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import pickle
import os
import numpy as np
from datetime import datetime
import pandas as pd
def save_models(models, model_type, directory):
print('Saving models...')
for i, model in enumerate(models):
with open(f'{directory}/{model_type}_FOLD_{i+1}.pkl', 'wb') as file:
pickle.dump(model, file)
def load_models(directory):
print('Loading models...')
models = []
# List all files in the directory
files = os.listdir(directory)
pkl_files = [file for file in files if file.endswith('.pkl')]
for file in pkl_files:
with open(os.path.join(directory, file), 'rb') as file:
model = pickle.load(file)
models.append(model)
return models
def get_predictions(models, X_test):
print('Forecasting test data...')
preds = []
for model in models:
preds.append(post_process(model.predict(X_test)))
return np.mean(preds, axis=0)
def post_process(predictions):
predictions = predictions.clip(0)
return predictions
def save_results(dataframe, file_name):
print('Saving results...')
today_date = datetime.now().strftime("%Y-%m-%d")
dataframe.to_excel(f'demand_predictions/{file_name}_{today_date}.xlsx', index=False)
def save_parquet(dataframe, path):
dataframe.to_parquet(path, index=False)
def load_parquet(path):
return pd.read_parquet(path)
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