def test_paired_dtw_distance(r, expected):
x, y = load_dataset("GunPoint", repository="wildboar/ucr-tiny")
assert_almost_equal(
paired_distance(x[0:3], x[30:33], metric="dtw", metric_params={"r":
r}),
expected,
)
def test_extra_tree_regressor():
x, y = load_dataset("GunPoint", repository="wildboar/ucr-tiny")
f = ExtraShapeletTreeRegressor(criterion="mse", random_state=123)
f.fit(x, y.astype(float))
assert_almost_equal(f.tree_.threshold[0], 2.052192203219023)
assert_almost_equal(f.tree_.threshold[6], 0.8963769152751806)
assert_almost_equal(f.predict(x), y.astype(float))
def test_extra_tree_classifier(criterion, expected_left, expected_right,
threshold):
x, y = load_dataset("GunPoint", repository="wildboar/ucr-tiny")
f = ExtraShapeletTreeClassifier(criterion=criterion, random_state=123)
f.fit(x, y)
assert (f.predict(x) == y).sum() == 191
assert_equal(f.tree_.left, expected_left)
assert_equal(f.tree_.right, expected_right)
assert_equal(f.tree_.left > 0, f.tree_.right > 0)
assert_almost_equal(f.tree_.threshold[f.tree_.left > 0], threshold)
def test(dataset, f, random_state=None):
x, y = load_dataset(
dataset,
repository="wildboar/tsereg",
merge_train_test=True,
preprocess=None,
)
x_train, x_test, y_train, y_test = train_test_split(
x, y, random_state=random_state)
f.fit(x_train, y_train)
print("%.6f" %
mean_squared_error(y_test, f.predict(x_test), squared=False))
def test_paired_subsequence_distance(metric, metric_params, expected_min_dist,
expected_min_ind):
x, y = load_dataset("GunPoint", repository="wildboar/ucr-tiny")
min_dist, min_ind = paired_subsequence_distance(
x[[2, 3, 8], 0:20],
x[40:43],
metric=metric,
metric_params=metric_params,
return_index=True,
)
assert_almost_equal(min_dist, expected_min_dist)
assert_equal(min_ind, expected_min_ind)
import matplotlib.pylab as plt
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from wildboar.datasets import load_dataset
from wildboar.ensemble import IsolationShapeletForest, ShapeletForestEmbedding
random_state = 1234
x, y = load_dataset("CBF", repository="wildboar/outlier:easy")
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.2, random_state=random_state, stratify=y
)
metric = "euclidean"
embedding = make_pipeline(
ShapeletForestEmbedding(
metric=metric, random_state=random_state, sparse_output=False
),
PCA(n_components=2, random_state=random_state),
)
isf = IsolationShapeletForest(
contamination=0.05,
metric=metric,
random_state=random_state,
n_jobs=-1,
)
isf.fit(x_train)
embedding.fit(x_train)
x_embedding = embedding.transform(x_test)
from sklearn.linear_model import RidgeClassifierCV
from sklearn.pipeline import make_pipeline
from wildboar.datasets import load_dataset
from wildboar.embed import RandomShapeletEmbedding
x_train, x_test, y_train, y_test = load_dataset("GunPoint",
merge_train_test=False)
pipe = make_pipeline(RandomShapeletEmbedding(metric="scaled_euclidean"),
RidgeClassifierCV())
pipe.fit(x_train, y_train)
print(pipe.score(x_test, y_test))
import matplotlib.pylab as plt
from sklearn.model_selection import train_test_split
from wildboar.datasets import load_dataset
from wildboar.ensemble import ShapeletForestClassifier
from wildboar.explain import IntervalImportance
x, y = load_dataset("LargeKitchenAppliances")
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.3,
random_state=123)
f = ShapeletForestClassifier(
n_shapelets=1,
n_estimators=100,
n_jobs=-1,
metric="scaled_euclidean",
random_state=123,
)
f.fit(x_train, y_train)
i = IntervalImportance(
n_interval=72,
scoring="accuracy",
domain="frequency",
verbose=True,
random_state=123,
)
i.fit(f, x_test, y_test)
ax = i.plot(
def test_shapelet_forest_classifier():
x_train, x_test, y_train, y_test = load_dataset(
"GunPoint", repository="wildboar/ucr-tiny", merge_train_test=False
)
clf = ShapeletForestClassifier(n_estimators=10, n_shapelets=10, random_state=1)
clf.fit(x_train, y_train)
branches = [
(
[1, -1, 3, 4, 5, -1, -1, 8, -1, 10, -1, -1, -1],
[2, -1, 12, 7, 6, -1, -1, 9, -1, 11, -1, -1, -1],
),
(
[1, -1, 3, 4, -1, 6, -1, 8, -1, 10, -1, -1, -1],
[2, -1, 12, 5, -1, 7, -1, 9, -1, 11, -1, -1, -1],
),
(
[1, 2, 3, 4, -1, -1, 7, -1, -1, 10, -1, 12, -1, -1, -1],
[14, 9, 6, 5, -1, -1, 8, -1, -1, 11, -1, 13, -1, -1, -1],
),
(
[1, 2, 3, 4, -1, -1, -1, 8, -1, 10, -1, 12, -1, -1, -1],
[14, 7, 6, 5, -1, -1, -1, 9, -1, 11, -1, 13, -1, -1, -1],
),
(
[1, 2, 3, -1, 5, -1, 7, -1, 9, -1, 11, -1, -1, -1, 15, -1, 17, -1, -1],
[14, 13, 4, -1, 6, -1, 8, -1, 10, -1, 12, -1, -1, -1, 16, -1, 18, -1, -1],
),
(
[1, 2, -1, 4, 5, -1, 7, -1, -1, 10, -1, -1, 13, 14, -1, -1, -1],
[12, 3, -1, 9, 6, -1, 8, -1, -1, 11, -1, -1, 16, 15, -1, -1, -1],
),
(
[1, 2, 3, 4, -1, -1, -1, 8, 9, -1, -1, 12, 13, -1, -1, -1, -1],
[16, 7, 6, 5, -1, -1, -1, 11, 10, -1, -1, 15, 14, -1, -1, -1, -1],
),
(
[1, 2, -1, 4, 5, 6, 7, -1, -1, -1, -1, 12, -1, -1, -1],
[14, 3, -1, 11, 10, 9, 8, -1, -1, -1, -1, 13, -1, -1, -1],
),
(
[1, 2, 3, -1, 5, -1, -1, 8, 9, 10, -1, 12, -1, -1, -1, -1, -1],
[16, 7, 4, -1, 6, -1, -1, 15, 14, 11, -1, 13, -1, -1, -1, -1, -1],
),
(
[1, 2, -1, 4, -1, 6, -1, 8, -1, 10, -1, 12, -1, -1, -1],
[14, 3, -1, 5, -1, 7, -1, 9, -1, 11, -1, 13, -1, -1, -1],
),
]
thresholds = [
(
[
3.728410228070656,
11.127575591141072,
7.383224794807461,
7.109350684315213,
0.9248183559076002,
6.08675185469423,
],
[
3.728410228070656,
11.127575591141072,
7.383224794807461,
7.109350684315213,
0.9248183559076002,
6.08675185469423,
],
),
(
[
2.468504005311855,
7.912505524900922,
1.0551252327034113,
0.8574299925766751,
0.5760307808209804,
0.009237440363224308,
],
[
2.468504005311855,
7.912505524900922,
1.0551252327034113,
0.8574299925766751,
0.5760307808209804,
0.009237440363224308,
],
),
(
[
3.909569808800988,
2.821010442496668,
1.8694668182965288,
0.034583372931197384,
0.8137102058624538,
0.7560554810866997,
2.713102595233928,
],
[
3.909569808800988,
2.821010442496668,
1.8694668182965288,
0.034583372931197384,
0.8137102058624538,
0.7560554810866997,
2.713102595233928,
],
),
(
[
5.391042553752862,
4.420547070721347,
2.2716225008196576,
0.6679258993537478,
1.5471177855226528,
1.2706259403508802,
6.379381672446367,
],
[
5.391042553752862,
4.420547070721347,
2.2716225008196576,
0.6679258993537478,
1.5471177855226528,
1.2706259403508802,
6.379381672446367,
],
),
(
[
2.784221806516613,
3.9613021926565697,
0.43050821107331483,
1.3603965501478146,
1.9817847740610532,
0.557171910946499,
0.023161212907754903,
3.2040403820972045,
0.25123702588573155,
],
[
2.784221806516613,
3.9613021926565697,
0.43050821107331483,
1.3603965501478146,
1.9817847740610532,
0.557171910946499,
0.023161212907754903,
3.2040403820972045,
0.25123702588573155,
],
),
(
[
9.06314095909644,
0.9301861459984877,
1.2749535932250209,
0.6602701901531287,
0.3105779260645574,
3.199344210068309,
1.7444498163002922,
0.9679068532147111,
],
[
9.06314095909644,
0.9301861459984877,
1.2749535932250209,
0.6602701901531287,
0.3105779260645574,
3.199344210068309,
1.7444498163002922,
0.9679068532147111,
],
),
(
[
10.684770463276237,
1.0443634502866903,
2.657944200018761,
0.31997645008775166,
8.506009151805937,
2.5790890876760417,
2.444351040739898,
0.8797498982567451,
],
[
10.684770463276237,
1.0443634502866903,
2.657944200018761,
0.31997645008775166,
8.506009151805937,
2.5790890876760417,
2.444351040739898,
0.8797498982567451,
],
),
(
[
8.903669489275785,
2.558013265746756,
1.9352062567009694,
0.6160338380839283,
1.1133147922166846,
2.6673841033247827,
0.6693157414483296,
],
[
8.903669489275785,
2.558013265746756,
1.9352062567009694,
0.6160338380839283,
1.1133147922166846,
2.6673841033247827,
0.6693157414483296,
],
),
(
[
2.9771351955856753,
3.4048368843307957,
2.847751510400112,
1.2655496884627422,
4.410184513977114,
2.3116642536119203,
0.5858765536466852,
0.7586458184224343,
],
[
2.9771351955856753,
3.4048368843307957,
2.847751510400112,
1.2655496884627422,
4.410184513977114,
2.3116642536119203,
0.5858765536466852,
0.7586458184224343,
],
),
(
[
6.260659343273105,
0.05120063347084325,
0.678745571123132,
5.913261089713139,
0.25431501853894734,
0.27996560751446015,
0.7309024510514174,
],
[
6.260659343273105,
0.05120063347084325,
0.678745571123132,
5.913261089713139,
0.25431501853894734,
0.27996560751446015,
0.7309024510514174,
],
),
]
for estimator, (left, right), (left_threshold, right_threshold) in zip(
clf.estimators_, branches, thresholds
):
assert_equal(left, estimator.tree_.left)
assert_equal(right, estimator.tree_.right)
assert_almost_equal(
left_threshold, estimator.tree_.threshold[estimator.tree_.left > 0]
)
assert_almost_equal(
right_threshold, estimator.tree_.threshold[estimator.tree_.right > 0]
)
def test_decision_path():
x_train, x_test, y_train, y_test = load_dataset(
"GunPoint", repository="wildboar/ucr-tiny", merge_train_test=False)
f = ShapeletTreeClassifier(random_state=123)
f.fit(x_test, y_test)
actual_decision_path = f.decision_path(x_train)
expected_decision_path = np.array(
[
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[
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[
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[
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[
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[
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[
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[
1,
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[
1,
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1,
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1,
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0,
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],
[
1,
0,
0,
0,
0,
0,
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0,
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0,
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],
],
dtype=bool,
)
assert actual_decision_path.dtype == np.bool_
assert_array_equal(actual_decision_path.toarray(), expected_decision_path)
import matplotlib.pylab as plt
import numpy as np
from wildboar.datasets import load_dataset
from wildboar.ensemble import ShapeletForestClassifier
from wildboar.explain.counterfactual import counterfactuals
x_train, x_test, y_train, y_test = load_dataset("TwoLeadECG",
repository="wildboar/ucr",
merge_train_test=False)
# x_train = x_train.repeat(2, axis=0).reshape(x_train.shape[0], 2, -1)
# print(x_train[0])
# x_test = x_test.repeat(2, axis=0).reshape(x_test.shape[0], 2, -1)
clf = ShapeletForestClassifier(metric="euclidean",
random_state=1,
n_jobs=-1,
n_estimators=100)
clf.fit(x_train, y_train)
print(clf.score(x_test, y_test))
y_pred = clf.predict(x_test)
class_ = clf.classes_[1]
print("Class: %s" % class_)
print("Pred: %r" % y_pred)
x_test = x_test[y_pred != class_][:10]
y_test = y_test[y_pred != class_][:10]
x_counterfactual, success, score = counterfactuals(clf,
x_test,
class_,
import numpy as np
from sklearn.model_selection import cross_validate
from wildboar.datasets import load_dataset
from wildboar.ensemble import ExtraShapeletTreesClassifier, ShapeletForestClassifier
random_state = 1234
x, y = load_dataset("Beef")
classifiers = {
"Shapelet forest":
ShapeletForestClassifier(
n_shapelets=10,
metric="scaled_euclidean",
n_jobs=-1,
random_state=random_state,
),
"Extra Shapelet Trees":
ExtraShapeletTreesClassifier(
metric="scaled_euclidean",
n_jobs=-1,
random_state=random_state,
),
}
for name, clf in classifiers.items():
score = cross_validate(clf, x, y, scoring="roc_auc_ovo", n_jobs=1)
print("Classifier: %s" % name)
print(" - fit-time: %.2f" % np.mean(score["fit_time"]))
print(" - test-score: %.2f" % np.mean(score["test_score"]))
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from wildboar.datasets import load_dataset
from wildboar.ensemble import ShapeletForestEmbedding
random_state = 1234
x, y = load_dataset("CBF")
pca = make_pipeline(
ShapeletForestEmbedding(
metric="scaled_euclidean",
sparse_output=False,
max_depth=5,
random_state=random_state,
),
PCA(n_components=2, random_state=random_state),
)
p = pca.fit_transform(x)
var = pca.steps[1][1].explained_variance_ratio_
labels, index = np.unique(y, return_inverse=True)
colors = plt.cm.rainbow(np.linspace(0, 1, len(labels)))
plt.scatter(p[:, 0], p[:, 1], color=colors[index, :])
plt.xlabel("Component 1 (%.2f variance explained)" % var[0])
plt.ylabel("Component 2 (%.2f variance explained)" % var[1])
plt.savefig("fig/sfe_pca.png")
import matplotlib.pylab as plt
from wildboar.datasets import load_dataset
from wildboar.distance import matrix_profile
x, y = load_dataset("TwoLeadECG")
x = x[:20].reshape(-1)
print(x.shape)
mp = matrix_profile(x.reshape(-1), window=20, exclude=0.2)
fig, ax = plt.subplots(nrows=2, sharex=True)
ax[0].plot(x, color="red", lw=0.5)
ax[1].plot(mp, color="blue", lw=0.5)
ax[0].set_title("Time series")
ax[1].set_title("Matrix profile")
ax[0].set_xlim(0, x.shape[-1])
ax[1].set_xlim(0, x.shape[-1])
plt.tight_layout()
plt.savefig("../fig/matrix_profile.png")
from wildboar.datasets import (
list_bundles,
list_collections,
list_datasets,
list_repositories,
load_dataset,
load_datasets,
)
print(list_datasets(repository="wildboar/ucr:no-missing"))
print(list_datasets(repository="wildboar/outlier:1.0:hard"))
print(list_repositories())
print(list_bundles("wildboar"))
for dataset, (x, y) in load_datasets(
repository="wildboar/ucr-tiny",
filter=["n_timestep>10", "n_samples<=200", "n_labels<=3"],
):
print(dataset)
for dataset in list_datasets(repository="wildboar/ucr", collection="bake-off"):
print(dataset)
print(list_collections("wildboar/ucr"))
load_dataset("GunPoint", repository="wildboar/ucr-tiny")
import numpy as np
from sklearn.model_selection import train_test_split
from wildboar.datasets import load_dataset
from wildboar.linear_model import RandomShapeletClassifier, RocketClassifier
x, y = load_dataset("ItalyPowerDemand",
merge_train_test=True,
preprocess="standardize")
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=123)
print(np.mean(x_train, axis=1))
f = RocketClassifier(
n_kernels=10000,
sampling="normal",
sampling_params={
"mean": 0,
"scale": 1
},
n_jobs=16,
random_state=123,
alphas=np.logspace(-3, 3, 10),
normalize=True,
)
f.fit(x_train, y_train)
print(f.score(x_test, y_test))
f = RandomShapeletClassifier(
n_shapelets=10000,
n_jobs=16,
metric="scaled_euclidean",
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from wildboar.datasets import load_dataset
from wildboar.ensemble import ShapeletForestClassifier, ShapeletForestEmbedding
random_state = 1234
x, y = load_dataset("Car")
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=random_state)
f_embedding = make_pipeline(
ShapeletForestEmbedding(sparse_output=False, random_state=random_state),
PCA(n_components=2, random_state=random_state),
)
f_embedding.fit(x_train)
x_embedding = f_embedding.transform(x_test)
classifiers = [
("Shapelet forest", ShapeletForestClassifier(random_state=random_state)),
("Nearest neighbors", KNeighborsClassifier()),
]
classes = np.unique(y)
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from wildboar.datasets import load_dataset
from wildboar.explain.counterfactual import counterfactuals
x_train, x_test, y_train, y_test = load_dataset("GunPoint",
repository="wildboar/ucr",
merge_train_test=False)
clf = KNeighborsClassifier(n_neighbors=1, metric="euclidean")
clf.fit(x_train, y_train)
x_counter, success, scores = counterfactuals(clf,
x_test,
y_test[::-1],
random_state=123,
scoring="euclidean")
print(scores)
print(np.sum(success))
print(np.sum(clf.predict(x_counter) == y_test[::-1]) / y_test.shape[0])
n_neighbors=1,
metric="euclidean",
),
"Shapelet forest":
ShapeletForestClassifier(
n_shapelets=10,
metric="scaled_euclidean",
random_state=random_state,
n_jobs=-1,
),
"Extra shapelet trees":
ExtraShapeletTreesClassifier(
metric="scaled_euclidean",
n_jobs=-1,
random_state=random_state,
),
}
repository = "wildboar/ucr-tiny"
datasets = list_datasets(repository)
df = pd.DataFrame(columns=classifiers.keys(), index=datasets, dtype=float)
for dataset in datasets:
print(dataset)
x, y = load_dataset(dataset, repository=repository)
for clf_name, clf in classifiers.items():
print(" ", clf_name)
score = cross_validate(clf, x, y, scoring="roc_auc_ovo", n_jobs=1)
df.loc[dataset, clf_name] = np.mean(score["test_score"])
df.to_csv("../tab/classification_cmp.csv", float_format="%.3f")
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from wildboar.datasets import load_dataset
from wildboar.datasets.outlier import EmmottLabeler
from wildboar.ensemble import IsolationShapeletForest
x, y = load_dataset("SwedishLeaf",
repository="wildboar/ucr",
merge_train_test=True)
labeler = EmmottLabeler(n_outliers=0.05,
difficulty=1,
variation="tight",
random_state=5)
x, y = labeler.fit_transform(x, y)
print(np.unique(y, return_counts=True))
x_train, x_test, y_train, y_test = train_test_split(x,
y,
random_state=10,
test_size=0.2,
stratify=y)
f = IsolationShapeletForest(
random_state=10,
n_estimators=100,
contamination=0.1,
metric="scaled_euclidean",
n_jobs=-1,
min_shapelet_size=0,
max_shapelet_size=1,
import matplotlib.pylab as plt
import numpy as np
from wildboar.datasets import load_dataset
from wildboar.embed import IntervalEmbedding
x, y = load_dataset("GunPoint")
fixed = IntervalEmbedding(n_interval=30, summarizer="auto", intervals="fixed")
x_t = fixed.fit_transform(x)
labels = ["%s" % start for (dim, (start, length, _)) in fixed.embedding_.features]
n_features = x_t.shape[1]
fig, ax = plt.subplots(nrows=4, figsize=(5.5, 7))
ax[0].plot(x[0])
ax[0].title.set_text("Time series")
colors = plt.cm.rainbow(np.linspace(0, 1, 3))
titles = ["Mean", "Variance", "Slope"]
for i in range(3):
ax[i + 1].bar(labels, x_t[0, i:n_features:3], color=colors[i, :])
plt.setp(ax[i + 1].get_xticklabels(), rotation="vertical", ha="center")
ax[i + 1].title.set_text(titles[i])
plt.tight_layout()
# plt.xlim([0, 150])
plt.savefig("../fig/interval.png")
def test_apply():
x_train, x_test, y_train, y_test = load_dataset(
"GunPoint", repository="wildboar/ucr-tiny", merge_train_test=False)
f = ShapeletTreeClassifier(random_state=123)
f.fit(x_test, y_test)
actual_apply = f.apply(x_train)
expected_apply = np.array(
[
29,
24,
7,
7,
24,
24,
34,
34,
20,
5,
12,
5,
5,
5,
24,
5,
15,
29,
5,
32,
12,
5,
5,
34,
2,
20,
5,
5,
20,
32,
19,
34,
20,
7,
24,
2,
33,
24,
34,
32,
15,
5,
5,
5,
32,
24,
29,
34,
29,
34,
],
dtype=int,
)
assert actual_apply.dtype == np.intp
assert_array_equal(actual_apply, expected_apply)
