Processes for Random Forest

proposals/fit_class_random_forest.json

@@ -0,0 +1,88 @@
+{
+    "id": "fit_class_random_forest",
+    "summary": "Train a random forest classification model",
+    "description": "Executes the fit of a random forest classification based on the user input of target and predictors. 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": "vector-cube"
+            }
+        },
+        {
+            "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": "vector-cube"
+            }
+        },
+        {
+            "name": "training",
+            "description": "The amount of training data to be used in the classification, given as a fraction. The sampling will be chosen randomly through the data object. The remaining data will be used as test data for the validation.",
+            "schema": {
+                "type": "number",
+                "exclusiveMinimum": 0,
+                "maximum": 1
+            }
+        },
+        {
+            "name": "num_trees",
+            "description": "The number of trees build within the Random Forest classification.",
+            "optional": true,
+            "default": 100,
+            "schema": {
+                "type": "integer",
+                "minimum": 1
+            }
+        },
+        {
+            "name": "mtry",
+            "description": "Specifies how many split variables will be used at a node. Default value is `null`, which corresponds to the number of predictors divided by 3.",
+            "optional": true,
+            "default": null,
+            "schema": [
+                {
+                    "type": "integer",
+                    "minimum": 1
+                },
+                {
+                    "type": "null"
+                }
+            ]
+        },
+        {
+            "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"
+        }
+    ]
+}

proposals/fit_regr_random_forest.json

@@ -0,0 +1,88 @@
+{
+    "id": "fit_regr_random_forest",
+    "summary": "Train a random forest regression model",
+    "description": "Executes the fit of a random forest regression based on the user input of target and predictors. 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": "vector-cube"
+            }
+        },
+        {
+            "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": "vector-cube"
+            }
+        },
+        {
+            "name": "training",
+            "description": "The amount of training data to be used in the regression, given as a fraction. The sampling will be randomly through the data object. The remaining data will be used as test data for the validation.",
+            "schema": {
+                "type": "number",
+                "exclusiveMinimum": 0,
+                "maximum": 1
+            }
+        },
+        {
+            "name": "num_trees",
+            "description": "The number of trees build within the Random Forest regression.",
+            "optional": true,
+            "default": 100,
+            "schema": {
+                "type": "integer",
+                "minimum": 1
+            }
+        },
+        {
+            "name": "mtry",
+            "description": "Specifies how many split variables will be used at a node. Default value is `null`, which corresponds to the number of predictors divided by 3.",
+            "optional": true,
+            "default": null,
+            "schema": [
+                {
+                    "type": "integer",
+                    "minimum": 1
+                },
+                {
+                    "type": "null"
+                }
+            ]
+        },
+        {
+            "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"
+        }
+    ]
+}

proposals/load_ml_model.json

@@ -1,7 +1,7 @@
 {
     "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 ``save_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"
@@ -36,7 +36,7 @@
         }
     ],
     "returns": {
-        "description": "A machine learning model to be used with machine learning processes.",
+        "description": "A machine learning model to be used with machine learning processes such as ``predict_random_forest()``.",
         "schema": {
             "type": "object",
             "subtype": "ml-model"

proposals/predict_random_forest.json

@@ -0,0 +1,42 @@
+{
+    "id": "predict_random_forest",
+    "summary": "Predict values from 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"
+            ]
+        }
+    }
+}

tests/.words

@@ -37,4 +37,5 @@ gdalwarp
 Lanczos
 sinc
 interpolants
-Hyndman
+Breiman
+Hyndman