189 lines
6.2 KiB
Python
189 lines
6.2 KiB
Python
import os
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import unicodedata
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import pandas as pd
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STATIC_PATH = './Static'
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# 从文件夹中读取所有xlsx文件，每个文件对应一个label
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# labelNames为label的名字，如果不提供则默认为文件名
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def load_data(folder, labelNames, isDir=True, fileClass='xlsx'):
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# 检查folder参数
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if folder is None:
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raise ValueError("The 'folder' parameter is required.")
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# 检查labelNames参数
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if labelNames is None:
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raise ValueError("The 'labelNames' parameter is required if 'folder' does not contain labels.")
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folder = os.path.join(STATIC_PATH, folder)
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# 看看有没有元数据文件夹
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if not os.path.isdir(folder):
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raise ValueError(f"The folder '{folder}' does not exist.")
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data = None
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if not isDir:
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data = load_from_file(folder=folder, labelNames=labelNames, fileClass=fileClass)
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else:
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data = load_from_folder(folder=folder, labelNames=labelNames, fileClass=fileClass)
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print(data)
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return data
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def load_from_folder(folder, labelNames, fileClass):
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all_features = []
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fileClass = '.' + fileClass
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for labelName in labelNames:
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subfolder = os.path.join(folder, labelName)
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if os.path.exists(subfolder) and os.path.isdir(subfolder):
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fileNames = [f for f in os.listdir(subfolder) if f.endswith(fileClass)]
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max_row_length = get_max_row_len(subfolder, fileNames)
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features = []
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for fileName in fileNames:
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file_path = os.path.join(subfolder, fileName)
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features.append(load_xlsx(file_path, labelName, max_row_length, 'zero'))
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if features:
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all_features.append(pd.concat(features, ignore_index=True))
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# 将所有标签的数据合并
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return pd.concat(all_features, ignore_index=True)
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def load_from_file(folder, labelNames, fileClass):
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# 构建期望的文件名（label + .扩展名），并在目录中进行健壮匹配（去除零宽字符、Unicode 规范化、大小写不敏感）
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expected_names = [f"{labelName}.{fileClass}" for labelName in labelNames]
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actual_file_names = []
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missing = []
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for expected in expected_names:
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match = _find_matching_file(folder, expected)
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if match is None:
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missing.append(expected)
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else:
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actual_file_names.append(match)
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if missing:
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available = sorted(os.listdir(folder))
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raise FileNotFoundError(
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"The following files were not found (after normalization): "
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+ ", ".join(missing)
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+ f". Available files: {available}"
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)
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# 获取数据的最大行数（使用实际匹配到的文件名）
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max_row_length = get_max_row_len(folder, actual_file_names)
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all_features = []
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for i, fileName in enumerate(actual_file_names):
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file_path = os.path.join(folder, fileName)
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features = load_xlsx(file_path, labelNames[i], max_row_length, 'zero')
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all_features.append(features)
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return pd.concat(all_features, ignore_index = True)
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def load_xlsx(fileName, labelName, max_row_length = 1000, fill_rule = None):
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df = pd.read_excel(fileName)
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# 提取偶数列
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features = df.iloc[0:, 1::2]
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# 复制 features DataFrame
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# features_copy = features.copy()
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# 使用 pd.concat 来追加副本到原始 DataFrame
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# features = pd.concat([features, features_copy], ignore_index=True, axis=1)
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# 计算变化率
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# first_value = features.iloc[0, :] # 获取第一行的数据
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# features_pct_change = (features - first_value) / first_value
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# features = features_pct_change
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features.dropna(inplace=True)
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features.reset_index(drop=True, inplace=True)
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features = features.T
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# 补全每一行到指定长度
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features = features.apply(lambda row: fill_to_len(row, max_row_length, fill_rule), axis=1)
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# 获取实际的列数
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actual_columns = features.shape[1]
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features['label'] = labelName
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# 使用实际的列数来创建列名
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features.columns = [f'feature{i+1}' for i in range(actual_columns)] + ['label']
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return features
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def fill_to_len(row, length = 1000, rule = None):
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fill_value = 0
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if rule == 'min':
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fill_value = row.min()
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elif rule == 'mean':
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fill_value = row.mean()
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elif rule == 'zero':
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fill_value = 0
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fill_values = pd.Series([fill_value] * (length - len(row)))
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return pd.concat([row, fill_values], ignore_index=True)
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def get_max_row_len(folder, filenames):
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max_len = 0
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for filename in filenames:
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df = pd.read_excel(os.path.join(folder, filename))
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max_len = max(max_len, df.shape[0])
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return max_len
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__all__ = ['load_data']
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# ---------- 内部工具函数：处理包含零宽字符或不同 Unicode 形式的文件名匹配 ----------
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def _strip_zero_width(s: str) -> str:
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# 移除常见零宽字符：U+200B, U+200C, U+200D, U+FEFF
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if not isinstance(s, str):
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return s
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return s.translate({
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0x200B: None, # ZERO WIDTH SPACE
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0x200C: None, # ZERO WIDTH NON-JOINER
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0x200D: None, # ZERO WIDTH JOINER
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0xFEFF: None, # ZERO WIDTH NO-BREAK SPACE
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})
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def _canonicalize_name(name: str) -> str:
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# 规范化到 NFKC，并移除零宽字符
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name = unicodedata.normalize('NFKC', name)
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name = _strip_zero_width(name)
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return name
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def _normalize_for_compare(name: str) -> str:
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# 进一步规范化用于宽松比较：
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# - 统一大小写
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# - 将下划线视为空格（与文件名用下划线代替空格的情况匹配）
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# - 折叠所有空白为一个空格，并去除首尾空格
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n = _canonicalize_name(name)
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n = n.replace('_', ' ')
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n = ' '.join(n.split())
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return n.lower()
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def _find_matching_file(folder: str, expected_name: str):
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# 首先进行严格匹配（规范化后相等）
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expected = _canonicalize_name(expected_name)
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try:
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entries = os.listdir(folder)
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except FileNotFoundError:
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return None
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for f in entries:
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if _canonicalize_name(f) == expected:
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return f
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# 次要策略：大小写不敏感比较
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expected_lower = expected.lower()
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for f in entries:
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if _canonicalize_name(f).lower() == expected_lower:
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return f
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# 宽松策略：将下划线当作空格处理，并折叠空白（用于匹配 "Crocodile grain" vs "Crocodile_grain"）
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expected_relaxed = _normalize_for_compare(expected_name)
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for f in entries:
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if _normalize_for_compare(f) == expected_relaxed:
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return f
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return None
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