goko 0.5.5

A lock-free, eventually consistent, concurrent covertree.
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

//! See the paper for how this works

use crate::plugins::discrete::tracker::*;
use crate::*;
use rand::prelude::*;
use rand::thread_rng;
use rayon::iter::repeatn;

/// Trains a baseline by sampling randomly from the training set (used to create the tree)
/// This baseline is _not_ realistic.
pub struct DirichletBaseline {
    sample_rate: usize,
    sequence_len: usize,
    num_sequences: usize,
    prior_weight: f64,
    observation_weight: f64,
}

impl Default for DirichletBaseline {
    fn default() -> DirichletBaseline {
        DirichletBaseline {
            sample_rate: 100,
            sequence_len: 0,
            num_sequences: 8,
            prior_weight: 1.0,
            observation_weight: 1.0,
        }
    }
}

impl DirichletBaseline {
    /// Sets a new maxium sequence length. Set this to be the window size if you're using windows, the lenght of the test set you've got,
    /// or leave it alone as the default limit is the number of points in the training set.
    ///
    /// We sample up to this cap, linearly interpolating above this. So, the baseline produced is fairly accurate for indexes below this
    /// and unreliable above this.
    pub fn set_sequence_len(&mut self, sequence_len: usize) {
        self.sequence_len = sequence_len;
    }
    /// Sets a new count of sequences to train over, default 100. Stats for each sequence are returned.
    pub fn set_num_sequences(&mut self, num_sequences: usize) {
        self.num_sequences = num_sequences;
    }
    /// Sets a new prior weight, default 1.0. The prior is multiplied by this to increase or decrease it's importance
    pub fn set_prior_weight(&mut self, prior_weight: f64) {
        self.prior_weight = prior_weight;
    }
    /// Sets a new observation weight, default 1.0. Each discrete observation is treated as having this value.
    pub fn set_observation_weight(&mut self, observation_weight: f64) {
        self.observation_weight = observation_weight;
    }
    /// Samples at the following rate, then interpolates for sequence lengths between the following.
    pub fn set_sample_rate(&mut self, sample_rate: usize) {
        self.sample_rate = sample_rate;
    }

    /// Trains the sequences up.
    pub fn train<D: PointCloud>(
        &self,
        reader: CoverTreeReader<D>,
    ) -> GokoResult<KLDivergenceBaseline> {
        let point_indexes = reader.point_cloud().reference_indexes();
        let sequence_len = if self.sequence_len == 0 {
            point_indexes.len()
        } else {
            self.sequence_len
        };

        let results: Vec<Vec<KLDivergenceStats>> = repeatn(reader, self.num_sequences)
            .map(|reader| {
                let mut tracker = BayesCategoricalTracker::new(
                    0,
                    reader,
                );
                (&point_indexes[..])
                    .choose_multiple(&mut thread_rng(), sequence_len)
                    .enumerate()
                    .filter_map(|(i, pi)| {
                        tracker.add_path(tracker.reader().known_path(*pi).unwrap());
                        if i % self.sample_rate == 0 {
                            Some(tracker.kl_div_stats())
                        } else {
                            None
                        }
                    })
                    .collect()
            })
            .collect();
        let len = results[0].len();
        let mut sequence_len = Vec::with_capacity(len);
        let mut stats: Vec<KLDivergenceBaselineStats> =
            std::iter::repeat_with(KLDivergenceBaselineStats::default)
                .take(len)
                .collect();

        for i in 0..len {
            for result_vec in &results {
                stats[i].add(&result_vec[i]);
            }
            sequence_len.push(results[0][i].sequence_len);
        }
        Ok(KLDivergenceBaseline {
            num_sequences: results.len(),
            stats,
            sequence_len,
        })
    }
}

/// Tracks the non-zero (all KL divergences above 1e-10)
#[derive(Debug, Default)]
pub struct KLDivergenceBaselineStats {
    /// The maximum non-zero KL divergence
    pub max: (f64, f64),
    /// The minimum non-zero KL divergence
    pub min: (f64, f64),
    /// The number of nodes that have a non-zero divergence
    pub nz_count: (f64, f64),
    /// The first moment, use this with the `nz_count` to get the mean
    pub moment1_nz: (f64, f64),
    /// The second moment, use this with the `nz_count` and first moment to get the variance
    pub moment2_nz: (f64, f64),
}

impl KLDivergenceBaselineStats {
    pub(crate) fn add(&mut self, stats: &KLDivergenceStats) {
        self.max.0 += stats.max;
        self.max.1 += stats.max * stats.max;
        self.min.0 += stats.min;
        self.min.1 += stats.min * stats.min;
        self.nz_count.0 += stats.nz_count as f64;
        self.nz_count.1 += (stats.nz_count * stats.nz_count) as f64;
        self.moment1_nz.0 += stats.moment1_nz;
        self.moment1_nz.1 += stats.moment1_nz * stats.moment1_nz;
        self.moment2_nz.0 += stats.moment2_nz;
        self.moment2_nz.1 += stats.moment2_nz * stats.moment2_nz;
    }

    fn to_mean_var(&self, count: f64) -> KLDivergenceBaselineStats {
        let max_mean = self.max.0 / count;
        let min_mean = self.min.0 / count;
        let nz_count_mean = self.nz_count.0 / count;
        let moment1_nz_mean = self.moment1_nz.0 / count;
        let moment2_nz_mean = self.moment2_nz.0 / count;

        let max_var = self.max.1 / count - max_mean * max_mean;
        let min_var = self.min.1 / count - min_mean * min_mean;
        let nz_count_var = self.nz_count.1 / count - nz_count_mean * nz_count_mean;
        let moment1_nz_var = self.moment1_nz.1 / count - moment1_nz_mean * moment1_nz_mean;
        let moment2_nz_var = self.moment2_nz.1 / count - moment2_nz_mean * moment2_nz_mean;

        KLDivergenceBaselineStats {
            max: (max_mean, max_var),
            min: (min_mean, min_var),
            nz_count: (nz_count_mean, nz_count_var),
            moment1_nz: (moment1_nz_mean, moment1_nz_var),
            moment2_nz: (moment2_nz_mean, moment2_nz_var),
        }
    }

    fn interpolate(mut self, other: &Self, w: f64) -> Self {
        self.max.0 += w * (other.max.0 - self.max.0);
        self.max.1 += w * (other.max.1 - self.max.1);

        self.min.0 += w * (other.min.0 - self.min.0);
        self.min.1 += w * (other.min.1 - self.min.1);

        self.nz_count.0 += w * (other.nz_count.0 - self.nz_count.0);
        self.nz_count.1 += w * (other.nz_count.1 - self.nz_count.1);

        self.moment1_nz.0 += w * (other.moment1_nz.0 - self.moment1_nz.0);
        self.moment1_nz.1 += w * (other.moment1_nz.1 - self.moment1_nz.1);

        self.moment2_nz.0 += w * (other.moment2_nz.0 - self.moment2_nz.0);
        self.moment2_nz.1 += w * (other.moment2_nz.1 - self.moment2_nz.1);
        self
    }
}

/// Computing the KL div of each node's prior and posterior is expensive.
pub struct KLDivergenceBaseline {
    /// The number of sequences we're have the stats from
    pub num_sequences: usize,
    /// The lenght of the sequences we have stats for. All other values are linearly interpolated between these.
    pub sequence_len: Vec<usize>,
    /// The actual stats objects. These are stored by the moments, but are returned by (mean,var)
    pub stats: Vec<KLDivergenceBaselineStats>,
}

impl KLDivergenceBaseline {
    /// Gets the stats object that stores an approximate mean and variance of the samples.
    pub fn stats(&self, i: usize) -> KLDivergenceBaselineStats {
        match self.sequence_len.binary_search(&i) {
            Ok(index) => self.stats[index].to_mean_var(self.num_sequences as f64),
            Err(index) => {
                if index == 0 {
                    KLDivergenceBaselineStats::default()
                } else if index == self.sequence_len.len() {
                    let stats1 = self.stats[index - 2].to_mean_var(self.num_sequences as f64);
                    let stats2 = self.stats[index - 1].to_mean_var(self.num_sequences as f64);
                    let weight = ((i - self.sequence_len[index - 2]) as f64)
                        / ((self.sequence_len[index - 1] - self.sequence_len[index - 2]) as f64);
                    stats1.interpolate(&stats2, weight)
                } else {
                    let stats1 = self.stats[index - 1].to_mean_var(self.num_sequences as f64);
                    let stats2 = self.stats[index].to_mean_var(self.num_sequences as f64);
                    let weight = ((i - self.sequence_len[index - 1]) as f64)
                        / ((self.sequence_len[index] - self.sequence_len[index - 1]) as f64);
                    stats1.interpolate(&stats2, weight)
                }
            }
        }
    }
}