Credits: this code and documentation was adapted from Paul Butler's sklearn-pandas
DataFrames
It is possible to use a dataframe as a training set, but it needs to be converted to an array first. DataFrameMapper is used to specify how this conversion proceeds. For example, PCA might be applied to some numerical dataframe columns, and one-hot-encoding to a categorical column.
Transformation Mapping
Consider this dataset:
julia> using ScikitLearn
julia> using DataFrames: DataFrame, missing
julia> @sk_import preprocessing: (LabelBinarizer, StandardScaler)
PyObject <class 'sklearn.preprocessing._data.StandardScaler'>
julia> data = DataFrame(pet=["cat", "dog", "dog", "fish", "cat", "dog", "cat", "fish"],
children=[4., 6, 3, 3, 2, 3, 5, 4],
salary=[90, 24, 44, 27, 32, 59, 36, 27])
8×3 DataFrame
│ Row │ pet │ children │ salary │
│ │ String │ Float64 │ Int64 │
├─────┼────────┼──────────┼────────┤
│ 1 │ cat │ 4.0 │ 90 │
│ 2 │ dog │ 6.0 │ 24 │
│ 3 │ dog │ 3.0 │ 44 │
│ 4 │ fish │ 3.0 │ 27 │
│ 5 │ cat │ 2.0 │ 32 │
│ 6 │ dog │ 3.0 │ 59 │
│ 7 │ cat │ 5.0 │ 36 │
│ 8 │ fish │ 4.0 │ 27 │
Map the Columns to Transformations
The mapper takes a list of pairs. The first is a column name from the DataFrame, or a list containing one or multiple columns (we will see an example with multiple columns later). The second is an object which will perform the transformation which will be applied to that column:
Note: ScikitLearn.DataFrameMapper won't be available until DataFrames is imported
julia> mapper = DataFrameMapper([(:pet, LabelBinarizer()),
([:children], StandardScaler())]);The difference between specifying the column selector as :column (as a single symbol) and [:column] (as a list with one element) is the shape of the array that is passed to the transformer. In the first case, a one dimensional array with be passed, while in the second case it will be a 2-dimensional array with one column, i.e. a column vector.
Test the Transformation
We can use the fit_transform! shortcut to both fit the model and see what transformed data looks like. In this and the other examples, output is rounded to two digits with round to account for rounding errors on different hardware:
julia> round.(fit_transform!(mapper, copy(data)), digits=2)
8×4 Array{Float64,2}:
1.0 0.0 0.0 0.21
0.0 1.0 0.0 1.88
0.0 1.0 0.0 -0.63
0.0 0.0 1.0 -0.63
1.0 0.0 0.0 -1.46
0.0 1.0 0.0 -0.63
1.0 0.0 0.0 1.04
0.0 0.0 1.0 0.21
Note that the first three columns are the output of the LabelBinarizer (corresponding to cat, dog, and fish respectively) and the fourth column is the standardized value for the number of children. In general, the columns are ordered according to the order given when the DataFrameMapper is constructed.
Now that the transformation is trained, we confirm that it works on new data:
julia> sample = DataFrame(pet = ["cat"], children = [5.])
1×2 DataFrame
│ Row │ pet │ children │
│ │ String │ Float64 │
├─────┼────────┼──────────┤
│ 1 │ cat │ 5.0 │
julia> round.(transform(mapper, sample), digits=2)
1×4 Array{Float64,2}:
1.0 0.0 0.0 1.04
Transform Multiple Columns
Transformations may require multiple input columns. In these cases, the column names can be specified in a list:
julia> @sk_import decomposition: PCA
PyObject <class 'sklearn.decomposition._pca.PCA'>
julia> mapper2 = DataFrameMapper([([:children, :salary], PCA(1))]);
Now running fit_transform! will run PCA on the children and salary columns and return the first principal component:
julia> round.(fit_transform!(mapper2, copy(data)), digits=1)
8×1 Array{Float64,2}:
47.6
-18.4
1.6
-15.4
-10.4
16.6
-6.4
-15.4
Multiple transformers for the same column
Multiple transformers can be applied to the same column specifying them in a list:
julia> @sk_import impute: SimpleImputer
PyObject <class 'sklearn.impute._base.SimpleImputer'>
julia> mapper3 = DataFrameMapper([([:age], [SimpleImputer(),
StandardScaler()])]; missing2NaN=true);
julia> data_3 = DataFrame(age= [1, missing, 3]);
julia> fit_transform!(mapper3, data_3)
3×1 Array{Float64,2}:
-1.224744871391589
0.0
1.224744871391589
Columns that don't need any transformation
Only columns that are listed in the DataFrameMapper are kept. To keep a column but don't apply any transformation to it, use nothing as transformer:
julia> mapper3 = DataFrameMapper([(:pet, LabelBinarizer()), (:children, nothing)]);
julia> round.(fit_transform!(mapper3, copy(data)))
8×4 Array{Float64,2}:
1.0 0.0 0.0 4.0
0.0 1.0 0.0 6.0
0.0 1.0 0.0 3.0
0.0 0.0 1.0 3.0
1.0 0.0 0.0 2.0
0.0 1.0 0.0 3.0
1.0 0.0 0.0 5.0
0.0 0.0 1.0 4.0
Cross-validation
Now that we can combine features from a DataFrame, we may want to use cross-validation to see whether our model works.
julia> @sk_import linear_model: LinearRegression
PyObject <class 'sklearn.linear_model._base.LinearRegression'>
julia> using ScikitLearn.CrossValidation: cross_val_score
julia> pipe = Pipelines.Pipeline([(:featurize, mapper), (:lm, LinearRegression())]);
julia> round.(cross_val_score(pipe, data, data[!,:salary]), digits=2)
3-element Array{Float64,1}:
-1.09
-5.3
-15.38