Added cross validation

ElM2D/ElM2D.py

@@ -264,6 +264,76 @@ def sort(self):
         return sorted_indices
 
 
+    def cross_validate(self, y=None, k=5, shuffle=True, seed=42):
+        """
+        Implementation of cross validation with K-Folds.
+        
+        Splits the formula_list into k equal sized partitions and returns five 
+        tuples of training and test sets. Returns a list of length k, each item 
+        containing 2 (4 with target data) numpy arrays of formulae of 
+        length n - n/k and n/k.
+
+        Parameters:
+            y=None: (optional) a numpy array of target properties to cross validate
+            k=5: Number of k-folds
+            shuffle=True: whether to shuffle the input formulae or not
+
+        Usage:
+            cvs = mapper.cross_validate()
+            for i, (X_train, X_test) in enumerate(cvs):
+                sub_mapper = ElM2D()
+                sub_mapper.fit(X_train)
+                sub_mapper.save(f"train_elm2d_{i}.pk")
+                sub_mapper.fit(X_test)
+                sub_mapper.save(f"test_elm2d_{i}.pk")
+            ...
+            cvs = mapper.cross_validate(y=df["target"])
+            for X_train, X_test, y_train, y_test in cvs:
+                clf.fit(X_train, y_train)
+                y_pred = clf.predict(X_test)
+                errors.append(mae(y_pred, y_test))
+            print(np.mean(errors))
+        """
+        inds = np.arange(len(self.formula_list)) # TODO Exception
+
+        if shuffle:
+            np.random.seed(seed) 
+            np.random.shuffle(inds)
+
+        formulas = self.formula_list.to_numpy(str)[inds]
+        splits = np.array_split(formulas, k)
+
+        X_ret = []
+
+        for i in range(k):
+            train_splits = np.delete(np.arange(k), i)
+            X_train = splits[train_splits[0]]
+
+            for index in train_splits[1:]:
+                X_train = np.concatenate((X_train, splits[index]))
+
+            X_test = splits[i]
+            X_ret.append((X_train, X_test))
+
+        if y is None:
+            return X_ret
+
+        y = y[inds]
+        y_splits = np.array_split(y, k)
+        y_ret = []
+
+        for i in range(k):
+            train_splits = np.delete(np.arange(k), i)
+            y_train = y_splits[train_splits[0]]
+
+            for index in train_splits[1:]:
+                y_train = np.concatenate((y_train, y_splits[index]))
+
+            y_test = y_splits[i]
+            y_ret.append((y_train, y_test))
+
+        return [(X_ret[i][0], X_ret[i][1], y_ret[i][0], y_ret[i][1]) for i in range(k)]
+
     def _process_list(self, formula_list, n_proc, metric="mod_petti"):
         '''
         Given an iterable list of formulas in composition form

README.md

@@ -114,8 +114,35 @@ mapper.export_dm("large_df_emb_UMAP.csv")
 mapper = ElM2D()
 mapper.import_dm("large_df_dm.csv")
 mapper.import_embedding("large_df_emb_UMAP.csv")
+mapper.formula_list = df["formula"]
 ```
 
+### Cross Validation
+
+```python
+cvs = mapper.cross_validation()
+for i, (X_train, X_test) in enumerate(cvs):
+    sub_mapper = ElM2D()
+
+    sub_mapper.fit(X_train)
+    sub_mapper.save(f"train_elm2d_{i}.pk")
+
+    sub_mapper.fit(X_test)
+    sub_mapper.save(f"test_elm2d_{i}.pk")
+...
+from sklearn.metrics import mean_average_error as mae
+
+cvs = mapper.cross_validation(y=df["target"])
+
+for X_train, X_test, y_train, y_test in cvs:
+    clf.fit(X_train, y_train)
+    y_pred = clf.predict(X_test)
+    errors.append(mae(y_pred, y_test))
+
+print(np.mean(errors))
+```
+
+
 ## Citing
 
 If you would like to cite this code in your work, please use the Chemistry of Materials reference

setup.py

@@ -2,13 +2,13 @@
 setup(
   name = 'ElM2D',        
   packages = ['ElM2D'],  
-  version = '0.2.9',      
+  version = '0.2.10',      
   license='GPL3',       
   description = 'A high performance mapping class to embed large datasets of ionic compositions with respect to the ElMD metric.',  
   author = 'Cameron Hagreaves',              
   author_email = 'cameron.h@rgreaves.me.uk', 
   url = 'https://github.com/lrcfmd/ElM2D/',   
-  download_url = 'https://github.com/lrcfmd/ElM2D/archive/0.2.9.tar.gz',    
+  download_url = 'https://github.com/lrcfmd/ElM2D/archive/0.2.10.tar.gz',    
   keywords = ['ChemInformatics', 'Materials Science', 'Machine Learning', 'Materials Representation'],   
   install_requires=[            
           'cython',