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from sklearn.metrics import pairwise_distances import pandas as pd import numpy as np from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split # Link the X vector with index index = df.index.values def get_top_5_person_who_resolved(df, row, distance_metric='cosine'): # Concatenate the input data into a single string input_data = ' '.join([str(row['ticket_category']), str(row['ticket_type']), str(row['ticket_item']),str(row['ticket_summary']), str(row['ticket_severity']),str(row['resolution_sla_violated']),str(row['reopen_count']), str(row['role_id']),str(row['ticket_resolution_time'])]) # Calculate the pairwise distances between the input vector and X input_vector_x = np.array(list(row[['ticket_category', 'ticket_type', 'ticket_item','ticket_summary', 'ticket_severity', 'resolution_sla_violated', 'reopen_count', 'role_id','ticket_resolution_time']])) if distance_metric == 'cosine': distances = pairwise_distances(input_vector_x.reshape(1, -1), df[['ticket_category', 'ticket_type', 'ticket_item','ticket_summary', 'ticket_severity', 'resolution_sla_violated', 'reopen_count', 'role_id','ticket_resolution_time']], metric='cosine')[0] elif distance_metric == 'euclidean': distances = pairwise_distances(input_vector_x.reshape(1, -1), df[['ticket_category', 'ticket_type', 'ticket_item','ticket_summary', 'ticket_severity', 'resolution_sla_violated', 'reopen_count', 'role_id','ticket_resolution_time']], metric='euclidean')[0] elif distance_metric == 'manhattan': distances = pairwise_distances(input_vector_x.reshape(1, -1), df[['ticket_category', 'ticket_type', 'ticket_item','ticket_summary', 'ticket_severity', 'resolution_sla_violated', 'reopen_count', 'role_id','ticket_resolution_time']], metric='manhattan')[0] else: raise ValueError('Invalid distance metric') # Get the indices of the top 5 closest tickets closest_indices = np.argsort(distances)[:5] # Get the person_who_resolved values for the closest tickets closest_person_who_resolved = df.iloc[closest_indices]['person_who_resolved'] # Get the actual person_who_resolved value for the input ticket actual_person_who_resolved = row['person_who_resolved'] return closest_person_who_resolved.tolist(), actual_person_who_resolved # Split the data into training and testing sets train_data, test_data = train_test_split(df, test_size=0.2, random_state=42) # Apply the function to each row of the test data to get the recommendations test_data['recommendations'], test_data['actual_person_who_resolved'] = zip(*test_data.apply(lambda row: get_top_5_person_who_resolved(train_data, row), axis=1)) # Remove duplicate values from recommendations test_data['recommendations'] = test_data['recommendations'].apply(lambda x: list(set(x))) # Calculate overall MAP score for the test data test_map_score = test_data.apply(lambda row: calculate_map(row['actual_person_who_resolved'], row['recommendations']), axis=1).mean() # Calculate overall Top-k Accuracy score for the test data test_topk_accuracy = test_data.apply(lambda row: calculate_topk_accuracy(row['actual_person_who_resolved'], row['recommendations'], k=5), axis=1).mean() print("Test MAP score:", test_map_score) print("Test Top-k Accuracy score:", test_topk_accuracy) # Getting unique values unique_values = test_data['recommendations'].apply(lambda x: list(set(x))) # Remove duplicate values in each list test_data['unique_top_5_person_who_resolved'] = unique_values.apply(lambda x: x[:5]) # Take only the first 5 unique values # Calculate overall MAP score for the updated test data test_map_score_updated = test_data.apply(lambda row: calculate_map(row['actual_person_who_resolved'], row['unique_top_5_person_who_resolved']), axis=1).mean() # Calculate overall Top-k Accuracy score for the updated test data test_topk_accuracy_updated = test_data.apply(lambda row: calculate_topk_accuracy(row['actual_person_who_resolved'], row['unique_top_5_person_who_resolved'], k=5), axis=1).mean() print("Updated Test MAP score:", test_map_score_updated) print("Updated Test Top-k Accuracy score:", test_topk_accuracy_updated)