Rewrites if len(..) == 0 into if .. (#48)

* Rewrites if len(..) == 0 into if ..

* fix wrong change

_unittests/ut_graph/test_graph_distance.py

@@ -129,7 +129,7 @@ def test_unittest_GraphDistance2(self):
         if distance is None:
             raise TypeError("expecting something different from None")
         allPaths = list(graph.enumerate_all_paths(True))
-        if len(allPaths) == 0:
+        if not allPaths:
             raise ValueError("the number of paths should not be null")
         if distance == 0:
             raise ValueError("expecting a distance > 0")

mlstatpy/garden/poulet.py

@@ -166,7 +166,7 @@ def local_proba_poisson_melange(params, coef, i):
             à la loi de paramètre ``params[i]``
         :return: valeur
         """
-        if len(proba_poisson_melange_tableau) == 0:
+        if not proba_poisson_melange_tableau:
             proba_poisson_melange_tableau.extend(
                 histogramme_poisson_melange(params, coef)
             )

mlstatpy/graph/graph_distance.py

@@ -246,7 +246,7 @@ def load_from_file(filename, add_loop):
             elif ve:
                 g = ve.groups()
                 vertex_label[g[0]] = g[1]
-        if len(vertex_label) == 0 or len(edge_list) == 0:
+        if not vertex_label or not edge_list:
             raise OSError(f"Unable to parse file {filename!r}.")  # pragma: no cover
         return GraphDistance(edge_list, vertex_label, add_loop)
 
@@ -510,7 +510,7 @@ def common_paths(
             add = {}
             for k, v in g.vertices.items():
                 v1, v2 = v.pair
-                if len(v.succE) == 0:
+                if not v.succE:
                     for e1 in v1.succE:
                         for e2 in v2.succE:
                             oe1 = self.edges[e1]
@@ -599,17 +599,17 @@ def clean_dead_ends(self):
     def enumerate_all_paths(self, edges_and_vertices, begin=None):
         if begin is None:
             begin = []
-        if len(self.vertices) > 0 and len(self.edges) > 0:
+        if self.vertices and self.edges:
             if edges_and_vertices:
-                last = begin[-1] if len(begin) > 0 else self.vertices[self.labelBegin]
+                last = begin[-1] if begin else self.vertices[self.labelBegin]
             else:
                 last = (
                     self.vertices[begin[-1].to]
-                    if len(begin) > 0
+                    if begin
                     else self.vertices[self.labelBegin]
                 )
 
-            if edges_and_vertices and len(begin) == 0:
+            if edges_and_vertices and not begin:
                 begin = [last]
 
             for ef in last.succE:

mlstatpy/graph/graphviz_helper.py

@@ -24,7 +24,7 @@ def run_graphviz(filename, image, engine="dot"):
     else:
         cmd = f'"{engine}" -Tpng "{filename}" -o "{image}"'
     out, err = run_cmd(cmd, wait=True)
-    if len(err) > 0:
+    if err:
         raise RuntimeError(
             f"Unable to run Graphviz\nCMD:\n{cmd}\nOUT:\n{out}\nERR:\n{err}"
         )

mlstatpy/ml/neural_tree.py

@@ -95,10 +95,11 @@ def copy(self):
     def _update_members(self, node=None, attr=None):
         "Updates internal members."
         if node is None or attr is None:
-            if len(self.nodes_attr) == 0:
-                self.size_ = self.dim
-            else:
-                self.size_ = max(d["output"] for d in self.nodes_attr) + 1
+            self.size_ = (
+                self.dim
+                if not self.nodes_attr
+                else (max(d["output"] for d in self.nodes_attr) + 1)
+            )
             self.output_to_node_ = {}
             self.input_to_node_ = {}
             for node2, attr2 in zip(self.nodes, self.nodes_attr):
@@ -150,7 +151,7 @@ def append(self, node, inputs):
             self.nodes.append(node)
             first_coef = (
                 0
-                if len(self.nodes_attr) == 0
+                if not self.nodes_attr
                 else self.nodes_attr[-1]["first_coef"]
                 + self.nodes_attr[-1]["coef_size"]
             )
@@ -173,7 +174,7 @@ def append(self, node, inputs):
             self.nodes.append(node)
             first_coef = (
                 0
-                if len(self.nodes_attr) == 0
+                if not self.nodes_attr
                 else self.nodes_attr[-1]["first_coef"]
                 + self.nodes_attr[-1]["coef_size"]
             )
@@ -702,7 +703,7 @@ def gradient_backward(self, graddx, X, inputs=False, cache=None):
 
         whole_gradx = numpy.zeros(pred.shape, dtype=numpy.float64)
         whole_gradw = numpy.zeros(shape, dtype=numpy.float64)
-        if len(graddx.shape) == 0:
+        if not graddx.shape:
             whole_gradx[-1] = graddx
         else:
             whole_gradx[-graddx.shape[0] :] = graddx

mlstatpy/nlp/completion.py

@@ -267,7 +267,7 @@ def find(self, prefix: str) -> "CompletionTrieNode":
         :param prefix: prefix
         :return: node or None for no result
         """
-        if len(prefix) == 0:
+        if not prefix:
             if not self.value:
                 return self
             else:

setup.py

@@ -18,7 +18,7 @@
         requirements = f.read().strip(" \n\r\t").split("\n")
 except FileNotFoundError:
     requirements = []
-if len(requirements) == 0 or requirements == [""]:
+if not requirements or requirements == [""]:
     requirements = ["numpy", "mlinsight", "onnxruntime", "skl2onnx"]
 
 try:
@@ -34,7 +34,7 @@
         for _ in [_.strip("\r\n ") for _ in f.readlines()]
         if _.startswith("__version__")
     ]
-    if len(line) > 0:
+    if line:
         version_str = line[0].split("=")[1].strip('" ')
 
 # see https://pypi.org/classifiers/