Python（sklearn） - SVRのすべてのテストタプルに対して同じ予測が得られるのはなぜですか？

Question

同様の質問に対する答えは、インスタンスSVR（）のパラメータ値を変更することを示唆していますが、それらを処理する方法はわかりません。

ここで私が使用していたコードです：

import json 
import numpy as np 
from sklearn.svm import SVR 

f = open('training_data.txt','r') 
data = json.loads(f.read()) 
f.close() 

f = open('predict_py.txt','r') 
data1 = json.loads(f.read()) 
f.close() 

features = [] 
response = [] 
predict = [] 


for row in data: 
    a = [] 
    a.append(row['star_power']) 
    a.append(row['view_count']) 
    a.append(row['like_count']) 
    a.append(row['dislike_count']) 
    a.append(row['sentiment_score']) 
    a.append(row['holidays']) 
    a.append(row['clashes']) 

    features.append(a) 

    response.append(row['collection']) 


for row in data1: 
    a = [] 
    a.append(row['star_power']) 
    a.append(row['view_count']) 
    a.append(row['like_count']) 
    a.append(row['dislike_count']) 
    a.append(row['sentiment_score']) 
    a.append(row['holidays']) 
    a.append(row['clashes']) 

    predict.append(a) 



X = np.array(features) 


Y = np.array(response) 


predict = np.array(predict) 
predict = predict.astype(float) 
X = X.astype(float) 
Y = Y.astype(float) 

svm = SVR() 
svm.fit(X,Y) 
print("svm prediction") 
svm_pred = svm.predict(predict) 
print(svm_pred) 

Here're私はコード

training_data.txt

predict_py.txt

で使用している2つのテキストファイルへのリンクは、出力：

要求に応じて2つのテキストファイルのサンプルを追加する210

：

1）training_data.txt：

[{"star_power":"1300","view_count":"50602729","like_count":"348059","dislike_count":"31748","holidays":"1","clashes":"0","sentiment_score":"0.32938596491228","collection":"383"},{"star_power":"1700","view_count":"36012808","like_count":"205694","dislike_count":"20130","holidays":"0","clashes":"0","sentiment_score":"0.1130303030303","collection":"300.68"},{"star_power":"0","view_count":"23892902","like_count":"86380","dislike_count":"4426","holidays":"0","clashes":"0","sentiment_score":"0.16004079254079","collection":"188.72"},{"star_power":"0","view_count":"27177685","like_count":"374671","dislike_count":"10372","holidays":"0","clashes":"0","sentiment_score":"0.16032407407407","collection":"132.85"},{"star_power":"500","view_count":"7481738","like_count":"42734","dislike_count":"1885","holidays":"0","clashes":"0","sentiment_score":"0.38622493734336","collection":"128.45"},{"star_power":"400","view_count":"16895259","like_count":"99158","dislike_count":"4188","holidays":"0","clashes":"0","sentiment_score":"0.22791203703704","collection":"127.48"},{"star_power":"200","view_count":"16646480","like_count":"63472","dislike_count":"13652","holidays":"1","clashes":"1","sentiment_score":"0.16873480902778","collection":"112.14"},{"star_power":"400","view_count":"18717042","like_count":"67497","dislike_count":"14165","holidays":"0","clashes":"0","sentiment_score":"0.30881006493506","collection":"109.14"}] 

2）

[{"star_power":"0","view_count":"3717403","like_count":"13399","dislike_count":"909","sentiment_score":"0.154167","holidays":"0","clashes":"0"},{"star_power":"0","view_count":"1640896","like_count":"2923","dislike_count":"328","sentiment_score":"0.109112","holidays":"0","clashes":"0"},{"star_power":"100","view_count":"14723084","like_count":"95088","dislike_count":"9816","sentiment_score":"0.352344","holidays":"0","clashes":"0"},{"star_power":"0","view_count":"584922","like_count":"4032","dislike_count":"212","sentiment_score":"0.3495","holidays":"0","clashes":"0"},{"star_power":"0","view_count":"14826843","like_count":"94788","dislike_count":"4169","sentiment_score":"0.208472","holidays":"0","clashes":"0"},{"star_power":"0","view_count":"1866184","like_count":"2750","dislike_count":"904","sentiment_score":"0.1275","holidays":"0","clashes":"0"},{"star_power":"200","view_count":"22006916","like_count":"184780","dislike_count":"13796","sentiment_score":"0.183611","holidays":"0","clashes":"0"},{"star_power":"0","view_count":"2645992","like_count":"4698","dislike_count":"1874","sentiment_score":"0.185487","holidays":"0","clashes":"0"},{"star_power":"0","view_count":"13886030","like_count":"116879","dislike_count":"6608","sentiment_score":"0.243479","holidays":"0","clashes":"0"},{"star_power":"0","view_count":"3102123","like_count":"36790","dislike_count":"769","sentiment_score":"0.065651","holidays":"0","clashes":"0"},{"star_power":"300","view_count":"16469439","like_count":"110054","dislike_count":"17892","sentiment_score":"0.178432","holidays":"0","clashes":"0"},{"star_power":"0","view_count":"6353017","like_count":"81236","dislike_count":"2154","sentiment_score":"0.0480556","holidays":"0","clashes":"0"},{"star_power":"0","view_count":"8679597","like_count":"89531","dislike_count":"6923","sentiment_score":"0.152083","holidays":"0","clashes":"0"}] 

に任意の提案をpredict_py.txt？ ありがとうございます。

Answer 1

コードを変更してデータを標準化します。

from sklearn.preprocessing import RobustScaler 
rbX = RobustScaler() 
X = rbX.fit_transform(X) 

rbY = RobustScaler() 
Y = rbY.fit_transform(Y) 

はその後だけRBXに応じpredict変換、予測の時点でfit()

svm = SVR() 
svm.fit(X,Y) 

を行います。

svm_pred = svm.predict(rbX.transform(predict)) 

ここでsvm_predは標準化された形式です。予測されたYを正しい形式にしたいので、svm_predをrbYに従って逆変換します。

svm_pred = rbY.inverse_transform(svm_pred) 

次に、svm_predを印刷します。それは満足のいく結果を与えるでしょう。