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02_explore.jl
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### A Pluto.jl notebook ###
# v0.19.9
using Markdown
using InteractiveUtils
# ╔═╡ 1f03b42a-035d-11ed-1fb3-058ad5b6b693
using Parquet, DataFrames, PlutoUI, Statistics, Plots, StatsPlots
# ╔═╡ 426d6d87-8944-43ba-bf63-d33eb0c8b863
path = "data/processed.parq"
# ╔═╡ a49ab4c2-4fa3-40e7-a51d-9b586ac3f9ff
data = DataFrame(read_parquet(path))
# ╔═╡ 5546cb94-9354-4bf9-91df-ac7b7bf34f5e
disallowmissing!(data);
# ╔═╡ 180a2603-06f5-43c2-ac2f-c77b433419e8
md"""
We have a lot of data to work with. An obvious thing would be to predict the `:species`, just like we would do with the Iris dataset. However, we could also try to predict the island, or sex, of a penguin.
If something is a feature or a target depends on our choice. We could decide that we want to predict species, and use island as a (categorical) feature, or the other way around. But before we do that, let's explore the data and try to get an idea how feasable those models would be.
A starting point might be the Floating point variables.
"""
# ╔═╡ 206c0081-6961-4edd-9b69-886c46a51fff
function isfloatvec(col)
typeof(col) <: Vector{Float64}
end
# ╔═╡ 1fc3d6d8-2b33-4b7e-934a-79c88bf5b87c
md"""
This function will return true if col is of type Vector{Float64}, or a subtype of that.
In general, `<:` means "is a subtype of".
This syntax could be useful if you would want to include the integers as well: you could write `<: Vector{Real}`
To give you an idea of the hierarchy of types, have a look at the cells below.
If you want to read more, look [here](https://docs.julialang.org/en/v1/manual/types/)
"""
# ╔═╡ b633b6e7-2977-42aa-b69d-cc3519cc2f35
Real <: Number
# ╔═╡ 7fb07576-9c01-438f-ba4e-ca0ee9f2d2ff
AbstractFloat <: Real
# ╔═╡ 0c7b4bcd-9c9b-478c-8db9-4e600ea9e77e
Float64 <: AbstractFloat
# ╔═╡ a734f9c2-25cc-4d76-bf6f-8ac3acc6f53f
Float64 <: Real
# ╔═╡ 0ebc3a6a-ab21-4ab6-8d77-94ce7a7b0b4d
Integer <: Real
# ╔═╡ 44c0ecc8-83af-42f3-97e5-2f1e3ebb075d
Integer <: Float64
# ╔═╡ 7f51f165-3f11-4d33-adae-fa79b0750dc9
md"""
In Python, you would want to do something like this to select the proper columns:
typevector = df.dtypes == np.float64
`df.loc[:, typevector]`
Here, df.dtypes == np.float64` will give you a boolean array with true and false values. The `.loc[]` format let's you select the correct columns.
In Julia, the approach would be like this.
"""
# ╔═╡ 363d915a-1eec-468b-85f7-dbbfb97ff59c
typevector = isfloatvec.(eachcol(data))
# ╔═╡ b76026ce-2248-4f6d-8753-c26f9b20c961
floatdata = select(data, typevector)
# ╔═╡ eef29a52-5e0f-4062-9f02-a92678365859
md"""
Note how I use the . after the function. This is Julia syntax for broadcasting.
This also happens in [Python](https://numpy.org/doc/stable/user/basics.broadcasting.html), only there you might have never noticed that it happens.
It simply means: apply the function to every element of the next thing.
e.g if you want the `sin` of every number in a vector, you would do:
x = [1.4, 2.7, 0.3]
sin.(x)
which is more explicit than the implicit broadcasting.
If you really insist on the more pythonic syntax, you could have also used the julia syntax that is equivalent to the .loc syntax: `data[!, typevector]` but I think the select is clearer.
Also, for a full side-by-side comparison, look [here](https://dataframes.juliadata.org/stable/man/comparisons/#Comparison-with-the-Python-package-pandas)
"""
# ╔═╡ c4a94bab-1497-4206-b516-556bf0b0c3e7
function normalize(col)
μ = Statistics.mean(col)
σ = Statistics.std(col)
(col .- μ) / σ
end
# ╔═╡ 315974a0-9f5a-4f36-8371-59da51f69828
normdata = transform(floatdata, names(floatdata) .=> normalize .=> names(floatdata))
# ╔═╡ e718dea8-a74a-447d-b6be-9b8ee385aea5
md"""
So, this was how I normalized the data.
Technically, you would not need to do `Statistics.mean`, but I think it is clearer because you might not have a clue that those functions come from the `Statistics` package.
Note that I didnt use the return keyword in the function. By default, the function returns the last line, the return keyword is optional (even though I think it is clearer to include it).
So, now we make our first plot. Let's do a kernel density estimate:
"""
# ╔═╡ 528e4767-79c0-440d-b5b7-9602eaf32afe
StatsPlots.density(normdata.culdep, group=data.species)
# ╔═╡ 2e14cb74-9c47-4c09-af82-6e8c29c0821e
md"""
Pretty much what you would expect, I think.
Let's do that for every column from our floatdata.
We will create a plotlist Vector, and with syntax that is pretty close to Python
you enumerate through the columnnames.
`push!` is the Julia equivalent of `plotlist.append(item)`
We are just adding plots to the vector, because why not.
"""
# ╔═╡ cd37ebc3-32c4-47dc-a4c0-6788374b25b9
function subplots(df::DataFrame, group::Vector)
plotlist = []
colnames = names(df)
for (i, col) in enumerate(eachcol(df))
push!(plotlist, StatsPlots.density(col, group=group, title=colnames[i]))
end
plotlist
end
# ╔═╡ 6c5de056-202c-4889-9101-b391ceea00f0
typeof(data.species)
# ╔═╡ fcee2705-3de5-4cf0-96f8-8138be9d1497
speciesplotlist = subplots(normdata, data.species)
# ╔═╡ d87c5cd8-5080-4276-ad11-1c9ee444029d
Plots.plot(speciesplotlist..., size=(1000, 800))
# ╔═╡ 03641c69-3d42-42a8-9828-a23bc3c35b81
md"""
We can now plot everything with the basic `Plots.plot` function.
Because you would need to do `Plots.plot(p1, p2, p3)` instead of `Plots.plot([p1, p2, p3])` we use the ... splatting operator. Look it up in the livedocs if you want to know more (just put your cursor in the dots in the cell above).
With this plot, it is pretty easy to spot the variables you would need to predict the species.
You can see how some features overlap a lot, but for example the Culmen Length helps to split of the Adele penguins, and culmen depth, mass or $\Delta$ 13 can discern between the Chinstrap and Gentoo penguins.
$(Resource("https://allisonhorst.github.io/palmerpenguins/reference/figures/culmen_depth.png"))
"""
# ╔═╡ b054107f-fe8d-476e-975f-2e8513641620
Plots.scatter(floatdata.cullen, floatdata.Δ13, group=data.species, alpha=0.5)
# ╔═╡ 2e6b0058-5dd9-4b6d-b539-91a318d8d8ae
md"""
Try out some other combinations of features for yourself.
But I hope it would be pretty straightforward from this, that you could use a fairly simple model to discern between the penguins species:
If the culmen length is smaller than ~42, it's Adele.
If the $\Delta$ 13 is above ~25, it's Gentoo.
You can probably fit a model [from the 90's here](https://en.wikipedia.org/wiki/1990s), like for example a SVM, random forest or even XGBoost. Neural networks here are an overkill; you only have 300ish items, which is not enough for neural networks to avoid overfitting.
"""
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
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# ╔═╡ 00000000-0000-0000-0000-000000000002
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