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require 'rserve'
require 'json'
require 'yaml'
require 'csv'
ENDPOINT = "Cell.association (Net cell association [mL/ug(Mg)])"
def predict params
neighbors = []
sim_sum = 0
weighted_sum = 0
match = nil
relevant_features = JSON.parse(File.read("./relevant-features.json"))
weights = relevant_features.values.collect{|v| v["r"]}
JSON.parse(File.read("./data.json")).each do |id,categories|
neighbor_values = categories["physchem"].select{|f,v| params.keys.include? f}.values
if params.values == neighbor_values
match = {id => categories}
else
sim = weighted_cosine_similarity(params.values,neighbor_values,weights)
if sim > 0.95
neighbor = categories
neighbor["similarity"] = sim
neighbor["sim"] = cosine_similarity(params.values,neighbor_values)
neighbor["id"] = id
sim_sum += sim
weighted_sum += sim*Math.log10(categories["tox"][ENDPOINT])
#weighted_sum += sim*categories["tox"][ENDPOINT]
neighbors << neighbor
end
end
end
neighbors.sort!{|a,b| b["similarity"] <=> a["similarity"]}
sim_sum == 0 ? prediction = nil : prediction = 10**(weighted_sum/sim_sum)
{
:query => params,
:match => match,
:prediction => {ENDPOINT => prediction},
:neighbors => neighbors
}
end
class Object
def numeric?
true if Float(self) rescue false
end
end
def euclidean_distance(a, b)
sq = a.zip(b).map{|a,b| (a - b) ** 2}
Math.sqrt(sq.inject(0) {|s,c| s + c})
end
def dot_product(a, b)
products = a.zip(b).map{|a, b| a * b}
products.inject(0) {|s,p| s + p}
end
def magnitude(point)
squares = point.map{|x| x ** 2}
Math.sqrt(squares.inject(0) {|s, c| s + c})
end
# http://stackoverflow.com/questions/1838806/euclidean-distance-vs-pearson-correlation-vs-cosine-similarity
def cosine_similarity(a, b)
dot_product(a, b) / (magnitude(a) * magnitude(b))
end
def weighted_cosine_similarity(a, b, w)
dot_product = 0
magnitude_a = 0
magnitude_b = 0
(0..a.size-1).each do |i|
dot_product += w[i].abs*a[i]*b[i]
magnitude_a += w[i].abs*a[i]**2
magnitude_b += w[i].abs*b[i]**2
end
dot_product/Math.sqrt(magnitude_a*magnitude_b)
end
#@endpoint = @data.collect{|r| r[5]}
def neighbors query
end
def csv2json
csv = CSV.read("data/MergedSheets_edit.csv")
csv.collect!{|row| row[0..36].collect{|c| c.numeric? ? c.to_f : c } }.compact
feature_names = [
"ID",
csv[0][1],
csv[0][2],
csv[0][3],
csv[6][4],
"#{csv[0][5]} (#{csv[6][5]} [#{csv[11][5]}])", # endpoint
"#{csv[0][6]} (#{csv[6][6]})", # endpoint
"#{csv[6][7]} [#{csv[11][7]}]",
"#{csv[6][8]} [#{csv[11][8]}]",
"#{csv[6][9]} [#{csv[11][9]}]",
]
(10..10+5*3).step(3) do |i|
feature_names += [
"#{csv[6][i]} [#{csv[11][i]}]",
"#{csv[6][i+1]} #{csv[8][i+1]} [#{csv[11][i+1]}]",
"#{csv[6][i+2]} #{csv[8][i+2]}",
]
end
feature_names += [
"#{csv[6][28]}",
"#{csv[6][29]} #{csv[8][29]}",
"#{csv[6][30]} #{csv[8][30]}",
]
(31..34).each do |i|
feature_names << "#{csv[6][i]} #{csv[8][i]} [#{csv[11][i]}]"
end
(35..36).each do |i|
feature_names << "#{csv[6][i]} #{csv[8][i]} #{csv[10][i]} [#{csv[11][i]}]"
end
data = {}
csv.drop(12).each do |row|
id = row.first
if id.match /^G/ # skip Ag, too many missing values
data[id] = {}
row.each_with_index do |col,i|
if i == 0
data[id][:composition] = {}
elsif i < 5
data[id][:composition][feature_names[i]] = col
elsif i == 5
data[id][:tox] ||= {}
data[id][:tox][feature_names[i]] = col
elsif i > 6
data[id][:physchem] ||= {}
data[id][:physchem][feature_names[i]] = col
end
end
end
end
File.open("data.json","w+"){|f| f.puts data.to_json}
data
end
#puts data.to_yaml
=begin
R.assign "endpoint", endpoint
(0..data[0].size).each do |c|
if data.collect{|r| r[c]}.uniq.size > 1
begin
R.assign "feature", data.collect{|r| r[c]}
R.eval "r <- cor(-log(endpoint),-log(feature),use='complete')"
r = R.eval("r").to_ruby
p "#{c}: #{r}" if r > 0.3 or r < -0.3
rescue
end
end
end
csv[0..13].each do |row|
row.each_with_index do |col,i|
features[i] = features[i].to_s+", "+col.to_s
end
end
puts features.select{|f| f.match(/Mean/)}.to_yaml
#n+=1
#p n,row.first unless row.first.match /^[G|S]/
=end
|
