I'm trying to instantiate my test set for classification, loading a dataset with 41 features and 1 label:
import numpy as np
f = open("mydataset")
dataset = np.genfromtxt(f, delimiter=',', dtype=None)
X = dataset[:, 0:40] # select columns 1 through 41
y = dataset[:, 41] # select column 42 (the labels)
Since mydataset is not homogeneous (not all elements have the same type), the function genfromtxt creates a 1D array (a list of tuples). So I get this error:
X = dataset[:, 0:40] # select columns 1 through 41
IndexError: too many indices for array
How can I solve this? Have I to transform the numpy array in 2D (if yes, in which way)? Or have I to use another way to select the right columns?
Thanks
You could define a compound dtype:
dt = np.dtype([('values',float,(41,)),('labels','S10')])
data=np.genfromtxt(f, delimiters=',',dtype=dt)
X = data['values']
Y = data['labels']
(not tested because I don't have a sample array this size).
And as I describe in a recent answer, https://stackoverflow.com/a/37126091/901925 ,
you could convert the dtype=None
data to this compound dtype with
data.view(dt)
though that requires that all the numbers be loaded as float (or all as ints). Often CSVs have a mix of float and integer columns, so the numeric fields of a None genfromtxt
call will be a mix of types.
Borrowing from that other answer, a general structured array might look like:
In [421]: data=np.array([('label1', 12, 23.2, 232.0), ('label2', 23, 2324.0, 324.0),
('label3', 34, 123.0, 2141.0), ('label4', 0, 2.0, 3.0)],
dtype=[('f0', '<U10'), ('f1', '<i4'), ('f2', '<f8'), ('f3', '<f8')])
4 fields with different dtypes.
Individual fields can be accessed by name: data['f0']
, or a list of names data[['f0','f3']]
. But the things you can do with the list of names is limited.
In [426]: data[['f2','f3']]=10
...
ValueError: multi-field assignment is not supported
You can do more if you make a copy, and more if you view it as homogeneous array:
In [427]: d23=data[['f2','f3']].copy()
In [428]: d23
Out[428]:
array([(23.2, 232.0), (2324.0, 324.0), (123.0, 2141.0), (2.0, 3.0)],
dtype=[('f2', '<f8'), ('f3', '<f8')])
In [429]: d23=d23.view((float,(2,)))
In [430]: d23
Out[430]:
array([[ 2.32000000e+01, 2.32000000e+02],
[ 2.32400000e+03, 3.24000000e+02],
[ 1.23000000e+02, 2.14100000e+03],
[ 2.00000000e+00, 3.00000000e+00]])
In [431]: d23+=34
In [432]: d23
Out[432]:
array([[ 57.2, 266. ],
[ 2358. , 358. ],
[ 157. , 2175. ],
[ 36. , 37. ]])
(changes to d23
do not affect the original data
).
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