[DOC] added usage examples to multiple estimator docstrings

sktime/classification/deep_learning/inceptiontime.py

@@ -41,6 +41,16 @@ class InceptionTimeClassifier(BaseDeepClassifier):
 
     Adapted from the implementation from Fawaz et. al
     https://github.com/hfawaz/InceptionTime/blob/master/classifiers/inception.py
+
+    Examples
+    --------
+    >>> from sktime.classification.deep_learning.inceptiontime import InceptionTimeClassifier # doctest: +SKIP # noqa
+    >>> from sktime.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
+    >>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
+    >>> itc = InceptionTimeClassifier(n_epochs=20,batch_size=4)  # doctest: +SKIP
+    >>> itc.fit(X_train, y_train)  # doctest: +SKIP
+    InceptionTimeClassifier(...)
     """
 
     _tags = {

sktime/classification/deep_learning/lstmfcn.py

@@ -52,6 +52,16 @@ class LSTMFCNClassifier(BaseDeepClassifier):
     ----------
     .. [1] Karim et al. Multivariate LSTM-FCNs for Time Series Classification, 2019
     https://arxiv.org/pdf/1801.04503.pdf
+
+    Examples
+    --------
+    >>> from sktime.classification.deep_learning.lstmfcn import LSTMFCNClassifier
+    >>> from sktime.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
+    >>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
+    >>> lstmfcn = FCNClassifier(n_epochs=20,batch_size=4)  # doctest: +SKIP
+    >>> lstmfcn.fit(X_train, y_train)  # doctest: +SKIP
+    FCNClassifier(...)
     """
 
     _tags = {

sktime/classification/distance_based/_shape_dtw.py

@@ -108,6 +108,22 @@ class ShapeDTW(BaseClassifier):
     .. [1] Jiaping Zhao and Laurent Itti, "shapeDTW: Shape Dynamic Time Warping",
         Pattern Recognition, 74, pp 171-184, 2018
         http://www.sciencedirect.com/science/article/pii/S0031320317303710,
+
+    Examples
+    --------
+    >>> from sktime.classification.distance_based import ShapeDTW
+    >>> from sktime.datasets import load_unit_test  # doctest: +SKIP
+    >>> X_train, y_train = load_unit_test(split="train")  # doctest: +SKIP
+    >>> X_test, y_test = load_unit_test(split="test")  # doctest: +SKIP
+    >>> clf = ShapeDTW(n_neighbors=1,
+    ... subsequence_length=30,
+    ... shape_descriptor_function="raw",
+    ... shape_descriptor_functions=None,
+    ... metric_params=None
+    ... )  # doctest: +SKIP
+    >>> clf.fit(X_train, y_train)  # doctest: +SKIP
+    ShapeDTW(...)
+    >>> y_pred = clf.predict(X_test)  # doctest: +SKIP
     """
 
     _tags = {

sktime/classification/distance_based/_time_series_neighbors_pyts.py

@@ -71,6 +71,25 @@ class KNeighborsTimeSeriesClassifierPyts(_PytsAdapter, BaseClassifier):
     ----------
     classes_ : array, shape = (n_classes,)
         An array of class labels known to the classifier.
+
+    Examples
+    --------
+    >>> from sktime.classification.distance_based import KNeighborsTimeSeriesClassifierPyts  # doctest: +SKIP # noqa
+    >>> from sktime.datasets import load_unit_test  # doctest: +SKIP
+    >>> X_train, y_train = load_unit_test(split="train")  # doctest: +SKIP
+    >>> X_test, y_test = load_unit_test(split="test")  # doctest: +SKIP
+    >>> clf = KNeighborsTimeSeriesClassifierPyts(n_neighbors=1,
+    ...        weights="uniform",
+    ...        algorithm="auto",
+    ...        leaf_size=30,
+    ...        p=2,
+    ...        metric="minkowski",
+    ...        metric_params=None,
+    ...        n_jobs=1
+    ...        )  # doctest: +SKIP
+    >>> clf.fit(X_train, y_train)  # doctest: +SKIP
+    KNeighborsTimeSeriesClassifierPyts(...)
+    >>> y_pred = clf.predict(X_test)  # doctest: +SKIP
     """
 
     _tags = {

sktime/classification/distance_based/_time_series_neighbors_tslearn.py

@@ -55,6 +55,24 @@ class KNeighborsTimeSeriesClassifierTslearn(_TslearnAdapter, BaseClassifier):
         Above 50, the output is sent to stdout.
         The frequency of the messages increases with the verbosity level.
         If it more than 10, all iterations are reported.
+
+    Examples
+    --------
+    >>> from sktime.classification.distance_based import KNeighborsTimeSeriesClassifierTslearn # doctest: +SKIP # noqa
+    >>> from sktime.datasets import load_unit_test  # doctest: +SKIP
+    >>> X_train, y_train = load_unit_test(split="train")  # doctest: +SKIP
+    >>> X_test, y_test = load_unit_test(split="test")  # doctest: +SKIP
+    >>> clf = KNeighborsTimeSeriesClassifierTslearn(
+    ...         n_neighbors=5,
+    ...         weights="uniform",
+    ...         metric="dtw",
+    ...         metric_params=None,
+    ...         n_jobs=None,
+    ...         verbose=0,
+    ...         )  # doctest: +SKIP
+    >>> clf.fit(X_train, y_train)  # doctest: +SKIP
+    KNeighborsTimeSeriesClassifierTslearn(...)
+    >>> y_pred = clf.predict(X_test)  # doctest: +SKIP
     """
 
     _tags = {

sktime/classification/ensemble/_ctsf.py

@@ -171,6 +171,21 @@ class labels (multi-output problem).
     ----------
     .. [1] Deng et. al, A time series forest for classification and feature extraction,
     Information Sciences, 239:2013.
+
+    Examples
+    --------
+    >>> from sktime.classification.ensemble import ComposableTimeSeriesForestClassifier
+    >>> from sktime.classification.kernel_based import RocketClassifier
+    >>> from sktime.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train") # doctest: +SKIP
+    >>> X_test, y_test = load_unit_test(split="test") # doctest: +SKIP
+    >>> clf = ComposableTimeSeriesForestClassifier(
+    ...     RocketClassifier(num_kernels=100),
+    ...     n_estimators=10,
+    ... ) # doctest: +SKIP
+    >>> clf.fit(X_train, y_train) # doctest: +SKIP
+    ComposableTimeSeriesForestClassifier(...)
+    >>> y_pred = clf.predict(X_test) # doctest: +SKIP
     """
 
     _tags = {
@@ -188,7 +203,7 @@ class labels (multi-output problem).
 
     def __init__(
         self,
-        estimator=None,
+        estimator,
         n_estimators=100,
         max_depth=None,
         min_samples_split=2,

sktime/classification/feature_based/_fresh_prince.py

@@ -57,6 +57,21 @@ class FreshPRINCE(BaseClassifier):
         scalable hypothesis tests (tsfresh–a python package)." Neurocomputing 307
         (2018): 72-77.
         https://www.sciencedirect.com/science/article/pii/S0925231218304843
+
+    >>> from sktime.classification.feature_based import FreshPRINCE
+    >>> from sktime.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
+    >>> X_test, y_test = load_unit_test(split="test", return_X_y=True) # doctest: +SKIP
+    >>> clf = FreshPRINCE(
+    ...     default_fc_parameters="comprehensive",
+    ...     n_estimators=200,
+    ...     save_transformed_data=False,
+    ...     verbose=0,
+    ...     n_jobs=1,
+    ...     ) # doctest: +SKIP
+    >>> clf.fit(X_train, y_train) # doctest: +SKIP
+    FreshPRINCE(...)
+    >>> y_pred = clf.predict(X_test) # doctest: +SKIP
     """
 
     _tags = {

sktime/classification/feature_based/_random_interval_classifier.py

@@ -49,6 +49,18 @@ class RandomIntervalClassifier(BaseClassifier):
     See Also
     --------
     RandomIntervals
+
+    >>> from sktime.classification.feature_based import RandomIntervalClassifier
+    >>> from sklearn.ensemble import RandomForestClassifier
+    >>> from sktime.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
+    >>> X_test, y_test = load_unit_test(split="test", return_X_y=True) # doctest: +SKIP
+    >>> clf = RandomIntervalClassifier(
+    ...     estimator=RandomForestClassifier(n_estimators=5)
+    ... ) # doctest: +SKIP
+    >>> clf.fit(X_train, y_train) # doctest: +SKIP
+    RandomIntervalClassifier(...)
+    >>> y_pred = clf.predict(X_test) # doctest: +SKIP
     """
 
     _tags = {

sktime/classification/feature_based/_tsfresh_classifier.py

@@ -62,6 +62,19 @@ class TSFreshClassifier(BaseClassifier):
         scalable hypothesis tests (tsfresh–a python package)." Neurocomputing 307
         (2018): 72-77.
         https://www.sciencedirect.com/science/article/pii/S0925231218304843
+
+    >>> from sktime.classification.feature_based import TSFreshClassifier
+    >>> from sklearn.ensemble import RandomForestClassifier
+    >>> from sktime.datasets import load_unit_test
+    >>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
+    >>> X_test, y_test = load_unit_test(split="test", return_X_y=True) # doctest: +SKIP
+    >>> clf = TSFreshClassifier(
+    ...     estimator=RandomForestClassifier(n_estimators=5),
+    ...     default_fc_parameters="efficient",
+    ... ) # doctest: +SKIP
+    >>> clf.fit(X_train, y_train) # doctest: +SKIP
+    TSFreshClassifier(...)
+    >>> y_pred = clf.predict(X_test) # doctest: +SKIP
     """
 
     _tags = {