python - SVM 在我的数据中提供了错误的结果。怎么修？

Question

我有一个数据集，其中包含 510 个用于训练的样本和 127 个用于测试的样本，每个样本都有 7680 个特征。我想设计一个模型来根据训练数据预测高度(厘米)-标签。目前，我使用 SVM，但它提供了非常糟糕的结果。你能看看我的代码并给我一些评论吗？您可以使用 dataset 在您的机器上尝试它和可运行的代码

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_回归

[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.]

Answer 1

这里有一个类似的方法。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的模型。然后我们将选择最佳(就 MSE 而言)模型并从测试数据集中预测值。

所有经过训练(拟合)的模型都将保存为 Pickle 文件，以便稍后可以使用 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}

代码:

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)