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@@ -7,4 +7,5 @@ venv | |
| .ipynb_checkpoints | ||
| build | ||
| author_rank.egg-info | ||
| htmlcov | ||
| htmlcov | ||
| *.graffle | ||
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| @@ -0,0 +1,29 @@ | ||
| """ | ||
| AuthorRank | ||
| ======== | ||
| AuthorRank is a Python package that implements a modification of PageRank to | ||
| find the most prestigious authors in a scientific collaboration network. | ||
| See https://github.com/adidier17/AuthorRank. | ||
| """ | ||
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| import sys | ||
| if sys.version_info[:2] < (3, 5): | ||
| m = "Python 3.5 or later is required for NetworkX (%d.%d detected)." | ||
| raise ImportError(m % sys.version_info[:2]) | ||
| del sys | ||
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| __author__ = "Valentino Constantinou, Annie Didier" | ||
| __version__ = "0.1.0" | ||
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| import author_rank.graph | ||
| from author_rank.graph import * | ||
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| import author_rank.score | ||
| from author_rank.score import * | ||
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| import author_rank.utils | ||
| from author_rank.utils import * | ||
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| # imports | ||
| from author_rank.score import top_authors as top | ||
| from author_rank.utils import emit_progress_bar, check_author_count | ||
| from collections import Counter | ||
| import itertools | ||
| import networkx as nx | ||
| from typing import List | ||
| from author_rank.utils import emit_progress_bar | ||
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| def create(documents: List[dict], authorship_key: str = "authors", keys: set = None, progress_bar: bool = False) -> 'nx.classes.digraph.DiGraph': | ||
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| """ | ||
| Creates a directed graph object from the list of input documents which are represented as dictionaries. | ||
| :param documents: a list of dictionaries which represent documents. | ||
| :param authorship_key: the key in the document which contains a list of dictionaries representing authors. | ||
| :param keys: a set that contains the keys to be used to create a UID for authors. | ||
| :param progress_bar: a boolean that indicates whether or not a progress bar should be emitted, default False. | ||
| :return: a networkx DiGraph object. | ||
| """ | ||
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| # if keys are not provided, set a default | ||
| # see https://florimond.dev/blog/articles/2018/08/python-mutable-defaults-are-the-source-of-all-evil/ | ||
| if keys is None: | ||
| keys = {"first_name", "last_name"} | ||
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| # get the authorship from each of the documents | ||
| # gets a list of lists | ||
| doc_authors = [i[authorship_key] for i in documents] | ||
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| # remove keys and values that are not used as part of an author UID | ||
| for doc in doc_authors: | ||
| for author in doc: | ||
| unwanted_keys = set(author) - set(keys) | ||
| for unwanted_key in unwanted_keys: | ||
| del author[unwanted_key] | ||
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| # create a UID for each author based on the remaining keys | ||
| # each unique combination of key values will serve as keys for each author | ||
| flattened_list = list(itertools.chain.from_iterable(doc_authors)) | ||
| author_uid_tuples = [tuple(d.values()) for d in flattened_list] | ||
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| # get overall counts of each author | ||
| counts = Counter(author_uid_tuples) | ||
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| # create lists for the edges | ||
| edges_all = list() | ||
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| # process each document and create the edges with the appropriate weights | ||
| progress = "=" | ||
| for doc in range(0, len(doc_authors)): | ||
| if len(doc_authors[doc]) > 1: | ||
| author_ids = [tuple(d.values()) for d in flattened_list] | ||
| pairs = (list(itertools.permutations(author_ids, 2))) | ||
| # calculate g_i_j_k | ||
| exclusivity = 1 / (len(doc_authors[doc]) - 1) | ||
| edges_all.extend([{"edge": (x[0], x[1]), "weight": exclusivity} for x in pairs]) | ||
| from typing import List, Tuple | ||
| import warnings | ||
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| class Graph: | ||
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| def __init__(self): | ||
| self.graph = nx.DiGraph() | ||
| self._is_fit = False | ||
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| def fit(self, documents: List[dict], authorship_key: str = "authors", | ||
| keys: set = None, progress_bar: bool = False) -> 'nx.classes.digraph.DiGraph': | ||
|
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||
| """ | ||
| Creates a directed graph object from the list of input documents which | ||
| are represented as dictionaries. | ||
| :param documents: a list of dictionaries which represent documents. | ||
| :param authorship_key: the key in the document which contains a list | ||
| of dictionaries representing authors. | ||
| :param keys: a set that contains the keys to be used to create a UID | ||
| for authors. | ||
| :param progress_bar: a boolean that indicates whether or not a progress | ||
| bar should be emitted, default False. | ||
| :return: a NetworkX DiGraph object. | ||
| """ | ||
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| # if keys are not provided, set a default | ||
| # see https://florimond.dev/blog/articles/2018/08/python-mutable-defaults-are-the-source-of-all-evil/ | ||
| if keys is None: | ||
| keys = {"first_name", "last_name"} | ||
|
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||
| # get the authorship from each of the documents | ||
| # gets a list of lists | ||
| doc_authors = [i[authorship_key] for i in documents] | ||
|
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||
| # remove keys and values that are not used as part of an author UID | ||
| for doc in doc_authors: | ||
| for author in doc: | ||
| unwanted_keys = set(author) - set(keys) | ||
| for unwanted_key in unwanted_keys: | ||
| del author[unwanted_key] | ||
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| # create a UID for each author based on the remaining keys | ||
| # unique combination of key values will serve as keys for each author | ||
| flattened_list = list(itertools.chain.from_iterable(doc_authors)) | ||
| author_uid_tuples = [tuple(d.values()) for d in flattened_list] | ||
| # ajd_matrix = np.empty(shape=()) | ||
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| # get overall counts of each author | ||
| counts = Counter(author_uid_tuples) | ||
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| acceptable_author_count = check_author_count(counts) | ||
| if acceptable_author_count is False: | ||
| warnings.warn("Number of authors in document set must be greater than one. " | ||
| "AuthorRank not fit to the data, please try again.", UserWarning) | ||
| else: | ||
| edges_all.extend([{"edge": (doc_authors[doc][0], doc_authors[doc][0]), "weight": 1}]) | ||
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| if progress_bar: | ||
| progress = emit_progress_bar(progress, doc+1, len(doc_authors)) | ||
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| # sort the edges for processing | ||
| edges_all_sorted = sorted(edges_all, key=lambda x: str(x["edge"])) | ||
| gb_object = itertools.groupby(edges_all_sorted, key=lambda x: x["edge"]) | ||
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| # normalize the edge weights and create the directed graph | ||
| normalized = {} | ||
| for k, v in gb_object: | ||
| try: | ||
| v = list(v) # need to reassign | ||
| numerator = sum(d["weight"] for d in list(v)) | ||
| denominator = counts[k[0]] | ||
| normalized[k] = numerator / denominator | ||
| except TypeError: | ||
| # this occurs when an author is compared to one-self, which is not a valid scenario for the graph | ||
| pass | ||
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| # create the directed graph | ||
| edge_list = [(k[0], k[1], v) for k, v in normalized.items()] | ||
| G = nx.DiGraph() | ||
| G.add_weighted_edges_from(edge_list) | ||
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| return G | ||
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| def export_to_json(graph: 'nx.classes.digraph.DiGraph'): | ||
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| """ | ||
| Returns the directed graph in JSON format, containing information | ||
| about nodes and their relationships to one another in the form of edges. | ||
| A wrapper around the NetworkX functionality. | ||
| :param graph: a networkx.DiGraph object | ||
| :return: a JSON format for the provided graph | ||
| """ | ||
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| return nx.readwrite.json_graph.node_link_data(graph) | ||
| # create lists for the edges | ||
| edges_all = list() | ||
|
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| # process each document, create the edges with the appropriate weights | ||
| progress = "=" | ||
| for doc in range(0, len(doc_authors)): | ||
| if len(doc_authors[doc]) > 1: | ||
| author_ids = [tuple(d.values()) for d in doc_authors[doc]] | ||
| pairs = (list(itertools.permutations(author_ids, 2))) | ||
| # calculate g_i_j_k | ||
| exclusivity = 1 / (len(doc_authors[doc]) - 1) | ||
| edges_all.extend([{"edge": (x[0], x[1]), "weight": exclusivity} for x in pairs]) | ||
| else: | ||
| edges_all.extend([{"edge": (doc_authors[doc][0], doc_authors[doc][0]), "weight": 1}]) | ||
|
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| if progress_bar: | ||
| progress = emit_progress_bar(progress, doc+1, len(doc_authors)) | ||
|
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| # sort the edges for processing | ||
| edges_all_sorted = sorted(edges_all, key=lambda x: str(x["edge"])) | ||
| gb_object = itertools.groupby(edges_all_sorted, key=lambda x: x["edge"]) | ||
|
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| # normalize the edge weights and create the directed graph | ||
| normalized = {} | ||
| for k, v in gb_object: | ||
| try: | ||
| v = list(v) # need to reassign | ||
| numerator = sum(d["weight"] for d in list(v)) | ||
| denominator = counts[k[0]] | ||
| normalized[k] = numerator / denominator | ||
| except TypeError: | ||
| # this occurs when an author is compared to one-self, which is | ||
| # not a valid scenario for the graph | ||
| pass | ||
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| # create the directed graph | ||
| edge_list = [(k[0], k[1], v) for k, v in normalized.items()] | ||
| self.graph.add_weighted_edges_from(edge_list) | ||
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| self._is_fit = True | ||
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| return self.graph | ||
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| def top_authors(self, n: int = 10, normalize_scores: bool = False) -> Tuple[List, List]: | ||
| """ | ||
| Calculates the top N authors in an AuthorRank graph and returns them | ||
| in sorted order. | ||
| :param n: an integer to specify the maximum number of authors to be | ||
| returned. | ||
| :param normalize_scores: a boolean to indicate whether or not to normalize | ||
| the scores between 0 and 1. | ||
| :return: a tuple which contains two lists, one for authors and the other | ||
| for their scores. | ||
| """ | ||
|
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| # check to see if AuthorRank has been fit | ||
| if self._is_fit is False: | ||
| warnings.warn("AuthorRank must first be fit on a set of documents " | ||
| "prior to calling top_authors.", UserWarning) | ||
| return list(), list() | ||
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| else: | ||
| top_authors, top_scores = top(self.graph, n=n, normalize_scores=normalize_scores) | ||
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| return top_authors, top_scores | ||
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| def as_json(self) -> dict: | ||
| """ | ||
| Returns the directed graph in JSON format, containing information | ||
| about nodes and their relationships to one another in the form of edges. | ||
| A wrapper around the NetworkX functionality. | ||
| :return: a JSON format for the provided graph | ||
| """ | ||
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| return nx.readwrite.json_graph.node_link_data(self.graph) | ||
|
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