add standard scaler and move heavy piece to not buildd in dev

pieces/StandardScalerPiece/metadata.json

@@ -0,0 +1,18 @@
+{
+    "name": "StandardScalerPiece",
+    "description": "Apply StandardScaler to the data",
+    "dependency": {
+        "requirements_file": "requirements_0.txt"
+    },
+    "tags": [
+        "preprocessing",
+        "scaler"
+    ],
+    "style": {
+        "node_label": "Standard Scaler",
+        "node_style": {
+            "backgroundColor": "#b3cde8"
+        },
+        "icon_class_name": "icon-park-outline:split"
+    }
+}

pieces/StandardScalerPiece/models.py

@@ -0,0 +1,26 @@
+from pydantic import BaseModel, Field
+from typing import List
+
+
+class InputModel(BaseModel):
+    """
+    Input data for TextSummarizerPiece
+    """
+    train_data: List[dict] = Field(
+        title="Train Data",
+        description="The train data to be scaled.",
+        json_schema_extra={"from_upstream": "always"}
+    )
+    test_data: List[dict] = Field(
+        title="Test Data",
+        description="The test data to be scaled.",
+        json_schema_extra={"from_upstream": "always"}
+    )
+
+
+class OutputModel(BaseModel):
+    """
+    Output data for TextSummarizerPiece
+    """
+    train_data: List[dict]
+    test_data: List[dict]

pieces/StandardScalerPiece/piece.py

@@ -0,0 +1,29 @@
+from domino.base_piece import BasePiece
+from .models import InputModel, OutputModel
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
+
+class StandardScalerPiece(BasePiece):
+
+    def piece_function(self, input_data: InputModel):
+        df_train = pd.DataFrame(input_data.train_data)
+        df_test = pd.DataFrame(input_data.test_data)
+
+        if "target" not in df_train.columns or "target" not in df_test.columns:
+            raise ValueError("Target column not found in data with name 'target'.")
+
+
+        scaler = StandardScaler()
+        scaler.fit(df_train.drop('target', axis=1))
+        X_train = scaler.transform(df_train.drop('target', axis=1))
+        X_test = scaler.transform(df_test.drop('target', axis=1))
+
+        df_train_scaled = pd.DataFrame(X_train, columns=df_train.drop('target', axis=1).columns)
+        df_train_scaled['target'] = df_train['target']
+        df_test_scaled = pd.DataFrame(X_test, columns=df_test.drop('target', axis=1).columns)
+        df_test_scaled['target'] = df_test['target']
+
+        return OutputModel(train_data=df_train_scaled.to_dict(orient='records'), test_data=df_test_scaled.to_dict(orient='records'))
+
+

pieces/TrainTestSplitPiece/metadata.json

@@ -5,7 +5,7 @@
         "requirements_file": "requirements_0.txt"
     },
     "tags": [
-        "default",
+        "preprocessing",
         "datasets"
     ],
     "style": {