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import sklearn |
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import pandas as pd |
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from tsai.basics import * |
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import config |
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from tsai.inference import load_learner |
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import pandas as pd |
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import platform |
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import pathlib |
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plt=platform.system() |
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if plt=='Linux': |
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pathlib.WindowsPath=pathlib.PosixPath |
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def get_inputs_from_user(): |
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return 0 |
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def preprocess_data(DataFrame:pd.DataFrame): |
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preproc=load_object() |
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return DataFrame |
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def preprocess_data_transform_generate_splits_Train(DataFrame:pd.DataFrame): |
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DataFrame=DataFrame.drop(config.DROP_COLOUMNS,axis=1) |
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preproc_pipe=load_object(config.PREPROCESSOR_PATH) |
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exp_pipe=load_object(config.SCALING_DATA) |
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DataFrame=preproc_pipe.fit_transform(DataFrame) |
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print("dataframe processed and ready for splitting") |
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splits=get_forecasting_splits(DataFrame,fcst_history=config.FCST_HISTORY,fcst_horizon=config.FCST_HORIZON,datetime_col=config.DATETIME_COL, |
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valid_size=config.VALID_SIZE,test_size=config.TEST_SIZE) |
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X,y=prepare_forecasting_data(DataFrame,fcst_history=config.FCST_HISTORY,fcst_horizon=config.FCST_HORIZON,x_vars=config.COLOUMNS,y_vars=config.COLOUMNS) |
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learn=TSForecaster(X,y,splits=splits, |
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batch_size=16,path='models', |
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arch='InceptionTimePlus', |
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pipelines=[preproc_pipe,exp_pipe], |
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metrics=[mse,mape], |
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cbs=ShowGraph() |
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) |
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lr_max=learn.lr_find().valley |
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learn.fit_one_cycle(n_epoch=config.N_EPOCH,lr_max=lr_max) |
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learn.export("model_in.pt") |
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return 0 |
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def inference_Aircomp(fcst_date:string,DataFrame:pd.DataFrame): |
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pre=load_object(config.AIR_PREPROCESSOR_PATH) |
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DataFrame=pre.fit_transform(DataFrame) |
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dates=pd.date_range(start=None,end=fcst_date,periods=config.FCST_HISTORY,freq=config.FREQUENCY) |
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new_df=DataFrame[DataFrame[config.AIR_DATETIME_COL].isin(dates)].reset_index(drop=True) |
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predict=load_learner(Path('models/AirInceptionTime.pt')) |
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new_df=predict.transform(new_df) |
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new_x,__=prepare_forecasting_data(new_df,fcst_history=config.FCST_HISTORY,fcst_horizon=0,x_vars=config.AIR_COLOUMNS,y_vars=config.AIR_COLOUMNS) |
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new_scaled_preds, *_ = predict.get_X_preds(new_x) |
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new_scaled_preds=to_np(new_scaled_preds).swapaxes(1,2).reshape(-1,len(config.AIR_COLOUMNS)) |
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dates=pd.date_range(start=fcst_date, periods=config.FCST_HORIZON+1,freq='1H')[1:] |
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preds_df=pd.DataFrame(dates,columns=[config.AIR_DATETIME_COL]) |
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preds_df.loc[:, config.AIR_COLOUMNS]=new_scaled_preds |
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preds_df=predict.inverse_transform(preds_df) |
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return preds_df |
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def inference_Energy(fcst_date:string,DataFrame:pd.DataFrame): |
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pre=load_object(config.ENER_PREPROCESSOR_PATH) |
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DataFrame[config.ENERGY_DATETIME]=pd.to_datetime(DataFrame[config.ENERGY_DATETIME],format='mixed') |
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DataFrame=pre.fit_transform(DataFrame) |
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dates=pd.date_range(start=None,end=fcst_date,periods=config.FCST_HISTORY,freq=config.FREQUENCY) |
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new_df=DataFrame[DataFrame[config.ENERGY_DATETIME].isin(dates)].reset_index(drop=True) |
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predict=load_learner(config.MODEL_PATH_ITP_ENER) |
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new_df=predict.transform(new_df) |
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new_x,__=prepare_forecasting_data(new_df,fcst_history=config.FCST_HISTORY,fcst_horizon=0,x_vars=config.ENERGY_COLOUMNS,y_vars=config.ENERGY_COLOUMNS) |
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new_scaled_preds, *_ = predict.get_X_preds(new_x) |
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new_scaled_preds=to_np(new_scaled_preds).swapaxes(1,2).reshape(-1,len(config.ENERGY_COLOUMNS)) |
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dates=pd.date_range(start=fcst_date, periods=config.FCST_HORIZON+1,freq='1H')[1:] |
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preds_df=pd.DataFrame(dates,columns=[config.ENERGY_DATETIME]) |
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preds_df.loc[:, config.ENERGY_COLOUMNS]=new_scaled_preds |
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preds_df=predict.inverse_transform(preds_df) |
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return preds_df |
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def inference_boiler(fcst_date:string,DataFrame:pd.DataFrame): |
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pre=load_object(config.BOILER_PREPROCESSOR_PATH) |
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DataFrame=pre.fit_transform(DataFrame) |
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dates=pd.date_range(start=None,end=fcst_date,periods=config.FCST_HISTORY,freq=config.FREQUENCY) |
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new_df=DataFrame[DataFrame[config.BOILER_DATETIME].isin(dates)].reset_index(drop=True) |
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predict=load_learner(config.MODEL_PATH_ITP_BOIL) |
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new_df=predict.transform(new_df) |
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new_x,__=prepare_forecasting_data(new_df,fcst_history=config.FCST_HISTORY,fcst_horizon=0,x_vars=config.BOILER_COLOUMNS,y_vars=config.BOILER_COLOUMNS) |
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new_scaled_preds, *_ = predict.get_X_preds(new_x) |
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new_scaled_preds=to_np(new_scaled_preds).swapaxes(1,2).reshape(-1,len(config.BOILER_COLOUMNS)) |
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dates=pd.date_range(start=fcst_date, periods=config.FCST_HORIZON+1,freq='1H')[1:] |
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preds_df=pd.DataFrame(dates,columns=[config.BOILER_DATETIME]) |
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preds_df.loc[:, config.BOILER_COLOUMNS]=new_scaled_preds |
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preds_df=predict.inverse_transform(preds_df) |
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return preds_df |
