Added variable window size for data (SV)GP
This commit is contained in:
Radu C. Martin
parent
8a5a56547a
commit
d98e21a2a1
2 changed files with 75 additions and 67 deletions
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@ -61,7 +61,7 @@ class PIDcontroller:
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class sysIDcontroller(object):
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def __init__(self, u_range = (-1, 1)):
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self.u_range = u_range
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id_P = 10000
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id_P = 30000
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id_I = 50000/(3 * 3600)
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id_D = 0
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self.PIDcontroller = PIDcontroller(P = id_P, I = id_I, D = id_D,
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@ -98,6 +98,20 @@ class Base_MPCcontroller(object):
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self.dict_cols.values()])
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self.X_log = []
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# Adaptive models update parameters
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self.model_update_frequency = (24 * 3600)/self.T_sample # once per day
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self.pts_since_update = 0
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# Model log
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self.model_log = []
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### Input range
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# Define an identification signal to be used first
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self.Pel = 2 * 6300
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self.COP = 5.0
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# Set up identification controller
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self.u_range = self.COP * self.Pel * np.array([-1, 1])
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# Complete measurement history
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# Columns are: [SolRad, OutsideTemp] (Disturbance), Heat(Input), Temperature (Output)
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@ -107,8 +121,9 @@ class Base_MPCcontroller(object):
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self.data = np.empty((0, len(self.data_cols)))
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# Dataset used for training
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self.dataset_train_minsize = 5 * (24*3600)/self.T_sample # 5 days worth
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self.dataset_train_minsize = 1 * (24*3600)//self.T_sample # 5 days worth
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self.dataset_train_maxsize = np.iinfo(np.int32).max # maximum 32bit int
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#self.dataset_train_maxsize = 5 * (24*3600)//self.T_sample # 5 days worth
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self.dataset_train = np.empty((0, self.n_states))
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# The current weather forcast
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@ -152,13 +167,7 @@ class Base_MPCcontroller(object):
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self.model = None
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self.id_mode = True
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# Define an identification signal to be used first
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self.Pel = 2 * 6300
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self.COP = 5.0
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# Set up identification controller
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u_range = self.COP * self.Pel * np.array([-1, 1])
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self.id_controller = sysIDcontroller(u_range)
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self.id_controller = sysIDcontroller(self.u_range)
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return
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@ -283,6 +292,23 @@ class Base_MPCcontroller(object):
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print("Initialized casadi solver")
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def _fit_scaler(self, nb_train_pts):
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# Get the min/max values from the dataset
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df = pd.DataFrame(self.data[-nb_train_pts:], columns = self.data_cols)
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# Get the range of data in the dataset
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df_range = df.describe().loc[['min', 'max'], :]
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# Manually overwrite the values for heat range
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in_names = self.dict_cols['u'][1]
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df_range.loc['min', in_names] = self.u_range[0]
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df_range.loc['max', in_names] = self.u_range[1]
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self.scaler = MinMaxScaler(feature_range = (-1, 1))
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self.scaler.fit(df_range.to_numpy())
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self.scaler_helper = ScalerHelper(self.scaler)
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return
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def _train_model(self):
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"""
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Placeholder function to silence linter warning
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@ -363,7 +389,6 @@ class Base_MPCcontroller(object):
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X = np.array(res['x'].reshape((self.N_horizon + 1, self.n_states)))
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self.X_log.append(X)
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df_X = pd.DataFrame(X)
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#import pdb; pdb.set_trace()
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u_idx = self.dict_cols['w'][0] * len(self.dict_cols['w'][1])
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# Take the first control action and apply it
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u = X[1, u_idx]
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@ -372,6 +397,22 @@ class Base_MPCcontroller(object):
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self._add_input_measurement(u)
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return u
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def update_model(self):
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self._add_measurement_set()
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if not self.id_mode and not self.recover_from_crash:
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self.pts_since_update += 1
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if self.pts_since_update >= self.model_update_frequency:
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print(f"Updating model after {self.pts_since_update} measurements")
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# Append old model to log
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self.model_log.append(self.model)
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# Train new model
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self._train_model()
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# Re-initialize CasADi solver
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self._setup_solver()
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self.pts_since_update = 0
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def save_data(self):
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df = pd.DataFrame(self.data, columns = self.data_cols)
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df.to_pickle("controller_df.pkl")
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@ -379,9 +420,7 @@ class Base_MPCcontroller(object):
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pickle.dump(self.scaler, open(Path("controller_scaler.pkl"), 'wb'))
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pickle.dump(self.model, open(Path("controller_model.pkl"), 'wb'))
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pickle.dump(self.X_log, open(Path("controller_X_log.pkl"), 'wb'))
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return
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pickle.dump(self.model_log, open(Path("controller_model_log.pkl"), 'wb'))
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class GP_MPCcontroller(Base_MPCcontroller):
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@ -394,15 +433,16 @@ class GP_MPCcontroller(Base_MPCcontroller):
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def _train_model(self):
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""" Identify model from gathered data """
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nb_train_pts = self.dataset_train_minsize - 1
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nb_train_pts = self.dataset_train_minsize
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###
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# Dataset
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###
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# Train the model on the last nb_train_pts
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df = pd.DataFrame(self.data[-nb_train_pts:], columns = self.data_cols)
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self.scaler = MinMaxScaler(feature_range = (-1, 1))
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self.scaler_helper = ScalerHelper(self.scaler)
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df_sc = get_scaled_df(df, self.dict_cols, self.scaler)
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self._fit_scaler(nb_train_pts)
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np_sc = self.scaler.transform(df.to_numpy())
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df_sc = pd.DataFrame(np_sc, index = df.index, columns = df.columns)
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df_gpr_train = data_to_gpr(df_sc, self.dict_cols)
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df_input_train = df_gpr_train.drop(columns = self.dict_cols['w'][1] + self.dict_cols['u'][1] + self.dict_cols['y'][1])
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@ -413,13 +453,13 @@ class GP_MPCcontroller(Base_MPCcontroller):
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data_train = (np_input_train, np_output_train)
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df_test = pd.DataFrame(self.data[nb_train_pts:], columns = self.data_cols)
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df_test_sc = get_scaled_df(df_test, self.dict_cols, self.scaler)
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df_gpr_test = data_to_gpr(df_test_sc, self.dict_cols)
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df_input_test = df_gpr_test.drop(columns = self.dict_cols['w'][1] + self.dict_cols['u'][1] + self.dict_cols['y'][1])
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df_output_test = df_gpr_test[self.dict_cols['y'][1]]
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np_input_test = df_input_test.to_numpy()
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np_output_test = df_output_test.to_numpy()
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#df_test = pd.DataFrame(self.data[nb_train_pts:], columns = self.data_cols)
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#df_test_sc = get_scaled_df(df_test, self.dict_cols, self.scaler)
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#df_gpr_test = data_to_gpr(df_test_sc, self.dict_cols)
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#df_input_test = df_gpr_test.drop(columns = self.dict_cols['w'][1] + self.dict_cols['u'][1] + self.dict_cols['y'][1])
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#df_output_test = df_gpr_test[self.dict_cols['y'][1]]
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#np_input_test = df_input_test.to_numpy()
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#np_output_test = df_output_test.to_numpy()
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###
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# Kernel
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@ -431,7 +471,7 @@ class GP_MPCcontroller(Base_MPCcontroller):
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squared_dims = np.arange(nb_rational_dims, nb_dims, 1)
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nb_squared_dims = len(squared_dims)
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default_lscale = 150
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default_lscale = 1
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while True:
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try:
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@ -533,21 +573,10 @@ class GP_MPCcontroller(Base_MPCcontroller):
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return
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def update_model(self):
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self._add_measurement_set()
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class SVGP_MPCcontroller(Base_MPCcontroller):
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def __init__(self, dict_cols, model = None, scaler = None, N_horizon = 10, recover_from_crash = False):
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super().__init__(dict_cols, model, scaler, N_horizon, recover_from_crash)
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# Adaptive models update parameters
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self.model_update_frequency = (24 * 3600)/self.T_sample # once per day
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self.pts_since_update = 0
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# Model log
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self.model_log = []
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###
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# GPflow model training and update
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@ -560,9 +589,11 @@ class SVGP_MPCcontroller(Base_MPCcontroller):
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# Dataset
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###
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df = pd.DataFrame(self.data[-nb_train_pts:], columns = self.data_cols)
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self.scaler = MinMaxScaler(feature_range = (-1, 1))
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self.scaler_helper = ScalerHelper(self.scaler)
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df_sc = get_scaled_df(df, self.dict_cols, self.scaler)
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print(f"Training model on {df.shape[0]} datapoints")
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self._fit_scaler(nb_train_pts)
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np_sc = self.scaler.transform(df.to_numpy())
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df_sc = pd.DataFrame(np_sc, index = df.index, columns = df.columns)
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df_gpr_train = data_to_gpr(df_sc, self.dict_cols)
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df_input_train = df_gpr_train.drop(columns = self.dict_cols['w'][1] + self.dict_cols['u'][1] + self.dict_cols['y'][1])
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@ -711,34 +742,6 @@ class SVGP_MPCcontroller(Base_MPCcontroller):
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return
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def update_model(self):
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self._add_measurement_set()
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if not self.id_mode and not self.recover_from_crash:
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self.pts_since_update += 1
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if self.pts_since_update >= self.model_update_frequency:
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print(f"Updating model after {self.pts_since_update} measurements")
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# Append old model to log
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self.model_log.append(self.model)
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# Train new model
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self._train_model()
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# Re-initialize CasADi solver
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self._setup_solver()
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self.pts_since_update = 0
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# Redefine save_data since now we're also saving the model log
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def save_data(self):
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df = pd.DataFrame(self.data, columns = self.data_cols)
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df.to_pickle("controller_df.pkl")
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pickle.dump(self.scaler, open(Path("controller_scaler.pkl"), 'wb'))
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pickle.dump(self.model, open(Path("controller_model.pkl"), 'wb'))
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pickle.dump(self.X_log, open(Path("controller_X_log.pkl"), 'wb'))
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pickle.dump(self.model_log, open(Path("controller_model_log.pkl"), 'wb'))
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return
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class TestController:
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def __init__(self):
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return
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@ -46,6 +46,11 @@ w_lags = 1
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u_lags = 2
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y_lags = 3
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print(f"[*] t_lags: {t_lags}")
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print(f"[*] w_lags: {w_lags}")
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print(f"[*] u_lags: {u_lags}")
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print(f"[*] y_lags: {y_lags}")
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dict_cols = {
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't': (t_lags, t_cols),
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'w': (w_lags, w_cols),
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