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Julia: Come for the Syntax, Stay for the Speed

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Re: Julia: Come for the Syntax, Stay for the Speed

#2

Post by plg »

Something I always try with new (to me) languages: write a short script to

(1) load a .txt file containing space-delimited columns of data;

(2) fit a linear model in which one column is predicted by a linear combination of the others;

(3) plot the predicted values again the actual values using dots and overlay a y=x line

Tried this with Julia a short while ago and basically gave up, couldn’t figure out how to get something to plot. Has the Julia-verse changed? Is it easier now?

I can do this in MATLAB in basically 3 or 4 lines of code. Python, not much more than that.

Re: Julia: Come for the Syntax, Stay for the Speed

#3

Post by jagtesh »

I've been following Julia for many years now and it hasn't quite picked up outside the Data Science/Research communities. I'm at a loss to understand why it hasn't gone mainstream. Technically, isn't this a superior language to everything out there if you are getting performance + readability?

To start the discussion, maybe one factor could be the lack of a package manager like npm/crate. What else?

Re: Julia: Come for the Syntax, Stay for the Speed

#4

Post by ahurmazda »

plg wrote: post #2

Something I always try with new (to me) languages: write a short script to (1) load a .txt file containing space-delimited columns of data; (2) fit a linear model in which one column is predicted by a linear combination of the others; (3) plot the predicted values again the actual values using dots and overlay a y=x line Tried this with Julia a short while ago and basically gave up, couldn’t figure out how to get som…

Not familiar with matlab but its not as terse as R

``` using GLM, CSV, Plots

data = CSV.read("data.csv", header=["x","y"], types=[Float64, Float64]) #returns dataframe

ols = lm(@formula(y ~ x), data)

ypred = predict(ols)

yall = Base.hcat(data.y, ypred)

plot(data.x, yall, linewidth=2, title="Linear regression", label=["y", "ypred"], xlabel="x", ylabel="y")

```

Re: Julia: Come for the Syntax, Stay for the Speed

#5

Post by [deleted] »

ahurmazda wrote: post #4
plg wrote: post #2

Something I always try with new (to me) languages: write a short script to (1) load a .txt file containing space-delimited columns of data; (2) fit a linear model in which one column is predicted by a linear combination of the others; (3) plot the predicted values again the actual values using dots and overlay a y=x line Tried this with Julia a short while ago and basically gave up, couldn’t figure out how to get som…

Not familiar with matlab but its not as terse as R ``` using GLM, CSV, Plots data = CSV.read("data.csv", header=["x","y"], types=[Float64, Float64]) #returns dataframe ols = lm(@formula(y ~ x), data) ypred = predict(ols) yall = Base.hcat(data.y, ypred) plot(data.x, yall, linewidth=2, title="Linear regression", label=["y", "ypred"], xlabel="x", ylabel="y") ```

[deleted]

Re: Julia: Come for the Syntax, Stay for the Speed

#6

Post by cbkeller »

ahurmazda wrote: post #4
plg wrote: post #2

Something I always try with new (to me) languages: write a short script to (1) load a .txt file containing space-delimited columns of data; (2) fit a linear model in which one column is predicted by a linear combination of the others; (3) plot the predicted values again the actual values using dots and overlay a y=x line Tried this with Julia a short while ago and basically gave up, couldn’t figure out how to get som…

Not familiar with matlab but its not as terse as R ``` using GLM, CSV, Plots data = CSV.read("data.csv", header=["x","y"], types=[Float64, Float64]) #returns dataframe ols = lm(@formula(y ~ x), data) ypred = predict(ols) yall = Base.hcat(data.y, ypred) plot(data.x, yall, linewidth=2, title="Linear regression", label=["y", "ypred"], xlabel="x", ylabel="y") ```

The comment I was going to reply to disappeared, but for something closer in form to the Matlab example that used to be here:

  using Plots, DelimitedFiles
  d = readdlm("data.tsv",'\t')
  A = [ones(10,1) d[:,1:2]]; B = copy(d[:,3]); X = A\B
  plot(B, seriestype=:scatter, color=:blue); plot!(A*X, seriestype=:scatter, color=:red)
I find Julia syntax feels closer to Matlab than to Python or R, just different enough to be frustrating for the first month or so (followed by a period of "oh, that's why Julia does it this way instead!")

Re: Julia: Come for the Syntax, Stay for the Speed

#7

Post by cbkeller »

jagtesh wrote: post #3

I've been following Julia for many years now and it hasn't quite picked up outside the Data Science/Research communities. I'm at a loss to understand why it hasn't gone mainstream. Technically, isn't this a superior language to everything out there if you are getting performance + readability? To start the discussion, maybe one factor could be the lack of a package manager like npm/crate. What else?

The initial precompilation times may give some new users the wrong idea, but those have been for me a total non-issue once I'm using the language regularly.

Re: Julia: Come for the Syntax, Stay for the Speed

#8

Post by DNF2 »

cbkeller wrote: post #6
ahurmazda wrote: post #4

Earlier quoted context omitted.

Not familiar with matlab but its not as terse as R ``` using GLM, CSV, Plots data = CSV.read("data.csv", header=["x","y"], types=[Float64, Float64]) #returns dataframe ols = lm(@formula(y ~ x), data) ypred = predict(ols) yall = Base.hcat(data.y, ypred) plot(data.x, yall, linewidth=2, title="Linear regression", label=["y", "ypred"], xlabel="x", ylabel="y") ```

The comment I was going to reply to disappeared, but for something closer in form to the Matlab example that used to be here: using Plots, DelimitedFiles d = readdlm("data.tsv",'\t') A = [ones(10,1) d[:,1:2]]; B = copy(d[:,3]); X = A\B plot(B, seriestype=:scatter, color=:blue); plot!(A*X, seriestype=:scatter, color=:red) I find Julia syntax feels closer to Matlab than to Python or R, just different enough to be frust…

No need to `copy` from `d[:,3]`. Slicing already creates copies (so you're copying twice), but even if it made a view you could still just write `B = d[:, 3]`.

Re: Julia: Come for the Syntax, Stay for the Speed

#9

Post by DNF2 »

plg wrote: post #2

Something I always try with new (to me) languages: write a short script to (1) load a .txt file containing space-delimited columns of data; (2) fit a linear model in which one column is predicted by a linear combination of the others; (3) plot the predicted values again the actual values using dots and overlay a y=x line Tried this with Julia a short while ago and basically gave up, couldn’t figure out how to get som…

That is strange. It is literally as easy as `using Plots; plot(...)`

Re: Julia: Come for the Syntax, Stay for the Speed

#10

Post by cbkeller »

DNF2 wrote: post #8
cbkeller wrote: post #6

Earlier quoted context omitted.

The comment I was going to reply to disappeared, but for something closer in form to the Matlab example that used to be here: using Plots, DelimitedFiles d = readdlm("data.tsv",'\t') A = [ones(10,1) d[:,1:2]]; B = copy(d[:,3]); X = A\B plot(B, seriestype=:scatter, color=:blue); plot!(A*X, seriestype=:scatter, color=:red) I find Julia syntax feels closer to Matlab than to Python or R, just different enough to be frust…

No need to `copy` from `d[:,3]`. Slicing already creates copies (so you're copying twice), but even if it made a view you could still just write `B = d[:, 3]`.

Ah, didn't realize slicing always made copies. A view/alias/whatever would have been fine for the example, but having multiple variable names referring to the same memory has caused me enough problems to try to avoid it by default.
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