45 add interpolation methods for input output

executables/bestest_hydronic_heat_pump/Dummy_shifting.py

@@ -0,0 +1,73 @@
+from physXAI.models.ann.ann_design import ClassicalANNModel
+from physXAI.preprocessing.preprocessing import PreprocessingSingleStep
+from physXAI.preprocessing.constructed import Feature
+from physXAI.utils.logging import Logger
+
+
+"""
+This script demonstrates the usage of different shifts. It is not physically meaningful.
+"""
+# Setup up logger for saving
+Logger.setup_logger(folder_name='Dummy_shifting_ann', override=True)
+
+# File path to data
+file_path = r"data/bestest_hydronic_heat_pump/pid_data.csv"
+
+# List of input features. Can include constructed features and lagged inputs
+inputs = ['reaTZon_y', 'reaTZon_y_lag1', 'reaTZon_y_lag2', 'weaSta_reaWeaTDryBul_y', 'weaSta_reaWeaTDryBul_y_lag1',
+          'weaSta_reaWeaHDirNor_y', 'oveHeaPumY_u', 'oveHeaPumY_u_lag1', 'oveHeaPumY_u_lag2']
+# Output feature
+output = 'Change(T_zone)'
+
+""" 
+The constructed features are automatically added to the data via 'physXAI.preprocessing.constructed.py' 
+Lagged inputs can be added directly based on the feature
+"""
+x1 = Feature('reaTZon_y')
+x1.lag(2)  # reaTZon_y_lag1, reaTZon_y_lag2
+x2 = Feature('weaSta_reaWeaTDryBul_y')
+x2.lag(1)  # weaSta_reaWeaTDryBul_y_lag1
+x3 = Feature('oveHeaPumY_u')
+x3.lag(2)  # oveHeaPumY_u_lag1, oveHeaPumY_u_lag2
+
+
+"""
+shift (Union[int, str, dict]): Time step of the input data used to predict the output.
+    - If a single int or str is given, it applies to all inputs.
+    - If a dict is provided, it can specify different shifts for individual inputs.
+    - If not all inputs are specified in the dict, unspecified inputs will use a default value (autocomplete).
+    Examples:
+        - shift = 0 or shift = 'current': Current time step will be used for prediction.
+        - shift = 1 or shift = 'previous': Previous values will be used for prediction.
+        - shift = 'mean_over_interval': Mean between current and previous time step will be used.
+        - shift = {
+            'inp_1': 1,
+            'inp_2': 'mean_over_interval',
+            '_default': 0,  # current time step will be used for all inputs not specified in the dict
+            # If no custom default value is given in dict, 'previous' will be used as default
+        }
+"""
+shift = {
+    'reaTZon_y': 'previous',  # for all lags of reaTZon_y, the shift will be set automatically
+    'weaSta_reaWeaHDirNor_y': 'mean_over_interval',
+    '_default': 0,
+}
+
+# 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, shift=shift, time_step=4)
+
+# Process Training data
+td = prep.pipeline(file_path)
+
+# Classical ANN
+m = ClassicalANNModel(epochs=50)
+
+# Training pipeline
+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)

physXAI/preprocessing/constructed.py

@@ -560,18 +560,24 @@ def get_feature(name: str) -> Union[FeatureBase, None]:
         return None
 
     @staticmethod
-    def process(df: DataFrame):
+    def process(df: DataFrame, feature_names: list[str] = None):
         """
         Processes the input DataFrame by applying all registered feature transformations in order.
         Each feature's `process` method is called, which typically adds a new column to `df`
         if it doesn't already exist.
 
         Args:
             df (DataFrame): The DataFrame to process and add features to.
+            feature_names (list[str]): optional parameter to only process those features given in feature_names
         """
 
-        for f in FeatureConstruction.features:
-            f.process(df)
+        if feature_names is None:
+            for f in FeatureConstruction.features:
+                f.process(df)
+        else:
+            for f in FeatureConstruction.features:
+                if f.feature in feature_names:
+                    f.process(df)
 
     @staticmethod
     def get_config() -> list: