Question

我即将使用Scikit-Learn中的支持向量回归来预测IMDB得分（电影费率）。问题是它总是为每个输入提供相同的预测结果。

当我预测使用数据训练时，它会给出不同的结果。但是在使用数据测试时，它总是给出相同的值。

数据培训预测：

Data training prediction http://image.prntscr.com/image/647c2a2db7c3419dbad364b340e2a49c.png

数据测试预测：

Data testing prediction http://image.prntscr.com/image/d4e2c8ff5d6447cfb73e888e79a897a5.png

我的代码：

import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sb
from sklearn import metrics as met


df = pd.read_csv("movie_metadata.csv")
df.head()


original = df.shape[0]
df = df.drop_duplicates(["movie_title"])
notDuplicated = df.shape[0]
df.reset_index(drop = True, inplace = True)
print(original, notDuplicated)


df["num_critic_for_reviews"].fillna(0, inplace = True)
df["num_critic_for_reviews"] = df["num_critic_for_reviews"].astype("int")

df["director_facebook_likes"].fillna(0, inplace = True)
df["director_facebook_likes"] = df["director_facebook_likes"].astype("int")

df["actor_3_facebook_likes"].fillna(0, inplace = True)
df["actor_3_facebook_likes"] = df["actor_3_facebook_likes"].astype(np.int64)

df["actor_2_facebook_likes"].fillna(0, inplace = True)
df["actor_2_facebook_likes"] = df["actor_2_facebook_likes"].astype(np.int64)

df["actor_1_facebook_likes"].fillna(0, inplace = True)
df["actor_1_facebook_likes"] = df["actor_1_facebook_likes"].astype(np.int64)

df["movie_facebook_likes"].fillna(0, inplace = True)
df["movie_facebook_likes"] = df["movie_facebook_likes"].astype(np.int64)

df["content_rating"].fillna("Not Rated", inplace = True)
df["content_rating"].replace('-', "Not Rated", inplace = True)
df["content_rating"] = df["content_rating"].astype("str")

df["imdb_score"].fillna(0.0, inplace = True)

df["title_year"].fillna(0, inplace = True)
df["title_year"].replace("NA", 0, inplace = True)
df["title_year"] = df["title_year"].astype("int")

df["genres"].fillna("", inplace = True)
df["genres"] = df["genres"].astype("str")


df2 = df[df["title_year"] >= 1980]
df2.reset_index(drop = True, inplace = True)

nRow = len(df2)
print("Number of data:", nRow)
nTrain = np.int64(np.floor(0.7 * nRow))
nTest = nRow - nTrain
print("Number of data training (70%):", nTrain, "\nNumber of data testing (30%):", nTest)

dataTraining = df2[0:nTrain]
dataTesting = df2[nTrain:nRow]
dataTraining.reset_index(drop = True, inplace = True)
dataTesting.reset_index(drop = True, inplace = True)


xTrain = dataTraining[["num_critic_for_reviews", "director_facebook_likes", "actor_3_facebook_likes", "actor_2_facebook_likes", "actor_1_facebook_likes", "movie_facebook_likes"]]
yTrain = dataTraining["imdb_score"]

xTest = dataTesting[["num_critic_for_reviews", "director_facebook_likes", "actor_3_facebook_likes", "actor_2_facebook_likes", "actor_1_facebook_likes", "movie_facebook_likes"]]
yTest = dataTesting["imdb_score"]

movieTitle = dataTesting["movie_title"].reset_index(drop = True)


from sklearn.svm import SVR

svrModel = SVR(kernel = "rbf", C = 1e3, gamma = 0.1, epsilon = 0.1)
svrModel.fit(xTrain,yTrain)


predicted = svrModel.predict(xTest)
[print(movieTitle[i], ":", predicted[i]) for i in range(10)]

Answer 1

将gamma从0.1更改为1e-8，同时保持其他所有内容相同。

当gamma设置为0.1时，唯一预测的数量为8，它们都接近6.37。当gamma设置为1e-8时，输出1366个唯一预测（xTest包含1368个总样本）。

为什么伽玛很重要？

直观地说，gamma参数定义了a的影响程度单个训练示例达到，低值意味着'远'和高值意味着'接近'。伽玛参数可以看作是由模型选择的样本的影响半径的倒数支持向量。

RBF SVM Parameters有更深层次的解释和示例。

这里也有类似的解释： Output of Scikit SVM in multiclass classification always gives same label

就个人而言，我会在脚本的底部使用GridSearchCV。以下是查找理想gamma和C值的示例：

from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV

#svrModel = SVR(kernel = "rbf", C = 1e3, gamma = 1e-8, epsilon = 0.1)
#svrModel.fit(xTrain,yTrain)


#predicted = svrModel.predict(xTest)
#[print(movieTitle[i], ":", predicted[i]) for i in range(10)]

#print('Unique predictions:', np.unique(predicted))

parameters = {
    "kernel": ["rbf"],
    "C": [1,10,10,100,1000],
    "gamma": [1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
    }

grid = GridSearchCV(SVR(), parameters, cv=5, verbose=2)
grid.fit(xTrain, yTrain)

Scikit-Learn SVR预测始终具有相同的价值

1 个答案: