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import os
import pandas as pd
import pickle
from prediction.model import Model
class Task:
def run(self, action, config):
# Call the right handler for the action
getattr(self, '_process_' + action)(**config)
def _process_training(self, data_path, output_path):
# Read the training data from a CSV file
data = pd.read_csv(data_path, index_col=0)
# Train the model
model = Model()
model.fit(data)
# Ensure that the output directory exists
if not os.path.exists(output_path):
os.makedirs(output_path)
# Save the trained model in a Pickle file
model_path = os.path.join(output_path, 'model.pickle')
with open(model_path, 'wb') as file:
pickle.dump(model, file)
def _process_application(self, data_path, model_path, output_path):
# Read the application data from a CSV file
data = pd.read_csv(data_path, index_col=0)
# Load the trained model from a Pickle file
with open(model_path, 'rb') as file:
model = pickle.load(file)
# Make predictions
predictions = model.predict(data)
# Ensure that the output directory exists
if not os.path.exists(output_path):
os.makedirs(output_path)
# Save the predictions in a CSV file
predictions_path = os.path.join(output_path, 'predictions.csv')
predictions.to_csv(predictions_path, header=True)
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