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Python 機械学習 Unknown label type: 'continuous'
Jupiter notebook上でパイソンを動かしています。
ボストンのデータを読み込み、ZN,INDUS,CRIMを用いてロジスティック回帰を行いたいです。
#課題4_主成分分析
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_boston
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import Lasso
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import sklearn
from sklearn.decomposition import PCA #主成分分析器
from sklearn import preprocessing
%matplotlib inline
boston = load_boston()
boston = pd.DataFrame(boston.data, columns=boston.feature_names)
#行列の標準化をすると,CHAS要素を削除するときに
#なぜかCRIMも消えてしまうため実行しない
#boston = boston.iloc[:, 1:].apply(lambda x: (x-x.mean())/x.std(), axis=0)
#主成分分析の実行
pca = PCA()
pca.fit(boston)
## データを主成分空間に写像
feature = pca.transform(boston)
pd.DataFrame(pca.explained_variance_ratio_, index=["PC{}".format(x + 1) for x in range(len(boston.columns))])
## PCA の固有値
koyuchi = pd.DataFrame(pca.explained_variance_, index=["PC{}".format(x + 1) for x in range(len(boston.columns))])
#列要素を削除
boston.drop("LSTAT",axis=1,inplace=True)
boston.drop("B",axis=1,inplace=True)
boston.drop("PTRATIO",axis=1,inplace=True)
boston.drop("TAX",axis=1,inplace=True)
boston.drop("RAD",axis=1,inplace=True)
boston.drop("DIS",axis=1,inplace=True)
boston.drop("AGE",axis=1,inplace=True)
boston.drop("RM",axis=1,inplace=True)
boston.drop("NOX",axis=1,inplace=True)
boston.drop("CHAS",axis=1,inplace=True)
X = preprocessing.scale(boston[["INDUS","CRIM",]])
Y =boston["ZN"]#正解データ:整数のものを選んだ
#トレーニングデータとテストデータを7:3に分ける
X_tr, X_te, Y_tr, Y_te = train_test_split(X, Y, test_size=0.3, random_state=7 )
from sklearn.linear_model import LogisticRegression
## ロジスティック回帰モデルのインスタンス
lr = LogisticRegression()
## トレーニングデータから，ロジスティック回帰モデルの重みを学習
lr.fit(X_tr, Y_tr)
## テストデータにおける検証を行う．
Y_pred = lr.predict(X_te)
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
#混同行列
print('confusion matrix = \n', confusion_matrix(y_true=Y_te, y_pred=Y_pred))
#正確度
print('accuracy = ', accuracy_score(y_true=Y_te, y_pred=Y_pred))
#汎化誤差
print('汎化誤差 = ', 1-accuracy_score(y_true=Y_te, y_pred=Y_pred))
#適合率
#print('precision = ', precision_score(y_true=Y_te, y_pred=Y_pred))
#F値
print('f1 score = ', f1_score(y_true=Y_te, y_pred=Y_pred , average='micro'))
#python
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Basic Data Types in Python | Python Web Development For Beginners
At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Table of Contents hide
III Built-in data types in Python
Mutable objects
The Size and declared value and its sequence of the object can able to be modified called mutable objects.
Mutable Data Types are list, dict, set, byte array
Immutable objects
The Size and declared value and its sequence of the object can able to be modified.
Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.
id() and type() is used to know the Identity and data type of the object
a**=25+**85j
type**(a)**
output**:<class’complex’>**
b**={1:10,2:“Pinky”****}**
id**(b)**
output**:**238989244168
Built-in data types in Python
a**=str(“Hello python world”)****#str**
b**=int(18)****#int**
c**=float(20482.5)****#float**
d**=complex(5+85j)****#complex**
e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**
f**=tuple((“python”,“easy”,“learning”))****#tuple**
g**=range(10)****#range**
h**=dict(name=“Vidu”,age=36)****#dict**
i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**
j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**
k**=bool(18)****#bool**
l**=bytes(8)****#bytes**
m**=bytearray(8)****#bytearray**
n**=memoryview(bytes(18))****#memoryview**
Numbers (int,Float,Complex)
Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.
#signed interger
age**=**18
print**(age)**
Output**:**18
Python supports 3 types of numeric data.
int (signed integers like 20, 2, 225, etc.)
float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)
complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)
A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).
String
The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.
# String Handling
‘Hello Python’
#single (') Quoted String
“Hello Python”
# Double (") Quoted String
“”“Hello Python”“”
‘’‘Hello Python’‘’
# triple (‘’') (“”") Quoted String
In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.
The operator “+” is used to concatenate strings and “*” is used to repeat the string.
“Hello”+“python”
output**:****‘Hello python’**
"python "*****2
'Output : Python python ’
#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type
1626775355
Why use Python for Software Development
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
5 Reasons to Utilize Python for Programming Web Apps
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Robust frameworks
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Progressive applications
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Summary
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
#python development services #python development company #python app development #python development #python in web development #python software development
1624934525
Python I: Data Types and Operators, variable assignment, and print()
Learn the Python basics so that you can use it for your data science projects.
This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.
I. About Python 🐍
Created by Dutch programmer Guido van Rossum at Centrum Wiskunde & Informatica, Python made its debut in 1991. Over thirty years it has gained popularity earned a reputation of being the “Swiss army knife of programming languages.” Here are a few reasons why:
- A robust community has cultivated a wealth of free libraries and packages that allow Python to interface with technologies in different domains. Python is popular for data science, but you can also use it for web development, finance, designing games, and more.
- Python is also a multi-paradigm language — in other words it permits both structured/functional and object-oriented programming.
- As a high-level language the syntax is designed to be intuitive and easy to read. This feature makes it attractive to beginners as well as large companies like Instagram and Google.
In emerging fields like data science, artificial intelligence, and machine learning, a robust community, plenty of packages, paradigm flexibility, and syntactical simplicity, allow beginners and professionals to focus on insights and innovation.
#python3 #variables-in-python #data-types-in-python #operators-in-python #python #python i: data types and operators, variable assignment, and print()
1602968400
Python Tricks Every Developer Should Know
Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Let’s get started
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName
>>> print(FirstName, LastName)
('Jordan', 'kalebu')
#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development
1602666000
How to Remove all Duplicate Files on your Drive via Python
Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
Intro
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
- md5
- sha1
- sha224, sha256, sha384 and sha512
#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips