Это сложная функция, но она прекрасно использует собственный примитив в Featuretools.
Первый шаг - загрузить CSV совпадений в набор сущностей Featuretools
es = ft.EntitySet()
matches_df = pd.read_csv("./matches.csv")
es.entity_from_dataframe(entity_id="matches",
index="match_id",
time_index="match_date",
dataframe=matches_df)
Затем мыопределить пользовательский примитив преобразования, который вычисляет средние цели, забитые в последних n играх.у него есть параметр, который контролирует количество прошедших игр и позволяет ли рассчитывать на домашнюю или выездную команду.Информация об определении пользовательских примитивов находится в нашей документации здесь и здесь .
from featuretools.variable_types import Numeric, Categorical
from featuretools.primitives import make_trans_primitive
def avg_goals_previous_n_games(home_team, away_team, home_goals, away_goals, which_team=None, n=1):
# make dataframe so it's easier to work with
df = pd.DataFrame({
"home_team": home_team,
"away_team": away_team,
"home_goals": home_goals,
"away_goals": away_goals
})
result = []
for i, current_game in df.iterrows():
# get the right team for this game
team = current_game[which_team]
# find all previous games that have been played
prev_games = df.iloc[:i]
# only get games the team participated in
participated = prev_games[(prev_games["home_team"] == team) | (prev_games["away_team"] == team)]
if participated.shape[0] < n:
result.append(None)
continue
# get last n games
last_n = participated.tail(n)
# calculate games per game
goal_as_home = (last_n["home_team"] == team) * last_n["home_goals"]
goal_as_away = (last_n["away_team"] == team) * last_n["away_goals"]
# calculate mean across all home and away games
mean = (goal_as_home + goal_as_away).mean()
result.append(mean)
return result
# custom function so the name of the feature prints out correctly
def make_name(self):
return "%s_goal_last_%d" % (self.kwargs['which_team'], self.kwargs['n'])
AvgGoalPreviousNGames = make_trans_primitive(function=avg_goals_previous_n_games,
input_types=[Categorical, Categorical, Numeric, Numeric],
return_type=Numeric,
cls_attributes={"generate_name": make_name, "uses_full_entity":True})
Теперь мы можем определять объекты, используя этот примитив.В этом случае мы должны будем сделать это вручную.
input_vars = [es["matches"]["home_team"], es["matches"]["away_team"], es["matches"]["home_goals"], es["matches"]["away_goals"]]
home_team_last1 = AvgGoalPreviousNGames(*input_vars, which_team="home_team", n=1)
home_team_last3 = AvgGoalPreviousNGames(*input_vars, which_team="home_team", n=3)
home_team_last5 = AvgGoalPreviousNGames(*input_vars, which_team="home_team", n=5)
away_team_last1 = AvgGoalPreviousNGames(*input_vars, which_team="away_team", n=1)
away_team_last3 = AvgGoalPreviousNGames(*input_vars, which_team="away_team", n=3)
away_team_last5 = AvgGoalPreviousNGames(*input_vars, which_team="away_team", n=5)
features = [home_team_last1, home_team_last3, home_team_last5,
away_team_last1, away_team_last3, away_team_last5]
Наконец, мы можем вычислить матрицу объектов
fm = ft.calculate_feature_matrix(entityset=es, features=features)
, которая возвращает
home_team_goal_last_1 home_team_goal_last_3 home_team_goal_last_5 away_team_goal_last_1 away_team_goal_last_3 away_team_goal_last_5
match_id
1 NaN NaN NaN NaN NaN NaN
2 2.0 NaN NaN 0.0 NaN NaN
3 1.0 NaN NaN 0.0 NaN NaN
4 3.0 1.000000 NaN 0.0 1.000000 NaN
5 1.0 1.333333 NaN 1.0 0.666667 NaN
6 2.0 2.000000 1.2 0.0 0.333333 0.8
7 1.0 0.666667 0.6 2.0 1.666667 1.6
8 2.0 1.000000 0.8 2.0 2.000000 2.0
9 0.0 1.000000 0.8 1.0 1.666667 1.6
10 3.0 2.000000 2.0 1.0 1.000000 0.8
11 3.0 2.333333 2.2 1.0 0.666667 1.0
12 2.0 2.666667 2.2 2.0 1.333333 1.2
Наконецмы также можем использовать эти определенные вручную функции в качестве входных данных для автоматизированного проектирования объектов с использованием Deep Feature Synthesis, которое объясняется здесь .Передавая вручную определенные объекты как seed_features, ft.dfs будет автоматически накладываться поверх них.
fm, feature_defs = ft.dfs(entityset=es,
target_entity="matches",
seed_features=features,
agg_primitives=[],
trans_primitives=["day", "month", "year", "weekday", "percentile"])
feature_defs равно
[<Feature: home_team>,
<Feature: away_team>,
<Feature: home_goals>,
<Feature: away_goals>,
<Feature: label>,
<Feature: home_team_goal_last_1>,
<Feature: home_team_goal_last_3>,
<Feature: home_team_goal_last_5>,
<Feature: away_team_goal_last_1>,
<Feature: away_team_goal_last_3>,
<Feature: away_team_goal_last_5>,
<Feature: DAY(match_date)>,
<Feature: MONTH(match_date)>,
<Feature: YEAR(match_date)>,
<Feature: WEEKDAY(match_date)>,
<Feature: PERCENTILE(home_goals)>,
<Feature: PERCENTILE(away_goals)>,
<Feature: PERCENTILE(home_team_goal_last_1)>,
<Feature: PERCENTILE(home_team_goal_last_3)>,
<Feature: PERCENTILE(home_team_goal_last_5)>,
<Feature: PERCENTILE(away_team_goal_last_1)>,
<Feature: PERCENTILE(away_team_goal_last_3)>,
<Feature: PERCENTILE(away_team_goal_last_5)>]
Матрица объектов равна
home_team away_team home_goals away_goals label home_team_goal_last_1 home_team_goal_last_3 home_team_goal_last_5 away_team_goal_last_1 away_team_goal_last_3 away_team_goal_last_5 DAY(match_date) MONTH(match_date) YEAR(match_date) WEEKDAY(match_date) PERCENTILE(home_goals) PERCENTILE(away_goals) PERCENTILE(home_team_goal_last_1) PERCENTILE(home_team_goal_last_3) PERCENTILE(home_team_goal_last_5) PERCENTILE(away_team_goal_last_1) PERCENTILE(away_team_goal_last_3) PERCENTILE(away_team_goal_last_5)
match_id
1 Arsenal Chelsea 2 0 1 NaN NaN NaN NaN NaN NaN 1 1 2014 2 0.666667 0.166667 NaN NaN NaN NaN NaN NaN
2 Arsenal Chelsea 1 0 1 2.0 NaN NaN 0.0 NaN NaN 2 1 2014 3 0.333333 0.166667 0.590909 NaN NaN 0.227273 NaN NaN
3 Arsenal Chelsea 0 3 2 1.0 NaN NaN 0.0 NaN NaN 3 1 2014 4 0.125000 0.958333 0.272727 NaN NaN 0.227273 NaN NaN
4 Chelsea Arsenal 1 1 X 3.0 1.000000 NaN 0.0 1.000000 NaN 4 1 2014 5 0.333333 0.500000 0.909091 0.333333 NaN 0.227273 0.500000 NaN
5 Chelsea Arsenal 2 0 1 1.0 1.333333 NaN 1.0 0.666667 NaN 5 1 2014 6 0.666667 0.166667 0.272727 0.555556 NaN 0.590909 0.277778 NaN
6 Chelsea Arsenal 2 1 1 2.0 2.000000 1.2 0.0 0.333333 0.8 6 1 2014 0 0.666667 0.500000 0.590909 0.722222 0.571429 0.227273 0.111111 0.214286
7 Arsenal Chelsea 2 2 X 1.0 0.666667 0.6 2.0 1.666667 1.6 7 1 2014 1 0.666667 0.791667 0.272727 0.111111 0.142857 0.909091 0.833333 0.785714
8 Arsenal Chelsea 0 1 2 2.0 1.000000 0.8 2.0 2.000000 2.0 8 1 2014 2 0.125000 0.500000 0.590909 0.333333 0.357143 0.909091 1.000000 1.000000
9 Arsenal Chelsea 1 3 2 0.0 1.000000 0.8 1.0 1.666667 1.6 9 1 2014 3 0.333333 0.958333 0.090909 0.333333 0.357143 0.590909 0.833333 0.785714
10 Chelsea Arsenal 3 1 1 3.0 2.000000 2.0 1.0 1.000000 0.8 10 1 2014 4 0.916667 0.500000 0.909091 0.722222 0.714286 0.590909 0.500000 0.214286
11 Chelsea Arsenal 2 2 X 3.0 2.333333 2.2 1.0 0.666667 1.0 11 1 2014 5 0.666667 0.791667 0.909091 0.888889 0.928571 0.590909 0.277778 0.428571
12 Chelsea Arsenal 4 1 1 2.0 2.666667 2.2 2.0 1.333333 1.2 12 1 2014 6 1.000000 0.500000 0.590909 1.000000 0.928571 0.909091 0.666667 0.571429