Benjamin Trent benwtrent

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benwtrent / recreation_attempt.es

Last active February 26, 2020 17:32

Trying to recreate wierd script bug

benwtrent / pipeline_and_composite

Created February 11, 2020 14:43

pipeline agg in composite example

	GET kibana_sample_data_ecommerce/_search
	{
	"size": 0,
	"aggs": {
	"user": {
	"composite": {
	"sources": [
	{
	"user": {
	"terms": {

benwtrent / ESModel.py

Last active February 11, 2020 17:53

model transformers

	from typing import List


	def add_if_exists(d: dict, k: str, v) -> dict:
	"""
	:param v:
	:param k:
	:type d: object
	"""
	if v is not None:

benwtrent / processor_definition.json

Last active November 12, 2019 17:09

benwtrent / mappings

Last active November 11, 2019 17:24

building out avg price prediction on a house given ashville air bnb listing data

	{
	"listings-ash" : {
	"aliases" : { },
	"mappings" : {
	"_meta" : {
	"created_by" : "ml-file-data-visualizer"
	},
	"properties" : {
	"@timestamp" : {
	"type" : "date"

benwtrent / LogParser.java

Created July 12, 2019 19:37

Java implementation of the Drain algorithm.

	import java.util.AbstractMap;
	import java.util.ArrayList;
	import java.util.Collections;
	import java.util.HashMap;
	import java.util.List;
	import java.util.Map;
	import java.util.regex.Pattern;

	/**
	* This class contains the parsing logic for implementing the DRAIN algorithm.

benwtrent / m2cgen_painless.py

Last active May 9, 2019 21:03

This is a giant script created via some hacking against m2cgen

	import xgboost as xgb
	from sklearn import datasets
	from sklearn.metrics import mean_squared_error
	import m2cgen as m2c


	diabetes = datasets.load_diabetes() # load data
	from sklearn.model_selection import train_test_split
	X_train, X_test, y_train, y_test = train_test_split(diabetes.data, diabetes.target, test_size=0.2, random_state=0)
	print(diabetes.feature_names)

benwtrent / painless_linear_regression.json

Created March 7, 2019 21:50

Linear Regression Model inference with Painless in ElasticSearch

	PUT _scripts/linear_regression_inference
	{
	"script": {
	"lang": "painless",
	"source": """
	double total = params.intercept;
	for (int i = 0; i < params.coefs.length; ++i) {
	total += params.coefs.get(i) * doc[params['x'+i]].value;
	}
	return total;

benwtrent / values_true.json

Created February 28, 2019 19:25

How to select terms buckets by all values being true

benwtrent / mat-to-csv

Created January 3, 2019 19:25

mat to csv octave: just a simple