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2e0eaa2 f586f77 2e0eaa2 f586f77 2e0eaa2 f586f77 2e0eaa2 f586f77 2e0eaa2 f586f77 2e0eaa2 f586f77 2e0eaa2 f586f77 2e0eaa2 f586f77 2e0eaa2 f586f77 2e0eaa2 f586f77 2e0eaa2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | from dataclasses import asdict
from sklearn.exceptions import NotFittedError
from sklearn.utils.validation import check_is_fitted
from logic import *
class UserError(Exception):
"""A mistake the student can fix; its message is shown in the UI."""
class Manager:
def __init__(self):
self._dataset: Dataset | None = None
self._model: BaseEstimator | None = None
self._model_config: ModelSettings | None = None
# Maps plot coordinates back to inputs; None when the inputs are the plot coordinates
self._projection: Projection | None = None
self._input_names: list[str] = []
def handle_set_dataset(self, dataset: dict) -> None:
self._reset_model()
try:
parsed_dataset = Dataset(
inputs=np.array(dataset["inputs"]),
outputs=np.array(dataset["outputs"]),
)
input_names = [str(name) for name in dataset.get("input_names", [])]
projection = dataset.get("projection")
parsed_projection = None if projection is None else Projection(
origin=np.array(projection["origin"]),
components=np.array(projection["components"]),
)
except Exception as e:
raise ValueError(f"Invalid dataset format: {e}")
self._dataset = parsed_dataset
self._projection = parsed_projection
n_inputs = parsed_dataset.inputs.shape[1] if parsed_dataset.inputs.ndim == 2 else 0
self._input_names = input_names or [f"x{i + 1}" for i in range(n_inputs)]
def handle_set_model_config(self, settings: dict) -> None:
self._reset_model()
try:
parsed_settings = ModelSettings(
type=settings["type"],
params=dict(settings.get("params", {})),
)
except Exception as e:
raise ValueError(f"Invalid model settings format: {e}")
self._model_config = parsed_settings
def handle_build_model(self) -> dict | None:
"""Fit the model; returns its metrics and learned parameters (None with no data)."""
self._reset_model()
if self._dataset is None:
raise ValueError("Dataset not set")
if self._model_config is None:
raise ValueError("Model settings not set")
try:
self._model = init_model(self._model_config)
except Exception as e:
# user error
raise UserError(f"Error initializing model: {e}")
if self._dataset.inputs.size > 0 and self._dataset.outputs.size > 0:
try:
train_model(self._model, self._dataset)
except Exception as e:
# user error
self._reset_model()
raise UserError(f"Error training model: {e}")
return describe_model(self._model, self._dataset, self._input_names)
return None
def handle_get_decision_boundary(self, settings: dict) -> dict:
if self._model is None:
return dict(x=[], y=[], labels=[])
try:
check_is_fitted(self._model)
except NotFittedError:
return dict(x=[], y=[], labels=[])
try:
parsed_settings = DecisionBoundarySettings(
xmin=settings["xmin"],
xmax=settings["xmax"],
ymin=settings["ymin"],
ymax=settings["ymax"],
resolution=settings.get("resolution", 500),
)
except Exception as e:
raise ValueError(f"Invalid decision boundary settings format: {e}")
try:
result = get_decision_boundary_values(
self._model,
parsed_settings,
self._projection.lift if self._projection else None,
)
except Exception as e:
raise UserError(f"Error computing decision boundary: {e}")
return asdict(result)
def _reset_model(self) -> None:
self._model = None
print("Model reset")
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