I’m working on a machine learning project to predict when to activate suppliers in an industrial purchasing process. I have two main datasets:

1. doc3: Contains daily information about purchase orders.
2. expedite2: Contains records of when suppliers were activated.

My goal is to create a model that can predict, on any given day, whether a supplier needs to be activated based on historical patterns.

Here’s a simplified version of my current data preparation function:

import pandas as pd

def optimized_join_and_prepare_data(doc3, expedite2):
    doc3['PROCESS_DATE'] = pd.to_datetime(doc3['PROCESS_DATE'])
    expedite2['LOG_DATE'] = pd.to_datetime(expedite2['LOG_DATE'])
    
    doc3['item_key'] = doc3['DOC_NUM'].astype(str) + '_' + doc3['DOC_ITEM'].astype(str) + '_' + doc3['SAP_CODE']
    expedite2['item_key'] = expedite2['DOC_NUM'].astype(str) + '_' + expedite2['DOC_ITEM'].astype(str) + '_' + expedite2['SAP_WEB_CODE']
    
    doc3 = doc3.sort_values('PROCESS_DATE')
    expedite2 = expedite2.sort_values('LOG_DATE')
    
    merged = pd.merge_asof(doc3, expedite2[['item_key', 'LOG_DATE', 'DATA01']], 
                           left_on='PROCESS_DATE', 
                           right_on='LOG_DATE', 
                           by='item_key',
                           direction='nearest', 
                           tolerance=pd.Timedelta('2d'))
        
    merged['LOG_DATE'] = merged['LOG_DATE'].fillna(pd.NaT)
    merged['DATA01'] = merged['DATA01'].fillna('NO_ACTIVATION')
    merged = merged.dropna(subset=['PROCESS_DATE', 'item_key'])
    
    return merged

final_data = optimized_join_and_prepare_data(doc3, expedite2)

My main concerns and questions are:

1. Is there a risk of data leakage in this approach, especially with the use of merge_asof and the 2-day tolerance?

2. If data leakage is present, how can I modify this function to ensure I’m only using past information for each prediction day while maintaining computational efficiency?

3. Are there any other time series-specific considerations I should keep in mind when preparing this data for a predictive model?

I’m dealing with a large dataset, so efficiency is a concern, but I want to ensure the integrity of my time series analysis. Any insights or suggestions for improvement would be greatly appreciated!

I tried several approaches to prepare my time series data:

1. Initially, I used a simple merge on the item key and date, but this resulted in many missing values and didn’t capture the temporal nature of the data.

2. Then, I implemented the merge_asof function as shown in my code. I expected this to align the activation data (from expedite2) with the nearest processing date (from doc3), within a 2-day window. This worked well in terms of efficiency and seemed to capture recent activations.

3. I also added historical features like previous activations and days since last activation using the shift function within each item group.

What I expected:

• A dataset where each row in doc3 is enriched with the most recent activation data.
• Historical features that capture past activation patterns for each item.
• An efficient process that can handle large datasets.

What actually happened:

• The function runs efficiently on my large dataset.
• I get a merged dataset with activation information and some historical features.
• However, I’m unsure if I’ve completely eliminated data leakage, especially with the 2-day tolerance in merge_asof.
• I’m concerned that the shift function for creating historical features might not be capturing long-term patterns effectively.

I’m looking for validation of my approach or suggestions for improvement, especially regarding data leakage prevention and more sophisticated time series feature engineering.