lzh
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
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import pandas as pd
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import numpy as np
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from sklearn.base import BaseEstimator, TransformerMixin
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"""数据整理
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Scikit learn 的preprocessing 模块目前只能将X, Y形态的数据整理成为需要的
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形似。这里我们需要对时间序列数据进行重拍，将其整理成为X, y形式。
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对于每一个观测，因变量y代表当前秒的变化率，自变量X代表前几秒的变化率和
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成交量变化情况。
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这里有两个办法进行这样的处理：
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1) 通过for 循环，将观测逐个加入到X, y矩阵和向量中。这种方法短平快，但是
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程序可重复利用率较低。
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2) 撰写preprocessing 模块，通过preprocessing 模块的fit/transform模式对
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数据进行转换。这样的方法看似麻烦，但是代码可重复利用率高，可以为后面操
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作节省很多工作。
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"""
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def embed_time_series(x, k):
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"""this function would transform an N dimensional time series into a
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tuple containing:
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1) an (n - k) by k matrix that is [X[i], x[i+1], ... x[i+k-1]],
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for i from 0 to n-k-1
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2) a vector of length (n - k) that is [x[k], x[k+1] ... x[n]]
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"""
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n = len(x)
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if k >= n:
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raise "Can not deal with k greater than the length of x"
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output_x = list(map(lambda i: list(x[i:(i+k)]),
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range(0, n-k)))
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return np.array(output_x)
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class TimeSeriesEmbedder(BaseEstimator, TransformerMixin):
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def __init__(self, k):
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self.k = k
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def fit(self, X, y= None):
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return self
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def transform(self, X, y = None):
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return embed_time_series(X, self.k)
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class ColumnExtractor(BaseEstimator, TransformerMixin):
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def __init__(self, column_name):
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self.column_name = column_name
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def fit(self, X, y=None):
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return self
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def transform(self, X, y=None):
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return X[self.column_name]
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class TimeSeriesDiff(BaseEstimator, TransformerMixin):
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def __init__(self, k=1):
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self.k = k
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def fit(self, X, y=None):
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return self
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def transform(self, X, y=None):
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if type(X) is pd.core.frame.DataFrame or type(X) is pd.core.series.Series:
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return X.diff(self.k) / X.shift(self.k)
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else:
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raise "Have to be a pandas data frame or Series object!"
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