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fix-embedding-notebook (#307)
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Co-authored-by: Egor Baturin <egoriyaa@github.com>
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@egoriyaa
egoriyaa and Egor Baturin committed May 7, 2024
1 parent d604429 commit 97515b9
Showing 1 changed file with 88 additions and 111 deletions.
199 changes: 88 additions & 111 deletions examples/210-embedding_models.ipynb
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"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Disabling SSL verification. Connections to this server are not verified and may be insecure!\n",
"Global seed set to 42\n"
]
},
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"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[0;31mSignature:\u001b[0m\n",
"\u001b[0mTSTCCEmbeddingModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mn_epochs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mlambda1\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mlambda2\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'TSTCCEmbeddingModel'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mDocstring:\u001b[0m\n",
"Fit TSTCC embedding model.\n",
"\n",
"Parameters\n",
"----------\n",
"x:\n",
" data with shapes (n_segments, n_timestamps, input_dims).\n",
"n_epochs:\n",
" The number of epochs. When this reaches, the training stops.\n",
"lr:\n",
" The learning rate.\n",
"temperature:\n",
" Temperature in NTXentLoss.\n",
"lambda1:\n",
" The relative weight of the first item in the loss (temporal contrasting loss).\n",
"lambda2:\n",
" The relative weight of the second item in the loss (contextual contrasting loss).\n",
"verbose:\n",
" Whether to print the training loss after each epoch.\n",
"\u001b[0;31mFile:\u001b[0m /workdir/src/etna/etna/transforms/embeddings/models/tstcc.py\n",
"\u001b[0;31mType:\u001b[0m function\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"?TSTCCEmbeddingModel.fit"
]
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"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 33.3s\n",
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]
}
],
"source": [
"emb_model = TSTCCEmbeddingModel(input_dims=1, tc_hidden_dim=16, depth=3, output_dims=6, device=\"cuda\")\n",
"import torch\n",
"\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"emb_model = TSTCCEmbeddingModel(input_dims=1, tc_hidden_dim=16, depth=3, output_dims=6, device=device)\n",
"training_params = {\"n_epochs\": 10}\n",
"smape_score = forecast_with_segment_embeddings(emb_model, training_params)"
]
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"name": "stdout",
"output_type": "stream",
"text": [
"SMAPE: 14.18648029957201\n"
"SMAPE: 14.214904390075835\n"
]
}
],
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"name": "stderr",
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]
}
],
"source": [
"emb_model = TS2VecEmbeddingModel(input_dims=1, hidden_dims=16, depth=3, output_dims=6, device=\"cuda\")\n",
"emb_model = TS2VecEmbeddingModel(input_dims=1, hidden_dims=16, depth=3, output_dims=6, device=device)\n",
"training_params = {\"n_epochs\": 10}\n",
"smape_score = forecast_with_segment_embeddings(emb_model, training_params)"
]
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"name": "stdout",
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"text": [
"SMAPE: 13.620591044127748\n"
"SMAPE: 13.549340740762041\n"
]
}
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"name": "stderr",
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]
}
],
"source": [
"emb_model = TSTCCEmbeddingModel(input_dims=1, tc_hidden_dim=16, depth=3, output_dims=6, device=\"cuda\")\n",
"emb_model = TSTCCEmbeddingModel(input_dims=1, tc_hidden_dim=16, depth=3, output_dims=6, device=device)\n",
"training_params = {\"n_epochs\": 10}\n",
"smape_score = forecast_with_window_embeddings(emb_model, training_params)"
]
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"name": "stdout",
"output_type": "stream",
"text": [
"SMAPE: 123.02843183461513\n"
"SMAPE: 104.68988621650867\n"
]
}
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"name": "stderr",
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]
}
],
"source": [
"emb_model = TS2VecEmbeddingModel(input_dims=1, hidden_dims=16, depth=3, output_dims=6, device=\"cuda\")\n",
"emb_model = TS2VecEmbeddingModel(input_dims=1, hidden_dims=16, depth=3, output_dims=6, device=device)\n",
"training_params = {\"n_epochs\": 10}\n",
"smape_score = forecast_with_window_embeddings(emb_model, training_params)"
]
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"name": "stdout",
"output_type": "stream",
"text": [
"SMAPE: 27.93511865651414\n"
"SMAPE: 29.776520212845234\n"
]
}
],
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