import numpy as np
from yellowbrick.datasets import load_energy
from yellowbrick.model_selection import ValidationCurve
from sklearn.tree import DecisionTreeRegressor
# Load a regression dataset
X, y = load_energy()# param_name参数名称# param_range参数范围# cv交叉认证指定折数# scoring评价指标
viz = ValidationCurve(
DecisionTreeRegressor(), param_name="max_depth",
param_range=np.arange(1,11), cv=10, scoring="r2")# Fit and show the visualizer
viz.fit(X, y)
viz.show();
import numpy as np
from yellowbrick.datasets import load_energy
from yellowbrick.model_selection import validation_curve
from sklearn.tree import DecisionTreeRegressor
# Load a regression dataset
X, y = load_energy()
viz = validation_curve(
DecisionTreeRegressor(), X, y, param_name="max_depth",
param_range=np.arange(1,11), cv=10, scoring="r2",)
import numpy as np
from sklearn.model_selection import StratifiedKFold
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from yellowbrick.datasets import load_game
from yellowbrick.model_selection import LearningCurve
# Load a classification dataset
X, y = load_game()# Encode the categorical data
X = OneHotEncoder().fit_transform(X)
y = LabelEncoder().fit_transform(y)# Create the learning curve visualizer
cv = StratifiedKFold(n_splits=12)
sizes = np.linspace(0.3,1.0,10)# Instantiate the classification model and visualizer
model = MultinomialNB()
visualizer = LearningCurve(
model, cv=cv, scoring='f1_weighted', train_sizes=sizes, n_jobs=4)
visualizer.fit(X, y)# Fit the data to the visualizer
visualizer.show();
from sklearn.linear_model import RidgeCV
from yellowbrick.datasets import load_energy
from yellowbrick.model_selection import LearningCurve
# Load a regression dataset
X, y = load_energy()# Instantiate the regression model and visualizer
model = RidgeCV()
visualizer = LearningCurve(model, scoring='r2')
visualizer.fit(X, y)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from yellowbrick.model_selection import LearningCurve
# Generate synthetic dataset with 5 random clusters
X, y = make_blobs(n_samples=1000, centers=5, random_state=42)# Instantiate the clustering model and visualizer
model = KMeans()
visualizer = LearningCurve(model, scoring="adjusted_rand_score", random_state=42)
visualizer.fit(X, y)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
from sklearn.linear_model import RidgeCV
from yellowbrick.datasets import load_energy
from yellowbrick.model_selection import learning_curve
# Load a regression dataset
X, y = load_energy()
learning_curve(RidgeCV(), X, y, scoring='r2');
from sklearn.model_selection import StratifiedKFold
from sklearn.naive_bayes import MultinomialNB
from yellowbrick.datasets import load_occupancy
from yellowbrick.model_selection import CVScores
# Load the classification dataset
X, y = load_occupancy()# Create a cross-validation strategy# 浇查验证策略
cv = StratifiedKFold(n_splits=12, random_state=42)# Instantiate the classification model and visualizer
model = MultinomialNB()# 交叉验证获得分数
visualizer = CVScores(model, cv=cv, scoring='f1_weighted')
visualizer.fit(X, y)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
from sklearn.linear_model import Ridge
from sklearn.model_selection import KFold
from yellowbrick.datasets import load_energy
from yellowbrick.model_selection import CVScores
# Load the regression dataset
X, y = load_energy()# Instantiate the regression model and visualizer
cv = KFold(n_splits=12, random_state=42)
model = Ridge()
visualizer = CVScores(model, cv=cv, scoring='r2')
visualizer.fit(X, y)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
from sklearn.linear_model import Ridge
from sklearn.model_selection import KFold
from yellowbrick.datasets import load_energy
from yellowbrick.model_selection import cv_scores
# Load the regression dataset
X, y = load_energy()# Instantiate the regression model and visualizer
cv = KFold(n_splits=12, random_state=42)
model = Ridge()
visualizer = cv_scores(model, X, y, cv=cv, scoring='r2');
from sklearn.ensemble import RandomForestClassifier
from yellowbrick.datasets import load_occupancy
from yellowbrick.model_selection import FeatureImportances
# Load the classification data set
X, y = load_occupancy()
model = RandomForestClassifier(n_estimators=10)# 显示特征对于分类的重要程度
viz = FeatureImportances(model)
viz.fit(X, y)
viz.show();
from sklearn.linear_model import Lasso
from yellowbrick.datasets import load_concrete
from yellowbrick.model_selection import FeatureImportances
# Load the regression dataset
dataset = load_concrete(return_dataset=True)
X, y = dataset.to_data()# Title case the feature for better display and create the visualizer
labels =list(map(lambda s: s.title(), dataset.meta['features']))
viz = FeatureImportances(Lasso(), labels=labels, relative=False)# Fit and show the feature importances
viz.fit(X, y)
viz.show();
from yellowbrick.model_selection import FeatureImportances
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
data = load_iris()
X, y = data.data, data.target
model = LogisticRegression(multi_class="auto", solver="liblinear")
viz = FeatureImportances(model, stack=True, relative=False)
viz.fit(X, y)
viz.show();
4.3 快速方法
可以通过关联的快速方法feature_importances实现上述相同功能。此方法将FeatureImportances使用关联的参数构建对象，将其拟合，然后 from sklearn.ensemble import AdaBoostClassifier（可选）立即显示它。
from sklearn.ensemble import AdaBoostClassifier
from yellowbrick.datasets import load_occupancy
from yellowbrick.model_selection import feature_importances
# Load dataset
X, y = load_occupancy()# Use the quick method and immediately show the figure
feature_importances(AdaBoostClassifier(), X, y);
from sklearn.svm import SVC
from sklearn.datasets import make_classification
from yellowbrick.model_selection import RFECV
# Create a dataset with only 3 informative features# 只有三个有效特征
X, y = make_classification(
n_samples=1000, n_features=25, n_informative=3, n_redundant=2,
n_repeated=0, n_classes=8, n_clusters_per_class=1, random_state=0)# Instantiate RFECV visualizer with a linear SVM classifier
visualizer = RFECV(SVC(kernel='linear', C=1))
visualizer.fit(X, y)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedKFold
from yellowbrick.model_selection import RFECV
from yellowbrick.datasets import load_credit
# Load classification dataset
X, y = load_credit()
cv = StratifiedKFold(2)
visualizer = RFECV(RandomForestClassifier(), cv=cv, scoring='f1_weighted')
visualizer.fit(X, y)# Fit the data to the visualizer
visualizer.show()# Finalize and render the figure
继承 extends 多态
继承是面向对象最经常使用的特征之一：继承语法是通过继承发、基类的域和方法 //继承就是从现有的类中生成一个新的类，这个新类拥有现有类的所有extends是使用继承的关键字：
在A类中定义属性和方法；
class A{
//定义属性
int age；
//定义方法
public void go
hive DDL语法汇总
１、对表重命名
hive> ALTER TABLE table_name RENAME TO new_table_name;
2、修改表备注
hive> ALTER TABLE table_name SET TBLPROPERTIES ('comment' = new_comm
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