Module ImportFrom alias ImportFrom alias ImportFrom alias Import alias identifier ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad Str Num Num Num Num body Assert CompareGt NameLoad Num Str Assert CompareGt NameLoad Num Str Assert CompareGt NameLoad Num Str Assign AttributeStore NameLoad attr Call NameLoad NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Return Call AttributeLoad NameLoad attr Str ListComp AttributeLoad NameLoad attr comprehension NameStore Call NameLoad Call NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Assert CompareEq Call NameLoad NameLoad Call NameLoad NameLoad Str If Call NameLoad NameLoad AttributeLoad NameLoad attr body Assert CompareEq SubscriptLoad AttributeLoad NameLoad attr Index Num Num Str Assign NameStore SubscriptLoad AttributeLoad NameLoad attr ExtSlice Slice Index Num Assign NameStore Call AttributeLoad NameLoad attr Assert CompareEq Call NameLoad NameLoad Num Str Assign NameStore SubscriptLoad NameLoad Index Num Assign NameStore SubscriptLoad NameLoad Index Num If AttributeLoad NameLoad attr body Assign NameStore Call AttributeLoad NameLoad attr NameLoad If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Call AttributeLoad Call NameLoad NameLoad NameLoad keyword NameLoad keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr attr orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Call AttributeLoad Call NameLoad NameLoad NameLoad keyword NameLoad keyword BinOpDiv BinOpDiv Num Call NameLoad NameLoad AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr keyword AttributeLoad NameLoad attr attr orelse Raise Call NameLoad BinOpAdd Str AttributeLoad NameLoad attr Assign NameStore NameLoad If AttributeLoad NameLoad attr body Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Slice Num orelse Assign NameStore AttributeLoad NameLoad attr Return Call NameLoad NameLoad NameLoad NameLoad NameLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr decorator_list decorator_list
N/A pyfora.algorithms.logistic.FullRankMajorizationSolver FullRankMajorizationSolver pyfora.algorithms.logistic.TrustRegionConjugateGradientSolver TrustRegionConjugateGradientSolver pyfora.algorithms.logistic.BinaryLogisticRegressionModel BinaryLogisticRegressionModel N/A pyfora.pure_modules.pure_pandas PurePandas BinaryLogisticRegressionFitter N/A object N/A N/A 
    BinaryLogisticRegressionFitter

    A logistic regression "fitter" ithat holds fitting parameters used to fit logit models.

    Args:
        C (float): Inverse of regularization strength; must be a positive float.
        hasIntercept (bool): If True, include an intercept (aka bias) term in
            the fitted models.
        method (string): one of 'newton-cg' (default) or 'majorization'
        interceptScale (float): When ``hasIntercept`` is True, feature vectors
            become ``[x, interceptScale]``, i.e. we add a "synthetic" feature
            with constant value ``interceptScale`` to all of the feature vectors.
            This synthetic feature is subject to regularization as all other
            features. To lessen the effect of regularization, users should
            increase this value.
        tol (float): Tolerance for stopping criteria. Fitting stops when
            the l2-norm of the parameters to update do not change more than ``tol``.
        maxIter (int): A hard limit on the number of update cycles allowed.
     __init__ N/A N/A self C hasIntercept method interceptScale tol maxIter splitLimit N/A True newton-cg 1.0 0.0001 100000.0 1000000 N/A N/A N/A tol 0 tol must be > 0 N/A N/A maxIter 0 maxIter must be > 0 N/A N/A C 0 C must be > 0 N/A N/A self regularizer N/A float C N/A N/A self hasIntercept hasIntercept N/A N/A self method method N/A N/A self interceptScale interceptScale N/A N/A self tol tol N/A N/A self maxIter maxIter N/A N/A self splitLimit splitLimit N/A _addScaleColumn N/A N/A self df N/A N/A N/A N/A N/A df pyfora_addColumn intercept N/A N/A self interceptScale N/A _ N/A xrange N/A len df N/A fit N/A N/A self X y N/A N/A N/A 
         fit a (regularized) logit model to the predictors ``X`` and responses ``y``.

        Args:
            X: a dataframe of feature vectors.
            y: a dataframe (with one column) which contains the "target" values,
                corresponding to the feature vectors in ``X``.

        Returns:
            A :class:`~pyfora.algorithms.BinaryLogisticRegressionModel.BinaryLogisticRegressionModel`
            which represents the fit model.

        Example::

            # fit a logit model without intercept using regularizer 1.0

            from pyfora.algorithms import BinaryLogisticRegressionFitter

            fitter = BinaryLogisticRegressionFitter(1.0, False)
            x = pandas.DataFrame({'x0': [-1,0,1], 'x1': [0,1,1]})
            y = pandas.DataFrame({'y': [0,1,1]})

            model = fitter.fit(x, y)

         N/A N/A N/A len X N/A len y len(X) doesn't match len(y) N/A N/A isinstance y N/A PurePandas PurePythonDataFrame N/A N/A N/A N/A N/A y shape N/A 1 1 for dataframe y, need y.shape[1] == 1 N/A y N/A N/A y iloc N/A N/A N/A 0 N/A classes N/A N/A y unique N/A N/A N/A len classes 2 need len(classes) == 2 N/A classZeroLabel N/A classes N/A 0 N/A classOneLabel N/A classes N/A 1 N/A N/A self hasIntercept N/A N/A X N/A N/A self _addScaleColumn X N/A N/A N/A self method newton-cg N/A N/A returnValue N/A N/A N/A TrustRegionConjugateGradientSolver X y classZeroLabel classZeroLabel C N/A self regularizer eps N/A self tol maxIters N/A self maxIter splitLimit N/A self splitLimit solve N/A N/A N/A N/A self method majorization N/A N/A returnValue N/A N/A N/A FullRankMajorizationSolver X y classZeroLabel classZeroLabel regularizer N/A N/A 1.0 N/A len X N/A self regularizer tol N/A self tol maxIters N/A self maxIter splitLimit N/A self splitLimit solve N/A N/A N/A Exception N/A bad method argument passed in:  N/A self method N/A intercept None N/A N/A self hasIntercept N/A N/A intercept N/A N/A returnValue weights N/A -1 N/A weights N/A N/A returnValue weights N/A -1 N/A N/A weights N/A returnValue weights N/A N/A BinaryLogisticRegressionModel weights classZeroLabel classOneLabel intercept N/A self interceptScale N/A returnValue iterations N/A N/A