def getModel():
job = input("Enter the JOB id of the saved model:")
run = input("Enter the RUN id of the saved model:")
job_name = "SS_job_" + str(job) + "/"
run_id = "run_"+str(run)
saveDir = getDir(client="SS", typ="TB")
model = saveDir+job_name+run_id
return model
def getData():
data_dir = getDir(client="SS", typ="ML")
df = pd.read_csv(data_dir + FILENM, sep="|")
df = df.sample(frac=1.0)
# No need for the HH during Testing
del df['hh_num']
# Drop any columns with NAN values
before = df.shape[0]
df = df.dropna(axis=0, how='any')
after = df.shape[0]
print("{}Deleted {:,.0f} rows out of {:,.0f} due to NAN".format(
"\n", (before - after), before))
return df
def getData():
data_dir = getDir(client="SS", typ="ML")
df = pd.read_csv(data_dir+FILENM, sep="|")
df = df.sample(frac = 1.0)
# Save the HH IDs before removing
hh = df['hh_num']
del df['hh_num']
# Remove a Label column if there is one
try:
del df['Label']
except:
pass
# Drop any columns with NAN values
before = df.shape[0]
df = df.dropna(axis=0, how='any')
after = df.shape[0]
print("{}Deleted {:,.0f} rows out of {:,.0f} due to NAN".format(
"\n",(before-after), before))
return df, hh
def get_models(job):
job_name = "SS_job_" + str(job) + "/"
saveDir = getDir(client="SS", typ="TB")
for model in glob.glob(saveDir + job_name + '*.meta'):
yield model