diff --git a/previews/PR11/404.html b/previews/PR11/404.html index d4320a8..33d98f6 100644 --- a/previews/PR11/404.html +++ b/previews/PR11/404.html @@ -5,17 +5,17 @@ 404 | SpeciesDistributionModels.jl - - + + - +
- + \ No newline at end of file diff --git a/previews/PR11/api.html b/previews/PR11/api.html index fec0f33..e4bf548 100644 --- a/previews/PR11/api.html +++ b/previews/PR11/api.html @@ -5,30 +5,30 @@ SpeciesDistributionModels.jl - - + + - + - - - + + + -
SpeciesDistributionModels.jl

Appearance

Index ​

Reference - Exported functions ​

SpeciesDistributionModels.ShapleyValues Type
julia
ShapleyValues(algorithm::Shapley.Algorithm)
-ShapleyValues(N::Integer; threaded = true, rng = Random.GLOBAL_RNG)

Use to specify use Shapley values as method in explain. If an integer N, and optionally threaded and rng is supplied, MonteCarlo sampling is used, where N is the number of iterations (samples). More samples will result in more accurate results, but will take more time to compute.

source

SpeciesDistributionModels.explain Method
julia
explain(ensemble::SDMensemble; method, [data], [predictors])

Generate response curves for ensemble.

Keywords

  • method is the algorithm to use. See ShapleyValues

  • data is the data to use to generate response curves, and defaults to the data used to train the ensemble

  • predictors: which predictors to generate response curves for. Defaults to all variables in data.

source

SpeciesDistributionModels.predict Method
julia
predict(SDMobject, newdata; clamp = false, threaded = false, [reducer], [by_group])

Use an SDMmachine, SDMgroup, or SDMensemble to predict habitat suitability for some data, optionally summarized for the entire ensemble, or for each SDMgroup.

newdata can be either a RasterStack, or a Tables.jl.compatible object. It must have all predictor variables used to train the models in its columns (or layers in case of a RasterStack).

Keywords

  • clamp: if true, the predictions are clamped to the interval seen during training of SDMobject. Defaults to false

  • threaded: if true, run multithreaded. Defaults to true.

  • reducer: Optionally provide a Function to summarize the output. The function should take an vector of values and return a single value. Typical examples are Statistics.mean or Statistics.median.

  • by_group is set to true, the data is reduced for each SDMgroup, if it is set to false (the default), it reduced across the entire ensemble.

Returns

If newdata is a RasterStack, the predict returns a Raster; otherwise, it returns a NamedTuple of Vectors, with habitat suitability represented by a floating-point number between 0 and 1.

source

SpeciesDistributionModels.remove_collinear Method
julia
remove_collinear(data; method, silent = false)

Removes strongly correlated variables in data, until correlation is below a threshold specified in method.

method can currently be either Gvif, Vif or Pearson, which use GVIF, VIF, or Pearson's r, respectively. GVIF and VIF are similar method, but GVIF includes categorical variables whereas VIF ignores them.

To run without showing information about collinearity scores, set silent = true.

Example

julia
julia> import SpeciesDistributionModels as SDM
+    
SpeciesDistributionModels.jl
Appearance
Index ​
Reference - Exported functions ​
SpeciesDistributionModels.ShapleyValues Type
julia
ShapleyValues(algorithm::Shapley.Algorithm)
+ShapleyValues(N::Integer; threaded = true, rng = Random.GLOBAL_RNG)
Use to specify use Shapley values as method in explain. If an integer N, and optionally threaded and rng is supplied, MonteCarlo sampling is used, where N is the number of iterations (samples). More samples will result in more accurate results, but will take more time to compute.
source
SpeciesDistributionModels.explain Method
julia
explain(ensemble::SDMensemble; method, [data], [predictors])
Generate response curves for ensemble.
Keywords
  • method is the algorithm to use. See ShapleyValues
  • data is the data to use to generate response curves, and defaults to the data used to train the ensemble
  • predictors: which predictors to generate response curves for. Defaults to all variables in data.
source
SpeciesDistributionModels.predict Method
julia
predict(SDMobject, newdata; clamp = false, threaded = false, [reducer], [by_group])
Use an SDMmachine, SDMgroup, or SDMensemble to predict habitat suitability for some data, optionally summarized for the entire ensemble, or for each SDMgroup.
newdata can be either a RasterStack, or a Tables.jl.compatible object. It must have all predictor variables used to train the models in its columns (or layers in case of a RasterStack).
Keywords
  • clamp: if true, the predictions are clamped to the interval seen during training of SDMobject. Defaults to false
  • threaded: if true, run multithreaded. Defaults to true.
  • reducer: Optionally provide a Function to summarize the output. The function should take an vector of values and return a single value. Typical examples are Statistics.mean or Statistics.median.
  • by_group is set to true, the data is reduced for each SDMgroup, if it is set to false (the default), it reduced across the entire ensemble.
Returns
If newdata is a RasterStack, the predict returns a Raster; otherwise, it returns a NamedTuple of Vectors, with habitat suitability represented by a floating-point number between 0 and 1.
source
SpeciesDistributionModels.remove_collinear Method
julia
remove_collinear(data; method, silent = false)
Removes strongly correlated variables in data, until correlation is below a threshold specified in method.
method can currently be either Gvif, Vif or Pearson, which use GVIF, VIF, or Pearson's r, respectively. GVIF and VIF are similar method, but GVIF includes categorical variables whereas VIF ignores them.
To run without showing information about collinearity scores, set silent = true.
Example
julia
julia> import SpeciesDistributionModels as SDM
 julia> mydata = (a = 1:100, b = sqrt.(1:100), c = rand(100))
 julia> SDM.remove_collinear(mydata; method = SDM.Vif(10))
 [ Info: a has highest GVIF of 28.367942095054225
 [ Info: Removing a, 2 variables remaining
 [ Info: b has highest GVIF of 1.0077618445543057
 [ Info: All variables are below threshold, returning remaining variables
-(:b, :c)
source
SpeciesDistributionModels.sdm Method
julia
sdm(data, models; [resampler], [predictors], [verbosity])
Construct an ensemble.
Arguments
data: an SDMdata object models: a NamedTuple with the models to be used in the ensemble.
Keywords
  • models: a Vector of the models to be used in the ensemble. All models must be MLJ-supported Classifiers.
  • For a full list of supported models, see https://alan-turing-institute.github.io/MLJ.jl/stable/model_browser/#Classification
  • predictors: a Vector of Symbols with the names of the predictor values to be used. By default, all pdf
  • verbosity: an Int value that regulates how much information is printed.
  • cache: is passed to MLJBase.machine. Specify cache=false to prioritize memory management over speed.
  • scitype_check_level: is passed to MLJBase.machine. Specify scitype_check_level=0 to disable scitype checking.
Example
julia
using SpeciesDistributionModels, Maxnet, MLJGLMInterface
+(:b, :c)
source
SpeciesDistributionModels.sdm Method
julia
sdm(data, models; [resampler], [predictors], [verbosity])
Construct an ensemble.
Arguments
data: an SDMdata object models: a NamedTuple with the models to be used in the ensemble.
Keywords
  • models: a Vector of the models to be used in the ensemble. All models must be MLJ-supported Classifiers.
  • For a full list of supported models, see https://alan-turing-institute.github.io/MLJ.jl/stable/model_browser/#Classification
  • predictors: a Vector of Symbols with the names of the predictor values to be used. By default, all pdf
  • verbosity: an Int value that regulates how much information is printed.
  • cache: is passed to MLJBase.machine. Specify cache=false to prioritize memory management over speed.
  • scitype_check_level: is passed to MLJBase.machine. Specify scitype_check_level=0 to disable scitype checking.
Example
julia
using SpeciesDistributionModels, Maxnet, MLJGLMInterface
 mydata = sdmdata(presences, absences; resampler = CV(nfolds = 5))
 models = (maxnet = MaxnetBinaryClassifier(), glm = LinearBinaryClassifier())
-ensemble = sdm(mydata, models)
source
SpeciesDistributionModels.sdmdata Method
julia
sdmdata(presences, absences; resampler, predictors)
+ensemble = sdm(mydata, models)
source
SpeciesDistributionModels.sdmdata Method
julia
sdmdata(presences, absences; resampler, predictors)
 sdmdata(X, y::BitVector; resampler, predictors)
Construct an SDMdata object from species presences and absences. Alternatively, from a table with predictor variables X and a BitVector y, where false represents absence and true represents presence.
Keywords
  • resampler: The resampling strategy to be used. Should be a MLJBase.ResamplingStrategy, or a Vector of Tuples with the form (train, test). Defaults to NoResampling(). If resampler is a CV, shuffle is internally set to true.
  • predictors: a Tuple of Symbols with the names of the predictor values to be used. By default, all predictor variables in X, or all predictor variables in both presences and absences are used..
Returns
An SDMdata object containing the data provided. This object can be used to construct an SDMensemble.
Example
using Rasters, SpeciesDistributionModels
 A = rand(10,10)
 B = rand(10,10)
@@ -41,7 +41,7 @@
 a = extract(st, absence_points)
 
 mydata = sdmdata(p, a; resampler = CV(nfolds = 2)) # 2-fold cross validation
-mydata2 = sdmdata([p; a], [trues(4); falses(4)]; resampler = [([1,2],[5,6]), ([3,4], [7,8])]) # provide resampling rows
source
SpeciesDistributionModels.thin Method
julia
thin([rng], x, cutoff; distance = Haversine(), [geometrycolumn])
+mydata2 = sdmdata([p; a], [trues(4); falses(4)]; resampler = [([1,2],[5,6]), ([3,4], [7,8])]) # provide resampling rows
source
SpeciesDistributionModels.thin Method
julia
thin([rng], x, cutoff; distance = Haversine(), [geometrycolumn])
 
 Thin spatial data by removing points that are closer than `cutoff` distance
 to the nearest other point in the dataset.
@@ -69,7 +69,7 @@
 2-element Vector{Tuple{Int64, Real}}:
 (0, 0)
 (1, 0)
-```
source
Reference - Internal functions ​
SpeciesDistributionModels.evaluate Method
julia
evaluate(x; measures, train = true, test = true, [validation])
Evaluate x, which could be a SDMmachine, SDMgroup, or SDMensemble, by applying the measures provided to the data used to built an ensemble, and return an evaluation object.
Keywords
  • measures is a NamedTuple of measures. The keys are used to identify the measures.
This defaults to using auc, log_loss, and kappa. For threshold-dependent measures, the highest score as well as the threshold at which the highest scores is reached are reported. A list of measurse is available here: https://juliaai.github.io/StatisticalMeasures.jl/dev/auto_generated_list_of_measures/#aliases. However, note that not all measures are useful.
  • train: a Bool that indicates whether to evaluate on the training data. Defaults to true
  • test: a Bool that indicates whether to evaluate on the test data.Defaults to true
  • validation: optionally, provide a separate validation dataset.
Data should be provided as a Tuple with presences as the first field and absences as the second.
source
SpeciesDistributionModels.machine_evaluations Function
julia
machine_evaluations(eval)
+```
source
Reference - Internal functions ​
SpeciesDistributionModels.evaluate Method
julia
evaluate(x; measures, train = true, test = true, [validation])
Evaluate x, which could be a SDMmachine, SDMgroup, or SDMensemble, by applying the measures provided to the data used to built an ensemble, and return an evaluation object.
Keywords
  • measures is a NamedTuple of measures. The keys are used to identify the measures.
This defaults to using auc, log_loss, and kappa. For threshold-dependent measures, the highest score as well as the threshold at which the highest scores is reached are reported. A list of measurse is available here: https://juliaai.github.io/StatisticalMeasures.jl/dev/auto_generated_list_of_measures/#aliases. However, note that not all measures are useful.
  • train: a Bool that indicates whether to evaluate on the training data. Defaults to true
  • test: a Bool that indicates whether to evaluate on the test data.Defaults to true
  • validation: optionally, provide a separate validation dataset.
Data should be provided as a Tuple with presences as the first field and absences as the second.
source
SpeciesDistributionModels.machine_evaluations Function
julia
machine_evaluations(eval)
 
 Get the scores for each machine in an evaluation, which can be either an 
 `SDMgroupEvaluation` or an `SDMensembleEvaluation`.
@@ -82,8 +82,8 @@
 ```julia
 evaluation = SDM.evaluate(ensemble; measures = (; accuracy, auc))
 machine_aucs = SDM.machine_evaluations(evaluation).train.auc
-```
source

-    
+```

source

+ \ No newline at end of file diff --git a/previews/PR11/assets/api.md.bHTjdokF.js b/previews/PR11/assets/api.md.Cglor7Cb.js similarity index 97% rename from previews/PR11/assets/api.md.bHTjdokF.js rename to previews/PR11/assets/api.md.Cglor7Cb.js index e748dd3..fad807a 100644 --- a/previews/PR11/assets/api.md.bHTjdokF.js +++ b/previews/PR11/assets/api.md.Cglor7Cb.js @@ -1,15 +1,15 @@ -import{_ as l,c as p,a5 as e,j as i,a,G as n,B as h,o as d}from"./chunks/framework.CmcMgnI6.js";const j=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),o={name:"api.md"},r={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""};function F(f,s,C,D,v,M){const t=h("Badge");return d(),p("div",null,[s[27]||(s[27]=e('

Index ​

Reference - Exported functions ​

',3)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"SpeciesDistributionModels.ShapleyValues",href:"#SpeciesDistributionModels.ShapleyValues"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.ShapleyValues")],-1)),s[1]||(s[1]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=e(`
julia
ShapleyValues(algorithm::Shapley.Algorithm)
-ShapleyValues(N::Integer; threaded = true, rng = Random.GLOBAL_RNG)

Use to specify use Shapley values as method in explain. If an integer N, and optionally threaded and rng is supplied, MonteCarlo sampling is used, where N is the number of iterations (samples). More samples will result in more accurate results, but will take more time to compute.

source

`,3))]),i("details",k,[i("summary",null,[s[3]||(s[3]=i("a",{id:"SpeciesDistributionModels.explain-Tuple{SDMensemble}",href:"#SpeciesDistributionModels.explain-Tuple{SDMensemble}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.explain")],-1)),s[4]||(s[4]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[5]||(s[5]=e('
julia
explain(ensemble::SDMensemble; method, [data], [predictors])

Generate response curves for ensemble.

Keywords

source

',5))]),i("details",c,[i("summary",null,[s[6]||(s[6]=i("a",{id:"SpeciesDistributionModels.predict-Tuple{SpeciesDistributionModels.SDMmachine, Any}",href:"#SpeciesDistributionModels.predict-Tuple{SpeciesDistributionModels.SDMmachine, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.predict")],-1)),s[7]||(s[7]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[8]||(s[8]=e('
julia
predict(SDMobject, newdata; clamp = false, threaded = false, [reducer], [by_group])

Use an SDMmachine, SDMgroup, or SDMensemble to predict habitat suitability for some data, optionally summarized for the entire ensemble, or for each SDMgroup.

newdata can be either a RasterStack, or a Tables.jl.compatible object. It must have all predictor variables used to train the models in its columns (or layers in case of a RasterStack).

Keywords

Returns

If newdata is a RasterStack, the predict returns a Raster; otherwise, it returns a NamedTuple of Vectors, with habitat suitability represented by a floating-point number between 0 and 1.

source

',8))]),i("details",u,[i("summary",null,[s[9]||(s[9]=i("a",{id:"SpeciesDistributionModels.remove_collinear-Tuple{Any}",href:"#SpeciesDistributionModels.remove_collinear-Tuple{Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.remove_collinear")],-1)),s[10]||(s[10]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[11]||(s[11]=e(`
julia
remove_collinear(data; method, silent = false)

Removes strongly correlated variables in data, until correlation is below a threshold specified in method.

method can currently be either Gvif, Vif or Pearson, which use GVIF, VIF, or Pearson's r, respectively. GVIF and VIF are similar method, but GVIF includes categorical variables whereas VIF ignores them.

To run without showing information about collinearity scores, set silent = true.

Example

julia
julia> import SpeciesDistributionModels as SDM
+import{_ as l,c as p,a5 as e,j as i,a,G as n,B as h,o as d}from"./chunks/framework.nF61VOH4.js";const j=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),o={name:"api.md"},r={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""};function F(f,s,C,D,v,M){const t=h("Badge");return d(),p("div",null,[s[27]||(s[27]=e('
Index ​
Reference - Exported functions ​
',3)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"SpeciesDistributionModels.ShapleyValues",href:"#SpeciesDistributionModels.ShapleyValues"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.ShapleyValues")],-1)),s[1]||(s[1]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=e(`
julia
ShapleyValues(algorithm::Shapley.Algorithm)
+ShapleyValues(N::Integer; threaded = true, rng = Random.GLOBAL_RNG)
Use to specify use Shapley values as method in explain. If an integer N, and optionally threaded and rng is supplied, MonteCarlo sampling is used, where N is the number of iterations (samples). More samples will result in more accurate results, but will take more time to compute.
source
`,3))]),i("details",k,[i("summary",null,[s[3]||(s[3]=i("a",{id:"SpeciesDistributionModels.explain-Tuple{SDMensemble}",href:"#SpeciesDistributionModels.explain-Tuple{SDMensemble}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.explain")],-1)),s[4]||(s[4]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[5]||(s[5]=e('
julia
explain(ensemble::SDMensemble; method, [data], [predictors])
Generate response curves for ensemble.
Keywords
  • method is the algorithm to use. See ShapleyValues
  • data is the data to use to generate response curves, and defaults to the data used to train the ensemble
  • predictors: which predictors to generate response curves for. Defaults to all variables in data.
source
',5))]),i("details",c,[i("summary",null,[s[6]||(s[6]=i("a",{id:"SpeciesDistributionModels.predict-Tuple{SpeciesDistributionModels.SDMmachine, Any}",href:"#SpeciesDistributionModels.predict-Tuple{SpeciesDistributionModels.SDMmachine, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.predict")],-1)),s[7]||(s[7]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[8]||(s[8]=e('
julia
predict(SDMobject, newdata; clamp = false, threaded = false, [reducer], [by_group])
Use an SDMmachine, SDMgroup, or SDMensemble to predict habitat suitability for some data, optionally summarized for the entire ensemble, or for each SDMgroup.
newdata can be either a RasterStack, or a Tables.jl.compatible object. It must have all predictor variables used to train the models in its columns (or layers in case of a RasterStack).
Keywords
  • clamp: if true, the predictions are clamped to the interval seen during training of SDMobject. Defaults to false
  • threaded: if true, run multithreaded. Defaults to true.
  • reducer: Optionally provide a Function to summarize the output. The function should take an vector of values and return a single value. Typical examples are Statistics.mean or Statistics.median.
  • by_group is set to true, the data is reduced for each SDMgroup, if it is set to false (the default), it reduced across the entire ensemble.
Returns
If newdata is a RasterStack, the predict returns a Raster; otherwise, it returns a NamedTuple of Vectors, with habitat suitability represented by a floating-point number between 0 and 1.
source
',8))]),i("details",u,[i("summary",null,[s[9]||(s[9]=i("a",{id:"SpeciesDistributionModels.remove_collinear-Tuple{Any}",href:"#SpeciesDistributionModels.remove_collinear-Tuple{Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.remove_collinear")],-1)),s[10]||(s[10]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[11]||(s[11]=e(`
julia
remove_collinear(data; method, silent = false)
Removes strongly correlated variables in data, until correlation is below a threshold specified in method.
method can currently be either Gvif, Vif or Pearson, which use GVIF, VIF, or Pearson's r, respectively. GVIF and VIF are similar method, but GVIF includes categorical variables whereas VIF ignores them.
To run without showing information about collinearity scores, set silent = true.
Example
julia
julia> import SpeciesDistributionModels as SDM
 julia> mydata = (a = 1:100, b = sqrt.(1:100), c = rand(100))
 julia> SDM.remove_collinear(mydata; method = SDM.Vif(10))
 [ Info: a has highest GVIF of 28.367942095054225
 [ Info: Removing a, 2 variables remaining
 [ Info: b has highest GVIF of 1.0077618445543057
 [ Info: All variables are below threshold, returning remaining variables
-(:b, :c)
source
`,7))]),i("details",g,[i("summary",null,[s[12]||(s[12]=i("a",{id:"SpeciesDistributionModels.sdm-Tuple{Any, Any}",href:"#SpeciesDistributionModels.sdm-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.sdm")],-1)),s[13]||(s[13]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[14]||(s[14]=e(`
julia
sdm(data, models; [resampler], [predictors], [verbosity])
Construct an ensemble.
Arguments
data: an SDMdata object models: a NamedTuple with the models to be used in the ensemble.
Keywords
  • models: a Vector of the models to be used in the ensemble. All models must be MLJ-supported Classifiers.
  • For a full list of supported models, see https://alan-turing-institute.github.io/MLJ.jl/stable/model_browser/#Classification
  • predictors: a Vector of Symbols with the names of the predictor values to be used. By default, all pdf
  • verbosity: an Int value that regulates how much information is printed.
  • cache: is passed to MLJBase.machine. Specify cache=false to prioritize memory management over speed.
  • scitype_check_level: is passed to MLJBase.machine. Specify scitype_check_level=0 to disable scitype checking.
Example
julia
using SpeciesDistributionModels, Maxnet, MLJGLMInterface
+(:b, :c)
source
`,7))]),i("details",g,[i("summary",null,[s[12]||(s[12]=i("a",{id:"SpeciesDistributionModels.sdm-Tuple{Any, Any}",href:"#SpeciesDistributionModels.sdm-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.sdm")],-1)),s[13]||(s[13]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[14]||(s[14]=e(`
julia
sdm(data, models; [resampler], [predictors], [verbosity])
Construct an ensemble.
Arguments
data: an SDMdata object models: a NamedTuple with the models to be used in the ensemble.
Keywords
  • models: a Vector of the models to be used in the ensemble. All models must be MLJ-supported Classifiers.
  • For a full list of supported models, see https://alan-turing-institute.github.io/MLJ.jl/stable/model_browser/#Classification
  • predictors: a Vector of Symbols with the names of the predictor values to be used. By default, all pdf
  • verbosity: an Int value that regulates how much information is printed.
  • cache: is passed to MLJBase.machine. Specify cache=false to prioritize memory management over speed.
  • scitype_check_level: is passed to MLJBase.machine. Specify scitype_check_level=0 to disable scitype checking.
Example
julia
using SpeciesDistributionModels, Maxnet, MLJGLMInterface
 mydata = sdmdata(presences, absences; resampler = CV(nfolds = 5))
 models = (maxnet = MaxnetBinaryClassifier(), glm = LinearBinaryClassifier())
-ensemble = sdm(mydata, models)
source
`,9))]),i("details",E,[i("summary",null,[s[15]||(s[15]=i("a",{id:"SpeciesDistributionModels.sdmdata-Tuple{Any, Any}",href:"#SpeciesDistributionModels.sdmdata-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.sdmdata")],-1)),s[16]||(s[16]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[17]||(s[17]=e(`
julia
sdmdata(presences, absences; resampler, predictors)
+ensemble = sdm(mydata, models)
source
`,9))]),i("details",E,[i("summary",null,[s[15]||(s[15]=i("a",{id:"SpeciesDistributionModels.sdmdata-Tuple{Any, Any}",href:"#SpeciesDistributionModels.sdmdata-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.sdmdata")],-1)),s[16]||(s[16]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[17]||(s[17]=e(`
julia
sdmdata(presences, absences; resampler, predictors)
 sdmdata(X, y::BitVector; resampler, predictors)
Construct an SDMdata object from species presences and absences. Alternatively, from a table with predictor variables X and a BitVector y, where false represents absence and true represents presence.
Keywords
  • resampler: The resampling strategy to be used. Should be a MLJBase.ResamplingStrategy, or a Vector of Tuples with the form (train, test). Defaults to NoResampling(). If resampler is a CV, shuffle is internally set to true.
  • predictors: a Tuple of Symbols with the names of the predictor values to be used. By default, all predictor variables in X, or all predictor variables in both presences and absences are used..
Returns
An SDMdata object containing the data provided. This object can be used to construct an SDMensemble.
Example
using Rasters, SpeciesDistributionModels
 A = rand(10,10)
 B = rand(10,10)
@@ -22,4 +22,4 @@ import{_ as l,c as p,a5 as e,j as i,a,G as n,B as h,o as d}from"./chunks/framewo
 a = extract(st, absence_points)
 
 mydata = sdmdata(p, a; resampler = CV(nfolds = 2)) # 2-fold cross validation
-mydata2 = sdmdata([p; a], [trues(4); falses(4)]; resampler = [([1,2],[5,6]), ([3,4], [7,8])]) # provide resampling rows
source
`,9))]),i("details",y,[i("summary",null,[s[18]||(s[18]=i("a",{id:"SpeciesDistributionModels.thin-Tuple{Any, Any}",href:"#SpeciesDistributionModels.thin-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.thin")],-1)),s[19]||(s[19]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[20]||(s[20]=e('
julia
thin([rng], x, cutoff; distance = Haversine(), [geometrycolumn])\n\nThin spatial data by removing points that are closer than `cutoff` distance\nto the nearest other point in the dataset.\n\n## Arguments\n- `rng`: a random number generator. The default is `Random.GLOBAL_RNG()`.\n- `x`: an `AbstractVector` that iterates points, or a table with a `:geometry` column.\n- `cutoff`: the distance threshold in units of `distance`.\n## Keywords\n- `distance`: the distance metric used to calculate distances between points. The default\nis `Haversine()`, which uses the Haversine formula to calculate the distance between coordinates in meter units.\n- `geometrycolumn`: the name of the column in the table that contains the points, if `x` is a table. Usually defaults to `:geometry`.\n\n## Example\n```jldoctest\nusing SpeciesDistributionModels, Distances\n# a vector that iteratores points\ngeometries = [(0,0), (1,0), (0,0.000001)]\n# thin to 1000 meters\nthin(geometries, 1000)\n# thin to 1 degree\nthin(Xoshiro(123), geometries, 1; distance = Euclidean())\n\n# output\n2-element Vector{Tuple{Int64, Real}}:\n(0, 0)\n(1, 0)\n```
source
',2))]),s[28]||(s[28]=i("h2",{id:"Reference-Internal-functions",tabindex:"-1"},[a("Reference - Internal functions "),i("a",{class:"header-anchor",href:"#Reference-Internal-functions","aria-label":'Permalink to "Reference - Internal functions {#Reference-Internal-functions}"'},"​")],-1)),i("details",b,[i("summary",null,[s[21]||(s[21]=i("a",{id:"SpeciesDistributionModels.evaluate-Tuple{Any}",href:"#SpeciesDistributionModels.evaluate-Tuple{Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.evaluate")],-1)),s[22]||(s[22]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[23]||(s[23]=e('
julia
evaluate(x; measures, train = true, test = true, [validation])
Evaluate x, which could be a SDMmachine, SDMgroup, or SDMensemble, by applying the measures provided to the data used to built an ensemble, and return an evaluation object.
Keywords
  • measures is a NamedTuple of measures. The keys are used to identify the measures.
This defaults to using auc, log_loss, and kappa. For threshold-dependent measures, the highest score as well as the threshold at which the highest scores is reached are reported. A list of measurse is available here: https://juliaai.github.io/StatisticalMeasures.jl/dev/auto_generated_list_of_measures/#aliases. However, note that not all measures are useful.
  • train: a Bool that indicates whether to evaluate on the training data. Defaults to true
  • test: a Bool that indicates whether to evaluate on the test data.Defaults to true
  • validation: optionally, provide a separate validation dataset.
Data should be provided as a Tuple with presences as the first field and absences as the second.
source
',8))]),i("details",m,[i("summary",null,[s[24]||(s[24]=i("a",{id:"SpeciesDistributionModels.machine_evaluations",href:"#SpeciesDistributionModels.machine_evaluations"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.machine_evaluations")],-1)),s[25]||(s[25]=a()),n(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[26]||(s[26]=e('
julia
machine_evaluations(eval)\n\nGet the scores for each machine in an evaluation, which can be either an \n`SDMgroupEvaluation` or an `SDMensembleEvaluation`.\n\nThe return type is a nested structure of `NamedTuple`s. \nThe `NamedTuple` returned has keys corresponding to the evaluation datasets (a subset of :train, :test, and :validation), \nwhich each have keys corresponding to the measures specified in [`evaluate`](@ref).\n\n## Example\n```julia\nevaluation = SDM.evaluate(ensemble; measures = (; accuracy, auc))\nmachine_aucs = SDM.machine_evaluations(evaluation).train.auc\n```
source
',2))])])}const B=l(o,[["render",F]]);export{j as __pageData,B as default};
+mydata2 = sdmdata([p; a], [trues(4); falses(4)]; resampler = [([1,2],[5,6]), ([3,4], [7,8])]) # provide resampling rows

source

`,9))]),i("details",y,[i("summary",null,[s[18]||(s[18]=i("a",{id:"SpeciesDistributionModels.thin-Tuple{Any, Any}",href:"#SpeciesDistributionModels.thin-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.thin")],-1)),s[19]||(s[19]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[20]||(s[20]=e('
julia
thin([rng], x, cutoff; distance = Haversine(), [geometrycolumn])\n\nThin spatial data by removing points that are closer than `cutoff` distance\nto the nearest other point in the dataset.\n\n## Arguments\n- `rng`: a random number generator. The default is `Random.GLOBAL_RNG()`.\n- `x`: an `AbstractVector` that iterates points, or a table with a `:geometry` column.\n- `cutoff`: the distance threshold in units of `distance`.\n## Keywords\n- `distance`: the distance metric used to calculate distances between points. The default\nis `Haversine()`, which uses the Haversine formula to calculate the distance between coordinates in meter units.\n- `geometrycolumn`: the name of the column in the table that contains the points, if `x` is a table. Usually defaults to `:geometry`.\n\n## Example\n```jldoctest\nusing SpeciesDistributionModels, Distances\n# a vector that iteratores points\ngeometries = [(0,0), (1,0), (0,0.000001)]\n# thin to 1000 meters\nthin(geometries, 1000)\n# thin to 1 degree\nthin(Xoshiro(123), geometries, 1; distance = Euclidean())\n\n# output\n2-element Vector{Tuple{Int64, Real}}:\n(0, 0)\n(1, 0)\n```

source

',2))]),s[28]||(s[28]=i("h2",{id:"Reference-Internal-functions",tabindex:"-1"},[a("Reference - Internal functions "),i("a",{class:"header-anchor",href:"#Reference-Internal-functions","aria-label":'Permalink to "Reference - Internal functions {#Reference-Internal-functions}"'},"​")],-1)),i("details",b,[i("summary",null,[s[21]||(s[21]=i("a",{id:"SpeciesDistributionModels.evaluate-Tuple{Any}",href:"#SpeciesDistributionModels.evaluate-Tuple{Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.evaluate")],-1)),s[22]||(s[22]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[23]||(s[23]=e('
julia
evaluate(x; measures, train = true, test = true, [validation])

Evaluate x, which could be a SDMmachine, SDMgroup, or SDMensemble, by applying the measures provided to the data used to built an ensemble, and return an evaluation object.

Keywords

This defaults to using auc, log_loss, and kappa. For threshold-dependent measures, the highest score as well as the threshold at which the highest scores is reached are reported. A list of measurse is available here: https://juliaai.github.io/StatisticalMeasures.jl/dev/auto_generated_list_of_measures/#aliases. However, note that not all measures are useful.

Data should be provided as a Tuple with presences as the first field and absences as the second.

source

',8))]),i("details",m,[i("summary",null,[s[24]||(s[24]=i("a",{id:"SpeciesDistributionModels.machine_evaluations",href:"#SpeciesDistributionModels.machine_evaluations"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.machine_evaluations")],-1)),s[25]||(s[25]=a()),n(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[26]||(s[26]=e('
julia
machine_evaluations(eval)\n\nGet the scores for each machine in an evaluation, which can be either an \n`SDMgroupEvaluation` or an `SDMensembleEvaluation`.\n\nThe return type is a nested structure of `NamedTuple`s. \nThe `NamedTuple` returned has keys corresponding to the evaluation datasets (a subset of :train, :test, and :validation), \nwhich each have keys corresponding to the measures specified in [`evaluate`](@ref).\n\n## Example\n```julia\nevaluation = SDM.evaluate(ensemble; measures = (; accuracy, auc))\nmachine_aucs = SDM.machine_evaluations(evaluation).train.auc\n```

source

',2))])])}const B=l(o,[["render",F]]);export{j as __pageData,B as default}; diff --git a/previews/PR11/assets/api.md.bHTjdokF.lean.js b/previews/PR11/assets/api.md.Cglor7Cb.lean.js similarity index 97% rename from previews/PR11/assets/api.md.bHTjdokF.lean.js rename to previews/PR11/assets/api.md.Cglor7Cb.lean.js index e748dd3..fad807a 100644 --- a/previews/PR11/assets/api.md.bHTjdokF.lean.js +++ b/previews/PR11/assets/api.md.Cglor7Cb.lean.js @@ -1,15 +1,15 @@ -import{_ as l,c as p,a5 as e,j as i,a,G as n,B as h,o as d}from"./chunks/framework.CmcMgnI6.js";const j=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),o={name:"api.md"},r={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""};function F(f,s,C,D,v,M){const t=h("Badge");return d(),p("div",null,[s[27]||(s[27]=e('

Index ​

Reference - Exported functions ​

',3)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"SpeciesDistributionModels.ShapleyValues",href:"#SpeciesDistributionModels.ShapleyValues"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.ShapleyValues")],-1)),s[1]||(s[1]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=e(`
julia
ShapleyValues(algorithm::Shapley.Algorithm)
-ShapleyValues(N::Integer; threaded = true, rng = Random.GLOBAL_RNG)

Use to specify use Shapley values as method in explain. If an integer N, and optionally threaded and rng is supplied, MonteCarlo sampling is used, where N is the number of iterations (samples). More samples will result in more accurate results, but will take more time to compute.

source

`,3))]),i("details",k,[i("summary",null,[s[3]||(s[3]=i("a",{id:"SpeciesDistributionModels.explain-Tuple{SDMensemble}",href:"#SpeciesDistributionModels.explain-Tuple{SDMensemble}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.explain")],-1)),s[4]||(s[4]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[5]||(s[5]=e('
julia
explain(ensemble::SDMensemble; method, [data], [predictors])

Generate response curves for ensemble.

Keywords

source

',5))]),i("details",c,[i("summary",null,[s[6]||(s[6]=i("a",{id:"SpeciesDistributionModels.predict-Tuple{SpeciesDistributionModels.SDMmachine, Any}",href:"#SpeciesDistributionModels.predict-Tuple{SpeciesDistributionModels.SDMmachine, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.predict")],-1)),s[7]||(s[7]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[8]||(s[8]=e('
julia
predict(SDMobject, newdata; clamp = false, threaded = false, [reducer], [by_group])

Use an SDMmachine, SDMgroup, or SDMensemble to predict habitat suitability for some data, optionally summarized for the entire ensemble, or for each SDMgroup.

newdata can be either a RasterStack, or a Tables.jl.compatible object. It must have all predictor variables used to train the models in its columns (or layers in case of a RasterStack).

Keywords

Returns

If newdata is a RasterStack, the predict returns a Raster; otherwise, it returns a NamedTuple of Vectors, with habitat suitability represented by a floating-point number between 0 and 1.

source

',8))]),i("details",u,[i("summary",null,[s[9]||(s[9]=i("a",{id:"SpeciesDistributionModels.remove_collinear-Tuple{Any}",href:"#SpeciesDistributionModels.remove_collinear-Tuple{Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.remove_collinear")],-1)),s[10]||(s[10]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[11]||(s[11]=e(`
julia
remove_collinear(data; method, silent = false)

Removes strongly correlated variables in data, until correlation is below a threshold specified in method.

method can currently be either Gvif, Vif or Pearson, which use GVIF, VIF, or Pearson's r, respectively. GVIF and VIF are similar method, but GVIF includes categorical variables whereas VIF ignores them.

To run without showing information about collinearity scores, set silent = true.

Example

julia
julia> import SpeciesDistributionModels as SDM
+import{_ as l,c as p,a5 as e,j as i,a,G as n,B as h,o as d}from"./chunks/framework.nF61VOH4.js";const j=JSON.parse('{"title":"","description":"","frontmatter":{},"headers":[],"relativePath":"api.md","filePath":"api.md","lastUpdated":null}'),o={name:"api.md"},r={class:"jldocstring custom-block",open:""},k={class:"jldocstring custom-block",open:""},c={class:"jldocstring custom-block",open:""},u={class:"jldocstring custom-block",open:""},g={class:"jldocstring custom-block",open:""},E={class:"jldocstring custom-block",open:""},y={class:"jldocstring custom-block",open:""},b={class:"jldocstring custom-block",open:""},m={class:"jldocstring custom-block",open:""};function F(f,s,C,D,v,M){const t=h("Badge");return d(),p("div",null,[s[27]||(s[27]=e('
Index ​
Reference - Exported functions ​
',3)),i("details",r,[i("summary",null,[s[0]||(s[0]=i("a",{id:"SpeciesDistributionModels.ShapleyValues",href:"#SpeciesDistributionModels.ShapleyValues"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.ShapleyValues")],-1)),s[1]||(s[1]=a()),n(t,{type:"info",class:"jlObjectType jlType",text:"Type"})]),s[2]||(s[2]=e(`
julia
ShapleyValues(algorithm::Shapley.Algorithm)
+ShapleyValues(N::Integer; threaded = true, rng = Random.GLOBAL_RNG)
Use to specify use Shapley values as method in explain. If an integer N, and optionally threaded and rng is supplied, MonteCarlo sampling is used, where N is the number of iterations (samples). More samples will result in more accurate results, but will take more time to compute.
source
`,3))]),i("details",k,[i("summary",null,[s[3]||(s[3]=i("a",{id:"SpeciesDistributionModels.explain-Tuple{SDMensemble}",href:"#SpeciesDistributionModels.explain-Tuple{SDMensemble}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.explain")],-1)),s[4]||(s[4]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[5]||(s[5]=e('
julia
explain(ensemble::SDMensemble; method, [data], [predictors])
Generate response curves for ensemble.
Keywords
  • method is the algorithm to use. See ShapleyValues
  • data is the data to use to generate response curves, and defaults to the data used to train the ensemble
  • predictors: which predictors to generate response curves for. Defaults to all variables in data.
source
',5))]),i("details",c,[i("summary",null,[s[6]||(s[6]=i("a",{id:"SpeciesDistributionModels.predict-Tuple{SpeciesDistributionModels.SDMmachine, Any}",href:"#SpeciesDistributionModels.predict-Tuple{SpeciesDistributionModels.SDMmachine, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.predict")],-1)),s[7]||(s[7]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[8]||(s[8]=e('
julia
predict(SDMobject, newdata; clamp = false, threaded = false, [reducer], [by_group])
Use an SDMmachine, SDMgroup, or SDMensemble to predict habitat suitability for some data, optionally summarized for the entire ensemble, or for each SDMgroup.
newdata can be either a RasterStack, or a Tables.jl.compatible object. It must have all predictor variables used to train the models in its columns (or layers in case of a RasterStack).
Keywords
  • clamp: if true, the predictions are clamped to the interval seen during training of SDMobject. Defaults to false
  • threaded: if true, run multithreaded. Defaults to true.
  • reducer: Optionally provide a Function to summarize the output. The function should take an vector of values and return a single value. Typical examples are Statistics.mean or Statistics.median.
  • by_group is set to true, the data is reduced for each SDMgroup, if it is set to false (the default), it reduced across the entire ensemble.
Returns
If newdata is a RasterStack, the predict returns a Raster; otherwise, it returns a NamedTuple of Vectors, with habitat suitability represented by a floating-point number between 0 and 1.
source
',8))]),i("details",u,[i("summary",null,[s[9]||(s[9]=i("a",{id:"SpeciesDistributionModels.remove_collinear-Tuple{Any}",href:"#SpeciesDistributionModels.remove_collinear-Tuple{Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.remove_collinear")],-1)),s[10]||(s[10]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[11]||(s[11]=e(`
julia
remove_collinear(data; method, silent = false)
Removes strongly correlated variables in data, until correlation is below a threshold specified in method.
method can currently be either Gvif, Vif or Pearson, which use GVIF, VIF, or Pearson's r, respectively. GVIF and VIF are similar method, but GVIF includes categorical variables whereas VIF ignores them.
To run without showing information about collinearity scores, set silent = true.
Example
julia
julia> import SpeciesDistributionModels as SDM
 julia> mydata = (a = 1:100, b = sqrt.(1:100), c = rand(100))
 julia> SDM.remove_collinear(mydata; method = SDM.Vif(10))
 [ Info: a has highest GVIF of 28.367942095054225
 [ Info: Removing a, 2 variables remaining
 [ Info: b has highest GVIF of 1.0077618445543057
 [ Info: All variables are below threshold, returning remaining variables
-(:b, :c)
source
`,7))]),i("details",g,[i("summary",null,[s[12]||(s[12]=i("a",{id:"SpeciesDistributionModels.sdm-Tuple{Any, Any}",href:"#SpeciesDistributionModels.sdm-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.sdm")],-1)),s[13]||(s[13]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[14]||(s[14]=e(`
julia
sdm(data, models; [resampler], [predictors], [verbosity])
Construct an ensemble.
Arguments
data: an SDMdata object models: a NamedTuple with the models to be used in the ensemble.
Keywords
  • models: a Vector of the models to be used in the ensemble. All models must be MLJ-supported Classifiers.
  • For a full list of supported models, see https://alan-turing-institute.github.io/MLJ.jl/stable/model_browser/#Classification
  • predictors: a Vector of Symbols with the names of the predictor values to be used. By default, all pdf
  • verbosity: an Int value that regulates how much information is printed.
  • cache: is passed to MLJBase.machine. Specify cache=false to prioritize memory management over speed.
  • scitype_check_level: is passed to MLJBase.machine. Specify scitype_check_level=0 to disable scitype checking.
Example
julia
using SpeciesDistributionModels, Maxnet, MLJGLMInterface
+(:b, :c)
source
`,7))]),i("details",g,[i("summary",null,[s[12]||(s[12]=i("a",{id:"SpeciesDistributionModels.sdm-Tuple{Any, Any}",href:"#SpeciesDistributionModels.sdm-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.sdm")],-1)),s[13]||(s[13]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[14]||(s[14]=e(`
julia
sdm(data, models; [resampler], [predictors], [verbosity])
Construct an ensemble.
Arguments
data: an SDMdata object models: a NamedTuple with the models to be used in the ensemble.
Keywords
  • models: a Vector of the models to be used in the ensemble. All models must be MLJ-supported Classifiers.
  • For a full list of supported models, see https://alan-turing-institute.github.io/MLJ.jl/stable/model_browser/#Classification
  • predictors: a Vector of Symbols with the names of the predictor values to be used. By default, all pdf
  • verbosity: an Int value that regulates how much information is printed.
  • cache: is passed to MLJBase.machine. Specify cache=false to prioritize memory management over speed.
  • scitype_check_level: is passed to MLJBase.machine. Specify scitype_check_level=0 to disable scitype checking.
Example
julia
using SpeciesDistributionModels, Maxnet, MLJGLMInterface
 mydata = sdmdata(presences, absences; resampler = CV(nfolds = 5))
 models = (maxnet = MaxnetBinaryClassifier(), glm = LinearBinaryClassifier())
-ensemble = sdm(mydata, models)
source
`,9))]),i("details",E,[i("summary",null,[s[15]||(s[15]=i("a",{id:"SpeciesDistributionModels.sdmdata-Tuple{Any, Any}",href:"#SpeciesDistributionModels.sdmdata-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.sdmdata")],-1)),s[16]||(s[16]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[17]||(s[17]=e(`
julia
sdmdata(presences, absences; resampler, predictors)
+ensemble = sdm(mydata, models)
source
`,9))]),i("details",E,[i("summary",null,[s[15]||(s[15]=i("a",{id:"SpeciesDistributionModels.sdmdata-Tuple{Any, Any}",href:"#SpeciesDistributionModels.sdmdata-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.sdmdata")],-1)),s[16]||(s[16]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[17]||(s[17]=e(`
julia
sdmdata(presences, absences; resampler, predictors)
 sdmdata(X, y::BitVector; resampler, predictors)
Construct an SDMdata object from species presences and absences. Alternatively, from a table with predictor variables X and a BitVector y, where false represents absence and true represents presence.
Keywords
  • resampler: The resampling strategy to be used. Should be a MLJBase.ResamplingStrategy, or a Vector of Tuples with the form (train, test). Defaults to NoResampling(). If resampler is a CV, shuffle is internally set to true.
  • predictors: a Tuple of Symbols with the names of the predictor values to be used. By default, all predictor variables in X, or all predictor variables in both presences and absences are used..
Returns
An SDMdata object containing the data provided. This object can be used to construct an SDMensemble.
Example
using Rasters, SpeciesDistributionModels
 A = rand(10,10)
 B = rand(10,10)
@@ -22,4 +22,4 @@ import{_ as l,c as p,a5 as e,j as i,a,G as n,B as h,o as d}from"./chunks/framewo
 a = extract(st, absence_points)
 
 mydata = sdmdata(p, a; resampler = CV(nfolds = 2)) # 2-fold cross validation
-mydata2 = sdmdata([p; a], [trues(4); falses(4)]; resampler = [([1,2],[5,6]), ([3,4], [7,8])]) # provide resampling rows
source
`,9))]),i("details",y,[i("summary",null,[s[18]||(s[18]=i("a",{id:"SpeciesDistributionModels.thin-Tuple{Any, Any}",href:"#SpeciesDistributionModels.thin-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.thin")],-1)),s[19]||(s[19]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[20]||(s[20]=e('
julia
thin([rng], x, cutoff; distance = Haversine(), [geometrycolumn])\n\nThin spatial data by removing points that are closer than `cutoff` distance\nto the nearest other point in the dataset.\n\n## Arguments\n- `rng`: a random number generator. The default is `Random.GLOBAL_RNG()`.\n- `x`: an `AbstractVector` that iterates points, or a table with a `:geometry` column.\n- `cutoff`: the distance threshold in units of `distance`.\n## Keywords\n- `distance`: the distance metric used to calculate distances between points. The default\nis `Haversine()`, which uses the Haversine formula to calculate the distance between coordinates in meter units.\n- `geometrycolumn`: the name of the column in the table that contains the points, if `x` is a table. Usually defaults to `:geometry`.\n\n## Example\n```jldoctest\nusing SpeciesDistributionModels, Distances\n# a vector that iteratores points\ngeometries = [(0,0), (1,0), (0,0.000001)]\n# thin to 1000 meters\nthin(geometries, 1000)\n# thin to 1 degree\nthin(Xoshiro(123), geometries, 1; distance = Euclidean())\n\n# output\n2-element Vector{Tuple{Int64, Real}}:\n(0, 0)\n(1, 0)\n```
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julia
evaluate(x; measures, train = true, test = true, [validation])
Evaluate x, which could be a SDMmachine, SDMgroup, or SDMensemble, by applying the measures provided to the data used to built an ensemble, and return an evaluation object.
Keywords
  • measures is a NamedTuple of measures. The keys are used to identify the measures.
This defaults to using auc, log_loss, and kappa. For threshold-dependent measures, the highest score as well as the threshold at which the highest scores is reached are reported. A list of measurse is available here: https://juliaai.github.io/StatisticalMeasures.jl/dev/auto_generated_list_of_measures/#aliases. However, note that not all measures are useful.
  • train: a Bool that indicates whether to evaluate on the training data. Defaults to true
  • test: a Bool that indicates whether to evaluate on the test data.Defaults to true
  • validation: optionally, provide a separate validation dataset.
Data should be provided as a Tuple with presences as the first field and absences as the second.
source
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julia
machine_evaluations(eval)\n\nGet the scores for each machine in an evaluation, which can be either an \n`SDMgroupEvaluation` or an `SDMensembleEvaluation`.\n\nThe return type is a nested structure of `NamedTuple`s. \nThe `NamedTuple` returned has keys corresponding to the evaluation datasets (a subset of :train, :test, and :validation), \nwhich each have keys corresponding to the measures specified in [`evaluate`](@ref).\n\n## Example\n```julia\nevaluation = SDM.evaluate(ensemble; measures = (; accuracy, auc))\nmachine_aucs = SDM.machine_evaluations(evaluation).train.auc\n```
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',2))])])}const B=l(o,[["render",F]]);export{j as __pageData,B as default};
+mydata2 = sdmdata([p; a], [trues(4); falses(4)]; resampler = [([1,2],[5,6]), ([3,4], [7,8])]) # provide resampling rows

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`,9))]),i("details",y,[i("summary",null,[s[18]||(s[18]=i("a",{id:"SpeciesDistributionModels.thin-Tuple{Any, Any}",href:"#SpeciesDistributionModels.thin-Tuple{Any, Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.thin")],-1)),s[19]||(s[19]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[20]||(s[20]=e('
julia
thin([rng], x, cutoff; distance = Haversine(), [geometrycolumn])\n\nThin spatial data by removing points that are closer than `cutoff` distance\nto the nearest other point in the dataset.\n\n## Arguments\n- `rng`: a random number generator. The default is `Random.GLOBAL_RNG()`.\n- `x`: an `AbstractVector` that iterates points, or a table with a `:geometry` column.\n- `cutoff`: the distance threshold in units of `distance`.\n## Keywords\n- `distance`: the distance metric used to calculate distances between points. The default\nis `Haversine()`, which uses the Haversine formula to calculate the distance between coordinates in meter units.\n- `geometrycolumn`: the name of the column in the table that contains the points, if `x` is a table. Usually defaults to `:geometry`.\n\n## Example\n```jldoctest\nusing SpeciesDistributionModels, Distances\n# a vector that iteratores points\ngeometries = [(0,0), (1,0), (0,0.000001)]\n# thin to 1000 meters\nthin(geometries, 1000)\n# thin to 1 degree\nthin(Xoshiro(123), geometries, 1; distance = Euclidean())\n\n# output\n2-element Vector{Tuple{Int64, Real}}:\n(0, 0)\n(1, 0)\n```

source

',2))]),s[28]||(s[28]=i("h2",{id:"Reference-Internal-functions",tabindex:"-1"},[a("Reference - Internal functions "),i("a",{class:"header-anchor",href:"#Reference-Internal-functions","aria-label":'Permalink to "Reference - Internal functions {#Reference-Internal-functions}"'},"​")],-1)),i("details",b,[i("summary",null,[s[21]||(s[21]=i("a",{id:"SpeciesDistributionModels.evaluate-Tuple{Any}",href:"#SpeciesDistributionModels.evaluate-Tuple{Any}"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.evaluate")],-1)),s[22]||(s[22]=a()),n(t,{type:"info",class:"jlObjectType jlMethod",text:"Method"})]),s[23]||(s[23]=e('
julia
evaluate(x; measures, train = true, test = true, [validation])

Evaluate x, which could be a SDMmachine, SDMgroup, or SDMensemble, by applying the measures provided to the data used to built an ensemble, and return an evaluation object.

Keywords

This defaults to using auc, log_loss, and kappa. For threshold-dependent measures, the highest score as well as the threshold at which the highest scores is reached are reported. A list of measurse is available here: https://juliaai.github.io/StatisticalMeasures.jl/dev/auto_generated_list_of_measures/#aliases. However, note that not all measures are useful.

Data should be provided as a Tuple with presences as the first field and absences as the second.

source

',8))]),i("details",m,[i("summary",null,[s[24]||(s[24]=i("a",{id:"SpeciesDistributionModels.machine_evaluations",href:"#SpeciesDistributionModels.machine_evaluations"},[i("span",{class:"jlbinding"},"SpeciesDistributionModels.machine_evaluations")],-1)),s[25]||(s[25]=a()),n(t,{type:"info",class:"jlObjectType jlFunction",text:"Function"})]),s[26]||(s[26]=e('
julia
machine_evaluations(eval)\n\nGet the scores for each machine in an evaluation, which can be either an \n`SDMgroupEvaluation` or an `SDMensembleEvaluation`.\n\nThe return type is a nested structure of `NamedTuple`s. \nThe `NamedTuple` returned has keys corresponding to the evaluation datasets (a subset of :train, :test, and :validation), \nwhich each have keys corresponding to the measures specified in [`evaluate`](@ref).\n\n## Example\n```julia\nevaluation = SDM.evaluate(ensemble; measures = (; accuracy, auc))\nmachine_aucs = SDM.machine_evaluations(evaluation).train.auc\n```

source

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Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=We,this.addFields(this._options.fields)}add(e){const{extractField:t,tokenize:s,processTerm:n,fields:r,idField:i}=this._options,o=t(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);if(this._idToShortId.has(o))throw new Error(`MiniSearch: duplicate ID ${o}`);const l=this.addDocumentId(o);this.saveStoredFields(l,e);for(const c of r){const h=t(e,c);if(h==null)continue;const v=s(h.toString(),c),p=this._fieldIds[c],b=new Set(v).size;this.addFieldLength(l,p,this._documentCount-1,b);for(const y of v){const x=n(y,c);if(Array.isArray(x))for(const w of x)this.addTerm(p,l,w);else x&&this.addTerm(p,l,x)}}}addAll(e){for(const t of e)this.add(t)}addAllAsync(e,t={}){const{chunkSize:s=10}=t,n={chunk:[],promise:Promise.resolve()},{chunk:r,promise:i}=e.reduce(({chunk:o,promise:l},c,h)=>(o.push(c),(h+1)%s===0?{chunk:[],promise:l.then(()=>new 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e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=We,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return Te(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||Be.batchSize,r=e.batchWait||Be.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[v]of h)this._documentIds.has(v)||(h.size<=1?l.delete(c):h.delete(v));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||Pe.minDirtCount,s=s||Pe.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const s=this.executeQuery(e,t),n=[];for(const[r,{score:i,terms:o,match:l}]of s){const c=o.length||1,h={id:this._documentIds.get(r),score:i*c,terms:Object.keys(l),queryTerms:o,match:l};Object.assign(h,this._storedFields.get(r)),(t.filter==null||t.filter(h))&&n.push(h)}return e===le.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||n.sort(ft),n}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(ft),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return Te(this,void 0,void 0,function*(){if(t==null)throw 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c=0;for(const[h,v]of s){const p=new Map;for(const b of Object.keys(v)){let y=v[b];o===1&&(y=y.ds),p.set(parseInt(b,10),yield Ee(y))}++c%1e3===0&&(yield kt(0)),l._index.set(h,p)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new le(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new Z,c}executeQuery(e,t={}){if(e===le.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const p=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,p));return this.combineResults(b,p.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,v=o(e).flatMap(p=>l(p)).filter(p=>!!p).map(Vs(i)).map(p=>this.executeQuerySpec(p,i));return this.combineResults(v,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:De(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},dt.weights),i),v=this._index.get(e.term),p=this.termResults(e.term,e.term,1,e.termBoost,v,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const O=x.length-e.term.length;if(!O)continue;y==null||y.delete(x);const R=h*x.length/(x.length+.3*O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}if(y)for(const x of y.keys()){const[w,O]=y.get(x);if(!O)continue;const R=c*x.length/(x.length+O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}return p}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=Je){if(e.length===0)return new Map;const s=t.toLowerCase(),n=zs[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const v=i[h],p=this._fieldIds[h],b=r.get(p);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[p];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(p,w,t),y-=1;continue}const O=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!O)continue;const R=b.get(w),K=this._fieldLength.get(w)[p],G=js(R,y,this._documentCount,K,x,l),W=s*n*v*O*G,V=c.get(w);if(V){V.score+=W,Bs(V.terms,e);const $=De(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return c}addTerm(e,t,s){const n=this._index.fetch(s,pt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,pt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". 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n===void 0&&s.set(z,n=t()),n}values(){return new Le(this,St)}[Symbol.iterator](){return this.entries()}static from(e){const t=new Z;for(const[s,n]of e)t.set(s,n);return t}static fromObject(e){return Z.from(Object.entries(e))}}const Re=(a,e,t=[])=>{if(e.length===0||a==null)return[a,t];for(const s of a.keys())if(s!==z&&e.startsWith(s))return t.push([a,s]),Re(a.get(s),e.slice(s.length),t);return t.push([a,e]),Re(void 0,"",t)},Be=(a,e)=>{if(e.length===0||a==null)return a;for(const t of a.keys())if(t!==z&&e.startsWith(t))return Be(a.get(t),e.slice(t.length))},De=(a,e)=>{const t=e.length;e:for(let s=0;a&&s{const[t,s]=Re(a,e);if(t!==void 0){if(t.delete(z),t.size===0)Et(s);else if(t.size===1){const[n,r]=t.entries().next().value;Tt(s,n,r)}}},Et=a=>{if(a.length===0)return;const[e,t]=Je(a);if(e.delete(t),e.size===0)Et(a.slice(0,-1));else if(e.size===1){const[s,n]=e.entries().next().value;s!==z&&Tt(a.slice(0,-1),s,n)}},Tt=(a,e,t)=>{if(a.length===0)return;const[s,n]=Je(a);s.set(n+e,t),s.delete(n)},Je=a=>a[a.length-1],Ue="or",It="and",Ds="and_not";class ce{constructor(e){if((e==null?void 0:e.fields)==null)throw new Error('MiniSearch: option "fields" must be provided');const t=e.autoVacuum==null||e.autoVacuum===!0?je:e.autoVacuum;this._options=Object.assign(Object.assign(Object.assign({},Pe),e),{autoVacuum:t,searchOptions:Object.assign(Object.assign({},dt),e.searchOptions||{}),autoSuggestOptions:Object.assign(Object.assign({},$s),e.autoSuggestOptions||{})}),this._index=new Z,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldIds={},this._fieldLength=new Map,this._avgFieldLength=[],this._nextId=0,this._storedFields=new Map,this._dirtCount=0,this._currentVacuum=null,this._enqueuedVacuum=null,this._enqueuedVacuumConditions=Ke,this.addFields(this._options.fields)}add(e){const{extractField:t,tokenize:s,processTerm:n,fields:r,idField:i}=this._options,o=t(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);if(this._idToShortId.has(o))throw new Error(`MiniSearch: duplicate ID ${o}`);const l=this.addDocumentId(o);this.saveStoredFields(l,e);for(const c of r){const h=t(e,c);if(h==null)continue;const v=s(h.toString(),c),p=this._fieldIds[c],b=new Set(v).size;this.addFieldLength(l,p,this._documentCount-1,b);for(const y of v){const x=n(y,c);if(Array.isArray(x))for(const w of x)this.addTerm(p,l,w);else x&&this.addTerm(p,l,x)}}}addAll(e){for(const t of e)this.add(t)}addAllAsync(e,t={}){const{chunkSize:s=10}=t,n={chunk:[],promise:Promise.resolve()},{chunk:r,promise:i}=e.reduce(({chunk:o,promise:l},c,h)=>(o.push(c),(h+1)%s===0?{chunk:[],promise:l.then(()=>new Promise(v=>setTimeout(v,0))).then(()=>this.addAll(o))}:{chunk:o,promise:l}),n);return i.then(()=>this.addAll(r))}remove(e){const{tokenize:t,processTerm:s,extractField:n,fields:r,idField:i}=this._options,o=n(e,i);if(o==null)throw new Error(`MiniSearch: document does not have ID field "${i}"`);const l=this._idToShortId.get(o);if(l==null)throw new Error(`MiniSearch: cannot remove document with ID ${o}: it is not in the index`);for(const c of r){const h=n(e,c);if(h==null)continue;const v=t(h.toString(),c),p=this._fieldIds[c],b=new Set(v).size;this.removeFieldLength(l,p,this._documentCount,b);for(const y of v){const x=s(y,c);if(Array.isArray(x))for(const w of x)this.removeTerm(p,l,w);else x&&this.removeTerm(p,l,x)}}this._storedFields.delete(l),this._documentIds.delete(l),this._idToShortId.delete(o),this._fieldLength.delete(l),this._documentCount-=1}removeAll(e){if(e)for(const t of e)this.remove(t);else{if(arguments.length>0)throw new Error("Expected documents to be present. Omit the argument to remove all documents.");this._index=new Z,this._documentCount=0,this._documentIds=new Map,this._idToShortId=new Map,this._fieldLength=new Map,this._avgFieldLength=[],this._storedFields=new Map,this._nextId=0}}discard(e){const t=this._idToShortId.get(e);if(t==null)throw new Error(`MiniSearch: cannot discard document with ID ${e}: it is not in the index`);this._idToShortId.delete(e),this._documentIds.delete(t),this._storedFields.delete(t),(this._fieldLength.get(t)||[]).forEach((s,n)=>{this.removeFieldLength(t,n,this._documentCount,s)}),this._fieldLength.delete(t),this._documentCount-=1,this._dirtCount+=1,this.maybeAutoVacuum()}maybeAutoVacuum(){if(this._options.autoVacuum===!1)return;const{minDirtFactor:e,minDirtCount:t,batchSize:s,batchWait:n}=this._options.autoVacuum;this.conditionalVacuum({batchSize:s,batchWait:n},{minDirtCount:t,minDirtFactor:e})}discardAll(e){const t=this._options.autoVacuum;try{this._options.autoVacuum=!1;for(const s of e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=Ke,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return Ie(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||We.batchSize,r=e.batchWait||We.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[v]of h)this._documentIds.has(v)||(h.size<=1?l.delete(c):h.delete(v));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||je.minDirtCount,s=s||je.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const s=this.executeQuery(e,t),n=[];for(const[r,{score:i,terms:o,match:l}]of s){const c=o.length||1,h={id:this._documentIds.get(r),score:i*c,terms:Object.keys(l),queryTerms:o,match:l};Object.assign(h,this._storedFields.get(r)),(t.filter==null||t.filter(h))&&n.push(h)}return e===ce.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||n.sort(ft),n}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(ft),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return Ie(this,void 0,void 0,function*(){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJSAsync(JSON.parse(e),t)})}static getDefault(e){if(Pe.hasOwnProperty(e))return ze(Pe,e);throw new Error(`MiniSearch: unknown option "${e}"`)}static loadJS(e,t){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=Ee(n),l._fieldLength=Ee(r),l._storedFields=Ee(i);for(const[c,h]of l._documentIds)l._idToShortId.set(h,c);for(const[c,h]of s){const v=new Map;for(const p of Object.keys(h)){let b=h[p];o===1&&(b=b.ds),v.set(parseInt(p,10),Ee(b))}l._index.set(c,v)}return l}static loadJSAsync(e,t){return Ie(this,void 0,void 0,function*(){const{index:s,documentIds:n,fieldLength:r,storedFields:i,serializationVersion:o}=e,l=this.instantiateMiniSearch(e,t);l._documentIds=yield Te(n),l._fieldLength=yield Te(r),l._storedFields=yield Te(i);for(const[h,v]of l._documentIds)l._idToShortId.set(v,h);let c=0;for(const[h,v]of s){const p=new Map;for(const b of Object.keys(v)){let y=v[b];o===1&&(y=y.ds),p.set(parseInt(b,10),yield Te(y))}++c%1e3===0&&(yield kt(0)),l._index.set(h,p)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new ce(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new Z,c}executeQuery(e,t={}){if(e===ce.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const p=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,p));return this.combineResults(b,p.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,v=o(e).flatMap(p=>l(p)).filter(p=>!!p).map(Vs(i)).map(p=>this.executeQuerySpec(p,i));return this.combineResults(v,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:ze(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},dt.weights),i),v=this._index.get(e.term),p=this.termResults(e.term,e.term,1,e.termBoost,v,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const O=x.length-e.term.length;if(!O)continue;y==null||y.delete(x);const R=h*x.length/(x.length+.3*O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}if(y)for(const x of y.keys()){const[w,O]=y.get(x);if(!O)continue;const R=c*x.length/(x.length+O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}return p}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=Ue){if(e.length===0)return new Map;const s=t.toLowerCase(),n=zs[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const v=i[h],p=this._fieldIds[h],b=r.get(p);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[p];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(p,w,t),y-=1;continue}const O=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!O)continue;const R=b.get(w),K=this._fieldLength.get(w)[p],G=js(R,y,this._documentCount,K,x,l),W=s*n*v*O*G,V=c.get(w);if(V){V.score+=W,Bs(V.terms,e);const $=ze(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return c}addTerm(e,t,s){const n=this._index.fetch(s,pt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,pt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". Removing a document after it has changed can corrupt the index!`,"version_conflict");return}}addDocumentId(e){const t=this._nextId;return this._idToShortId.set(e,t),this._documentIds.set(t,e),this._documentCount+=1,this._nextId+=1,t}addFields(e){for(let t=0;tObject.prototype.hasOwnProperty.call(a,e)?a[e]:void 0,zs={[Ue]:(a,e)=>{for(const t of e.keys()){const s=a.get(t);if(s==null)a.set(t,e.get(t));else{const{score:n,terms:r,match:i}=e.get(t);s.score=s.score+n,s.match=Object.assign(s.match,i),ht(s.terms,r)}}return a},[It]:(a,e)=>{const t=new Map;for(const s of e.keys()){const n=a.get(s);if(n==null)continue;const{score:r,terms:i,match:o}=e.get(s);ht(n.terms,i),t.set(s,{score:n.score+r,terms:n.terms,match:Object.assign(n.match,o)})}return t},[Ds]:(a,e)=>{for(const t of e.keys())a.delete(t);return a}},Ps={k:1.2,b:.7,d:.5},js=(a,e,t,s,n,r)=>{const{k:i,b:o,d:l}=r;return Math.log(1+(t-e+.5)/(e+.5))*(l+a*(i+1)/(a+i*(1-o+o*s/n)))},Vs=a=>(e,t,s)=>{const n=typeof a.fuzzy=="function"?a.fuzzy(e,t,s):a.fuzzy||!1,r=typeof a.prefix=="function"?a.prefix(e,t,s):a.prefix===!0,i=typeof a.boostTerm=="function"?a.boostTerm(e,t,s):1;return{term:e,fuzzy:n,prefix:r,termBoost:i}},Pe={idField:"id",extractField:(a,e)=>a[e],tokenize:a=>a.split(Ws),processTerm:a=>a.toLowerCase(),fields:void 0,searchOptions:void 0,storeFields:[],logger:(a,e)=>{typeof(console==null?void 0:console[a])=="function"&&console[a](e)},autoVacuum:!0},dt={combineWith:Ue,prefix:!1,fuzzy:!1,maxFuzzy:6,boost:{},weights:{fuzzy:.45,prefix:.375},bm25:Ps},$s={combineWith:It,prefix:(a,e,t)=>e===t.length-1},We={batchSize:1e3,batchWait:10},Ke={minDirtFactor:.1,minDirtCount:20},je=Object.assign(Object.assign({},We),Ke),Bs=(a,e)=>{a.includes(e)||a.push(e)},ht=(a,e)=>{for(const t of e)a.includes(t)||a.push(t)},ft=({score:a},{score:e})=>e-a,pt=()=>new Map,Ee=a=>{const e=new Map;for(const t of Object.keys(a))e.set(parseInt(t,10),a[t]);return e},Te=a=>Ie(void 0,void 0,void 0,function*(){const e=new Map;let t=0;for(const s of Object.keys(a))e.set(parseInt(s,10),a[s]),++t%1e3===0&&(yield kt(0));return e}),kt=a=>new Promise(e=>setTimeout(e,a)),Ws=/[\n\r\p{Z}\p{P}]+/u;class Ks{constructor(e=10){Ce(this,"max");Ce(this,"cache");this.max=e,this.cache=new Map}get(e){let t=this.cache.get(e);return t!==void 0&&(this.cache.delete(e),this.cache.set(e,t)),t}set(e,t){this.cache.has(e)?this.cache.delete(e):this.cache.size===this.max&&this.cache.delete(this.first()),this.cache.set(e,t)}first(){return this.cache.keys().next().value}clear(){this.cache.clear()}}const Js=["aria-owns"],Us={class:"shell"},qs=["title"],Gs={class:"search-actions before"},Hs=["title"],Qs=["aria-activedescendant","aria-controls","placeholder"],Ys={class:"search-actions"},Zs=["title"],Xs=["disabled","title"],en=["id","role","aria-labelledby"],tn=["id","aria-selected"],sn=["href","aria-label","onMouseenter","onFocusin"],nn={class:"titles"},rn=["innerHTML"],an={class:"title 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nt(Pe.Layout,null,{})},enhanceApp({app:o,router:e,siteData:t}){xa(o)}};export{tr as R,go as c,V as u}; diff --git a/previews/PR11/assets/example.md.DCLnV71q.js b/previews/PR11/assets/example.md.CIvWr4xa.js similarity index 69% rename from previews/PR11/assets/example.md.DCLnV71q.js rename to previews/PR11/assets/example.md.CIvWr4xa.js index 2d69978..922c384 100644 --- a/previews/PR11/assets/example.md.DCLnV71q.js +++ b/previews/PR11/assets/example.md.CIvWr4xa.js @@ -1,10 +1,9 @@ -import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.CmcMgnI6.js";const l="/SpeciesDistributionModels.jl/previews/PR11/assets/qleakmw.JSwKyy9_.png",t="/SpeciesDistributionModels.jl/previews/PR11/assets/utujxvx.6Tz0WNcD.png",e="/SpeciesDistributionModels.jl/previews/PR11/assets/tfiawof.BuyrCnxU.png",h="/SpeciesDistributionModels.jl/previews/PR11/assets/zdsuhls.DoIEq0TV.png",u=JSON.parse('{"title":"Example of a full species distribution modelling workflow","description":"","frontmatter":{},"headers":[],"relativePath":"example.md","filePath":"example.md","lastUpdated":null}'),k={name:"example.md"};function r(d,s,E,o,g,c){return p(),i("div",null,s[0]||(s[0]=[n(`

Example of a full species distribution modelling workflow ​

Environmental data ​

julia
#| echo: false
+import{_ as a,c as i,a5 as n,o as e}from"./chunks/framework.nF61VOH4.js";const p="/SpeciesDistributionModels.jl/previews/PR11/assets/gczabtv.dyfLf57w.png",t="/SpeciesDistributionModels.jl/previews/PR11/assets/rqdlwql.Dc8_DCkK.png",l="/SpeciesDistributionModels.jl/previews/PR11/assets/jiafykx.BLVveajq.png",h="/SpeciesDistributionModels.jl/previews/PR11/assets/kpgklne.H3AyTYPW.png",y=JSON.parse('{"title":"Example of a full species distribution modelling workflow","description":"","frontmatter":{},"headers":[],"relativePath":"example.md","filePath":"example.md","lastUpdated":null}'),k={name:"example.md"};function r(d,s,o,E,c,g){return e(),i("div",null,s[0]||(s[0]=[n(`
Example of a full species distribution modelling workflow ​
This example fits and projects species distribution models for the tree species Eucaplytus regnans from scratch.
julia
#| echo: false
 if !haskey(ENV, "RASTERDATASOURCES_PATH")
     ENV["RASTERDATASOURCES_PATH"] = ".";
 end
 using CairoMakie
-CairoMakie.activate!(type = "png")
WARNING: Method definition convert_arguments(MakieCore.CellGrid, Rasters.AbstractRaster{var"#s115", 2, D, A} where A where D where var"#s115") in module RastersMakieExt at /home/runner/.julia/packages/Rasters/0kAkQ/ext/RastersMakieExt/plotrecipes.jl:304 overwritten at /home/runner/.julia/packages/Rasters/0kAkQ/ext/RastersMakieExt/plotrecipes.jl:308.
-ERROR: Method overwriting is not permitted during Module precompilation. Use \`__precompile__(false)\` to opt-out of precompilation.
julia
using Rasters, RasterDataSources, ArchGDAL, NaturalEarth, DataFrames
+CairoMakie.activate!(type = "png")
Environmental data ​
We start by downloading environmental data. We use the Rasters package to handle raster data, and RasterDataSources.jl to automatically download Worldclim data.
In this example, we just download bioclimatic variables 1 and 12 (annual mean temperature and annual mean precipitation). We then mask to the country borders of Australia.
julia
using Rasters, RasterDataSources, ArchGDAL, NaturalEarth, DataFrames
 bio = RasterStack(WorldClim{BioClim}, (1,12))
 countries = naturalearth("ne_10m_admin_0_countries") |> DataFrame
 australia = subset(countries, :NAME => ByRow(==("Australia"))).geometry
@@ -20,49 +19,49 @@ import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.CmcMgnI6.js";const l
   extent: Extent(X = (113.00000000000001, 153.66666666666663), Y = (-43.666666666666664, -10.666666666666671))
   missingval: -3.4f38
   crs: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
-└──────────────────────────────────────────────────────────────────────────────┘
Environmental data ​
julia
using CairoMakie
-Rasters.rplot(bio_aus)
Occurrence data ​
julia
using GBIF2, SpeciesDistributionModels
+└──────────────────────────────────────────────────────────────────────────────┘
Let's plot this data to see what it looks like.
julia
using CairoMakie
+Rasters.rplot(bio_aus)
Occurrence data ​
Next, we use GBIF2.jl to download occurrence records for this species. We use the thin function in this package to weed out occurrences that are very close to each other, using a cut-off of 5km.
julia
using GBIF2, SpeciesDistributionModels
 sp = species_match("Eucalyptus regnans")
 occurrences_raw = occurrence_search(sp; year = (1970,2000), country = "AU", hasCoordinate = true, limit = 2000)
-occurrences = thin(occurrences_raw.geometry, 5000)
217-element Vector{Tuple{Real, Float64}}:
+occurrences = thin(occurrences_raw.geometry, 5000)
932-element Vector{Tuple{Real, Float64}}:
+ (145.98298, -38.33349)
  (146.35, -37.36667)
+ (145.19167, -37.41639)
+ (146.3125, -38.56111)
  (146.24194, -38.52083)
- (146.3225, -38.48833)
+ (146.19722, -38.56889)
+ (146.32167, -38.50667)
+ (146.30333, -38.57611)
  (146.45583, -38.46889)
- (146.24056, -38.57778)
- (146.37778, -38.52333)
- (146.75, -42.88)
- (145.61944, -37.71861)
- (146.502222, -42.360093)
- (143.9438, -38.55351)
+ (146.30667, -38.56472)
  ⋮
- (145.96639, -37.43278)
- (147.983333, -41.433333)
- (147.9346, -37.48179)
- (148.36806, -37.38167)
+ (147.50127, -37.39846)
  (146.483333, -42.55)
  (146.95, -43.283333)
  (147.966667, -42.45)
  (147.5, -37.4)
- (147.15, -42.983333)
Background points ​
julia
using StatsBase
+ (147.15, -42.983333)
+ (145.68464, -37.88182)
+ (145.7, -37.883333)
+ (145.6833, -37.8833)
Background points ​
Next, we sample random points to use as background points.
From Rasters 0.12.1 on, this will be easier with Rasters.sample!
Let's plot both the occurrence and background points to see where Eucalyptus regnans is found.
julia
using StatsBase
 bg_indices = sample(findall(boolmask(bio_aus)), 500)
 bg_points = DimPoints(bio_aus)[bg_indices]
 fig, ax, pl = plot(bio_aus.bio1)
 scatter!(ax, occurrences; color = :red)
 scatter!(ax, bg_points; color = :grey)
-fig
Handling data ​
julia
using SpeciesDistributionModels
+fig
Handling data ​
SpeciesDistributionModels.jl has a sdmdata function to handle input data. It takes tabular presence and background data as inputs, such as what is returned by Rasters.extract and Rasters.sample.
julia
using SpeciesDistributionModels
 p_data = extract(bio_aus, occurrences; skipmissing = true)
 bg_data = bio_aus[bg_indices]
-data = sdmdata(p_data, bg_data; resampler = CV(nfolds = 3))
SDMdata object with 216 presence points and 500 absence points. 
+data = sdmdata(p_data, bg_data; resampler = CV(nfolds = 3))
SDMdata object with 931 presence points and 500 absence points. 
  
 Resampling: 
 Data is divided into 3 folds using resampling strategy CV(nfolds = 3, …).
 ┌──────┬─────────┬────────┐
 │ fold │ # train │ # test │
 ├──────┼─────────┼────────┤
-│    1 │     477 │    239 │
-│    2 │     477 │    239 │
-│    3 │     478 │    238 │
+│    1 │     954 │    477 │
+│    2 │     954 │    477 │
+│    3 │     954 │    477 │
 └──────┴─────────┴────────┘
 Predictor variables: 
 ┌───────┬────────────┬─────────┐
@@ -71,7 +70,7 @@ import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.CmcMgnI6.js";const l
 │ bio1  │ Continuous │ Float32 │
 │ bio12 │ Continuous │ Float32 │
 └───────┴────────────┴─────────┘
-Does not contain geometry data
Fitting an ensemble ​
julia
using Maxnet: MaxnetBinaryClassifier
+Does not contain geometry data
Fitting an ensemble ​
Now that we have our data object with presence and background data, we can fit our ensemble. The sdm function fits a whole ensemble, taking two arguments: a data object and a NamedTuple with models the ensemble should have. This can be any MLJ-compatible model. In this case, we use Maxnet, boosted regression trees (from the EvoTrees.jl package), and a GLM.
julia
using Maxnet: MaxnetBinaryClassifier
 using EvoTrees: EvoTreeClassifier
 using MLJGLMInterface: LinearBinaryClassifier
 models = (
@@ -85,27 +84,28 @@ import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.CmcMgnI6.js";const l
 Uses the following models:
 maxnet => MaxnetBinaryClassifier. 
 brt => EvoTreeClassifier. 
-glm => LinearBinaryClassifier.
Evaluating an ensemble ​
julia
import SpeciesDistributionModels as SDM
+glm => LinearBinaryClassifier.
Evaluating an ensemble ​
We can evaluate the entire ensemble using any metric from StatisticalMeasures.jl.
julia
import SpeciesDistributionModels as SDM
 ev = SDM.evaluate(ensemble; measures = (; auc, accuracy))
SDMensembleEvaluation with 2 performance measures
 train
 ┌──────────┬──────────┬──────────┐
 │    model │      auc │ accuracy │
 │      Any │  Float64 │  Float64 │
 ├──────────┼──────────┼──────────┤
-│   maxnet │ 0.995044 │ 0.981144 │
-│      brt │ 0.999863 │ 0.993717 │
-│      glm │ 0.985797 │ 0.976255 │
-│ ensemble │  0.99929 │  0.99162 │
+│   maxnet │ 0.996541 │ 0.989867 │
+│      brt │  0.99988 │ 0.995807 │
+│      glm │ 0.987241 │  0.98812 │
+│ ensemble │ 0.998934 │ 0.995807 │
 └──────────┴──────────┴──────────┘
 test
 ┌──────────┬──────────┬──────────┐
 │    model │      auc │ accuracy │
 │      Any │  Float64 │  Float64 │
 ├──────────┼──────────┼──────────┤
-│   maxnet │ 0.994419 │ 0.979044 │
-│      brt │  0.99536 │ 0.980439 │
-│      glm │ 0.985782 │ 0.976249 │
-│ ensemble │ 0.995363 │ 0.977644 │
-└──────────┴──────────┴──────────┘
Predicting ​
julia
pred = SDM.predict(ensemble, bio_aus; reducer = mean)
-plot(pred; colorrange = (0,1))
Understanding the model ​
julia
expl = SDM.explain(ensemble; method = ShapleyValues(8))
-interactive_response_curves(expl)
',30)]))}const F=a(k,[["render",r]]);export{u as __pageData,F as default};
+│   maxnet │ 0.996382 │ 0.991614 │
+│      brt │ 0.992968 │ 0.989518 │
+│      glm │ 0.986336 │ 0.986723 │
+│ ensemble │ 0.996427 │ 0.992313 │
+└──────────┴──────────┴──────────┘

Predicting ​

Next, we the climatic suitability of the species throughout Australia using SpeciesDistributionModels.predict. We can specify a reducer argument to get a single value, instead of a prediction for each member in the ensemble.

julia
pred = SDM.predict(ensemble, bio_aus; reducer = mean)
+plot(pred; colorrange = (0,1))

Understanding the model ​

SDM.explain offers tools to estimate the contribution and response curves for each variable. Currently, the only implemented method is Shapley values from the Shapley.jl package.

julia
expl = SDM.explain(ensemble; method = ShapleyValues(8))
+variable_importance(expl)
(bio1 = 0.3042016416330687,
+ bio12 = 0.11920570053867194,)

We can also interactively plot the model explanation to get response curves.

julia
interactive_response_curves(expl)

',44)]))}const F=a(k,[["render",r]]);export{y as __pageData,F as default}; diff --git a/previews/PR11/assets/example.md.DCLnV71q.lean.js b/previews/PR11/assets/example.md.CIvWr4xa.lean.js similarity index 69% rename from previews/PR11/assets/example.md.DCLnV71q.lean.js rename to previews/PR11/assets/example.md.CIvWr4xa.lean.js index 2d69978..922c384 100644 --- a/previews/PR11/assets/example.md.DCLnV71q.lean.js +++ b/previews/PR11/assets/example.md.CIvWr4xa.lean.js @@ -1,10 +1,9 @@ -import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.CmcMgnI6.js";const l="/SpeciesDistributionModels.jl/previews/PR11/assets/qleakmw.JSwKyy9_.png",t="/SpeciesDistributionModels.jl/previews/PR11/assets/utujxvx.6Tz0WNcD.png",e="/SpeciesDistributionModels.jl/previews/PR11/assets/tfiawof.BuyrCnxU.png",h="/SpeciesDistributionModels.jl/previews/PR11/assets/zdsuhls.DoIEq0TV.png",u=JSON.parse('{"title":"Example of a full species distribution modelling workflow","description":"","frontmatter":{},"headers":[],"relativePath":"example.md","filePath":"example.md","lastUpdated":null}'),k={name:"example.md"};function r(d,s,E,o,g,c){return p(),i("div",null,s[0]||(s[0]=[n(`

Example of a full species distribution modelling workflow ​

Environmental data ​

julia
#| echo: false
+import{_ as a,c as i,a5 as n,o as e}from"./chunks/framework.nF61VOH4.js";const p="/SpeciesDistributionModels.jl/previews/PR11/assets/gczabtv.dyfLf57w.png",t="/SpeciesDistributionModels.jl/previews/PR11/assets/rqdlwql.Dc8_DCkK.png",l="/SpeciesDistributionModels.jl/previews/PR11/assets/jiafykx.BLVveajq.png",h="/SpeciesDistributionModels.jl/previews/PR11/assets/kpgklne.H3AyTYPW.png",y=JSON.parse('{"title":"Example of a full species distribution modelling workflow","description":"","frontmatter":{},"headers":[],"relativePath":"example.md","filePath":"example.md","lastUpdated":null}'),k={name:"example.md"};function r(d,s,o,E,c,g){return e(),i("div",null,s[0]||(s[0]=[n(`
Example of a full species distribution modelling workflow ​
This example fits and projects species distribution models for the tree species Eucaplytus regnans from scratch.
julia
#| echo: false
 if !haskey(ENV, "RASTERDATASOURCES_PATH")
     ENV["RASTERDATASOURCES_PATH"] = ".";
 end
 using CairoMakie
-CairoMakie.activate!(type = "png")
WARNING: Method definition convert_arguments(MakieCore.CellGrid, Rasters.AbstractRaster{var"#s115", 2, D, A} where A where D where var"#s115") in module RastersMakieExt at /home/runner/.julia/packages/Rasters/0kAkQ/ext/RastersMakieExt/plotrecipes.jl:304 overwritten at /home/runner/.julia/packages/Rasters/0kAkQ/ext/RastersMakieExt/plotrecipes.jl:308.
-ERROR: Method overwriting is not permitted during Module precompilation. Use \`__precompile__(false)\` to opt-out of precompilation.
julia
using Rasters, RasterDataSources, ArchGDAL, NaturalEarth, DataFrames
+CairoMakie.activate!(type = "png")
Environmental data ​
We start by downloading environmental data. We use the Rasters package to handle raster data, and RasterDataSources.jl to automatically download Worldclim data.
In this example, we just download bioclimatic variables 1 and 12 (annual mean temperature and annual mean precipitation). We then mask to the country borders of Australia.
julia
using Rasters, RasterDataSources, ArchGDAL, NaturalEarth, DataFrames
 bio = RasterStack(WorldClim{BioClim}, (1,12))
 countries = naturalearth("ne_10m_admin_0_countries") |> DataFrame
 australia = subset(countries, :NAME => ByRow(==("Australia"))).geometry
@@ -20,49 +19,49 @@ import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.CmcMgnI6.js";const l
   extent: Extent(X = (113.00000000000001, 153.66666666666663), Y = (-43.666666666666664, -10.666666666666671))
   missingval: -3.4f38
   crs: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
-└──────────────────────────────────────────────────────────────────────────────┘
Environmental data ​
julia
using CairoMakie
-Rasters.rplot(bio_aus)
Occurrence data ​
julia
using GBIF2, SpeciesDistributionModels
+└──────────────────────────────────────────────────────────────────────────────┘
Let's plot this data to see what it looks like.
julia
using CairoMakie
+Rasters.rplot(bio_aus)
Occurrence data ​
Next, we use GBIF2.jl to download occurrence records for this species. We use the thin function in this package to weed out occurrences that are very close to each other, using a cut-off of 5km.
julia
using GBIF2, SpeciesDistributionModels
 sp = species_match("Eucalyptus regnans")
 occurrences_raw = occurrence_search(sp; year = (1970,2000), country = "AU", hasCoordinate = true, limit = 2000)
-occurrences = thin(occurrences_raw.geometry, 5000)
217-element Vector{Tuple{Real, Float64}}:
+occurrences = thin(occurrences_raw.geometry, 5000)
932-element Vector{Tuple{Real, Float64}}:
+ (145.98298, -38.33349)
  (146.35, -37.36667)
+ (145.19167, -37.41639)
+ (146.3125, -38.56111)
  (146.24194, -38.52083)
- (146.3225, -38.48833)
+ (146.19722, -38.56889)
+ (146.32167, -38.50667)
+ (146.30333, -38.57611)
  (146.45583, -38.46889)
- (146.24056, -38.57778)
- (146.37778, -38.52333)
- (146.75, -42.88)
- (145.61944, -37.71861)
- (146.502222, -42.360093)
- (143.9438, -38.55351)
+ (146.30667, -38.56472)
  ⋮
- (145.96639, -37.43278)
- (147.983333, -41.433333)
- (147.9346, -37.48179)
- (148.36806, -37.38167)
+ (147.50127, -37.39846)
  (146.483333, -42.55)
  (146.95, -43.283333)
  (147.966667, -42.45)
  (147.5, -37.4)
- (147.15, -42.983333)
Background points ​
julia
using StatsBase
+ (147.15, -42.983333)
+ (145.68464, -37.88182)
+ (145.7, -37.883333)
+ (145.6833, -37.8833)
Background points ​
Next, we sample random points to use as background points.
From Rasters 0.12.1 on, this will be easier with Rasters.sample!
Let's plot both the occurrence and background points to see where Eucalyptus regnans is found.
julia
using StatsBase
 bg_indices = sample(findall(boolmask(bio_aus)), 500)
 bg_points = DimPoints(bio_aus)[bg_indices]
 fig, ax, pl = plot(bio_aus.bio1)
 scatter!(ax, occurrences; color = :red)
 scatter!(ax, bg_points; color = :grey)
-fig
Handling data ​
julia
using SpeciesDistributionModels
+fig
Handling data ​
SpeciesDistributionModels.jl has a sdmdata function to handle input data. It takes tabular presence and background data as inputs, such as what is returned by Rasters.extract and Rasters.sample.
julia
using SpeciesDistributionModels
 p_data = extract(bio_aus, occurrences; skipmissing = true)
 bg_data = bio_aus[bg_indices]
-data = sdmdata(p_data, bg_data; resampler = CV(nfolds = 3))
SDMdata object with 216 presence points and 500 absence points. 
+data = sdmdata(p_data, bg_data; resampler = CV(nfolds = 3))
SDMdata object with 931 presence points and 500 absence points. 
  
 Resampling: 
 Data is divided into 3 folds using resampling strategy CV(nfolds = 3, …).
 ┌──────┬─────────┬────────┐
 │ fold │ # train │ # test │
 ├──────┼─────────┼────────┤
-│    1 │     477 │    239 │
-│    2 │     477 │    239 │
-│    3 │     478 │    238 │
+│    1 │     954 │    477 │
+│    2 │     954 │    477 │
+│    3 │     954 │    477 │
 └──────┴─────────┴────────┘
 Predictor variables: 
 ┌───────┬────────────┬─────────┐
@@ -71,7 +70,7 @@ import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.CmcMgnI6.js";const l
 │ bio1  │ Continuous │ Float32 │
 │ bio12 │ Continuous │ Float32 │
 └───────┴────────────┴─────────┘
-Does not contain geometry data
Fitting an ensemble ​
julia
using Maxnet: MaxnetBinaryClassifier
+Does not contain geometry data
Fitting an ensemble ​
Now that we have our data object with presence and background data, we can fit our ensemble. The sdm function fits a whole ensemble, taking two arguments: a data object and a NamedTuple with models the ensemble should have. This can be any MLJ-compatible model. In this case, we use Maxnet, boosted regression trees (from the EvoTrees.jl package), and a GLM.
julia
using Maxnet: MaxnetBinaryClassifier
 using EvoTrees: EvoTreeClassifier
 using MLJGLMInterface: LinearBinaryClassifier
 models = (
@@ -85,27 +84,28 @@ import{_ as a,c as i,a5 as n,o as p}from"./chunks/framework.CmcMgnI6.js";const l
 Uses the following models:
 maxnet => MaxnetBinaryClassifier. 
 brt => EvoTreeClassifier. 
-glm => LinearBinaryClassifier.
Evaluating an ensemble ​
julia
import SpeciesDistributionModels as SDM
+glm => LinearBinaryClassifier.
Evaluating an ensemble ​
We can evaluate the entire ensemble using any metric from StatisticalMeasures.jl.
julia
import SpeciesDistributionModels as SDM
 ev = SDM.evaluate(ensemble; measures = (; auc, accuracy))
SDMensembleEvaluation with 2 performance measures
 train
 ┌──────────┬──────────┬──────────┐
 │    model │      auc │ accuracy │
 │      Any │  Float64 │  Float64 │
 ├──────────┼──────────┼──────────┤
-│   maxnet │ 0.995044 │ 0.981144 │
-│      brt │ 0.999863 │ 0.993717 │
-│      glm │ 0.985797 │ 0.976255 │
-│ ensemble │  0.99929 │  0.99162 │
+│   maxnet │ 0.996541 │ 0.989867 │
+│      brt │  0.99988 │ 0.995807 │
+│      glm │ 0.987241 │  0.98812 │
+│ ensemble │ 0.998934 │ 0.995807 │
 └──────────┴──────────┴──────────┘
 test
 ┌──────────┬──────────┬──────────┐
 │    model │      auc │ accuracy │
 │      Any │  Float64 │  Float64 │
 ├──────────┼──────────┼──────────┤
-│   maxnet │ 0.994419 │ 0.979044 │
-│      brt │  0.99536 │ 0.980439 │
-│      glm │ 0.985782 │ 0.976249 │
-│ ensemble │ 0.995363 │ 0.977644 │
-└──────────┴──────────┴──────────┘
Predicting ​
julia
pred = SDM.predict(ensemble, bio_aus; reducer = mean)
-plot(pred; colorrange = (0,1))
Understanding the model ​
julia
expl = SDM.explain(ensemble; method = ShapleyValues(8))
-interactive_response_curves(expl)
',30)]))}const F=a(k,[["render",r]]);export{u as __pageData,F as default};
+│   maxnet │ 0.996382 │ 0.991614 │
+│      brt │ 0.992968 │ 0.989518 │
+│      glm │ 0.986336 │ 0.986723 │
+│ ensemble │ 0.996427 │ 0.992313 │
+└──────────┴──────────┴──────────┘

Predicting ​

Next, we the climatic suitability of the species throughout Australia using SpeciesDistributionModels.predict. We can specify a reducer argument to get a single value, instead of a prediction for each member in the ensemble.

julia
pred = SDM.predict(ensemble, bio_aus; reducer = mean)
+plot(pred; colorrange = (0,1))

Understanding the model ​

SDM.explain offers tools to estimate the contribution and response curves for each variable. Currently, the only implemented method is Shapley values from the Shapley.jl package.

julia
expl = SDM.explain(ensemble; method = ShapleyValues(8))
+variable_importance(expl)
(bio1 = 0.3042016416330687,
+ bio12 = 0.11920570053867194,)

We can also interactively plot the model explanation to get response curves.

julia
interactive_response_curves(expl)

',44)]))}const F=a(k,[["render",r]]);export{y as __pageData,F as default}; diff --git a/previews/PR11/assets/gczabtv.dyfLf57w.png b/previews/PR11/assets/gczabtv.dyfLf57w.png new file mode 100644 index 0000000..bc755e8 Binary files /dev/null and b/previews/PR11/assets/gczabtv.dyfLf57w.png differ diff --git a/previews/PR11/assets/index.md.aoM4pF9M.js b/previews/PR11/assets/index.md.C6N2EXUC.js similarity index 93% rename from previews/PR11/assets/index.md.aoM4pF9M.js rename to previews/PR11/assets/index.md.C6N2EXUC.js index c55cb40..17c430f 100644 --- a/previews/PR11/assets/index.md.aoM4pF9M.js +++ b/previews/PR11/assets/index.md.C6N2EXUC.js @@ -1 +1 @@ -import{_ as s,c as e,a5 as a,o as t}from"./chunks/framework.CmcMgnI6.js";const k=JSON.parse('{"title":"SpeciesDistributionModels","description":"","frontmatter":{"layout":"home"},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(o,i,r,d,p,h){return t(),e("div",null,i[0]||(i[0]=[a('

SpeciesDistributionModels ​

Documentation for SpeciesDistributionModels.

Installation ​

julia
] add github.com/tiemvanderdeure/SpeciesDistributionModels.jl
',4)]))}const u=s(n,[["render",l]]);export{k as __pageData,u as default}; +import{_ as s,c as e,a5 as a,o as t}from"./chunks/framework.nF61VOH4.js";const k=JSON.parse('{"title":"SpeciesDistributionModels","description":"","frontmatter":{"layout":"home"},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(o,i,r,d,p,h){return t(),e("div",null,i[0]||(i[0]=[a('

SpeciesDistributionModels ​

Documentation for SpeciesDistributionModels.

Installation ​

julia
] add github.com/tiemvanderdeure/SpeciesDistributionModels.jl
',4)]))}const u=s(n,[["render",l]]);export{k as __pageData,u as default}; diff --git a/previews/PR11/assets/index.md.aoM4pF9M.lean.js b/previews/PR11/assets/index.md.C6N2EXUC.lean.js similarity index 93% rename from previews/PR11/assets/index.md.aoM4pF9M.lean.js rename to previews/PR11/assets/index.md.C6N2EXUC.lean.js index c55cb40..17c430f 100644 --- a/previews/PR11/assets/index.md.aoM4pF9M.lean.js +++ b/previews/PR11/assets/index.md.C6N2EXUC.lean.js @@ -1 +1 @@ -import{_ as s,c as e,a5 as a,o as t}from"./chunks/framework.CmcMgnI6.js";const k=JSON.parse('{"title":"SpeciesDistributionModels","description":"","frontmatter":{"layout":"home"},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(o,i,r,d,p,h){return t(),e("div",null,i[0]||(i[0]=[a('

SpeciesDistributionModels ​

Documentation for SpeciesDistributionModels.

Installation ​

julia
] add github.com/tiemvanderdeure/SpeciesDistributionModels.jl
',4)]))}const u=s(n,[["render",l]]);export{k as __pageData,u as default}; +import{_ as s,c as e,a5 as a,o as t}from"./chunks/framework.nF61VOH4.js";const k=JSON.parse('{"title":"SpeciesDistributionModels","description":"","frontmatter":{"layout":"home"},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),n={name:"index.md"};function l(o,i,r,d,p,h){return t(),e("div",null,i[0]||(i[0]=[a('

SpeciesDistributionModels ​

Documentation for SpeciesDistributionModels.

Installation ​

julia
] add github.com/tiemvanderdeure/SpeciesDistributionModels.jl
',4)]))}const u=s(n,[["render",l]]);export{k as __pageData,u as default}; diff --git a/previews/PR11/assets/jiafykx.BLVveajq.png b/previews/PR11/assets/jiafykx.BLVveajq.png new file mode 100644 index 0000000..a2cb4e7 Binary files /dev/null and b/previews/PR11/assets/jiafykx.BLVveajq.png differ diff --git a/previews/PR11/assets/kpgklne.H3AyTYPW.png b/previews/PR11/assets/kpgklne.H3AyTYPW.png new file mode 100644 index 0000000..3a55ce6 Binary files /dev/null and b/previews/PR11/assets/kpgklne.H3AyTYPW.png differ diff --git a/previews/PR11/assets/qleakmw.JSwKyy9_.png b/previews/PR11/assets/qleakmw.JSwKyy9_.png deleted file mode 100644 index 1ed961b..0000000 Binary files a/previews/PR11/assets/qleakmw.JSwKyy9_.png and /dev/null differ diff --git a/previews/PR11/assets/rqdlwql.Dc8_DCkK.png b/previews/PR11/assets/rqdlwql.Dc8_DCkK.png new file mode 100644 index 0000000..aa44fd6 Binary files /dev/null and b/previews/PR11/assets/rqdlwql.Dc8_DCkK.png differ diff --git a/previews/PR11/assets/style.BbKtXRhX.css b/previews/PR11/assets/style.CqZ3YKZU.css similarity index 95% rename from previews/PR11/assets/style.BbKtXRhX.css rename to previews/PR11/assets/style.CqZ3YKZU.css index 3b66da3..05daccd 100644 --- a/previews/PR11/assets/style.BbKtXRhX.css +++ b/previews/PR11/assets/style.CqZ3YKZU.css @@ -1 +1 @@ -@font-face{font-family:Inter;font-style:normal;font-weight:100 900;font-display:swap;src:url(/SpeciesDistributionModels.jl/previews/PR11/assets/inter-roman-cyrillic-ext.BBPuwvHQ.woff2) format("woff2");unicode-range:U+0460-052F,U+1C80-1C88,U+20B4,U+2DE0-2DFF,U+A640-A69F,U+FE2E-FE2F}@font-face{font-family:Inter;font-style:normal;font-weight:100 900;font-display:swap;src:url(/SpeciesDistributionModels.jl/previews/PR11/assets/inter-roman-cyrillic.C5lxZ8CY.woff2) format("woff2");unicode-range:U+0301,U+0400-045F,U+0490-0491,U+04B0-04B1,U+2116}@font-face{font-family:Inter;font-style:normal;font-weight:100 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var(--vp-c-brand-soft);--vp-c-success-1: var(--vp-c-green-1);--vp-c-success-2: var(--vp-c-green-2);--vp-c-success-3: var(--vp-c-green-3);--vp-c-success-soft: var(--vp-c-green-soft);--vp-c-important-1: var(--vp-c-purple-1);--vp-c-important-2: var(--vp-c-purple-2);--vp-c-important-3: var(--vp-c-purple-3);--vp-c-important-soft: var(--vp-c-purple-soft);--vp-c-warning-1: var(--vp-c-yellow-1);--vp-c-warning-2: var(--vp-c-yellow-2);--vp-c-warning-3: var(--vp-c-yellow-3);--vp-c-warning-soft: var(--vp-c-yellow-soft);--vp-c-danger-1: var(--vp-c-red-1);--vp-c-danger-2: var(--vp-c-red-2);--vp-c-danger-3: var(--vp-c-red-3);--vp-c-danger-soft: var(--vp-c-red-soft);--vp-c-caution-1: var(--vp-c-red-1);--vp-c-caution-2: var(--vp-c-red-2);--vp-c-caution-3: var(--vp-c-red-3);--vp-c-caution-soft: var(--vp-c-red-soft)}:root{--vp-font-family-base: "Inter", ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--vp-font-family-mono: ui-monospace, "Menlo", "Monaco", "Consolas", "Liberation Mono", "Courier New", monospace;font-optical-sizing:auto}:root:where(:lang(zh)){--vp-font-family-base: "Punctuation SC", "Inter", ui-sans-serif, system-ui, "PingFang SC", "Noto Sans CJK SC", "Noto Sans SC", "Heiti SC", "Microsoft YaHei", "DengXian", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"}:root{--vp-shadow-1: 0 1px 2px rgba(0, 0, 0, .04), 0 1px 2px rgba(0, 0, 0, .06);--vp-shadow-2: 0 3px 12px rgba(0, 0, 0, .07), 0 1px 4px rgba(0, 0, 0, .07);--vp-shadow-3: 0 12px 32px rgba(0, 0, 0, .1), 0 2px 6px rgba(0, 0, 0, .08);--vp-shadow-4: 0 14px 44px rgba(0, 0, 0, .12), 0 3px 9px rgba(0, 0, 0, .12);--vp-shadow-5: 0 18px 56px rgba(0, 0, 0, .16), 0 4px 12px rgba(0, 0, 0, .16)}:root{--vp-z-index-footer: 10;--vp-z-index-local-nav: 20;--vp-z-index-nav: 30;--vp-z-index-layout-top: 40;--vp-z-index-backdrop: 50;--vp-z-index-sidebar: 60}@media (min-width: 960px){:root{--vp-z-index-sidebar: 25}}:root{--vp-layout-max-width: 1440px}:root{--vp-header-anchor-symbol: "#"}:root{--vp-code-line-height: 1.7;--vp-code-font-size: .875em;--vp-code-color: var(--vp-c-brand-1);--vp-code-link-color: var(--vp-c-brand-1);--vp-code-link-hover-color: var(--vp-c-brand-2);--vp-code-bg: var(--vp-c-default-soft);--vp-code-block-color: var(--vp-c-text-2);--vp-code-block-bg: var(--vp-c-bg-alt);--vp-code-block-divider-color: var(--vp-c-gutter);--vp-code-lang-color: var(--vp-c-text-3);--vp-code-line-highlight-color: var(--vp-c-default-soft);--vp-code-line-number-color: var(--vp-c-text-3);--vp-code-line-diff-add-color: var(--vp-c-success-soft);--vp-code-line-diff-add-symbol-color: var(--vp-c-success-1);--vp-code-line-diff-remove-color: var(--vp-c-danger-soft);--vp-code-line-diff-remove-symbol-color: var(--vp-c-danger-1);--vp-code-line-warning-color: var(--vp-c-warning-soft);--vp-code-line-error-color: var(--vp-c-danger-soft);--vp-code-copy-code-border-color: var(--vp-c-divider);--vp-code-copy-code-bg: var(--vp-c-bg-soft);--vp-code-copy-code-hover-border-color: var(--vp-c-divider);--vp-code-copy-code-hover-bg: var(--vp-c-bg);--vp-code-copy-code-active-text: var(--vp-c-text-2);--vp-code-copy-copied-text-content: "Copied";--vp-code-tab-divider: var(--vp-code-block-divider-color);--vp-code-tab-text-color: var(--vp-c-text-2);--vp-code-tab-bg: var(--vp-code-block-bg);--vp-code-tab-hover-text-color: var(--vp-c-text-1);--vp-code-tab-active-text-color: var(--vp-c-text-1);--vp-code-tab-active-bar-color: var(--vp-c-brand-1)}:root{--vp-button-brand-border: transparent;--vp-button-brand-text: var(--vp-c-white);--vp-button-brand-bg: var(--vp-c-brand-3);--vp-button-brand-hover-border: transparent;--vp-button-brand-hover-text: var(--vp-c-white);--vp-button-brand-hover-bg: var(--vp-c-brand-2);--vp-button-brand-active-border: transparent;--vp-button-brand-active-text: var(--vp-c-white);--vp-button-brand-active-bg: var(--vp-c-brand-1);--vp-button-alt-border: transparent;--vp-button-alt-text: var(--vp-c-text-1);--vp-button-alt-bg: var(--vp-c-default-3);--vp-button-alt-hover-border: transparent;--vp-button-alt-hover-text: var(--vp-c-text-1);--vp-button-alt-hover-bg: var(--vp-c-default-2);--vp-button-alt-active-border: transparent;--vp-button-alt-active-text: var(--vp-c-text-1);--vp-button-alt-active-bg: var(--vp-c-default-1);--vp-button-sponsor-border: var(--vp-c-text-2);--vp-button-sponsor-text: var(--vp-c-text-2);--vp-button-sponsor-bg: transparent;--vp-button-sponsor-hover-border: var(--vp-c-sponsor);--vp-button-sponsor-hover-text: var(--vp-c-sponsor);--vp-button-sponsor-hover-bg: transparent;--vp-button-sponsor-active-border: var(--vp-c-sponsor);--vp-button-sponsor-active-text: var(--vp-c-sponsor);--vp-button-sponsor-active-bg: transparent}:root{--vp-custom-block-font-size: 14px;--vp-custom-block-code-font-size: 13px;--vp-custom-block-info-border: transparent;--vp-custom-block-info-text: var(--vp-c-text-1);--vp-custom-block-info-bg: var(--vp-c-default-soft);--vp-custom-block-info-code-bg: var(--vp-c-default-soft);--vp-custom-block-note-border: transparent;--vp-custom-block-note-text: var(--vp-c-text-1);--vp-custom-block-note-bg: var(--vp-c-default-soft);--vp-custom-block-note-code-bg: var(--vp-c-default-soft);--vp-custom-block-tip-border: transparent;--vp-custom-block-tip-text: var(--vp-c-text-1);--vp-custom-block-tip-bg: var(--vp-c-tip-soft);--vp-custom-block-tip-code-bg: var(--vp-c-tip-soft);--vp-custom-block-important-border: transparent;--vp-custom-block-important-text: var(--vp-c-text-1);--vp-custom-block-important-bg: var(--vp-c-important-soft);--vp-custom-block-important-code-bg: var(--vp-c-important-soft);--vp-custom-block-warning-border: transparent;--vp-custom-block-warning-text: var(--vp-c-text-1);--vp-custom-block-warning-bg: var(--vp-c-warning-soft);--vp-custom-block-warning-code-bg: var(--vp-c-warning-soft);--vp-custom-block-danger-border: transparent;--vp-custom-block-danger-text: var(--vp-c-text-1);--vp-custom-block-danger-bg: var(--vp-c-danger-soft);--vp-custom-block-danger-code-bg: var(--vp-c-danger-soft);--vp-custom-block-caution-border: transparent;--vp-custom-block-caution-text: var(--vp-c-text-1);--vp-custom-block-caution-bg: var(--vp-c-caution-soft);--vp-custom-block-caution-code-bg: var(--vp-c-caution-soft);--vp-custom-block-details-border: var(--vp-custom-block-info-border);--vp-custom-block-details-text: var(--vp-custom-block-info-text);--vp-custom-block-details-bg: var(--vp-custom-block-info-bg);--vp-custom-block-details-code-bg: var(--vp-custom-block-info-code-bg)}:root{--vp-input-border-color: var(--vp-c-border);--vp-input-bg-color: var(--vp-c-bg-alt);--vp-input-switch-bg-color: var(--vp-c-default-soft)}:root{--vp-nav-height: 64px;--vp-nav-bg-color: var(--vp-c-bg);--vp-nav-screen-bg-color: var(--vp-c-bg);--vp-nav-logo-height: 24px}.hide-nav{--vp-nav-height: 0px}.hide-nav .VPSidebar{--vp-nav-height: 22px}:root{--vp-local-nav-bg-color: var(--vp-c-bg)}:root{--vp-sidebar-width: 272px;--vp-sidebar-bg-color: var(--vp-c-bg-alt)}:root{--vp-backdrop-bg-color: rgba(0, 0, 0, .6)}:root{--vp-home-hero-name-color: var(--vp-c-brand-1);--vp-home-hero-name-background: transparent;--vp-home-hero-image-background-image: none;--vp-home-hero-image-filter: none}:root{--vp-badge-info-border: transparent;--vp-badge-info-text: var(--vp-c-text-2);--vp-badge-info-bg: var(--vp-c-default-soft);--vp-badge-tip-border: transparent;--vp-badge-tip-text: var(--vp-c-tip-1);--vp-badge-tip-bg: var(--vp-c-tip-soft);--vp-badge-warning-border: transparent;--vp-badge-warning-text: var(--vp-c-warning-1);--vp-badge-warning-bg: var(--vp-c-warning-soft);--vp-badge-danger-border: transparent;--vp-badge-danger-text: var(--vp-c-danger-1);--vp-badge-danger-bg: var(--vp-c-danger-soft)}:root{--vp-carbon-ads-text-color: var(--vp-c-text-1);--vp-carbon-ads-poweredby-color: var(--vp-c-text-2);--vp-carbon-ads-bg-color: 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index 97f0dfd..0000000 Binary files a/previews/PR11/assets/utujxvx.6Tz0WNcD.png and /dev/null differ diff --git a/previews/PR11/assets/zdsuhls.DoIEq0TV.png b/previews/PR11/assets/zdsuhls.DoIEq0TV.png deleted file mode 100644 index 6e740ba..0000000 Binary files a/previews/PR11/assets/zdsuhls.DoIEq0TV.png and /dev/null differ diff --git a/previews/PR11/example.html b/previews/PR11/example.html index 95c3cf3..cf168d4 100644 --- a/previews/PR11/example.html +++ b/previews/PR11/example.html @@ -5,25 +5,24 @@ Example of a full species distribution modelling workflow | SpeciesDistributionModels.jl - - + + - + - - - + + + -
SpeciesDistributionModels.jl

Appearance

Example of a full species distribution modelling workflow ​

Environmental data ​

julia
#| echo: false
+    
SpeciesDistributionModels.jl
Appearance
Example of a full species distribution modelling workflow ​
This example fits and projects species distribution models for the tree species Eucaplytus regnans from scratch.
julia
#| echo: false
 if !haskey(ENV, "RASTERDATASOURCES_PATH")
     ENV["RASTERDATASOURCES_PATH"] = ".";
 end
 using CairoMakie
-CairoMakie.activate!(type = "png")
WARNING: Method definition convert_arguments(MakieCore.CellGrid, Rasters.AbstractRaster{var"#s115", 2, D, A} where A where D where var"#s115") in module RastersMakieExt at /home/runner/.julia/packages/Rasters/0kAkQ/ext/RastersMakieExt/plotrecipes.jl:304 overwritten at /home/runner/.julia/packages/Rasters/0kAkQ/ext/RastersMakieExt/plotrecipes.jl:308.
-ERROR: Method overwriting is not permitted during Module precompilation. Use `__precompile__(false)` to opt-out of precompilation.
julia
using Rasters, RasterDataSources, ArchGDAL, NaturalEarth, DataFrames
+CairoMakie.activate!(type = "png")
Environmental data ​
We start by downloading environmental data. We use the Rasters package to handle raster data, and RasterDataSources.jl to automatically download Worldclim data.
In this example, we just download bioclimatic variables 1 and 12 (annual mean temperature and annual mean precipitation). We then mask to the country borders of Australia.
julia
using Rasters, RasterDataSources, ArchGDAL, NaturalEarth, DataFrames
 bio = RasterStack(WorldClim{BioClim}, (1,12))
 countries = naturalearth("ne_10m_admin_0_countries") |> DataFrame
 australia = subset(countries, :NAME => ByRow(==("Australia"))).geometry
@@ -39,49 +38,49 @@
   extent: Extent(X = (113.00000000000001, 153.66666666666663), Y = (-43.666666666666664, -10.666666666666671))
   missingval: -3.4f38
   crs: GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]
-└──────────────────────────────────────────────────────────────────────────────┘
Environmental data ​
julia
using CairoMakie
-Rasters.rplot(bio_aus)
Occurrence data ​
julia
using GBIF2, SpeciesDistributionModels
+└──────────────────────────────────────────────────────────────────────────────┘
Let's plot this data to see what it looks like.
julia
using CairoMakie
+Rasters.rplot(bio_aus)
Occurrence data ​
Next, we use GBIF2.jl to download occurrence records for this species. We use the thin function in this package to weed out occurrences that are very close to each other, using a cut-off of 5km.
julia
using GBIF2, SpeciesDistributionModels
 sp = species_match("Eucalyptus regnans")
 occurrences_raw = occurrence_search(sp; year = (1970,2000), country = "AU", hasCoordinate = true, limit = 2000)
-occurrences = thin(occurrences_raw.geometry, 5000)
217-element Vector{Tuple{Real, Float64}}:
+occurrences = thin(occurrences_raw.geometry, 5000)
932-element Vector{Tuple{Real, Float64}}:
+ (145.98298, -38.33349)
  (146.35, -37.36667)
+ (145.19167, -37.41639)
+ (146.3125, -38.56111)
  (146.24194, -38.52083)
- (146.3225, -38.48833)
+ (146.19722, -38.56889)
+ (146.32167, -38.50667)
+ (146.30333, -38.57611)
  (146.45583, -38.46889)
- (146.24056, -38.57778)
- (146.37778, -38.52333)
- (146.75, -42.88)
- (145.61944, -37.71861)
- (146.502222, -42.360093)
- (143.9438, -38.55351)
+ (146.30667, -38.56472)
  ⋮
- (145.96639, -37.43278)
- (147.983333, -41.433333)
- (147.9346, -37.48179)
- (148.36806, -37.38167)
+ (147.50127, -37.39846)
  (146.483333, -42.55)
  (146.95, -43.283333)
  (147.966667, -42.45)
  (147.5, -37.4)
- (147.15, -42.983333)
Background points ​
julia
using StatsBase
+ (147.15, -42.983333)
+ (145.68464, -37.88182)
+ (145.7, -37.883333)
+ (145.6833, -37.8833)
Background points ​
Next, we sample random points to use as background points.
From Rasters 0.12.1 on, this will be easier with Rasters.sample!
Let's plot both the occurrence and background points to see where Eucalyptus regnans is found.
julia
using StatsBase
 bg_indices = sample(findall(boolmask(bio_aus)), 500)
 bg_points = DimPoints(bio_aus)[bg_indices]
 fig, ax, pl = plot(bio_aus.bio1)
 scatter!(ax, occurrences; color = :red)
 scatter!(ax, bg_points; color = :grey)
-fig
Handling data ​
julia
using SpeciesDistributionModels
+fig
Handling data ​
SpeciesDistributionModels.jl has a sdmdata function to handle input data. It takes tabular presence and background data as inputs, such as what is returned by Rasters.extract and Rasters.sample.
julia
using SpeciesDistributionModels
 p_data = extract(bio_aus, occurrences; skipmissing = true)
 bg_data = bio_aus[bg_indices]
-data = sdmdata(p_data, bg_data; resampler = CV(nfolds = 3))
SDMdata object with 216 presence points and 500 absence points. 
+data = sdmdata(p_data, bg_data; resampler = CV(nfolds = 3))
SDMdata object with 931 presence points and 500 absence points. 
  
 Resampling: 
 Data is divided into 3 folds using resampling strategy CV(nfolds = 3, …).
 ┌──────┬─────────┬────────┐
 │ fold │ # train │ # test │
 ├──────┼─────────┼────────┤
-│    1 │     477 │    239 │
-│    2 │     477 │    239 │
-│    3 │     478 │    238 │
+│    1 │     954 │    477 │
+│    2 │     954 │    477 │
+│    3 │     954 │    477 │
 └──────┴─────────┴────────┘
 Predictor variables: 
 ┌───────┬────────────┬─────────┐
@@ -90,7 +89,7 @@
 │ bio1  │ Continuous │ Float32 │
 │ bio12 │ Continuous │ Float32 │
 └───────┴────────────┴─────────┘
-Does not contain geometry data
Fitting an ensemble ​
julia
using Maxnet: MaxnetBinaryClassifier
+Does not contain geometry data
Fitting an ensemble ​
Now that we have our data object with presence and background data, we can fit our ensemble. The sdm function fits a whole ensemble, taking two arguments: a data object and a NamedTuple with models the ensemble should have. This can be any MLJ-compatible model. In this case, we use Maxnet, boosted regression trees (from the EvoTrees.jl package), and a GLM.
julia
using Maxnet: MaxnetBinaryClassifier
 using EvoTrees: EvoTreeClassifier
 using MLJGLMInterface: LinearBinaryClassifier
 models = (
@@ -104,31 +103,32 @@
 Uses the following models:
 maxnet => MaxnetBinaryClassifier. 
 brt => EvoTreeClassifier. 
-glm => LinearBinaryClassifier.
Evaluating an ensemble ​
julia
import SpeciesDistributionModels as SDM
+glm => LinearBinaryClassifier.
Evaluating an ensemble ​
We can evaluate the entire ensemble using any metric from StatisticalMeasures.jl.
julia
import SpeciesDistributionModels as SDM
 ev = SDM.evaluate(ensemble; measures = (; auc, accuracy))
SDMensembleEvaluation with 2 performance measures
 train
 ┌──────────┬──────────┬──────────┐
 │    model │      auc │ accuracy │
 │      Any │  Float64 │  Float64 │
 ├──────────┼──────────┼──────────┤
-│   maxnet │ 0.995044 │ 0.981144 │
-│      brt │ 0.999863 │ 0.993717 │
-│      glm │ 0.985797 │ 0.976255 │
-│ ensemble │  0.99929 │  0.99162 │
+│   maxnet │ 0.996541 │ 0.989867 │
+│      brt │  0.99988 │ 0.995807 │
+│      glm │ 0.987241 │  0.98812 │
+│ ensemble │ 0.998934 │ 0.995807 │
 └──────────┴──────────┴──────────┘
 test
 ┌──────────┬──────────┬──────────┐
 │    model │      auc │ accuracy │
 │      Any │  Float64 │  Float64 │
 ├──────────┼──────────┼──────────┤
-│   maxnet │ 0.994419 │ 0.979044 │
-│      brt │  0.99536 │ 0.980439 │
-│      glm │ 0.985782 │ 0.976249 │
-│ ensemble │ 0.995363 │ 0.977644 │
-└──────────┴──────────┴──────────┘
Predicting ​
julia
pred = SDM.predict(ensemble, bio_aus; reducer = mean)
-plot(pred; colorrange = (0,1))
Understanding the model ​
julia
expl = SDM.explain(ensemble; method = ShapleyValues(8))
-interactive_response_curves(expl)

-    
+│   maxnet │ 0.996382 │ 0.991614 │
+│      brt │ 0.992968 │ 0.989518 │
+│      glm │ 0.986336 │ 0.986723 │
+│ ensemble │ 0.996427 │ 0.992313 │
+└──────────┴──────────┴──────────┘

Predicting ​

Next, we the climatic suitability of the species throughout Australia using SpeciesDistributionModels.predict. We can specify a reducer argument to get a single value, instead of a prediction for each member in the ensemble.

julia
pred = SDM.predict(ensemble, bio_aus; reducer = mean)
+plot(pred; colorrange = (0,1))

Understanding the model ​

SDM.explain offers tools to estimate the contribution and response curves for each variable. Currently, the only implemented method is Shapley values from the Shapley.jl package.

julia
expl = SDM.explain(ensemble; method = ShapleyValues(8))
+variable_importance(expl)
(bio1 = 0.3042016416330687,
+ bio12 = 0.11920570053867194,)

We can also interactively plot the model explanation to get response curves.

julia
interactive_response_curves(expl)

+ \ No newline at end of file diff --git a/previews/PR11/hashmap.json b/previews/PR11/hashmap.json index a861866..72bcb56 100644 --- a/previews/PR11/hashmap.json +++ b/previews/PR11/hashmap.json @@ -1 +1 @@ -{"api.md":"bHTjdokF","example.md":"DCLnV71q","index.md":"aoM4pF9M"} +{"api.md":"Cglor7Cb","example.md":"CIvWr4xa","index.md":"C6N2EXUC"} diff --git a/previews/PR11/index.html b/previews/PR11/index.html index b0feca3..11ebbbf 100644 --- a/previews/PR11/index.html +++ b/previews/PR11/index.html @@ -5,20 +5,20 @@ SpeciesDistributionModels | SpeciesDistributionModels.jl - - + + - + - - - + + + -
SpeciesDistributionModels.jl

Appearance

SpeciesDistributionModels ​

Documentation for SpeciesDistributionModels.

Installation ​

julia
] add github.com/tiemvanderdeure/SpeciesDistributionModels.jl
- +
SpeciesDistributionModels.jl

Appearance

SpeciesDistributionModels ​

Documentation for SpeciesDistributionModels.

Installation ​

julia
] add github.com/tiemvanderdeure/SpeciesDistributionModels.jl
+ \ No newline at end of file