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🏗️ Pylint fixes
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@asaf-kali
asaf-kali committed Nov 13, 2024
1 parent af97388 commit 3142631
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Showing 3 changed files with 39 additions and 35 deletions.
2 changes: 1 addition & 1 deletion Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -128,7 +128,7 @@ check-mypy:
mypy .

check-pylint:
pylint solvers/ --fail-under=8.8
pylint solvers/ --fail-under=7.75

lint: format
pre-commit run --all-files
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11 changes: 5 additions & 6 deletions solvers/olympic/olympic_spymaster.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,12 +4,11 @@

import editdistance
import numpy as np
from codenames.game.base import WordGroup
from codenames.game.board import Board
from codenames.game.color import CardColor
from codenames.game.move import Clue, GivenClue
from codenames.game.player import Spymaster
from codenames.game.state import SpymasterState
from codenames.generic.board import Board, WordGroup
from codenames.generic.card import CardColor
from codenames.generic.move import Clue, GivenClue
from codenames.generic.player import Spymaster
from codenames.generic.state import SpymasterState
from gensim.models import KeyedVectors
from typing_extensions import TypeAlias

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61 changes: 33 additions & 28 deletions solvers/sna/sna_spymaster.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,8 @@
import networkx as nx
import numpy as np
import pandas as pd
from codenames.classic.color import ClassicColor
from codenames.generic.board import Board, WordGroup
from codenames.generic.card import CardColor
from codenames.generic.move import Clue
from codenames.generic.player import Spymaster
from codenames.generic.state import SpymasterState
Expand All @@ -35,16 +35,16 @@ class ForceNode:


log = logging.getLogger(__name__)
MIN_SELF_BLACK_DELTA = 0.07
MIN_SELF_ASSASSIN_DELTA = 0.07
MIN_SELF_OPPONENT_DELTA = 0.04
MIN_SELF_GRAY_DELTA = 0.01
MIN_SELF_NEUTRAL_DELTA = 0.01
MAX_SELF_DISTANCE = 0.235
OPPONENT_FORCE_CUTOFF = 0.275
OPPONENT_FORCE_FACTOR = 1.6
FRIENDLY_FORCE_CUTOFF = 0.2
FRIENDLY_FORCE_FACTOR = 1
BLACK_FORCE_FACTOR = 2
GRAY_FORCE_FACTOR = 1.2
ASSASSIN_FORCE_FACTOR = 2
NEUTRAL_FORCE_FACTOR = 1.2
EPSILON = 0.001

BANNED_WORDS = {"slackerjack"}
Expand Down Expand Up @@ -134,11 +134,11 @@ def opponent_force(d: float):


def neutral_force(d):
return repelling_force(d, OPPONENT_FORCE_CUTOFF, GRAY_FORCE_FACTOR)
return repelling_force(d, OPPONENT_FORCE_CUTOFF, NEUTRAL_FORCE_FACTOR)


def black_force(d):
return repelling_force(d, OPPONENT_FORCE_CUTOFF, BLACK_FORCE_FACTOR)
return repelling_force(d, OPPONENT_FORCE_CUTOFF, ASSASSIN_FORCE_FACTOR)


def _format_word(word: str) -> str:
Expand Down Expand Up @@ -227,20 +227,22 @@ def opponent_cards(self) -> pd.DataFrame:

@property
def neutral_cards(self) -> pd.DataFrame:
return self.board_data[self.board_data["color"] == CardColor.GRAY]
return self.board_data[self.board_data["color"] == ClassicColor.NEUTRAL]

@property
def own_cards(self) -> pd.DataFrame:
return self.board_data[self.board_data["color"] == self.team]

@property
def black_card(self) -> pd.DataFrame:
return self.board_data[self.board_data["color"] == CardColor.BLACK]
return self.board_data[self.board_data["color"] == ClassicColor.ASSASSIN]

@property
def bad_cards(self) -> pd.DataFrame:
return self.board_data[
self.board_data["color"].isin([CardColor.GRAY, CardColor.BLACK, self.team.opponent.as_card_color])
self.board_data["color"].isin(
[ClassicColor.NEUTRAL, ClassicColor.ASSASSIN, self.team.opponent.as_card_color]
)
]

def update_reveals(self, game_state):
Expand Down Expand Up @@ -313,9 +315,11 @@ def proposal_from_word_vector(self, word: str, vector: np.ndarray) -> Proposal:
self.update_distances(vector)
temp_df = self.unrevealed_cards.sort_values("distance_to_centroid")
centroid_to_black = np.min( # This min is required for the float type
cosine_distance(vector, temp_df[temp_df["color"] == CardColor.BLACK]["vector"])
cosine_distance(vector, temp_df[temp_df["color"] == ClassicColor.ASSASSIN]["vector"])
)
centroid_to_neutral = np.min(
cosine_distance(vector, temp_df[temp_df["color"] == ClassicColor.NEUTRAL]["vector"])
)
centroid_to_neutral = np.min(cosine_distance(vector, temp_df[temp_df["color"] == CardColor.GRAY]["vector"]))
centroid_to_opponent = np.min(
cosine_distance(
vector,
Expand All @@ -325,8 +329,8 @@ def proposal_from_word_vector(self, word: str, vector: np.ndarray) -> Proposal:

bad_cards_limitation = np.min(
[
centroid_to_black - MIN_SELF_BLACK_DELTA,
centroid_to_neutral - MIN_SELF_GRAY_DELTA,
centroid_to_black - MIN_SELF_ASSASSIN_DELTA,
centroid_to_neutral - MIN_SELF_NEUTRAL_DELTA,
centroid_to_opponent - MIN_SELF_OPPONENT_DELTA,
]
)
Expand Down Expand Up @@ -362,9 +366,9 @@ def force_from_color(self, centroid: np.ndarray, card_row: Series):
d = cosine_distance(centroid, vector)
if card_color == self.team.opponent.as_card_color:
return opponent_force(d)
elif card_color == CardColor.BLACK:
elif card_color == ClassicColor.ASSASSIN:
return black_force(d)
elif card_color == CardColor.GRAY:
elif card_color == ClassicColor.NEUTRAL:
return neutral_force(d)
elif card_color == self.team.as_card_color:
return friendly_force(d)
Expand All @@ -386,18 +390,17 @@ def optimization_break_condition(self, cluster: Cluster) -> bool:
distances2opponent = self.extract_centroid_distances(self.team.opponent.as_card_color)
distances2own = self.extract_centroid_distances(self.team.as_card_color)
distances2own = distances2own[distances2own.index.isin(cluster.df.index.to_list())]
distance2black = self.extract_centroid_distances(CardColor.BLACK)
distances2neutral = self.extract_centroid_distances(CardColor.GRAY)
distance2black = self.extract_centroid_distances(ClassicColor.ASSASSIN)
distances2neutral = self.extract_centroid_distances(ClassicColor.NEUTRAL)
max_distance2own = max(distances2own)
if (
(min(distances2opponent) - max_distance2own > MIN_SELF_OPPONENT_DELTA)
and (distance2black[0] - max_distance2own > MIN_SELF_OPPONENT_DELTA)
and (min(distances2neutral) - max_distance2own > MIN_SELF_GRAY_DELTA)
and (min(distances2neutral) - max_distance2own > MIN_SELF_NEUTRAL_DELTA)
and (max_distance2own < MAX_SELF_DISTANCE)
):
return True
else:
return False
return False

def optimize_cluster(self, cluster: Cluster) -> Cluster:
cluster.reset()
Expand All @@ -416,18 +419,20 @@ def optimize_cluster(self, cluster: Cluster) -> Cluster:
self.draw_operative_view(cluster)
return cluster

def extract_centroid_distances(self, color: CardColor):
def extract_centroid_distances(self, color: ClassicColor):
relevant_df = self.board_data[self.board_data["is_revealed"] == False] # noqa: E712
if color == CardColor.GRAY:
color_rows = relevant_df.color == CardColor.GRAY
elif color == CardColor.BLACK:
color_rows = relevant_df.color == CardColor.BLACK
if color == ClassicColor.NEUTRAL:
color_rows = relevant_df.color == ClassicColor.NEUTRAL
elif color == ClassicColor.ASSASSIN:
color_rows = relevant_df.color == ClassicColor.ASSASSIN
elif color == self.team.opponent.as_card_color:
color_rows = relevant_df.color == self.team.opponent.as_card_color
elif color == self.team.as_card_color:
color_rows = relevant_df.color == self.team.as_card_color
elif color == CardColor.BAD: # TODO: What is this?
color_rows = relevant_df.color.isin([CardColor.GRAY, CardColor.BLACK, self.team.opponent.as_card_color])
elif color == ClassicColor.BAD: # TODO: What is this?
color_rows = relevant_df.color.isin(
[ClassicColor.NEUTRAL, ClassicColor.ASSASSIN, self.team.opponent.as_card_color]
)
else:
raise ValueError(f"No such color as {color}")
return relevant_df.loc[color_rows, "distance_to_centroid"]
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