From Power Chord to the Power of Models - Oredev
•
1 like•360 views
Who were the most influential bands of Rock history? Which bands could not exist if there was no Velvet Underground? How much Shoegazing subgenre is related to the Drone music? Rock music history was perhaps full of drugs and alcohol but we are sobering up to represent it in terms of (social) networks and find mathematical relationship between artists, trends and subgenres. Full of DataViz and interesting relationships, we will pick up a few common clustering and network analysis algorithms to analyse the publicly available Wiki data. Expect lots of air guitar power chords and virtuoso solos.
Recommended
More Related Content
More from Ali Kheyrollahi
More from Ali Kheyrollahi (15)
Recently uploaded
Recently uploaded (20)
From Power Chord to the Power of Models - Oredev
- 1. From Power Chords to the Power of Models @aliostad Ali Kheyrollahi
- 2. > stackoverflow > £1.5 bln global fashion destination > 35% every year
- 3. 8 Local pop music 9 Local pop music “Cheelee pom!” 10 Boney M “Rasputin” 11 Blondie “Heart of Glass”
- 7. Data Source - Wiki 4,990,2794,990,279 English Articles 37,583,879 Articles
- 8. Data Source - Wiki vs Britannica Feng Zhu (assistant prof at Harvard): “There has been lots of research on the accuracy of Wikipedia, and the results are mixed—some studies show it is just as good as the experts, others show [that] Wikipedia is not accurate at all.” “… the editors [of Britannica] are still not found to be more objective than the crowd in articles that are sufficiently revised.”
- 9. Data Source - Wikipedia in scholar papers 0 45000 90000 135000 180000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Source: Google Scholar
- 10. Data Acquisition - Wiki List of Rock Genres Rock Genres Rock Artists Store Store HTML Capture Links Store HTML Python scripts Postgres
- 11. Data Source - Content vs. Data Hyphen U+002D figure dash U+2012 minus sign U+2015 em dash U+2014 en dash U+2013
- 12. Data Exploration
- 13. Data Exploration “I personally … literally just look at the screen, just like the matrix” Claudia Perlich, multi-award winner Data Scientist
- 14. Data Exploration “… the dirty little secret that I have won all of them because I have found something wrong with the data… I would like to play around with dataset and get initimately familiar with dataset and its properties.“ Claudia Perlich
- 15. Album Genre
- 17. Data Models
- 19. Data Models Model Mathematical representation of a concept based on parameters that impact that concept • Rating of a native app • Stackoverflow score • Credit score • Fraud check
- 20. “All models are wrong… but some are useful. George Box Data Models Model
- 21. Data Models Graph 101 Social Network Analysis and Graph Theory • Nodes/vertices and edges/lines • Directedness: • Directed • Undirected • Degree, InDegree/OutDegree • Weight A B
- 22. Data Models Centrality 1 2 4 2 2 1 Same degree Different betweenness Degree
- 23. Graph Codez import networkx as nx g = nx.Graph() g.add_edge(‘a’, ‘b’) g.add_edge(‘b’, ‘c’) … print len(g[‘b’]) # degree c = nx.betweenness_centrality(g, normalized=True) # c -> dictionary of node names and their score DiGraph()
- 25. Data Models Cited Influence Howlin’ Wolf Captain Beefheart 1940 1964
- 26. Data Models Cited Influence Most influential Rock Artists Based on out-degree The Beatles => 188 Black Sabbath => 127 Led Zeppelin => 118 Jimi Hendrix => 114 Bob Dylan => 94 Pink Floyd => 86 Iron Maiden => 77 Metallica => 77 The Rolling Stones => 66 The Beach Boys => 65 Neil Young => 63 Nirvana => 62 Slayer => 60 Queen => 59
- 27. Data Models Cited Influence Most influential Rock Artists Based on Betweenness Centrality Jimi Hendrix => 53476.2014921 The Beatles => 47511.7957531 Bob Dylan => 38107.0298185 Led Zeppelin => 32701.7223273 Nirvana => 29733.9066836 Metallica => 29356.6009213 Queen => 28989.2844223 Robert Smith => 28880.670718 Elvis Presley => 28463.2891497 Slade => 27656.487307 Iron Maiden => 22449.6697023 Ramones => 22437.6112965 Rush => 21125.9481602 Neil Young => 19913.887522
- 28. Data Models Cited Influence Most influential Artists Based on Betweenness Centrality Metallica => 566.06 Iron Maiden => 419.21 Corey Taylor => 146.0 Led Zeppelin => 122.73 Slipknot => 116.58 King Diamond => 94.7 Machine Head => 85.12 Rush => 70.41 Black Sabbath => 68.0 Van Halen => 54.56 Deep Purple => 53.5 Megadeth => 42.63 Guns N' Roses => 24.25 Heavy Metal Nirvana => 490.08 Muse => 114.5 Weezer => 97.33 Pixies => 94.17 Sonic Youth => 78.5 Rivers Cuomo => 69.5 Siouxsie and the Banshees => 51.67 The Smiths => 51.5 Jeff Buckley => 46.17 The Offspring => 43.0 Placebo => 42.0 My Chemical Romance => 34.0 The Smashing Pumpkins => 32.33 Alternative Rock Rush => 54.0 Marillion => 34.0 Pink Floyd => 33.0 Yes => 20.0 Porcupine Tree => 19.5 Dream Theater => 19.0 Chris Squire => 16.5 Primus => 15.0 Tool => 12.0 Mahavishnu Orchestra => 8.0 Geddy Lee => 7.0 Neil Peart => 5.0 Keith Emerson => 5.0 Progressive Rock
- 30. Data Models Page Rank The Beatles => 0.00837723421839 Blind Lemon Jefferson => 0.00837369035189 Josh White => 0.00824945015047 Bessie Smith => 0.00717743996144 Louis Armstrong => 0.00692897940193 James P. Johnson => 0.00628676810257 Little Richard => 0.00584677302727 Muddy Waters => 0.005773172933 Tampa Red => 0.00572032424174 Robert Johnson => 0.00523579252974 Big Bill Broonzy => 0.00516075834679 Moon Mullican => 0.0050657751593 Black Sabbath => 0.00498789229732 Elvis Presley => 0.00497932058047 Duke Ellington => 0.00465800760107 Bo Diddley => 0.0044496675634 Jimmy Page => 0.00437658472459 Frank Zappa => 0.00431978608953 Miles Davis => 0.00396303890974 Jimi Hendrix => 0.00391117233916 Sister Rosetta Tharpe => 0.00390833570401 Bing Crosby => 0.00385435213525 Bob Dylan => 0.00358608821536 James Brown => 0.00349870931123
- 31. Other Models
- 32. Weighted graph Album Genres Krautrock Psychedelic Rock Experimental Rock 1 1 1
- 33. Genre Affinity Indie Rock Shoegazing Alternative Rock Dream Pop 22 25 24 12 Post-rock
- 34. Genre Affinity Gothic Metal Doom Metal Black Metal Heavy Metal 13 34 27 12 Stoner Metal
- 36. Clustering in Networks u1 u2 u3 u4 u5 u1 1 0 0 1 u2 1 1 1 0 u3 0 1 0 1 u4 0 1 0 1 u5 1 0 1 1 Adjacency Matrix (Similarity Matrix) u1 u2 u3 u4 u5 u1 2 u2 3 u3 2 u4 2 u5 3 Degree Matrix 1 5 4 2 3
- 37. Clustering in Networks u1 u2 u3 u4 u5 u1 2 u2 3 u3 2 u4 2 u5 3 Spectral Clustering: Using Eigenvectors of the Laplacian Matrix − u1 u2 u3 u4 u5 u1 1 0 0 1 u2 1 1 1 0 u3 0 1 0 1 u4 0 1 0 1 u5 1 0 1 1 = u1 u2 u3 u4 u5 u1 2 -1 0 0 -1 u2 -1 3 -1 -1 0 u3 0 -1 2 0 -1 u4 0 -1 0 2 -1 u5 -1 0 -1 -1 3 Degree Matrix Adjacency Matrix (Similarity Matrix) Laplacian Matrix
- 38. Clustering in Networks Eigenvector: a vector (v) that by getting multiplied in matrix A does not result in changing its direction (similar to being multiplied by scalar λ) u1 u2 u3 u4 u5 -0.7 0.3 -0.2 -0.1 0.7 -0.7 0.3 -0.2 -0.1 0.7
- 39. Spectral Clustering Codez from sklearn.cluster import spectral_clustering import numpy as np A = [[0.0 for x in n] for x in n] … # build adjacency matrix res = spectral_clustering(np.matrix(A), n_clusters) # res -> list of cluster indices e.g. [1,1,0,5,…]
- 40. Spectral Clustering Results Folk Rock Country Rock Blues Folk Country Americana Roots Rock Blues Rock Southern Rock Power Metal Progressive Metal Symphonic Metal Black Metal Melodic Death Metal Groove Metal Nu Metal Thrash Metal Death Metal Metalcore Industrial Metal Gothic Metal Christian Metal Doom Metal Speed Metal Alternative Rock Indie Rock New Wave Synthpop Electronica Rock R&B Pop Pop Rock Funk Soul Heavy Metal Hard Rock Alternative Metal
- 42. word2vec Model Skip-gram: a proximity-based probability model trained using Neural Networks (Deep Learning) Pink Floyd were an English rock band formed in London X XX
- 44. word2vec Demo
- 45. Album Genre Model Fun Happy Saturday We Are Friends Electronic Frozen Blood In My Veins Redneck Dance Chaos and Mayhem Basement Dub Sentiment Analysis in text Predicting the genre based on name of the album
- 46. Deep Learning Basics 1) Traditional Neural Networks with many layers 2) Often uses convolution as the node function 3) Training on Big Data can take weeks even on GPU 0) A method of supervised learning 4) Huge success attributed to improved training, powerful computation and above all Big Data 5) Pooling, Dropout and local connections important
- 48. Deep Learning TensorFlow “Wish you were here” => [123, 101, 42, 1969 ] => [123, 101, 42, 1969, 0, 0, 0, … 0 ] Rock => [0, 0, 0, 1, 0, 0, 0, 0 ] => [[100000000000],[000000010000], … ]
- 50. Wrap-up
- 51. References •All pictures from wikipedia.org used under Creative Commons •Source of all data is from wikipedia.org collected online using a single call and then stored and processed •Efficient Estimation of Word Representations in Vector Space. Mikolov et. al. http://arxiv.org/abs/1301.3781 •Gensim's word2vec •networkx lib •word2vec blog post (500K docs): Five crazy abstractions my Deep Learning word2vec model just did •word2vec on Rock music blog: Daft Punk+Tool=Muse: word2vec model trained on a small Rock music corpus •code for word2vec on wiki data •Highcharts: highcharts •word2vec paper: PDF •Automatic real-time road marking recognition using a feature-driven approach PDF •Video of the road marking recognition: here and here and here •Future of Programming - Rise of the Scientific Programmer (and fall of the craftsman) •Deep Learning articles •code for Deep Learning genre analysis •…