y_func=last,
forecast=200)), ('features', FeatureRep()),
('lin', LinearRegression())])
# fit and score
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
print("N series in train: ", len(X_train))
print("N series in test: ", len(X_test))
print("N segments in train: ", clf.N_train)
print("N segments in test: ", clf.N_test)
print("Score: ", score)
# generate some predictions
y, y_p = clf.transform_predict(X, y) # all predictions
ytr, ytr_p = clf.transform_predict(X_train, y_train) # training predictions
yte, yte_p = clf.transform_predict(X_test, y_test) # test predictions
# note - the first few segments in the test set won't have predictions (gap)
# we plot the 'gap' for the visualization to hopefully make the situation clear
Ns = len(y)
ts = np.arange(Ns) # segment number
ttr = ts[0:len(ytr)]
tte = ts[(Ns - len(yte)):Ns]
tga = ts[len(ytr):(Ns - len(yte))]
yga = y[len(ytr):(Ns - len(yte))]
# plot the results
plt.plot(ttr, ytr, '.', label="training")
plt.plot(tga, yga, '.', label="gap")
pipe = Pype([('seg', Segment(width=200, overlap=0.5, y_func=last)),
('features', FeatureRep()), ('lin', LinearRegression())])
# fit and score
pipe.fit(X_train, y_train)
score = pipe.score(X_test, y_test)
print("N series in train: ", len(X_train))
print("N series in test: ", len(X_test))
print("N segments in train: ", pipe.N_train)
print("N segments in test: ", pipe.N_test)
print("Score: ", score)
# generate some predictions
ytr, ytr_p = pipe.transform_predict(X_train, y_train) # training predictions
yte, yte_p = pipe.transform_predict(X_test, y_test) # test predictions
xtr = np.arange(len(ytr)) # segment number
xte = np.arange(len(yte)) + len(xtr)
# plot the amazing results
plt.plot(xtr, ytr, '.', label="training")
plt.plot(xte, yte, '.', label="actual")
plt.plot(xte, yte_p, label="predicted")
plt.xlabel("Segment Number")
plt.ylabel("Target")
plt.legend()
plt.show()
# # now try a cross validation
# X = [np.arange(4000) / 100.]
verbose=10)))
])
#scorer=scorer1
X_train, X_test, y_train, y_test, matlab_train, matlab_test = train_test_split(
new_features_seg_included,
new_labels_seg_included,
new_matlab_seg_included,
test_size=0.10,
random_state=10)
clf.fit(X_train, y_train)
#clf1.fit(X_train, y_train)
score = clf.score(X_test, y_test)
predict = clf.predict(X_test)
test1, test2 = clf.transform_predict(X_test, y_test)
train1, train2 = clf.transform_predict(X_train, y_train)
test21 = inverse_transform(test2, y_test, chosenwidth, 1, order='F')
test11 = inverse_transform(test1, y_test, chosenwidth, 1, order='F')
train11 = inverse_transform(train1, y_train, chosenwidth, 1, order='F')
train21 = inverse_transform(train2, y_train, chosenwidth, 1, order='F')
# and do a cross validation
scoring = make_scorer(f1_score, average='macro')
if change_labels == 1 and conservative == 1:
freeze2 = [np.argmax(test21[i]) for i in range(len(test21))]
freeze2_conserve = [
test21[i].shape[0] -
np.argmax(np.flip(np.int_(np.logical_not(test21[i]))))
for i in range(len(test21))
test_size=0.25,
random_state=42)
##############################################
# OPTION 1: Use the score SegPipe score method
##############################################
pipe.fit(X_train, y_train)
score = pipe.score(X_test, y_test)
print("Accuracy score: ", score)
######################################################################
# OPTION 2: generate true and predicted target values for the segments
######################################################################
y_true, y_pred = pipe.transform_predict(X_test, y_test)
# use any of the sklearn scorers
f1_macro = f1_score(y_true, y_pred, average='macro')
print("F1 score: ", f1_macro)
cm = confusion_matrix(y_true, y_pred)
plot_confusion_matrix(cm, data['y_labels'])
##########################################
# OPTION 3: scoring during model selection
##########################################
# model selection using the built-in score method for the final estimator
cv_scores = cross_validate(pipe, X, y, cv=4, return_train_score=True)
print("CV Scores: ", pd.DataFrame(cv_scores))
def test_pipe_regression():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [np.random.rand(1000)]
pipe = Pype([('seg', SegmentXY()), ('ftr', FeatureRep()),
('ridge', Ridge())])
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# context data, single time seres
Xt = [np.random.rand(1000, 10)]
Xc = [np.random.rand(3)]
X = TS_Data(Xt, Xc)
y = [np.random.rand(1000)]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# multiple time seres
Xt = [
np.random.rand(1000, 10),
np.random.rand(100, 10),
np.random.rand(500, 10)
]
Xc = np.random.rand(3, 3)
X = TS_Data(Xt, Xc)
y = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# cross val
Xt = np.array([np.random.rand(1000, 10)] * 5)
Xc = np.random.rand(5, 3)
X = TS_Data(Xt, Xc)
y = np.array([np.random.rand(1000)] * 5)
cross_validate(pipe, X, y, cv=3)
# transform pipe
pipe = Pype([('seg', SegmentXY()), ('ftr', FeatureRep()),
('scaler', StandardScaler())])
Xt = [
np.random.rand(1000, 10),
np.random.rand(100, 10),
np.random.rand(500, 10)
]
Xc = np.random.rand(3, 3)
X = TS_Data(Xt, Xc)
y = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]
pipe.fit(X, y)
pipe.transform(X, y)
pipe.fit_transform(X, y)
def test_pipe_PadTrunc():
# no context data, single time series
X = [np.random.rand(1000, 10)]
y = [5]
pipe = Pype([('trunc', PadTrunc()), ('ftr', FeatureRep()),
('rf', RandomForestClassifier(n_estimators=10))])
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# context data, single time seres
Xt = [np.random.rand(1000, 10)]
Xc = [np.random.rand(3)]
X = TS_Data(Xt, Xc)
y = [5]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# multiple time series
Xt = [
np.random.rand(1000, 10),
np.random.rand(100, 10),
np.random.rand(500, 10)
]
Xc = np.random.rand(3, 3)
X = TS_Data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# univariate data
Xt = [np.random.rand(1000), np.random.rand(100), np.random.rand(500)]
Xc = np.random.rand(3)
X = TS_Data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.transform_predict(X, y)
pipe.predict(X)
pipe.score(X, y)
# transform pipe
pipe = Pype([('trunc', PadTrunc()), ('ftr', FeatureRep()),
('scaler', StandardScaler())])
Xt = [
np.random.rand(1000, 10),
np.random.rand(100, 10),
np.random.rand(500, 10)
]
Xc = np.random.rand(3, 3)
X = TS_Data(Xt, Xc)
y = [1, 2, 3]
pipe.fit(X, y)
pipe.transform(X, y)
pipe.fit_transform(X, y)