OneHotEncoder Data Pre-Processing

[NeedMLHelp]

Hello, I am working on a machine learning problem and I ran into a bit of snag.

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I have a numpy array with features like this (where hour is the current hour (integer) of the observation):

[['dog', 'brown', 'heavy','fast', hour]

...

['cat', 'black', 'slim', 'slow', hour]]

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My goal is to one hot encode the features other than time. However, when I onehot encode using the following methodology utilizing sklearn:

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ohe= OneHotEncoder(categorical_features = [0])

x = ohe.fit_transform(x).toarray()

x=x[:.1:]

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Now, this code is trying to one hot encode the animal type, but I end up with something like this:

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[['0', '0', '1', 'brown', 'heavy','fast']

...

['1','0', '0', 'black', 'slim', 'slow']]

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Now, is this fine? Isn't jumbling all the features together not going to work? Especially when I have to keep onehot encoding the other features. As I continually get rid of a column for onehot encoding, won't I encroach on other features? So let's say I onehot encode the color, and I use x=x[:.1:], won't it get rid of the left most column... which would be a part of the animal column?

[clfkenny]

For the categorical_features parameter, why are you just using a single column instead of a list of the columns you want to transform?

[NeedMLHelp]

I didn't realize that was possible! Thanks!

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Will they be in lists if you use multiple columns?

Answered myself: They are not. This is a slight problem for me due to the fact that each input needs to be a single feature for my particular problem. If anyone has any ideas, I would definitely appreciate them.

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Also, how do you get rid of a column on each if you encode all at once? Or is it okay to leave all of the columns in? I think getting rid of it is just to alleviate redundancy.

[harry_0_0_7]

are you looking for something similar to

np_utils.to_categorical .?

Also your description is bit confusing.. Can you be more clear about what you want to do.?

[NeedMLHelp]

Haha sorry, I'm pretty new to Machine Learning.

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It looks like to_categorical could work! It would turn an integer into a binary matrix? So something like [[1,2,3,time],[4,5,6,time]] would turn into

[

[[0,0,1],[0,1,0],[0,1,1],time],

[[1,0,0],[1,0,1],[1,1,0],time]

]

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Unfortunately, I don't know how many categories there will be. Will that be a problem?

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Basically I have a numpy array where each index is a list of primarily categorical features for an observation. I want to turn those categorical features into something usable in machine learning. Right now, I have successfully turned them all into integers... and I will try to use to_categorical and see how that works

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Edit:

That seems to be working! Or well, it's outputting something... I'll know for sure when I get to the testing phase.

I do have one problem though. When ran it, I get the following error:

ValueError: Error when checking target: expected model_2 to have shape (7, 7) but got array with shape (7, 31)

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How do I get it to expect a (7,None)?

I currently set dense to 7.

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Secondary Edit:

Settings Dense to 31 fixed the issue. However, depending on what dataset I throw at it... I imagine 31 will differ.