为机器学习选择正确类型的数据

Question

我一直对机器学习非常好奇，我正在用它来学习。

我能够毫无问题地编译代码，并生成图形。

我想使用不同的数据源。目前他们正在使用股票价格：

d1 = datetime.datetime(2003, 01, 01)
d2 = datetime.datetime(2008, 01, 01)
symbol_dict = {
'TOT': 'Total',
'XOM': 'Exxon',
'CVX': 'Chevron',
'COP': 'ConocoPhillips',
...
...
}
symbols, names = np.array(symbol_dict.items()).T
quotes = [finance.quotes_historical_yahoo(symbol, d1, d2, asobject=True)
for symbol in symbols]
open = np.array([q.open for q in quotes]).astype(np.float)
close = np.array([q.close for q in quotes]).astype(np.float)

1. quotes返回什么？我知道这是每股股票的价格，但我得到了这样的东西：

[rec.array([(datetime.date(2003，1，2)，2003，173217.0，28.12235692134198、28.5、28.564279672963064、28.0982524398083、12798800.0、28.5)，(datetime.date(2003，1，3)，2003，1、3、731218.0、28.329084507042257、28.53、28.634476056338034、28.28890140845071、9221900.0、28.53)，(日期时间日期(2003年1月6日)，2003年1日，6日，731221.0，28.482778999450247，29.23，29.406761957119297，28.45064046179219，11925100.0，29.23)，…，

1. 我想输入我自己的数据集。你能给我一个我可以输入到quotes的数据集的例子吗

整个代码都在这里：

http://scikit-learn.org/dev/auto_examples/applications/plot_stock_market.html

Answer 1

如果您在ipython中执行finance.quotes_historical_yahoo?，它会告诉您：

In [53]: finance.quotes_historical_yahoo?
Type:       function
String Form:<function quotes_historical_yahoo at 0x10f311d70>
File:       /Users/dvelkov/src/matplotlib/lib/matplotlib/finance.py
Definition: finance.quotes_historical_yahoo(ticker, date1, date2, asobject=False, adjusted=True, cachename=None)
Docstring:
Get historical data for ticker between date1 and date2.  date1 and
date2 are datetime instances or (year, month, day) sequences.
See :func:`parse_yahoo_historical` for explanation of output formats
and the *asobject* and *adjusted* kwargs.
...(more stuff)

所以我们检查parse_yahoo_historical:

In [54]: finance.parse_yahoo_historical?
Type:       function
String Form:<function parse_yahoo_historical at 0x10f996ed8>
File:       /Users/dvelkov/src/matplotlib/lib/matplotlib/finance.py
Definition: finance.parse_yahoo_historical(fh, adjusted=True, asobject=False)
Docstring:
Parse the historical data in file handle fh from yahoo finance.
*adjusted*
If True (default) replace open, close, high, and low prices with
their adjusted values. The adjustment is by a scale factor, S =
adjusted_close/close. Adjusted prices are actual prices
multiplied by S.
Volume is not adjusted as it is already backward split adjusted
by Yahoo. If you want to compute dollars traded, multiply volume
by the adjusted close, regardless of whether you choose adjusted
= True|False.

*asobject*
If False (default for compatibility with earlier versions)
return a list of tuples containing
d, open, close, high, low, volume
If None (preferred alternative to False), return
a 2-D ndarray corresponding to the list of tuples.
Otherwise return a numpy recarray with
date, year, month, day, d, open, close, high, low,
volume, adjusted_close
where d is a floating poing representation of date,
as returned by date2num, and date is a python standard
library datetime.date instance.
The name of this kwarg is a historical artifact.  Formerly,
True returned a cbook Bunch
holding 1-D ndarrays.  The behavior of a numpy recarray is
very similar to the Bunch.

在您的情况下，您使用的是asobject=True，因此您得到的格式是date, year, month, day, d, open, close, high, low, volume, adjusted_close。