给出先前Python的朴素贝叶斯分类图

Question

我需要绘制来自多元正态分布的600个样本（来自两个类别的300个），然后根据两个不同类别将这些样本绘制在散点图上。我使用以下代码成功执行了此操作：

dist7=np.random.multivariate_normal([3,2],[[1.5,0],[0,1.5]],300) #generated the samples
dist8=np.random.multivariate_normal([8,5],[[3,0],[0,3]],300)

class7=pd.DataFrame(dist7, columns=['x', 'y']) #put into a data frame
class8=pd.DataFrame(dist8, columns=['x', 'y'])

class7.insert(2, 'Class', 'class 7' ) #labeled the classes
class8.insert(2,'Class', 'class 8')

data4 = [class7, class8]
question4 = pd.concat(data4, ignore_index =True)

sns.scatterplot(question4['x'], question4['y'], hue=question4['Class'], palette=['b','r'])

现在，考虑到先前的分配比率为1：2，我需要绘制相似的图。我该怎么办？

Answer 1

很难告诉您您在这里做什么，但是我会刺中它。我认为您正在这里进行重采样练习。此外，故意不平衡您的数据似乎很愚蠢。通常人们希望拥有平衡的数据集！无论如何，直接查看此代码以查看如何对数据重新采样。

import numpy as np
import pandas as pd

df_train = pd.read_csv('C:\\Users\\ryans\\OneDrive\\Desktop\\Briefcase\\PDFs\\1-ALL PYTHON & R CODE SAMPLES\\Resample Imbalanced Datasets\\train.csv')

target_count = df_train.target.value_counts()
print('Class 0:', target_count[0])
print('Class 1:', target_count[1])
print('Proportion:', round(target_count[0] / target_count[1], 2), ': 1')

target_count.plot(kind='bar', title='Count (target)');

from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Remove 'id' and 'target' columns
labels = df_train.columns[2:]

X = df_train[labels]
y = df_train['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

model = XGBClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

model = XGBClassifier()
model.fit(X_train[['ps_calc_01']], y_train)
y_pred = model.predict(X_test[['ps_calc_01']])

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

from sklearn.metrics import confusion_matrix
from matplotlib import pyplot as plt

conf_mat = confusion_matrix(y_true=y_test, y_pred=y_pred)
print('Confusion matrix:\n', conf_mat)

labels = ['Class 0', 'Class 1']
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(conf_mat, cmap=plt.cm.Blues)
fig.colorbar(cax)
ax.set_xticklabels([''] + labels)
ax.set_yticklabels([''] + labels)
plt.xlabel('Predicted')
plt.ylabel('Expected')
plt.show()

# Class count
count_class_0, count_class_1 = df_train.target.value_counts()

# Divide by class
df_class_0 = df_train[df_train['target'] == 0]
df_class_1 = df_train[df_train['target'] == 1]

# Random under-sampling
df_class_0_under = df_class_0.sample(count_class_1)
df_test_under = pd.concat([df_class_0_under, df_class_1], axis=0)

print('Random under-sampling:')
print(df_test_under.target.value_counts())

df_test_under.target.value_counts().plot(kind='bar', title='Count (target)');


df_class_1_over = df_class_1.sample(count_class_0, replace=True)
df_test_over = pd.concat([df_class_0, df_class_1_over], axis=0)

print('Random over-sampling:')
print(df_test_over.target.value_counts())

df_test_over.target.value_counts().plot(kind='bar', title='Count (target)');


import imblearn

# For ease of visualization, let's create a small unbalanced sample dataset using the make_classification method:
from sklearn.datasets import make_classification

X, y = make_classification(
    n_classes=2, class_sep=1.5, weights=[0.9, 0.1],
    n_informative=3, n_redundant=1, flip_y=0,
    n_features=20, n_clusters_per_class=1,
    n_samples=100, random_state=10
)

df = pd.DataFrame(X)
df['target'] = y
df.target.value_counts().plot(kind='bar', title='Count (target)');


def plot_2d_space(X, y, label='Classes'):   
    colors = ['#1F77B4', '#FF7F0E']
    markers = ['o', 's']
    for l, c, m in zip(np.unique(y), colors, markers):
        plt.scatter(
            X[y==l, 0],
            X[y==l, 1],
            c=c, label=l, marker=m
        )
    plt.title(label)
    plt.legend(loc='upper right')
    plt.show()

from sklearn.decomposition import PCA

pca = PCA(n_components=2)
X = pca.fit_transform(X)

plot_2d_space(X, y, 'Imbalanced dataset (2 PCA components)')


from imblearn.under_sampling import RandomUnderSampler

rus = RandomUnderSampler(return_indices=True)
X_rus, y_rus, id_rus = rus.fit_sample(X, y)

print('Removed indexes:', id_rus)

plot_2d_space(X_rus, y_rus, 'Random under-sampling')

from imblearn.over_sampling import RandomOverSampler

ros = RandomOverSampler()
X_ros, y_ros = ros.fit_sample(X, y)

print(X_ros.shape[0] - X.shape[0], 'new random picked points')

plot_2d_space(X_ros, y_ros, 'Random over-sampling')


from imblearn.under_sampling import TomekLinks

tl = TomekLinks(return_indices=True, ratio='majority')
X_tl, y_tl, id_tl = tl.fit_sample(X, y)

print('Removed indexes:', id_tl)

plot_2d_space(X_tl, y_tl, 'Tomek links under-sampling')

from imblearn.under_sampling import ClusterCentroids

cc = ClusterCentroids(ratio={0: 10})
X_cc, y_cc = cc.fit_sample(X, y)

plot_2d_space(X_cc, y_cc, 'Cluster Centroids under-sampling')

from imblearn.over_sampling import SMOTE

smote = SMOTE(ratio='minority')
X_sm, y_sm = smote.fit_sample(X, y)

plot_2d_space(X_sm, y_sm, 'SMOTE over-sampling')


from imblearn.combine import SMOTETomek

smt = SMOTETomek(ratio='auto')
X_smt, y_smt = smt.fit_sample(X, y)

plot_2d_space(X_smt, y_smt, 'SMOTE + Tomek links')


# reference:
# https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets

# data set:
# https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/data