Debug

Author: Gossty

qiita_db/support_files/patches/test_db_sql/93.sql

@@ -1,9 +1,10 @@
 INSERT INTO qiita.allocation_equations(equation_name, expression)
-        VALUES  ('mem_model1', 'k * np.log(x) + x * a + b'),
-('mem_model2', 'k * np.log(x) + b * np.log(x)**2 + a'),
-('mem_model3', 'k * np.log(x) + b * np.log(x)**2 + a * np.log(x)**3'),
-('mem_model4', 'k * np.log(x) + b * np.log(x)**2 + a * np.log(x)**2.5'),
-('time_model1', 'a + b + np.log(x) * k'),
-('time_model2', 'a + b * x + np.log(x) * k'),
-('time_model3', 'a + b * np.log(x)**2 + np.log(x) * k'),
-('time_model4', 'a * np.log(x)**3 + b * np.log(x)**2 + np.log(x) * k');
+        VALUES 
+        ('mem_model1', '(k * (np.log(x))) + (x * a) + b'),
+('mem_model2', '(k * (np.log(x))) + (b * ((np.log(x))**2)) + a'),
+('mem_model3', '(k * (np.log(x))) + (b * ((np.log(x))**2)) + (a * ((np.log(x))**3))'),
+('mem_model4', '(k * (np.log(x))) + (b * ((np.log(x))**2)) + (a * ((np.log(x))**2.5))'),
+('time_model1', 'a + b + ((np.log(x)) * k)'),
+('time_model2', 'a + (b * x) + ((np.log(x)) * k)'),
+('time_model3', 'a + (b * ((np.log(x))**2)) + ((np.log(x)) * k)'),
+('time_model4', '(a * ((np.log(x))**3)) + (b * ((np.log(x))**2)) + ((np.log(x)) * k)');

qiita_db/test/test_meta_util.py

@@ -532,13 +532,18 @@ def test_update_resource_allocation_redis(self):
             "model: "
             "k * log(x) + "
             "b * log(x)^2 + "
-            "a * log(x)^3" in title_mem
+            "a * log(x)^2.5" in title_mem
         )
 
         title_time_str = 'resources$#%s$#%s$#%s$#%s:%s' % (
                         cname, sname, version, col_name, 'title_time')
         title_time = str(r_client.get(title_time_str))
-        self.assertTrue("model: a + b + log(x) * k" in title_time)
+        self.assertTrue(
+            "model: "
+            "a * log(x)^3 + "
+            "b * log(x)^2 + "
+            "log(x) * k" in title_time
+        )
 
 
 if __name__ == '__main__':

qiita_db/test/test_util.py

@@ -1353,11 +1353,11 @@ def test_minimize_const(self):
             self.df, k, a, b, bm, self.col_name, 'MaxRSSRaw')[-1]
         failures = failures_df.shape[0]
 
-        self.assertEqual(bm_name, 'mem_model3',
+        self.assertEqual(bm_name, 'mem_model4',
                          msg=f"""Best memory model
                          doesn't match
-                         {bm_name} != 'mem_model3'""")
-        self.assertEqual(bm, mem_models['mem_model3'],
+                         {bm_name} != 'mem_model4'""")
+        self.assertEqual(bm, mem_models['mem_model4'],
                          msg=f"""Best memory model
                                  doesn't match
                                  Coefficients:{k} {a} {b}
@@ -1372,18 +1372,17 @@ def test_minimize_const(self):
         failures_df = qdb.util._resource_allocation_success_failures(
             self.df, k, a, b, bm, self.col_name, 'ElapsedRaw')[-1]
         failures = failures_df.shape[0]
-
-        self.assertEqual(bm_name, 'time_model1',
+        self.assertEqual(bm_name, 'time_model4',
                          msg=f"""Best time model
                          doesn't match
-                         {bm_name} != 'time_model1'""")
+                         {bm_name} != 'time_model4'""")
 
-        self.assertEqual(bm, qdb.util.time_model1,
+        self.assertEqual(bm, time_models[bm_name],
                          msg=f"""Best time model
                                 doesn't match
                                 Coefficients:{k} {a} {b}
                                 """)
-        self.assertEqual(failures, 1, "Number of failures must be 1")
+        self.assertEqual(failures, 0, "Number of failures must be 0")
 
     def test_MaxRSS_helper(self):
         tests = [

qiita_db/util.py

@@ -2405,7 +2405,7 @@ def _retrieve_equations():
             if 'mem' in models[1]:
                 memory_models[models[1]] = lambda x, k, a, b: eval(models[2])
             else:
-                time_models[models[2]] = lambda x, k, a, b: eval(models[2])
+                time_models[models[1]] = lambda x, k, a, b: eval(models[2])
         return (memory_models, time_models)