Reducing the computation time and adding holidays features

[predictops.git] / predictops / engine.py

diff --git a/predictops/engine.py b/predictops/engine.py
index dedd2652cdd9efc97eaf24d26c1d3bd66a1398ec..e7bbf1c5aa58221da7a8aaa71788cf0339258cbc 100644 (file)
--- a/predictops/engine.py
+++ b/predictops/engine.py
@@ -6,7 +6,10 @@ from pathlib import Path
 from shutil import rmtree
 
 from .source.ephemeris import Ephemeris
 from shutil import rmtree
 
 from .source.ephemeris import Ephemeris

+from .source.holidays import Holidays

 from .source.meteofrance import MeteoFrance
 from .source.meteofrance import MeteoFrance

+from .learn.learning import Learning
+from .learn.preprocessing import Preprocessing

 from .target.target import Target
 
 fileConfig((Path.cwd() / 'config') / 'logging.cfg')
 from .target.target import Target
 
 fileConfig((Path.cwd() / 'config') / 'logging.cfg')


@@ -64,6 +67,17 @@ class Engine:
             for date in dated_features:
                 self._X.setdefault(date,{}).update(dated_features[date])
 
             for date in dated_features:
                 self._X.setdefault(date,{}).update(dated_features[date])
 

+        if self._config['FEATURES'].getboolean('holidays'):
+            holidays = Holidays(config_file =
+                                eval(self._config['FEATURE_CONFIG']['holidays']))
+
+            holidays.start = self._start
+            holidays.end = self._end
+
+            dated_features = holidays.dated_features
+            for date in dated_features:
+                self._X.setdefault(date,{}).update(dated_features[date])
+

 
     def add_target(self):
         self._target = Target(config_file = eval(self._config['TARGET']['config']),
 
     def add_target(self):
         self._target = Target(config_file = eval(self._config['TARGET']['config']),


@@ -71,6 +85,18 @@ class Engine:
                               timestep = self._timestep)
 
 
                               timestep = self._timestep)
 
 

+    def add_preprocessing(self):
+        self._preproc = Preprocessing(config_file = self._config,
+                                      dict_features = self.X,
+                                      dict_target = self.y)
+
+
+    def learn(self):
+        history = self._config['HISTORY_KNOWLEDGE'].getint('nb_lines')
+        self._learner = Learning(config_file = eval(self._config['LEARNER']['config']),
+                                 X = self._preproc.dataframe, y = list(self.y.values())[history:])
+
+

     @property
     def X(self):
         return self._X
     @property
     def X(self):
         return self._X