# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import os
from pathlib import Path
from typing import Dict, Optional, List, Union, Iterator
import numpy as np
from gluonts.core.component import validated
from gluonts.dataset.common import Dataset
from gluonts.dataset.util import forecast_start
from gluonts.model.forecast import SampleForecast, QuantileForecast
from gluonts.model.predictor import RepresentablePredictor
from gluonts.time_feature import get_seasonality
# https://stackoverflow.com/questions/25329955/check-if-r-is-installed-from-python
from subprocess import Popen, PIPE
proc = Popen(["which", "R"], stdout=PIPE, stderr=PIPE)
R_IS_INSTALLED = proc.wait() == 0
try:
import rpy2.robjects.packages as rpackages
from rpy2 import rinterface, robjects
from rpy2.rinterface import RRuntimeError
except ImportError as e:
rpy2_error_message = str(e)
RPY2_IS_INSTALLED = False
else:
RPY2_IS_INSTALLED = True
USAGE_MESSAGE = """
The RForecastPredictor is a thin wrapper for calling the R forecast package.
In order to use it you need to install R and run
pip install 'rpy2>=2.9.*,<3.*'
R -e 'install.packages(c("forecast", "nnfor"),\
repos="https://cloud.r-project.org")'
"""
SAMPLE_FORECAST_METHODS = ["ets", "arima"]
QUANTILE_FORECAST_METHODS = ["tbats", "thetaf", "stlar"]
POINT_FORECAST_METHODS = ["croston", "mlp"]
SUPPORTED_METHODS = (
SAMPLE_FORECAST_METHODS
+ QUANTILE_FORECAST_METHODS
+ POINT_FORECAST_METHODS
)
[docs]class RForecastPredictor(RepresentablePredictor):
"""
Wrapper for calling the `R forecast package.
<http://pkg.robjhyndman.com/forecast/>`_.
The `RForecastPredictor` is a thin wrapper for calling the R forecast
package. In order to use it you need to install R and run::
pip install 'rpy2>=2.9.*,<3.*'
R -e 'install.packages(c("forecast", "nnfor"), repos="https://cloud.r-project.org")'
Parameters
----------
freq
The granularity of the time series (e.g. '1H')
prediction_length
Number of time points to be predicted.
method
The method from rforecast to be used one of
"ets", "arima", "tbats", "croston", "mlp", "thetaf".
period
The period to be used (this is called `frequency` in the R forecast
package), result to a tentative reasonable default if not specified
(for instance 24 for hourly freq '1H')
trunc_length
Maximum history length to feed to the model (some models become slow
with very long series).
params
Parameters to be used when calling the forecast method default.
Note that currently only `output_type = 'samples'` is supported.
""" # noqa: E501
@validated()
def __init__(
self,
freq: str,
prediction_length: int,
method_name: str = "ets",
period: Optional[int] = None,
trunc_length: Optional[int] = None,
params: Optional[Dict] = None,
) -> None:
super().__init__(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"
f" {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.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 _unlist(self, l):
if type(l).__name__.endswith("Vector"):
return [self._unlist(x) for x in l]
else:
return l
def _run_r_forecast(self, d, params, save_info):
buf = []
def save_to_buf(x):
buf.append(x)
def dont_save(x):
pass
f = save_to_buf if save_info else dont_save
# save output from the R console in buf
self._rinterface.set_writeconsole_regular(f)
self._rinterface.set_writeconsole_warnerror(f)
make_ts = self._stats_pkg.ts
r_params = self._robjects.vectors.ListVector(params)
vec = self._robjects.FloatVector(d["target"])
ts = make_ts(vec, frequency=self.period)
forecast = self._r_method(ts, r_params)
forecast_dict = dict(
zip(forecast.names, map(self._unlist, list(forecast)))
)
if "quantiles" in forecast_dict or "upper_quantiles" in forecast_dict:
def from_interval_to_level(interval: int, side: str):
if side == "upper":
level = 50 + interval / 2
elif side == "lower":
level = 50 - interval / 2
else:
raise ValueError
return level / 100
# Post-processing quantiles on then Python side for the convenience
# of asserting and debugging.
upper_quantiles = [
str(from_interval_to_level(interval, side="upper"))
for interval in params["intervals"]
]
lower_quantiles = [
str(from_interval_to_level(interval, side="lower"))
for interval in params["intervals"]
]
# Median forecasts would be available at two places: Lower 0 and
# Higher 0 (0-prediction interval)
forecast_dict["quantiles"] = dict(
zip(
lower_quantiles + upper_quantiles[1:],
forecast_dict["lower_quantiles"]
+ forecast_dict["upper_quantiles"][1:],
)
)
# `QuantileForecast` allows "mean" as the key; we store them as
# well since they can differ from median.
forecast_dict["quantiles"].update(
{"mean": forecast_dict.pop("mean")}
)
self._rinterface.set_writeconsole_regular(
self._rinterface.consolePrint
)
self._rinterface.set_writeconsole_warnerror(
self._rinterface.consolePrint
)
return forecast_dict, buf
[docs] def predict(
self,
dataset: Dataset,
num_samples: int = 100,
intervals: Optional[List] = None,
save_info: bool = False,
**kwargs,
) -> Iterator[Union[SampleForecast, QuantileForecast]]:
if self.method_name in POINT_FORECAST_METHODS:
print(
"Overriding `output_types` to `mean` since"
f" {self.method_name} is a point forecast method."
)
elif self.method_name in QUANTILE_FORECAST_METHODS:
print(
"Overriding `output_types` to `quantiles` since "
f"{self.method_name} is a quantile forecast method."
)
for data in dataset:
if self.trunc_length:
shift_by = max(data["target"].shape[0] - self.trunc_length, 0)
data["start"] = data["start"] + shift_by
data["target"] = data["target"][-self.trunc_length :]
params = self.params.copy()
params["num_samples"] = num_samples
if self.method_name in POINT_FORECAST_METHODS:
params["output_types"] = ["mean"]
elif self.method_name in QUANTILE_FORECAST_METHODS:
params["output_types"] = ["quantiles", "mean"]
if intervals is None:
# This corresponds to quantiles: 0.05 to 0.95 in steps of
# 0.05.
params["intervals"] = list(range(0, 100, 10))
else:
params["intervals"] = np.sort(intervals).tolist()
forecast_dict, console_output = self._run_r_forecast(
data, params, save_info=save_info
)
if self.method_name in QUANTILE_FORECAST_METHODS:
quantile_forecasts_dict = forecast_dict["quantiles"]
yield QuantileForecast(
forecast_arrays=np.array(
list(quantile_forecasts_dict.values())
),
forecast_keys=list(quantile_forecasts_dict.keys()),
start_date=forecast_start(data),
item_id=data.get("item_id", None),
)
else:
if self.method_name in POINT_FORECAST_METHODS:
# Handling special cases outside of R is better, since it
# is more visible and is easier to change. Repeat mean
# forecasts `num_samples` times.
samples = np.reshape(
forecast_dict["mean"] * params["num_samples"],
(params["num_samples"], self.prediction_length),
)
else:
samples = np.array(forecast_dict["samples"])
expected_shape = (
params["num_samples"],
self.prediction_length,
)
assert (
samples.shape == expected_shape
), f"Expected shape {expected_shape} but found {samples.shape}"
info = (
{"console_output": "\n".join(console_output)}
if save_info
else None
)
yield SampleForecast(
samples,
forecast_start(data),
info=info,
item_id=data.get("item_id", None),
)