As part of the course “Python Developer. Basic ” prepared a translation of useful material for you.
We also invite everyone to an open webinar on the topic "Three whales: map (), filter () and zip ()" . Can you write code that requires loops but no loops? Can. Could it be faster than if we were using loops in Python? Can. To implement the plan, you need to know the words "callback", "iterator" and "lambda". It will be difficult, but interesting. Join us.
We add clustering algorithms using scikit-learn, Keras and others packages to Photonai. We'll show you how to @dataclass
improve your Python code with 12 examples . For this, we use code from the Photonai package for Machine Learning.
Upgrade to Python 3.7 or later
@dataclass
Python 3.7. Python 3.7 Docker-, /.bashrc_profile
/bashrc.txt
.
devdir='<path-to-projects>/photon/photonai/dockerSeasons/dev/'
testdir='<path-to-projects>/photon/photonai/dockerSeasons/test/'
echo $devdir
echo $testdir
export testdir
export devdir
#
alias updev="(cd $devdir; docker-compose up) &"
alias downdev="(cd $devdir; docker-compose down) &"
alias builddev="(cd $devdir; docker-compose build) &"
#
alias uptest="(cd $testdir; docker-compose up) & "
alias downtest="(cd $testdir; docker-compose down) &"
alias buildtest="cd $testdir; docker-compose build) &"
/bashrc.txt
touch/bashrc.txt
. ( MacOS Linux Unix.)
: ˜/.bashrc_profile
˜/bashrc.txt
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: Docker GitHub.
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Python 3.7 @dataclass
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https://medium.com/swlh/future-proof-your-python-code-20ef2b75e9f5
https://realpython.com/python-type-checking/
https://docs.python.org/3/library/typing.html
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photon/photonai/base/hyperpipe.py
, @dataclass.
### Example #1
class Data:
def __init__(self, X=None, y=None, kwargs=None):
self.X = X
self.y = y
self.kwargs = kwargs
1, =>
from dataclasses import dataclass
from typing import Dict
import numpy as np
@dataclass
class Data:
X: np.ndarray = None # The field declaration: X
y: np.array = None # The field declaration: y
kwargs: Dict = None # The field declaration: kwargs
: , . any
, .
eq()
?
### Example #2
data1 = Data()
data2 = Data()
data1 == data1
2, =>
True
! repr()
str
?
### Example #3
print(data1)
data1
, =>
Data(X=None, y=None, kwargs=None)
Data(X=None, y=None, kwargs=None)
! hash()
init
?
Example #4
@dataclass(unsafe_hash=True)
class Data:
X: np.ndarray = None
y: np.array = None
kwargs: Dict = None
data3 = Data(1,2,3)
{data3:1}
4, =>
{Data(X=1, y=2, kwargs=3): 1}
!
: init
(X, y, kwargs). , , Python 3.7.
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eq()
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inspect
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### Example #5
from inspect import signature
print(signature(data3.__init__))
5, =>
(X: numpy.ndarray = None, y: <built-in function array> = None,
kwargs: Dict = None) -> None
!
photon/photonai/base/hyperpipe.py
### Example #6
class CrossValidation:
def __init__(self, inner_cv, outer_cv,
eval_final_performance, test_size,
calculate_metrics_per_fold,
calculate_metrics_across_folds):
self.inner_cv = inner_cv
self.outer_cv = outer_cv
self.eval_final_performance = eval_final_performance
self.test_size = test_size
self.calculate_metrics_per_fold = calculate_metrics_per_fold
self.calculate_metrics_across_folds =
calculate_metrics_across_folds
self.outer_folds = None
self.inner_folds = dict()Example #6 Output=>
6, =>
from dataclasses import dataclass
@dataclass
class CrossValidation:
inner_cv: int
outer_cv: int
eval_final_performance: bool = True
test_size: float = 0.2
calculate_metrics_per_fold: bool = True
calculate_metrics_across_folds: bool = False
Note:(Example #6) As any signature, keyword arguments fields with default values must be declared last.
Note:(Example #6) class CrossValidation: Readability has increased substantially by using @dataclass and type hinting.
### Example #7
cv1 = CrossValidation()
7, =>
TypeError: __init__() missing 2 required positional arguments: 'inner_cv' and 'outer_cv'
Note:(Example #7) inner_cv and outer_cv are positional arguments. With any signature, you declare a non-default field after a default one. (Hint: If this were allowed, inheritance from a parent class breaks.)((Why? Goggle interview question #666.))
### Example #8
cv1 = CrossValidation(1,2)
cv2 = CrossValidation(1,2)
cv3 = CrossValidation(3,2,test_size=0.5)
print(cv1)
cv3
8, =>
CrossValidation(inner_cv=1, outer_cv=2, eval_final_performance=True, test_size=0.2, calculate_metrics_per_fold=True, calculate_metrics_across_folds=False)
CrossValidation(inner_cv=3, outer_cv=2, eval_final_performance=True, test_size=0.5, calculate_metrics_per_fold=True, calculate_metrics_across_folds=False)
### Example #9
cv1 == cv2
9, =>
True
### Example #10
cv1 == cv3
10, =>
False
### Example #11
from inspect import signature
print(signature(cv3.__init__))
cv3
11, =>
(inner_cv: int, outer_cv: int, eval_final_performance: bool = True, test_size: float = 0.2, calculate_metrics_per_fold: bool = True, calculate_metrics_across_folds: bool = False) -> None
CrossValidation(inner_cv=3, outer_cv=2, eval_final_performance=True, test_size=0.5, calculate_metrics_per_fold=True, calculate_metrics_across_folds=False)
Note: (Example #11) The inspect function shows the signature of the class object while the__str__ default shows the instance state variables and their values.
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self.outer_folds = None
self.inner_folds = dict()
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init
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CrossValidation
:
### Example 12
from dataclasses import dataclass
@dataclass
class CrossValidation:
inner_cv: int
outer_cv: int
eval_final_performance: bool = True
test_size: float = 0.2
calculate_metrics_per_fold: bool = True
calculate_metrics_across_folds: bool = False
def __post_init__(self):
self.outer_folds = None
self.inner_folds = dict()
@dataclass
:
https://realpython.com/python-data-classes/
https://blog.usejournal.com/new-buzzword-in-python-is-here-dataclasses-843dd1d372a5
12 « » , @dataclass
Photonai Machine Learning. , @dataclass
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«Python Developer. Basic».
« : map(), filter() zip()».