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coSharing.py
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coSharing.py
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import pandas as pd
import numpy as np
import networkx as nx
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfTransformer
from scipy.sparse import csr_matrix
from pandas.api.types import CategoricalDtype
# Data assumptions:
# - data: Pandas dataframe with columns ['userid', 'feature_shared']
def coSharing(data):
temp = data.groupby('feature_shared', as_index=False).count()
data = data.loc[data['feature_shared'].isin(temp.loc[temp['userid']>1]['feature_shared'].to_list())]
data['value'] = 1
ids = dict(zip(list(data.feature_shared.unique()), list(range(data.feature_shared.unique().shape[0]))))
data['feature_shared'] = data['feature_shared'].apply(lambda x: ids[x]).astype(int)
del ids
userid = dict(zip(list(data.userid.astype(str).unique()), list(range(data.userid.unique().shape[0]))))
data['userid'] = data['userid'].astype(str).apply(lambda x: userid[x]).astype(int)
person_c = CategoricalDtype(sorted(data.userid.unique()), ordered=True)
thing_c = CategoricalDtype(sorted(data.feature_shared.unique()), ordered=True)
row = data.userid.astype(person_c).cat.codes
col = data.feature_shared.astype(thing_c).cat.codes
sparse_matrix = csr_matrix((data["value"], (row, col)), shape=(person_c.categories.size, thing_c.categories.size))
del row, col, person_c, thing_c
vectorizer = TfidfTransformer()
tfidf_matrix = vectorizer.fit_transform(sparse_matrix)
similarities = cosine_similarity(tfidf_matrix, dense_output=False)
df_adj = pd.DataFrame(similarities.toarray())
del similarities
df_adj.index = userid.keys()
df_adj.columns = userid.keys()
G = nx.from_pandas_adjacency(df_adj)
del df_adj
G.remove_edges_from(nx.selfloop_edges(G))
G.remove_nodes_from(list(nx.isolates(G)))
return G