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zeekay
dda5b2a367
The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
49 lines
2.0 KiB
Plaintext
49 lines
2.0 KiB
Plaintext
class SignificantFeatureJob(Job):
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"""Compute the most significant feature."""
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name = "significant_feature"
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def __init__(self, metrics_df: pd.DataFrame) -> None:
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self.metrics_df = metrics_df
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def run(self, task_id: int | None = None) -> tuple[Explanation, pd.DataFrame]:
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"""Fit a RandomForestRegressor to compute the shapely values."""
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x_features = self.metrics_df.drop(columns=["score"])
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target = self.metrics_df["score"]
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# Convert categorical features to one-hot encoding
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categorical_features = ["backbone", "stride"]
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encoder = OneHotEncoder(sparse_output=False)
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x_encoded = pd.DataFrame(encoder.fit_transform(x_features[categorical_features]))
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x_encoded.columns = encoder.get_feature_names_out(categorical_features)
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# Combine encoded categorical features with the rest of the data
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x_features = x_features.drop(columns=categorical_features)
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x_features = pd.concat([x_features, x_encoded], axis=1)
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# We don't split the data as the random forest is a proxy to compute the shapely values
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# Train the model
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model = RandomForestRegressor()
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model.fit(X=x_features, y=target)
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# Initialize the SHAP explainer
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explainer = shap.Explainer(model, x_features)
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# Calculate SHAP values
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shap_values = explainer(x_features)
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return shap_values, x_features
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@staticmethod
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def collect(results: list[tuple[Explanation, pd.DataFrame]]) -> Explanation:
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"""We only need to run this once."""
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return results[0]
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@staticmethod
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def save(results: tuple[Explanation, pd.DataFrame]) -> None:
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"""Save the results in a plot."""
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shapely_values, features = results
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shap.summary_plot(shapely_values, features, show=False, plot_type="violin")
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file_path = Path("runs") / SignificantFeatureJob.name
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file_path.mkdir(parents=True, exist_ok=True)
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plt.savefig(file_path / "summary_plot.png")
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