<jupyter_start><jupyter_text>TPCH<jupyter_code>model_meta = (v1, os.path.join(os.getcwd(), 'logs/tpch/version_0/checkpoints/epoch=95-step=1309151.ckpt'))
version, checkpoint = model_meta
estimater = ge.QueryEstimater(
    checkpoint, 
    dataset='tpch:optimized', 
    encoder=version.encoder, 
    model_cls=version.model.CNNRegressor
)
results = estimater.evaluate()
json.dumps(ge.evaluate_metrics(results))
ge.display_results(results)
model_meta = (v1, os.path.join(os.getcwd(), 'logs/tpch/version_0/checkpoints/epoch=95-step=1309151.ckpt'))
version, checkpoint = model_meta
estimater = ge.QueryEstimater(
    checkpoint, 
    dataset='tpcd:optimized', 
    encoder=version.encoder, 
    model_cls=version.model.CNNRegressor
)
results = estimater.evaluate()
json.dumps(ge.evaluate_metrics(results))<jupyter_output>100%|██████████| 343/343 [00:19<00:00, 17.72it/s]<jupyter_text>ALL<jupyter_code>model_meta = (v1, os.path.join(os.getcwd(), 'logs/all/version_0/checkpoints/epoch=16-step=987087.ckpt'))
version, checkpoint = model_meta
estimater = ge.QueryEstimater(
    checkpoint, 
    dataset='tpch:optimized', 
    encoder=version.encoder, 
    model_cls=version.model.CNNRegressor
)
results = estimater.evaluate()
json.dumps(ge.evaluate_metrics(results))
model_meta = (v1, os.path.join(os.getcwd(), 'logs/all/version_0/checkpoints/epoch=16-step=987087.ckpt'))
version, checkpoint = model_meta
estimater = ge.QueryEstimater(
    checkpoint, 
    dataset='tpcd:optimized', 
    encoder=version.encoder, 
    model_cls=version.model.CNNRegressor
)
results = estimater.evaluate()
json.dumps(ge.evaluate_metrics(results))
cp_path = os.path.join(os.getcwd(), 'v7/logs/three/version_1/checkpoints/')
cps = [os.path.join(cp_path, f) for f in os.listdir(cp_path)]
cps = sorted(cps, key= lambda s: int(epoch_num_regex.search(s)[1]))

v = v7
for cp in tqdm(cps):
    qe = ge.QueryEstimater(cp, dataset=tpcd, encoder=v.encoder, model_cls=v.model.GereltRegressor)
    r = qe.evaluate(df=tpcd_sample)
    print(int(epoch_num_regex.search(cp)[1]), json.dumps(ge.evaluate_metrics(r)))
cp_path = os.path.join(os.getcwd(), 'v4/logs/three/version_0/checkpoints/')
cps = [os.path.join(cp_path, f) for f in os.listdir(cp_path)]
cps = sorted(cps, key= lambda s: int(epoch_num_regex.search(s)[1]))

v = v4
for cp in tqdm(cps):
    qe = ge.QueryEstimater(cp, dataset=tpcd, encoder=v.encoder, model_cls=v.model.GereltRegressor)
    r = qe.evaluate(df=tpcd_sample)
    print(int(epoch_num_regex.search(cp)[1]), json.dumps(ge.evaluate_metrics(r)))<jupyter_output><empty_output>