A UI for mapping model bindings.

examples/Model use

@@ -15,8 +15,22 @@
       "targetHandle": "bundle"
     },
     {
-      "id": "Train model 3 Model inference 1",
-      "source": "Train model 3",
+      "id": "Model inference 1 View tables 1",
+      "source": "Model inference 1",
+      "sourceHandle": "output",
+      "target": "View tables 1",
+      "targetHandle": "bundle"
+    },
+    {
+      "id": "Train/test split 1 Train model 1",
+      "source": "Train/test split 1",
+      "sourceHandle": "output",
+      "target": "Train model 1",
+      "targetHandle": "bundle"
+    },
+    {
+      "id": "Train model 1 Model inference 1",
+      "source": "Train model 1",
       "sourceHandle": "output",
       "target": "Model inference 1",
       "targetHandle": "bundle"
@@ -28,7 +42,30 @@
       "data": {
         "__execution_delay": 0.0,
         "collapsed": null,
-        "display": null,
+        "display": {
+          "dataframes": {
+            "df": {
+              "columns": [
+                "x",
+                "y"
+              ]
+            },
+            "df_test": {
+              "columns": [
+                "x",
+                "y"
+              ]
+            },
+            "df_train": {
+              "columns": [
+                "x",
+                "y"
+              ]
+            }
+          },
+          "other": {},
+          "relations": []
+        },
         "error": null,
         "meta": {
           "inputs": {
@@ -134,7 +171,46 @@
       "data": {
         "__execution_delay": 0.0,
         "collapsed": null,
-        "display": null,
+        "display": {
+          "dataframes": {
+            "df": {
+              "columns": [
+                "x",
+                "y"
+              ]
+            },
+            "df_test": {
+              "columns": [
+                "x",
+                "y"
+              ]
+            },
+            "df_train": {
+              "columns": [
+                "x",
+                "y"
+              ]
+            }
+          },
+          "other": {
+            "model": {
+              "model": {
+                "inputs": [
+                  "Input__embedding_1_x"
+                ],
+                "loss_inputs": [
+                  "Activation_2_x",
+                  "Input__label_1_y"
+                ],
+                "outputs": [
+                  "Activation_2_x"
+                ]
+              },
+              "type": "model"
+            }
+          },
+          "relations": []
+        },
         "error": null,
         "meta": {
           "inputs": {
@@ -194,7 +270,7 @@
         },
         "params": {
           "epochs": "1000",
-          "input_mapping": "{\"Input__embedding_1_x\": {\"df\": \"df_train\", \"column\": \"x\"}, \"Input__label_1_y\": {\"df\": \"df_train\", \"column\": \"y\"  }}",
+          "input_mapping": "{\"map\": {\"Input__embedding_1_x\": {\"df\": \"df_train\", \"column\": \"x\"}, \"Input__label_1_y\": {\"df\": \"df_train\", \"column\": \"y\"  }}}",
           "model_workspace": "Model definition",
           "save_as": "model"
         },
@@ -205,8 +281,8 @@
       "height": 519.0,
       "id": "Train model 3",
       "position": {
-        "x": 687.3818749999999,
-        "y": -34.16902777777775
+        "x": 722.5912720951791,
+        "y": -784.0614755260641
       },
       "type": "basic",
       "width": 640.0
@@ -216,7 +292,7 @@
         "__execution_delay": 0.0,
         "collapsed": null,
         "display": null,
-        "error": null,
+        "error": "'Input__embedding_1_x'",
         "meta": {
           "inputs": {
             "bundle": {
@@ -267,9 +343,9 @@
           "type": "basic"
         },
         "params": {
-          "input_mapping": "{\"Input__embedding_1_x\": {\"df\": \"df_test\", \"column\": \"x\"}}",
+          "input_mapping": "{\"map\": {\"Input__embedding_1_x\": {\"df\": \"df_test\", \"column\": \"x\"}}}",
           "model_name": "model",
-          "output_mapping": "{\"Activation_2_x\": {\"df\": \"df_test\", \"column\": \"predicted\"}}"
+          "output_mapping": "{\"map\": {\"Activation_2_x\": {\"df\": \"df_test\", \"column\": \"predicted\"}}}"
         },
         "status": "done",
         "title": "Model inference"
@@ -283,6 +359,1014 @@
       },
       "type": "basic",
       "width": 410.0
+    },
+    {
+      "data": {
+        "display": {
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+          },
+          "other": {
+            "model": "ModelConfig(model=Sequential(\n  (0) - Linear(in_features=4, out_features=4, bias=True): Input__embedding_1_x -> Linear_1_x\n  (1) - <function leaky_relu at 0x710fc8f0fba0>: Linear_1_x -> Activation_2_x\n  (2) - Identity(): Activation_2_x -> Activation_2_x\n), model_inputs=['Input__embedding_1_x'], model_outputs=['Activation_2_x'], loss_inputs=['Activation_2_x', 'Input__label_1_y'], loss=Sequential(\n  (0) - <function mse_loss at 0x710fc8f316c0>: Activation_2_x, Input__label_1_y -> MSE_loss_1_loss\n  (1) - Identity(): MSE_loss_1_loss -> loss\n), optimizer=SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    lr: 0.1\n    maximize: False\n    momentum: 0\n    nesterov: False\n    weight_decay: 0\n))"
+          },
+          "relations": []
+        },
+        "error": null,
+        "meta": {
+          "inputs": {
+            "bundle": {
+              "name": "bundle",
+              "position": "left",
+              "type": {
+                "type": "<class 'lynxkite_graph_analytics.core.Bundle'>"
+              }
+            }
+          },
+          "name": "View tables",
+          "outputs": {},
+          "params": {
+            "limit": {
+              "default": 100.0,
+              "name": "limit",
+              "type": {
+                "type": "<class 'int'>"
+              }
+            }
+          },
+          "position": {
+            "x": 471.0,
+            "y": 424.0
+          },
+          "type": "table_view"
+        },
+        "params": {
+          "limit": 100.0
+        },
+        "status": "planned",
+        "title": "View tables"
+      },
+      "dragHandle": ".bg-primary",
+      "height": 600.0,
+      "id": "View tables 1",
+      "position": {
+        "x": 2017.4630208327735,
+        "y": -223.54449620081252
+      },
+      "type": "table_view",
+      "width": 582.0
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+    {
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+            }
+          },
+          "other": {
+            "model": {
+              "model": {
+                "inputs": [],
+                "loss_inputs": [
+                  "Activation_2_x",
+                  "Input__label_1_y"
+                ],
+                "outputs": [
+                  "Activation_2_x",
+                  "Input__label_1_y"
+                ]
+              },
+              "type": "model"
+            }
+          },
+          "relations": []
+        },
+        "error": "Mapping is unset.",
+        "meta": {
+          "inputs": {
+            "bundle": {
+              "name": "bundle",
+              "position": "left",
+              "type": {
+                "type": "<class 'lynxkite_graph_analytics.core.Bundle'>"
+              }
+            }
+          },
+          "name": "Train model",
+          "outputs": {
+            "output": {
+              "name": "output",
+              "position": "right",
+              "type": {
+                "type": "None"
+              }
+            }
+          },
+          "params": {
+            "epochs": {
+              "default": 1.0,
+              "name": "epochs",
+              "type": {
+                "type": "<class 'int'>"
+              }
+            },
+            "input_mapping": {
+              "default": null,
+              "name": "input_mapping",
+              "type": {
+                "type": "<class 'lynxkite_graph_analytics.pytorch_model_ops.ModelMapping'>"
+              }
+            },
+            "model_workspace": {
+              "default": null,
+              "name": "model_workspace",
+              "type": {
+                "type": "<class 'str'>"
+              }
+            },
+            "save_as": {
+              "default": "model",
+              "name": "save_as",
+              "type": {
+                "type": "<class 'str'>"
+              }
+            }
+          },
+          "position": {
+            "x": 723.0,
+            "y": 370.0
+          },
+          "type": "basic"
+        },
+        "params": {
+          "epochs": "2",
+          "input_mapping": "{\"map\":{\"Activation_2_x\":{\"df\":\"df_train\"},\"Input__label_1_y\":{\"df\":\"df_train\",\"column\":\"y\"}}}",
+          "model_workspace": "Model definition",
+          "save_as": "model"
+        },
+        "status": "done",
+        "title": "Train model"
+      },
+      "dragHandle": ".bg-primary",
+      "height": 473.0,
+      "id": "Train model 1",
+      "position": {
+        "x": 712.1212754578014,
+        "y": 42.33722689912529
+      },
+      "type": "basic",
+      "width": 577.0
     }
   ]
 }

lynxkite-app/web/src/index.css

@@ -256,6 +256,14 @@ body {
       cursor: pointer;
     }
   }
+
+  .model-mapping-param {
+    border: 1px solid var(--fallback-bc, oklch(var(--bc) / 0.2));
+    border-collapse: separate;
+    border-radius: 5px;
+    padding: 5px 10px;
+    width: 100%;
+  }
 }
 
 .params-expander {

lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx

@@ -1,15 +1,127 @@
-const BOOLEAN = "<class 'bool'>";
+// @ts-ignore
+import ArrowsHorizontal from "~icons/tabler/arrows-horizontal.jsx";
 
+const BOOLEAN = "<class 'bool'>";
+const MODEL_MAPPING =
+  "<class 'lynxkite_graph_analytics.pytorch_model_ops.ModelMapping'>";
 function ParamName({ name }: { name: string }) {
   return (
     <span className="param-name bg-base-200">{name.replace(/_/g, " ")}</span>
   );
 }
 
+function getModelBindings(data: any): string[] {
+  function bindingsOfModel(m: any): string[] {
+    return [...m.inputs, ...m.outputs, ...m.loss_inputs];
+  }
+  const bindings = new Set<string>();
+  const other = data?.display?.other ?? data?.display?.value?.other ?? {};
+  for (const e of Object.values(other) as any[]) {
+    if (e.type === "model") {
+      for (const b of bindingsOfModel(e.model)) {
+        bindings.add(b);
+      }
+    }
+  }
+  const list = [...bindings];
+  list.sort();
+  return list;
+}
+
+function parseJsonOrEmpty(json: string): object {
+  try {
+    const j = JSON.parse(json);
+    if (typeof j === "object") {
+      return j;
+    }
+  } catch (e) {}
+  return {};
+}
+
+function ModelMapping({ value, onChange, data }: any) {
+  const v: any = parseJsonOrEmpty(value);
+  v.map ??= {};
+  const dfs =
+    data?.display?.dataframes ?? data?.display?.value?.dataframes ?? {};
+  const bindings = getModelBindings(data);
+  return (
+    <table className="model-mapping-param">
+      <tbody>
+        <tr>
+          <td>mm</td>
+        </tr>
+        {bindings.length > 0 ? (
+          bindings.map((binding: string) => (
+            <tr key={binding}>
+              <td>{binding}</td>
+              <td>
+                <ArrowsHorizontal />
+              </td>
+              <td>
+                <select
+                  className="select select-ghost"
+                  value={v.map?.[binding]?.df}
+                  onChange={(evt) => {
+                    const df = evt.currentTarget.value;
+                    if (df === "unbound") {
+                      const map = { ...v.map, [binding]: undefined };
+                      onChange(JSON.stringify({ map }));
+                    } else {
+                      const columnSpec = {
+                        column: dfs[df][0],
+                        ...(v.map?.[binding] || {}),
+                        df,
+                      };
+                      const map = { ...v.map, [binding]: columnSpec };
+                      onChange(JSON.stringify({ map }));
+                    }
+                  }}
+                >
+                  <option key="unbound" value="unbound">
+                    unbound
+                  </option>
+                  {Object.keys(dfs).map((df: string) => (
+                    <option key={df} value={df}>
+                      {df}
+                    </option>
+                  ))}
+                </select>
+              </td>
+              <td>
+                <select
+                  className="select select-ghost"
+                  value={v.map?.[binding]?.column}
+                  onChange={(evt) => {
+                    const column = evt.currentTarget.value;
+                    const columnSpec = { ...(v.map?.[binding] || {}), column };
+                    const map = { ...v.map, [binding]: columnSpec };
+                    onChange(JSON.stringify({ map }));
+                  }}
+                >
+                  {dfs[v.map?.[binding]?.df]?.columns.map((col: string) => (
+                    <option key={col} value={col}>
+                      {col}
+                    </option>
+                  ))}
+                </select>
+              </td>
+            </tr>
+          ))
+        ) : (
+          <tr>
+            <td>no bindings</td>
+          </tr>
+        )}
+      </tbody>
+    </table>
+  );
+}
+
 interface NodeParameterProps {
   name: string;
   value: any;
   meta: any;
+  data: any;
   onChange: (value: any, options?: { delay: number }) => void;
 }
 
@@ -17,6 +129,7 @@ export default function NodeParameter({
   name,
   value,
   meta,
+  data,
   onChange,
 }: NodeParameterProps) {
   return (
@@ -65,6 +178,11 @@ export default function NodeParameter({
             {name.replace(/_/g, " ")}
           </label>
         </div>
+      ) : meta?.type?.type === MODEL_MAPPING ? (
+        <>
+          <ParamName name={name} />
+          <ModelMapping value={value} data={data} onChange={onChange} />
+        </>
       ) : (
         <>
           <ParamName name={name} />

lynxkite-app/web/src/workspace/nodes/NodeWithParams.tsx

@@ -62,6 +62,7 @@ function NodeWithParams(props: any) {
               name={name}
               key={name}
               value={value}
+              data={props.data}
               meta={metaParams?.[name]}
               onChange={(value: any, opts?: UpdateOptions) =>
                 setParam(name, value, opts || {})

lynxkite-core/src/lynxkite/core/ops.py

@@ -112,6 +112,13 @@ class Result:
     display: ReadOnlyJSON | None = None
     error: str | None = None
 
+    def default_display(self) -> ReadOnlyJSON | None:
+        """Automatically extracts basic data from the output."""
+        if hasattr(self.output, "default_display"):
+            return self.output.default_display()
+        else:
+            return None
+
 
 MULTI_INPUT = Input(name="multi", type="*")
 
@@ -140,6 +147,11 @@ def _param_to_type(name, value, type):
                 return None if value == "" else _param_to_type(name, value, type)
             case (type, types.NoneType):
                 return None if value == "" else _param_to_type(name, value, type)
+    if issubclass(type, pydantic.BaseModel):
+        try:
+            return type.model_validate_json(value)
+        except pydantic.ValidationError:
+            return None
     return value
 
 
@@ -174,9 +186,10 @@ class Op(BaseConfig):
         """Returns the parameters converted to the expected type."""
         res = {}
         for p in params:
-            res[p] = params[p]
             if p in self.params:
                 res[p] = _param_to_type(p, params[p], self.params[p].type)
+            else:
+                res[p] = params[p]
         return res
 
 

lynxkite-core/src/lynxkite/core/workspace.py

@@ -58,13 +58,13 @@ class WorkspaceNode(BaseConfig):
 
     def publish_result(self, result: ops.Result):
         """Sends the result to the frontend. Call this in an executor when the result is available."""
-        self.data.display = result.display
+        self.data.display = result.display or result.default_display()
         self.data.error = result.error
         self.data.status = NodeStatus.done
         if hasattr(self, "_crdt"):
             with self._crdt.doc.transaction():
-                self._crdt["data"]["display"] = result.display
-                self._crdt["data"]["error"] = result.error
+                self._crdt["data"]["display"] = self.data.display
+                self._crdt["data"]["error"] = self.data.error
                 self._crdt["data"]["status"] = NodeStatus.done
 
     def publish_error(self, error: Exception | str | None):

lynxkite-graph-analytics/src/lynxkite_graph_analytics/core.py

@@ -106,6 +106,7 @@ class Bundle:
         )
 
     def to_dict(self, limit: int = 100):
+        """JSON-serializable representation of the bundle, including some data."""
         return {
             "dataframes": {
                 name: {
@@ -115,7 +116,23 @@ class Bundle:
                 for name, df in self.dfs.items()
             },
             "relations": [dataclasses.asdict(relation) for relation in self.relations],
-            "other": self.other,
+            "other": {k: str(v) for k, v in self.other.items()},
+        }
+
+    def default_display(self):
+        """JSON-serializable information about the bundle, metadata only."""
+        return {
+            "dataframes": {
+                name: {
+                    "columns": sorted(str(c) for c in df.columns),
+                }
+                for name, df in self.dfs.items()
+            },
+            "relations": [dataclasses.asdict(relation) for relation in self.relations],
+            "other": {
+                k: getattr(v, "default_display", lambda: {})()
+                for k, v in self.other.items()
+            },
         }
 
 

lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py

@@ -368,21 +368,26 @@ def train_model(
     bundle: core.Bundle,
     *,
     model_workspace: str,
-    input_mapping: str,
+    input_mapping: pytorch_model_ops.ModelMapping,
     epochs: int = 1,
     save_as: str = "model",
 ):
     """Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
+    assert model_workspace, "Model workspace is unset."
+    print(f"input_mapping: {input_mapping}")
     ws = load_ws(model_workspace)
-    input_mapping = json.loads(input_mapping)
-    inputs = pytorch_model_ops.to_tensors(bundle, input_mapping)
+    inputs = (
+        pytorch_model_ops.to_tensors(bundle, input_mapping) if input_mapping else {}
+    )
     m = pytorch_model_ops.build_model(ws, inputs)
+    bundle = bundle.copy()
+    bundle.other[save_as] = m
+    if input_mapping is None:
+        return ops.Result(bundle, error="Mapping is unset.")
     t = tqdm(range(epochs), desc="Training model")
     for _ in t:
         loss = m.train(inputs)
         t.set_postfix({"loss": loss})
-    bundle = bundle.copy()
-    bundle.other[save_as] = m
     return bundle
 
 
@@ -391,18 +396,18 @@ def model_inference(
     bundle: core.Bundle,
     *,
     model_name: str = "model",
-    input_mapping: str = "",
-    output_mapping: str = "",
+    input_mapping: pytorch_model_ops.ModelMapping,
+    output_mapping: pytorch_model_ops.ModelMapping,
 ):
     """Executes a trained model."""
+    if input_mapping is None or output_mapping is None:
+        return ops.Result(bundle, error="Mapping is unset.")
     m = bundle.other[model_name]
-    input_mapping = json.loads(input_mapping)
-    output_mapping = json.loads(output_mapping)
     inputs = pytorch_model_ops.to_tensors(bundle, input_mapping)
     outputs = m.inference(inputs)
     bundle = bundle.copy()
-    for k, v in output_mapping.items():
-        bundle.dfs[v["df"]][v["column"]] = outputs[k].detach().numpy().tolist()
+    for k, v in output_mapping.map.items():
+        bundle.dfs[v.df][v.column] = outputs[k].detach().numpy().tolist()
     return bundle
 
 

lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch_model_ops.py

@@ -1,6 +1,8 @@
 """Boxes for defining PyTorch models."""
 
 import graphlib
+
+import pydantic
 from lynxkite.core import ops, workspace
 from lynxkite.core.ops import Parameter as P
 import torch
@@ -128,6 +130,15 @@ def _to_id(s: str) -> str:
     return "".join(c if c.isalnum() else "_" for c in s)
 
 
+class ColumnSpec(pydantic.BaseModel):
+    df: str
+    column: str
+
+
+class ModelMapping(pydantic.BaseModel):
+    map: dict[str, ColumnSpec]
+
+
 @dataclass
 class ModelConfig:
     model: torch.nn.Module
@@ -169,6 +180,16 @@ class ModelConfig:
         c.model = self.model.copy()
         return c
 
+    def default_display(self):
+        return {
+            "type": "model",
+            "model": {
+                "inputs": self.model_inputs,
+                "outputs": self.model_outputs,
+                "loss_inputs": self.loss_inputs,
+            },
+        }
+
 
 def build_model(
     ws: workspace.Workspace, inputs: dict[str, torch.Tensor]
@@ -241,9 +262,9 @@ def build_model(
                 loss_layers.append(
                     (torch.nn.functional.mse_loss, f"{xi}, {yi} -> {nid}_loss")
                 )
-    cfg["model_inputs"] = used_inputs & inputs.keys()
-    cfg["model_outputs"] = loss_inputs - inputs.keys()
-    cfg["loss_inputs"] = loss_inputs
+    cfg["model_inputs"] = list(used_inputs & inputs.keys())
+    cfg["model_outputs"] = list(loss_inputs - inputs.keys())
+    cfg["loss_inputs"] = list(loss_inputs)
     # Make sure the trained output is output from the last model layer.
     outputs = ", ".join(cfg["model_outputs"])
     layers.append((torch.nn.Identity(), f"{outputs} -> {outputs}"))
@@ -266,11 +287,9 @@ def build_model(
     return ModelConfig(**cfg)
 
 
-def to_tensors(b: core.Bundle, m: dict[str, dict]) -> dict[str, torch.Tensor]:
+def to_tensors(b: core.Bundle, m: ModelMapping) -> dict[str, torch.Tensor]:
     """Converts a tensor to the correct type for PyTorch."""
     tensors = {}
-    for k, v in m.items():
-        tensors[k] = torch.tensor(
-            b.dfs[v["df"]][v["column"]].to_list(), dtype=torch.float32
-        )
+    for k, v in m.map.items():
+        tensors[k] = torch.tensor(b.dfs[v.df][v.column].to_list(), dtype=torch.float32)
     return tensors