creating unit test cases

.gitignore

@@ -1,3 +1,4 @@
+logs/
 ipynb_checkpoints/
 mlruns
 mlartifacts

src/data/utils.py

@@ -30,26 +30,44 @@ def load_feature(
 @logger.catch
 def download_dataset(
     name: str,
+    new_name: str,
+    path: pathlib.Path,
+    send_to_aws: bool,
 ) -> None:
     """Dowload the dataset using Kaggle's API.
 
     Args:
         name (str): the dataset's name.
+        new_name (str): the dataset file's new name.
+        path (pathlib.Path): the path where the dataset will be stored locally.
+        send_to_aws (bool): whether the dataset will be send to an AWS S3 bucket or not.
     """
-    kaggle_user = kaggle_credentials.KAGGLE_USERNAME
-    kaggle_key = kaggle_credentials.KAGGLE_KEY
-    path = '../data/'
+    os.environ["KAGGLE_USERNAME"] = kaggle_credentials.KAGGLE_USERNAME
+    os.environ["KAGGLE_KEY"] = kaggle_credentials.KAGGLE_KEY
+
     logger.info(f"Downloading dataset {name} and saving into the folder {path}.")
 
     # Downloading data using the Kaggle API through the terminal
-    os.system(f'export KAGGLE_USERNAME={kaggle_user}; export KAGGLE_KEY={kaggle_key};')
-    os.system(f'kaggle datasets download -d {name} -p {path} --unzip')
+    # os.system(f'export KAGGLE_USERNAME={kaggle_user}; export KAGGLE_KEY={kaggle_key};')
+    os.system(f'kaggle datasets download -d {name} --unzip')
+    os.system(
+        f'mv ObesityDataSet.csv {pathlib.Path.joinpath(path, new_name)}'
+    )
 
     # Sending the dataset to the AWS S3 bucket
-    if aws_credentials.S3 != "YOUR_S3_BUCKET_URL":
-        send_dataset_to_s3()
-
+    if send_to_aws:
+        if aws_credentials.S3 != "YOUR_S3_BUCKET_URL":
+            send_dataset_to_s3(
+                file_path=path,
+                file_name=new_name,
+            )
+        else:
+            logger.warning(
+                "The S3 Bucket url was not specified in the 'credentials.yaml' file. " +
+                "Therefore, the dataset will not be send to S3 and it will be kept saved locally."
+            )
 
+@logger.catch
 def send_dataset_to_s3(
     file_path: pathlib.Path,
     file_name: str,
@@ -71,3 +89,5 @@ def send_dataset_to_s3(
         aws_credentials.S3,
         file_name,
     )
+
+    os.remove(pathlib.Path.joinpath(file_path, file_name))

src/model/inference.py

@@ -49,16 +49,21 @@ def load(self) -> None:
             logger.critical(f"Couldn't load the model using the flavor {model_settings.MODEL_FLAVOR}.")
             raise NotImplementedError()
 
-    def predict(self, x: np.ndarray) -> np.ndarray:
+    def predict(self, x: np.ndarray, transform_to_str: bool = True) -> np.ndarray:
         """Uses the trained model to make a prediction on a given feature array.
 
         Args:
             x (np.ndarray): the features array.
+            transform_to_str (bool): whether to transform the prediction integer to
+                string or not. Defaults to True.
 
         Returns:
             np.ndarray: the predictions array.
         """
         prediction = self.model.predict(x)
-        prediction = label_encoder.inverse_transform(prediction)
+
+        if transform_to_str:
+            prediction = label_encoder.inverse_transform(prediction)
+
         logger.info(f"Prediction: {prediction}.")
         return prediction

tests/unit/test_api.py

@@ -0,0 +1,60 @@
+import json
+from pathlib import Path
+from typing import Dict
+
+import requests
+
+from src.config.model import model_settings
+from src.config.settings import general_settings
+
+with open(
+    f"{Path.joinpath(general_settings.RESEARCH_ENVIRONMENT_PATH, 'VERSION')}",
+    "r",
+    encoding="utf-8"
+) as f:
+    CODE_VERSION = f.readline().strip()
+
+def test_version_endpoint() -> None:
+    """
+    Unit case to test the API's version endpoint.
+    """
+    desired_keys = ["model_version", "code_version"]
+
+    response = requests.get("http://127.0.0.1:8000/version", timeout=100)
+    content = json.loads(response.text)
+
+    assert response.status_code == 200
+    assert isinstance(content, Dict)
+    assert all(dk in content.keys() for dk in desired_keys)
+    assert model_settings.VERSION == content[desired_keys[0]]
+    assert CODE_VERSION == content[desired_keys[1]]
+
+def test_inference_endpoint() -> None:
+    """
+    Unit case to test the API's inference endpoint.
+    """
+    desired_classes = [["Normal_Weight"]]
+    desired_keys = ["predictions"]
+
+    data = {
+        "Age": 21,
+        "CAEC": "Sometimes",
+        "CALC": "no",
+        "FAF": 0,
+        "FCVC": 2,
+        "Gender": "Female",
+        "Height": 1.62,
+        "MTRANS": "Public_Transportation",
+        "SCC": "no",
+        "SMOKE": "False",
+        "TUE": 1,
+        "Weight": 64
+    }
+
+    response = requests.get("http://127.0.0.1:8000/predict", json=data, timeout=100)
+    content = json.loads(response.text)
+
+    assert response.status_code == 200
+    assert isinstance(content, Dict)
+    assert all(dk in content.keys() for dk in desired_keys)
+    assert content[desired_keys[0]] == desired_classes