1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
|
require_relative "setup.rb"
class ValidationTest < MiniTest::Test
def test_fminer_crossvalidation
dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
model = Model::LazarFminerClassification.create dataset
cv = ClassificationCrossValidation.create model
refute_empty cv.validation_ids
assert cv.accuracy > 0.8, "Crossvalidation accuracy lower than 0.8"
assert cv.weighted_accuracy > cv.accuracy, "Weighted accuracy (#{cv.weighted_accuracy}) larger than unweighted accuracy(#{cv.accuracy}) "
end
def test_classification_crossvalidation
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
p cv.nr_unpredicted
p cv.accuracy
#assert cv.weighted_accuracy > cv.accuracy, "Weighted accuracy should be larger than unweighted accuracy."
end
def test_regression_crossvalidation
dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.medi.csv"
#dataset = Dataset.from_csv_file "#{DATA_DIR}/EPAFHM.csv"
params = {
:prediction_algorithm => "OpenTox::Algorithm::Regression.weighted_average",
:neighbor_algorithm => "fingerprint_neighbors",
:neighbor_algorithm_parameters => {
:type => "MACCS",
:min_sim => 0.7,
}
}
model = Model::LazarRegression.create dataset, params
cv = RegressionCrossValidation.create model
cv.validation_ids.each do |vid|
model = Model::Lazar.find(Validation.find(vid).model_id)
assert_equal params[:neighbor_algorithm_parameters][:type], model[:neighbor_algorithm_parameters][:type]
assert_equal params[:neighbor_algorithm_parameters][:min_sim], model[:neighbor_algorithm_parameters][:min_sim]
refute_equal params[:neighbor_algorithm_parameters][:training_dataset_id], model[:neighbor_algorithm_parameters][:training_dataset_id]
end
#`inkview #{cv.plot}`
#puts JSON.pretty_generate(cv.misclassifications)#.collect{|l| l.join ", "}.join "\n"
#`inkview #{cv.plot}`
assert cv.rmse < 30, "RMSE > 30"
#assert cv.weighted_rmse < cv.rmse, "Weighted RMSE (#{cv.weighted_rmse}) larger than unweighted RMSE(#{cv.rmse}) "
assert cv.mae < 12
#assert cv.weighted_mae < cv.mae
end
def test_repeated_crossvalidation
dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
model = Model::LazarClassification.create dataset
repeated_cv = RepeatedCrossValidation.create model
repeated_cv.crossvalidations.each do |cv|
assert cv.accuracy > 0.7
end
end
def test_crossvalidation_parameters
dataset = Dataset.from_csv_file "#{DATA_DIR}/hamster_carcinogenicity.csv"
params = {
:neighbor_algorithm_parameters => {
:min_sim => 0.3,
:type => "FP3"
}
}
model = Model::LazarClassification.create dataset, params
model.save
cv = ClassificationCrossValidation.create model
params = model.neighbor_algorithm_parameters
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
end
end
def test_physchem_regression_crossvalidation
skip
@descriptors = OpenTox::Algorithm::Descriptor::OBDESCRIPTORS.keys
refute_empty @descriptors
# UPLOAD DATA
training_dataset = OpenTox::Dataset.from_csv_file File.join(DATA_DIR,"EPAFHM.medi.csv")
feature_dataset = Algorithm::Descriptor.physchem training_dataset, @descriptors
feature_dataset.save
scaled_feature_dataset = feature_dataset.scale
scaled_feature_dataset.save
model = Model::LazarRegression.create training_dataset
model.neighbor_algorithm = "physchem_neighbors"
model.neighbor_algorithm_parameters = {
:feature_calculation_algorithm => "OpenTox::Algorithm::Descriptor.physchem",
:descriptors => @descriptors,
:feature_dataset_id => scaled_feature_dataset.id,
:min_sim => 0.3
}
model.save
cv = RegressionCrossValidation.create model
p cv
end
end
|
