doc fixes

seglearn/transform.py

@@ -99,9 +99,8 @@ class SegmentX(BaseEstimator, XyTransformerMixin):
     width : int > 0
         width of segments (number of samples)
     overlap : float range [0,1)
-        amount of overlap between segments. must be in range: 0 <= overlap < 1.
-        shuffle : bool, optional
-        shuffle the segments before fitting the ``est`` pipeline (recommended)
+        amount of overlap between segments. must be in range: 0 <= overlap <= 1
+        (note: setting overlap to 1.0 results in the segments to being advanced by a single sample)
     shuffle : bool, optional
         shuffle the segments after transform (recommended for batch optimizations)
     random_state : int, default = None
@@ -123,7 +122,7 @@ def __init__(self, width=100, overlap=0.5, shuffle=False, random_state=None):
 
         self.f_labels = None
         self.step = int(self.width * (1. - self.overlap))
-        self.step = self.step if self.step >= 1 else 1
+        self.step = max(1, self.step)
 
     def _validate_params(self):
         if not self.width >= 1:
@@ -230,7 +229,8 @@ class SegmentXY(BaseEstimator, XyTransformerMixin):
     width : int > 0
         width of segments (number of samples)
     overlap : float range [0,1)
-        amount of overlap between segments. must be in range: 0 <= overlap < 1.
+        amount of overlap between segments. must be in range: 0 <= overlap <= 1
+        (note: setting overlap to 1.0 results in the segments to being advanced by a single sample)
     y_func : function
         returns target from array of target segments (eg ``last``, ``middle``, or ``mean``)
     shuffle : bool, optional
@@ -255,7 +255,7 @@ def __init__(self, width=100, overlap=0.5, y_func=last, shuffle=False, random_st
         self._validate_params()
 
         self.step = int(self.width * (1. - self.overlap))
-        self.step = self.step if self.step >= 1 else 1
+        self.step = max(1, self.step)
 
     def _validate_params(self):
         if not self.width >= 1:
@@ -275,7 +275,7 @@ def fit(self, X, y=None):
             There is no need of a target in a transformer, yet the pipeline API requires this
             parameter.
 
-        Returnsself._validate_params()
+        Returns
         -------
         self : object
             Returns self.
@@ -356,7 +356,8 @@ class SegmentXYForecast(BaseEstimator, XyTransformerMixin):
     width : int > 0
         width of segments (number of samples)
     overlap : float range [0,1)
-        amount of overlap between segments. must be in range: 0 <= overlap < 1.
+        amount of overlap between segments. must be in range: 0 <= overlap <= 1
+        (note: setting overlap to 1.0 results in the segments to being advanced by a single sample)
     forecast : int
         The number of samples ahead in time to forecast
     y_func : function
@@ -385,7 +386,7 @@ def __init__(self, width=100, overlap=0.5, forecast=10, y_func=last, shuffle=Fal
         self._validate_params()
 
         self.step = int(self.width * (1. - self.overlap))
-        self.step = self.step if self.step >= 1 else 1
+        self.step = max(1, self.step)
 
     def _validate_params(self):
         if not self.width >= 1: