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‎__site/assets/end-to-end/glm/code/ex8.jl

+4-3
Original file line numberDiff line numberDiff line change
@@ -1,12 +1,13 @@
11
# This file was generated, do not modify it. # hide
22
ŷ = MLJ.predict(LinearModel, X)
3-
yhatResponse = [ŷ[i,1] for i in 1:nrow(y)]
3+
yhatResponse = [ŷ[i,1].μ for i in 1:nrow(y)]
44
residuals = y .- yhatResponse
55
r = report(LinearModel)
66

77
k = collect(keys(fp.fitted_params_given_machine))[3]
8-
println("\n Coefficients: ", fp.fitted_params_given_machine[k].coefs)
8+
println("\n Coefficients: ", fp.fitted_params_given_machine[k].coef)
99
println("\n y \n ", y[1:5,1])
1010
println("\n ŷ \n ", ŷ[1:5])
1111
println("\n yhatResponse \n ", yhatResponse[1:5])
12-
println("\n Residuals \n ", y[1:5,1] .- yhatResponse[1:5])
12+
println("\n Residuals \n ", y[1:5,1] .- yhatResponse[1:5])
13+
println("\n Standard Error per Coefficient \n", r.report_given_machine[k].stderror)

‎__site/assets/end-to-end/glm/code/output/ex1.res

+1-1
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,3 @@
11
LinearBinaryClassifier(
22
fit_intercept = true,
3-
link = GLM.LogitLink()) @975
3+
link = GLM.LogitLink()) @403

‎__site/assets/end-to-end/glm/code/output/ex10.res

+3-3
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,5 @@
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(standardizer = (mean_and_std_given_feature = Dict(:wage => (9.500506478338009, 1.3430670761078416),:unemp => (7.597214581091511, 2.763580873344848),:tuition => (0.8146082493518824, 0.33950381985971717),:score => (50.88902933684601, 8.701909614072397)),),
2-
one_hot_encoder = (fitresult = OneHotEncoderResult @113,),
2+
one_hot_encoder = (fitresult = OneHotEncoderResult @199,),
33
linear_binary_classifier = Any[],
4-
machines = Machine[Machine{Standardizer} @402, Machine{OneHotEncoder} @008, Machine{LinearBinaryClassifier{LogitLink}} @719],
5-
fitted_params_given_machine = OrderedCollections.LittleDict{Any,Any,Array{Any,1},Array{Any,1}}(Machine{Standardizer} @402 => (mean_and_std_given_feature = Dict(:wage => (9.500506478338009, 1.3430670761078416),:unemp => (7.597214581091511, 2.763580873344848),:tuition => (0.8146082493518824, 0.33950381985971717),:score => (50.88902933684601, 8.701909614072397)),),Machine{OneHotEncoder} @008 => (fitresult = OneHotEncoderResult @113,),Machine{LinearBinaryClassifier{LogitLink}} @719 => (coef = [0.20250729378868743, 0.130752939109129, 0.344951624939835, 0.9977565847160846, -0.502231510298459, -0.4785005626021652, -0.20440507809955, -0.06922751403500076, 0.05892864973017095, -0.0834474982820323, -0.002315143333859721, 0.4617765395578658, 0.38432629581007743], intercept = -1.0766338905793655)),)
4+
machines = Machine[Machine{Standardizer} @204, Machine{OneHotEncoder} @981, Machine{LinearBinaryClassifier{LogitLink}} @865],
5+
fitted_params_given_machine = OrderedCollections.LittleDict{Any,Any,Array{Any,1},Array{Any,1}}(Machine{Standardizer} @204 => (mean_and_std_given_feature = Dict(:wage => (9.500506478338009, 1.3430670761078416),:unemp => (7.597214581091511, 2.763580873344848),:tuition => (0.8146082493518824, 0.33950381985971717),:score => (50.88902933684601, 8.701909614072397)),),Machine{OneHotEncoder} @981 => (fitresult = OneHotEncoderResult @199,),Machine{LinearBinaryClassifier{LogitLink}} @865 => (coef = [0.20250729378868743, 0.130752939109129, 0.344951624939835, 0.9977565847160846, -0.502231510298459, -0.4785005626021652, -0.20440507809955, -0.06922751403500076, 0.05892864973017095, -0.0834474982820323, -0.002315143333859721, 0.4617765395578658, 0.38432629581007743], intercept = -1.0766338905793655)),)

‎__site/assets/end-to-end/glm/code/output/ex7.res

+3-3
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@@ -1,6 +1,6 @@
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(standardizer = (mean_and_std_given_feature = Dict(:V1 => (0.0024456300706479973, 1.1309193246154066),:V2 => (-0.015561621122145304, 1.1238897897565245),:V5 => (0.0077036209704558975, 1.1421493464876622),:V3 => (0.02442889884313839, 2.332713568319154),:V4 => (0.15168404285157286, 6.806065861835239)),),
2-
one_hot_encoder = (fitresult = OneHotEncoderResult @676,),
2+
one_hot_encoder = (fitresult = OneHotEncoderResult @631,),
33
linear_regressor = (coef = [1.0207869497405524, 1.03242891546997, 0.009406292423317668, 0.026633915171207462, 0.29985915636370225],
44
intercept = 0.015893883995789806,),
5-
machines = Machine[Machine{Standardizer} @801, Machine{OneHotEncoder} @227, Machine{LinearRegressor} @459],
6-
fitted_params_given_machine = OrderedCollections.LittleDict{Any,Any,Array{Any,1},Array{Any,1}}(Machine{Standardizer} @801 => (mean_and_std_given_feature = Dict(:V1 => (0.0024456300706479973, 1.1309193246154066),:V2 => (-0.015561621122145304, 1.1238897897565245),:V5 => (0.0077036209704558975, 1.1421493464876622),:V3 => (0.02442889884313839, 2.332713568319154),:V4 => (0.15168404285157286, 6.806065861835239)),),Machine{OneHotEncoder} @227 => (fitresult = OneHotEncoderResult @676,),Machine{LinearRegressor} @459 => (coef = [1.0207869497405524, 1.03242891546997, 0.009406292423317668, 0.026633915171207462, 0.29985915636370225], intercept = 0.015893883995789806)),)
5+
machines = Machine[Machine{Standardizer} @577, Machine{OneHotEncoder} @580, Machine{LinearRegressor} @579],
6+
fitted_params_given_machine = OrderedCollections.LittleDict{Any,Any,Array{Any,1},Array{Any,1}}(Machine{Standardizer} @577 => (mean_and_std_given_feature = Dict(:V1 => (0.0024456300706479973, 1.1309193246154066),:V2 => (-0.015561621122145304, 1.1238897897565245),:V5 => (0.0077036209704558975, 1.1421493464876622),:V3 => (0.02442889884313839, 2.332713568319154),:V4 => (0.15168404285157286, 6.806065861835239)),),Machine{OneHotEncoder} @580 => (fitresult = OneHotEncoderResult @631,),Machine{LinearRegressor} @579 => (coef = [1.0207869497405524, 1.03242891546997, 0.009406292423317668, 0.026633915171207462, 0.29985915636370225], intercept = 0.015893883995789806)),)

‎__site/assets/end-to-end/glm/code/output/ex8.out

+17-6
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@@ -1,6 +1,17 @@
1-
MethodError: no method matching -(::Float64, ::Distributions.Normal{Float64})
2-
Closest candidates are:
3-
-(::Float64, !Matched::Float64) at float.jl:403
4-
-(::Float64) at float.jl:393
5-
-(!Matched::PyCall.PyObject, ::Any) at /Users/GD/.julia/packages/PyCall/zqDXB/src/pyoperators.jl:13
6-
...
1+
2+
Coefficients: [1.0207869497405524, 1.03242891546997, 0.009406292423317668, 0.026633915171207462, 0.29985915636370225]
3+
4+
y
5+
[-2.0446341129015, -0.461528671336098, 0.458261960749596, 2.2746223981481, -0.969887403007307]
6+
7+
ŷ
8+
Distributions.Normal{Float64}[Distributions.Normal{Float64}(μ=-1.6915415373374758, σ=0.9580569656804974), Distributions.Normal{Float64}(μ=1.412005563203644, σ=0.9580569656804974), Distributions.Normal{Float64}(μ=0.47362968068623923, σ=0.9580569656804974), Distributions.Normal{Float64}(μ=0.7266938985590492, σ=0.9580569656804974), Distributions.Normal{Float64}(μ=-1.8396459459760566, σ=0.9580569656804974)]
9+
10+
yhatResponse
11+
[-1.6915415373374758, 1.412005563203644, 0.47362968068623923, 0.7266938985590492, -1.8396459459760566]
12+
13+
Residuals
14+
[-0.3530925755640242, -1.8735342345397419, -0.01536771993664321, 1.547928499589051, 0.8697585429687495]
15+
16+
Standard Error per Coefficient
17+
[0.01587640310780568, 0.015862782503144917, 0.01515900587321476, 0.01515667698600387, 0.016546721612329368, 0.015148210698700702]

‎__site/assets/end-to-end/glm/code/output/ex9.out

-4
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@@ -1,4 +0,0 @@
1-
MethodError: no method matching (::MLJBase.RMS)(::Array{Distributions.Normal{Float64},1}, ::Array{Float64,1})
2-
Closest candidates are:
3-
Any(!Matched::AbstractArray{#s490,1} where #s490<:Real, ::AbstractArray{#s489,1} where #s489<:Real) at /Users/GD/.julia/packages/MLJBase/CcEkh/src/measures/continuous.jl:75
4-
Any(!Matched::AbstractArray{#s490,1} where #s490<:Real, ::AbstractArray{#s489,1} where #s489<:Real, !Matched::AbstractArray{#s488,1} where #s488<:Real) at /Users/GD/.julia/packages/MLJBase/CcEkh/src/measures/continuous.jl:86

‎__site/assets/end-to-end/glm/code/output/ex9.res

+1-1
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@@ -1 +1 @@
1-
nothing
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0.9573

‎__site/assets/getting-started/learning-networks-2/code/ex6.jl

+2-2
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surrogate = Deterministic()
33
mach = machine(surrogate, Xs, ys; predict=ŷ)
44

5-
fit!(ŷ)
6-
ŷ(X[test[1:5], :])
5+
fit!(mach)
6+
predict(mach, X[test[1:5], :])

‎__site/assets/getting-started/learning-networks-2/code/output/ex2.res

+1-1
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@@ -1 +1 @@
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Source @693 ⏎ `AbstractArray{Continuous,1}`
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Source @644 ⏎ `AbstractArray{Continuous,1}`

‎__site/assets/getting-started/learning-networks-2/code/output/ex3.res

+4-4
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Node{Machine{UnivariateBoxCoxTransformer}} @075
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Node{Machine{UnivariateBoxCoxTransformer}} @347
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args:
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1: Source @693
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1: Source @644
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formula:
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transform(
6-
Machine{UnivariateBoxCoxTransformer} @607,
7-
Source @693)
6+
Machine{UnivariateBoxCoxTransformer} @156,
7+
Source @644)

‎__site/assets/getting-started/learning-networks-2/code/output/ex4.res

+5-5
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Node{Machine{RidgeRegressor}} @093
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Node{Machine{RidgeRegressor}} @064
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args:
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1: Node{Machine{Standardizer}} @319
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1: Node{Machine{Standardizer}} @070
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formula:
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predict(
6-
Machine{RidgeRegressor} @208,
6+
Machine{RidgeRegressor} @310,
77
transform(
8-
Machine{Standardizer} @996,
9-
Source @834))
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Machine{Standardizer} @498,
9+
Source @912))

‎__site/assets/getting-started/learning-networks-2/code/output/ex5.res

+6-6
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Node{Machine{UnivariateBoxCoxTransformer}} @037
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Node{Machine{UnivariateBoxCoxTransformer}} @605
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args:
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1: Node{Machine{RidgeRegressor}} @093
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1: Node{Machine{RidgeRegressor}} @064
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formula:
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inverse_transform(
6-
Machine{UnivariateBoxCoxTransformer} @607,
6+
Machine{UnivariateBoxCoxTransformer} @156,
77
predict(
8-
Machine{RidgeRegressor} @208,
8+
Machine{RidgeRegressor} @310,
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transform(
10-
Machine{Standardizer} @996,
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Source @834)))
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Machine{Standardizer} @498,
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Source @912)))

‎__site/assets/literate/A-learning-networks-2.md

+2-2
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@@ -73,8 +73,8 @@ As we show next, a learning network needs to be exported to create a new stand-a
7373
surrogate = Deterministic()
7474
mach = machine(surrogate, Xs, ys; predict=ŷ)
7575
76-
fit!(ŷ)
77-
ŷ(X[test[1:5], :])
76+
fit!(mach)
77+
predict(mach, X[test[1:5], :])
7878
```
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To form a model out of that network is easy using the `@from_network` macro.

‎__site/assets/literate/A-learning-networks-2_script.jl

+2-2
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@@ -35,8 +35,8 @@ ŷ = inverse_transform(box_mach, ẑ)
3535
surrogate = Deterministic()
3636
mach = machine(surrogate, Xs, ys; predict=ŷ)
3737

38-
fit!(ŷ)
39-
ŷ(X[test[1:5], :])
38+
fit!(mach)
39+
predict(mach, X[test[1:5], :])
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4141
@from_network mach begin
4242
mutable struct CompositeModel

‎__site/assets/literate/EX-GLM.md

+3-2
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@@ -82,16 +82,17 @@ We can quickly read the results of our models in MLJ. Remember to compute the a
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8383
```julia:ex8
8484
ŷ = MLJ.predict(LinearModel, X)
85-
yhatResponse = [ŷ[i,1] for i in 1:nrow(y)]
85+
yhatResponse = [ŷ[i,1].μ for i in 1:nrow(y)]
8686
residuals = y .- yhatResponse
8787
r = report(LinearModel)
8888
8989
k = collect(keys(fp.fitted_params_given_machine))[3]
90-
println("\n Coefficients: ", fp.fitted_params_given_machine[k].coefs)
90+
println("\n Coefficients: ", fp.fitted_params_given_machine[k].coef)
9191
println("\n y \n ", y[1:5,1])
9292
println("\n ŷ \n ", ŷ[1:5])
9393
println("\n yhatResponse \n ", yhatResponse[1:5])
9494
println("\n Residuals \n ", y[1:5,1] .- yhatResponse[1:5])
95+
println("\n Standard Error per Coefficient \n", r.report_given_machine[k].stderror)
9596
```
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9798
and get the accuracy

‎__site/assets/literate/EX-GLM_script.jl

+3-2
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@@ -42,16 +42,17 @@ fit!(LinearModel)
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fp = fitted_params(LinearModel)
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4444
ŷ = MLJ.predict(LinearModel, X)
45-
yhatResponse = [ŷ[i,1] for i in 1:nrow(y)]
45+
yhatResponse = [ŷ[i,1].μ for i in 1:nrow(y)]
4646
residuals = y .- yhatResponse
4747
r = report(LinearModel)
4848

4949
k = collect(keys(fp.fitted_params_given_machine))[3]
50-
println("\n Coefficients: ", fp.fitted_params_given_machine[k].coefs)
50+
println("\n Coefficients: ", fp.fitted_params_given_machine[k].coef)
5151
println("\n y \n ", y[1:5,1])
5252
println("\n ŷ \n ", ŷ[1:5])
5353
println("\n yhatResponse \n ", yhatResponse[1:5])
5454
println("\n Residuals \n ", y[1:5,1] .- yhatResponse[1:5])
55+
println("\n Standard Error per Coefficient \n", r.report_given_machine[k].stderror)
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round(rms(yhatResponse, y[:,1]), sigdigits=4)
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‎__site/assets/literate/EX-crabs-xgb.md

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measure=cross_entropy);
150150
```
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152-
actually it doesn't look like it's changing much...:
152+
it looks like the `gamma` parameter substantially affects model performance:
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```julia:ex14
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@show round(minimum(curve.measurements), sigdigits=3)

‎__site/data/categorical/index.html

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"BB"
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"CC"</code></pre><div class="page-foot">
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<div class="copyright">
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&copy; Thibaut Lienart, Anthony Blaom and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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&copy; Thibaut Lienart, Anthony Blaom, Sebastian Vollmer and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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</div>
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</div><!-- CONTENT ENDS HERE -->

‎__site/data/dataframe/index.html

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"PetalWidth"</code></pre>
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<p>and note the use of <code>.</code> in <code>.&#61;&gt;</code> to indicate that we broadcast the function over each column.<div class="page-foot">
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<div class="copyright">
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&copy; Thibaut Lienart, Anthony Blaom and collaborators. Last modified: July 20, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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&copy; Thibaut Lienart, Anthony Blaom, Sebastian Vollmer and collaborators. Last modified: July 20, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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</div><!-- CONTENT ENDS HERE -->

‎__site/data/loading/index.html

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│ 2 │ 0 │ missing │ 0 │ 0 │ 0 │
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│ 3 │ 1 │ 0 │ 1 │ 1 │ 0 │</code></pre><div class="page-foot">
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<div class="copyright">
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&copy; Thibaut Lienart, Anthony Blaom and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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&copy; Thibaut Lienart, Anthony Blaom, Sebastian Vollmer and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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‎__site/data/processing/index.html

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ax2.set_title("Gas")</code></pre>
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<img src="/DataScienceTutorials.jl/assets/data/processing/code/output/D0-processing-g3.svg" alt="processing3"><div class="page-foot">
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<div class="copyright">
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&copy; Thibaut Lienart, Anthony Blaom and collaborators. Last modified: July 20, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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&copy; Thibaut Lienart, Anthony Blaom, Sebastian Vollmer and collaborators. Last modified: July 20, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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‎__site/data/scitype/index.html

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</code></pre>
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<p>You can also specify multiple rules, see <a href="https://alan-turing-institute.github.io/MLJScientificTypes.jl/stable/#Automatic-type-conversion-for-tabular-data-1">the docs</a> for more information.<div class="page-foot">
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<div class="copyright">
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&copy; Thibaut Lienart, Anthony Blaom and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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&copy; Thibaut Lienart, Anthony Blaom, Sebastian Vollmer and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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‎__site/end-to-end/AMES/index.html

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<pre><code class="language-julia">preds = predict(mtm, rows=test)
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rmsl(y[test], preds)</code></pre><pre><code class="plaintext">0.13932972436067093</code></pre><div class="page-foot">
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<div class="copyright">
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&copy; Thibaut Lienart, Anthony Blaom and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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&copy; Thibaut Lienart, Anthony Blaom, Sebastian Vollmer and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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</div>
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</div><!-- CONTENT ENDS HERE -->

‎__site/end-to-end/HouseKingCounty/index.html

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<p>Tuning helps a fair bit&#33;</p>
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<div class="page-foot">
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<div class="copyright">
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&copy; Thibaut Lienart, Anthony Blaom and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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‎__site/end-to-end/airfoil/index.html

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<img src="/DataScienceTutorials.jl/assets/end-to-end/airfoil/code/output/airfoil_heatmap.svg" alt="Hyperparameter heatmap">
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&copy; Thibaut Lienart, Anthony Blaom and collaborators. Last modified: July 14, 2020. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a>.
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‎__site/end-to-end/boston-flux/index.html

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<p>This evaluated the model at each value of our range. The best value is:</p>
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<pre><code class="language-julia">MLJ.fitted_params(m).best_model.batch_size</code></pre><pre><code class="plaintext">2</code></pre><div class="page-foot">
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‎__site/end-to-end/boston-lgbm/index.html

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@show rms_score</code></pre><pre><code class="plaintext">rms_score = 3.744
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</code></pre><div class="page-foot">
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‎__site/end-to-end/crabs-xgb/index.html

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r = range(xgb, :gamma, lower=0, upper=10)
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curve = learning_curve!(xgbm, range=r, resolution=30,
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measure=cross_entropy);</code></pre>
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<p>actually it doesn&#39;t look like it&#39;s changing much...:</p>
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<p>it looks like the <code>gamma</code> parameter substantially affects model performance:</p>
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<pre><code class="language-julia">@show round(minimum(curve.measurements), sigdigits=3)
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@show round(maximum(curve.measurements), sigdigits=3)</code></pre><pre><code class="plaintext">round(minimum(curve.measurements), sigdigits = 3) = 0.211
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<pre><code class="language-julia">ŷ = predict_mode(mtm, rows=test)
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round(accuracy(ŷ, y[test]), sigdigits=3)</code></pre><div class="page-foot">
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