Invalid initital conditions

[mo2561057]

respy/respy/simulate.py

Lines 189 to 255 in 8c4d69f

	def _extend_data_with_sampled_characteristics(df, optim_paras, options):
	"""Sample initial observations from initial conditions.
	The function iterates over all state space dimensions and replaces NaNs with values
	sampled from initial conditions. In the case of an n-step-ahead simulation with
	sampling all state space dimensions are sampled. For the other two simulation
	methods, potential NaNs in the data are replaced with sampled characteristics.
	Characteristics are sampled regardless of the simulation type which keeps randomness
	across the types constant.
	Parameters
	----------
	df : pandas.DataFrame
	A pandas DataFrame which contains only an index for n-step-ahead simulation with
	sampling. For the other simulation methods, it contains information on
	individuals which is allowed to have missing information in the first period.
	optim_paras : dict
	options : dict
	Returns
	-------
	df : pandas.DataFrame
	A pandas DataFrame with no missings at all.
	"""
	# Sample characteristics only for the first period.
	fp = df.query("period == 0").copy()
	index = fp.index
	for observable in optim_paras["observables"]:
	level_dict = optim_paras["observables"][observable]
	sampled_char = _sample_characteristic(fp, options, level_dict, use_keys=False)
	fp[observable] = fp[observable].fillna(
	pd.Series(data=sampled_char, index=index), downcast="infer"
	)
	for choice in optim_paras["choices_w_exp"]:
	level_dict = optim_paras["choices"][choice]["start"]
	sampled_char = _sample_characteristic(fp, options, level_dict, use_keys=True)
	fp[f"exp_{choice}"] = fp[f"exp_{choice}"].fillna(
	pd.Series(data=sampled_char, index=index), downcast="infer"
	)
	for lag in reversed(range(1, optim_paras["n_lagged_choices"] + 1)):
	level_dict = optim_paras[f"lagged_choice_{lag}"]
	sampled_char = _sample_characteristic(fp, options, level_dict, use_keys=False)
	fp[f"lagged_choice_{lag}"] = fp[f"lagged_choice_{lag}"].fillna(
	pd.Series(data=sampled_char, index=index), downcast="infer"
	)
	# Sample types and map them to individuals for all periods.
	if optim_paras["n_types"] >= 2:
	level_dict = optim_paras["type_prob"]
	types = _sample_characteristic(fp, options, level_dict, use_keys=False)
	fp["type"] = fp["type"].fillna(
	pd.Series(data=types, index=index), downcast="infer"
	)
	# Update data in the first period with sampled characteristics.
	df = df.combine_first(fp)
	# Types are invariant and we have to fill the DataFrame for one-step-ahead.
	if optim_paras["n_types"] >= 2:
	df["type"] = df["type"].fillna(method="ffill")
	return df

I am not entirely sure whether I am doing something wrong but to me it seems like this function also samples invalid states.
That leads to a indexing error in the simulation procedure since we can not assign a position in the state space to some inititial conditions.
I used a simple random model as returned by the random model function in the test directory.
Specifically the function returned initial states where working was a lagged choice but where the agent had no prior work experience!

[tobiasraabe]

It seems like your initial conditions are wrong. Can you post them? What branch are you working with?

[mo2561057]

Im using the master branch. I just used a randomly generated model from the random model function in respy!