import sys
sys.path.append("/home/app/code/")
from pyspark.sql import SparkSession
from pyspark.sql import SQLContext
from extract import Extract
from transform import Transformer
from load import Load
if __name__ == '__main__':
spark = SparkSession \
.builder \
.appName("Covid App") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
sqlContext = SQLContext(spark)
df = Extract(spark)
df = df.extract_covid_data()
transformer = Transformer(df, sqlContext)
transformer.data_types_transformations()
transformed_df = transformer.dimensions_transfomations()
transformed_df = transformer.fill_na(transformed_df)
loader = Load(transformed_df)
loader.load_data()
ap.add_argument("-t",
"--train-model",
type=int,
default=-1,
help="(Optional) Whether to train a previous model.")
ap.add_argument("-w",
"--weights",
type=str,
help="(Optional) Path of weights file")
args = vars(ap.parse_args())
gWeightsPath = "output/" + args["weights"] + ".gener" + ".hdf5"
dWeightsPath = "output/" + args["weights"] + ".discrim" + ".hdf5"
# Load data
_X = Load.load_data()
# Convert to np arrays
X = np.array(_X)
# Scale data to [-1, 1] range
X = (X.astype("float32") - 127.5) / 127.5
# Initialize optimizer and model
print("Loading model...")
loadPath = args["load_model"] > 0 or args["train_model"] > 0
opt = SGD(lr=0.001, momentum=0.5, nesterov=True)
modelG = Models.buildGenerator(vectSize=100,
weightsPath=gWeightsPath if loadPath else None)
modelD = Models.buildDiscriminator(
numChannels=3,
def back_menu(self):
""" I display the back-office menu. """
self.clear()
self.views.header_admin()
option = self.views.admin_choice()
if option == "0":
self.main_menu()
elif option == "1":
self.clear()
self.views.header_admin()
self.db.db_create()
self.views.pause()
self.back_menu()
elif option == "2":
self.clear()
self.views.header_admin()
self.db.tables_create()
self.views.pause()
self.back_menu()
elif option == "3":
self.clear()
self.views.header_admin()
self.db.tables_delete()
self.views.pause()
self.back_menu()
elif option == "4":
self.clear()
self.views.header_admin()
extraction = Extract()
extraction.extract()
self.views.pause()
self.back_menu()
elif option == "5":
self.clear()
self.views.header_admin()
transform = Transform()
transform.transform_basic()
self.views.pause()
self.back_menu()
elif option == "6":
self.clear()
self.views.header_admin()
load = Load()
load.load_data()
self.views.pause()
self.back_menu()
elif option == "7":
self.clear()
self.views.header_admin()
extraction = Extract()
transform = Transform()
load = Load()
extraction.extract()
transform.transform_basic()
load.load_data()
self.views.pause()
self.back_menu()
elif option == "Q" or option == "q":
sys.exit
else:
print("""
Vous devez taper A ou Q
Merci de réessayer.""")
time.sleep(2)
self.back_menu()
