Python code for ACDWM (Adaptive Chunk-based Dynamic Weighted Majority)
Author: Yang Lu
Contact: lylylytc@gmail.com
Run Python 3 by:
main.py dataset_name.npzThe dataset can be chosen by:
drifting_gaussian_abrupt.npz drifting_gaussian_gradual.npz elec2_abrupt.npz elec2_gradual.npz hyperP_abrupt.npz hyperP_gradual.npz moving_gaussian_abrupt.npz moving_gaussian_gradual.npz noaa_abrupt.npz noaa_gradual.npz rotcb_abrupt.npz rotcb_gradual.npz rotsp_abrupt.npz rotsp_gradual.npz sea_abrupt.npz sea_gradual.npz
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Moving Gaussian: This data stream consists of two Gaussian distributed classes with identity covariance and 2 dimensions. The initial coordinates of the mean of the two classes are [5,0] and [7,0]. They gradually move to [-5,0] and [-3,0] from the beginning to the half of the stream and then move back to the initial coordinates.
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Drifting Gaussian: It is a linear combination of three Gaussian components and one of them is the minority class. The mean and variance of the Gaussian components are varying along all time.
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SEA: It contains three attributes ranging from 0 to 10. Only the first two attributes are related to the class that is determined by
$attr_1+attr_2\le \alpha$ . The third attribute is treated as noise. The control parameter$\alpha$ is set at 15 for the first 1/3 and the last 1/3 chunks, and 5 for the second 1/3 chunks. -
Hyper Plane: The gradually changed concepts are calculated by
$f(\mathbf{x})=\sum_{i=1}^{d-1}a_i\cdot\frac{x_i+x_{i+1}}{x_i}$ , where the dimension$d=10$ and$a_i$ is used to control the decision hyperplane. -
Spiral: There are four spirals are rotating with a size-fixed two dimensional window. The position of the spirals are used to predict the class.
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Checkerboard: It is a non-linear XOR classification problem. The data stream is produced by selecting from a size-fixed window in the rotating checkerboard.
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Electricity: This dataset contains the changes of electricity price according to the time and demand in New South Wales, Australian. The class label is determined by the change of price over the last 24 hours.
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Weather: This dataset contains the weather information over 50 years in Bellevue and Nebraska. The task is to predict whether a day is rainy or not.
In the experiments, the imbalance ratio is changed by two prior drift modes:
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Abrupt drift: The imbalance ratio is initially set at 0.01. After half of the data stream, the imbalance ratio suddenly changes to 0.99, namely the majority class becomes to the minority class with imbalance ratio 0.01. The prequential measures are reset at the position of the abrupt drift.
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Gradual drift: The imbalance ratio is initially set at 0.01. After 1/3 of the data stream, the imbalance ratio starts to gradually increase until 0.99 at 2/3 of the data stream. The prequential measures are reset at the starting and ending position of the gradual drift.
The imbalance ratio here refers to the percentage of positive class samples. To control the imbalance ratio, undersampling is done on every 1000 samples in the original data stream. The majority class is undersampled if the original imbalance ratio on this chunk is smaller than the assigned imbalance ratio, and the minority class is undersampled vice versa. As the original imbalance ratio of each dataset is different, the drift position after undersampling is also different.
Please cite the paper if the codes are helpful for you research.
Yang Lu, Yiu-ming Cheung, and Yuan Yan Tang, “Adaptive Chunk-based Dynamic Weighted Majority for Imbalanced Data Streams with Concept Drift”, in IEEE Transactions on Neural Networks and Learning Systems, DOI:10.1109/TNNLS.2019.2951814.