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import copy
from pathlib import Path
import warnings
import lightning.pytorch as pl
import numpy as np
import pandas as pd
import torch
from prophet.serialize import model_to_json, model_from_json
from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
import pickle
# at beginning of the script
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class Model_Load:
def __init__(self):
pass
def energy_model_load(self,model_option):
if model_option=='TFT':
# best_model_path='models/consumer_final_10/lightning_logs/lightning_logs/version_0/checkpoints/epoch=5-step=49260.ckpt'
# best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)
filename="models/cpu_energy_tft_model_v1.sav"
best_tft=pickle.load(open(filename, 'rb'))
print('Model Load Sucessfully.')
return best_tft
elif model_option=='Prophet':
best_model_path='models/fb_energy_model.json'
with open(best_model_path, 'r') as fin:
model = model_from_json(fin.read())
return model
# elif model_option=='ten consumer':
# best_model_path='consumer_10/lightning_logs/lightning_logs/version_0/checkpoints/epoch=11-step=98544.ckpt'
# best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)
# print('Model Load Sucessfully.')
# elif model_option=='fifty consumer':
# raise Exception('Model not present')
def store_model_load(self,model_option):
if model_option=='TFT':
# best_model_path="models/store_item_10_lead_1_v2/lightning_logs/lightning_logs/version_2/checkpoints/epoch=7-step=4472.ckpt"
# best_model_path="models/store_item_10_lead_1_v3/lightning_logs/lightning_logs/version_0/checkpoints/epoch=7-step=4472.ckpt"
# best_tft = TemporalFusionTransformer.load_from_checkpoint(best_model_path)
best_tft=pickle.load(open("models/cpu_finalized_model_v1.sav", 'rb'))
print('Model Load Sucessfully.')
return best_tft
elif model_option=='Prophet':
best_model_path='models/fb_store_model_new.json'
with open(best_model_path, 'r') as fin:
model = model_from_json(fin.read())
return model
# elif model_option=='Item 50 TFT':
# raise Exception('Model not present')
# elif model_option=='FB Prophet':
# raise Exception('Model not present')
if __name__=='__main__':
obj=Model_Load()
obj.load()
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