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J. Fernando Sánchez 3 years ago
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.gitignore vendored
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.*
*.pyc
__pycache__
dist
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LICENSE.txt
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MANIFEST.in
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include requirements.txt
include test-requirements.txt
include extra-requirements.txt
include README.md
include LICENSE.txt
graft keepit

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Makefile
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release:
python setup.py sdist
python setup.py bdist_wheel
twine upload --skip-existing dist/*

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README.md
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# KEEP IT
This is a **WORK IN PROGRESS**.
`keepit` provides advanced memoization to disk for functions.
In other words, it records the results of important functions between executions.
`keepit` saves the results of calling a function to disk, so calling the function with the exact same parameters will re-use the stored copy of the results, leading to much faster times.
Example usage:
```
import pandas as pd
from keepit import keepit
@keepit('myresults.tsv')
def expensive_function(number=1):
df = pd.DataFrame()
# Perform a really expensive operation, maybe access to disk?
return df
# When a results file for the function does not exist
# this may take a long time
expensive_function(number=1)
# Now a myresults.tsv_{some hash) has been generated
# This is almost instantaneous:
expensive_function(number=1)
# Files are specific to each parameter execution,
# so this will again take a long time:
expensive_function(number=42)
# After this, we should have two files, one for number=1,
# and another one for number=42.
```

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extra-requirements.txt
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keepit/VERSION
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0.2.2

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keepit/__init__.py
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import os
import unicodedata
import inspect
import re
import pandas as pd
from functools import wraps, partial
from glob import glob
import logging
import pickle
import hashlib
from collections import namedtuple
from .backends import Pickle, Entry
logger = logging.getLogger(__name__)
def _slugify(value):
"""
Normalizes string, converts to lowercase, removes non-alpha characters,
and converts spaces to hyphens.
Source:
http://stackoverflow.com/questions/295135/turn-a-string-into-a-valid-filename-in-python
"""
value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')
value = re.sub(r'[^\w\s-]', '', value.decode('utf-8', 'ignore'))
value = value.strip().lower()
value = re.sub(r'[-\s]+', '-', value)
return value
def hash_df(df):
'''Hashes a pandas dataframe'''
return hashlib.sha256(pd.util.hash_pandas_object(df, index=True).values).hexdigest()
def hash_object(obj):
return hashlib.sha256(pickle.dumps(obj)).hexdigest()
def hash_element(elem):
if isinstance(elem, pd.DataFrame):
return hash_df(elem)
pick = pickle.dumps(elem)
return hashlib.sha256(pick).hexdigest()
def func_hasher(f, *args, fname=None, element_hasher=hash_element, **kwargs):
sig = inspect.signature(f)
func = partial(f, *args, **kwargs)
bound = sig.bind_partial(*args, **kwargs)
bound.apply_defaults()
reqs = {}
for k, v in bound.arguments.items():
reqs[k] = v
fname = fname or '{}_{}'.format(f.__name__, f.__module__)
args_hash = hash_object(reqs)
name = '{}_{}'.format(fname, args_hash)
return _slugify(name), func, reqs
Result = namedtuple('Result', ['func', 'args', 'value'])
class HashedFunc:
def __init__(self, func, fname=None, tags=[], backend=Pickle()):
self.func = func
self.tags = tags
self.backend = backend
self.sig = inspect.signature(func)
self.fname = fname or '{}_{}'.format(self.func.__name__,
self.func.__module__)
def hash(self, *args, **kwargs):
return func_hasher(self.func, *args, fname=self.fname, **kwargs, element_hasher=self.hash_element)
def hash_element(self, elem):
return hash_element(elem)
def __call__(self, *args, cache_force=False, tags=[], **kwargs):
if os.environ.get('no_cache'):
return self.func(*args, **kwargs)
func_id, func, requirements = self.hash(*args, **kwargs)
print(func_id)
if cache_force or not self.backend.exists(func_id):
res = func()
e = Entry(tags=[self.fname, ],
id=func_id,
content=Result(func_id, requirements, res))
self.backend.put(e)
# hash = self.hash_element(res)
# self.backend.put(hash, res, tags=self.tags+tags)
# for req_hash, req_value in requirements.items():
# if not self.backend.find(req_hash):
# self.backend.put(req_hash, req_value)
else:
res = self.backend.get(func_id).content.value
return res
def drop(self, *args, **kwargs):
func_id, func, requirements = self.hash(*args, **kwargs)
if self.backend.exists(func_id):
self.backend.remove(func_id)
def drop_all(self):
for f in self.list():
self.backend.remove(f.id)
def list(self):
return list(self.backend.find(tags=[self.fname, ]))
def keepit(fname=None, hasher=HashedFunc, **kwargs):
def outer(of):
return hasher(of, fname=fname, **kwargs)
return outer
def diff(df1, df2):
return pd.concat([df1, df2]).drop_duplicates(keep=False)

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keepit/backends/__init__.py
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import os
import pickle
import time
import sqlite3
from glob import glob
from pathlib import Path
ROOT = os.path.join(Path.home(), '.keepit')
ROOT = os.path.abspath(os.path.basename(__file__))
CACHE_DIR = os.environ.get('CACHE_DIR', os.path.join(ROOT, '_cache'))
class NotFound(Exception):
pass
class BackEnd():
def exists(self, oid):
raise NotImplementedError()
def read(self, oid):
raise NotImplementedError()
def put(self, entry):
raise NotImplementedError()
def remove(self, oid):
raise NotImplementedError()
def find(self, *args, **kwargs):
return list(self.ifind(*args, **kwargs))
def ifind(self, oid=None, tags=[]):
raise NotImplementedError()
def erase_all(self):
for entry in self.find():
self.remove(entry.id)
class Entry:
def __init__(self, id, content, tags=[], timestamp=None):
self.id = id
self.timestamp = time.localtime(timestamp or time.time())
self.content = content
self.tags = set(tags)
def __repr__(self):
return str(self)
def __str__(self):
return '{} @ {} [{}]'.format(self.id, time.strftime('%Y-%m-%d %H:%M', self.timestamp), ','.join(self.tags))
class Pickle(BackEnd):
res_folder = 'keepit_cache'
def __init__(self):
pass
def _filename(self, oid):
return os.path.join(self.res_folder, "{}.pickle".format(oid))
def _open(self, fpath, abs=False):
if not abs:
fpath = self._filename(fpath)
with open(fpath, 'rb') as f:
return pickle.load(f)
def put(self, entry):
if not os.path.exists(self.res_folder):
os.makedirs(self.res_folder)
with open(self._filename(entry.id), 'wb') as f:
pickle.dump(entry, f)
def exists(self, oid):
return os.path.exists(self._filename(oid))
def get(self, oid):
s = self._open(oid)
return s
def remove(self, oid):
return os.remove(self._filename(oid))
def ifind(self, oid=None, tags=[]):
target = set(tags)
for f in glob(os.path.join(self.res_folder, '*')):
e = self._open(f, abs=True)
if (not oid or f.id == oid) and e.tags.issuperset(tags):
yield e

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keepit/version.py
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import os
import logging
logger = logging.getLogger(__name__)
ROOT = os.path.dirname(__file__)
DEFAULT_FILE = os.path.join(ROOT, 'VERSION')
def read_version(versionfile=DEFAULT_FILE):
try:
with open(versionfile) as f:
return f.read().strip()
except IOError: # pragma: no cover
logger.error('Running an unknown version of senpy. Be careful!.')
return '0.0'
__version__ = read_version()

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requirements.txt
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setup.py
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from setuptools import setup
with open('keepit/VERSION') as f:
__version__ = f.read().strip()
assert __version__
def parse_requirements(filename):
""" load requirements from a pip requirements file """
with open(filename, 'r') as f:
lineiter = list(line.strip() for line in f)
return [line for line in lineiter if line and not line.startswith("#")]
install_reqs = parse_requirements("requirements.txt")
test_reqs = parse_requirements("test-requirements.txt")
# read the contents of your README file
from os import path
this_directory = path.abspath(path.dirname(__file__))
with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='keepit',
python_requires='>3.3',
packages=['keepit'], # this must be the same as the name above
version=__version__,
license='Apache License 2.0',
description=('advanced memoization/caching of functions with data analytics in mind'),
long_description=long_description,
long_description_content_type='text/markdown',
author='J. Fernando Sanchez',
author_email='balkian@gmail.com',
url='https://github.com/balkian/keepit', # use the URL to the github repo
download_url='https://github.com/balkian/keepit/archive/{}.tar.gz'.format(
__version__),
keywords=['data analysis', 'memoization', 'cache'],
classifiers=[
'Programming Language :: Python :: 3',
],
install_requires=install_reqs,
tests_require=test_reqs,
setup_requires=['pytest-runner', ],
include_package_data=True,
entry_points={
'console_scripts':
['keepit = keepit.__main__:main',]
})

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test-requirements.txt
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tests/create_custom.py
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from keepit import keepit
ORIGIN = None
@keepit('myfunction')
def custom_function():
return ORIGIN
if __name__ == '__main__':
ORIGIN = 'main'
custom_function()

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tests/test.tsv
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int str bool other
0 0 hola True
1 1 adiós False 5.0
1 int str bool other
2 0 0 hola True
3 1 1 adiós False 5.0

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tests/test_main.py
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import sys
import os
import subprocess
from unittest import TestCase
from keepit import keepit, diff, hash_df
from keepit.backends import Pickle
import pandas as pd
# df = pd.DataFrame([[0, 'hola', True, None], [1, 'adiós', False, 5]], columns=['int', 'str', 'bool', 'other'])
this_directory = os.path.abspath(os.path.dirname(__file__))
count = 0
@keepit()
def prueba(first, name='something', value=29):
global count
count += 1
return pd.DataFrame([[count, first, name, value], ], columns=['time', 'first', 'name', 'value'])
@keepit()
def prueba_df():
return pd.read_csv(os.path.join(this_directory, 'test.tsv'), sep='\t')
@keepit()
def prueba_df_argument(df):
return count
class TestMain(TestCase):
def setUpClass():
back = Pickle()
back.erase_all()
assert not back.find()
assert (not os.path.exists(back.res_folder)) or (not os.listdir(back.res_folder))
def tearDownClass():
back = Pickle()
back.erase_all()
def test_basic(self):
try:
prueba()
except TypeError:
pass
p1 = prueba('hello')
print(p1)
p2 = prueba('hello')
print(p2)
print(diff(p1, p2))
assert p1.equals(p2) # Columns and rows are equal
p3 = prueba('other')
print(p3)
assert not p1.equals(p3)
p4 = prueba('hello', name='different')
print(p4)
assert not p1.equals(p4)
def test_list_and_drop(self):
assert len(prueba.list()) > 0
prueba.drop_all()
assert len(prueba.list()) == 0
def test_df(self):
df1 = prueba_df()
df2 = prueba_df()
assert df1.equals(df2)
assert hash_df(df1) == hash_df(df2)
prueba_df.drop_all()
def test_different_program(self):
'''A value saved by a different interpreter should be reusable.'''
from create_custom import custom_function
custom_function.drop_all()
subprocess.check_call([sys.executable, os.path.join(this_directory, 'create_custom.py')])
# create_custom returns different results when run as a script.
# We make sure we are using the "stored" value, and not the result from the imported function
assert custom_function() == 'main'
def test_df_arg(self):
df1 = pd.read_csv(os.path.join(this_directory, 'test.tsv'), sep='\t')
res1 = prueba_df_argument(df1)
df2 = pd.read_csv(os.path.join(this_directory, 'test.tsv'), sep='\t')
res2 = prueba_df_argument(df2)
assert res1 == res2
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