def test_fit_cards():
with test_past_data_session_scope() as ts:
df_cards = fit_card_points(gameweek=1, season="1819", dbsession=ts)
assert isinstance(df_cards, pd.Series)
assert len(df_cards) > 0
assert all(df_cards <= 0)
assert all(df_cards >= -3)
def test_get_fitted_player_model():
pm = get_player_model()
assert isinstance(pm, pystan.model.StanModel)
with test_past_data_session_scope() as ts:
fpm = get_fitted_player_model(pm, "FWD", "1819", 12, ts)
assert isinstance(fpm, pd.DataFrame)
assert len(fpm) > 0
def test_get_ratings_dict():
with test_past_data_session_scope() as ts:
rd = get_result_dict("1819", 10, ts)
teams = set(rd["home_team"]) | set(rd["away_team"])
d = get_ratings_dict("1819", teams, ts)
assert isinstance(d, dict)
assert len(d) >= 20
def test_fit_bonus():
with test_past_data_session_scope() as ts:
df_bonus = fit_bonus_points(gameweek=1, season="1819", dbsession=ts)
assert len(df_bonus) == 2
for df in df_bonus:
assert isinstance(df, pd.Series)
assert len(df) > 0
assert all(df <= 3)
assert all(df >= 0)
def test_get_fitted_player_model():
pm = NumpyroPlayerModel()
assert isinstance(pm, NumpyroPlayerModel)
cpm = ConjugatePlayerModel()
assert isinstance(cpm, ConjugatePlayerModel)
with test_past_data_session_scope() as ts:
fpm = fit_player_data("FWD", "1819", 12, model=pm, dbsession=ts)
assert isinstance(fpm, pd.DataFrame)
assert len(fpm) > 0
fcpm = fit_player_data("FWD", "1819", 12, model=cpm, dbsession=ts)
assert isinstance(fcpm, pd.DataFrame)
assert len(fcpm) > 0
def test_get_player_history_df():
"""
test that we only consider gameweeks up to the specified gameweek
(gw 12 in 1819 season).
"""
with test_past_data_session_scope() as ts:
df = get_player_history_df(season="1819", gameweek=12, dbsession=ts)
assert len(df) > 0
result_ids = df.match_id.unique()
for result_id in result_ids:
if result_id == 0:
continue
fixture_id = (ts.query(Result).filter_by(
result_id=int(result_id)).first().fixture_id)
fixture = ts.query(Fixture).filter_by(
fixture_id=fixture_id).first()
assert fixture.season in ["1718", "1819"]
if fixture.season == "1819":
assert fixture.gameweek < 12
def test_get_player_scores():
"""Test utility function used by fit bonus, save and card points to get player
scores rows filtered by season, gameweek and minutese played values"""
with test_past_data_session_scope() as ts:
df = get_player_scores(season="1819", gameweek=12, dbsession=ts)
# check type and columns
assert len(df) > 0
assert isinstance(df, pd.DataFrame)
req_cols = [
"player_id",
"minutes",
"saves",
"bonus",
"yellow_cards",
"red_cards",
]
for col in req_cols:
assert col in df.columns
# test player scores correctly filtered by gameweek and season
for _, row in df.iterrows():
assert row["season"] in ["1718", "1819"]
if row["season"] == "1819":
assert row["gameweek"] < 12
# test filtering on min minutes
df = get_player_scores(season="1819",
gameweek=12,
min_minutes=10,
dbsession=ts)
assert len(df) > 0
assert all(df["minutes"] >= 10)
# test filtering on max minutes
df = get_player_scores(season="1819",
gameweek=12,
max_minutes=10,
dbsession=ts)
assert len(df) > 0
assert all(df["minutes"] <= 10)
def test_fixture_probabilities():
with test_past_data_session_scope() as ts:
df = fixture_probabilities(20, "1819", dbsession=ts)
assert isinstance(df, pd.DataFrame)
assert len(df) == 10
def test_get_fitted_team_model():
with test_past_data_session_scope() as ts:
model_team = get_fitted_team_model("1819", 10, ts)
assert isinstance(
model_team,
bpl.extended_dixon_coles.ExtendedDixonColesMatchPredictor)
def test_get_result_dict():
with test_past_data_session_scope() as ts:
d = get_result_dict("1819", 10, ts)
assert isinstance(d, dict)
assert len(d) > 0
def test_get_fitted_team_model():
with test_past_data_session_scope() as ts:
model_team = get_fitted_team_model("1819", 10, ts)
assert isinstance(model_team, bpl.models.BPLModel)
def test_get_ratings_df():
with test_past_data_session_scope() as ts:
df = get_ratings_df("1819", ts)
assert isinstance(df, pd.DataFrame)
assert len(df) >= 20
