From 30792ce0630d8f0768379d8e7e0b8556fbfa9894 Mon Sep 17 00:00:00 2001
From: Daniel Friesel <daniel.friesel@uos.de>
Date: Thu, 6 Jan 2022 08:10:40 +0100
Subject: add preliminary xgboost support

---
 lib/functions.py  |  2 +-
 lib/model.py      |  6 ++++++
 lib/parameters.py | 20 ++++++++++++++++++++
 3 files changed, 27 insertions(+), 1 deletion(-)

(limited to 'lib')

diff --git a/lib/functions.py b/lib/functions.py
index 5358d8e..f4e1709 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -443,7 +443,7 @@ class SKLearnRegressionFunction(ModelFunction):
         for i, param in enumerate(param_list):
             if not self.ignore_index[i]:
                 actual_param_list.append(param)
-        return self.regressor.predict([actual_param_list])
+        return self.regressor.predict(np.array([actual_param_list]))
 
 
 class AnalyticFunction(ModelFunction):
diff --git a/lib/model.py b/lib/model.py
index e84e680..1270cf6 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -160,6 +160,7 @@ class AnalyticModel:
                     with_sklearn_cart = bool(
                         int(os.getenv("DFATOOL_DTREE_SKLEARN_CART", "0"))
                     )
+                    with_xgboost = bool(int(os.getenv("DFATOOL_USE_XGBOOST", "0")))
                     loss_ignore_scalar = bool(
                         int(os.getenv("DFATOOL_DTREE_LOSS_IGNORE_SCALAR", "0"))
                     )
@@ -173,6 +174,7 @@ class AnalyticModel:
                         with_function_leaves=with_function_leaves,
                         with_nonbinary_nodes=with_nonbinary_nodes,
                         with_sklearn_cart=with_sklearn_cart,
+                        with_xgboost=with_xgboost,
                         loss_ignore_scalar=loss_ignore_scalar,
                     )
             self.fit_done = True
@@ -324,6 +326,7 @@ class AnalyticModel:
                         with_sklearn_cart = bool(
                             int(os.getenv("DFATOOL_DTREE_SKLEARN_CART", "0"))
                         )
+                        with_xgboost = bool(int(os.getenv("DFATOOL_USE_XGBOOST", "0")))
                         loss_ignore_scalar = bool(
                             int(os.getenv("DFATOOL_DTREE_LOSS_IGNORE_SCALAR", "0"))
                         )
@@ -344,6 +347,7 @@ class AnalyticModel:
                             with_function_leaves=with_function_leaves,
                             with_nonbinary_nodes=with_nonbinary_nodes,
                             with_sklearn_cart=with_sklearn_cart,
+                            with_xgboost=with_xgboost,
                             loss_ignore_scalar=loss_ignore_scalar,
                         )
                     else:
@@ -423,6 +427,7 @@ class AnalyticModel:
         with_function_leaves=False,
         with_nonbinary_nodes=True,
         with_sklearn_cart=False,
+        with_xgboost=False,
         loss_ignore_scalar=False,
     ):
 
@@ -445,6 +450,7 @@ class AnalyticModel:
             with_function_leaves=with_function_leaves,
             with_nonbinary_nodes=with_nonbinary_nodes,
             with_sklearn_cart=with_sklearn_cart,
+            with_xgboost=with_xgboost,
             loss_ignore_scalar=loss_ignore_scalar,
             threshold=threshold,
         )
diff --git a/lib/parameters.py b/lib/parameters.py
index 38e36b2..ca28cbb 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -876,6 +876,7 @@ class ModelAttribute:
         with_function_leaves=False,
         with_nonbinary_nodes=True,
         with_sklearn_cart=False,
+        with_xgboost=False,
         loss_ignore_scalar=False,
         threshold=100,
     ):
@@ -908,6 +909,25 @@ class ModelAttribute:
             )
             return
 
+        if with_xgboost:
+            from xgboost import XGBRegressor
+
+            # TODO retrieve parameters from env
+            xgb = XGBRegressor(
+                n_estimators=100,
+                max_depth=10,
+                eta=0.2,
+                subsample=0.7,
+                gamma=0.01,
+                alpha=0.0006,
+            )
+            fit_parameters, ignore_index = param_to_ndarray(parameters, with_nan=False)
+            xgb.fit(fit_parameters, data)
+            self.model_function = df.SKLearnRegressionFunction(
+                np.mean(data), xgb, ignore_index
+            )
+            return
+
         if loss_ignore_scalar and not with_function_leaves:
             logger.warning(
                 "build_dtree called with loss_ignore_scalar=True, with_function_leaves=False. This does not make sense."
-- 
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