问题描述
我想在 this notebook 之后进行主成分分析，以从其成分（通过 Quandl 找到）重建 DJIA（我正在使用 alpha_ventage）。然而，在重建将主成分与其权重相乘的值时，我似乎创建了比原始数据帧更多的值
kernel_pca = KernelPCA(n_components=5).fit(df_z_components)
pca_5 = kernel_pca.transform(-daily_df_components)
weights = fn_weighted_average(kernel_pca.lambdas_)
reconstructed_values = np.dot(pca_5,weights)
实际上，daily_df_components 是通过 quandl API 从 DJIA 的组件创建的，它似乎比我用来获取 DJIA 指数的库 alpha_ventage 拥有更多的数据。
这是完整的代码
"""
Obtaining the components data from quandl
"""
import quandl
QUANDL_API_KEY = 'MYKEY'
quandl.ApiConfig.api_key = QUANDL_API_KEY
SYMBOLS = [
'AAPL','MMM','BA','AXP','CAT','CVX','CSCO','KO','DD','XOM','GS','HD','IBM','INTC','JNJ','JPM','MCD','MRK','MSFT','NKE','PFE','PG','UNH','UTX','TRV','VZ','V','WMT','WBA','dis'
]
wiki_symbols = ['WIKI/%s'%symbol for symbol in SYMBOLS]
df_components = quandl.get(
wiki_symbols,start_date='2017-01-01',end_date='2017-12-31',column_index=11)
df_components.columns = SYMBOLS
filled_df_components = df_components.fillna(method='ffill')
daily_df_components = filled_df_components.resample('24h').ffill()
daily_df_components = daily_df_components.fillna(method='bfill')
"""
Download the all-time DJIA dataset
"""
from alpha_vantage.timeseries import TimeSeries
# Update your Alpha Vantage API key here...
ALPHA_VANTAGE_API_KEY = 'MYKEY'
ts = TimeSeries(key=ALPHA_VANTAGE_API_KEY,output_format='pandas')
df,Meta_data = ts.get_inTraday(symbol='DIA',interval='1min',outputsize='full')
# Finding eigenvectors and eigen values
fn_weighted_average = lambda x: x/x.sum()
weighted_values = fn_weighted_average(fitted_pca.lambdas_)[:5]
from sklearn.decomposition import KernelPCA
fn_z_score = lambda x: (x - x.mean())/x.std()
df_z_components = daily_df_components.apply(fn_z_score)
fitted_pca = KernelPCA().fit(df_z_components)
# Reconstructing the Dow Average with PCA
import numpy as np
kernel_pca = KernelPCA(n_components=5).fit(df_z_components)
pca_5 = kernel_pca.transform(-daily_df_components)
weights = fn_weighted_average(kernel_pca.lambdas_)
reconstructed_values = np.dot(pca_5,weights)
# Combine PCA and Index to compare
df_combined = djia_2020_weird.copy()
df_combined['pca_5'] = reconstructed_values
但它返回：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-100-2808dc14f789> in <module>()
9 # Combine PCA and Index to compare
10 df_combined = djia_2020_weird.copy()
---> 11 df_combined['pca_5'] = reconstructed_values
12 df_combined = df_combined.apply(fn_z_score)
13 df_combined.plot(figsize=(12,8));
3 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/construction.py in sanitize_index(data,index)
746 if len(data) != len(index):
747 raise ValueError(
--> 748 "Length of values "
749 f"({len(data)}) "
750 "does not match length of index "
ValueError: Length of values (361) does not match length of index (14)
确实，reconstructed_values 是 361 长而 df_combined 是 14 值长...
这是最后一个数据框：
DJI
date
2021-01-21 NaN
2021-01-22 311.37
2021-01-23 310.03
2021-01-24 310.03
2021-01-25 310.03
2021-01-26 309.01
2021-01-27 309.49
2021-01-28 302.17
2021-01-29 305.25
2021-01-30 299.20
2021-01-31 299.20
2021-02-01 299.20
2021-02-02 302.13
2021-02-03 307.86
可能是notebook作者可以拿到他感兴趣的一整年的数据，我跑数据的时候好像只有两个月？
解决方法
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