Add try catch

hloeffler · Mar 13, 2018 · 82dda8b · 82dda8b
1 parent a24f3ab
commit 82dda8b
Showing 1 changed file with 40 additions and 28 deletions.
diff --git a/server/ai/src/learn.py b/server/ai/src/learn.py
@@ -94,29 +94,35 @@ def do_classification(self, header, csv_data):
                 continue
             payload['predictions'].append(predict_payload)
 
-        t2 = time.time()
-        name = "Extended Naive Bayes"
-        clf = ExtendedNaiveBayes(self.family,path_to_data=self.path_to_data)
-        predictions = clf.predict_proba(header,csv_data)
-        predict_payload = {'name': name,'locations': [], 'probabilities': []}
-        for tup in predictions:
-            predict_payload['locations'].append(str(self.naming['from'][tup[0]]))
-            predict_payload['probabilities'].append(round(tup[1],2))
-        payload['predictions'].append(predict_payload)
-        self.logger.debug("{} {:d} ms".format(name,int(1000 * (t2 - time.time()))))
+        try:
+            t2 = time.time()
+            name = "Extended Naive Bayes"
+            clf = ExtendedNaiveBayes(self.family,path_to_data=self.path_to_data)
+            predictions = clf.predict_proba(header,csv_data)
+            predict_payload = {'name': name,'locations': [], 'probabilities': []}
+            for tup in predictions:
+                predict_payload['locations'].append(str(self.naming['from'][tup[0]]))
+                predict_payload['probabilities'].append(round(tup[1],2))
+            payload['predictions'].append(predict_payload)
+            self.logger.debug("{} {:d} ms".format(name,int(1000 * (t2 - time.time()))))
+        except Exception as e:
+            self.logger.error(str(e))
 
-        t2 = time.time()
-        name = "Extended Naive Bayes2"
-        clf = ExtendedNaiveBayes2(self.family, path_to_data=self.path_to_data)
-        predictions = clf.predict_proba(header, csv_data)
-        predict_payload = {'name': name, 'locations': [], 'probabilities': []}
-        for tup in predictions:
-            predict_payload['locations'].append(
-                str(self.naming['from'][tup[0]]))
-            predict_payload['probabilities'].append(round(tup[1], 2))
-        payload['predictions'].append(predict_payload)
-        self.logger.debug("{} {:d} ms".format(
-            name, int(1000 * (t2 - time.time()))))
+        try:
+            t2 = time.time()
+            name = "Extended Naive Bayes2"
+            clf = ExtendedNaiveBayes2(self.family, path_to_data=self.path_to_data)
+            predictions = clf.predict_proba(header, csv_data)
+            predict_payload = {'name': name, 'locations': [], 'probabilities': []}
+            for tup in predictions:
+                predict_payload['locations'].append(
+                    str(self.naming['from'][tup[0]]))
+                predict_payload['probabilities'].append(round(tup[1], 2))
+            payload['predictions'].append(predict_payload)
+            self.logger.debug("{} {:d} ms".format(
+                name, int(1000 * (t2 - time.time()))))
+        except Exception as e:
+            self.logger.error(str(e))
 
         self.logger.debug("{:d} ms".format(int(1000 * (t - time.time()))))
         return payload
@@ -202,16 +208,22 @@ def learn(self, fname):
         t2 = time.time()
         name = "Extended Naive Bayes"
         clf = ExtendedNaiveBayes(self.family, path_to_data=self.path_to_data)
-        clf.fit(fname)
-        self.logger.debug("learned {}, {:d} ms".format(
-            name, int(1000 * (t2 - time.time()))))
+        try:
+            clf.fit(fname)
+            self.logger.debug("learned {}, {:d} ms".format(
+                name, int(1000 * (t2 - time.time()))))
+        except Exception as e:
+            self.logger.error(str(e))
 
         t2 = time.time()
         name = "Extended Naive Bayes2"
         clf = ExtendedNaiveBayes2(self.family, path_to_data=self.path_to_data)
-        clf.fit(fname)
-        self.logger.debug("learned {}, {:d} ms".format(
-            name, int(1000 * (t2 - time.time()))))
+        try:
+            clf.fit(fname)
+            self.logger.debug("learned {}, {:d} ms".format(
+                name, int(1000 * (t2 - time.time()))))
+        except Exception as e:
+            self.logger.error(str(e))
 
         self.logger.debug("{:d} ms".format(int(1000 * (t - time.time()))))