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| import random | |
| import networkx as nx | |
| import pandas as pd | |
| paradigm_list = ["Interaction and Dialogue", "Concepts and Ideas", "Character and Role", "Settings and Scenes", | |
| "Actions and Tasks", "Imaginative Elements", "Technical Terms", "Emotional Expressions"] | |
| file_path = 'New_frequent_semantic_categorized.xlsx' | |
| def build_data(data): | |
| data_processed = data.copy() | |
| for col in data_processed.columns: | |
| none_count = 0 | |
| for i in range(len(data_processed)): | |
| if data_processed[col][i] == '[None]': | |
| none_count += 1 | |
| if none_count > 1: | |
| data_processed.at[i, col] = None | |
| return data_processed | |
| def build_graph(G, data_processed): | |
| start_node = random.choice(list(G.nodes)) | |
| random_walk_path = random_walk(G, start_node, len(G.nodes)) | |
| # Creating a dictionary from the random walk path, ensuring all categories are covered | |
| random_walk_dict = {} | |
| # Select sentences from categories in the random walk | |
| for category in random_walk_path: | |
| sentences = data_processed[category].dropna().tolist() | |
| if sentences and sentences != ['[None]']: | |
| selected_sentence = random.choice(sentences) | |
| else: | |
| selected_sentence = '[None]' | |
| random_walk_dict[category] = selected_sentence | |
| # Ensuring all categories are included | |
| for category in G.nodes: | |
| if category not in random_walk_dict: | |
| sentences = data_processed[category].dropna().tolist() | |
| if sentences and sentences != ['[None]']: | |
| selected_sentence = random.choice(sentences) | |
| else: | |
| selected_sentence = '[None]' | |
| random_walk_dict[category] = selected_sentence | |
| return random_walk_dict | |
| def random_walk(graph, start_node, steps): | |
| path = [start_node] | |
| for _ in range(steps - 1): | |
| neighbors = list(graph.neighbors(path[-1])) | |
| if neighbors: | |
| next_node = random.choice(neighbors) | |
| path.append(next_node) | |
| else: | |
| break | |
| return path | |
| def add_edges_based_on_shared_content(graph, dataframe): | |
| for i in range(len(dataframe)): | |
| row = dataframe.iloc[i] | |
| for col1 in dataframe.columns: | |
| for col2 in dataframe.columns: | |
| if col1 != col2 and pd.notna(row[col1]) and pd.notna(row[col2]) and row[col1] != '[None]' and row[ | |
| col2] != '[None]': | |
| graph.add_edge(col1, col2) | |
| def random_walk_function(file_path='New_frequent_semantic_categorized.xlsx'): | |
| data = pd.read_excel(file_path) | |
| data_processed = build_data(data) | |
| G = nx.Graph() | |
| G.add_nodes_from(data.columns) | |
| add_edges_based_on_shared_content(G, data_processed) | |
| random_walk_dict = build_graph(G, data_processed) | |
| return random_walk_dict | |
| if __name__ == '__main__': | |
| random_walk_dict = random_walk_function() |