file stringlengths 6 44 | content stringlengths 38 162k |
|---|---|
reader.py | """This module contains the Reader class."""
from .builtin_datasets import BUILTIN_DATASETS
class Reader:
"""The Reader class is used to parse a file containing ratings.
Such a file is assumed to specify only one rating per line, and each line
needs to respect the following structure: ::
user ... |
__init__.py | from pkg_resources import get_distribution
from . import dump, model_selection
from .builtin_datasets import get_dataset_dir
from .dataset import Dataset
from .prediction_algorithms import (
AlgoBase,
BaselineOnly,
CoClustering,
KNNBaseline,
KNNBasic,
KNNWithMeans,
KNNWithZScore,
NMF,... |
builtin_datasets.py | """This module contains built-in datasets that can be automatically
downloaded."""
import errno
import os
import zipfile
from collections import namedtuple
from os.path import join
from urllib.request import urlretrieve
def get_dataset_dir():
"""Return folder where downloaded datasets and other data are stored.... |
__main__.py | #!/usr/bin/env python
import argparse
import os
import random as rd
import shutil
import sys
import numpy as np
import surprise.dataset as dataset
from surprise import __version__
from surprise.builtin_datasets import get_dataset_dir
from surprise.dataset import Dataset
from surprise.model_selection import cross_val... |
trainset.py | """This module contains the Trainset class."""
import numpy as np
class Trainset:
"""A trainset contains all useful data that constitute a training set.
It is used by the :meth:`fit()
<surprise.prediction_algorithms.algo_base.AlgoBase.fit>` method of every
prediction algorithm. You should not try t... |
utils.py | """The utils module contains the get_rng function."""
import numbers
import numpy as np
def get_rng(random_state):
"""Return a 'validated' RNG.
If random_state is None, use RandomState singleton from numpy. Else if
it's an integer, consider it's a seed and initialized an rng with that
seed. If it... |
search.py | from abc import ABC, abstractmethod
from itertools import product
import numpy as np
from joblib import delayed, Parallel
from ..dataset import DatasetUserFolds
from ..utils import get_rng
from .split import get_cv
from .validation import fit_and_score
class BaseSearchCV(ABC):
"""Base class for hyper parameter... |
__init__.py | from .search import GridSearchCV, RandomizedSearchCV
from .split import (
KFold,
LeaveOneOut,
PredefinedKFold,
RepeatedKFold,
ShuffleSplit,
train_test_split,
)
from .validation import cross_validate
__all__ = [
"KFold",
"ShuffleSplit",
"train_test_split",
"RepeatedKFold",
"... |
validation.py | """
The validation module contains the cross_validate function, inspired from
the mighty scikit learn.
"""
import time
import numpy as np
from joblib import delayed, Parallel
from .. import accuracy
from .split import get_cv
def cross_validate(
algo,
data,
measures=["rmse", "mae"],
cv=None,
re... |
split.py | """
The :mod:`model_selection.split<surprise.model_selection.split>` module
contains various cross-validation iterators. Design and tools are inspired from
the mighty scikit learn.
The available iterators are:
.. autosummary::
:nosignatures:
KFold
RepeatedKFold
ShuffleSplit
LeaveOneOut
Predef... |
knns.py | """
the :mod:`knns` module includes some k-NN inspired algorithms.
"""
import heapq
import numpy as np
from .algo_base import AlgoBase
from .predictions import PredictionImpossible
# Important note: as soon as an algorithm uses a similarity measure, it should
# also allow the bsl_options parameter because of the ... |
algo_base.py | """
The :mod:`surprise.prediction_algorithms.algo_base` module defines the base
class :class:`AlgoBase` from which every single prediction algorithm has to
inherit.
"""
import heapq
from .. import similarities as sims
from .optimize_baselines import baseline_als, baseline_sgd
from .predictions import Prediction, Predi... |
random_pred.py | """ Algorithm predicting a random rating.
"""
import numpy as np
from .algo_base import AlgoBase
class NormalPredictor(AlgoBase):
"""Algorithm predicting a random rating based on the distribution of the
training set, which is assumed to be normal.
The prediction :math:`\\hat{r}_{ui}` is generated from... |
predictions.py | """
The :mod:`surprise.prediction_algorithms.predictions` module defines the
:class:`Prediction` named tuple and the :class:`PredictionImpossible`
exception.
"""
from collections import namedtuple
class PredictionImpossible(Exception):
r"""Exception raised when a prediction is impossible.
When raised, the ... |
__init__.py | """
The :mod:`prediction_algorithms` package includes the prediction algorithms
available for recommendation.
The available prediction algorithms are:
.. autosummary::
:nosignatures:
random_pred.NormalPredictor
baseline_only.BaselineOnly
knns.KNNBasic
knns.KNNWithMeans
knns.KNNWithZScore
... |
baseline_only.py | """
This class implements the baseline estimation.
"""
from .algo_base import AlgoBase
class BaselineOnly(AlgoBase):
r"""Algorithm predicting the baseline estimate for given user and item.
:math:`\hat{r}_{ui} = b_{ui} = \mu + b_u + b_i`
If user :math:`u` is unknown, then the bias :math:`b_u` is assumed... |
linux_dependencies.py | import os
import traceback
import sys
print("before function process")
def process(version):
print("inside fun process")
currentDirectory = os.path.dirname(os.path.abspath(__file__))
print(currentDirectory)
try:
from os.path import expanduser
import platform
import subprocess
... |
dependencies.py | import os
import traceback
def process(version):
currentDirectory = os.path.dirname(os.path.abspath(__file__))
try:
import win32com.client
from os.path import expanduser
import platform
import subprocess
import sys
import demoji
try:
print('Do... |
__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains... |
visualization.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains... |
local_pipeline.py | import docker
import json
import logging
def read_json(file_path):
data = None
with open(file_path,'r') as f:
data = json.load(f)
return data
def run_pipeline(inputconfig):
inputconfig = json.loads(inputconfig)
logfilepath = input... |
build_container.py | import os
import shutil
import sys
import subprocess
from os.path import expanduser
import platform
import json
def createDockerImage(model_name,model_version,module,folderpath):
command = 'docker pull python:3.8-slim-buster'
os.system(command);
subprocess.check_call(["docker", "build", "-t",module+'_'+model_name... |
git_upload.py | import os
import sys
import json
from pathlib import Path
import subprocess
import shutil
import argparse
def create_and_save_yaml(git_storage_path, container_label,usecasepath):
file_name_prefix = 'gh-acr-'
yaml_file = f"""\
name: gh-acr-{container_label}
on:
push:
branches: main
paths: {con... |
__init__.py | '''
*
* =============================================================================
* COPYRIGHT NOTICE
* =============================================================================
* @ Copyright HCL Technologies Ltd. 2021, 2022,2023
* Proprietary and confidential. All information contained herein is, and
* remains... |
kafka_consumer.py | from kafka import KafkaConsumer
from json import loads
import pandas as pd
import json
import os,sys
import time
import multiprocessing
from os.path import expanduser
import platform
import datetime
modelDetails = {}
class Process(multiprocessing.Process):
def __init__(self, modelSignature,jsonData,predictedData,mo... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.