Merge branch 'master' into deploy

dtaidistance/dtw_weighted.py

@@ -294,8 +294,8 @@ def dt_windows(features, targets, classifier, patternlen, max_clfs, min_ig, min_
         clf = classifier()
         cur_features = features[:, idx_s:idx_e]
         clf.fit(cur_features, targets, ignore_features=ignore_features, min_ig=min_ig)
-        logger.debug(f"Learned classifier {len(clfss) + 1}: idx = f{idx}/{idx_s}:f{idx+patternlen}/{idx_e}, "
-                     f"nb nodes = {clf.tree_.nb_nodes}, used features = {clf.tree_.used_features}")
+        # logger.debug(f"Learned classifier {len(clfss) + 1}: idx = f{idx}/{idx_s}:f{idx+patternlen}/{idx_e}, "
+        #              f"nb nodes = {clf.tree_.nb_nodes}, used features = {clf.tree_.used_features}")
         if clf.tree_.nb_nodes <= 1:
             continue
         clf.set_features(list(range(idx_s, idx_e)))
@@ -304,8 +304,8 @@ def dt_windows(features, targets, classifier, patternlen, max_clfs, min_ig, min_
     clfs = []
     for clf_idx, clf in enumerate(clfss):
         score = clf.score(max_kd)
-        logger.debug(f"Clf[{clf_idx:<2}] - Score = {score}, Entropy = {clf.avg_impurity()}, "
-                     f"depth = {clf.tree_.depth}, nbnodes = {clf.tree_.nb_nodes}")
+        # logger.debug(f"Clf[{clf_idx:<2}] - Score = {score}, Entropy = {clf.avg_impurity()}, "
+        #              f"depth = {clf.tree_.depth}, nbnodes = {clf.tree_.nb_nodes}")
         clfs.append((score, -clf.tree_.nb_nodes, clf))
     clfs.sort(reverse=True)
     min_score = clfs[-1][0]
@@ -321,7 +321,7 @@ def dt_windows(features, targets, classifier, patternlen, max_clfs, min_ig, min_
 
     if max_clfs is not None:
         clfs = clfs[:max_clfs]
-    logger.debug(f"Kept {len(clfs)} classifiers with score >= {minallowed_score}, clfs_use = {clfs_use}")
+    # logger.debug(f"Kept {len(clfs)} classifiers with score >= {minallowed_score}, clfs_use = {clfs_use}")
     for clf_score, clf_nbnodes, clf in clfs:
         new_cl_values, used_features = decisiontree_to_clweights(clf, min_purity)
         # if len(used_features) == 0:
@@ -347,14 +347,14 @@ def dt_onewindow(features, targets, classifier, max_clfs, min_ig, min_purity):
     while not_empty and not (max_clfs is not None and len(clfs) >= max_clfs):
         clf = classifier()
         clf.fit(features, targets, ignore_features=ignore_features, min_ig=min_ig)
-        logger.debug(f"Learned classifier {len(clfs) + 1}: nb nodes = {clf.tree_.nb_nodes}")
+        # logger.debug(f"Learned classifier {len(clfs) + 1}: nb nodes = {clf.tree_.nb_nodes}")
         if clf.tree_.nb_nodes <= 1:
             not_empty = False
             continue
         clfs.append(clf)
         new_cl_values, used_features = decisiontree_to_clweights(clf, min_purity)
         if len(used_features) == 0:
-            logger.debug(f"No features used, ignore all features in tree: {clf.tree_.used_features}")
+            # logger.debug(f"No features used, ignore all features in tree: {clf.tree_.used_features}")
             used_features.update(clf.tree_.used_features)
         update_cl_values(cl_values, new_cl_values)
         update_importances(importances, new_cl_values, clf_w)
@@ -423,15 +423,16 @@ def decisiontree_to_clweights(clf, min_purity=1.0):
 
 
 def clweights_updatefrompath(cl_values, path):
-    logger.debug(f"Path to CL: {path}")
+    # logger.debug(f"Path to CL: {path}")
     used_features = set()
     for feature, threshold, leq in path:
         index = feature // 2
         dneg = ((feature % 2) == 0)
         if leq:  # f <= t
-            logger.debug(f"Ignore: CL with f{index} <= {threshold} (d is negative={dneg}, feature={feature})")
+            # logger.debug(f"Ignore: CL with f{index} <= {threshold} (d is negative={dneg}, feature={feature})")
+            pass
         else:  # f > t
-            logger.debug(f"Accept: CL with f{index} >  {threshold} (d is negative={dneg}, feature={feature})")
+            # logger.debug(f"Accept: CL with f{index} >  {threshold} (d is negative={dneg}, feature={feature})")
             cl_values[index][0 if dneg else 1].append(threshold)
             used_features.add(feature)
     return used_features

dtaidistance/preprocessing.py

@@ -78,7 +78,7 @@ def smoothing(series, smooth):
         b, a = signal.butter(N=2, Wn=Wn, btype='low', analog=False, output='ba')
     except ValueError as exc:
         raise ValueError("Cannot construct filter, change the smoothing factor. "
-                         f"Requires 0<smooth<0.5 (now {smooth=}, {Wn=})") from exc
+                         "Requires 0<smooth<0.5 (now Smooth={}, Wn={})".format(smooth, Wn)) from exc
     try:
         series = signal.filtfilt(b, a, series, axis=axis, method="gust")
     except ValueError as exc:

tests/test_alignment.py

@@ -150,7 +150,7 @@ def test_substitution_function():
 
 if __name__ == "__main__":
     directory = Path(os.environ.get('TESTDIR', Path(__file__).parent))
-    print(f"Saving files to {directory}")
+    print("Saving files to {}".format(directory))
     test_sequences1()
     test_sequences2()
     test_sequences3()

tests/test_barycenter.py

@@ -492,7 +492,7 @@ def test_ndim_kmeans2():
     logger.setLevel(logging.DEBUG)
     logger.addHandler(logging.StreamHandler(sys.stdout))
     directory = Path(os.environ.get('TESTDIR', Path(__file__).parent))
-    print(f"Saving files to {directory}")
+    print("Saving files to {}".format(directory))
     # test_pair()
     # test_trace()
     # test_trace_mask()

tests/test_bugsvis.py

@@ -116,7 +116,7 @@ def test_bug4():
     logger.addHandler(sh)
     logger.propagate = 0
     directory = Path(os.environ.get('TESTDIR', Path(__file__).parent))
-    print(f"Saving files to {directory}")
+    print("Saving files to {}".format(directory))
     # test_bug1()
     test_bug2()
     # test_bug3()

tests/test_clustering.py

@@ -293,7 +293,7 @@ def test_clustering_centroid():
     logger.setLevel(logging.DEBUG)
     logger.addHandler(logging.StreamHandler(sys.stdout))
     directory = Path(os.environ.get('TESTDIR', Path(__file__).parent))
-    print(f"Saving files to {directory}")
+    print("Saving files to {}".format(directory))
     # test_clustering_tree()
     # test_clustering_tree_ndim()
     # test_clustering_tree_maxdist()

tests/test_dtw_weighted.py

@@ -105,8 +105,8 @@ def test_distance2():
         prototypeidx = 0
         ml_values, cl_values, clfs, importances = \
             dtww.series_to_dt(s, l, prototypeidx, max_clfs=50, savefig=savefig)
-        logger.debug(f"ml_values = {dict(ml_values)}")
-        logger.debug(f"cl_values = {dict(cl_values)}")
+        # logger.debug(f"ml_values = {dict(ml_values)}")
+        # logger.debug(f"cl_values = {dict(cl_values)}")
         weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values, only_max=False, strict_cl=True)
 
         if not dtwvis.test_without_visualization():
@@ -165,8 +165,8 @@ def test_distance4():
         prototypeidx = 0
         ml_values, cl_values, clf, importances = \
             dtww.series_to_dt(s, l, prototypeidx, window=2, min_ig=0.1, savefig=savefig)
-        logger.debug(f"ml_values = {dict(ml_values)}")
-        logger.debug(f"cl_values = {dict(cl_values)}")
+        # logger.debug(f"ml_values = {dict(ml_values)}")
+        # logger.debug(f"cl_values = {dict(cl_values)}")
         weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values,
                                                        only_max=False, strict_cl=True)
         if directory:
@@ -191,8 +191,8 @@ def test_distance5():
 
         prototypeidx = 0
         ml_values, cl_values, clf, importances = dtww.series_to_dt(s, l, prototypeidx, window=4)
-        logger.debug(f"ml_values = {dict(ml_values)}")
-        logger.debug(f"cl_values = {dict(cl_values)}")
+        # logger.debug(f"ml_values = {dict(ml_values)}")
+        # logger.debug(f"cl_values = {dict(cl_values)}")
         weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values,
                                                        only_max=False, strict_cl=True)
         if directory:
@@ -218,8 +218,8 @@ def test_distance6():
         labels[l == l[prototypeidx]] = 1
         ml_values, cl_values, clf, importances = \
             dtww.series_to_dt(s, labels, prototypeidx, window=0, min_ig=0.1, savefig=savefig)
-        logger.debug(f"ml_values = {dict(ml_values)}")
-        logger.debug(f"cl_values = {dict(cl_values)}")
+        # logger.debug(f"ml_values = {dict(ml_values)}")
+        # logger.debug(f"cl_values = {dict(cl_values)}")
         weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values,
                                                        only_max=False, strict_cl=True)
         if directory:

tests/test_warping.py

@@ -85,7 +85,7 @@ def test_warping_path1():
         path3 = [(0, 0), (1, 0), (2, 1), (3, 2), (4, 3), (5, 4), (5, 5), (6, 6), (7, 7), (8, 8), (9, 9), (10, 10)]
         assert len(path1) == len(path3)
         assert len(path2) == len(path3)
-        assert d1 == pytest.approx(d2), f"{d1} != {d2}"
+        assert d1 == pytest.approx(d2), "{} != {}".format(d1, d2)
         assert all(ai1 == bi1 and ai2 == bi2 for ((ai1, ai2), (bi1, bi2)) in zip(path1, path3))
         assert all(ai1 == bi1 and ai2 == bi2 for ((ai1, ai2), (bi1, bi2)) in zip(path2, path3))
 
@@ -296,7 +296,7 @@ def test_subsequence():
     with util_numpy.test_uses_numpy() as np:
         np.set_printoptions(precision=2, linewidth=120)
     directory = Path(os.environ.get('TESTDIR', Path(__file__).parent))
-    print(f"Saving files to {directory}")
+    print("Saving files to {}".format(directory))
     # test_normalize()
     test_normalize2()
     # test_normalize2_prob()