45 add interpolation methods for input output

executables/bestest_hydronic_heat_pump/different_sampling_methods.py

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+from physXAI.models.ann.ann_design import ClassicalANNModel
+from physXAI.preprocessing.preprocessing import PreprocessingSingleStep
+from physXAI.preprocessing.constructed import Feature, FeatureExp
+from physXAI.utils.logging import Logger
+
+"""
+This script demonstrates the usage of different sampling methods. It is not physically meaningful.
+
+When creating a Feature, a sampling method can be specified.
+For constructed features, no sampling method is necessary. It is assigned based on their corresponding base feature(s).
+
+sampling_method (Union[str, int]): Time step of the input data used to predict the output.
+    - if None: Feature.get_default_sampling_method() is used
+    - if 'current' or 0: Current time step will be used for prediction.
+    - if 'previous' or 1: Previous time step will be used for prediction.
+    - if 'mean_over_interval': Mean between current and previous time step will be used.
+
+    Specify default sampling method using Feature.set_default_sampling_method(<your default sampling>).
+    If no default sampling method is specified by the user, 'previous' is used as default.
+
+Constructed features must be built from features with sampling methods that apply the same time shift. Therefore, 
+constructed features can either base on features which have solely the sampling method 'current' (no time shift applied)
+or on features which have one of the sampling methods ['previous','mean_over_interval'] (time shift of one unit applied).
+"""
+
+Feature.set_default_sampling_method(0)
+
+# Setup up logger for saving
+Logger.setup_logger(folder_name='different_sampling_methods_ann', override=True)
+
+# File path to data
+file_path = r"data/bestest_hydronic_heat_pump/pid_data.csv"
+
+# List of input features. Can include names of constructed features and lagged inputs
+inputs = ['reaTZon_y', 'reaTZon_y_lag1', 'reaTZon_y_lag2', 'weaSta_reaWeaTDryBul_y', 'weaSta_reaWeaTDryBul_y_lag1',
+          Feature('weaSta_reaWeaHDirNor_y', sampling_method='mean_over_interval'), 'oveHeaPumY_u',
+          'oveHeaPumY_u_lag1', 'oveHeaPumY_u_lag2']
+# Output feature. Can include names of constructed features as well
+output = ['Change(T_air)']
+
+""" 
+The constructed features are automatically added to the data via 'physXAI.preprocessing.constructed.py' 
+Lagged inputs can be added directly based on the feature
+"""
+# create lags of reaTZon_y: reaTZon_y_lag1, reaTZon_y_lag2
+x1 = Feature('reaTZon_y', sampling_method='previous')
+lx1 = x1.lag(2)  # for all lags of reaTZon_y, the shift will be set automatically as 'previous'
+
+# create lag of weaSta_reaWeaTDryBul_y: weaSta_reaWeaTDryBul_y_lag1
+x2 = Feature('weaSta_reaWeaTDryBul_y')
+lx2 = x2.lag(1)
+
+# create lag of oveHeaPumY_u: oveHeaPumY_u_lag1, oveHeaPumY_u_lag2
+x3 = Feature('oveHeaPumY_u', sampling_method='mean_over_interval')
+x3.lag(2)
+
+# dummy Features
+y = x1 + lx1[0]
+z = y + x1
+z.rename('example_feature_two')  # since z is a constructed feature based on x1, its sampling_method will be previous
+e = FeatureExp(x1-273.15, 'exp')  # reduce x1 by 273.15, otherwise values are too high
+
+# x1 and x3 have sampling methods 'previous' and 'mean_over_interval'.
+# Since both of them apply a time shift of one, they can be combined in constructed features
+a = x1 + x3
+
+inputs.extend([z, e, a])  # add dummy features to inputs
+
+# construct output
+change_tair = x1 - lx1[0]
+change_tair.rename('Change(T_air)')
+
+# Create Training data
+# Time step defines target sampling: if original sampling of data is in 15min intervals, it is resampled to 1h intervals
+# for time_step=4. Hence, if the shift method of an input is defined as 'mean_over_interval', the mean over the last
+# hour is taken as input
+prep = PreprocessingSingleStep(inputs, output, time_step=4)
+
+# Process Training data
+td = prep.pipeline(file_path)
+
+# Build & train Classical ANN
+m = ClassicalANNModel(epochs=50)
+model = m.pipeline(td)
+
+# Log setup of preprocessing and model as json
+Logger.log_setup(prep, m)
+# Log training data as pickle
+Logger.save_training_data(td)