import sys, os
import glob
import numpy as np
import pandas as pd

# path arguments
ingestion_program = sys.argv[1] 
train_data = sys.argv[2] 
output_dir = sys.argv[3] 
input_dir = sys.argv[4] 
submission_program = sys.argv[5] 

# read the data however you like
csv_files = glob.glob(train_data + '/*.csv')
data = {name.split('/')[-1]: pd.read_csv(os.path.join(name)) for name in csv_files}


# our file with the solution
sys.path.append(submission_program)
import submission_model

model = submission_model.Model()

# you can rewrite your model interface

pred = model.run(data)

# write your prediction to the output_dir
answer_path = os.path.join(output_dir, "prediction.csv")
pred.to_csv(answer_path, index=False)

3ccfc8d352bca80bf916cae3adefe74f452e4faf
<mask token>

<mask token>
sys.path.append(submission_program)
<mask token>
pred.to_csv(answer_path, index=False)

<mask token>
ingestion_program = sys.argv[1]
train_data = sys.argv[2]
output_dir = sys.argv[3]
input_dir = sys.argv[4]
submission_program = sys.argv[5]
csv_files = glob.glob(train_data + '/*.csv')
data = {name.split('/')[-1]: pd.read_csv(os.path.join(name)) for name in
    csv_files}
sys.path.append(submission_program)
<mask token>
model = submission_model.Model()
pred = model.run(data)
answer_path = os.path.join(output_dir, 'prediction.csv')
pred.to_csv(answer_path, index=False)

import sys, os
import glob
import numpy as np
import pandas as pd
ingestion_program = sys.argv[1]
train_data = sys.argv[2]
output_dir = sys.argv[3]
input_dir = sys.argv[4]
submission_program = sys.argv[5]
csv_files = glob.glob(train_data + '/*.csv')
data = {name.split('/')[-1]: pd.read_csv(os.path.join(name)) for name in
    csv_files}
sys.path.append(submission_program)
import submission_model
model = submission_model.Model()
pred = model.run(data)
answer_path = os.path.join(output_dir, 'prediction.csv')
pred.to_csv(answer_path, index=False)

import sys, os
import glob
import numpy as np
import pandas as pd

# path arguments
ingestion_program = sys.argv[1] 
train_data = sys.argv[2] 
output_dir = sys.argv[3] 
input_dir = sys.argv[4] 
submission_program = sys.argv[5] 

# read the data however you like
csv_files = glob.glob(train_data + '/*.csv')
data = {name.split('/')[-1]: pd.read_csv(os.path.join(name)) for name in csv_files}


# our file with the solution
sys.path.append(submission_program)
import submission_model

model = submission_model.Model()

# you can rewrite your model interface

pred = model.run(data)

# write your prediction to the output_dir
answer_path = os.path.join(output_dir, "prediction.csv")
pred.to_csv(answer_path, index=False)
