ValueError: y data should have at least 1 samples, but found 0 #290
Description
Hi, I have been facing an error while analyzing for 'Turbine Ideal Energy'. I'm facing this error if I have less than 2 years of SCADA data. Even if I have data which is short by a day, it throws the error. I'm wondering if we have to use 2 years or more data or it's just a bug. Could you look into this issue please? I have pasted the entire error for reference. Also, I have similar error with other codes as well except AEP when I used data less than 2 years. Thank you very much for consideration.
ValueError Traceback (most recent call last)
Cell In[15], line 6
1 # We can choose to save key plots to a file by setting enable_plotting=True and
2 # specifying a directory to save the images. For now we turn off this feature.
3 # ta.run(reanalysis_subset=['era5', 'merra2'], enable_plotting=False, plot_dir=None,
4 # wind_bin_thresh=wind_bin_thresh, max_power_filter=max_power_filter,
5 # correction_threshold=correction_threshold)
----> 6 ta.run(reanalysis_products=['era5', 'merra2'])
File ~\AppData\Local\anaconda3\envs\openoa-env\lib\site-packages\openoa\logging.py:33, in logged_method_call.<locals>._wrapper(self, *args, **kwargs)
31 logger = logging.getLogger(the_method.__module__)
32 logger.debug(f"{self.__class__.__name__}#{id(self)}.{the_method.__name__}: {msg}")
---> 33 return the_method(self, *args, **kwargs)
File ~\AppData\Local\anaconda3\envs\openoa-env\lib\site-packages\openoa\analysis\turbine_long_term_gross_energy.py:255, in TurbineLongTermGrossEnergy.run(self, num_sim, reanalysis_products, uncertainty_scada, wind_bin_threshold, max_power_filter, correction_threshold)
253 self.filter_sum_impute_scada() # Setup daily scada data
254 self.setupturbine_model_dict() # Setup daily data to be fit using the GAM
--> 255 self.fit_model() # Fit daily turbine energy to atmospheric data
256 self.apply_model(i) # Apply fitting result to long-term reanalysis data
258 # Log the completion of the run
File ~\AppData\Local\anaconda3\envs\openoa-env\lib\site-packages\openoa\logging.py:33, in logged_method_call.<locals>._wrapper(self, *args, **kwargs)
31 logger = logging.getLogger(the_method.__module__)
32 logger.debug(f"{self.__class__.__name__}#{id(self)}.{the_method.__name__}: {msg}")
---> 33 return the_method(self, *args, **kwargs)
File ~\AppData\Local\anaconda3\envs\openoa-env\lib\site-packages\openoa\analysis\turbine_long_term_gross_energy.py:519, in TurbineLongTermGrossEnergy.fit_model(self)
516 df["energy_imputed"] = df["energy_imputed"] * self._run.scada_data_fraction
518 # Consider wind speed, wind direction, and air density as features
--> 519 mod_results[t] = functions.gam_3param(
520 windspeed_col="WMETR_HorWdSpd",
521 wind_direction_col="WMETR_HorWdDir",
522 air_density_col="WMETR_AirDen",
523 power_col="energy_imputed",
524 data=df,
525 )
526 self._model_results = mod_results
File ~\AppData\Local\anaconda3\envs\openoa-env\lib\site-packages\openoa\utils\_converters.py:294, in dataframe_method.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
292 # Update the args and kwargs as need and call the function
293 args, kwargs = _update_arguments(args, kwargs, arg_ix_list, data_cols, arg_list)
--> 294 return func(*args, **kwargs)
296 # When no data is provided, then convert the Series arguments, update args and kwargs,
297 # appropriately, then call the function
298 df, arg_list = series_to_df(*arg_list, names=data_cols)
File ~\AppData\Local\anaconda3\envs\openoa-env\lib\site-packages\openoa\utils\power_curve\functions.py:187, in gam_3param(windspeed_col, wind_direction_col, air_density_col, power_col, n_splines, data)
184 y = data[power_col]
186 # Fit the model
--> 187 model = LinearGAM(n_splines=n_splines).fit(X, y)
189 # Wrap the prediction function in a closure to pack input variables
190 @dataframe_method(data_cols=["windspeed_col", "wind_direction_col", "air_density_col"])
191 def predict(
192 windspeed_col: str | pd.Series,
(...)
195 data: pd.DataFrame = None,
196 ):
File ~\AppData\Local\anaconda3\envs\openoa-env\lib\site-packages\pygam\pygam.py:887, in GAM.fit(self, X, y, weights)
884 self._validate_params()
886 # validate data
--> 887 y = check_y(y, self.link, self.distribution, verbose=self.verbose)
888 X = check_X(X, verbose=self.verbose)
889 check_X_y(X, y)
File ~\AppData\Local\anaconda3\envs\openoa-env\lib\site-packages\pygam\utils.py:234, in check_y(y, link, dist, min_samples, verbose)
212 """
213 tool to ensure that the targets:
214 - are in the domain of the link function
(...)
230 y : array containing validated y-data
231 """
232 y = np.ravel(y)
--> 234 y = check_array(
235 y,
236 force_2d=False,
237 min_samples=min_samples,
238 ndim=1,
239 name='y data',
240 verbose=verbose,
241 )
243 with warnings.catch_warnings():
244 warnings.simplefilter("ignore")
File ~\AppData\Local\anaconda3\envs\openoa-env\lib\site-packages\pygam\utils.py:203, in check_array(array, force_2d, n_feats, ndim, min_samples, name, verbose)
201 n = array.shape[0]
202 if n < min_samples:
--> 203 raise ValueError(
204 '{} should have at least {} samples, '
205 'but found {}'.format(name, min_samples, n)
206 )
208 return array
ValueError: y data should have at least 1 samples, but found 0EDIT: I put the traceback in python code bracket to make it easier for me to read.