Added functions for testing accuracy of current regression model and associated test.

compiler/characterizer/linear_regression.py

@@ -19,13 +19,16 @@ class linear_regression(regression_model):
     def __init__(self, sram, spfile, corner):
         super().__init__(sram, spfile, corner)
 
+    def get_model(self):
+        return Ridge()
+
     def generate_model(self, features, labels):
         """
         Supervised training of model.
         """
         
         #model = LinearRegression()
-        model = Ridge()
+        model = self.get_model()
         model.fit(features, labels)
         return model
         

compiler/characterizer/regression_model.py

@@ -150,6 +150,43 @@ class regression_model(simulation):
 
         return models
     
+    def score_model(self):
+        num_inputs = 9 #FIXME - should be defined somewhere else
+        self.output_names = get_data_names(data_path)[num_inputs:]
+        data = get_scaled_data(data_path)
+        features, labels = data[:, :num_inputs], data[:,num_inputs:]
+
+        output_num = 0
+        models = {}
+        debug.info(1, "Output name, score")
+        for o_name in self.output_names:
+            output_label = labels[:,output_num]
+            model = self.generate_model(features, output_label)
+            scr = model.score(features, output_label)
+            debug.info(1, "{}, {}".format(o_name, scr))
+            output_num+=1
+    
+    
+    def cross_validation(self):
+        from sklearn.model_selection import cross_val_score
+        untrained_model = self.get_model()
+        
+        num_inputs = 9 #FIXME - should be defined somewhere else
+        self.output_names = get_data_names(data_path)[num_inputs:]
+        data = get_scaled_data(data_path)
+        features, labels = data[:, :num_inputs], data[:,num_inputs:]
+
+        output_num = 0
+        models = {}
+        debug.info(1, "Output name, mean_accuracy, std_dev")
+        for o_name in self.output_names:
+            output_label = labels[:,output_num]
+            scores = cross_val_score(untrained_model, features, output_label, cv=5)
+            debug.info(1, "{}, {}, {}".format(o_name, scores.mean(), scores.std()))
+            output_num+=1
+        
+            
+    
     # Fixme - only will work for sklearn regression models
     def save_model(self, model_name, model):
         try:

compiler/tests/21_regression_model_test.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+# See LICENSE for licensing information.
+#
+# Copyright (c) 2016-2021 Regents of the University of California and The Board
+# of Regents for the Oklahoma Agricultural and Mechanical College
+# (acting for and on behalf of Oklahoma State University)
+# All rights reserved.
+#
+import unittest
+from testutils import *
+import sys, os
+sys.path.append(os.getenv("OPENRAM_HOME"))
+import globals
+from globals import OPTS
+from sram_factory import factory
+import debug
+
+# @unittest.skip("SKIPPING 21_regression_model_test")
+class regression_model_test(openram_test):
+    """ Compare the accuracy of the analytical model with a spice simulation. """
+
+    def runTest(self):
+        config_file = "{}/tests/configs/config".format(os.getenv("OPENRAM_HOME"))
+        globals.init_openram(config_file)
+        OPTS.analytical_delay = False
+        OPTS.netlist_only = True
+
+        # This is a hack to reload the characterizer __init__ with the spice version
+        from importlib import reload
+        import characterizer
+        reload(characterizer)
+        from characterizer import linear_regression
+        from sram import sram
+        from sram_config import sram_config
+        c = sram_config(word_size=1,
+                        num_words=16,
+                        num_banks=1)
+        c.words_per_row=1
+        c.recompute_sizes()
+        debug.info(1, "Testing timing for sample 1bit, 16words SRAM with 1 bank")
+        s = factory.create(module_type="sram", sram_config=c)
+
+        tempspice = OPTS.openram_temp + "temp.sp"
+        s.sp_write(tempspice)
+
+        probe_address = "1" * s.s.addr_size
+        probe_data = s.s.word_size - 1
+        debug.info(1, "Probe address {0} probe data bit {1}".format(probe_address, probe_data))
+
+        corner = (OPTS.process_corners[0], OPTS.supply_voltages[0], OPTS.temperatures[0])
+        
+        m = linear_regression(s.s, tempspice, corner)
+        m.cross_validation()
+
+        # Only compare the delays
+        # spice_delays = {key:value for key, value in spice_data.items() if 'delay' in key}
+        # spice_delays['min_period'] = spice_data['min_period']
+        # model_delays = {key:value for key, value in model_data.items() if 'delay' in key}
+        # model_delays['min_period'] = model_data['min_period']
+        # debug.info(1,"Spice Delays={}".format(spice_delays))
+        # debug.info(1,"Model Delays={}".format(model_delays))
+
+        # if OPTS.tech_name == "freepdk45":
+            # error_tolerance = 0.25
+        # elif OPTS.tech_name == "scn4m_subm":
+            # error_tolerance = 0.25
+        # else:
+            # self.assertTrue(False) # other techs fail
+
+        # print('spice_delays', spice_delays)
+        # print('model_delays', model_delays)
+
+        # # Check if no too many or too few results
+        # self.assertTrue(len(spice_delays.keys())==len(model_delays.keys()))
+
+        # self.assertTrue(self.check_golden_data(spice_delays,model_delays,error_tolerance))
+
+        globals.end_openram()
+
+# run the test from the command line
+if __name__ == "__main__":
+    (OPTS, args) = globals.parse_args()
+    del sys.argv[1:]
+    header(__file__, OPTS.tech_name)
+    unittest.main(testRunner=debugTestRunner())