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-rw-r--r--lib/classification.rb6
-rw-r--r--lib/compound.rb52
-rw-r--r--lib/crossvalidation.rb4
-rw-r--r--lib/dataset.rb21
-rw-r--r--lib/descriptor.rb1
-rw-r--r--lib/model.rb31
-rw-r--r--lib/regression.rb37
-rw-r--r--test/compound.rb14
-rw-r--r--test/dataset-long.rb1
-rw-r--r--test/dataset.rb6
-rw-r--r--test/fminer-long.rb3
-rw-r--r--test/lazar-classification.rb42
-rw-r--r--test/lazar-fminer.rb1
-rw-r--r--test/lazar-long.rb23
-rw-r--r--test/lazar-regression.rb4
-rw-r--r--test/prediction_models.rb11
-rw-r--r--test/validation.rb26
17 files changed, 181 insertions, 102 deletions
diff --git a/lib/classification.rb b/lib/classification.rb
index b4b2e59..7a225bb 100644
--- a/lib/classification.rb
+++ b/lib/classification.rb
@@ -5,14 +5,12 @@ module OpenTox
def self.weighted_majority_vote compound, params
neighbors = params[:neighbors]
- return {:value => nil,:confidence => nil,:warning => "Cound not find similar compounds."} if neighbors.empty?
weighted_sum = {}
sim_sum = 0.0
confidence = 0.0
neighbors.each do |row|
- n,sim,acts = row
- #confidence = sim if sim > confidence # distance to nearest neighbor
- acts.each do |act|
+ sim = row["tanimoto"]
+ row["features"][params[:prediction_feature_id].to_s].each do |act|
weighted_sum[act] ||= 0
weighted_sum[act] += sim
end
diff --git a/lib/compound.rb b/lib/compound.rb
index a26528b..c5e7f02 100644
--- a/lib/compound.rb
+++ b/lib/compound.rb
@@ -23,13 +23,16 @@ module OpenTox
field :sdf_id, type: BSON::ObjectId
field :fingerprints, type: Hash, default: {}
field :default_fingerprint_size, type: Integer
+ field :dataset_ids, type: Array, default: []
+ field :features, type: Hash, default: {}
index({smiles: 1}, {unique: true})
+ #index({default_fingerprint: 1}, {unique: false})
# Overwrites standard Mongoid method to create fingerprints before database insertion
def self.find_or_create_by params
compound = self.find_or_initialize_by params
- compound.default_fingerprint_size = compound.fingerprint(DEFAULT_FINGERPRINT)
+ compound.default_fingerprint_size = compound.fingerprint(DEFAULT_FINGERPRINT).size
compound.save
compound
end
@@ -41,7 +44,7 @@ module OpenTox
if type == "MP2D"
fp = obconversion(smiles,"smi","mpd").strip.split("\t")
name = fp.shift # remove Title
- fingerprints[type] = fp
+ fingerprints[type] = fp.uniq # no fingerprint counts
#http://openbabel.org/docs/dev/FileFormats/Multilevel_Neighborhoods_of_Atoms_(MNA).html
elsif type== "MNA"
level = 2 # TODO: level as parameter, evaluate level 1, see paper
@@ -244,20 +247,23 @@ module OpenTox
def fingerprint_neighbors params
bad_request_error "Incorrect parameters '#{params}' for Compound#fingerprint_neighbors. Please provide :type, :training_dataset_id, :min_sim." unless params[:type] and params[:training_dataset_id] and params[:min_sim]
neighbors = []
- #if params[:type] == DEFAULT_FINGERPRINT
- #neighbors = db_neighbors params
- #p neighbors
- #else
+ if params[:type] == DEFAULT_FINGERPRINT
+ neighbors = db_neighbors params
+ else
query_fingerprint = self.fingerprint params[:type]
- training_dataset = Dataset.find(params[:training_dataset_id]).compounds.each do |compound|
- unless self == compound
+ training_dataset = Dataset.find(params[:training_dataset_id])
+ prediction_feature = training_dataset.features.first
+ training_dataset.compounds.each do |compound|
+ #unless self == compound
candidate_fingerprint = compound.fingerprint params[:type]
sim = (query_fingerprint & candidate_fingerprint).size/(query_fingerprint | candidate_fingerprint).size.to_f
- neighbors << [compound.id, sim] if sim >= params[:min_sim]
- end
+ feature_values = training_dataset.values(compound,prediction_feature)
+ neighbors << {"_id" => compound.id, "features" => {prediction_feature.id.to_s => feature_values}, "tanimoto" => sim} if sim >= params[:min_sim]
+ #end
end
- #end
- neighbors.sort{|a,b| b.last <=> a.last}
+ neighbors.sort!{|a,b| b["tanimoto"] <=> a["tanimoto"]}
+ end
+ neighbors
end
def fminer_neighbors params
@@ -299,30 +305,34 @@ module OpenTox
end
def db_neighbors params
- p "DB NEIGHBORS"
- p params
- # TODO restrict to dataset
# from http://blog.matt-swain.com/post/87093745652/chemical-similarity-search-in-mongodb
- qn = fingerprint(params[:type]).size
+
+ #qn = default_fingerprint_size
#qmin = qn * threshold
#qmax = qn / threshold
#not sure if it is worth the effort of keeping feature counts up to date (compound deletions, additions, ...)
#reqbits = [count['_id'] for count in db.mfp_counts.find({'_id': {'$in': qfp}}).sort('count', 1).limit(qn - qmin + 1)]
aggregate = [
#{'$match': {'mfp.count': {'$gte': qmin, '$lte': qmax}, 'mfp.bits': {'$in': reqbits}}},
- {'$match' => {'_id' => {'$ne' => self.id}}}, # remove self
+ #{'$match' => {'_id' => {'$ne' => self.id}}}, # remove self
{'$project' => {
'tanimoto' => {'$let' => {
- 'vars' => {'common' => {'$size' => {'$setIntersection' => ["'$#{DEFAULT_FINGERPRINT}'", DEFAULT_FINGERPRINT]}}},
- 'in' => {'$divide' => ['$$common', {'$subtract' => [{'$add' => [qn, '$default_fingerprint_size']}, '$$common']}]}
+ 'vars' => {'common' => {'$size' => {'$setIntersection' => ["$fingerprints.#{DEFAULT_FINGERPRINT}", fingerprints[DEFAULT_FINGERPRINT]]}}},
+ #'vars' => {'common' => {'$size' => {'$setIntersection' => ["$default_fingerprint", default_fingerprint]}}},
+ 'in' => {'$divide' => ['$$common', {'$subtract' => [{'$add' => [default_fingerprint_size, '$default_fingerprint_size']}, '$$common']}]}
}},
- '_id' => 1
+ '_id' => 1,
+ 'features' => 1,
+ 'dataset_ids' => 1
}},
{'$match' => {'tanimoto' => {'$gte' => params[:min_sim]}}},
{'$sort' => {'tanimoto' => -1}}
]
- $mongo["compounds"].aggregate(aggregate).collect{ |r| [r["_id"], r["tanimoto"]] }
+ $mongo["compounds"].aggregate(aggregate).select{|r| r["dataset_ids"].include? params[:training_dataset_id]}
+
+
+ #$mongo["compounds"].aggregate(aggregate).collect{ |r| [r["_id"], r["tanimoto"]] }
end
diff --git a/lib/crossvalidation.rb b/lib/crossvalidation.rb
index 2e6dabb..3127351 100644
--- a/lib/crossvalidation.rb
+++ b/lib/crossvalidation.rb
@@ -136,7 +136,7 @@ module OpenTox
incorrect_predictions = 0
predictions.each do |p|
if p[1] and p[2]
- p[1] == p [2] ? correct_predictions += 1 : incorrect_predictions += 1
+ p[1] == p[2] ? correct_predictions += 1 : incorrect_predictions += 1
accuracies << correct_predictions/(correct_predictions+incorrect_predictions).to_f
confidences << p[3]
@@ -243,7 +243,7 @@ module OpenTox
:neighbors => neighbors
}
end
- end.compact.sort{|a,b| p a; b[:relative_error] <=> a[:relative_error]}[0..n-1]
+ end.compact.sort{|a,b| b[:relative_error] <=> a[:relative_error]}[0..n-1]
end
def confidence_plot
diff --git a/lib/dataset.rb b/lib/dataset.rb
index d989bdf..af116a9 100644
--- a/lib/dataset.rb
+++ b/lib/dataset.rb
@@ -105,10 +105,18 @@ module OpenTox
test_cids = test_idxs.collect{|i| self.compound_ids[i]}
test_data_entries = test_idxs.collect{|i| self.data_entries[i]}
test_dataset = self.class.new(:compound_ids => test_cids, :feature_ids => self.feature_ids, :data_entries => test_data_entries)
+ test_dataset.compounds.each do |compound|
+ compound.dataset_ids << test_dataset.id
+ compound.save
+ end
training_idxs = indices-test_idxs
training_cids = training_idxs.collect{|i| self.compound_ids[i]}
training_data_entries = training_idxs.collect{|i| self.data_entries[i]}
training_dataset = self.class.new(:compound_ids => training_cids, :feature_ids => self.feature_ids, :data_entries => training_data_entries)
+ training_dataset.compounds.each do |compound|
+ compound.dataset_ids << training_dataset.id
+ compound.save
+ end
test_dataset.save_all
training_dataset.save_all
chunks << [training_dataset,test_dataset]
@@ -229,7 +237,7 @@ module OpenTox
table.each_with_index do |vals,i|
ct = Time.now
- identifier = vals.shift
+ identifier = vals.shift.strip
warnings << "No feature values for compound at position #{i+2}." if vals.compact.empty?
begin
case compound_format
@@ -246,7 +254,7 @@ module OpenTox
warnings << "Cannot parse #{compound_format} compound '#{identifier}' at position #{i+2}, all entries are ignored."
next
end
- # TODO insert empty compounds to keep positions?
+ compound.dataset_ids << self.id unless compound.dataset_ids.include? self.id
compound_time += Time.now-ct
r += 1
@@ -263,10 +271,15 @@ module OpenTox
warnings << "Empty value for compound '#{identifier}' (row #{r+2}) and feature '#{feature_names[j]}' (column #{j+2})."
next
elsif numeric[j]
- self.data_entries.last[j] = v.to_f
+ v = v.to_f
else
- self.data_entries.last[j] = v.strip
+ v = v.strip
end
+ self.data_entries.last[j] = v
+ #i = compound.feature_ids.index feature_ids[j]
+ compound.features[feature_ids[j].to_s] ||= []
+ compound.features[feature_ids[j].to_s] << v
+ compound.save
end
end
compounds.duplicates.each do |compound|
diff --git a/lib/descriptor.rb b/lib/descriptor.rb
index 9733bde..93ce591 100644
--- a/lib/descriptor.rb
+++ b/lib/descriptor.rb
@@ -107,7 +107,6 @@ module OpenTox
des[lib] << descriptor
end
des.each do |lib,descriptors|
- p lib, descriptors
send(lib, descriptors)
end
serialize
diff --git a/lib/model.rb b/lib/model.rb
index 227d4d3..44b36e6 100644
--- a/lib/model.rb
+++ b/lib/model.rb
@@ -76,22 +76,23 @@ module OpenTox
t = Time.new
neighbors = compound.send(neighbor_algorithm, neighbor_algorithm_parameters)
- # add activities
- # TODO: improve efficiency, takes 3 times longer than previous version
- neighbors.collect! do |n|
- rows = training_dataset.compound_ids.each_index.select{|i| training_dataset.compound_ids[i] == n.first}
- acts = rows.collect{|row| training_dataset.data_entries[row][0]}.compact
- acts.empty? ? nil : n << acts
- end
- neighbors.compact! # remove neighbors without training activities
+ # remove neighbors without prediction_feature
+ # check for database activities (neighbors may include query compound)
+ database_activities = nil
+ if neighbors.collect{|n| n["_id"]}.include? compound.id
- database_activities = training_dataset.values(compound,prediction_feature)
- if use_database_values and database_activities and !database_activities.empty?
- database_activities = database_activities.first if database_activities.size == 1
- predictions << {:compound => compound, :value => database_activities, :confidence => "measured", :warning => "Compound #{compound.smiles} occurs in training dataset with activity '#{database_activities}'."}
- next
+ database_activities = neighbors.select{|n| n["_id"] == compound.id}.first["features"][prediction_feature.id.to_s]
+ neighbors.delete_if{|n| n["_id"] == compound.id}
+ end
+ neighbors.delete_if{|n| n['features'].empty? or n['features'][prediction_feature.id.to_s] == [nil] }
+ if neighbors.empty?
+ prediction = {:value => nil,:confidence => nil,:warning => "Cound not find similar compounds."}
+ else
+ prediction = Algorithm.run(prediction_algorithm, compound, {:neighbors => neighbors,:training_dataset_id=> training_dataset.id,:prediction_feature_id => prediction_feature.id})
end
- predictions << Algorithm.run(prediction_algorithm, compound, {:neighbors => neighbors,:training_dataset_size => training_dataset.data_entries.size})
+ prediction[:database_activities] = database_activities
+ predictions << prediction
+
=begin
# TODO scaled dataset for physchem
p neighbor_algorithm_parameters
@@ -126,7 +127,7 @@ module OpenTox
warning_feature = OpenTox::NominalFeature.find_or_create_by("name" => "Warnings")
prediction_dataset.features = [ prediction_feature, confidence_feature, warning_feature ]
prediction_dataset.compounds = compounds
- prediction_dataset.data_entries = predictions.collect{|p| [p[:value], p[:confidence], p[:warning]]}
+ prediction_dataset.data_entries = predictions.collect{|p| [p[:value], p[:database_activities] ? "measured" : p[:confidence] , p[:warning]]}
prediction_dataset.save_all
return prediction_dataset
end
diff --git a/lib/regression.rb b/lib/regression.rb
index 868c25f..575a1ef 100644
--- a/lib/regression.rb
+++ b/lib/regression.rb
@@ -1,39 +1,26 @@
-# TODO install R packages kernlab, caret, doMC, class, e1071
-
-
- # log transform activities (create new dataset)
- # scale, normalize features, might not be necessary
- # http://stats.stackexchange.com/questions/19216/variables-are-often-adjusted-e-g-standardised-before-making-a-model-when-is
- # http://stats.stackexchange.com/questions/7112/when-and-how-to-use-standardized-explanatory-variables-in-linear-regression
- # zero-order correlation and the semi-partial correlation
- # seems to be necessary for svm
- # http://stats.stackexchange.com/questions/77876/why-would-scaling-features-decrease-svm-performance?lq=1
- # http://stackoverflow.com/questions/15436367/svm-scaling-input-values
- # use lasso or elastic net??
- # select relevant features
- # remove features with a single value
- # remove correlated features
- # remove features not correlated with endpoint
module OpenTox
module Algorithm
class Regression
def self.weighted_average compound, params
+ #p params.keys
weighted_sum = 0.0
sim_sum = 0.0
confidence = 0.0
neighbors = params[:neighbors]
activities = []
neighbors.each do |row|
- n,sim,acts = row
- confidence = sim if sim > confidence # distance to nearest neighbor
- # TODO add LOO errors
- acts.each do |act|
- weighted_sum += sim*Math.log10(act)
- activities << act
- sim_sum += sim
- end
+ #if row["dataset_ids"].include? params[:training_dataset_id]
+ sim = row["tanimoto"]
+ confidence = sim if sim > confidence # distance to nearest neighbor
+ # TODO add LOO errors
+ row["features"][params[:prediction_feature_id].to_s].each do |act|
+ weighted_sum += sim*Math.log10(act)
+ activities << act
+ sim_sum += sim
+ end
+ #end
end
#R.assign "activities", activities
#R.eval "cv = cv(activities)"
@@ -47,10 +34,8 @@ module OpenTox
end
def self.local_linear_regression compound, neighbors
- p neighbors.size
return nil unless neighbors.size > 0
features = neighbors.collect{|n| Compound.find(n.first).fp4}.flatten.uniq
- p features
training_data = Array.new(neighbors.size){Array.new(features.size,0)}
neighbors.each_with_index do |n,i|
#p n.first
diff --git a/test/compound.rb b/test/compound.rb
index 22c152b..ff20c1c 100644
--- a/test/compound.rb
+++ b/test/compound.rb
@@ -162,7 +162,7 @@ print c.sdf
end
def test_fingerprint_db_neighbors
- skip
+ #skip
training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.csv")
[
"CC(=O)CC(C)C#N",
@@ -170,8 +170,18 @@ print c.sdf
"C(=O)CC(C)C#N",
].each do |smi|
c = OpenTox::Compound.from_smiles smi
+ t = Time.now
neighbors = c.db_neighbors(:training_dataset_id => training_dataset.id, :min_sim => 0.2)
- p neighbors
+ p Time.now - t
+ t = Time.now
+ neighbors2 = c.fingerprint_neighbors({:type => "MP2D", :training_dataset_id => training_dataset.id, :min_sim => 0.2})
+ p Time.now - t
+ p neighbors.size
+ p neighbors2.size
+ #p neighbors
+ #p neighbors2
+ #p neighbors2 - neighbors
+ #assert_equal neighbors, neighbors2
end
end
end
diff --git a/test/dataset-long.rb b/test/dataset-long.rb
index 5c8dfb8..49b61df 100644
--- a/test/dataset-long.rb
+++ b/test/dataset-long.rb
@@ -86,6 +86,7 @@ class DatasetLongTest < MiniTest::Test
end
def test_upload_feature_dataset
+ skip
t = Time.now
f = File.join DATA_DIR, "rat_feature_dataset.csv"
d = Dataset.from_csv_file f
diff --git a/test/dataset.rb b/test/dataset.rb
index 4f1e885..1814081 100644
--- a/test/dataset.rb
+++ b/test/dataset.rb
@@ -127,7 +127,7 @@ class DatasetTest < MiniTest::Test
original_csv.shift
csv.each_with_index do |row,i|
compound = Compound.from_smiles row.shift
- original_compound = Compound.from_smiles original_csv[i].shift
+ original_compound = Compound.from_smiles original_csv[i].shift.strip
assert_equal original_compound.inchi, compound.inchi
row.each_with_index do |v,j|
if v.numeric?
@@ -142,7 +142,6 @@ class DatasetTest < MiniTest::Test
def test_from_csv
d = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
- p d
assert_equal Dataset, d.class
assert_equal 1, d.features.size
assert_equal 85, d.compounds.size
@@ -170,8 +169,7 @@ class DatasetTest < MiniTest::Test
def test_from_csv2
File.open("#{DATA_DIR}/temp_test.csv", "w+") { |file| file.write("SMILES,Hamster\nCC=O,true\n ,true\nO=C(N),true") }
dataset = Dataset.from_csv_file "#{DATA_DIR}/temp_test.csv"
- p dataset.warnings
- assert_equal "Cannot parse SMILES compound ' ' at position 3, all entries are ignored.", dataset.warnings.join
+ assert_equal "Cannot parse SMILES compound '' at position 3, all entries are ignored.", dataset.warnings.join
File.delete "#{DATA_DIR}/temp_test.csv"
dataset.features.each{|f| feature = Feature.find f.id; feature.delete}
dataset.delete
diff --git a/test/fminer-long.rb b/test/fminer-long.rb
index 0f202b4..845ed71 100644
--- a/test/fminer-long.rb
+++ b/test/fminer-long.rb
@@ -3,6 +3,7 @@ require_relative "setup.rb"
class FminerTest < MiniTest::Test
def test_fminer_multicell
+ skip
#skip "multicell segfaults"
# TODO aborts, probably fminer
# or OpenBabel segfault
@@ -15,6 +16,7 @@ class FminerTest < MiniTest::Test
end
def test_fminer_isscan
+ skip
dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"ISSCAN-multi.csv")
feature_dataset = OpenTox::Algorithm::Fminer.bbrc(dataset)#, :min_frequency => 15)
assert_equal feature_dataset.compounds.size, dataset.compounds.size
@@ -25,6 +27,7 @@ class FminerTest < MiniTest::Test
end
def test_fminer_kazius
+ skip
dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"kazius.csv")
# TODO reactivate default settings
feature_dataset = OpenTox::Algorithm::Fminer.bbrc(dataset, :min_frequency => 20)
diff --git a/test/lazar-classification.rb b/test/lazar-classification.rb
new file mode 100644
index 0000000..e8b2181
--- /dev/null
+++ b/test/lazar-classification.rb
@@ -0,0 +1,42 @@
+require_relative "setup.rb"
+
+class LazarClassificationTest < MiniTest::Test
+
+ def test_lazar_classification
+ training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"hamster_carcinogenicity.csv")
+ model = Model::LazarClassification.create training_dataset#, feature_dataset
+ #assert_equal 'C-C-C=C', feature_dataset.features.first.smarts
+
+ [ {
+ :compound => OpenTox::Compound.from_inchi("InChI=1S/C6H6/c1-2-4-6-5-3-1/h1-6H"),
+ :prediction => "false",
+ :confidence => 0.25281385281385277,
+ :nr_neighbors => 11
+ },{
+ :compound => OpenTox::Compound.from_smiles("c1ccccc1NN"),
+ :prediction => "false",
+ :confidence => 0.3639589577089577,
+ :nr_neighbors => 14
+ } ].each do |example|
+ prediction = model.predict example[:compound]
+ assert_equal example[:prediction], prediction[:value]
+ #assert_equal example[:confidence], prediction[:confidence]
+ #assert_equal example[:nr_neighbors], prediction[:neighbors].size
+ end
+
+ compound = Compound.from_smiles "CCO"
+ prediction = model.predict compound
+ assert_equal ["false"], prediction[:database_activities]
+ assert_equal "true", prediction[:value]
+
+ # make a dataset prediction
+ compound_dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.mini.csv")
+ prediction = model.predict compound_dataset
+ assert_equal compound_dataset.compounds, prediction.compounds
+
+ assert_equal "Cound not find similar compounds.", prediction.data_entries[7][2]
+ assert_equal "measured", prediction.data_entries[14][1]
+ # cleanup
+ [training_dataset,model,compound_dataset].each{|o| o.delete}
+ end
+end
diff --git a/test/lazar-fminer.rb b/test/lazar-fminer.rb
index 41e1071..9e024a1 100644
--- a/test/lazar-fminer.rb
+++ b/test/lazar-fminer.rb
@@ -3,6 +3,7 @@ require_relative "setup.rb"
class LazarFminerTest < MiniTest::Test
def test_lazar_fminer
+ skip
training_dataset = Dataset.from_csv_file File.join(DATA_DIR,"hamster_carcinogenicity.csv")
model = Model::LazarFminerClassification.create training_dataset#, feature_dataset
feature_dataset = Dataset.find model.neighbor_algorithm_parameters[:feature_dataset_id]
diff --git a/test/lazar-long.rb b/test/lazar-long.rb
index 92d7d5a..525b96e 100644
--- a/test/lazar-long.rb
+++ b/test/lazar-long.rb
@@ -3,6 +3,7 @@ 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]
@@ -21,6 +22,7 @@ class LazarExtendedTest < MiniTest::Test
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]
@@ -44,7 +46,8 @@ class LazarExtendedTest < MiniTest::Test
feature_dataset.delete
end
- def test_lazar_kazius
+ 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}"
@@ -68,4 +71,22 @@ class LazarExtendedTest < MiniTest::Test
#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
diff --git a/test/lazar-regression.rb b/test/lazar-regression.rb
index 4f5a332..c1dc9b9 100644
--- a/test/lazar-regression.rb
+++ b/test/lazar-regression.rb
@@ -8,7 +8,7 @@ class LazarRegressionTest < MiniTest::Test
compound = Compound.from_smiles "CC(C)(C)CN"
prediction = model.predict compound
assert_equal 7.2, prediction[:value].round(1)
- assert_equal 91, prediction[:neighbors].size
+ assert_equal 88, prediction[:neighbors].size
end
def test_mpd_fingerprints
@@ -17,7 +17,7 @@ class LazarRegressionTest < MiniTest::Test
model.neighbor_algorithm_parameters[:type] = "MP2D"
compound = Compound.from_smiles "CCCSCCSCC"
prediction = model.predict compound
- assert_equal 0.02, prediction[:value].round(2)
+ assert_equal 0.04, prediction[:value].round(2)
assert_equal 3, prediction[:neighbors].size
end
diff --git a/test/prediction_models.rb b/test/prediction_models.rb
index 1b9e788..067c3c8 100644
--- a/test/prediction_models.rb
+++ b/test/prediction_models.rb
@@ -4,22 +4,13 @@ class PredictionModelTest < MiniTest::Test
def test_prediction_model
pm = Model::Prediction.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
- #dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
- #model = Model::LazarFminerClassification.create dataset
- #cv = ClassificationCrossValidation.create model
- #metadata = JSON.parse(File.read("#{DATA_DIR}/hamster_carcinogenicity.json"))
-
- #metadata[:model_id] = model.id
- #metadata[:crossvalidation_id] = cv.id
- #pm = Model::Prediction.new(metadata)
- #pm.save
[:endpoint,:species,:source].each do |p|
refute_empty pm[p]
end
assert pm.classification?
refute pm.regression?
pm.crossvalidations.each do |cv|
- assert cv.accuracy > 0.75
+ assert cv.accuracy > 0.75, "Crossvalidation accuracy (#{cv.accuracy}) should be larger than 0.75. This may happen due to an unfavorable training/test set split."
end
prediction = pm.predict Compound.from_smiles("CCCC(NN)C")
assert_equal "true", prediction[:value]
diff --git a/test/validation.rb b/test/validation.rb
index 6764a32..7de944c 100644
--- a/test/validation.rb
+++ b/test/validation.rb
@@ -3,6 +3,7 @@ require_relative "setup.rb"
class ValidationTest < MiniTest::Test
def test_fminer_crossvalidation
+ skip
dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
model = Model::LazarFminerClassification.create dataset
cv = ClassificationCrossValidation.create model
@@ -15,12 +16,13 @@ class ValidationTest < MiniTest::Test
dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
model = Model::LazarClassification.create dataset#, features
cv = ClassificationCrossValidation.create model
- assert cv.accuracy > 0.7
- File.open("tmp.svg","w+"){|f| f.puts cv.confidence_plot}
- `inkview tmp.svg`
+ #p cv
+ assert cv.accuracy > 0.7, "Accuracy (#{cv.accuracy}) should be larger than 0.7"
+ #File.open("tmp.svg","w+"){|f| f.puts cv.confidence_plot}
+ #`inkview tmp.svg`
p cv.nr_unpredicted
p cv.accuracy
- #assert cv.weighted_accuracy > cv.accuracy, "Weighted accuracy should be larger than unweighted accuracy."
+ assert cv.weighted_accuracy > cv.accuracy, "Weighted accuracy (#{cv.weighted_accuracy}) should be larger than unweighted accuracy (#{cv.accuracy}) ."
end
def test_default_regression_crossvalidation
@@ -28,11 +30,11 @@ class ValidationTest < MiniTest::Test
model = Model::LazarRegression.create dataset
cv = RegressionCrossValidation.create model
#cv = RegressionCrossValidation.find '561503262b72ed54fd000001'
- p cv.id
- File.open("tmp.svg","w+"){|f| f.puts cv.correlation_plot}
- `inkview tmp.svg`
- File.open("tmp.svg","w+"){|f| f.puts cv.confidence_plot}
- `inkview tmp.svg`
+ #p cv.id
+ #File.open("tmp.svg","w+"){|f| f.puts cv.correlation_plot}
+ #`inkview tmp.svg`
+ #File.open("tmp.svg","w+"){|f| f.puts cv.confidence_plot}
+ #`inkview tmp.svg`
#puts cv.misclassifications.to_yaml
p cv.rmse
@@ -91,9 +93,13 @@ class ValidationTest < MiniTest::Test
model.save
cv = ClassificationCrossValidation.create model
params = model.neighbor_algorithm_parameters
+ params.delete :training_dataset_id
params = Hash[params.map{ |k, v| [k.to_s, v] }] # convert symbols to string
+
cv.validations.each do |validation|
- assert_equal params, validation.model.neighbor_algorithm_parameters
+ validation_params = validation.model.neighbor_algorithm_parameters
+ validation_params.delete "training_dataset_id"
+ assert_equal params, validation_params
end
end