revised task api tests

setup.cfg

@@ -27,6 +27,7 @@ filterwarnings =
     ignore::RuntimeWarning
     ignore::PendingDeprecationWarning
     ignore::FutureWarning
+    ignore::numpy.VisibleDeprecationWarning
 
 addopts=-v
         --durations=10

tests/autokeras/tasks/image_test.py

@@ -1,22 +1,38 @@
 from unittest import mock
 
-from autokeras.tasks import image
+import autokeras as ak
 from tests import utils
 
 
-@mock.patch('autokeras.auto_model.AutoModel.__init__')
-def test_image_classifier(auto_model, tmp_path):
-    image.ImageClassifier(directory=tmp_path, max_trials=2, seed=utils.SEED)
-    assert auto_model.called
+@mock.patch('autokeras.AutoModel.fit')
+def test_img_clf_fit_call_auto_model_fit(fit, tmp_path):
+    auto_model = ak.ImageClassifier(directory=tmp_path, seed=utils.SEED)
 
+    auto_model.fit(
+        x=utils.generate_data(num_instances=100, shape=(32, 32, 3)),
+        y=utils.generate_one_hot_labels(num_instances=100, num_classes=10))
 
-@mock.patch('autokeras.auto_model.AutoModel.__init__')
-def test_image_regressor(auto_model, tmp_path):
-    image.ImageRegressor(directory=tmp_path, max_trials=2, seed=utils.SEED)
-    assert auto_model.called
+    assert fit.is_called
 
 
-@mock.patch('autokeras.auto_model.AutoModel.__init__')
-def test_image_segmenter(auto_model, tmp_path):
-    image.ImageSegmenter(directory=tmp_path, max_trials=2, seed=utils.SEED)
-    assert auto_model.called
+@mock.patch('autokeras.AutoModel.fit')
+def test_img_reg_fit_call_auto_model_fit(fit, tmp_path):
+    auto_model = ak.ImageRegressor(directory=tmp_path, seed=utils.SEED)
+
+    auto_model.fit(
+        x=utils.generate_data(num_instances=100, shape=(32, 32, 3)),
+        y=utils.generate_data(num_instances=100, shape=(1,)))
+
+    assert fit.is_called
+
+
+@mock.patch('autokeras.AutoModel.fit')
+def test_img_seg_fit_call_auto_model_fit(fit, tmp_path):
+    auto_model = ak.tasks.image.ImageSegmenter(
+        directory=tmp_path, seed=utils.SEED)
+
+    auto_model.fit(
+        x=utils.generate_data(num_instances=100, shape=(32, 32, 3)),
+        y=utils.generate_data(num_instances=100, shape=(32, 32)))
+
+    assert fit.is_called

tests/autokeras/tasks/structure_data_test.py

@@ -4,79 +4,51 @@
 import pandas
 import pytest
 
-from autokeras.tasks import structured_data
+import autokeras as ak
 from tests import utils
 
 
-def test_structured_data_unknown_str_in_col_type(tmp_path):
+def test_raise_error_unknown_str_in_col_type(tmp_path):
     with pytest.raises(ValueError) as info:
-        structured_data.StructuredDataClassifier(
-            column_types=utils.FALSE_COLUMN_TYPES_FROM_CSV,
+        ak.StructuredDataClassifier(
+            column_types={'age': 'num', 'parch': 'categorical'},
             directory=tmp_path,
-            max_trials=1,
             seed=utils.SEED)
-    assert 'Column_types should be either "categorical"' in str(info.value)
-
 
-def test_structured_data_col_name_type_mismatch(tmp_path):
-    with pytest.raises(ValueError) as info:
-        structured_data.StructuredDataClassifier(
-            column_names=utils.COLUMN_NAMES_FROM_NUMPY,
-            column_types=utils.COLUMN_TYPES_FROM_CSV,
-            directory=tmp_path,
-            max_trials=1,
-            seed=utils.SEED)
-    assert 'Column_names and column_types are mismatched.' in str(info.value)
+    assert 'Column_types should be either "categorical"' in str(info.value)
 
 
-@mock.patch('autokeras.auto_model.AutoModel.fit')
-@mock.patch('autokeras.auto_model.AutoModel.__init__')
-def test_structured_classifier(init, fit, tmp_path):
-    num_data = 500
-    train_x = utils.generate_structured_data(num_data)
-    train_y = utils.generate_one_hot_labels(num_instances=num_data, num_classes=3)
+@mock.patch('autokeras.AutoModel.fit')
+def test_structured_clf_fit_call_auto_model_fit(fit, tmp_path):
+    auto_model = ak.StructuredDataClassifier(directory=tmp_path, seed=utils.SEED)
 
-    clf = structured_data.StructuredDataClassifier(
-        column_names=utils.COLUMN_NAMES_FROM_NUMPY,
-        directory=tmp_path,
-        max_trials=1,
-        seed=utils.SEED)
-    clf.fit(train_x, train_y, epochs=2, validation_data=(train_x, train_y))
+    auto_model.fit(
+        x=utils.generate_structured_data(num_instances=100),
+        y=utils.generate_one_hot_labels(num_instances=100, num_classes=3))
 
-    assert init.called
-    assert fit.called
+    assert fit.is_called
 
 
-@mock.patch('autokeras.auto_model.AutoModel.fit')
-@mock.patch('autokeras.auto_model.AutoModel.__init__')
-def test_structured_regressor(init, fit, tmp_path):
-    num_data = 500
-    train_x = utils.generate_structured_data(num_data)
-    train_y = utils.generate_data(num_instances=100, shape=(1,))
+@mock.patch('autokeras.AutoModel.fit')
+def test_structured_reg_fit_call_auto_model_fit(fit, tmp_path):
+    auto_model = ak.StructuredDataRegressor(directory=tmp_path, seed=utils.SEED)
 
-    clf = structured_data.StructuredDataRegressor(
-        column_names=utils.COLUMN_NAMES_FROM_NUMPY,
-        directory=tmp_path,
-        max_trials=1,
-        seed=utils.SEED)
-    clf.fit(train_x, train_y, epochs=2, validation_data=(train_x, train_y))
+    auto_model.fit(
+        x=utils.generate_structured_data(num_instances=100),
+        y=utils.generate_data(num_instances=100, shape=(1,)))
 
-    assert init.called
-    assert fit.called
+    assert fit.is_called
 
 
-@mock.patch('autokeras.auto_model.AutoModel.fit')
-@mock.patch('autokeras.auto_model.AutoModel.__init__')
-def test_structured_data_classifier_from_csv(init, fit, tmp_path):
-    clf = structured_data.StructuredDataClassifier(
-        directory=tmp_path,
-        max_trials=1,
-        seed=utils.SEED)
+@mock.patch('autokeras.AutoModel.fit')
+def test_structured_data_clf_convert_csv_to_df_and_np(fit, tmp_path):
+    auto_model = ak.StructuredDataClassifier(directory=tmp_path, seed=utils.SEED)
 
-    clf.fit(x=utils.TRAIN_FILE_PATH, y='survived', epochs=2,
-            validation_data=(utils.TEST_FILE_PATH, 'survived'))
+    auto_model.fit(x=utils.TRAIN_FILE_PATH,
+                   y='survived',
+                   epochs=2,
+                   validation_data=(utils.TEST_FILE_PATH, 'survived'))
 
-    assert init.called
     _, kwargs = fit.call_args_list[0]
     assert isinstance(kwargs['x'], pandas.DataFrame)
     assert isinstance(kwargs['y'], np.ndarray)

tests/autokeras/tasks/text_test.py

@@ -1,16 +1,28 @@
 from unittest import mock
 
-from autokeras.tasks import text
+import numpy as np
+
+import autokeras as ak
 from tests import utils
 
 
-@mock.patch('autokeras.auto_model.AutoModel.__init__')
-def test_text_classifier(auto_model, tmp_path):
-    text.TextClassifier(directory=tmp_path, max_trials=2, seed=utils.SEED)
-    assert auto_model.called
+@mock.patch('autokeras.AutoModel.fit')
+def test_txt_clf_fit_call_auto_model_fit(fit, tmp_path):
+    auto_model = ak.TextClassifier(directory=tmp_path, seed=utils.SEED)
+
+    auto_model.fit(
+        x=np.array(['a b c', 'b b c']),
+        y=np.array([1, 2]))
+
+    assert fit.is_called
+
+
+@mock.patch('autokeras.AutoModel.fit')
+def test_txt_reg_fit_call_auto_model_fit(fit, tmp_path):
+    auto_model = ak.TextRegressor(directory=tmp_path, seed=utils.SEED)
 
+    auto_model.fit(
+        x=np.array(['a b c', 'b b c']),
+        y=np.array([1.0, 2.0]))
 
-@mock.patch('autokeras.auto_model.AutoModel.__init__')
-def test_text_regressor(auto_model, tmp_path):
-    text.TextRegressor(directory=tmp_path, max_trials=2, seed=utils.SEED)
-    assert auto_model.called
+    assert fit.is_called