Retain data in tabular form

src/MLJGLMInterface.jl

@@ -18,7 +18,7 @@
 import MLJModelInterface: metadata_pkg, metadata_model, Table, Continuous, Count, Finite,
     OrderedFactor, Multiclass, @mlj_model
 using Distributions: Bernoulli, Distribution, Poisson
-using StatsModels: ConstantTerm, Term, FormulaTerm, term
+using StatsModels: ConstantTerm, Term, FormulaTerm, term, modelcols
 using Tables
 import GLM
 
@@ -97,20 +97,6 @@
 ## Helper functions
 ###
 
-"""
-    augment_X(X::AbstractMatrix, b::Bool)
-
-Augment the matrix `X` with a column of ones if the intercept should
-be fitted (`b==true`) and return `X` otherwise.
-"""
-function augment_X(X::AbstractMatrix, b::Bool)
-    if b
-        return hcat(X, ones(float(Int), size(X, 1), 1))
-    else
-        return X
-    end
-end
-
 _to_vector(v::Vector) = v
 _to_vector(v) = collect(v)
 _to_array(v::AbstractArray) = v
@@ -203,38 +189,15 @@
     return nothing
 end
 
-function _matrix_and_features(model, Xcols, handle_intercept=false)
-    col_names = Tables.columnnames(Xcols)
-    n, p = Tables.rowcount(Xcols), length(col_names)
-    augment = handle_intercept && model.fit_intercept
-
-    if !handle_intercept # i.e This only runs during `fit`
-        check_sample_size(model, n, p)
-    end
-
-    if p == 0
-        Xmatrix = Matrix{float(Int)}(undef, n, p)
-    else
-        Xmatrix = Tables.matrix(Xcols)
-    end
-
-    Xmatrix = augment_X(Xmatrix, augment)
-
-    return Xmatrix, col_names
-end
-
-_to_columns(t::Tables.AbstractColumns) = t
-_to_columns(t) = Tables.Columns(t)
-
 """
     prepare_inputs(model, X; handle_intercept=false)
 
 Handle `model.offsetcol` and `model.fit_intercept` if `handle_intercept=true`.
 `handle_intercept` is disabled for fitting since the StatsModels.@formula handles the intercept.
 """
-function prepare_inputs(model, X; handle_intercept=false)
-    Xcols = _to_columns(X)
-    table_features = Tables.columnnames(Xcols)
+function prepare_inputs(model, X)
+    Xcols = Tables.columntable(X)
+    table_features = Base.keys(Xcols)
     p = length(table_features)
     p >= 1 || throw(
         ArgumentError("`X` must contain at least one feature column.")
@@ -253,9 +216,11 @@
         end
     end
     Xminoffset, offset = split_X_offset(Xcols, model.offsetcol)
-    Xminoffset_cols = _to_columns(Xminoffset)
-    Xmatrix, features = _matrix_and_features(model, Xminoffset_cols , handle_intercept)
-    return Xmatrix, offset, _to_array(features)
+    features = Tables.columnnames(Xminoffset)
+
+    check_sample_size(model, length(first(Xminoffset)), p)
+
+    return Xminoffset, offset, _to_array(features)
 end
 
 """
@@ -285,14 +250,6 @@
     end
     if :coef_table in reportkeys
         coef_table = GLM.coeftable(glm_model)
-        # Update the variable names in the `coef_table` with the actual variable
-        # names seen during fit.
-        if length(coef_table.rownms) == length(features)
-            # This means `fit_intercept` is false
-            coef_table.rownms = string.(features)
-        else
-            coef_table.rownms = [string.(features); "(Intercept)"]
-        end
         report_dict[:coef_table] = coef_table
     end
     if :glm_model in reportkeys
@@ -364,7 +321,9 @@
 #### FIT FUNCTIONS
 ####
 
-struct FitResult{V<:AbstractVector, T, R}
+struct FitResult{F, V<:AbstractVector, T, R}
+    "Formula containing all coefficients and their types"
+    formula::F
     "Vector containg coeficients of the predictors and intercept"
     coefs::V
     "An estimate of the dispersion parameter of the glm model. "
@@ -373,20 +332,23 @@
     params::R
 end
 
+FitResult(fitted_glm, features) = FitResult(GLM.formula(fitted_glm), GLM.coef(fitted_glm), GLM.dispersion(fitted_glm.model), (features = features,))
+
 dispersion(fr::FitResult) = fr.dispersion
 params(fr::FitResult) = fr.params
+coefs(fr::FitResult) = fr.coefs
 
 function MMI.fit(model::LinearRegressor, verbosity::Int, X, y, w=nothing)
     # apply the model
-    Xmatrix, offset, features = prepare_inputs(model, X)
+    X_col_table, offset, features = prepare_inputs(model, X)
     y_ = isempty(offset) ? y : y .- offset
     wts = check_weights(w, y_)
-    data = glm_data(model, Xmatrix, y_, features)
+    data = merge(X_col_table, (; y = y_))
     form = glm_formula(model, features)
-    fitted_lm = GLM.lm(form, data; model.dropcollinear, wts).model
-    fitresult = FitResult(
-        GLM.coef(fitted_lm), GLM.dispersion(fitted_lm), (features = features,)
-    )
+    fitted_lm = GLM.lm(form, data; model.dropcollinear, wts)
+
+    fitresult = FitResult(fitted_lm, features)
+
     # form the report
     report = glm_report(fitted_lm, features, model.report_keys)
     cache = nothing
@@ -396,11 +358,11 @@
 
 function MMI.fit(model::LinearCountRegressor, verbosity::Int, X, y, w=nothing)
     # apply the model
-    Xmatrix, offset, features = prepare_inputs(model, X)
-    data = glm_data(model, Xmatrix, y, features)
+    X_col_table, offset, features = prepare_inputs(model, X)
+    data = merge(X_col_table, (; y))
     wts = check_weights(w, y)
     form = glm_formula(model, features)
-    fitted_glm_frame = GLM.glm(
+    fitted_glm = GLM.glm(
         form, data, model.distribution, model.link;
         offset,
         model.maxiter,
@@ -409,10 +371,9 @@
         model.minstepfac,
         wts
     )
-    fitted_glm = fitted_glm_frame.model
-    fitresult = FitResult(
-        GLM.coef(fitted_glm), GLM.dispersion(fitted_glm), (features = features,)
-    )
+
+    fitresult = FitResult(fitted_glm, features)
+
     # form the report
     report = glm_report(fitted_glm, features, model.report_keys)
     cache = nothing
@@ -422,13 +383,13 @@
 
 function MMI.fit(model::LinearBinaryClassifier, verbosity::Int, X, y, w=nothing)
     # apply the model
-    decode = y[1]
+    decode = MMI.classes(y)
     y_plain = MMI.int(y) .- 1 # 0, 1 of type Int
     wts = check_weights(w, y_plain)
-    Xmatrix, offset, features = prepare_inputs(model, X)
-    data = glm_data(model, Xmatrix, y_plain, features)
+    X_col_table, offset, features = prepare_inputs(model, X)
+    data = merge(X_col_table, (; y = y_plain))
     form = glm_formula(model, features)
-    fitted_glm_frame = GLM.glm(
+    fitted_glm = GLM.glm(
         form, data, Bernoulli(), model.link;
         offset,
         model.maxiter,
@@ -437,10 +398,9 @@
         model.minstepfac,
         wts
     )
-    fitted_glm = fitted_glm_frame.model
-    fitresult = FitResult(
-        GLM.coef(fitted_glm), GLM.dispersion(fitted_glm), (features = features,)
-    )
+
+    fitresult = FitResult(fitted_glm, features)
+
     # form the report
     report = glm_report(fitted_glm, features, model.report_keys)
     cache = nothing
@@ -452,7 +412,6 @@
 glm_fitresult(::LinearCountRegressor, fitresult) = fitresult
 glm_fitresult(::LinearBinaryClassifier, fitresult) = fitresult[1]
 
-coefs(fr::FitResult) = fr.coefs
 
 function MMI.fitted_params(model::GLM_MODELS, fitresult)
     result = glm_fitresult(model, fitresult)
@@ -468,38 +427,33 @@
     return (; features, coef=coef_, intercept)
 end
 
-
 ####
 #### PREDICT FUNCTIONS
 ####
-
 glm_link(model) = model.link
 glm_link(::LinearRegressor) = GLM.IdentityLink()
 
-# more efficient than MLJBase fallback
-function MMI.predict_mean(model::GLM_MODELS, fitresult, Xnew)
-    Xmatrix, offset, _ = prepare_inputs(model, Xnew; handle_intercept=true)
-    result = glm_fitresult(model, fitresult) # ::FitResult
-    coef = coefs(result)
-    p = size(Xmatrix, 2)
-    if p != length(coef)
-        throw(
-            DimensionMismatch(
-                "The number of features in training and prediction datasets must be equal"
-            )
-        )
-    end
-    link = glm_link(model)
-    return glm_predict(link, coef, Xmatrix, model.offsetcol, offset)
+function glm_predict(link, terms, coef, offsetcol::Nothing, Xnew)
+    mm = modelcols(terms, Xnew)
+    η = mm * coef
+    μ = GLM.linkinv.(link, η)
+    return μ
 end
 
-# barrier function to aid performance
-function glm_predict(link, coef, Xmatrix, offsetcol, offset)
-    η = offsetcol === nothing ? (Xmatrix * coef) : (Xmatrix * coef .+ offset)
+function glm_predict(link, terms, coef, offsetcol::Symbol, Xnew)
+    mm = modelcols(terms, Xnew)
+    offset = Tables.getcolumn(Xnew, offsetcol)
+    η = mm * coef .+ offset
     μ = GLM.linkinv.(link, η)
     return μ
 end
 
+# More efficient fallback. predict_mean is not defined for LinearBinaryClassifier
+function MMI.predict_mean(model::Union{LinearRegressor, LinearCountRegressor}, fitresult, Xnew) 
+    p = glm_predict(glm_link(model), fitresult.formula.rhs, fitresult.coefs, model.offsetcol, Xnew)
+    return p
+end
+
 function MMI.predict(model::LinearRegressor, fitresult, Xnew)
     μ = MMI.predict_mean(model, fitresult, Xnew)
     σ̂ = dispersion(fitresult)
@@ -512,8 +466,8 @@
 end
 
 function MMI.predict(model::LinearBinaryClassifier, (fitresult, decode), Xnew)
-    π = MMI.predict_mean(model, (fitresult, decode), Xnew)
-    return MMI.UnivariateFinite(MMI.classes(decode), π, augment=true)
+    p = glm_predict(glm_link(model), fitresult.formula.rhs, fitresult.coefs, model.offsetcol, Xnew)
+    return MMI.UnivariateFinite(decode, p, augment=true)
 end
 
 # NOTE: predict_mode uses MLJBase's fallback
@@ -539,23 +493,23 @@
 
 metadata_model(
     LinearRegressor,
-    input = Table(Continuous),
+    input = Table(Continuous, Finite),
     target = AbstractVector{Continuous},
     supports_weights = true,
     path = "$PKG.LinearRegressor"
 )
 
 metadata_model(
     LinearBinaryClassifier,
-    input = Table(Continuous),
+    input = Table(Continuous, Finite),
     target = AbstractVector{<:Finite{2}},
     supports_weights = true,
     path = "$PKG.LinearBinaryClassifier"
 )
 
 metadata_model(
     LinearCountRegressor,
-    input = Table(Continuous),
+    input = Table(Continuous, Finite),
     target = AbstractVector{Count},
     supports_weights = true,
     path = "$PKG.LinearCountRegressor"