In this post, we will provide an example of Cross Validation using the K-Fold method with the python scikit learn library.  The K-Fold Cross Validation example would have k parameters equal to 5. By using a ‘for’ loop, we will fit each model using 4 folds for training data and 1 fold for testing data, and then we will call the accuracy_score method from scikit learn to determine the accuracy of the model.

The example is divided into the following steps:

• Step 1: Import the libraries and load into the environment Open, High, Low, Close data for EURUSD
• Step 2: Create features with the create_features() function
• Step 3: Run the model with the Validation Set approach
• Step 4: Run the model with the K-Fold Cross Validation approach

import pandas as pd
from sklearn.model_selection import KFold
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn import tree
import matplotlib.pyplot as plt
import seaborn as sns

# Read csv file to load into the environment OHLC data from EURUSD.
eurusd_ohlc = pd.read_csv("C:/Users/Nicolas/Documents/Machine Learning Course/eurusd.csv")
 
# The create_features function, receives the eurusd_ohlc parameter  and create new features to use in a machine learning model
 
def create_features(fx_data):
    '''
    Parameters:
        fx_data: has Open-High-Low-Close data for currency pair EURUSD between  2001-08-21 to 2019-09-21
    Return:
     fx_data: dataframe with original and new data with the features of the model
        target: target variable to predict, which contains the direction of the price.  The values can be 1 for up direction and -1 for down direction.
    '''
    
    # Convert all columns of the stock_data data frame to numeric columns
    fx_data = fx_data.apply(pd.to_numeric)
    
    # Reverse the index to have old values at top of the dataframe 
    fx_data = fx_data.sort_values('Date')
    
    # Create features to use in the machine learning model
    fx_data['High-Low'] = fx_data['High'] - fx_data['Low']
    fx_data['pct_change'] = fx_data['Close'].pct_change()
    fx_data['ret_5'] = fx_data['pct_change'].rolling(5).mean()
    
    # Calculate RSI Indicator
    close = fx_data['Close']
    # Get the difference in price from previous step
    delta = close.diff()
    # Get rid of the first row, which is Nan since it did not have a previous 
    # row to calculate the differences
    delta = delta[1:] 
 
    # Make the positive gains (up) and negative gains (down) Series
    up, down = delta.copy(), delta.copy()
    up[up < 0] = 0
    down[down > 0] = 0
 
    # Calculate the EWMA
    roll_up = up.rolling(center=False,window=14).mean()
    roll_down = abs(down).rolling(center=False,window=14).mean()
 
    # Calculate the RSI based on EWMA
    RS = roll_up / roll_down
    RSI = 100.0 - (100.0 / (1.0 + RS))
    
    fx_data['RSI'] = RSI
    fx_data.dropna(inplace=True)
    # Create the target variable that take the values of 1 if the stock price go up or -1 if the stock price go down
    target = np.where(fx_data['Close'].shift(-1) > fx_data['Close'], 1, -1)
    
    return fx_data, target
 
features, target = create_features(eurusd_ohlc)
 
# Validation Set approach : take 80% of the data as the training set and 20 % as the test set. X is a dataframe with  the input variable
X = features[['High-Low', 'pct_change', 'ret_5','RSI']]
 
# Y is the target or output variable 
y = target
 
length_to_split = int(len(features) * 0.8)
# Splitting the X and y into train and test datasets
X_train, X_test = X[:length_to_split], X[length_to_split:]
y_train, y_test = y[:length_to_split], y[length_to_split:]
 
# Print the size of the train and test dataset
print(X_train.shape, X_test.shape)
print(y_train.shape, y_test.shape)
    
clf = tree.DecisionTreeClassifier(random_state=20)
 
# Create the model on train dataset
model = clf.fit(X_train, y_train)
 
# Calculate the accuracy
print(accuracy_score(y_test, model.predict(X_test), normalize=True)*100)
 
# KFold Cross Validation approach
kf = KFold(n_splits=5,shuffle=False)
kf.split(X)    
     
# Initialize the accuracy of the models to blank list. The accuracy of each model will be appended to this list
accuracy_model = []
 
# Iterate over each train-test split
for train_index, test_index in kf.split(X):
    # Split train-test
    X_train, X_test = X.iloc[train_index], X.iloc[test_index]
    y_train, y_test = y[train_index], y[test_index]
    # Train the model
    model = clf.fit(X_train, y_train)
    # Append to accuracy_model the accuracy of the model
    accuracy_model.append(accuracy_score(y_test, model.predict(X_test), normalize=True)*100)

# Print the accuracy    
print(accuracy_model)

(3989, 4) (998, 4)
(3989,) (998,)
51.4028
[50.501, 52.004, 48.9468, 46.1384, 51.3541]

These 4 lines above are the outputs of the print() messages. (3989, 4) (998, 4) are the size of the X_train and X_test dataset where 3989 is the number of observations in the train dataset and 4 is the number of features in the train dataset. 998 is the number of observations in the test dataset, and 4 is the number of features in the test dataset.

(3989,) (998,) are the size of y_train and y_test. 51.4028 is the accuracy score with the Validation set approach and [50.501, 52.004, 48.9468, 46.1384, 51.3541] is the accuracy_model list which show the accuracy in each iteration using the K-Fold Cross Validation method.

K-Fold Cross Validation gives a better idea of how the model will perform with new or live data, because we have used 5 different testing sets to obtain measures of the model performance. 

Finally we use a bar plot to visualize the score measure in each iteration:

### Visualize accuracy for each iteration
 
scores = pd.DataFrame(accuracy_model,columns=['Scores'])
 
sns.set(style="white", rc={"lines.linewidth": 3})
sns.barplot(x=['Iter1','Iter2','Iter3','Iter4','Iter5'],y="Scores",data=scores)
plt.show()
sns.set()

K-Fold Cross Validation Scores