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Commit Notebook Markdown Changes #83

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90 changes: 51 additions & 39 deletions docs/src/notebooks/UsingMLJ/01_basics/notebook.md
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,18 @@ Notebook supporting the video series "Using MLJ".
To run the code in this tutorial in a live Julia session, first follow the instructions
given [here](@ref instructions).

### Video Timings

- 00:00 Introduction
- 01:05 Goals
- 01:48 Prerequisites and Getting Help
- 02:21 Supervised Learning Recap
- 05:31 Models and Machines
- 09:28 **Live Coding:** Regression
- 28:55 Scientific Types
- 32:29 **Live Coding:** Scitypes and Classification
- 55:45 End

We start by inspecting the packages, and their exact versions, in the currently active
package environment:

Expand All @@ -30,7 +42,7 @@ Info Packages marked with ⌃ have new versions available and may be upgradable.

````

# Part I. Regression
## Part I. Regression

````@julia
using MLJ
Expand Down Expand Up @@ -187,8 +199,8 @@ fitted_params(mach)
````
(forest = Ensemble of Decision Trees
Trees: 100
Avg Leaves: 147.25
Avg Depth: 14.49,)
Avg Leaves: 146.21
Avg Depth: 14.61,)
````

````@julia
Expand All @@ -207,9 +219,9 @@ predict(mach, X)[1:3]

````
3-element Vector{Float64}:
26.984
22.682999999999993
35.31
27.011
22.410999999999994
34.27
````

Predict in the `test` rows:
Expand All @@ -225,7 +237,7 @@ mae(ypred, y[test])
````

````
4.410905940594058
4.574237623762377
````

`mae` is actually just an alias:
Expand Down Expand Up @@ -272,14 +284,14 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: RandomForestRegressor-162
Tag: RandomForestRegressor-336
Extract:
┌────────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
├────────────┼───────────┼─────────────┤
│ LPLoss( │ predict │ 4.41 │
│ LPLoss( │ predict │ 4.57 │
│ p = 1) │ │ │
│ RSquared() │ predict │ 0.369 │
│ RSquared() │ predict │ 0.33 │
└────────────┴───────────┴─────────────┘
Apply `describe` to this result for a named tuple summary.
````
Expand All @@ -301,21 +313,21 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: RandomForestRegressor-267
Tag: RandomForestRegressor-817
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
│ A │ LPLoss( │ predict │ 2.99 │ 0.92 │
│ A │ LPLoss( │ predict │ 3.05 │ 0.89 │
│ │ p = 1) │ │ │ │
│ B │ RSquared() │ predict │ 0.67 │ 0.17 │
│ B │ RSquared() │ predict │ 0.65 │ 0.16 │
└───┴────────────┴───────────┴─────────────┴─────────┘
┌───┬──────────────────────────────────────────┐
│ │ per_fold │
├───┼──────────────────────────────────────────┤
│ A │ [2.14, 2.11, 3.32, 2.5, 4.9, 2.96] │
│ B │ [0.74, 0.871, 0.73, 0.813, 0.454, 0.392] │
└───┴──────────────────────────────────────────┘
┌───┬─────────────────────────────────────────┐
│ │ per_fold │
├───┼─────────────────────────────────────────┤
│ A │ [2.27, 2.36, 3.25, 2.48, 4.97, 2.96] │
│ B │ [0.723, 0.81, 0.742, 0.806, 0.441, 0.4] │
└───┴─────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````

Expand All @@ -338,21 +350,21 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: RandomForestRegressor-760
Tag: RandomForestRegressor-621
Extract:
┌───┬────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────┼───────────┼─────────────┼─────────┤
│ A │ LPLoss( │ predict │ 2.49 │ 0.14 │
│ A │ LPLoss( │ predict │ 2.48 │ 0.1 │
│ │ p = 1) │ │ │ │
│ B │ RSquared() │ predict │ 0.823 │ 0.033 │
│ B │ RSquared() │ predict │ 0.817 │ 0.038 │
└───┴────────────┴───────────┴─────────────┴─────────┘
┌───┬────────────────────────────────────────────────────────────────────────┐
│ │ per_fold │
├───┼────────────────────────────────────────────────────────────────────────┤
│ A │ [2.64, 2.35, 2.17, 2.66, 2.86, 2.27, 2.67, 2.47, 2.54, 2.28] │
│ B │ [0.752, 0.874, 0.897, 0.758, 0.777, 0.853, 0.835, 0.792, 0.824, 0.864] │
└───┴────────────────────────────────────────────────────────────────────────┘
┌───┬──────────────────────────────────────────────────────────────────────┐
│ │ per_fold │
├───┼──────────────────────────────────────────────────────────────────────┤
│ A │ [2.37, 2.74, 2.27, 2.42, 2.75, 2.54, 2.36, 2.48, 2.41, 2.5] │
│ B │ [0.87, 0.702, 0.857, 0.848, 0.824, 0.72, 0.843, 0.834, 0.839, 0.834] │
└───┴──────────────────────────────────────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````

Expand All @@ -362,8 +374,8 @@ e.uncertainty_radius_95

````
2-element Vector{Float64}:
0.14383105341435692
0.032989951374955834
0.10349121844149421
0.03773282475420102
````

# Interlude on scientific types
Expand Down Expand Up @@ -400,7 +412,7 @@ scitype(["cat", "mouse", "dog"])
AbstractVector{Textual} (alias for AbstractArray{ScientificTypesBase.Textual, 1})
````

# Part II. Classification
## Part II. Classification

New data set for classification, the Adult Dataset (census data):

Expand Down Expand Up @@ -608,8 +620,8 @@ first(yprob, 5)
5-element CategoricalDistributions.UnivariateFiniteVector{ScientificTypesBase.Multiclass{2}, InlineStrings.String7, UInt32, Float64}:
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.91, >50K=>0.09)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.33, >50K=>0.67)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.89, >50K=>0.11)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>0.28, >50K=>0.72)
UnivariateFinite{ScientificTypesBase.Multiclass{2}}(<=50K=>1.0, >50K=>0.0)
````

Expand All @@ -620,8 +632,8 @@ yprob[3]
````
UnivariateFinite{ScientificTypesBase.Multiclass{2}}
┌ ┐
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.91
>50K ┤■■■ 0.09
<=50K ┤■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 0.89
>50K ┤■■■■ 0.11
└ ┘
````

Expand All @@ -632,15 +644,15 @@ accuracy(ypoint, y[test])
````

````
0.7823616727235502
0.7821057480677689
````

````@julia
log_loss(yprob, y[test])
````

````
1.5655836383744342
1.53133125245395
````

Evaluate with one command:
Expand All @@ -660,12 +672,12 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: RandomForestClassifier-308
Tag: RandomForestClassifier-321
Extract:
┌──────────────────────┬──────────────┬─────────────┐
│ measure │ operation │ measurement │
├──────────────────────┼──────────────┼─────────────┤
│ Accuracy() │ predict_mode │ 0.781 │
│ Accuracy() │ predict_mode │ 0.782 │
│ LogLoss( │ predict │ 1.55 │
│ tol = 2.22045e-16) │ │ │
└──────────────────────┴──────────────┴─────────────┘
Expand Down
23 changes: 20 additions & 3 deletions docs/src/notebooks/UsingMLJ/02_model_composition/notebook.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,20 @@ Notebook supporting the video series "Using MLJ".
To run the code in this tutorial in a live Julia session, first follow the instructions
given [here](@ref instructions).

### Video Timings

- 00:00 Introduction
- 00:08 Goals
- 01:19 Prerequisites and Getting Help
- 02:00 Composite Models Defined
- 04:13 Model Pipelines
- 05:59 Data Leakage
- 08:37 Target Transformations
- 12:43 Live Coding: Pipelines
- 18:42 Live Coding: Target Transformations
- 20:35 Other Model Wrappers
- 25:02 End

````@julia
using MLJ
````
Expand Down Expand Up @@ -146,7 +160,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-639
Tag: DeterministicPipeline-500
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
Expand Down Expand Up @@ -211,7 +225,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: TransformedTargetModelDeterministic-399
Tag: TransformedTargetModelDeterministic-470
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
Expand Down Expand Up @@ -254,7 +268,10 @@ evaluations = evaluate(
PerformanceEvaluation("new lambda & normalized target", 18460.0 ± 440.0)
````

Here's a pretty view of these results:
(To provide multiple models, tagged with strings, to `evaluate` requires MLJBase 1.12.0
or higher.)

Here's a pretty view of these results (needs MLJBase 1.13.0 or higher):

````@julia
describe.(evaluations) |> pretty
Expand Down
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