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Signed-off-by: András Schmelczer <andras@schmelczer.dev>

example/predict.py

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+
+from models import DomainPrediction
+from typing import Iterable, List, Dict
+from sklearn.pipeline import Pipeline 
+from helper import preprocess
+import re
+# from sus.use_model import use_model
+from config import model_key
+
+
+# @use_model(model_key, version="latest")
+def predict(text: str, model: Pipeline, cut_off_probability: float=0.2) -> List[DomainPrediction]:
+    assert 0 <= cut_off_probability <= 1
+
+    feature_names = model.named_steps['vectorizer'].get_feature_names_out()
+
+    token_mapping = {
+        preprocess(original): original
+        for original in re.sub(r'[^a-zA-Z0-9]', ' ', text).split(' ')
+    }
+    
+    features = model.named_steps['vectorizer'].transform([text])
+    prediction = model.named_steps['classifier'].predict_proba(features)[0]
+    best_classes = sorted(enumerate(prediction), key=lambda v: v[1], reverse=True)
+
+    results: List[DomainPrediction] = []
+    for class_index, probability in best_classes:
+        weights = model.named_steps['classifier'].feature_log_prob_[class_index]
+       
+        results.append(DomainPrediction(
+            domain=model.named_steps['classifier'].classes_[class_index],
+            probability=round(probability * 100),
+            explanation=_get_explanation(
+                feature_names=feature_names,
+                features=features.A[0],
+                weights=weights,
+                token_mapping=token_mapping
+            )
+        ))
+
+        if sum(r.probability for r in results) >= cut_off_probability:
+            break
+    
+    return results
+
+
+def _get_explanation(feature_names: Iterable[str], features: Iterable[float], weights: Iterable[float], token_mapping: Dict[str, str]) -> List[str]:
+    influential = [
+        (value * weight, name)
+        for name, value, weight in zip(feature_names, features, weights)
+        if value
+    ]
+    
+    most_influential = sorted(influential, reverse=True)[:5]
+
+    return [token_mapping[v[1]] for v in most_influential]