From 204cc78c5050f9ae3d4f182d37646a9465affbce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 26 Nov 2024 06:23:36 +0100 Subject: [PATCH 1/3] DOC Use text label instead of emoticon in ML map --- doc/images/ml_map.svg | 2 +- doc/machine_learning_map.rst | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/images/ml_map.svg b/doc/images/ml_map.svg index 2dedc6cf054df..0651a89172801 100644 --- a/doc/images/ml_map.svg +++ b/doc/images/ml_map.svg @@ -1,4 +1,4 @@ -
START
START
>50
samples
>50...
get
more
data
get...
NO
NO
predicting a
category
predicting...
YES
YES
do you have
labeled
data
do you hav...
YES
YES
predicting a
quantity
predicting...
NO
NO
just
looking
just...
NO
NO
predicting
structure
predicting...
NO
NO
tough
luck
tough...
<100K
samples
<100K...
YES
YES
SGD
Classifier
SGD...
NO
NO
Linear
SVC
Linear...
YES
YES
text
data
text...
😭
😭
Kernel
Approximation
Kernel...
😭
😭
KNeighbors
Classifier
KNeighbors...
NO
NO
SVC
SVC
Ensemble
Classifiers
Ensemble...
😭
😭
Naive
Bayes
Naive...
YES
YES
classification
classification
number of
categories
known
number of...
NO
NO
<10K
samples
<10K...
<10K
samples
<10K...
NO
NO
NO
NO
YES
YES
MeanShift
MeanShift
VBGMM
VBGMM
YES
YES
MiniBatch
KMeans
MiniBatch...
NO
NO
clustering
clustering
KMeans
KMeans
YES
YES
Spectral
Clustering
Spectral...
GMM
GMM
😭
😭
<100K
samples
<100K...
YES
YES
few features
should be
important
few features...
YES
YES
SGD
Regressor
SGD...
NO
NO
Lasso
Lasso
ElasticNet
ElasticNet
YES
YES
RidgeRegression
RidgeRegression
SVR(kernel="linear")
SVR(kernel="linea...
NO
NO
SVR(kernel="rbf")
SVR(kernel="rbf...
Ensemble
Regressors
Ensemble...
😭
😭
regression
regression
Ramdomized
PCA
Ramdomized...
YES
YES
<10K
samples
<10K...
😭
😭
Kernel
Approximation
Kernel...
NO
NO
IsoMap
IsoMap
Spectral
Embedding
Spectral...
YES
YES
LLE
LLE
😭
😭
dimensionality
reduction
dimensionality...
scikit-learn
algorithm cheat sheet
scikit-learn...
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+
START
START
>50
samples
>50...
get
more
data
get...
NO
NO
predicting a
category
predicting...
YES
YES
do you have
labeled
data
do you hav...
YES
YES
predicting a
quantity
predicting...
NO
NO
just
looking
just...
NO
NO
predicting
structure
predicting...
NO
NO
tough
luck
tough...
<100K
samples
<100K...
YES
YES
SGD
Classifier
SGD...
NO
NO
Linear
SVC
Linear...
YES
YES
text
data
text...
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KNeighbors
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KNeighbors...
NO
NO
SVC
SVC
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Naive
Bayes
Naive...
YES
YES
classification
classification
number of
categories
known
number of...
NO
NO
<10K
samples
<10K...
<10K
samples
<10K...
NO
NO
NO
NO
YES
YES
MeanShift
MeanShift
VBGMM
VBGMM
YES
YES
MiniBatch
KMeans
MiniBatch...
NO
NO
clustering
clustering
KMeans
KMeans
YES
YES
Spectral
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Spectral...
GMM
GMM
<100K
samples
<100K...
YES
YES
few features
should be
important
few features...
YES
YES
SGD
Regressor
SGD...
NO
NO
Lasso
Lasso
ElasticNet
ElasticNet
YES
YES
RidgeRegression
RidgeRegression
SVR(kernel="linear")
SVR(kernel="linea...
NO
NO
SVR(kernel="rbf")
SVR(kernel="rbf...
Ensemble
Regressors
Ensemble...
regression
regression
Ramdomized
PCA
Ramdomized...
YES
YES
<10K
samples
<10K...
Kernel
Approximation
Kernel...
NO
NO
IsoMap
IsoMap
Spectral
Embedding
Spectral...
YES
YES
LLE
LLE
dimensionality
reduction
dimensionality...
scikit-learn
algorithm cheat sheet
scikit-learn...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
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TRY...
TRY
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diff --git a/doc/machine_learning_map.rst b/doc/machine_learning_map.rst index a03bb963cb046..880523a0cfc5b 100644 --- a/doc/machine_learning_map.rst +++ b/doc/machine_learning_map.rst @@ -11,10 +11,10 @@ data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any estimator in -the chart below to see its documentation. The 😭 emoji is to be read as "if this -estimator does not achieve the desired outcome, then follow the arrow and try the next -one". Use scroll wheel to zoom in and out, and click and drag to pan around. You can -also download the chart: :download:`ml_map.svg `. +the chart below to see its documentation. The "Try next" orange arrows are to be read as +"if this estimator does not achieve the desired outcome, then follow the arrow and try +the next one". Use scroll wheel to zoom in and out, and click and drag to pan around. +You can also download the chart: :download:`ml_map.svg `. .. raw:: html From 4f89ab2021a60f8b906eb9382b34e20a67926f76 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 26 Nov 2024 11:32:21 +0100 Subject: [PATCH 2/3] replace links --- doc/images/ml_map.README.rst | 14 +++++++++----- doc/images/ml_map.svg | 2 +- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/doc/images/ml_map.README.rst b/doc/images/ml_map.README.rst index 8d82c175dad58..645d2980591c2 100644 --- a/doc/images/ml_map.README.rst +++ b/doc/images/ml_map.README.rst @@ -13,8 +13,12 @@ for exporting the chart are: - Transparent Background: False - Appearance: Light -Each node in the chart that contains an estimator should have a link, where the root -directory is at `../../`. Note that after updating or re-exporting the SVG, the links -may be prefixed with e.g. `https://app.diagrams.net/`. Remember to check and remove -them, for instance by replacing all occurrences of `https://app.diagrams.net/../../` -with `../../`. +Note that estimators nodes are clickable and should go to the estimator +documentation. After updating or re-exporting the SVG with draw.io, the links +may be prefixed with e.g. `https://app.diagrams.net/`. Remember to check and +remove them, for instance by replacing all occurrences of +`https://app.diagrams.net/./` with `./` with the following command: + +.. prompt:: bash + + perl -pi -e 's@https://app.diagrams.net/\./@./@g' doc/images/ml_map.svg diff --git a/doc/images/ml_map.svg b/doc/images/ml_map.svg index 0651a89172801..c329e0fcce24b 100644 --- a/doc/images/ml_map.svg +++ b/doc/images/ml_map.svg @@ -1,4 +1,4 @@ -
START
START
>50
samples
>50...
get
more
data
get...
NO
NO
predicting a
category
predicting...
YES
YES
do you have
labeled
data
do you hav...
YES
YES
predicting a
quantity
predicting...
NO
NO
just
looking
just...
NO
NO
predicting
structure
predicting...
NO
NO
tough
luck
tough...
<100K
samples
<100K...
YES
YES
SGD
Classifier
SGD...
NO
NO
Linear
SVC
Linear...
YES
YES
text
data
text...
Kernel
Approximation
Kernel...
KNeighbors
Classifier
KNeighbors...
NO
NO
SVC
SVC
Ensemble
Classifiers
Ensemble...
Naive
Bayes
Naive...
YES
YES
classification
classification
number of
categories
known
number of...
NO
NO
<10K
samples
<10K...
<10K
samples
<10K...
NO
NO
NO
NO
YES
YES
MeanShift
MeanShift
VBGMM
VBGMM
YES
YES
MiniBatch
KMeans
MiniBatch...
NO
NO
clustering
clustering
KMeans
KMeans
YES
YES
Spectral
Clustering
Spectral...
GMM
GMM
<100K
samples
<100K...
YES
YES
few features
should be
important
few features...
YES
YES
SGD
Regressor
SGD...
NO
NO
Lasso
Lasso
ElasticNet
ElasticNet
YES
YES
RidgeRegression
RidgeRegression
SVR(kernel="linear")
SVR(kernel="linea...
NO
NO
SVR(kernel="rbf")
SVR(kernel="rbf...
Ensemble
Regressors
Ensemble...
regression
regression
Ramdomized
PCA
Ramdomized...
YES
YES
<10K
samples
<10K...
Kernel
Approximation
Kernel...
NO
NO
IsoMap
IsoMap
Spectral
Embedding
Spectral...
YES
YES
LLE
LLE
dimensionality
reduction
dimensionality...
scikit-learn
algorithm cheat sheet
scikit-learn...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
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+
START
START
>50
samples
>50...
get
more
data
get...
NO
NO
predicting a
category
predicting...
YES
YES
do you have
labeled
data
do you hav...
YES
YES
predicting a
quantity
predicting...
NO
NO
just
looking
just...
NO
NO
predicting
structure
predicting...
NO
NO
tough
luck
tough...
<100K
samples
<100K...
YES
YES
SGD
Classifier
SGD...
NO
NO
Linear
SVC
Linear...
YES
YES
text
data
text...
Kernel
Approximation
Kernel...
KNeighbors
Classifier
KNeighbors...
NO
NO
SVC
SVC
Ensemble
Classifiers
Ensemble...
Naive
Bayes
Naive...
YES
YES
classification
classification
number of
categories
known
number of...
NO
NO
<10K
samples
<10K...
<10K
samples
<10K...
NO
NO
NO
NO
YES
YES
MeanShift
MeanShift
VBGMM
VBGMM
YES
YES
MiniBatch
KMeans
MiniBatch...
NO
NO
clustering
clustering
KMeans
KMeans
YES
YES
Spectral
Clustering
Spectral...
GMM
GMM
<100K
samples
<100K...
YES
YES
few features
should be
important
few features...
YES
YES
SGD
Regressor
SGD...
NO
NO
Lasso
Lasso
ElasticNet
ElasticNet
YES
YES
RidgeRegression
RidgeRegression
SVR(kernel="linear")
SVR(kernel="linea...
NO
NO
SVR(kernel="rbf")
SVR(kernel="rbf...
Ensemble
Regressors
Ensemble...
regression
regression
Ramdomized
PCA
Ramdomized...
YES
YES
<10K
samples
<10K...
Kernel
Approximation
Kernel...
NO
NO
IsoMap
IsoMap
Spectral
Embedding
Spectral...
YES
YES
LLE
LLE
dimensionality
reduction
dimensionality...
scikit-learn
algorithm cheat sheet
scikit-learn...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
NEXT
TRY...
TRY
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TRY
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From 853a360e96d5c71e106b69587f0f58f02e738096 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 26 Nov 2024 17:28:24 +0100 Subject: [PATCH 3/3] Update doc/machine_learning_map.rst Co-authored-by: Thomas J. Fan --- doc/machine_learning_map.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/machine_learning_map.rst b/doc/machine_learning_map.rst index 880523a0cfc5b..e63ab1b1ddce6 100644 --- a/doc/machine_learning_map.rst +++ b/doc/machine_learning_map.rst @@ -11,7 +11,7 @@ data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any estimator in -the chart below to see its documentation. The "Try next" orange arrows are to be read as +the chart below to see its documentation. The **Try next** orange arrows are to be read as "if this estimator does not achieve the desired outcome, then follow the arrow and try the next one". Use scroll wheel to zoom in and out, and click and drag to pan around. You can also download the chart: :download:`ml_map.svg `.