def __init__(
self,
freq: str,
prediction_length: int,
method_name: str = "ets",
period: int = None,
trunc_length: Optional[int] = None,
params: Optional[Dict] = None,
) -> None:
super().__init__(freq=freq, prediction_length=prediction_length)
try:
from rpy2 import robjects, rinterface
import rpy2.robjects.packages as rpackages
from rpy2.rinterface import RRuntimeError
except ImportError as e:
raise ImportError(str(e) + USAGE_MESSAGE) from e
self._robjects = robjects
self._rinterface = rinterface
self._rinterface.initr()
self._rpackages = rpackages
this_dir = os.path.dirname(os.path.realpath(__file__))
this_dir = this_dir.replace("\\", "/") # for windows
r_files = [
n[:-2] for n in os.listdir(f"{this_dir}/R/") if n[-2:] == ".R"
]
for n in r_files:
try:
path = Path(this_dir, "R", f"{n}.R")
robjects.r(f'source("{path}")'.replace("\\", "\\\\"))
except RRuntimeError as er:
raise RRuntimeError(str(er) + USAGE_MESSAGE) from er
supported_methods = ["ets", "arima", "tbats", "croston", "mlp"]
assert (
method_name in supported_methods
), f"method {method_name} is not supported please use one of {supported_methods}"
self.method_name = method_name
self._stats_pkg = rpackages.importr("stats")
self._r_method = robjects.r[method_name]
self.prediction_length = prediction_length
self.freq = freq
self.period = period if period is not None else get_seasonality(freq)
self.trunc_length = trunc_length
self.params = {
"prediction_length": self.prediction_length,
"output_types": ["samples"],
"frequency": self.period,
}
if params is not None:
self.params.update(params)
def __init__(
self,
freq: str,
prediction_length: int,
method_name: str = "ets",
period: int = None,
num_eval_samples: int = 100,
trunc_length: Optional[int] = None,
params: Optional[Dict] = None,
) -> None:
try:
from rpy2 import robjects, rinterface
import rpy2.robjects.packages as rpackages
from rpy2.rinterface import RRuntimeError
except ImportError as e:
raise ImportError(str(e) + USAGE_MESSAGE) from e
self._robjects = robjects
self._rinterface = rinterface
self._rinterface.initr()
self._rpackages = rpackages
this_dir = os.path.dirname(os.path.realpath(__file__))
r_files = [
n[:-2] for n in os.listdir(f"{this_dir}/R/") if n[-2:] == ".R"
]
for n in r_files:
try:
robjects.r(f'source("{this_dir}/R/{n}.R")')
except RRuntimeError as er:
raise RRuntimeError(str(er) + USAGE_MESSAGE) from er
supported_methods = ["ets", "arima", "tbats", "croston", "mlp"]
assert (
method_name in supported_methods
), f"method {method_name} is not supported please use one of {supported_methods}"
self.method_name = method_name
self._stats_pkg = rpackages.importr('stats')
self._r_method = robjects.r[method_name]
self.prediction_length = prediction_length
self.freq = freq
self.period = period if period is not None else get_seasonality(freq)
self.num_samples = num_eval_samples
self.trunc_length = trunc_length
self.params = {
'prediction_length': self.prediction_length,
'output_types': ['samples'],
'num_samples': self.num_samples,
'frequency': self.period,
}
if params is not None:
self.params.update(params)
def __init__(
self,
freq: str,
prediction_length: int,
method_name: str = "ets",
period: int = None,
trunc_length: Optional[int] = None,
params: Optional[Dict] = None,
) -> None:
super().__init__(freq=freq, prediction_length=prediction_length)
if not R_IS_INSTALLED:
raise ImportError("R is not Installed! \n " + USAGE_MESSAGE)
if not RPY2_IS_INSTALLED:
raise ImportError(rpy2_error_message + USAGE_MESSAGE)
self._robjects = robjects
self._rinterface = rinterface
self._rinterface.initr()
self._rpackages = rpackages
this_dir = os.path.dirname(os.path.realpath(__file__))
this_dir = this_dir.replace("\\", "/") # for windows
r_files = [
n[:-2] for n in os.listdir(f"{this_dir}/R/") if n[-2:] == ".R"
]
for n in r_files:
try:
path = Path(this_dir, "R", f"{n}.R")
robjects.r(f'source("{path}")'.replace("\\", "\\\\"))
except RRuntimeError as er:
raise RRuntimeError(str(er) + USAGE_MESSAGE) from er
assert (
method_name in SUPPORTED_METHODS
), f"method {method_name} is not supported please use one of {SUPPORTED_METHODS}"
self.method_name = method_name
self._stats_pkg = rpackages.importr("stats")
self._r_method = robjects.r[method_name]
self.prediction_length = prediction_length
self.freq = freq
self.period = period if period is not None else get_seasonality(freq)
self.trunc_length = trunc_length
self.params = {
"prediction_length": self.prediction_length,
"output_types": ["samples"],
"frequency": self.period,
}
if params is not None:
self.params.update(params)
