Add a CrossValidation module with all the required methods.

machine_learning/cross_validation.py

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+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from sklearn.model_selection import LeaveOneGroupOut, StratifiedKFold
+
+class CrossValidation():
+    """This code implements a CrossValidation class for creating cross validation splits.
+    """
+    
+    
+    def __init__(self, data=None, cv_method='logo'):
+        """This method initializes the cv_method argument and optionally prepares the data if supplied.
+
+        Args:
+            cv_method (str, optional): String of cross validation method; options are 'logo', 'half_logo' and '5kfold'. 
+                Defaults to 'logo'.
+            data (DataFrame, optional): Pandas DataFrame with target, pid columns and other features as columns. 
+                Defaults to None.
+        """
+        
+        self.initialize_cv_method(cv_method)
+        
+        if data is not None:
+            self.prepare_data(data)
+        
+        
+    def prepare_data(self, data):
+        """Prepares the data ready to be passed to the cross-validation algorithm, depending on the cv_method type. 
+            For example, if cv_method is set to 'half_logo' new columns 'pid_index', 'pid_count', 'pid_half' 
+            are added and used in the process.
+
+        Args:
+            data (_type_): Pandas DataFrame with target, pid columns and other features as columns.
+        """
+        self.data = data
+        if self.cv_method == "logo":
+            data_X, data_y, data_groups = data.drop(["target", "pid"], axis=1), data["target"], data["pid"]
+            
+        elif self.cv_method == "half_logo":
+            data['pid_index'] = data.groupby('pid').cumcount()
+            data['pid_count'] = data.groupby('pid')['pid'].transform('count')
+
+            data["pid_index"] = (data['pid_index'] / data['pid_count'] + 1).round()
+            data["pid_half"] = data["pid"] + "_" +  data["pid_index"].astype(int).astype(str)
+
+            data_X, data_y, data_groups = data.drop(["target", "pid", "pid_index", "pid_half"], axis=1), data["target"], data["pid_half"]
+           
+        elif self.cv_method == "5kfold":
+            data_X, data_y, data_groups = data.drop(["target", "pid"], axis=1), data["target"], data["pid"]
+
+        self.X, self.y, self.groups = data_X, data_y, data_groups
+
+
+    def initialize_cv_method(self, cv_method):
+        """Initializes the given cv_method type. Depending on the type, the appropriate splitting technique is used.
+        
+        Args:
+            cv_method (str): The type of cross-validation method to use; options are 'logo', 'half_logo' and '5kfold'.
+
+        Raises:
+            ValueError: If cv_method is not in the list of available methods, it raises an ValueError.
+        """
+        
+        self.cv_method = cv_method
+        if self.cv_method not in ["logo", "half_logo", "5kfold"]:
+            raise ValueError("Invalid cv_method input. Correct values are: 'logo', 'half_logo', '5kfold'")
+        
+        if self.cv_method in ["logo", "half_logo"]:
+            self.cv = LeaveOneGroupOut()
+        elif self.cv_method == "5kfold":
+            self.cv = StratifiedKFold(n_splits=5, shuffle=True)
+
+
+    def get_splits(self):
+        """Returns a generator object containing the cross-validation splits. 
+
+        Raises:
+            ValueError: Raises ValueError if no data has been set.
+
+        """
+        if not self.data.empty:
+            return self.cv.split(self.X, self.y, self.groups)
+        else: 
+            raise ValueError("No data has been set. Use 'prepare_data(data)' method to set the data.")
+        
+        
+    def get_data(self):
+        """data getter
+
+        Returns:
+            Pandas DataFrame: Returns the data from the class instance.
+        """
+        return self.data
+    
+    
+    def get_x_y_groups(self):
+        """X, y, and groups data getter
+
+        Returns:
+            Pandas DataFrame: Returns the data from the class instance.
+        """
+        return self.X, self.y, self.groups
+    
+    
+    def get_train_test_sets(self, split):
+        """Gets train and test sets, dependent on the split parameter. This method can be used in a specific splitting context,
+        where by index we can get train and test sets.
+
+        Args:
+            split (tuple of indices): It represents one iteration of the split generator (see get_splits method). 
+
+        Returns:
+            tuple of Pandas DataFrames: This method returns train_X, train_y, test_X, test_y, with correctly indexed rows by split param.
+        """
+        return self.X.iloc[split[0]], self.y.iloc[split[0]], self.X.iloc[split[1]], self.y.iloc[split[1]]
+    
+