Update svm.py

superstyl/svm.py

@@ -1,3 +1,6 @@
+
+
+
 import sklearn.svm as sk
 import sklearn.metrics as metrics
 import sklearn.decomposition as decomp
@@ -101,20 +104,27 @@ def train_svm(train, test, cross_validate=None, k=10, dim_reduc=None, norms=True
             estimators.append(('sampling', over.RandomOverSampler(random_state=42)))
 
         if balance in ['SMOTE', 'SMOTETomek']:
-            # Adjust n_neighbors for SMOTE/SMOTETomek based on smallest class size: 
+            # Adjust n_neighbors for SMOTE based on smallest class size: 
             # Ensures that the resampling method does not attempt to use more neighbors than available samples
             # in the minority class, which produced the error.
             min_class_size = min(Counter(classes).values())
             n_neighbors = min(5, min_class_size - 1)  # Default n_neighbors in SMOTE is 5
             # In case we have to temper with the n_neighbors, we print a warning message to the user
             # (might be written more clearly, but we want a short message, right?)
-            if n_neighbors >= min_class_size:
+            if 0 < n_neighbors >= min_class_size:
                 print(
-                    f"Warning: Adjusting n_neighbors for SMOTE / SMOTETomek to {n_neighbors} due to small class size.")
-            if balance == 'SMOTE':
-                estimators.append(('sampling', over.SMOTE(n_neighbors=n_neighbors, random_state=42)))
-            elif balance == 'SMOTETomek':
-                estimators.append(('sampling', comb.SMOTETomek(n_neighbors=n_neighbors, random_state=42)))
+                    f"Warning: Adjusting n_neighbors for SMOTE to {n_neighbors} due to small class size.")
+
+            if n_neighbors == 0:
+                print(
+                    f"Warning: at least one class only has a single individual; cannot apply SMOTE(Tomek) due to small class size.")
+            else:
+
+                if balance == 'SMOTE':
+                    estimators.append(('sampling', over.SMOTE(k_neighbors=n_neighbors, random_state=42)))
+
+                elif balance == 'SMOTETomek':
+                    estimators.append(('sampling', comb.SMOTETomek(random_state=42, smote=over.SMOTE(k_neighbors=n_neighbors, random_state=42))))
 
     print(".......... choosing SVM ........")
 
@@ -255,7 +265,6 @@ def train_svm(train, test, cross_validate=None, k=10, dim_reduc=None, norms=True
 
     return results
 
-
 # Following function from Aneesha Bakharia
 # https://aneesha.medium.com/visualising-top-features-in-linear-svm-with-scikit-learn-and-matplotlib-3454ab18a14d
 

tests/test_train_svm.py

@@ -53,8 +53,51 @@ def test_train_svm(self):
         self.assertEqual(results["misattributions"].to_dict(), expected_results["misattributions"])
         self.assertEqual(list(results.keys()), expected_keys)
 
+        #TODO: quick tests for SMOTE, SMOTETOMEK, to improve
         # WHEN
-        #results = superstyl.train_svm(train, test, final_pred=False, balance="SMOTETomek")
+        results = superstyl.train_svm(train, test, final_pred=False, balance="SMOTETomek")
+        # THEN
+        self.assertEqual(results["confusion_matrix"].to_dict(), expected_results["confusion_matrix"])
+        self.assertEqual(results["classification_report"], expected_results["classification_report"])
+        self.assertEqual(results["misattributions"].to_dict(), expected_results["misattributions"])
+        self.assertEqual(list(results.keys()), expected_keys)
+
+        # WHEN
+        results = superstyl.train_svm(train, test, final_pred=False, balance="SMOTE")
+        # THEN
+        self.assertEqual(results["confusion_matrix"].to_dict(), expected_results["confusion_matrix"])
+        self.assertEqual(results["classification_report"], expected_results["classification_report"])
+        self.assertEqual(results["misattributions"].to_dict(), expected_results["misattributions"])
+        self.assertEqual(list(results.keys()), expected_keys)
+
+        # now, when it can be applied, but needs to be recomputed, because of a maximum possible number of
+        # neighbors < 5
+        train2 = pandas.DataFrame({'author': {'Dupont_Letter1.txt': 'Dupont', 'Dupont_Letter2.txt': 'Dupont', 'Smith_Letter1.txt': 'Smith', 'Smith_Letter2.txt': 'Smith'},
+                           'lang': {'Dupont_Letter1.txt': 'NA', 'Dupont_Letter2.txt': 'NA', 'Smith_Letter1.txt': 'NA', 'Smith_Letter2.txt': 'NA'},
+                           'this': {'Dupont_Letter1.txt': 0.0, 'Dupont_Letter2.txt': 0.0, 'Smith_Letter1.txt': 0.25, 'Smith_Letter2.txt': 0.2},
+                           'is': {'Dupont_Letter1.txt': 0.0, 'Dupont_Letter2.txt': 0.0, 'Smith_Letter1.txt': 0.25, 'Smith_Letter2.txt': 0.2},
+                           'the': {'Dupont_Letter1.txt': 0.0, 'Dupont_Letter2.txt': 0.0, 'Smith_Letter1.txt': 0.25, 'Smith_Letter2.txt': 0.2},
+                           'text': {'Dupont_Letter1.txt': 0.0, 'Dupont_Letter2.txt': 0.0, 'Smith_Letter1.txt': 0.25, 'Smith_Letter2.txt': 0.2},
+                           'voici': {'Dupont_Letter1.txt': 1/3, 'Dupont_Letter2.txt': 1/3, 'Smith_Letter1.txt': 0.0, 'Smith_Letter2.txt': 0.0},
+                           'le': {'Dupont_Letter1.txt': 1/3, 'Dupont_Letter2.txt': 1/3, 'Smith_Letter1.txt': 0.0, 'Smith_Letter2.txt': 0.0},
+                           'texte': {'Dupont_Letter1.txt': 1/3, 'Dupont_Letter2.txt': 1/3, 'Smith_Letter1.txt': 0.0, 'Smith_Letter2.txt': 0.0},
+                           'also': {'Dupont_Letter1.txt': 0.0, 'Dupont_Letter2.txt': 0.0, 'Smith_Letter1.txt': 0.0, 'Smith_Letter2.txt': 0.2}})
+
+        # WHEN
+        results = superstyl.train_svm(train2, test, final_pred=True, balance="SMOTETomek")
+        # THEN
+        expected_preds = {'filename': {0: 'Dupont_Letter1.txt', 1: 'Smith_Letter1.txt',
+                                                        2: 'Smith_Letter2.txt'},
+                                           'author': {0: 'Dupont', 1: 'Smith', 2: 'Smith'},
+                                           'Decision function': {0: -0.883772535448984, 1: 0.8756912342726781,
+                                                                 2: 0.873288374519472}}
+
+        self.assertEqual(results['final_predictions'].to_dict()["author"], expected_preds["author"])
+
+        # WHEN
+        results = superstyl.train_svm(train2, test, final_pred=True, balance="SMOTE")
+        # THEN
+        self.assertEqual(results['final_predictions'].to_dict()["author"], expected_preds["author"])
 
 
         # This is only the first minimal tests for this function