def test_different_pipelines():
random_seed = 1233
X_train, y_train = load_gunpoint(return_X_y=True)
steps = [
('segment', RandomIntervalSegmenter(n_intervals='sqrt')),
('transform',
FeatureUnion([
('mean',
RowwiseTransformer(
FunctionTransformer(func=np.mean, validate=False))),
('std',
RowwiseTransformer(
FunctionTransformer(func=np.std, validate=False))),
('slope',
RowwiseTransformer(
FunctionTransformer(func=time_series_slope,
validate=False))),
])),
]
pipe = Pipeline(steps, random_state=random_seed)
a = pipe.fit_transform(X_train)
tran = RandomIntervalFeatureExtractor(
n_intervals='sqrt',
features=[np.mean, np.std, time_series_slope],
random_state=random_seed)
b = tran.fit_transform(X_train)
np.testing.assert_array_equal(a, b)
np.testing.assert_array_equal(pipe.steps[0][1].intervals_, tran.intervals_)
def rise_benchmarking():
for i in range(0, len(benchmark_datasets)):
dataset = benchmark_datasets[i]
print(str(i) + " problem = " + dataset)
rise = fb.RandomIntervalSpectralForest(n_trees=100)
exp.run_experiment(overwrite=True,
datasets_dir_path=data_dir,
results_path=results_dir,
cls_name="PythonRISE",
classifier=rise,
dataset=dataset,
train_file=False)
steps = [('segment',
RandomIntervalSegmenter(n_intervals=1, min_length=5)),
('transform',
FeatureUnion([('acf',
RowwiseTransformer(
FunctionTransformer(func=acf_coefs,
validate=False))),
('ps',
RowwiseTransformer(
FunctionTransformer(func=powerspectrum,
validate=False)))])),
('tabularise', Tabulariser()),
('clf', DecisionTreeClassifier())]
base_estimator = Pipeline(steps)
rise = TimeSeriesForestClassifier(base_estimator=base_estimator,
n_estimators=100)
exp.run_experiment(overwrite=True,
datasets_dir_path=data_dir,
results_path=results_dir,
cls_name="PythonRISEComposite",
classifier=rise,
dataset=dataset,
train_file=False)
def test_rowwise_transformer_function_transformer_series_to_primitives():
X, y = load_gunpoint(return_X_y=True)
ft = FunctionTransformer(func=np.mean, validate=False)
t = RowwiseTransformer(ft)
Xt = t.fit_transform(X, y)
assert Xt.shape == X.shape
assert isinstance(Xt.iloc[0, 0], float) # check series-to-primitive transforms
def test_different_implementations():
random_seed = 1233
X_train, y_train = load_gunpoint(return_X_y=True)
# Compare with chained transformations.
tran1 = RandomIntervalSegmenter(n_intervals='sqrt', random_state=random_seed)
tran2 = RowwiseTransformer(FunctionTransformer(func=np.mean, validate=False))
A = tran2.fit_transform(tran1.fit_transform(X_train))
tran = RandomIntervalFeatureExtractor(n_intervals='sqrt', features=[np.mean], random_state=random_seed)
B = tran.fit_transform(X_train)
np.testing.assert_array_equal(A, B)
# Compare with transformer pipeline using TSFeatureUnion.
steps = [
('segment', RandomIntervalSegmenter(n_intervals='sqrt', check_input=False)),
('transform', TSFeatureUnion([
('mean', RowwiseTransformer(FunctionTransformer(func=np.mean, validate=False))),
('std', RowwiseTransformer(FunctionTransformer(func=np.std, validate=False))),
])),
]
pipe = TSPipeline(steps, random_state=random_seed)
a = pipe.fit_transform(X_train)
n_ints = a.shape[1] // 2 # Rename columns for comparing re-ordered arrays.
a.columns = [*a.columns[:n_ints] + '_mean', *a.columns[n_ints:n_ints * 2] + '_std']
a = a.reindex(np.sort(a.columns), axis=1)
tran = RandomIntervalFeatureExtractor(n_intervals='sqrt', features=[np.mean, np.std],
random_state=random_seed)
b = tran.fit_transform(X_train)
b = b.reindex(np.sort(b.columns), axis=1)
np.testing.assert_array_equal(a, b)
def _test_TimeSeriesForest_predictions(n_estimators=None, n_intervals=None, random_state=None):
# fully modular implementation using pipeline with FeatureUnion
steps = [
('segment', RandomIntervalSegmenter(n_intervals=n_intervals, check_input=False)),
('transform', TSFeatureUnion([
('mean', RowwiseTransformer(FunctionTransformer(func=np.mean, validate=False))),
('std', RowwiseTransformer(FunctionTransformer(func=np.std, validate=False))),
('slope', RowwiseTransformer(FunctionTransformer(func=time_series_slope, validate=False)))
])),
('clf', DecisionTreeClassifier())
]
base_estimator = TSPipeline(steps)
clf1 = TimeSeriesForestClassifier(base_estimator=base_estimator,
random_state=random_state,
n_estimators=n_estimators)
clf1.fit(X_train, y_train)
a = clf1.predict_proba(X_test)
# default, semi-modular implementation using RandomIntervalFeatureExtractor internally
clf2 = TimeSeriesForestClassifier(random_state=random_state,
n_estimators=n_estimators)
clf2.set_params(**{'base_estimator__transform__n_intervals': n_intervals})
clf2.fit(X_train, y_train)
b = clf2.predict_proba(X_test)
np.testing.assert_array_equal(a, b)
def test_heterogenous_pipeline_column_ensmbler():
X_train, y_train = load_basic_motions("TRAIN", return_X_y=True)
X_test, y_test = load_basic_motions("TEST", return_X_y=True)
n_intervals = 3
steps = [('segment', RandomIntervalSegmenter(n_intervals=n_intervals)),
('transform',
FeatureUnion([('mean',
RowwiseTransformer(
FunctionTransformer(func=np.mean,
validate=False))),
('std',
RowwiseTransformer(
FunctionTransformer(func=np.std,
validate=False)))])),
('clf', DecisionTreeClassifier())]
clf1 = Pipeline(steps, random_state=1)
# dims 0-3 with alternating classifiers.
ct = ColumnEnsembleClassifier([
("RandomIntervalTree", clf1, [0]),
("KNN4", KNNTSC(n_neighbors=1), [4]),
("BOSSEnsemble1 ", BOSSEnsemble(ensemble_size=3), [1]),
("KNN2", KNNTSC(n_neighbors=1), [2]),
("BOSSEnsemble3", BOSSEnsemble(ensemble_size=3), [3]),
])
ct.fit(X_train, y_train)
ct.score(X_test, y_test)
def _test_pipeline_predictions(n_intervals=None, random_state=None):
steps = [('segment',
RandomIntervalSegmenter(n_intervals=n_intervals,
check_input=False)),
('transform',
FeatureUnion([('mean',
RowwiseTransformer(
FunctionTransformer(func=np.mean,
validate=False))),
('std',
RowwiseTransformer(
FunctionTransformer(func=np.std,
validate=False)))])),
('clf', DecisionTreeClassifier())]
clf1 = Pipeline(steps, random_state=random_state)
clf1.fit(X_train, y_train)
a = clf1.predict(X_test)
steps = [('transform',
RandomIntervalFeatureExtractor(n_intervals=n_intervals,
features=[np.mean, np.std])),
('clf', DecisionTreeClassifier())]
clf2 = Pipeline(steps, random_state=random_state)
clf2.fit(X_train, y_train)
b = clf2.predict(X_test)
np.array_equal(a, b)
def test_pipeline_predictions(n_intervals, n_estimators):
random_state = 1234
# Due to tie-breaking/floating point rounding in the final decision tree classifier, the results depend on the
# exact column order of the input data
# Compare pipeline predictions outside of ensemble.
steps = [('segment', RandomIntervalSegmenter(n_intervals=n_intervals)),
('transform',
FeatureUnion([('mean',
RowwiseTransformer(
FunctionTransformer(func=np.mean,
validate=False))),
('std',
RowwiseTransformer(
FunctionTransformer(func=np.std,
validate=False))),
('slope',
RowwiseTransformer(
FunctionTransformer(func=time_series_slope,
validate=False)))])),
('clf', DecisionTreeClassifier())]
clf1 = Pipeline(steps, random_state=random_state)
clf1.fit(X_train, y_train)
a = clf1.predict(X_test)
steps = [('transform',
RandomIntervalFeatureExtractor(
n_intervals=n_intervals,
features=[np.mean, np.std, time_series_slope])),
('clf', DecisionTreeClassifier())]
clf2 = Pipeline(steps, random_state=random_state)
clf2.fit(X_train, y_train)
b = clf2.predict(X_test)
np.array_equal(a, b)
def test_rowwise_transformer_transform_inverse_transform():
X, y = load_gunpoint(return_X_y=True)
t = RowwiseTransformer(StandardScaler())
Xt = t.fit_transform(X)
Xit = t.inverse_transform(Xt)
assert Xit.shape == X.shape
assert isinstance(Xit.iloc[0, 0], (pd.Series, np.ndarray)) # check series-to-series transforms
np.testing.assert_array_almost_equal(tabularise(X).values, tabularise(Xit).values, decimal=5)
def test_FeatureUnion():
X, y = load_gunpoint(return_X_y=True)
ft = FunctionTransformer(func=np.mean, validate=False)
t = RowwiseTransformer(ft)
fu = FeatureUnion([
('mean', t),
('std',
RowwiseTransformer(FunctionTransformer(func=np.std, validate=False)))
])
Xt = fu.fit_transform(X, y)
assert Xt.shape == (X.shape[0], X.shape[1] * len(fu.transformer_list))
def test_rowwise_transformer_sklearn_transfomer():
mu = 10
sd = 5
X = generate_df_from_array(np.random.normal(loc=mu, scale=5, size=(100,)), n_rows=10, n_cols=1)
t = StandardScaler(with_mean=True, with_std=True)
r = RowwiseTransformer(t)
Xt = r.fit_transform(X)
assert Xt.shape == X.shape
assert isinstance(Xt.iloc[0, 0], (pd.Series, np.ndarray)) # check series-to-series transform
np.testing.assert_almost_equal(Xt.iloc[0, 0].mean(), 0) # check standardisation
np.testing.assert_almost_equal(Xt.iloc[0, 0].std(), 1, decimal=2)
def test_rowwise_transformer_function_transformer_series_to_series():
X, y = load_gunpoint(return_X_y=True)
# series-to-series transform function
def powerspectrum(x):
fft = np.fft.fft(x)
ps = fft.real * fft.real + fft.imag * fft.imag
return ps[:ps.shape[0] // 2]
ft = FunctionTransformer(func=powerspectrum, validate=False)
t = RowwiseTransformer(ft)
Xt = t.fit_transform(X, y)
assert Xt.shape == X.shape
assert isinstance(Xt.iloc[0, 0], (pd.Series, np.ndarray)) # check series-to-series transforms
def test_RowwiseTransformer_pipeline():
# using pure sklearn
mean_func = lambda X: pd.DataFrame([np.mean(row) for row in X])
first_func = lambda X: pd.DataFrame([row[0] for row in X])
column_transformer = ColumnTransformer(
[('mean', FunctionTransformer(func=mean_func, validate=False), 'ts'),
('first', FunctionTransformer(func=first_func, validate=False), 'ts_copy')])
estimator = RandomForestClassifier(n_estimators=2, random_state=1)
strategy = [
('feature_extract', column_transformer),
('rfestimator', estimator)]
model = Pipeline(steps=strategy)
model.fit(X_train, y_train)
expected = model.predict(X_test)
# using sktime with sklearn pipeline
first_func = lambda X: pd.DataFrame([row[0] for row in X])
column_transformer = ColumnTransformer(
[('mean', RowwiseTransformer(FunctionTransformer(func=np.mean, validate=False)), 'ts'),
('first', FunctionTransformer(func=first_func, validate=False), 'ts_copy')])
estimator = RandomForestClassifier(n_estimators=2, random_state=1)
strategy = [
('feature_extract', column_transformer),
('rfestimator', estimator)]
model = Pipeline(steps=strategy)
model.fit(X_train, y_train)
got = model.predict(X_test)
np.testing.assert_array_equal(expected, got)
def test_FeatureUnion_pipeline():
# pipeline with segmentation plus multiple feature extraction
steps = [
('segment', RandomIntervalSegmenter(n_intervals=3, check_input=False)),
('transform', FeatureUnion([
('mean', RowwiseTransformer(FunctionTransformer(func=np.mean, validate=False))),
('std', RowwiseTransformer(FunctionTransformer(func=np.std, validate=False)))
])),
('clf', DecisionTreeClassifier())
]
clf = Pipeline(steps)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
assert y_pred.shape[0] == y_test.shape[0]
np.testing.assert_array_equal(np.unique(y_pred), np.unique(y_test))
def test_different_implementations():
random_seed = 1233
X_train, y_train = load_gunpoint(return_X_y=True)
# Compare with chained transformations.
tran1 = RandomIntervalSegmenter(n_intervals='sqrt',
random_state=random_seed)
tran2 = RowwiseTransformer(
FunctionTransformer(func=np.mean, validate=False))
A = tran2.fit_transform(tran1.fit_transform(X_train))
tran = RandomIntervalFeatureExtractor(n_intervals='sqrt',
features=[np.mean],
random_state=random_seed)
B = tran.fit_transform(X_train)
np.testing.assert_array_equal(A, B)
def set_classifier(cls, resampleId):
"""
Basic way of determining the classifier to build. To differentiate settings just and another elif. So, for example, if
you wanted tuned TSF, you just pass TuneTSF and set up the tuning mechanism in the elif.
This may well get superceded, it is just how e have always done it
:param cls: String indicating which classifier you want
:return: A classifier.
"""
if cls.lower() == 'pf':
return pf.ProximityForest(random_state = resampleId)
elif cls.lower() == 'pt':
return pf.ProximityTree(random_state = resampleId)
elif cls.lower() == 'ps':
return pf.ProximityStump(random_state = resampleId)
elif cls.lower() == 'rise':
return fb.RandomIntervalSpectralForest(random_state = resampleId)
elif cls.lower() == 'tsf':
return ib.TimeSeriesForest(random_state = resampleId)
elif cls.lower() == 'boss':
return db.BOSSEnsemble()
elif cls.lower() == 'st':
return st.ShapeletTransformClassifier(time_contract_in_mins=1500)
elif cls.lower() == 'dtwcv':
return nn.KNeighborsTimeSeriesClassifier(metric="dtwcv")
elif cls.lower() == 'ee' or cls.lower() == 'elasticensemble':
return dist.ElasticEnsemble()
elif cls.lower() == 'tsfcomposite':
#It defaults to TSF
return ensemble.TimeSeriesForestClassifier()
elif cls.lower() == 'risecomposite':
steps = [
('segment', RandomIntervalSegmenter(n_intervals=1, min_length=5)),
('transform', FeatureUnion([
('acf', RowwiseTransformer(FunctionTransformer(func=acf_coefs, validate=False))),
('ps', RowwiseTransformer(FunctionTransformer(func=powerspectrum, validate=False)))
])),
('tabularise', Tabulariser()),
('clf', DecisionTreeClassifier())
]
base_estimator = Pipeline(steps)
return ensemble.TimeSeriesForestClassifier(base_estimator=base_estimator, n_estimators=100)
else:
raise Exception('UNKNOWN CLASSIFIER')
def tsf_benchmarking():
for i in range(0, len(benchmark_datasets)):
dataset = benchmark_datasets[i]
print(str(i) + " problem = " + dataset)
tsf = ib.TimeSeriesForest(n_trees=100)
exp.run_experiment(overwrite=False,
datasets_dir_path=data_dir,
results_path=results_dir,
cls_name="PythonTSF",
classifier=tsf,
dataset=dataset,
train_file=False)
steps = [
('segment', RandomIntervalSegmenter(n_intervals='sqrt')),
('transform',
FeatureUnion([('mean',
RowwiseTransformer(
FunctionTransformer(func=np.mean,
validate=False))),
('std',
RowwiseTransformer(
FunctionTransformer(func=np.std,
validate=False))),
('slope',
RowwiseTransformer(
FunctionTransformer(func=time_series_slope,
validate=False)))])),
('clf', DecisionTreeClassifier())
]
base_estimator = Pipeline(steps)
tsf = TimeSeriesForestClassifier(base_estimator=base_estimator,
n_estimators=100)
exp.run_experiment(overwrite=False,
datasets_dir_path=data_dir,
results_path=results_dir,
cls_name="PythonTSFComposite",
classifier=tsf,
dataset=dataset,
train_file=False)
def test_RowwiseTransformer_pipeline():
X_train, y_train = load_basic_motions("TRAIN", return_X_y=True)
X_test, y_test = load_basic_motions("TEST", return_X_y=True)
# using pure sklearn
def rowwise_mean(X):
if isinstance(X, pd.Series):
X = pd.DataFrame(X)
Xt = pd.concat([pd.Series(col.apply(np.mean))
for _, col in X.items()], axis=1)
return Xt
def rowwise_first(X):
if isinstance(X, pd.Series):
X = pd.DataFrame(X)
Xt = pd.concat([pd.Series(tabularise(col).iloc[:, 0])
for _, col in X.items()], axis=1)
return Xt
# specify column as a list, otherwise pandas Series are selected and passed on to the transformers
transformer = ColumnTransformer([
('mean', FunctionTransformer(func=rowwise_mean, validate=False), ['dim_0']),
('first', FunctionTransformer(func=rowwise_first, validate=False), ['dim_1'])
])
estimator = RandomForestClassifier(n_estimators=2, random_state=1)
steps = [
('extract', transformer),
('classify', estimator)
]
model = Pipeline(steps=steps)
model.fit(X_train, y_train)
expected = model.predict(X_test)
# using sktime with sklearn pipeline
transformer = ColumnTransformer([
('mean', RowwiseTransformer(FunctionTransformer(func=np.mean, validate=False)), ['dim_0']),
('first', FunctionTransformer(func=rowwise_first, validate=False), ['dim_1'])
])
estimator = RandomForestClassifier(n_estimators=2, random_state=1)
steps = [
('extract', transformer),
('classify', estimator)
]
model = Pipeline(steps=steps)
model.fit(X_train, y_train)
actual = model.predict(X_test)
np.testing.assert_array_equal(expected, actual)
def test_Pipeline_random_state():
steps = [('transform', RandomIntervalFeatureExtractor(features=[np.mean])),
('clf', DecisionTreeClassifier())]
pipe = Pipeline(steps)
# Check that pipe is initiated without random_state
assert pipe.random_state is None
assert pipe.get_params()['random_state'] is None
# Check that all components are initiated without random_state
for step in pipe.steps:
assert step[1].random_state is None
assert step[1].get_params()['random_state'] is None
# Check that if random state is set, it's set to itself and all its random components
rs = 1234
pipe.set_params(**{'random_state': rs})
assert pipe.random_state == rs
assert pipe.get_params()['random_state'] == rs
for step in pipe.steps:
assert step[1].random_state == rs
assert step[1].get_params()['random_state'] == rs
# Check specific results
X_train, y_train = load_gunpoint(return_X_y=True)
X_test, y_test = load_gunpoint("TEST", return_X_y=True)
steps = [
('segment', RandomIntervalSegmenter(n_intervals=3)),
('extract',
RowwiseTransformer(FunctionTransformer(func=np.mean,
validate=False))),
('clf', DecisionTreeClassifier())
]
pipe = Pipeline(steps, random_state=rs)
pipe.fit(X_train, y_train)
y_pred_first = pipe.predict(X_test)
N_ITER = 10
for _ in range(N_ITER):
pipe = Pipeline(steps, random_state=rs)
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
np.testing.assert_array_equal(y_pred_first, y_pred)
return acf(x, nlags=nlags).ravel()
def powerspectrum(x, **kwargs):
x = np.asarray(x).ravel()
fft = np.fft.fft(x)
ps = fft.real * fft.real + fft.imag * fft.imag
return ps[:ps.shape[0] // 2].ravel()
rise_steps = [
('segment', RandomIntervalSegmenter(n_intervals=1, min_length=5)),
('transform',
FeatureUnion([
('ar',
RowwiseTransformer(FunctionTransformer(func=ar_coefs,
validate=False))),
('acf',
RowwiseTransformer(
FunctionTransformer(func=acf_coefs, validate=False))),
('ps',
RowwiseTransformer(
FunctionTransformer(func=powerspectrum, validate=False)))
])), ('tabularise', Tabulariser()), ('clf', DecisionTreeClassifier())
]
base_estimator = Pipeline(rise_steps)
# ('RISE', TimeSeriesForestClassifier(base_estimator=base_estimator, n_estimators=100, bootstrap=True)),
classifiers = [('TimeSeriesForest', TimeSeriesForest()),
('ProximityForest', ProximityForest(n_trees=100)),
('BOSS', BOSSEnsemble()),
('RandomIntervalSpectralForest', RandomIntervalSpectralForest())
]
