Merge 0eac74a into 5dab9d1

examples/plot_function_transformer.py

@@ -0,0 +1,40 @@
+'''
+==================================
+Simple FunctionTransformer Example
+==================================
+
+This example demonstrates how to execute arbitrary functions on time series data using the
+FunctionTransformer.
+
+'''
+
+# Author: Matthias Gazzari
+# License: BSD
+
+from seglearn.transform import FunctionTransformer, SegmentXY
+from seglearn.base import TS_Data
+
+import numpy as np
+
+def choose_cols(Xt, cols):
+	return [time_series[:, cols] for time_series in Xt]
+
+# Two multivariate time series with 4 and 3 samples of 3 variables each
+X = [
+	np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]]),
+	np.array([[30, 40, 50], [60, 70, 80], [90, 100, 110]]),
+]
+# Time series target
+y = [
+	np.array([True, False, False, True]),
+	np.array([False, True, False]),
+]
+
+trans = FunctionTransformer(choose_cols, func_kwargs={"cols":[0,1]})
+X = trans.fit_transform(X, y)
+
+segment = SegmentXY(width=3, overlap=1)
+X = segment.fit_transform(X, y)
+
+print("X:", X)
+print("y: ", y)

seglearn/__init__.py

@@ -15,6 +15,7 @@
 __all__ = ['TS_Data', 'FeatureRep', 'FeatureRepMix', 'PadTrunc', 'Interp', 'Pype', 'SegmentX',
            'SegmentXY', 'SegmentXYForecast', 'TemporalKFold', 'temporal_split', 'check_ts_data',
            'check_ts_data_with_ts_target', 'ts_stats', 'get_ts_data_parts', 'all_features',
-           'base_features', 'load_watch', 'TargetRunLengthEncoder', '__version__']
+           'base_features', 'load_watch', 'TargetRunLengthEncoder', 'FunctionTransformer',
+           '__version__']
 
 __author__ = 'David Burns david.mo.burns@gmail.com'

seglearn/tests/test_transform.py

@@ -1,6 +1,8 @@
 # Author: David Burns
 # License: BSD
 
+import pytest
+
 import numpy as np
 
 import seglearn.transform as transform
@@ -447,3 +449,78 @@ def test_feature_rep_mix():
     Xt = uni_union.transform(X)
     assert Xt.shape[0] == len(X)
     assert len(uni_union.f_labels) == Xt.shape[1]
+
+
+def test_function_transform():
+    constant = 10
+    identity = transform.FunctionTransformer()
+    def replace(Xt, value):
+        return np.ones(Xt.shape) * value
+    custom = transform.FunctionTransformer(replace, func_kwargs={"value": constant})
+
+    # univariate ts
+    X = np.random.rand(100, 10)
+    y = np.ones(100)
+
+    identity.fit(X, y)
+    Xtrans = identity.transform(X)
+    assert Xtrans is X
+
+    custom.fit(X, y)
+    Xtrans = custom.transform(X)
+    assert np.array_equal(Xtrans, np.ones(X.shape) * constant)
+
+    # multivariate ts
+    X = np.random.rand(100, 10, 4)
+    y = np.ones(100)
+
+    identity.fit(X, y)
+    Xtrans = identity.transform(X)
+    assert Xtrans is X
+
+    custom.fit(X, y)
+    Xtrans = custom.transform(X)
+    assert np.array_equal(Xtrans, np.ones(X.shape) * constant)
+
+    # ts with univariate contextual data
+    Xt = np.random.rand(100, 10, 4)
+    Xc = np.random.rand(100)
+    X = TS_Data(Xt, Xc)
+    y = np.ones(100)
+
+    identity.fit(X, y)
+    Xtrans = identity.transform(X)
+    assert Xtrans is X
+
+    custom.fit(X, y)
+    Xtrans = custom.transform(X)
+    Xtt, Xtc = get_ts_data_parts(Xtrans)
+    assert np.array_equal(Xtt, np.ones(Xt.shape) * constant)
+    assert Xtc is Xc
+
+    # ts with multivariate contextual data
+    Xt = np.random.rand(100, 10, 4)
+    Xc = np.random.rand(100, 3)
+    X = TS_Data(Xt, Xc)
+    y = np.ones(100)
+
+    identity.fit(X, y)
+    Xtrans = identity.transform(X)
+    assert Xtrans is X
+
+    custom.fit(X, y)
+    Xtrans = custom.transform(X)
+    Xtt, Xtc = get_ts_data_parts(Xtrans)
+    assert np.array_equal(Xtt, np.ones(Xt.shape) * constant)
+    assert Xtc is Xc
+
+    # test resampling
+    def resample(Xt):
+        return Xt.reshape(1, -1)
+
+    illegal_resampler = transform.FunctionTransformer(resample)
+    X = np.random.rand(100, 10)
+    y = np.ones(100)
+    illegal_resampler.fit(X, y)
+    with pytest.raises(ValueError):
+        Xtrans = illegal_resampler.transform(X)

seglearn/transform.py

@@ -16,7 +16,7 @@
 from .util import get_ts_data_parts, check_ts_data
 
 __all__ = ['SegmentX', 'SegmentXY', 'SegmentXYForecast', 'PadTrunc', 'Interp', 'FeatureRep',
-           'FeatureRepMix']
+           'FeatureRepMix', 'FunctionTransformer']
 
 
 class XyTransformerMixin(object):
@@ -1141,3 +1141,73 @@ def transform(self, X):
             fts = np.column_stack([fts, Xc])
 
         return fts
+
+class FunctionTransformer(BaseEstimator, TransformerMixin):
+    '''
+    Transformer for applying a custom function to time series data.
+
+    Parameters
+    ----------
+    func : function, optional (default=None)
+        the function to be applied to Xt, the time series part of X (contextual variables Xc are
+        passed through unaltered) - X remains unchanged if no function is supplied
+    func_kwargs : dictionary, optional (default={})
+        keyword arguments to be passed to the function call
+
+    Returns
+    -------
+    self : object
+        returns self
+    '''
+
+    def __init__(self, func=None, func_kwargs={}):
+        self.func = func
+        self.func_kwargs = func_kwargs
+
+    def fit(self, X, y=None):
+        '''
+        Fit the transform
+
+        Parameters
+        ----------
+        X : array-like, shape [n_samples, ...]
+            time series data and (optionally) contextual data
+        y : None
+            there is no need of a target in a transformer, yet the pipeline API requires this
+
+        Returns
+        -------
+        self : object
+            returns self
+        '''
+        check_ts_data(X, y)
+        return self
+
+    def transform(self, X):
+        '''
+        Transforms the time series data based on the provided function. Note this transformation
+        must not change the number of samples in the data.
+
+        Parameters
+        ----------
+        X : array-like, shape [n_samples, ...]
+            time series data and (optionally) contextual data
+
+        Returns
+        -------
+        Xt : array-like, shape [n_samples, ...]
+            transformed time series data
+
+        '''
+        if self.func is None:
+            return X
+        else:
+            Xt, Xc = get_ts_data_parts(X)
+            n_samples = len(Xt)
+            Xt = self.func(Xt, **self.func_kwargs)
+            if len(Xt) != n_samples:
+                raise ValueError("Changing the number of samples inside a FunctionTransformer is"
+                                 "disabled.")
+            if Xc is not None:
+                Xt = TS_Data(Xt, Xc)
+            return Xt