将变量列表传递给 ridge.fit() - 不可散列的类型？

Question

我有一个名为 training 的数据集，它是在使用 Pandas 进行操作时读取的。大约有 150 个变量，所以我将它们放在一个列表中，我想将它们传递给岭回归；但是，我收到一条错误消息“不可散列的类型：列表”

我可能遗漏了一些明显的东西，因为这是我第一次使用 python（用于 R 和 Stata）。

代码如下：

  # Variables to use (potentially) -- for dummies, one has already been taken out to avoid dummy var trap
continuous_vars = ['VehicleAge','VehOdo', 'MMRAcquisitionAuctionAveragePrice', 'MMRAcquisitionAuctionCleanPrice', 'MMRAcquisitionRetailAveragePrice', 'MMRAcquisitonRetailCleanPrice', 'MMRCurrentAuctionAveragePrice', 'MMRCurrentAuctionCleanPrice', 'MMRCurrentRetailAveragePrice', 'MMRCurrentRetailCleanPrice', 'VehBCost', 'IsOnlineSale', 'WarrantyCost', 'reliability_score', 'num_bought']
make_cats = ['BUICK', 'CADILLAC', 'CHEVROLET', 'CHRYSLER', 'DODGE', 'FORD', 'GMC', 'HONDA', 'HUMMER', 'HYUNDAI', 'INFINITI', 'ISUZU', 'JEEP', 'KIA', 'LEXUS', 'LINCOLN', 'MAZDA', 'MERCURY', 'MINI', 'MITSUBISHI', 'NISSAN', 'OLDSMOBILE', 'PLYMOUTH', 'PONTIAC', 'SATURN', 'SCION', 'SUBARU', 'SUZUKI', 'TOYOTA', 'TOYOTA SCION', 'VOLKSWAGEN', 'VOLVO']
state_cats = ['AR', 'AZ', 'CA', 'CO', 'FL', 'GA', 'IA', 'ID', 'IL', 'IN', 'KY', 'LA', 'MA', 'MD', 'MI', 'MN', 'MO', 'MS', 'NC', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'SC', 'TN', 'TX', 'UT', 'VA', 'WA', 'WV']
auction_cats = ['ADESA', 'MANHEIM', 'OTHER']
trans_cats = ['AUTO']
color_cats = ['BEIGE', 'BLACK', 'BLUE', 'BROWN', 'GOLD', 'GREEN', 'GREY', 'MAROON', 'NOT AVAIL', 'ORANGE', 'OTHER', 'PURPLE', 'RED', 'SILVER', 'WHITE', 'YELLOW']
wheel_cats = ['Alloy', 'Covers', 'Special']
nat_cats = ['AMERICAN', 'OTHER', 'OTHER ASIAN', 'TOP LINE ASIAN']
size_cats =['COMPACT', 'CROSSOVER', 'LARGE', 'LARGE SUV', 'LARGE TRUCK', 'MEDIUM', 'MEDIUM SUV', 'SMALL SUV', 'SMALL TRUCK', 'SPECIALTY', 'SPORTS', 'VAN']
year_cats = ['2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010_x']

all_vars = continuous_vars + make_cats + state_cats + auction_cats + trans_cats + color_cats + wheel_cats + nat_cats + size_cats + year_cats
hashable = all_vars
## Ridge Regression 
ridge_reg = Ridge(alpha=1)

ridge_reg.fit(training[hashable], training['IsBadBuy'])

更新 我更新了代码以反映一些建议。这是新的错误消息：

  File "C:\Anaconda\lib\site-packages\pandas\core\indexing.py", line 1068, in _convert_to_indexer
    raise KeyError('%s not in index' % objarr[mask])
KeyError: "['reliability_score' 'num_bought' 'BUICK' 'CADILLAC' 'CHEVROLET' 'CHRYSLER'\n 'DODGE' 'FORD' 'GMC' 'HONDA' 'HUMMER' 'HYUNDAI' 'INFINITI' 'ISUZU' 'JEEP'\n 'KIA' 'LEXUS' 'LINCOLN' 'MAZDA' 'MERCURY' 'MINI' 'MITSUBISHI' 'NISSAN'\n 'OLDSMOBILE' 'PLYMOUTH' 'PONTIAC' 'SATURN' 'SCION' 'SUBARU' 'SUZUKI'\n 'TOYOTA' 'TOYOTA SCION' 'VOLKSWAGEN' 'VOLVO' 'AR' 'AZ' 'CA' 'CO' 'FL' 'GA'\n 'IA' 'ID' 'IL' 'IN' 'KY' 'LA' 'MA' 'MD' 'MI' 'MN' 'MO' 'MS' 'NC' 'NE' 'NH'\n 'NJ' 'NM' 'NV' 'NY' 'OH' 'OK' 'OR' 'PA' 'SC' 'TN' 'TX' 'UT' 'VA' 'WA' 'WV'\n 'ADESA' 'MANHEIM' 'OTHER' 'AUTO' 'BEIGE' 'BLACK' 'BLUE' 'BROWN' 'GOLD'\n 'GREEN' 'GREY' 'MAROON' 'NOT AVAIL' 'ORANGE' 'OTHER' 'PURPLE' 'RED'\n 'SILVER' 'WHITE' 'YELLOW' 'Alloy' 'Covers' 'Special' 'AMERICAN' 'OTHER'\n 'OTHER ASIAN' 'TOP LINE ASIAN' 'COMPACT' 'CROSSOVER' 'LARGE' 'LARGE SUV'\n 'LARGE TRUCK' 'MEDIUM' 'MEDIUM SUV' 'SMALL SUV' 'SMALL TRUCK' 'SPECIALTY'\n 'SPORTS' 'VAN' '2001' '2002' '2003' '2004' '2005' '2006' '2007' '2008'\n '2009' '2010_x'] not in index"

Answer 1

您所做的是创建一个列表列表，您希望将它们全部连接起来：

all_vars = continuous_vars+ make_cats+ state_cats+ auction_cats+ trans_cats+ color_cats+ wheel_cats+ nat_cats+ size_cats+ year_cats

这将正确地从 pandas 中选择特征列

比较以下：

In [84]:

[auction_cats, wheel_cats]
Out[84]:
[['ADESA', 'MANHEIM', 'OTHER'], ['Alloy', 'Covers', 'Special']]
In [85]:

auction_cats+wheel_cats
Out[85]:
['ADESA', 'MANHEIM', 'OTHER', 'Alloy', 'Covers', 'Special']

Answer 2

几天前我遇到了同样的问题。事实证明，您不能将列表类型用作字典中的键。我通过将列表元素分配给一个新变量并将该变量用作键来解决这个问题。

hashableVar = all_vars
training[hashableVar]