whitespace

Author: tlienart

src/mlj/classifiers.jl

@@ -1,6 +1,6 @@
-#=  ===================
-    LOGISTIC CLASSIFIER
-    =================== =#
+#= ===================
+   LOGISTIC CLASSIFIER
+   =================== =#
 
 @with_kw_noshow mutable struct LogisticClassifier <: MLJBase.Probabilistic
     lambda::Real             = 1.0
@@ -19,9 +19,9 @@ glr(m::LogisticClassifier) = LogisticRegression(m.lambda, m.gamma; penalty=m.pen
 
 descr(::Type{LogisticClassifier}) = "Classifier corresponding to the loss function ``L(y, Xθ) + λ|θ|₂²/2 + γ|θ|₁`` where `L` is the logistic loss."
 
-#=  ======================
-    MULTINOMIAL CLASSIFIER
-    ====================== =#
+#= ======================
+   MULTINOMIAL CLASSIFIER
+   ====================== =#
 
 @with_kw_noshow mutable struct MultinomialClassifier <: MLJBase.Probabilistic
     lambda::Real             = 1.0

src/mlj/regressors.jl

@@ -1,6 +1,6 @@
-#=  ======================
-    LINEAR REGRESSOR (OLS)
-    ====================== =#
+#= ======================
+   LINEAR REGRESSOR (OLS)
+   ====================== =#
 
 @with_kw_noshow mutable struct LinearRegressor <: MLJBase.Deterministic
     fit_intercept::Bool    = true
@@ -11,9 +11,9 @@ glr(m::LinearRegressor) = LinearRegression(fit_intercept=m.fit_intercept)
 
 descr(::Type{LinearRegressor}) = "Regression with objective function ``|Xθ - y|₂²/2``."
 
-#=  ===============
-    RIDGE REGRESSOR
-    =============== =#
+#= ===============
+   RIDGE REGRESSOR
+   =============== =#
 
 @with_kw_noshow mutable struct RidgeRegressor <: MLJBase.Deterministic
     lambda::Real             = 1.0
@@ -27,9 +27,9 @@ glr(m::RidgeRegressor) = RidgeRegression(m.lambda, fit_intercept=m.fit_intercept
 
 descr(::Type{RidgeRegressor}) = "Regression with objective function ``|Xθ - y|₂²/2 + λ|θ|₂²/2``."
 
-#=  ===============
-    LASSO REGRESSOR
-    =============== =#
+#= ===============
+   LASSO REGRESSOR
+   =============== =#
 
 @with_kw_noshow mutable struct LassoRegressor <: MLJBase.Deterministic
     lambda::Real             = 1.0
@@ -43,9 +43,9 @@ glr(m::LassoRegressor) = LassoRegression(m.lambda, fit_intercept=m.fit_intercept
 
 descr(::Type{LassoRegressor}) = "Regression with objective function ``|Xθ - y|₂²/2 + λ|θ|₁``."
 
-#=  =====================
-    ELASTIC NET REGRESSOR
-    ===================== =#
+#= =====================
+   ELASTIC NET REGRESSOR
+   ===================== =#
 
 @with_kw_noshow mutable struct ElasticNetRegressor <: MLJBase.Deterministic
     lambda::Real             = 1.0
@@ -61,9 +61,9 @@ glr(m::ElasticNetRegressor) = ElasticNetRegression(m.lambda, m.gamma,
 
 descr(::Type{ElasticNetRegressor}) = "Regression with objective function ``|Xθ - y|₂²/2 + λ|θ|₂²/2 + γ|θ|₁``."
 
-#=  ==========================
-    ROBUST REGRESSOR (General)
-    ========================== =#
+#= ==========================
+   ROBUST REGRESSOR (General)
+   ========================== =#
 
 @with_kw_noshow mutable struct RobustRegressor <: MLJBase.Deterministic
     rho::RobustRho           = HuberRho(0.1)
@@ -81,9 +81,9 @@ glr(m::RobustRegressor) = RobustRegression(m.rho, m.lambda, m.gamma; penalty=m.p
 
 descr(::Type{RobustRegressor}) = "Robust regression with objective ``∑ρ(Xθ - y) + λ|θ|₂² + γ|θ|₁`` for a given robust `ρ`."
 
-#=  ===============
-    HUBER REGRESSOR
-    =============== =#
+#= ===============
+   HUBER REGRESSOR
+   =============== =#
 
 @with_kw_noshow mutable struct HuberRegressor <: MLJBase.Deterministic
     delta::Real              = 0.5
@@ -101,9 +101,9 @@ glr(m::HuberRegressor) = HuberRegression(m.delta, m.lambda, m.gamma; penalty=m.p
 
 descr(::Type{HuberRegressor}) = "Robust regression with objective ``∑ρ(Xθ - y) + λ|θ|₂² + γ|θ|₁`` where `ρ` is the Huber Loss."
 
-#=  ==================
-    QUANTILE REGRESSOR
-    ================== =#
+#= ==================
+   QUANTILE REGRESSOR
+   ================== =#
 
 @with_kw_noshow mutable struct QuantileRegressor <: MLJBase.Deterministic
     delta::Real              = 0.5
@@ -121,9 +121,9 @@ glr(m::QuantileRegressor) = QuantileRegression(m.delta, m.lambda, m.gamma; penal
 
 descr(::Type{QuantileRegressor}) = "Robust regression with objective ``∑ρ(Xθ - y) + λ|θ|₂² + γ|θ|₁`` where `ρ` is the Quantile Loss."
 
-#=  ==================================
-    LEAST ABSOLUTE DEVIATION REGRESSOR
-    ================================== =#
+#= ==================================
+   LEAST ABSOLUTE DEVIATION REGRESSOR
+   ================================== =#
 
 @with_kw_noshow mutable struct LADRegressor <: MLJBase.Deterministic
     lambda::Real             = 1.0