"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "python/prophet/serialize.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.

serialize.py  (prophet-1.1)	:	serialize.py  (prophet-1.1.1)
skipping to change at line 13		skipping to change at line 13
# 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 collections import OrderedDict		from collections import OrderedDict
from copy import deepcopy		from copy import deepcopy
from io import StringIO		from io import StringIO
import json		import json
		from pathlib import Path
import numpy as np		import numpy as np
import pandas as pd		import pandas as pd
from prophet.forecaster import Prophet		from prophet.forecaster import Prophet
from prophet import __version__
		about = {}
		here = Path(__file__).parent.resolve()
		with open(here / "__version__.py", "r") as f:
		exec(f.read(), about)
SIMPLE_ATTRIBUTES = [		SIMPLE_ATTRIBUTES = [
'growth', 'n_changepoints', 'specified_changepoints', 'changepoint_range',		'growth', 'n_changepoints', 'specified_changepoints', 'changepoint_range',
'yearly_seasonality', 'weekly_seasonality', 'daily_seasonality',		'yearly_seasonality', 'weekly_seasonality', 'daily_seasonality',
'seasonality_mode', 'seasonality_prior_scale', 'changepoint_prior_scale',		'seasonality_mode', 'seasonality_prior_scale', 'changepoint_prior_scale',
'holidays_prior_scale', 'mcmc_samples', 'interval_width', 'uncertainty_sampl es',		'holidays_prior_scale', 'mcmc_samples', 'interval_width', 'uncertainty_sampl es',
'y_scale', 'logistic_floor', 'country_holidays', 'component_modes'		'y_scale', 'logistic_floor', 'country_holidays', 'component_modes'
]		]
PD_SERIES = ['changepoints', 'history_dates', 'train_holiday_names']		PD_SERIES = ['changepoints', 'history_dates', 'train_holiday_names']
skipping to change at line 103		skipping to change at line 108
for k, v in fit_kwargs['init'].items():		for k, v in fit_kwargs['init'].items():
if isinstance(v, np.ndarray):		if isinstance(v, np.ndarray):
fit_kwargs['init'][k] = v.tolist()		fit_kwargs['init'][k] = v.tolist()
elif isinstance(v, np.floating):		elif isinstance(v, np.floating):
fit_kwargs['init'][k] = float(v)		fit_kwargs['init'][k] = float(v)
model_dict['fit_kwargs'] = fit_kwargs		model_dict['fit_kwargs'] = fit_kwargs
# Params (Dict[str, np.ndarray])		# Params (Dict[str, np.ndarray])
model_dict['params'] = {k: v.tolist() for k, v in model.params.items()}		model_dict['params'] = {k: v.tolist() for k, v in model.params.items()}
# Attributes that are skipped: stan_fit, stan_backend		# Attributes that are skipped: stan_fit, stan_backend
model_dict['__prophet_version'] = __version__		model_dict['__prophet_version'] = about["__version__"]
return model_dict		return model_dict
def model_to_json(model):		def model_to_json(model):
"""Serialize a Prophet model to json string.		"""Serialize a Prophet model to json string.
Model must be fitted. Skips Stan objects that are not needed for predict.		Model must be fitted. Skips Stan objects that are not needed for predict.
Can be deserialized with model_from_json.		Can be deserialized with model_from_json.
Parameters		Parameters
skipping to change at line 152		skipping to change at line 157
if model_dict[attribute] is None:		if model_dict[attribute] is None:
setattr(model, attribute, None)		setattr(model, attribute, None)
else:		else:
s = pd.read_json(StringIO(model_dict[attribute]), typ='series', orie nt='split')		s = pd.read_json(StringIO(model_dict[attribute]), typ='series', orie nt='split')
if s.name == 'ds':		if s.name == 'ds':
if len(s) == 0:		if len(s) == 0:
s = pd.to_datetime(s)		s = pd.to_datetime(s)
s = s.dt.tz_localize(None)		s = s.dt.tz_localize(None)
setattr(model, attribute, s)		setattr(model, attribute, s)
for attribute in PD_TIMESTAMP:		for attribute in PD_TIMESTAMP:
setattr(model, attribute, pd.Timestamp.utcfromtimestamp(model_dict[attri bute]))		setattr(model, attribute, pd.Timestamp.utcfromtimestamp(model_dict[attri bute]).tz_localize(None))
for attribute in PD_TIMEDELTA:		for attribute in PD_TIMEDELTA:
setattr(model, attribute, pd.Timedelta(seconds=model_dict[attribute]))		setattr(model, attribute, pd.Timedelta(seconds=model_dict[attribute]))
for attribute in PD_DATAFRAME:		for attribute in PD_DATAFRAME:
if model_dict[attribute] is None:		if model_dict[attribute] is None:
setattr(model, attribute, None)		setattr(model, attribute, None)
else:		else:
df = pd.read_json(StringIO(model_dict[attribute]), typ='frame', orie nt='table', convert_dates=['ds'])		df = pd.read_json(StringIO(model_dict[attribute]), typ='frame', orie nt='table', convert_dates=['ds'])
if attribute == 'train_component_cols':		if attribute == 'train_component_cols':
# Special handling because of named index column		# Special handling because of named index column
df.columns.name = 'component'		df.columns.name = 'component'
End of changes. 4 change blocks.
3 lines changed or deleted		8 lines changed or added

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