This Repository contains several pretrained models and scripts to train new models to get embeddings of Time-Date data. Autoencoder Model's layers are based on Cosine Activation function from "Time2Vec" paper.
Import Date2VecConvert Class from Model.py and use the object's __call__ method to get embedding.
from Model import Date2VecConvert
import torch
# Date2Vec embedder object
# Loads a pretrained model
d2v = Date2VecConvert(model_path="./d2v_model/d2v_98291_17.169918439404636.pth")
# Date-Time is 13:23:30 2019-7-23
x = torch.Tensor([[13, 23, 30, 2019, 7, 23]]).float()
# Get embeddings
embed = d2v(x)
print(embed, embed.shape)Output:
tensor([ 1.5502, -3.2361, -1.6112, -5.6936, -1.5411, -2.2469, -2.9789,
2.2282, 1.3976, -0.7998, -0.5832, 0.8173, 7.9928, -1.7402,
-0.4342, 2.2130, -10.9533, 1.2677, 0.0374, 0.0504, -1.1214,
-1.0377, 1.3404, -5.4184, -0.1232, -2.2410, -3.4778, 1.8836,
0.0324, 0.4323, -1.0348, 6.7379, 0.0173, 0.9126, 0.1675,
0.2571, -0.9084, -0.3421, -0.9645, -0.9206, -0.1205, 0.6614,
0.7904, -0.6719, 0.6717, -0.0115, -0.4214, -0.6423, 0.9074,
-0.5311, 0.7155, 0.9098, -0.6888, 0.8095, -0.2459, 0.9649,
0.4060, 0.9995, 0.6519, 0.9981, -0.0737, -0.1282, 0.1721,
-0.8690]) torch.Size([64])- Experiment.py contains scripts for training, evaluating models tracking metrics using Tensorboard logs.
- Two different Experiment setups are available:
- Next Date prediction.
- Same Date prediction (reconstruction).
from Model import Date2Vec
from Experiment import Date2VecExperiment
import os
act = 'cos'
optim = 'adam'
os.system("mkdir ./models/d2v_{}".format(act))
configure("logs/d2v_{}".format(act))
# k is the embedding dimension
# act is the periodic activation function for hidden layer
m = Date2Vec(k=64, act=act)
exp = Date2VecExperiment(m, act, lr=0.001, cuda=True, optim=optim)
exp.train()
exp.test()- Training Loss (MSE v/s Batches)
- Validation Loss, first set (MSE v/s Validation Steps)
- Validation Loss, second set (MAE v/s Validation Steps)
- Models for only Time and only Date.
*Surya Kant Sahu - ojus1
This project is licensed under the MIT License - LICENSE.md
- Packages used for Machine learning model: Python: Pandas, PyTorch
- Paper Title: "Time2Vec: Learning a Vector Representation of Time" - https://arxiv.org/pdf/1907.05321.pdf