Music Taste Analysis¶
When I met my first girlfriend's friends for the first time, somebody innocently tried to make conversation with me by asking my music taste. I clammed up. How could I answer such a complicated question convincingly? There are so many things! I became annoyed with myself for not knowing what to say, not even any genre names. How uncool. My frustration leaked out, and my then-girlfriend later told me someone had asked what was wrong with me. (Hi, Sam.)
But now when someone asks, I have the perfect answer. "Would you like to see my data analysis!?" Enjoy lol.
How to Use¶
My first pass at this depended upon Watsonbox's Exportify, but I decided I didn't like his version because of bugs and inadequate output detail. So I went and forked it, cleaned up the code, and hosted it myself.
As such, the code here depends on .csv inputs in the format output by my version.
- To get started, hop on over there, sign in to Spotify to give the app access to your playlists, and export whatever you like.
- Next, either download this
.ipynbfile and run the notebook yourself or launch it in Binder. - Either put the downloaded
.csvin the same directory as the notebook, or upload it in Binder. - Open the
.ipynbthrough your browser, update thefilenamevariable in the first code cell to point to your playlist instead, andshift+enterin each following code cell to generate the corresponding plot. (Or selectCell->Run Allfrom the menu to make all graphs at once.)
Read the Data¶
For years I've been accumulating my favorite songs in a single master playlist called music that tickles my fancy. It's thousands of songs. This is what I'll be analyzing. Let's take a look at the first few rows to get a sense of what we're dealing with.
filename = 'music_that_tickles_my_fancy.csv'
from matplotlib import pyplot
import seaborn
import pandas
from collections import defaultdict
from scipy.stats import pareto, gamma
from datetime import date
# read the data
data = pandas.read_csv(filename)
print("total songs:", data.shape[0])
print(data[:3])
total songs: 5499
Track URI Track Name \
0 spotify:track:3T9HSgS5jBFdXIBPav51gj Fanfare for the Common Man
1 spotify:track:2bdZDXDoFLzazaomjzoER8 Highschool Lover
2 spotify:track:1fE3ddAlmjJ99IIfLgZjTy I Need a Dollar
Album Name \
0 Copland Conducts Copland - Expanded Edition (F...
1 Virgin Suicides
2 I Need A Dollar
Artist Name(s) Release Date Duration (ms) \
0 Aaron Copland,London Symphony Orchestra 1963 196466
1 Air 2000 162093
2 Aloe Blacc 2010-03-16 244373
Popularity Explicit Added By Added At ... Key Loudness \
0 36 False pvlkmrv 2014-12-28T00:57:17Z ... 10 -15.727
1 0 False pvlkmrv 2014-12-28T00:59:35Z ... 1 -15.025
2 0 False pvlkmrv 2014-12-28T01:03:38Z ... 8 -11.829
Mode Speechiness Acousticness Instrumentalness Liveness Valence \
0 1 0.0381 0.986 0.954 0.0575 0.0377
1 0 0.0302 0.952 0.959 0.2520 0.0558
2 0 0.0387 0.178 0.000 0.0863 0.9620
Tempo Time Signature
0 104.036 4
1 130.052 4
2 95.509 4
[3 rows x 24 columns]
Artist Bar Chart¶
Number of songs binned by artist.
# count songs per artist
artists = defaultdict(int)
for i,song in data.iterrows():
if isinstance(song['Artist Name(s)'], str):
for musician in song['Artist Name(s)'].split(','):
artists[musician] += 1
# sort for chart
artists = pandas.DataFrame(artists.items(), columns=['Artist', 'Num Songs']
).sort_values('Num Songs', ascending=False).reset_index(drop=True)
print("number of unique artists:", artists.shape[0])
pyplot.figure(figsize=(18, 6))
pyplot.bar(artists['Artist'], artists['Num Songs'])
pyplot.xticks(visible=False)
pyplot.xlabel(artists.columns[0])
pyplot.ylabel(artists.columns[1])
pyplot.title('everybody')
pyplot.show()
number of unique artists: 2733
Note I've attributed songs with multiple artists to multiple bars, so the integral here is the number of unique song-artist pairs, not just the number of songs.
It seems to follow a Pareto distribution. Let's try to fit one.
# Let's find the best parameters. Need x, y data 'sampled' from the distribution for
# parameter fit.
y = []
for i in range(artists.shape[0]):
for j in range(artists['Num Songs'][i]):
y.append(i) # just let y have index[artist] repeated for each song
# sanity check. If the dataframe isn't sorted properly, y isn't either.
#pyplot.figure()
#pyplot.hist(y, bins=30)
# The documentation is pretty bad, but this is okay:
# https://stackoverflow.com/questions/6620471/fitting-empirical-distribution-to-theoretical-
# ones-with-scipy-python
param = pareto.fit(y, 100)
pareto_fitted = len(y)*pareto.pdf(range(artists.shape[0]), *param)
# param = gamma.fit(y) # gamma fits abysmally; see for yourself by uncommenting
# gamma_fitted = len(y)*gamma.pdf(range(artists.shape[0]), *param)
pyplot.figure(figsize=(18, 6))
pyplot.bar(artists['Artist'], artists['Num Songs'])
pyplot.plot(pareto_fitted, color='r')
#pyplot.plot(gamma_fitted, color='g')
pyplot.xticks(visible=False)
pyplot.xlabel(artists.columns[0])
pyplot.ylabel(artists.columns[1])
pyplot.title('everybody');
Best fit is still too sharp for the data, and I tried for a good long while to get it to fit better, so I conclude this doesn't quite fit a power law.
Let's plot the top 50 artists so we can actually read who they are.
pyplot.figure(figsize=(18, 10))
pyplot.bar(artists['Artist'][:50], artists['Num Songs'][:50])
pyplot.xticks(rotation=80)
pyplot.xlabel(artists.columns[0])
pyplot.ylabel(artists.columns[1])
pyplot.title('top 50');
Volume Added Over Time¶
My proclivity to add songs to this playlist is a proxy for my interest in listening to music generally. How has it waxed and waned over time?
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters() # to suppress warning
# Plot of added volume over time
parse_date = lambda d:(int(d[:4]), int(d[5:7]), int(d[8:10]))
pyplot.figure(figsize=(10, 6))
pyplot.hist([date(*parse_date(d)) for d in data['Added At']], bins=30)
pyplot.title('volume added over time');
The initial spike is from when I first stared using Spotify as the home for this collection and manually added hundreds from my previous list. There's also a funny Covid bump from when I was woodworking and listening a lot.
Eclecticness Measure (Frequency Transform)¶
This one is a personal favorite. I want to know how many of my songs are one-offs from that artist for me--just individual pieces I found fantastic and ended up adding after a few listens--, how many are two-offs, et cetera. I know it must be heavily skewed toward the low numbers.
# bar chart of first bar chart == hipster diversity factor
frequency = defaultdict(int)
for n in artists['Num Songs']:
frequency[n] += n
frequency = pandas.DataFrame(frequency.items(), columns=['Unique Count', 'Volume']
).sort_values('Volume', ascending=False)
print("number of song-artist pairs represented in the eclecticness chart:",
sum(frequency['Volume']))
pyplot.figure(figsize=(10, 6))
pyplot.bar(frequency['Unique Count'].values, frequency['Volume'].values)
pyplot.title('volume of songs binned by |songs from that artist|')
pyplot.xlabel('quasi-frequency domain')
pyplot.ylabel(frequency.columns[1]);
number of song-artist pairs represented in the eclecticness chart: 6268
So, yes, it's much more common for an artist to make it in my list a few times than many times. In fact, the plurality of my top songs come from unique artists.
Conversely, this view also makes stark those few musicians from whom I've collected dozens.
Note that here, as in the artist bar charts, some songs are doubly-counted, because in cases artists collaborated I listed the song in both bins.
Genres Bar Chart¶
Alright, enough messing around. All the above were possible with the output from Watsonbox's Exportify. Let's get to the novel stuff you came here for.
People describe music by genre. As we'll see, genre names are flippin' hilarious and extremely varied, but in theory if I cluster around a few, that should give you a flavor of my tastes.
# count songs per genre
genres = defaultdict(int)
for i,song in data.iterrows():
if type(song['Genres']) is str: # some times there aren't any, and this is NaN
for genre in song['Genres'].split(','):
if len(genre) > 0: # empty string seems to be a legit genre
genres[genre] += 1
# sort for chart
genres = pandas.DataFrame(genres.items(), columns=['Genre', 'Num Songs']
).sort_values('Num Songs', ascending=False).reset_index(drop=True)
print("number of unique genres:", genres.shape[0])
pyplot.figure(figsize=(18, 6))
pyplot.bar(genres['Genre'], genres['Num Songs'])
pyplot.xticks(visible=False)
pyplot.xlabel(genres.columns[0])
pyplot.ylabel(genres.columns[1])
pyplot.title('All the genera');
number of unique genres: 373
So many! Let's do the same thing as with the artists and for giggles see if it fits a power law.
y = []
for i in range(genres.shape[0]):
for j in range(genres['Num Songs'][i]):
y.append(i)
# sanity check
#pyplot.figure()
#pyplot.hist(y, bins=30)
param = pareto.fit(y, 100)
pareto_fitted = len(y)*pareto.pdf(range(genres.shape[0]), *param)
pyplot.figure(figsize=(18, 6))
pyplot.bar(genres['Genre'], genres['Num Songs'])
pyplot.plot(pareto_fitted, color='r')
pyplot.xticks(visible=False)
pyplot.xlabel(genres.columns[0])
pyplot.ylabel(genres.columns[1])
pyplot.title('All the genera');
Still too sharp, but fits better than with the artists.
Let's look at the top 50 so we can read the names.
pyplot.figure(figsize=(18, 10))
pyplot.bar(genres['Genre'][:50], genres['Num Songs'][:50])
pyplot.xticks(rotation=80)
pyplot.xlabel(genres.columns[0])
pyplot.ylabel(genres.columns[1])
pyplot.title('top 50');
"Indie poptimism" lol. wtf? "Dreamo", "Vapor soul", "Freak folk", "Tropical house", "Post-grunge", "Hopebeat", "Noise pop", "Mellow gold"
These are too good. Next time someone asks me my music taste, I'm definitely using these.
If these are the most popular names, what are the really unique ones at the bottom of the chart?
pyplot.figure(figsize=(18, 1))
pyplot.bar(genres['Genre'][-50:], genres['Num Songs'][-50:])
pyplot.xticks(rotation=80)
pyplot.xlabel(genres.columns[0])
pyplot.ylabel(genres.columns[1])
pyplot.title('bottom 50');
"hauntology", "psychadelic folk", "stomp and whittle", "dark trap", "filthstep", "shamanic", "deep underground hip hop", "future garage", "crunk", "sexy drill", "hard house"
That was fun.
Release Dates¶
Which era of music do I prefer?
years = defaultdict(int)
for i,song in data.iterrows():
if isinstance(song['Release Date'], str): # somebody found a NaN release date!
years[song['Release Date'][:4]] += 1
years = pandas.DataFrame(years.items(), columns=['Year', 'Num Songs']
).sort_values('Year')
pyplot.figure(figsize=(10, 6))
pyplot.bar(years['Year'], years['Num Songs'])
pyplot.xticks(years['Year'], [y if i % 2 == 0 else '' for i,y in enumerate(years['Year'])], rotation=80)
pyplot.xlabel(years.columns[0])
pyplot.ylabel(years.columns[1])
pyplot.title('Songs per year');
It seems to follow a Gamma distribution! This makes sense because I'm more likely to have heard things that are nearer me in time, and it takes a while for them to get through my process and become favorites.
Let's fit that gamma to the time-reversed data.
# Some years are missing, so transform to a dataframe that covers full time period.
eldest = int(years['Year'].values[0])
youngest = int(years['Year'].values[-1])
missing_years = [str(x) for x in range(eldest+1, youngest) if
str(x) not in years['Year'].values]
ago = pandas.concat((years, pandas.DataFrame.from_dict(
{'Year': missing_years, 'Num Songs': [0 for x in range(len(missing_years))]})
)).sort_values('Year', ascending=False).reset_index(drop=True)
y = []
for i in range(ago.shape[0]):
for j in range(int(ago['Num Songs'][i])):
y.append(i)
# sanity check histogram to make sure I'm constructing y properly
#pyplot.figure()
#pyplot.hist(y, bins=30)
param = gamma.fit(y, 10000)
gamma_fitted = len(y)*gamma.pdf(range(ago.shape[0]), *param)
pyplot.figure(figsize=(10, 6))
pyplot.bar(range(len(ago['Year'])), ago['Num Songs'])
pyplot.plot(gamma_fitted, color='g')
pyplot.xlabel('Years Ago')
pyplot.ylabel(ago.columns[1])
pyplot.title('Songs per year (in absolute time)');
print('Oldest Hall of Fame')
print(data[['Track Name', 'Artist Name(s)', 'Release Date']].sort_values(
'Release Date')[:10])
Oldest Hall of Fame
Track Name \
2985 That's Amore
2945 Autumn Nocturne
3743 The Elements (Music By Sir Arthur Sullivan)
2416 Take Five
3131 Skating In Central Park
3100 I Guess I'll Hang My Tears Out To Dry - Rudy V...
4257 Oye Cómo Va
2625 Stand By Me
0 Fanfare for the Common Man
3183 In A Sentimental Mood
Artist Name(s) Release Date
2985 Dean Martin 1954
2945 Lou Donaldson 1958
3743 Tom Lehrer 1959-01-01
2416 The Dave Brubeck Quartet 1959-12-14
3131 Bill Evans,Jim Hall 1962
3100 Dexter Gordon 1962
4257 Tito Puente 1962-01-01
2625 Ben E. King 1962-08-20
0 Aaron Copland,London Symphony Orchestra 1963
3183 Duke Ellington,John Coltrane 1963-02
Pretty good fit! I seem to be extra partial to music from about 5 years ago. We'll see whether the present or maybe the further past catches up.
Popularity Contest¶
I was happy to find popularity listed as a field in Spotify's track JSON. It's a percentile between 0 and 100, rather than an absolute number of plays. Still, it can be used to give a notion of how hipster I am.
popularity = defaultdict(int)
for i,song in data.iterrows():
popularity[song['Popularity']] += 1
popularity = pandas.DataFrame(popularity.items(), columns=['Popularity', 'Num Songs']
).sort_values('Popularity')
pyplot.figure(figsize=(10, 6))
pyplot.bar(popularity['Popularity'].values, popularity['Num Songs'].values)
pyplot.xlabel(popularity.columns[0])
pyplot.ylabel(popularity.columns[1])
pyplot.title('popularity distribution');
print("Average song popularity: ", popularity['Popularity'].mean())
print("Median song popularity: ", popularity['Popularity'].median())
print("Max song popularity: ", popularity['Popularity'].max())
Average song popularity: 46.02150537634409 Median song popularity: 46.0 Max song popularity: 94
pyplot.figure(figsize=(10,6))
pyplot.hist(data['Duration (ms)']/1000, bins=50);
pyplot.xlabel('Duration (s)')
pyplot.ylabel('Num Songs')
pyplot.title('Histogram of song lengths')
mean = data['Duration (ms)'].mean()/1000
median = data['Duration (ms)'].median()/1000
print("Average song length: " + str(int(mean//60)) + (":" if mean%60 >=10 else ":0")
+ str(mean%60))
print("Median song length: " + str(int(median//60)) + (":" if median%60 >=10 else ":0")
+ str(median%60))
Average song length: 4:01.9582615020912897 Median song length: 3:51.80000000000001
Median is lower than the mean, so I'm skewed right. That is, I like a few really long songs. What are they?
print("Longest Hall of Fame:")
print(data[['Track Name', 'Artist Name(s)', 'Release Date', 'Duration (ms)']].sort_values(
'Duration (ms)', ascending=False)[:10])
Longest Hall of Fame:
Track Name \
5236 Echoes
3150 Concierto De Aranjuez
691 Irene
1908 The Return of the King (From The Lord of the R...
4227 Boléro (Ravel)
460 The Cure For Pain
2344 Shine On You Crazy Diamond (Pts. 1-5)
140 Two Step - Live At Piedmont Park Atlanta GA - ...
5055 Rivers
3469 Má vlast (My Country): No. 2 Vltava [Moldau]
Artist Name(s) Release Date \
5236 Pink Floyd 1971-11-11
3150 Jim Hall 1974
691 Beach House 2012-05-15
1908 The City of Prague Philharmonic Orchestra 2004-01-01
4227 London Symphony Orchestra 1995
460 mewithoutYou 2002-01-01
2344 Pink Floyd 1975-09-12
140 Dave Matthews Band 2007-12-11
5055 Tarek Musa 2010-01-30
3469 Bedřich Smetana,Polish National Radio Symphony... 1994-08-05
Duration (ms)
5236 1412451
3150 1154040
691 1017013
1908 976893
4227 934067
460 908840
2344 811077
140 808226
5055 807437
3469 794000
Musical Features¶
In the interest of understanding user tastes and providing the best possible music recommendations, Spotify has done some really sophisticated analysis of actual track content, which has only gotten more extensive in recent years. Music is a time series, but most similarity metrics (and most ML methods generally) require inputs to be vectors, that is: points in some feature-space. So they've transformed the tracks to numerical metrics like Energy and Valence (continuous) and Key (discrete).
For the continuous metrics, here are distributions for my songs.
pyplot.figure(figsize=(20,20))
for i,category in enumerate(['Tempo', 'Acousticness', 'Instrumentalness', 'Liveness',
'Valence', 'Speechiness', 'Loudness', 'Energy', 'Danceability']):
pyplot.subplot(3, 3, i+1)
pyplot.hist(data[category], bins=30)
pyplot.text(pyplot.xlim()[1] - (pyplot.xlim()[1] - pyplot.xlim()[0])*0.3,
pyplot.ylim()[1]*0.9, r'$\mu=$'+str(data[category].mean())[:7], fontsize=12)
pyplot.xlabel('Value')
pyplot.ylabel('Num Songs')
pyplot.title(category)
pyplot.tight_layout(h_pad=2)
My Valence is somewhat negatively skewed; do I have an affinity for sadder songs?
Now let's look at the discrete music features.
pyplot.figure(figsize=(15,4))
pyplot.subplot(1, 3, 1)
seaborn.countplot(data, x='Time Signature', hue='Time Signature', legend=False)
pyplot.xlabel('Beats per bar')
pyplot.ylabel('Num Songs')
pyplot.title('Time Signature')
pyplot.subplot(1, 3, 2)
seaborn.countplot(data, x='Key', hue='Key', palette='husl', legend=False)
pyplot.xticks(ticks=pyplot.xticks()[0], labels=['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B'])
pyplot.ylabel('Num Songs')
pyplot.title('Key')
pyplot.subplot(1, 3, 3)
seaborn.countplot(data, x='Mode', hue='Mode', legend=False)
pyplot.xticks(ticks=pyplot.xticks()[0], labels=['minor', 'major'])
pyplot.ylabel('Num Songs')
pyplot.title('Major vs Minor Key');
pyplot.tight_layout(w_pad=2)
Musicians seem to favor C major and eschew D#. More than a third of my songs are in a minor key. I don't have a baseline to compare against here, but this might contribute to my lower Valence.
Looks like the vast majority of my music is 4/4 time with a good few in 3/4. I wasn't even aware there were any with 5 beats. What are those?
print('5:\n', data.loc[data['Time Signature']==5][
['Track Name', 'Artist Name(s)', 'Release Date']][:20])
5:
Track Name \
76 Yachts - A Man Called Adam mix
120 Good Morning Fire Eater
223 Carry On
233 Vanishing Grace
244 Elysium
273 Lately
386 Evenstar
447 Make A Fist
459 (B)
567 Animals
726 All That Remains
733 Crush The Camera
1059 Cold Sparks
1160 You Are Gonna Die
1186 Everything in Its Right Place
1191 The Tourist
1192 I Am Citizen Insane
1832 Have I Always Loved You?
1969 Resonance
2130 Pray
Artist Name(s) Release Date
76 Coco Steel Lovebomb 2000-10-31
120 Copeland 2008-01-01
223 fun. 2012-02-21
233 Gustavo Santaolalla 2013-06-07
244 Klaus Badelt,Lisa Gerrard,Gavin Greenaway,The ... 2000-04-25
273 Memoryhouse 2011-09-13
386 Howard Shore 2002-12-02
447 Phantogram 2011
459 mewithoutYou 2002-01-01
567 Muse 2012-09-24
726 Rogue Wave 2010
733 Rogue Wave 2005-08-23
1059 Mutemath 2011-09-30
1160 Marc Streitenfeld 2011
1186 Radiohead 2000-10-02
1191 Radiohead 1997-06-17
1192 Radiohead 2003-06-09
1832 Copeland 2014-11-17
1969 Home 2014-07-01
2130 Sam Smith 2017-10-06
Make A Fist is totally 5/4, and so is Animals. Funny how I didn't notice the strange energetic time signature until now. But Carry On is definitely 4/4, as is Yachts, and Pray is 6/8. So Spotify's algorithm isn't perfect at this, which is expected.
What are 0 and 1?
print('0:\n', data.loc[data['Time Signature']==0][
['Track Name', 'Artist Name(s)', 'Release Date']][:10])
print('\n1:\n', data.loc[data['Time Signature']==1][
['Track Name', 'Artist Name(s)', 'Release Date']][:20])
0:
Track Name Artist Name(s) Release Date
1362 Small Memory Jon Hopkins 2009-05-05
1:
Track Name \
71 Clair De Lune
119 Top Of The Hill
227 I Am the Very Model of a Modern
239 The Last of Us (You and Me)
362 Bowery
503 The Eternal City
564 Prelude
601 Þú ert jörðin
604 Raein
1276 Campfire Song Song
1328 Mylo Xyloto
1368 Anagram
1911 The Fellowship (From The Lord of the Rings: Th...
1951 Monsoon
1994 Meet Me in the Woods
2032 Only Songs
2176 Old Casino
2189 Work This Time
2586 I Don't Think So
2665 Other Worlds
Artist Name(s) Release Date
71 Claude Debussy 2014-10-13
119 Conduits 2013-04-16
227 The Pirates Of Penzance 1983-02-18
239 Gustavo Santaolalla,Alan Umstead 2013-06-07
362 Local Natives 2013-01-29
503 Michele McLaughlin 2007-12-04
564 Muse 2012-09-24
601 Ólafur Arnalds 2010-05-07
604 Ólafur Arnalds 2009-08-28
1276 Spongebob Squarepants 2009-07-14
1328 Coldplay 2011-10-24
1368 Young the Giant 2014-01-17
1911 The City of Prague Philharmonic Orchestra 2004-01-01
1951 Hippo Campus 2017-02-24
1994 Lord Huron 2015-04-07
2032 The Wild Reeds 2017-04-07
2176 Coastgaard 2016-02-26
2189 King Gizzard & The Lizard Wizard 2014-03-07
2586 Ben Phipps 2016-09-30
2665 Bassnectar,Dorfex Bos 2017-12-01
Looks like there is only one song with 0 time signature. It's a piano piece with a tempo that rises and falls. This category might be for variable tempo, or unknown.
Claire De Lune is 9/8 time, so sort of waltzish but not really.
The Major General's Song is 4/4, but there are some stops in there and a lot of speaking, so I understand how that might be difficult to pick out. Same with Campfire Song Song lol.
Top of the Hill really sounds like 7/4 to me (1-2-123 sort of beat).
Þú ert jörðin is actually properly 1/4 time according to the internet, and relistening I understand how that could be the case. It's like there are little riffs each bar following a quadruplet pattern, but the major beats really only come every bar.
The Last of Us (You and Me) seems similar. It might be properly 1/4 time.
So it looks like this category is for actual single beats and unusual time signatures that Spotify isn't sure what to do with.
Joint Analysis¶
I mostly just want to showcase what's possible. Let's plot Energy and Popularity together to see whether there is a relationship.
x = 'Energy'
y = 'Popularity'
axes = seaborn.jointplot(x=data[x], y=data[y], kind='hex', color='r')
axes.set_axis_labels(x, y, fontsize=20);
The density is pretty scattered, doesn't the whole plot, meaning the relationship here is actually pretty weak. Surprising.
The Final Frontier¶
Finally, I'm going to follow this guy's example and feed the dimension-reduced data to a one-class SVM to get a sense of what the frontier of my normal taste looks like in that space, heat-map-of-the-universe-style.
t-SNE is a method for visualizing high-dimensional data in low-dimension. Songs which are more alike will be nearer each other in the feature space, but we can't visualize a space with that many dimensions. What we can do is reconstitute the points in 2D, attempting to preserve the pairwise distances, the notions of similarity, between songs.
show_percent = 2
from sklearn.manifold import TSNE
from random import random
from sklearn.svm import OneClassSVM
import numpy
# Create a dataframe of only the numerical features, all normalized so embedding
# doesn't get confused by scale differences
numerical_data = data.drop(['Track URI', 'Track Name', 'Album Name', 'Artist Name(s)',
'Explicit', 'Added By', 'Added At', 'Genres', 'Record Label'], axis=1)
numerical_data['Release Date'] = pandas.to_numeric(
numerical_data['Release Date'].str.slice(0,4))
numerical_data = (numerical_data - numerical_data.mean())/numerical_data.std()
print('using:', list(numerical_data.columns))
# If you like, only include a subset of these, because the results with all
# is really hard to interpret
#tsne_data = numerical_data[['Popularity', 'Energy', 'Acousticness',
# 'Valence', 'Loudness']]
#print("\nConsidering similarity with respect to the following features:")
#print(tsne_data.dtypes)
# Takes a 2D data embedding and an svm trained on it and plots the decision boundary
def plotFrontier(embedded, svm, technique_name, scale):
# get all the points in the space, and query the svm on them
xx, yy = numpy.meshgrid(numpy.linspace(min(embedded[:,0])*scale,
max(embedded[:,0])*scale, 500),
numpy.linspace(min(embedded[:,1])*scale,
max(embedded[:,1])*scale, 500))
Z = svm.decision_function(numpy.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape) # positive Z means yes. negative means outliers.
pyplot.figure(figsize=(20,20))
pyplot.title('Decision boundary of One-class SVM in '+technique_name+' space')
pyplot.contourf(xx, yy, Z, levels=numpy.linspace(Z.min(), 0, 7), cmap=pyplot.cm.Blues_r)
pyplot.contour(xx, yy, Z, levels=[0], linewidths=2, colors='green') # the +/- boundary
pyplot.contourf(xx, yy, Z, levels=[0, Z.max()], colors='lightgreen')
pyplot.scatter(embedded[:, 0], embedded[:, 1], s=10, c='grey')
for i,song in data.iterrows():
if random() < show_percent*0.005: # randomly label % of points
#if song['Artist Name(s)'] in ['Coldplay']:
x, y = embedded[i]
pyplot.annotate(song['Track Name'], (x,y), size=10,
xytext=(-30,30), textcoords='offset points',
ha='center',va='bottom',
arrowprops={'arrowstyle':'->', 'color':'red'})
tsne_embedded = TSNE(n_components=2).fit_transform(numerical_data)
svm_tsne = OneClassSVM(gamma='scale')
svm_tsne.fit(tsne_embedded)
plotFrontier(tsne_embedded, svm_tsne, 't-SNE', 1.2)
using: ['Release Date', 'Duration (ms)', 'Popularity', 'Danceability', 'Energy', 'Key', 'Loudness', 'Mode', 'Speechiness', 'Acousticness', 'Instrumentalness', 'Liveness', 'Valence', 'Tempo', 'Time Signature']
The point scatter looks really different every time this runs, because it's stochastic. The clusters don't necessarily have sensible interpretations, though you might be able to label a few of them. It's good to see some notionally similar pieces ending up near each other. You can try this with a subset of these dimensions to try to make the result more interpretable.
Modifying the parameters of the SVM changes its fit significantly, so I'm not sure this is the best model. Gamma too large just clearly overfits the data. Gamma too small just makes the decision boundary a boring ellipse. Using gamma='scale' as the docs recommend is a more interesting middle ground, but still the SVM seems to believe that a great many of the songs I love fall outside the boundary.
I'll try a different dimensionality reduction technique. The original author uses Principle Component Analysis to feed the SVM.
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca_embedded = pca.fit_transform(numerical_data)
print("% variance explained by successive PCA dimensions:",
pca.explained_variance_ratio_)
svm_pca = OneClassSVM(gamma='scale')
svm_pca.fit(pca_embedded)
plotFrontier(pca_embedded, svm_pca, 'PCA', 1)
% variance explained by successive PCA dimensions: [0.21903801 0.09219339]
Ideally, songs falling nearer the center here, like Cheeseburger in Paradise and RAC's We Belong, are those that most characterize my taste numerically, and the odd ones, like Pink Floyd's Comfortably Numb and The Fellowship of the Ring orchestral suite, fall on the outside.
So in the end my music taste is a blob that doesn't even fit the data very well. And that's the point: Like many things, it's too complicated to boil down. You can't answer the question fully. But understanding elements of the answer can aid the process of discovery, and that's valuable. It's why Spotify is such a force at music recommendation. It's why Data Science.