From 9da5d7daaff901f63dcc9d4be4108eb68d96d170 Mon Sep 17 00:00:00 2001
From: "pasquale c." <343guiltyspark@outlook.it>
Date: Wed, 30 Oct 2024 01:09:07 +0100
Subject: [PATCH] reducing number of epochs and trying to extende patch
coverage
---
src/direct_mlj.jl | 6 +++---
test/direct_mlj_interface.jl | 25 ++++++++++++++++---------
2 files changed, 19 insertions(+), 12 deletions(-)
diff --git a/src/direct_mlj.jl b/src/direct_mlj.jl
index 82c6d81..f2d4a20 100644
--- a/src/direct_mlj.jl
+++ b/src/direct_mlj.jl
@@ -68,7 +68,7 @@ MMI.selectrows(::LaplaceModels, I, Xmatrix) = (Xmatrix[:, I],)
"""
- function features_shape(model::LaplaceRegression, X, y)
+ function dataset_shape(model::LaplaceRegression, X, y)
Compute the the number of features of the X input dataset and the number of variables to predict from the y output dataset.
@@ -80,7 +80,7 @@ Compute the the number of features of the X input dataset and the number of var
# Returns
- (input size, output size)
"""
-function features_shape(model::LaplaceModels, X, y)
+function dataset_shape(model::LaplaceModels, X, y)
#X = X isa Tables.MatrixTable ? MLJBase.matrix(X) : X
n_input = size(X, 1)
dims = size(y)
@@ -142,7 +142,7 @@ function MMI.fit(m::LaplaceModels, verbosity, X, y)
y, decode = y
if (m.model === nothing)
- shape = features_shape(m, X, y)
+ shape = dataset_shape(m, X, y)
m.model = default_build(11, shape)
diff --git a/test/direct_mlj_interface.jl b/test/direct_mlj_interface.jl
index e2ec402..88e26e3 100644
--- a/test/direct_mlj_interface.jl
+++ b/test/direct_mlj_interface.jl
@@ -11,8 +11,9 @@ cv = MLJBase.CV(; nfolds=3)
@testset "Regression" begin
@info " testing interface for LaplaceRegressor"
flux_model = Chain(Dense(4, 10, relu), Dense(10, 10, relu), Dense(10, 1))
- model = LaplaceRegressor(; model=flux_model, epochs=50)
+ model = LaplaceRegressor(; model=flux_model, epochs=20)
+ #testing more complex dataset
X, y = MLJ.make_regression(100, 4; noise=0.5, sparse=0.2, outliers=0.1)
#train, test = partition(eachindex(y), 0.7); # 70:30 split
mach = MLJ.machine(model, X, y)
@@ -21,9 +22,9 @@ cv = MLJBase.CV(; nfolds=3)
MLJBase.predict_mode(mach, X) # point predictions
MLJBase.fitted_params(mach) #fitted params function
MLJBase.training_losses(mach) #training loss history
- model.epochs = 100 #changing number of epochs
+ model.epochs = 40 #changing number of epochs
MLJBase.fit!(mach; verbosity=0) #testing update function
- model.epochs = 50 #changing number of epochs to a lower number
+ model.epochs = 30 #changing number of epochs to a lower number
MLJBase.fit!(mach; verbosity=0) #testing update function
model.fit_prior_nsteps = 200 #changing LaplaceRedux fit steps
MLJBase.fit!(mach; verbosity=0) #testing update function (the laplace part)
@@ -41,15 +42,21 @@ cv = MLJBase.CV(; nfolds=3)
#testing default mlp builder
- model = LaplaceRegressor(; model=nothing, epochs=50)
+ model = LaplaceRegressor(; model=nothing, epochs=20)
mach = MLJ.machine(model, X, y)
- MLJBase.fit!(mach; verbosity=0)
+ MLJBase.fit!(mach; verbosity=1)
yhat = MLJBase.predict(mach, X) # probabilistic predictions
MLJBase.predict_mode(mach, X) # point predictions
MLJBase.fitted_params(mach) #fitted params function
MLJBase.training_losses(mach) #training loss history
model.epochs = 100 #changing number of epochs
- MLJBase.fit!(mach; verbosity=0) #testing update function
+ MLJBase.fit!(mach; verbosity=1) #testing update function
+
+ #testing dataset_shape for one dimensional function
+ X, y = MLJ.make_regression(100, 1; noise=0.5, sparse=0.2, outliers=0.1)
+ model = LaplaceRegressor(; model=nothing, epochs=20)
+ mach = MLJ.machine(model, X, y)
+ MLJBase.fit!(mach; verbosity=0)
@@ -60,7 +67,7 @@ end
# Define the model
flux_model = Chain(Dense(4, 10, relu), Dense(10, 3))
- model = LaplaceClassifier(; model=flux_model, epochs=50)
+ model = LaplaceClassifier(; model=flux_model, epochs=20)
X, y = MLJ.@load_iris
mach = MLJ.machine(model, X, y)
@@ -76,9 +83,9 @@ end
MLJBase.pdf.(yhat, "virginica") # probabilities for the "verginica" class
MLJBase.fitted_params(mach) # fitted params
MLJBase.training_losses(mach) #training loss history
- model.epochs = 100 #changing number of epochs
+ model.epochs = 40 #changing number of epochs
MLJBase.fit!(mach; verbosity=0) #testing update function
- model.epochs = 50 #changing number of epochs to a lower number
+ model.epochs = 30 #changing number of epochs to a lower number
MLJBase.fit!(mach; verbosity=0) #testing update function
model.fit_prior_nsteps = 200 #changing LaplaceRedux fit steps
MLJBase.fit!(mach; verbosity=0) #testing update function (the laplace part)