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fixed metrics function and adding some test about it
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Original file line number | Diff line number | Diff line change |
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using Test | ||
using Revise | ||
using Random | ||
using DataFrames | ||
using SoleLogics | ||
using SoleModels | ||
using SoleData | ||
using SoleData.DimensionalDatasets | ||
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n_instances = 20 | ||
rng = MersenneTwister(42) | ||
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# Dataset Construction | ||
attributes = ["fever","pressure"] | ||
attributes_values = [ | ||
[rand(rng,collect(36:0.5:40)) for i in 1:n_instances], | ||
[rand(rng,collect(60:2:130)) for i in 1:n_instances], | ||
] | ||
y_true = [attributes_values[1][i] >= 37.5 && attributes_values[2][i] <= 100 ? "sick" : "not sick" for i in 1:n_instances] | ||
dataset = DataFrame(; NamedTuple([Symbol(attributes[i]) => attributes_values[i] for i in 1:length(attributes)])...) | ||
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# Logiset Definition | ||
nvars = nvariables(dataset) | ||
features = collect(Iterators.flatten([[UnivariateMin(i_var)] for i_var in 1:nvars])) | ||
logiset = scalarlogiset(dataset, features; use_full_memoization = false, use_onestep_memoization = false) | ||
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# Rule Definition: max[V1] >= 38, max[V2] < 110 | ||
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# Build a formula on scalar conditions | ||
condition1 = ScalarCondition(features[1], >=, 38.0) | ||
condition2 = ScalarCondition(features[2], <, 110) | ||
antecedentrule = Atom(condition1) ∧ Atom(condition2) | ||
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# Build consequent | ||
consequentrule = "sick" | ||
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# Build Rule without info | ||
rule = Rule(antecedentrule, consequentrule) | ||
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inforule = metrics(rule) | ||
inforulelogiset = metrics(rule,logiset) | ||
inforuley = metrics(rule,Y = y_true) | ||
inforuleall = metrics(rule,logiset,y_true) | ||
newrule = metrics(rule; return_model=true) | ||
newrulelogiset = metrics(rule,logiset; return_model=true) | ||
newruley = metrics(rule,Y = y_true; return_model=true) | ||
newruleall = metrics(rule,logiset,y_true; return_model=true) | ||
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@test inforule == NamedTuple() | ||
@test inforulelogiset == NamedTuple() | ||
@test inforuley == NamedTuple() | ||
@test inforuleall == (ninstances = 8, accuracy = 1.0,) | ||
@test SoleModels.info(newrule) == NamedTuple() | ||
@test SoleModels.info(newrule) == NamedTuple() | ||
@test SoleModels.info(newrulelogiset) == NamedTuple() | ||
@test SoleModels.info(newruley) == NamedTuple() | ||
@test SoleModels.info(newruleall) == (ninstances = 8, accuracy = 1.0,) | ||
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# Build Rule with info | ||
rule = Rule(antecedentrule,consequentrule,(; supporting_labels = y_true)) | ||
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inforule = metrics(rule) | ||
inforulelogiset = metrics(rule,logiset) | ||
inforuley = metrics(rule,Y = y_true) | ||
inforuleall = metrics(rule,logiset,y_true) | ||
newrule = metrics(rule; return_model=true) | ||
newrulelogiset = metrics(rule,logiset; return_model=true) | ||
newruley = metrics(rule,Y = y_true; return_model=true) | ||
newruleall = metrics(rule,logiset,y_true; return_model=true) | ||
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@test inforule == (ninstances = 20,) | ||
@test inforulelogiset == (ninstances = 8, accuracy = 1.0,) | ||
@test inforuley == (ninstances = 20,) | ||
@test inforuleall == (ninstances = 8, accuracy = 1.0,) | ||
@test SoleModels.info(newrule) == (ninstances = 20,) | ||
@test SoleModels.info(newrule) == (ninstances = 20,) | ||
@test SoleModels.info(newrulelogiset) == (ninstances = 8, accuracy = 1.0,) | ||
@test SoleModels.info(newruley) == (ninstances = 20,) | ||
@test SoleModels.info(newruleall) == (ninstances = 8, accuracy = 1.0,) | ||
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# Build Rule with info | ||
supp_preds = apply(Rule(antecedentrule,consequentrule),logiset) | ||
rule = Rule(antecedentrule,consequentrule,(; supporting_labels = y_true, supporting_predictions = supp_preds)) | ||
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inforule = metrics(rule) | ||
inforulelogiset = metrics(rule,logiset) | ||
inforuley = metrics(rule,Y = y_true) | ||
inforuleall = metrics(rule,logiset,y_true) | ||
newrule = metrics(rule; return_model=true) | ||
newrulelogiset = metrics(rule,logiset; return_model=true) | ||
newruley = metrics(rule,Y = y_true; return_model=true) | ||
newruleall = metrics(rule,logiset,y_true; return_model=true) | ||
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@test inforule == (ninstances = 8, accuracy = 1.0) | ||
@test inforulelogiset == (ninstances = 8, accuracy = 1.0,) | ||
@test inforuley == (ninstances = 8, accuracy = 1.0) | ||
@test inforuleall == (ninstances = 8, accuracy = 1.0,) | ||
@test SoleModels.info(newrule) == (ninstances = 8, accuracy = 1.0) | ||
@test SoleModels.info(newrule) == (ninstances = 8, accuracy = 1.0) | ||
@test SoleModels.info(newrulelogiset) == (ninstances = 8, accuracy = 1.0,) | ||
@test SoleModels.info(newruley) == (ninstances = 8, accuracy = 1.0) | ||
@test SoleModels.info(newruleall) == (ninstances = 8, accuracy = 1.0,) | ||
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