/
make.jl
executable file
·103 lines (95 loc) · 3.57 KB
/
make.jl
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
if Base.HOME_PROJECT[] !== nothing
Base.HOME_PROJECT[] = abspath(Base.HOME_PROJECT[])
end
using Pkg
using Documenter
using MLJ
import MLJIteration
import IterationControl
import EarlyStopping
import MLJBase
import MLJTuning
import MLJModels
import MLJEnsembles
import ScientificTypes
import MLJModelInterface
import ScientificTypesBase
import Distributions
using CategoricalArrays # avoid types like CategoricalArrays.Categorica
using LossFunctions
import CategoricalDistributions
const MMI = MLJModelInterface
# using Literate
# Literate.markdown("common_mlj_workflows.jl", ".",
# codefence = "```@example workflows" => "```")
pages = [
"Home" => "index.md",
"About MLJ" => "about_mlj.md",
"Learning MLJ" => "learning_mlj.md",
"Getting Started" => "getting_started.md",
"Common MLJ Workflows" => "common_mlj_workflows.md",
"Working with Categorical Data" => "working_with_categorical_data.md",
"Model Search" => "model_search.md",
"Loading Model Code" => "loading_model_code.md",
"Machines" => "machines.md",
"Evaluating Model Performance" => "evaluating_model_performance.md",
"Performance Measures" => "performance_measures.md",
"Weights" => "weights.md",
"Tuning Models" => "tuning_models.md",
"Learning Curves" => "learning_curves.md",
"Preparing Data" => "preparing_data.md",
"Transformers and Other Unsupervised models" => "transformers.md",
"More on Probablistic Predictors" => "more_on_probabilistic_predictors.md",
"Composing Models" => "composing_models.md",
"Linear Pipelines" => "linear_pipelines.md",
"Target Transformations" => "target_transformations.md",
"Homogeneous Ensembles" => "homogeneous_ensembles.md",
"Model Stacking" => "model_stacking.md",
"Controlling Iterative Models" => "controlling_iterative_models.md",
"Generating Synthetic Data" => "generating_synthetic_data.md",
"OpenML Integration" => "openml_integration.md",
"Acceleration and Parallelism" => "acceleration_and_parallelism.md",
"Simple User Defined Models" => "simple_user_defined_models.md",
"Quick-Start Guide to Adding Models" =>
"quick_start_guide_to_adding_models.md",
"Adding Models for General Use" => "adding_models_for_general_use.md",
"Modifying Behavior" => "modifying_behavior.md",
"Internals" => "internals.md",
"List of Supported Models" => "list_of_supported_models.md",
"Third Party Packages" => "third_party_packages.md",
"Glossary" => "glossary.md",
"MLJ Cheatsheet" => "mlj_cheatsheet.md",
"Known Issues" => "known_issues.md",
"FAQ" => "frequently_asked_questions.md",
"Julia BlogPost" => "julia_blogpost.md",
"Index of Methods" => "api.md",
]
for (k, v) in pages
println("$k\t=>$v")
end
makedocs(
sitename = "MLJ",
format = Documenter.HTML(),
modules = [MLJ,
MLJBase,
MLJTuning,
MLJModels,
MLJEnsembles,
ScientificTypes,
MLJModelInterface,
ScientificTypesBase,
MLJIteration,
EarlyStopping,
IterationControl,
CategoricalDistributions],
pages = pages)
# By default Documenter does not deploy docs just for PR
# this causes issues with how we're doing things and ends
# up choking the deployment of the docs, so here we
# force the environment to ignore this so that Documenter
# does indeed deploy the docs
ENV["TRAVIS_PULL_REQUEST"] = "false"
deploydocs(
repo = "github.com/alan-turing-institute/MLJ.jl.git",
push_preview=true
)