Create rule S6973: Specify important hyperparameters when instantiati…

…ng a Scikit-learn estimator (#3888)

rules/S6973/metadata.json

@@ -0,0 +1,2 @@
+{
+}

rules/S6973/python/metadata.json

@@ -0,0 +1,24 @@
+{
+  "title": "Important hyperparameters should be specified for Scikit-Learn estimators",
+  "type": "CODE_SMELL",
+  "status": "ready",
+  "remediation": {
+    "func": "Constant\/Issue",
+    "constantCost": "5min"
+  },
+  "tags": [
+  ],
+  "defaultSeverity": "Major",
+  "ruleSpecification": "RSPEC-6973",
+  "sqKey": "S6973",
+  "scope": "All",
+  "defaultQualityProfiles": ["Sonar way"],
+  "quickfix": "unknown",
+  "code": {
+    "impacts": {
+      "MAINTAINABILITY": "HIGH",
+      "RELIABILITY": "LOW"
+    },
+    "attribute": "CONVENTIONAL"
+  }
+}

rules/S6973/python/rule.adoc

@@ -0,0 +1,139 @@
+This rule raises an issue when a Scikit-learn model is instantiated without specifying the important hyperparameters. 
+
+
+
+== Why is this an issue?
+
+When instantiating a Scikit-learn estimator, it will use default values for the hyperparameters that are not specified.
+Relying on the default values can lead to non-reproducible results across diffferent versions of the library. 
+
+Furthermore, the default values might not be the best choice for the specific problem at hand and can lead to suboptimal performance.
+
+Here are the estimators and the parameters considered by this rule : 
+[cols="1,1"]
+|===
+|*Estimator*
+|*Hyperparameters*
+
+|AdaBoostClassifier
+|learning_rate
+|AdaBoostRegressor
+|learning_rate
+|GradientBoostingClassifier
+|learning_rate
+|GradientBoostingRegressor
+|learning_rate
+|HistGradientBoostingClassifier
+|learning_rate
+|HistGradientBoostingRegressor
+|learning_rate
+|RandomForestClassifier
+|min_samples_leaf, max_features
+|RandomForestRegressor
+|min_samples_leaf, max_features
+|ElasticNet
+|alpha, l1_ratio
+|NearestNeighbors
+|n_neighbors
+|KNeighborsClassifier
+|n_neighbors
+|KNeighborsRegressor
+|n_neighbors
+|NuSVC
+|nu, kernel, gamma
+|NuSVR
+|C, kernel, gamma
+|SVC
+|C, kernel, gamma
+|SVR
+|C, kernel, gamma
+|DecisionTreeClassifier
+|ccp_alpha
+|DecisionTreeRegressor
+|ccp_alpha
+
+|MLPClassifier
+|hidden_layer_sizes
+|MLPRegressor
+|hidden_layer_sizes
+
+|PolynomialFeatures
+|degree, interaction_only
+|===
+
+== How to fix it
+Specify the hyperparameters when instantiating the estimator.
+
+=== Code examples
+
+==== Noncompliant code example
+
+[source,python,diff-id=1,diff-type=noncompliant]
+----
+from sklearn.neighbors import KNeighborsClassifier
+
+clf = KNeighborsClassifier() # Noncompliant : n_neighbors is not specified, different values can change the behaviour of the predictor significantly
+----
+
+==== Compliant solution
+
+[source,python,diff-id=1,diff-type=compliant]
+----
+from sklearn.neighbors import KNeighborsClassifier
+
+clf = KNeighborsClassifier( # Compliant
+    n_neighbors=5
+)
+----
+
+ifdef::env-github,rspecator-view[]
+
+(visible only on this page)
+
+== Implementation specification 
+
+Implementation will be quite tricky if we want to avoid false positives.
+
+Abort if : 
+
+- In a Pipeline/make_pipeline used for hyperparameter search
+
+https://github.com/SERG-Delft/dslinter/blob/main/dslinter/checkers/hyperparameters_scikitlearn.py#L48-L70[List of DSLinter estimators]
+
+Possible baby step : only check for some estimators ( for exemple the meta-learners)
+
+Ignore parameters : 
+
+- n_jobs
+
+- that ends in `param` ?
+
+=== Message 
+
+Specify all hyperparameters when instantiating a Scikit-learn estimator.
+
+=== Issue location
+
+Primary : name of the estimator
+
+No secondary location
+=== Quickfix
+
+There is a possible quickfix : add all the missing parameters at their default values
+
+endif::env-github,rspecator-view[]
+
+== Resources
+
+=== Articles & blog posts
+
+* Probst, P., Boulesteix, A. L., & Bischl, B. (2019). Tunability: Importance of
+   Hyperparameters of Machine Learning Algorithms. Journal of Machine Learning Research,
+   20(53), 1-32.
+* van Rijn, J. N., & Hutter, F. (2018, July). Hyperparameter importance across datasets.
+   In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &
+   Data Mining (pp. 2367-2376).
+
+=== External coding guidelines
+* Code Smells for Machine Learning Applications - https://hynn01.github.io/ml-smells/posts/codesmells/11-hyperparameter-not-explicitly-set/[Hyperparameter not Explicitly Set]
+