refact: formatando arquivos python

heltonricardo · Dec 13, 2023 · d642e18 · d642e18
1 parent b1acb78
commit d642e18
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Showing 3 changed files with 53 additions and 38 deletions.
diff --git a/main.py b/main.py
@@ -25,10 +25,12 @@ def load_model():
     """
     Loads a pre-trained model from an MLflow server.
 
-    This function connects to an MLflow server using the provided tracking URI, username,
-    and password.
-    It retrieves the latest version of the 'fetal_health' model registered on the server.
-    The function then loads the model using the specified run ID and returns the loaded model.
+    This function connects to an MLflow server using the provided tracking URI,
+    username, and password.
+    It retrieves the latest version of the 'fetal_health' model registered on
+    the server.
+    The function then loads the model using the specified run ID and returns
+    the loaded model.
 
     Returns:
         loaded_model: The loaded pre-trained model.
@@ -37,7 +39,8 @@ def load_model():
         None
     """
     print("reading model...")
-    MLFLOW_TRACKING_URI = "https://dagshub.com/renansantosmendes/mlops-ead.mlflow"
+    MLFLOW_TRACKING_URI = \
+        "https://dagshub.com/renansantosmendes/mlops-ead.mlflow"
     MLFLOW_TRACKING_USERNAME = "renansantosmendes"
     MLFLOW_TRACKING_PASSWORD = "b63baf8c662a23fa00deb74ba86600278769e5dd"
     os.environ["MLFLOW_TRACKING_USERNAME"] = MLFLOW_TRACKING_USERNAME
@@ -59,8 +62,8 @@ def load_model():
 @app.on_event(event_type="startup")
 def startup_event():
     """
-    A function that is called when the application starts up. It loads a model into the
-    global variable `loaded_model`.
+    A function that is called when the application starts up. It loads a model
+    into the global variable `loaded_model`.
 
     Parameters:
         None
@@ -78,8 +81,8 @@ def api_health():
     A function that represents the health endpoint of the API.
 
     Returns:
-        dict: A dictionary containing the status of the API, with the key "status" and
-        the value "healthy".
+        dict: A dictionary containing the status of the API, with the key
+        "status" and the value "healthy".
     """
     return {"status": "healthy"}
 
@@ -90,7 +93,8 @@ def predict(request: FetalHealthData):
     Predicts the fetal health based on the given request data.
 
     Args:
-        request (FetalHealthData): The request data containing the fetal health parameters.
+        request (FetalHealthData): The request data containing the fetal
+        health parameters.
 
     Returns:
         dict: A dictionary containing the prediction of the fetal health.

diff --git a/test_train.py b/test_train.py
@@ -11,8 +11,8 @@ def sample_data():
     A fixture function that returns a sample dataset.
 
     Returns:
-        pandas.DataFrame: A DataFrame containing sample data with three columns: 'feature1',
-        'feature2', and 'fetal_health'.
+        pandas.DataFrame: A DataFrame containing sample data with three
+        columns: 'feature1', 'feature2', and 'fetal_health'.
     """
     data = pd.DataFrame(
         {
@@ -26,8 +26,8 @@ def sample_data():
 
 def test_read_data():
     """
-    This function tests the `read_data` function. It checks whether the returned data is not
-    empty for both features (X) and labels (y).
+    This function tests the `read_data` function. It checks whether the
+    returned data is not empty for both features (X) and labels (y).
 
     Parameters:
     None
@@ -43,8 +43,8 @@ def test_read_data():
 
 def test_create_model():
     """
-    Generate the function comment for the given function body in a markdown code block with
-    the correct language syntax.
+    Generate the function comment for the given function body in a markdown
+    code block with the correct language syntax.
     """
     X, _ = read_data()
     model = create_model(X)
@@ -56,12 +56,12 @@ def test_create_model():
 
 def test_train_model(sample_data):
     """
-    Generate a function comment for the given function body in a markdown code block with
-    the correct language syntax.
+    Generate a function comment for the given function body in a markdown code
+    block with the correct language syntax.
 
     Parameters:
-        sample_data (pandas.DataFrame): The input data containing features and target
-        variable.
+        sample_data (pandas.DataFrame): The input data containing features and
+        target variable.
 
     Returns:
         None

diff --git a/train.py b/train.py
@@ -15,8 +15,8 @@ def reset_seeds():
     """
     Reset the seeds for random number generators.
 
-    This function sets the seeds for the `os`, `tf.random`, `np.random`, and `random`
-    modules to ensure reproducibility in random number generations.
+    This function sets the seeds for the `os`, `tf.random`, `np.random`, and
+    `random` modules to ensure reproducibility in random number generations.
 
     Parameters:
         None
@@ -32,15 +32,17 @@ def reset_seeds():
 
 def read_data():
     """
-    Reads the data from a CSV file and returns the feature matrix X and target vector y.
+    Reads the data from a CSV file and returns the feature matrix X and target
+    vector y.
 
     Returns:
-        X (pandas.DataFrame): The feature matrix of shape (n_samples, n_features).
+        X (pandas.DataFrame): The feature matrix of shape (n_samp, n_feat).
         y (pandas.Series): The target vector of shape (n_samples,).
     """
-    data = pd.read_csv(
-        "https://raw.githubusercontent.com/heltonricardo/fetal-health-classifier/main/fetal_health_reduced.csv"
-    )
+    data = pd.read_csv("""
+        https://raw.githubusercontent.com/heltonricardo/
+        fetal-health-classifier/main/fetal_health_reduced.csv"""
+                       )
     X = data.drop(["fetal_health"], axis=1)
     y = data["fetal_health"]
     return X, y
@@ -77,11 +79,12 @@ def process_data(X, y):
 
 def create_model(X):
     """
-    Creates a neural network model for classification based on the given input data.
+    Creates a neural network model for classification based on the given input
+    data.
 
     Parameters:
-        X (numpy.ndarray): The input data array. It should have a shape of (num_samples,
-        num_features).
+        X (numpy.ndarray): The input data array. It should have a shape of
+        (num_samples, num_features).
 
     Returns:
         tensorflow.keras.models.Sequential: The created neural network model.
@@ -94,7 +97,9 @@ def create_model(X):
     model.add(Dense(3, activation="softmax"))
 
     model.compile(
-        loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]
+        loss="sparse_categorical_crossentropy",
+        optimizer="adam",
+        metrics=["accuracy"]
     )
     return model
 
@@ -104,14 +109,17 @@ def config_mlflow():
     Configures the MLflow settings for tracking experiments.
 
     Sets the MLFLOW_TRACKING_USERNAME and MLFLOW_TRACKING_PASSWORD environment
-    variables to provide authentication for accessing the MLflow tracking server.
+    variables to provide authentication for accessing the MLflow tracking
+    server.
 
-    Sets the MLflow tracking URI to 'https://dagshub.com/renansantosmendes/mlops-ead.mlflow'
+    Sets the MLflow tracking URI to
+    'https://dagshub.com/renansantosmendes/mlops-ead.mlflow'
     to specify the location where the experiment data will be logged.
 
-    Enables autologging of TensorFlow models by calling `mlflow.tensorflow.autolog()`.
-    This will automatically log the TensorFlow models, input examples, and model signatures
-    during training.
+    Enables autologging of TensorFlow models by calling
+    `mlflow.tensorflow.autolog()`.
+    This will automatically log the TensorFlow models, input examples, and
+    model signatures during training.
 
     Parameters:
         None
@@ -120,8 +128,11 @@ def config_mlflow():
         None
     """
     os.environ["MLFLOW_TRACKING_USERNAME"] = "renansantosmendes"
-    os.environ["MLFLOW_TRACKING_PASSWORD"] = "6d730ef4a90b1caf28fbb01e5748f0874fda6077"
-    mlflow.set_tracking_uri("https://dagshub.com/renansantosmendes/mlops-ead.mlflow")
+    os.environ["MLFLOW_TRACKING_PASSWORD"] = \
+        "6d730ef4a90b1caf28fbb01e5748f0874fda6077"
+    mlflow.set_tracking_uri(
+        "https://dagshub.com/renansantosmendes/mlops-ead.mlflow"
+    )
 
     mlflow.tensorflow.autolog(
         log_models=True, log_input_examples=True, log_model_signatures=True