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-rw-r--r--test/lazar-long.rb92
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diff --git a/test/lazar-long.rb b/test/lazar-long.rb
deleted file mode 100644
index 525b96e..0000000
--- a/test/lazar-long.rb
+++ /dev/null
@@ -1,92 +0,0 @@
-require_relative "setup.rb"
-
-class LazarExtendedTest < MiniTest::Test
-
- def test_lazar_bbrc_ham_minfreq
- skip
- dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"hamster_carcinogenicity.csv")
- model = Model::LazarFminerClassification.create(dataset, :min_frequency => 5)
- feature_dataset = Dataset.find model.neighbor_algorithm_parameters[:feature_dataset_id]
- assert_equal dataset.compounds.size, feature_dataset.compounds.size
- assert_equal model.feature_calculation_parameters, {"min_frequency"=>5}
- #TODO check frequencies, features and confidence
- #assert_equal 41, feature_dataset.features.size
- #assert_equal 'N-C=N', feature_dataset.features.first.smarts
- compound = OpenTox::Compound.from_inchi("InChI=1S/C6H6/c1-2-4-6-5-3-1/h1-6H")
- prediction = model.predict compound
- assert_equal "false", prediction[:value]
- #assert_equal 0.12380952380952381, prediction[:confidence]
- dataset.delete
- model.delete
- feature_dataset.delete
- end
-
- def test_lazar_bbrc_large_ds
- skip
- dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"multi_cell_call_no_dup.csv")
- model = Model::LazarFminerClassification.create dataset
- feature_dataset = Dataset.find model.neighbor_algorithm_parameters[:feature_dataset_id]
- model.save
- p model.id
- assert_equal dataset.compounds.size, feature_dataset.compounds.size
- #assert_equal 52, feature_dataset.features.size
- #assert_equal '[#17&A]-[#6&A]', feature_dataset.features.first.name
- compound = OpenTox::Compound.from_inchi("InChI=1S/C10H9NO2S/c1-8-2-4-9(5-3-8)13-6-10(12)11-7-14/h2-5H,6H2,1H3")
- prediction = model.predict compound
- assert_equal "1", prediction[:value]
- #p prediction
- #prediction = prediction_dataset.data_entries.first
- #assert_in_delta 0.025, prediction[:confidence], 0.001
- #assert_equal 0.025885845574483608, prediction[:confidence]
- # with compound change in training_dataset see:
- # https://github.com/opentox/opentox-test/commit/0e78c9c59d087adbd4cc58bab60fb29cbe0c1da0
- #assert_equal 0.02422364949075546, prediction[:confidence]
- dataset.delete
- model.delete
- feature_dataset.delete
- end
-
- def test_lazar_fminer_kazius
- skip
- t = Time.now
- dataset = Dataset.from_csv_file File.join(DATA_DIR,"kazius.csv")
- p "Dataset upload: #{Time.now-t}"
- t = Time.now
- model = Model::LazarFminerClassification.create(dataset, :min_frequency => 100)
- p "Feature mining: #{Time.now-t}"
- t = Time.now
- feature_dataset = Dataset.find model.neighbor_algorithm_parameters[:feature_dataset_id]
- assert_equal feature_dataset.compounds.size, dataset.compounds.size
- #model = Model::Lazar.find('55bcf5bf7a7838381200017e')
- #p model.id
- #prediction_times = []
- 2.times do
- compound = Compound.from_smiles("Clc1ccccc1NN")
- prediction = model.predict compound
- p prediction
- #assert_equal "1", prediction[:value]
- #assert_in_delta 0.019858401199860445, prediction[:confidence], 0.001
- end
- #dataset.delete
- #feature_dataset.delete
- end
-
- def test_lazar_kazius
- t = Time.now
- dataset = Dataset.from_csv_file File.join(DATA_DIR,"kazius.csv")
- p "Dataset upload: #{Time.now-t}"
- t = Time.now
- model = Model::LazarClassification.create(dataset)
- p "Feature mining: #{Time.now-t}"
- t = Time.now
- 2.times do
- compound = Compound.from_smiles("Clc1ccccc1NN")
- prediction = model.predict compound
- #p prediction
- assert_equal "1", prediction[:value]
- #assert_in_delta 0.019858401199860445, prediction[:confidence], 0.001
- end
- dataset.delete
- end
-
-end