I am trying to do something very similar to that previous question but I get an error.
I have a pandas dataframe containing features,label I need to do some convertion to send the features and the label variable into a machine learning object:
import pandas
import milk
from scikits.statsmodels.tools import categorical
then I have:
trainedData=bigdata[bigdata['meta']<15]
untrained=bigdata[bigdata['meta']>=15]
#print trainedData
#extract two columns from trainedData
#convert to numpy array
features=trainedData.ix[:,['ratio','area']].as_matrix(['ratio','area'])
un_features=untrained.ix[:,['ratio','area']].as_matrix(['ratio','area'])
print 'features'
print features[:5]
##label is a string:single, touching,nuclei,dust
print 'labels'
labels=trainedData.ix[:,['type']].as_matrix(['type'])
print labels[:5]
#convert single to 0, touching to 1, nuclei to 2, dusts to 3
#
tmp=categorical(labels,drop=True)
targets=categorical(labels,drop=True).argmax(1)
print targets
The output console yields first:
features
[[ 0.38846334 0.97681855]
[ 3.8318634 0.5724734 ]
[ 0.67710876 1.01816444]
[ 1.12024943 0.91508699]
[ 7.51749674 1.00156707]]
labels
[[single]
[touching]
[single]
[single]
[nuclei]]
I meet then the following error:
Traceback (most recent call last):
File "/home/claire/Applications/ProjetPython/projet particule et objet/karyotyper/DAPI-Trainer02-MILK.py", line 83, in <module>
tmp=categorical(labels,drop=True)
File "/usr/local/lib/python2.6/dist-packages/scikits.statsmodels-0.3.0rc1-py2.6.egg/scikits/statsmodels/tools/tools.py", line 206, in categorical
tmp_dummy = (tmp_arr[:,None]==data).astype(float)
AttributeError: 'bool' object has no attribute 'astype'
Is it possible to convert the category variable 'type' within the dataframe into int type ? 'type' can take the values 'single', 'touching','nuclei','dusts' and I need to convert with int values such 0, 1, 2, 3.
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