COIM 0.0.2

Description:

COIM 0.0.2

COIM
Constrain Operator for Inferential Models is a simple tool for pre and pos processing of data to eliminate redunduncy in datasets caused by dependency rules between the variables/columns.
Usage
To start using COIM, import into your code the operator class, which orquestrates the constrains and define an instance.
from COIM import ConstrainOperator
CO=ConstrainOperator()

To add a new constrain, use the add_rule method from ConstrainOperator class.
from COIM import SomeConstrain
SC=SomeConstrain(**parameters)
CO.add_rule(SC)

Each constrain will require their own specific parameters, refer to section Available constrains to know each of them. However, all constrains receive the parameter "labels", which is a list with the new names to be used on the encoded columns.
Then you can encode your dataframe to use the new corrected variables to feed your model.
new_df=CO.encode_dataframe(df)
After running your model, you can regenerate the data in the original format, decoding the acquired values and errors.
decoded_df, decoded_errors=CO.decode_dataframe(predicted_df, errors)
That will yield the predictions for the original variables as if they had been fed to the model themselves, but with rather more consistent results
Available constrains

"add_scalar":

a+K=b
base_variable = a
target_variable = b
constant = K


"mul_scalar":

a∗K=b
base_variable = a
target_variable = b
constant = K


"const_sum":

∑Wi⋅ai=K
variables = [a1,a2,⋯,an]
reference_variable = aj
constant_sum = K
weights = [W1,W2,⋯,Wn] or W if W1=W2=⋯=Wn


"custom_func":

to be used when none of the above is applicable and you have to develop your own functions to operate the dataframe
variables : list of the variables to be used
validate_function: Function to assert if the received dataframe follows the given constrain. (df[DataFrame], variables[list], labels[list])->bool
format_function: Write a string that describes the constrain equation. (variables[list], labels[list])->str
encode_dataframe: Create the new custom columns in the dataframe. (df[DataFrame], variables[list], labels[list])->DataFrame
decode_dataframe: Restore the original columns in the dataframe and calculate the propagated errors. (df[DataFrame], variables[list], labels[list], errors[DataFrame])->DataFrame, DataFrame



Future additions
In the foreseeable future, some new constrains will be implemented, those are:

Variable sum
Constant and variable products
Conditionals

Theoretical foundation
All of the worked out mathematics for the developed constrains can be found at the calculations pdf