summaryrefslogtreecommitdiff
path: root/lib/validation-statistics.rb
blob: 2202b79fdc7886fc899bf7ca8657378200fc7e55 (plain)
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
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
module OpenTox
  module Validation
    module ClassificationStatistics

      def statistics 
        self.accept_values = model.prediction_feature.accept_values
        self.confusion_matrix = Array.new(accept_values.size){Array.new(accept_values.size,0)}
        self.weighted_confusion_matrix = Array.new(accept_values.size){Array.new(accept_values.size,0)}
        nr_instances = 0
        predictions.each do |cid,pred|
          # TODO
          # use predictions without probabilities (single neighbor)??
          # use measured majority class??
          if pred[:measurements].uniq.size == 1 and pred[:probabilities]
            m = pred[:measurements].first
            if pred[:value] == m
              if pred[:value] == accept_values[0]
                confusion_matrix[0][0] += 1
                weighted_confusion_matrix[0][0] += pred[:probabilities][pred[:value]]
                nr_instances += 1
              elsif pred[:value] == accept_values[1]
                confusion_matrix[1][1] += 1
                weighted_confusion_matrix[1][1] += pred[:probabilities][pred[:value]]
                nr_instances += 1
              end
            elsif pred[:value] != m
              if pred[:value] == accept_values[0]
                confusion_matrix[0][1] += 1
                weighted_confusion_matrix[0][1] += pred[:probabilities][pred[:value]]
                nr_instances += 1
              elsif pred[:value] == accept_values[1]
                confusion_matrix[1][0] += 1
                weighted_confusion_matrix[1][0] += pred[:probabilities][pred[:value]]
                nr_instances += 1
              end
            end
          end
        end
        self.true_rate = {}
        self.predictivity = {}
        accept_values.each_with_index do |v,i|
          self.true_rate[v] = confusion_matrix[i][i]/confusion_matrix[i].reduce(:+).to_f
          self.predictivity[v] = confusion_matrix[i][i]/confusion_matrix.collect{|n| n[i]}.reduce(:+).to_f
        end
        confidence_sum = 0
        weighted_confusion_matrix.each do |r|
          r.each do |c|
            confidence_sum += c
          end
        end
        self.accuracy = (confusion_matrix[0][0]+confusion_matrix[1][1])/nr_instances.to_f
        self.weighted_accuracy = (weighted_confusion_matrix[0][0]+weighted_confusion_matrix[1][1])/confidence_sum.to_f
        $logger.debug "Accuracy #{accuracy}"
        save
        {
          :accept_values => accept_values,
          :confusion_matrix => confusion_matrix,
          :weighted_confusion_matrix => weighted_confusion_matrix,
          :accuracy => accuracy,
          :weighted_accuracy => weighted_accuracy,
          :true_rate => self.true_rate,
          :predictivity => self.predictivity,
        }
      end

      def probability_plot format: "pdf"
        #unless probability_plot_id

          #tmpdir = File.join(ENV["HOME"], "tmp")
          tmpdir = "/tmp"
          #p tmpdir
          FileUtils.mkdir_p tmpdir
          tmpfile = File.join(tmpdir,"#{id.to_s}_probability.#{format}")
          accuracies = []
          probabilities = []
          correct_predictions = 0
          incorrect_predictions = 0
          pp = []
          predictions.values.select{|p| p["probabilities"]}.compact.each do |p|
            p["measurements"].each do |m|
              pp << [ p["probabilities"][p["value"]], p["value"] == m ]
            end
          end
          pp.sort_by!{|p| 1-p.first}
          pp.each do |p|
            p[1] ? correct_predictions += 1 : incorrect_predictions += 1
            accuracies << correct_predictions/(correct_predictions+incorrect_predictions).to_f
            probabilities << p[0]
          end
          R.assign "accuracy", accuracies
          R.assign "probability", probabilities
          R.eval "image = qplot(probability,accuracy)+ylab('Accumulated accuracy')+xlab('Prediction probability')+ylim(c(0,1))+scale_x_reverse()+geom_line()"
          R.eval "ggsave(file='#{tmpfile}', plot=image)"
          file = Mongo::Grid::File.new(File.read(tmpfile), :filename => "#{self.id.to_s}_probability_plot.svg")
          plot_id = $gridfs.insert_one(file)
          update(:probability_plot_id => plot_id)
        #end
        $gridfs.find_one(_id: probability_plot_id).data
      end
    end

    module RegressionStatistics

      def statistics
        self.rmse = 0
        self.mae = 0
        self.within_prediction_interval = 0
        self.out_of_prediction_interval = 0
        x = []
        y = []
        predictions.each do |cid,pred|
          if pred[:value] and pred[:measurements] 
            x << pred[:measurements].median
            y << pred[:value]
            error = pred[:value]-pred[:measurements].median
            self.rmse += error**2
            self.mae += error.abs
            if pred[:prediction_interval]
              if pred[:measurements].median >= pred[:prediction_interval][0] and pred[:measurements].median <= pred[:prediction_interval][1]
                self.within_prediction_interval += 1
              else
                self.out_of_prediction_interval += 1
              end
            end
          else
            warnings << "No training activities for #{Compound.find(compound_id).smiles} in training dataset #{model.training_dataset_id}."
            $logger.debug "No training activities for #{Compound.find(compound_id).smiles} in training dataset #{model.training_dataset_id}."
          end
        end
        R.assign "measurement", x
        R.assign "prediction", y
        R.eval "r <- cor(measurement,prediction,use='pairwise')"
        self.r_squared = R.eval("r").to_ruby**2
        self.mae = self.mae/predictions.size
        self.rmse = Math.sqrt(self.rmse/predictions.size)
        $logger.debug "R^2 #{r_squared}"
        $logger.debug "RMSE #{rmse}"
        $logger.debug "MAE #{mae}"
        $logger.debug "#{percent_within_prediction_interval.round(2)}% of measurements within prediction interval"
        save
        {
          :mae => mae,
          :rmse => rmse,
          :r_squared => r_squared,
          :within_prediction_interval => within_prediction_interval,
          :out_of_prediction_interval => out_of_prediction_interval,
        }
      end

      def percent_within_prediction_interval
        100*within_prediction_interval.to_f/(within_prediction_interval+out_of_prediction_interval)
      end

      def correlation_plot format: "png"
        unless correlation_plot_id
          tmpfile = "/tmp/#{id.to_s}_correlation.#{format}"
          x = []
          y = []
          feature = Feature.find(predictions.first.last["prediction_feature_id"])
          predictions.each do |sid,p|
            x << p["measurements"].median
            y << p["value"]
          end
          R.assign "measurement", x
          R.assign "prediction", y
          R.eval "all = c(measurement,prediction)"
          R.eval "range = c(min(all), max(all))"
          title = feature.name
          title += "[#{feature.unit}]" if feature.unit and !feature.unit.blank?
          R.eval "image = qplot(prediction,measurement,main='#{title}',xlab='Prediction',ylab='Measurement',asp=1,xlim=range, ylim=range)"
          R.eval "image = image + geom_abline(intercept=0, slope=1)"
          R.eval "ggsave(file='#{tmpfile}', plot=image)"
          file = Mongo::Grid::File.new(File.read(tmpfile), :filename => "#{id.to_s}_correlation_plot.#{format}")
          plot_id = $gridfs.insert_one(file)
          update(:correlation_plot_id => plot_id)
        end
        $gridfs.find_one(_id: correlation_plot_id).data
      end

      def worst_predictions n: 5, show_neigbors: true, show_common_descriptors: false
        worst_predictions = predictions.sort_by{|sid,p| -(p["value"] - p["measurements"].median).abs}[0,n]
        worst_predictions.collect do |p|
          substance = Substance.find(p.first)
          prediction = p[1]
          if show_neigbors
            neighbors = prediction["neighbors"].collect do |n|
              common_descriptors = []
              if show_common_descriptors
                common_descriptors = n["common_descriptors"].collect do |d|
                  f=Feature.find(d)
                  {
                    :id => f.id.to_s,
                    :name => "#{f.name} (#{f.conditions})",
                    :p_value => d[:p_value],
                    :r_squared => d[:r_squared],
                  }
                end
              else
                common_descriptors = n["common_descriptors"].size
              end
              {
                :name => Substance.find(n["_id"]).name,
                :id => n["_id"].to_s,
                :common_descriptors => common_descriptors
              }
            end
          else
            neighbors = prediction["neighbors"].size
          end
          {
            :id => substance.id.to_s,
            :name => substance.name,
            :feature => Feature.find(prediction["prediction_feature_id"]).name,
            :error => (prediction["value"] - prediction["measurements"].median).abs,
            :prediction => prediction["value"],
            :measurements => prediction["measurements"],
            :neighbors => neighbors
          }
        end
      end
    end
  end
end