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Generating synthetic time series dataset to study the influence of class label noise on classification performance

Table of contents

Introduction

This module generates synthetic univariate time series datasets, which contain different levels of label noise. The generated datasets include remote sensing dataset specificity with a polygon concept. The idea is to take into account the field campaign protocols where sample labels are assigned with polygons describing an homogeneous area (for example a crop field). This code supports the following journal and conference papers:

@article{pelletier2017effect,
  title={Effect of training class label noise on classification performances for land cover mapping with satellite image time series},
  author={Pelletier, Charlotte and Valero, Silvia and Inglada, Jordi and Champion, Nicolas and Marais Sicre, Claire and Dedieu, G{\'e}rard},
  journal={Remote Sensing},
  volume={9},
  number={2},
  pages={173},
  year={2017},
  publisher={Multidisciplinary Digital Publishing Institute}
}
@inproceedings{pelletier2017new,
  title={New iterative learning strategy to improve classification systems by using outlier detection techniques},
  author={Pelletier, Charlotte and Valero, Silvia and Inglada, Jordi and Dedieu, G{\'e}rard and Champion, Nicolas},
  booktitle={2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  pages={3676--3679},
  year={2017},
  organization={IEEE}
}

Context

The automatic production of land cover maps obtained by the supervised classification of satellite image time series relies on the availability of accurate reference databases. In remote sensing, these reference databases come generally from several sources including field campaigns, thematic maps or photointerpretation of high spatial resolution images. Although most of classification algorithms made the assumption that reference databases are gold standard, it is well known that they contain errors, artifacts and imprecisions. These errors lead to the presence of class label noise on both training and testing samples. In other words some samples are assigned a wrong class label.

This code generates univariate time series representing vegetation profiles (closed from Normalized Difference Vegetation Index) for various vegetation classes (mainly crops). The generation of urban and water time series profiles is also possible. The generated datasets might be used for testing the robustness of various classification systems in a control environment where class label noise is completely known.

Prerequisites

The code relies on Pyton 3.8
And use:

  • Numpy
  • Pandas
  • Tables
  • h5py (HDF5 for Python)
  • Jupyter Notebook (only for use Jupyter Notebook)
  • Matplotlib (only for visualisation)
  • Scikit-Learn, use to train traditional machine learning algorithms including Support Vector Machines (SVM) and Random Forest (RF)
  • Keras & Tensorflow, use to train deep learning architectures such as Temporal Convolutional Neural Network (TempCNN)

Installation

Use the package manager pip to install numpy, pandas.

pip install numpy
pip install pandas
pip install tables
pip install h5py
pip install jupyter 
pip install matplotlib
pip install sklearn
pip install keras==2.4.3 tensorflow==2.2.0

Quick use

There are a set of Jupyter notebooks available in the notebook folder, for example:

  • ExampleUseGeneratingData.ipynb generates a dataset by using the class GenLabelNoiseTS. It requires a file name where to save the generated dataset, for example dataset.h5, a directory path to save the results, and a list of class (if not specified Wheat and Barley classes are used). It can also take two optional parameters: csv and v for a verbose mode. Data matrix X and the associated label Y (see next section for the format) can be obtained with the getDataXY() method. A method getNoiseDataXY(0.05, None) generates noisy data where 5 % of the instances are randomly corrupted (a dictionary can also be used to add systematic class label noise, see the next section). The getTestData() method creates also Xtest and Ytest data that can be used as test instances (when training supervised classification algorithms). Finally, the defaultVisualisation() method depicts the mean temporal profile for each class.
  • ExampleUseEvaluation.ipynb trains four supervised classification algorithms (Support Vector Machines with linear and Radial Basis Function kernel, Random Forests and Temporal Convolutional Neural Network) on the data contains in a h5 file. Each classification algorithm is trained on the original data and the data corrupted by different types and levels of class label noise. The training operation is repeated 10 times (on 10 different datasets) to evaluate the variance. The notebook outputs a Figure showing the overall accuracy of each algorithm (and one standard deviation) as a function of the noise level.

Data generation

The following Python code is used to generate a 2-class dataset (wheat and barley) composed of the original data (X,Y), the original data corrupted by 5 % of random class label noise (Xnoise,Ynoise), and some non noisy test data (Xtest,Ytest). X (of size (n,l)) is the data matrix (numpy array) composed of n time series of length l (X[i,j] represents the NDVI value of the i-th observation at time j), and Y is the label vector (of size n) associated with each time series.

from GenLabelNoiseTS.GenLabelNoiseTS import *

path = './somePath/'
# Example with a list of two class and systematic change.
generator = GenLabelNoiseTS(filename="dataset.h5", classList=('Wheat','Barley'), csv=True, verbose=True, dir=path/to/dir)
(X,Y) = generator.getDataXY()
(Xnoise,Ynoise) = generator.getNoiseDataXY(0.05,None)
(Xtest,Ytest) = (generator.getTestData())
generator.defaultVisualisation()

Generating a dataset using a Python command line

A dataset can be generated with the following Python command line.

# Generating a non-noisy synthetic dataset composed of original data and some corrupted version with a random class label noise applied to each class
python gen_data.py -d path/to/dir -f dataset.h5 --noClass 10 --noise random --noise.level [0.05,0.1,0.15,0.2,0.25,0.3] --save_csv -v --vis
# Generating a non-noisy synthetic dataset composed of original data and some corrupted version with a systematic class label noise applied to each class
python gen_data.py -d path/to/dir -f dataset.h5 --noClass 10 --noise {'Wheat':('Barley','Soy'),'Barley':'Soy'} --noise.level [0.05,0.1,0.15,0.2,0.25,0.3] --save_csv -v --vis
# The dictionary is used to add a systematic noise to the original data. In this example, the wheat labels are always changed to either barley or soy.
# The dictionay should not contain any space: {'Wheat':('Barley','Soy'),'Barley':'Soy'}

This command generates a dataset in the specified directory path/to/dir. The complete dataset is stored in the HDF5 dataset.h5. It includes non-noisy data as well as noisy data contaminated by different level of noise (noise.level).
The following options are mandatory:

  • -d path/to/dir: directory path
  • -f fileName: file name (should be a .h5 file)
  • --noClass 10: number of classes presents in the generated dataset
  • --noise random: type of class label noise added to the data. Either random or a dictionary for systematic noise. The dictionary needs to be in a Python format: {'Wheat':('Barley','Soy'),'Barley':'Soy'}.
  • --noise.level [0.05,0.1]: a list containing the noise levels added to the data.

The following options are optional: ---config config_file: a configuration file can be used to parameterize the double logistic function parameters (please refer to the Remote Sensing journal paper).

  • --save_csv: the data will also be saved in csv files (one file per type of noise and per level of noise)
  • -v: verbose mode
  • --vis: saving default visualisation (one Figure per class depicting all the NDVI profiles and one Figure depicting mean averaged NDVI profiles per class)

Configuration file

The configuration file initFile.csv is a data frame (converted into csv file) with the following format:

  • The first column must be named: class_names.
  • The 0 and 1 columns contain the number of samples and the number of polygons for each class. The following columns contain parameter values used to parametrized a double logistic function (please refer to the Remote Sensing journal paper).
  • The last row (line 13 in the folloing example) must contain days of year (dates). The days of year are inserted from column 0.
      class_names    0   1      2       3  ...    21     22     23    24    25
0            Corn  500  10   0.57   0.720  ...   NaN    NaN    NaN   NaN   NaN
1   Corn_Ensilage  500  10   0.57   0.720  ...   NaN    NaN    NaN   NaN   NaN
2         Sorghum  500  10   0.62   0.770  ...   NaN    NaN    NaN   NaN   NaN
3       Sunflower  500  10   0.67   0.820  ...   NaN    NaN    NaN   NaN   NaN
4             Soy  500  10   0.67   0.820  ...   NaN    NaN    NaN   NaN   NaN
5           Wheat  500  10   0.52   0.670  ...   NaN    NaN    NaN   NaN   NaN
6        Rapeseed  500  10   0.70   0.800  ...  12.0  135.0  145.0   5.0  15.0
7          Barley  500  10   0.52   0.670  ...   NaN    NaN    NaN   NaN   NaN
8       Wheat_Soy  500  10   0.50   0.550  ...  15.0  280.0  300.0  25.0  35.0
9       Evergreen  500  10   0.01   0.015  ...   NaN    NaN    NaN   NaN   NaN
10      Decideous  500  10   0.20   0.350  ...   NaN    NaN    NaN   NaN   NaN
11          Water  500  10   0.01   0.020  ...   NaN    NaN    NaN   NaN   NaN
12          Build  500  10   0.01   0.020  ...   NaN    NaN    NaN   NaN   NaN
13            NaN    1  26  51.00  76.000  ...   NaN    NaN    NaN   NaN   NaN

Data visualisation

The following code retrieves the data contained in the dataset.h5 file and displays several visualisation figures on the non-noisy data.

from GenLabelNoiseTS.GenLabelNoiseTS import *

generator = GenLabelNoiseTS(filename="dataset.h5", dir='pathToData' + 'Run' + str(1) + '/', csv=True,
                                verbose=False)
generator.visualisation(typePlot='mean')
generator.visualisation(typePlot='mean', className='Corn')
generator.visualisation(typePlot='all', className='Corn')
generator.visualisation(typePlot='random', className='Corn', noProfile=20)
generator.visualisation(typePlot='randomPoly', className='Corn')

Algorithm performance evaluation

To evaluate the influence of class label noise on the performance of supervised classification algorithms, we created three synthetic datasets of 2 classes, 5 classes and 10 classes. Each class has 500 training instances and 500 testing instances. The training instances are corrupted by 5 % to 100 % (5 % step) random class label noise (and systematic class label noise for 5-class dataset). This setting is the same than the one published in the Remote Sensing MDPI journal. We describe below how the published results can be reproduced.

Generating datasets

We first generate the three datasets, which contain different types and levels of class label noise by using the following Python command line:

python gen_datasets.py

This command creates

  • 2-class (TwoClass), 5-class (FiveClass), and 10-class (TenClass) datasets.
  • Ten variants of the datasets (ten runs) are created.
  • One run includes the original training data, training data corrupted using 20 levels of class label noise (from 5 % to 100 %, with a step of 5 %), and (non-noisy) testing data.
  • All the datasets are corrupted by a random class label noise, and the 5-class datasets are also corrupted by a systematic class label noise.

The created datasets are stored in the data folder (at the root of the project) with the following tree structure:

  • data
    • TwoClass
      • Run1
        • data.csv
        • dataFrame.h5
        • random_0.csv
        • ...
        • random_100.csv
      • Run2
      • ...
      • Run10
    • FiveClass
      • Run1
        • data.csv
        • dataFrame.h5
        • random_0.csv
        • ...
        • random_100.csv
        • systematic_0_98494941304801395478184421979593253002.csv
        • ...
        • systematic_100_98494941304801395478184421979593253002.csv
      • Run2
      • ...
      • Run10
    • TenClass
      • Run1
        • data.csv
        • dataFrame.h5
        • random_0.csv
        • ...
        • random_100.csv
      • Run2
      • ...
      • Run10

We obtain the following averaged NDVI temporal profiles (displayed for thirteen classes): Plots Results Generating Data

The 2-class dataset uses wheat and barley, the 5-class dataset uses sorghum, soy, sunflower, corn and corn ensilage, and the 10-class dataset uses wheat, barley, rapeseed, sorghum, soy, sunflower, corn, corn ensilage, deciduous and evergreen.

Evaluation of classification algorithm performance

We evaluate here the performance of four classification algorithms: Support Vector Machines with linear kernel (SVM-Linear), with Radial Basis Function (SVM-RBF), Random Forest (RF) and Temporal Convolutional Neural Network (TempCNN). The following code saves the overall accuracy (OA) scores obtained on each dataset:

from EvalAlgo.EvalAlgo import *

pathTwoClass = './data/TwoClass/'
pathFiveClass = './data/FiveClass/'
pathTenClass = './data/TenClass/'

systematicChange = False
noclass = 2
seed = 0

EvalAlgo(pathTwoClass, noclass, seed, systematicChange)
visualisationEval(pathTwoClass+"/random") # shows performance for the 2-class dataset on random class label noise

For each dataset and type of class label noise, the results are saved into two csv file

  • runOA_RF.csv: OA per run and per noise level
  • meanOA_RF.csv: a mean run OA score computed for each noise level. This is thus a summary of the OA scores contained in runOA_RF.csv; it is used for the visualisation of the classification algorithm performane.

The following results are obtained: they are similar to the one published in the Remote Sensing journal (initially obtained by running a Matlab implementation and by using OpenCV implementation for the classification algorithms). It also adds the performance of TemCNN for the different datasets and noise levels (see the code here). Plots Results Evaluation

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

Contributors

  • Martin Dautriche - undergraduate computer science student at Univ. Bretagne Sud
  • Dr. Charlotte Pelletier - Ass. Professor in computer science at Univ. Bretagne Sud / IRISA

License

GNU AGPLv3