Metadata-Version: 2.1
Name: building-energy-forecastor
Version: 0.0.2
Summary: Pour l'instant fait pas grand chose
Home-page: https://gitlab.com/KZSLAB/building_energy_forecastor.git
Author: Moi Je
Author-email: arthur.feyt@kaizen-solutions.net
License: UNKNOWN
Description: # Forecastor for Buildings' Consumption
        
        Go check ce lien pour rÃ©diger le README: 
        [Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
        
        ### Installation
        Command line:
        *pip install building_energy_forecastor*
        
        ### Features' list for preprocessing data from *src/building_preprocess*
        * **day_of_week(date_serie)**: Takes a pandas.Series of dates and returns a pandas.Series of corresponding week days
        (['Monday', 'Tuesday', ...]).
        * **set_time_index(df, timeindex='Timestamp')**: Set the time column as index of the dataframe df. By default the column's
        label is 'Timestamp'.
        * **time_to_cycle(df, timeindex='Timestamp')**: From the 3rd competitor of the [Forecast challenge](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/)
        by Schneider Electric. Add column to a copy of df containing cosinus and sinus functions of the time of the day, the month of the year and the day of the year.
        * **add_weather(df, weather, timeindex='Timestamp', freq_temp='D')**: From the 3rd competitor of the [Forecast challenge](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/) by Schneider Electric.
        Adds the weather data to the training dataset (*df* here) merging the two dataframes on the 'Timestamp' and rouding the time
        value in weather to the precised freq_temp ('D' by default).
        * **fill_temperature(df, tempindex='Temperature')**: fill the NaN values in the tempindex column by computing the mean on the
        two closest framing values.
        
        ### Model functions from *src/building_model*
        * **building_regressor()**: Returns a linear regressor from Scikit-learn.
        * **building_train(reg, X, y)**: Trains the regressor with X the data and Y the targeted values.
        * **building_prediction(reg, X)**: Returns a pandas.DataFrame showing the prediction of the regressor *reg* given the data *X*.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
