def predict():
if request.method == 'POST':
# step:1 Extract Post data from request body as JSON
json_data = request.get_json()
print(f'User input from UI: {json_data}')
_logger.info(f'Inputs: {json_data}')
# # step:2 Validate the input using marshmallow schema
#input_data,errors = validate_inputs(input_data=json_data)
# step 3: model prediction
result = make_prediction(input_data=json_data)
_logger.info(f'Outputs: {result}')
# step 4: Convert numpy ndarray to list
predictions = round(result.get('prediction')[0],2)
#print(f'prediction from model ==== {predictions}')
#version = result.get('version')
return jsonify({'prediction': predictions}), 200
def predict():
if request.method = 'POST':
# Step 1: Extract POST data from request body as jason
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
# Step 2: Validate the input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: Model prediction_app
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
# Step 4: Convert numpy array to list
predictions = result.get('prediction').tolist()
version = result.get('version')
# Step 5: Return the response as JSON
return jsonify({'predictions': predictions,
'version': version,
'errors': errors})
def predict():
if request.method == "POST":
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.debug(f"Inputs: {json_data}")
# Step 2: Validate the input using marshmallow schema
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: Model prediction
result = make_prediction(input_data=input_data)
_logger.debug(f"Outputs: {result}")
# Step 4: Convert numpy ndarray to list
predictions = result.get("predictions").tolist()
version = result.get("version")
# Step 5: Return the response as JSON
return jsonify(
{"predictions": predictions, "version": version, "errors": errors}
)
def test_model_prediction_differential(
*,
save_file: str = 'test_data_predictions.csv'):
"""
This test compares the prediction result similarity of
the current model with the previous model's results.
"""
# Given
# Load the saved previous model predictions
previous_model_df = pd.read_csv(f'{config.PACKAGE_ROOT}/{save_file}')
previous_model_predictions = previous_model_df.predictions.values
test_data = load_dataset(file_name=model_config.TESTING_DATA_FILE)
multiple_test_input = test_data[99:600]
# When
current_result = make_prediction(input_data=multiple_test_input)
current_model_predictions = current_result.get('predictions')
# Then
# diff the current model vs. the old model
assert len(previous_model_predictions) == len(
current_model_predictions)
# Perform the differential test
for previous_value, current_value in zip(
previous_model_predictions, current_model_predictions):
# convert numpy float64 to Python float.
previous_value = previous_value.item()
current_value = current_value.item()
# rel_tol is the relative tolerance – it is the maximum allowed
# difference between a and b, relative to the larger absolute
# value of a or b. For example, to set a tolerance of 5%, pass
# rel_tol=0.05.
assert math.isclose(previous_value,
current_value,
rel_tol=model_config.ACCEPTABLE_MODEL_DIFFERENCE)
def test_make_predictions():
data = load_dataset(file_name=config.DATASET_FILE)
X_train, X_test, y_train, y_test = train_test_split(
data.drop(config.TARGET, axis=1),
data[config.TARGET],
test_size=0.2,
random_state=0) # must be 0
test_data = X_test
original_data_length = len(test_data)
pred = make_prediction(input_data=test_data)
# Check predictions have been made
assert pred is not None
# Check accuracy
assert accuracy_score(y_test, pred['predictions'].round()) < 1
# Check number of predictions is the same as length of test data
# Change to != when more validation methods are added as some rows
# may get removed.
assert len(pred.get('predictions')) == original_data_length
def predict():
if request.method == 'POST':
# Step 1: リクエスト本文からJSONとしてPOSTデータを抽出
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
# Step 2: marshmallowスキーマを使用して入力を検証
input_data, errors = validate_inputs(input_data=json_data)
# Step 3: モデル予測
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
# Step 4: numpy ndarrayをリストに変換
predictions = result.get('predictions').tolist()
version = result.get('version')
# Step 5: 応答をJSONとして返す
return jsonify({
'predictions': predictions,
'version': version,
'errors': errors
})
def test_get_multiple_predictions_with_forecast_data(forecast_input_data):
subject = make_prediction(input_data=forecast_input_data)
assert subject is not None
assert len(subject.get('predictions')) == len(forecast_input_data)
def test_get_single_prediction_with_forecast_data(forecast_input_data):
single_test_json = forecast_input_data[0:1]
subject = make_prediction(input_data=single_test_json)
assert subject is not None
assert isinstance(subject.get('predictions')[0], float)
@prediction_app.route('/v1/predict/regression', methods=['POST'])
def predict():
if request.method == 'POST':
# Step 1: Extract POST data from request body as JSON
json_data = request.get_json()
_logger.debug(f'Inputs: {json_data}')
# Step 2: Validate the input using marshmallow schema
<<<<<<< HEAD
input_data, errors = validate_inputs(input_data=json_data)
=======
input_data, errors = validate_inputs(input_json=json_data)
>>>>>>> 6162c318b58b225e0061fccd6c64cd67fe205c1b
# Step 3: Model prediction
result = make_prediction(input_data=input_data)
_logger.debug(f'Outputs: {result}')
# Step 4: Convert numpy ndarray to list
predictions = result.get('predictions').tolist()
version = result.get('version')
# Step 5: Return the response as JSON
return jsonify({'predictions': predictions,
'version': version,
'errors': errors})
@prediction_app.route('/predict/classifier', methods=['POST'])
def predict_image():
if request.method == 'POST':
