from sklearn.model_selection import TimeSeriesSplit
from sklearn.naive_bayes import GaussianNB
from yellowbrick.classifier import ClassificationReport
from yellowbrick.datasets import load_occupancy
# Load the classification dataset
X, y = load_occupancy()# Specify the target classes
classes =["unoccupied","occupied"]# Create the training and test data# 建立时序数据
tscv = TimeSeriesSplit()for train_index, test_index in tscv.split(X):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]# Instantiate the classification model and visualizer# 建立分类器
model = GaussianNB()# support 表示是否添加support方块格中显示数据。{True，False，None，'percent'，'count'}，默认值：无# support-'percent'表示百分比显示数据，'count'表示按个数显示数据
visualizer = ClassificationReport(model, classes=classes, support=True)
visualizer.fit(X_train, y_train)# Fit the visualizer and the model
visualizer.score(X_test, y_test)# Evaluate the model on the test data
visualizer.show();# Finalize and show the figure
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/model_selection/_split.py:752: FutureWarning: You should specify a value for 'n_splits' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.
warnings.warn(NSPLIT_WARNING, FutureWarning)
from sklearn.model_selection import TimeSeriesSplit
from sklearn.naive_bayes import GaussianNB
from yellowbrick.datasets import load_occupancy
from yellowbrick.classifier import classification_report
# Load the classification data set
X, y = load_occupancy()# Specify the target classes
classes =["unoccupied","occupied"]# Create the training and test data
tscv = TimeSeriesSplit()for train_index, test_index in tscv.split(X):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]# Instantiate the visualizer
visualizer = classification_report(
GaussianNB(), X_train, y_train, X_test, y_test, classes=classes, support=True);
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/model_selection/_split.py:752: FutureWarning: You should specify a value for 'n_splits' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.
warnings.warn(NSPLIT_WARNING, FutureWarning)
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split as tts
from sklearn.linear_model import LogisticRegression
from yellowbrick.classifier import ConfusionMatrix
# We'll use the handwritten digits data set from scikit-learn.# Each feature of this dataset is an 8x8 pixel image of a handwritten number.# Digits.data converts these 64 pixels into a single array of features#我们将使用scikit learn中的手写数字数据集。#该数据集的每个特征都是手写数字的8x8像素图像。# Digits.data 将这64个像素转换为一个维度数组
digits = load_digits()
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = tts(X, y, test_size =0.2, random_state=11)
X_test.shape,y_test.shape
model = LogisticRegression(multi_class="auto", solver="liblinear")# The ConfusionMatrix visualizer taxes a model# 混淆矩阵分类号
cm = ConfusionMatrix(model, classes=[0,1,2,3,4,5,6,7,8,9])# Fit fits the passed model. This is unnecessary if you pass the visualizer a pre-fitted model
cm.fit(X_train, y_train)# To create the ConfusionMatrix, we need some test data. Score runs predict() on the data# and then creates the confusion_matrix from scikit-learn.#为了创建ConfusionMatrix，我们需要一些测试数据。对数据执行Score runs predict（）然后从scikit learn创建混淆矩阵。
cm.score(X_test, y_test)# 图中显示的每一类的个数
cm.show();
from yellowbrick.datasets import load_credit
from yellowbrick.classifier import confusion_matrix
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split as tts
#Load the classification dataset
X, y = load_credit()#Create the train and test data
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)# Instantiate the visualizer with the classification model
confusion_matrix(
LogisticRegression(),
X_train, y_train, X_test, y_test,
classes=['not_defaulted','defaulted']);
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from yellowbrick.classifier import ROCAUC
from yellowbrick.datasets import load_spam
# Load the classification dataset
X, y = load_spam()# Create the training and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)# Instantiate the visualizer with the classification model# 初始分类器
model = LogisticRegression(multi_class="auto", solver="liblinear")# 绘制ROCAUC
visualizer = ROCAUC(model, classes=["not_spam","is_spam"])
visualizer.fit(X_train, y_train)# Fit the training data to the visualizer
visualizer.score(X_test, y_test)# Evaluate the model on the test data
visualizer.show();# Finalize and show the figure
from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder, LabelEncoder
from yellowbrick.classifier import ROCAUC
from yellowbrick.datasets import load_game
# Load multi-class classification dataset
X, y = load_game()# Encode the non-numeric columns
X = OrdinalEncoder().fit_transform(X)
y = LabelEncoder().fit_transform(y)# Create the train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)# Instaniate the classification model and visualizer
model = RidgeClassifier()# 多类
visualizer = ROCAUC(model, classes=["win","loss","draw"])
visualizer.fit(X_train, y_train)# Fit the training data to the visualizer
visualizer.score(X_test, y_test)# Evaluate the model on the test data
visualizer.show();# Finalize and render the figure
from yellowbrick.classifier.rocauc import roc_auc
from yellowbrick.datasets import load_credit
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
#Load the classification dataset
X, y = load_credit()#Create the train and test data
X_train, X_test, y_train, y_test = train_test_split(X,y)# Instantiate the visualizer with the classification model
model = LogisticRegression()
roc_auc(model, X_train, y_train, X_test=X_test, y_test=y_test, classes=['not_defaulted','defaulted']);
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import train_test_split as tts
from yellowbrick.classifier import PrecisionRecallCurve
from yellowbrick.datasets import load_spam
# Load the dataset and split into train/test splits
X, y = load_spam()
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, shuffle=True)# Create the visualizer, fit, score, and show it
viz = PrecisionRecallCurve(RidgeClassifier())
viz.fit(X_train, y_train)
viz.score(X_test, y_test)
viz.show();
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
from sklearn.model_selection import train_test_split as tts
from yellowbrick.classifier import PrecisionRecallCurve
from yellowbrick.datasets import load_game
# Load dataset and encode categorical variables
X, y = load_game()
X = OrdinalEncoder().fit_transform(X)
y = LabelEncoder().fit_transform(y)
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, shuffle=True)# Create the visualizer, fit, score, and show it
viz = PrecisionRecallCurve(RandomForestClassifier(n_estimators=10))
viz.fit(X_train, y_train)
viz.score(X_test, y_test)
viz.show();
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
from sklearn.model_selection import train_test_split as tts
from yellowbrick.classifier import PrecisionRecallCurve
from yellowbrick.datasets import load_game
# Load dataset and encode categorical variables
X, y = load_game()
X = OrdinalEncoder().fit_transform(X)
encoder = LabelEncoder()
y = encoder.fit_transform(y)
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, shuffle=True)# Create the visualizer, fit, score, and show it
viz = PrecisionRecallCurve(
MultinomialNB(), per_class=True, iso_f1_curves=True,
fill_area=False, micro=False, classes=encoder.classes_
)
viz.fit(X_train, y_train)
viz.score(X_test, y_test)
viz.show();
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
from sklearn.model_selection import train_test_split as tts
from yellowbrick.classifier import PrecisionRecallCurve
from yellowbrick.datasets import load_game
# Load dataset and encode categorical variables
X, y = load_game()
X = OrdinalEncoder().fit_transform(X)
encoder = LabelEncoder()
y = encoder.fit_transform(y)
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, shuffle=True)# Create the visualizer, fit, score, and show it
viz = PrecisionRecallCurve(
MultinomialNB(), per_class=True, iso_f1_curves=True,
fill_area=False, micro=False, classes=encoder.classes_
)
viz.fit(X_train, y_train)
viz.score(X_test, y_test)
viz.show();
from yellowbrick.datasets import load_game
from yellowbrick.target import ClassBalance
# Load the classification dataset
X, y = load_game()# Instantiate the visualizer
visualizer = ClassBalance(labels=["draw","loss","win"])
visualizer.fit(y)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
from sklearn.model_selection import TimeSeriesSplit
from yellowbrick.datasets import load_occupancy
from yellowbrick.target import ClassBalance
# Load the classification dataset
X, y = load_occupancy()# Create the training and test data
tscv = TimeSeriesSplit()for train_index, test_index in tscv.split(X):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]# Instantiate the visualizer
visualizer = ClassBalance(labels=["unoccupied","occupied"])
visualizer.fit(y_train, y_test)# Fit the data to the visualizer
visualizer.show();
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/model_selection/_split.py:752: FutureWarning: You should specify a value for 'n_splits' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.
warnings.warn(NSPLIT_WARNING, FutureWarning)
from yellowbrick.datasets import load_game
from yellowbrick.target import class_balance
# Load the dataset
X, y = load_game()# Use the quick method and immediately show the figure
class_balance(y);
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from yellowbrick.classifier import ClassPredictionError
# Create classification dataset
X, y = make_classification(
n_samples=1000, n_classes=5, n_informative=3, n_clusters_per_class=1,
random_state=36,)
classes =["apple","kiwi","pear","banana","orange"]# Perform 80/20 training/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20,
random_state=42)# Instantiate the classification model and visualizer
visualizer = ClassPredictionError(
RandomForestClassifier(random_state=42, n_estimators=10), classes=classes
)# Fit the training data to the visualizer
visualizer.fit(X_train, y_train)# Evaluate the model on the test data
visualizer.score(X_test, y_test)# Draw visualization
visualizer.show();
from sklearn.ensemble import RandomForestClassifier
from yellowbrick.classifier import ClassPredictionError
from yellowbrick.datasets import load_credit
X, y = load_credit()
classes =['account in default','current with bills']# Perform 80/20 training/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20,
random_state=42)# Instantiate the classification model and visualizer
visualizer = ClassPredictionError(
RandomForestClassifier(n_estimators=10), classes=classes
)# Fit the training data to the visualizer
visualizer.fit(X_train, y_train)# Evaluate the model on the test data
visualizer.score(X_test, y_test)# Draw visualization
visualizer.show();
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split as tts
from yellowbrick.classifier import class_prediction_error
from yellowbrick.datasets import load_occupancy
# Load the dataset and split into train/test splits
X, y = load_occupancy()
X_train, X_test, y_train, y_test = tts(
X, y, test_size=0.2, shuffle=True)
class_prediction_error(
LinearSVC(random_state=42),
X_train, y_train, X_test, y_test,
classes=["vacant","occupied"]);
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
"the number of iterations.", ConvergenceWarning)
from sklearn.linear_model import LogisticRegression
from yellowbrick.classifier import DiscriminationThreshold
from yellowbrick.datasets import load_spam
# Load a binary classification dataset
X, y = load_spam()# Instantiate the classification model and visualizer
model = LogisticRegression(multi_class="auto", solver="liblinear")
visualizer = DiscriminationThreshold(model)
visualizer.fit(X, y)# Fit the data to the visualizer
visualizer.show();# Finalize and render the figure
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
"the number of iterations.", ConvergenceWarning)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
"the number of iterations.", ConvergenceWarning)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
"the number of iterations.", ConvergenceWarning)
from yellowbrick.classifier.threshold import discrimination_threshold
from yellowbrick.datasets import load_occupancy
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
#Load the classification dataset
X, y = load_occupancy()# Instantiate the visualizer with the classification model
model = KNeighborsClassifier(3)# exclude设定不显示的指标# argmax阈值评价的指标，可选'precision','recall','fscore',None,默认'f1score'
discrimination_threshold(model, X, y,exclude='queue_rate', argmax='precision');
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I have centos 6.3 minimal running as web server. I’m looking to install gui to my server to vnc to my server. You can insta
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