add simple decisiontree test

2 files changed, 156 insertions, 3 deletions

diff --git a/lib/model.py b/lib/model.py
index 451a39a..83c31b1 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -422,7 +422,7 @@ class ModelAttribute:
         # )
 
     # None -> kein split notwendig
-    # andernfalls: Parameter, anhand dessen eine Decision Tree-Ebene aufgespannt wird
+    # andernfalls: Parameter-Index, anhand dessen eine Decision Tree-Ebene aufgespannt wird
     # (Kinder sind wiederum ModelAttributes, in denen dieser Parameter konstant ist)
     def get_split_param_index(self):
         if not self.param_names:
diff --git a/test/test_ptamodel.py b/test/test_ptamodel.py
index bcbb19a..dad4328 100755
--- a/test/test_ptamodel.py
+++ b/test/test_ptamodel.py
@@ -338,8 +338,6 @@ class TestSynthetic(unittest.TestCase):
         static_quality = validator.kfold(lambda m: m.get_static(), 10)
         param_quality = validator.kfold(lambda m: m.get_fitted()[0], 10)
 
-        print(static_quality)
-
         # static quality reflects normal distribution scale for non-parameterized data
 
         # the Root Mean Square Deviation must not be greater the scale (i.e., standard deviation) of the normal distribution
@@ -669,6 +667,161 @@ class TestFromFile(unittest.TestCase):
             param_model("RX", "power", param=[1, None, None]), 48647, places=-1
         )
 
+    def test_decisiontrees_rf24(self):
+        raw_data = RawData(["test-data/20191024-152648-nrf24l01.tar"])
+        preprocessed_data = raw_data.get_preprocessed_data()
+        by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
+        model = PTAModel(by_name, parameters, arg_count)
+        self.assertEqual(model.states(), "RX STANDBY1".split(" "))
+        self.assertEqual(
+            model.transitions(),
+            "setAutoAck setDataRate setPALevel setup startListening stopListening write".split(
+                " "
+            ),
+        )
+        static_model = model.get_static()
+        self.assertAlmostEqual(static_model("RX", "power"), 47964, places=0)
+        self.assertAlmostEqual(static_model("STANDBY1", "power"), 128, places=0)
+        self.assertAlmostEqual(static_model("setAutoAck", "power"), 151, places=0)
+        self.assertAlmostEqual(static_model("setDataRate", "power"), 146, places=0)
+        self.assertAlmostEqual(static_model("setPALevel", "power"), 147, places=0)
+        self.assertAlmostEqual(static_model("setup", "power"), 153, places=0)
+        self.assertAlmostEqual(static_model("startListening", "power"), 18954, places=0)
+        self.assertAlmostEqual(static_model("stopListening", "power"), 2426, places=0)
+        self.assertAlmostEqual(static_model("write", "power"), 17629, places=0)
+
+        self.assertAlmostEqual(static_model("setAutoAck", "duration"), 90, places=0)
+        self.assertAlmostEqual(static_model("setDataRate", "duration"), 240, places=0)
+        self.assertAlmostEqual(static_model("setPALevel", "duration"), 160, places=0)
+        self.assertAlmostEqual(static_model("setup", "duration"), 6550, places=0)
+        self.assertAlmostEqual(
+            static_model("startListening", "duration"), 470, places=0
+        )
+        self.assertAlmostEqual(static_model("stopListening", "duration"), 510, places=0)
+        self.assertAlmostEqual(static_model("write", "duration"), 11230, places=0)
+
+        self.assertAlmostEqual(
+            model.attr_by_name["write"]["duration"].stats.param_dependence_ratio(
+                "auto_ack!"
+            ),
+            1,
+            places=2,
+        )
+        self.assertAlmostEqual(
+            model.attr_by_name["write"]["power"].stats.param_dependence_ratio(
+                "auto_ack!"
+            ),
+            0.99,
+            places=2,
+        )
+
+        param_model, param_info = model.get_fitted()
+
+        self.assertAlmostEqual(
+            param_model(
+                "write", "duration", param=[0, 76, 1000, 0, 10, None, None, 1500, 0]
+            ),
+            1090,
+            places=0,
+        )
+
+        # only bitrate is relevant
+        self.assertAlmostEqual(
+            param_model(
+                "write",
+                "duration",
+                param=[0, None, 1000, None, None, None, None, None, None],
+            ),
+            1090,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model(
+                "write",
+                "duration",
+                param=[0, None, 250, None, None, None, None, None, None],
+            ),
+            2057,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model(
+                "write",
+                "duration",
+                param=[0, None, 2000, None, None, None, None, None, None],
+            ),
+            929,
+            places=0,
+        )
+
+        # auto_ack == 1 has a different write duration, still only bitrate is relevant
+        self.assertAlmostEqual(
+            param_model(
+                "write", "duration", param=[1, 76, 1000, 0, 10, None, None, 1500, 0]
+            ),
+            22284,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model(
+                "write",
+                "duration",
+                param=[1, None, 1000, None, None, None, None, None, None],
+            ),
+            22284,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model(
+                "write",
+                "duration",
+                param=[1, None, 250, None, None, None, None, None, None],
+            ),
+            33229,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model(
+                "write",
+                "duration",
+                param=[1, None, 2000, None, None, None, None, None, None],
+            ),
+            20459,
+            places=0,
+        )
+
+        """
+        param_model, param_info = model.get_fitted()
+        self.assertEqual(param_info("POWERDOWN", "power"), None)
+        self.assertEqual(
+            param_info("RX", "power")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * np.sqrt(parameter(datarate))",
+        )
+        self.assertAlmostEqual(
+            param_info("RX", "power")["function"].model_args[0], 48530.7, places=0
+        )
+        self.assertAlmostEqual(
+            param_info("RX", "power")["function"].model_args[1], 117, places=0
+        )
+        self.assertEqual(param_info("STANDBY1", "power"), None)
+        self.assertEqual(
+            param_info("TX", "power")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * parameter(txpower) + regression_arg(3) * 1/(parameter(datarate)) * parameter(txpower)",
+        )
+        self.assertEqual(
+            param_info("epilogue", "timeout")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))",
+        )
+        self.assertEqual(
+            param_info("stopListening", "duration")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))",
+        )
+
+        self.assertAlmostEqual(
+            param_model("RX", "power", param=[1, None, None]), 48647, places=-1
+        )
+        """
+
     def test_singlefile_mmparam(self):
         raw_data = RawData(["test-data/20161221_123347_mmparam.tar"])
         preprocessed_data = raw_data.get_preprocessed_data()