renaming

image_prediction/classifier/classifier.py

@@ -8,13 +8,14 @@ from image_prediction.utils import get_logger
 logger = get_logger()
 
 
-class Estimator:
+class Classifier:
     def __init__(self, estimator_adapter: EstimatorAdapter, classes: Mapping[int, str]):
         """Abstraction layer over different estimator backends (e.g. keras or scikit-learn). For each backend to be used
         an EstimatorAdapter must be implemented.
 
         Args:
-            estimator_adapter: adapter for a given estimator backend
+            estimator_adapter: adapter for a given estimator backend; expected to be a classifier that returns numeric
+                labels as predictions
             classes: mapping from a numerical label to a human-readable label for classes
         """
         self.__estimator_adapter = estimator_adapter

image_prediction/classifier/image_classifier.py

@@ -3,18 +3,18 @@ from typing import List
 
 from PIL.Image import Image
 
-from image_prediction.estimator.estimator import Estimator
+from image_prediction.classifier.classifier import Classifier
 from image_prediction.estimator.preprocessor.preprocessor import Preprocessor
 from image_prediction.estimator.preprocessor.preprocessors.basic import BasicPreprocessor
 from image_prediction.utils import chunk_iterable
 
 
-class Predictor:
-    def __init__(self, estimator: Estimator, preprocessor: Preprocessor = None):
+class ImageClassifier:
+    def __init__(self, estimator: Classifier, preprocessor: Preprocessor = None):
         self.estimator = estimator
         self.preprocessor = preprocessor if preprocessor else BasicPreprocessor()
         self.pipe = lambda batch: self.estimator(self.preprocessor(batch))
 
-    def predict(self, images: List[Image], batch_size=2):
+    def predict(self, images: List[Image], batch_size=16):
         batches = chunk_iterable(images, chunk_size=batch_size)
         return chain(*map(self.pipe, batches))

image_prediction/flask.py

@@ -49,7 +49,7 @@ def make_prediction_server(predict_fn: Callable):
             except KeyError:
                 raise
 
-        logger.debug("Running predictor on document...")
+        logger.debug("Running classifier on document...")
         try:
             predictions = process()
             response = jsonify(predictions)

test/unit_tests/classifier_test.py

@@ -10,8 +10,8 @@ logger.setLevel(logging.DEBUG)
 
 @pytest.mark.parametrize("estimator_type", ["mock", "keras"])
 @pytest.mark.parametrize("batch_size", [0, 1, 2, 16, 32, 64])
-def test_predict(estimator, input_batch, expected_predictions):
-    predictions = estimator.predict(input_batch)
+def test_predict(classifier, input_batch, expected_predictions):
+    predictions = classifier.predict(input_batch)
     assert predictions == expected_predictions
 
 

test/unit_tests/conftest.py

@@ -4,22 +4,22 @@ import numpy as np
 import pytest
 from PIL import Image
 
+from image_prediction.classifier.classifier import Classifier
+from image_prediction.classifier.image_classifier import ImageClassifier
 from image_prediction.estimator.adapter.adapters.keras import KerasEstimatorAdapter
 from image_prediction.estimator.adapter.adapters.mock import DummyEstimator, EstimatorAdapterMock
-from image_prediction.estimator.estimator import Estimator
 from image_prediction.exceptions import UnknownEstimatorAdapter
-from image_prediction.predictor.predictor import Predictor
 
 
 @pytest.fixture
-def predictor(estimator, monkeypatch, expected_predictions):
-    return Predictor(estimator)
+def predictor(classifier, monkeypatch, expected_predictions):
+    return ImageClassifier(classifier)
 
 
 @pytest.fixture
-def estimator(estimator_adapter, classes):
-    estimator = Estimator(estimator_adapter, classes)
-    return estimator
+def classifier(estimator_adapter, classes):
+    classifier = Classifier(estimator_adapter, classes)
+    return classifier
 
 
 @pytest.fixture

test/unit_tests/image_classifier_test.py

@@ -4,8 +4,7 @@ from image_prediction.utils import chunk_iterable
 
 
 @pytest.mark.parametrize("estimator_type", ["mock", "keras"])
-# @pytest.mark.parametrize("batch_size", [0, 1, 2, 16, 32, 64])
-@pytest.mark.parametrize("batch_size", [0, 1, 2, 4])
+@pytest.mark.parametrize("batch_size", [0, 1, 2, 16, 32, 64])
 def test_predict(predictor, images, expected_predictions):
     predictions = list(predictor.predict(images))
     assert predictions == expected_predictions