else:
model_suffix = time.strftime("%d_%m_%Y")
directory = 'model_' + model_suffix
if not os.path.exists(directory):
os.makedirs(directory)
##########################
### Load the data
##########################
if args.input_data:
print('Loading data from {0}'.format(args.input_data))
df = pd.read_csv(args.input_data,
parse_dates='Full_date',
low_memory=False)
df['Full_date'] = rd.date_lookup(df['Full_date'])
else:
print('Reading and loading data. Saving to {}'.format(directory +
'/all_data.csv'))
df = rd.read_data(read_weather_station=False,
read_water_sensor=False,
add_each_beach_data=True)
df.to_csv(directory + '/all_data.csv', index=False)
###############################
### Prepare Predictors
###############################
if args.input_processed:
print('Using Preprocessed data from {0} and {1}'.format(
args.input_processed, args.input_meta))
datafilename = args.input_processed
if __name__ == '__main__':
# Command Line Argument parsing
parser = argparse.ArgumentParser(description='Process beach data.')
parser.add_argument('-i',
'--input',
type=str,
metavar='input_filename',
help='input CSV filename')
parser.add_argument('-v', '--verbose', action='count', default=0)
args = parser.parse_args()
# Load the data
if args.input:
df = pd.read_csv(args.input, parse_dates='Full_date')
df['Full_date'] = rd.date_lookup(df['Full_date'])
else:
df = rd.read_data(read_weather_station=False, read_water_sensor=False)
# Extract the EPA (technically USGS) model performance from the data
epa_model_df = df[['Drek_Prediction', 'Escherichia.coli']].dropna()
# Prepare the data
predictors, meta_info = prepare_data(df)
timestamps = meta_info['Full_date']
classes = meta_info['Escherichia.coli'] > 235
print('Using the following columns as predictors:')
for c in predictors.columns:
print('\t' + str(c))
if args.suffix:
model_suffix = args.suffix
else:
model_suffix = time.strftime("%d_%m_%Y")
directory = 'model_'+model_suffix
if not os.path.exists(directory):
os.makedirs(directory)
##########################
### Load the data
##########################
if args.input_data:
print('Loading data from {0}'.format(args.input_data))
df = pd.read_csv(args.input_data, parse_dates='Full_date', low_memory=False)
df['Full_date'] = rd.date_lookup(df['Full_date'])
else:
print('Reading and loading data. Saving to {}'.format(directory+'/all_data.csv'))
df = rd.read_data(read_weather_station=False, read_water_sensor=False, add_each_beach_data=True)
df.to_csv(directory+'/all_data.csv', index=False)
###############################
### Prepare Predictors
###############################
if args.input_processed:
print('Using Preprocessed data from {0} and {1}'.format(args.input_processed, args.input_meta ))
datafilename = args.input_processed
metadatafilename = args.input_meta
data_processed = pd.read_csv(datafilename)
meta_info = pd.read_csv(metadatafilename, parse_dates='Full_date')
meta_info['Full_date'] = rd.date_lookup(meta_info['Full_date'])
return predictors, meta_info
if __name__ == '__main__':
# Command Line Argument parsing
parser = argparse.ArgumentParser(description='Process beach data.')
parser.add_argument('-i', '--input', type=str,
metavar='input_filename', help='input CSV filename')
parser.add_argument('-v', '--verbose', action='count', default=0)
args = parser.parse_args()
# Load the data
if args.input:
df = pd.read_csv(args.input, parse_dates='Full_date')
df['Full_date'] = rd.date_lookup(df['Full_date'])
else:
df = rd.read_data(read_weather_station=False, read_water_sensor=False)
# Extract the EPA (technically USGS) model performance from the data
epa_model_df = df[['Drek_Prediction', 'Escherichia.coli']].dropna()
# Prepare the data
predictors, meta_info = prepare_data(df)
timestamps = meta_info['Full_date']
classes = meta_info['Escherichia.coli'] > 235
print('Using the following columns as predictors:')
for c in predictors.columns:
print('\t' + str(c))