Spaces:
Running
Running
Some more mock-ups for ODE-GNN demo.
Browse files
examples/ODE-GNN
CHANGED
@@ -77,20 +77,6 @@
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"target": "Concatenate 1",
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"targetHandle": "b"
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},
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{
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"id": "Concatenate 1 Neural ODE 1",
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"source": "Concatenate 1",
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"sourceHandle": "x",
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"target": "Neural ODE 1",
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"id": "Neural ODE 1 MSE loss 1",
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"source": "Neural ODE 1",
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"sourceHandle": "x",
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"target": "MSE loss 1",
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"targetHandle": "x"
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{
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"id": "Input: label 1 MSE loss 1",
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"source": "Input: label 1",
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"sourceHandle": "loss",
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"target": "Optimizer 1",
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"targetHandle": "loss"
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}
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],
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"env": "PyTorch model",
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"height": 200.0,
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"id": "Graph conv 1",
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"type": "basic",
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"type": "basic"
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"params": {
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"times":
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"status": "planned",
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"title": "Repeat"
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"type": "tensor"
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"name": "Neural ODE",
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"x": {
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"params": {},
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"status": "planned",
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"title": "Neural ODE"
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},
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"dragHandle": ".bg-primary",
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"height": 200.0,
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"id": "Neural ODE 1",
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}
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"target": "Concatenate 1",
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{
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"id": "Input: label 1 MSE loss 1",
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"source": "Input: label 1",
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"sourceHandle": "loss",
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"target": "Optimizer 1",
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"targetHandle": "loss"
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{
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"id": "Concatenate 1 Neural ODE 2",
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"source": "Concatenate 1",
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"target": "Neural ODE 2",
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"id": "Neural ODE 2 MSE loss 1",
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"target": "MSE loss 1",
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"env": "PyTorch model",
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"height": 200.0,
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"id": "Graph conv 1",
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"type": "basic",
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"meta": {
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"params": {
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"times": "5"
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"status": "planned",
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"title": "Repeat"
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examples/Using a model
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"x": 995.0,
|
443 |
+
"y": 350.0
|
444 |
+
},
|
445 |
+
"type": "basic"
|
446 |
+
},
|
447 |
+
"params": {
|
448 |
+
"epochs": 1.0,
|
449 |
+
"model_mapping": "input: data_train",
|
450 |
+
"model_name": "model"
|
451 |
+
},
|
452 |
+
"status": "planned",
|
453 |
+
"title": "Train model"
|
454 |
+
},
|
455 |
+
"dragHandle": ".bg-primary",
|
456 |
+
"height": 342.0,
|
457 |
+
"id": "Train model 1",
|
458 |
+
"position": {
|
459 |
+
"x": 1358.7213662492159,
|
460 |
+
"y": 352.03096133771896
|
461 |
+
},
|
462 |
+
"type": "basic",
|
463 |
+
"width": 296.0
|
464 |
+
}
|
465 |
+
]
|
466 |
+
}
|
lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py
CHANGED
@@ -342,3 +342,41 @@ def create_graph(bundle: core.Bundle, *, relations: str = None) -> core.Bundle:
|
|
342 |
core.RelationDefinition(**r) for r in json.loads(relations).values()
|
343 |
]
|
344 |
return ops.Result(output=bundle, display=bundle.to_dict(limit=100))
|
|
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|
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|
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|
|
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|
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|
|
|
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|
|
342 |
core.RelationDefinition(**r) for r in json.loads(relations).values()
|
343 |
]
|
344 |
return ops.Result(output=bundle, display=bundle.to_dict(limit=100))
|
345 |
+
|
346 |
+
|
347 |
+
@op("Define model")
|
348 |
+
def define_model(*, model_workspace: str, save_as: str = "model"):
|
349 |
+
"""Reads a PyTorch model workspace and returns it as a model in a bundle."""
|
350 |
+
return None
|
351 |
+
|
352 |
+
|
353 |
+
@op("Biomedical foundation graph (PLACEHOLDER)")
|
354 |
+
def biomedical_foundation_graph(*, filter_nodes: str):
|
355 |
+
"""Loads the gigantic Lynx-maintained knowledge graph. Includes drugs, diseases, genes, proteins, etc."""
|
356 |
+
return None
|
357 |
+
|
358 |
+
|
359 |
+
@op("Train model")
|
360 |
+
def train_model(
|
361 |
+
bundle: core.Bundle, *, model_name: str, model_mapping: str, epochs: int = 1
|
362 |
+
):
|
363 |
+
"""Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
|
364 |
+
return None
|
365 |
+
|
366 |
+
|
367 |
+
@op("Model inference")
|
368 |
+
def model_inference(
|
369 |
+
bundle: core.Bundle,
|
370 |
+
*,
|
371 |
+
model_name: str,
|
372 |
+
model_mapping: str,
|
373 |
+
save_output_as: str = "prediction",
|
374 |
+
):
|
375 |
+
"""Executes a trained model."""
|
376 |
+
return None
|
377 |
+
|
378 |
+
|
379 |
+
@op("Train/test split")
|
380 |
+
def train_test_split(bundle: core.Bundle, *, table_name: str, test_ratio: float = 0.1):
|
381 |
+
"""Splits a dataframe in the bundle into separate "_train" and "_test" dataframes."""
|
382 |
+
return None
|
lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch_model_ops.py
CHANGED
@@ -33,7 +33,29 @@ reg("Input: sequential", outputs=["y"])
|
|
33 |
reg("Input: zeros", outputs=["x"])
|
34 |
|
35 |
reg("LSTM", inputs=["x", "h"], outputs=["x", "h"])
|
36 |
-
reg(
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
37 |
reg("Attention", inputs=["q", "k", "v"], outputs=["x", "weights"])
|
38 |
reg("LayerNorm", inputs=["x"])
|
39 |
reg("Dropout", inputs=["x"], params=[P.basic("p", 0.5)])
|
|
|
33 |
reg("Input: zeros", outputs=["x"])
|
34 |
|
35 |
reg("LSTM", inputs=["x", "h"], outputs=["x", "h"])
|
36 |
+
reg(
|
37 |
+
"Neural ODE",
|
38 |
+
inputs=["x"],
|
39 |
+
params=[
|
40 |
+
P.basic("relative_tolerance"),
|
41 |
+
P.basic("absolute_tolerance"),
|
42 |
+
P.options(
|
43 |
+
"method",
|
44 |
+
[
|
45 |
+
"dopri8",
|
46 |
+
"dopri5",
|
47 |
+
"bosh3",
|
48 |
+
"fehlberg2",
|
49 |
+
"adaptive_heun",
|
50 |
+
"euler",
|
51 |
+
"midpoint",
|
52 |
+
"rk4",
|
53 |
+
"explicit_adams",
|
54 |
+
"implicit_adams",
|
55 |
+
],
|
56 |
+
),
|
57 |
+
],
|
58 |
+
)
|
59 |
reg("Attention", inputs=["q", "k", "v"], outputs=["x", "weights"])
|
60 |
reg("LayerNorm", inputs=["x"])
|
61 |
reg("Dropout", inputs=["x"], params=[P.basic("p", 0.5)])
|