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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,110 @@ | ||
| { | ||
| "id": "fit_class_random_forest", | ||
| "summary": "Train a random forest classification model", | ||
| "description": "Executes the fit of a random forest classification based on training data. The process does not include a separate split of the data in test, validation and training data. The Random Forest classification model is based on the approach by Breiman (2001).", | ||
| "categories": [ | ||
| "machine learning" | ||
| ], | ||
| "experimental": true, | ||
| "parameters": [ | ||
| { | ||
| "name": "predictors", | ||
| "description": "The predictors for the classification model as a vector data cube. Aggregated to the features (vectors) of the target input variable.", | ||
| "schema": [ | ||
| { | ||
| "type": "object", | ||
| "subtype": "datacube", | ||
| "dimensions": [ | ||
| { | ||
| "type": "geometry" | ||
| }, | ||
| { | ||
| "type": "bands" | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "type": "object", | ||
| "subtype": "datacube", | ||
| "dimensions": [ | ||
| { | ||
| "type": "geometry" | ||
| }, | ||
| { | ||
| "type": "other" | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "name": "target", | ||
| "description": "The training sites for the classification model as a vector data cube. This is associated with the target variable for the Random Forest model. The geometry has to associated with a value to predict (e.g. fractional forest canopy cover).", | ||
| "schema": { | ||
| "type": "object", | ||
| "subtype": "datacube", | ||
| "dimensions": [ | ||
| { | ||
| "type": "geometry" | ||
| } | ||
| ] | ||
| } | ||
| }, | ||
| { | ||
| "name": "max_variables", | ||
| "description": "Specifies how many split variables will be used at a node.\n\nThe following options are available:\n\n- *integer*: The given number of variables are considered for each split.\n- `all`: All variables are considered for each split.\n- `log2`: The logarithm with base 2 of the number of variables are considered for each split.\n- `onethird`: A third of the number of variables are considered for each split.\n- `sqrt`: The square root of the number of variables are considered for each split. This is often the default for classification.", | ||
| "schema": [ | ||
| { | ||
| "type": "integer", | ||
| "minimum": 1 | ||
| }, | ||
| { | ||
| "type": "string", | ||
| "enum": [ | ||
| "all", | ||
| "log2", | ||
| "onethird", | ||
| "sqrt" | ||
| ] | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "name": "num_trees", | ||
| "description": "The number of trees build within the Random Forest classification.", | ||
| "optional": true, | ||
| "default": 100, | ||
| "schema": { | ||
| "type": "integer", | ||
| "minimum": 1 | ||
| } | ||
| }, | ||
| { | ||
| "name": "seed", | ||
| "description": "A randomization seed to use for the random sampling in training. If not given or `null`, no seed is used and results may differ on subsequent use.", | ||
| "optional": true, | ||
| "default": null, | ||
| "schema": { | ||
| "type": [ | ||
| "integer", | ||
| "null" | ||
| ] | ||
| } | ||
| } | ||
| ], | ||
| "returns": { | ||
| "description": "A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.", | ||
| "schema": { | ||
| "type": "object", | ||
| "subtype": "ml-model" | ||
| } | ||
| }, | ||
| "links": [ | ||
| { | ||
| "href": "https://doi.org/10.1023/A:1010933404324", | ||
| "title": "Breiman (2001): Random Forests", | ||
| "type": "text/html", | ||
| "rel": "about" | ||
| } | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,110 @@ | ||
| { | ||
| "id": "fit_regr_random_forest", | ||
| "summary": "Train a random forest regression model", | ||
| "description": "Executes the fit of a random forest regression based on training data. The process does not include a separate split of the data in test, validation and training data. The Random Forest regression model is based on the approach by Breiman (2001).", | ||
| "categories": [ | ||
| "machine learning" | ||
| ], | ||
| "experimental": true, | ||
| "parameters": [ | ||
| { | ||
| "name": "predictors", | ||
| "description": "The predictors for the regression model as a vector data cube. Aggregated to the features (vectors) of the target input variable.", | ||
| "schema": [ | ||
| { | ||
| "type": "object", | ||
| "subtype": "datacube", | ||
| "dimensions": [ | ||
| { | ||
| "type": "geometry" | ||
| }, | ||
| { | ||
| "type": "bands" | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "type": "object", | ||
| "subtype": "datacube", | ||
| "dimensions": [ | ||
| { | ||
| "type": "geometry" | ||
| }, | ||
| { | ||
| "type": "other" | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "name": "target", | ||
| "description": "The training sites for the regression model as a vector data cube. This is associated with the target variable for the Random Forest model. The geometry has to associated with a value to predict (e.g. fractional forest canopy cover).", | ||
| "schema": { | ||
| "type": "object", | ||
| "subtype": "datacube", | ||
| "dimensions": [ | ||
| { | ||
| "type": "geometry" | ||
| } | ||
| ] | ||
| } | ||
| }, | ||
| { | ||
| "name": "max_variables", | ||
| "description": "Specifies how many split variables will be used at a node.\n\nThe following options are available:\n\n- *integer*: The given number of variables are considered for each split.\n- `all`: All variables are considered for each split.\n- `log2`: The logarithm with base 2 of the number of variables are considered for each split.\n- `onethird`: A third of the number of variables are considered for each split. This is often the default for regression.\n- `sqrt`: The square root of the number of variables are considered for each split.", | ||
| "schema": [ | ||
| { | ||
| "type": "integer", | ||
| "minimum": 1 | ||
| }, | ||
| { | ||
| "type": "string", | ||
| "enum": [ | ||
| "all", | ||
| "log2", | ||
| "onethird", | ||
| "sqrt" | ||
| ] | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "name": "num_trees", | ||
| "description": "The number of trees build within the Random Forest regression.", | ||
| "optional": true, | ||
| "default": 100, | ||
| "schema": { | ||
| "type": "integer", | ||
| "minimum": 1 | ||
| } | ||
| }, | ||
| { | ||
| "name": "seed", | ||
| "description": "A randomization seed to use for the random sampling in training. If not given or `null`, no seed is used and results may differ on subsequent use.", | ||
| "optional": true, | ||
| "default": null, | ||
| "schema": { | ||
| "type": [ | ||
| "integer", | ||
| "null" | ||
| ] | ||
| } | ||
| } | ||
| ], | ||
| "returns": { | ||
| "description": "A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.", | ||
| "schema": { | ||
| "type": "object", | ||
| "subtype": "ml-model" | ||
| } | ||
| }, | ||
| "links": [ | ||
| { | ||
| "href": "https://doi.org/10.1023/A:1010933404324", | ||
| "title": "Breiman (2001): Random Forests", | ||
| "type": "text/html", | ||
| "rel": "about" | ||
| } | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| { | ||
| "id": "load_ml_model", | ||
| "summary": "Load a ML model", | ||
| "description": "Loads a machine learning model from a STAC Item.\n\nSuch a model could be trained and saved as part of a previous batch job with processes such as ``fit_regr_random_forest()`` and ``save_ml_model()``.", | ||
| "categories": [ | ||
| "machine learning", | ||
| "import" | ||
| ], | ||
| "experimental": true, | ||
| "parameters": [ | ||
| { | ||
| "name": "id", | ||
| "description": "The STAC Item to load the machine learning model from. The STAC Item must implement the `ml-model` extension.", | ||
| "schema": [ | ||
| { | ||
| "title": "URL", | ||
| "type": "string", | ||
| "format": "uri", | ||
| "subtype": "uri", | ||
| "pattern": "^https?://" | ||
| }, | ||
| { | ||
| "title": "Batch Job ID", | ||
| "description": "Loading a model by batch job ID is possible only if a single model has been saved by the job. Otherwise, you have to load a specific model from a batch job by URL.", | ||
| "type": "string", | ||
| "subtype": "job-id", | ||
| "pattern": "^[\\w\\-\\.~]+$" | ||
| }, | ||
| { | ||
| "title": "User-uploaded File", | ||
| "type": "string", | ||
| "subtype": "file-path", | ||
| "pattern": "^[^\r\n\\:'\"]+$" | ||
| } | ||
| ] | ||
| } | ||
| ], | ||
| "returns": { | ||
| "description": "A machine learning model to be used with machine learning processes such as ``predict_random_forest()``.", | ||
| "schema": { | ||
| "type": "object", | ||
| "subtype": "ml-model" | ||
| } | ||
| }, | ||
| "links": [ | ||
| { | ||
| "href": "https://github.com/stac-extensions/ml-model", | ||
| "title": "STAC ml-model extension", | ||
| "type": "text/html", | ||
| "rel": "about" | ||
| } | ||
| ] | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,42 @@ | ||
| { | ||
| "id": "predict_random_forest", | ||
| "summary": "Predict values based on a Random Forest model", | ||
| "description": "Applies a Random Forest machine learning model to an array and predict a value for it.", | ||
| "categories": [ | ||
| "machine learning", | ||
| "reducer" | ||
| ], | ||
| "experimental": true, | ||
| "parameters": [ | ||
| { | ||
| "name": "data", | ||
| "description": "An array of numbers.", | ||
| "schema": { | ||
| "type": "array", | ||
| "items": { | ||
| "type": [ | ||
| "number", | ||
| "null" | ||
| ] | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "name": "model", | ||
| "description": "A model object that can be trained with the processes ``fit_regr_random_forest()`` (regression) and ``fit_class_random_forest()`` (classification).", | ||
| "schema": { | ||
| "type": "object", | ||
| "subtype": "ml-model" | ||
| } | ||
| } | ||
| ], | ||
| "returns": { | ||
| "description": "The predicted value. Returns `null` if any of the given values in the array is a no-data value.", | ||
| "schema": { | ||
| "type": [ | ||
| "number", | ||
| "null" | ||
| ] | ||
| } | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,44 @@ | ||
| { | ||
| "id": "save_ml_model", | ||
| "summary": "Save a ML model", | ||
| "description": "Saves a machine learning model as part of a batch job.\n\nThe model will be accompanied by a separate STAC Item that implements the [ml-model extension](https://github.com/stac-extensions/ml-model).", | ||
| "categories": [ | ||
| "machine learning", | ||
| "import" | ||
| ], | ||
| "experimental": true, | ||
| "parameters": [ | ||
| { | ||
| "name": "data", | ||
| "description": "The data to store as a machine learning model.", | ||
| "schema": { | ||
| "type": "object", | ||
| "subtype": "ml-model" | ||
| } | ||
| }, | ||
| { | ||
| "name": "options", | ||
| "description": "Additional parameters to create the file(s).", | ||
| "schema": { | ||
| "type": "object", | ||
| "additionalParameters": false | ||
| }, | ||
| "default": {}, | ||
| "optional": true | ||
| } | ||
| ], | ||
| "returns": { | ||
| "description": "Returns `false` if the process failed to store the model, `true` otherwise.", | ||
| "schema": { | ||
| "type": "boolean" | ||
| } | ||
| }, | ||
| "links": [ | ||
| { | ||
| "href": "https://github.com/stac-extensions/ml-model", | ||
| "title": "STAC ml-model extension", | ||
| "type": "text/html", | ||
| "rel": "about" | ||
| } | ||
| ] | ||
| } |