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## Click Models | ||
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### Create Datasets | ||
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```Python | ||
from ultr_toolbox.click_models.data import ClickDataset | ||
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train_dataset = ClickDataset(train_df) | ||
val_dataset = ClickDataset(val_df) | ||
test_dataset = ClickDataset(test_df) | ||
``` | ||
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### Train neural click models | ||
``` | ||
from ultr_toolbox.click_models.metrics import Perplexity | ||
from ultr_toolbox.click_models.neural import PositionBasedModel, NeuralTrainer | ||
model = PositionBasedModel() | ||
trainer = NeuralTrainer(model) | ||
trainer.fit(train_dataset, val_dataset) | ||
metrics = trainer.test(test_dataset, metrics=[Perplexity()]) | ||
``` | ||
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### Train PyClick models | ||
``` | ||
from pyclick.click_models import PBM | ||
from ultr_toolbox.click_models.metrics import Perplexity | ||
from ultr_toolbox.click_models.em import PyClickTrainer | ||
model = PBM() | ||
trainer = PyClickTrainer(model) | ||
trainer.fit(train_dataset, val_dataset) | ||
metrics = trainer.test(test_dataset, metrics=[Perplexity()]) | ||
``` | ||
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### Train naive models based on click statistics | ||
``` | ||
from ultr_toolbox.click_models.metrics import Perplexity | ||
from ultr_toolbox.click_models.stats import StatsTrainer, RankDocumentBasedModel | ||
model = RankDocumentBasedModel() | ||
trainer = StatsTrainer(model) | ||
trainer.fit(train_dataset, val_dataset) | ||
metrics = trainer.test(test_dataset, metrics=[Perplexity()]) | ||
``` |