Release/0.6.0

RELEASE.md

@@ -1,3 +1,8 @@
+# Release 0.6.0
+
+* support for newer versions of scikit-learn
+* classification report now returns dict in line with scikit-learn
+
 # Release 0.5.0
 
 * Plotting now backed by pygraphviz. This allows:

causalnex/__init__.py

@@ -30,6 +30,6 @@
 causalnex toolkit for causal reasoning (Bayesian Networks / Inference)
 """
 
-__version__ = "0.5.0"
+__version__ = "0.6.0"
 
 __all__ = ["structure", "discretiser", "evaluation", "inference", "network", "plots"]

causalnex/evaluation/evaluation.py

@@ -27,7 +27,7 @@
 # limitations under the License.
 """Evaluation metrics for causal models."""
 
-from typing import List, Tuple
+from typing import Dict, List, Tuple
 
 import pandas as pd
 from sklearn import metrics
@@ -112,9 +112,7 @@ def roc_auc(
     return roc, auc
 
 
-def classification_report(
-    bn: BayesianNetwork, data: pd.DataFrame, node: str
-) -> pd.DataFrame:
+def classification_report(bn: BayesianNetwork, data: pd.DataFrame, node: str) -> Dict:
     """
     Build a report showing the main classification metrics.
 
@@ -160,7 +158,7 @@ def classification_report(
         >>>                         'traffic': ['light', 'heavy', 'heavy', 'light']
         >>>                         })
         >>> from causalnex.evaluation import classification_report
-        >>> classification_report(bn, test_data, "traffic").to_dict()
+        >>> classification_report(bn, test_data, "traffic")
         {'precision': {
             'macro avg': 0.8333333333333333, 'micro avg': 0.75,
             'traffic_heavy': 0.6666666666666666,
@@ -204,4 +202,4 @@ def classification_report(
         output_dict=True,
     )
 
-    return pd.DataFrame.from_dict(report, orient="index")
+    return report

causalnex/inference/inference.py

@@ -163,6 +163,11 @@ def _do(self, observation: str, state: Dict[Hashable, float]) -> None:
         if sum(state.values()) != 1.0:
             raise ValueError("The cpd for the provided observation must sum to 1")
 
+        if max(state.values()) > 1.0 or min(state.values()) < 0:
+            raise ValueError(
+                "The cpd for the provided observation must be between 0 and 1"
+            )
+
         if not set(state.keys()) == set(self._cpds_original[observation]):
             raise ValueError(
                 "The cpd states do not match expected states: expected {expected}, found {found}".format(