transdim

Made by Xinyu Chen • 🌐 https://twitter.com/chenxy346

Machine learning models make important developments in the field of spatiotemporal data modeling - like how to forecast near-future traffic states of road networks. But what happens when these models are built with incomplete data commonly collected in real-world systems?

About this Project

In the transdim (transportation data imputation) project, we build machine learning models to help address some of the toughest challenges of spatiotemporal data modeling - from missing data imputation to time series prediction. The strategic aim of this project is creating accurate and efficient solutions for spatiotemporal traffic data imputation and prediction tasks.

In a hurry? Please check out our contents as follows.

Tasks and Challenges

Missing data are there, whether we like them or not. The really interesting question is how to deal with incomplete data.

Figure 1: Two classical missing patterns in a spatiotemporal setting.

We create three missing data mechanisms on real-world data.

• Missing data imputation 🔥

  • Random missing (RM): Each sensor lost observations at completely random. (★★★)
  • Non-random missing (NM): Each sensor lost observations during several days. (★★★★)
  • Blockout missing (BM): All sensors lost their observations at several consecutive time points. (★★★★)

Figure 2: Tensor completion framework for spatiotemporal missing traffic data imputation.

• Spatiotemporal prediction 🔥
  • Forecasting without missing values. (★★★)
  • Forecasting with incomplete observations. (★★★★★)

Figure 3: Illustration of our proposed Low-Rank Autoregressive Tensor Completion (LATC) imputer/predictor with a prediction window τ (green nodes: observed values; white nodes: missing values; red nodes/panel: prediction; blue panel: training data to construct the tensor).

Implementation

Open data

In this repository, we have adapted the publicly available data sets into our experiments. If you want to view or use these data sets, please download them at the ../datasets/ folder in advance, and then run the following codes in your Python console:

import scipy.io

tensor = scipy.io.loadmat('../datasets/Guangzhou-data-set/tensor.mat')
tensor = tensor['tensor']
random_matrix = scipy.io.loadmat('../datasets/Guangzhou-data-set/random_matrix.mat')
random_matrix = random_matrix['random_matrix']
random_tensor = scipy.io.loadmat('../datasets/Guangzhou-data-set/random_tensor.mat')
random_tensor = random_tensor['random_tensor']

If you want to view the original data, please check out the following links:

• Gdata: Guangzhou urban traffic speed data set.
• Bdata: Birmingham parking data set.
• Hdata: Hangzhou metro passenger flow data set.
• Sdata: Seattle freeway traffic speed data set.
• Ndata: NYC taxi data set.

In particular, we take into account large-scale traffic data imputation/prediction on PeMS-4W and PeMS-8W data sets:

• PeMS-4W/8W/12W: Large-scale traffic speed data sets in California, USA.

You can download the data sets from Zenodo and place them at the folder of datasets (data path example: ../datasets/California-data-set/pems-4w.csv). Then you can open data in Python by using Pandas:

import pandas as pd

data = pd.read_csv('../datasets/California-data-set/pems-4w.csv', header = None)

For model evaluation, we mask certain entries of the "observed" data as missing values and then perform imputation for these "missing" values.

Model implementation

In our experiments, we have implemented some machine learning models mainly on Numpy, and written these Python codes with Jupyter Notebook. So, if you want to evaluate these models, please download and run these notebooks directly (prerequisite: download the data sets in advance).

• Our models

Task	Jupyter Notebook	Gdata	Bdata	Hdata	Sdata	Ndata
Missing Data Imputation	BTMF	✅	✅	✅	✅	🔶
	BGCP	✅	✅	✅	✅	✅
	LRTC-TNN	✅	✅	✅	✅	🔶
	BTTF	🔶	🔶	🔶	🔶	✅
Single-Step Prediction	BTMF	✅	✅	✅	✅	🔶
	BTTF	🔶	🔶	🔶	🔶	✅
Multi-Step Prediction	BTMF	✅	✅	✅	✅	🔶
	BTTF	🔶	🔶	🔶	🔶	✅

• Baselines

Task	Jupyter Notebook	Gdata	Bdata	Hdata	Sdata	Ndata
Missing Data Imputation	BayesTRMF	✅	✅	✅	✅	🔶
	TRMF	✅	✅	✅	✅	🔶
	BPMF	✅	✅	✅	✅	🔶
	HaLRTC	✅	✅	✅	✅	🔶
	TF-ALS	✅	✅	✅	✅	✅
	BayesTRTF	🔶	🔶	🔶	🔶	✅
	BPTF	🔶	🔶	🔶	🔶	✅
Single-Step Prediction	BayesTRMF	✅	✅	✅	✅	🔶
	TRMF	✅	✅	✅	✅	🔶
	BayesTRTF	🔶	🔶	🔶	🔶	✅
	TRTF	🔶	🔶	🔶	🔶	✅
Multi-Step Prediction	BayesTRMF	✅	✅	✅	✅	🔶
	TRMF	✅	✅	✅	✅	🔶
	BayesTRTF	🔶	🔶	🔶	🔶	✅
	TRTF	🔶	🔶	🔶	🔶	✅

• ✅ — Cover
• 🔶 — Does not cover
• 🚧 — Under development

Imputation/Prediction performance

• Imputation example (on Guangzhou data)

(a) Time series of actual and estimated speed within two weeks from August 1 to 14.

(b) Time series of actual and estimated speed within two weeks from September 12 to 25.

The imputation performance of BGCP (CP rank r=15 and missing rate α=30%) under the fiber missing scenario with third-order tensor representation, where the estimated result of road segment #1 is selected as an example. In the both two panels, red rectangles represent fiber missing (i.e., speed observations are lost in a whole day).

• Prediction example

Quick Start

This is an imputation example of Low-Rank Tensor Completion with Truncated Nuclear Norm minimization (LRTC-TNN). One notable thing is that unlike the complex equations in our paper, our Python implementation is extremely easy to work with.

• First, import some necessary packages:

import numpy as np
from numpy.linalg import inv as inv

• Define the operators of tensor unfolding (ten2mat) and matrix folding (mat2ten) using Numpy:

def ten2mat(tensor, mode):
    return np.reshape(np.moveaxis(tensor, mode, 0), (tensor.shape[mode], -1), order = 'F')

def mat2ten(mat, tensor_size, mode):
    index = list()
    index.append(mode)
    for i in range(tensor_size.shape[0]):
        if i != mode:
            index.append(i)
    return np.moveaxis(np.reshape(mat, list(tensor_size[index]), order = 'F'), 0, mode)

• Define Singular Value Thresholding (SVT) for Truncated Nuclear Norm (TNN) minimization:

def svt_tnn(mat, alpha, rho, theta):
    tau = alpha / rho
    [m, n] = mat.shape
    if 2 * m < n:
        u, s, v = np.linalg.svd(mat @ mat.T, full_matrices = 0)
        s = np.sqrt(s)
        idx = np.sum(s > tau)
        mid = np.zeros(idx)
        mid[:theta] = 1
        mid[theta:idx] = (s[theta:idx] - tau) / s[theta:idx]
        return (u[:,:idx] @ np.diag(mid)) @ (u[:,:idx].T @ mat)
    elif m > 2 * n:
        return svt_tnn(mat.T, tau, theta).T
    u, s, v = np.linalg.svd(mat, full_matrices = 0)
    idx = np.sum(s > tau)
    vec = s[:idx].copy()
    vec[theta:] = s[theta:] - tau
    return u[:,:idx] @ np.diag(vec) @ v[:idx,:]

• Define performance metrics (i.e., RMSE, MAPE):

def compute_rmse(var, var_hat):
    return np.sqrt(np.sum((var - var_hat) ** 2) / var.shape[0])

def compute_mape(var, var_hat):
    return np.sum(np.abs(var - var_hat) / var) / var.shape[0]

• Define LRTC-TNN:

def LRTC(dense_tensor, sparse_tensor, alpha, rho, theta, epsilon, maxiter):
    """Low-Rank Tenor Completion with Truncated Nuclear Norm, LRTC-TNN."""
    
    dim = np.array(sparse_tensor.shape)
    pos_missing = np.where(sparse_tensor == 0)
    pos_test = np.where((dense_tensor != 0) & (sparse_tensor == 0))
    
    X = np.zeros(np.insert(dim, 0, len(dim))) # \boldsymbol{\mathcal{X}}
    T = np.zeros(np.insert(dim, 0, len(dim))) # \boldsymbol{\mathcal{T}}
    Z = sparse_tensor.copy()
    last_tensor = sparse_tensor.copy()
    snorm = np.sqrt(np.sum(sparse_tensor ** 2))
    it = 0
    while True:
        rho = min(rho * 1.05, 1e5)
        for k in range(len(dim)):
            X[k] = mat2ten(svt_tnn(ten2mat(Z - T[k] / rho, k), alpha[k], rho, np.int(np.ceil(theta * dim[k]))), dim, k)
        Z[pos_missing] = np.mean(X + T / rho, axis = 0)[pos_missing]
        T = T + rho * (X - np.broadcast_to(Z, np.insert(dim, 0, len(dim))))
        tensor_hat = np.einsum('k, kmnt -> mnt', alpha, X)
        tol = np.sqrt(np.sum((tensor_hat - last_tensor) ** 2)) / snorm
        last_tensor = tensor_hat.copy()
        it += 1
        if (it + 1) % 50 == 0:
            print('Iter: {}'.format(it + 1))
            print('RMSE: {:.6}'.format(compute_rmse(dense_tensor[pos_test], tensor_hat[pos_test])))
            print()
        if (tol < epsilon) or (it >= maxiter):
            break

    print('Imputation MAPE: {:.6}'.format(compute_mape(dense_tensor[pos_test], tensor_hat[pos_test])))
    print('Imputation RMSE: {:.6}'.format(compute_rmse(dense_tensor[pos_test], tensor_hat[pos_test])))
    print()
    
    return tensor_hat

• Let us try it on Guangzhou urban traffic speed data set (Gdata):

import scipy.io

tensor = scipy.io.loadmat('../datasets/Guangzhou-data-set/tensor.mat')
dense_tensor = tensor['tensor']
random_tensor = scipy.io.loadmat('../datasets/Guangzhou-data-set/random_tensor.mat')
random_tensor = random_tensor['random_tensor']

missing_rate = 0.2

### Random missing (RM) scenario:
binary_tensor = np.round(random_tensor + 0.5 - missing_rate)
sparse_tensor = np.multiply(dense_tensor, binary_tensor)

• Run the imputation experiment:

import time
start = time.time()
alpha = np.ones(3) / 3
rho = 1e-5
theta = 0.30
epsilon = 1e-4
maxiter = 200
LRTC(dense_tensor, sparse_tensor, alpha, rho, theta, epsilon, maxiter)
end = time.time()
print('Running time: %d seconds'%(end - start))

This example is from ../experiments/Imputation-LRTC-TNN.ipynb, you can check out this Jupyter Notebook for advanced usage.

Toy Examples

• Time series forecasting

  • Structured low-rank matrix completion

• Time series imputation

Our Publications

• Xinyu Chen, Yixian Chen, Lijun Sun (2020). Scalable low-rank autoregressive tensor learning for spatiotemporal traffic data imputation. arXiv: 2008.03194. [preprint] [data] [Python code]

• Xinyu Chen, Lijun Sun (2020). Low-rank autoregressive tensor completion for multivariate time series forecasting. arXiv: 2006.10436. [preprint] [data & Python code]

• Xinyu Chen, Jinming Yang, Lijun Sun (2020). A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 117: 102673. [preprint] [doi] [data & Python code]

• Xinyu Chen, Lijun Sun (2019). Bayesian temporal factorization for multidimensional time series prediction. arXiv: 1910.06366. [preprint] [slide] [data & Python code]

• Xinyu Chen, Zhaocheng He, Yixian Chen, Yuhuan Lu, Jiawei Wang (2019). Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model. Transportation Research Part C: Emerging Technologies, 104: 66-77. [preprint] [doi] [slide] [data] [Matlab code]

• Xinyu Chen, Zhaocheng He, Lijun Sun (2019). A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 98: 73-84. [preprint] [doi] [data] [Matlab code] [Python code]

• Xinyu Chen, Zhaocheng He, Jiawei Wang (2018). Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition. Transportation Research Part C: Emerging Technologies, 86: 59-77. [doi] [data]

  This project is from the above papers, please cite these papers if they help your research.

Collaborators

Xinyu Chen 💻

Jinming Yang 💻

Yixian Chen 💻

Lijun Sun 💻

Tianyang Han 💻

• Principal Investigator (PI)

Lijun Sun 💻

See the list of contributors who participated in this project.

Our transdim is still under development. More machine learning models and technical features are going to be added and we always welcome contributions to help make transdim better. If you have any suggestion about this project or want to collaborate with us, please feel free to contact Xinyu Chen (email: [email protected]) and send your suggestion/statement. We would like to thank everyone who has helped this project in any way.

Recommended email subjects:

• Suggestion on transdim from [+ your name]
• Collaboration statement on transdim from [+ your name]

Acknowledgements

This research is supported by the Institute for Data Valorization (IVADO).

License

This work is released under the MIT license.