Python sklearn PCA.explained_variance_ratio_不等於1

我正在使用sklearn的PCA模塊。我正在使用下面的代碼來設置分析。Python sklearn PCA.explained_variance_ratio_不等於1

from sklearn.decomposition import PCA 
pca = PCA(n_components=9) 
p = pca.fit([row[:-1] for row in norm])

norm這裏是我的歸一化數據集，並在最後一列的唯一標識符，這就是爲什麼我在最後一行刪除它。這個數據集中有9個特徵，所以我預計有9個組成部分不會有無法解釋的變化。然而，當我打電話p.explained_variance_.cumsum()，我得到：

[ 0.06589563 0.08608778 0.09578116 0.10150195 0.10703567 0.11036608 
    0.11241904 0.11422285 0.11591605]

我誤解一些關於PCA？我之前沒有任何問題使用過這個模塊，但已經有一段時間了。我是否設置錯了？我把我的任何識別信息的數據都在這裏發佈。以下是似乎正在複製該問題的一部分數據。

[0.3888888888888889, 0.3888888888888889, 0.3888888888888889, 0.436943311456892, 0.7905900031193156, 0.5020468092219706, 0.8389717734280283, 0.7604923090797432, 0.8206054422776056, '0'] 
[0.3888888888888889, 0.3888888888888889, 0.2222222222222222, 0.4457200178477334, 0.8114779465247448, 0.506899600792241, 0.8368566485573798, 0.760617288778523, 0.8195489478905984, '1'] 
[0.2777777777777778, 0.2777777777777778, 0.05555555555555555, 0.4426231291814084, 0.7883413226205706, 0.5037172133121759, 0.8370362549229062, 0.7599752704033258, 0.8184218722901648, '2'] 
[0.1111111111111111, 0.1111111111111111, 0.16666666666666666, 0.4651807845446571, 0.7983379003654792, 0.5250604537887904, 0.8463875215362144, 0.7533582308429306, 0.8241548325954007, '3'] 
[0.5000000000000001, 0.5000000000000001, 0.3333333333333333, 0.4457200178477334, 0.7878040593905666, 0.506899600792241, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '4'] 
[0.3888888888888889, 0.3888888888888889, 0.2222222222222222, 0.44943322185630036, 0.7843622888520198, 0.5055757644148106, 0.8351253941103399, 0.7604171267769607, 0.8185442945328569, '5'] 
[0.3888888888888889, 0.3888888888888889, 0.3333333333333333, 0.4424914587425397, 0.7877430312713435, 0.5029950110274568, 0.836692391332608, 0.760611529525946, 0.8198150075184326, '6'] 
[0.3333333333333333, 0.05555555555555555, 0.7777777777777778, 0.4389415113841421, 0.7878040593905666, 0.506899600792241, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '7'] 
[0.4444444444444444, 0.4444444444444444, 0.4444444444444444, 0.42770705188736874, 0.7976039510596705, 0.5057230657076256, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '8'] 
[0.2222222222222222, 0.2777777777777778, 0.5000000000000001, 0.43182322765312314, 0.7971732873351607, 0.5072390458086798, 0.84541364942531, 0.7613416598875292, 0.8239037851005895, '9']

來源

2017-06-19 bendl

這裏的問題比CV社區更適合。我想知道'p = pca.fit（[row [： - 1] for norm]）'的用途是什麼。 – Toni

我也在那裏發帖，最終決定這是更好的位置，因爲我認爲問題的根源比我的代碼更符合理論 – bendl

是的，沒問題。我在表格上不是很大，並且不知道在哪裏發表我的答案。可能你應該考慮刪除你在任何社區的帖子。我看到你張貼了數據集的一部分，但我沒有機會玩它。 – Toni

變形蟲堆交換變形蟲最終給了我答案 - 這是一個簡單的錯字。我打電話給p.explained_variance_.cumsum()，但正確的方法調用是p.explained_variance_ratio_.cumsum()。當然，方差不一定要求和！

來源

2017-06-21 13:03:39 bendl

我很高興你有你的答案...正確...這是解釋方差的百分比... – Toni

這裏是虹膜數據集包括碎石圖爲例：

試圖與您剛剛發佈的數據集來重現您的問題：

d = matrix([[0.3888888888888889, 0.3888888888888889, 0.3888888888888889, 0.436943311456892, 0.7905900031193156, 0.5020468092219706, 0.8389717734280283, 0.7604923090797432, 0.8206054422776056, '0'], 
[0.3888888888888889, 0.3888888888888889, 0.2222222222222222, 0.4457200178477334, 0.8114779465247448, 0.506899600792241, 0.8368566485573798, 0.760617288778523, 0.8195489478905984, '1'], 
[0.2777777777777778, 0.2777777777777778, 0.05555555555555555, 0.4426231291814084, 0.7883413226205706, 0.5037172133121759, 0.8370362549229062, 0.7599752704033258, 0.8184218722901648, '2'], 
[0.1111111111111111, 0.1111111111111111, 0.16666666666666666, 0.4651807845446571, 0.7983379003654792, 0.5250604537887904, 0.8463875215362144, 0.7533582308429306, 0.8241548325954007, '3'], 
[0.5000000000000001, 0.5000000000000001, 0.3333333333333333, 0.4457200178477334, 0.7878040593905666, 0.506899600792241, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '4'], 
[0.3888888888888889, 0.3888888888888889, 0.2222222222222222, 0.44943322185630036, 0.7843622888520198, 0.5055757644148106, 0.8351253941103399, 0.7604171267769607, 0.8185442945328569, '5'], 
[0.3888888888888889, 0.3888888888888889, 0.3333333333333333, 0.4424914587425397, 0.7877430312713435, 0.5029950110274568, 0.836692391332608, 0.760611529525946, 0.8198150075184326, '6'], 
[0.3333333333333333, 0.05555555555555555, 0.7777777777777778, 0.4389415113841421, 0.7878040593905666, 0.506899600792241, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '7'], 
[0.4444444444444444, 0.4444444444444444, 0.4444444444444444, 0.42770705188736874, 0.7976039510596705, 0.5057230657076256, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '8'], 
[0.2222222222222222, 0.2777777777777778, 0.5000000000000001, 0.43182322765312314, 0.7971732873351607, 0.5072390458086798, 0.84541364942531, 0.7613416598875292, 0.8239037851005895, '9']])

來源

2017-06-21 03:35:33 Toni

Python sklearn PCA.explained_variance_ratio_不等於1

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