Minor changes in constants.py

Author: ypriverol

fslite/fs/constants.py

@@ -1,14 +1,23 @@
 """
 This file contains a list of constants used in the feature selection and machine learning methods.
 """
+from typing import Dict, List, Union
 
 FS_METHODS = {
     'univariate': {
         "title": 'Univariate Feature Selection',
         "methods": [
             {
                 'name': 'anova',
-                'description': 'ANOVA univariate feature selection (F-classification)'
+                'description': 'Univariate ANOVA feature selection (f-classification)'
+            },
+            {
+                'name': 'u_corr',
+                'description': 'Univariate correlation'
+            },
+            {
+                'name': 'f_regression',
+                'description': 'Univariate f-regression'
             }
         ]
     },
@@ -68,7 +77,7 @@ def get_fs_methods():
     """
     return FS_METHODS
 
-def get_fs_method_details(method_name: str):
+def get_fs_method_details(method_name: str) -> Union[Dict, None]:
     """
     Get the details of the feature selection method, this function search in all-methods definitions
     and get the details of the method with the given name. If the method is not found, it returns None.
@@ -82,3 +91,25 @@ def get_fs_method_details(method_name: str):
             if method['name'].lower() == method_name.lower():
                 return method
     return None
+
+def get_fs_univariate_methods() -> List:
+    """
+    Get the list of univariate methods implemented in the library
+    :return: list
+    """
+    univariate_methods = FS_METHODS['univariate']
+    univariate_names = [method["name"] for method in univariate_methods["methods"]]
+    return univariate_names
+
+def is_valid_univariate_method(method_name: str) -> bool:
+    """
+    This method check if the given method name is a supported univariate method
+    :param method_name method name
+    :return: boolean
+    """
+    for method in FS_METHODS["univariate"]["methods"]:
+        if method["name"].lower() == method_name:
+            return True
+    return False
+
+

fslite/fs/fdataframe.py

@@ -221,7 +221,6 @@ def select_features_by_index(self, feature_indexes: List[int]) -> 'FSDataFrame':
     def to_pandas(self) -> DataFrame:
         """
         Return the DataFrame representation of the FSDataFrame.
-
         :return: Pandas DataFrame.
         """
 

fslite/fs/univariate.py

@@ -5,6 +5,7 @@
 import pandas as pd
 from sklearn.feature_selection import SelectKBest, f_classif, f_regression
 
+from fslite.fs.constants import get_fs_univariate_methods, is_valid_univariate_method
 from fslite.fs.fdataframe import FSDataFrame
 
 logging.basicConfig(format="%(levelname)s (%(name)s %(lineno)s): %(message)s")
@@ -100,6 +101,9 @@ def univariate_filter(df: FSDataFrame,
     :return: Filtered DataFrame with selected features
     """
 
+    if not is_valid_univariate_method(univariate_method):
+        raise NotImplementedError("The provided method {} is not implented !! please select one from this list {}".format(univariate_method, get_fs_univariate_methods()))
+
     selected_features = []
 
     if univariate_method == 'anova':

fslite/tests/test_univariate_methods.py

@@ -16,7 +16,7 @@ def test_univariate_filter_corr():
     # create FSDataFrame instance
     fs_df = FSDataFrame(df=df,sample_col='Sample',label_col='label')
 
-    fsdf_filtered = univariate_filter(fs_df,univariate_method='u_corr', corr_threshold=0.3)
+    fsdf_filtered = univariate_filter(fs_df, univariate_method='u_corr', corr_threshold=0.3)
 
     assert fs_df.count_features() == 500
     assert fsdf_filtered.count_features() == 211