From 91da40e2ffc0f547fd7f5224208d656370d78832 Mon Sep 17 00:00:00 2001
From: Isak Samsten <isak@samsten.se>
Date: Wed, 7 Dec 2022 19:39:01 +0000
Subject: [PATCH] Check that SAX and PAA are fitted (#69)

---
 docs/more/versions/v1.1.rst    | 12 ++++++++++++
 src/wildboar/transform/_sax.py |  6 +++++-
 2 files changed, 17 insertions(+), 1 deletion(-)

diff --git a/docs/more/versions/v1.1.rst b/docs/more/versions/v1.1.rst
index bf7b668b3f..80771a5ca5 100644
--- a/docs/more/versions/v1.1.rst
+++ b/docs/more/versions/v1.1.rst
@@ -16,6 +16,10 @@ Wildboar 1.1 requires Python 3.8+, numpy 1.17.3+, scipy 1.3.2+ and scikit-learn
 Version 1.1.1
 =============
 
+Major changes
+-------------
+Correctly depend on the oldest supported Numpy release.
+
 Changelog
 ---------
 
@@ -28,6 +32,14 @@ Changelog
 
      - |Fix| Correctly return ``max_motif`` motifs from :func:`annotate.motifs`.
 
+  .. grid-item-card:: 
+    
+     :mod:`wildboar.transform`
+     ^^^ 
+
+     - |Fix| Check that :class:`transform.SAX` and :class:`transform.PAA` is fitted
+       before ``transform``.
+
 
 Version 1.1.0
 =============
diff --git a/src/wildboar/transform/_sax.py b/src/wildboar/transform/_sax.py
index e23b6c2c7e..d33efb4b4f 100644
--- a/src/wildboar/transform/_sax.py
+++ b/src/wildboar/transform/_sax.py
@@ -7,7 +7,7 @@
 import numpy as np
 from scipy.stats import norm, uniform
 from sklearn.base import TransformerMixin
-from sklearn.utils.validation import check_scalar
+from sklearn.utils.validation import check_is_fitted, check_scalar
 
 from wildboar.utils.validation import check_array, check_option
 
@@ -147,6 +147,7 @@ def fit(self, x, y=None):
         return self
 
     def transform(self, x):
+        check_is_fitted(self)
         x = self._validate_data(x, reset=False, dtype=float)
         x_paa = self.paa_.transform(x)
         thresholds = self.binning_.get_thresholds(x, estimate=self.estimate)
@@ -156,6 +157,7 @@ def transform(self, x):
         return x_out
 
     def inverse_transform(self, x):
+        check_is_fitted(self)
         if self.estimate:
             raise ValueError("Unable to inverse_transform with estimate=True")
 
@@ -208,10 +210,12 @@ def fit(self, x, y=None):
         return self
 
     def transform(self, x):
+        check_is_fitted(self)
         x = self._validate_data(x, dtype=float, reset=False)
         return self.interval_transform_.transform(x)
 
     def inverse_transform(self, x):
+        check_is_fitted(self)
         x = check_array(x, dtype=float)
 
         x_inverse = np.empty((x.shape[0], self.n_timesteps_in_), dtype=float)