From 3cc735536f69d77a769b2b898e95a5074ec1f32a Mon Sep 17 00:00:00 2001 From: Fengdi Guo Date: Fri, 19 Jan 2024 08:53:13 -0500 Subject: [PATCH 1/3] feat: upload playaround dataset [PDS-463] --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 78a0cc4e..3f931222 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,4 @@ -/datasets/*[!.py] +# /datasets/*[!.py] .ipynb_checkpoints __pycache__ training/* From 82518dc226c0c9cb086523989caefd17f96aca17 Mon Sep 17 00:00:00 2001 From: Fengdi Guo Date: Fri, 19 Jan 2024 08:56:41 -0500 Subject: [PATCH 2/3] feat: add data [PDS-463] --- datasets/UCR/Car/Car_TEST.tsv | 60 ++++++++++++++++++++++++++++++++++ datasets/UCR/Car/Car_TRAIN.tsv | 60 ++++++++++++++++++++++++++++++++++ datasets/UCR/Car/README.md | 13 ++++++++ 3 files changed, 133 insertions(+) create mode 100755 datasets/UCR/Car/Car_TEST.tsv create mode 100755 datasets/UCR/Car/Car_TRAIN.tsv create mode 100755 datasets/UCR/Car/README.md diff 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1.0570681 1.093044 1.1230553 1.1572828 1.1933688 1.2219413 1.1803826 1.196855 1.2305737 1.2659446 1.2978241 1.3336782 1.3241417 1.3241417 diff --git a/datasets/UCR/Car/README.md b/datasets/UCR/Car/README.md new file mode 100755 index 00000000..93f345a0 --- /dev/null +++ b/datasets/UCR/Car/README.md @@ -0,0 +1,13 @@ +# Car + +Train size: 60 + +Test size: 60 + +Missing value: No + +Number of classses: 4 + +Time series length: 577 + +Data donated by Jean Gao. \ No newline at end of file From d7fc09e6b1bc079f6aeafbbe4d851958f35406ef Mon Sep 17 00:00:00 2001 From: fd-guo Date: Wed, 21 Feb 2024 19:02:57 +0000 Subject: [PATCH 3/3] feat: add validation during training [PDS-488] Add validation processing --- models/encoder.py | 4 +- ts2vec.py | 72 ++++++++++- ts2vec_backup.py | 319 ++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 388 insertions(+), 7 deletions(-) create mode 100644 ts2vec_backup.py diff --git a/models/encoder.py b/models/encoder.py index cedb13b0..9f924f3c 100644 --- a/models/encoder.py +++ b/models/encoder.py @@ -24,7 +24,7 @@ def generate_binomial_mask(B, T, p=0.5): return torch.from_numpy(np.random.binomial(1, p, size=(B, T))).to(torch.bool) class TSEncoder(nn.Module): - def __init__(self, input_dims, output_dims, hidden_dims=64, depth=10, mask_mode='binomial'): + def __init__(self, input_dims, output_dims, hidden_dims=64, kernel_size=3, depth=10, mask_mode='binomial'): super().__init__() self.input_dims = input_dims self.output_dims = output_dims @@ -34,7 +34,7 @@ def __init__(self, input_dims, output_dims, hidden_dims=64, depth=10, mask_mode= self.feature_extractor = DilatedConvEncoder( hidden_dims, [hidden_dims] * depth + [output_dims], - kernel_size=3 + kernel_size=kernel_size ) self.repr_dropout = nn.Dropout(p=0.1) diff --git a/ts2vec.py b/ts2vec.py index de909bd5..f5418ba6 100644 --- a/ts2vec.py +++ b/ts2vec.py @@ -15,6 +15,7 @@ def __init__( input_dims, output_dims=320, hidden_dims=64, + kernel_size=3, depth=10, device='cuda', lr=0.001, @@ -47,7 +48,7 @@ def __init__( self.max_train_length = max_train_length self.temporal_unit = temporal_unit - self._net = TSEncoder(input_dims=input_dims, output_dims=output_dims, hidden_dims=hidden_dims, depth=depth).to(self.device) + self._net = TSEncoder(input_dims=input_dims, output_dims=output_dims, hidden_dims=hidden_dims, kernel_size=kernel_size, depth=depth).to(self.device) self.net = torch.optim.swa_utils.AveragedModel(self._net) self.net.update_parameters(self._net) @@ -57,7 +58,7 @@ def __init__( self.n_epochs = 0 self.n_iters = 0 - def fit(self, train_data, n_epochs=None, n_iters=None, verbose=False): + def fit(self, train_data, val_data, n_epochs=None, n_iters=None, verbose=False): ''' Training the TS2Vec model. Args: @@ -91,11 +92,13 @@ def fit(self, train_data, n_epochs=None, n_iters=None, verbose=False): optimizer = torch.optim.AdamW(self._net.parameters(), lr=self.lr) loss_log = [] + loss_val_log = [] while True: if n_epochs is not None and self.n_epochs >= n_epochs: break - + + # Train -------------------------------------------------------------------------------- cum_loss = 0 n_epoch_iters = 0 @@ -150,14 +153,73 @@ def fit(self, train_data, n_epochs=None, n_iters=None, verbose=False): cum_loss /= n_epoch_iters loss_log.append(cum_loss) + + # calculate loss on validation data + loss_val = self.evaluate(val_data) + loss_val_log.append(loss_val) if verbose: - print(f"Epoch #{self.n_epochs}: loss={cum_loss}") + print(f"Epoch #{self.n_epochs}: train loss={cum_loss}") + print(f"Epoch #{self.n_epochs}: val loss={loss_val}") + print(" ") self.n_epochs += 1 if self.after_epoch_callback is not None: self.after_epoch_callback(self, cum_loss) - return loss_log + return loss_log, loss_val_log + + def evaluate(self, val_data): + ''' Evaluate the TS2Vec model on validation data. + + Args: + val_data (numpy.ndarray): The validation data. It should have a shape of (n_instance, n_timestamps, n_features). All missing data should be set to NaN. + + Returns: + loss_val: validation loss. + ''' + assert val_data.ndim == 3 + self.net.eval() + + temporal_missing = np.isnan(val_data).all(axis=-1).any(axis=0) + if temporal_missing[0] or temporal_missing[-1]: + val_data = centerize_vary_length_series(val_data) + + val_data = val_data[~np.isnan(val_data).all(axis=2).all(axis=1)] + val_dataset = TensorDataset(torch.from_numpy(val_data).to(torch.float)) + val_loader = DataLoader(val_dataset, batch_size=min(self.batch_size, len(val_dataset)), shuffle=True, drop_last=True) + + cum_loss_val=0 + with torch.no_grad(): + for batch in val_loader: + x = batch[0] + if self.max_train_length is not None and x.size(1) > self.max_train_length: + window_offset = np.random.randint(x.size(1) - self.max_train_length + 1) + x = x[:, window_offset : window_offset + self.max_train_length] + x = x.to(self.device) + + ts_l = x.size(1) + crop_l = np.random.randint(low=2 ** (self.temporal_unit + 1), high=ts_l+1) + crop_left = np.random.randint(ts_l - crop_l + 1) + crop_right = crop_left + crop_l + crop_eleft = np.random.randint(crop_left + 1) + crop_eright = np.random.randint(low=crop_right, high=ts_l + 1) + crop_offset = np.random.randint(low=-crop_eleft, high=ts_l - crop_eright + 1, size=x.size(0)) + + out1 = self._net(take_per_row(x, crop_offset + crop_eleft, crop_right - crop_eleft)) + out1 = out1[:, -crop_l:] + + out2 = self._net(take_per_row(x, crop_offset + crop_left, crop_eright - crop_left)) + out2 = out2[:, :crop_l] + + loss_val = hierarchical_contrastive_loss( + out1, + out2, + temporal_unit=self.temporal_unit + ) + + cum_loss_val += loss_val.item() + + return cum_loss_val def _eval_with_pooling(self, x, mask=None, slicing=None, encoding_window=None): out = self.net(x.to(self.device, non_blocking=True), mask) diff --git a/ts2vec_backup.py b/ts2vec_backup.py new file mode 100644 index 00000000..de909bd5 --- /dev/null +++ b/ts2vec_backup.py @@ -0,0 +1,319 @@ +import torch +import torch.nn.functional as F +from torch.utils.data import TensorDataset, DataLoader +import numpy as np +from models import TSEncoder +from models.losses import hierarchical_contrastive_loss +from utils import take_per_row, split_with_nan, centerize_vary_length_series, torch_pad_nan +import math + +class TS2Vec: + '''The TS2Vec model''' + + def __init__( + self, + input_dims, + output_dims=320, + hidden_dims=64, + depth=10, + device='cuda', + lr=0.001, + batch_size=16, + max_train_length=None, + temporal_unit=0, + after_iter_callback=None, + after_epoch_callback=None + ): + ''' Initialize a TS2Vec model. + + Args: + input_dims (int): The input dimension. For a univariate time series, this should be set to 1. + output_dims (int): The representation dimension. + hidden_dims (int): The hidden dimension of the encoder. + depth (int): The number of hidden residual blocks in the encoder. + device (int): The gpu used for training and inference. + lr (int): The learning rate. + batch_size (int): The batch size. + max_train_length (Union[int, NoneType]): The maximum allowed sequence length for training. For sequence with a length greater than , it would be cropped into some sequences, each of which has a length less than . + temporal_unit (int): The minimum unit to perform temporal contrast. When training on a very long sequence, this param helps to reduce the cost of time and memory. + after_iter_callback (Union[Callable, NoneType]): A callback function that would be called after each iteration. + after_epoch_callback (Union[Callable, NoneType]): A callback function that would be called after each epoch. + ''' + + super().__init__() + self.device = device + self.lr = lr + self.batch_size = batch_size + self.max_train_length = max_train_length + self.temporal_unit = temporal_unit + + self._net = TSEncoder(input_dims=input_dims, output_dims=output_dims, hidden_dims=hidden_dims, depth=depth).to(self.device) + self.net = torch.optim.swa_utils.AveragedModel(self._net) + self.net.update_parameters(self._net) + + self.after_iter_callback = after_iter_callback + self.after_epoch_callback = after_epoch_callback + + self.n_epochs = 0 + self.n_iters = 0 + + def fit(self, train_data, n_epochs=None, n_iters=None, verbose=False): + ''' Training the TS2Vec model. + + Args: + train_data (numpy.ndarray): The training data. It should have a shape of (n_instance, n_timestamps, n_features). All missing data should be set to NaN. + n_epochs (Union[int, NoneType]): The number of epochs. When this reaches, the training stops. + n_iters (Union[int, NoneType]): The number of iterations. When this reaches, the training stops. If both n_epochs and n_iters are not specified, a default setting would be used that sets n_iters to 200 for a dataset with size <= 100000, 600 otherwise. + verbose (bool): Whether to print the training loss after each epoch. + + Returns: + loss_log: a list containing the training losses on each epoch. + ''' + assert train_data.ndim == 3 + + if n_iters is None and n_epochs is None: + n_iters = 200 if train_data.size <= 100000 else 600 # default param for n_iters + + if self.max_train_length is not None: + sections = train_data.shape[1] // self.max_train_length + if sections >= 2: + train_data = np.concatenate(split_with_nan(train_data, sections, axis=1), axis=0) + + temporal_missing = np.isnan(train_data).all(axis=-1).any(axis=0) + if temporal_missing[0] or temporal_missing[-1]: + train_data = centerize_vary_length_series(train_data) + + train_data = train_data[~np.isnan(train_data).all(axis=2).all(axis=1)] + + train_dataset = TensorDataset(torch.from_numpy(train_data).to(torch.float)) + train_loader = DataLoader(train_dataset, batch_size=min(self.batch_size, len(train_dataset)), shuffle=True, drop_last=True) + + optimizer = torch.optim.AdamW(self._net.parameters(), lr=self.lr) + + loss_log = [] + + while True: + if n_epochs is not None and self.n_epochs >= n_epochs: + break + + cum_loss = 0 + n_epoch_iters = 0 + + interrupted = False + for batch in train_loader: + if n_iters is not None and self.n_iters >= n_iters: + interrupted = True + break + + x = batch[0] + if self.max_train_length is not None and x.size(1) > self.max_train_length: + window_offset = np.random.randint(x.size(1) - self.max_train_length + 1) + x = x[:, window_offset : window_offset + self.max_train_length] + x = x.to(self.device) + + ts_l = x.size(1) + crop_l = np.random.randint(low=2 ** (self.temporal_unit + 1), high=ts_l+1) + crop_left = np.random.randint(ts_l - crop_l + 1) + crop_right = crop_left + crop_l + crop_eleft = np.random.randint(crop_left + 1) + crop_eright = np.random.randint(low=crop_right, high=ts_l + 1) + crop_offset = np.random.randint(low=-crop_eleft, high=ts_l - crop_eright + 1, size=x.size(0)) + + optimizer.zero_grad() + + out1 = self._net(take_per_row(x, crop_offset + crop_eleft, crop_right - crop_eleft)) + out1 = out1[:, -crop_l:] + + out2 = self._net(take_per_row(x, crop_offset + crop_left, crop_eright - crop_left)) + out2 = out2[:, :crop_l] + + loss = hierarchical_contrastive_loss( + out1, + out2, + temporal_unit=self.temporal_unit + ) + + loss.backward() + optimizer.step() + self.net.update_parameters(self._net) + + cum_loss += loss.item() + n_epoch_iters += 1 + + self.n_iters += 1 + + if self.after_iter_callback is not None: + self.after_iter_callback(self, loss.item()) + + if interrupted: + break + + cum_loss /= n_epoch_iters + loss_log.append(cum_loss) + if verbose: + print(f"Epoch #{self.n_epochs}: loss={cum_loss}") + self.n_epochs += 1 + + if self.after_epoch_callback is not None: + self.after_epoch_callback(self, cum_loss) + + return loss_log + + def _eval_with_pooling(self, x, mask=None, slicing=None, encoding_window=None): + out = self.net(x.to(self.device, non_blocking=True), mask) + if encoding_window == 'full_series': + if slicing is not None: + out = out[:, slicing] + out = F.max_pool1d( + out.transpose(1, 2), + kernel_size = out.size(1), + ).transpose(1, 2) + + elif isinstance(encoding_window, int): + out = F.max_pool1d( + out.transpose(1, 2), + kernel_size = encoding_window, + stride = 1, + padding = encoding_window // 2 + ).transpose(1, 2) + if encoding_window % 2 == 0: + out = out[:, :-1] + if slicing is not None: + out = out[:, slicing] + + elif encoding_window == 'multiscale': + p = 0 + reprs = [] + while (1 << p) + 1 < out.size(1): + t_out = F.max_pool1d( + out.transpose(1, 2), + kernel_size = (1 << (p + 1)) + 1, + stride = 1, + padding = 1 << p + ).transpose(1, 2) + if slicing is not None: + t_out = t_out[:, slicing] + reprs.append(t_out) + p += 1 + out = torch.cat(reprs, dim=-1) + + else: + if slicing is not None: + out = out[:, slicing] + + return out.cpu() + + def encode(self, data, mask=None, encoding_window=None, causal=False, sliding_length=None, sliding_padding=0, batch_size=None): + ''' Compute representations using the model. + + Args: + data (numpy.ndarray): This should have a shape of (n_instance, n_timestamps, n_features). All missing data should be set to NaN. + mask (str): The mask used by encoder can be specified with this parameter. This can be set to 'binomial', 'continuous', 'all_true', 'all_false' or 'mask_last'. + encoding_window (Union[str, int]): When this param is specified, the computed representation would the max pooling over this window. This can be set to 'full_series', 'multiscale' or an integer specifying the pooling kernel size. + causal (bool): When this param is set to True, the future informations would not be encoded into representation of each timestamp. + sliding_length (Union[int, NoneType]): The length of sliding window. When this param is specified, a sliding inference would be applied on the time series. + sliding_padding (int): This param specifies the contextual data length used for inference every sliding windows. + batch_size (Union[int, NoneType]): The batch size used for inference. If not specified, this would be the same batch size as training. + + Returns: + repr: The representations for data. + ''' + assert self.net is not None, 'please train or load a net first' + assert data.ndim == 3 + if batch_size is None: + batch_size = self.batch_size + n_samples, ts_l, _ = data.shape + + org_training = self.net.training + self.net.eval() + + dataset = TensorDataset(torch.from_numpy(data).to(torch.float)) + loader = DataLoader(dataset, batch_size=batch_size) + + with torch.no_grad(): + output = [] + for batch in loader: + x = batch[0] + if sliding_length is not None: + reprs = [] + if n_samples < batch_size: + calc_buffer = [] + calc_buffer_l = 0 + for i in range(0, ts_l, sliding_length): + l = i - sliding_padding + r = i + sliding_length + (sliding_padding if not causal else 0) + x_sliding = torch_pad_nan( + x[:, max(l, 0) : min(r, ts_l)], + left=-l if l<0 else 0, + right=r-ts_l if r>ts_l else 0, + dim=1 + ) + if n_samples < batch_size: + if calc_buffer_l + n_samples > batch_size: + out = self._eval_with_pooling( + torch.cat(calc_buffer, dim=0), + mask, + slicing=slice(sliding_padding, sliding_padding+sliding_length), + encoding_window=encoding_window + ) + reprs += torch.split(out, n_samples) + calc_buffer = [] + calc_buffer_l = 0 + calc_buffer.append(x_sliding) + calc_buffer_l += n_samples + else: + out = self._eval_with_pooling( + x_sliding, + mask, + slicing=slice(sliding_padding, sliding_padding+sliding_length), + encoding_window=encoding_window + ) + reprs.append(out) + + if n_samples < batch_size: + if calc_buffer_l > 0: + out = self._eval_with_pooling( + torch.cat(calc_buffer, dim=0), + mask, + slicing=slice(sliding_padding, sliding_padding+sliding_length), + encoding_window=encoding_window + ) + reprs += torch.split(out, n_samples) + calc_buffer = [] + calc_buffer_l = 0 + + out = torch.cat(reprs, dim=1) + if encoding_window == 'full_series': + out = F.max_pool1d( + out.transpose(1, 2).contiguous(), + kernel_size = out.size(1), + ).squeeze(1) + else: + out = self._eval_with_pooling(x, mask, encoding_window=encoding_window) + if encoding_window == 'full_series': + out = out.squeeze(1) + + output.append(out) + + output = torch.cat(output, dim=0) + + self.net.train(org_training) + return output.numpy() + + def save(self, fn): + ''' Save the model to a file. + + Args: + fn (str): filename. + ''' + torch.save(self.net.state_dict(), fn) + + def load(self, fn): + ''' Load the model from a file. + + Args: + fn (str): filename. + ''' + state_dict = torch.load(fn, map_location=self.device) + self.net.load_state_dict(state_dict) +