/pearson.py (7a6cc1ce0ca8ab13c12325ce4ac45044544ed9a1) (1575 bytes) (mode 100644) (type blob)
import redis as rd
import numpy as np
from scipy.stats import pearsonr
metrics = ['clustering_coefficient',
'degree',
'average_neighbor_degree',
'iterated_average_neighbor_degree',
'betweenness_centrality',
'eccentricity',
'average_shortest_path_length',
'corrected_clustering_coefficient',
'corrected_average_neighbor_degree',
'corrected_iterated_average_neighbor_degree']
rdb = rd.StrictRedis(host='localhost', port=6379, db=0)
correlations = {}
for metric1 in metrics:
correlations[metric1] = {}
for metric2 in metrics:
correlations[metric1][metric2] = (0,0)
if metric1 == metric2:
correlations[metric1][metric2] = (1,0)
continue
dict_metric1 = dict(rdb.zrange(metric1, 0, -1, withscores=True, score_cast_func=float))
dict_metric2 = dict(rdb.zrange(metric2, 0, -1, withscores=True, score_cast_func=float))
values_metric1 = []
values_metric2 = []
for key in sorted(dict_metric1.iterkeys()):
values_metric1.append(dict_metric1[key])
for key in sorted(dict_metric2.iterkeys()):
values_metric2.append(dict_metric2[key])
correlations[metric1][metric2] = pearsonr(values_metric1,values_metric2)
for source in correlations:
for target in correlations[source]:
rdb.hset("correlations:"+source+":"+target, "correlation", correlations[source][target][0])
rdb.hset("correlations:"+source+":"+target, "confidence", correlations[source][target][1])
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README.md |
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__init__.py |
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advancedscores.py |
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data |
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file_importer.py |
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graph tool test.py |
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indexing.py |
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log |
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metric_calculator.py |
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metrics.py |
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normalizations.py |
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pearson.py |
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start.py |
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statistics.py |
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