darabos commited on
Commit
f369afc
·
1 Parent(s): 2bb2d1d

Split out ml_ops.py, add "View vector" box.

Browse files
examples/Model use CHANGED
@@ -41,6 +41,13 @@
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  "sourceHandle": "output",
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  "target": "View tables 1",
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  "targetHandle": "bundle"
 
 
 
 
 
 
 
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  }
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  ],
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  "env": "LynxKite Graph Analytics",
@@ -358,12 +365,14 @@
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  },
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  "df_test": {
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  "columns": [
 
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  "x",
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  "y"
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  ]
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  },
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  "df_train": {
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  "columns": [
 
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  "x",
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  "y"
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  ]
@@ -546,12 +555,14 @@
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  },
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  "df_test": {
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  "columns": [
 
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  "x",
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  "y"
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  ]
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  },
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  "df_train": {
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  "columns": [
 
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  "x",
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  "y"
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  ]
@@ -610,7 +621,7 @@
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  "default": null,
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  "name": "input_mapping",
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  "type": {
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- "type": "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelTrainingInputMapping'>"
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  }
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  },
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  "model_name": {
@@ -624,7 +635,7 @@
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  "type": "basic"
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  },
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  "params": {
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- "epochs": "501",
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  "input_mapping": "{\"map\":{\"Input__tensor_1_output\":{\"df\":\"df_train\",\"column\":\"x\"},\"Input__tensor_3_output\":{\"df\":\"df_train\",\"column\":\"y\"}}}",
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  "model_name": "model"
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  },
@@ -764,12 +775,14 @@
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  },
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  "df_test": {
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  "columns": [
 
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  "x",
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  "y"
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  ]
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  },
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  "df_train": {
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  "columns": [
 
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  "x",
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  "y"
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  ]
@@ -826,7 +839,7 @@
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  "default": null,
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  "name": "input_mapping",
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  "type": {
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- "type": "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelInferenceInputMapping'>"
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  }
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  },
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  "model_name": {
@@ -840,7 +853,7 @@
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  "default": null,
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  "name": "output_mapping",
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  "type": {
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- "type": "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelOutputMapping'>"
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  }
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  }
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  },
@@ -1462,507 +1475,157 @@
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1910
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1911
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1912
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1913
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1914
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1915
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1916
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1917
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1918
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1920
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1921
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1922
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1924
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1925
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1926
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1927
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1928
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1929
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1930
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1931
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1932
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1933
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1934
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1935
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1936
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1937
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1938
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1939
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1940
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1941
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1942
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1943
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1944
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1945
- 0.012246524915099144,
1946
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1947
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1948
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1949
- 0.012172140181064606,
1950
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1951
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1952
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1953
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1954
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1955
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1956
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1957
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1958
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1959
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1960
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1961
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1962
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1963
- 0.011920991353690624,
1964
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1965
- 0.011886196210980415
1966
  ],
1967
  "type": "line"
1968
  }
@@ -1989,12 +1652,14 @@
1989
  },
1990
  "df_test": {
1991
  "columns": [
 
1992
  "x",
1993
  "y"
1994
  ]
1995
  },
1996
  "df_train": {
1997
  "columns": [
 
1998
  "x",
1999
  "y"
2000
  ]
@@ -2471,426 +2136,528 @@
2471
  },
2472
  "df_test": {
2473
  "columns": [
 
2474
  "x",
2475
  "y",
2476
  "pred"
2477
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2478
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2479
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2480
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2481
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2482
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2483
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2485
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2488
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2490
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2493
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2498
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2505
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2528
  ]
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2530
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2531
  "df_train": {
2532
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2533
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2535
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2536
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2548
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2550
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2551
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2554
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2555
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2558
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2559
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2560
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2562
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2563
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2564
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2565
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2566
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2567
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2568
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2570
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2572
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2574
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2575
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2576
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2578
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2579
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2580
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2582
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2583
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2584
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2586
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2587
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2588
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2590
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2591
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2594
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2595
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2598
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2599
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2602
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2603
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2606
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2610
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2614
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2642
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2646
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2658
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2670
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2674
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2682
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2686
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2694
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2695
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2698
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2699
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2700
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2706
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2707
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2711
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2714
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2715
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2716
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2731
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2735
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2763
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2774
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2786
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2787
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2790
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2791
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2794
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@@ -3221,6 +2988,7 @@
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@@ -3228,6 +2996,7 @@
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@@ -3300,6 +3069,229 @@
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3303
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41
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  "target": "View tables 1",
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+ "target": "View vectors 1",
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621
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623
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625
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635
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839
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853
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3133
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+ ],
3135
+ [
3136
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3137
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3138
+ "",
3139
+ -8.997444152832031
3140
+ ]
3141
+ ],
3142
+ "symbolSize": 15.811388300841896,
3143
+ "type": "scatter"
3144
+ }
3145
+ ],
3146
+ "title": {
3147
+ "text": "UMAP projection of pred"
3148
+ },
3149
+ "tooltip": {
3150
+ "show": false
3151
+ },
3152
+ "visualMap": {
3153
+ "calculable": true,
3154
+ "dimension": 3,
3155
+ "inRange": {
3156
+ "color": [
3157
+ "#440154",
3158
+ "#482777",
3159
+ "#3E4989",
3160
+ "#31688E",
3161
+ "#26828E",
3162
+ "#1F9E89",
3163
+ "#35B779",
3164
+ "#6CCE59",
3165
+ "#B4DE2C",
3166
+ "#FDE725"
3167
+ ]
3168
+ },
3169
+ "max": -7.914520263671875,
3170
+ "min": -12.964160919189453,
3171
+ "right": 10,
3172
+ "top": "center"
3173
+ },
3174
+ "xAxis": [
3175
+ {
3176
+ "type": "value"
3177
+ }
3178
+ ],
3179
+ "yAxis": [
3180
+ {
3181
+ "type": "value"
3182
+ }
3183
+ ]
3184
+ },
3185
+ "error": null,
3186
+ "input_metadata": [
3187
+ {
3188
+ "dataframes": {
3189
+ "df": {
3190
+ "columns": [
3191
+ "x",
3192
+ "y"
3193
+ ]
3194
+ },
3195
+ "df_test": {
3196
+ "columns": [
3197
+ "index",
3198
+ "pred",
3199
+ "x",
3200
+ "y"
3201
+ ]
3202
+ },
3203
+ "df_train": {
3204
+ "columns": [
3205
+ "index",
3206
+ "x",
3207
+ "y"
3208
+ ]
3209
+ },
3210
+ "training": {
3211
+ "columns": [
3212
+ "training_loss"
3213
+ ]
3214
+ }
3215
+ },
3216
+ "other": {
3217
+ "model": {
3218
+ "model": {
3219
+ "inputs": [
3220
+ "Input__tensor_1_output"
3221
+ ],
3222
+ "loss_inputs": [
3223
+ "Input__tensor_3_output",
3224
+ "Output_1_x"
3225
+ ],
3226
+ "outputs": [
3227
+ "Output_1_x"
3228
+ ],
3229
+ "trained": true
3230
+ },
3231
+ "type": "model"
3232
+ }
3233
+ },
3234
+ "relations": []
3235
+ }
3236
+ ],
3237
+ "meta": {
3238
+ "inputs": {
3239
+ "bundle": {
3240
+ "name": "bundle",
3241
+ "position": "left",
3242
+ "type": {
3243
+ "type": "<class 'lynxkite_graph_analytics.core.Bundle'>"
3244
+ }
3245
+ }
3246
+ },
3247
+ "name": "View vectors",
3248
+ "outputs": {},
3249
+ "params": {
3250
+ "label_column": {
3251
+ "default": "",
3252
+ "name": "label_column",
3253
+ "type": {
3254
+ "type": "<class 'str'>"
3255
+ }
3256
+ },
3257
+ "table_name": {
3258
+ "default": "nodes",
3259
+ "name": "table_name",
3260
+ "type": {
3261
+ "type": "<class 'str'>"
3262
+ }
3263
+ },
3264
+ "vector_column": {
3265
+ "default": "",
3266
+ "name": "vector_column",
3267
+ "type": {
3268
+ "type": "<class 'str'>"
3269
+ }
3270
+ }
3271
+ },
3272
+ "position": {
3273
+ "x": 943.0,
3274
+ "y": 315.0
3275
+ },
3276
+ "type": "visualization"
3277
+ },
3278
+ "params": {
3279
+ "label_column": "",
3280
+ "table_name": "df_test",
3281
+ "vector_column": "pred"
3282
+ },
3283
+ "status": "done",
3284
+ "title": "View vectors"
3285
+ },
3286
+ "dragHandle": ".bg-primary",
3287
+ "height": 668.0,
3288
+ "id": "View vectors 1",
3289
+ "position": {
3290
+ "x": 3091.4609779299844,
3291
+ "y": 659.497108066971
3292
+ },
3293
+ "type": "visualization",
3294
+ "width": 613.0
3295
  }
3296
  ]
3297
  }
lynxkite-app/web/src/workspace/Workspace.tsx CHANGED
@@ -352,7 +352,7 @@ function LynxKiteFlow() {
352
  onConnect={onConnect}
353
  proOptions={{ hideAttribution: true }}
354
  maxZoom={1}
355
- minZoom={0.3}
356
  zoomOnScroll={false}
357
  preventScrolling={false}
358
  defaultEdgeOptions={{
 
352
  onConnect={onConnect}
353
  proOptions={{ hideAttribution: true }}
354
  maxZoom={1}
355
+ minZoom={0.2}
356
  zoomOnScroll={false}
357
  preventScrolling={false}
358
  defaultEdgeOptions={{
lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx CHANGED
@@ -4,10 +4,10 @@ import ArrowsHorizontal from "~icons/tabler/arrows-horizontal.jsx";
4
 
5
  const BOOLEAN = "<class 'bool'>";
6
  const MODEL_TRAINING_INPUT_MAPPING =
7
- "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelTrainingInputMapping'>";
8
  const MODEL_INFERENCE_INPUT_MAPPING =
9
- "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelInferenceInputMapping'>";
10
- const MODEL_OUTPUT_MAPPING = "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelOutputMapping'>";
11
  function ParamName({ name }: { name: string }) {
12
  return <span className="param-name bg-base-200">{name.replace(/_/g, " ")}</span>;
13
  }
 
4
 
5
  const BOOLEAN = "<class 'bool'>";
6
  const MODEL_TRAINING_INPUT_MAPPING =
7
+ "<class 'lynxkite_graph_analytics.ml_ops.ModelTrainingInputMapping'>";
8
  const MODEL_INFERENCE_INPUT_MAPPING =
9
+ "<class 'lynxkite_graph_analytics.ml_ops.ModelInferenceInputMapping'>";
10
+ const MODEL_OUTPUT_MAPPING = "<class 'lynxkite_graph_analytics.ml_ops.ModelOutputMapping'>";
11
  function ParamName({ name }: { name: string }) {
12
  return <span className="param-name bg-base-200">{name.replace(/_/g, " ")}</span>;
13
  }
lynxkite-app/web/src/workspace/nodes/NodeWithVisualization.tsx CHANGED
@@ -8,6 +8,10 @@ const NodeWithVisualization = (props: any) => {
8
  useEffect(() => {
9
  const opts = props.data?.display?.value;
10
  if (!opts || !chartsRef.current) return;
 
 
 
 
11
  chartsInstanceRef.current = echarts.init(chartsRef.current, null, {
12
  renderer: "canvas",
13
  width: "auto",
 
8
  useEffect(() => {
9
  const opts = props.data?.display?.value;
10
  if (!opts || !chartsRef.current) return;
11
+ if (opts.tooltip?.formatter === "GET_THIRD_VALUE") {
12
+ // We can't pass a function from the backend, and can't get good tooltips otherwise.
13
+ opts.tooltip.formatter = (params: any) => params.value[2];
14
+ }
15
  chartsInstanceRef.current = echarts.init(chartsRef.current, null, {
16
  renderer: "canvas",
17
  width: "auto",
lynxkite-graph-analytics/pyproject.toml CHANGED
@@ -16,6 +16,7 @@ dependencies = [
16
  "polars[gpu]>=1.14.0",
17
  "torch>=2.6.0",
18
  "torch-geometric>=2.6.1",
 
19
  ]
20
 
21
  [project.optional-dependencies]
 
16
  "polars[gpu]>=1.14.0",
17
  "torch>=2.6.0",
18
  "torch-geometric>=2.6.1",
19
+ "cuml-cu12>=25.2.1",
20
  ]
21
 
22
  [project.optional-dependencies]
lynxkite-graph-analytics/src/lynxkite_graph_analytics/__init__.py CHANGED
@@ -14,6 +14,7 @@ from .core import * # noqa (easier access for core classes)
14
  from . import lynxkite_ops # noqa (imported to trigger registration)
15
  from . import networkx_ops # noqa (imported to trigger registration)
16
  from . import pytorch # noqa (imported to trigger registration)
 
17
 
18
  if os.environ.get("LYNXKITE_BIONEMO_INSTALLED", "").strip().lower() == "true":
19
  from . import bionemo_ops # noqa (imported to trigger registration)
 
14
  from . import lynxkite_ops # noqa (imported to trigger registration)
15
  from . import networkx_ops # noqa (imported to trigger registration)
16
  from . import pytorch # noqa (imported to trigger registration)
17
+ from . import ml_ops # noqa (imported to trigger registration)
18
 
19
  if os.environ.get("LYNXKITE_BIONEMO_INSTALLED", "").strip().lower() == "true":
20
  from . import bionemo_ops # noqa (imported to trigger registration)
lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py CHANGED
@@ -2,15 +2,11 @@
2
 
3
  import enum
4
  import os
5
- import pathlib
6
  import fsspec
7
  from lynxkite.core import ops
8
  from collections import deque
9
 
10
- from tqdm import tqdm
11
  from . import core
12
- from .pytorch import pytorch_core
13
- from lynxkite.core import workspace
14
  import grandcypher
15
  import joblib
16
  import matplotlib
@@ -323,127 +319,7 @@ def create_graph(bundle: core.Bundle, *, relations: str = None) -> core.Bundle:
323
  return ops.Result(output=bundle, display=bundle.to_dict(limit=100))
324
 
325
 
326
- def load_ws(model_workspace: str):
327
- cwd = pathlib.Path()
328
- path = cwd / model_workspace
329
- assert path.is_relative_to(cwd)
330
- assert path.exists(), f"Workspace {path} does not exist"
331
- ws = workspace.load(path)
332
- return ws
333
-
334
-
335
  @op("Biomedical foundation graph (PLACEHOLDER)")
336
  def biomedical_foundation_graph(*, filter_nodes: str):
337
  """Loads the gigantic Lynx-maintained knowledge graph. Includes drugs, diseases, genes, proteins, etc."""
338
  return None
339
-
340
-
341
- @op("Define model")
342
- def define_model(
343
- bundle: core.Bundle,
344
- *,
345
- model_workspace: str,
346
- save_as: str = "model",
347
- ):
348
- """Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
349
- assert model_workspace, "Model workspace is unset."
350
- ws = load_ws(model_workspace)
351
- # Build the model without inputs, to get its interface.
352
- m = pytorch_core.build_model(ws)
353
- m.source_workspace = model_workspace
354
- bundle = bundle.copy()
355
- bundle.other[save_as] = m
356
- return bundle
357
-
358
-
359
- # These contain the same mapping, but they get different UIs.
360
- # For inputs, you select existing columns. For outputs, you can create new columns.
361
- class ModelInferenceInputMapping(pytorch_core.ModelMapping):
362
- pass
363
-
364
-
365
- class ModelTrainingInputMapping(pytorch_core.ModelMapping):
366
- pass
367
-
368
-
369
- class ModelOutputMapping(pytorch_core.ModelMapping):
370
- pass
371
-
372
-
373
- @op("Train model")
374
- @ops.slow
375
- def train_model(
376
- bundle: core.Bundle,
377
- *,
378
- model_name: str = "model",
379
- input_mapping: ModelTrainingInputMapping,
380
- epochs: int = 1,
381
- ):
382
- """Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
383
- m = bundle.other[model_name].copy()
384
- inputs = pytorch_core.to_tensors(bundle, input_mapping)
385
- t = tqdm(range(epochs), desc="Training model")
386
- losses = []
387
- for _ in t:
388
- loss = m.train(inputs)
389
- t.set_postfix({"loss": loss})
390
- losses.append(loss)
391
- m.trained = True
392
- bundle = bundle.copy()
393
- bundle.dfs["training"] = pd.DataFrame({"training_loss": losses})
394
- bundle.other[model_name] = m
395
- return bundle
396
-
397
-
398
- @op("Model inference")
399
- @ops.slow
400
- def model_inference(
401
- bundle: core.Bundle,
402
- *,
403
- model_name: str = "model",
404
- input_mapping: ModelInferenceInputMapping,
405
- output_mapping: ModelOutputMapping,
406
- ):
407
- """Executes a trained model."""
408
- if input_mapping is None or output_mapping is None:
409
- return ops.Result(bundle, error="Mapping is unset.")
410
- m = bundle.other[model_name]
411
- assert m.trained, "The model is not trained."
412
- inputs = pytorch_core.to_tensors(bundle, input_mapping)
413
- outputs = m.inference(inputs)
414
- bundle = bundle.copy()
415
- copied = set()
416
- for k, v in output_mapping.map.items():
417
- if not v.df or not v.column:
418
- continue
419
- if v.df not in copied:
420
- bundle.dfs[v.df] = bundle.dfs[v.df].copy()
421
- copied.add(v.df)
422
- bundle.dfs[v.df][v.column] = outputs[k].detach().numpy().tolist()
423
- return bundle
424
-
425
-
426
- @op("Train/test split")
427
- def train_test_split(bundle: core.Bundle, *, table_name: str, test_ratio: float = 0.1):
428
- """Splits a dataframe in the bundle into separate "_train" and "_test" dataframes."""
429
- df = bundle.dfs[table_name]
430
- test = df.sample(frac=test_ratio)
431
- train = df.drop(test.index)
432
- bundle = bundle.copy()
433
- bundle.dfs[f"{table_name}_train"] = train
434
- bundle.dfs[f"{table_name}_test"] = test
435
- return bundle
436
-
437
-
438
- @op("View loss", view="visualization")
439
- def view_loss(
440
- bundle: core.Bundle,
441
- ):
442
- loss = bundle.dfs["training"].training_loss.tolist()
443
- v = {
444
- "title": {"text": "Training loss"},
445
- "xAxis": {"type": "category"},
446
- "yAxis": {"type": "value"},
447
- "series": [{"data": loss, "type": "line"}],
448
- }
449
- return v
 
2
 
3
  import enum
4
  import os
 
5
  import fsspec
6
  from lynxkite.core import ops
7
  from collections import deque
8
 
 
9
  from . import core
 
 
10
  import grandcypher
11
  import joblib
12
  import matplotlib
 
319
  return ops.Result(output=bundle, display=bundle.to_dict(limit=100))
320
 
321
 
 
 
 
 
 
 
 
 
 
322
  @op("Biomedical foundation graph (PLACEHOLDER)")
323
  def biomedical_foundation_graph(*, filter_nodes: str):
324
  """Loads the gigantic Lynx-maintained knowledge graph. Includes drugs, diseases, genes, proteins, etc."""
325
  return None
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
lynxkite-graph-analytics/src/lynxkite_graph_analytics/ml_ops.py ADDED
@@ -0,0 +1,192 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Operations for machine learning."""
2
+
3
+ import numpy as np
4
+ from . import core
5
+ from lynxkite.core import workspace
6
+ from .pytorch import pytorch_core
7
+ from lynxkite.core import ops
8
+ from tqdm import tqdm
9
+ import cuml
10
+ import joblib
11
+ import pandas as pd
12
+ import pathlib
13
+
14
+
15
+ mem = joblib.Memory(".joblib-cache")
16
+ op = ops.op_registration(core.ENV)
17
+
18
+
19
+ def load_ws(model_workspace: str):
20
+ cwd = pathlib.Path()
21
+ path = cwd / model_workspace
22
+ assert path.is_relative_to(cwd)
23
+ assert path.exists(), f"Workspace {path} does not exist"
24
+ ws = workspace.load(path)
25
+ return ws
26
+
27
+
28
+ @op("Define model")
29
+ def define_model(
30
+ bundle: core.Bundle,
31
+ *,
32
+ model_workspace: str,
33
+ save_as: str = "model",
34
+ ):
35
+ """Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
36
+ assert model_workspace, "Model workspace is unset."
37
+ ws = load_ws(model_workspace)
38
+ # Build the model without inputs, to get its interface.
39
+ m = pytorch_core.build_model(ws)
40
+ m.source_workspace = model_workspace
41
+ bundle = bundle.copy()
42
+ bundle.other[save_as] = m
43
+ return bundle
44
+
45
+
46
+ # These contain the same mapping, but they get different UIs.
47
+ # For inputs, you select existing columns. For outputs, you can create new columns.
48
+ class ModelInferenceInputMapping(pytorch_core.ModelMapping):
49
+ pass
50
+
51
+
52
+ class ModelTrainingInputMapping(pytorch_core.ModelMapping):
53
+ pass
54
+
55
+
56
+ class ModelOutputMapping(pytorch_core.ModelMapping):
57
+ pass
58
+
59
+
60
+ @op("Train model")
61
+ @ops.slow
62
+ def train_model(
63
+ bundle: core.Bundle,
64
+ *,
65
+ model_name: str = "model",
66
+ input_mapping: ModelTrainingInputMapping,
67
+ epochs: int = 1,
68
+ ):
69
+ """Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
70
+ m = bundle.other[model_name].copy()
71
+ inputs = pytorch_core.to_tensors(bundle, input_mapping)
72
+ t = tqdm(range(epochs), desc="Training model")
73
+ losses = []
74
+ for _ in t:
75
+ loss = m.train(inputs)
76
+ t.set_postfix({"loss": loss})
77
+ losses.append(loss)
78
+ m.trained = True
79
+ bundle = bundle.copy()
80
+ bundle.dfs["training"] = pd.DataFrame({"training_loss": losses})
81
+ bundle.other[model_name] = m
82
+ return bundle
83
+
84
+
85
+ @op("Model inference")
86
+ @ops.slow
87
+ def model_inference(
88
+ bundle: core.Bundle,
89
+ *,
90
+ model_name: str = "model",
91
+ input_mapping: ModelInferenceInputMapping,
92
+ output_mapping: ModelOutputMapping,
93
+ ):
94
+ """Executes a trained model."""
95
+ if input_mapping is None or output_mapping is None:
96
+ return ops.Result(bundle, error="Mapping is unset.")
97
+ m = bundle.other[model_name]
98
+ assert m.trained, "The model is not trained."
99
+ inputs = pytorch_core.to_tensors(bundle, input_mapping)
100
+ outputs = m.inference(inputs)
101
+ bundle = bundle.copy()
102
+ copied = set()
103
+ for k, v in output_mapping.map.items():
104
+ if not v.df or not v.column:
105
+ continue
106
+ if v.df not in copied:
107
+ bundle.dfs[v.df] = bundle.dfs[v.df].copy()
108
+ copied.add(v.df)
109
+ bundle.dfs[v.df][v.column] = outputs[k].detach().numpy().tolist()
110
+ return bundle
111
+
112
+
113
+ @op("Train/test split")
114
+ def train_test_split(bundle: core.Bundle, *, table_name: str, test_ratio: float = 0.1):
115
+ """Splits a dataframe in the bundle into separate "_train" and "_test" dataframes."""
116
+ df = bundle.dfs[table_name]
117
+ test = df.sample(frac=test_ratio).reset_index()
118
+ train = df.drop(test.index).reset_index()
119
+ bundle = bundle.copy()
120
+ bundle.dfs[f"{table_name}_train"] = train
121
+ bundle.dfs[f"{table_name}_test"] = test
122
+ return bundle
123
+
124
+
125
+ @op("View loss", view="visualization")
126
+ def view_loss(bundle: core.Bundle):
127
+ loss = bundle.dfs["training"].training_loss.tolist()
128
+ v = {
129
+ "title": {"text": "Training loss"},
130
+ "xAxis": {"type": "category"},
131
+ "yAxis": {"type": "value"},
132
+ "series": [{"data": loss, "type": "line"}],
133
+ }
134
+ return v
135
+
136
+
137
+ @op("Reduce dimensions")
138
+ def reduce_dimensions(bundle: core.Bundle):
139
+ return
140
+
141
+
142
+ VIRIDIS = [
143
+ "#440154",
144
+ "#482777",
145
+ "#3E4989",
146
+ "#31688E",
147
+ "#26828E",
148
+ "#1F9E89",
149
+ "#35B779",
150
+ "#6CCE59",
151
+ "#B4DE2C",
152
+ "#FDE725",
153
+ ]
154
+
155
+
156
+ @op("View vectors", view="visualization")
157
+ def view_vectors(
158
+ bundle: core.Bundle,
159
+ *,
160
+ table_name: str = "nodes",
161
+ vector_column: str = "",
162
+ label_column: str = "",
163
+ ):
164
+ vec = np.stack(bundle.dfs[table_name][vector_column].to_numpy())
165
+ proj = cuml.manifold.umap.UMAP(n_components=2).fit_transform(vec)
166
+ color = cuml.manifold.umap.UMAP(n_components=1).fit_transform(vec)
167
+ data = [[*p.tolist(), "", c.item()] for p, c in zip(proj, color)]
168
+ if label_column:
169
+ for i, row in enumerate(bundle.dfs[table_name][label_column]):
170
+ data[i][2] = row
171
+ size = 50 / len(data) ** 0.5
172
+ v = {
173
+ "title": {
174
+ "text": f"UMAP projection of {vector_column}",
175
+ },
176
+ "visualMap": {
177
+ "min": color[:, 0].min().item(),
178
+ "max": color[:, 0].max().item(),
179
+ "right": 10,
180
+ "top": "center",
181
+ "calculable": True,
182
+ "dimension": 3,
183
+ "inRange": {"color": VIRIDIS},
184
+ },
185
+ "tooltip": {"trigger": "item", "formatter": "GET_THIRD_VALUE"}
186
+ if label_column
187
+ else {"show": False},
188
+ "xAxis": [{"type": "value"}],
189
+ "yAxis": [{"type": "value"}],
190
+ "series": [{"type": "scatter", "symbolSize": size, "data": data}],
191
+ }
192
+ return v
lynxkite-graph-analytics/uv.lock CHANGED
@@ -104,6 +104,15 @@ wheels = [
104
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105
  ]
106
 
 
 
 
 
 
 
 
 
 
107
  [[package]]
108
  name = "certifi"
109
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161
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162
  ]
163
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
164
  [[package]]
165
  name = "colorama"
166
  version = "0.4.6"
@@ -221,30 +251,111 @@ wheels = [
221
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222
  ]
223
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
224
  [[package]]
225
  name = "cuda-python"
226
- version = "12.6.0"
227
  source = { registry = "https://pypi.org/simple" }
 
 
 
228
  wheels = [
229
- { url = "https://files.pythonhosted.org/packages/6f/84/0b9ba455780219c930017c7e479edb6b4f038199c6842efe3a96fd3d68d2/cuda_python-12.6.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:11fae55fd27977f787eae6c447ad08155c5aff81eff246079f2c01759ec7cac3", size = 24613998 },
230
- { url = "https://files.pythonhosted.org/packages/ca/1f/ef67d7988816c4cf403e6cea7f35e19bac5bcadcd4b67665b3bc138cde6b/cuda_python-12.6.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:94dea2aedc5fbe1f89635b1b8f7c7a90b498da27a69e0ca0e1e46d1f78939b39", size = 24988739 },
231
- { url = "https://files.pythonhosted.org/packages/fe/38/68d6d3aa34f79526eebd3aef4700cc0b7b64d40b8800d2c16f90862cf8e2/cuda_python-12.6.0-cp311-cp311-win_amd64.whl", hash = "sha256:12b6f4136a4161ba97507dd5a371469471ea0b3f53e1b745979fcb77b4644526", size = 10002325 },
232
- { url = "https://files.pythonhosted.org/packages/ca/27/d8743b4ab32b35845ae647e91e25e0783495c3d8f76ef0ebec4267d93ac0/cuda_python-12.6.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:78063e643cbde653d8d541f14fd591fc1d631e3010fdda8ab1132d95bc29a693", size = 23902462 },
233
- { url = "https://files.pythonhosted.org/packages/e7/15/f2e6cd28c5523c638ebd704c3268abb60e684506c1f4813e3dd4dcad167f/cuda_python-12.6.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eb755a0825c42ff75d6b113c852b3b9ab580f1a6cde4cb19b57d0495925cd5cc", size = 24167826 },
234
- { url = "https://files.pythonhosted.org/packages/65/d7/aef9de64f2df946637d1e7fc202598ec44f2a4206fe1dfd39661e28612ea/cuda_python-12.6.0-cp312-cp312-win_amd64.whl", hash = "sha256:932681507b0f2005e67fb296db242562670a1f311bc61b9a2dc437c2f7046737", size = 10127468 },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
235
  ]
236
 
237
  [[package]]
238
  name = "cudf-polars-cu12"
239
- version = "24.12.0"
240
  source = { registry = "https://pypi.org/simple" }
241
  dependencies = [
242
  { name = "polars" },
243
  { name = "pylibcudf-cu12" },
244
  ]
245
  wheels = [
246
- { url = "https://files.pythonhosted.org/packages/0f/6e/3d0b2d19ac853347f435f49815d471d461315d8f6e74f165624f16ddc7db/cudf_polars_cu12-24.12.0-py3-none-any.whl", hash = "sha256:3d2058f75251fd4921618bb1d4cfba0c99b670a12756df0d3f51559aca2298fa", size = 79456 },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
247
  ]
 
248
 
249
  [[package]]
250
  name = "cupy-cuda12x"
@@ -263,6 +374,18 @@ wheels = [
263
  { url = "https://files.pythonhosted.org/packages/14/11/8bc53cab1466605ba88ba3c7243078a784ee2cd8974f5602a491882af9af/cupy_cuda12x-13.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:88ef1478f00ae252da0026e7f04f70c9bb6a2dc130ba5f1e5bc5e8069a928bf5", size = 69499349 },
264
  ]
265
 
 
 
 
 
 
 
 
 
 
 
 
 
266
  [[package]]
267
  name = "cycler"
268
  version = "0.12.1"
@@ -272,6 +395,110 @@ wheels = [
272
  { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321 },
273
  ]
274
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
275
  [[package]]
276
  name = "fastrlock"
277
  version = "0.8.3"
@@ -451,6 +678,18 @@ wheels = [
451
  { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 },
452
  ]
453
 
 
 
 
 
 
 
 
 
 
 
 
 
454
  [[package]]
455
  name = "iniconfig"
456
  version = "2.0.0"
@@ -558,26 +797,105 @@ wheels = [
558
 
559
  [[package]]
560
  name = "libcudf-cu12"
561
- version = "24.12.0"
562
  source = { registry = "https://pypi.org/simple" }
563
  dependencies = [
564
  { name = "libkvikio-cu12" },
565
  { name = "nvidia-nvcomp-cu12" },
566
  ]
567
  wheels = [
568
- { url = "https://files.pythonhosted.org/packages/fa/ea/efccbbeb4012cdd247e3d12ed19c4cd70a770e6c22c1456d8cb569818083/libcudf_cu12-24.12.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:1e78a247f31c6045221f3142a5fd15210d53c91043c5a4e260b67b5ddff43164", size = 454436993 },
569
- { url = "https://files.pythonhosted.org/packages/88/93/dc3a27c3904aa12a32def0df330f15a85d0f01e0420b18bc0efa8b3245ba/libcudf_cu12-24.12.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:47b7537a314b4462c24938f4e9118ea65bfe2de7440e99ecf278a38a14abf9ab", size = 457847164 },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
570
  ]
 
571
 
572
  [[package]]
573
  name = "libkvikio-cu12"
574
- version = "24.12.1"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
575
  source = { registry = "https://pypi.org/simple" }
576
  wheels = [
577
- { url = "https://files.pythonhosted.org/packages/24/17/8dbef99dc5a73dbf56dea090ba1c345cc4343bf8d13f331a8091d0e1c362/libkvikio_cu12-24.12.1-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:7ed5d27263204a237ea7a14ce176ed885888c8daf47341ae0fbcecd55fb2c694", size = 1875299 },
578
- { url = "https://files.pythonhosted.org/packages/0a/31/3be8facaf2f15849629a030e8fb1696ec80228270860b9ffd869a17a6a71/libkvikio_cu12-24.12.1-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:c4f333dbbffc35ba94a028db3b24ddb1c3dfddff9c6fb0f17488dc662a86f481", size = 1989095 },
579
  ]
580
 
 
 
 
 
 
 
 
 
 
581
  [[package]]
582
  name = "llvmlite"
583
  version = "0.43.0"
@@ -596,6 +914,15 @@ wheels = [
596
  { url = "https://files.pythonhosted.org/packages/c6/94/dea10e263655ce78d777e78d904903faae39d1fc440762be4a9dc46bed49/llvmlite-0.43.0-cp312-cp312-win_amd64.whl", hash = "sha256:7a2872ee80dcf6b5dbdc838763d26554c2a18aa833d31a2635bff16aafefb9c9", size = 28107442 },
597
  ]
598
 
 
 
 
 
 
 
 
 
 
599
  [[package]]
600
  name = "lynxkite-core"
601
  version = "0.1.0"
@@ -609,6 +936,7 @@ name = "lynxkite-graph-analytics"
609
  version = "0.1.0"
610
  source = { virtual = "." }
611
  dependencies = [
 
612
  { name = "fsspec" },
613
  { name = "grand-cypher" },
614
  { name = "joblib" },
@@ -633,6 +961,7 @@ gpu = [
633
 
634
  [package.metadata]
635
  requires-dist = [
 
636
  { name = "fsspec", specifier = ">=2025.2.0" },
637
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638
  { name = "joblib", specifier = ">=1.4.2" },
@@ -649,6 +978,18 @@ requires-dist = [
649
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650
  ]
651
 
 
 
 
 
 
 
 
 
 
 
 
 
652
  [[package]]
653
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654
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740
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741
  ]
742
 
 
 
 
 
 
 
 
 
 
743
  [[package]]
744
  name = "mpmath"
745
  version = "1.3.0"
@@ -749,6 +1099,47 @@ wheels = [
749
  { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198 },
750
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751
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
752
  [[package]]
753
  name = "multidict"
754
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857
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858
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859
 
 
 
 
 
 
 
 
 
 
 
 
 
860
  [[package]]
861
  name = "numpy"
862
  version = "2.0.2"
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959
  source = { registry = "https://pypi.org/simple" }
960
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961
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962
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963
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964
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966
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967
 
968
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1080
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1081
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1082
 
 
 
 
 
 
 
 
 
 
1083
  [[package]]
1084
  name = "nvidia-nccl-cu12"
1085
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@@ -1090,12 +1504,11 @@ wheels = [
1090
 
1091
  [[package]]
1092
  name = "nvidia-nvcomp-cu12"
1093
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1094
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1095
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1096
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1099
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1100
 
1101
  [[package]]
@@ -1151,7 +1564,7 @@ wheels = [
1151
 
1152
  [[package]]
1153
  name = "nx-cugraph-cu12"
1154
- version = "24.12.0"
1155
  source = { registry = "https://pypi.org/simple" }
1156
  dependencies = [
1157
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@@ -1159,7 +1572,7 @@ dependencies = [
1159
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1160
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1161
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1162
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1163
 
1164
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1165
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1228
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1229
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1230
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1231
  [[package]]
1232
  name = "pillow"
1233
  version = "11.1.0"
@@ -1288,15 +1714,16 @@ wheels = [
1288
 
1289
  [[package]]
1290
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1291
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1292
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1293
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1294
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1295
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1302
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1430
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1431
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1433
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1434
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1435
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1442
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1450
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1468
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1894
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1907
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