Merge pull request #103 from quantumblacklabs/release_0_9_2

Release 0.9.2
mckinsey · Mar 11, 2021 · b64dab0 · b64dab0
2 parents 3a6844a + 20498ad
commit b64dab0
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diff --git a/RELEASE.md b/RELEASE.md
@@ -1,5 +1,9 @@
 # Upcoming release
 
+# Release 0.9.2
+* Remove Boston housing dataset from "sklearn tutorial", see #91 for more information.
+* Update pylint version to 2.7
+* Improve speed and non-stochasticity of tests
 
 # Release 0.9.1
 * Fixed bug where the sklearn tutorial documentation wasn't rendering.

diff --git a/causalnex/__init__.py b/causalnex/__init__.py
@@ -30,6 +30,6 @@
 causalnex toolkit for causal reasoning (Bayesian Networks / Inference)
 """
 
-__version__ = "0.9.1"
+__version__ = "0.9.2"
 
 __all__ = ["structure", "discretiser", "evaluation", "inference", "network", "plots"]
diff --git a/causalnex/inference/inference.py b/causalnex/inference/inference.py
@@ -198,10 +198,8 @@ def do_intervention(
             ValueError: if performing intervention would create an isolated node.
         """
         if not any(
-            [
-                node in inspect.getargs(f.__code__)[0][1:]
-                for _, f in self._node_functions.items()
-            ]
+            node in inspect.getargs(f.__code__)[0][1:]
+            for _, f in self._node_functions.items()
         ):
             raise ValueError(
                 "Do calculus cannot be applied because it would result in an isolate"
@@ -289,7 +287,7 @@ def template() -> float:
             return self._cpds[arg_spec.args[0]][  # target name
                 arg_spec.locals[arg_spec.args[0]]
             ][  # target state
-                tuple([(arg, arg_spec.locals[arg]) for arg in arg_spec.args[1:]])
+                tuple((arg, arg_spec.locals[arg]) for arg in arg_spec.args[1:])
             ]  # conditions
 
         code = template.__code__

diff --git a/causalnex/network/network.py b/causalnex/network/network.py
@@ -450,7 +450,7 @@ def _predict_from_complete_data(
         transformed_data[
             "{node}_prediction".format(node=node)
         ] = transformed_data.apply(
-            lambda row: cpd[tuple([row[parent] for parent in parents])].idxmax()
+            lambda row: cpd[tuple(row[parent] for parent in parents)].idxmax()
             if parents
             else cpd[""].idxmax(),
             axis=1,
@@ -528,7 +528,7 @@ def _predict_probability_from_complete_data(
         def lookup_probability(row, s):
             """Retrieve probability from CPD"""
             if parents:
-                return cpd[tuple([row[parent] for parent in parents])].loc[s]
+                return cpd[tuple(row[parent] for parent in parents)].loc[s]
             return cpd.at[s, ""]
 
         for state in self.node_states[node]:

diff --git a/causalnex/structure/pytorch/core.py b/causalnex/structure/pytorch/core.py
@@ -50,7 +50,6 @@
 
 
 # Problem in pytorch 1.6 (_forward_unimplemented), fixed in next release:
-# pylint: disable=abstract-method
 class NotearsMLP(nn.Module, BaseEstimator):
     """
     Class for NOTEARS MLP (Multi-layer Perceptron) model.

diff --git a/causalnex/structure/pytorch/nonlinear.py b/causalnex/structure/pytorch/nonlinear.py
@@ -42,7 +42,6 @@
 
 
 # Problem in pytorch 1.6 (_forward_unimplemented), fixed in next release:
-# pylint: disable=abstract-method
 class LocallyConnected(nn.Module):
     """
     Local linear layer, i.e. Conv1dLocal() with filter size 1.