Metadata-Version: 1.2
Name: likelycause2
Version: 0.1.6
Summary: Likely cause finds creative ways to identify causes
Home-page: https://github.com/Ana-c-ng/likelycause
Author: Ana Garcia
Author-email: ana.n.garcia2@gmail.com
License: MIT
Description: # Likelycause2
        
        Likelycause is an utility package that uses several functions to attribute causes to variations. Using a combination of arithmetical decompositions and bayesian techniques, this was built to facilitate the workflow of a data-analyst working for the private sector.
        
        ## What the package contains
        This package has everything built under the likelycause2 module, so all the functions should be called using “likelycause2.”. Currently, we have 1 auxiliary function and 1 causal function.
        
        ### Auxiliary functions
        - likelycause2.last_period: The last period function is a utility function that builds variation variables in a dataframe._
        
        ### Causal functions
        - likelycause2.bayes_suspects: The bayes_suspects function calculates the conditional probabilities of the event and each suspicious causes or a combination of those causes.  It also suggests an attribution to each individual cause, by adjusting the intersections of causes
        
        ## Likelycause2.last_period
        
        ### Description:
        The last period function is a utility function that builds variation variables in a dataframe.
        Variations are defined between moment t and a moment in the past.
        
        ### Arguments:
        
        - df (pd.DataFrame): the dataframe
        - unique_id (string): unique identifier of each line. Must be unique, and can only be 1 column
        - interval (string): what is the interval you want to calculate variations for. Accepts days, weeks and hours
        - periods (int): number of periods you want to look back on that interval. For last variations, for example, the argument period would be 1
        - date_column (string): the date column in your dateframe. Must be a datetime. To convert, use pandas.to_datetime function
        - to_past (list): list of columns you want to calculate the variations for
        
        ### Returns:
        Returns the dataframe that was inputed with additinal columns named v+name of the columns in the to_past argument. Those columns represent the variation of that variable between moment t and t-periods. This variation is calculated as (Variable in moment t)/(Variable in moment t-periods).
        
        
        ## Likelycause2.bayes_suspects
        
        ### Description:
        The bayes_suspects function calculates the conditional probabilities of the event and each suspicious causes or a combination of those causes. 
        It also suggests an attribution to each individual cause, by adjusting the intersections of causes
        
        ### Arguments:
        
        - df (pd.DataFrame): the dataframe
        - event (string): name of the column that contains the event that we want to explain
        - suspects (list): list with name of the columns that contains the potential causes for what we want to explain
        - point (dictionary): dictionary with the point for which we want to calculate the probability. Must be a combination of the cause and all the individual points of suspects
        
        ### Returns:
        Returns a dataframe with all the possible probabilities combinations, and the conditional probabilities:
        
        - name: name of that conditional combination. If it has one event, it represents P(event|a). If it has 2 events it represents P(event1 & event2|a)
        - prob_ba: P(cause | event)
        - prob_a: P(cause)
        - prob_b: P(event)
        - pbayes: confitional probability
        - pbayes_attribution: suggested probability attribution if we want to attribute to individual causes
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Requires-Python: >=2.7
