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desision trees experiment

pull/25/head
Alexey Velikiy 3 years ago
parent
27cee51551
commit
42e7da5efd
5 changed files with 294 additions and 209 deletions
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  1. 2
      client/package.json
  2. 2
      client/yarn.lock
  3. 387
      server/Cargo.lock
  4. 4
      server/Cargo.toml
  5. 108
      server/src/services/analytic_service/analytic_unit/pattern_analytic_unit.rs

2
client/package.json
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@ -9,7 +9,7 @@
"lint": "vue-cli-service lint"
},
"dependencies": {
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"@chartwerk/scatter-pod": "^0.2.4",
"@kyvg/vue3-notification": "^2.3.4",
"@types/lodash": "^4.14.176",

2
client/yarn.lock
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@ -232,7 +232,7 @@
version "0.1.0"
resolved "https://codeload.github.com/chartwerk/core/tar.gz/532eddbc8ad938091b1d9ec1693cec5eddfdbfc2"
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387
server/Cargo.lock generated
Unescape View File

@ -37,15 +37,6 @@ version = "1.0.44"
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version = "0.11.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "6ea71e48b3eab4c4b153e8e35dcaeac132720809ef68359097b8cb54a18edd70"
dependencies = [
"ndarray",
"num-complex",
"num-traits 0.2.14",
"serde 1.0.130",
"smallvec",
]
[[package]]
name = "static_assertions"
version = "1.1.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a2eb9349b6444b326872e140eb1cf5e7c522154d69e7a0ffb0fb81c06b37543f"
[[package]]
name = "strength_reduce"
version = "0.2.3"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "a3ff2f71c82567c565ba4b3009a9350a96a7269eaa4001ebedae926230bc2254"
[[package]]
name = "strsim"
version = "0.8.0"
@ -1574,7 +1595,7 @@ version = "3.2.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "dac1c663cfc93810f88aed9b8941d48cabf856a1b111c29a40439018d870eb22"
dependencies = [
"cfg-if",
"cfg-if 1.0.0",
"libc",
"rand 0.8.4",
"redox_syscall",
@ -1643,7 +1664,7 @@ version = "1.12.0"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "c2c2416fdedca8443ae44b4527de1ea633af61d8f7169ffa6e72c5b53d24efcc"
dependencies = [
"autocfg",
"autocfg 1.0.1",
"bytes",
"libc",
"memchr",
@ -1737,7 +1758,7 @@ version = "0.1.29"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "375a639232caf30edfc78e8d89b2d4c375515393e7af7e16f01cd96917fb2105"
dependencies = [
"cfg-if",
"cfg-if 1.0.0",
"log",
"pin-project-lite",
"tracing-core",
@ -1752,6 +1773,16 @@ dependencies = [
"lazy_static",
]
[[package]]
name = "transpose"
version = "0.2.1"
source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "95f9c900aa98b6ea43aee227fd680550cdec726526aab8ac801549eadb25e39f"
dependencies = [
"num-integer",
"strength_reduce",
]
[[package]]
name = "try-lock"
version = "0.2.3"

4
server/Cargo.toml
Unescape View File

@ -23,11 +23,11 @@ libsqlite3-sys = { version = "*", features = ["bundled"] }
futures = "0.3.17"
chrono = "0.4.19"
linfa = "0.5.0"
linfa-svm = { version="0.5.0", features=["serde"] }
gbdt = "0.1.1"
ndarray = "0.15.3"
bincode = "1.3.3"
async-trait = "0.1.51"
rustfft = "6.0.1"
# TODO: remove this from prod
# plotlib = "0.5.1"

108
server/src/services/analytic_service/analytic_unit/pattern_analytic_unit.rs
Unescape View File

@ -3,10 +3,12 @@ use std::{collections::VecDeque, fmt, sync::Arc};
use futures::future;
use parking_lot::Mutex;
use linfa::prelude::*;
use linfa;
use linfa_svm::Svm;
use gbdt::config::Config;
use gbdt::decision_tree::{Data, DataVec, PredVec};
use gbdt::gradient_boost::GBDT;
use ndarray::Array;
@ -16,13 +18,20 @@ use super::types::{AnalyticUnit, AnalyticUnitConfig, LearningResult, PatternConf
use async_trait::async_trait;
use rustfft::{self, FftPlanner, num_complex::Complex};
// TODO: move to config
const DETECTION_STEP: u64 = 10;
const FFT_LEN: usize = 64;
// TODO: convert to vector
pub const FEATURES_SIZE: usize = 4 + 16 * 2;
pub type Features = Vec<f64>;
#[derive(Clone)]
pub struct LearningResults {
// TODO: replace with RWLock
model: Arc<Mutex<Svm<f64, bool>>>,
model: Arc<Mutex<GBDT>>,
pub learning_train: LearningTrain,
@ -55,9 +64,7 @@ impl fmt::Debug for LearningResults {
}
}
pub const FEATURES_SIZE: usize = 4;
pub type Features = [f64; FEATURES_SIZE];
fn nan_to_zero(n: f64) -> f64 {
if n.is_nan() {
@ -111,12 +118,44 @@ fn get_features(xs: &Vec<f64>) -> Features {
let sd = sum.sqrt();
// TODO: add autocorrelation
// TODO: add FFT
// TODO: add DWT
return [
let mut planner = FftPlanner::<f64>::new();
// TODO: move 128 to config
let fft = planner.plan_fft_forward(FFT_LEN);
let mut c_buffer = vec![Complex{ re: 0.0f64, im: 0.0f64 }; FFT_LEN];
for i in 0..FFT_LEN.min(xs.len()) {
c_buffer[i].re = xs[i];
}
fft.process(&mut c_buffer);
// https://docs.rs/rustfft/6.0.1/rustfft/index.html#normalization
let norm_factor = (FFT_LEN.min(xs.len()) as f64).sqrt();
for i in 0..FFT_LEN.min(xs.len()) {
c_buffer[i] /= norm_factor;
}
return vec![
min, max, mean, sd,
c_buffer[0].re, c_buffer[0].im,
c_buffer[1].re, c_buffer[1].im,
c_buffer[2].re, c_buffer[2].im,
c_buffer[3].re, c_buffer[3].im,
c_buffer[4].re, c_buffer[4].im,
c_buffer[5].re, c_buffer[5].im,
c_buffer[6].re, c_buffer[6].im,
c_buffer[7].re, c_buffer[7].im,
c_buffer[8].re, c_buffer[8].im,
c_buffer[9].re, c_buffer[9].im,
c_buffer[10].re, c_buffer[10].im,
c_buffer[11].re, c_buffer[11].im,
c_buffer[12].re, c_buffer[12].im,
c_buffer[13].re, c_buffer[13].im,
c_buffer[14].re, c_buffer[14].im,
c_buffer[15].re, c_buffer[15].im,
// 0f64,0f64,
// 0f64,0f64,0f64, 0f64
];
@ -202,6 +241,19 @@ impl AnalyticUnit for PatternAnalyticUnit {
}
async fn learn(&mut self, ms: MetricService, ss: SegmentsService) -> LearningResult {
// TODO: move to config
let mut cfg = Config::new();
cfg.set_feature_size(FEATURES_SIZE);
cfg.set_max_depth(3);
cfg.set_iterations(50);
cfg.set_shrinkage(0.1);
cfg.set_loss("LogLikelyhood");
cfg.set_debug(false);
cfg.set_data_sample_ratio(1.0);
cfg.set_feature_sample_ratio(1.0);
cfg.set_training_optimization_level(2);
// be careful if decide to store detections in db
let segments = ss.get_segments_inside(0, u64::MAX / 2).unwrap();
let has_segments_label = segments
@ -263,19 +315,22 @@ impl AnalyticUnit for PatternAnalyticUnit {
anti_patterns.push(xs);
}
let records = Array::from_shape_fn((records_raw.len(), FEATURES_SIZE), |(i, j)| {
records_raw[i][j]
});
let targets = Array::from_vec(targets_raw.clone());
let mut train_dv = Vec::new();
assert_eq!(records_raw.len(), targets_raw.len());
for i in 0..train_dv.len() {
let data = Data::new_training_data(
records_raw[i].iter().map(|e| *e as f32).collect(),
1.0,
if targets_raw[i] { 1.0 } else { -1.0 },
Some(0.5)
);
train_dv.push(data);
}
let train = linfa::Dataset::new(records, targets);
let model = Svm::<_, bool>::params()
.pos_neg_weights(50000., 5000.)
.gaussian_kernel(80.0)
.fit(&train)
.unwrap();
let mut model = GBDT::new(&cfg);
model.fit(&mut train_dv);
let avg_pattern_length = pattern_length_size_sum / (&patterns.len() + &anti_patterns.len());
@ -346,14 +401,13 @@ impl AnalyticUnit for PatternAnalyticUnit {
for v in window.iter() {
vs.push(*v);
}
let fs = get_features(&vs);
let fs = get_features(&vs).iter().map(|e| *e as f32).collect();
let lk = lr.model.lock();
let p = lk.predict(Array::from_vec(fs.to_vec()));
if p {
1
} else {
-1
}
let data_t1 = Data::new_test_data(fs, Some(0.5));
let mut test_dv = Vec::new();
test_dv.push(data_t1);
lk.predict(&test_dv)[0]
};
let score = positive_corr * self.config.correlation_score

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