# Path: epistasis/models/utils.py
# def arghandler(method):
#     """Points methods to argument handlers. Assumes each argument has a
#     corresponding method attached to the object named "_{argument}". These
#     methods given default values to arguments.
# 
#     Ignores self and kwargs
#     """
#     @wraps(method)
#     def inner(self, *args, **kwargs):
#         # Get method name
#         name = method.__name__
# 
#         # Inspect function for arguments to update.
#         out = inspect.signature(method)
# 
#         # Construct kwargs from signature.
#         kws = {key: val.default for key, val in out.parameters.items()}
#         kws.pop('self')
# 
#         # Try to remove kwargs.
#         try:
#             kws.pop('kwargs')
#         except:
#             pass
# 
#         # Update kwargs with user specified kwargs.
#         kws.update(**kwargs)
# 
#         # Handle each argument
#         for arg in kws:
#             # Get handler function.
#             handler_name = "_{}".format(arg)
#             handler = getattr(self, handler_name)
#             kws[arg] = handler(data=kws[arg], method=name)
# 
#         return method(self, **kws)
#     return inner
# 
# class FittingError(Exception):
#     """Exception Subclass for X matrix errors."""
# 
# Path: epistasis/models/nonlinear/minimizer.py
# class FunctionMinimizer(Minimizer):
#     """Minimizer object that fits data using a function.
#     """
#     def __init__(self, function, **p0):
#         # Do some inspection to get the parameters from the nonlinear
#         # function argument list.
#         func_signature = inspect.signature(function)
#         func_params = list(func_signature.parameters.keys())
# 
#         if func_params[0] != "x":
#             raise Exception("First argument of the nonlinear function must "
#                             "be `x`.")
# 
#         # Construct lmfit parameters object
#         self.parameters = Parameters()
#         for p in func_params[1:]:
#             # Get starting value of parameter if given.
#             val = None
#             if p in p0:
#                 val = p0[p]
#             # Add parameter.
#             self.parameters.add(name=p, value=val)
# 
#         # Set function
#         self._function = function
# 
#     def function(self, x, *args, **kwargs):
#         """Execute the function."""
#         return self._function(x, *args, **kwargs)
# 
#     def predict(self, x):
#         """Call function"""
#         return self._function(x, **self.parameters)
# 
#     def transform(self, x, y):
#         """Transform y onto the x scale."""
#         ymodel = self.predict(x)
#         return (y - ymodel) + x
# 
#     def fit(self, x, y):
#         """Fit the function"""
#         # Store residual steps in case fit fails.
#         last_residual_set = None
# 
#         # Residual function to minimize.
#         def residual(params, func, x, y=None):
#             # Fit model
#             parvals = list(params.values())
#             ymodel = func(x, *parvals)
# 
#             # Store items in case of error.
#             nonlocal last_residual_set
#             last_residual_set = (params, ymodel)
# 
#             return y - ymodel
# 
#         # Minimize the above residual function.
#         try:
#             self.minimizer = lmfit.minimize(
#                 residual,
#                 self.parameters,
#                 args=[self._function, x],
#                 kws={'y': y})
# 
#             # Point to nonlinear.
#             self.parameters = self.minimizer.params
# 
#         # If fitting fails, print what happened
#         except Exception as e:
#             # if e is ValueError
#             print("ERROR! Some of the transformed phenotypes are invalid.")
#             print("\nParameters:")
#             print("----------")
#             print(last_residual_set[0].pretty_print())
#             print("\nTransformed phenotypes:")
#             print("----------------------")
#             print(last_residual_set[1])
#             raise e


import warnings
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, RegressorMixin
from gpmap import GenotypePhenotypeMap
from epistasis.mapping import EpistasisMap
from epistasis.models.base import BaseModel
from epistasis.models.utils import (arghandler, FittingError)
from epistasis.models.linear import (EpistasisLinearRegression, EpistasisLasso)
from epistasis.stats import pearson
from .minimizer import FunctionMinimizer
        self.parameters = self.minimizer.parameters

    @arghandler
    def fit_transform(self, X=None, y=None, **kwargs):
        self.fit(X=X, y=y, **kwargs)

        linear_phenotypes = self.transform(X=X, y=y)

        # Transform map.
        gpm = GenotypePhenotypeMap.read_dataframe(
            dataframe=self.gpm.data,
            wildtype=self.gpm.wildtype,
            mutations=self.gpm.mutations
        )

        gpm.data['phenotypes'] = linear_phenotypes
        return gpm

    def predict(self, X=None):
        x = self.Additive.predict(X=X)
        y = self.minimizer.predict(x)
        return y

    def predict_transform(self, X=None, y=None):
        if y is None:
            x = self.Additive.predict(X=X)
        else:
            x = y
        return self.minimizer.predict(x)


# Path: epistasis/models/utils.py
# def arghandler(method):
#     """Points methods to argument handlers. Assumes each argument has a
#     corresponding method attached to the object named "_{argument}". These
#     methods given default values to arguments.
# 
#     Ignores self and kwargs
#     """
#     @wraps(method)
#     def inner(self, *args, **kwargs):
#         # Get method name
#         name = method.__name__
# 
#         # Inspect function for arguments to update.
#         out = inspect.signature(method)
# 
#         # Construct kwargs from signature.
#         kws = {key: val.default for key, val in out.parameters.items()}
#         kws.pop('self')
# 
#         # Try to remove kwargs.
#         try:
#             kws.pop('kwargs')
#         except:
#             pass
# 
#         # Update kwargs with user specified kwargs.
#         kws.update(**kwargs)
# 
#         # Handle each argument
#         for arg in kws:
#             # Get handler function.
#             handler_name = "_{}".format(arg)
#             handler = getattr(self, handler_name)
#             kws[arg] = handler(data=kws[arg], method=name)
# 
#         return method(self, **kws)
#     return inner
# 
# class FittingError(Exception):
#     """Exception Subclass for X matrix errors."""
# 
# Path: epistasis/models/nonlinear/minimizer.py
# class FunctionMinimizer(Minimizer):
#     """Minimizer object that fits data using a function.
#     """
#     def __init__(self, function, **p0):
#         # Do some inspection to get the parameters from the nonlinear
#         # function argument list.
#         func_signature = inspect.signature(function)
#         func_params = list(func_signature.parameters.keys())
# 
#         if func_params[0] != "x":
#             raise Exception("First argument of the nonlinear function must "
#                             "be `x`.")
# 
#         # Construct lmfit parameters object
#         self.parameters = Parameters()
#         for p in func_params[1:]:
#             # Get starting value of parameter if given.
#             val = None
#             if p in p0:
#                 val = p0[p]
#             # Add parameter.
#             self.parameters.add(name=p, value=val)
# 
#         # Set function
#         self._function = function
# 
#     def function(self, x, *args, **kwargs):
#         """Execute the function."""
#         return self._function(x, *args, **kwargs)
# 
#     def predict(self, x):
#         """Call function"""
#         return self._function(x, **self.parameters)
# 
#     def transform(self, x, y):
#         """Transform y onto the x scale."""
#         ymodel = self.predict(x)
#         return (y - ymodel) + x
# 
#     def fit(self, x, y):
#         """Fit the function"""
#         # Store residual steps in case fit fails.
#         last_residual_set = None
# 
#         # Residual function to minimize.
#         def residual(params, func, x, y=None):
#             # Fit model
#             parvals = list(params.values())
#             ymodel = func(x, *parvals)
# 
#             # Store items in case of error.
#             nonlocal last_residual_set
#             last_residual_set = (params, ymodel)
# 
#             return y - ymodel
# 
#         # Minimize the above residual function.
#         try:
#             self.minimizer = lmfit.minimize(
#                 residual,
#                 self.parameters,
#                 args=[self._function, x],
#                 kws={'y': y})
# 
#             # Point to nonlinear.
#             self.parameters = self.minimizer.params
# 
#         # If fitting fails, print what happened
#         except Exception as e:
#             # if e is ValueError
#             print("ERROR! Some of the transformed phenotypes are invalid.")
#             print("\nParameters:")
#             print("----------")
#             print(last_residual_set[0].pretty_print())
#             print("\nTransformed phenotypes:")
#             print("----------------------")
#             print(last_residual_set[1])
#             raise e


# Path: epistasis/models/nonlinear/ordinary.py
import warnings
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, RegressorMixin
from gpmap import GenotypePhenotypeMap
from epistasis.mapping import EpistasisMap
from epistasis.models.base import BaseModel
from epistasis.models.utils import (arghandler, FittingError)
from epistasis.models.linear import (EpistasisLinearRegression, EpistasisLasso)
from epistasis.stats import pearson
from .minimizer import FunctionMinimizer

        self.parameters = self.minimizer.parameters

    @arghandler
    def fit_transform(self, X=None, y=None, **kwargs):
        self.fit(X=X, y=y, **kwargs)

        linear_phenotypes = self.transform(X=X, y=y)

        # Transform map.
        gpm = GenotypePhenotypeMap.read_dataframe(
            dataframe=self.gpm.data,
            wildtype=self.gpm.wildtype,
            mutations=self.gpm.mutations
        )

        gpm.data['phenotypes'] = linear_phenotypes
        return gpm

    def predict(self, X=None):
        x = self.Additive.predict(X=X)
        y = self.minimizer.predict(x)
        return y

    def predict_transform(self, X=None, y=None):
        if y is None:
            x = self.Additive.predict(X=X)
        else:
            x = y
        return self.minimizer.predict(x)


    @arghandler