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
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
|
# Get a list of all prediction models
# @param [Header] Accept one of text/uri-list,
# @return [text/uri-list] list of all prediction models
get "/api/model/?" do
models = Model::Validation.all
case @accept
when "text/uri-list"
uri_list = models.collect{|model| uri("/api/model/#{model.id}")}
return uri_list.join("\n") + "\n"
when "application/json"
models = JSON.parse models.to_json
list = []
models.each{|m| list << uri("/api/model/#{m["_id"]["$oid"]}")}
return list.to_json
else
halt 400, "Mime type #{@accept} is not supported."
end
end
get "/api/model/:id/?" do
model = Model::Validation.find params[:id]
halt 400, "Model with id: #{params[:id]} not found." unless model
model["training_dataset"] = model.model.training_dataset.id.to_s
return model.to_json
end
post "/api/model/:id/?" do
if request.content_type == "application/x-www-form-urlencoded"
identifier = params[:identifier].strip.gsub(/\A"|"\Z/,'')
compound = Compound.from_smiles identifier
model = Model::Validation.find params[:id]
prediction = model.predict(compound)
output = {:compound => {:id => compound.id, :inchi => compound.inchi, :smiles => compound.smiles},
:model => model,
:prediction => prediction
}
return 200, output.to_json
elsif request.content_type =~ /^multipart\/form-data/ && request.content_length.to_i > 0
@task = Task.new
@task.save
task = Task.run do
m = Model::Validation.find params[:id]
@task.update_percent(0.1)
dataset = Batch.from_csv_file params[:fileName][:tempfile]
compounds = dataset.compounds
$logger.debug compounds.size
identifiers = dataset.identifiers
ids = dataset.ids
type = (m.regression? ? "Regression" : "Classification")
# add header for regression
if type == "Regression"
unit = (type == "Regression") ? "(#{m.unit})" : ""
converted_unit = (type == "Regression") ? "#{m.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
if ids.blank?
header = "ID,Input,Endpoint,Unique SMILES,inTrainingSet,Measurements #{unit},Prediction #{unit},Prediction #{converted_unit},"\
"Prediction Interval Low #{unit},Prediction Interval High #{unit},"\
"Prediction Interval Low #{converted_unit},Prediction Interval High #{converted_unit},"\
"inApplicabilityDomain,Note\n"
else
header = "ID,Original ID,Input,Endpoint,Unique SMILES,inTrainingSet,Measurements #{unit},Prediction #{unit},Prediction #{converted_unit},"\
"Prediction Interval Low #{unit},Prediction Interval High #{unit},"\
"Prediction Interval Low #{converted_unit},Prediction Interval High #{converted_unit},"\
"inApplicabilityDomain,Note\n"
end
end
# add header for classification
if type == "Classification"
av = m.prediction_feature.accept_values
if ids.blank?
header = "ID,Input,Endpoint,Unique SMILES,inTrainingSet,Measurements,Prediction,"\
"predProbability #{av[0]},predProbability #{av[1]},inApplicabilityDomain,Note\n"
else
header = "ID,Original ID,Input,Endpoint,Unique SMILES,inTrainingSet,Measurements,Prediction,"\
"predProbability #{av[0]},predProbability #{av[1]},inApplicabilityDomain,Note\n"
end
end
# predict compounds
p = 100.0/compounds.size
counter = 1
predictions = []
compounds.each_with_index do |cid,idx|
compound = Compound.find cid
#$logger.debug compound.inspect
if Prediction.where(compound: compound.id, model: m.id).exists?
prediction_object = Prediction.find_by(compound: compound.id, model: m.id)
prediction = prediction_object.prediction
prediction_id = prediction_object.id
# in case prediction object was created by single prediction
if prediction_object.csv.blank?
prediction_object[:csv] = prediction_to_csv(m,compound,prediction)
prediction_object.save
end
# identifier
identifier = identifiers[idx]
else
prediction = m.predict(compound)
# save prediction object
prediction_object = Prediction.new
prediction_id = prediction_object.id
prediction_object[:compound] = compound.id
prediction_object[:model] = m.id
# add additionally fields for html representation
unless prediction[:value].blank? || type == "Classification"
prediction[:prediction_value] = "#{prediction[:value].delog10.signif(3)} #{unit}"
prediction["converted_prediction_value"] = "#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)} #{converted_unit}"
end
unless prediction[:prediction_interval].blank?
interval = prediction[:prediction_interval]
prediction[:interval] = "#{interval[1].delog10.signif(3)} - #{interval[0].delog10.signif(3)} #{unit}"
prediction[:converted_interval] = "#{compound.mmol_to_mg(interval[1].delog10).signif(3)} - #{compound.mmol_to_mg(interval[0].delog10).signif(3)} #{converted_unit}"
end
prediction["unit"] = unit
prediction["converted_unit"] = converted_unit
if prediction[:measurements].is_a?(Array)
prediction["measurements_string"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{value.delog10.signif(3)} #{unit}"} : prediction[:measurements].join("</br>")
prediction["converted_measurements"] = prediction[:measurements].collect{|value| "#{compound.mmol_to_mg(value.delog10).signif(3)} #{unit =~ /mmol\/L/ ? "(mg/L)" : "(mg/kg_bw/day)"}"} if type == "Regression"
else
output["measurements_string"] = (type == "Regression") ? "#{prediction[:measurements].delog10.signif(3)} #{unit}}" : prediction[:measurements]
output["converted_measurements"] = "#{compound.mmol_to_mg(prediction[:measurements].delog10).signif(3)} #{(unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" if type == "Regression"
end
# store in prediction_object
prediction_object[:prediction] = prediction
prediction_object[:csv] = prediction_to_csv(m,compound,prediction)
prediction_object.save
# identifier
identifier = identifiers[idx]
end
# collect prediction_object ids with identifier
predictions << {"#{identifier}" => prediction_id}
$logger.debug predictions.inspect
@task.update_percent((counter*p).ceil > 100 ? 100 : (counter*p).ceil)
counter += 1
end
# write csv
@task[:csv] = header
# write predictions
# save task
# append predictions as last action otherwise they won't save
# mongoid works with shallow copy via #dup
@task[:predictions] = {m.id.to_s => predictions}
@task[:dataset_id] = dataset.id
@task[:model_id] = m.id
@task.save
end#main task
tid = @task.id.to_s
return 202, "//#{$host_with_port}/task/#{tid}".to_json
else
halt 400, "No accepted content type"
end
end
|