90 lines
2.1 KiB
Python
90 lines
2.1 KiB
Python
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.model_selection import train_test_split
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def data_format(data_path, is_column=False, rate=0.25):
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"""_summary_
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Args:
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data_path (_type_): 数据路径
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is_column (bool, optional): 是否为列数据. Defaults to False.
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rate (float, optional): 实验集划分的比例. Defaults to 0.25.
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md:模式,0为分类,1为预测
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Returns:X_train, X_test, Y_train, Y_test
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_type_: np.array
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"""
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# 读入数据
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X = data_load_forecast(data_path, is_column)
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# 归一化数据
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sc = MinMaxScaler(feature_range=(-1, 1))
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X = sc.fit_transform(X)
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# 分离Y
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# 分离第 128 个元素
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Y = X[:, -1]
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# 分离前 127 个元素
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X = X[:, :-1]
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# 划分数据集，75%用于训练，25%用于测试
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X_train, X_test, Y_train, Y_test = train_test_split(
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X, Y, test_size=rate, random_state=7)
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return X_train, X_test, Y_train, Y_test
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def data_load_forecast(data_path, is_column=False):
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"""
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数据加载
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data_path: 数据路径
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is_column: 是否是列数据
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return:X,Y
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"""
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# 读取csv文件
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df = pd.read_csv(data_path)
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# 进行数据清洗
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data_clean(df, is_column)
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df = df[df['output'] == 1]
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# 去除第一列
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df = df.drop(df.columns[0], axis=1)
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# 初始化X,Y
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X = []
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# 遍历DataFrame的每一行
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for index, row in df.iterrows():
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# 获取前127个数据项
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X.append(row.iloc[0:128])
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return np.array(X)
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def data_clean(data, is_column=False):
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"""_summary_
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Args:
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data (_type_): csv数据
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is_column (bool, optional): 清除含有NaN数据的列. Defaults to False.即清除含有NaN数据的行
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Returns:
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_type_: 清洗过的数据
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"""
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if not is_column:
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data = data.dropna(axis=0)
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return data
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else:
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data = data.dropna(axis=1)
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return data
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if __name__ == '__main__':
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# 加载数据
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X_train, X_test, Y_train, Y_test = data_format(
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'data/archive/PowerQualityDistributionDataset1.csv')
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print(X_train.shape)
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