Delete Pipline

Pipline/.vscode/settings.json

@@ -1,6 +0,0 @@
-{
-    "editor.defaultColorDecorators": true,
-    "debug.javascript.autoAttachFilter": "always",
-    "intellicodeApiExamples.loggingLevel": "ALL",
-    "vsintellicode.modelDownloadPath": ""
-}

Pipline/Generate.py

@@ -1,119 +0,0 @@
-import sklearn
-import pandas as pd
-from tsai.basics import *
-import config
-from tsai.inference import load_learner
-
-import pandas as pd       
-
-
-
-def get_inputs_from_user():
-    
-    return 0
-
-def preprocess_data(DataFrame:pd.DataFrame):
-    preproc=load_object()        
-    return DataFrame
-
-def preprocess_data_transform_generate_splits_Train(DataFrame:pd.DataFrame):
-    DataFrame=DataFrame.drop(config.DROP_COLOUMNS,axis=1)
-    preproc_pipe=load_object(config.PREPROCESSOR_PATH)
-    exp_pipe=load_object(config.SCALING_DATA)
-    DataFrame=preproc_pipe.fit_transform(DataFrame)
-    
-    print("dataframe processed and ready for splitting")
-    
-    splits=get_forecasting_splits(DataFrame,fcst_history=config.FCST_HISTORY,fcst_horizon=config.FCST_HORIZON,datetime_col=config.DATETIME_COL,
-                                  valid_size=config.VALID_SIZE,test_size=config.TEST_SIZE)
-    
-    X,y=prepare_forecasting_data(DataFrame,fcst_history=config.FCST_HISTORY,fcst_horizon=config.FCST_HORIZON,x_vars=config.COLOUMNS,y_vars=config.COLOUMNS)
-    
-    learn=TSForecaster(X,y,splits=splits,
-        batch_size=16,path='models',
-        arch='InceptionTimePlus',#"PatchTST" when PatchTST is to be used
-        pipelines=[preproc_pipe,exp_pipe],
-        #arch_config=config.ARCH_CONFIG, #uncomment only if PatchTST is used
-        metrics=[mse,mape],
-        cbs=ShowGraph()
-    )
-    
-    lr_max=learn.lr_find().valley
-    
-    learn.fit_one_cycle(n_epoch=config.N_EPOCH,lr_max=lr_max)
-    learn.export("model_in.pt")
-    return 0
-
-#when using PatchTst model use the below function 
-def inference_Aircomp(fcst_date:string,DataFrame:pd.DataFrame):
-    pre=load_object(config.AIR_PREPROCESSOR_PATH)
-    DataFrame=pre.fit_transform(DataFrame)
-    
-    dates=pd.date_range(start=None,end=fcst_date,periods=config.FCST_HISTORY,freq=config.FREQUENCY)
-    new_df=DataFrame[DataFrame[config.AIR_DATETIME_COL].isin(dates)].reset_index(drop=True)
-    
-
-    predict=load_learner(config.MODEL_PATH_ITP_AIR)
-    new_df=predict.transform(new_df)
-    
-    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)
-    
-    new_scaled_preds, *_ = predict.get_X_preds(new_x)
-
-    new_scaled_preds=to_np(new_scaled_preds).swapaxes(1,2).reshape(-1,len(config.AIR_COLOUMNS))
-    
-    dates=pd.date_range(start=fcst_date, periods=config.FCST_HORIZON+1,freq='1H')[1:]
-    preds_df=pd.DataFrame(dates,columns=[config.AIR_DATETIME_COL])
-    preds_df.loc[:, config.AIR_COLOUMNS]=new_scaled_preds
-    preds_df=predict.inverse_transform(preds_df)
-    
-    return preds_df
-
-def inference_Energy(fcst_date:string,DataFrame:pd.DataFrame):
-    pre=load_object(config.ENER_PREPROCESSOR_PATH)
-    DataFrame[config.ENERGY_DATETIME]=pd.to_datetime(DataFrame[config.ENERGY_DATETIME],format='mixed')   
-    DataFrame=pre.fit_transform(DataFrame)
-    
-    dates=pd.date_range(start=None,end=fcst_date,periods=config.FCST_HISTORY,freq=config.FREQUENCY)
-    new_df=DataFrame[DataFrame[config.ENERGY_DATETIME].isin(dates)].reset_index(drop=True)
-    
-
-    predict=load_learner(config.MODEL_PATH_ITP_ENER)
-    new_df=predict.transform(new_df)
-    
-    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)
-    
-    new_scaled_preds, *_ = predict.get_X_preds(new_x)
-
-    new_scaled_preds=to_np(new_scaled_preds).swapaxes(1,2).reshape(-1,len(config.ENERGY_COLOUMNS))
-    
-    dates=pd.date_range(start=fcst_date, periods=config.FCST_HORIZON+1,freq='1H')[1:]
-    preds_df=pd.DataFrame(dates,columns=[config.ENERGY_DATETIME])
-    preds_df.loc[:, config.ENERGY_COLOUMNS]=new_scaled_preds
-    preds_df=predict.inverse_transform(preds_df)
-    
-    return preds_df
-
-def inference_boiler(fcst_date:string,DataFrame:pd.DataFrame):
-    pre=load_object(config.BOILER_PREPROCESSOR_PATH)  
-    DataFrame=pre.fit_transform(DataFrame)
-    
-    dates=pd.date_range(start=None,end=fcst_date,periods=config.FCST_HISTORY,freq=config.FREQUENCY)
-    new_df=DataFrame[DataFrame[config.BOILER_DATETIME].isin(dates)].reset_index(drop=True)
-
-    
-    predict=load_learner(config.MODEL_PATH_ITP_BOIL)
-    new_df=predict.transform(new_df)
-    
-    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)
-    
-    new_scaled_preds, *_ = predict.get_X_preds(new_x)
-
-    new_scaled_preds=to_np(new_scaled_preds).swapaxes(1,2).reshape(-1,len(config.BOILER_COLOUMNS))
-    
-    dates=pd.date_range(start=fcst_date, periods=config.FCST_HORIZON+1,freq='1H')[1:]
-    preds_df=pd.DataFrame(dates,columns=[config.BOILER_DATETIME])
-    preds_df.loc[:, config.BOILER_COLOUMNS]=new_scaled_preds
-    preds_df=predict.inverse_transform(preds_df)
-    
-    return preds_df

Pipline/app.py

@@ -1,66 +0,0 @@
-import gradio as gr
-import pandas as pd
-import tsai
-import Generate
-
-example_df_aircomp=pd.read_csv('D:/project/aircompressordata.csv')
-example_df_ener=pd.read_csv('D:/project/energymeter.csv')
-example_df_boiler=pd.read_csv('D:/project/Boiler1.csv')
-
-demo=gr.Blocks(title="EcoForecast")
-
-def pred_air(date):
-    preds=Generate.inference_Aircomp(date,example_df_aircomp)
-    return preds
-
-def pred_ener(date):
-    preds=Generate.inference_Energy(date,example_df_ener)
-    return preds
-
-def pred_boiler(date):
-    preds=Generate.inference_boiler(date,example_df_boiler)
-    return preds
-
-def plotgraphs(dataframe):
-    plots=0
-    return plots
-
-with demo:  
-    gr.Markdown("Tool for predicting the next seven days of data in the future using the last 200 points of data incoming")
-    with gr.Tabs():
-        with gr.TabItem("Air compressor data"):
-            with gr.Row():
-                Air_input=gr.Text(placeholder="Enter date and like the example only",show_label=False)
-                air_dataframe_input=gr.Dataframe(example_df_aircomp.head(100))
-                Air_dataframe_output=gr.Dataframe()
-                Air_plots=gr.Plot()
-            with gr.Column():
-                Aircomp_output_btn=gr.Button("Forecast")
-                Air_plot_forecast=gr.Button("Plot")
-                
-        with gr.TabItem("Energymeter data"):
-            with gr.Row():
-                ener_input=gr.Text(placeholder="Enter the date and time in example format only",show_label=False)
-                ener_dataframe_input=gr.Dataframe(example_df_ener.head(100))
-                Ener_dataframe_output=gr.Dataframe()
-                Ener_plots=gr.Plot()
-            with gr.Column():
-                Energy_output_btn=gr.Button("Forecast")
-                Ener_plot_forecast=gr.Button("Plot")
-                
-        with gr.TabItem("Boiler data"):
-            with gr.Row():
-                boiler_input=gr.Text(placeholder="Enter the date and time in example format only",show_label=False)
-                ener_dataframe_input=gr.Dataframe(example_df_boiler.head(100))
-                boiler_dataframe_output=gr.Dataframe()
-                boil_plots=gr.Plot()
-            with gr.Column():
-                Boiler_output_btn=gr.Button("Forecast")
-                boiler_plot_forecast=gr.Button("Plot")
-                
-    Aircomp_output_btn.click(pred_air,inputs=Air_input,outputs=Air_dataframe_output)
-    Energy_output_btn.click(pred_ener,inputs=ener_input,outputs=Ener_dataframe_output)
-    Boiler_output_btn.click(pred_boiler,inputs=boiler_input,outputs=boiler_dataframe_output)
-    Air_plot_forecast.click()
-    
-demo.launch(share=True)

Pipline/config.py

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-MODEL_PATH_ITP_AIR='/project/Pipline/models/AirInceptionTime.pt'
-MODEL_PATH_ITP_ENER='/project/Pipline/models/EnerInceptionTime.pt'
-MODEL_PATH_ITP_BOIL='/project/Pipline/models/BoilerInceptionTime.pt'
-
-DATA_PATH='/project/Pipline/data'
-AIR_PREPROCESSOR_PATH='/project/Pipline/data/preproc_pipe.pkl'
-AIR_SCALING_DATA='/project/Pipline/data/exp_pipe.pkl'
-
-ENER_PREPROCESSOR_PATH='/project/Pipline/data/Enerpreproc_pipe.pkl'
-ENER_SCALING_DATA='/project/Pipline/data/Ener_exp_pipe.pkl' 
-
-AIR_DATETIME_COL='timestamp'
-#the current model is only for 1 device and of a specific appliance only
-AIR_COLOUMNS=['air_inlet_temp_deg_f', 'average_cfm', 'average_kw',
-       'compressor_room_air_generated_cfm',
-       'compressor_room_energy_cosumed_kwh', 'specific_power_kw_100cfm',
-       'percentage_loading_based_on_air_supplied_design_600cfm']
-
-#the below columns provide no relavant information to the model
-AIR_DROP_COLOUMNS=['data_id','ideal_specific_power_kw_100cfm','device_id']
-
-ENERGY_DATETIME='parameter_timestamp'
-ENERGY_COLOUMNS=['current_ir', 'electrical_energy', 'frequency', 'power', 'powerfactor',
-       'pressure', 'temperature', 'voltage_vb',
-       'voltage_vr', 'voltage_vy']
-ENERGY_DROP_COLOUMNS=["location","current_ib","current_iy","device_1_state","device_2_state","device_id","device_name",'id',"device_type"]
-
-BOILER_PREPROCESSOR_PATH='/project/Pipline/data/boiler_preproc_pipe.pkl'
-BOILER_DATETIME='DateString'
-BOILER_COLOUMNS=['Boiler2_Feed Water Temp (T-4) (°F)','Boiler2_Gas Flow (G-2) (MMBtu)', 'Boiler2_Make Up Flow (W-17) (kGal)','Boiler2_Steam Flow (S-1) (lbs)']
-#Frequency of the incoming data
-FREQUENCY='1H'
-METHOD='ffill'
-VALUE=0
-
-#No. of values needed to be looked back
-FCST_HISTORY=200
-
-#No. of Timestamps predicted in the future
-FCST_HORIZON=168 #1 Week
-VALID_SIZE=0.1
-TEST_SIZE=0.2
-
-#Training Parameters for a new Model
-ARCH_CONFIG=dict(
-    n_layers=3,
-    n_heads=4,
-    d_model=16,
-    d_ff=128,
-    attn_dropout=0.0,
-    dropout=0.3,
-    patch_len=24,
-    stride=2,
-    padding_patch=True,
-)#Only needed when using PatchTst
-
-N_EPOCH=100

Pipline/data/Ener_exp_pipe.pkl

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-size 581

Pipline/data/Enerpreproc_pipe.pkl

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-oid sha256:55ccbfdb0999fec534f2076803bcc45fd26daa098d86cad281cf8dbe417fb83a
-size 917

Pipline/data/boiler_preproc_pipe.pkl

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-size 918

Pipline/data/exp_pipe.pkl

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-size 1029

Pipline/data/preproc_pipe.pkl

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-size 1499

Pipline/forecast.py

@@ -1,228 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-import sklearn
-from tsai.basics import *
-#my_setup(sklearn)
-
-import pandas as pd
-df=pd.read_csv('D:/project/aircompressordata.csv')
-df=df.drop(['data_id','ideal_specific_power_kw_100cfm','device_id'],axis=1)
-print(df.columns)
-df.head(5)
-
-
-# In[3]:
-
-
-datetime_col="timestamp"
-freq='1H'
-coloumns=df.columns[:7]
-method='ffill'
-value=0
-
-preproc_pipe=sklearn.pipeline.Pipeline([
-    ('shrinker',TSShrinkDataFrame()),
-    ('drop_duplicates',TSDropDuplicates(datetime_col=datetime_col)),
-    ('add_mts',TSAddMissingTimestamps(datetime_col=datetime_col,freq=freq)),
-    ('fill_missing',TSFillMissing(columns=coloumns,method=method,value=value)),
-    ],
-    verbose=True)
-mkdir('data', exist_ok=True,parents=True)
-save_object(preproc_pipe,'data/preproc_pipe.pkl')
-preproc_pipe=load_object('data/preproc_pipe.pkl')
-
-df=preproc_pipe.fit_transform(df)
-
-
-# In[4]:
-
-
-df.head()
-
-
-# In[5]:
-
-
-fcst_history=200
-fcst_horizon=72
-valid_size=0.1
-test_size=0.2
-
-splits=get_forecasting_splits(df,fcst_history=fcst_history,fcst_horizon=fcst_horizon,datetime_col=datetime_col,
-                              valid_size=valid_size,test_size=test_size)
-
-splits
-
-
-# In[6]:
-
-
-coloumns=df.columns[1:]
-train_split=splits[0]
-
-exp_pipe=sklearn.pipeline.Pipeline([
-    ('scaler',TSStandardScaler(columns=coloumns)),
-    ],
-    verbose=True)
-
-save_object(exp_pipe,'data/exp_pipe.pkl')
-exp_pipe=load_object('data/exp_pipe.pkl')
-
-df_scaled=exp_pipe.fit_transform(df,scaler__idxs=train_split)
-
-df_scaled
-
-
-# In[7]:
-
-
-x_vars=df.columns[1:]
-y_vars=df.columns[1:]
-
-
-# In[8]:
-
-
-X,y=prepare_forecasting_data(df,fcst_history=fcst_history,fcst_horizon=fcst_horizon,x_vars=x_vars,y_vars=y_vars)
-X.shape , y.shape
-
-
-# In[9]:
-
-
-arch_config=dict(
-    n_layers=3,
-    n_heads=4,
-    d_model=16,
-    d_ff=128,
-    attn_dropout=0.0,
-    dropout=0.3,
-    patch_len=24,
-    stride=2,
-    padding_patch=True,
-)
-
-
-# In[10]:
-
-
-learn=TSForecaster(X,y,splits=splits,
-                   batch_size=16,path="models",
-                   pipelines=[preproc_pipe,exp_pipe],
-                   arch="PatchTST",
-                   arch_config=arch_config,
-                   metrics=[mse,mae],
-                   cbs=ShowGraph())
-
-
-# In[11]:
-
-
-learn.summary()
-
-
-# In[12]:
-
-
-lr_max=learn.lr_find().valley
-
-
-# In[ ]:
-
-
-n_epochs=100
-
-learn.fit_one_cycle(n_epoch=n_epochs,lr_max=lr_max)
-learn.export('PatchTST.pt')
-
-
-# In[14]:
-
-
-from tsai.inference import load_learner
-from sklearn.metrics import mean_squared_error, mean_absolute_error
-from sklearn.metrics import mean_absolute_percentage_error
-
-learn=load_learner('models/PatchTST.pt')
-y_test_preds, *_=learn.get_X_preds(X[splits[2]])
-y_test_preds=to_np(y_test_preds)
-print(y_test_preds.shape)
-
-y_test=y[splits[2]]
-
-print(mean_squared_error(y_test.flatten(),y_test_preds.flatten()))
-print(mean_absolute_error(y_test.flatten(),y_test_preds.flatten()))
-print(mean_absolute_percentage_error(y_test.flatten(),y_test_preds.flatten()))
-
-
-# In[15]:
-
-
-X_test=X[splits[2]]
-plot_forecast(X_test,y_test,y_test_preds,sel_vars=True)
-
-
-# In[16]:
-
-
-fcst_date="2023-07-31 23:00:00"
-dates=pd.date_range(start=None,end=fcst_date,periods=fcst_history,freq=freq)
-dates
-
-
-# In[17]:
-
-
-#df=pd.read_csv('D:/project/aircompressordata.csv')
-#df=preproc_pipe.fit_transform(df)
-
-new_df=df[df[datetime_col].isin(dates)].reset_index(drop=True)
-new_df
-
-
-# In[18]:
-
-
-from tsai.inference import load_learner
-
-predict=load_learner('models/PatchTST.pt')
-new_df=predict.transform(new_df)
-
-new_df
-
-
-# In[19]:
-
-
-x_feat=new_df.columns[1:]
-new_x,__=prepare_forecasting_data(new_df,fcst_history=fcst_history,fcst_horizon=0,x_vars=x_vars,y_vars=y_vars)
-new_x.shape
-
-
-# In[20]:
-
-
-new_scaled_preds, *_ = learn.get_X_preds(new_x)
-
-new_scaled_preds=to_np(new_scaled_preds).swapaxes(1,2).reshape(-1,len(y_vars))
-dates=pd.date_range(start=fcst_date, periods=fcst_horizon+1,freq='1H')[1:]
-preds_df=pd.DataFrame(dates,columns=[datetime_col])
-preds_df.loc[:, y_vars]=new_scaled_preds
-preds_df=learn.inverse_transform(preds_df)
-
-preds_df
-
-
-# In[1]:
-
-
-from tsai.export import get_nb_name; nb_name=get_nb_name(locals())
-from tsai.imports import  create_scripts; create_scripts(nb_name)
-
-
-# In[ ]:
-
-
-
-

Pipline/models/AirInceptionTime.pt

@@ -1,3 +0,0 @@
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-oid sha256:d4c78a71c41299fab531348291d4143c80b1dba25a40408825ec469495b0a319
-size 134990062

Pipline/models/BoilerInceptionTime.pt

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-size 74149517

Pipline/models/EnerInceptionTime.pt

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-oid sha256:699db947208b539c0c7fc90b8c17cd83882eb29413f7da5e0a83de110d62e5fc
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Pipline/test.py

@@ -1,16 +0,0 @@
-import pandas as pd
-import Generate
-import config
-
-Aircomp_df=pd.read_csv('D:/project/aircompressordata.csv')
-#df=Aircomp_df.drop(config.DROP_COLOUMNS,axis=1)
-Energy_df=pd.read_csv('D:/project/energymeter.csv')
-boiler_df=pd.read_csv('D:/project/Boiler1.csv')
-'''
-preds=Generate.inference_Aircomp("2023-07-31 23:00:00",Aircomp_df)
-print(preds)
-preds=Generate.inference_Energy("2023-07-13 12:00:50",Energy_df)
-print(preds)
-'''
-preds=Generate.inference_boiler("2023-04-30 01:59:00",boiler_df)
-print(preds)