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 |
|---|---|---|---|---|---|
import hashlib
from typing import Optional, Callable
from pydantic import constr, Field, parse_obj_as
from .base_model import BaseModel
from .utils import HashableSequence, HashableSet
from .results import Result, Results
from .qgraph import QueryGraph
from .kgraph import KnowledgeGraph
from .shared import LogEntry,... | /reasoner-pydantic-4.1.4.tar.gz/reasoner-pydantic-4.1.4/reasoner_pydantic/message.py | 0.873754 | 0.237598 | message.py | pypi |
from enum import Enum
from typing import Any, Optional, Union
from pydantic.class_validators import validator
from pydantic.types import confloat, conint
from .base_model import BaseModel
from .utils import HashableSequence, nonzero_validator
from .shared import BiolinkPredicate
def constant(s: str):
"""Generat... | /reasoner-pydantic-4.1.4.tar.gz/reasoner-pydantic-4.1.4/reasoner_pydantic/workflow.py | 0.927042 | 0.155559 | workflow.py | pypi |
from enum import Enum
from pydantic.class_validators import validator
from reasoner_pydantic.utils import HashableMapping
from typing import Any, Optional
from pydantic import Field
from .base_model import BaseModel
from .utils import HashableMapping, HashableSequence, nonzero_validator
from .shared import BiolinkEn... | /reasoner-pydantic-4.1.4.tar.gz/reasoner-pydantic-4.1.4/reasoner_pydantic/qgraph.py | 0.919719 | 0.344195 | qgraph.py | pypi |
import collections.abc
from typing import Dict, List, Generic, Set, TypeVar
from pydantic.generics import GenericModel
KeyType = TypeVar("KeyType")
ValueType = TypeVar("ValueType")
class HashableMapping(
GenericModel,
Generic[KeyType, ValueType],
collections.abc.MutableMapping,
):
"""
Custom cla... | /reasoner-pydantic-4.1.4.tar.gz/reasoner-pydantic-4.1.4/reasoner_pydantic/utils.py | 0.870308 | 0.303435 | utils.py | pypi |
from typing import Optional
from pydantic import Field
from .shared import (
Attribute,
BiolinkEntity,
BiolinkPredicate,
CURIE,
EdgeIdentifier,
Qualifier,
ResourceRoleEnum,
)
from .base_model import BaseModel
from .utils import HashableMapping, HashableSet
class Node(BaseModel):
"""K... | /reasoner-pydantic-4.1.4.tar.gz/reasoner-pydantic-4.1.4/reasoner_pydantic/kgraph.py | 0.898882 | 0.187077 | kgraph.py | pypi |
import copy
from typing import Optional
from pydantic import Field, parse_obj_as
from .base_model import BaseModel
from .utils import HashableMapping, HashableSet, HashableSequence
from .shared import Attribute, CURIE
class EdgeBinding(BaseModel):
"""Edge binding."""
id: str = Field(
...,
t... | /reasoner-pydantic-4.1.4.tar.gz/reasoner-pydantic-4.1.4/reasoner_pydantic/results.py | 0.82741 | 0.311911 | results.py | pypi |
import re
from typing import List, Union, overload
def _space_case(arg: str):
"""Convert string to space case.
"ThisCase" is replaced with "this case".
"""
# replace "_" with " "
tmp = re.sub("_", " ", arg)
# replace "xYz" with "x yz"
tmp = re.sub(
r"(?<=[a-z])(?=[A-Z][a-z])",
... | /reasoner_transpiler-2.0.2-py3-none-any.whl/reasoner_transpiler/util.py | 0.61173 | 0.402421 | util.py | pypi |
from functools import reduce
from operator import and_, or_
class Query():
"""Cypher query segment."""
def __init__(
self,
string,
qids=None,
references=None,
qgraph=None,
):
"""Initialize."""
self._string = string
self._... | /reasoner_transpiler-2.0.2-py3-none-any.whl/reasoner_transpiler/nesting.py | 0.906784 | 0.203866 | nesting.py | pypi |
# reassembler
## A Python implementation of the various OS IPv4 packet fragment reassembly engines.
### One Packet in => Six Packets out
This module will reassemble fragmented packets using common used fragmentation reassembly techniques. It then generates 6 pcap files. It also prints the payloads to the screen and ... | /reassembler-2.1.1.tar.gz/reassembler-2.1.1/README.md | 0.451568 | 0.883387 | README.md | pypi |
# Reaver: Modular Deep Reinforcement Learning Framework
[](https://youtu.be/gEyBzcPU5-w)
[![CollectMineralShards](https://user-images.githubusercontent.com/195271/48730941-70d8fc80-ec44-11e8-95ae-acff6f5a... | /reaver-2.1.9.tar.gz/reaver-2.1.9/README.md | 0.433981 | 0.938294 | README.md | pypi |
<a href="https://colab.research.google.com/github/ykanematsu/reaxfit/blob/main/reaxfit_sample.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# ReaxFF sample
- A sample notebook for [reaxfit](https://github.com/ykanematsu/reaxfit).
- Potential ener... | /reaxfit-0.2.1.tar.gz/reaxfit-0.2.1/reaxfit_sample.ipynb | 0.403802 | 0.970576 | reaxfit_sample.ipynb | pypi |
import logging
from typing import TYPE_CHECKING
from osp.core.namespaces import emmo
from osp.core.session import SimWrapperSession
from .celery_workflow_engine import CeleryWorkflowEngine
if TYPE_CHECKING:
from typing import UUID, Any, Dict, List, Optional
from pydantic import BaseSettings
from osp.c... | /reaxpro-workflow-service-1.0.0.tar.gz/reaxpro-workflow-service-1.0.0/osp/wrappers/celery_workflow_wrapper/celery_workflow_wrapper.py | 0.87916 | 0.218253 | celery_workflow_wrapper.py | pypi |
import logging
import tempfile
from typing import TYPE_CHECKING
from celery import Celery, signals
from celery.result import allow_join_result
from osp.core.namespaces import emmo, get_entity
from osp.core.session import CoreSession
from osp.core.utils import export_cuds, import_cuds
from osp.settings import AppConfi... | /reaxpro-workflow-service-1.0.0.tar.gz/reaxpro-workflow-service-1.0.0/osp/wrappers/celery_workflow_wrapper/celery_workflow_engine.py | 0.810741 | 0.181444 | celery_workflow_engine.py | pypi |
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional
from uuid import UUID
from pydantic import BaseModel, Field
class RemoteWorker(str):
"""Identifier of the remote celery worker."""
class RemoteTaskName(str):
"""Task name which can be executed on remote worker.... | /reaxpro-workflow-service-1.0.0.tar.gz/reaxpro-workflow-service-1.0.0/osp/app/models.py | 0.931197 | 0.258373 | models.py | pypi |
from typing import Optional
from fastapi_plugins import RedisSettings
from pydantic import Field, SecretStr
class AppConfig(RedisSettings):
"""Main configuration for based on redis-settings"""
worker_name: str = Field(
"simphony-workflows",
description="Name of the worker displayed in the s... | /reaxpro-workflow-service-1.0.0.tar.gz/reaxpro-workflow-service-1.0.0/osp/settings/__init__.py | 0.904675 | 0.397295 | __init__.py | pypi |
import tempfile
import warnings
from enum import Enum
from pathlib import Path
from typing import Union, List, TYPE_CHECKING, Optional
from uuid import UUID
from osp.core.namespaces import emmo, crystallography
from osp.core.session import CoreSession
from osp.core.utils import export_cuds
from osp.models.utils.genera... | /reaxpro_wrappers-1.7.0-py3-none-any.whl/osp/models/multiscale/co_pt111_meso.py | 0.842701 | 0.298075 | co_pt111_meso.py | pypi |
import tempfile
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, List, Optional, Union
from uuid import UUID
from pydantic import AnyUrl, BaseModel, Field, root_validator
from pydantic.dataclasses import dataclass
from osp.core.namespaces import emmo
from osp.core.session import CoreSession
... | /reaxpro_wrappers-1.7.0-py3-none-any.whl/osp/models/ams/energy_landscape_refinement.py | 0.758824 | 0.260742 | energy_landscape_refinement.py | pypi |
import os
import yaml
from osp.core.cuds import Cuds
from osp.tools.io_functions import raise_warning
from osp.core.namespaces import emmo
def AMS_default_setting(root_cuds_object: Cuds, accuracy_level: str,
calculation_type: str, setting: str) -> str:
"""Set default setting of a Wrapper o... | /reaxpro_wrappers-1.7.0-py3-none-any.whl/osp/tools/set_functions.py | 0.566139 | 0.302249 | set_functions.py | pypi |
[中文 README](./README.zh.md)
# reb -- Regular Expression Beautiful
To make **information extraction with patterns** easier, reb tries to improve traditional re in some ways:
* Maintainability
* Reusability
* Readability
For that, seve... | /reb-0.1.2.tar.gz/reb-0.1.2/README.md | 0.785185 | 0.91734 | README.md | pypi |
from src.utils.utils import Utils
from pydicom.dataset import FileDataset
from pydicom.dataelem import DataElement
from pydicom.pixel_data_handlers.util import convert_color_space
from typing import Dict, Union, List, Tuple
import matplotlib.pyplot as plt
import cv2
import json
class Image:
def __init__(self, di... | /rebadicom-0.0.1.tar.gz/rebadicom-0.0.1/src/image.py | 0.86609 | 0.540621 | image.py | pypi |
import rebase.util.api_request as api_request
import json
import pandas as pd
import requests
class SiteTemplate():
def __init__(self, latitude, longitude):
self.latitude = latitude
self.longitude = longitude
class Site():
base_path = 'platform/v1'
@classmethod
def create(cls, site... | /rebase-toolkit-0.0.4b0.tar.gz/rebase-toolkit-0.0.4b0/rebase/api/site.py | 0.610337 | 0.150403 | site.py | pypi |
# rebasin
[](https://www.python.org/downloads/release/python-370/)
An implementati... | /rebasin-0.0.47.tar.gz/rebasin-0.0.47/README.md | 0.854703 | 0.911771 | README.md | pypi |
import enum
import errno
import os
import pathlib
import re
from timeit import default_timer
from typing import List, AnyStr
from urllib.parse import uses_relative, uses_netloc, uses_params
import requests
from six.moves.urllib_parse import urlparse
from urllib3.util import parse_url
from rebotics_sdk.advanced import... | /rebotics_sdk-0.25.10.tar.gz/rebotics_sdk-0.25.10/rebotics_sdk/utils.py | 0.494873 | 0.177704 | utils.py | pypi |
import json
from datetime import datetime
import click
import pytz
from dateutil import parser as dp
from prettytable import PrettyTable
def extract_fields_to_render(d, max_column_length, keys_to_skip, key_prefix=None, depth=0, max_depth=1):
fields_to_render = []
if depth > max_depth:
return []
... | /rebotics_sdk-0.25.10.tar.gz/rebotics_sdk-0.25.10/rebotics_sdk/cli/renderers.py | 0.467818 | 0.258078 | renderers.py | pypi |
import pathlib
try:
import click
except ImportError:
raise Exception("To use authenticated role provider you have to install rebotics_sdk[shell]")
from rebotics_sdk.cli.utils import app_dir, ReboticsScriptsConfiguration
from rebotics_sdk.providers import (
RetailerProvider, CvatProvider, AdminProvider, D... | /rebotics_sdk-0.25.10.tar.gz/rebotics_sdk-0.25.10/rebotics_sdk/cli/authenticated_provider.py | 0.613121 | 0.160891 | authenticated_provider.py | pypi |
import os
import pathlib
import typing
import zipfile
from typing import AnyStr, Type, Union
import py7zr
class ArchiveFacade:
def __init__(self, archive):
self.archive = archive
def read(self, arcname) -> typing.IO:
raise NotImplementedError()
def write(self, filename, arcname):
... | /rebotics_sdk-0.25.10.tar.gz/rebotics_sdk-0.25.10/rebotics_sdk/rcdb/archivers.py | 0.675336 | 0.244414 | archivers.py | pypi |
import inspect
import typing
import warnings
if typing.TYPE_CHECKING:
from rebotics_sdk.rcdb.fields import BaseField
from rebotics_sdk.rcdb.fields import ImageField, StringField, FeatureVectorField
class Options:
fields: typing.Dict[str, 'BaseField']
def __init__(self, cls):
self.cls = cls
... | /rebotics_sdk-0.25.10.tar.gz/rebotics_sdk-0.25.10/rebotics_sdk/rcdb/entries.py | 0.738763 | 0.179459 | entries.py | pypi |
from typing import BinaryIO
from rebotics_sdk.advanced import remote_loaders
from rebotics_sdk.constants import RCDB
class FileUploadError(Exception):
def __init__(self, msg, response):
super(FileUploadError, self).__init__(msg)
self.response = response
class PresignedURLFileUploader:
def _... | /rebotics_sdk-0.25.10.tar.gz/rebotics_sdk-0.25.10/rebotics_sdk/advanced/flows.py | 0.844313 | 0.182207 | flows.py | pypi |
import os
from collections import OrderedDict
from typing import Optional, BinaryIO
import requests
import six
from requests_toolbelt import MultipartEncoder, MultipartEncoderMonitor
from tqdm import tqdm
class ProgressBar(tqdm):
def update_to(self, n):
"""
identical to update, except `n` should... | /rebotics_sdk-0.25.10.tar.gz/rebotics_sdk-0.25.10/rebotics_sdk/advanced/remote_loaders.py | 0.769167 | 0.154951 | remote_loaders.py | pypi |
import io
import typing
from .base import ReboticsBaseProvider, remote_service, PageResult
class HawkeyeProvider(ReboticsBaseProvider):
@remote_service('/api-token-auth/', raw=True)
def token_auth(self, username, password, **kwargs):
response = self.session.post(data={
'username': userna... | /rebotics_sdk-0.25.10.tar.gz/rebotics_sdk-0.25.10/rebotics_sdk/providers/hawkeye.py | 0.577019 | 0.157428 | hawkeye.py | pypi |
from __future__ import division, print_function
__all__ = ["IntegrateOp"]
import pkg_resources
import numpy as np
import theano
from theano import gof
import theano.tensor as tt
from .build_utils import (
get_compile_args,
get_cache_version,
get_header_dirs,
get_librebound_path,
get_librebound... | /rebound_pymc3-0.0.3.tar.gz/rebound_pymc3-0.0.3/rebound_pymc3/integrate.py | 0.767516 | 0.264168 | integrate.py | pypi |
__all__ = ["ReboundOp"]
import numpy as np
import theano
from theano import gof
import theano.tensor as tt
class ReboundOp(gof.Op):
__props__ = ()
def __init__(self, **rebound_args):
self.rebound_args = rebound_args
super(ReboundOp, self).__init__()
def make_node(self, masses, initia... | /rebound_pymc3-0.0.3.tar.gz/rebound_pymc3-0.0.3/rebound_pymc3/python_impl.py | 0.613005 | 0.461988 | python_impl.py | pypi |
from enum import Enum
from functools import lru_cache
from os import access, chmod, R_OK
import os.path as path
from pathlib import Path
import platform
from shutil import copy as copy_file, which
import stat
from subprocess import check_call, check_output
import sys
from tempfile import TemporaryDirectory
from colora... | /rebuild_audio_file-0.1.1-py3-none-any.whl/raf/__init__.py | 0.408631 | 0.17676 | __init__.py | pypi |
import copy
import pandas as pd
from typing import Any, AnyStr, List, Dict, Optional
from rebyu.util.logger import Logger
class BaseStep(object):
"""
Base Class for Step
The BaseStep class is the foundation of Rebyu Steps which are encapsulated functions to run
within a pipeline with a given rule-... | /pipeline/base.py | 0.889523 | 0.449755 | base.py | pypi |
from collections import OrderedDict
from collections.abc import Iterable, Mapping, MutableMapping, MutableSequence
from typing import Any
def rec_avro_schema(namespace='rec_avro'):
"""
Generates an avro schema (as python object) suitable for storing arbitrary
python nested data structure.
For fastavr... | /rec_avro-0.0.4-py3-none-any.whl/rec_avro/core.py | 0.875282 | 0.39423 | core.py | pypi |
import os
import torch
from .model_pipeline import train_model, valid_model, test_model
from .utils import beautify_json
from .dataset import BaseDataset,MultiTaskDataset
from loguru import logger
import torch.utils.data as D
import wandb
class RankTraniner:
"""
Rank Trainer
"""
def __init__(self, num_... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/old_ranktrainer.py | 0.49048 | 0.228146 | old_ranktrainer.py | pypi |
import os
import torch
from .model_pipeline import train_model, valid_model, test_model, train_graph_model, test_graph_model, train_sequence_model, test_sequence_model
from .utils import beautify_json
from .dataset import BaseDataset,MultiTaskDataset
from loguru import logger
import torch.utils.data as D
import wandb
i... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/gpt_ranktrainer.py | 0.881053 | 0.362828 | gpt_ranktrainer.py | pypi |
from typing import Dict, List
import torch
import time
from tqdm import tqdm
from sklearn.metrics import roc_auc_score, log_loss
from loguru import logger
from .utils import get_gpu_usage, evaluate_recall, get_recall_predict
import faiss
import wandb
def train_model(model: torch.nn.Module,
train_loade... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/model_pipeline.py | 0.950709 | 0.427038 | model_pipeline.py | pypi |
import os
import torch
from .model_pipeline import train_model, test_model, train_graph_model, test_graph_model, train_sequence_model, \
test_sequence_model
from .utils import beautify_json
from .dataset import BaseDataset, MultiTaskDataset
from loguru import logger
import torch.utils.data as D
import wandb
from ty... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/trainer.py | 0.893606 | 0.427935 | trainer.py | pypi |
from dgl import DGLGraph
import numpy as np
import torch
from torch.utils.data import Dataset
import random
class GeneralGraphDataset(Dataset):
def __init__(self, df, num_user, num_item, phase='train'):
self.df = df
self.n_item = self.df['item_id'].nunique()
self.phase = phase
self... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/dataset/graph_dataset.py | 0.650134 | 0.452959 | graph_dataset.py | pypi |
from .base_dataset import BaseDataset
from .multi_task_dataset import MultiTaskDataset
from .sequence_dataset import SequenceDataset, SequenceDatasetV2
import torch.utils.data as D
def get_base_dataloader(train_df, valid_df, test_df, schema, batch_size=512 * 3):
train_dataset = BaseDataset(schema, train_df)
e... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/dataset/process_data.py | 0.791418 | 0.310629 | process_data.py | pypi |
import torch
from torch.utils.data import Dataset
import random
class SequenceDataset(Dataset):
def __init__(self, config, df, enc_dict=None, phase='train'):
self.config = config
self.df = df
self.enc_dict = enc_dict
self.max_length = self.config['max_length']
self.user_col... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/dataset/sequence_dataset.py | 0.573081 | 0.230422 | sequence_dataset.py | pypi |
import pandas as pd
import numpy as np
import torch
from torch.utils.data import Dataset
from typing import Dict
from collections import defaultdict
class BaseDataset(Dataset):
"""
This class implements a BaseDataset that inherits from Pytorch's Dataset class for loading and encoding data.
Args:
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/dataset/base_dataset.py | 0.943178 | 0.731898 | base_dataset.py | pypi |
import torch
from collections import defaultdict
import pandas as pd
import numpy as np
from .base_dataset import BaseDataset
class MultiTaskDataset(BaseDataset):
"""
A dataset class for multi-task learning.
Args:
config: A dictionary containing the dataset configuration.
df: A Pandas Data... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/dataset/multi_task_dataset.py | 0.889712 | 0.482551 | multi_task_dataset.py | pypi |
import onnx
from onnx_tf.backend import prepare
import torch
import os
def construct_demmy_data(schema: dict) -> tuple:
"""
Construct dummy data for the model input.
Args:
schema (dict): A dictionary containing the schema of the input data.
Returns:
tuple: A tuple containing the dummy input ... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/serving/ranking_server.py | 0.905196 | 0.641829 | ranking_server.py | pypi |
import torch
from torch import nn
from torch.nn.init import xavier_normal_, constant_
import numpy as np
from .layers import EmbeddingLayer
from loguru import logger
class BaseModel(nn.Module):
def __init__(self, enc_dict: dict, embedding_dim: int) -> None:
"""
A base class for a neural network mo... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/base_model.py | 0.97338 | 0.544801 | base_model.py | pypi |
import dgl
import os
import numpy as np
import torch
from torch import nn
import random
from typing import Dict, List, Tuple, Union
def seed_everything(seed: int = 1029) -> None:
"""Set the random seed for reproducibility.
Args:
seed (int, optional): The random seed. Defaults to 1029.
"""
ra... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/utils.py | 0.923618 | 0.465752 | utils.py | pypi |
from typing import Dict, List
import torch
from torch import nn
from ..layers import MLP, MultiHeadSelfAttention
from ..utils import get_feature_num
from ..base_model import BaseModel
class AITM(BaseModel):
def __init__(self,
embedding_dim: int = 32,
tower_dims: List[int] = [400,... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/multi_task/aitm.py | 0.958236 | 0.412767 | aitm.py | pypi |
import torch
from torch import nn
from ..layers import EmbeddingLayer
from ..utils import get_feature_num, get_linear_input
import numpy as np
from ..base_model import BaseModel
class MMOE(BaseModel):
def __init__(self,
num_task=2,
n_expert=3,
embedding_dim=40,
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/multi_task/mmoe.py | 0.880155 | 0.307319 | mmoe.py | pypi |
from typing import Dict, List
import torch
from torch import nn
from ..utils import get_feature_num, get_linear_input
import numpy as np
from ..base_model import BaseModel
class ShareBottom(BaseModel):
def __init__(self,
num_task: int = 2,
embedding_dim: int = 40,
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/multi_task/sharebottom.py | 0.947575 | 0.396915 | sharebottom.py | pypi |
from torch import nn
from ..layers import MLP
from ..utils import get_feature_num
from ..base_model import BaseModel
class ESSM(BaseModel):
def __init__(self,
embedding_dim=40,
hidden_dim=[128, 64],
dropouts=[0.2, 0.2],
enc_dict=None,
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/multi_task/essm.py | 0.941422 | 0.275702 | essm.py | pypi |
import torch
from torch import nn
from ..layers import EmbeddingLayer
from ..utils import get_feature_num, get_linear_input
import numpy as np
from ..base_model import BaseModel
class MLMMOE(BaseModel):
def __init__(self,
num_task=2,
n_expert=3,
embedding_dim=40,
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/multi_task/mlmmoe.py | 0.836655 | 0.298856 | mlmmoe.py | pypi |
import torch
from torch import nn
from ..layers import EmbeddingLayer
from ..utils import get_feature_num, get_linear_input
import numpy as np
from ..base_model import BaseModel
class OMOE(BaseModel):
def __init__(self,
num_task=2,
n_expert=3,
embedding_dim=40,
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/multi_task/omoe.py | 0.896455 | 0.308763 | omoe.py | pypi |
from typing import List
from torch import nn
import torch
class NextItNetLayer(nn.Module):
def __init__(self, channels: int, dilations: List[int], one_masked: bool, kernel_size: int, feat_drop: float = 0.0):
"""
Args:
channels: Number of input channels
dilations: List of di... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/layers/conv.py | 0.98525 | 0.605682 | conv.py | pypi |
import copy
import math
import torch
from torch import nn
import torch.nn.functional as fn
class MultiHeadAttention(nn.Module):
"""
Multi-head Self-attention layers, a attention score dropout layer is introduced.
Args:
input_tensor (torch.Tensor): the input of the multi-head self-attention layer
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/layers/trainformer.py | 0.948751 | 0.585812 | trainformer.py | pypi |
from typing import Dict, Union, Optional
import torch
from torch import nn
class EmbeddingLayer(nn.Module):
def __init__(self,
enc_dict: Dict[str, Dict[str, Union[int, str]]],
embedding_dim: int) -> None:
"""
Initialize EmbeddingLayer instance.
Args:
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/layers/embedding.py | 0.964321 | 0.452838 | embedding.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiInterestSelfAttention(nn.Module):
def __init__(self, embedding_dim: int, num_attention_heads: int, d: int = None) -> None:
super(MultiInterestSelfAttention, self).__init__()
self.embedding_dim = embedding_dim
sel... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/layers/multi_interest.py | 0.94388 | 0.608507 | multi_interest.py | pypi |
import torch
from torch import nn
from itertools import combinations
class InnerProductLayer(nn.Module):
""" output: product_sum_pooling (bs x 1),
Bi_interaction_pooling (bs * dim),
inner_product (bs x f2/2),
elementwise_product (bs x f2/2 x emb_dim)
"""
d... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/layers/interaction.py | 0.945538 | 0.4231 | interaction.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
from itertools import product
import dgl.function as fn
class FiGNN_Layer(nn.Module):
def __init__(self,
num_fields,
embedding_dim,
gnn_layers=3,
reuse_graph_layer=False,
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/layers/graph.py | 0.938336 | 0.317069 | graph.py | pypi |
from typing import List, Union
import torch.nn as nn
from .activation import get_activation
class MLP(nn.Module):
"""Customizable Multi-Layer Perceptron"""
def __init__(self,
input_dim: int,
output_dim: Union[int, None] = None,
hidden_units: List[int] = [],
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/layers/deep.py | 0.957942 | 0.587411 | deep.py | pypi |
import torch
from torch import nn
import numpy as np
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, dropout_rate=0.):
super(ScaledDotProductAttention, self).__init__()
self.dropout = None
if dropout_rate > 0:
self.dropo... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/layers/attention.py | 0.961144 | 0.445409 | attention.py | pypi |
from torch import nn
import torch
import torch.nn.functional as F
import numpy as np
class MaskedAveragePooling(nn.Module):
"""
This module takes as input an embedding matrix,
applies masked pooling, i.e. ignores zero-padding,
and computes the average embedding vector for each input.
"""
def... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/layers/sequence.py | 0.980534 | 0.725819 | sequence.py | pypi |
from typing import Dict
import torch
from torch import nn
from rec_pangu.models.utils import generate_graph
from rec_pangu.models.layers import SRGNNCell, TransformerEncoder
from rec_pangu.models.base_model import SequenceBaseModel
class GCSAN(SequenceBaseModel):
def __init__(self, enc_dict, config):
supe... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/gcsan.py | 0.937024 | 0.271457 | gcsan.py | pypi |
from typing import Dict
import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from rec_pangu.models.base_model import SequenceBaseModel
class SINE(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(SINE, self).__init__(enc_dict, config)
self.layer_norm_e... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/sine.py | 0.950675 | 0.474996 | sine.py | pypi |
from typing import Dict
import torch
from torch import nn
import torch.nn.functional as F
from rec_pangu.models.base_model import SequenceBaseModel
class CMI(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(CMI, self).__init__(enc_dict, config)
self.hidden_size = self.config.get('h... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/cmi.py | 0.771499 | 0.330985 | cmi.py | pypi |
from typing import Dict
import torch
from rec_pangu.models.layers import CapsuleNetwork
from rec_pangu.models.base_model import SequenceBaseModel
class MIND(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(MIND, self).__init__(enc_dict, config)
self.capsule = CapsuleNetwork(self.em... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/mind.py | 0.946262 | 0.419529 | mind.py | pypi |
from typing import Dict
import torch
from rec_pangu.models.layers import MultiInterestSelfAttention, CapsuleNetwork
from rec_pangu.models.base_model import SequenceBaseModel
class ComirecSA(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(ComirecSA, self).__init__(enc_dict, config)
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/comirec.py | 0.957942 | 0.530601 | comirec.py | pypi |
from typing import Dict
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from rec_pangu.models.base_model import SequenceBaseModel
class Re4(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(Re4, self).__init__(enc_dict, config)
self.num_interests... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/re4.py | 0.941156 | 0.465448 | re4.py | pypi |
from typing import Dict
import torch
from torch import nn
from rec_pangu.models.utils import generate_graph
from rec_pangu.models.layers import SRGNNCell
from rec_pangu.models.base_model import SequenceBaseModel
class SRGNN(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(SRGNN, self).__ini... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/srgnn.py | 0.922404 | 0.496521 | srgnn.py | pypi |
from typing import Dict
import torch
from torch import nn
from rec_pangu.models.base_model import SequenceBaseModel
class NARM(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(NARM, self).__init__(enc_dict, config)
self.n_layers = self.config.get('n_layers', 2)
self.dropout... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/narm.py | 0.954594 | 0.36832 | narm.py | pypi |
from typing import Dict
import torch
from rec_pangu.models.base_model import SequenceBaseModel
from rec_pangu.models.layers import NextItNetLayer
class NextItNet(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(NextItNet, self).__init__(enc_dict, config)
self.dilations = self.confi... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/nextitnet.py | 0.933817 | 0.288839 | nextitnet.py | pypi |
from typing import Dict
import torch
from torch import nn
from rec_pangu.models.layers import TransformerEncoder
from rec_pangu.models.base_model import SequenceBaseModel
class SASRec(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(SASRec, self).__init__(enc_dict, config)
self.n_l... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/sasrec.py | 0.956125 | 0.198763 | sasrec.py | pypi |
from typing import Dict
import torch
import torch.nn as nn
import copy
import torch.nn.functional as F
import math
from rec_pangu.models.base_model import SequenceBaseModel
class IOCRec(SequenceBaseModel):
def __init__(self, enc_dict, config):
super(IOCRec, self).__init__(enc_dict, config)
self.in... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/iocrec.py | 0.938955 | 0.177383 | iocrec.py | pypi |
from typing import Dict
import torch
from torch import nn
import torch.nn.functional as F
from rec_pangu.models.utils import generate_graph
from rec_pangu.models.layers import SRGNNCell
from rec_pangu.models.base_model import SequenceBaseModel
class NISER(SequenceBaseModel):
def __init__(self, enc_dict, config):
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/sequence/niser.py | 0.91985 | 0.503296 | niser.py | pypi |
from typing import Dict, List
from torch import nn
import torch
from ..layers import KMaxPooling, get_activation
from ..utils import get_feature_num
from ..base_model import BaseModel
class CCPM(BaseModel):
def __init__(self,
embedding_dim: int = 32,
hidden_units: List[int] = [64... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/ccpm.py | 0.957646 | 0.491578 | ccpm.py | pypi |
from typing import Dict
from torch import nn
import torch
from ..layers import EmbeddingLayer, MLP
from ..utils import get_feature_num
from ..base_model import BaseModel
class AFN(BaseModel):
def __init__(self,
embedding_dim=32,
dnn_hidden_units=[64, 64, 64],
afn... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/afn.py | 0.953057 | 0.394609 | afn.py | pypi |
import torch
from ..layers import MLP, LR_Layer, SENET_Layer, BilinearInteractionLayer
from ..utils import get_feature_num, get_linear_input
from ..base_model import BaseModel
# Fixme: change the current code of AFM with the right version.
class AFM(BaseModel):
def __init__(self,
embedding_dim=3... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/afm.py | 0.908124 | 0.395251 | afm.py | pypi |
from typing import Dict, List
from torch import nn
import torch
from ..layers import MLP
from ..utils import get_feature_num, get_linear_input
from ..base_model import BaseModel
class AOANet(BaseModel):
def __init__(self,
embedding_dim: int = 32,
dnn_hidden_units: List[int] = [64... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/aoanet.py | 0.960584 | 0.460168 | aoanet.py | pypi |
import torch
from typing import Dict, List
from ..layers import LR_Layer, MLP, InnerProductLayer
from ..utils import get_dnn_input_dim
from ..base_model import BaseModel
class NFM(BaseModel):
def __init__(self,
embedding_dim: int = 32,
hidden_units: List[int] = [64, 64, 64],
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/nfm.py | 0.953966 | 0.472988 | nfm.py | pypi |
from typing import Dict, List
import torch
from ..layers import LR_Layer, MLP, BilinearInteractionLayer, SENET_Layer
from ..utils import get_feature_num, get_linear_input
from ..base_model import BaseModel
class FiBiNet(BaseModel):
def __init__(self,
embedding_dim: int = 32,
hidd... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/fibinet.py | 0.955734 | 0.485722 | fibinet.py | pypi |
from typing import Dict, List
from torch import nn
import torch
from ..layers import MLP, LR_Layer, MultiHeadSelfAttention
from ..utils import get_feature_num, get_linear_input
from ..base_model import BaseModel
class AutoInt(BaseModel):
def __init__(self,
embedding_dim: int = 32,
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/autoint.py | 0.960212 | 0.47792 | autoint.py | pypi |
import torch
from typing import Dict, List
from ..layers import MLP, LR_Layer, CompressedInteractionNet
from ..utils import get_feature_num, get_linear_input
from ..base_model import BaseModel
class xDeepFM(BaseModel):
def __init__(self,
embedding_dim: int = 32,
dnn_hidden_units:... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/xdeepfm.py | 0.93734 | 0.489198 | xdeepfm.py | pypi |
import torch
from typing import Dict, List
from ..layers import MLP, LR_Layer
from ..utils import get_dnn_input_dim, get_linear_input
from ..base_model import BaseModel
class WDL(BaseModel):
def __init__(self,
embedding_dim: int = 32,
hidden_units: List[int] = [64, 64, 64],
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/wdl.py | 0.953827 | 0.496094 | wdl.py | pypi |
from typing import Dict, List
import torch
from ..layers import MaskBlock, MLP
from ..utils import get_dnn_input_dim, get_linear_input
from ..base_model import BaseModel
class MaskNet(BaseModel):
def __init__(self,
embedding_dim: int = 32,
block_num: int = 3,
use... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/masknet.py | 0.964431 | 0.499878 | masknet.py | pypi |
from typing import Dict, List
import torch
from ..layers import FM_Layer, MLP
from ..utils import get_dnn_input_dim, get_linear_input
from ..base_model import BaseModel
class DeepFM(BaseModel):
def __init__(self,
embedding_dim: int = 32,
hidden_units: List[int] = [64, 64, 64],
... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/deepfm.py | 0.951549 | 0.459197 | deepfm.py | pypi |
from typing import Dict, List
from torch import nn
import torch
from ..layers import CrossNet
from ..utils import get_linear_input, get_feature_num
from ..base_model import BaseModel
class DCN(BaseModel):
def __init__(self,
embedding_dim: int = 32,
hidden_units: List[int] = [64, ... | /rec_pangu-0.4.1-py3-none-any.whl/rec_pangu/models/ranking/dcn.py | 0.942889 | 0.505127 | dcn.py | pypi |
import numpy as np
from rec_rnn_a3c.src.supervised_rnn import SupervisedBloomRNN
import tensorflow as tf
class RewardModel(object):
def __init__(self, optimizer, input_fns, params, scope='reward_network'):
self.optimizer = optimizer
if input_fns:
self.train_input_fn = input_fns['train... | /rec_rnn_a3c-0.29.tar.gz/rec_rnn_a3c-0.29/rec_rnn_a3c/src/reward_worker.py | 0.750461 | 0.224874 | reward_worker.py | pypi |
from copy import deepcopy
import numpy as np
import tensorflow as tf
import time
from rec_rnn_a3c.src.supervised_rnn import SupervisedRNN
tf.logging.set_verbosity(tf.logging.INFO)
class SupervisedRNNModel(object):
def __init__(self, optimizer, params, scope='supervised_rnn'):
self.optimizer = optimizer
... | /rec_rnn_a3c-0.29.tar.gz/rec_rnn_a3c-0.29/rec_rnn_a3c/src/supervised_rnn_model.py | 0.668015 | 0.223261 | supervised_rnn_model.py | pypi |
import os
import shutil
import typing as t
from rec_spotify.config import Config
from rec_spotify.utils import clear_unwanted
class Collection(object):
"""Represents playlist or album."""
def __init__(self, id: str, kind: str = "playlist") -> None:
self._id = id
self._kind = kind
sel... | /rec_spotify-1.8-py3-none-any.whl/rec_spotify/items.py | 0.768168 | 0.173831 | items.py | pypi |
import datetime
import eyed3
import requests
from rec_spotify.items import Track
class Lyrics(object):
"Class for finding and embedding lyrics in audio files."
API_ENDPOINT = "https://spotify-lyric-api.herokuapp.com/?trackid={track_id}"
BASE_DATETIME = datetime.datetime(1970, 1, 1)
@classmethod
... | /rec_spotify-1.8-py3-none-any.whl/rec_spotify/lyrics.py | 0.67971 | 0.169578 | lyrics.py | pypi |
import math
import re
import shutil
import tempfile
import typing as t
import requests
from pydub import AudioSegment
from pydub.silence import detect_leading_silence
def parse_spotify_url(url: str) -> tuple[str, str] | None:
"Parses a Spotify URL and returns the type of link and its ID."
match = re.search(r... | /rec_spotify-1.8-py3-none-any.whl/rec_spotify/utils.py | 0.47098 | 0.260251 | utils.py | pypi |
CONFIG_VALID = "[[green bold]OK[/green bold]] Configuration: {filepath}"
CONFIG_NOT_FOUND = (
":grey_question: The configuration files are not found. Lets create them first!"
)
CONFIG_CREATED = ":white_check_mark: The configuration file located at {filepath} has been created. Please restart the program."
SELECT_SP... | /rec_spotify-1.8-py3-none-any.whl/rec_spotify/messages.py | 0.505371 | 0.228393 | messages.py | pypi |
from sklearn.decomposition import TruncatedSVD
import pandas as pd
from scipy import spatial
import numpy as np
from src.rec_system.data.recipes import get_dish_id
class InternalStatusError(Exception):
pass
class Recommender:
def __init__(
self,
data: pd.DataFrame
):
self... | /rec_system_trb-0.0.6.tar.gz/rec_system_trb-0.0.6/src/rec_system/engine/recommender.py | 0.799638 | 0.472683 | recommender.py | pypi |
import pandas as pd
COLUMNS_TO_DROP = ['W skali od 1 do 10 jak bardzo lubisz słone jedzenie',
'W skali od 1 do 10 jak bardzo lubisz słodkie jedzenie',
'W skali od 1 do 10 jak bardzo lubisz gorzkie jedzenie',
'W skali od 1 do 10 jak bardzo lubisz mięso',
... | /rec_system_trb-0.0.6.tar.gz/rec_system_trb-0.0.6/src/rec_system/data/survey.py | 0.4206 | 0.236164 | survey.py | pypi |
import requests
import pandas as pd
import traceback
def get_all_recipes(api_key: str, to_csv: bool = False,
destination: str = "./src/rec_system/data/all_recipes.csv") -> pd.DataFrame:
"""Converts results to be compatible with recommendation model.
Parameters:
... | /rec_system_trb-0.0.6.tar.gz/rec_system_trb-0.0.6/src/rec_system/data/recipes.py | 0.620622 | 0.263303 | recipes.py | pypi |
import glob
import os
from logging import getLogger
import rec_to_binaries.trodes_data as td
from rec_to_binaries.adjust_timestamps import fix_timestamp_lag
logger = getLogger(__name__)
def extract_trodes_rec_file(data_dir,
animal,
out_dir=None,
... | /rec_to_binaries-0.7.5-py3-none-any.whl/rec_to_binaries/core.py | 0.609524 | 0.260637 | core.py | pypi |
from logging import getLogger
import numpy as np
import pandas as pd
from rec_to_binaries.create_system_time import infer_systime
from rec_to_binaries.read_binaries import (readTrodesExtractedDataFile,
write_trodes_extracted_datafile)
from scipy.stats import linregress
logge... | /rec_to_binaries-0.7.5-py3-none-any.whl/rec_to_binaries/adjust_timestamps.py | 0.803791 | 0.396448 | adjust_timestamps.py | pypi |
import functools
import struct
import numpy as np
import pandas as pd
class TrodesBinaryFormatError(RuntimeError):
pass
class TrodesBinaryReader:
def __init__(self, path):
self.path = path
with open(path, 'rb') as file:
# reading header
# read first line to make sure... | /rec_to_binaries-0.7.5-py3-none-any.whl/rec_to_binaries/binary_utils.py | 0.546254 | 0.151561 | binary_utils.py | pypi |
# Microscope Installation Guide
This guide will walk through a complete recOrder installation consisting of:
1. Checking pre-requisites for compatibility.
2. Installing Meadowlark DS5020 and liquid crystals.
3. Installing and launching the latest stable version of `recOrder` via `pip`.
4. Installing a compatible vers... | /recOrder-napari-0.4.0.tar.gz/recOrder-napari-0.4.0/docs/microscope-installation-guide.md | 0.406862 | 0.909023 | microscope-installation-guide.md | pypi |
# `recOrder` development guide
## Install `recOrder` for development
1. Install [conda](https://github.com/conda-forge/miniforge) and create a virtual environment:
```sh
conda create -y -n recOrder python=3.9
conda activate recOrder
```
2. Clone the `recOrder` directory:
```sh
git clone htt... | /recOrder-napari-0.4.0.tar.gz/recOrder-napari-0.4.0/docs/development-guide.md | 0.903106 | 0.98355 | development-guide.md | pypi |
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