revert changes to ugly enum to have icons

src/constants.py

@@ -1,9 +1,11 @@
 
 class MetricNames:
+    raw_error: str = "raw-error"
     normalized_error: str = "normalized-error"
     fit_time_per_1K_rows: str = "fit-time-per-1K-rows"
     inference_time_per_1K_rows: str = "inference-time-per-1K-rows"
 
+
 class ProblemTypes:
     col_name: str = "problem_type"
     regression: str = "Regression"

src/display/utils.py

@@ -63,22 +63,37 @@ model_type_emoji = {
     MethodTypes.other: "❓",
 }
 
+def _make_model_details(name: str):
+    return ModelDetails(name=f"{model_type_emoji[name]} {name}", symbol=model_type_emoji[name])
+class ModelType(Enum):
+    T1 = _make_model_details(MethodTypes.foundational)
+    T2 = _make_model_details(MethodTypes.finetuned)
+    T3 = _make_model_details(MethodTypes.automl)
+    T4 = _make_model_details(MethodTypes.boosted_tree)
+    T5 = _make_model_details(MethodTypes.other)
 
-@dataclass
-class ModelType:
-    name: str
-    symbol: str
+    Unknown = ModelDetails(name="", symbol="?")
 
     def to_str(self, separator=" "):
-        return f"{self.symbol}{separator}{self.name}"
-
-    @classmethod
-    def from_str(cls, name: str):
-        symbol = model_type_emoji.get(name, "❓")
-        return cls(
-            name=name,
-            symbol=symbol,
-        )
+        return f"{self.value.symbol}{separator}{self.value.name}"
+
+    @staticmethod
+    def from_str(type):
+        if MethodTypes.foundational in type or model_type_emoji[MethodTypes.foundational] in type:
+            return ModelType.T1
+        if MethodTypes.finetuned in type or model_type_emoji[MethodTypes.finetuned] in type:
+            return ModelType.T2
+        if MethodTypes.automl in type or model_type_emoji[MethodTypes.automl] in type:
+            return ModelType.T3
+        if MethodTypes.boosted_tree in type or model_type_emoji[MethodTypes.boosted_tree] in type:
+            return ModelType.T4
+        if MethodTypes.other in type or model_type_emoji[MethodTypes.other] in type:
+            return ModelType.T5
+        return ModelType.T5
+
+    def to_str(self, separator=" "):
+        return f"{self.value.symbol}{separator}{self.value.name}"
+
 
 class WeightType(Enum):
     Adapter = ModelDetails("Adapter")

src/leaderboard/read_evals.py

@@ -19,7 +19,7 @@ class ModelConfig:
     """Represents the model configuration of a model"""
     model: str
     model_link: str = ""
-    model_type: ModelType = ModelType.from_str("?")
+    model_type: ModelType = ModelType.Unknown
     code_link: str = ""
     precision: Precision = Precision.Unknown
     license: str = "?"
@@ -48,8 +48,8 @@ class ModelConfig:
             ModelInfoColumn.model.name: self.model,
             'model_w_link': model_hyperlink(self.model_link, self.code_link, self.model),
             ModelInfoColumn.precision.name: self.precision.value.name,
-            ModelInfoColumn.model_type.name: self.model_type.name,
-            ModelInfoColumn.model_type_symbol.name: self.model_type.symbol,
+            ModelInfoColumn.model_type.name: self.model_type.value.name,
+            ModelInfoColumn.model_type_symbol.name: self.model_type.value.symbol,
             # ModelInfoColumn.model.model_link: model_hyperlink(self.full_model),
             ModelInfoColumn.license.name: self.license,
             ModelInfoColumn.likes.name: self.likes,

src/utils.py

@@ -192,6 +192,7 @@ def get_grouped_dfs(root_dir='results', ds_properties='results/dataset_propertie
     # grouped_results_overall = grouped_results_overall.rename(columns={'model':'Model'})
     # grouped_results.to_csv(f'artefacts/grouped_results_by_model.csv')
     grouped_dfs = {}
+    # for col_name in ["domain", 'term_length', 'frequency', 'univariate']:
     for col_name in [ProblemTypes.col_name]:
         grouped_dfs[col_name] = group_by(df, col_name)
         # print(f"Grouping by {col_name}:\n {grouped_dfs.head(20)}")