├── .gitignore ├── LICENSE ├── README.md ├── analysis.py ├── data └── livelo.npy ├── data_loader └── timeseries_class.py ├── main.py ├── model ├── lstm.py ├── mlp.py └── time2vec.py └── test.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # Distribution / packaging 7 | .Python 8 | build/ 9 | develop-eggs/ 10 | dist/ 11 | downloads/ 12 | eggs/ 13 | .eggs/ 14 | lib/ 15 | lib64/ 16 | parts/ 17 | sdist/ 18 | var/ 19 | wheels/ 20 | pip-wheel-metadata/ 21 | share/python-wheels/ 22 | *.egg-info/ 23 | .installed.cfg 24 | *.egg 25 | MANIFEST 26 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # pytorch_time2vec 2 | 3 | This repository contains a Pytorch implementation of the Time2Vec Algorithm [1]. It 4 | makes use of the Punta Salute 2009 dataset (historical levels of water in Venice) 5 | as in [2]. The dataset shows the hourly water level (cm). Furthermore, we have 6 | followed the analysis provided in [2] and thus a Bootstrapping script can be 7 | found in the current repository. 8 | 9 | 10 | ## Code organization 11 | 12 | - **main.py** - Implements a simple time series prediction training and testing example 13 | on the Livelo dataset. 14 | - **analysis.py** Implements a Bootstrapping and estimates the distributions of 15 | the samples on the test dataset. 16 | - **model/network.py** Implements an LSTM and an LSTM equipped with a Time2Vec layer 17 | - **model/time2vec** Implents the Time2Vec layer 18 | - **data/livelo.npy** Raw dataset of Punta Salute 2009 19 | 20 | 21 | ## Example of usage 22 | The time2vec layer can be used at will. In this repository, we provide a simple 23 | example of how to use it along with an LSTM to predict the hourly water level 24 | of Venice based on historical data (2009). 25 | 26 | To run the simple LSTM model type in: 27 | ``` 28 | $ python (or python3 depending on your system`s configuration) main.py --model lstm 29 | ``` 30 | 31 | And to run the T2V-LSTM: 32 | ``` 33 | $ python main.py --model tv-lstm 34 | ``` 35 | 36 | When you run either of the two aforementioned examples the script will store 37 | the results of the test prediction to the directory **results** (you will need 38 | to create this directory before running the main.py script). 39 | 40 | If you'd like to run the Bootstrapping to get the distributions of the 41 | test predictions you can run the **analysis.py** script (this script requires 42 | the results of the test predictions for both LSTM and T2V-LSTM models). 43 | ``` 44 | $ python analysis.py 45 | ``` 46 | You can control more parameters such as the number of epochs or batches and 47 | the length of the learned sequence by passing command line arguments to the 48 | **main.py** script. 49 | 50 | 51 | ## Requirements 52 | - Python 3 53 | - Numpy 54 | - Matplotlib 55 | - Pytorch 56 | 57 | 58 | ## Platform specifications 59 | The current implementation has been tested and ran on the following software 60 | configuration: 61 | - GCC 8.3.0 62 | - Ubuntu Linux 5.3.0-40-generic 63 | - Python 3.6.9 64 | - Numpy 1.18.2 65 | - Matplotlib 3.1.1 66 | - Pytorch 1.4.0 (No GPU) 67 | 68 | 69 | ## References 70 | 71 | [1] "Time2Vec: Learning a vector representation of time", Kazemi et al., 2019 72 | 73 | [2] [Time2Vec for Time Series features encoding](https://towardsdatascience.com/time2vec-for-time-series-features-encoding-a03a4f3f937e) 74 | -------------------------------------------------------------------------------- /analysis.py: -------------------------------------------------------------------------------- 1 | # Results analysis script 2 | # This script is based on the implementation provided by Marco Cerliani 3 | # https://towardsdatascience.com/time2vec-for-time-series-features-encoding-a03a4f3f937e 4 | # Copyright (C) 2020 Georgios Is. Detorakis (gdetor@protonmail.com) 5 | 6 | # This program is free software: you can redistribute it and/or modify 7 | # it under the terms of the GNU General Public License as published 8 | # by the Free Software Foundation, either version 3 of the License, or 9 | # (at your option) any later version. 10 | 11 | # This program is distributed in the hope that it will be useful, 12 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 13 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 14 | # GNU General Public License for more details. 15 | 16 | # You should have received a copy of the GNU General Public License 17 | # along with this program. If not, see . 18 | import numpy as np 19 | import matplotlib.pylab as plt 20 | 21 | 22 | def bootstraping(x): 23 | """ 24 | Bootstrapping (random sampling with replacement) to estimate the sampling 25 | distribution of x (input data). 26 | 27 | Args: 28 | x (ndarray): Input data 29 | 30 | Returns: 31 | Bootstrapped sample distribution as Nump array. 32 | """ 33 | sample = [] 34 | for _ in range(1000): 35 | sample_mean = np.random.choice(x, 100).mean() 36 | sample.append(sample_mean) 37 | return np.array(sample) 38 | 39 | 40 | if __name__ == '__main__': 41 | y_lstm = np.load("./results/lstm_prediction.npy")[:, :, 0].flatten() 42 | y_tvlstm = np.load("./results/tvlstm_prediction.npy")[:, :, 0].flatten() 43 | 44 | sample_lstm = [] 45 | for _ in range(1000): 46 | sample_mean = np.random.choice(y_lstm, 100).mean() 47 | sample_lstm.append(sample_mean) 48 | 49 | sample_tvlstm = [] 50 | for _ in range(1000): 51 | sample_mean = np.random.choice(y_tvlstm, 100).mean() 52 | sample_tvlstm.append(sample_mean) 53 | 54 | # Estimate the quantiles of the Bootstraped distributions in order to 55 | # check for distributions overlapping. 56 | print("LSTM Quantile: %f" % (np.quantile(sample_lstm, 0.9))) 57 | print("T2VLSTM Quantile: %f" % (np.quantile(sample_tvlstm, 0.1))) 58 | 59 | # Plot prediction distributions 60 | fig = plt.figure(figsize=(7, 7)) 61 | ax = fig.add_subplot(111) 62 | ax.hist(y_tvlstm, bins=40, alpha=0.5, color='k', label="T2VLSTM") 63 | ax.hist(y_lstm, bins=40, alpha=0.5, color='m', label="LSTM") 64 | ax.legend() 65 | ax.grid() 66 | plt.show() 67 | 68 | # Plot bootstraped prediction distributions 69 | fig = plt.figure(figsize=(7, 7)) 70 | ax = fig.add_subplot(111) 71 | ax.hist(sample_tvlstm, bins=40, alpha=0.5, color='k', label="T2VLSTM") 72 | ax.hist(sample_lstm, bins=40, alpha=0.5, color='m', label="LSTM") 73 | ax.legend() 74 | ax.grid() 75 | plt.show() 76 | -------------------------------------------------------------------------------- /data/livelo.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/gdetor/pytorch_time2vec/c0ca0fb96b14b961d7a97009a2348ee5141c6cec/data/livelo.npy -------------------------------------------------------------------------------- /data_loader/timeseries_class.py: -------------------------------------------------------------------------------- 1 | # Time series Pytorch DataLoader Class 2 | # Copyright (C) 2020 Georgios Is. Detorakis (gdetor@protonmail.com) 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published 6 | # by the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | from numpy import load, float32, random, expand_dims 17 | from numpy import nan_to_num, log, abs, sign, isnan, count_nonzero 18 | from torch import from_numpy, is_tensor 19 | from sklearn.preprocessing import MinMaxScaler, StandardScaler 20 | from torch.utils.data import Dataset 21 | 22 | 23 | from scipy.stats import boxcox 24 | 25 | 26 | def mu_law(x, mu=255): 27 | """ Compute the mu law (companding algorithm). It reduces the dynamic 28 | range of the input signal x. This can be used in cases where the original 29 | signal x has, for instance, 16-bit integer values and we need to pass it 30 | through a softmax layer (as in Wavenet) to get all possible probabilities 31 | [65536 (2**16)]. Passing the signal through mu_law it will encode 32 | (compress) the values to 256 (if mu=255). 33 | """ 34 | mulaw = sign(x) * log(1 + mu * abs(x)) / log(1 + mu) 35 | return mulaw 36 | 37 | 38 | class timeseries(Dataset): 39 | """ Timeseries Class. This class preprocesses timeseries data and provides 40 | a Pytorch DataLoader for training and testing models. 41 | """ 42 | def __init__(self, fname, win_size=10, horizon=1, dim_size=1, scale=False, 43 | ab=(0, 1), standarize=False, power_transform=False, 44 | train=True, noise=False, var=1.0, mulaw=False): 45 | """ 46 | Constructor method of timeseries class. 47 | 48 | Args: 49 | fname (string): Raw data filename (must be numpy file-npy) 50 | win_size (int): Window size (chunk of timeseries) 51 | horizon (int): Prediction horizon (default=1) 52 | dim_size (int): (Features dimension or number of variables in 53 | case of multivariable timeseries) 54 | scale (bool): Scale the raw data 55 | ab (floats tuple): Interval to scale the raw data 56 | standarize (bool): Standarize the raw data 57 | power_transform (bool): Apply a power transform to the raw data 58 | train (bool): True to split the raw data into two sets 59 | (80% of the raw data for training) 60 | noise (bool): Add white noise to the data 61 | var (float): Variance of the additive white noise (noise 62 | argument should be True) 63 | mulaw (bool): Apply a mu-law companding algorithm 64 | 65 | Returns: 66 | 67 | """ 68 | self.horizon = horizon 69 | # Load the data 70 | data = load(fname).astype(float32) 71 | if data.ndim != 1: 72 | data = data[:, :dim_size] 73 | 74 | # Check for NaNs 75 | if count_nonzero(isnan(data)): 76 | print("WARNING: NaN detected in the raw data!") 77 | 78 | # Remove NaNs 79 | data = nan_to_num(data, nan=0.0).astype(float32) 80 | 81 | perc = int(data.shape[0] * 0.7) 82 | if train is True: 83 | data = data[:perc] 84 | else: 85 | data = data[perc:] 86 | self.shape = data.shape # Keep data shape 87 | self.win_len = win_size # Prediction horizon 88 | 89 | # Add white noise to the data 90 | if noise is True: 91 | data += random.normal(0, var, data.shape) 92 | 93 | # Convert data Numpy array to Torch Tensor 94 | self.data = from_numpy(data) 95 | 96 | # Ensure the data are positive when Box-Cox transform is enabled 97 | if scale is False and power_transform is True: 98 | scale = True 99 | 100 | # Scale the data [0, 1] 101 | if scale: 102 | scaler = MinMaxScaler(feature_range=(ab[0], ab[1]), copy=True) 103 | if len(self.data.shape) == 1: 104 | self.data = scaler.fit_transform(self.data.reshape(-1, 1)) 105 | self.data = self.data[:, 0] 106 | else: 107 | self.data = scaler.fit_transform(self.data) 108 | self.data = self.data.astype(float32) 109 | 110 | # Standarize the data (x - mu) / sigma 111 | if standarize is True: 112 | standarizer = StandardScaler() 113 | if len(self.data.shape) == 1: 114 | self.data = standarizer.fit_transform(self.data.reshape(-1, 1)) 115 | self.data = self.data[:, 0] 116 | else: 117 | self.data = standarizer.fit_transform(self.data) 118 | self.data = self.data.astype(float32) 119 | 120 | # Apply a Box-Cox (power) transform 121 | if power_transform is True: 122 | self.data, lamda = boxcox(self.data.flatten()) 123 | self.data = self.data.reshape(self.shape) 124 | 125 | if mulaw is True: 126 | self.data = mu_law(self.data) 127 | 128 | # Final data tensor length 129 | self.size = len(self.data) - (win_size + 1) 130 | 131 | def __len__(self): 132 | """ Return the length of data. """ 133 | return len(self.data) 134 | 135 | def __getitem__(self, idx): 136 | """ Get an item from data. Slide the window based on the horizon. 137 | """ 138 | if is_tensor(idx): 139 | idx = idx.tolist() 140 | idx %= self.size 141 | x = self.data[idx:idx+self.win_len] 142 | if self.horizon == 1: 143 | y = self.data[idx+self.win_len] 144 | else: 145 | y = self.data[idx+1:idx+self.win_len+1] 146 | if self.data.ndim == 1: 147 | x = expand_dims(x, axis=1) 148 | y = expand_dims(y, axis=0) 149 | return x, y, idx 150 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # Example of how to use Time2Vec Pytorch implementation. 2 | # Copyright (C) 2020 Georgios Is. Detorakis (gdetor@protonmail.com) 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published 6 | # by the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | import sys 17 | import argparse 18 | from numpy import array, save 19 | 20 | import torch 21 | from torch import nn, device, no_grad, manual_seed, backends 22 | from torch.optim import Adam 23 | from torch.utils.data import DataLoader 24 | 25 | from data_loader.timeseries_class import timeseries 26 | from model.lstm import T2VLSTM, LSTM 27 | from model.mlp import MLP, T2VMLP 28 | 29 | import matplotlib.pylab as plt 30 | 31 | backends.cudnn.deterministic = True 32 | backends.cudnn.benchmark = False 33 | 34 | 35 | manual_seed(135) 36 | 37 | 38 | def run_experiment(model, data_path, sequence_length, epochs, batch_size, 39 | cdevice): 40 | dev = device(cdevice) 41 | 42 | ts = timeseries(data_path, win_size=sequence_length, 43 | scale=False, standarize=False, train=True) 44 | dataloader = DataLoader(ts, batch_size=batch_size, shuffle=False, 45 | drop_last=True) 46 | 47 | if model == 'tvlstm': 48 | print("Time2Vec - LSTM") 49 | net = T2VLSTM(128, 1, 32, 1, sequence_length, dev=dev).to(dev) 50 | elif model == 'lstm': 51 | print("LSTM") 52 | net = LSTM(1, 32, 1, sequence_length, dev=dev).to(dev) 53 | elif model == 'mlp': 54 | print("MLP") 55 | net = MLP(seq_len=sequence_length).to(dev) 56 | elif model == 'tvmlp': 57 | print("T2VMLP") 58 | net = T2VMLP(seq_len=sequence_length, tv_dim=100, dev=dev).to(dev) 59 | print(net) 60 | else: 61 | print("Model is not specified!") 62 | sys.exit(-1) 63 | 64 | optimizer = Adam(net.parameters(), lr=0.001) 65 | criterion = nn.MSELoss() 66 | 67 | print("Start training the model ... ") 68 | length_data = len(dataloader) 69 | loss_track = [] 70 | for e in range(epochs): 71 | total_loss = 0 72 | for x, y, _ in dataloader: 73 | x = x.to(dev) 74 | y = y.to(dev) 75 | 76 | optimizer.zero_grad() 77 | 78 | y_hat = net(x) 79 | loss = criterion(y_hat, y) 80 | 81 | loss.backward() 82 | optimizer.step() 83 | total_loss += loss.item() 84 | loss_track.append(total_loss / length_data) 85 | print("[Epoch: %d Total Loss: %f]" % (e, loss_track[e])) 86 | 87 | fig = plt.figure() 88 | ax = fig.add_subplot(111) 89 | ax.plot(loss_track, 'k', lw=2) 90 | ax.set_xlabel("Epochs", fontsize=16) 91 | ax.set_ylabel("Loss", fontsize=16) 92 | plt.show() 93 | 94 | print("Testing the trained model ...") 95 | ts = timeseries(data_path, win_size=sequence_length, 96 | scale=False, standarize=False, train=False) 97 | dataloader = DataLoader(ts, batch_size=batch_size, shuffle=False, 98 | drop_last=True) 99 | 100 | net.eval() 101 | with no_grad(): 102 | error = 0 103 | y_pred = [] 104 | for x, y, _ in dataloader: 105 | x = x.to(dev) 106 | y = y.to(dev) 107 | 108 | y_hat = net(x) 109 | tmp_error = nn.functional.l1_loss(y_hat, y) 110 | error += tmp_error.item() 111 | y_pred.append(y_hat.detach().cpu().numpy()) 112 | error /= len(dataloader) 113 | print(error) 114 | if model == 'tvlstm': 115 | save("results/tvlstm_prediction", array(y_pred)) 116 | elif model == 'lstm': 117 | save("results/lstm_prediction", array(y_pred)) 118 | elif model == 'mlp': 119 | save("results/mlp_prediction", array(y_pred)) 120 | elif model == 'tvmlp': 121 | save("results/tvmlp_prediction", array(y_pred)) 122 | else: 123 | print("Nothing stored!") 124 | 125 | 126 | if __name__ == '__main__': 127 | parser = argparse.ArgumentParser(description="Time2Vec Pytorch Impl") 128 | parser.add_argument('--epochs', 129 | type=int, 130 | default=100, 131 | help='number of epochs (default: 100)') 132 | parser.add_argument('--sequence-len', 133 | type=int, 134 | default=23, 135 | help='historical data points') 136 | parser.add_argument('--batch-size', 137 | type=int, 138 | default=16, 139 | help='batch size') 140 | parser.add_argument('--device', 141 | type=str, 142 | default='cuda:0', 143 | help="computational device (GPU or CPU)") 144 | parser.add_argument('--model', 145 | type=str, 146 | default='tvlstm', 147 | help='model type (lstm, tvlstm, mlp, tvmlp)') 148 | parser.add_argument('--data-path', 149 | type=str, 150 | default='./data/livelo.npy', 151 | help='the path where the input data can be found') 152 | args = parser.parse_args() 153 | 154 | if args.device == "cuda:0" and torch.cuda.is_available() is not True: 155 | print("There is no CUDA device available!") 156 | print("Fallback to CPU ... ") 157 | args.device = "cpu" 158 | 159 | run_experiment(args.model, 160 | args.data_path, 161 | args.sequence_len, 162 | args.epochs, 163 | args.batch_size, 164 | args.device) 165 | -------------------------------------------------------------------------------- /model/lstm.py: -------------------------------------------------------------------------------- 1 | # LSTM and T2V-LSTM classes 2 | # Copyright (C) 2020 Georgios Is. Detorakis (gdetor@protonmail.com) 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published 6 | # by the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | from torch import nn, randn 17 | from .time2vec import Time2Vec 18 | 19 | 20 | def init_weights(layer): 21 | """ 22 | Initialize weights using Xavier's method (uniform). 23 | 24 | Args: 25 | layer (torch object): Torch Layer 26 | 27 | Returns: 28 | 29 | """ 30 | for w in layer._all_weights: 31 | for p in w: 32 | if 'weight' in p: 33 | nn.init.xavier_uniform_(layer.__getattr__(p).data) 34 | 35 | 36 | class T2VLSTM(nn.Module): 37 | """ 38 | LSTM class equipped with Time2Vec layer. The input is firstly passed to 39 | the Time2Vec layer and then is forwarded to the LSTM. 40 | """ 41 | def __init__(self, t2v_size, input_size, hidden_size, num_layers, 42 | seq_len=1, dev='cpu'): 43 | """ 44 | Constructor of T2VLSTM class 45 | 46 | Args: 47 | t2v_size (int): Number of units in Time2Vec layer 48 | input_size (int): Input size of LSTM 49 | hidden_size (int): Number of units per hidden layer of LSTM 50 | num_layers (int): Number of LSTM layers 51 | seq_len (int): Sequence length 52 | dev (torch device): CPU or GPU device 53 | 54 | Returns: 55 | 56 | """ 57 | super(T2VLSTM, self).__init__() 58 | self.n_layers = num_layers 59 | self.hidden_dim = hidden_size 60 | self.dev = dev 61 | 62 | # Time2Vec Layer 63 | self.t2v = Time2Vec(seq_len, t2v_size, dev=self.dev) 64 | # LSTM 65 | self.lstm = nn.LSTM(t2v_size+1, hidden_size, 1, batch_first=True) 66 | # FC layer 67 | self.fc = nn.Linear(hidden_size * seq_len, 1) 68 | 69 | # Initialize all layers 70 | nn.init.xavier_uniform_(self.t2v.W) 71 | nn.init.xavier_uniform_(self.t2v.W0) 72 | nn.init.uniform_(self.t2v.b0, -0.01, 0.01) 73 | nn.init.uniform_(self.t2v.b, -0.01, 0.01) 74 | nn.init.xavier_uniform_(self.fc.weight) 75 | 76 | # LSTM Initialization - Weights and Bias 77 | for layer in self.lstm._all_weights: 78 | for p in layer: 79 | if 'weight' in p: 80 | nn.init.xavier_uniform_(self.lstm.__getattr__(p).data) 81 | 82 | for names in self.lstm._all_weights: 83 | for name in filter(lambda n: "bias" in n, names): 84 | bias = self.lstm.__getattr__(name) 85 | n = bias.size(0) 86 | start, end = n//4, n//2 87 | bias.data[start:end].fill_(1.) 88 | # Flag is used for initializing the states of LSTM (h0, c0) 89 | self.flag = 0 90 | 91 | def forward(self, x): 92 | """ 93 | Forward method of T2VLSTM class. 94 | 95 | Args: 96 | x (tensor): Input tensor (batch_size, sequence_lengh, features_dim) 97 | 98 | Returns: 99 | Tensor of size (batch_size, sequence_lengh, features_dim) 100 | """ 101 | batch_size = x.shape[0] 102 | # Initialize LSTM states 103 | if self.flag == 0: 104 | self.h0 = nn.Parameter(randn(self.n_layers*1, batch_size, 105 | self.hidden_dim), 106 | requires_grad=True).to(self.dev) 107 | self.c0 = nn.Parameter(randn(self.n_layers*1, batch_size, 108 | self.hidden_dim), 109 | requires_grad=True).to(self.dev) 110 | self.flag = 1 111 | # Pass the input signal through Time2Vec layer 112 | out = self.t2v(x) 113 | out, (self.h, self.c) = self.lstm(out, (self.h0, self.c0)) 114 | m, n = out.shape[1], out.shape[2] 115 | out = out.reshape(-1, m * n) 116 | out = self.fc(out) 117 | return out 118 | 119 | 120 | class LSTM(nn.Module): 121 | """ 122 | Standard LSTM class 123 | """ 124 | def __init__(self, input_size, hidden_size, num_layers, seq_len=1, 125 | dev='cpu'): 126 | """ 127 | Constructor of LSTM class 128 | 129 | Args: 130 | input_size (int): Input size of LSTM 131 | hidden_size (int): Number of units per hidden layer of LSTM 132 | num_layers (int): Number of LSTM layers 133 | seq_len (int): Sequence length 134 | dev (torch device): CPU or GPU device 135 | 136 | Returns: 137 | 138 | """ 139 | super(LSTM, self).__init__() 140 | self.n_layers = num_layers 141 | self.hidden_dim = hidden_size 142 | self.dev = dev 143 | 144 | # Define LSTM 145 | self.lstm = nn.LSTM(input_size, hidden_size, num_layers, 146 | batch_first=True) 147 | # Define a FC layer 148 | self.fc = nn.Linear(hidden_size * seq_len, 1) 149 | 150 | # Initialize FC layer weights 151 | nn.init.xavier_uniform_(self.fc.weight) 152 | 153 | # Initialize LSTM's weights and biases 154 | for layer in self.lstm._all_weights: 155 | for p in layer: 156 | if 'weight' in p: 157 | nn.init.xavier_uniform_(self.lstm.__getattr__(p).data) 158 | 159 | for names in self.lstm._all_weights: 160 | for name in filter(lambda n: "bias" in n, names): 161 | bias = self.lstm.__getattr__(name) 162 | n = bias.size(0) 163 | start, end = n//4, n//2 164 | bias.data[start:end].fill_(1.) 165 | # Flag is used for initializing the states of LSTM (h0, c0) 166 | self.flag = 0 167 | 168 | def forward(self, x): 169 | """ 170 | Forward method of LSTM class. 171 | 172 | Args: 173 | x (tensor): Input tensor (batch_size, sequence_lengh, features_dim) 174 | 175 | Returns: 176 | Tensor of size (batch_size, sequence_lengh, features_dim) 177 | """ 178 | batch_size = x.shape[0] 179 | # Initialize LSTM states 180 | if self.flag == 0: 181 | self.h0 = nn.Parameter(randn(self.n_layers*1, batch_size, 182 | self.hidden_dim), 183 | requires_grad=True).to(self.dev) 184 | self.c0 = nn.Parameter(randn(self.n_layers*1, batch_size, 185 | self.hidden_dim), 186 | requires_grad=True).to(self.dev) 187 | self.flag = 1 188 | out, (self.h, self.c) = self.lstm(x, (self.h0, self.c0)) 189 | m, n = out.shape[1], out.shape[2] 190 | out = out.reshape(-1, m*n) 191 | out = self.fc(out) 192 | return out 193 | -------------------------------------------------------------------------------- /model/mlp.py: -------------------------------------------------------------------------------- 1 | from torch import nn 2 | from model.time2vec import Time2Vec 3 | 4 | 5 | class MLP(nn.Module): 6 | """ 7 | Multi-Layer Perceptron class for time series forecasting. 8 | """ 9 | def __init__(self, seq_len=1): 10 | """ 11 | Constructor of MLP class. 12 | 13 | Args: 14 | seq_len (int): Sequence length 15 | tv_dim (int): Time2Vec dimension 16 | 17 | Returns: 18 | 19 | """ 20 | super(MLP, self).__init__() 21 | 22 | self.fc1 = nn.Linear(seq_len, 64) 23 | self.bn1 = nn.BatchNorm1d(64) 24 | self.fc2 = nn.Linear(64, 8) 25 | self.bn2 = nn.BatchNorm1d(8) 26 | self.fc3 = nn.Linear(8, 1) 27 | 28 | self.relu = nn.ReLU() 29 | self.tanh = nn.Tanh() 30 | 31 | def forward(self, x): 32 | """ 33 | Forward method of MLP class. 34 | 35 | Args: 36 | x (torch tensor): The input sequence (sequence length x number 37 | of features) 38 | 39 | Returns: 40 | A pytorch tensor that contains the prediction. 41 | """ 42 | x = x.view(-1, x.shape[1] * x.shape[2]) 43 | out = self.relu(self.fc1(x)) 44 | out = self.bn1(out) 45 | out = self.relu(self.fc2(out)) 46 | out = self.bn2(out) 47 | out = self.tanh(self.fc3(out)) 48 | return out 49 | 50 | 51 | class T2VMLP(nn.Module): 52 | """ 53 | Multi-Layer Perceptron class for time series forecasting. 54 | """ 55 | def __init__(self, seq_len=1, tv_dim=100, dev="cuda:0"): 56 | """ 57 | Constructor of MLP class. 58 | 59 | Args: 60 | seq_len (int): Sequence length 61 | tv_dim (int): Time2Vec dimension 62 | 63 | Returns: 64 | 65 | """ 66 | super(T2VMLP, self).__init__() 67 | 68 | self.t2v = Time2Vec(seq_len, tv_dim, dev=dev) 69 | self.fc1 = nn.Linear((tv_dim+1)*seq_len, 64) 70 | self.bn1 = nn.BatchNorm1d(64) 71 | self.fc2 = nn.Linear(64, 8) 72 | self.bn2 = nn.BatchNorm1d(8) 73 | self.fc3 = nn.Linear(8, 1) 74 | 75 | self.relu = nn.ReLU() 76 | self.tanh = nn.Tanh() 77 | 78 | def forward(self, x): 79 | """ 80 | Forward method of MLP class. 81 | 82 | Args: 83 | x (torch tensor): The input sequence 84 | (batch_size, seq_length, number of features) 85 | 86 | Returns: 87 | A pytorch tensor that contains the prediction. 88 | """ 89 | out = self.relu(self.t2v(x)) 90 | out = out.reshape(-1, out.shape[1] * out.shape[2]) 91 | out = self.relu(self.fc1(out)) 92 | out = self.bn1(out) 93 | out = self.relu(self.fc2(out)) 94 | out = self.bn2(out) 95 | out = self.tanh(self.fc3(out)) 96 | return out 97 | -------------------------------------------------------------------------------- /model/time2vec.py: -------------------------------------------------------------------------------- 1 | # time2vec class 2 | # Copyright (C) 2020 Georgios Is. Detorakis (gdetor@protonmail.com) 3 | 4 | # This program is free software: you can redistribute it and/or modify 5 | # it under the terms of the GNU General Public License as published 6 | # by the Free Software Foundation, either version 3 of the License, or 7 | # (at your option) any later version. 8 | 9 | # This program is distributed in the hope that it will be useful, 10 | # but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | # GNU General Public License for more details. 13 | 14 | # You should have received a copy of the GNU General Public License 15 | # along with this program. If not, see . 16 | 17 | from torch import nn, cat, device 18 | from torch import bmm, sin, rand 19 | 20 | 21 | class Time2Vec(nn.Linear): 22 | """ 23 | time2vec_layer class implements a Time2Vec layer based on the work: 24 | [1] "Time2Vec: Learning a Vector Representation of Time", 25 | Kazemi et al., 2019. 26 | """ 27 | def __init__(self, in_features, out_features, bias=True, func=sin, 28 | dev="cuda:0", const=None, init_vals=[-0.05, 0.05]): 29 | """ 30 | Constructor of time2vec_layer. 31 | 32 | Note: Currently the parameters are initialized either as constants 33 | or randomly drawn from uniform distribution. If the end-user needs 34 | a different initialization distribution they can replace the 35 | distributions at lines: 63, 70, 81, 89. 36 | 37 | Args: 38 | in_features (int): Number of input features 39 | out_features (int): Number of output features 40 | bias (bool): True to add bias - False to not add bias 41 | term. 42 | func (object): Torch periodic function (sin, cos, etc) 43 | dev (str): Computational Device (GPU or CPU) 44 | const (float): If a number is provided the parameters are 45 | initialized with constants values given by 46 | the provided number. 47 | init_vals (list): A list contains the intervals for the 48 | uniform initialization of the parameters. 49 | 50 | Returns: 51 | 52 | """ 53 | super(Time2Vec, self).__init__(in_features, out_features, bias) 54 | self.out_features = out_features 55 | self.dev = device(dev) 56 | 57 | # Define and register the essential Time2Vec parameters 58 | # w0 and b0 correspond to omega_0 and phi_0 in [1] 59 | self.W0 = nn.Parameter(rand(1, 1).to(self.dev)) 60 | if const is not None: 61 | nn.init.constant_(self.W0, const) 62 | else: 63 | nn.init.uniform_(self.W0, a=init_vals[0], b=init_vals[1]) 64 | self.register_parameter("W0", self.W0) 65 | 66 | self.b0 = nn.Parameter(rand(in_features, 1).to(self.dev)) 67 | if const is not None: 68 | nn.init.constant_(self.b0, const) 69 | else: 70 | nn.init.uniform_(self.b0, a=init_vals[0], b=init_vals[1]) 71 | self.register_parameter("b0", self.b0) 72 | 73 | # W and b correspond to omega_i and phi_i for i != 0 74 | # omega_i and phi_i represent the frequency and phase-shift in case 75 | # func=sin. 76 | self.W = nn.Parameter(rand(out_features, 77 | out_features).to(self.dev)) 78 | if const is not None: 79 | nn.init.constant_(self.W, const) 80 | else: 81 | nn.init.uniform_(self.W, a=init_vals[0], b=init_vals[1]) 82 | self.register_parameter("W", self.W) 83 | 84 | self.b = nn.Parameter(rand(in_features, 85 | out_features).to(self.dev)) 86 | if const is not None: 87 | nn.init.constant_(self.b, const) 88 | else: 89 | nn.init.uniform_(self.b, a=init_vals[0], b=init_vals[1]) 90 | self.register_parameter("b", self.b) 91 | 92 | # Nonlinear period function 93 | self.f = func 94 | 95 | # Disable gradients for intrinsic weights and biases (see nn.Linear) 96 | self.weight.requires_grad = False 97 | self.bias.requires_grad = False 98 | 99 | def forward(self, tau): 100 | """ 101 | Time2Vec layer forward method. The input signal (tensor) contains 102 | a sequence with dimension -> features_dim. 103 | 104 | Args: 105 | tau (tensor): Input signal (torch tensor) 106 | (batch_size, sequence_length, features_dim) 107 | 108 | Returns: 109 | A tensor that contains the concatenation of a predictive and a 110 | progressive signal, respectively. 111 | 112 | Notes: 113 | For instance, if the time2vec layer is being used in timeseries 114 | prediction the sequence length is the length of the timeseries 115 | chunk and the features dimension is 1 if the prediction horizon 116 | is one. 117 | """ 118 | batch_size = tau.shape[0] 119 | W = self.W.repeat(batch_size, 1, 1) 120 | b = self.b.repeat(batch_size, 1, 1) 121 | # Progressive signal 122 | res_progressive = tau * self.W0 + self.b0 123 | x = tau.repeat_interleave(self.out_features, dim=-1) 124 | # Predictive signal 125 | res_predictive = self.f(bmm(x, W) + b) 126 | out = cat([res_predictive, res_progressive], 2) 127 | return out 128 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | from model.time2vec import Time2Vec 4 | 5 | np.random.seed(13) 6 | torch.manual_seed(13) 7 | 8 | 9 | if __name__ == '__main__': 10 | dev = torch.device("cpu") 11 | x = torch.from_numpy(np.ones((2, 3, 1), 'f')) 12 | tv = Time2Vec(1, 3, dev=dev) 13 | res = tv(x) 14 | print(res.shape) 15 | print(res) 16 | --------------------------------------------------------------------------------