def test_lambda(self):
'''
Test Lambda function on AWS
'''
# prepare input
json_list_list_x = prepare_to_lambda(dataset)
# REQUEST
resp = requests.post(URL_PREDICT, json=json_list_list_x)
#print("status code : ", resp.status_code)
#print(resp.json())
y_multi_pred_out = retrieve_from_lambda(resp)
# Assert if the result of TFLite model is consistent with the TF model.
np.testing.assert_almost_equal(y_multi_pred,
y_multi_pred_out,
decimal=3)
print("Done. PAST DAYS : TensorFlow and TensorFlow Lite matches.")
# prepare input
json_list_list_x = prepare_to_lambda_future(dataset)
# REQUEST
resp = requests.post(URL_PREDICT, json=json_list_list_x)
y_future_pred_out = retrieve_from_lambda(resp)
# Assert if the result of TFLite model is consistent with the TF model.
np.testing.assert_almost_equal(y_future_pred,
y_future_pred_out,
decimal=3)
print("Done. FUTURE DAYS : TensorFlow and TensorFlow Lite matches.")
def test_lambda_interface(self):
'''
Test interface with lambda AWS (IN/OUT)
'''
# test past days
json_list_list_x = prepare_to_lambda(dataset)
# simulate input to lambda (double dumps ? why ? i don't know yet)
json_list_list_x = json.dumps(json_list_list_x)
# simulate lambda
event = {"body": json_list_list_x}
# lambda
context = None
response = predict(event, context)
# Retrieve from lambda in App code
y_multi_pred_out = retrieve_from_lambda(response)
# Assert if the result of TFLite model is consistent with the TF model.
np.testing.assert_almost_equal(y_multi_pred,
y_multi_pred_out,
decimal=3)
print("Done. PAST DAYS : TensorFlow and TensorFlow Lite matches.")
# test future days
json_list_list_x = prepare_to_lambda_future(dataset)
json_list_list_x = json.dumps(
json_list_list_x) # dumps again : I dont know why
event = {"body": json_list_list_x}
context = None
response = predict(event, context)
y_future_pred_out = retrieve_from_lambda(response)
# Assert if the result of TFLite model is consistent with the TF model.
np.testing.assert_almost_equal(y_future_pred,
y_future_pred_out,
decimal=3)
print("Done. FUTURE DAYS : TensorFlow and TensorFlow Lite matches.")
def update_pred_pos_kr(df_feat_kr, from_disk=False):
'''
Update prediction data positive cases France
'''
# check if last prediction is after last known date
if os.path.isfile(PATH_DF_PLOT_PRED_KR):
df_plot_pred = pd.read_csv(PATH_DF_PLOT_PRED_KR)
df_plot_pred.index = df_plot_pred["date"]
if df_plot_pred["date"].min() <= df_feat_kr["date"].max():
from_disk = False
else:
from_disk = False
# if no prediction or if from disk
if (not settings.PREDICT) | from_disk:
df_plot_pred = pd.read_csv(PATH_DF_PLOT_PRED_KR)
df_plot_pred.index = df_plot_pred["date"]
return df_plot_pred
# prepare features
dataset, data_std, data_mean = prepare_dataset_kr(df_feat_kr)
# predict next days
if settings.MODEL_TFLITE:
json_list_list_x = prepare_to_lambda_future(dataset)
resp = requests.post(URL_PREDICT_KR, json=json_list_list_x)
print("status code : ", resp.status_code)
if resp.status_code == 200:
y_multi_pred = retrieve_from_lambda(resp)
else:
print("AWS Lamdba future pred ERROR!")
df_plot_pred = pd.read_csv(PATH_DF_PLOT_PRED_KR)
df_plot_pred.index = df_plot_pred["date"]
return df_plot_pred
else:
# prepare data : very last days
x_multi = np.array([dataset[-PAST_HISTORY:, :]])
# load model
multi_step_model = tf.keras.models.load_model(PATH_MDL_MULTI_STEP_KR)
y_multi_pred = multi_step_model.predict(x_multi)
# convert in positive cases
y_pos_pred = y_multi_pred * data_std[4] + data_mean[4]
# pos pred next 3 days from last day : date, pos, total (sum)
str_date_pred_0 = df_feat_kr.date.max()
str_date_pred_1 = add_days(str_date_pred_0, FUTURE_TARGET)
list_dates_pred = generate_list_dates(str_date_pred_0, str_date_pred_1)
# figure
df_plot_pred = pd.DataFrame(index=list_dates_pred,
columns=["date"],
data=list_dates_pred)
df_plot_pred["pos"] = y_pos_pred[0].astype(int)
arr_nb_pred = df_plot_pred["pos"].cumsum().values
df_plot_pred["nb_cases"] = df_feat_kr["nb_cases"].max() + arr_nb_pred
# save for future pred
df_plot_pred.to_csv(PATH_DF_PLOT_PRED_KR, index=False)
return df_plot_pred