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* Implement TS2VecModel (#253) * add ts2vec model * delete unnecessary utils * add multiscale mode * revert to common encode in model class * lints * reformat save method, add _is_fitted attr * fix embeddings shapes * fix * one more fix * pass numpy array to fit * add tests checking nans in embeddings * update changelog --------- Co-authored-by: Egor Baturin <egoriyaa@github.com> * Implement EmbeddingSegmentTransform and EmbeddingWindowTransform (#265) * add transforms * update changelog * fix ts2vec tests * fix * update rst, encoding_params * fix * fix * fix * fix docstring * add training_params * add freeze method * fix inference tests * lints * fix lisence * fix lisence, fix docs * fix quotes --------- Co-authored-by: Egor Baturin <egoriyaa@github.com> * Implement TSTCC (#294) * add tstcc * add einops package * remove pd.testing in inference tests * fix * add verbose param, refactor logging, fix warning * fix logging loss * add changelog * catch torch warning * fix * catch nn.Conv1d warning --------- Co-authored-by: Egor Baturin <egoriyaa@github.com> * lints * fix * Add tutorial how to work with embedding models (#304) * fix tstcc * move lr param from __init__ to fit * add tutorial * fix notebook * update changelog * fix changelog * lints * fix notebook * update readme * fix readme * fix readme * write comment in libs/ts2vec/ts2vec.py * fix notebook * remove multiscale option in ts2vec * lints * fix notebook --------- Co-authored-by: Egor Baturin <egoriyaa@github.com> * fix atol in inference tests * downgrade poetry --------- Co-authored-by: Egor Baturin <egoriyaa@github.com>
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| from etna.libs.ts2vec.ts2vec import TS2Vec |
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| """ | ||
| MIT License | ||
| Copyright (c) 2022 Zhihan Yue | ||
| Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| of this software and associated documentation files (the "Software"), to deal | ||
| in the Software without restriction, including without limitation the rights | ||
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
| copies of the Software, and to permit persons to whom the Software is | ||
| furnished to do so, subject to the following conditions: | ||
| The above copyright notice and this permission notice shall be included in all | ||
| copies or substantial portions of the Software. | ||
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| SOFTWARE. | ||
| """ | ||
| # Note: Copied from ts2vec repository (https://github.com/yuezhihan/ts2vec/tree/main) | ||
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| import torch | ||
| from torch import nn | ||
| import torch.nn.functional as F | ||
| import numpy as np | ||
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| class SamePadConv(nn.Module): | ||
| def __init__(self, in_channels, out_channels, kernel_size, dilation=1, groups=1): | ||
| super().__init__() | ||
| self.receptive_field = (kernel_size - 1) * dilation + 1 | ||
| padding = self.receptive_field // 2 | ||
| self.conv = nn.Conv1d( | ||
| in_channels, out_channels, kernel_size, | ||
| padding=padding, | ||
| dilation=dilation, | ||
| groups=groups | ||
| ) | ||
| self.remove = 1 if self.receptive_field % 2 == 0 else 0 | ||
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| def forward(self, x): | ||
| out = self.conv(x) | ||
| if self.remove > 0: | ||
| out = out[:, :, : -self.remove] | ||
| return out | ||
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| class ConvBlock(nn.Module): | ||
| def __init__(self, in_channels, out_channels, kernel_size, dilation, final=False): | ||
| super().__init__() | ||
| self.conv1 = SamePadConv(in_channels, out_channels, kernel_size, dilation=dilation) | ||
| self.conv2 = SamePadConv(out_channels, out_channels, kernel_size, dilation=dilation) | ||
| self.projector = nn.Conv1d(in_channels, out_channels, 1) if in_channels != out_channels or final else None | ||
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| def forward(self, x): | ||
| residual = x if self.projector is None else self.projector(x) | ||
| x = F.gelu(x) | ||
| x = self.conv1(x) | ||
| x = F.gelu(x) | ||
| x = self.conv2(x) | ||
| return x + residual | ||
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| class DilatedConvEncoder(nn.Module): | ||
| def __init__(self, in_channels, channels, kernel_size): | ||
| super().__init__() | ||
| self.net = nn.Sequential(*[ | ||
| ConvBlock( | ||
| channels[i - 1] if i > 0 else in_channels, | ||
| channels[i], | ||
| kernel_size=kernel_size, | ||
| dilation=2 ** i, | ||
| final=(i == len(channels) - 1) | ||
| ) | ||
| for i in range(len(channels)) | ||
| ]) | ||
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| def forward(self, x): | ||
| return self.net(x) |
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