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TCGPR

Citation

If you use this code in your research, please cite the following:

  • Processing (paper): Tianliang Li(李天亮)&, Bin Cao(曹斌)&, Tianhao Su(苏天昊)&, ..., Lingyan Feng^(冯凌燕), Tongyi Zhang^(张统一). ( SMALL, Under REVIEW)
  • Patent: Zhang Tongyi (张统一), Cao Bin (曹斌), Yuan Hao, Wei Qinghua, Dong Ziqiang. Authorized Chinese Patent.

Source Code

PyPI - TCGPR

This Python-based library is compatible with Windows, Linux, and macOS operating systems.

Patent

Patent Image

Algorithm Introduction

For detailed algorithm information, refer to the Introduction.

Installation

To install TCGPR, use pip:

pip install PyTcgpr

Checking Installation

You can check the installation details with:

pip show PyTcgpr

Updating the Package

Update TCGPR to the latest version using:

pip install --upgrade PyTcgpr

Running the Algorithm

1. Data Screening | Partition

from PyTcgpr import TCGPR
dataSet = "data.csv"
initial_set_cap = 3
sampling_cap = 2
up_search = 500
CV = 'LOOCV'
Task = 'Partition'

TCGPR.fit(
    filePath = dataSet, 
    initial_set_cap = initial_set_cap, 
    Task = Task, 
    sampling_cap = sampling_cap,
    up_search = up_search, 
    CV = CV
)
# Note: Mission is set to 'DATA' by default. No need to declare it explicitly.

2. Data Screening | Identification

from PyTcgpr import TCGPR
dataSet = "data.csv"
sampling_cap = 2
up_search = 500
Task = 'Identification'
CV = 'LOOCV'

TCGPR.fit(
    filePath = dataSet, 
    Task = Task, 
    sampling_cap = sampling_cap,
    up_search = up_search, 
    CV = CV
)
# Note: 'Mission' is 'DATA' by default; no need to declare it. 'initial_set_cap' is masked in this case.

3. Feature Selection Module

from PyTcgpr import TCGPR
dataSet = "data.csv"
sampling_cap = 2
Mission = 'FEATURE'
up_search = 500
CV = 'LOOCV'

TCGPR.fit(
    filePath = dataSet, 
    Mission = Mission, 
    sampling_cap = sampling_cap,
    up_search = up_search, 
    CV = CV
)
# Note: For feature selection, 'Mission' must be explicitly set to 'FEATURE'.

Parameters

:param Mission: str, default='DATA'
    The task to perform:
    - 'DATA' for data screening
    - 'FEATURE' for feature selection

:param filePath: str
    Path to the input dataset in CSV format.

:param initial_set_cap: int or list
    Initial set capacity. For 'Partition' under 'DATA', defaults to 3.
    Can also be a list specifying the indices of the initial set.

:param sampling_cap: int, default=1
    Number of data points or features added at each iteration.

:param measure: str, default='Pearson'
    Correlation criteria. Can be 'Pearson' (R values) or 'Determination' (R² values).

:param ratio: float
    Tolerance ratio for correlation. Varies based on the mission and task.

:param target: int, default=1
    Used in feature selection. Specifies the number of targets in regression tasks.

:param weight: float, default=0.2
    Weight factor for calculating the GGMF score.

:param up_search: int, default=500
    Upper boundary for brute-force search.

:param exploit_coef: float, default=2
    Constraint on the variance in the Cal_EI function.

:param exploit_model: bool, default=False
    If True, only R values will be used for the search (GGMF will not be considered).

:param CV: int or str, default=10
    Cross-validation setting. Can be an integer (e.g., 5, 10) or 'LOOCV' for leave-one-out cross-validation.

Output

The algorithm will output a CSV file containing the processed dataset: Dataset_remained_by_TCGPR.csv.

Maintainers

This project is maintained by Bin Cao. If you encounter any issues or have suggestions, feel free to open an issue on GitHub or contact:

Contributing

We welcome contributions and suggestions! You can submit issues for questions, bugs, and feature requests, or submit a pull request directly. We are also open to research collaborations—please get in touch if you're interested!