def test_evaluate_no_exog_against_with_exog():
"""Check that adding exogenous data produces different results."""
y, X = load_longley()
forecaster = ARIMA(suppress_warnings=True)
cv = SlidingWindowSplitter()
scoring = MeanAbsolutePercentageError(symmetric=True)
out_exog = evaluate(forecaster, cv, y, X=X, scoring=scoring)
out_no_exog = evaluate(forecaster, cv, y, X=None, scoring=scoring)
scoring_name = f"test_{scoring.name}"
assert np.all(out_exog[scoring_name] != out_no_exog[scoring_name])
def test_evaluate_no_exog_against_with_exog():
# Check that adding exogenous data produces different results
y, X = load_longley()
forecaster = ARIMA(suppress_warnings=True)
cv = SlidingWindowSplitter()
scoring = sMAPE()
out_exog = evaluate(forecaster, cv, y, X=X, scoring=scoring)
out_no_exog = evaluate(forecaster, cv, y, X=None, scoring=scoring)
scoring_name = f"test_{scoring.name}"
assert np.all(out_exog[scoring_name] != out_no_exog[scoring_name])
def _fit_and_score(params):
# Clone forecaster.
forecaster = clone(self.forecaster)
# Set parameters.
forecaster.set_params(**params)
# Evaluate.
out = evaluate(
forecaster,
cv,
y,
X,
strategy=self.strategy,
scoring=scoring,
fit_params=fit_params,
)
# Filter columns.
out = out.filter(items=[scoring_name, "fit_time", "pred_time"],
axis=1)
# Aggregate results.
out = out.mean()
out = out.add_prefix("mean_")
# Add parameters to output table.
out["params"] = params
return out
def test_evaluate_common_configs(CV, fh, window_length, step_length, strategy,
scoring):
"""Test evaluate common configs."""
y = make_forecasting_problem(n_timepoints=30, index_type="int")
forecaster = NaiveForecaster()
cv = CV(fh, window_length, step_length=step_length)
out = evaluate(forecaster=forecaster,
y=y,
cv=cv,
strategy=strategy,
scoring=scoring)
_check_evaluate_output(out, cv, y, scoring)
# check scoring
actual = out.loc[:, f"test_{scoring.name}"]
n_splits = cv.get_n_splits(y)
expected = np.empty(n_splits)
for i, (train, test) in enumerate(cv.split(y)):
f = clone(forecaster)
f.fit(y.iloc[train], fh=fh)
expected[i] = scoring(y.iloc[test], f.predict(), y_train=y.iloc[train])
np.testing.assert_array_equal(actual, expected)
def _get_expected_scores(forecaster, cv, param_grid, y, X, scoring):
scores = np.zeros(len(param_grid))
for i, params in enumerate(param_grid):
f = clone(forecaster)
f.set_params(**params)
out = evaluate(f, cv, y, X=X, scoring=scoring)
scores[i] = out.loc[:, f"test_{scoring.name}"].mean()
return scores
def test_evaluate():
y = load_airline()
forecaster = NaiveForecaster(strategy="drift", sp=12)
cv = ExpandingWindowSplitter(
initial_window=24,
step_length=24,
fh=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
window_length=10,
)
df = evaluate(forecaster=forecaster, y=y, cv=cv, strategy="update")
# just making sure the function is running
assert isinstance(df, pd.DataFrame)
def _score_forecasters(forecasters, cv, y):
"""Will evaluate all the forecasters on y and return the name of best."""
scoring = check_scoring(None)
scoring_name = f"test_{scoring.name}"
score = None
for name, forecaster in forecasters:
results = evaluate(forecaster, cv, y)
results = results.mean()
new_score = float(results[scoring_name])
if not score or new_score < score:
score = new_score
best_name = name
return best_name
def test_evaluate_initial_window():
initial_window = 20
y = make_forecasting_problem(n_timepoints=30, index_type="int")
forecaster = NaiveForecaster()
fh = 1
cv = SlidingWindowSplitter(fh=fh, initial_window=initial_window)
scoring = sMAPE()
out = evaluate(
forecaster=forecaster, y=y, cv=cv, strategy="update", scoring=scoring
)
_check_evaluate_output(out, cv, y, scoring)
assert out.loc[0, "len_train_window"] == initial_window
# check scoring
actual = out.loc[0, f"test_{scoring.name}"]
train, test = next(cv.split(y))
f = clone(forecaster)
f.fit(y.iloc[train], fh=fh)
expected = scoring(y.iloc[test], f.predict())
np.testing.assert_equal(actual, expected)
fh = np.arange(1, 14 + 1)
y = pd.Series(data=trend_newCases.values, index=total_de_casos_amazonas.date)
y.index.freq = 'D'
model = LGBMRegressor(random_state=4,
learning_rate=0.04591301953670739,
num_leaves=45,
min_child_samples=1,
subsample=0.05,
colsample_bytree=0.9828905761860228,
subsample_freq=1,
n_estimators=685)
reg = make_reduction(estimator=model, window_length=14)
cv = ExpandingWindowSplitter(initial_window=60)
cross_val = evaluate(forecaster=reg,
y=y,
cv=cv,
strategy="refit",
return_data=True)
reg.fit(y)
y_pred = reg.predict(fh).round()
def show_figure11():
fig = go.Figure()
fig.add_trace(
go.Bar(x=total_de_casos_amazonas['date'].tail(30),
y=total_de_casos_amazonas['newCases'].tail(30),
hoverinfo='skip'))
fig.update_traces(marker_color='gray')
