Integrate Recurrency Baseline

[JuliaGast]

• [x] for polecat
• [x] for mini-yago

[JuliaGast]

• [x] load and reshape dataset
• [x] integrate negative sampling
• [x] specify seeds
• [x] restructure code s.t. it fits the tgb setup
• [x] evaluation
• [x] log evaluation scores
• [x] log run times

[shenyangHuang]

let's discuss tomorrow but starting with MRR ranking over all possible destination could be a good starting point to get an idea of what kind of performance we have on the dataset. We don't want datasets where methods already has overly high performance to begin with

[JuliaGast]

todos:

• [x] find out why the performance is significantly slower than when I run my original code (for yago)
• [x] check if the mrr scores are similar for yago. careful: slightly different test timesteps (5 instead of 6 test timesteps)

[JuliaGast]

YAGO: without training:

   {
        "model": "RecurrencyBaseline",
        "train_flag": false,
        "data": "tkgl-yago",
        "run": 1,
        "seed": 1,
        "mrr": 0.8588335514068604,
        "hits10": 0.9259546399116516,
        "test_time": 175.950186,
        "tot_train_val_time": 190.072089
    }

with training:

    {
        "model": "RecurrencyBaseline",
        "train_flag": "True",
        "data": "tkgl-yago",
        "run": 1,
        "seed": 1,
        "mrr": 0.9091410040855408,
        "hits10": 0.9302738904953003,
        "test_time": 104.039734,
        "tot_train_val_time": 922.78976
    }

POLECAT mini:

    {
        "model": "RecurrencyBaseline",
        "train_flag": false,
        "data": "tkgl-polecat",
        "run": 1,
        "seed": 1,
        "mrr": 0.14627420902252197,
        "hits10": 0.21407249569892883,
        "test_time": 38.191202,
        "tot_train_val_time": 44.85462
    },
    {
        "model": "RecurrencyBaseline",
        "train_flag": "True",
        "data": "tkgl-polecat",
        "run": 1,
        "seed": 1,
        "mrr": 0.1447719931602478,
        "hits10": 0.21252413094043732,
        "test_time": 19.861222,
        "tot_train_val_time": 210.635383
    }
]