堆栈内存溢出
  繁体   English   中英

SVM在我的数据中提供了不好的结果。 怎么修？

[英]SVM provided a bad result in my data. How to fix?

 原文  2018-03-04 10:11:39  0   3    python/ machine-learning/ scikit-learn/ svm/ linear-regression

问题描述

我有一个数据集，其中包含510用于训练的样本和127用于测试的样本，每个样本都有7680特征。 我想设计一个模型来根据训练数据预测身高（cm）-标签。 目前，我使用了SVM，但结果却非常糟糕。 您能否看一下我的代码并给我一些评论。 您可以使用数据集和可运行的代码在计算机上尝试 

import numpy as np
from sklearn.svm import SVR

# Training Data
train_X = np.loadtxt('trainX.txt') # 510 x 7680
train_Y = np.loadtxt('trainY.txt') # 510 x 1
test_X = np.loadtxt('testX.txt')   # 127 x 7680
test_Y = np.loadtxt('testY.txt')   # 127 x 1

my_svr = SVR(C=1000, epsilon=0.2)
my_svr.fit(train_X,train_Y)

p_regression = my_svr.predict(test_X)
print (p_regression)
print (test_Y)

一些结果：

p_regression 

[15.67367165 16.35094166 13.10510262 14.03943211 12.7116549  11.45071423
 13.27225207  9.44959181 10.45775627 13.23953143 14.95568324 11.35994414
 10.69531821 12.42556347 14.54712287 12.25965911  9.04101931 14.03604126
 12.41237627 13.51951317 10.36302674  9.86389635 11.41448842 15.67146184
 14.74764672 11.22794536 12.04429175 12.48199183 14.29790809 16.21724184
 10.94478135  9.68210872 14.8663311   8.62974573 15.17281425 12.97230127
  9.46515876 16.24388177 10.35742683 15.65336366 11.04652502 16.35094166
 14.03943211 10.29066405 13.27225207  9.44959181 10.45775627 13.23953143
 14.95568324 11.35994414 10.69531821 12.42556347 14.54712287 12.25965911
  9.04101931 14.03604126 12.41237627 13.51951317 10.36302674  9.86389635
 11.41448842 15.67146184 14.74764672 11.22794536 12.04429175 12.48199183
 14.29790809 16.21724184 10.94478135  9.68210872 14.8663311   8.62974573
 15.17281425 12.97230127  9.46515876 16.24388177 10.35742683 15.65336366
 11.04652502 16.35094166 14.03943211 10.29066405 13.27225207  9.44959181
 10.45775627 13.23953143 14.95568324 11.35994414 10.69531821 12.42556347
 14.54712287 12.25965911  9.04101931 14.03604126 12.41237627 13.51951317
 10.36302674  9.86389635 11.41448842 15.67146184 14.74764672 11.22794536
 12.04429175 12.48199183 14.29790809 16.21724184 10.94478135  9.68210872
 14.8663311   8.62974573 15.17281425 12.97230127  9.46515876 16.24388177
 10.35742683 15.65336366 11.04652502 16.35094166 14.03943211 10.29066405
 13.27225207  9.44959181 10.45775627 13.23953143 14.95568324 11.35994414
 10.69531821]

test_Y 

[13. 14. 13. 15. 15. 17. 13. 17. 16. 12. 17.  6.  4.  3.  4.  6.  6.  8.
  9. 18.  3.  6.  4.  6.  7.  8. 11. 11. 13. 12. 12. 14. 13. 12. 15. 15.
 16. 15. 17. 18. 17. 14. 15. 17. 13. 17. 16. 12. 17.  6.  4.  3.  4.  6.
  6.  8.  9. 18.  3.  6.  4.  6.  7.  8. 11. 11. 13. 12. 12. 14. 13. 12.
 15. 15. 16. 15. 17. 18. 17. 14. 15. 17. 13. 17. 16. 12. 17.  6.  4.  3.
  4.  6.  6.  8.  9. 18.  3.  6.  4.  6.  7.  8. 11. 11. 13. 12. 12. 14.
 13. 12. 15. 15. 16. 15. 17. 18. 17. 14. 15. 17. 13. 17. 16. 12. 17.  6.
  4.]

3 个解决方案

解决方案1
 1  2018-03-04 13:02:17

我同意@George- "there is something "wrong" with the test set" 。 我也得到了类似的MSE结果-大约。 21。

我还尝试将训练和测试数据集放在一起，并将其提供给GridSearchCV。

这些尝试的结果如下： 

In [33]: print_grid_results(grid)
----------------------------- [SVR_rbf] ------------------------------
Score:          48.98%
Parameters:     {'SVR_rbf__C': 5, 'SVR_rbf__max_iter': 500}
**********************************************************************
---------------------------- [SVR_linear] ----------------------------
Score:          64.07%
Parameters:     {'SVR_linear__C': 0.1, 'SVR_linear__max_iter': 500}
**********************************************************************
------------------------------ [Ridge] -------------------------------
Score:          63.98%
Parameters:     {'Ridge__alpha': 100, 'Ridge__max_iter': 200}
**********************************************************************
------------------------------ [Lasso] -------------------------------
Score:          60.36%
Parameters:     {'Lasso__alpha': 0.001, 'Lasso__max_iter': 1000}
**********************************************************************
--------------------------- [RandomForest] ---------------------------
Score:          44.01%
Parameters:     {'RandomForest__max_depth': 5, 'RandomForest__n_estimators': 100}
**********************************************************************

同样，不同的分组也会给出非常不同的测试分数： 

In [43]: clf = grid['SVR_linear']

In [44]: {k:v for k,v in clf.cv_results_.items() if k.endswith('_test_score')}
Out[44]:
{'mean_test_score': array([0.64067998, 0.63919104, 0.6391681 , 0.64067998, 0.63919104, 0.6391681 , 0.64067998, 0.63919104, 0.6391681 ]),
 'rank_test_score': array([1, 4, 7, 1, 4, 7, 1, 4, 7]),
 'split0_test_score': array([0.98557453, 0.98876705, 0.98883802, 0.98557453, 0.98876705, 0.98883802, 0.98557453, 0.98876705, 0.98883802]),
 'split1_test_score': array([0.69915178, 0.69750946, 0.69740475, 0.69915178, 0.69750946, 0.69740475, 0.69915178, 0.69750946, 0.69740475]),
 'split2_test_score': array([0.23568677, 0.22964765, 0.22961214, 0.23568677, 0.22964765, 0.22961214, 0.23568677, 0.22964765, 0.22961214]),
 'std_test_score': array([0.30903146, 0.31275403, 0.31278954, 0.30903146, 0.31275403, 0.31278954, 0.30903146, 0.31275403, 0.31278954])}

这是完整的代码： 

import os
#import contextlib
from pathlib import Path
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import SGDRegressor, Ridge, Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.externals import joblib


def get_data_split(path='.'):
    p = Path(path)
    kwargs = dict(delim_whitespace=True, header=None)
    X_train = pd.read_csv(list(p.glob('trainX.txt*'))[0], **kwargs)
    y_train = pd.read_csv(list(p.glob('trainY.txt*'))[0], **kwargs)
    X_test = pd.read_csv(list(p.glob('testX.txt*'))[0], **kwargs)
    y_test = pd.read_csv(list(p.glob('testY.txt*'))[0], **kwargs)
    return X_train, y_train[0], X_test, y_test[0]


def get_data(path='.'):
    p = Path(path)
    kwargs = dict(delim_whitespace=True, header=None)
    X_train = pd.read_csv(list(p.glob('trainX.txt*'))[0], **kwargs)
    y_train = pd.read_csv(list(p.glob('trainY.txt*'))[0], **kwargs)
    X_test = pd.read_csv(list(p.glob('testX.txt*'))[0], **kwargs)
    y_test = pd.read_csv(list(p.glob('testY.txt*'))[0], **kwargs)
    return (pd.concat([X_train, X_test], ignore_index=True),
            pd.concat([y_train, y_test], ignore_index=True)[0])


def fit_all_classifiers_grid(X, y, classifiers, **common_grid_kwargs):
    grids = {}
    for clf in classifiers:
        print('{:-^70}'.format(' [' + clf['name'] + '] '))
        pipe = Pipeline([
                    ("scale", StandardScaler()),
                    (clf['name'], clf['clf'])   ])
        grids[clf['name']] = (GridSearchCV(pipe,
                                           param_grid=clf['param_grid'],
                                           **common_grid_kwargs)
                                  .fit(X, y))
        # saving single trained model ...
        joblib.dump(grids[clf['name']], './{}.pkl'.format(clf['name']))
    return grids


def test_dataset(grid, X_test, y_test):
    res = {}
    for name, clf in grid.items():
        y_pred = clf.predict(X_test)
        res[name] = {'MSE': mean_squared_error(y_test, y_pred),
                       'R2': r2_score(y_test, y_pred)
                      }
    return res


def print_grid_results(grids):
    for name, clf in grids.items():
        print('{:-^70}'.format(' [' + name + '] '))
        print('Score:\t\t{:.2%}'.format(clf.best_score_))
        print('Parameters:\t{}'.format(clf.best_params_))
        print('*' * 70)




classifiers = [
    {   'name':     'SVR_rbf',
        'clf':      SVR(),
        'title':    "SVR_rbf",
        'param_grid': {
            'SVR_rbf__C':           [0.1, 1, 5],
            'SVR_rbf__max_iter':    [500, 1000, 5000]
        } 
    },
    {   'name':     'SVR_linear',
        'clf':      SVR(kernel='linear'),
        'title':    "SVR_rbf",
        'param_grid': {
            'SVR_linear__C':           [0.1, 1, 5],
            'SVR_linear__max_iter':    [500, 1000, 5000]
        } 
    },
    {   'name':     'Ridge',
        'clf':      Ridge(),
        'title':    "Ridge",
        'param_grid': {
            'Ridge__alpha':         [0.1, 1, 5, 10, 50, 100],
            'Ridge__max_iter':      [200, 500]
        } 
    },
    {   'name':     'Lasso',
        'clf':      Lasso(),
        'title':    "Lasso",
        'param_grid':  {
            'Lasso__alpha':         [0.001, 0.01, 0.1, 1, 5, 10],
            'Lasso__max_iter':      [1000, 5000]
        } 
    },
    {   'name':     'RandomForest',
        'clf':      RandomForestRegressor(),
        'title':    "RandomForest",
        'param_grid':  {
            'RandomForest__n_estimators':   [10, 100],
            'RandomForest__max_depth':      [3, 5],
        } 
    },
]


def main(path):
    #path = r'D:\data\work\.ML\SO\49094242-SVM provided a bad result in my data'
    os.chdir(path)

    X, y = get_data(path)
    grid = fit_all_classifiers_grid(X, y, classifiers, cv=3, verbose=2, n_jobs=-1)
    print_grid_results(grid)

    #X_train, y_train, X_test, y_test = get_data_split(path)
    #grid = fit_all_classifiers_grid(X_train, y_train, classifiers, cv=2, verbose=2, n_jobs=-1)
    #res = test_dataset(grid, X_test, y_test)
    #print(res)

PS对不起使用名称classifier而不是regressor -我只是在搜索最佳分类器的地方重用了旧代码。

解决方案2
 1 已采纳  2018-03-05 19:08:21

这是一种类似的方法。 我们将数据集分为train和test 。 train数据集将用于调整超参数和拟合不同模型。 然后，我们将选择最佳（就MSE而言）模型，并根据test数据集预测值。

所有经过训练（拟合）的模型都将另存为Pickle文件，因此以后可以使用joblib.load()方法加载它们。

输出： 

----------------------------- [SVR_rbf] ------------------------------
Fitting 3 folds for each of 4 candidates, totalling 12 fits
---------------------------- [SVR_linear] ----------------------------
Fitting 3 folds for each of 4 candidates, totalling 12 fits
------------------------------ [Ridge] -------------------------------
Fitting 3 folds for each of 7 candidates, totalling 21 fits
------------------------------ [Lasso] -------------------------------
Fitting 3 folds for each of 6 candidates, totalling 18 fits
--------------------------- [RandomForest] ---------------------------
Fitting 3 folds for each of 3 candidates, totalling 9 fits
----------------------------- [SVR_rbf] ------------------------------
Score:      44.88%
Parameters: {'SVR_rbf__C': 10, 'SVR_rbf__max_iter': 500}
**********************************************************************
---------------------------- [SVR_linear] ----------------------------
Score:      33.40%
Parameters: {'SVR_linear__C': 0.01, 'SVR_linear__max_iter': 1000}
**********************************************************************
------------------------------ [Ridge] -------------------------------
Score:      34.83%
Parameters: {'Ridge__alpha': 500, 'Ridge__max_iter': 200}
**********************************************************************
------------------------------ [Lasso] -------------------------------
Score:      22.90%
Parameters: {'Lasso__alpha': 0.1, 'Lasso__max_iter': 1000}
**********************************************************************
--------------------------- [RandomForest] ---------------------------
Score:      36.87%
Parameters: {'RandomForest__max_depth': 5, 'RandomForest__n_estimators': 250}
**********************************************************************
Mean Squared Error: {'SVR_rbf': 5.375, 'SVR_linear': 7.036, 'Ridge': 7.02, 'Lasso': 8.108, 'RandomForest': 9.475}

码： 

import os
#import contextlib
from operator import itemgetter
from pathlib import Path
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import SGDRegressor, Ridge, Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.externals import joblib


def get_data(path='.'):
    p = Path(path)
    kwargs = dict(delim_whitespace=True, header=None)
    X_train = pd.read_csv(list(p.glob('trainX.txt*'))[0], **kwargs)
    y_train = pd.read_csv(list(p.glob('trainY.txt*'))[0], **kwargs)
    X_test = pd.read_csv(list(p.glob('testX.txt*'))[0], **kwargs)
    y_test = pd.read_csv(list(p.glob('testY.txt*'))[0], **kwargs)
    return (pd.concat([X_train, X_test], ignore_index=True),
            pd.concat([y_train, y_test], ignore_index=True)[0])


def get_data_split(path='.', test_size=0.25):
    X, y = get_data(path)
    return train_test_split(X, y, test_size=test_size)


def tune_models_hyperparams(X, y, models, **common_grid_kwargs):
    grids = {}
    for model in models:
        print('{:-^70}'.format(' [' + model['name'] + '] '))
        pipe = Pipeline([
                    ("scale", StandardScaler()),
                    (model['name'], model['model'])   ])
        grids[model['name']] = (GridSearchCV(pipe,
                                           param_grid=model['param_grid'],
                                           **common_grid_kwargs)
                                  .fit(X, y))
        # saving single trained model ...
        joblib.dump(grids[model['name']], './{}.pkl'.format(model['name']))
    return grids


def get_best_model(grid, X_test, y_test,
                        metric_func=mean_squared_error):
    res = {name : round(metric_func(y_test, model.predict(X_test)), 3)
           for name, model in grid.items()}
    print('Mean Squared Error:', res)
    best_model_name = min(res, key=itemgetter(1))
    return grid[best_model_name]


def test_dataset(grid, X_test, y_test):
    res = {}
    for name, model in grid.items():
        y_pred = model.predict(X_test)
        res[name] = {'MSE': mean_squared_error(y_test, y_pred),
                       'R2': r2_score(y_test, y_pred)
                      }
    return res

def predict(grid, X_test, model_name):
    return grid[model_name].predict(X_test)


def print_grid_results(grids):
    for name, model in grids.items():
        print('{:-^70}'.format(' [' + name + '] '))
        print('Score:\t\t{:.2%}'.format(model.best_score_))
        print('Parameters:\t{}'.format(model.best_params_))
        print('*' * 70)


models = [
    {   'name':     'SVR_rbf',
        'model':    SVR(),
        'title':    "SVR_rbf",
        'param_grid': {
            'SVR_rbf__C':           [0.1, 1, 5, 10],
            'SVR_rbf__max_iter':    [500]
        } 
    },
    {   'name':     'SVR_linear',
        'model':      SVR(kernel='linear'),
        'title':    "SVR_rbf",
        'param_grid': {
            'SVR_linear__C':           [0.01, 0.1, 1, 5],
            'SVR_linear__max_iter':    [1000]
        } 
    },
    {   'name':     'Ridge',
        'model':    Ridge(),
        'title':    "Ridge",
        'param_grid': {
            'Ridge__alpha':         [0.1, 0.5, 5, 10, 50, 100, 500],
            'Ridge__max_iter':      [200]
        } 
    },
    {   'name':     'Lasso',
        'model':    Lasso(),
        'title':    "Lasso",
        'param_grid':  {
            'Lasso__alpha':         [0.0001, 0.001, 0.01, 0.1, 1, 10],
            'Lasso__max_iter':      [1000]
        } 
    },
    {   'name':     'RandomForest',
        'model':    RandomForestRegressor(),
        'title':    "RandomForest",
        'param_grid':  {
            'RandomForest__n_estimators':   [50, 250, 500],
            'RandomForest__max_depth':      [5],
        } 
    },
]


def main(path):
    os.chdir(str(path))

    X_train, X_test, y_train, y_test = \
        get_data_split(path, test_size=127/510.)
    grid = tune_models_hyperparams(X_train, y_train, models, cv=3,
                                   verbose=2, n_jobs=-1)
    print_grid_results(grid)
    model = get_best_model(grid, X_test, y_test)
    df = pd.DataFrame({'predicted': model.predict(X_test)})
    df.to_csv('predicted.csv', index=False)

if __name__ == "__main__":
    p =  Path(__file__).parent.resolve()
    main(p)

解决方案3
 0  2018-03-04 10:33:56

根据您的数据集，看来您的功能太高了。 在开始使用SVM进行处理之前，最好使用功能分组算法。

暂无
暂无

声明:本站的技术帖子网页，遵循CC BY-SA 4.0协议，如果您需要转载，请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.

相关问题 输出仅是数据的最后一行。 如何四处移动代码以解决此问题？ 我的数据中有对象。 我如何查看它在 Python 中的内容？ 如何修复 Python ValueError：错误的编组数据？ 如何正确格式化数据以运行SVM算法？ 我的带有ModelMultipleChoiceField的表单没有保存数据。 如何修正线性SVM的误报率？ 如何从连续的数据流中提取所需的信息？ 使用散点图显示所有数据时，numpy 切片有问题。 我该如何解决？ 如何在我的案例中构建我的训练数据，以便在scikit-learn中训练分类器中的SVM？ SVM 错误的输入形状
相关标签
 
粤ICP备18138465号  © 2020-2024 STACKOOM.COM