109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
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from .predictor import PRFPredictor
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import pandas as pd
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import numpy as np
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from typing import Union, List, Dict
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__version__ = '0.3.0'
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__author__ = ''
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__email__ = ''
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__all__ = ['PRFPredictor', 'predict_prf', '__version__', '__author__', '__email__']
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def predict_prf(
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sequence: Union[str, List[str], None] = None,
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data: Union[pd.DataFrame, None] = None,
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window_size: int = 3,
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gb_threshold: float = 0.1,
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model_dir: str = None
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) -> pd.DataFrame:
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"""
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PRF位点预测函数
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Args:
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sequence: 单个或多个DNA序列，用于滑动窗口预测
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data: DataFrame数据，必须包含'399bp'列，用于区域预测
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window_size: 滑动窗口大小（默认为3）
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gb_threshold: GB模型概率阈值（默认为0.1）
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model_dir: 模型文件目录路径（可选）
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Returns:
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pandas.DataFrame: 预测结果
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Examples:
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# 1. 单条序列滑动窗口预测
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>>> from FScanpy import predict_prf
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>>> sequence = "ATGCGTACGT..."
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>>> results = predict_prf(sequence=sequence)
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# 2. 多条序列滑动窗口预测
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>>> sequences = ["ATGCGTACGT...", "GCTATAGCAT..."]
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>>> results = predict_prf(sequence=sequences)
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# 3. DataFrame区域预测
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>>> import pandas as pd
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>>> data = pd.DataFrame({
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... '399bp': ['ATGCGT...', 'GCTATAG...']
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... })
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>>> results = predict_prf(data=data)
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"""
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predictor = PRFPredictor(model_dir=model_dir)
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# 验证输入参数
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if sequence is None and data is None:
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raise ValueError("必须提供sequence或data参数之一")
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if sequence is not None and data is not None:
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raise ValueError("sequence和data参数不能同时提供")
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# 滑动窗口预测模式
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if sequence is not None:
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if isinstance(sequence, str):
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# 单条序列预测
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return predictor.predict_full(
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sequence, window_size, gb_threshold)
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elif isinstance(sequence, (list, tuple)):
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# 多条序列预测
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results = []
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for i, seq in enumerate(sequence, 1):
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try:
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result = predictor.predict_full(
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seq, window_size, gb_threshold)
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result['Sequence_ID'] = f'seq_{i}'
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results.append(result)
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except Exception as e:
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print(f"警告：序列 {i} 预测失败 - {str(e)}")
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return pd.concat(results, ignore_index=True) if results else pd.DataFrame()
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# 区域化预测模式
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else:
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if not isinstance(data, pd.DataFrame):
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raise ValueError("data参数必须是pandas DataFrame类型")
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if '399bp' not in data.columns:
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raise ValueError("DataFrame必须包含'399bp'列")
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# 调用区域预测函数
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try:
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results = predictor.predict_region(
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data['399bp'], gb_threshold)
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# 添加原始数据的其他列
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for col in data.columns:
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if col not in ['399bp', '33bp']:
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results[col] = data[col].values
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return results
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except Exception as e:
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print(f"警告：区域预测失败 - {str(e)}")
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# 创建空结果
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results = pd.DataFrame({
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'GB_Probability': [0.0] * len(data),
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'CNN_Probability': [0.0] * len(data),
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'Voting_Probability': [0.0] * len(data)
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})
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# 添加原始数据列
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for col in data.columns:
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results[col] = data[col].values
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return results
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