"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "python/prophet/models.py" between
prophet-1.1.tar.gz and prophet-1.1.1.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

models.py  (prophet-1.1)	:	models.py  (prophet-1.1.1)
# -*- coding: utf-8 -*-		# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.		# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the		# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.		# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function		from __future__ import absolute_import, division, print_function
from abc import abstractmethod, ABC		from abc import abstractmethod, ABC
from tempfile import mkdtemp
from typing import Tuple		from typing import Tuple
from collections import OrderedDict		from collections import OrderedDict
from enum import Enum		from enum import Enum
from pathlib import Path		from pathlib import Path
import os
import pickle
import pkg_resources		import pkg_resources
import platform		import platform
import logging		import logging
logger = logging.getLogger('prophet.models')		logger = logging.getLogger('prophet.models')
PLATFORM = "unix"		PLATFORM = "win" if platform.platform().startswith("Win") else "unix"
if platform.platform().startswith("Win"):
PLATFORM = "win"
class IStanBackend(ABC):		class IStanBackend(ABC):
def __init__(self):		def __init__(self):
self.model = self.load_model()		self.model = self.load_model()
self.stan_fit = None		self.stan_fit = None
self.newton_fallback = True		self.newton_fallback = True
def set_options(self, **kwargs):		def set_options(self, **kwargs):
"""		"""
Specify model options as kwargs.		Specify model options as kwargs.
skipping to change at line 95		skipping to change at line 90
(stan_init, stan_data) = self.prepare_data(stan_init, stan_data)		(stan_init, stan_data) = self.prepare_data(stan_init, stan_data)
if 'inits' not in kwargs and 'init' in kwargs:		if 'inits' not in kwargs and 'init' in kwargs:
kwargs['inits'] = self.prepare_data(kwargs['init'], stan_data)[0]		kwargs['inits'] = self.prepare_data(kwargs['init'], stan_data)[0]
args = dict(		args = dict(
data=stan_data,		data=stan_data,
inits=stan_init,		inits=stan_init,
algorithm='Newton' if stan_data['T'] < 100 else 'LBFGS',		algorithm='Newton' if stan_data['T'] < 100 else 'LBFGS',
iter=int(1e4),		iter=int(1e4),
output_dir = mkdtemp(),
)		)
args.update(kwargs)		args.update(kwargs)
try:		try:
self.stan_fit = self.model.optimize(**args)		self.stan_fit = self.model.optimize(**args)
except RuntimeError as e:		except RuntimeError as e:
# Fall back on Newton		# Fall back on Newton
if self.newton_fallback and args['algorithm'] != 'Newton':		if not self.newton_fallback or args['algorithm'] == 'Newton':
logger.warning(
'Optimization terminated abnormally. Falling back to Newton.
'
)
args['algorithm'] = 'Newton'
self.stan_fit = self.model.optimize(**args)
else:
raise e		raise e
		logger.warning('Optimization terminated abnormally. Falling back to
		Newton.')
		args['algorithm'] = 'Newton'
		self.stan_fit = self.model.optimize(**args)
params = self.stan_to_dict_numpy(		params = self.stan_to_dict_numpy(
self.stan_fit.column_names, self.stan_fit.optimized_params_np)		self.stan_fit.column_names, self.stan_fit.optimized_params_np)
for par in params:		for par in params:
params[par] = params[par].reshape((1, -1))		params[par] = params[par].reshape((1, -1))
return params		return params
def sampling(self, stan_init, stan_data, samples, **kwargs) -> dict:		def sampling(self, stan_init, stan_data, samples, **kwargs) -> dict:
(stan_init, stan_data) = self.prepare_data(stan_init, stan_data)		(stan_init, stan_data) = self.prepare_data(stan_init, stan_data)
if 'inits' not in kwargs and 'init' in kwargs:		if 'inits' not in kwargs and 'init' in kwargs:
skipping to change at line 193		skipping to change at line 183
import numpy as np		import numpy as np
output = OrderedDict()		output = OrderedDict()
prev = None		prev = None
start = 0		start = 0
end = 0		end = 0
two_dims = len(data.shape) > 1		two_dims = len(data.shape) > 1
for cname in column_names:		for cname in column_names:
if "." in cname:		parsed = cname.split(".") if "." in cname else cname.split("[")
parsed = cname.split(".")
else:
parsed = cname.split("[")
curr = parsed[0]		curr = parsed[0]
if prev is None:		if prev is None:
prev = curr		prev = curr
if curr != prev:		if curr != prev:
if prev in output:		if prev in output:
raise RuntimeError(		raise RuntimeError(
"Found repeated column name"		"Found repeated column name"
)		)
if two_dims:		if two_dims:
output[prev] = np.array(data[:, start:end])		output[prev] = np.array(data[:, start:end])
else:		else:
output[prev] = np.array(data[start:end])		output[prev] = np.array(data[start:end])
prev = curr		prev = curr
start = end		start = end
end += 1		end += 1
else:
end += 1
if prev in output:		if prev in output:
raise RuntimeError(		raise RuntimeError(
"Found repeated column name"		"Found repeated column name"
)		)
if two_dims:		if two_dims:
output[prev] = np.array(data[:, start:end])		output[prev] = np.array(data[:, start:end])
else:		else:
output[prev] = np.array(data[start:end])		output[prev] = np.array(data[start:end])
return output		return output
class StanBackendEnum(Enum):		class StanBackendEnum(Enum):
CMDSTANPY = CmdStanPyBackend		CMDSTANPY = CmdStanPyBackend
@staticmethod		@staticmethod
def get_backend_class(name: str) -> IStanBackend:		def get_backend_class(name: str) -> IStanBackend:
try:		try:
return StanBackendEnum[name].value		return StanBackendEnum[name].value
except KeyError as e:		except KeyError as e:
raise ValueError("Unknown stan backend: {}".format(name)) from e		raise ValueError(f"Unknown stan backend: {name}") from e
End of changes. 9 change blocks.
25 lines changed or deleted		8 lines changed or added

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