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Merge pull request #267 from FluxML/entity-embeddings
Introduce EntityEmbeddings
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,152 @@ | ||
| """ | ||
| File containing ordinal encoder and entity embedding encoder. Borrows code from the MLJTransforms package. | ||
| """ | ||
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||
| ### Ordinal Encoder | ||
| """ | ||
| **Private Method** | ||
| Fits an ordinal encoder to the table `X`, using only the columns with indices in `featinds`. | ||
| Returns a dictionary mapping each column index to a dictionary mapping each level in that column to an integer. | ||
| """ | ||
| function ordinal_encoder_fit(X; featinds) | ||
| # 1. Define mapping per column per level dictionary | ||
| mapping_matrix = Dict() | ||
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| # 2. Use feature mapper to compute the mapping of each level in each column | ||
| for i in featinds | ||
| feat_col = Tables.getcolumn(Tables.columns(X), i) | ||
| feat_levels = levels(feat_col) | ||
| # Check if feat levels is already ordinal encoded in which case we skip | ||
| (Set([float(i) for i in 1:length(feat_levels)]) == Set(feat_levels)) && continue | ||
| # Compute the dict using the given feature_mapper function | ||
| mapping_matrix[i] = | ||
| Dict{Any, AbstractFloat}( | ||
| value => float(index) for (index, value) in enumerate(feat_levels) | ||
| ) | ||
| end | ||
| return mapping_matrix | ||
| end | ||
|
|
||
| """ | ||
| **Private Method** | ||
| Checks that all levels in `test_levels` are also in `train_levels`. If not, throws an error. | ||
| """ | ||
| function check_unkown_levels(train_levels, test_levels) | ||
| # test levels must be a subset of train levels | ||
| if !issubset(test_levels, train_levels) | ||
| # get the levels in test that are not in train | ||
| lost_levels = setdiff(test_levels, train_levels) | ||
| error( | ||
| "While transforming, found novel levels for the column: $(lost_levels) that were not seen while training.", | ||
| ) | ||
| end | ||
| end | ||
|
|
||
| """ | ||
| **Private Method** | ||
| Transforms the table `X` using the ordinal encoder defined by `mapping_matrix`. | ||
| Returns a new table with the same column names as `X`, but with categorical columns replaced by integer columns. | ||
| """ | ||
| function ordinal_encoder_transform(X, mapping_matrix) | ||
| isnothing(mapping_matrix) && return X | ||
| isempty(mapping_matrix) && return X | ||
| feat_names = Tables.schema(X).names | ||
| numfeats = length(feat_names) | ||
| new_feats = [] | ||
| for ind in 1:numfeats | ||
| col = Tables.getcolumn(Tables.columns(X), ind) | ||
|
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||
| # Create the transformation function for each column | ||
| if ind in keys(mapping_matrix) | ||
| train_levels = keys(mapping_matrix[ind]) | ||
| test_levels = levels(col) | ||
| check_unkown_levels(train_levels, test_levels) | ||
| level2scalar = mapping_matrix[ind] | ||
| new_col = recode(col, level2scalar...) | ||
| push!(new_feats, new_col) | ||
| else | ||
| push!(new_feats, col) | ||
| end | ||
| end | ||
|
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||
| transformed_X = NamedTuple{tuple(feat_names...)}(tuple(new_feats)...) | ||
| # Attempt to preserve table type | ||
| transformed_X = Tables.materializer(X)(transformed_X) | ||
| return transformed_X | ||
| end | ||
|
|
||
| """ | ||
| **Private Method** | ||
| Combine ordinal_encoder_fit and ordinal_encoder_transform and return both X and ordinal_mappings | ||
| """ | ||
| function ordinal_encoder_fit_transform(X; featinds) | ||
| ordinal_mappings = ordinal_encoder_fit(X; featinds = featinds) | ||
| return ordinal_encoder_transform(X, ordinal_mappings), ordinal_mappings | ||
| end | ||
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| ## Entity Embedding Encoder (assuming precomputed weights) | ||
| """ | ||
| **Private method.** | ||
| Function to generate new feature names: feat_name_0, feat_name_1,..., feat_name_n | ||
| """ | ||
| function generate_new_feat_names(feat_name, num_inds, existing_names) | ||
| conflict = true # will be kept true as long as there is a conflict | ||
| count = 1 # number of conflicts+1 = number of underscores | ||
|
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||
| new_column_names = [] | ||
| while conflict | ||
| suffix = repeat("_", count) | ||
| new_column_names = [Symbol("$(feat_name)$(suffix)$i") for i in 1:num_inds] | ||
| conflict = any(name -> name in existing_names, new_column_names) | ||
| count += 1 | ||
| end | ||
| return new_column_names | ||
| end | ||
|
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||
|
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| """ | ||
| Given X and a dict of mapping_matrices that map each categorical column to a matrix, use the matrix to transform | ||
| each level in each categorical columns using the columns of the matrix. | ||
| This is used with the embedding matrices of the entity embedding layer in entity enabled models to implement entity embeddings. | ||
| """ | ||
| function embedding_transform(X, mapping_matrices) | ||
| (isempty(mapping_matrices)) && return X | ||
| feat_names = Tables.schema(X).names | ||
| new_feat_names = Symbol[] | ||
| new_cols = [] | ||
| for feat_name in feat_names | ||
| col = Tables.getcolumn(Tables.columns(X), feat_name) | ||
| # Create the transformation function for each column | ||
| if feat_name in keys(mapping_matrices) | ||
| level2vector = mapping_matrices[feat_name] | ||
| new_multi_col = map(x -> level2vector[:, Int.(unwrap(x))], col) | ||
| new_multi_col = [col for col in eachrow(hcat(new_multi_col...))] | ||
| push!(new_cols, new_multi_col...) | ||
| feat_names_with_inds = generate_new_feat_names( | ||
| feat_name, | ||
| size(level2vector, 1), | ||
| feat_names, | ||
| ) | ||
| push!(new_feat_names, feat_names_with_inds...) | ||
| else | ||
| # Not to be transformed => left as is | ||
| push!(new_feat_names, feat_name) | ||
| push!(new_cols, col) | ||
| end | ||
| end | ||
|
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||
| transformed_X = NamedTuple{tuple(new_feat_names...)}(tuple(new_cols)...) | ||
| # Attempt to preserve table type | ||
| transformed_X = Tables.materializer(X)(transformed_X) | ||
| return transformed_X | ||
| end |
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