update up to chapter 9
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Bogumił Kamiński
170
ch08.jl
170
ch08.jl
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# Bogumił Kamiński, 2022
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# Codes for chapter 6
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# Codes for chapter 8
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# Code for section 6.1
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# Code for section 8.1
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if isfile("puzzles.csv.bz2")
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@info "file already present"
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@@ -25,22 +25,27 @@ end
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readlines("puzzles.csv")
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# Code for section 6.2
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# Code for section 8.2
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using CSV
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using DataFrames
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puzzles = CSV.read("puzzles.csv", DataFrame);
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CSV.read(plain, DataFrame);
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puzzles2 = CSV.read(plain, DataFrame;
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header=["PuzzleId", "FEN", "Moves",
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"Rating","RatingDeviation",
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"Popularity", "NbPlays",
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"Themes","GameUrl"]);
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puzzles == puzzles2
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compressed = nothing
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plain = nothing
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# Code for listing 6.1
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# Code for listing 8.1
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puzzles
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# Code for listing 6.2
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# Code for listing 8.2
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describe(puzzles)
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@@ -52,7 +57,13 @@ nrow(puzzles)
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names(puzzles)
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# Code for section 6.3
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CSV.write("puzzles2.csv", puzzles)
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read("puzzles2.csv")
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read("puzzles2.csv") == read("puzzles.csv")
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# Code for section 8.3
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puzzles.Rating
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@@ -101,148 +112,3 @@ plot(histogram(puzzles.Rating, label="Rating"),
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plot([histogram(puzzles[!, col]; label=col) for
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col in ["Rating", "RatingDeviation",
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"Popularity", "NbPlays"]]...)
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# Code for section 6.4
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using Statistics
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plays_lo = median(puzzles.NbPlays)
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puzzles.NbPlays .> plays_lo
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puzzles.NbPlays > plays_lo
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rating_lo = 1500
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rating_hi = quantile(puzzles.Rating, 0.99)
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rating_lo .< puzzles.Rating .< rating_hi
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row_selector = (puzzles.NbPlays .> plays_lo) .&&
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(rating_lo .< puzzles.Rating .< rating_hi)
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sum(row_selector)
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count(row_selector)
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# Code for listing 6.3
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good = puzzles[row_selector, ["Rating", "Popularity"]]
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# Code for plotting histograms
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plot(histogram(good.Rating; label="Rating"),
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histogram(good.Popularity; label="Popularity"))
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# Code for column selectors
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puzzles[1, "Rating"]
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puzzles[:, "Rating"]
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row1 = puzzles[1, ["Rating", "Popularity"]]
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row1["Rating"]
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row1[:Rating]
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row1[1]
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row1.Rating
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row1."Rating"
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good = puzzles[row_selector, ["Rating", "Popularity"]]
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good[1, "Rating"]
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good[1, :]
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good[:, "Rating"]
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good[:, :]
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names(puzzles, ["Rating", "Popularity"])
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names(puzzles, [:Rating, :Popularity])
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names(puzzles, [4, 6])
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names(puzzles, [false, false, false, true, false, true, false, false, false])
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names(puzzles, r"Rating")
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names(puzzles, Not([4, 6]))
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names(puzzles, Not(r"Rating"))
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names(puzzles, Between("Rating", "Popularity"))
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names(puzzles, :)
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names(puzzles, All())
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names(puzzles, Cols(r"Rating", "NbPlays"))
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names(puzzles, Cols(startswith("P")))
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names(puzzles, startswith("P"))
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names(puzzles, Real)
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names(puzzles, AbstractString)
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puzzles[:, names(puzzles, Real)]
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# Code for row subsetting
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df1 = puzzles[:, ["Rating", "Popularity"]];
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df2 = puzzles[!, ["Rating", "Popularity"]];
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df1 == df2
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df1 == puzzles
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df2 == puzzles
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df1.Rating === puzzles.Rating
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df1.Popularity === puzzles.Popularity
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df2.Rating === puzzles.Rating
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df2.Popularity === puzzles.Popularity
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@benchmark $puzzles[:, ["Rating", "Popularity"]]
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@benchmark $puzzles[!, ["Rating", "Popularity"]]
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puzzles[1, 1]
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puzzles[[1], 1]
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puzzles[1, [1]]
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puzzles[[1], [1]]
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# Code for making views
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@view puzzles[1, 1]
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@view puzzles[[1], 1]
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@view puzzles[1, [1]]
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@view puzzles[[1], [1]]
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@btime $puzzles[$row_selector, ["Rating", "Popularity"]];
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@btime @view $puzzles[$row_selector, ["Rating", "Popularity"]];
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parentindices(@view puzzles[row_selector, ["Rating", "Popularity"]])
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# Code for section 6.5
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describe(good)
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rating_mapping = Dict{Int, Vector{Int}}()
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for (i, rating) in enumerate(good.Rating)
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if haskey(rating_mapping, rating)
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push!(rating_mapping[rating], i)
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else
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rating_mapping[rating] = [i]
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end
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end
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rating_mapping
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good[rating_mapping[2108], :]
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unique(good[rating_mapping[2108], :].Rating)
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using Statistics
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mean(good[rating_mapping[2108], "Popularity"])
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ratings = unique(good.Rating)
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mean_popularities = map(ratings) do rating
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indices = rating_mapping[rating]
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popularities = good[indices, "Popularity"]
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return mean(popularities)
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end
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scatter(ratings, mean_popularities;
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xlabel="rating", ylabel="mean popularity", legend=false)
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import Loess
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model = Loess.loess(ratings, mean_popularities);
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ratings_predict = float.(sort(ratings))
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popularity_predict = Loess.predict(model, ratings_predict)
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plot!(ratings_predict, popularity_predict; width=5, color="black")
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