predictops/engine.py

   1 from configparser import ConfigParser
   2 from datetime import datetime, timedelta
   3 from logging import getLogger
   4 from logging.config import fileConfig
   5 from pathlib import Path
   6 from shutil import rmtree
   7
   8 from .source.ephemeris import Ephemeris
   9 from .source.meteofrance import MeteoFrance
  10 from .learn.learning import Learning
  11 from .learn.preprocessing import Preprocessing
  12 from .target.target import Target
  13
  14 fileConfig((Path.cwd() / 'config') / 'logging.cfg')
  15 logger = getLogger()
  16
  17
  18 class Engine:
  19
  20     def __init__(self, config_file = (Path.cwd() / 'config') / 'learn.cfg'):
  21         self._config = ConfigParser()
  22         self._config.read(config_file)
  23         self._start = datetime.strptime(self._config['DATETIME']['start'],
  24                                         '%m/%d/%Y %H:%M:%S')
  25         self._end = datetime.strptime(self._config['DATETIME']['end'],
  26                                         '%m/%d/%Y %H:%M:%S')
  27
  28         self._timestep = timedelta(hours =
  29                                    self._config['DATETIME'].getfloat('hourStep'))
  30
  31         self._X = {}
  32
  33
  34
  35     def clean(self):
  36         # Cleaning the data directory
  37         logger.info("Cleaning and restoring data directory")
  38         directory  = Path.cwd() / 'data'
  39         if directory.is_dir():
  40             rmtree(directory)
  41         p = Path(Path.cwd() / 'data')
  42         p.mkdir()
  43
  44
  45     def add_features(self):
  46         if self._config['FEATURES'].getboolean('meteofrance'):
  47             meteofeature = MeteoFrance(config_file =
  48                                        eval(self._config['FEATURE_CONFIG']['meteofrance']))
  49
  50             meteofeature.start = self._start
  51             meteofeature.end = self._end
  52
  53             meteofeature.update()
  54             dated_features = meteofeature.dated_features
  55             for date in dated_features:
  56                 self._X.setdefault(date,{}).update(dated_features[date])
  57
  58         if self._config['FEATURES'].getboolean('ephemeris'):
  59             ephemerides = Ephemeris(config_file =
  60                                     eval(self._config['FEATURE_CONFIG']['ephemeris']))
  61
  62             ephemerides.start = self._start
  63             ephemerides.end = self._end
  64
  65             dated_features = ephemerides.dated_features
  66             for date in dated_features:
  67                 self._X.setdefault(date,{}).update(dated_features[date])
  68
  69
  70     def add_target(self):
  71         self._target = Target(config_file = eval(self._config['TARGET']['config']),
  72                               start = self._start, end = self._end,
  73                               timestep = self._timestep)
  74
  75
  76     def add_preprocessing(self):
  77         self._preproc = Preprocessing(config_file = self._config,
  78                                       dict_features = self.X,
  79                                       dict_target = self.y)
  80
  81
  82     def learn(self):
  83         history = self._config['HISTORY_KNOWLEDGE'].getint('nb_lines')
  84         self._learner = Learning(config_file = eval(self._config['LEARNER']['config']),
  85                                  X = self._preproc.dataframe, y = list(self.y.values())[history:])
  86
  87
  88     @property
  89     def X(self):
  90         return self._X
  91
  92     @X.setter
  93     def X(self, x):
  94         self._X = x
  95
  96
  97     @property
  98     def y(self):
  99         return self._target.y
 100
 101     @y.setter
 102     def end(self, y):
 103         self._target.y = y