PyError with OneHotEncoder (Julia 0.6.0 on Windows10)

[ValdarT]

I'm getting a PyError with this code.

using DataFrames
using ScikitLearn
@sk_import preprocessing: OneHotEncoder

df = DataFrame(A = 1:4, B = ["M", "F", "F", "M"])

mapper = DataFrameMapper([([:B], OneHotEncoder())]);

fit_transform!(mapper, df)

ERROR: PyError (ccall(@pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, arg, C_NULL)) <type 'exceptions.ValueError'>
ValueError('could not convert string to float: M',)
  File "C:\Users\...\.julia\v0.6\Conda\deps\usr\lib\site-packages\sklearn\preprocessing\data.py", line 1844, in fit
    self.fit_transform(X)
  File "C:\Users\...\.julia\v0.6\Conda\deps\usr\lib\site-packages\sklearn\preprocessing\data.py", line 1902, in fit_transform
    self.categorical_features, copy=True)
  File "C:\Users\...\.julia\v0.6\Conda\deps\usr\lib\site-packages\sklearn\preprocessing\data.py", line 1697, in _transform_selected
    X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)
  File "C:\Users\...\.julia\v0.6\Conda\deps\usr\lib\site-packages\sklearn\utils\validation.py", line 382, in check_array
    array = np.array(array, dtype=dtype, order=order, copy=copy)

It seems specific to OneHotEncoder. For example, LabelBinarizer works fine like this:

mapper = DataFrameMapper([(:B, LabelBinarizer())]);

I'm on Windows 10 using Julia 0.6.0.
Package versions:

- Conda                         0.5.3
- DataArrays                    0.5.3
- DataFrames                    0.10.0
- PyCall                        1.14.0
- ScikitLearn                   0.3.0
- ScikitLearnBase               0.3.0

I let ScikitLearn.jl automatically handle the installation of Python dependencies. The installed versions are:

python                    2.7.13
numpy                     1.13.0
scikit-learn              0.18.2

[cstjean]

It's probably a bug, but have you checked if the equivalent code works in Python?

You can use ScikitLearn.Preprocessing.DictEncoder() until this gets fixed. The semantics are a bit different, but for single-column input matrices it should be the same:

DictEncoder()

For every unique row in the input matrix, associate a 0/1 binary column in the output matrix. This is similar to OneHotEncoder, but considers the entire row as a single value for one-hot-encoding. It works with any hashable datatype.

It is common to use it inside a DataFrameMapper, with a particular subset of the columns.

[cstjean]

Thank you for the detailed bug report!

[ValdarT]

Sorry, my mistake. Turns out OneHotEncoder only accepts integer values. Rather unexpected and weird in my opinion but clearly stated in the docs. At least I'm not the only one: scikit-learn-contrib/sklearn-pandas#63. : )

However, I still get an 'invalid Array dimensions' error with this code

using DataFrames
using ScikitLearn
@sk_import preprocessing: OneHotEncoder

df = DataFrame(A = 1:4, B = ["M", "F", "F", "M"])

mapper = DataFrameMapper([([:A], OneHotEncoder())]);

fit_transform!(mapper, df)

invalid Array dimensions

Stacktrace:
 [1] Array{Float64,N} where N(::Tuple{Int64}) at .\boot.jl:317
 [2] py2array(::Type{T} where T, ::PyCall.PyObject) at C:\Users\...\.julia\v0.6\PyCall\src\conversions.jl:381
 [3] convert(::Type{Array{Float64,2}}, ::PyCall.PyObject) at C:\Users\...\.julia\v0.6\PyCall\src\numpy.jl:480
 [4] transform(::ScikitLearn.DataFrameMapper, ::DataFrames.DataFrame) at C:\Users\...\.julia\v0.6\ScikitLearn\src\dataframes.jl:150
 [5] #fit_transform!#16(::Array{Any,1}, ::Function, ::ScikitLearn.DataFrameMapper, ::DataFrames.DataFrame, ::Void) at C:\Users\...\.julia\v0.6\ScikitLearnBase\src\ScikitLearnBase.jl:152
 [6] fit_transform!(::ScikitLearn.DataFrameMapper, ::DataFrames.DataFrame) at C:\Users\...\.julia\v0.6\ScikitLearnBase\src\ScikitLearnBase.jl:152

although this code in Python works fine

import pandas as pd
from sklearn_pandas import DataFrameMapper
from sklearn.preprocessing import OneHotEncoder

df = pd.DataFrame({'A': [1,2,3,4], 'B': ["M", "F", "F", "M"]})
mapper = DataFrameMapper([(['A'], OneHotEncoder())])

mapper.fit_transform(df)

[ValdarT]

Fortunately the change to OneHotEncoder for accepting strings is in the works: scikit-learn/scikit-learn#4920

[cstjean]

Figured it out; OneHotEncoder returns a sparse matrix by default, which PyCall doesn't know how to convert (JuliaPy/PyCall.jl#204). It would have to be fixed there. Or at the very least, there should be a clearer error message on that end.

Fortunately, you can solve the problem with OneHotEncoder(sparse=false).

Turns out OneHotEncoder only accepts integer values

Use DictEncoder! It's pure Julia, so it'll be way faster than OneHotEncoder, and it will work with any hashable type (almost anything).

[ValdarT]

Thank you!

Use DictEncoder!

Will do.

Hopefully we can soon replace all the preprocessing steps with pure Julia implementations. The work at JuliaML seems to get there step-by-step.