multiregression: Initial MicroPython code

Author: jonnor

examples/multiregression_trees/multiregression_run.py

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+
+"""MicroPython code for doing multi-output regression with emlearn_trees
+"""
+
+import os
+
+import emlearn_trees
+import array
+
+class MultiRegressor():
+    """Convenience wrapper for a collection of tree-based regression models"""
+
+    def __init__(self, max_trees=10, max_nodes=1000, max_leaves=1000):
+        self.models = []
+
+        self.max_trees = max_trees
+        self.max_nodes = max_nodes
+        self.max_leaves = max_leaves        
+
+        # temporary buffer for invididual model output
+        self._output = array.array('f', [0.0])
+
+    def load(self, path):
+        """Load a directory of model files"""
+
+        for filename in os.listdir(path):
+            if not filename.endswith('.csv'):
+                print('Warning: Ignoring unknown file in model directory', filename)
+                continue
+
+            model_path = path + '/' + filename
+
+            # TODO: support reading neccesary capacity from file
+            model = emlearn_trees.new(self.max_trees, self.max_nodes, self.max_leaves)
+
+            with open(model_path, 'r') as f:
+                emlearn_trees.load_model(model, f)
+
+            self.models.append(model)
+
+    def predict(self, features : array.array, outputs : array.array):
+        assert len(self.models), 'no models'
+
+        for i, model in self.models():
+            model.predict(features, self._output)
+            outputs[i] = self._output[0]
+
+def main():
+    
+    # FIXME: read paths from sys.argv
+    model = MultiRegressor()
+    model.load('models')
+
+    outputs = array.array('f', [0.0 for _ in range(model.models)])  
+
+    import npyfile
+    (n_samples, n_features), data = npyfile.load('data.npy')
+
+    # TODO: write output to a file
+    for row in range(n_samples):
+        offset = row*n_features
+        f = data[offset:offset+n_features]
+        model.predict(f, outputs)
+
+    
+
+if __name__ == '__main__':
+    main()