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Scaling input data with StandardScaler or MinMaxScaler might help.Empty shape tuple for cython.arrayCannot create writable memory view from read-only memoryviewCannot assign to read-only memoryviewCan only create a buffer that is contiguous in memory.Buffer view does not expose stridesunknown dtype code in numpy.pxd (%d)numpy.core.multiarray failed to import<strided and direct or indirect>sklearn.linear_model._sgd_fastTotal training time: %.2f seconds.unable to allocate array data.__pyx_unpickle_Classificationndarray is not C contiguousitemsize <= 0 for cython.array__pyx_unpickle_LossFunctionSquaredEpsilonInsensitiveInvalid shape in axis %d: %d.Cannot index with type '%s'__pyx_unpickle_Regression<contiguous and indirect><MemoryView of %r at 0x%x><MemoryView of %r object><contiguous and direct>validation_mask_view<strided and indirect>no_improvement_countvalidation_score_cb<strided and direct>standard_interceptcline_in_tracebackEpsilonInsensitive__pyx_unpickle_Enumaverage_interceptConvergence after standard_weightsn_iter_no_changevalidation_mask__setstate_cython____pyx_PickleErrorintercept_decaydtype_is_objectaverage_weightsallocate_bufferView.MemoryViewearly_stoppingClassificationsample_weight__reduce_cython____pyx_getbufferlearning_ratefit_interceptModifiedHuberstringsourcesample_index__pyx_checksumpenalty_typeoptimal_initinitial_eta0class_weightSquaredHingeRuntimeErrorLossFunctionplain_sgd epochs took average_sgdSquaredLossPickleErrorMemoryErrorImportErrorx_data_ptrweight_posweight_negq_data_ptr__pyx_vtable____pyx_resultn_featuresmax_weightmax_changebest_scoreValueErrorRegressionIndexErrorx_ind_ptrthreshold__reduce_ex____pyx_state_plain_sgdn_samplesinterceptenumeratebest_lossTypeErrorMAX_DLOSS__setstate____pyx_typemax_iterl1_ratioitemsizeis_hingeinfinity__getstate__Ellipsis, Avg. loss: weightsverboset_startsumlossshuffle secondspower_tnonzeromemviewfortranfloat64epsilondatasetaverage-- Epoch %dupdateunpackstruct__reduce__ps_ptrpickle__name__n_iter___import__formatencodezerosw_ptrstartshapescorerangeprintordernumpyflagserrorepochdtypedlosscount__class__alphaHuberHingeASCIIxnnztypwtime__test__stopstepsqrtsizeseedpackndimnamemode__main__losseta0__dict__baseNorm: , NNZs: , Bias: tolsysobj__new__eta_Lognpid.6f.2fywutqpifdc, T: OCBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'memviewslice is already initialized!sklearn/linear_model/_sgd_fast.pyxUnable to initialize pickling for %s%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectinvalid vtable found for imported typeDoes not understand character buffer dtype format string ('%c')Argument '%.200s' has incorrect type (expected %.200s, got %.200s)sklearn/linear_model/_sgd_fast.cView.MemoryView.memoryview.__str__View.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.setitem_indexedView.MemoryView.array.memview.__get__View.MemoryView.array.__getattr__View.MemoryView.array.__getitem__View.MemoryView.array.__setitem__Subscript deletion not supported by %.200ssklearn.linear_model._sgd_fast.SquaredEpsilonInsensitive.__reduce__sklearn.linear_model._sgd_fast.EpsilonInsensitive.__reduce__sklearn.linear_model._sgd_fast.Huber.__reduce__sklearn.linear_model._sgd_fast.SquaredHinge.__reduce__sklearn.linear_model._sgd_fast.Hinge.__reduce__sklearn.linear_model._sgd_fast.ModifiedHuber.__reduce__sklearn.linear_model._sgd_fast.SquaredLoss.__reduce__sklearn.linear_model._sgd_fast.Log.__reduce____int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)value too large to convert to npy_uint32can't convert negative value to npy_uint32%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%.200s() takes %.8s %zd positional argument%.1s (%zd given)sklearn.linear_model._sgd_fast.LossFunction.dlosssklearn.linear_model._sgd_fast.Hinge.__init__sklearn.linear_model._sgd_fast.SquaredHinge.__init__sklearn.linear_model._sgd_fast.Huber.__init__sklearn.linear_model._sgd_fast.EpsilonInsensitive.__init__sklearn.linear_model._sgd_fast.SquaredEpsilonInsensitive.__init__Unexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Buffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)View.MemoryView.memoryview.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_objectAcquisition count is %d (line %d)calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionView.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__View.MemoryView.memoryview.strides.__get__View.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView._memoryviewslice.__setstate_cython__PyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.array.__getbuffer__View.MemoryView.memoryview.__getbuffer__View.MemoryView.array.get_memviewView.MemoryView.memoryview_cwrapperView.MemoryView.memoryview.is_sliceView.MemoryView.memoryview_fromsliceView.MemoryView.memoryview_copy_from_sliceView.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectsklearn.linear_model._sgd_fast.Classification.__reduce_cython__sklearn.linear_model._sgd_fast.LossFunction.__reduce_cython__sklearn.linear_model._sgd_fast.Regression.__reduce_cython__View.MemoryView.Enum.__reduce_cython__View.MemoryView.memoryview.__repr__value too large to convert to intc-string too long to convert to Pythonsklearn.linear_model._sgd_fast.average_sgdtoo many values to unpack (expected %zd)need more than %zd value%.1s to unpacksklearn.linear_model._sgd_fast.plain_sgdCannot convert %.200s to %.200sView.MemoryView.get_slice_from_memviewView.MemoryView.memoryview.is_f_contigView.MemoryView.memoryview.is_c_contigView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalarobject of type 'NoneType' has no len()hasattr(): attribute name must be stringsklearn.linear_model._sgd_fast.__pyx_unpickle_LossFunction__set_statesklearn.linear_model._sgd_fast.LossFunction.__setstate_cython__sklearn.linear_model._sgd_fast.__pyx_unpickle_Classification__set_statesklearn.linear_model._sgd_fast.Classification.__setstate_cython__sklearn.linear_model._sgd_fast.__pyx_unpickle_Regression__set_statesklearn.linear_model._sgd_fast.Regression.__setstate_cython__'NoneType' object is not subscriptableView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__Cannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.copysklearn.linear_model._sgd_fast.__pyx_unpickle_LossFunctionsklearn.linear_model._sgd_fast.__pyx_unpickle_RegressionView.MemoryView.__pyx_unpickle_Enumsklearn.linear_model._sgd_fast.__pyx_unpickle_Classificationinteger division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointer'NoneType' object is not iterableView.MemoryView.memoryview.__setitem__View.MemoryView.array.__cinit__Argument '%.200s' must not be Noneexpected bytes, NoneType foundAll dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceStep may not be zero (axis %d)local variable '%s' referenced before assignmentView.MemoryView.memoryview.__getitem__View.MemoryView.memoryview.__cinit__Buffer exposes suboffsets but no stridesBuffer not compatible with direct access in dimension %d.Item size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)sklearn.linear_model._sgd_fast._plain_sgdView.MemoryView.memoryview.setitem_slice_assignmentsklearn.linear_model._sgd_fastcompiletime version %s of module '%.100s' does not match runtime version %ssklearn/utils/_weight_vector.pxdsklearn/utils/_seq_dataset.pxd_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimeinit sklearn.linear_model._sgd_fastsklearn.linear_model._sgd_fast._memoryviewsliceInternal class for passing memoryview slices to Pythonsklearn.linear_model._sgd_fast.memoryviewsklearn.linear_model._sgd_fast.Enumsklearn.linear_model._sgd_fast.arraysklearn.linear_model._sgd_fast.SquaredEpsilonInsensitiveEpsilon-Insensitive loss.

    loss = max(0, |y - p| - epsilon)^2
    sklearn.linear_model._sgd_fast.EpsilonInsensitiveEpsilon-Insensitive loss (used by SVR).

    loss = max(0, |y - p| - epsilon)
    sklearn.linear_model._sgd_fast.HuberHuber regression loss

    Variant of the SquaredLoss that is robust to outliers (quadratic near zero,
    linear in for large errors).

    https://en.wikipedia.org/wiki/Huber_Loss_Function
    sklearn.linear_model._sgd_fast.SquaredHingeSquared Hinge loss for binary classification tasks with y in {-1,1}

    Parameters
    ----------

    threshold : float > 0.0
        Margin threshold. When threshold=1.0, one gets the loss used by
        (quadratically penalized) SVM.
    sklearn.linear_model._sgd_fast.HingeHinge loss for binary classification tasks with y in {-1,1}

    Parameters
    ----------

    threshold : float > 0.0
        Margin threshold. When threshold=1.0, one gets the loss used by SVM.
        When threshold=0.0, one gets the loss used by the Perceptron.
    sklearn.linear_model._sgd_fast.ModifiedHuberModified Huber loss for binary classification with y in {-1, 1}

    This is equivalent to quadratically smoothed SVM with gamma = 2.

    See T. Zhang 'Solving Large Scale Linear Prediction Problems Using
    Stochastic Gradient Descent', ICML'04.
    sklearn.linear_model._sgd_fast.SquaredLossSquared loss traditional used in linear regression.sklearn.linear_model._sgd_fast.LogLogistic regression loss for binary classification with y in {-1, 1}sklearn.linear_model._sgd_fast.ClassificationBase class for loss functions for classificationsklearn.linear_model._sgd_fast.RegressionBase class for loss functions for regressionsklearn.linear_model._sgd_fast.LossFunctionBase class for convex loss functionsð?ð¿ÀÀà?2@2Àððÿ¢”m¢”mBÿÿÿÿÿÿïÿÿÿÿÿÿïÿ�÷ư>@€ÿÿÿÿÿÿÿ
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Ð#hjH! I	þÿÿoø ÿÿÿoðÿÿošùÿÿoêÍ#&w6wFwVwfwvw†w–w¦w¶wÆwÖwæwöwxx&x6xFxVxfxvx†x–x¦x¶xÆxÖxæxöxyy&y6yFyVyfyvy†y–y¦y¶yÆyÖyæyöyzz&z6zFzVzfzvz†z–z¦z¶zÆzÖzæzöz{{&{6{F{V{f{v{†{–{¦{¶{Æ{Ö{æ{ö{||&|6|F|V|f|v|†|–|¦|¶|Æ|Ö|æ|ö|}}&}6}F}V}f}v}†}–}¦}¶}Æ}Ö}æ}ö}~~&~6~F~V~f~v~†~–~¦~¶~Æ~Ö~æ~ö~&6FVfv`Ô#Average SGD for generic loss functions and penalties.

    Parameters
    ----------
    weights : ndarray[double, ndim=1]
        The allocated coef_ vector.
    intercept : double
        The initial intercept.
    average_weights : ndarray[double, ndim=1]
        The average weights as computed for ASGD
    average_intercept : double
        The average intercept for ASGD
    loss : LossFunction
        A concrete ``LossFunction`` object.
    penalty_type : int
        The penalty 2 for L2, 1 for L1, and 3 for Elastic-Net.
    alpha : float
        The regularization parameter.
    C : float
        Maximum step size for passive aggressive.
    l1_ratio : float
        The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
        l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
    dataset : SequentialDataset
        A concrete ``SequentialDataset`` object.
    validation_mask : ndarray[unsigned char, ndim=1]
        Equal to True on the validation set.
    early_stopping : boolean
        Whether to use a stopping criterion based on the validation set.
    validation_score_cb : callable
        A callable to compute a validation score given the current
        coefficients and intercept values.
        Used only if early_stopping is True.
    n_iter_no_change : int
        Number of iteration with no improvement to wait before stopping.
    max_iter : int
        The maximum number of iterations (epochs).
    tol: double
        The tolerance for the stopping criterion.
    dataset : SequentialDataset
        A concrete ``SequentialDataset`` object.
    fit_intercept : int
        Whether or not to fit the intercept (1 or 0).
    verbose : int
        Print verbose output; 0 for quite.
    shuffle : boolean
        Whether to shuffle the training data before each epoch.
    weight_pos : float
        The weight of the positive class.
    weight_neg : float
        The weight of the negative class.
    seed : np.uint32_t
        Seed of the pseudorandom number generator used to shuffle the data.
    learning_rate : int
        The learning rate:
        (1) constant, eta = eta0
        (2) optimal, eta = 1.0/(alpha * t).
        (3) inverse scaling, eta = eta0 / pow(t, power_t)
        (4) adaptive decrease
        (5) Passive Aggressive-I, eta = min(alpha, loss/norm(x))
        (6) Passive Aggressive-II, eta = 1.0 / (norm(x) + 0.5*alpha)
    eta0 : double
        The initial learning rate.
    power_t : double
        The exponent for inverse scaling learning rate.
    t : double
        Initial state of the learning rate. This value is equal to the
        iteration count except when the learning rate is set to `optimal`.
        Default: 1.0.
    average : int
        The number of iterations before averaging starts. average=1 is
        equivalent to averaging for all iterations.

    Returns
    -------
    weights : array, shape=[n_features]
        The fitted weight vector.
    intercept : float
        The fitted intercept term.
    average_weights : array shape=[n_features]
        The averaged weights across iterations
    average_intercept : float
        The averaged intercept across iterations
    n_iter_ : int
        The actual number of iter (epochs).
    Plain SGD for generic loss functions and penalties.

    Parameters
    ----------
    weights : ndarray[double, ndim=1]
        The allocated coef_ vector.
    intercept : double
        The initial intercept.
    loss : LossFunction
        A concrete ``LossFunction`` object.
    penalty_type : int
        The penalty 2 for L2, 1 for L1, and 3 for Elastic-Net.
    alpha : float
        The regularization parameter.
    C : float
        Maximum step size for passive aggressive.
    l1_ratio : float
        The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
        l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
    dataset : SequentialDataset
        A concrete ``SequentialDataset`` object.
    validation_mask : ndarray[unsigned char, ndim=1]
        Equal to True on the validation set.
    early_stopping : boolean
        Whether to use a stopping criterion based on the validation set.
    validation_score_cb : callable
        A callable to compute a validation score given the current
        coefficients and intercept values.
        Used only if early_stopping is True.
    n_iter_no_change : int
        Number of iteration with no improvement to wait before stopping.
    max_iter : int
        The maximum number of iterations (epochs).
    tol: double
        The tolerance for the stopping criterion.
    fit_intercept : int
        Whether or not to fit the intercept (1 or 0).
    verbose : int
        Print verbose output; 0 for quite.
    shuffle : boolean
        Whether to shuffle the training data before each epoch.
    weight_pos : float
        The weight of the positive class.
    weight_neg : float
        The weight of the negative class.
    seed : np.uint32_t
        Seed of the pseudorandom number generator used to shuffle the data.
    learning_rate : int
        The learning rate:
        (1) constant, eta = eta0
        (2) optimal, eta = 1.0/(alpha * t).
        (3) inverse scaling, eta = eta0 / pow(t, power_t)
        (4) adaptive decrease
        (5) Passive Aggressive-I, eta = min(alpha, loss/norm(x))
        (6) Passive Aggressive-II, eta = 1.0 / (norm(x) + 0.5*alpha)
    eta0 : double
        The initial learning rate.
    power_t : double
        The exponent for inverse scaling learning rate.
    t : double
        Initial state of the learning rate. This value is equal to the
        iteration count except when the learning rate is set to `optimal`.
        Default: 1.0.
    intercept_decay : double
        The decay ratio of intercept, used in updating intercept.

    Returns
    -------
    weights : array, shape=[n_features]
        The fitted weight vector.
    intercept : float
        The fitted intercept term.
    n_iter_ : int
        The actual number of iter (epochs).
    Evaluate the derivative of the loss function with respect to
        the prediction `p`.

        Parameters
        ----------
        p : double
            The prediction, p = w^T x
        y : double
            The true value (aka target)
        Returns
        -------
        double
            The derivative of the loss function with regards to `p`.
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