Time Series Data Splitting with User Behavior Consideration

Splitting time series data into training and testing sets is a crucial step in machine learning model development. However, when user behavior is involved, the process becomes more complex due to potential data leakage issues. In this article, we will explore how to properly split time series data while considering user behavior.

Introduction

Time series data represents information that varies over time, such as sales figures or sensor readings. Machine learning models are often trained on these datasets to predict future values. However, when user behavior is involved, the dataset becomes more complex and requires careful handling to avoid data leakage issues.

Data leakage occurs when there is a correlation between the training and testing sets, causing the model to learn patterns that are not representative of the test data. This can result in biased or inaccurate predictions on unseen data.

Problem Statement

The problem presented in the question is as follows:

• We have a dataset containing dated information about user purchases.
• We want to split the data at 70:30, but we need to ensure that there is no data leakage.
• The data is sorted and user behavior should be taken into consideration.

Solution

To solve this problem, we can use the following steps:

1. Group by ‘Time’: Group the dataset by date using the groupby function in pandas.
2. Calculate cumulative percentage: Calculate the cumulative count of items for each date group and divide it by the total number of rows to get a percentage.
3. Find maximum train date: Find the maximum date that falls below the 70% threshold.
4. Split dataset into training and testing sets: Split the original dataset into two sets based on the maximum train date.

Code

Here is the complete code to solve this problem:

import pandas as pd

# Sample dataset
data = {
    'Time': ['2023-08-15', '2023-08-15', '2023-08-15', '2023-08-16', '2023-08-16', '2023-08-16', '2023-08-16'],
    'user_id': [1, 1, 2, 1, 2, 3, 3],
    'product': ['prod1', 'prod2', 'prod1', 'prod3', 'prod4', 'prod1', 'prod4']
}

df = pd.DataFrame(data)

total_rows = df.shape[0]

# Step 1: Group by 'Time' and calculate cumulative count
df_grouped = df.groupby('Time')['product'].count().cumsum()

# Step 2: Calculate percentage for each date group
df_grouped['percent'] = df_grouped / total_rows

# Step 3: Find maximum train date that falls below the 70% threshold
train_mask = (df_grouped['percent'] < .70)
max_train_date = df_grouped[train_mask]['Time'].max()

# Step 4: Split dataset into training and testing sets based on the maximum train date
train_set_mask = (df['Time'] <= max_train_date)
test_set_mask = (df['Time'] > max_train_date)

df_train = df[train_set_mask].copy()
df_test = df[test_set_mask].copy()

print("Training set:")
print(df_train)
print("\nTesting set:")
print(df_test)

Conclusion

In this article, we explored how to properly split time series data while considering user behavior. We used the following steps:

• Group by ‘Time’ and calculate cumulative count of items for each date group.
• Calculate percentage for each date group and find the maximum train date that falls below the 70% threshold.
• Split the dataset into training and testing sets based on the maximum train date.

This approach ensures that there is no data leakage issue and provides a more accurate representation of the test data.

Last modified on 2024-07-31