micaleel

A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

• Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
• Evaluation function tells how good the machine learning model is.
• Optimisation is the method to search for the most optimal learning model.

micaleel

# Elasticsearch Cheatsheet - an overview of commonly used Elasticsearch API commands

# cat paths
/_cat/allocation
/_cat/shards
/_cat/shards/{index}
/_cat/master
/_cat/nodes
/_cat/indices
/_cat/indices/{index}

micaleel

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

micaleel

Movies Recommendation:

• MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
• Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
• Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
• Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

• Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

micaleel

import unittest, os, os.path, sys, urllib
import tornado.database
import tornado.options
from tornado.options import options
from tornado.testing import AsyncHTTPTestCase

# add application root to sys.path
APP_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(os.path.join(APP_ROOT, '..'))

micaleel

ratings = pd.read_csv('ratings_small.csv') # loading data from csv
"""
ratings_small.csv has 4 columns - userId, movieId, ratings, and timestammp
it is most generic data format for CF related data
"""

val_indx = get_cv_idxs(len(ratings))  # index for validation set
wd = 2e-4 # weight decay
n_factors = 50 # n_factors - dimension of embedding matrix (D)