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# -*- coding: utf-8 -*-
"""Shared code to do with ranks
Author: Gertjan van den Burg
Copyright (c) 2020 - The Alan Turing Institute
License: See the LICENSE file.
"""
import colorama
import json
import numpy as np
import sys
import termcolor
from scipy.stats import rankdata
colorama.init()
def load_data(filename):
with open(filename, "r") as fp:
return json.load(fp)
def compute_ranks(results, keep_methods=None, higher_better=True):
"""Compute the ranks
Parameters
----------
results : dict
Mapping from dataset name to dict, where each dict in turn is a map
from method name to a score value.
keep_methods: list
Methods to include in the ranks
higher_better: bool
Whether a higher or a lower value is considered better
Returns
-------
avg_ranks : dict
Map from method name to average rank
all_ranks: dict
Map from dataset name to dictionary, which is in turn a map from method
name to rank for that dataset and that method.
"""
vec_ranks = []
all_ranks = {}
for dset in results:
methods = results[dset].keys()
methods = sorted(methods)
methods = [m for m in methods if m in keep_methods]
assert methods == keep_methods
if higher_better:
values = [-results[dset][m] for m in methods]
else:
values = [results[dset][m] for m in methods]
if any(np.isnan(v) for v in values):
print(
"Skipping dataset %s because of nans" % dset, file=sys.stderr
)
continue
ranks = rankdata(values, method="average")
vec_ranks.append(ranks)
rank_dict = {m: ranks[i] for i, m in enumerate(methods)}
all_ranks[dset] = rank_dict
avg_ranks = np.mean(vec_ranks, axis=0)
avg_ranks = {m: r for m, r in zip(methods, avg_ranks)}
return avg_ranks, all_ranks
def warning(msg):
termcolor.cprint(msg, "yellow", file=sys.stderr)
def preprocess_data(data, _type):
methods = set([m for dset in data.keys() for m in data[dset].keys()])
methods = sorted(methods)
if _type == "best":
warning(
"\nWarning: Filtering out RBOCPDMS due to insufficient results.\n"
)
methods = [m for m in methods if not m == "rbocpdms"]
data_w_methods = {}
for dset in data:
data_w_methods[dset] = {}
for method in methods:
data_w_methods[dset][method] = data[dset][method]
data_no_missing = {}
for dset in data_w_methods:
if any((x is None for x in data_w_methods[dset].values())):
continue
data_no_missing[dset] = data_w_methods[dset]
return data_no_missing, methods
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