almost ready

fixing load_csv

Add debug print statement to load_file function

updating load_csv to handle dataframes

ping

Enhance load_file function to ensure proper handling of unnamed columns by checking if the column name is a string before dropping. This improves data integrity during file uploads.

Enhance load_file function to check if the upload is already a DataFrame, improving handling of file uploads. This change simplifies the logic for processing uploaded data and ensures compatibility with existing DataFrame objects.

Refactor portfolio file loading to streamline assignment of DataFrames. The load_file function now returns both export and portfolio DataFrames in a single call, improving code clarity and efficiency.

chaning for to while for lineup add

Update salary field references in exposure_spread.py to ensure consistency in salary eligibility checks for player replacements.

adjusting salary declaration in exposure increase

Add update-pm-files.ps1 script for EC2 Instance Connect file updates and enhance exposure_spread.py with salary eligibility checks for player replacements

Lets try this

checking again

checking to see if this fixes the exposure increase issue

Refactor SE Score calculation in predict_dupes function to adjust the order of operations, ensuring player diversity is factored in after the score computation. This change enhances the accuracy of the SE Score while maintaining code clarity.

Refactor SE Score calculation in predict_dupes function to incorporate player diversity, enhancing the accuracy of the score. Removed redundant print statement for cleaner output.

Add print statement to display the top 10 SE Scores in predict_dupes function for debugging purposes

Adding SE score

ping

Refactor exposure_spread function to improve player replacement logic by implementing a while loop for better handling of row selections and ensuring replacements are made only when necessary. This enhances the clarity and efficiency of the code.

Enhance exposure_spread function by refining replacement logic for players based on exposure targets. This update improves the handling of player replacements, ensuring that changes are only applied when necessary and maintaining clarity in the code structure.

Remove unnecessary break statement in exposure_spread function to enhance lineup modification logic and improve code clarity.

Refactor exposure_spread function to streamline lineup modification logic, ensuring that changes are applied correctly based on the current change counter and improving overall code clarity.

Refactor exposure_spread function to improve list comparison for comparable_player_list, ensuring clarity and consistency in checks for non-empty lists.

fixing exposure management

recent changes

updating for recent changes

Enhance Lineup Edge calculation in predict_dupes.py by normalizing the edge values based on the maximum and minimum edges. This adjustment improves the scaling of the Lineup Edge, leading to more accurate portfolio predictions.

ping

Correct Lineup Edge calculation in predict_dupes.py by changing the adjustment from addition to subtraction based on duplicate ownership. This modification refines the edge computation, improving the accuracy of portfolio predictions.

Update Lineup Edge calculation in predict_dupes.py to include a normalized adjustment based on duplicate ownership. This change improves the accuracy of the edge computation by factoring in the average number of duplicates, enhancing overall portfolio predictions.

Refactor similarity score calculation in predict_dupes.py by removing the reference to duplicate adjustments. This change clarifies the computation of player selection diversity while preserving the overall prediction model's integrity.

Refactor Lineup Edge calculation in predict_dupes.py by removing the duplicate adjustment and simplifying the formula. This change enhances clarity in the computation while maintaining the integrity of the prediction model.

ping

Refactor Lineup Edge calculation in predict_dupes.py to incorporate both raw edge and adjusted edge values. This change enhances the accuracy of the prediction model by averaging the adjusted edge with the raw edge, improving overall portfolio predictions.

Adjust Lineup Edge_Raw calculation in predict_dupes.py to include a constant offset, enhancing the accuracy of the edge computation for portfolio predictions.

Refactor Lineup Edge calculation in predict_dupes.py to utilize the raw edge value for duplicate adjustment. This change clarifies the computation and maintains the integrity of the prediction model.

Update Lineup Edge calculation in predict_dupes.py to separate raw edge computation. Adjusted the formula to remove the scaling based on duplicates, enhancing clarity and maintaining prediction accuracy.

ping

Refine Lineup Edge calculation in predict_dupes.py to adjust the edge based on the mean of duplicates. This update improves the accuracy of the prediction model by incorporating a scaling factor related to the average number of duplicates.

Refine Lineup Edge calculation in predict_dupes.py to average the adjusted edge based on the number of duplicates. This update enhances the prediction model's accuracy and performance.

diverting

Refine Lineup Edge calculation in predict_dupes.py to improve accuracy. Adjusted the formula to incorporate a divisor of (Dupes + 1) and modified the scaling factor, enhancing the prediction model's performance.

ping

Refine Lineup Edge calculation in predict_dupes.py to enhance accuracy. Updated the formula to adjust the edge based on the number of duplicates, improving the prediction model's performance.

Refactor Lineup Edge calculation in predict_dupes.py to improve accuracy. Updated the formula to average the adjusted edge based on the number of duplicates, enhancing the overall performance of the prediction model.

Implement vectorized calculations for salary, median, and ownership in app.py to enhance performance and memory efficiency. Refactor reassess_edge and stratification_function to minimize DataFrame copies and improve memory management. Update filtering logic to use boolean masks for better efficiency.

Add memory-efficient ranking calculation in predict_dupes.py

Update diversity score calculation to use chunked processing for improved performance in predict_dupes.py