teen learn

최초 작성일 : 2021-12-08
categories: Python Machine Learning

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회귀, 지도학습, 회귀모델, 경사하강법 (비용 최소화하기), 'pycaret(파이 캐럿)',

평가지표, 'EDA'해보기, '데이터 셋 분리 -> ML 모형만들기 -> ML 모형 평가' 해보기

오늘은 큰 틀에서 봤을 때 이렇게 10가지에 알아보고 직접 해보았다.

회귀

- 회귀는 통계학에서 메인  - 유전적 특성  --> 부모(독립변수)의 키가 크더라도 자식(종속변수)의 키가 대를 이어서 무한정 커지지 않고,  부모의 키가 작더라도 자식의 키가 대를 이어서 무한정 작지는 않다.  

단순 선형 회귀

--> 단순선형회귀 (변수, feature, 컬럼명)  f(x)(y)(종속변수, 반응변) = 기울기(독립변수, 설명변수) 

회귀계수

model <- lm (판매량 ~ 광고비)  summary(model)  아파트 가격 <- 학군(A, 지역, 근처 지하철역의 갯수(얼마나 관계 있느냐??),   연도(신축, 구축), 방의 개수 (X), 아파트 단지 크기(X) ~ ~ ~ ~   -->  도메인 잘 활용  --> A(1), A(2), A(3), ... 독립변수의 값에 영향을 미치는 걸   회귀 계수 (Regression Coefficients)  아파트 가격 = A1의 회귀계수 * (등급) + A2의 회귀계수 * (지역) + A3 ~~ 

지도학습

--> 분류 (이산값) = Category (성공, 실패)  --> 수치 예측 (=회귀) / 연속값 (키, 몸무게, 판매량) 

선형 회귀 모델(13-14개)

--> 문제점: 오차합 (RSS) ==> 오차를 줄일수 있도록 최적화 할수 있는 규제(Regularization)  --> 전통적인 모형   --> 릿지(Ridge) + 라쏘(Lassso) = 엘라스틱넷(ElasticNet)  ----->   --> 로지스틱 회귀!! (비선형 문제) ****   - 오류합 계산 방법  1. 절대값을 취해서 더하는 방식   2. 오류값의 제곱을 구해서 더하는 방식 (RSS: Residual sum of squared)  ->  RSS(w0, w1) = 1/N  

비용최소화를 위해 경사하강법을 쓰는 경우

 예를 들어 조선소는 1년에 수주를   ---> 많으면 1건 (몇조원),  ---->    y 총 사업비용    3-4만개   2010년 행의 개수 1            10만원, 천만원,   2011년-1  2011년-2  2012년   그래서 비용 최소화하기 --> 경사하강법 (Gradient Descent) 

여기서 부터는 실습 코드들과 결과 화면이다.

• y = 4x + 6 + error 시뮬레이션 데이터 값 생성

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 랩덤값 만들기 y = 4 * X + 6 + np.random.rand(100, 1)  plt.scatter(X,y) 

<결과 화면>

<데이터 확인>

X_shape, y.shape 

<결과 확인>

((100, 1), (100, 1)) 

<경사하강법으로 최적의 기울기 찾기>

# 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 

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) 

<결과 화면>

(100, 1) (1, 1) (1, 1) [[0.]] [[0.]] 

<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>

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))) 

<결과 화면>

w1:3.9462, w0:6.5590 Total Cost:0.0803 

<산점도 그래프 코드>

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

<결과 화면>

<'paycaret' 설치 코드>

!pip install pycaret 

<결과 화면>

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/root/.cache/pip/wheels/3d/bb/b7/62b60fb451b33b0df1ab8006697fba7a6a49709a629055cf77   Building wheel for umap-learn (setup.py) ... done   Created wheel for umap-learn: filename=umap_learn-0.5.2-py3-none-any.whl size=82709 sha256=f4bae757148b4cf4930e495a816ecb3f6fcc3a16d1014c85ce052bb2acccb378   Stored in directory: /root/.cache/pip/wheels/84/1b/c6/aaf68a748122632967cef4dffef68224eb16798b6793257d82   Building wheel for pynndescent (setup.py) ... done   Created wheel for pynndescent: filename=pynndescent-0.5.5-py3-none-any.whl size=52603 sha256=e19d78d031a739792e30a3bf2d93865296b6eb66226835f66a8287b1330882f1   Stored in directory: /root/.cache/pip/wheels/af/e9/33/04db1436df0757c42fda8ea6796d7a8586e23c85fac355f476 Successfully built htmlmin imagehash alembic databricks-cli pyLDAvis pyod umap-learn pynndescent 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   Attempting uninstall: scipy     Found existing installation: scipy 1.4.1     Uninstalling scipy-1.4.1:       Successfully uninstalled scipy-1.4.1   Attempting uninstall: joblib     Found existing installation: joblib 1.1.0     Uninstalling joblib-1.1.0:       Successfully uninstalled joblib-1.1.0   Attempting uninstall: scikit-learn     Found existing installation: scikit-learn 1.0.1     Uninstalling scikit-learn-1.0.1:       Successfully uninstalled scikit-learn-1.0.1   Attempting uninstall: requests     Found existing installation: requests 2.23.0     Uninstalling requests-2.23.0:       Successfully uninstalled requests-2.23.0   Attempting uninstall: PyYAML     Found existing installation: PyYAML 3.13     Uninstalling PyYAML-3.13:       Successfully uninstalled PyYAML-3.13   Attempting uninstall: pandas-profiling     Found existing installation: pandas-profiling 1.4.1     Uninstalling pandas-profiling-1.4.1:       Successfully uninstalled pandas-profiling-1.4.1   Attempting uninstall: mlxtend     Found existing installation: mlxtend 0.14.0     Uninstalling mlxtend-0.14.0:       Successfully uninstalled mlxtend-0.14.0   Attempting uninstall: lightgbm     Found existing installation: lightgbm 2.2.3     Uninstalling lightgbm-2.2.3:       Successfully uninstalled lightgbm-2.2.3   Attempting uninstall: imbalanced-learn     Found existing installation: imbalanced-learn 0.8.1     Uninstalling imbalanced-learn-0.8.1:       Successfully uninstalled imbalanced-learn-0.8.1 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 

'pycaret'을 '구글코랩'에서 설치했기 때문에 이코드를 반드시 입력해서

'코랩 모드'로 바꿔줘야 된다.

<코랩 모드 코드>

from pycaret.utils import enable_colab enable_colab() 

<결과 화면>

Colab mode enabled. 

<파이캐롯 데이터 셋 확인 코드>

from pycaret.datasets import get_data dataset = get_data('diamond') 

<결과 화면>

아래 부터는 그냥 이어지는 '실습용 코드' 정도로 보면 된다.

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)) 

<결과 화면>

Data for Modeling: (5400, 8) Unseen Data For Predictions: (600, 8) 

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

<결과 화면>

 

 

best = compare_models(exclude = ['ransac']) 

<결과 화면>

• 사이킷런 LinearRegression 클래스

--> LinearRegression(fit_intercept = True, normalize = False)

평가지표

• MAE(Mean Absolute Error) 오차 차이를 절댓값으로 변환해 평균한 것
• MSE(Mean Squared Error) 오차 차이를 제곱해 평균한 것
• MSLE(MSE) 로그를 적용
• RMSE

=========

• R^2: 분산 기반으로 예측 성능을 평가 함. R2 - 예측값(분산) / 실제값 분산

====

• 사이킷런 회귀 평가 모형 함수
• 기본제공: MAE, MSE, R^2
• RMSE <-- 사용자 정의 함수 구현 및 활용

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

<결과 화면>

EDA

• 종속변수가 기준, y값, medv

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]) 

<결과 화면>

• 두 연속형 변수를 활용한 산점도 그리고, 회귀식 그리고
• 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 

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 

<결과 화면>

((506,), (506, 5)) 

데이터셋 분리

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

# 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 

<결과 화면>

((354, 5), (152, 5), (354,), (152,)) 

ML 모형 만들기

lr = LinearRegression() lr.fit(X_train, y_train)   y_preds = lr.predict(X_test) y_preds 

<결과 화면>

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

from sklearn.metrics import mean_squared_error, r2_score mse = mean_squared_error(y_test, y_preds)  print("MSE: {0:.3f}".format(mse)) 

<결과 화면>

MSE: 21.369 

y = 상수값 + rm 기울기 x rm의 값

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) 

<결과 화면>

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