def test_single_dataset_single_strategy_against_sklearn(
dataset, cv, metric_func, estimator, results_cls, tmpdir):
"""Test against sklearn."""
# set up orchestration
task = TSCTask(target="class_val")
# create strategies
clf = make_reduction_pipeline(estimator)
strategy = TSCStrategy(clf)
# result backend
if results_cls in [HDDResults]:
# for hard drive results, create temporary directory using pytest's
# tmpdir fixture
tempdir = tmpdir.mkdir("results/")
path = tempdir.dirpath()
results = results_cls(path=path)
elif results_cls in [RAMResults]:
results = results_cls()
else:
raise ValueError()
orchestrator = Orchestrator(datasets=[dataset],
tasks=[task],
strategies=[strategy],
cv=cv,
results=results)
orchestrator.fit_predict(save_fitted_strategies=False)
evaluator = Evaluator(results)
# create metric classes for evaluation and set metric kwargs
if metric_func in [accuracy_score]:
kwargs = {} # empty kwargs for simple pairwise metrics
metric = PairwiseMetric(func=metric_func, name="metric")
elif metric_func in [f1_score]:
kwargs = {"average": "macro"} # set kwargs for composite metrics
metric = AggregateMetric(func=metric_func, name="metric", **kwargs)
else:
raise ValueError()
metrics = evaluator.evaluate(metric=metric)
actual = metrics["metric_mean"].iloc[0]
# compare against sklearn cross_val_score
data = dataset.load() # load data
X = data.loc[:, task.features]
y = data.loc[:, task.target]
expected = cross_val_score(clf,
X,
y,
scoring=make_scorer(metric_func, **kwargs),
cv=cv).mean()
# compare results
np.testing.assert_array_equal(actual, expected)
def test_stat():
"""Test sign ranks."""
data = load_gunpoint(split="train", return_X_y=False)
dataset = RAMDataset(dataset=data, name="gunpoint")
task = TSCTask(target="class_val")
fc = ComposableTimeSeriesForestClassifier(n_estimators=1, random_state=1)
strategy_fc = TSCStrategy(fc, name="tsf")
pf = KNeighborsTimeSeriesClassifier()
strategy_pf = TSCStrategy(pf, name="pf")
# result backend
results = RAMResults()
orchestrator = Orchestrator(
datasets=[dataset],
tasks=[task],
strategies=[strategy_pf, strategy_fc],
cv=SingleSplit(random_state=1),
results=results,
)
orchestrator.fit_predict(save_fitted_strategies=False)
analyse = Evaluator(results)
metric = PairwiseMetric(func=accuracy_score, name="accuracy")
_ = analyse.evaluate(metric=metric)
ranks = analyse.rank(ascending=True)
pf_rank = ranks.loc[ranks.strategy == "pf",
"accuracy_mean_rank"].item() # 1
fc_rank = ranks.loc[ranks.strategy == "tsf",
"accuracy_mean_rank"].item() # 2
rank_array = [pf_rank, fc_rank]
rank_array_test = [1, 2]
_, sign_test_df = analyse.sign_test()
sign_array = [
[sign_test_df["pf"][0], sign_test_df["pf"][1]],
[sign_test_df["tsf"][0], sign_test_df["tsf"][1]],
]
sign_array_test = [[1, 1], [1, 1]]
np.testing.assert_equal([rank_array, sign_array],
[rank_array_test, sign_array_test])
def test_automated_orchestration_vs_manual(data_loader):
"""Test orchestration."""
data = data_loader(return_X_y=False)
dataset = RAMDataset(dataset=data, name="data")
task = TSCTask(target="class_val")
# create strategies
# clf = TimeSeriesForestClassifier(n_estimators=1, random_state=1)
clf = make_reduction_pipeline(
RandomForestClassifier(n_estimators=2, random_state=1))
strategy = TSCStrategy(clf)
# result backend
results = RAMResults()
orchestrator = Orchestrator(
datasets=[dataset],
tasks=[task],
strategies=[strategy],
cv=SingleSplit(random_state=1),
results=results,
)
orchestrator.fit_predict(save_fitted_strategies=False)
result = next(results.load_predictions(cv_fold=0,
train_or_test="test")) # get
# only first item of iterator
actual = result.y_pred
# expected output
task = TSCTask(target="class_val")
cv = SingleSplit(random_state=1)
train_idx, test_idx = next(cv.split(data))
train = data.iloc[train_idx, :]
test = data.iloc[test_idx, :]
strategy.fit(task, train)
expected = strategy.predict(test)
# compare results
np.testing.assert_array_equal(actual, expected)
HOME = os.path.expanduser("~")
DATA_PATH = os.path.join(HOME, "Documents/Research/data/Univariate_ts")
RESULTS_PATH = "results"
# Alternatively, we can use a helper function to create them automatically
datasets = make_datasets(path=DATA_PATH,
dataset_cls=UEADataset,
names=UNIVARIATE_DATASETS)
tasks = [TSCTask(target="target") for _ in range(len(datasets))]
results = HDDResults(path=RESULTS_PATH)
orchestrator = Orchestrator(
datasets=datasets,
tasks=tasks,
strategies=STRATEGIES,
cv=PresplitFilesCV(cv=UEAStratifiedCV(n_splits=30)),
results=results)
orchestrator.fit_predict(save_fitted_strategies=False,
verbose=True,
overwrite_predictions=True,
save_timings=True)
evaluator = Evaluator(results=results)
metric = PairwiseMetric(func=accuracy_score, name="accuracy")
evaluator.evaluate(metric)
evaluator.metrics_by_strategy_dataset.to_csv(os.path.join(
RESULTS_PATH, "accuracy.csv"),
header=True)
bootstrap_sample_subset=False,
n_jobs=-1)
strategies = [
TSCStrategy(estimator=make_reduction_pipeline(estimator=estimator),
name="rotf")
]
# define results output
results = HDDResults(path=RESULTS_PATH)
# results = RAMResults()
# run orchestrator
orchestrator = Orchestrator(datasets=datasets,
tasks=tasks,
strategies=strategies,
cv=PresplitFilesCV(),
results=results)
start = time.time()
orchestrator.fit_predict(save_fitted_strategies=False,
overwrite_fitted_strategies=False,
overwrite_predictions=True,
predict_on_train=False,
verbose=True)
elapsed = time.time() - start
print(elapsed)
# evaluate predictions
evaluator = Evaluator(results=results)
metric = PairwiseMetric(func=accuracy_score, name="accuracy")