quantlab.model.view.PredictorView

class quantlab.model.view.PredictorView(predictor)

Bases: Component

Root class of the views of config.predictor.

Every protocol member is the wrapped predictor’s, through the view:

labels	the wrapped predictor’s
train_bounds	the wrapped predictor’s
test_bounds	the wrapped predictor’s
fitted_train_bounds	the wrapped predictor’s
label_delays	the wrapped predictor’s
label_scales	the wrapped predictor’s
predict_window	the wrapped predictor’s, then _view_predictions
collect, load	called on the wrapped predictor; return the view
train	the wrapped predictor’s checkpoint
check_checkpoint	the wrapped predictor’s
config	config_cls filled from the view’s attributes of the same names
from_config	the config’s fields, components rebuilt, passed to the constructor

A subclass declares config_cls (a subclass of PredictorViewConfig), keeps each config field as an attribute of the same name, takes the fields as constructor arguments, and overrides _view_predictions to change the predictions; a view that predicts differently (a wider request, a record) overrides predict_window, and one that changes a label’s scale or the checkpoint handling overrides that member.

Parameters:

predictorPredictor

The wrapped predictor.

Attributes:

predictorPredictor

The wrapped predictor.

Examples

With model a loaded predictor:

>>> class Doubled(PredictorView):
...     def _view_predictions(self, predictions):
...         return predictions * 2
>>> view = Doubled(model)
>>> view.train_bounds == model.train_bounds, view.label_scales == model.label_scales
(True, True)
>>> window = ("2024-01-01", "2024-01-12")
>>> bool(view.predict_window(*window).equals(model.predict_window(*window) * 2))
True
>>> view.load("ckpt.joblib") is view
True

check_checkpoint(path)

Validate path with the wrapped predictor, without loading it.

Examples

>>> view.check_checkpoint("ckpt.joblib")  # raises if it does not fit

collect()

Collect the wrapped predictor’s training data; returns the view.

Examples

>>> view.collect() is view
True

property config

config_cls filled from the view’s attributes; get_config() serialises it.

Examples

>>> view.config.predictor is model
True

config_cls

The config class the view is serialised and rebuilt with.

alias of PredictorViewConfig

property fitted_train_bounds

The wrapped predictor’s fitted (start, end) training window.

Examples

>>> view.fitted_train_bounds == model.fitted_train_bounds
True

classmethod from_config(config, run_dir=None)

Rebuild the view and the predictor it wraps from get_config().

The component fields are rebuilt by the component rule, with run_dir passed down, and every field is passed to the constructor by name.

Examples

>>> from quantlab.model.predefined.smoothed import SmoothedPredictor
>>> smoothed = SmoothedPredictor(None, halflife_bars=2.5)
>>> rebuilt = SmoothedPredictor.from_config(smoothed.get_config())
>>> rebuilt.halflife_bars, rebuilt.warmup_bars, rebuilt.predictor
(2.5, 25, None)

property label_delays

The wrapped predictor’s label delays; a view moves no bar.

Examples

>>> view.label_delays == model.label_delays
True

property label_scales

The wrapped predictor’s label scales.

Examples

>>> view.label_scales == model.label_scales
True

property labels

The wrapped predictor’s labels.

Examples

>>> view.labels is model.labels
True

load(path)

Load path into the wrapped predictor; returns the view.

Examples

>>> view.load("ckpt.joblib") is view
True

predict_window(start, end)

The wrapped predictor’s predictions over the window, through _view_predictions.

Examples

>>> bool(view.predict_window(*window).equals(model.predict_window(*window) * 2))
True

property test_bounds

The wrapped predictor’s (start, end) test window.

Examples

>>> view.test_bounds == model.test_bounds
True

train()

Train the wrapped predictor; returns its checkpoint.

Examples

>>> view.train() == model.train()
True

property train_bounds

The wrapped predictor’s (start, end) training window.

Examples

>>> view.train_bounds == model.train_bounds
True