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require_relative "setup.rb"
class ExperimentTest < MiniTest::Test
def test_regression_experiment
skip
datasets = [
"EPAFHM.medi.csv",
#"EPAFHM.csv",
#"FDA_v3b_Maximum_Recommended_Daily_Dose_mmol.csv",
"LOAEL_mmol_corrected_smiles.csv"
]
experiment = Experiment.create(
:name => "Default regression for datasets #{datasets}.",
:dataset_ids => datasets.collect{|d| Dataset.from_csv_file(File.join(DATA_DIR, d)).id},
:model_settings => [
{
:algorithm => "OpenTox::Model::LazarRegression",
}
]
)
#experiment.run
puts experiment.report.to_yaml
assert_equal datasets.size, experiment.results.size
experiment.results.each do |dataset_id, result|
assert_equal 1, result.size
result.each do |r|
assert_kind_of BSON::ObjectId, r[:model_id]
assert_kind_of BSON::ObjectId, r[:repeated_crossvalidation_id]
end
end
end
def test_classification_experiment
skip
datasets = [ "hamster_carcinogenicity.csv" ]
experiment = Experiment.create(
:name => "Fminer vs fingerprint classification for datasets #{datasets}.",
:dataset_ids => datasets.collect{|d| Dataset.from_csv_file(File.join(DATA_DIR, d)).id},
:model_settings => [
{
:algorithm => "OpenTox::Model::LazarClassification",
},{
:algorithm => "OpenTox::Model::LazarClassification",
:neighbor_algorithm_parameter => {:min_sim => 0.3}
},
#{
#:algorithm => "OpenTox::Model::LazarFminerClassification",
#}
]
)
#experiment.run
=begin
experiment = Experiment.find "55f944a22b72ed7de2000000"
=end
puts experiment.report.to_yaml
experiment.results.each do |dataset_id, result|
assert_equal 2, result.size
result.each do |r|
assert_kind_of BSON::ObjectId, r[:model_id]
assert_kind_of BSON::ObjectId, r[:repeated_crossvalidation_id]
end
end
end
def test_regression_fingerprints
skip
#=begin
datasets = [
"EPAFHM.medi.csv",
#"LOAEL_mmol_corrected_smiles.csv"
]
min_sims = [0.3,0.7]
#min_sims = [0.7]
#types = ["FP2","FP3","FP4","MACCS","MP2D"]
types = ["MP2D","FP3"]
experiment = Experiment.create(
:name => "Fingerprint regression with different types for datasets #{datasets}.",
:dataset_ids => datasets.collect{|d| Dataset.from_csv_file(File.join(DATA_DIR, d)).id},
)
types.each do |type|
min_sims.each do |min_sim|
experiment.model_settings << {
:model_algorithm => "OpenTox::Model::LazarRegression",
:prediction_algorithm => "OpenTox::Algorithm::Regression.weighted_average",
:neighbor_algorithm => "fingerprint_neighbors",
:neighbor_algorithm_parameters => {
:type => type,
:min_sim => min_sim,
}
}
end
end
experiment.run
#=end
=begin
experiment = Experiment.find '56029cb92b72ed673d000000'
=end
p experiment.id
experiment.results.each do |dataset,result|
result.each do |r|
params = Model::Lazar.find(r["model_id"])[:neighbor_algorithm_parameters]
RepeatedCrossValidation.find(r["repeated_crossvalidation_id"]).crossvalidations.each do |cv|
cv.validation_ids.each do |vid|
model_params = Model::Lazar.find(Validation.find(vid).model_id)[:neighbor_algorithm_parameters]
assert_equal params[:type], model_params[:type]
assert_equal params[:min_sim], model_params[:min_sim]
refute_equal params[:training_dataset_id], model_params[:training_dataset_id]
end
end
end
end
puts experiment.report.to_yaml
p experiment.summary
end
def test_mpd_fingerprints
skip
datasets = [
"EPAFHM.medi.csv",
]
types = ["FP2","MP2D"]
experiment = Experiment.create(
:name => "FP2 vs MP2D fingerprint regression for datasets #{datasets}.",
:dataset_ids => datasets.collect{|d| Dataset.from_csv_file(File.join(DATA_DIR, d)).id},
)
types.each do |type|
experiment.model_settings << {
:algorithm => "OpenTox::Model::LazarRegression",
:neighbor_algorithm => "fingerprint_neighbors",
:neighbor_algorithm_parameter => {
:type => type,
:min_sim => 0.7,
}
}
end
experiment.run
p experiment.id
=begin
=end
#experiment = Experiment.find '55ffd0c02b72ed123c000000'
p experiment
puts experiment.report.to_yaml
end
def test_multiple_datasets
skip
datasets = [
"EPAFHM.medi.csv",
"LOAEL_mmol_corrected_smiles.csv"
]
min_sims = [0.3]
types = ["FP2"]
experiment = Experiment.create(
:name => "Fingerprint regression with mutiple datasets #{datasets}.",
:dataset_ids => datasets.collect{|d| Dataset.from_csv_file(File.join(DATA_DIR, d)).id},
)
types.each do |type|
min_sims.each do |min_sim|
experiment.model_settings << {
:model_algorithm => "OpenTox::Model::LazarRegression",
:prediction_algorithm => "OpenTox::Algorithm::Regression.weighted_average",
:neighbor_algorithm => "fingerprint_neighbors",
:neighbor_algorithm_parameters => {
:type => type,
:min_sim => min_sim,
}
}
end
end
experiment.run
p experiment.id
experiment.results.each do |dataset,result|
result.each do |r|
params = Model::Lazar.find(r["model_id"])[:neighbor_algorithm_parameters]
RepeatedCrossValidation.find(r["repeated_crossvalidation_id"]).crossvalidations.each do |cv|
cv.validation_ids.each do |vid|
model_params = Model::Lazar.find(Validation.find(vid).model_id)[:neighbor_algorithm_parameters]
assert_equal params[:type], model_params[:type]
assert_equal params[:min_sim], model_params[:min_sim]
refute_equal params[:training_dataset_id], model_params[:training_dataset_id]
end
end
end
end
puts experiment.report.to_yaml
p experiment.summary
end
def test_mpd_mna_regression_fingerprints
skip
datasets = [
"EPAFHM.medi.csv",
#"hamster_carcinogenicity.csv"
]
min_sims = [0.0,0.3]
types = ["MP2D","MNA"]
neighbor_algos = [
"fingerprint_neighbors",
"fingerprint_count_neighbors",
]
experiment = Experiment.create(
:name => "MNA vs MPD descriptors",
:dataset_ids => datasets.collect{|d| Dataset.from_csv_file(File.join(DATA_DIR, d)).id},
)
types.each do |type|
min_sims.each do |min_sim|
neighbor_algos.each do |neighbor_algo|
experiment.model_settings << {
:model_algorithm => "OpenTox::Model::LazarRegression",
:prediction_algorithm => "OpenTox::Algorithm::Regression.weighted_average",
:neighbor_algorithm => neighbor_algo,
:neighbor_algorithm_parameters => {
:type => type,
:min_sim => min_sim,
}
}
end
end
end
experiment.run
#=end
=begin
experiment = Experiment.find '56029cb92b72ed673d000000'
=end
p experiment.id
puts experiment.report.to_yaml
#p experiment.summary
experiment.results.each do |dataset,result|
result.each do |r|
p r
# TODO fix r["model_id"]
params = Model::Lazar.find(r["model_id"])[:neighbor_algorithm_parameters]
RepeatedCrossValidation.find(r["repeated_crossvalidation_id"]).crossvalidations.each do |cv|
cv.validation_ids.each do |vid|
model_params = Model::Lazar.find(Validation.find(vid).model_id)[:neighbor_algorithm_parameters]
assert_equal params[:type], model_params[:type]
assert_equal params[:min_sim], model_params[:min_sim]
refute_equal params[:training_dataset_id], model_params[:training_dataset_id]
end
end
end
end
end
def test_mpd_mna_classification_fingerprints
skip
datasets = [
#"EPAFHM.medi.csv",
"hamster_carcinogenicity.csv"
]
min_sims = [0.0,0.3]
types = ["MP2D","MNA"]
neighbor_algos = [
"fingerprint_count_neighbors",
"fingerprint_neighbors",
]
experiment = Experiment.create(
:name => "MNA vs MPD descriptors",
:dataset_ids => datasets.collect{|d| Dataset.from_csv_file(File.join(DATA_DIR, d)).id},
)
types.each do |type|
min_sims.each do |min_sim|
neighbor_algos.each do |neighbor_algo|
experiment.model_settings << {
:model_algorithm => "OpenTox::Model::LazarClassification",
:prediction_algorithm => "OpenTox::Algorithm::Classification.weighted_majority_vote",
:neighbor_algorithm => neighbor_algo,
:neighbor_algorithm_parameters => {
:type => type,
:min_sim => min_sim,
}
}
end
end
end
experiment.run
#=end
=begin
experiment = Experiment.find '56029cb92b72ed673d000000'
=end
p experiment.id
puts experiment.report.to_yaml
#p experiment.summary
experiment.results.each do |dataset,result|
result.each do |r|
# TODO fix r["model_id"]
params = Model::Lazar.find(r["model_id"])[:neighbor_algorithm_parameters]
RepeatedCrossValidation.find(r["repeated_crossvalidation_id"]).crossvalidations.each do |cv|
cv.validation_ids.each do |vid|
model_params = Model::Lazar.find(Validation.find(vid).model_id)[:neighbor_algorithm_parameters]
assert_equal params[:type], model_params[:type]
assert_equal params[:min_sim], model_params[:min_sim]
refute_equal params[:training_dataset_id], model_params[:training_dataset_id]
end
end
end
end
end
end
|