code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
value |
|---|---|---|---|---|---|
from __future__ import annotations
from typing import Callable
import xarray as xr
from risus.engine import target_number, combat, single_action_conflict
def make_target_number_table(
max_potency: int,
n_faces: int = 6,
**kwargs
) -> xr.DataArray:
"""Make a dataframe comparing potencies... | /risus_py-0.0.12-py3-none-any.whl/risus/table.py | 0.914896 | 0.420183 | table.py | pypi |
import datetime
import enum
import re
from typing import List, Optional
from pydantic import AnyUrl, EmailStr, Field, HttpUrl, datetime_parse, root_validator
from .common import BaseModel
# Validate zipcode is in 5 digit or 5 digit + 4 digit format
# e.g. 94612, 94612-1234
ZIPCODE_RE = re.compile(r"^[0-9]{5}(?:-[0-9... | /rit-housing-data-schema-0.1.0.tar.gz/rit-housing-data-schema-0.1.0/rit_housing_data_schema/apartment.py | 0.653127 | 0.177241 | apartment.py | pypi |
import logging
from contextlib import contextmanager
from unicodedata import normalize, category
from itertools import cycle, chain
from time import time
from json import JSONEncoder
logger = logging.getLogger(__name__)
class Node(object):
"""
An utility structure. Has no meaning outside
Allows to speci... | /rita-dsl-0.7.4.tar.gz/rita-dsl-0.7.4/rita/utils.py | 0.643665 | 0.190366 | utils.py | pypi |
import logging
import ply.yacc as yacc
from functools import partial
from rita.lexer import RitaLexer
from rita import macros
logger = logging.getLogger(__name__)
def stub(*args, **kwargs):
return None
def either(a, b):
yield a
yield b
def load_macro(name, config):
try:
return partial(... | /rita-dsl-0.7.4.tar.gz/rita-dsl-0.7.4/rita/parser.py | 0.444083 | 0.179567 | parser.py | pypi |
import logging
import re
import json
from functools import partial
from itertools import groupby, chain
from concurrent.futures import ThreadPoolExecutor, as_completed
from typing import Any, TYPE_CHECKING, Mapping, Callable
from rita.utils import ExtendedOp
from rita.types import Rules, Patterns
logger = logging.ge... | /rita-dsl-0.7.4.tar.gz/rita-dsl-0.7.4/rita/engine/translate_standalone.py | 0.467332 | 0.250592 | translate_standalone.py | pypi |
import json
import logging
from platform import system
from ctypes import (c_char_p, c_int, c_uint, c_long, Structure, cdll, POINTER)
from typing import Any, TYPE_CHECKING, Tuple, List, AnyStr
from rita.engine.translate_standalone import rules_to_patterns, RuleExecutor
from rita.types import Rules
logger = logging.... | /rita-dsl-0.7.4.tar.gz/rita-dsl-0.7.4/rita/engine/translate_rust.py | 0.500977 | 0.185504 | translate_rust.py | pypi |
import logging
from functools import partial
from typing import Any, TYPE_CHECKING, Mapping, Callable, Generator, AnyStr
from rita.utils import ExtendedOp
from rita.types import Rules, Patterns
logger = logging.getLogger(__name__)
SpacyPattern = Generator[Mapping[AnyStr, Any], None, None]
ParseFn = Callable[[Any, "... | /rita-dsl-0.7.4.tar.gz/rita-dsl-0.7.4/rita/engine/translate_spacy.py | 0.525369 | 0.240931 | translate_spacy.py | pypi |
rithm
=====
[](https://github.com/lycantropos/rithm/actions/workflows/ci.yml "Github Actions")
[](https://codecov.io/gh/lycantropos/rithm "Codecov")
[
from feature_engine.selection import DropFeatures
from feature_engine.transformation import LogTransformer
from feature_engine.wrappers i... | /rithousingpackage-0.0.4-py3-none-any.whl/regression_model/pipeline.py | 0.704364 | 0.202956 | pipeline.py | pypi |
from pathlib import Path
from typing import Dict, List, Sequence
from pydantic import BaseModel
from strictyaml import YAML, load
import regression_model
# Project Directories
PACKAGE_ROOT = Path(regression_model.__file__).resolve().parent
ROOT = PACKAGE_ROOT.parent
CONFIG_FILE_PATH = PACKAGE_ROOT / "config.yml"
DAT... | /rithousingpackage-0.0.4-py3-none-any.whl/regression_model/config/core.py | 0.815049 | 0.348091 | core.py | pypi |
from typing import List, Optional, Tuple
import numpy as np
import pandas as pd
from pydantic import BaseModel, ValidationError
from regression_model.config.core import config
def drop_na_inputs(*, input_data: pd.DataFrame) -> pd.DataFrame:
"""Check model inputs for na values and filter."""
validated_data =... | /rithousingpackage-0.0.4-py3-none-any.whl/regression_model/processing/validation.py | 0.803598 | 0.42173 | validation.py | pypi |
from datetime import timedelta
from pathlib import Path
import numpy as np
import torch
from ritm_annotation.data.datasets import (
BerkeleyDataset,
DavisDataset,
GrabCutDataset,
PascalVocDataset,
SBDEvaluationDataset,
)
from ritm_annotation.utils.serialization import load_model
def get_time_met... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/inference/utils.py | 0.683314 | 0.357399 | utils.py | pypi |
from copy import deepcopy
import cv2
import numpy as np
class Clicker(object):
def __init__(
self,
gt_mask=None,
init_clicks=None,
ignore_label=-1,
click_indx_offset=0,
):
self.click_indx_offset = click_indx_offset
if gt_mask is not None:
se... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/inference/clicker.py | 0.533397 | 0.259829 | clicker.py | pypi |
import math
from typing import List
import numpy as np
import torch
from ritm_annotation.inference.clicker import Click
from .base import BaseTransform
class Crops(BaseTransform):
def __init__(self, crop_size=(320, 480), min_overlap=0.2):
super().__init__()
self.crop_height, self.crop_width = c... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/inference/transforms/crops.py | 0.797872 | 0.392453 | crops.py | pypi |
from typing import List
import torch
from ritm_annotation.inference.clicker import Click
from ritm_annotation.utils.misc import (
clamp_bbox,
expand_bbox,
get_bbox_from_mask,
get_bbox_iou,
)
from .base import BaseTransform
class ZoomIn(BaseTransform):
def __init__(
self,
target_... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/inference/transforms/zoom_in.py | 0.793866 | 0.182207 | zoom_in.py | pypi |
import numpy as np
import torch
import torch.nn.functional as F
from scipy.optimize import fmin_l_bfgs_b
from .base import BasePredictor
class BRSBasePredictor(BasePredictor):
def __init__(
self, model, device, opt_functor, optimize_after_n_clicks=1, **kwargs
):
super().__init__(model, device... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/inference/predictors/brs.py | 0.836354 | 0.271964 | brs.py | pypi |
import torch
import torch.nn.functional as F
from torchvision import transforms
from ritm_annotation.inference.transforms import (
AddHorizontalFlip,
LimitLongestSide,
SigmoidForPred,
)
class BasePredictor(object):
def __init__(
self,
model,
device,
net_clicks_limit=No... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/inference/predictors/base.py | 0.751739 | 0.352954 | base.py | pypi |
import torch
from ritm_annotation.model.losses import SigmoidBinaryCrossEntropyLoss
class BRSMaskLoss(torch.nn.Module):
def __init__(self, eps=1e-5):
super().__init__()
self._eps = eps
def forward(self, result, pos_mask, neg_mask):
pos_diff = (1 - result) * pos_mask
pos_targe... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/inference/predictors/brs_losses.py | 0.901653 | 0.535463 | brs_losses.py | pypi |
import numpy as np
import torch
from ritm_annotation.model.metrics import _compute_iou
from .brs_losses import BRSMaskLoss
class BaseOptimizer:
def __init__(
self,
optimizer_params,
prob_thresh=0.49,
reg_weight=1e-3,
min_iou_diff=0.01,
brs_loss=BRSMaskLoss(),
... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/inference/predictors/brs_functors.py | 0.784814 | 0.304662 | brs_functors.py | pypi |
from ritm_annotation.inference.transforms import ZoomIn
from ritm_annotation.model.is_hrnet_model import HRNetModel
from .base import BasePredictor
from .brs import (
FeatureBRSPredictor,
HRNetFeatureBRSPredictor,
InputBRSPredictor,
)
from .brs_functors import InputOptimizer, ScaleBiasOptimizer
def get_p... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/inference/predictors/__init__.py | 0.572723 | 0.200969 | __init__.py | pypi |
import logging
import random
from pathlib import Path
import cv2
from albumentations.augmentations.geometric import longest_max_size
from ritm_annotation.data.base import ISDataset
from ritm_annotation.data.sample import DSample
from ritm_annotation.utils.exp_imports.default import *
logger = logging.getLogger(__nam... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/cli/finetune/dataset.py | 0.526343 | 0.159872 | dataset.py | pypi |
from ritm_annotation.utils.exp_imports.default import *
MODEL_NAME = "sbd_hrnet18"
def init_model(cfg, dry_run=False):
model_cfg = edict()
model_cfg.crop_size = (320, 480)
model_cfg.num_max_points = 24
model_cfg.default_num_epochs = 220
model = HRNetModel(
width=18,
ocr_width=64... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/models/iter_mask/hrnet18_sbd_itermask_3p.py | 0.758779 | 0.151498 | hrnet18_sbd_itermask_3p.py | pypi |
import math
import random
from functools import lru_cache
import cv2
import numpy as np
from .sample import DSample
class BasePointSampler:
def __init__(self):
self._selected_mask = None
self._selected_masks = None
def sample_object(self, sample: DSample):
raise NotImplementedError
... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/points_sampler.py | 0.647352 | 0.258478 | points_sampler.py | pypi |
import pickle
import random
import numpy as np
import torch
from torchvision import transforms
from .points_sampler import MultiPointSampler
from .sample import DSample
class ISDataset(torch.utils.data.dataset.Dataset):
def __init__(
self,
augmentator=None,
points_sampler=MultiPointSampl... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/base.py | 0.829527 | 0.281578 | base.py | pypi |
import random
import cv2
import numpy as np
from albumentations import DualTransform, ImageOnlyTransform
from albumentations.augmentations import functional as F
from albumentations.augmentations.geometric import functional as FG
from albumentations.core.serialization import SERIALIZABLE_REGISTRY
from albumentations.c... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/transforms.py | 0.728845 | 0.174833 | transforms.py | pypi |
from copy import deepcopy
import numpy as np
from albumentations import ReplayCompose
from ritm_annotation.data.transforms import remove_image_only_transforms
from ritm_annotation.utils.misc import get_labels_with_sizes
class DSample:
def __init__(
self,
image,
encoded_masks,
obj... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/sample.py | 0.63023 | 0.245277 | sample.py | pypi |
import pickle as pkl
from pathlib import Path
import cv2
import numpy as np
from ritm_annotation.data.base import ISDataset
from ritm_annotation.data.sample import DSample
class PascalVocDataset(ISDataset):
def __init__(self, dataset_path, split="train", dry_run=False, **kwargs):
super().__init__(**kwar... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/datasets/pascalvoc.py | 0.455925 | 0.290402 | pascalvoc.py | pypi |
import json
import pickle
import random
from copy import deepcopy
from pathlib import Path
import cv2
import numpy as np
from ritm_annotation.data.base import ISDataset
from ritm_annotation.data.sample import DSample
class CocoLvisDataset(ISDataset):
def __init__(
self,
dataset_path,
spl... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/datasets/coco_lvis.py | 0.422028 | 0.167866 | coco_lvis.py | pypi |
from pathlib import Path
import cv2
import numpy as np
from ritm_annotation.data.base import ISDataset
from ritm_annotation.data.sample import DSample
class ImagesDirDataset(ISDataset):
def __init__(
self,
dataset_path,
images_dir_name="images",
masks_dir_name="masks",
**... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/datasets/images_dir.py | 0.712432 | 0.331282 | images_dir.py | pypi |
import json
import random
from collections import defaultdict
from pathlib import Path
import cv2
import numpy as np
from ritm_annotation.data.base import ISDataset
from ritm_annotation.data.sample import DSample
class LvisDataset(ISDataset):
def __init__(
self,
dataset_path,
split="trai... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/datasets/lvis.py | 0.493164 | 0.194884 | lvis.py | pypi |
import os
import pickle as pkl
import random
from pathlib import Path
import cv2
import numpy as np
from ritm_annotation.data.base import ISDataset
from ritm_annotation.data.sample import DSample
class OpenImagesDataset(ISDataset):
def __init__(self, dataset_path, split="train", dry_run=False, **kwargs):
... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/datasets/openimages.py | 0.451327 | 0.270985 | openimages.py | pypi |
import os
import pickle as pkl
import random
from pathlib import Path
import cv2
import numpy as np
from ritm_annotation.data.base import ISDataset
from ritm_annotation.data.sample import DSample
from ritm_annotation.utils.misc import get_labels_with_sizes
class ADE20kDataset(ISDataset):
def __init__(
s... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/datasets/ade20k.py | 0.408749 | 0.176885 | ade20k.py | pypi |
import pickle as pkl
from pathlib import Path
import cv2
import numpy as np
from scipy.io import loadmat
from ritm_annotation.data.base import ISDataset
from ritm_annotation.data.sample import DSample
from ritm_annotation.utils.misc import (
get_bbox_from_mask,
get_labels_with_sizes,
)
class SBDDataset(ISDa... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/datasets/sbd.py | 0.446495 | 0.214712 | sbd.py | pypi |
import json
import random
from pathlib import Path
import cv2
import numpy as np
from ritm_annotation.data.base import ISDataset
from ritm_annotation.data.sample import DSample
class CocoDataset(ISDataset):
def __init__(
self,
dataset_path,
split="train",
stuff_prob=0.0,
... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/data/datasets/coco.py | 0.430746 | 0.185652 | coco.py | pypi |
import inspect
from copy import deepcopy
from functools import wraps
import torch.nn as nn
def serialize(init):
parameters = list(inspect.signature(init).parameters)
@wraps(init)
def new_init(self, *args, **kwargs):
params = deepcopy(kwargs)
for pname, value in zip(parameters[1:], args):... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/utils/serialization.py | 0.710628 | 0.218993 | serialization.py | pypi |
import importlib
import logging
import numpy as np
import torch
logger = logging.getLogger(__name__)
def get_dims_with_exclusion(dim, exclude=None):
dims = list(range(dim))
if exclude is not None:
dims.remove(exclude)
return dims
def save_checkpoint(
net, checkpoints_path, epoch=None, pre... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/utils/misc.py | 0.45423 | 0.21264 | misc.py | pypi |
from functools import lru_cache
import cv2
import numpy as np
def visualize_instances(
imask,
bg_color=255,
boundaries_color=None,
boundaries_width=1,
boundaries_alpha=0.8,
):
num_objects = imask.max() + 1
palette = get_palette(num_objects)
if bg_color is not None:
palette[0] ... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/utils/vis.py | 0.603932 | 0.350408 | vis.py | pypi |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ritm_annotation.utils import misc
class NormalizedFocalLossSigmoid(nn.Module):
def __init__(
self,
axis=-1,
alpha=0.25,
gamma=2,
max_mult=-1,
eps=1e-12,
from_sigmoid=... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/model/losses.py | 0.942889 | 0.360517 | losses.py | pypi |
import numpy as np
import torch
from ritm_annotation.utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoc... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/model/metrics.py | 0.784979 | 0.182899 | metrics.py | pypi |
import numpy as np
import torch
from torch import nn as nn
import ritm_annotation.model.initializer as initializer
def select_activation_function(activation):
if isinstance(activation, str):
if activation.lower() == "relu":
return nn.ReLU
elif activation.lower() == "softplus":
... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/model/ops.py | 0.927851 | 0.403861 | ops.py | pypi |
import numpy as np
import torch
import torch.nn as nn
class Initializer(object):
def __init__(self, local_init=True, gamma=None):
self.local_init = local_init
self.gamma = gamma
def __call__(self, m):
if getattr(m, "__initialized", False):
return
if (
... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/model/initializer.py | 0.872157 | 0.308255 | initializer.py | pypi |
import numpy as np
import torch
import torch.nn as nn
from ritm_annotation.model.modifiers import LRMult
from ritm_annotation.model.ops import BatchImageNormalize, DistMaps, ScaleLayer
class ISModel(nn.Module):
def __init__(
self,
use_rgb_conv=True,
with_aux_output=False,
norm_rad... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/model/is_model.py | 0.880264 | 0.225502 | is_model.py | pypi |
import torch
import torch._utils
import torch.nn as nn
import torch.nn.functional as F
class SpatialGather_Module(nn.Module):
"""
Aggregate the context features according to the initial
predicted probability distribution.
Employ the soft-weighted method to aggregate the context.
"""
def __ini... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/model/modeling/ocr.py | 0.93711 | 0.494751 | ocr.py | pypi |
import os
import numpy as np
import torch
import torch._utils
import torch.nn as nn
import torch.nn.functional as F
from .ocr import SpatialGather_Module, SpatialOCR_Module
from .resnetv1b import BasicBlockV1b, BottleneckV1b
relu_inplace = True
class HighResolutionModule(nn.Module):
def __init__(
self,... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/model/modeling/hrnet_ocr.py | 0.842313 | 0.341363 | hrnet_ocr.py | pypi |
import torch
import torch.nn as nn
GLUON_RESNET_TORCH_HUB = "rwightman/pytorch-pretrained-gluonresnet"
class BasicBlockV1b(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
dilation=1,
downsample=None,
previous_dilation=1,
... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/model/modeling/resnetv1b.py | 0.955579 | 0.424412 | resnetv1b.py | pypi |
import torch.nn as nn
from ritm_annotation.model import ops
class ConvHead(nn.Module):
def __init__(
self,
out_channels,
in_channels=32,
num_layers=1,
kernel_size=3,
padding=1,
norm_layer=nn.BatchNorm2d,
):
super(ConvHead, self).__init__()
... | /ritm_annotation-0.3.0.tar.gz/ritm_annotation-0.3.0/ritm_annotation/model/modeling/basic_blocks.py | 0.936532 | 0.190573 | basic_blocks.py | pypi |
(function($){
/**
* The bgiframe is chainable and applies the iframe hack to get
* around zIndex issues in IE6. It will only apply itself in IE6
* and adds a class to the iframe called 'bgiframe'. The iframe
* is appeneded as the first child of the matched element(s)
* with a tabIndex and zIndex of -1.
*
* ... | /ritremixerator-0.3.tar.gz/ritremixerator-0.3/dorrie/comps/media/jquery/development-bundle/external/bgiframe/jquery.bgiframe.js | 0.502441 | 0.568536 | jquery.bgiframe.js | pypi |
from . import Item
from . import ResourceCoefficient
class WorkProduction():
"""Calculate work productivity based on parameters
Sources:
http://wiki.rivalregions.com/Work_formulas
"""
# Input
resource = None
user_level = 0
work_exp = 0
factory_level = 0
resource_max = 0
... | /rival_regions_calc-1.1.2-py3-none-any.whl/rival_regions_calc/work_production.py | 0.845688 | 0.20264 | work_production.py | pypi |
rivalcfg: Configure SteelSeries gaming mice
===========================================
|Github| |Discord| |PYPI Version| |Github Actions| |Black| |License|
Rivalcfg is a **Python library** and a **CLI utility program** that allows you
to configure SteelSeries gaming mice on Linux and Windows (probably works on
BSD a... | /rivalcfg-4.10.0.tar.gz/rivalcfg-4.10.0/README.rst | 0.521471 | 0.685088 | README.rst | pypi |
# RIVALGAN 
[Background](#background)
[The Dataset](#the-dataset)
[Implementation Overview](#implementation-overview) <br/>
[Usage](#usage)<br/>
[Visualizing the Data Augmentation Process](#visualizing-the-data-augmentation-process)<br/>
[GitHub Folder Structure](#github-folder-str... | /rivalgan-0.4.tar.gz/rivalgan-0.4/README.md | 0.588889 | 0.939858 | README.md | pypi |
from django.contrib.contenttypes.models import ContentType
from rest_framework.generics import get_object_or_404
from rest_framework.response import Response
from rest_framework.status import HTTP_200_OK, HTTP_400_BAD_REQUEST
from river.core.workflowregistry import workflow_registry
from river.models import Workflow, S... | /river_admin-0.7.0-py3-none-any.whl/river_admin/views/workflow_view.py | 0.476092 | 0.197929 | workflow_view.py | pypi |
from django.contrib.auth.models import Permission, Group
from django.contrib.contenttypes.models import ContentType
from rest_framework import serializers
from river.models import Function, OnApprovedHook, State, TransitionApprovalMeta, \
OnTransitHook, TransitionMeta, Transition, Workflow, TransitionApproval, DONE... | /river_admin-0.7.0-py3-none-any.whl/river_admin/views/serializers.py | 0.611498 | 0.19546 | serializers.py | pypi |
from django.contrib.auth.models import Group
from django.core.management.base import BaseCommand
from django.db import transaction
from river.models import TransitionApprovalMeta
from examples.shipping_example.models import Shipping
INITIALIZED = "Initialized"
SHIPPED = "Shipped"
ARRIVED = "Arrived"
RETURN_INITIALIZE... | /river_admin-0.7.0-py3-none-any.whl/examples/shipping_example/management/commands/bootstrap_shipping_example.py | 0.681621 | 0.198413 | bootstrap_shipping_example.py | pypi |
import importlib
import sys
import pluggy
gen_hookspec = pluggy.HookspecMarker("generator")
dut_hookspec = pluggy.HookspecMarker("dut")
class RandomGeneratorSpec(object):
""" Test generator specification"""
@gen_hookspec
def pre_gen(self, spec_config, output_dir):
"""
This stage is us... | /river_core-1.4.0.tar.gz/river_core-1.4.0/river_core/sim_hookspecs.py | 0.715424 | 0.489686 | sim_hookspecs.py | pypi |
import logging
import colorlog
# a theme is just a dict of strings to represent each level
THEME = {
logging.CRITICAL: " critical ",
logging.ERROR: " error ",
logging.WARNING: " command ",
logging.INFO: " info ",
logging.DEBUG: " debug "
}
class Log:
"""
this cl... | /river_core-1.4.0.tar.gz/river_core-1.4.0/river_core/log.py | 0.45641 | 0.225843 | log.py | pypi |
import math
from river import metrics
__all__ = ["PrevalenceThreshold"]
class PrevalenceThreshold(metrics.base.BinaryMetric):
r"""Prevalence Threshold (PT).
The relationship between a positive predicted value and its target prevalence
is propotional - though not linear in all but a special case. In con... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/prevalence_threshold.py | 0.922779 | 0.76986 | prevalence_threshold.py | pypi |
from river import metrics
class KappaM(metrics.base.MultiClassMetric):
r"""Kappa-M score.
The Kappa-M statistic compares performance with the majority class classifier.
It is defined as
$$
\kappa_{m} = (p_o - p_e) / (1 - p_e)
$$
where $p_o$ is the empirical probability of agreement on th... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/kappa.py | 0.967054 | 0.695435 | kappa.py | pypi |
import math
from scipy.special import factorial
from river import metrics
__all__ = ["Q0", "Q2"]
class Q0(metrics.base.MultiClassMetric):
r"""Q0 index.
Dom's Q0 measure [^2] uses conditional entropy to calculate the goodness of
a clustering solution. However, this term only evaluates the homogeneity o... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/q0.py | 0.913467 | 0.783326 | q0.py | pypi |
import math
from river import metrics
__all__ = ["VariationInfo"]
class VariationInfo(metrics.base.MultiClassMetric):
r"""Variation of Information.
Variation of Information (VI) [^1] [^2] is an information-based clustering measure.
It is presented as a distance measure for comparing partitions (or clus... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/variation_info.py | 0.90282 | 0.789112 | variation_info.py | pypi |
import math
from river import utils
from . import base
class XieBeni(base.ClusteringMetric):
"""Xie-Beni index (XB).
The Xie-Beni index [^1] has the form of (Compactness)/(Separation), which defines the
inter-cluster separation as the minimum squared distance between cluster centers,
and the intra-... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/xiebeni.py | 0.931544 | 0.675055 | xiebeni.py | pypi |
import math
from river import utils
from . import base
__all__ = ["MSSTD", "RMSSTD"]
class MSSTD(base.ClusteringMetric):
"""Mean Squared Standard Deviation.
This is the pooled sample variance of all the attributes, which measures
only the compactness of found clusters.
Examples
--------
... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/rmsstd.py | 0.91502 | 0.482368 | rmsstd.py | pypi |
import math
from river import metrics
from . import base
from .ssw import SSW
class BIC(base.ClusteringMetric):
r"""Bayesian Information Criterion (BIC).
In statistics, the Bayesian Information Criterion (BIC) [^1], or Schwarz Information
Criterion (SIC), is a criterion for model selection among a fini... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/bic.py | 0.907392 | 0.851181 | bic.py | pypi |
import math
from river import metrics
from . import base
from .ssb import SSB
from .ssw import SSW
__all__ = ["CalinskiHarabasz", "Hartigan", "WB"]
class CalinskiHarabasz(base.ClusteringMetric):
"""Calinski-Harabasz index (CH).
The Calinski-Harabasz index (CH) index measures the criteria simultaneously
... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/ssq_based.py | 0.916393 | 0.672963 | ssq_based.py | pypi |
import abc
import numbers
import typing
from river import base, stats, utils
from river.base.typing import FeatureName
__all__ = ["ClusteringMetric"]
class ClusteringMetric(abc.ABC):
"""
Mother class of all internal clustering metrics.
"""
# Define the format specification used for string represent... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/base.py | 0.888378 | 0.512266 | base.py | pypi |
import math
from river import stats, utils
from . import base
class R2(base.ClusteringMetric):
"""R-Squared
R-Squared (RS) [^1] is the complement of the ratio of sum of squared distances between objects
in different clusters to the total sum of squares. It is an intuitive and simple formulation
of ... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/r2.py | 0.94502 | 0.706209 | r2.py | pypi |
from river import stats, utils
from . import base
class SSB(base.ClusteringMetric):
"""Sum-of-Squares Between Clusters (SSB).
The Sum-of-Squares Between Clusters is the weighted mean of the squares of distances
between cluster centers to the mean value of the whole dataset.
Examples
--------
... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/ssb.py | 0.946621 | 0.672224 | ssb.py | pypi |
import math
from river import utils
from . import base
class DaviesBouldin(base.ClusteringMetric):
"""Davies-Bouldin index (DB).
The Davies-Bouldin index (DB) [^1] is an old but still widely used inernal validaion measure.
DB uses intra-cluster variance and inter-cluster center distance to find the wor... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/daviesbouldin.py | 0.912199 | 0.651466 | daviesbouldin.py | pypi |
import math
from river import metrics, utils
from . import base
__all__ = ["BallHall", "Cohesion", "SSW", "Xu"]
class SSW(base.MeanClusteringMetric):
"""Sum-of-Squares Within Clusters (SSW).
Mean of sum of squared distances from data points to their assigned cluster centroids.
The bigger the better.
... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/ssw.py | 0.919697 | 0.67178 | ssw.py | pypi |
import math
from river import stats, utils
from . import base
class SD(base.ClusteringMetric):
"""The SD validity index (SD).
The SD validity index (SD) [^1] is a more recent clustering validation measure. It is composed of
two terms:
* Scat(NC) stands for the scattering within clusters,
* Di... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/sd_validation.py | 0.910679 | 0.779343 | sd_validation.py | pypi |
import math
from river import stats, utils
from . import base
class IIndex(base.ClusteringMetric):
"""I-Index (I).
I-Index (I) [^1] adopts the maximum distance between cluster centers. It also shares the type of
formulation numerator-separation/denominator-compactness. For compactness, the distance fro... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/i_index.py | 0.901929 | 0.552962 | i_index.py | pypi |
import math
from river import stats, utils
from . import base
__all__ = ["GD43", "GD53"]
class GD43(base.ClusteringMetric):
r"""Generalized Dunn's index 43 (GD43).
The Generalized Dunn's indices comprise a set of 17 variants of the original
Dunn's index devised to address sensitivity to noise in the l... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/generalized_dunn.py | 0.906218 | 0.81946 | generalized_dunn.py | pypi |
import math
from river import stats, utils
from . import base
class PS(base.ClusteringMetric):
r"""Partition Separation (PS).
The PS index [^1] was originally developed for fuzzy clustering. This index
only comprises a measure of separation between prototypes. Although classified
as a batch cluster... | /river_extra-0.14.0-py3-none-any.whl/river_extra/metrics/cluster/ps.py | 0.886033 | 0.765769 | ps.py | pypi |
import random
import typing
from river.tree.nodes.htc_nodes import LeafMajorityClass
from river.tree.nodes.htr_nodes import LeafAdaptive, LeafMean, LeafModel
from river.tree.nodes.leaf import HTLeaf
class ETLeaf(HTLeaf):
"""The Extra Tree leaves change the way in which the splitters are updated
(by using sub... | /river_extra-0.14.0-py3-none-any.whl/river_extra/tree/nodes/et_nodes.py | 0.882719 | 0.450359 | et_nodes.py | pypi |
import abc
import collections
import random
import sys
from river import stats
from river.tree.splitter import Splitter
from river.tree.utils import BranchFactory
class RandomSplitter(Splitter):
def __init__(self, seed, buffer_size):
super().__init__()
self.seed = seed
self.buffer_size = ... | /river_extra-0.14.0-py3-none-any.whl/river_extra/tree/splitter/random_splitter.py | 0.638159 | 0.284297 | random_splitter.py | pypi |
import abc
import collections
import copy
import math
import random
import sys
import typing
from river import base, drift, metrics, tree
from ..tree.nodes.et_nodes import ETLeafAdaptive, ETLeafMean, ETLeafModel
from ..tree.splitter import RegRandomSplitter
class ExtraTrees(base.Ensemble, metaclass=abc.ABCMeta):
... | /river_extra-0.14.0-py3-none-any.whl/river_extra/ensemble/online_extra_trees.py | 0.582847 | 0.257246 | online_extra_trees.py | pypi |
from __future__ import annotations
import collections
import inspect
import typing
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from river import base
__all__ = ["PyTorch2RiverBase", "PyTorch2RiverRegressor", "PyTorch2RiverClassifier"]
class PyTorch2RiverBase(base.Estimator, base.Mult... | /river_extra-0.14.0-py3-none-any.whl/river_extra/compat/pytorch.py | 0.960221 | 0.409191 | pytorch.py | pypi |
import re
from .auth_credentials import AuthCredentials
from .cargo_client import CargoClient
from .errors import CantFindMatchHistory
from .esports_lookup_cache import EsportsLookupCache
from .gamepedia_client import GamepediaClient
from .site import Site
ALL_ESPORTS_WIKIS = ['lol', 'halo', 'smite', 'vg', 'rl', 'pub... | /river_mwclient-0.5.0.tar.gz/river_mwclient-0.5.0/river_mwclient/esports_client.py | 0.509276 | 0.193909 | esports_client.py | pypi |
from pytz import timezone, utc
from datetime import datetime
class WikiTime(object):
"""
Leaguepedia and the other esports wikis us an EXTREMELY simplified time zone model.
In this model, there are only three time zones: PST, CET, and KST.
Additionally, the wiki itself does not know anything about day... | /river_mwclient-0.5.0.tar.gz/river_mwclient-0.5.0/river_mwclient/wiki_time.py | 0.692434 | 0.434941 | wiki_time.py | pypi |
import json
import re
from unidecode import unidecode
from .errors import EsportsCacheKeyError
from .site import Site
from .cargo_client import CargoClient
class EsportsLookupCache(object):
def __init__(self, site: Site, cargo_client: CargoClient = None):
self.site = site
self.cargo_client = carg... | /river_mwclient-0.5.0.tar.gz/river_mwclient-0.5.0/river_mwclient/esports_lookup_cache.py | 0.569494 | 0.167185 | esports_lookup_cache.py | pypi |
import abc
import collections
import inspect
from typing import Any, Callable, Deque, Optional, Type, Union, cast
import torch
from river import base
from river_torch.utils import get_loss_fn, get_optim_fn
class DeepEstimator(base.Estimator):
"""
Abstract base class that implements basic functionality of
... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/base.py | 0.944982 | 0.573947 | base.py | pypi |
from typing import Callable, List, Type, Union
import pandas as pd
import torch
from river import base
from river.base.typing import RegTarget
from river_torch.base import DeepEstimator
from river_torch.utils.tensor_conversion import (
df2tensor,
dict2tensor,
float2tensor,
)
class _TestModule(torch.nn.M... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/regression/regressor.py | 0.958673 | 0.647798 | regressor.py | pypi |
from typing import Any, Callable, List, Type, Union
import pandas as pd
import torch
from river.base.typing import RegTarget
from river_torch.base import RollingDeepEstimator
from river_torch.regression import Regressor
from river_torch.utils.tensor_conversion import (
deque2rolling_tensor,
float2tensor,
)
... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/regression/rolling_regressor.py | 0.952286 | 0.619903 | rolling_regressor.py | pypi |
import math
from typing import Any, Callable, Type, Union
import pandas as pd
import torch
from river import stats, utils
from scipy.special import ndtr
from river_torch.anomaly import ae
from river_torch.utils import dict2tensor
class ProbabilityWeightedAutoencoder(ae.Autoencoder):
"""
Wrapper for PyTorch ... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/anomaly/probability_weighted_ae.py | 0.953373 | 0.720835 | probability_weighted_ae.py | pypi |
import abc
import numpy as np
from river import base, utils
from river.anomaly import HalfSpaceTrees
from river.anomaly.base import AnomalyDetector
from river.stats import Mean, Min
class AnomalyScaler(base.Wrapper, AnomalyDetector):
"""Wrapper around an anomaly detector that scales the output of the model
t... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/anomaly/scaler.py | 0.911088 | 0.614452 | scaler.py | pypi |
from typing import Any, Callable, List, Type, Union
import numpy as np
import pandas as pd
import torch
from river import anomaly
from torch import nn
from river_torch.base import RollingDeepEstimator
from river_torch.utils.tensor_conversion import deque2rolling_tensor
class _TestLSTMAutoencoder(nn.Module):
def... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/anomaly/rolling_ae.py | 0.962839 | 0.558267 | rolling_ae.py | pypi |
from typing import Any, Callable, Type, Union
import numpy as np
import pandas as pd
import torch
from river.anomaly.base import AnomalyDetector
from torch import nn
from river_torch.base import DeepEstimator
from river_torch.utils import dict2tensor
from river_torch.utils.tensor_conversion import df2tensor
class _... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/anomaly/ae.py | 0.97362 | 0.662906 | ae.py | pypi |
from typing import Callable, Union
import torch
import torch.nn.functional as F
from torch import nn, optim
ACTIVATION_FNS = {
"selu": nn.SELU,
"relu": nn.ReLU,
"leaky_relu": nn.LeakyReLU,
"gelu": nn.GELU,
"tanh": nn.Tanh,
"sigmoid": nn.Sigmoid,
"elu": nn.ELU,
"linear": nn.Identity,
}
... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/utils/params.py | 0.932982 | 0.57063 | params.py | pypi |
from typing import Deque, Dict, Optional, Union
import numpy as np
import pandas as pd
import torch
from ordered_set import OrderedSet
from river import base
from river.base.typing import ClfTarget, RegTarget
def dict2tensor(
x: dict, device: str = "cpu", dtype: torch.dtype = torch.float32
) -> torch.Tensor:
... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/utils/tensor_conversion.py | 0.953188 | 0.619443 | tensor_conversion.py | pypi |
import math
import warnings
from typing import Callable, Dict, List, Type, Union, cast
import pandas as pd
import torch
from ordered_set import OrderedSet
from river import base
from river.base.typing import ClfTarget
from torch import nn
from torch.utils.hooks import RemovableHandle
from river_torch.base import Deep... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/classification/classifier.py | 0.951391 | 0.506836 | classifier.py | pypi |
import math
from typing import Callable, Dict, List, Type, Union
import pandas as pd
import torch
from river.base.typing import ClfTarget
from torch import nn
from river_torch.base import RollingDeepEstimator
from river_torch.classification import Classifier
from river_torch.utils.tensor_conversion import (
deque... | /river_torch-0.1.2.tar.gz/river_torch-0.1.2/river_torch/classification/rolling_classifier.py | 0.959078 | 0.498962 | rolling_classifier.py | pypi |
</br>
<p align="center">
<img height="80px" src="docs/img/logo.svg" alt="river_logo">
</p>
</br>
<p align="center">
<!-- Tests -->
<a href="https://github.com/online-ml/river/actions?query=workflow%3Atests+branch%3Amaster">
<img src="https://github.com/online-ml/river/workflows/tests/badge.svg?branch=maste... | /river-0.7.0.tar.gz/river-0.7.0/README.md | 0.601477 | 0.93276 | README.md | pypi |
# rivers2stratigraphy
[](https://travis-ci.org/sededu/rivers2stratigraphy)
[](https://ci.appveyor.com/project/amoodie/rivers2stratigrap... | /rivers2stratigraphy-0.3.17.tar.gz/rivers2stratigraphy-0.3.17/README.md | 0.454714 | 0.967533 | README.md | pypi |
import collections
def recursive_update(source, overrides, overwrite_nones=False):
"""Update a nested dictionary or similar mapping.
Modify ``source`` in place.
"""
for key, value in overrides.items():
if value is not None and not overwrite_nones or (overwrite_nones is True):
if i... | /rivery_cli-0.4.0-py3-none-any.whl/rivery_cli/utils/utils.py | 0.679285 | 0.188287 | utils.py | pypi |
import bson
import datetime
import uuid
import collections
import base64
from bson import ObjectId
import calendar
import decimal
import simplejson as json
def _datetime_to_millis(dtm):
"""Convert datetime to milliseconds since epoch UTC."""
if dtm.utcoffset() is not None:
dtm = dtm - dtm.utcoffset()
... | /rivery_cli-0.4.0-py3-none-any.whl/rivery_cli/utils/bson_utils.py | 0.639061 | 0.208773 | bson_utils.py | pypi |
import yaml
import click
import pathlib
def get_context():
""" Get the context from click application"""
try:
return click.get_current_context(silent=True).obj or {}
except:
return {}
def import_model(loader, node):
""" Import yaml from the specific paths in the project """
ctx =... | /rivery_cli-0.4.0-py3-none-any.whl/rivery_cli/utils/yaml_loaders.py | 0.449151 | 0.202148 | yaml_loaders.py | pypi |
import uuid
import bson
import click
import simplejson as json
from rivery_cli.globals import global_keys, global_settings
from rivery_cli.rivery_session import RiverySession
from rivery_cli.utils import logicode_utils
class RiverConverter(object):
"""
River yaml converter.
convert the river from the
... | /rivery_cli-0.4.0-py3-none-any.whl/rivery_cli/converters/entities/rivers.py | 0.676086 | 0.214465 | rivers.py | pypi |
from __future__ import unicode_literals
import json
import redis
from rivescript.sessions import SessionManager
__author__ = 'Noah Petherbridge'
__copyright__ = 'Copyright 2017, Noah Petherbridge'
__license__ = 'MIT'
__status__ = 'Beta'
__version__ = '0.1.0'
class RedisSessionManager(SessionManager):
"... | /rivescript_redis-0.1.0.tar.gz/rivescript_redis-0.1.0/rivescript_redis.py | 0.78016 | 0.175485 | rivescript_redis.py | pypi |
# RiveScript-Python
[![Build Status][1]][2] [![Read the docs][3]][4] [![PyPI][5]][6]
## Introduction
This is a RiveScript interpreter for the Python programming language. RiveScript
is a scripting language for chatterbots, making it easy to write
trigger/response pairs for building up a bot's intelligence.
This lib... | /rivescript-1.15.0.tar.gz/rivescript-1.15.0/README.md | 0.509764 | 0.888904 | README.md | pypi |
# RiveScript Deparse
This example purely consists of additional documentation and examples of the
`deparse()` method of RiveScript.
## Relevant Methods
* `rs.deparse()`
This method exports the current in-memory representation of the RiveScript
brain as a JSON-serializable data structure. See [Schema](#schema) f... | /rivescript-1.15.0.tar.gz/rivescript-1.15.0/eg/deparse/README.md | 0.717903 | 0.920718 | README.md | pypi |
# rivet
A user-friendly Python-to-S3 interface. Adds quality of life and convenience features around `boto3`, including the handling of reading and writing to files in proper formats. While there is nothing that you can do with `rivet` that you can't do with `boto3`, `rivet`'s primary focus is ease-of-use. By handling... | /rivet-1.6.0.tar.gz/rivet-1.6.0/README.md | 0.613584 | 0.943138 | README.md | pypi |
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