pm25predict's Introduction

PM2.5 Predict

Prototype RNN approach to predicting PM2.5 time series

Experimentation

Supply pandas dataframe of features, indexed by timeseries sampled by hour.

    from pm25predict import ModelLoader, DatasetLoader, ModelContext
        
    # load data from influxdb
    df = InfluxDataLoader(database='gams', tables=['indoor', 'outdoor']).df

    # create dataset with dataframe features, df, 
    # supply labels with dataframe with single column, df[['pm25]]
    dataset_loader = DatasetLoader(df, df[['pm25']], look_back=3, ratio=0.9)

    # create model
    model_context = ModelContext(look_back=3)
    
    # train model 
    model_context.train(dataset_loader, nb_epoch=50)

    # test model on 10% of data, this will print RMSE errors
    testPredict = model_context.test(dataset_loader)

    # get training and testing labels for measurement
    train_labels, test_labels = dataset_loader.get_labels()

Online prediction

Data pipeline is captured via indoor and outdoor data sources to influx db.

indoor sensor data
outdoor atmosphere API

Cruncher will need to supply last 3+2 hours (look_back=3) data as Dataframe and create dataset.

    from pm25predict import ModelLoader, DatasetLoader, ModelContext

    # load dataset with ratio = 0.0 for all testing features
    dataset_loader = DatasetLoader(df, df[['pm25']], look_back=3, ratio=0.)

    # load current model
    model_path = "path/to/models/"
    loader = ModelLoader(model_path)

    # make prediction
    predict = loader.model_context.predict(dataset_loader)

    # load dataset with ratio = 1.0 for all training features
    dataset_loader = DatasetLoader(df, df[['pm25']], look_back=3, ratio=1.)

    # update model
    loader.model_context.update(dataset_loader)

    # save model
    loader.save_model()