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Rearrange documentation for predicting function
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‎README.md‎

Lines changed: 11 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -79,18 +79,6 @@ Ties should have either equivalent rank or score.
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[[[24.68943500312503, 8.179213704945203]], [[22.826045021875203, 8.179213704945203]], [[24.68943500312503, 8.179213704945203]], [[27.795084971874736, 8.263160757613477]]]
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```
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## Choosing Models
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The default model is `PlackettLuce`. You can import alternate models from `openskill.models` like so:
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```python
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>>> from openskill.models import BradleyTerryFull
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>>> a1 = b1 = c1 = d1 = Rating()
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>>> rate([[a1], [b1], [c1], [d1]], rank=[4, 1, 3, 2], model=BradleyTerryFull)
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[[[17.09430584957905, 7.5012190693964005]], [[32.90569415042095, 7.5012190693964005]], [[22.36476861652635, 7.5012190693964005]], [[27.63523138347365, 7.5012190693964005]]]
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```
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## Predicting Winners
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You can compare two or more teams to get the probabilities of each team winning.
@@ -106,6 +94,17 @@ You can compare two or more teams to get the probabilities of each team winning.
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1.0
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```
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## Choosing Models
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The default model is `PlackettLuce`. You can import alternate models from `openskill.models` like so:
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```python
102+
>>> from openskill.models import BradleyTerryFull
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>>> a1 = b1 = c1 = d1 = Rating()
104+
>>> rate([[a1], [b1], [c1], [d1]], rank=[4, 1, 3, 2], model=BradleyTerryFull)
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[[[17.09430584957905, 7.5012190693964005]], [[32.90569415042095, 7.5012190693964005]], [[22.36476861652635, 7.5012190693964005]], [[27.63523138347365, 7.5012190693964005]]]
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```
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### Available Models
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- `BradleyTerryFull`: Full Pairing for Bradley-Terry
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- `BradleyTerryPart`: Partial Pairing for Bradley-Terry

‎benchmark/benchmark.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -158,7 +158,7 @@ def predict_os_match(match: dict):
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def win_probability(team1, team2):
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delta_mu = sum(r.mu for r in team1) - sum(r.mu for r in team2)
161-
sum_sigma = sum(r.sigma ** 2 for r in itertools.chain(team1, team2))
161+
sum_sigma = sum(r.sigma**2 for r in itertools.chain(team1, team2))
162162
size = len(team1) + len(team2)
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denom = math.sqrt(size * (trueskill.BETA * trueskill.BETA) + sum_sigma)
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ts = trueskill.global_env()

‎docs/manual.rst‎

Lines changed: 13 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -86,19 +86,6 @@ Ties should have either equivalent rank or score.
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>>> result
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[[[24.68943500312503, 8.179213704945203]], [[22.826045021875203, 8.179213704945203]], [[24.68943500312503, 8.179213704945203]], [[27.795084971874736, 8.263160757613477]]]
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89-
Choosing Models
90-
---------------
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The default model is ``PlackettLuce``. You can import alternate models
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from ``openskill.models`` like so:
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95-
.. code:: python
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>>> from openskill.models import BradleyTerryFull
98-
>>> a1 = b1 = c1 = d1 = Rating()
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>>> rate([[a1], [b1], [c1], [d1]], rank=[4, 1, 3, 2], model=BradleyTerryFull)
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[[[17.09430584957905, 7.5012190693964005]], [[32.90569415042095, 7.5012190693964005]], [[22.36476861652635, 7.5012190693964005]], [[27.63523138347365, 7.5012190693964005]]]
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Predicting Winners
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------------------
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@@ -115,6 +102,19 @@ You can compare two or more teams to get the probabilities of each team winning.
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>>> sum(predictions)
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1.0
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Choosing Models
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---------------
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The default model is ``PlackettLuce``. You can import alternate models
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from ``openskill.models`` like so:
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.. code:: python
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>>> from openskill.models import BradleyTerryFull
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>>> a1 = b1 = c1 = d1 = Rating()
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>>> rate([[a1], [b1], [c1], [d1]], rank=[4, 1, 3, 2], model=BradleyTerryFull)
116+
[[[17.09430584957905, 7.5012190693964005]], [[32.90569415042095, 7.5012190693964005]], [[22.36476861652635, 7.5012190693964005]], [[27.63523138347365, 7.5012190693964005]]]
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Available Models
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~~~~~~~~~~~~~~~~
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‎openskill/models/bradley_terry_full.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -46,9 +46,9 @@ def calculate(self):
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for j, j_players in enumerate(i_team):
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mu = j_players.mu
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sigma = j_players.sigma
49-
mu += (sigma ** 2 / i_sigma_squared) * omega
49+
mu += (sigma**2 / i_sigma_squared) * omega
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sigma *= math.sqrt(
51-
max(1 - (sigma ** 2 / i_sigma_squared) * delta, self.EPSILON),
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max(1 - (sigma**2 / i_sigma_squared) * delta, self.EPSILON),
5252
)
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intermediate_result_per_team.append([mu, sigma])
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result.append(intermediate_result_per_team)

‎openskill/models/bradley_terry_part.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -46,9 +46,9 @@ def od_reduce(od, q_team_ratings):
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for j, j_players in enumerate(i_team):
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mu = j_players.mu
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sigma = j_players.sigma
49-
mu += (sigma ** 2 / i_sigma_squared) * i_omega
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mu += (sigma**2 / i_sigma_squared) * i_omega
5050
sigma *= math.sqrt(
51-
max(1 - (sigma ** 2 / i_sigma_squared) * i_delta, self.EPSILON),
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max(1 - (sigma**2 / i_sigma_squared) * i_delta, self.EPSILON),
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)
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intermediate_result_per_team.append([mu, sigma])
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return intermediate_result_per_team

‎openskill/models/plackett_luce.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -38,7 +38,7 @@ def calculate(self):
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omega -= i_mu_over_ce_over_sum_q / self.a[q]
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4040
omega *= i_sigma_squared / self.c
41-
delta *= i_sigma_squared / self.c ** 2
41+
delta *= i_sigma_squared / self.c**2
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4343
gamma = self.gamma(self.c, len(self.team_ratings), *i_team_ratings)
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delta *= gamma
@@ -47,9 +47,9 @@ def calculate(self):
4747
for j, j_players in enumerate(i_team):
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mu = j_players.mu
4949
sigma = j_players.sigma
50-
mu += (sigma ** 2 / i_sigma_squared) * omega
50+
mu += (sigma**2 / i_sigma_squared) * omega
5151
sigma *= math.sqrt(
52-
max(1 - (sigma ** 2 / i_sigma_squared) * delta, self.EPSILON),
52+
max(1 - (sigma**2 / i_sigma_squared) * delta, self.EPSILON),
5353
)
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intermediate_result_per_team.append([mu, sigma])
5555
result.append(intermediate_result_per_team)

‎openskill/models/thurstone_mosteller_full.py‎

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Original file line numberDiff line numberDiff line change
@@ -63,9 +63,9 @@ def calculate(self):
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for j, j_players in enumerate(i_team):
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mu = j_players.mu
6565
sigma = j_players.sigma
66-
mu += (sigma ** 2 / i_sigma_squared) * omega
66+
mu += (sigma**2 / i_sigma_squared) * omega
6767
sigma *= math.sqrt(
68-
max(1 - (sigma ** 2 / i_sigma_squared) * delta, self.EPSILON),
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max(1 - (sigma**2 / i_sigma_squared) * delta, self.EPSILON),
6969
)
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intermediate_result_per_team.append([mu, sigma])
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result.append(intermediate_result_per_team)

‎openskill/models/thurstone_mosteller_part.py‎

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Original file line numberDiff line numberDiff line change
@@ -62,9 +62,9 @@ def od_reduce(od, q_team_ratings):
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for j, j_players in enumerate(i_team):
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mu = j_players.mu
6464
sigma = j_players.sigma
65-
mu += (sigma ** 2) / i_sigma_squared * i_omega
65+
mu += (sigma**2) / i_sigma_squared * i_omega
6666
sigma *= math.sqrt(
67-
max(1 - (sigma ** 2) / i_sigma_squared * i_delta, self.EPSILON)
67+
max(1 - (sigma**2) / i_sigma_squared * i_delta, self.EPSILON)
6868
)
6969
intermediate_result_per_team.append([mu, sigma])
7070
return intermediate_result_per_team

‎openskill/rate.py‎

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Original file line numberDiff line numberDiff line change
@@ -66,7 +66,7 @@ def team_rating(game: List[List[Rating]], **options) -> List[List[Union[int, flo
6666
for index, team in enumerate(game):
6767
team_result = []
6868
mu_i = reduce(lambda x, y: x + y, map(lambda p: p.mu, team))
69-
sigma_squared = reduce(lambda x, y: x + y, map(lambda p: p.sigma ** 2, team))
69+
sigma_squared = reduce(lambda x, y: x + y, map(lambda p: p.sigma**2, team))
7070
team_result.extend([mu_i, sigma_squared, team, rank[index]])
7171
result.append(team_result)
7272
return result
@@ -136,7 +136,7 @@ def predict_win(teams: List[List[Rating]], **options) -> List[Union[int, float]]
136136
pairwise_probabilities.append(
137137
phi_major(
138138
(mu_a - mu_b)
139-
/ math.sqrt(n * beta(**options) ** 2 + sigma_a ** 2 + sigma_b ** 2)
139+
/ math.sqrt(n * beta(**options) ** 2 + sigma_a**2 + sigma_b**2)
140140
)
141141
)
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