pipelinex.extras.ops package

Subpackages

Submodules

pipelinex.extras.ops.allennlp_ops module

class pipelinex.extras.ops.allennlp_ops.AllennlpReaderToDict(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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)[source]

Bases: object

__init__(func, args={}, force_return=None)[source]

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

pipelinex.extras.ops.argparse_ops.namespace(d)[source]

pipelinex.extras.ops.numpy_ops module

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

Bases: object

__init__(channel_first=False)[source]

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

pipelinex.extras.ops.numpy_ops.reverse_channel(a, channel_first=False)[source]
pipelinex.extras.ops.numpy_ops.to_channel_first_arr(a)[source]
pipelinex.extras.ops.numpy_ops.to_channel_last_arr(a)[source]

pipelinex.extras.ops.opencv_ops module

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

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

__init__(*args, **kwargs)[source]

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

fn = 'cvtColor'
class pipelinex.extras.ops.opencv_ops.CvBGR2HSV(*args, **kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

__init__(*args, **kwargs)[source]

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

fn = 'cvtColor'
class pipelinex.extras.ops.opencv_ops.CvBilateralFilter(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'bilateralFilter'
class pipelinex.extras.ops.opencv_ops.CvBlur(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'blur'
class pipelinex.extras.ops.opencv_ops.CvBoxFilter(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'boxFilter'
class pipelinex.extras.ops.opencv_ops.CvCanny(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'Canny'
class pipelinex.extras.ops.opencv_ops.CvCvtColor(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'cvtColor'
class pipelinex.extras.ops.opencv_ops.CvDiagonalEdgeFilter2d(kernel_type=2, **kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvModuleL2

__init__(kernel_type=2, **kwargs)[source]

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

class pipelinex.extras.ops.opencv_ops.CvDictToDict(**kwargs)[source]

Bases: pipelinex.utils.DictToDict

module = <module 'cv2.cv2' from '/home/docs/checkouts/readthedocs.org/user_builds/pipelinex/envs/latest/lib/python3.6/site-packages/cv2/cv2.cpython-36m-x86_64-linux-gnu.so'>
class pipelinex.extras.ops.opencv_ops.CvDilate(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'dilate'
class pipelinex.extras.ops.opencv_ops.CvEqualizeHist(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'equalizeHist'
class pipelinex.extras.ops.opencv_ops.CvErode(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'erode'
class pipelinex.extras.ops.opencv_ops.CvFilter2d(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'filter2D'
class pipelinex.extras.ops.opencv_ops.CvGaussianBlur(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'GaussianBlur'
class pipelinex.extras.ops.opencv_ops.CvHoughLinesP(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'HoughLinesP'
class pipelinex.extras.ops.opencv_ops.CvLine(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'line'
class pipelinex.extras.ops.opencv_ops.CvMedianBlur(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'medianBlur'
class pipelinex.extras.ops.opencv_ops.CvModuleConcat(*modules)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvModuleListMerge

class pipelinex.extras.ops.opencv_ops.CvModuleL1(*modules)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvModuleStack

class pipelinex.extras.ops.opencv_ops.CvModuleL2(*modules)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvModuleStack

class pipelinex.extras.ops.opencv_ops.CvModuleListMerge(*modules)[source]

Bases: object

__init__(*modules)[source]

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

class pipelinex.extras.ops.opencv_ops.CvModuleMean(*modules)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvModuleStack

class pipelinex.extras.ops.opencv_ops.CvModuleStack(*modules)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvModuleListMerge

class pipelinex.extras.ops.opencv_ops.CvModuleSum(*modules)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvModuleStack

class pipelinex.extras.ops.opencv_ops.CvResize(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

fn = 'resize'
class pipelinex.extras.ops.opencv_ops.CvScale(width, height)[source]

Bases: object

__init__(width, height)[source]

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

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

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

__init__(ddepth='CV_64F', **kwargs)[source]

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

fn = 'Sobel'
class pipelinex.extras.ops.opencv_ops.CvThreshold(type='THRESH_BINARY', **kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.CvDictToDict

__init__(type='THRESH_BINARY', **kwargs)[source]

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

fn = 'threshold'
class pipelinex.extras.ops.opencv_ops.NpAbs(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'abs'
class pipelinex.extras.ops.opencv_ops.NpConcat(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'concatenate'
class pipelinex.extras.ops.opencv_ops.NpDictToDict(**kwargs)[source]

Bases: pipelinex.utils.DictToDict

module = <module 'numpy' from '/home/docs/checkouts/readthedocs.org/user_builds/pipelinex/envs/latest/lib/python3.6/site-packages/numpy/__init__.py'>
class pipelinex.extras.ops.opencv_ops.NpFullLike(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'full_like'
class pipelinex.extras.ops.opencv_ops.NpMean(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'mean'
class pipelinex.extras.ops.opencv_ops.NpOnesLike(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'ones_like'
class pipelinex.extras.ops.opencv_ops.NpSqrt(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'sqrt'
class pipelinex.extras.ops.opencv_ops.NpSquare(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'square'
class pipelinex.extras.ops.opencv_ops.NpStack(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'stack'
class pipelinex.extras.ops.opencv_ops.NpSum(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'sum'
class pipelinex.extras.ops.opencv_ops.NpZerosLike(**kwargs)[source]

Bases: pipelinex.extras.ops.opencv_ops.NpDictToDict

fn = 'zeros_like'
pipelinex.extras.ops.opencv_ops.expand_repeat(a, repeats=1, axis=None)[source]
pipelinex.extras.ops.opencv_ops.fit_to_1(a)[source]
pipelinex.extras.ops.opencv_ops.fit_to_uint8(a)[source]
pipelinex.extras.ops.opencv_ops.mix_up(*imgs)[source]
pipelinex.extras.ops.opencv_ops.overlay(*imgs)[source]
pipelinex.extras.ops.opencv_ops.sum_up(*imgs)[source]

pipelinex.extras.ops.pandas_ops module

class pipelinex.extras.ops.pandas_ops.DfAddRowStat(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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)[source]

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)[source]

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)[source]

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)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

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

Bases: object

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

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

class pipelinex.extras.ops.pandas_ops.DfBaseMethod(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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)[source]

Bases: object

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

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

method = None
class pipelinex.extras.ops.pandas_ops.DfColApply(func, **kwargs)[source]

Bases: object

__init__(func, **kwargs)[source]

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)[source]

Bases: object

__init__(new_col_name=None, new_col_values=None, col_id=None, sort=False)[source]

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)[source]

Bases: object

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

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

class pipelinex.extras.ops.pandas_ops.DfDrop(**kwargs)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'drop'
class pipelinex.extras.ops.pandas_ops.DfDropDuplicates(**kwargs)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'drop_duplicates'
class pipelinex.extras.ops.pandas_ops.DfDropFilter(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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

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

Bases: object

__init__(func, **kwargs)[source]

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

class pipelinex.extras.ops.pandas_ops.DfDuplicate(columns)[source]

Bases: object

__init__(columns)[source]

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

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

Bases: object

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

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)[source]

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)[source]

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)[source]

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)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

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

Bases: pipelinex.extras.ops.pandas_ops.DfFilter

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

Bases: object

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

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

class pipelinex.extras.ops.pandas_ops.DfGetColIndexes(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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

class pipelinex.extras.ops.pandas_ops.DfGetCols[source]

Bases: object

class pipelinex.extras.ops.pandas_ops.DfGetDummies(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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

class pipelinex.extras.ops.pandas_ops.DfGroupby(**kwargs)[source]

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)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'head'
class pipelinex.extras.ops.pandas_ops.DfMap(arg, prefix='', suffix='', **kwargs)[source]

Bases: object

__init__(arg, prefix='', suffix='', **kwargs)[source]

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

class pipelinex.extras.ops.pandas_ops.DfMerge(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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)[source]

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)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'pipe'
class pipelinex.extras.ops.pandas_ops.DfQuery(**kwargs)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'query'
class pipelinex.extras.ops.pandas_ops.DfRelative(focus, columns, groupby=None)[source]

Bases: object

__init__(focus, columns, groupby=None)[source]

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

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

Bases: object

__init__(index=None, columns=None, copy=True, level=None)[source]

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)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'resample'
class pipelinex.extras.ops.pandas_ops.DfResetIndex(**kwargs)[source]

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)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'rolling'
class pipelinex.extras.ops.pandas_ops.DfRowApply(func, **kwargs)[source]

Bases: object

__init__(func, **kwargs)[source]

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

class pipelinex.extras.ops.pandas_ops.DfSample(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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

class pipelinex.extras.ops.pandas_ops.DfSelectDtypes(**kwargs)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseMethod

method = 'select_dtypes'
class pipelinex.extras.ops.pandas_ops.DfSelectDtypesCols(**kwargs)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfSelectDtypes

class pipelinex.extras.ops.pandas_ops.DfSetIndex(**kwargs)[source]

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)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'shift'
class pipelinex.extras.ops.pandas_ops.DfSlice(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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

class pipelinex.extras.ops.pandas_ops.DfSortValues(**kwargs)[source]

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)[source]

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)[source]
Available values for output:

distance affinity laplacian eigenvalues eigenvectors n_connected

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

Bases: object

__init__(cols, **kwargs)[source]

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)[source]

Bases: pipelinex.extras.ops.pandas_ops.DfBaseTask

method = 'tail'
class pipelinex.extras.ops.pandas_ops.DfToDatetime(cols=None, **kwargs)[source]

Bases: object

__init__(cols=None, **kwargs)[source]

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

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

Bases: object

__init__(cols, **kwargs)[source]

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

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

Bases: object

__init__(cols, **kwargs)[source]

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)[source]

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)[source]

Bases: object

__init__(row_oriented=True, index_name='index', reset_index=True)[source]

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

class pipelinex.extras.ops.pandas_ops.SrMap(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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)[source]
pipelinex.extras.ops.pandas_ops.degree_matrix(affinity_2darr)[source]
pipelinex.extras.ops.pandas_ops.distance_matrix(coo_2darr, ord=None)[source]
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)[source]
pipelinex.extras.ops.pandas_ops.eigen(a, return_values=True, values_as_square_matrix=False, return_vectors=False, sort=False)[source]
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)[source]
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)[source]
pipelinex.extras.ops.pandas_ops.nested_dict_to_df(d, row_oriented=True, index_name='index', reset_index=True)[source]
pipelinex.extras.ops.pandas_ops.row_vector_to_square_matrix(a)[source]

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')[source]

Bases: torch.nn.modules.loss.CrossEntropyLoss

forward(input, target)[source]

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

Bases: pipelinex.extras.ops.pytorch_ops.ModuleListMerge

forward(input)[source]

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)[source]

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)[source]

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

Bases: pipelinex.extras.ops.pytorch_ops.ModuleListMerge

forward(input)[source]

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)[source]

Bases: pipelinex.extras.ops.pytorch_ops.ModuleConcat

__init__(*modules)[source]

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)[source]

Bases: torch.nn.modules.container.Sequential

__init__(batchnorm=None, activation=None, *args, **kwargs)[source]

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

Bases: torch.nn.modules.container.Sequential

forward(input)[source]

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

Bases: pipelinex.extras.ops.pytorch_ops.ModuleListMerge

forward(input)[source]

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

Bases: pipelinex.extras.ops.pytorch_ops.ModuleListMerge

forward(input)[source]

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)[source]

Bases: pipelinex.extras.ops.pytorch_ops.ModuleSum

__init__(*modules)[source]

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')[source]

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)[source]

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)[source]

Bases: object

__init__(keepdim=False)[source]

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

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

Bases: object

__init__(keepdim=False)[source]

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

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

Bases: object

__init__(keepdim=False)[source]

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

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

Bases: torch.nn.modules.module.Module

__init__(dim, keepdim=False)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(size, desc=False, compare=None, dtype=None)[source]

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

forward(input)[source]

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)[source]

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)[source]

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)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(min=None, max=None)[source]

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

forward(input)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(max=None)[source]

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

forward(input)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(min=None)[source]

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

forward(input)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(weight=1, bias=0)[source]

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

forward(input)[source]

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)[source]

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)[source]

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)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(dim=1)[source]

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

forward(input)[source]

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[source]

Bases: torch.nn.modules.module.Module

forward(input)[source]

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[source]

Bases: torch.nn.modules.module.Module

forward(input)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(func=None)[source]

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

forward(input)[source]

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)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalAvgPool2d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalAvgPool3d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalMaxPool1d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalMaxPool2d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalMaxPool3d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalMinPool1d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalMinPool2d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalMinPool3d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalRangePool1d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalRangePool2d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalRangePool3d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalSumPool1d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalSumPool2d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorGlobalSumPool3d(keepdim=False)[source]

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

training: bool
class pipelinex.extras.ops.pytorch_ops.TensorIdentity[source]

Bases: torch.nn.modules.module.Module

forward(input)[source]

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[source]

Bases: torch.nn.modules.module.Module

forward(input)[source]

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)[source]

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

forward(input)[source]

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)[source]

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)[source]

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)[source]

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)[source]

Bases: pipelinex.extras.ops.pytorch_ops.StatModule

forward(input)[source]

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)[source]

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

forward(input)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(lower=1, upper=1)[source]

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

forward(input)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(dim=1)[source]

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

forward(input)[source]

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)[source]

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

forward(input)[source]

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[source]

Bases: torch.nn.modules.module.Module

forward(input)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(start=0, end=None, step=1)[source]

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

forward(input)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(dim=None)[source]

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

forward(input)[source]

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)[source]

Bases: pipelinex.extras.ops.pytorch_ops.StatModule

forward(input)[source]

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)[source]

Bases: torch.nn.modules.module.Module

__init__(dim)[source]

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

forward(input)[source]

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)[source]
pipelinex.extras.ops.pytorch_ops.element_wise_average(tt_list)[source]
pipelinex.extras.ops.pytorch_ops.element_wise_prod(tt_list)[source]
pipelinex.extras.ops.pytorch_ops.element_wise_sum(tt_list)[source]
pipelinex.extras.ops.pytorch_ops.nl_loss(input, *args, **kwargs)[source]
pipelinex.extras.ops.pytorch_ops.setup_conv_params(kernel_size=1, dilation=None, padding=None, stride=None, raise_error=False, *args, **kwargs)[source]
pipelinex.extras.ops.pytorch_ops.step_binary(input, output_size, compare=<built-in method ge of type object>)[source]
pipelinex.extras.ops.pytorch_ops.tensor_max(input, dim, keepdim=False)[source]
pipelinex.extras.ops.pytorch_ops.tensor_min(input, dim, keepdim=False)[source]
pipelinex.extras.ops.pytorch_ops.to_array(input)[source]
pipelinex.extras.ops.pytorch_ops.to_channel_first_tensor(a)[source]
pipelinex.extras.ops.pytorch_ops.to_channel_last_tensor(a)[source]

pipelinex.extras.ops.shap_ops module

class pipelinex.extras.ops.shap_ops.ExplainModel(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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

class pipelinex.extras.ops.shap_ops.Scale(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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

pipelinex.extras.ops.skimage_ops module

class pipelinex.extras.ops.skimage_ops.SkimageMarkBoundaries(**kwargs)[source]

Bases: pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict

fn = 'mark_boundaries'
class pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict(**kwargs)[source]

Bases: pipelinex.utils.DictToDict

module = <module 'skimage.segmentation' from '/home/docs/checkouts/readthedocs.org/user_builds/pipelinex/envs/latest/lib/python3.6/site-packages/skimage/segmentation/__init__.py'>
class pipelinex.extras.ops.skimage_ops.SkimageSegmentationFelzenszwalb(**kwargs)[source]

Bases: pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict

fn = 'felzenszwalb'
class pipelinex.extras.ops.skimage_ops.SkimageSegmentationQuickshift(**kwargs)[source]

Bases: pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict

fn = 'quickshift'
class pipelinex.extras.ops.skimage_ops.SkimageSegmentationSlic(**kwargs)[source]

Bases: pipelinex.extras.ops.skimage_ops.SkimageSegmentationDictToDict

fn = 'slic'
class pipelinex.extras.ops.skimage_ops.SkimageSegmentationWatershed(**kwargs)[source]

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)[source]

Bases: pipelinex.extras.ops.sklearn_ops.ZeroToZeroTransformer

__init__(cols=None, target_col=None, **kwargs)[source]

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

fit(df)[source]
fit_transform(df)[source]

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

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

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

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

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

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

class pipelinex.extras.ops.sklearn_ops.DfTrainTestSplit(**kwargs)[source]

Bases: object

__init__(**kwargs)[source]

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

class pipelinex.extras.ops.sklearn_ops.EstimatorTransformer[source]

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

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

Bases: pipelinex.extras.ops.sklearn_ops.EstimatorTransformer

__init__(zero_to_zero=False, **kwargs)[source]

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

fit_transform(X)[source]

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