u001b[0;36mClosure.closure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclosure\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ClosureResult:\n\u001b[0;32m--> 126\u001b[0m step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m step_output\u001b[38;5;241m.\u001b[39mclosure_loss \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarning_cache\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`training_step` returned `None`. If this was on purpose, ignore this warning...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py:308\u001b[0m, in \u001b[0;36m_AutomaticOptimization._training_step\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\n\u001b[1;32m 307\u001b[0m \u001b[38;5;66;03m# manually capture logged metrics\u001b[39;00m\n\u001b[0;32m--> 308\u001b[0m training_step_output \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtraining_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mpost_training_step()\n\u001b[1;32m 311\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_result_cls\u001b[38;5;241m.\u001b[39mfrom_training_step_output(training_step_output, trainer\u001b[38;5;241m.\u001b[39maccumulate_grad_batches)\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:288\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[0;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 288\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 290\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 291\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py:366\u001b[0m, in \u001b[0;36mStrategy.training_step\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprecision_plugin\u001b[38;5;241m.\u001b[39mtrain_step_context():\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, TrainingStep)\n\u001b[0;32m--> 366\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","Cell \u001b[0;32mIn[12], line 31\u001b[0m, in \u001b[0;36mCBOWModel.training_step\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m 29\u001b[0m target \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(target)\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 30\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m(context)\n\u001b[0;32m---> 31\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCrossEntropyLoss\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_log_error\u001b[38;5;241m.\u001b[39mappend(loss\u001b[38;5;241m.\u001b[39mitem())\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/nn/modules/loss.py:1174\u001b[0m, in \u001b[0;36mCrossEntropyLoss.forward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m 1173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor, target: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m-> 1174\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcross_entropy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1175\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1176\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabel_smoothing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel_smoothing\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/Developer/miniconda3/envs/pytorch_env/lib/python3.10/site-packages/torch/nn/functional.py:3029\u001b[0m, in \u001b[0;36mcross_entropy\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)\u001b[0m\n\u001b[1;32m 3027\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size_average \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m reduce \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3028\u001b[0m reduction \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[0;32m-> 3029\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcross_entropy_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_Reduction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_enum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreduction\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_smoothing\u001b[49m\u001b[43m)\u001b[49m\n","\u001b[0;31mRuntimeError\u001b[0m: MPS backend out of memory (MPS allocated: 7.13 GB, other allocations: 1.86 GB, max allowed: 9.07 GB). Tried to allocate 1.73 GB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure)."]}],"source":["ratings_med_df, token_mapping = convert_movies_to_tokens(ratings_med_df)\n","display(ratings_med_df.head(4))\n","\n","sequences = get_sequences_from_df(ratings_med_df)\n","print(sequences[0][:10])\n","\n","vocab_size = ratings_med_df[\"movieId\"].unique().count()\n","window_size = 1\n","\n","# Create dataset and dataloader\n","dataset = CBOWDataset(sequences, window_size)\n","dataloader = DataLoader(dataset, batch_size=64, shuffle=False, collate_fn=collate_fn)\n","\n","embedding_dim = 20\n","model = CBOWModel(vocab_size, embedding_dim)\n","trainer = pytl.Trainer(max_epochs=1)\n","trainer.fit(model, dataloader)\n","\n","fig, ax = plt.subplots(figsize=(6, 4))\n","ax.plot(moving_average(model.train_log_error, 100), label=\"train\")\n","ax.set_title(f\"Training error\")\n","ax.set_xlabel(\"Batches\")\n","ax.set_ylabel(\"LL\")\n","ax.legend()\n","fig.show()\n","\n","embedding_matrix = model.embeddings.weight\n","similarities = calculate_cosine_similarity(embedding_matrix.cpu()).detach().numpy()\n","similarities[np.triu_indices(similarities.shape[0], k=1)] = np.nan\n","\n","labels = token_mapping.join(names_df, on=\"movieId\").sort(\"token\")[\"title\"].to_list()\n","\n","fig, ax = plt.subplots(figsize=(10, 10))\n","sns.heatmap(\n"," data=similarities,\n"," ax=ax,\n"," xticklabels=labels,\n"," yticklabels=labels,\n",")\n","fig.show()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"ename":"NotImplementedError","evalue":"","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m\n","\u001b[0;31mNotImplementedError\u001b[0m: "]}],"source":["raise NotImplementedError"]}],"metadata":{"kernelspec":{"display_name":"pytorch_env","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":"3.10.10"},"orig_nbformat":2},"nbformat":4,"nbformat_minor":2} diff --git a/paper_list.md b/paper_list.md index abfa938..9f5438b 100644 --- a/paper_list.md +++ b/paper_list.md @@ -243,4 +243,10 @@ Read 2024/03 * Using a learning rate decay of $\alpha_t=\alpha/\sqrt{t}$ is common. * Results * Performs similarly to SGD with momentum on dense MNIST - * Performas similarly to AdaGrad on sparse problems (best of both worlds) \ No newline at end of file + * Performas similarly to AdaGrad on sparse problems (best of both worlds) + +### The LambdaLoss Framework for Ranking Metric Optimization [2018] +https://dl.acm.org/doi/10.1145/3269206.3271784 +Read 2024/03 +* Summary + * Formulates a general framework for optimise learn to rank problems \ No newline at end of file diff --git a/unfinished/GaussianProcesses/gaussian_processes_from_scratch.ipynb b/unfinished/GaussianProcesses/gaussian_processes_from_scratch.ipynb new file mode 100644 index 0000000..6e464ff --- /dev/null +++ b/unfinished/GaussianProcesses/gaussian_processes_from_scratch.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Exploration of Gaussian processes\n","\n"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["import numpy as np\n","\n","import matplotlib.pyplot as plt\n","plt.style.use(\"seaborn-v0_8-whitegrid\")\n","\n","rng = np.random.RandomState(0)"]},{"cell_type":"markdown","metadata":{},"source":["\n","https://en.wikipedia.org/wiki/Radial_basis_function_kernel\n","\n","$$\n","K(\\mathbf {x} ,\\mathbf {x'} )=\\exp \\left(-{\\frac {\\|\\mathbf {x} -\\mathbf {x'} \\|^{2}}{2\\sigma ^{2}}}\\right)\n","$$\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["def rbf_kernel(X1, X2, length_scale=1.0, variance=1.0):\n"," sqdist = np.sum(X1**2, 1).reshape(-1, 1) + np.sum(X2**2, 1) - 2 * np.dot(X1, X2.T)\n"," return variance * np.exp(-0.5 / length_scale**2 * sqdist)\n"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[[1.]]\n","[[0.60653066]]\n","[[0.36787944]]\n"]}],"source":["print(rbf_kernel(X1=np.array([[1, 2]]), X2=np.array([[1, 2]])))\n","print(rbf_kernel(X1=np.array([[0, 0]]), X2=np.array([[0, 1]])))\n","print(rbf_kernel(X1=np.array([[0, 0]]), X2=np.array([[1, 1]])))"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["class GaussianProcessRegressor:\n"," def __init__(self, kernel, noise=1e-10):\n"," self.kernel = kernel\n"," self.noise = noise\n"," \n"," def fit(self, X_train, y_train):\n"," self.X_train = X_train\n"," self.y_train = y_train\n"," self.K = self.kernel(X_train, X_train) + self.noise * np.eye(len(X_train))\n"," self.L = np.linalg.cholesky(self.K)\n"," self.alpha = np.linalg.solve(self.L.T, np.linalg.solve(self.L, y_train))\n"," \n"," def predict(self, X_test):\n"," K_s = self.kernel(self.X_train, X_test)\n"," K_ss = self.kernel(X_test, X_test) + self.noise * np.eye(len(X_test))\n"," \n"," # Mean of the predictive distribution\n"," mu_s = K_s.T.dot(self.alpha)\n"," \n"," # Covariance of the predictive distribution\n"," v = np.linalg.solve(self.L, K_s)\n"," cov_s = K_ss - v.T.dot(v)\n"," \n"," return mu_s, cov_s\n"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["X_train = np.random.uniform(-5, 5, 10).reshape(-1, 1)\n","y_train = np.sin(X_train) + 0.1 * np.random.randn(10, 1)\n","\n","X_test = np.linspace(-5, 5, 100).reshape(-1, 1)\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["gp = GaussianProcessRegressor(kernel=rbf_kernel)\n","gp.fit(X_train, y_train)\n"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["mu_s, cov_s = gp.predict(X_test)\n","std_s = np.sqrt(np.diag(cov_s))\n"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(10, 6))\n","plt.plot(X_train, y_train, 'ro', label='Training Data')\n","plt.plot(X_test, np.sin(X_test), 'b-', label='True Function')\n","plt.plot(X_test, mu_s, 'k-', label='GP Mean')\n","plt.fill_between(X_test.ravel(), mu_s.ravel() - 1.96 * std_s, mu_s.ravel() + 1.96 * std_s, alpha=0.2, label='95% Confidence Interval')\n","plt.legend()\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"project_env","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":"3.11.0"},"orig_nbformat":2},"nbformat":4,"nbformat_minor":2}