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Browse files- streamlit_simulation/app.py +14 -18
streamlit_simulation/app.py
CHANGED
@@ -133,10 +133,11 @@ init_session_state()
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# ============================== Loaders ==============================
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@st.cache_resource
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def load_transformer_model_and_dataset():
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@@ -152,15 +153,9 @@ def load_transformer_model_and_dataset():
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model.to(device)
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model.eval()
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csv_path = hf_hub_download(
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repo_id="dlaj/energy-forecasting-files",
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filename="data/processed/energy_consumption_aggregated_cleaned.csv",
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repo_type="dataset"
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)
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# Datasets
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train_dataset = InformerDataset(data_split="train", forecast_horizon=FORECAST_HORIZON, random_seed=13, csv_path=
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test_dataset = InformerDataset(data_split="test", forecast_horizon=FORECAST_HORIZON, random_seed=13, csv_path=
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test_dataset.scaler = train_dataset.scaler
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return model, test_dataset, device
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@@ -168,14 +163,15 @@ def load_transformer_model_and_dataset():
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@st.cache_data
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def load_data():
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repo_id="dlaj/energy-forecasting-files",
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filename="data/processed/energy_consumption_aggregated_cleaned.csv",
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repo_type="dataset"
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)
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df = pd.read_csv(csv_path, parse_dates=["date"])
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return df
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# ============================== Utility Functions ==============================
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# ============================== Loaders ==============================
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CSV_PATH_HF = hf_hub_download(
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repo_id="dlaj/energy-forecasting-files",
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filename="data/processed/energy_consumption_aggregated_cleaned.csv",
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repo_type="dataset"
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)
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@st.cache_resource
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def load_transformer_model_and_dataset():
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model.to(device)
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model.eval()
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# Datasets
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train_dataset = InformerDataset(data_split="train", forecast_horizon=FORECAST_HORIZON, random_seed=13, csv_path=CSV_PATH_HF)
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test_dataset = InformerDataset(data_split="test", forecast_horizon=FORECAST_HORIZON, random_seed=13, csv_path=CSV_PATH_HF)
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test_dataset.scaler = train_dataset.scaler
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return model, test_dataset, device
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@st.cache_data
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def load_data():
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df = pd.read_csv(CSV_PATH_HF, parse_dates=["date"])
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return df
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#Load lightgbm model
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@st.cache_data
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def load_lightgbm_model():
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with open(MODEL_PATH_LIGHTGBM, "rb") as f:
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return pickle.load(f)
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# ============================== Utility Functions ==============================
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