Auto-generate a classifier Capabilities summary #997

.all-contributorsrc

@@ -1224,6 +1224,17 @@
                 "doc"
             ]
     }
+    {
+      "login": "BINAYKUMAR943"
+      "name":"Binay Kumar"
+      "avatar_url":"https://avatars.githubusercontent.com/u/38756834?v=4
+      "profile":"https://github.com/BINAYKUMAR943"
+      "contributions":[
+        "code",
+        "doc",
+        "test"
+      ]
+    }
   ],
   "projectName": "sktime",
   "projectOwner": "alan-turing-institute",

sktime/contrib/classifier_capabilities_table.py

@@ -0,0 +1,55 @@
+# -*- coding: utf-8 -*-
+"""Auto-generate a classifier capabilites summary."""
+import pandas as pd
+from sktime.registry import all_estimators
+
+# List of columns in the table
+df_columns = [
+    "Classifier Category",
+    "Classifier Name",
+    "multivariate",
+    "unequal_length",
+    "missing_values",
+    "train_estimate",
+    "contractable",
+]
+# creates dataframe as df
+df = pd.DataFrame([], columns=df_columns)
+# Loop through all the classifiers
+for classiName, classiClass in all_estimators(estimator_types="classifier"):
+    category = str(classiClass).split(".")[2]
+    try:
+        # capabilites of each of the classifier classifier
+        cap_dict = classiClass.capabilities
+        multivariate = str(cap_dict["multivariate"])
+        unequal_length = str(cap_dict["unequal_length"])
+        missing_values = str(cap_dict["missing_values"])
+        train_estimate = str(cap_dict["train_estimate"])
+        contractable = str(cap_dict["contractable"])
+        # Adding capabilites for each classifier in the table
+        df = df.append(
+            {
+                "Classifier Category": category,
+                "Classifier Name": classiName,
+                "multivariate": multivariate,
+                "unequal_length": unequal_length,
+                "missing_values": missing_values,
+                "train_estimate": train_estimate,
+                "contractable": contractable,
+            },
+            ignore_index=True,
+        )
+    except AttributeError:
+        df = df.append(
+            {
+                "Classifier Category": category,
+                "Classifier Name": classiName,
+                "multivariate": "N/A",
+                "unequal_length": "N/A",
+                "missing_values": "N/A",
+                "train_estimate": "N/A",
+                "contractable": "N/A",
+            },
+            ignore_index=True,
+        )
+df.to_html("Classifier_Capabilities.html", index=False, escape=False)