marquisthunder’s gists

marquisthunder / lightgbm_rfe.py

Created March 10, 2023 05:25 — forked from c-bata/lightgbm_rfe.py

Recursive Feature Elimination for LightGBM. This class accepts missing values and Optuna LightGBM tuner.

	import numpy as np
	import pandas as pd

	#import lightgbm as lgb
	from optuna.integration import lightgbm as lgb

	from sklearn.model_selection import train_test_split
	from sklearn.utils import check_X_y, safe_sqr
	from sklearn.feature_selection.base import SelectorMixin
	from lightgbm import Booster

marquisthunder / pymc3-fosscon2017.ipynb

Created August 27, 2017 08:28 — forked from AustinRochford/pymc3-fosscon2017.ipynb

Open Source Bayesian Inference in Python with PyMC3

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marquisthunder / BagOfLittleBootstraps.jl

Last active August 19, 2017 09:40 — forked from jiahao/BagOfLittleBootstraps.jl

Julia implementation of the Bag of Little Bootstraps (BLB) method (documented as, e.g., Algorithm 1 in arXiv:1112.5016) #BLB

	using Distributions

	export Measure, DiscreteMeasure, Estimator, EstimatorQuality,
	blb, randsubset

	abstract Measure

	type DiscreteMeasure{S<:Number,T<:Number} <: Measure
	points :: Vector{S}
	weights :: Vector{T}

marquisthunder / create_bokeh_network.py

Last active November 14, 2021 16:15 — forked from luerhard/BokehGraph.py

[bokeh-nx]Script to create interactive bokeh networkx plots. #bokeh #networkx

	import networkx as nx
	from collections import namedtuple
	from math import sqrt
	import bokeh
	from bokeh.models import HoverTool
	from bokeh.plotting import show, figure
	from bokeh.colors import RGB
	import random

	#corresponding package on pypi is confusingly called python-louvain

marquisthunder / maybe.py

Created July 6, 2016 23:58 — forked from senko/maybe.py

A Pythonic implementation of the Maybe monad

	# maybe.py - a Pythonic implementation of the Maybe monad
	# Copyright (C) 2014. Senko Rasic <[email protected]>
	#
	# Permission is hereby granted, free of charge, to any person obtaining a copy
	# of this software and associated documentation files (the "Software"), to deal
	# in the Software without restriction, including without limitation the rights
	# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	# copies of the Software, and to permit persons to whom the Software is
	# furnished to do so, subject to the following conditions:
	#

marquisthunder / marisa_count_vectorizer.py

Last active August 19, 2017 09:36 — forked from kmike/marisa_count_vectorizer.py

[marisa_trie]marisa_count_vectorizer for sklearn #sklearn #trie

	import numpy as np
	import marisa_trie
	from sklearn.feature_extraction.text import CountVectorizer
	from sklearn.externals import six

	class MarisaCountVectorizer(CountVectorizer):

	# ``CountVectorizer.fit`` method calls ``fit_transform`` so
	# ``fit`` is not provided
	def fit_transform(self, raw_documents, y=None):

marquisthunder / lda-advi-ae.ipynb

Created July 2, 2016 23:01 — forked from taku-y/lda-advi-ae.ipynb

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marquisthunder / bayesian_neural_network.ipynb

Created May 26, 2016 22:33 — forked from anonymous/bayesian_neural_network.ipynb

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marquisthunder / pymc3

Created May 26, 2016 00:22 — forked from ceshine/pymc3

Bayesian Logistic Regression on the Kaggle Titanic dataset via PyMC3

	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# Bayesian Logistic Regression on the Kaggle titanic dataset"
	]
	},
	{

marquisthunder / avazu_ftrl.go

Created May 26, 2016 00:22 — forked from ceshine/avazu_ftrl.go

Kaggle Avazu Challenge: FTRL-Proximal with L1 & L2 implemented in Go (single-threaded)

	// Based on tinrtgu's Python script here:
	// https://www.kaggle.com/c/avazu-ctr-prediction/forums/t/10927/beat-the-benchmark-with-less-than-1mb-of-memory
	package main

	import (
	"encoding/csv"
	"os"
	"strconv"
	"hash/fnv"
	"math"