Python_files package
Submodules
Python_files.cascade_class module
this is the class version of the previous code
- class Python_files.cascade_class.Cascade(json)
Bases:
object
- Get_N()
- Get_T()
- Get_alpha()
- Get_beta()
- Get_history()
- Get_mu()
- Get_p()
- Set_N(value)
- Set_T(value)
- Set_beta(value)
- Set_history(value)
- Set_p(value)
- compute_MAP(prior_params=[0.02, 0.0002, 0.01, 0.001, - 0.1], max_n_star=1, display=False)
Returns the pair of the estimated logdensity of a posteriori and parameters (as a numpy array)
history – (n,2) numpy array containing marked time points (t_i,m_i) t – current time (i.e end of observation window) alpha – power parameter of the power-law mark distribution mu – min value parameter of the power-law mark distribution prior_params – list (mu_p, mu_beta, sig_p, sig_beta, corr) of hyper parameters of the prior
– where: – mu_p: is the prior mean value of p – mu_beta: is the prior mean value of beta – sig_p: is the prior standard deviation of p – sig_beta: is the prior standard deviation of beta – corr: is the correlation coefficient between p and beta
max_n_star – maximum authorized value of the branching factor (defines the upper bound of p) display – verbose flag to display optimization iterations (see ‘disp’ options of optim.optimize)
- loglikelihood()
Returns the loglikelihood of a Hawkes process with exponential kernel computed with a linear time complexity
params – parameter tuple (p,beta) of the Hawkes process history – (n,2) numpy array containing marked time points (t_i,m_i) t – current time (i.e end of observation window)
Python_files.hawkes_estimator module
The aim of this code is to estimate the cascade’s parameters.
Python_files.hawkes_tools module
The aim of this code is to provide statistical tools for cascade’s parameters estimation.
- Python_files.hawkes_tools.compute_MAP(history, t, alpha, mu, prior_params=[0.02, 0.0002, 0.01, 0.001, - 0.1], max_n_star=1, display=False)
Returns the pair of the estimated logdensity of a posteriori and parameters (as a numpy array)
history – (n,2) numpy array containing marked time points (t_i,m_i) t – current time (i.e end of observation window) alpha – power parameter of the power-law mark distribution mu – min value parameter of the power-law mark distribution prior_params – list (mu_p, mu_beta, sig_p, sig_beta, corr) of hyper parameters of the prior
– where: – mu_p: is the prior mean value of p – mu_beta: is the prior mean value of beta – sig_p: is the prior standard deviation of p – sig_beta: is the prior standard deviation of beta – corr: is the correlation coefficient between p and beta
max_n_star – maximum authorized value of the branching factor (defines the upper bound of p) display – verbose flag to display optimization iterations (see ‘disp’ options of optim.optimize)
- Python_files.hawkes_tools.loglikelihood(params, history, t)
Returns the loglikelihood of a Hawkes process with exponential kernel computed with a linear time complexity
params – parameter tuple (p,beta) of the Hawkes process history – (n,2) numpy array containing marked time points (t_i,m_i) t – current time (i.e end of observation window)
Python_files.learner module
The aim of this code is to estimate the cascade’s parameters.
Python_files.logger module
- class Python_files.logger.KafkaHandler(hostlist, topic='logs', tls=None)
Bases:
logging.Handler
Class to instantiate the kafka logging facility.
- close()
Close the producer and clean up.
- emit(record)
Emit the provided record to the kafka_client producer.
- flush(timeout=None)
Flush the objects.
- class Python_files.logger.Logger(columns=[{'field': 't', 'length': 5, 'align': '>', 'name': 'time'}, {'field': 'source', 'length': 9, 'align': '^', 'name': 'source'}, {'field': 'level', 'length': 8, 'align': '^', 'name': 'level'}, {'field': 'message', 'length': None, 'align': '<', 'name': 'message'}], log_sep=None, skip_line=True, levels={}, sources={})
Bases:
object
- accept_field(field, fields)
- add_postprocessor(field, f)
- add_preprocessor(field, f)
- default_columns = [{'field': 't', 'length': 5, 'align': '>', 'name': 'time'}, {'field': 'source', 'length': 9, 'align': '^', 'name': 'source'}, {'field': 'level', 'length': 8, 'align': '^', 'name': 'level'}, {'field': 'message', 'length': None, 'align': '<', 'name': 'message'}]
- draw_line(c)
- get_terminal_width()
- log(entry, log_sep=None)
- print(entry, log_sep=None)
- Python_files.logger.get_logger(name, debug=False, topic='logs', broker_list='localhost:9091,localhost:9092', levels=[])
- Python_files.logger.postprocess_color(colors, default_color='white')
- Python_files.logger.preprocess_time()
Python_files.predictor module
The aim of this code is predict the number of retweet thanks to the estimated parameters.
Python_files.predictor_tools module
This code is aimed to provide tools for prediction process.
- Python_files.predictor_tools.predictions(params, history, alpha, mu, T=None)
Returns the expected total numbers of points for a set of time points
params – parameter tuple (p,beta) of the Hawkes process history – (n,2) numpy array containing marked time points (t_i,m_i) alpha – power parameter of the power-law mark distribution mu – min value parameter of the power-law mark distribution T – 1D-array of times (i.e ends of observation window)