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
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"""Classes that define a user's choice behavior over a slate of documents."""
import abc
from typing import Sequence, Text
import edward2 as ed # type: ignore
from gym import spaces
import numpy as np
from recsim_ng.core import value
from recsim_ng.lib.tensorflow import entity
from recsim_ng.lib.tensorflow import fie... | /recsim_ng-0.1.2-py3-none-any.whl/recsim_ng/entities/choice_models/selectors.py | 0.981753 | 0.802517 | selectors.py | pypi |
from typing import Optional, Sequence, Text
from gym import spaces
import numpy as np
from recsim_ng.core import value
from recsim_ng.lib.tensorflow import entity
from recsim_ng.lib.tensorflow import field_spec
import tensorflow as tf
Value = value.Value
ValueSpec = value.ValueSpec
Space = field_spec.Space
class Ta... | /recsim_ng-0.1.2-py3-none-any.whl/recsim_ng/entities/choice_models/affinities.py | 0.969628 | 0.585042 | affinities.py | pypi |
from typing import Callable, Optional, Sequence, Text
from gym import spaces
import numpy as np
from recsim_ng.core import value
from recsim_ng.entities.state_models import state
from recsim_ng.lib.tensorflow import field_spec
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
Value ... | /recsim_ng-0.1.2-py3-none-any.whl/recsim_ng/entities/state_models/estimation.py | 0.970282 | 0.890342 | estimation.py | pypi |
import abc
from typing import Optional, Text
import edward2 as ed # type: ignore
from recsim_ng.core import value
from recsim_ng.lib.tensorflow import entity as entity_lib
from recsim_ng.lib.tensorflow import field_spec
import tensorflow_probability as tfp
tfd = tfp.distributions
Entity = entity_lib.Entity
Value = ... | /recsim_ng-0.1.2-py3-none-any.whl/recsim_ng/entities/state_models/state.py | 0.900157 | 0.222521 | state.py | pypi |
"""Metrics entity for bandit simulation."""
from typing import Text
from recsim_ng.core import value
from recsim_ng.entities.state_models import state
import tensorflow as tf
FieldSpec = value.FieldSpec
Value = value.Value
ValueSpec = value.ValueSpec
class BanditMetrics(state.StateModel):
"""The base entity for ... | /recsim_ng-0.1.2-py3-none-any.whl/recsim_ng/entities/bandits/metrics.py | 0.937261 | 0.37319 | metrics.py | pypi |
"""Abstract classes that encode a user's state and dynamics."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import numpy as np
import six
from typing import List, Optional
def softmax(vector):
"""Computes the softmax of a vector."""
no... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/choice_model.py | 0.970562 | 0.568236 | choice_model.py | pypi |
r"""An example of main function for training in RecSim.
Use the interest evolution environment and a slateQ agent for illustration.
To run locally:
python main.py --base_dir=/tmp/interest_evolution \
--gin_bindings=simulator.runner_lib.Runner.max_steps_per_episode=50
"""
from __future__ import absolute_import
fro... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/main.py | 0.765067 | 0.449695 | main.py | pypi |
"""Abstract interface for recommender system agents."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
from absl import logging
import six
@six.add_metaclass(abc.ABCMeta)
class AbstractRecommenderAgent(object):
"""Abstract class to model a r... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agent.py | 0.966718 | 0.595257 | agent.py | pypi |
"""Abstract classes that encode a user's state and dynamics."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
from gym import spaces
import numpy as np
import six
@six.add_metaclass(abc.ABCMeta)
class AbstractResponse(object):
"""Abstract c... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/user.py | 0.963083 | 0.532729 | user.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
from absl import logging
import gin
from gym import spaces
import numpy as np
from recsim import choice_model
from recsim import document
from recsim import user
from recsim import utils
... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/environments/interest_exploration.py | 0.923545 | 0.352759 | interest_exploration.py | pypi |
"""Agent that picks items with highest pCTR given the true user choice model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
import numpy as np
from recsim import agent
from recsim import choice_model as cm
class GreedyPCTRA... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/greedy_pctr_agent.py | 0.962918 | 0.643133 | greedy_pctr_agent.py | pypi |
"""A Tabular Q-learning implementation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
from absl import logging
from gym import spaces
import numpy as np
from recsim import agent
from recsim.agents import agent_utils
class TabularQAg... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/tabular_q_agent.py | 0.91086 | 0.673853 | tabular_q_agent.py | pypi |
"""Agent that picks topics based on the UCB1 algorithm given past responses."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import gin
import numpy as np
from recsim import agent
from recsim.agents.bandits import algorithms
from recsim.... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/cluster_bandit_agent.py | 0.885712 | 0.482551 | cluster_bandit_agent.py | pypi |
"""Convenience primitives relating to the implementation of agents."""
from gym import spaces
import numpy as np
class GymSpaceWalker(object):
"""Class for recursively applying a given function to a gym space.
Gym spaces have nested structure in terms of container spaces (e.g. Dict and
Tuple) containing basic ... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/agent_utils.py | 0.925255 | 0.855066 | agent_utils.py | pypi |
"""Classes for Bandit Algorithms."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
class MABAlgorithm(object):
"""Base class for Multi-armed bandit (MAB) algorithms.
We implement multi-armed bandit algorithms with confidence width ... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/bandits/algorithms.py | 0.955465 | 0.639708 | algorithms.py | pypi |
"""Classes for Bandit Algorithms for Generalized Linear Models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import numpy as np
from scipy import special
import six
@six.add_metaclass(abc.ABCMeta)
class GLMAlgorithm(object):
"""Base class... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/bandits/glm_algorithms.py | 0.958693 | 0.628179 | glm_algorithms.py | pypi |
"""Helper class to collect cluster click and impression counts."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from gym import spaces
import numpy as np
from recsim.agents.layers import sufficient_statistics
class ClusterClickStatsLayer(sufficient_sta... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/layers/cluster_click_statistics.py | 0.882225 | 0.476214 | cluster_click_statistics.py | pypi |
"""Temporally aggregated reinforcement learning agent."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from gym import spaces
import numpy as np
from recsim import agent
from recsim.agents import agent_utils
class TemporalAggregationLayer(agent.Abstra... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/layers/temporal_aggregation.py | 0.945336 | 0.602442 | temporal_aggregation.py | pypi |
"""Agent that picks topics based on the UCB1 algorithm given past responses."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gin
import numpy as np
from recsim import agent
from recsim.agents.bandits import algorithms
@gin.configurable
class Ab... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/layers/abstract_click_bandit.py | 0.934731 | 0.548129 | abstract_click_bandit.py | pypi |
"""Helper classe to record fixed length history of observations."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from gym import spaces
from recsim.agents.layers import sufficient_statistics
class FixedLengthHistoryLayer(sufficient_statistics.Sufficien... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/layers/fixed_length_history.py | 0.955392 | 0.519887 | fixed_length_history.py | pypi |
"""Helper classes to record user response history on recommendations."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
from gym import spaces
from recsim import agent
class SufficientStatisticsLayer(agent.AbstractHierarchicalAgentLayer):
""... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/agents/layers/sufficient_statistics.py | 0.953966 | 0.619673 | sufficient_statistics.py | pypi |
r"""An executable class to run agents in the simulator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from absl import flags, logging
import gin
from gym import spaces
import numpy as np
from recsim.simulator import environment
... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/simulator/runner_lib.py | 0.917478 | 0.328206 | runner_lib.py | pypi |
"""A wrapper for using Gym environment."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import gym
from gym import spaces
import numpy as np
from recsim.simulator import environment
def _dummy_metrics_aggregator(responses, metrics, in... | /recsim_no_tf-0.2.3-py3-none-any.whl/recsim/simulator/recsim_gym.py | 0.964296 | 0.466238 | recsim_gym.py | pypi |
import theano, numpy
import theano.tensor as T
import time
import sys
from collections import defaultdict
class BPR(object):
def __init__(self, rank, n_users, n_items, lambda_u = 0.0025, lambda_i = 0.0025, lambda_j = 0.00025, lambda_bias = 0.0, learning_rate = 0.05):
"""
Creates a new object for... | /recsys_bpr-0.1.1-py3-none-any.whl/recsys_bpr/bpr.py | 0.68215 | 0.670622 | bpr.py | pypi |
from collections import defaultdict
import urllib, csv
def load_data_from_csv(csv_file, users_to_i = {}, items_to_i = {}):
"""
Loads data from a CSV file located at `csv_file`
where each line is of the form:
user_id_1, item_id_1
...
user_id_n, item_id_n
Initial mappings... | /recsys_bpr-0.1.1-py3-none-any.whl/recsys_bpr/utils.py | 0.783326 | 0.449876 | utils.py | pypi |
from typing import Optional, Tuple
import torch
from torch import Tensor
def _check_topk_validity(preds: Tensor, k: Optional[int] = None) -> int:
_max_k = preds.shape[-1]
if k is None:
k = _max_k
if not (isinstance(k, int) and k > 0):
raise ValueError(f'`k` has to be a positive integer o... | /recsys_metrics-0.0.4-py3-none-any.whl/recsys_metrics/utils.py | 0.943152 | 0.62019 | utils.py | pypi |
from typing import Optional
import torch
from torch import Tensor
from recsys_metrics.utils import _check_ranking_inputs, _check_preds_unexpectedness_inputs, _reduce_tensor
# Ref: https://eugeneyan.com/writing/serendipity-and-accuracy-in-recommender-systems/#serendipity
def serendipity(preds: Tensor, target: Tensor... | /recsys_metrics-0.0.4-py3-none-any.whl/recsys_metrics/serendipity.py | 0.878001 | 0.559711 | serendipity.py | pypi |
import torch
from recsys_metrics.mean_average_precision import mean_average_precision
from torchmetrics.functional import retrieval_average_precision
from benchmark.benchmark import benchmark, benchmark_batch, sanity_check
# ns = [128, 256, 512, 1024, 2048, 4096]
# ks = [1, 50, 100]
def bench_map_single(ns, ks, num... | /recsys_metrics-0.0.4-py3-none-any.whl/benchmark/mean_average_precision.py | 0.792946 | 0.280019 | mean_average_precision.py | pypi |
import torch
from recsys_metrics.normalized_dcg import normalized_dcg
from torchmetrics.functional import retrieval_normalized_dcg
from benchmark.benchmark import benchmark, benchmark_batch, sanity_check
# ns = [128, 256, 512, 1024, 2048, 4096]
# ks = [1, 50, 100]
def bench_ndcg_single(ns, ks, number=10_000, seed=4... | /recsys_metrics-0.0.4-py3-none-any.whl/benchmark/normalized_dcg.py | 0.753376 | 0.22718 | normalized_dcg.py | pypi |
import torch
from recsys_metrics.hit_rate import hit_rate
from torchmetrics.functional import retrieval_hit_rate
from benchmark.benchmark import benchmark, benchmark_batch, sanity_check
# ns = [128, 256, 512, 1024, 2048, 4096]
# ks = [1, 50, 100]
def bench_hr_single(ns, ks, number=10_000, seed=42):
def init_cal... | /recsys_metrics-0.0.4-py3-none-any.whl/benchmark/hit_rate.py | 0.741019 | 0.215888 | hit_rate.py | pypi |
import torch
from recsys_metrics.recall import recall
from torchmetrics.functional import retrieval_recall
from benchmark.benchmark import benchmark, benchmark_batch, sanity_check
# ns = [128, 256, 512, 1024, 2048, 4096]
# ks = [1, 50, 100]
def bench_recall_single(ns, ks, number=10_000, seed=42):
def init_callb... | /recsys_metrics-0.0.4-py3-none-any.whl/benchmark/recall.py | 0.735547 | 0.192577 | recall.py | pypi |
import torch
from recsys_metrics.precision import precision
from torchmetrics.functional import retrieval_precision
from benchmark.benchmark import benchmark, benchmark_batch, sanity_check
# ns = [128, 256, 512, 1024, 2048, 4096]
# ks = [1, 50, 100]
def bench_precision_single(ns, ks, number=10_000, seed=42):
de... | /recsys_metrics-0.0.4-py3-none-any.whl/benchmark/precision.py | 0.768038 | 0.247441 | precision.py | pypi |
import torch
from recsys_metrics.mean_reciprocal_rank import mean_reciprocal_rank
from torchmetrics.functional import retrieval_reciprocal_rank
from benchmark.benchmark import benchmark, benchmark_batch, sanity_check
# ns = [128, 256, 512, 1024, 2048, 4096]
# ks = [1, 50, 100]
def bench_mrr_single(ns, ks, number=10... | /recsys_metrics-0.0.4-py3-none-any.whl/benchmark/mean_reciprocal_rank.py | 0.785555 | 0.235493 | mean_reciprocal_rank.py | pypi |
import tensorflow as tf
from datetime import datetime
from recsys_models.data.sampling import uniform_sample_from_df
def train_model(session, model, train_df, validation_mat, test_mat,
n_iterations=2500, batch_size=512,
min_epochs=10, max_epochs=200, stopping_threshold=1e-5,
... | /recsys_models-0.1.3.tar.gz/recsys_models-0.1.3/recsys_models/pipeline.py | 0.85564 | 0.591664 | pipeline.py | pypi |
import os
import gc
import pandas as pd
import numpy as np
from datetime import datetime
import random
def sample_unobserved(df, user_interactions_dict, n_items, size=500000, use_original_actions=False):
'''
Samples unobserved items for each (user, item) interaction pair.
Creates <size> pairwise comparison... | /recsys_models-0.1.3.tar.gz/recsys_models-0.1.3/recsys_models/data/sampling.py | 0.827445 | 0.458652 | sampling.py | pypi |
import os
import gc
import pandas as pd
import numpy as np
from datetime import datetime
USER_ITEM_COLS = {
'allrecipes': ['user_id', 'recipe_id'],
'movielens': ['user_id', 'item_id']
}
def print_basic_stats(df, user_col, item_col):
'''
Prints basic summary stats for a DF:
# users
# it... | /recsys_models-0.1.3.tar.gz/recsys_models-0.1.3/recsys_models/data/__init__.py | 0.565779 | 0.292823 | __init__.py | pypi |
from torch import nn
import torch.nn.functional as F
def _reduce(x, reduction='elementwise_mean'):
if reduction is 'none':
return x
elif reduction is 'elementwise_mean':
return x.mean()
elif reduction is 'sum':
return x.sum()
else:
raise ValueError('No such reduction {} defined'.format(reducti... | /recsys-recoder-0.4.0.tar.gz/recsys-recoder-0.4.0/recoder/losses.py | 0.968672 | 0.583856 | losses.py | pypi |
import numpy as np
import recoder.utils as utils
from recoder.data import RecommendationDataLoader
from multiprocessing import Process, Queue
def average_precision(x, y, k, normalize=True):
x = x[:k]
x_in_y = np.isin(x, y, assume_unique=True).astype(np.int)
tp = x_in_y.cumsum() # true positives at every pos... | /recsys-recoder-0.4.0.tar.gz/recsys-recoder-0.4.0/recoder/metrics.py | 0.859649 | 0.46393 | metrics.py | pypi |
import annoy as an
import pickle
import glog as log
class EmbeddingsIndex(object):
"""
An abstract Embeddings Index from which to fetch embeddings and
execute nearest neighbor search on the items represented by the embeddings
All ``EmbeddingsIndex`` should implement this interface.
"""
def get_embeddi... | /recsys-recoder-0.4.0.tar.gz/recsys-recoder-0.4.0/recoder/embedding.py | 0.852107 | 0.507629 | embedding.py | pypi |
import torch
from torch import nn
import torch.nn.functional as F
def activation(x, act):
if act == 'none': return x
func = getattr(torch, act)
return func(x)
class FactorizationModel(nn.Module):
"""
Base class for factorization models. All subclasses should implement
the following methods.
"""
def... | /recsys-recoder-0.4.0.tar.gz/recsys-recoder-0.4.0/recoder/nn.py | 0.947684 | 0.69125 | nn.py | pypi |
import numpy as np
from scipy.sparse import coo_matrix
def unzip(l):
"""
Returns the inverse operation of `zip` on `list`.
Args:
l (list): the list to unzip
"""
return list(map(list, zip(*l)))
def normalize(x, axis=None):
"""
Returns the normalization of `x` along `axis`.
Args:
x (np.array... | /recsys-recoder-0.4.0.tar.gz/recsys-recoder-0.4.0/recoder/utils.py | 0.865934 | 0.703282 | utils.py | pypi |
from recoder.embedding import EmbeddingsIndex
import numpy as np
import recoder.utils as utils
class Recommender(object):
"""
Base Recommender that provide recommendations given users history of interactions.
All Recommenders should implement the ``recommend`` function.
"""
def recommend(self, users_hist... | /recsys-recoder-0.4.0.tar.gz/recsys-recoder-0.4.0/recoder/recommender.py | 0.936995 | 0.515254 | recommender.py | pypi |
# %% auto 0
__all__ = ['SequentialDataset', 'load_dataloaders']
# %% ../dataset_torch.ipynb 2
import torch
import recsys_slates_dataset.data_helper as data_helper
from torch.utils.data import Dataset, DataLoader
import torch
import json
import numpy as np
import logging
logging.basicConfig(format='%(asctime)s %(messa... | /recsys_slates_dataset-1.0.3-py3-none-any.whl/recsys_slates_dataset/dataset_torch.py | 0.729038 | 0.676793 | dataset_torch.py | pypi |
# %% auto 0
__all__ = ['SlateDataModule', 'CallbackPrintRecommendedCategory', 'Hitrate']
# %% ../lightning_helper.ipynb 3
import recsys_slates_dataset.dataset_torch as dataset_torch
import recsys_slates_dataset.data_helper as data_helper
import pytorch_lightning as pl
import logging
class SlateDataModule(pl.Lightning... | /recsys_slates_dataset-1.0.3-py3-none-any.whl/recsys_slates_dataset/lightning_helper.py | 0.562898 | 0.385693 | lightning_helper.py | pypi |
d = { 'settings': { 'branch': 'main',
'doc_baseurl': '/recsys_slates_dataset/',
'doc_host': 'http://opensource.finn.no',
'git_url': 'https://github.com/finn-no/recsys_slates_dataset/tree/main/',
'lib_path': 'recsys_slates_dataset'},
'syms': { 'recsys_sl... | /recsys_slates_dataset-1.0.3-py3-none-any.whl/recsys_slates_dataset/_modidx.py | 0.433022 | 0.178884 | _modidx.py | pypi |
__author__ = 'Daniel Andersson'
__maintainer__ = __author__
__email__ = 'daniel.4ndersson@gmail.com'
__contact__ = __email__
__copyright__ = 'Copyright (c) 2017 Daniel Andersson'
__license__ = 'MIT'
__url__ = 'https://github.com/Penlect/rectangle-packer'
__version__ = '2.0.1'
# Built-in
from typing import Iterable, Tu... | /rectangle_packer-2.0.1-pp37-pypy37_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl/rpack/__init__.py | 0.924351 | 0.306806 | __init__.py | pypi |
from typing import Tuple
import matplotlib.patches as patches
from matplotlib import pylab as plt
from .solution import Solution
class Visualizer:
"""
A floorplan visualizer.
"""
def __init__(self) -> None:
# Default font size is 12
plt.rcParams["font.size"] = 14
def visualize... | /rectangle_packing_solver-0.0.5-py3-none-any.whl/rectangle_packing_solver/visualizer.py | 0.946708 | 0.452415 | visualizer.py | pypi |
import random
import signal
import sys
from typing import Any, List, Optional, Tuple
import simanneal
from tqdm.auto import tqdm
from .problem import Problem
from .sequence_pair import SequencePair
from .solution import Solution
def exit_handler(signum, frame) -> None: # type: ignore
"""
Since `simaaneal`... | /rectangle_packing_solver-0.0.5-py3-none-any.whl/rectangle_packing_solver/solver.py | 0.765944 | 0.353721 | solver.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from rectifai.models.posenet.settings import *
def traverse_to_targ_keypoint(
edge_id, source_keypoint, target_keypoint_id, scores, offsets, output_stride, displacements
):
height = scores.shape[1]
width = scores.sh... | /rectif-ai-0.1.tar.gz/rectif-ai-0.1/rectifai/models/posenet/decode.py | 0.69035 | 0.386706 | decode.py | pypi |
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
def _get_padding(kernel_size, stride, dilation):
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
class InputConv(nn.Module):
def __init__(self, inp, outp, k=3, stride=1, dil... | /rectif-ai-0.1.tar.gz/rectif-ai-0.1/rectifai/models/posenet/network.py | 0.932731 | 0.540196 | network.py | pypi |
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchsummary import summary
from rectifai.models.posturenet import PostureNetwork
from rectifai.settings import *
from rectifai.models.posturenet.config import *
from rectifai.data.dataset.posturenet import PostureDataset
# Fully connect... | /rectif-ai-0.1.tar.gz/rectif-ai-0.1/rectifai/train/posturenet.py | 0.824956 | 0.461502 | posturenet.py | pypi |
import torch
import cv2
import time
import argparse
import subprocess
from rectifai.models import posenet, posturenet
from rectifai.models.posenet.decode import *
from rectifai.tools.utils.posenet import *
from rectifai.models.posturenet.config import input_size
parser = argparse.ArgumentParser()
parser.add_argument(... | /rectif-ai-0.1.tar.gz/rectif-ai-0.1/rectifai/predictors/demo_webcam.py | 0.47926 | 0.192027 | demo_webcam.py | pypi |
import cv2
import numpy as np
import math
import subprocess
import time
from rectifai.models.posenet.settings import *
def valid_resolution(width, height, output_stride=16):
target_width = (int(width) // output_stride) * output_stride + 1
target_height = (int(height) // output_stride) * output_stride + 1
... | /rectif-ai-0.1.tar.gz/rectif-ai-0.1/rectifai/tools/utils/posenet.py | 0.498291 | 0.272377 | posenet.py | pypi |
# rectpack [](https://travis-ci.org/secnot/rectpack)
Rectpack is a collection of heuristic algorithms for solving the 2D knapsack problem,
also known as the bin packing problem. In essence packing a set of rectangles into the
smallest number of ... | /rectpack-0.2.2.tar.gz/rectpack-0.2.2/README.md | 0.731634 | 0.962321 | README.md | pypi |
from __future__ import annotations
import argparse
import itertools
import logging
import random
import subprocess as sp
import sys
import time
import traceback
from dataclasses import dataclass
from typing import Callable, Literal
from simpleeval import EvalWithCompoundTypes
MAX_DELAY = 366 * 24 * 60 * 60
VERSION ... | /recur_command-0.2.0.tar.gz/recur_command-0.2.0/recur.py | 0.715722 | 0.151906 | recur.py | pypi |
# RecurrentJS in Python
Following in the footsteps of [Andrej Kaparthy](http://cs.stanford.edu/people/karpathy/), here is a Python implementation of [recurrentJS](http://cs.stanford.edu/people/karpathy/recurrentjs/) ([Github](https://github.com/karpathy/recurrentjs)).
### Why ?
While Python has great automatic diff... | /recurrent-js-python-0.0.1.tar.gz/recurrent-js-python-0.0.1/README.md | 0.557364 | 0.85446 | README.md | pypi |
from collections import namedtuple
from functools import wraps
from packaging import version
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
# constants
Config = namedtuple('EfficientAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
# ... | /recurrent_memory_transformer_pytorch-0.5.5-py3-none-any.whl/recurrent_memory_transformer_pytorch/attend.py | 0.924338 | 0.272775 | attend.py | pypi |
import sched
import functools
from datetime import datetime, timedelta
__all__ = ("RecurringScheduler",)
class RecurringJob:
def __init__(self, interval, rsched):
self.interval = interval
self.unit = None
self._rsched = rsched
self._at_time = None
self._prev_run = None
... | /recurring_sched-0.0.1.tar.gz/recurring_sched-0.0.1/recurring_sched.py | 0.778313 | 0.251906 | recurring_sched.py | pypi |
import typing
import re
from abc import ABC, abstractmethod
from pathlib import Path
import gzip
import io
import pickle
import pdb
import requests
class Corpus(ABC):
"""
Class to get a corpus
"""
name = ''
url = ''
decode=True
"""decode the raw bytes from the request in download_corpus""... | /recurse-words-0.2.1.tar.gz/recurse-words-0.2.1/recurse_words/corpi.py | 0.651687 | 0.201479 | corpi.py | pypi |
import multiprocessing as mp
from itertools import repeat
import pickle
from pathlib import Path
import typing
from tqdm import tqdm
import pygraphviz as pgv
import networkx as nx
from recurse_words.corpi import get_corpus
from recurse_words.recursers.recurser import Recurser
class Graph_Recurser(Recurser):
"""
... | /recurse-words-0.2.1.tar.gz/recurse-words-0.2.1/recurse_words/recursers/graph.py | 0.643217 | 0.200479 | graph.py | pypi |
# %% auto 0
__all__ = ['get_preorder_traversal', 'get_graph_edges', 'get_graph']
# %% ../01_graph.ipynb 4
from .node import Node
# %% ../01_graph.ipynb 5
import networkx as nx
from typing import List, Dict
# %% ../01_graph.ipynb 6
def get_preorder_traversal(
history: List[int] # list of node ids recording when ea... | /recursion_visualizer-0.0.1-py3-none-any.whl/recursion_visualizer/graph.py | 0.900549 | 0.628379 | graph.py | pypi |
from abc import ABC, abstractmethod
from enum import Enum
from collections.abc import Callable, Iterator
# Possible iteration orders.
Order = Enum('Order', 'PRE POST')
Direction = Enum('Direction', 'FORWARD REVERSE')
class Recursive(ABC):
"""Abstract base class for classes that provide the __recur__() method"""... | /recursive-abc-0.2.0.tar.gz/recursive-abc-0.2.0/recur/abc.py | 0.89177 | 0.248124 | abc.py | pypi |
from types import CodeType, FunctionType, MethodType
import sys
DECORATOR_LIST_FIELD_NAME = "__wraped_with_"
def mount_to_module(module_to_mount, object_to_mount, name_in_module):
"""Mount Given function to given module.
Args:
module_to_mount(module): module to mount function.
object_to_mou... | /recursive_decorator-1.0.7.tar.gz/recursive_decorator-1.0.7/recursive_decorator/utils.py | 0.606265 | 0.274718 | utils.py | pypi |
from __future__ import annotations
import argparse
import glob
import logging
import os
import sys
import xarray
from recursive_diff.recursive_diff import recursive_diff
LOGFORMAT = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
def argparser() -> argparse.ArgumentParser:
"""Return precompi... | /recursive_diff-1.1.0.tar.gz/recursive_diff-1.1.0/recursive_diff/ncdiff.py | 0.683736 | 0.209126 | ncdiff.py | pypi |
import uuid
from recursive_parse import recursive_parse
"""
data schema:
GameID: uuid
Map: str
Duration: float (minutes)
PlayerName: str
IsWinner: bool
Race: str ('Protoss', 'Terran', 'Zerg')
UnitName: str
BirthTime: float (minutes)
DeathTime: float (minutes)
"""
def parse_data(players, timeline, stats, metadata, ... | /recursive-parse-0.0.2.tar.gz/recursive-parse-0.0.2/recursive_parse/hsc_analysis.py | 0.526586 | 0.258303 | hsc_analysis.py | pypi |
# recurtx
[](https://pypi.org/project/recurtx/)
[](https://badge.fury.io/py/recurtx)
[](https://github.co... | /recurtx-0.0.9.tar.gz/recurtx-0.0.9/README.md | 0.595963 | 0.804329 | README.md | pypi |
import functools
from typing import AnyStr, Generator, Tuple
from redis import Redis
from redis.lock import Lock
from ._base import _BaseMapping
from .typing import TTL, KeyType
__author__ = 'Memory_Leak<irealing@163.com>'
class RedCache(_BaseMapping):
def __init__(self, redis: Redis):
super().__init_... | /red_cache-0.2.5-py3-none-any.whl/red_cache/objects.py | 0.828592 | 0.19789 | objects.py | pypi |
import os
import sys
import ftputil
import ftputil.session
from functools import wraps
from urllib.parse import urlparse
import jsonschema
def parse_ftp(url):
"""
Parses the given FTP URL and extracts the FTP host and path.
:param url: The FTP URL to parse.
:return: A tuple containing the FTP host an... | /red_connector_ftp-1.2-py3-none-any.whl/red_connector_ftp/commons/helpers.py | 0.547101 | 0.208985 | helpers.py | pypi |
import json
import os
import stat
import subprocess
from tempfile import NamedTemporaryFile
from argparse import ArgumentParser
import jsonschema
import pexpect
from red_connector_ssh.helpers import DEFAULT_PORT, graceful_error, check_remote_dir_available, \
InvalidAuthenticationError, find_executable, create_ssh... | /red_connector_ssh-1.3-py3-none-any.whl/red_connector_ssh/mount_dir.py | 0.559531 | 0.173183 | mount_dir.py | pypi |
import os
from fabric.api import env
from config import CustomConfig
from functions import gather_remotes
# Import all tasks
import local
from deploy import deploy, migrate
GIT_REPO_NAME = 'project-git'
GIT_WORKING_DIR = '/srv/active'
def setup_env(project_path):
"""
Sets up the env settings for the fabric... | /red-fab-deploy-0.0.8.tar.gz/red-fab-deploy-0.0.8/fab_deploy/__init__.py | 0.458591 | 0.167491 | __init__.py | pypi |
from fabric.api import env
from fabric.tasks import Task
from utils import get_security_group
from api import get_ec2_connection
class FirewallSync(Task):
"""
Update security group policies of AWS based on info read from server.ini
Under each section defining a type of server, you will find 'open-ports... | /red-fab-deploy-0.0.8.tar.gz/red-fab-deploy-0.0.8/fab_deploy/amazon/manage.py | 0.499756 | 0.190762 | manage.py | pypi |
from fabric.api import local, env, execute, run
from fabric.tasks import Task
from fabric.context_managers import cd
class AddGitRemote(Task):
"""
Adds a remote to your git repo.
Requires two arguments:
* **remote_name**: The name this remote should have in your repo.
* **user_and_host**: The co... | /red-fab-deploy-0.0.8.tar.gz/red-fab-deploy-0.0.8/fab_deploy/local/git.py | 0.563738 | 0.169234 | git.py | pypi |
import sys
import inspect
import types
from functools import wraps
from fabric.tasks import Task, WrappedCallableTask
from fabric.api import env, settings
from . import functions
class PlatformCallableTask(WrappedCallableTask):
def __init__(self, callable, platform, *args, **kwargs):
self.env_kwargs = {... | /red-fab-deploy2-0.2.5.tar.gz/red-fab-deploy2-0.2.5/fab_deploy2/tasks.py | 0.539469 | 0.252638 | tasks.py | pypi |
import abc
import json
import pickle
from datetime import timedelta
from typing import AnyStr, AsyncGenerator, Tuple, Callable, Union, TypeVar, Any
from aredis import StrictRedis
_Ret = TypeVar('_Ret')
TTL = Union[int, timedelta]
Encoder = Callable[[Any], AnyStr]
Decoder = Callable[[bytes], Any]
KeyType = Union[AnySt... | /red_helper-0.1.1-py3-none-any.whl/red_helper/types.py | 0.670932 | 0.162015 | types.py | pypi |
from typing import AnyStr, AsyncGenerator, Tuple, Optional
from aredis import StrictRedis
from ._base import _BaseMapping
from .types import RedCollection, TTL
from ._exc import UnsupportedOperation
class RedHelper(_BaseMapping):
def __init__(self, redis: StrictRedis):
super().__init__(redis, "")
... | /red_helper-0.1.1-py3-none-any.whl/red_helper/objects.py | 0.851073 | 0.207014 | objects.py | pypi |
import numpy as np
import scipy.optimize as op
import matplotlib.pyplot as plt
from .psd import psd, psd_from_fft2
def chi2_simple(data_1, data_2, err):
""" Simplest chi2 function comparing two sets of datas.
Parameters
==========
data_1, data_2 : array_like
Set of data to be compared.
e... | /red_magic-0.0.11-py3-none-any.whl/red_magic/chi2.py | 0.81409 | 0.812904 | chi2.py | pypi |
import numpy as np
from matplotlib.widgets import (
LassoSelector, RectangleSelector,
Button, RadioButtons
)
from matplotlib.path import Path
class Data_Selector(object):
def __init__(self, ax, line, proceed_func=None, alpha_other=0.3):
self.canvas = ax.figure.canvas
self.line = l... | /red_magic-0.0.11-py3-none-any.whl/red_magic/graphic_selection.py | 0.637144 | 0.248691 | graphic_selection.py | pypi |
import matplotlib.pyplot as plt
from matplotlib.widgets import TextBox, Button
from scipy.optimize import minimize
from tqdm import tqdm
class Manual_fitting():
# induce a bug with the text_boxes list not restarting at each call of
# a new instance of Manual_fitting
# to be fix later i guess :/
t... | /red_magic-0.0.11-py3-none-any.whl/red_magic/manual_fitting.py | 0.479016 | 0.327991 | manual_fitting.py | pypi |
import json
import requests
from datetime import timedelta
from .models.enum_user_role import EnumUserRole
from .models.enum_user_type import EnumUserType
from .exceptions import RedOctoberDecryptException
from .exceptions import RedOctoberRemoteException
class RedOctober(object):
""" It provides Python bindi... | /red-october-0.2.1b173.tar.gz/red-october-0.2.1b173/red_october/red_october.py | 0.863536 | 0.312311 | red_october.py | pypi |
import warnings
from collections import OrderedDict
from textwrap import dedent
from typing import Union, Optional, List
import logging
import pandas as pd
from red_panda.pandas import PANDAS_TOCSV_KWARGS
from red_panda.aws import (
REDSHIFT_RESERVED_WORDS,
REDSHIFT_COPY_KWARGS,
)
from red_panda.utils import ... | /red-panda-1.0.2.tar.gz/red-panda-1.0.2/red_panda/red_panda.py | 0.791821 | 0.231495 | red_panda.py | pypi |
import logging
from typing import Union, Optional, List
import pandas as pd
import psycopg2
from red_panda.typing import QueryResult, TemplateQueryResult
from red_panda.pandas import PANDAS_TOCSV_KWARGS
from red_panda.aws.templates.redshift import (
SQL_NUM_SLICES,
SQL_TABLE_INFO,
SQL_TABLE_INFO_SIMPLIFIE... | /red-panda-1.0.2.tar.gz/red-panda-1.0.2/red_panda/aws/redshift.py | 0.859251 | 0.16502 | redshift.py | pypi |
REDSHIFT_RESERVED_WORDS = [
"aes128",
"aes256",
"all",
"allowoverwrite",
"analyse",
"analyze",
"and",
"any",
"array",
"as",
"asc",
"authorization",
"backup",
"between",
"binary",
"blanksasnull",
"both",
"bytedict",
"bzip2",
"case",
"cas... | /red-panda-1.0.2.tar.gz/red-panda-1.0.2/red_panda/aws/__init__.py | 0.430626 | 0.463323 | __init__.py | pypi |
# red-postgres
Piccolo Postgres integration for Red-DiscordBot, although it could be used with any dpy bot as an easy wrapper for making postgres with cogs more modular.
:
if type.lower() == "eth":
return '0x8322fff2'
elif type == 'ERC20':
return '0xf47261b0' + address[2... | /red_py_sdk-0.1.10-py3-none-any.whl/redpysdk/starkex_utils.py | 0.49707 | 0.164047 | starkex_utils.py | pypi |
from typing import Tuple
import mpmath
import sympy
from sympy.core.numbers import igcdex
# A type that represents a point (x,y) on an elliptic curve.
ECPoint = Tuple[int, int]
def pi_as_string(digits: int) -> str:
"""
Returns pi as a string of decimal digits without the decimal point ("314...").
"""
... | /red_py_sdk-0.1.10-py3-none-any.whl/redpysdk/math_utils.py | 0.950995 | 0.644169 | math_utils.py | pypi |
from typing import List, Union
from dataclasses import dataclass, field
import hodgepodge.helpers
import itertools
@dataclass(frozen=True)
class Tactic:
external_id: str
name: str
@property
def id(self):
return self.external_id
@dataclass(frozen=True)
class Technique:
external_id: str
... | /red_raccoon-0.0.0.tar.gz/red_raccoon-0.0.0/red_raccoon/integrations/mitre_attack_evaluations/types.py | 0.944009 | 0.512205 | types.py | pypi |
from red_raccoon.integrations.mitre_attack_evaluations.types import Tactic, Technique, Evaluation, Step, Test, \
Observation, Screenshot
import hodgepodge.helpers
import logging
import re
import os
logger = logging.getLogger(__name__)
_RE_VENDOR_AND_GROUP = re.compile(r'([\w_-]+)\.1\.([\w_-]+)\..+')
def parse_... | /red_raccoon-0.0.0.tar.gz/red_raccoon-0.0.0/red_raccoon/integrations/mitre_attack_evaluations/parsers.py | 0.5144 | 0.435301 | parsers.py | pypi |
from typing import List, Dict, Union
from dataclasses import dataclass, field
from red_raccoon.integrations.mitre_attack import MITRE_ATTACK_ENTERPRISE, MITRE_ATTACK_MOBILE, \
MITRE_ATTACK_PRE_ATTACK, MITRE_CAPEC, MITRE_CWE, NIST_MOBILE_THREAT_CATALOGUE
import red_raccoon.integrations.stix.parsers
import red_racco... | /red_raccoon-0.0.0.tar.gz/red_raccoon-0.0.0/red_raccoon/integrations/mitre_attack/types.py | 0.879438 | 0.205276 | types.py | pypi |
import logging
import hodgepodge.helpers
import red_raccoon.helpers
import red_raccoon.integrations.stix.parsers
from hodgepodge.helpers import ensure_type
from red_raccoon.integrations.mitre_attack import MITRE_ATTACK_ENTERPRISE, MITRE_ATTACK_PRE_ATTACK, \
MITRE_ATTACK_MOBILE, MATRIX, TACTIC
from red_raccoon.i... | /red_raccoon-0.0.0.tar.gz/red_raccoon-0.0.0/red_raccoon/integrations/mitre_attack/parsers.py | 0.417746 | 0.168651 | parsers.py | pypi |
from typing import List, Union
from dataclasses import dataclass, field
from red_raccoon.integrations.atomic_red_team import DEFAULT_COMMAND_TIMEOUT, \
DEFAULT_COMMAND_TIMEOUT_FOR_DEPENDENCY_RESOLUTION
from red_raccoon.platforms import WINDOWS, LINUX, MACOS, CURRENT_OS_TYPE
from red_raccoon.commands import DEFAULT... | /red_raccoon-0.0.0.tar.gz/red_raccoon-0.0.0/red_raccoon/integrations/atomic_red_team/types.py | 0.867696 | 0.177152 | types.py | pypi |
import logging
import yaml
import os
import hodgepodge.helpers
import hodgepodge.files
import hodgepodge.path
import red_raccoon.integrations.atomic_red_team.parsers as parsers
from hodgepodge import UTF8
from hodgepodge.helpers import ensure_type
from hodgepodge.toolkits.filesystem.search.api import FilesystemSearch... | /red_raccoon-0.0.0.tar.gz/red_raccoon-0.0.0/red_raccoon/integrations/atomic_red_team/api.py | 0.443841 | 0.159381 | api.py | pypi |
from typing import List, Union
from dataclasses import dataclass, field
from hodgepodge.helpers import ensure_type
from red_raccoon.integrations.mitre_attack_navigator import DEFAULT_LAYER_NAME, DEFAULT_LAYER_DESCRIPTION, \
DEFAULT_LAYER_DOMAIN, DEFAULT_LAYER_VERSION, DEFAULT_COLOR
import hodgepodge.helpers
impor... | /red_raccoon-0.0.0.tar.gz/red_raccoon-0.0.0/red_raccoon/integrations/mitre_attack_navigator/types.py | 0.838018 | 0.270757 | types.py | pypi |
from __future__ import annotations
import argparse
import re
import json
import discord.ui
from discord import ButtonStyle
from red_star.rs_errors import CommandSyntaxError
from urllib.parse import urlparse
from typing import TYPE_CHECKING
if TYPE_CHECKING:
import discord
from red_star.config_manager import Js... | /red_star-3.0.2-py3-none-any.whl/red_star/rs_utils.py | 0.803097 | 0.181444 | rs_utils.py | pypi |
from __future__ import annotations
import datetime
import json
import discord.utils
from discord.ext import tasks
from random import choice
from red_star.plugin_manager import BasePlugin
from red_star.rs_errors import CommandSyntaxError, ChannelNotFoundError, DataCarrier
from red_star.rs_utils import respond, RSArgumen... | /red_star-3.0.2-py3-none-any.whl/red_star/plugins/motd.py | 0.591605 | 0.159577 | motd.py | pypi |
import shlex
import discord
from string import capwords
from red_star.plugin_manager import BasePlugin
from red_star.rs_errors import CommandSyntaxError
from red_star.rs_utils import respond, is_positive, find_role, group_items, RSArgumentParser
from red_star.command_dispatcher import Command
class RoleCommands(BaseP... | /red_star-3.0.2-py3-none-any.whl/red_star/plugins/roles.py | 0.467332 | 0.279862 | roles.py | pypi |
import discord
from red_star.plugin_manager import BasePlugin
from red_star.rs_utils import respond
from red_star.command_dispatcher import Command
from red_star.rs_errors import CommandSyntaxError
from random import randint
from collections import defaultdict
from enum import Enum
import re
roll_tokens = re.compile... | /red_star-3.0.2-py3-none-any.whl/red_star/plugins/diceroll.py | 0.481941 | 0.322619 | diceroll.py | pypi |
import discord
from asyncio import sleep
from string import capwords
from red_star.plugin_manager import BasePlugin
from red_star.rs_errors import UserPermissionError
from red_star.rs_utils import respond
from red_star.rs_version import version
from red_star.command_dispatcher import Command
# Yes, I know about multil... | /red_star-3.0.2-py3-none-any.whl/red_star/plugins/info.py | 0.707506 | 0.456591 | info.py | pypi |
from red_star.plugin_manager import BasePlugin
from red_star.rs_errors import CommandSyntaxError, UserPermissionError
from red_star.rs_utils import respond, RSArgumentParser
from red_star.command_dispatcher import Command
import shlex
import discord
class Voting(BasePlugin):
name = "voting"
version = "1.0"
... | /red_star-3.0.2-py3-none-any.whl/red_star/plugins/voting.py | 0.544075 | 0.157817 | voting.py | pypi |
from red_star.plugin_manager import BasePlugin
from red_star.rs_utils import respond
from red_star.command_dispatcher import Command
from red_star.rs_errors import ChannelNotFoundError, CommandSyntaxError
import discord
import shlex
class ChannelManagerCommands(BasePlugin):
name = "channel_manager_commands"
v... | /red_star-3.0.2-py3-none-any.whl/red_star/plugins/channel_manager_commands.py | 0.645567 | 0.274838 | channel_manager_commands.py | pypi |
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