number crunching in python
DESCRIPTION
"Number Crunching in Python": slides presented at EuroPython 2012, Florence, Italy Slides have been authored by me and by Dr. Enrico Franchi. Scientific and Engineering Computing, Numpy NDArray implementation and some working case studies are reported.TRANSCRIPT
LOREMI P S U M
NUMBER CRUNCHING IN PYTHONEnrico Franchi ([email protected]) &Valerio Maggio ([email protected])
DOLORS I T OUTLINE
• Scientific and Engineering Computing
• Common FP pitfalls
• Numpy NDArray (Memory and Indexing)
• Case Studies
DOLORS I T OUTLINE
• Scientific and Engineering Computing
• Common FP pitfalls
• Numpy NDArray (Memory and Indexing)
• Case Studies
DOLORS I T OUTLINE
• Scientific and Engineering Computing
• Common FP pitfalls
• Numpy NDArray (Memory and Indexing)
• Case Studies
number-crunching: n. [common] Computations of a numerical nature, esp. those that make extensive use of floating-point numbers. This term is in widespread informal use outside hackerdom and even in mainstream slang, but has additional hackish connotations: namely, that the computations are mindless and involve massive use of brute force. This is not always evil, esp. if it involves ray tracing or fractals or some other use that makes pretty pictures, esp. if such pictures can be used as screen backgrounds. See also crunch.
number-crunching: n. [common] Computations of a numerical nature, esp. those that make extensive use of floating-point numbers. This term is in widespread informal use outside hackerdom and even in mainstream slang, but has additional hackish connotations: namely, that the computations are mindless and involve massive use of brute force. This is not always evil, esp. if it involves ray tracing or fractals or some other use that makes pretty pictures, esp. if such pictures can be used as screen backgrounds. See also crunch.
We are not evil.
number-crunching: n. [common] Computations of a numerical nature, esp. those that make extensive use of floating-point numbers. This term is in widespread informal use outside hackerdom and even in mainstream slang, but has additional hackish connotations: namely, that the computations are mindless and involve massive use of brute force. This is not always evil, esp. if it involves ray tracing or fractals or some other use that makes pretty pictures, esp. if such pictures can be used as screen backgrounds. See also crunch.
We are not evil. Just chaotic neutral.
AMETM E N T I
T U M ALTERNATIVES• Matlab (IDE, numeric computations oriented, high quality algorithms,
lots of packages, poor GP programming support, commercial)
• Octave (Matlab clone)
• R (stats oriented, poor general purpose programming support)
• Fortran/C++ (very low level, very fast, more complex to use)
• In general, these tools either are low level GP or high level DSLs
HIS EX,T E M P O
R PYTHON• Numpy (low-level numerical computations) +
Scipy (lots of additional packages)
• IPython (wonderfull command line interpreter) + IPython Notebook (“Mathematica-like” interactive documents)
• HDF5 (PyTables, H5Py), Databases
• Specific libraries for machine learning, etc.
• General Purpose Object Oriented Programming
TOOLSCUS E D
TOOLSCUS E D
TOOLSCUS E D
DENIQUE
G U B E RG R E N
Our Code
Numpy
Atlas/MKL
Improvements
Improvements
Algorithms are fast because of highly optimized C/Fortran code
4 30 LOAD_GLOBAL 1 (dot) 33 LOAD_FAST 0 (a) 36 LOAD_FAST 1 (b) 39 CALL_FUNCTION 2 42 STORE_FAST 2 (c)
NUMPY STACKc = a · b
ndar
ray
ndarray
Memory
behavior
shape, stride, flags
(i0, . . . , in�1) ! I
Shape: (d0, …, dn-1)
4x3
An n-dimensional array references some (usually contiguous memory area)
An n-dimensional array has property such as its shape or the
data-type of the elements containes
Is an object, so there is some behavior, e.g., the def. of __add__ and similar stuff
N-dimensional arrays are homogeneous
(i0, . . . , in�1) ! I
C-contiguousF-contiguous
Shape: (d0, …, dn)
IC =n�1X
k=0
ik
n�1Y
j=k+1
dj
IF =n�1X
k=0
ik
k�1Y
j=0
dj
Shape: (d0, …, dk ,…, dn-1)
Shape: (d0, …, dk ,…, dn-1)
IC = i0 · d0 + i14x3
IF = i0 + i1 · d1
Elem
ent L
ayou
t in
Mem
ory
Strid
e
C-contiguous F-contiguous
sF (k) =k�1Y
j=0
dj
IF =nX
k=0
ik · sF (k)
sC(k) =n�1Y
j=k+1
dj
IC =n�1X
k=0
ik · sC(k)
Stride
C-contiguousF-contiguous
C-contiguous
(s0 = d0, s1 = 1) (s0 = 1, s1 = d1)
IC =n�1X
k=0
ik
n�1Y
j=k+1
dj IF =n�1X
k=0
ik
k�1Y
j=0
dj
ndarray
Memory
behavior
shape, stride, flags
ndarray
behavior
shape, stride, flags
View View
View View
View
s
C-contiguous
ndarray
behavior
(1,4)
Memory
C-contiguous
ndarray
behavior
(1,4)
Memory
ndarray
Memory
behavior
shape, stride, flags
matrix
Memory
behavior
shape, stride, flags
ndarray
matrix
Basic
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nced
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Vect
orize
!
Don’t use explicit for loops unless you have to!
PART II: NUMBER CRUNCHING IN ACTION
PART II: NUMBER CRUNCHING IN ACTION
General Disclaimer: All the Maths appearing in the next slides is only intended to better introduce the considered case studies. Speakers are not responsible for any possible disease or “brain consumption” caused by too much formulas.
So BEWARE; use this information at your own risk! It's intention is solely educational. We would strongly encourage you to use this information in cooperation with a medical or health professional.
Awfu
l Mat
hs
BEFORE STARTINGWhat do you need to get started:
• A handful Unix Command-line tool:
• Linux / Mac OSX Users: Your’re done.
• Windows Users: It should be the time to change your OS :-)
• [I]Python (You say?!)
• A DBMS:
• Relational: e.g., SQLite3, PostgreSQL
• No-SQL: e.g., MongoDB
MINIMS C R I PT O R E M
LOREMI P S U M
BENCHMARKING
LOREMI P S U M
• Vectorization (NumPy vs. “pure” Python
• Loops and Math functions (i.e., sin(x))
• Matrix-Vector Product
• Different implementations of Matrix-Vector Product
CASE STUDIES ON NUMERICAL EFFICIENCY
Hw In
fo
Vect
oriza
tion:
sin
(x)
Vect
oriza
tion:
sin
(x)
Vect
oriza
tion:
sin
(x)
Vect
oriza
tion:
sin
(x)
Vect
oriza
tion:
sin
(x)
Vect
oriza
tion:
sin
(x)
NumPy, Winssi
n(x)
: Res
ults
NumPy, Winsfatality
sin(
x): R
esul
ts
NumPy, Winsfatality
sin(
x): R
esul
ts
NumPy, Winsfatality
sin(
x): R
esul
ts
Mat
rix-V
ecto
r Pro
duct
dot
dot
dot
dot
dot
dot
NumPy, Winsdo
t: R
esul
ts
NumPy, Winsfatality
dot:
Res
ults
LOREMI P S U M
NUMBER CRUNCHING APPLICATIONS
MACHINE LEARNING• Machine Learing = Learning by Machine(s)
• Algorithms and Techniques to gain insights from data or a dataset
• Supervised or Unsupervised Learning
• Machine Learning is actively being used today, perhaps in many more places than you’d expected
• Mail Spam Filtering
• Search Engine Results Ranking
• Preference Selection
• e.g., Amazon “Customers Who Bought This Item Also Bought”
NAM IN,S E A
N O
LOREMI P S U M
CLUSTERING: BRIEF INTRODUCTION
• Clustering is a type of unsupervised learning that automatically forms clusters (groups) of similar things. It’s like automatic classification. You can cluster almost anything, and the more similar the items are in the cluster, the better your clusters are.
• k-means is an algorithm that will find k clusters for a given dataset.
• The number of clusters k is user defined.
• Each cluster is described by a single point known as the centroid.
• Centroid means it’s at the center of all the points in the cluster.
from scipy.cluster.vq import kmeans, vqK-
mea
ns
from scipy.cluster.vq import kmeans, vqK-
mea
ns
from scipy.cluster.vq import kmeans, vqK-
mea
ns
from scipy.cluster.vq import kmeans, vqK-
mea
ns
from scipy.cluster.vq import kmeans, vqK-
mea
ns
K-m
eans
plo
tfrom scipy.cluster.vq import kmeans, vq
K-m
eans
plo
tfrom scipy.cluster.vq import kmeans, vq
LOREMI P S U M
EXAMPLE:CLUSTERING POINTS ON A MAP
Here’s the situation: your friend <NAME> wants you to take him out in the greater Portland, Oregon, area (US) for his birthday. A number of other friends are going to come also, so you need to provide a plan that everyone can follow. Your friend has given you a list of places he wants to go. This list is long; it has 70 establishments in it.
Yaho
o AP
I: ge
oGra
b
�s�s �f�fLatitude and Longitude Coordinates of two points (s and f)
���� Corresponding differences
��̂ = arccos(sin�s sin�f + cos�s cos�f cos��)Spherical Distance Measure
Sphe
rical
Dist
ance
Mea
sure
kmea
ns w
ith dis
tLSC
• Problem: Given an input matrix A, calculate if possible, its inverse matrix.
• Definition: In linear algebra, a n-by-n (square) matrix A is invertible (a.k.a. is nonsingular or nondegenerate) if there exists a n-by-n matrix B (A-1) such that: AB = BA = In
TRIVIAL EXAMPLE:INVERSE MATRIX
✓ Eigen Decomposition: • If A is nonsingular, i.e., it can be eigendecomposed and none of its
eigenvalue is equal to zero
✓ Cholesky Decomposition:• If A is positive definite, where is the Conjugate transpose matrix
of L (i.e., L is a lower triangular matrix)
✓ LU Factorization: (with L and U Lower (Upper) Triangular Matrix)
✓ Analytic Solution: (writing the Matrix of Cofactors), a.k.a. Cramer Method
A�1 = Q⇤Q�1
A�1 = (L⇤)�1L�1
A�1 = 1det(A) (C
T )i,j =1
det(A) (Cji) =1
det(A)
0
BBB@
C1,1 C1,2 · · · C1,n
C2,1 C2,2 · · · C2,n...
.... . .
...Cm,1 Cm,2 · · · Cm,n
1
CCCA
L⇤
A = LU
Solu
tion(
s)
C =
0
@C1,1 C1,2 C1,3
C2,1 C2,2 C2,3
C3,1 C3,2 C3,3
1
A
Exam
ple
C =
0
@C1,1 C1,2 C1,3
C2,1 C2,2 C2,3
C3,1 C3,2 C3,3
1
A
Exam
ple
C�1 =1
det(C)⇤
⇤
0
@(C2,2C3,3 � C2,3C3,2) (C1,3C3,2 � C1,2C3,3) (C1,2C2,3 � C1,3C2,2)(C2,3C3,1 � C2,1C3,3) (C1,1C3,3 � C1,3C3,1) (C1,3C2,1 � C1,1C2,3)(C2,1C3,2 � C2,2C3,1) (C3,1C1,2 � C1,1C3,2) (C1,1C2,2 � C1,2C2,1)
1
A
C =
0
@C1,1 C1,2 C1,3
C2,1 C2,2 C2,3
C3,1 C3,2 C3,3
1
A
Exam
pledet(C) = C1,1(C2,2C3,3 � C2,3C3,2)
+C1,2(C1,3C3,2 � C1,2C3,3)
+C1,3(C1,2C2,3 � C1,3C2,2)
C�1 =1
det(C)⇤
⇤
0
@(C2,2C3,3 � C2,3C3,2) (C1,3C3,2 � C1,2C3,3) (C1,2C2,3 � C1,3C2,2)(C2,3C3,1 � C2,1C3,3) (C1,1C3,3 � C1,3C3,1) (C1,3C2,1 � C1,1C2,3)(C2,1C3,2 � C2,2C3,1) (C3,1C1,2 � C1,1C3,2) (C1,1C2,2 � C1,2C2,1)
1
A
Hom
e M
ade
Duplicated Code
Hom
e M
ade
Duplicated CodeTemplate Method Pattern
Hom
e M
ade
Duplicated CodeTemplate Method Pattern
However, we still have to implementfrom scratch computational functions!!
Reinventing the wheel!
Hom
e M
ade
Num
pyfrom numpy import linalg
Type: functionString Form:<function inv at 0x105f72b90>File: /Library/Python/2.7/site-packages/numpy/linalg/linalg.pyDefinition: linalg.inv(a)Source:def inv(a): """ Compute the (multiplicative) inverse of a matrix. [...]
Parameters ---------- a : array_like, shape (M, M) Matrix to be inverted.
Returns ------- ainv : ndarray or matrix, shape (M, M) (Multiplicative) inverse of the matrix `a`.
Raises ------ LinAlgError If `a` is singular or not square.
[...] """ a, wrap = _makearray(a) return wrap(solve(a, identity(a.shape[0], dtype=a.dtype)))Unde
r the
hoo
d
• Alternative built-in solutions to the same problem:
Num
py A
ltern
ative
s
Thanks for your kind attention.
Vect
oriza
tion:
i+=
2
Vect
oriza
tion:
i+=
2
Vect
oriza
tion:
i+=
2
Vect
oriza
tion:
i+=
2
NumPy, Winsi+
=2: R
esul
ts
fatalityNumPy, Wins
i+=2
: Res
ults
Create k points for starting centroids (often randomly)
While any point has changed cluster assignment for every point in dataset: for every centroid:
d = distance(centroid,point) assign(point, nearest(cluster))
for each cluster: mean = average(cluster) centroid[cluster] = mean
K-m
eans