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 |
|---|---|---|---|---|---|
```
# default_exp models.layers.tcn
```
# TCN
> Temporal Convolutional Network.
Temporal Convolutional Network (TCN) is a popular architecture. Studies have shown that TCN outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets.
```
#hide
from nbdev.showdoc import *
from f... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/layers/models.layers.tcn.ipynb | 0.891457 | 0.838548 | models.layers.tcn.ipynb | pypi |
```
# default_exp models.layers.common
```
# Common Layers
> Common layers.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import numpy as np
import torch
import torch.nn.functional as F
#export
class FeaturesLinear(torch.nn.Module):
"""
Reference... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/layers/models.layers.common.ipynb | 0.937247 | 0.831622 | models.layers.common.ipynb | pypi |
```
# default_exp models.layers.ou_noise
```
# OU Noise Layer
> Implementation of Ornstein Uhlenbeck Noise.
```
#hide
from nbdev.showdoc import *
#export
import torch
#export
class OUNoise(object):
"""
Ornstein-Uhlenbeck Noise
"""
def __init__(self,
embedded_action_size,
... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/models/layers/models.layers.ou_noise.ipynb | 0.609175 | 0.72309 | models.layers.ou_noise.ipynb | pypi |
```
# default_exp evaluation.metrics
```
# Metrics
> Metrics.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import torch
#export
def NDCG(true, pred):
match = pred.eq(true).nonzero(as_tuple=True)[1]
ncdg = torch.log(torch.Tensor([2])).div(torch.l... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/evaluation/evaluation.metrics.ipynb | 0.455925 | 0.782829 | evaluation.metrics.ipynb | pypi |
```
# default_exp datasets.aotm
```
# AOTM dataset
> AOTM dataset.
```
#hide
from nbdev.showdoc import *
#export
from typing import List, Optional, Callable, Union, Any, Tuple
import os
import os.path as osp
from collections.abc import Sequence
import sys
import numpy as np
import pandas as pd
from datetime import... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.aotm.ipynb | 0.579876 | 0.625295 | datasets.aotm.ipynb | pypi |
```
# default_exp datasets.retailrocket
```
# RetailRocket Dataset
> RetailRocket dataset.
```
#hide
from nbdev.showdoc import *
#export
from typing import List, Optional, Callable, Union, Any, Tuple
import os
import os.path as osp
from collections.abc import Sequence
import sys
import numpy as np
import pandas as... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.retailrocket.ipynb | 0.705988 | 0.69047 | datasets.retailrocket.ipynb | pypi |
```
# default_exp datasets.movielens
```
# MovieLens Dataset
> Implementation of MovieLens datasets.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
from typing import Any, Iterable, List, Optional, Tuple, Union, Callable
import os
import pandas as pd
fro... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.movielens.ipynb | 0.745398 | 0.811003 | datasets.movielens.ipynb | pypi |
```
# default_exp datasets.gowalla
```
# Gowalla dataset
> Gowalla dataset.
```
#hide
from nbdev.showdoc import *
#export
import os
import os.path as osp
import numpy as np
import pandas as pd
from pandas import Timedelta
from recohut.datasets.bases.common import SessionDatasetv3
from recohut.utils.common_utils imp... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.gowalla.ipynb | 0.487063 | 0.586256 | datasets.gowalla.ipynb | pypi |
```
# default_exp datasets.diginetica
```
# Diginetica dataset
> Diginetica dataset.
```
#hide
from nbdev.showdoc import *
#export
import numpy as np
from recohut.datasets.bases import common as base
from recohut.utils.common_utils import download_url
#export
class DigineticaDataset(base.SessionDatasetv2):
url... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.diginetica.ipynb | 0.558207 | 0.595169 | datasets.diginetica.ipynb | pypi |
```
# default_exp datasets.music30
```
# Music30 dataset
> Music30 dataset.
```
#hide
from nbdev.showdoc import *
#export
from typing import List, Optional, Callable, Union, Any, Tuple
import os
import os.path as osp
from collections.abc import Sequence
import sys
import numpy as np
import pandas as pd
from dateti... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.music30.ipynb | 0.568296 | 0.619932 | datasets.music30.ipynb | pypi |
```
# default_exp datasets.nowplaying
```
# NowPlaying dataset
> NowPlaying dataset.
```
#hide
from nbdev.showdoc import *
#export
from typing import List, Optional, Callable, Union, Any, Tuple
import os
import os.path as osp
from collections.abc import Sequence
import sys
import numpy as np
import pandas as pd
fr... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.nowplaying.ipynb | 0.550366 | 0.608391 | datasets.nowplaying.ipynb | pypi |
```
# default_exp datasets.synthetic
```
# Synthetic Datasets
> Methods to generate synthetic sample datasets for concept testing and validations.
```
#hide
from nbdev.showdoc import *
#export
import numpy as np
import pandas as pd
#export
class Synthetic():
def __init__(self, version='v1'):
self.version = ve... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.synthetic.ipynb | 0.640411 | 0.883789 | datasets.synthetic.ipynb | pypi |
```
# default_exp datasets.lastfm
```
# LastFM dataset
> LastFM dataset.
```
#hide
from nbdev.showdoc import *
#export
import os
import os.path as osp
import numpy as np
import pandas as pd
from pandas import Timedelta
from recohut.datasets.bases.common import SessionDatasetv3
from recohut.utils.common_utils import... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.lastfm.ipynb | 0.45302 | 0.606702 | datasets.lastfm.ipynb | pypi |
```
# default_exp datasets.tmall
```
# Tmall dataset
> Tmall dataset.
```
#hide
from nbdev.showdoc import *
#export
import numpy as np
from recohut.datasets.bases import common as base
from recohut.utils.common_utils import download_url
#export
class TmallDataset(base.SessionGraphDataset):
train_url = "https:/... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.tmall.ipynb | 0.496826 | 0.563978 | datasets.tmall.ipynb | pypi |
```
# default_exp datasets.criteo
```
# Criteo
> Criteo dataset.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import math
import shutil
import struct
from collections import defaultdict
from functools import lru_cache
from pathlib import Path
import lmd... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.criteo.ipynb | 0.821188 | 0.807005 | datasets.criteo.ipynb | pypi |
```
# default_exp datasets.tianchi
```
# Tianchi dataset
> Tianchi dataset.
```
#hide
from nbdev.showdoc import *
#export
import os
import os.path as osp
import sys
import numpy as np
import pandas as pd
from recohut.datasets.bases.common import Dataset
from recohut.utils.common_utils import download_url, extract_z... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.tianchi.ipynb | 0.424531 | 0.735238 | datasets.tianchi.ipynb | pypi |
```
# default_exp datasets.weeplaces
```
# Weeplaces
> Implementation of Weeplaces dataset.
This dataset is collected from Weeplaces, a website that aims to visualize users’ check-in activities in location-based social networks (LBSN). It is now integrated with the APIs of other location-based social networking servic... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.weeplaces.ipynb | 0.654564 | 0.746855 | datasets.weeplaces.ipynb | pypi |
```
# default_exp datasets.avazu
```
# Avazu
> Avazu dataset.
```
#hide
from nbdev.showdoc import *
from fastcore.nb_imports import *
from fastcore.test import *
#export
import shutil
import struct
from collections import defaultdict
from pathlib import Path
import lmdb
import numpy as np
import torch.utils.data
fr... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.avazu.ipynb | 0.539711 | 0.755152 | datasets.avazu.ipynb | pypi |
```
# default_exp datasets.sample_session
```
# Sample Session dataset
> Small sample of session dataset.
```
#hide
from nbdev.showdoc import *
#export
from typing import List, Optional, Callable, Union, Any, Tuple
import os
import os.path as osp
from collections.abc import Sequence
import sys
import csv
import pic... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/datasets/datasets.sample_session.ipynb | 0.575111 | 0.585723 | datasets.sample_session.ipynb | pypi |
```
# default_exp utils.common_utils
```
# Common utils
> A collection of utilities often used.
```
#hide
from nbdev.showdoc import *
#export
import sys
import os
import ssl
import os.path as osp
from six.moves import urllib
import errno
import tarfile
import zipfile
import bz2
import gzip
import pandas as pd
from t... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/utils/utils.common_utils.ipynb | 0.476092 | 0.73264 | utils.common_utils.ipynb | pypi |
```
# default_exp rl.memory
```
# RL Memory
> Reinforcement Learning Memory module, including Experience Replay.
```
#hide
from nbdev.showdoc import *
#export
import random
from collections import deque
#export
class ReplayMemory(object):
"""
Replay Memory
"""
def __init__(self, buffer_size: int):
... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/rl/rl.memory.ipynb | 0.620392 | 0.763748 | rl.memory.ipynb | pypi |
```
# default_exp rl.agents.ddpg
```
# DDPG
> An implementation of DDPG, Deep Deterministic Policy Gradient.
Reference:
1. https://github.com/massquantity/DBRL/blob/master/dbrl/models/ddpg.py
2. https://www.cnblogs.com/massquantity/p/13842139.html
**Deterministic Policy Gradient (DPG)** is a type of Actor-Critic RL a... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/rl/agents/rl.agents.ddpg.ipynb | 0.906794 | 0.907763 | rl.agents.ddpg.ipynb | pypi |
```
# default_exp rl.envs.recsys
```
# RecSys RL Env
> OpenAI Gym's Box environment that simulates a recommendation system by picking item and giving feedback signals.
```
#hide
from nbdev.showdoc import *
#export
import os
import gym
import numpy as np
from sklearn.decomposition import NMF
#export
class Env(gym.En... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/rl/envs/rl.envs.recsys.ipynb | 0.709523 | 0.828627 | rl.envs.recsys.ipynb | pypi |
```
# default_exp trainers.pl_trainer
#hide
!pip install pytorch-lightning
%cd /content
!rm -rf recohut
!git clone --branch US632593 https://github.com/RecoHut-Projects/recohut.git
%cd recohut
!pip install -U .
!apt-get -qq install tree
!pip install -q watermark
```
# PL Trainer
> Implementation of trainer for trainin... | /recohut-0.0.11.tar.gz/recohut-0.0.11/nbs/trainers/pl_trainer.ipynb | 0.672547 | 0.478894 | pl_trainer.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/US632593/tutorials/quick_tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install pytorch-lightning
%cd /content
!rm -rf recohut
!git clone --branch US... | /recohut-0.0.11.tar.gz/recohut-0.0.11/tutorials/quick_tutorial.ipynb | 0.695648 | 0.798776 | quick_tutorial.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/master/tutorials/modeling/T948935_deep_vae_recsys_filmtrust_tf.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Deep Variational Models on FilmTrust Dataset in Tensorf... | /recohut-0.0.11.tar.gz/recohut-0.0.11/tutorials/modeling/T948935_deep_vae_recsys_filmtrust_tf.ipynb | 0.701509 | 0.937555 | T948935_deep_vae_recsys_filmtrust_tf.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/master/tutorials/modeling/T936914_siren_ml1m_torch_gpu.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# SiReN on ML-1m in PyTorch
## **Step 1 - Setup the environment... | /recohut-0.0.11.tar.gz/recohut-0.0.11/tutorials/modeling/T936914_siren_ml1m_torch_gpu.ipynb | 0.802594 | 0.906901 | T936914_siren_ml1m_torch_gpu.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/master/tutorials/modeling/group_rec_ddpg_ml1m_pytorch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Deep Reinforcement Learning based Group Recommender System
`Act... | /recohut-0.0.11.tar.gz/recohut-0.0.11/tutorials/modeling/group_rec_ddpg_ml1m_pytorch.ipynb | 0.870377 | 0.983263 | group_rec_ddpg_ml1m_pytorch.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/master/tutorials/modeling/siren_movielens_pytorch_cpu.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Sign-Aware Recommendation Using Graph Neural Networks (SiReN) on... | /recohut-0.0.11.tar.gz/recohut-0.0.11/tutorials/modeling/siren_movielens_pytorch_cpu.ipynb | 0.678966 | 0.915129 | siren_movielens_pytorch_cpu.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/master/tutorials/modeling/T973437_matching_models_ml1m_tf.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Candidate selection (Item matching) models in Tensorflow on ... | /recohut-0.0.11.tar.gz/recohut-0.0.11/tutorials/modeling/T973437_matching_models_ml1m_tf.ipynb | 0.649801 | 0.912903 | T973437_matching_models_ml1m_tf.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/master/tutorials/modeling/T541654_group_rec_ddpg_ml1m_pytorch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Group Recommendations with Actor-critic RL Agent in MDP ... | /recohut-0.0.11.tar.gz/recohut-0.0.11/tutorials/modeling/T541654_group_rec_ddpg_ml1m_pytorch.ipynb | 0.755727 | 0.860428 | T541654_group_rec_ddpg_ml1m_pytorch.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/master/nbs/utils/logging.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#hide
!pip install -q nbdev
!pip install -q git+https://github.com/RecoHut-Projects/recohu... | /recohut-0.0.11.tar.gz/recohut-0.0.11/tutorials/utils/utils.logging.ipynb | 0.453988 | 0.866444 | utils.logging.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/master/nbs/utils/config.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#hide
!pip install -q nbdev
!git clone https://github.com/RecoHut-Projects/recohut.git
%cd r... | /recohut-0.0.11.tar.gz/recohut-0.0.11/tutorials/utils/utils.config.ipynb | 0.499756 | 0.758701 | utils.config.ipynb | pypi |
<a href="https://colab.research.google.com/github/RecoHut-Projects/recohut/blob/master/templates/_template_module.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Header
```
!pip install nbdev
!pip install twine
import os
project_name = "reco... | /recohut-0.0.11.tar.gz/recohut-0.0.11/templates/_template_module.ipynb | 0.486332 | 0.76259 | _template_module.ipynb | pypi |
# Recollection Overview
Recollection is a state recalling system. It allows for state of objects
to be snap-shotted exposing functionality to then 'rollback' to any
previously stored state.
Recollection gives two distinctly different exposures of this mechanism. If
performance is not the most critical concern and you... | /recollection-0.9.3.tar.gz/recollection-0.9.3/README.md | 0.724286 | 0.838548 | README.md | pypi |
from docopt import docopt
import os
import pystache
import yaml
def parse_args(arguments):
"""
Parse the docopt output.
params:
arguments: the docopt output dictionary
returns:
colorfile: the YAML file containing a colorscheme specification
indir: the directory to read templates from
... | /recolor_dots-0.1.3.tar.gz/recolor_dots-0.1.3/recolor_dots/main.py | 0.524151 | 0.243395 | main.py | pypi |
import warnings
import logging
import numpy as np
import pandas as pd
from tqdm import tqdm
from multiprocessing import cpu_count
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
from reComBat.src.utils import compute_init_values_parametric,compute_values_non_parametric, compute_weights, pa... | /reComBat-0.1.4-py3-none-any.whl/reComBat/reComBat.py | 0.834407 | 0.478102 | reComBat.py | pypi |
import warnings
import numpy as np
from joblib import Parallel, delayed
def compute_init_values_parametric(Z,batches_one_hot):
'''
Compute the starting values of the Bayesian optimization.
'''
gamma_hat = np.array([np.mean(Z[batches_one_hot[:,i]==1],axis=0) for i in range(batches_one_hot.shape[1])])
... | /reComBat-0.1.4-py3-none-any.whl/reComBat/src/utils.py | 0.563618 | 0.523177 | utils.py | pypi |
import warnings
import logging
import numpy as np
import pandas as pd
from tqdm import tqdm
from joblib import Parallel, delayed
from multiprocessing import cpu_count
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
class reComBat(object):
"""
reCombatClass
Parameters
----... | /reComBat-0.1.4-py3-none-any.whl/reComBat/src/reComBat_single_class.py | 0.798972 | 0.444565 | reComBat_single_class.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class SearchItemSegments(Request):
"""
Full-text personalized search that returns Segments from a Segmentation. The results are based on the provided `searchQuery` and also on th... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/search_item_segments.py | 0.919484 | 0.66838 | search_item_segments.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class RecommendNextItems(Request):
"""
Returns items that shall be shown to a user as next recommendations when the user e.g. scrolls the page down (*infinite scroll*) or goes to the ... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/recommend_next_items.py | 0.901627 | 0.410284 | recommend_next_items.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class RecommendItemsToUser(Request):
"""
Based on the user's past interactions (purchases, ratings, etc.) with the items, recommends top-N items that are most likely to be of high val... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/recommend_items_to_user.py | 0.908946 | 0.853486 | recommend_items_to_user.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class AddPurchase(Request):
"""
Adds a purchase of the given item made by the given user.
Required parameters:
:param user_id: User who purchased the item
:... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/add_purchase.py | 0.912072 | 0.511534 | add_purchase.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class AddCartAddition(Request):
"""
Adds a cart addition of the given item made by the given user.
Required parameters:
:param user_id: User who added the item to th... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/add_cart_addition.py | 0.906004 | 0.472136 | add_cart_addition.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class MergeUsers(Request):
"""
Merges interactions (purchases, ratings, bookmarks, detail views ...) of two different users under a single user ID. This is especially useful for onlin... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/merge_users.py | 0.809351 | 0.427158 | merge_users.py | pypi |
from typing import List
from recombee_api_client.api_requests.request import Request
class Batch(Request):
"""
Batch request for submitting an arbitrary sequence of requests
In many cases, it may be desirable to execute multiple requests at once. By example, when synchronizing the catalog of items in pe... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/batch.py | 0.895548 | 0.592077 | batch.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class SetViewPortion(Request):
"""
Sets viewed portion of an item (for example a video or article) by a user (at a session).
If you send a new request with the same (`userId`, `it... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/set_view_portion.py | 0.883889 | 0.445168 | set_view_portion.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class SearchItems(Request):
"""
Full-text personalized search. The results are based on the provided `searchQuery` and also on the user's past interactions (purchases, ratings, etc.) ... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/search_items.py | 0.922613 | 0.835752 | search_items.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class RecommendItemsToItem(Request):
"""
Recommends a set of items that are somehow related to one given item, *X*. A typical scenario is when the user *A* is viewing *X*. Then you ma... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/recommend_items_to_item.py | 0.926087 | 0.783119 | recommend_items_to_item.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class RecommendItemSegmentsToItemSegment(Request):
"""
Recommends Segments from a result Segmentation that are the most relevant to a particular Segment from a context Segmentation.
... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/recommend_item_segments_to_item_segment.py | 0.913717 | 0.714286 | recommend_item_segments_to_item_segment.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class RecommendUsersToItem(Request):
"""
Recommends users that are likely to be interested in the given item.
It is also possible to use POST HTTP method (for example in the ... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/recommend_users_to_item.py | 0.917164 | 0.803675 | recommend_users_to_item.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class InsertToSeries(Request):
"""
Inserts an existing item/series into a series of the given seriesId at a position determined by time.
Required parameters:
:param ... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/insert_to_series.py | 0.929592 | 0.612541 | insert_to_series.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class RecommendUsersToUser(Request):
"""
Gets users similar to the given user, based on the user's past interactions (purchases, ratings, etc.) and values of properties.
It i... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/recommend_users_to_user.py | 0.930078 | 0.820433 | recommend_users_to_user.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class AddDetailView(Request):
"""
Adds a detail view of the given item made by the given user.
Required parameters:
:param user_id: User who viewed the item
... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/add_detail_view.py | 0.906323 | 0.272269 | add_detail_view.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class ListUsers(Request):
"""
Gets a list of IDs of users currently present in the catalog.
Optional parameters:
:param filter: Boolean-returning [ReQL](https://docs... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/list_users.py | 0.929015 | 0.782849 | list_users.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class RecommendItemSegmentsToItem(Request):
"""
Recommends Segments from a Segmentation that are the most relevant to a particular item.
Based on the used Segmentation, this ... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/recommend_item_segments_to_item.py | 0.913399 | 0.690282 | recommend_item_segments_to_item.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class CreatePropertyBasedSegmentation(Request):
"""
Creates a Segmentation that splits the items into segments based on values of a particular item property.
A segment is cre... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/create_property_based_segmentation.py | 0.92788 | 0.486454 | create_property_based_segmentation.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class ListItems(Request):
"""
Gets a list of IDs of items currently present in the catalog.
Optional parameters:
:param filter: Boolean-returning [ReQL](https://docs... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/list_items.py | 0.936161 | 0.798226 | list_items.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class AddBookmark(Request):
"""
Adds a bookmark of the given item made by the given user.
Required parameters:
:param user_id: User who bookmarked the item
... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/add_bookmark.py | 0.894046 | 0.298747 | add_bookmark.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class RecommendItemSegmentsToUser(Request):
"""
Recommends the top Segments from a Segmentation for a particular user, based on the user's past interactions.
Based on the use... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/recommend_item_segments_to_user.py | 0.925112 | 0.68119 | recommend_item_segments_to_user.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class AddRating(Request):
"""
Adds a rating of the given item made by the given user.
Required parameters:
:param user_id: User who submitted the rating
:pa... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/add_rating.py | 0.917603 | 0.398699 | add_rating.py | pypi |
from recombee_api_client.api_requests.request import Request
from typing import Union, List
import uuid
DEFAULT = uuid.uuid4()
class CreateAutoReqlSegmentation(Request):
"""
Segment the items using a [ReQL](https://docs.recombee.com/reql.html) expression.
For each item, the expression should return a... | /recombee-api-client-4.1.0.tar.gz/recombee-api-client-4.1.0/recombee_api_client/api_requests/create_auto_reql_segmentation.py | 0.927712 | 0.534066 | create_auto_reql_segmentation.py | pypi |
# Recombinator - Statistical Resampling in Python
## Overview
Recombinator is a Python package for statistical resampling in Python. It provides various algorithms for the iid bootstrap, the block bootstrap, as well as optimal block-length selection.
## Algorithms
* I.I.D. bootstrap: Standard i.i.d. bootstrap f... | /recombinator-0.0.6.1.tar.gz/recombinator-0.0.6.1/README.md | 0.599485 | 0.969757 | README.md | pypi |
import numpy
import pandas
from networkx import DiGraph
def ped2graph(path):
"""
This function takes a pedigree file as input and encodes it as graphs.
The pedigree file can be a table in .ped format (headerless and space-separated),
in .tsv format (with header and tab-separated) or in .xlsx form... | /recombulator_x-1.1.2-py3-none-any.whl/recombulatorx/io.py | 0.558929 | 0.565419 | io.py | pypi |
from typing import Any, Dict, List
import joblib
import polars as pl
class ModelInstance:
def __init__(self, model_artifact_bucket: str, group_column: str, rank_column: str):
"""Initialize a ModelInstance object.
Args:
model_artifact_bucket (str): The path to the serialized model art... | /recommendation_model_server-0.1.1-py3-none-any.whl/recommendation_model_server/model.py | 0.942354 | 0.477371 | model.py | pypi |
from scipy.optimize import fmin_cg
import numpy as np
import pandas as pd
def normalize(df, unknown='?', max_value=5, min_value=0):
""" Normalize the columns of the data frame 'df'
Also return a matrix containing 0s for missing postions and 1s for others
"""
normalized_df = pd.DataFrame()
R_d... | /recommendation_system-1.0.0.tar.gz/recommendation_system-1.0.0/recommendation_system/recommendation_system.py | 0.618204 | 0.610221 | recommendation_system.py | pypi |
import numpy as np
import wikipedia
from transformers import DistilBertTokenizer, DistilBertModel #BertTokenizer, BertModel
import torch
import json
import fire
from scipy.spatial import distance
from pkg_resources import resource_stream
import transformers
transformers.logging.set_verbosity_error()
def get_text(movie... | /recommendation1000Movies-1.tar.gz/recommendation1000Movies-1/recommending_movies/movie_recommendations.py | 0.410993 | 0.270382 | movie_recommendations.py | pypi |
import pandas as pd
# currently tested on movies dataset
class FactorizationBased:
def __init__(
self,
dataframe,
product_title,
id_column: str,
genre_column: str,
ratings_column: str,
):
"""
Shaped based on user ratings(no of clicks, liked..) an... | /recommender-blackboxes-0.0.2.tar.gz/recommender-blackboxes-0.0.2/recommender/content.py | 0.679072 | 0.404655 | content.py | pypi |
import numpy as np
import pandas as pd
from typing import Optional
from sklearn.preprocessing import MinMaxScaler
def simple(csv: str, operation_column: str, ascending: Optional[bool] = False):
"""
Sorts given operation column
:param csv: csv file path location
:param operation_column: column to opera... | /recommender-blackboxes-0.0.2.tar.gz/recommender-blackboxes-0.0.2/recommender/popularity.py | 0.898009 | 0.679923 | popularity.py | pypi |
<p align="center">
<a href="https://github.com/tranlyvu/recommender"><img src="https://github.com/tranlyvu/recommender/blob/dev/img/recommender_logo.png" width="300" height="300"></a>
</p>
<p align="center">
<a href="https://pypi.org/project/recommender-engine/"><img src="https://img.shields.io/pypi/v/recommender-e... | /recommender-engine-0.3.0.tar.gz/recommender-engine-0.3.0/README.md | 0.617628 | 0.886371 | README.md | pypi |
import numpy as np
import pandas as pd
import random
def LeaveMembersOut(*lists, groups=None, n_val=1, n_test=1, seed=None):
"""Returns indices to split data into train, val, and test sets.
Returns indices of train, test, and validation sets based on the given
number of validation and test items per grou... | /recommender_pkg-0.0.6-py3-none-any.whl/recpkg/model_selection.py | 0.884033 | 0.6306 | model_selection.py | pypi |
import numpy as np
import random
import tensorflow as tf
from tensorflow import keras
from sklearn.base import BaseEstimator
class Recommender(BaseEstimator):
"""Abstract class for recommenders."""
class KerasRecommender(Recommender):
"""Abstract class for recommenders built with Keras models.
Args:
... | /recommender_pkg-0.0.6-py3-none-any.whl/recpkg/recommenders.py | 0.954594 | 0.497742 | recommenders.py | pypi |
import numpy as np
def dcg_score(items):
"""Calculate the discounted cumulative gain.
Args:
items (List[float, ...]): The list of ranked items.
Returns:
float: The DCG score.
"""
return sum([s/np.log2(i+2) for i, s in enumerate(items)])
def ndcg_score(items):
"""Calculate t... | /recommender_pkg-0.0.6-py3-none-any.whl/recpkg/metrics.py | 0.78403 | 0.579133 | metrics.py | pypi |
import numpy as np
import pandas as pd
import random
import re
import seaborn as sns
import tensorflow as tf
import tqdm
from .metrics import perform_groupwise_evaluation
from .preprocessing import get_standard_layers
def plot_metric_history(history_df, title=""):
"""Plot each metric versus epochs.
Args:
... | /recommender_pkg-0.0.6-py3-none-any.whl/recpkg/evaluation.py | 0.897464 | 0.380183 | evaluation.py | pypi |
import math
import numpy as np
from tensorflow import keras
import tqdm
from .recommenders import Recommender, KerasRecommender
class FunkSVD(Recommender):
"""Recommender implementing Funk SVD.
Funk SVD without global baselines.
Args:
user (ndarray): An array of the users.
item (ndarray)... | /recommender_pkg-0.0.6-py3-none-any.whl/recpkg/explicit.py | 0.928242 | 0.576989 | explicit.py | pypi |
import numpy as np
from sklearn.base import BaseEstimator
from tensorflow import keras
from .recommenders import KerasRecommender
class ItemPopularity(BaseEstimator):
"""Recommender based solely on interactions per item."""
def fit(self, X=None, y=None):
"""Fit the recommender from the training datas... | /recommender_pkg-0.0.6-py3-none-any.whl/recpkg/implicit.py | 0.955465 | 0.655557 | implicit.py | pypi |
import numpy as np
import pandas as pd
import random
def LeaveMembersOut(*lists, groups=None, n_val=1, n_test=1, seed=None):
"""Returns indices to split data into train, val, and test sets.
Returns indices of train, test, and validation sets based on the given
number of validation and test items per grou... | /recommender_pkg-0.0.6-py3-none-any.whl/recommender_pkg/model_selection.py | 0.884033 | 0.6306 | model_selection.py | pypi |
import numpy as np
import random
import tensorflow as tf
from tensorflow import keras
from sklearn.base import BaseEstimator
class Recommender(BaseEstimator):
"""Abstract class for recommenders."""
class KerasRecommender(Recommender):
"""Abstract class for recommenders built with Keras models.
Args:
... | /recommender_pkg-0.0.6-py3-none-any.whl/recommender_pkg/recommenders.py | 0.954594 | 0.497742 | recommenders.py | pypi |
import numpy as np
def dcg_score(items):
"""Calculate the discounted cumulative gain.
Args:
items (List[float, ...]): The list of ranked items.
Returns:
float: The DCG score.
"""
return sum([s/np.log2(i+2) for i, s in enumerate(items)])
def ndcg_score(items):
"""Calculate t... | /recommender_pkg-0.0.6-py3-none-any.whl/recommender_pkg/metrics.py | 0.78403 | 0.579133 | metrics.py | pypi |
import numpy as np
import pandas as pd
import random
import re
import seaborn as sns
import tensorflow as tf
from tqdm import tqdm
from .metrics import perform_groupwise_evaluation
from .preprocessing import get_standard_layers
def plot_metric_history(history_df, title=""):
"""Plot each metric versus epochs.
... | /recommender_pkg-0.0.6-py3-none-any.whl/recommender_pkg/evaluation.py | 0.879186 | 0.37648 | evaluation.py | pypi |
import math
import numpy as np
from tensorflow import keras
from tqdm import tqdm
from .recommenders import Recommender, KerasRecommender
class FunkSVD(Recommender):
"""Recommender implementing Funk SVD.
Funk SVD without global baselines.
Args:
user (ndarray): An array of the users.
item... | /recommender_pkg-0.0.6-py3-none-any.whl/recommender_pkg/explicit.py | 0.92175 | 0.54692 | explicit.py | pypi |
import numpy as np
from sklearn.base import BaseEstimator
from tensorflow import keras
from .recommenders import KerasRecommender
class ItemPopularity(BaseEstimator):
"""Recommender based solely on interactions per item."""
def fit(self, X=None, y=None):
"""Fit the recommender from the training datas... | /recommender_pkg-0.0.6-py3-none-any.whl/recommender_pkg/implicit.py | 0.955465 | 0.655557 | implicit.py | pypi |
from functools import lru_cache, wraps
import logging
import pandas as pd
import numpy as np
from reco_utils.common.constants import (
DEFAULT_USER_COL,
DEFAULT_ITEM_COL,
DEFAULT_RATING_COL,
DEFAULT_LABEL_COL,
)
logger = logging.getLogger(__name__)
def user_item_pairs(
user_df,
item_df,
... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/pandas_df_utils.py | 0.868325 | 0.452415 | pandas_df_utils.py | pypi |
import os
import re
import shutil
import warnings
import pandas as pd
import gzip
import random
import logging
import _pickle as cPickle
from reco_utils.dataset.download_utils import maybe_download, download_path
logger = logging.getLogger()
def data_preprocessing(
reviews_file,
meta_file,
train_file,
... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/amazon_reviews.py | 0.420124 | 0.205217 | amazon_reviews.py | pypi |
import pandas as pd
import numpy as np
import math
from reco_utils.common.constants import DEFAULT_ITEM_COL, DEFAULT_USER_COL
try:
from pyspark.sql.functions import col, broadcast
except ImportError:
pass # so the environment without spark doesn't break
def process_split_ratio(ratio):
"""Generate spli... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/split_utils.py | 0.883889 | 0.505676 | split_utils.py | pypi |
from reco_utils.dataset import blob_utils
from azure.storage.blob import BlockBlobService
from io import StringIO
import pandas as pd
import numpy as np
import json
def load_pandas_df(
azure_storage_account_name="azureopendatastorage",
azure_storage_sas_token="sv=2019-02-02&ss=bfqt&srt=sco&sp=rlcup&se=2025-0... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/covid_utils.py | 0.788868 | 0.496643 | covid_utils.py | pypi |
import numpy as np
try:
from pyspark.sql import Window
from pyspark.sql.functions import (
col,
row_number,
broadcast,
rand,
collect_list,
size,
)
except ImportError:
pass # skip this import if we are in pure python environment
from reco_utils.common.c... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/spark_splitters.py | 0.835886 | 0.628236 | spark_splitters.py | pypi |
import os
import random
import logging
import json
import numpy as np
import re
from tqdm import tqdm
from nltk.tokenize import RegexpTokenizer
from reco_utils.dataset.download_utils import maybe_download, download_path, unzip_file
URL_MIND_LARGE_TRAIN = (
"https://mind201910small.blob.core.windows.net/release/... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/mind.py | 0.657098 | 0.163145 | mind.py | pypi |
import os
import re
import shutil
import warnings
import pandas as pd
from zipfile import ZipFile
from reco_utils.dataset.download_utils import maybe_download, download_path
from reco_utils.common.notebook_utils import is_databricks
from reco_utils.common.constants import (
DEFAULT_USER_COL,
DEFAULT_ITEM_COL,
... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/movielens.py | 0.636805 | 0.312331 | movielens.py | pypi |
import pandas as pd
import requests
import logging
logger = logging.getLogger(__name__)
API_URL_WIKIPEDIA = "https://en.wikipedia.org/w/api.php"
API_URL_WIKIDATA = "https://query.wikidata.org/sparql"
SESSION = None
def get_session(session=None):
"""Get session object
Args:
session (requests.Sessio... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/wikidata.py | 0.504639 | 0.156459 | wikidata.py | pypi |
import os
import logging
import requests
import math
import zipfile
from contextlib import contextmanager
from tempfile import TemporaryDirectory
from tqdm import tqdm
log = logging.getLogger(__name__)
def maybe_download(url, filename=None, work_directory=".", expected_bytes=None):
"""Download a file if it is n... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/download_utils.py | 0.536556 | 0.171338 | download_utils.py | pypi |
import pandas as pd
import os
import tarfile
try:
from pyspark.sql.types import StructType, StructField, IntegerType, StringType
except ImportError:
pass # so the environment without spark doesn't break
from reco_utils.dataset.download_utils import maybe_download, download_path
from reco_utils.common.noteb... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/dataset/criteo.py | 0.786377 | 0.574753 | criteo.py | pypi |
from math import ceil, floor
import logging
logger = logging.getLogger(__name__)
def qps_to_replicas(
target_qps, processing_time, max_qp_replica=1, target_utilization=0.7
):
"""Provide a rough estimate of the number of replicas to support a given
load (queries per second)
Args:
target_qps (... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/azureml/aks_utils.py | 0.947709 | 0.519765 | aks_utils.py | pypi |
import argparse
import os
import shutil
import papermill as pm
import tensorflow as tf
print("TensorFlow version:", tf.VERSION)
try:
from azureml.core import Run
run = Run.get_context()
except ImportError:
run = None
from reco_utils.common.constants import (
DEFAULT_USER_COL,
DEFAULT_ITEM_COL,
... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/azureml/wide_deep_training.py | 0.584034 | 0.171408 | wide_deep_training.py | pypi |
try:
from pyspark.mllib.evaluation import RegressionMetrics, RankingMetrics
from pyspark.sql import Window, DataFrame
from pyspark.sql.functions import col, row_number, expr
import pyspark.sql.functions as F
except ImportError:
pass # skip this import if we are in pure python environment
from re... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/evaluation/spark_evaluation.py | 0.871898 | 0.387719 | spark_evaluation.py | pypi |
import numpy as np
import pandas as pd
from functools import wraps
from sklearn.metrics import (
mean_squared_error,
mean_absolute_error,
r2_score,
explained_variance_score,
roc_auc_score,
log_loss,
)
from reco_utils.common.constants import (
DEFAULT_USER_COL,
DEFAULT_ITEM_COL,
DEF... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/evaluation/python_evaluation.py | 0.905585 | 0.400339 | python_evaluation.py | pypi |
import pandas as pd
import numpy as np
import pandas as pd
from reco_utils.common.constants import (
DEFAULT_USER_COL,
DEFAULT_ITEM_COL,
DEFAULT_PREDICTION_COL,
)
from reco_utils.common.general_utils import invert_dictionary
def surprise_trainset_to_df(
trainset, col_user="uid", col_item="iid", col_... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/recommender/surprise/surprise_utils.py | 0.825132 | 0.444927 | surprise_utils.py | pypi |
import numpy as np
from scipy.linalg import sqrtm
from numba import njit, jit, prange
from IPython import embed
from .geoimc_utils import length_normalize
from reco_utils.common.python_utils import binarize as conv_binary
class PlainScalarProduct(object):
"""
Module that implements plain scalar product
a... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/recommender/geoimc/geoimc_predict.py | 0.878275 | 0.410874 | geoimc_predict.py | pypi |
import warnings
import logging
from scipy.io import loadmat
import pandas as pd
import numpy as np
from scipy.sparse import coo_matrix, isspmatrix_csr
from sklearn.model_selection import train_test_split
from sklearn import datasets
from sklearn.preprocessing import normalize
from numba import jit, prange
from reco_u... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/recommender/geoimc/geoimc_data.py | 0.694095 | 0.603172 | geoimc_data.py | pypi |
import os
import itertools
from collections import Counter, OrderedDict
import numpy as np
from sklearn.cluster import KMeans
from scipy.sparse import coo_matrix, csr_matrix, isspmatrix_csr
from numba import njit, jit, prange
from pymanopt import Problem
from pymanopt.manifolds import Stiefel, Product, PositiveDefinite... | /recommender_utils-2021.2.post1623854186-py3-none-any.whl/reco_utils/recommender/geoimc/geoimc_algorithm.py | 0.677047 | 0.339143 | geoimc_algorithm.py | pypi |
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