From ecad7381a27e936a080a2c7582ac31fdca755c40 Mon Sep 17 00:00:00 2001 From: Maxim Divilkovskiy Date: Thu, 23 May 2024 18:26:36 +0300 Subject: [PATCH] Final presentation --- .../code/ckpt_s100.pth | Bin 0 -> 471050 bytes .../code/lib_score_based_model.ipynb | 1122 +++++++++++++++++ ...out.tfevents.1716300730.sizzziy-pc.24193.0 | Bin 0 -> 88 bytes ...out.tfevents.1716300744.sizzziy-pc.24193.1 | Bin 0 -> 3164284 bytes ...ut.tfevents.1716302058.sizzziy-pc.214066.0 | Bin 0 -> 3164284 bytes ....out.tfevents.1716384282.sizzziy-pc.7729.0 | Bin 0 -> 59239 bytes ....out.tfevents.1716384329.sizzziy-pc.7729.1 | Bin 0 -> 125816 bytes ....out.tfevents.1716384357.sizzziy-pc.7729.2 | Bin 0 -> 267789 bytes ....out.tfevents.1716384536.sizzziy-pc.7729.3 | Bin 0 -> 1991667 bytes .../paper/figures/NonIdeal.png | Bin 0 -> 45634 bytes .../paper/figures/NonIdealBetter.png | Bin 0 -> 38622 bytes .../Divilkovskiy2024Presentation_ru.pdf | Bin 1120436 -> 1135047 bytes .../Divilkovskiy2024Presentation_ru.tex | 68 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zIXw1wci8>819BQxvPMlC96fGKr#}enT{W1`NgTUNW&w~k;%D)k5^}W#AJcQH|pTf^VEw(gI#rIod zsr%ivXngDr*r|}XE`ArxP{_cO1)pF_&uAgsa43Fk?1cHRT=4h&SLB9oz(Z5wW5%uI z35|!S&dMHw5~i?1vKB8$*TC|*ZLq23ApDZ=3U7v5p`Geix-(6MUF8h 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict()\n", + "Cell \u001b[0;32mIn[7], line 188\u001b[0m, in \u001b[0;36mScoreBasedModel.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 186\u001b[0m avg_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.\u001b[39m\n\u001b[1;32m 187\u001b[0m num_items \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 188\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata_loader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/utils/data/dataloader.py:439\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterator\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\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[43m_get_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/utils/data/dataloader.py:387\u001b[0m, in \u001b[0;36mDataLoader._get_iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_worker_number_rationality()\n\u001b[0;32m--> 387\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_MultiProcessingDataLoaderIter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.11/site-packages/torch/utils/data/dataloader.py:1022\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter.__init__\u001b[0;34m(self, loader)\u001b[0m\n\u001b[1;32m 1019\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_workers \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 1020\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_workers):\n\u001b[1;32m 1021\u001b[0m \u001b[38;5;66;03m# No certainty which module multiprocessing_context is\u001b[39;00m\n\u001b[0;32m-> 1022\u001b[0m index_queue \u001b[38;5;241m=\u001b[39m \u001b[43mmultiprocessing_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mQueue\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[var-annotated]\u001b[39;00m\n\u001b[1;32m 1023\u001b[0m \u001b[38;5;66;03m# Need to `cancel_join_thread` here!\u001b[39;00m\n\u001b[1;32m 1024\u001b[0m \u001b[38;5;66;03m# See sections (2) and (3b) above.\u001b[39;00m\n\u001b[1;32m 1025\u001b[0m index_queue\u001b[38;5;241m.\u001b[39mcancel_join_thread()\n", + "File \u001b[0;32m/usr/lib/python3.11/multiprocessing/context.py:103\u001b[0m, in \u001b[0;36mBaseContext.Queue\u001b[0;34m(self, maxsize)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''Returns a queue object'''\u001b[39;00m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mqueues\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Queue\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Queue(maxsize, ctx\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m/usr/lib/python3.11/multiprocessing/context.py:237\u001b[0m, in \u001b[0;36mDefaultContext.get_context\u001b[0;34m(self, method)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_default_context \u001b[38;5;241m=\u001b[39m context\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_actual_context \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 237\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_actual_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "model = ScoreBasedModel(time_series_data, N=0.8, window_size=3000)\n", + "model.train()\n", + "pred = model.predict()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def calc_mu(X):\n", + " T = X.shape[0]\n", + " N = X.shape[1]\n", + " res = np.zeros(N)\n", + " for t in range(T):\n", + " res += X[t]\n", + " return res / T\n", + "\n", + "def calc_sigma(X):\n", + " T = X.shape[0]\n", + " N = X.shape[1]\n", + " mu = calc_mu(X)\n", + " res = np.zeros((N, N))\n", + " for t in range(T):\n", + " res += np.outer(X[t] - mu, (X[t] - mu).T)\n", + " return res / T\n", + "\n", + "def calc_alternative_answer(X):\n", + " T = X.shape[0]\n", + " return 2 / (T - 1) * np.sum(X[:-1], axis=0) - X[-1]\n", + "\n", + "def project_to_psd_cone(A):\n", + " eigvals, eigvecs = np.linalg.eigh(A)\n", + " eigvals[eigvals < 0] = 0\n", + " A_psd = np.dot(eigvecs, np.dot(np.diag(eigvals), eigvecs.T))\n", + " return A_psd\n", + "\n", + "def get_two_answers(T, X_, SigmaT1):\n", + " SigmaT1 = project_to_psd_cone(SigmaT1)\n", + " SigmaT = calc_sigma(X_)\n", + " muT = calc_mu(X_)\n", + " A = (T/(T+1) * SigmaT - SigmaT1) * (T+1)**2/T\n", + " U, S, Vh = np.linalg.svd(A, full_matrices=False)\n", + " return (-np.sqrt(S[0]) * U.T[0].reshape(3, 1) + muT.reshape(3, 1)).T, (np.sqrt(S[0]) * U.T[0].reshape(3, 1) + muT.reshape(3, 1)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": { + "id": "XGCqrCiwFsLM" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 9971, 30)\n", + "(9971, 3, 3)\n", + "(9971, 1, 3, 3)\n", + "- - - - -\n", + "(1994, 1, 3, 3)\n", + "(1, 3, 3)\n", + "[[[49.58680466 48.88134113 49.13710343]\n", + " [48.88134113 49.80956365 49.7015127 ]\n", + " [49.13710343 49.7015127 51.54845223]]]\n", + "[[[52.65282894 51.53439021 51.64469314]\n", + " [51.53439021 52.10009989 51.83780833]\n", + " [51.64469314 51.83780833 53.38147626]]]\n", + "[[[53.80685298 52.17695173 53.2247633 ]\n", + " [52.17695173 52.2476886 52.90142121]\n", + " [53.2247633 52.90142121 55.38410835]]]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "N = 0.8\n", + "num_series = time_series_data.shape[1]\n", + "X = get_distance_matrix(time_series_data, window_size=30)\n", + "X = X.reshape(X.shape[0], 1, X.shape[1], X.shape[2])\n", + "print(X.shape)\n", + "N = int(N * len(X))\n", + "X_train, Y_train, X_test, Y_test = X[:N], X[1:N+1], X[N:-1], X[N+1:]\n", + "\n", + "plt.plot(time_series_data['Sine_1'][:30])\n", + "\n", + "print('- - - - -')\n", + "print(pred.shape)\n", + "print(X_test[1].shape)\n", + "print(X_test[0])\n", + "print(X_test[1])\n", + "print(X_test[2])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", 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