def glicko_update_test():
table1 = Table(sample_with_time_1,
['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
table2 = Table(sample_with_time_2,
['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
gRanker = GlickoRanker()
gRanker.update(table1)
gRanker.update(table2)
def glicko_test():
# This is illustrating example used in "Example of the Glicko-2 system".
gRanker = GlickoRanker(rd=200)
itemRatingLut = {
'Bob': 1400,
'Cyan': 1550,
'Dijkstra': 1700
}
itemRDLut = {
'Bob': 30,
'Cyan': 100,
'Dijkstra': 300
}
gRanker.setup(itemRatingLut=itemRatingLut, itemRDLut=itemRDLut)
df = pd.DataFrame(
{
'host': ['Alice', 'Alice', 'Alice'],
'visit': ['Bob', 'Cyan', 'Dijkstra'],
'hscore': [2, 0, 0],
'vscore': [0, 2, 2]
},
columns=['host', 'visit', 'hscore', 'vscore'])
tb = Table(df, col=['host', 'visit', 'hscore', 'vscore'])
gRanker.update(tb)
assert_equal(gRanker.data.itemnum, 4)
idx = gRanker.data.itemlut['Alice'] # don't hack this in production code.
assert_almost_equal(gRanker.miu[idx] * gRanker.factor + gRanker.baseline,
1464.06,
places=1)
assert_almost_equal(gRanker.phi[idx] * gRanker.factor, 151.52, places=1)
assert_almost_equal(gRanker.sigma[idx], 0.05999, places=4)
def trueskill_prob_win_test():
table = Table(sample_with_time_1,
['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
tsRanker = TrueSkillRanker()
tsRanker.update(table)
assert_true(tsRanker.prob_win(4, 1) > 0.5)
def __init__(self, K=10, xi=400, baseline=1500, drawMargin=0):
self.K = K
self.xi = xi
self.baseline = baseline
self.drawMargin = drawMargin
self.data = Table()
self.indexScoreLut = []
def elo_prob_win_test():
table = Table(sample_with_time_1,
['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
eloRanker = EloRanker()
eloRanker.update(table)
assert_true(eloRanker.prob_win(4, 1) > 0.5)
def massey_rank_test():
data = Table(sample_paired, col=[0, 1, 2, 3])
r = MasseyRanker(table=data)
rst = r.rank(ascending=False)
assert_array_almost_equal(rst.loc[:, 'rating'].values,
np.array([18.2, 18.0, -3.4, -8.0, -24.8]),
decimal=2)
def colley_rank_test():
data = Table(sample_paired, col=[0, 1, 2, 3])
r = ColleyRanker(table=data)
rst = r.rank(ascending=False)
assert_array_almost_equal(rst.loc[:, 'rating'].values,
np.array([0.79, 0.65, 0.50, 0.36, 0.21]),
decimal=2)
def trueskill_another_test():
tsRanker = TrueSkillRanker(baseline=25,
rd=25 / 3.0,
performanceRd=25 / 6.0)
df = pd.DataFrame(
{
'host': ['Alice'],
'visit': ['Bob'],
'hscore': [1],
'vscore': [0]
},
columns=['host', 'visit', 'hscore', 'vscore'])
t = Table(df, col=['host', 'visit', 'hscore', 'vscore'])
tsRanker.update(t)
idx = tsRanker.data.itemlut['Alice']
assert_almost_equal(tsRanker.rd * (tsRanker.indexMiuLut[idx] - 3) +
tsRanker.baseline,
29.396,
places=3)
assert_almost_equal(tsRanker.rd *
tsRanker.indexSigmaSqrLut[idx]**(1 / 2.0),
7.171,
places=3)
idx = tsRanker.data.itemlut['Bob']
assert_almost_equal(tsRanker.rd * (tsRanker.indexMiuLut[idx] - 3) +
tsRanker.baseline,
20.604,
places=3)
assert_almost_equal(tsRanker.rd *
tsRanker.indexSigmaSqrLut[idx]**(1 / 2.0),
7.171,
places=3)
def trueskill_leaderboard_test():
table = Table(sample_with_time_1,
['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
tsRanker = TrueSkillRanker()
tsRanker.update(table)
lb = tsRanker.leaderboard()
return lb
def glicko_leaderboard_test():
table = Table(sample_with_time_1,
['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
gRanker = GlickoRanker()
gRanker.update(table)
lb = gRanker.leaderboard()
return lb
def elo_leaderboard_test():
table = Table(sample_with_time_1,
['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
eloRanker = EloRanker()
eloRanker.update(table)
lb = eloRanker.leaderboard()
assert_equal(lb.rating.values.sum(), 6000)
return lb
def elo_update_test():
table = Table(sample_with_time_1.iloc[:-1, :],
['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
eloRanker = EloRanker()
eloRanker.update(table)
eloRanker.update_single(2, 3, 0, 1)
lb = eloRanker.leaderboard()
assert_array_almost_equal(lb.rating.values,
elo_leaderboard_test().rating.values)
def trueskill_update_test():
table = Table(sample_with_time_1.iloc[:-1, :],
['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
tsRanker = TrueSkillRanker()
tsRanker.update(table)
tsRanker.update_single(2, 3, 0, 1)
lb = tsRanker.leaderboard()
assert_array_almost_equal(lb.rating.values,
trueskill_leaderboard_test().rating.values)
def eld_rank_update_test():
data1 = Table(sample_with_time_1,
col=['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
data2 = Table(sample_with_time_2,
col=['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
ranker = EloRanker(data1)
r0 = ranker.rank(ascending=False)
r1 = ranker.update(data2)
data3 = Table(pd.concat([sample_with_time_1, sample_with_time_2]),
col=['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
ranker = EloRanker(data3)
r2 = ranker.rank(ascending=False)
assert_almost_equal(r1.rating.values, r2.rating.values)
assert_raises(AssertionError, assert_array_equal, r0.rating.values,
r2.rating.values)
def run_rank(self) -> dict:
"""
计算评价信息到outTable并返回
"""
if len(self.rawTable) == 0:
data = None
else:
data = Table(self.rawTable, col=[0, 1, 2, 3])
if len(self.rawTable) == 0:
data2 = None
else:
data2 = Table(self.rawTable2, col=[0, 1, 2, 3])
# 封装的评价方式，具体请参考rankit库作者的github
maseey = MasseyRanker()
keener = KeenerRanker()
# 处理无人打分的情况导致的异常
if data2 != None and data != None:
maseeyRank = maseey.rank(data2)
keenerRank = keener.rank(data)
mergedRank = borda_count_merge([maseeyRank, keenerRank])
self.outTable = {}
for index, i in mergedRank.iterrows():
self.outTable[i[0]] = i[-1]
return self.outTable
if data2 != None:
maseeyRank = maseey.rank(data2)
mergedRank = maseeyRank
self.outTable = {}
for index, i in mergedRank.iterrows():
self.outTable[i[0]] = i[-1]
return self.outTable
if data != None:
keenerRank = keener.rank(data)
mergedRank = keenerRank
self.outTable = {}
for index, i in mergedRank.iterrows():
self.outTable[i[0]] = i[-1]
return self.outTable
return {}
def od_rank_test():
data = Table(sample_paired, col=[0, 1, 2, 3])
r = ODRanker(table=data)
rst = r.rank(output='summary', ascending=False)
print('OD rank: overall rank:')
print(rst)
rst = r.rank(output='offence', ascending=False)
print('OD rank: offence rank:')
print(rst)
rst = r.rank(output='defence', ascending=True)
print('OD rank: defence rank:')
print(rst)
def difference_rank_score_difference_test():
table = Table(sample_paired, col=[0, 1, 2, 3])
ranker = DifferenceRanker(table)
rank = ranker.rank()
rank = rank.set_index('name')
score_diff = ranker.score_diff(sample_paired.primary.values,
sample_paired.secondary.values)
t = sample_paired.merge(rank, left_on='primary', right_index=True).\
merge(rank, left_on='secondary', right_index=True).\
sort_index()
score_diff_2 = t.rating_x - t.rating_y
assert_almost_equal(score_diff, score_diff_2)
def run_rank(self):
if len(self.rawTable) == 0:
data = None
else:
data = Table(self.rawTable, col=[0, 1, 2, 3])
if len(self.rawTable) == 0:
data2 = None
else:
data2 = Table(self.rawTable2, col=[0, 1, 2, 3])
maseey = MasseyRanker()
keener = KeenerRanker()
if data2 != None and data != None:
maseeyRank = maseey.rank(data2)
keenerRank = keener.rank(data)
mergedRank = borda_count_merge([maseeyRank, keenerRank])
self.outTable = {}
for index, i in mergedRank.iterrows():
self.outTable[i[0]] = i[-1]
return self.outTable
if data2 != None:
maseeyRank = maseey.rank(data2)
mergedRank = maseeyRank
self.outTable = {}
for index, i in mergedRank.iterrows():
self.outTable[i[0]] = i[-1]
return self.outTable
if data != None:
keenerRank = keener.rank(data)
mergedRank = keenerRank
self.outTable = {}
for index, i in mergedRank.iterrows():
self.outTable[i[0]] = i[-1]
return self.outTable
return {}
def __init__(self,
baseline=1500,
rd=500,
performanceRd=250,
drawProbability=0.1,
drawMargin=0):
self.miu = baseline / rd
self.sigma = 1
self.baseline = baseline
self.rd = rd
self.performanceRd = performanceRd
self.drawMargin = drawMargin
self.drawProbability = drawProbability
self.data = Table()
self.indexMiuLut = []
self.indexSigmaSqrLut = []
def trueskill_test():
tsRanker = TrueSkillRanker(baseline=25,
rd=25 / 3.0,
performanceRd=25 / 6.0)
df = pd.DataFrame(
{
'host': ['Alice'],
'visit': ['Bob'],
'hscore': [1],
'vscore': [1]
},
columns=['host', 'visit', 'hscore', 'vscore'])
t = Table(df, col=['host', 'visit', 'hscore', 'vscore'])
tsRanker.update(t)
assert_array_almost_equal(np.array(
[tsRanker.rd * j**(1 / 2.0) for j in tsRanker.indexSigmaSqrLut]),
np.array([6.458, 6.458]),
decimal=3)
def __init__(self,
baseline=1500,
rd=350,
votality=0.06,
tau=0.5,
epsilon=0.000001,
drawMargin=0):
self.baseline = baseline
self.rd = rd
self.votality = votality
self.tau = tau
self.epsilon = epsilon
self.drawMargin = drawMargin
self.data = Table()
self.miu = [] # normalized rating
self.phi = [] # normalized rd
self.sigma = [] # votality
self.factor = 400 / math.log(10)
def difference_rank_test():
data = Table(sample_paired, col=[0,1,2,3])
r = DifferenceRanker()
rst = r.rank(data)
def od_rank_test():
data = Table(sample_paired, col=[0,1,2,3])
r1 = ODRanker(method='summary')
rst = r1.rank(data)
r2 = ODRanker(method='defence')
rst = r2.rank(data)
def markov_rank_test():
data = Table(sample_paired, col=[0,1,2,3])
r = MarkovRanker()
rst = r.rank(data)
def keener_rank_test():
data = Table(sample_paired, col=[0,1,2,3])
r = KeenerRanker()
rst = r.rank(data)
def keener_rank_test():
data = Table(sample_paired, col=[0, 1, 2, 3])
r = KeenerRanker(table=data)
rst = r.rank(ascending=False)
print('Keener rank:')
print(rst)
def markov_rank_test():
data = Table(sample_paired, col=[0, 1, 2, 3])
r = MarkovRanker(table=data)
rst = r.rank(ascending=False)
print('Markov rank:')
print(rst)
def difference_rank_test():
data = Table(sample_paired, col=[0, 1, 2, 3])
r = DifferenceRanker(table=data)
rst = r.rank(ascending=False)
print('Difference rank:')
print(rst)
def elo_rank_test():
data = Table(sample_with_time_1,
col=['primary', 'secondary', 'rate1', 'rate2'],
timecol='date')
ranker = EloRanker(data)
ranker.rank(ascending=False)