def _run_with_data(self, data, pre_period, post_period, model_args, alpha,
estimation):
# Zoom in on data in modeling range
if data.shape[1] == 1: # no exogenous values provided
raise ValueError("data contains no exogenous variables")
data_modeling = data.copy()
df_pre = data_modeling.loc[pre_period[0]:pre_period[1], :]
df_post = data_modeling.loc[post_period[0]:post_period[1], :]
# Standardize all variables
orig_std_params = (0, 1)
if model_args["standardize_data"]:
sd_results = standardize_all_variables(data_modeling, pre_period,
post_period)
df_pre = sd_results["data_pre"]
df_post = sd_results["data_post"]
orig_std_params = sd_results["orig_std_params"]
# Construct model and perform inference
model = construct_model(df_pre, model_args)
self.model = model
trained_model = model_fit(model, estimation, model_args["niter"])
self.model = trained_model
inferences = compile_posterior_inferences(trained_model, data, df_pre,
df_post, None, alpha,
orig_std_params, estimation)
# "append" to 'CausalImpact' object
self.inferences = inferences["series"]
def _run_with_data(self, data, pre_period, post_period, model_args, alpha,
estimation):
# Zoom in on data in modeling range
if data.shape[1] == 1: # no exogenous values provided
raise ValueError("data contains no exogenous variables")
non_null = pd.isnull(data.iloc[:, 1]).to_numpy().nonzero()
first_non_null = non_null[0]
if first_non_null.size > 0:
pre_period[0] = max(pre_period[0], data.index[first_non_null[0]])
data_modeling = data.copy()
df_pre = data_modeling.loc[pre_period[0]:pre_period[1], :]
df_post = data_modeling.loc[post_period[0]:post_period[1], :]
# Standardize all variables
orig_std_params = (0, 1)
if model_args["standardize_data"]:
sd_results = standardize_all_variables(data_modeling, pre_period,
post_period)
df_pre = sd_results["data_pre"]
df_post = sd_results["data_post"]
orig_std_params = sd_results["orig_std_params"]
# Construct model and perform inference
ucm_model = construct_model(self, df_pre, model_args)
res = model_fit(self, ucm_model, estimation, model_args["niter"])
inferences = compile_posterior_inferences(res, data, df_pre, df_post, None,
alpha, orig_std_params,
estimation)
# "append" to 'CausalImpact' object
self.inferences = inferences["series"]
self.model = ucm_model
def _run_with_data(self, data, pre_period, post_period, model_args, alpha):
# Zoom in on data in modeling range
first_non_null = pd.isnull(data.iloc[:, 1]).nonzero()[0]
if len(first_non_null) > 0:
pre_period[0] = max(pre_period[0], data.index[first_non_null[0]])
data_modeling = data.iloc[pre_period[0]:post_period[1], :]
# Standardize all variables?
orig_std_params = np.identity
if model_args["standardize_data"]:
sd_results = standardize_all_variables(data_modeling)
data_modeling = sd_results["data"]
orig_std_params = sd_results["orig_std_params"]
# Set observed response in post-period to NA
data_modeling.iloc[post_period[0]:, 1] = np.nan
# Construct model and perform inference
ucm_model = construct_model(data_modeling, model_args)
# Compile posterior inferences
if ucm_model is not None:
data_post = data.iloc[post_period[0]:post_period[1], :]
inferences = compile_posterior_inferences(ucm_model, data_post,
alpha, orig_std_params)
else:
inferences = compile_na_inferences(data.iloc[:, 1])
# Extend <series> to cover original range
# (padding with NA as necessary)
empty = pd.DataFrame(index=data.index)
inferences["series"] = pd.merge(inferences["series"], empty,
left_index=True, right_index=True,
how="outer")
if len(inferences["series"]) != len(data):
raise ValueError("""inferences['series'] must have the same number
of rows as 'data'""")
# Replace <y.model> by full original response
inferences["series"].iloc[:, 0] = data[:, 0]
# Assign response-variable names
inferences["series"].iloc[:, 0].name = "response"
inferences["series"].iloc[:, 1].name = "cum.response"
# Return 'CausalImpact' object
params = {"pre_period": pre_period, "post_period": post_period,
"model_args": model_args, "alpha": alpha}
self.inferences = inferences["series"]
self.summary = inferences["summary"]
self.report = inferences["report"]
self.model = model
self.params = params