def setUp(self):
self.flask_app = app.test_client()
# Adding users to authenticate against
self.username = "******"
self.password = "******"
app.config['USERS'][self.username] = self.password
app.config['MODEL'] = ModelWrapper.load(GIT_COMMIT)
# in case of using data versioning, don't forget to pull test data from remote data repo
# read held back test data
self.test_data = pd.read_csv("resources/test.csv")
self.min_performance = 0.8
self.predictor = app.config['MODEL']
from flask import jsonify, request
from services.infrastructure.environment import training_host_url, debug_mode
from services.infrastructure.flask import init_flask
from services.infrastructure.git_info import GIT_COMMIT
from services.infrastructure.logging import initialize_logging
from services.infrastructure.environment import prediction_auth
from model.model_wrapper import ModelWrapper
# initialize flask application
TRAINING_POD_URL = training_host_url()
app, _executor, auth = init_flask()
app.config['USERS'] = prediction_auth()
app.config["MODEL"] = ModelWrapper.load(GIT_COMMIT)
@app.route('/predict', methods=['POST'])
@auth.login_required
def predict():
"""Provides the endpoint for getting new predictions from the developed model using a POST
request json.
HTTP Request Parameters
-----------------------
data : json
Json post containing at least the *ModelWrapper.source_features*. Otherwise a error will
be thrown
HTTP Response
logging.getLogger(__name__).info("Persisting the model...")
classification_model.save(model_name)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Training model and saving it")
parser.add_argument("--input",
"-i",
type=str,
help="Input file for training",
default="resources/train.csv")
parser.add_argument("--output",
"-o",
type=str,
help="output model name",
default="local")
parsed_args = parser.parse_args()
train(model_name=GIT_COMMIT, train_data=parsed_args.input)
model = ModelWrapper.load(GIT_COMMIT)
# load test Dataframe
test_df = pd.read_csv("resources/test.csv")
print(test_df.to_dict(orient='list'))
res = model.prep_and_predict(test_df.to_dict(orient='list'))
print(res, test_df['Species'].values)
print(accuracy_score(res['prediction'], test_df["Species"].values))