Data Science In Go: A Cheat Sheet

Introduction

Go is the future for doing data science. In this cheatsheet, we look at 2 families of libraries that will allow you to do that.

They are:

gorgonia.org/tensor

and

gonum.org/v1/gonum/mat

. The gonum libraries will be referred to as

gonum/mat

For this cheatsheet, assume the following:

import ts "gorgonia.org/tensor"

Note on panic and error behaviour:
1. Most

tensor

operations return

error

.
2.

gonum

has a good policy of when errors are returned and when panics happen.

What To Use

I ever only want a

float64

matrix or vector

use

gonum/mat

I want to focus on doing statistical/scientific work

use

gonum/mat

I want to focus on doing machine learning work

use

gonum/mat

gorgonia.org/tensor

I want to focus on deep learning work

use

gorgonia.org/tensor

I want multidimensional arrays

use

gorgonia.org/tensor

, or

[]mat.Matrix

I want to work with different data types

use

gorgonia.org/tensor

I want to wrangle data like in Pandas or R - with data frames

use

kniren/gota

Default Values

Numpy

a = np.Zeros((2,3))

gonum/mat

a := mat.NewDense(2, 3, nil)

tensor

a := ts.New(ts.Of(Float32), ts.WithShape(2,3))

A Range...

Numpy

a = np.arange(0, 9).reshape(3,3)

gonum

a := mat.NewDense(3, 3, floats.Span(make([]float64, 9), 0, 8)

tensor

a := ts.New(ts.WithBacking(ts.Range(ts.Int, 0, 9), ts.WithShape(3,3))

Identity Matrices

Numpy

a = np.eye(3,3)

gonum/mat

a := mat.NewDiagonal(3, []float64{1, 1, 1})

tensor

a := ts.I(3, 3, 0)

Elementwise Arithmetic Operations

Addition

Numpy

c = a + b

c = np.add(a, b)

a += b              # in-place

np.add(a, b, out=c) # reuse array

gonum/mat

c.Add(a, b)

a.Add(a, b) // in-place

tensor

var c *ts.Dense; c, err = a.Add(b)

var c ts.Tensor; c, err = ts.Add(a, b)

a.Add(b, ts.UseUnsafe())      // in-place

a.Add(b, ts.WithReuse(c))     // reuse tensor

ts.Add(a, b, ts.UseUnsafe())  // in-place

ts.Add(a, b, ts.WithReuse(c)) // reuse

Note: The operations all returns a result and an error, omitted for brevity here. It's good habit to check for errors.

Subtraction

Numpy

c = a - b

c = np.subtract(a, b)

gonum/mat

c.Sub(a, b)

tensor

c, err:= a.Sub(b)

c, err = ts.Sub(a, b)

Multiplication

Numpy

c = a * b

c = np.multiply(a, b)

gonum/mat

c.MulElem(a, b)

tensor

c, err := a.Mul(b)

c, err := ts.Mul(a, b)

Division

Numpy

c = a / b

c = np.divide(a, b)

gonum/mat

c.DivElem(a, b)

tensor

c, err := a.Div(b)

c, err := ts.Div(a, b)

Note: When encountering division by 0 for non-floats, an error will be returned, and the value at which the offending value will be 0 in the result.

Note: All variations of arithmetic operations follow the patterns available in Addition for all examples.

Note on Shapes
In all of these functions,

and

has to be of the same shape. In Numpy operations with dissimilar shapes will throw an exception. With

gonum/mat

it'd panic. With

tensor

, it will be returned as an error.

Aggregation

Sum

Numpy

s = a.sum()

s = np.sum(a)

gonum/mat

var s float64 = mat.Sum(a)

tensor

var s *ts.Dense = a.Sum()

var s ts.Tensor = ts.Sum(a)

Note: The result, which is a scalar value in this case, can be retrieved by calling

s.ScalarValue()

Sum Along An Axis

Numpy

s = a.sum(axis=0)

s = np.sum(a, axis=0)

gonum/mat
Write a loop, with manual aid from

mat.Col

and

mat.Row

Note: There's no performance loss by writing a loop. In fact there arguably may be a cognitive gain in being aware of what one is doing.

tensor

var s *ts.Dense = a.Sum(0)

var s ts.Tensor = ts.Sum(a, 0)

Argmax/Argmin

Numpy

am = a.argmax()

am = np.argmax(a)

gonum
Write a loop, using

mat.Col

and

mat.Row

tensor

var am *ts.Dense; am, err = a.Argmax(ts.AllAxes)

var am ts.Tensor; am, err = ts.Argmax(a, ts.AllAxes)

Argmax/Argmin Along An Axis

Numpy

am = a.argmax(axis=0)

am = np.argmax(a, axis=0)

gonum
Write a loop, using

mat.Col

and

mat.Row

tensor

var am *ts.Dense; am, err = a.Argmax(0)

var am ts.Tensor; am, err = ts.Argmax(a, 0)

Data Structure Creation

Numpy

a = np.array([1, 2, 3])

gonum/mat

a := mat.NewDense(1, 3, []float64{1, 2, 3})

tensor

a := ts.New(ts.WithBacking([]int{1, 2, 3})

Creating a float64 matrix

Numpy

a = np.array([[0, 1, 2], [3, 4, 5]], dtype='float64')

gonum/mat

a := mat.NewDense(2, 3, []float64{0, 1, 2, 3, 4, 5})

tensor

a := ts.New(ts.WithBacking([]float64{0, 1, 2, 3, 4, 5}, ts.WithShape(2, 3))

Creating a float32 3-D array

Numpy

a = np.array([[[0, 1, 2], [3, 4, 5]], [[100, 101, 102], [103, 104, 105]]], dtype='float32')

tensor

a := ts.New(ts.WithShape(2, 2, 3), ts.WithBacking([]float32{0, 1, 2, 3, 4, 5, 100, 101, 102, 103, 104, 105}))

Note: The the

tensor

package is imported as

ts

Additionally,

gonum/mat

actually offers many different data structures, each being useful to a particular subset of computations. The examples given in this document mainly assumes a dense matrix.

gonum Types

`mat.Matrix`	Abstract data type representing any `float64` matrix
`*mat.Dense`	Data type representing a dense `float64` matrix

tensor Types

`tensor.Tensor`	An abstract data type representing any kind of tensors. Package functions work on these types.
`*tensor.Dense`	A representation of a densely packed multidimensional array. Methods return `*tensor.Dense` instead of `tensor.Tensor`
`*tensor.CS`	A representation of compressed sparse row/column matrices.
`*tensor.MA`	Coming soon - representation of masked multidimensional array. Methods return `*tensor.MA` instead of `tensor.Tensor`
`tensor.DenseTensor`	Utility type that represents densely packed multidimensional arrays
`tensor.MaskedTensor`	Utility type that represents densely packed multidimensional arrays that are masked by a slice of `bool`
`tensor.Sparse`	Utility type that represents any sparsely packed multi-dim arrays (for now: only `*CS` )

Metadata

Metadata	Numpy	gonum	tensor
Shape	`a.shape`	`a.Dims()`	`a.Shape()`
Strides	`a.strides`		`a.Strides()`
Dims	`a.ndim`		`a.Dims()`

Tensor Manipulation

Zero-op Transpose

Numpy

aT = a.T

gonum/mat

aT := a.T()

tensor

a.T()

Transpose With Data Movement

Numpy

aT = np.transpose(a)

gonum/mat

b := a.T(); aT := mat.DenseCopyOf(b)

tensor

aT, err := ts.Transpose(a)

a.T(); err := a.Transpose()

Reshape

Numpy

b = a.reshape(2,3)

gonum/mat

b := NewDense(2, 3, a.RawMatrix().Data)

tensor

err := a.Reshape(2,3)

Note on reshaping when using gonum: the matrix

mustn't be a view.

Linear Algebra

Inner Product of Vectors

Numpy

c = np.inner(a, b)

gonum

var c float64 = mat.Dot(a, b)

tensor

var c interface{} = ts.Inner(a, b)

var c interface{} = a.Inner(b)

Note: The

tensor

package comes with specialized execution engines for

float64

and

float32

which will return

float64

float32

without returning an

interface{}

Matrix-Vector Multiplication

Numpy

mv = np.dot(m, v)

mv = np.matmul(m, v)

mv = m @ v

mv = m.dot(v)

gonum

mv.Mul(m, v)

tensor

var mv ts.Tensor; mv, _ = ts.MatVecMul(m, v)

var mv *Dense; mv, _ = m.MatVecMul(v)

Matrix-Matrix Multiplication

Numpy

mm = np.dot(m1, m2)

mm = np.matmul(m1, m2)

mm = m1 @ m2

mm = m1.dot(m2)

gonum

mm.Mul(m1, m2)

tensor

var mm Tensor; mm, _ = ts.MatMul(m1, m2)

var mm *ts.Dense; mm, _ = m1.MatMul(m2)

Magic

Numpy

c = np.dot(a, b)

c = a.dot(b)

tensor

var c ts.Tensor; c, _ = ts.Dot(a, b)

var c *ts.Dense; c, _ = a.Dot(b)

Note: The

Dot

function and method in package

tensor

works similarly to

dot

in Numpy - depending on the number of dimensions of the inputs, different functions will be called. You should treat it as a "magic" function that does products of two multi-dimensional arrays.

gonum

has a whole suite of linear-algebra functions and structures that are too many to enumerate here. You should check it out too.

Combinations

Concatenation

Numpy

c = np.concatenate((a, b), axis=0)

gonum/mat

c.Stack(a,b)

tensor

var c ts.Tensor; c, err = ts.Concat(0, a, b)

var c *ts.Dense; c, err = a.Concat(0, b)

Vstack

Numpy

c = np.vstack((a, b))

gonum/mat

c.Stack(a,b)

tensor

var c *ts.Dense; c, err = a.Vstack(0, b)

Hstack

Numpy

c = np.hstack((a, b))

gonum/mat

c.Augment(a,b)

tensor

var c *ts.Dense; c, err = a.Hstack(0, b)

Stack onto a New Axis

Numpy

c = np.stack((a, b))

gonum/mat

var stacked []mat.Matrix; stacked = append(stacked, a, b)

tensor

var c ts.Tensor; c, _ = ts.Stack(0, a, b)

var c *ts.Dense; c, _ = a.Stack(0,b)

Note: Unlike in Numpy,

Stack

tensor

is a little more strict on the axis. It has to be specified.

Repeats

Numpy

c = np.repeat(a, 2) # returns a flat array

c = np.repeat(a, 2, axis=0) # repeats along axis 0

c = np.repeat(a, 2, axis=1) # repeats along axis 1

gonum
Unsupported for now

tensor

var c ts.Tensor; c, _ = ts.Repeat(a, ts.AllAxes,  2) // returns a flat array

c = ts.Repeat(a, 0, 2) // repeats along axis 0

c = ts.Repeat(a, 1, 2) // repeats along axis 1

Data Access

Value At (Assuming Matrices)

Numpy

val = a[0, 0]

gonum/mat

var va float64 := a.At(0, 0)

tensor

var val interface{}; val, _ = a.At(0,0)

Slice Row or Column (Assuming Matrices)

Numpy

row = a[0]

col = a[:, 0]

gonum/mat

var row mat.Vector = a.RowView(0)

var col mat.Vector = a.ColView(0)

tensor

var row ts.View = a.Slice(s(0))

var col ts.View = a.Slice(nil, s(0))

Advanced Slicing (Assuming 9x9 Matrices)

Numpy

b = a[1:4, 3:6]

gonum/mat

var b mat.Matrix = a.Slice(1,4, 3,6)

tensor

var b ts.View = a.Slice(rs(1,4), rs(3,6))

Advanced Slicing With Steps

Numpy

b = a[1:4:1, 3:6:2]

gonum/mat
Unsupported

tensor

var b ts.View = a.Slice(rs(1,4,1), rs(3,6,2))

Getting Underlying Data

Numpy

b = a.ravel()

gonum/mat

var b []float64 = a.RawMatrix().Data

tensor

var b interface{} = a.Data()

Setting One Value (Assuming Matrices)

Numpy

a[r, c] = 100

gonum/mat

a.Set(r, c, 100)

tensor

a.SetAt(100, r, c)

Setting Row/Col (Assuming 3x3 Matrix)

Numpy

a[r] = [1, 2, 3]

a[:, c] = [1, 2, 3]

gonum/mat

a.SetRow(r, []float64{1, 2, 3})

a.SetCol(c, []float64{1, 2, 3})

tensor
No simple method - requires

Iterator

s and multiple lines of code.

Note: in the

tensor

examples, the

a.Slice

method take a list of

tensor.Slice

which is an interface defined here.

, and

rs

in the examples simply represent types that implement the

tensor.Slice

type. A

nil

is treated as a

in Python. There are no default

tensor.Slice

types provided, and it is up to the user to define their own.

Created By

chewxy

Languages: English

Published: 12th October, 2017
Last Updated: 25th October, 2020
Rated: 4 out of 5 stars based on 1 ratings

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Data Science In Go: A Cheat Sheet by chewxy

Introduction

What To Use

Default Values

Elementwise Arithmetic Operations

Aggregation

Data Structure Creation

gonum Types

tensor Types

Metadata

Tensor Manipulation

Linear Algebra

Combinations

Data Access

Created By

Metadata

Favourited By

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Data Science In Go: A Cheat Sheet by chewxy

Introd­uction

What To Use

Default Values

Elemen­twise Arithmetic Operations

Aggreg­ation

Data Structure Creation

gonum Types

tensor Types

Metadata

Tensor Manipu­lation

Linear Algebra

Combin­ations

Data Access

Created By

Metadata

Favourited By

Comments

Add a Comment

Related Cheat Sheets

Latest Cheat Sheet

Random Cheat Sheet

About Cheatography

Behind the Scenes

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Introduction

Elementwise Arithmetic Operations

Aggregation

Tensor Manipulation

Combinations