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