Gael Varoquaux GaelVaroquaux

Introductory quote:

"Machine learning people use hugely complex algorithms on trivially simple datasets. Biology does trivially simple algorithms on hugely complex datasets."

Concepts of reproducible science

Replicability

Notes on scikit-learn round table

Q:What possible additions to scikit-learn are important to you?

Xavier Dupré (Microsoft): keep .fit in the API but X can be a stream from Spark for example. Transparent for the user. Gaël: indexable and len is n_samples, is that good enough? Answer: X accessible through sequential iterator.
Jean-François Puget (IBM): IBM is betting on Spark at the scale of the company. Most machine learning applications have small data but some don't. How can the bridge b/w scikit-learn and Spark get better? How to get scikit-learn used in a distributed environment? Not all algorithms can work out-of-core, need distributed algorithm.
Jean-Paul Smet (Nexedi): Nexedi is an example company. Wendelin.core helps us removing the overhead, and enabling out of core computing. Next step of the story in a year.

	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.linear_model import Lasso, lars_path

	np.random.seed(42)

	def gen_data(n, m, k):
	X = np.random.randn(n, m)
	w = np.zeros((m, 1))
	i = np.arange(0, m)

	"""
	Uses C++ map containers for fast dict-like behavior with keys being
	integers, and values float.
	"""
	# Author: Gael Varoquaux
	# License: BSD

	# XXX: this needs Cython 17.1 or later. Elsewhere you will get a C++ compilation error.

	import numpy as np

	import numpy as np
	import time

	from sklearn import cluster
	from sklearn import datasets


	lfw = datasets.fetch_lfw_people()
	X_lfw = lfw.data[:, :5]
	eps = 8. # This choice of EPS gives 44 clusters

	### Keybase proof

	I hereby claim:

	* I am GaelVaroquaux on github.
	* I am gaelvaroquaux (https://keybase.io/gaelvaroquaux) on keybase.
	* I have a public key whose fingerprint is 44B8 B843 6321 47EB 59A9 8992 6C52 6A43 ABE0 36FC

	To claim this, I am signing this object:

	'''
	Non-parametric computation of entropy and mutual-information

	Adapted by G Varoquaux for code created by R Brette, itself
	from several papers (see in the code).

	This code is maintained at https://github.com/mutualinfo/mutual_info
	Please download the latest code there, to have improvements and
	bug fixes.

	import numpy as np
	import matplotlib.pyplot as plt

	from sklearn.linear_model import Ridge, Lasso
	from sklearn.cross_validation import ShuffleSplit
	from sklearn.grid_search import GridSearchCV
	from sklearn.utils import check_random_state
	from sklearn import datasets


	"""Persistence strategies comparison script.

	This script compute the speed, memory used and disk space used when dumping and
	loading arbitrary data. The data are taken among:
	- scikit-learn Labeled Faces in the Wild dataset (LFW)
	- a fully random numpy array with 10000x10000 shape
	- a dictionary with 1M random keys/values
	- a list containing 10M random value
	The compared persistence strategies are: