게시 됨 업데이트 됨python / theory35분안에 읽기 (약 5286 단어)

Regression in python(01)

Chepter 5 _파이썬 머신러닝 완벽 가이드

ref. & copyright(c) Book


회귀

  • Regression: 여러개의 독립변수와 한개의 종속변수 간의 상관관계를 모델링 하는 기법

  • Regression conefficients : 독립변수의 값에 영향을 미치는 회기 계수로 선형 회기 식의 기울기에 해당

  • 러닝머신의 관점

    • 독립변수 : 피처
    • 종속변수 : 결정값

= > 주어진 피처와 결정값 데이터 기반에서 학습을 통해 최적의 **회귀계수** 를 찾아 내는 것이 목표


✌지도학습 2가지 유형

  1. CLASSIFICATION + category, 이산 값 일때
  2. REGRESSION + 숫자, 연속 값 일때

⚡ 회귀의 4가지 유형

  1. 독립변수 개수 - 단일 회귀 - 다중 회귀
  2. 회귀 계수의 결합 - 선형 회귀 : 실제 값과 예측 값의 차이 (오류의 제곱값)를 최소화 하는 직선형 회귀선을 최적화 하는 방식
    • Regularization(규제방법) : 일반적 선형 회귀의 과적합 문제를 해결 하기 위해 회귀 계수를 조정 하는것 (패널티 값 적용) - 비선형 회귀
  • 일반선형회귀 : 예측값과 실제값의 RSS를 최소화 할 수 있도록 회귀계수 최적화 (Regularization X)
  • Ridge(릿지) : 선형 회귀 + L2 Regularization
    • L2 : 상대적으로 큰 회귀 계수 값의 예측 영향도를 감소 시키기 위해 회귀 계수값을 더 작게 만듦.
  • Lasso(라쏘) :
  • ElasticNet(엘라스틱넷) :
  • Rogistic Regression(로지스틱 회귀) :

Ref. scikit-learn

책에 나온 회귀들

딥러닝을 하고 싶다면 볼 것.


y = 4x + 6 + error 시뮬레이션 데이터 값 생성

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import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

np.random.seed(123) # 실험 재현성

X = 2 * np.random.rand(100, 1) # 100개의 랜덤값 만들기
y = 4 * X + 6 + np.random.rand(100, 1)

plt.scatter(X, y)

산점도 그래프 

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X.shape, y.shape
((100, 1), (100, 1))

경사하강법으로 최적의 기울기 찾기

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# w1과, w0를 업데이트할 w1_update, w0_update 값 반환

def get_weight_updates(w1, w0, X, y, learning_rate=0.01):
  N = len(y)

  # w1_update, w0_update 초기화
  w1_update = np.zeros_like(w1)
  w0_update = np.zeros_like(w0)

  # 예측 배열 계산하고, 예측값과 실젯 값의 차이 계산
  y_pred = np.dot(X, w1.T) + w0
  diff = y - y_pred # 실제갓, 예측값 == 오차

  # w0_update를 dot 행렬 연산으로 구하기 위해 모두 1 값을 가진 행렬 생성
  w0_factors = np.ones((N, 1))
  
  # w1과 w0을 업데이트할 w1_update, w0_update 계산 
  w1_update = -(2/N) * learning_rate * (np.dot(X.T, diff))
  w0_update = -(2/N) * learning_rate * (np.dot(w0_factors.T, diff))

  return w1_update, w0_update
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w0 = np.zeros((1, 1))
w1 = np.zeros((1, 1))

y_pred = np.dot(X, w1.T) + w0
diff = y-y_pred
print(diff.shape)

w0_factors = np.ones((100, 1))
w1_update = -(2/100) * 0.01 * (np.dot(X.T, diff))
w0_update = -(2/100) * 0.01 * (np.dot(w0_factors.T, diff))

print(w1_update.shape, w0_update.shape)
print(w1, w0)
(100, 1)
(1, 1) (1, 1)
[[0.]] [[0.]]

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# 입력 인자 반복문 코드 

def gradient_descent_steps(X, y, iters = 100000):

  # w0와 w1을 모두 0으로 초기화
  w0 = np.zeros((1, 1))
  w1 = np.zeros((1, 1))

  # iters 만큼 반복 수행 # get_weight_updates
  for ind in range(iters):
    w1_update, w0_update = get_weight_updates(w1, w0, X, y, learning_rate=0.01)
    w1 = w1 - w1_update
    w0 = w0 - w0_update
  
  return w1, w0
  • 예측 오차 비용 계산하는 함수 생성 및 경사 하강법 수행
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def get_cost(y, y_pred):
  N = len(y)

  cost = np.sum(np.square(y - y_pred)) / N 
  return cost 

w1, w0 = gradient_descent_steps(X, y, iters = 100000)
print("w1:{0:.4f}, w0:{1:.4f}".format(w1[0, 0], w0[0, 0]))

y_pred = w1[0,0] * X + w0
print("Total Cost:{0:.4f}".format(get_cost(y, y_pred)))
w1:3.9462, w0:6.5590
Total Cost:0.0803

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plt.scatter(X, y)
plt.plot(X, y_pred, color = "r")

산점도 그래프_Line

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import pandas as pd


bostonDF = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv")
bostonDF.head()

EDA

  • 종속변수가 기준, y값, medv
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import matplotlib.pyplot as plt
import seaborn as sns

fig, ax = plt.subplots(figsize = (16, 8), ncols = 4, nrows = 2)
lm_features = ["rm", "zn", "indus", "nox", "age", "ptratio", "lstat", "rad"]

for i, feature in enumerate(lm_features):
  row = int(i/4)
  col = i%4
  print("row is {}, col is {}".format(row, col))
  sns.regplot(x = feature, y = "medv", data = bostonDF, ax = ax[row][col])

Multi Graphes

  • 두 연속형 변수를 활용한 산점도나 회귀식 가능.
  • 박스플롯 (x: 명목형, y: medv)

rm 3.4
chas 3.0
rad 0.4
zn 0.1
b 0.0
tax -0.0
age 0.0
indus 0.0
crim -0.1
lstat -0.6
ptratio -0.9
dis -1.7
nox -19.8 


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from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression # model

y_target = bostonDF["medv"] # 종속변수, Y
X_data = bostonDF.drop(['medv', 'rad', 'zn', 'b', 'tax', 'age', 'indus', 'crim', 'lstat'], axis = 1, inplace = False) # 독립변수

y_target.shape, X_data.shape
((506,), (506, 5))

데이터셋 분리

  • 예측, 시뮬레이션, 가상의 데이터를 가지고 예측 & 시뮬레이션
  • 예측한 결괏값 vs 실젯값 비교
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# 임의 샘플링

X_train, X_test, y_train, y_test = train_test_split(X_data, y_target, test_size = 0.3, random_state=156)
X_train.shape, X_test.shape, y_train.shape, y_test.shape
((354, 5), (152, 5), (354,), (152,))

ML 모형 만들기

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lr = LinearRegression()
lr.fit(X_train, y_train)


y_preds = lr.predict(X_test)
y_preds
array([26.78074859, 16.40377991, 34.38443472, 19.13328473, 32.89690238,
       19.25298249, 28.32071818, 22.76654888,  9.87108567, 14.66339227,
       21.55844556, 17.27788854, 28.55574467, 38.50512646, 23.60848806,
       24.03347202, 23.82317119, 15.9119451 , 28.65132167, 20.98388455,
       20.29188703, 18.37003455, 18.58675839, 14.89143225, 35.24799305,
        7.70600921, 19.39133905, 15.97963635, 16.90296718, 15.484303  ,
       29.67753869, 17.58268684, 16.91992352, 22.47407959, 16.57706526,
       18.5381101 , 13.34337954, 24.11893098, 15.48185399, 24.3234222 ,
       36.24776797, 19.60882283, 20.95016211,  6.85667164, 20.32077896,
       23.05614583, 24.65371876, 35.25609168, 22.32959594, 25.96437918,
       27.29101785, 43.32992941, 41.76994078, 19.34288261, 24.8690423 ,
       25.99270875, 20.76285715, 33.13792328, 25.00439224, 16.82906893,
       22.80895172, 23.72489982, 24.53360315, 11.82722067, 17.55728132,
       37.43371362, 33.37256916, 25.65966256, 20.90725715, 21.09529467,
       15.22097444, 30.6234335 , 37.42143489, 26.22092177, 16.71532104,
       32.62735407, 23.41004013, 23.86575538, 18.75430877, 15.9914079 ,
       30.87778491, 16.04423898, 19.01496945, 20.04269634, 28.30832805,
       15.1948795 , 30.47430322, 33.93480059, 23.87721263, 29.7167635 ,
       29.85142798, 19.10737457, 28.49523963, 27.69846662, 25.49534489,
       24.59255802, 12.34870184, 26.65951587, 31.26197918, 17.86101862,
       27.3059424 , 18.18058484, 15.67184217, 13.17304165, 17.91281425,
       23.48894551, 24.53921273, 28.14530028, 16.05340908, 24.22120622,
       21.94517346, 26.62930956, 11.39298015, 18.53099857, 22.75407122,
       33.6679728 , 23.35342973, 20.85267956, 19.69347759, 28.12264641,
       28.56541499, 17.91759633, 27.83520695, 33.8011824 , 21.75436813,
       26.6360736 , 14.70682076, 19.99114889, 21.81029849, 31.72247354,
       21.33041025, 23.52438417, 35.55842163, 20.54294729, 38.34696416,
       19.25750865, 17.07595035, 18.31764392, 17.66658651, 23.12171447,
       19.58446231, 19.90774119, 14.84809066, 19.50652744, 38.83812958,
       15.26095952, 28.56874885, 17.62298514, 22.46794555, 23.28435884,
       18.8439135 , 31.16286012])

모형 평가

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from sklearn.metrics import mean_squared_error, r2_score
mse = mean_squared_error(y_test, y_preds)

print("MSE: {0:.3f}".format(mse))
MSE: 21.369

y = 상수값 + rm 기울기 x rm의 값 + 

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import numpy as np 

print("절편 값:", lr.intercept_) # 절편 값
print("회귀 계수값", np.round(lr.coef_, 1))

coeff_df = pd.Series(data=np.round(lr.coef_, 1), index = X_data.columns)
coeff_df.sort_values(ascending=False)
절편 값: 26.830373506191982
회귀 계수값 [  4.3 -33.1   6.5  -1.1  -1.2]





rm          6.5
chas        4.3
dis        -1.1
ptratio    -1.2
nox       -33.1
dtype: float64

아직 배우지 않았지만, 유용한 기능

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Installing collected packages: tangled-up-in-unicode, smmap, scipy, multimethod, joblib, websocket-client, visions, scikit-learn, requests, python-editor, Mako, imagehash, gitdb, querystring-parser, PyYAML, pynndescent, pydantic, prometheus-flask-exporter, phik, htmlmin, gunicorn, gitpython, funcy, docker, databricks-cli, alembic, umap-learn, scikit-plot, pyod, pyLDAvis, pandas-profiling, mlxtend, mlflow, lightgbm, kmodes, imbalanced-learn, Boruta, pycaret
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
Successfully installed Boruta-0.3 Mako-1.1.6 PyYAML-6.0 alembic-1.4.1 databricks-cli-0.16.2 docker-5.0.3 funcy-1.16 gitdb-4.0.9 gitpython-3.1.24 gunicorn-20.1.0 htmlmin-0.1.12 imagehash-4.2.1 imbalanced-learn-0.7.0 joblib-1.0.1 kmodes-0.11.1 lightgbm-3.3.1 mlflow-1.22.0 mlxtend-0.19.0 multimethod-1.6 pandas-profiling-3.1.0 phik-0.12.0 prometheus-flask-exporter-0.18.6 pyLDAvis-3.2.2 pycaret-2.3.5 pydantic-1.8.2 pynndescent-0.5.5 pyod-0.9.5 python-editor-1.0.4 querystring-parser-1.2.4 requests-2.26.0 scikit-learn-0.23.2 scikit-plot-0.3.7 scipy-1.5.4 smmap-5.0.0 tangled-up-in-unicode-0.1.0 umap-learn-0.5.2 visions-0.7.4 websocket-client-1.2.3

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from pycaret.utils import enable_colab
enable_colab()
Colab mode enabled.

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from pycaret.datasets import get_data
dataset = get_data('diamond')
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data = dataset.sample(frac=0.9, random_state=786)
data_unseen = dataset.drop(data.index)

data.reset_index(drop=True, inplace=True)
data_unseen.reset_index(drop=True, inplace=True)

print('Data for Modeling: ' + str(data.shape))
print('Unseen Data For Predictions: ' + str(data_unseen.shape))
Data for Modeling: (5400, 8)
Unseen Data For Predictions: (600, 8)

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from pycaret.regression import *
exp_reg101 = setup(data = data, target = 'Price', session_id=123)

Regression in python(01)

https://yoonhwa-p.github.io/2021/12/08/python/Rgression_py/

Author

YoonHwa

Posted on

2021-12-08

Updated on

2021-12-13

Licensed under

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