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92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
import sys
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import json
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import glob
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from graphviz import Graph
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# appending a path
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sys.path.append('../')
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# importing customised module
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import summa.edits
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from summa.edits import csv_to_matrix, custom_summarize
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# main
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# ------------------------------------------------------------------------
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def print_score(name, scored_sentences):
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print(name)
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for s in sorted_sentences:
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print(s.text)
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print(s.score)
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print()
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print()
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if __name__ == '__main__':
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paths = glob.glob('../summa/data/*.csv')
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matrices = []
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for path in paths:
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# CSV 2 MATRIC
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matrix = csv_to_matrix(path)
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matrices.append(matrix)
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# print(json.dumps(matrix, indent=4))
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# we give summa the whole text to tokenize, from the csv keys
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text = " ".join(matrix.keys())
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# random walk with pagerank
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scored_sentences, weighted_graph = custom_summarize(text, matrix)
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# sort the scored sentences
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sorted_sentences = sorted(scored_sentences, key=lambda s: s.score, reverse=True)
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# print them
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print_score(path, sorted_sentences)
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# create a graph
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graphviz_graph = Graph(path, engine='fdp', format='svg')
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graphviz_graph.attr('graph', overlap='false')
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for i, node1 in enumerate(weighted_graph.nodes()):
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graphviz_graph.node(str(i), node1)
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for j, node2 in enumerate(weighted_graph.nodes()):
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if weighted_graph.edge_weight((node1, node2)) != weighted_graph.edge_weight((node2, node1)):
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print("THIS IS NOT SYMMETRICAL")
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if i < j:
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original_weight = weighted_graph.edge_weight((node1, node2))
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graphviz_graph.edge(str(i), str(j), weight = str(original_weight * 10), penwidth = str(original_weight))
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elif i > j:
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original_weight = weighted_graph.edge_weight((node2, node1))
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graphviz_graph.edge(str(j), str(i), weight = str(original_weight * 10), penwidth = str(original_weight))
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graphviz_graph.render(path + '_graph')
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# # compute the average matrix
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# matrix_keys = matrices[0].keys()
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# average_matrix = {}
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# for i in matrix_keys:
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# average_matrix[i] = {}
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# for j in matrix_keys:
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# average_matrix[i][j] = sum([matrix[i][j] for matrix in matrices]) / len(matrices)
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# # random walk with pagerank
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# scored_sentences = custom_summarize(text, average_matrix)
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# # sort the scored sentences
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# sorted_sentences = sorted(scored_sentences, key=lambda s: s.score, reverse=True)
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# # print them
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# print_score('average', sorted_sentences)
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