pipelinex.extras.ops package

Subpackages

• pipelinex.extras.ops.ignite package
  • Subpackages
    • pipelinex.extras.ops.ignite.declaratives package
      • Submodules
      • pipelinex.extras.ops.ignite.declaratives.declarative_trainer module
    • pipelinex.extras.ops.ignite.handlers package
      • Submodules
      • pipelinex.extras.ops.ignite.handlers.flexible_checkpoint module
      • pipelinex.extras.ops.ignite.handlers.time_limit module
    • pipelinex.extras.ops.ignite.metrics package
      • Submodules
      • pipelinex.extras.ops.ignite.metrics.cohen_kappa_score module
      • pipelinex.extras.ops.ignite.metrics.fbeta_score module
      • pipelinex.extras.ops.ignite.metrics.utils module

Submodules

pipelinex.extras.ops.allennlp_ops module

class pipelinex.extras.ops.allennlp_ops.AllennlpReaderToDict(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

pipelinex.extras.ops.argparse_ops module

class pipelinex.extras.ops.argparse_ops.FeedArgsDict(func, args={}, force_return=None)

Bases: object

__init__(func, args={}, force_return=None)

Initialize self. See help(type(self)) for accurate signature.

pipelinex.extras.ops.argparse_ops.namespace(d)

pipelinex.extras.ops.numpy_ops module

class pipelinex.extras.ops.numpy_ops.ReverseChannel(channel_first=False)

Bases: object

__init__(channel_first=False)

Initialize self. See help(type(self)) for accurate signature.

pipelinex.extras.ops.numpy_ops.reverse_channel(a, channel_first=False)

pipelinex.extras.ops.numpy_ops.to_channel_first_arr(a)

pipelinex.extras.ops.numpy_ops.to_channel_last_arr(a)

pipelinex.extras.ops.opencv_ops module

class pipelinex.extras.ops.opencv_ops.CvBGR2Gray(*args, **kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

fn = 'cvtColor'

class pipelinex.extras.ops.opencv_ops.CvBGR2HSV(*args, **kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

fn = 'cvtColor'

class pipelinex.extras.ops.opencv_ops.CvBilateralFilter(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'bilateralFilter'

class pipelinex.extras.ops.opencv_ops.CvBlur(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'blur'

class pipelinex.extras.ops.opencv_ops.CvBoxFilter(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'boxFilter'

class pipelinex.extras.ops.opencv_ops.CvCanny(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'Canny'

class pipelinex.extras.ops.opencv_ops.CvCvtColor(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'cvtColor'

class pipelinex.extras.ops.opencv_ops.CvDiagonalEdgeFilter2d(kernel_type=2, **kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvModuleL2

__init__(kernel_type=2, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.opencv_ops.CvDictToDict(**kwargs)

Bases: pipelinex.utils.DictToDict

module = <module 'cv2.cv2' from '/home/docs/checkouts/readthedocs.org/user_builds/pipelinex/envs/0.4.9/lib/python3.6/site-packages/cv2/cv2.cpython-36m-x86_64-linux-gnu.so'>

class pipelinex.extras.ops.opencv_ops.CvDilate(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'dilate'

class pipelinex.extras.ops.opencv_ops.CvEqualizeHist(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'equalizeHist'

class pipelinex.extras.ops.opencv_ops.CvErode(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'erode'

class pipelinex.extras.ops.opencv_ops.CvFilter2d(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'filter2D'

class pipelinex.extras.ops.opencv_ops.CvGaussianBlur(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'GaussianBlur'

class pipelinex.extras.ops.opencv_ops.CvHoughLinesP(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'HoughLinesP'

class pipelinex.extras.ops.opencv_ops.CvLine(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'line'

class pipelinex.extras.ops.opencv_ops.CvMedianBlur(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'medianBlur'

class pipelinex.extras.ops.opencv_ops.CvModuleConcat(*modules)

Bases: pipelinex.extras.ops.opencv_ops.CvModuleListMerge

class pipelinex.extras.ops.opencv_ops.CvModuleL1(*modules)

Bases: pipelinex.extras.ops.opencv_ops.CvModuleStack

class pipelinex.extras.ops.opencv_ops.CvModuleL2(*modules)

Bases: pipelinex.extras.ops.opencv_ops.CvModuleStack

class pipelinex.extras.ops.opencv_ops.CvModuleListMerge(*modules)

Bases: object

__init__(*modules)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.opencv_ops.CvModuleMean(*modules)

Bases: pipelinex.extras.ops.opencv_ops.CvModuleStack

class pipelinex.extras.ops.opencv_ops.CvModuleStack(*modules)

Bases: pipelinex.extras.ops.opencv_ops.CvModuleListMerge

class pipelinex.extras.ops.opencv_ops.CvModuleSum(*modules)

Bases: pipelinex.extras.ops.opencv_ops.CvModuleStack

class pipelinex.extras.ops.opencv_ops.CvResize(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'resize'

class pipelinex.extras.ops.opencv_ops.CvScale(width, height)

Bases: object

__init__(width, height)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.opencv_ops.CvSobel(ddepth='CV_64F', **kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

__init__(ddepth='CV_64F', **kwargs)

Initialize self. See help(type(self)) for accurate signature.

fn = 'Sobel'

class pipelinex.extras.ops.opencv_ops.CvThreshold(type='THRESH_BINARY', **kwargs)

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

__init__(type='THRESH_BINARY', **kwargs)

Initialize self. See help(type(self)) for accurate signature.

fn = 'threshold'

class pipelinex.extras.ops.opencv_ops.NpAbs(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'abs'

class pipelinex.extras.ops.opencv_ops.NpConcat(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'concatenate'

class pipelinex.extras.ops.opencv_ops.NpDictToDict(**kwargs)

Bases: pipelinex.utils.DictToDict

module = <module 'numpy' from '/home/docs/checkouts/readthedocs.org/user_builds/pipelinex/envs/0.4.9/lib/python3.6/site-packages/numpy/__init__.py'>

class pipelinex.extras.ops.opencv_ops.NpFullLike(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'full_like'

class pipelinex.extras.ops.opencv_ops.NpMean(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'mean'

class pipelinex.extras.ops.opencv_ops.NpOnesLike(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'ones_like'

class pipelinex.extras.ops.opencv_ops.NpSqrt(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'sqrt'

class pipelinex.extras.ops.opencv_ops.NpSquare(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'square'

class pipelinex.extras.ops.opencv_ops.NpStack(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'stack'

class pipelinex.extras.ops.opencv_ops.NpSum(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'sum'

class pipelinex.extras.ops.opencv_ops.NpZerosLike(**kwargs)

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'zeros_like'

pipelinex.extras.ops.opencv_ops.expand_repeat(a, repeats=1, axis=None)

pipelinex.extras.ops.opencv_ops.fit_to_1(a)

pipelinex.extras.ops.opencv_ops.fit_to_uint8(a)

pipelinex.extras.ops.opencv_ops.mix_up(*imgs)

pipelinex.extras.ops.opencv_ops.overlay(*imgs)

pipelinex.extras.ops.opencv_ops.sum_up(*imgs)

pipelinex.extras.ops.pandas_ops module

class pipelinex.extras.ops.pandas_ops.DfAddRowStat(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfAgg(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'agg'

class pipelinex.extras.ops.pandas_ops.DfAggregate(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'aggregate'

class pipelinex.extras.ops.pandas_ops.DfApply(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'apply'

class pipelinex.extras.ops.pandas_ops.DfApplymap(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'applymap'

class pipelinex.extras.ops.pandas_ops.DfAssignColumns(names=None, name_fmt='{:03d}')

Bases: object

__init__(names=None, name_fmt='{:03d}')

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfBaseMethod(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

method = None

class pipelinex.extras.ops.pandas_ops.DfBaseTask(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: object

__init__(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

method = None

class pipelinex.extras.ops.pandas_ops.DfColApply(func, **kwargs)

Bases: object

__init__(func, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfConcat(new_col_name=None, new_col_values=None, col_id=None, sort=False)

Bases: object

__init__(new_col_name=None, new_col_values=None, col_id=None, sort=False)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfCondReplace(flag, columns, value=nan, replace_if_flag=True, **kwargs)

Bases: object

__init__(flag, columns, value=nan, replace_if_flag=True, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfDrop(**kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'drop'

class pipelinex.extras.ops.pandas_ops.DfDropDuplicates(**kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'drop_duplicates'

class pipelinex.extras.ops.pandas_ops.DfDropFilter(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfDtypesApply(func, **kwargs)

Bases: object

__init__(func, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfDuplicate(columns)

Bases: object

__init__(columns)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfEval(expr, parser='pandas', engine=None, truediv=True)

Bases: object

__init__(expr, parser='pandas', engine=None, truediv=True)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfEwm(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'ewm'

class pipelinex.extras.ops.pandas_ops.DfExpanding(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'expanding'

class pipelinex.extras.ops.pandas_ops.DfFillna(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'fillna'

class pipelinex.extras.ops.pandas_ops.DfFilter(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

class pipelinex.extras.ops.pandas_ops.DfFilterCols(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfFilter

class pipelinex.extras.ops.pandas_ops.DfFocusTransform(focus, columns, groupby=None, keep_others=False, func='max', **kwargs)

Bases: object

__init__(focus, columns, groupby=None, keep_others=False, func='max', **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfGetColIndexes(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfGetCols

Bases: object

class pipelinex.extras.ops.pandas_ops.DfGetDummies(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfGroupby(**kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'groupby'

class pipelinex.extras.ops.pandas_ops.DfHead(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'head'

class pipelinex.extras.ops.pandas_ops.DfMap(arg, prefix='', suffix='', **kwargs)

Bases: object

__init__(arg, prefix='', suffix='', **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfMerge(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfNgroup(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'ngroup'

class pipelinex.extras.ops.pandas_ops.DfPipe(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'pipe'

class pipelinex.extras.ops.pandas_ops.DfQuery(**kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'query'

class pipelinex.extras.ops.pandas_ops.DfRelative(focus, columns, groupby=None)

Bases: object

__init__(focus, columns, groupby=None)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfRename(index=None, columns=None, copy=True, level=None)

Bases: object

__init__(index=None, columns=None, copy=True, level=None)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfResample(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'resample'

class pipelinex.extras.ops.pandas_ops.DfResetIndex(**kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'reset_index'

class pipelinex.extras.ops.pandas_ops.DfRolling(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'rolling'

class pipelinex.extras.ops.pandas_ops.DfRowApply(func, **kwargs)

Bases: object

__init__(func, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfSample(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfSelectDtypes(**kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'select_dtypes'

class pipelinex.extras.ops.pandas_ops.DfSelectDtypesCols(**kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfSelectDtypes

class pipelinex.extras.ops.pandas_ops.DfSetIndex(**kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'set_index'

class pipelinex.extras.ops.pandas_ops.DfShift(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'shift'

class pipelinex.extras.ops.pandas_ops.DfSlice(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfSortValues(**kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'sort_values'

class pipelinex.extras.ops.pandas_ops.DfSpatialFeatures(output='distance', coo_cols=['X', 'Y'], groupby=None, ord=None, unit_distance=1.0, affinity_scale=1.0, binary_affinity=False, min_affinity=1e-06, col_name_fmt='feat_{:03d}', keep_others=True, sort=True)

Bases: object

__init__(output='distance', coo_cols=['X', 'Y'], groupby=None, ord=None, unit_distance=1.0, affinity_scale=1.0, binary_affinity=False, min_affinity=1e-06, col_name_fmt='feat_{:03d}', keep_others=True, sort=True)

Available values for output:

distance affinity laplacian eigenvalues eigenvectors n_connected

class pipelinex.extras.ops.pandas_ops.DfStrftime(cols, **kwargs)

Bases: object

__init__(cols, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfTail(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'tail'

class pipelinex.extras.ops.pandas_ops.DfToDatetime(cols=None, **kwargs)

Bases: object

__init__(cols=None, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfToTimedelta(cols, **kwargs)

Bases: object

__init__(cols, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfTotalSeconds(cols, **kwargs)

Bases: object

__init__(cols, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.DfTransform(groupby=None, columns=None, keep_others=False, method=None, **kwargs)

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'transform'

class pipelinex.extras.ops.pandas_ops.NestedDictToDf(row_oriented=True, index_name='index', reset_index=True)

Bases: object

__init__(row_oriented=True, index_name='index', reset_index=True)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pandas_ops.SrMap(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

pipelinex.extras.ops.pandas_ops.affinity_matrix(coo_2darr, ord=None, unit_distance=1.0, affinity_scale=1.0, binary_affinity=False, min_affinity=1e-06, zero_diag=True)

pipelinex.extras.ops.pandas_ops.degree_matrix(affinity_2darr)

pipelinex.extras.ops.pandas_ops.distance_matrix(coo_2darr, ord=None)

pipelinex.extras.ops.pandas_ops.distance_to_affinity(dist_2darr, unit_distance=1.0, affinity_scale=1.0, binary_affinity=False, min_affinity=1e-06)

pipelinex.extras.ops.pandas_ops.eigen(a, return_values=True, values_as_square_matrix=False, return_vectors=False, sort=False)

pipelinex.extras.ops.pandas_ops.laplacian_eigen(coo_2darr, return_values=True, return_vectors=False, ord=None, unit_distance=1.0, affinity_scale=1.0, binary_affinity=False, min_affinity=1e-06, sort=False)

pipelinex.extras.ops.pandas_ops.laplacian_matrix(coo_2darr, ord=None, unit_distance=1.0, affinity_scale=1.0, binary_affinity=False, min_affinity=1e-06)

pipelinex.extras.ops.pandas_ops.nested_dict_to_df(d, row_oriented=True, index_name='index', reset_index=True)

pipelinex.extras.ops.pandas_ops.row_vector_to_square_matrix(a)

pipelinex.extras.ops.pytorch_ops module

class pipelinex.extras.ops.pytorch_ops.CrossEntropyLoss2d(weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean')

Bases: torch.nn.modules.loss.CrossEntropyLoss

forward(input, target)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

ignore_index: int

class pipelinex.extras.ops.pytorch_ops.ModuleAvg(*args: torch.nn.modules.module.Module)

class pipelinex.extras.ops.pytorch_ops.ModuleAvg(arg: OrderedDict[str, Module])

Bases: pipelinex.extras.ops.pytorch_ops.ModuleListMerge

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.ModuleBottleneck2d(in_channels, out_channels, kernel_size=(1, 1), stride=(1, 1), mid_channels=None, batch_norm=None, activation=None, **kwargs)

Bases: torch.nn.modules.container.Sequential

__init__(in_channels, out_channels, kernel_size=(1, 1), stride=(1, 1), mid_channels=None, batch_norm=None, activation=None, **kwargs)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool

class pipelinex.extras.ops.pytorch_ops.ModuleConcat(*args: torch.nn.modules.module.Module)

class pipelinex.extras.ops.pytorch_ops.ModuleConcat(arg: OrderedDict[str, Module])

Bases: pipelinex.extras.ops.pytorch_ops.ModuleListMerge

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.ModuleConcatSkip(*modules)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConcat

__init__(*modules)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool

class pipelinex.extras.ops.pytorch_ops.ModuleConvWrap(batchnorm=None, activation=None, *args, **kwargs)

Bases: torch.nn.modules.container.Sequential

__init__(batchnorm=None, activation=None, *args, **kwargs)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

core = None

training: bool

class pipelinex.extras.ops.pytorch_ops.ModuleListMerge(*args: torch.nn.modules.module.Module)

class pipelinex.extras.ops.pytorch_ops.ModuleListMerge(arg: OrderedDict[str, Module])

Bases: torch.nn.modules.container.Sequential

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.ModuleProd(*args: torch.nn.modules.module.Module)

class pipelinex.extras.ops.pytorch_ops.ModuleProd(arg: OrderedDict[str, Module])

Bases: pipelinex.extras.ops.pytorch_ops.ModuleListMerge

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.ModuleSum(*args: torch.nn.modules.module.Module)

class pipelinex.extras.ops.pytorch_ops.ModuleSum(arg: OrderedDict[str, Module])

Bases: pipelinex.extras.ops.pytorch_ops.ModuleListMerge

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.ModuleSumSkip(*modules)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleSum

__init__(*modules)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool

class pipelinex.extras.ops.pytorch_ops.NLLoss(weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean')

Bases: torch.nn.modules.loss.NLLLoss

The negative likelihood loss. To compute Cross Entropy Loss, there are 3 options. NLLoss with torch.nn.Softmax torch.nn.NLLLoss with torch.nn.LogSoftmax torch.nn.CrossEntropyLoss

forward(input, target)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

ignore_index: int

class pipelinex.extras.ops.pytorch_ops.Pool1dMixIn(keepdim=False)

Bases: object

__init__(keepdim=False)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pytorch_ops.Pool2dMixIn(keepdim=False)

Bases: object

__init__(keepdim=False)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pytorch_ops.Pool3dMixIn(keepdim=False)

Bases: object

__init__(keepdim=False)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.pytorch_ops.StatModule(dim, keepdim=False)

Bases: torch.nn.modules.module.Module

__init__(dim, keepdim=False)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

training: bool

class pipelinex.extras.ops.pytorch_ops.StepBinary(size, desc=False, compare=None, dtype=None)

Bases: torch.nn.modules.module.Module

__init__(size, desc=False, compare=None, dtype=None)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorAvgPool1d(batchnorm=None, activation=None, *args, **kwargs)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConvWrap

core

alias of torch.nn.modules.pooling.AvgPool1d

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorAvgPool2d(batchnorm=None, activation=None, *args, **kwargs)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConvWrap

core

alias of torch.nn.modules.pooling.AvgPool2d

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorAvgPool3d(batchnorm=None, activation=None, *args, **kwargs)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConvWrap

core

alias of torch.nn.modules.pooling.AvgPool3d

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorClamp(min=None, max=None)

Bases: torch.nn.modules.module.Module

__init__(min=None, max=None)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorClampMax(max=None)

Bases: torch.nn.modules.module.Module

__init__(max=None)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorClampMin(min=None)

Bases: torch.nn.modules.module.Module

__init__(min=None)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorConstantLinear(weight=1, bias=0)

Bases: torch.nn.modules.module.Module

__init__(weight=1, bias=0)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorConv1d(batchnorm=None, activation=None, *args, **kwargs)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConvWrap

core

alias of torch.nn.modules.conv.Conv1d

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorConv2d(batchnorm=None, activation=None, *args, **kwargs)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConvWrap

core

alias of torch.nn.modules.conv.Conv2d

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorConv3d(batchnorm=None, activation=None, *args, **kwargs)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConvWrap

core

alias of torch.nn.modules.conv.Conv3d

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorCumsum(dim=1)

Bases: torch.nn.modules.module.Module

__init__(dim=1)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorExp

Bases: torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorFlatten

Bases: torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorForward(func=None)

Bases: torch.nn.modules.module.Module

__init__(func=None)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalAvgPool1d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool1dMixIn, pipelinex.extras.ops.pytorch_ops.TensorMean

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalAvgPool2d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool2dMixIn, pipelinex.extras.ops.pytorch_ops.TensorMean

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalAvgPool3d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool3dMixIn, pipelinex.extras.ops.pytorch_ops.TensorMean

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalMaxPool1d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool1dMixIn, pipelinex.extras.ops.pytorch_ops.TensorMax

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalMaxPool2d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool2dMixIn, pipelinex.extras.ops.pytorch_ops.TensorMax

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalMaxPool3d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool3dMixIn, pipelinex.extras.ops.pytorch_ops.TensorMax

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalMinPool1d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool1dMixIn, pipelinex.extras.ops.pytorch_ops.TensorMin

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalMinPool2d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool2dMixIn, pipelinex.extras.ops.pytorch_ops.TensorMin

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalMinPool3d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool3dMixIn, pipelinex.extras.ops.pytorch_ops.TensorMin

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalRangePool1d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool1dMixIn, pipelinex.extras.ops.pytorch_ops.TensorRange

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalRangePool2d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool2dMixIn, pipelinex.extras.ops.pytorch_ops.TensorRange

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalRangePool3d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool3dMixIn, pipelinex.extras.ops.pytorch_ops.TensorRange

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalSumPool1d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool1dMixIn, pipelinex.extras.ops.pytorch_ops.TensorSum

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalSumPool2d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool2dMixIn, pipelinex.extras.ops.pytorch_ops.TensorSum

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorGlobalSumPool3d(keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.Pool3dMixIn, pipelinex.extras.ops.pytorch_ops.TensorSum

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorIdentity

Bases: torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorLog

Bases: torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorMax(dim, keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.StatModule, torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorMaxPool1d(batchnorm=None, activation=None, *args, **kwargs)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConvWrap

core

alias of torch.nn.modules.pooling.MaxPool1d

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorMaxPool2d(batchnorm=None, activation=None, *args, **kwargs)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConvWrap

core

alias of torch.nn.modules.pooling.MaxPool2d

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorMaxPool3d(batchnorm=None, activation=None, *args, **kwargs)

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConvWrap

core

alias of torch.nn.modules.pooling.MaxPool3d

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorMean(dim, keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.StatModule

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorMin(dim, keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.StatModule, torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorNearestPad(lower=1, upper=1)

Bases: torch.nn.modules.module.Module

__init__(lower=1, upper=1)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorProba(dim=1)

Bases: torch.nn.modules.module.Module

__init__(dim=1)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorRange(dim, keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.StatModule, torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorSkip

Bases: torch.nn.modules.module.Module

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorSlice(start=0, end=None, step=1)

Bases: torch.nn.modules.module.Module

__init__(start=0, end=None, step=1)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorSqueeze(dim=None)

Bases: torch.nn.modules.module.Module

__init__(dim=None)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorSum(dim, keepdim=False)

Bases: pipelinex.extras.ops.pytorch_ops.StatModule

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class pipelinex.extras.ops.pytorch_ops.TensorUnsqueeze(dim)

Bases: torch.nn.modules.module.Module

__init__(dim)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

pipelinex.extras.ops.pytorch_ops.as_tuple(x)

pipelinex.extras.ops.pytorch_ops.element_wise_average(tt_list)

pipelinex.extras.ops.pytorch_ops.element_wise_prod(tt_list)

pipelinex.extras.ops.pytorch_ops.element_wise_sum(tt_list)

pipelinex.extras.ops.pytorch_ops.nl_loss(input, *args, **kwargs)

pipelinex.extras.ops.pytorch_ops.setup_conv_params(kernel_size=1, dilation=None, padding=None, stride=None, raise_error=False, *args, **kwargs)

pipelinex.extras.ops.pytorch_ops.step_binary(input, output_size, compare=<built-in method ge of type object>)

pipelinex.extras.ops.pytorch_ops.tensor_max(input, dim, keepdim=False)

pipelinex.extras.ops.pytorch_ops.tensor_min(input, dim, keepdim=False)

pipelinex.extras.ops.pytorch_ops.to_array(input)

pipelinex.extras.ops.pytorch_ops.to_channel_first_tensor(a)

pipelinex.extras.ops.pytorch_ops.to_channel_last_tensor(a)

pipelinex.extras.ops.shap_ops module

class pipelinex.extras.ops.shap_ops.ExplainModel(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.shap_ops.Scale(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

pipelinex.extras.ops.skimage_ops module

class pipelinex.extras.ops.skimage_ops.SkimageMarkBoundaries(**kwargs)

Bases: pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict

fn = 'mark_boundaries'

class pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict(**kwargs)

Bases: pipelinex.utils.DictToDict

module = <module 'skimage.segmentation' from '/home/docs/checkouts/readthedocs.org/user_builds/pipelinex/envs/0.4.9/lib/python3.6/site-packages/skimage/segmentation/__init__.py'>

class pipelinex.extras.ops.skimage_ops.SkimageSegmentationFelzenszwalb(**kwargs)

Bases: pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict

fn = 'felzenszwalb'

class pipelinex.extras.ops.skimage_ops.SkimageSegmentationQuickshift(**kwargs)

Bases: pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict

fn = 'quickshift'

class pipelinex.extras.ops.skimage_ops.SkimageSegmentationSlic(**kwargs)

Bases: pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict

fn = 'slic'

class pipelinex.extras.ops.skimage_ops.SkimageSegmentationWatershed(**kwargs)

Bases: pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict

fn = 'watershed'

pipelinex.extras.ops.sklearn_ops module

class pipelinex.extras.ops.sklearn_ops.DfBaseTransformer(cols=None, target_col=None, **kwargs)

Bases: pipelinex.extras.ops.sklearn_ops.ZeroToZeroTransformer

__init__(cols=None, target_col=None, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

fit(df)

fit_transform(df)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

• X (array-like of shape (n_samples, n_features)) – Input samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

• **fit_params (dict) – Additional fit parameters.

Returns

X_new – Transformed array.

Return type

ndarray array of shape (n_samples, n_features_new)

transform(df)

class pipelinex.extras.ops.sklearn_ops.DfMinMaxScaler(cols=None, target_col=None, **kwargs)

Bases: pipelinex.extras.ops.sklearn_ops.DfBaseTransformer, sklearn.preprocessing._data.MinMaxScaler

class pipelinex.extras.ops.sklearn_ops.DfQuantileTransformer(cols=None, target_col=None, **kwargs)

Bases: pipelinex.extras.ops.sklearn_ops.DfBaseTransformer, sklearn.preprocessing._data.QuantileTransformer

class pipelinex.extras.ops.sklearn_ops.DfStandardScaler(cols=None, target_col=None, **kwargs)

Bases: pipelinex.extras.ops.sklearn_ops.DfBaseTransformer, sklearn.preprocessing._data.StandardScaler

class pipelinex.extras.ops.sklearn_ops.DfTrainTestSplit(**kwargs)

Bases: object

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

class pipelinex.extras.ops.sklearn_ops.EstimatorTransformer

Bases: sklearn.base.TransformerMixin, sklearn.base.BaseEstimator

class pipelinex.extras.ops.sklearn_ops.ZeroToZeroTransformer(zero_to_zero=False, **kwargs)

Bases: pipelinex.extras.ops.sklearn_ops.EstimatorTransformer

__init__(zero_to_zero=False, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

fit_transform(X)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

• X (array-like of shape (n_samples, n_features)) – Input samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

• **fit_params (dict) – Additional fit parameters.

Returns

X_new – Transformed array.

Return type

ndarray array of shape (n_samples, n_features_new)

transform(X)

pipelinex.extras.ops.sklearn_ops.extract_from_df(df, cols, target_col)