class NuSVRImpl():
def __init__(self, nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=(- 1)):
self._hyperparams = {
'nu': nu,
'C': C,
'kernel': kernel,
'degree': degree,
'gamma': gamma,
'coef0': coef0,
'shrinking': shrinking,
'tol': tol,
'cache_size': cache_size,
'verbose': verbose,
'max_iter': max_iter}
self._wrapped_model = Op(**self._hyperparams)
def fit(self, X, y=None):
if (y is not None):
self._wrapped_model.fit(X, y)
else:
self._wrapped_model.fit(X)
return self
def predict(self, X):
return self._wrapped_model.predict(X)
def __init__(self,
nu=0.5,
C=1.0,
kernel='rbf',
degree=3,
gamma='auto_deprecated',
coef0=0.0,
shrinking=True,
tol=0.001,
cache_size=200,
verbose=False,
max_iter=(-1)):
self._hyperparams = {
'nu': nu,
'C': C,
'kernel': kernel,
'degree': degree,
'gamma': gamma,
'coef0': coef0,
'shrinking': shrinking,
'tol': tol,
'cache_size': cache_size,
'verbose': verbose,
'max_iter': max_iter
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
'MinCovDet':MinCovDet(), 'MinMaxScaler':MinMaxScaler(), 'MiniBatchDictionaryLearning':MiniBatchDictionaryLearning(), 'MiniBatchKMeans':MiniBatchKMeans(), 'MiniBatchSparsePCA':MiniBatchSparsePCA(), 'MultiTaskElasticNet':MultiTaskElasticNet(), 'MultiTaskElasticNetCV':MultiTaskElasticNetCV(), 'MultiTaskLasso':MultiTaskLasso(), 'MultiTaskLassoCV':MultiTaskLassoCV(), 'MultinomialNB':MultinomialNB(), 'NMF':NMF(), 'NearestCentroid':NearestCentroid(), 'NearestNeighbors':NearestNeighbors(), 'Normalizer':Normalizer(), 'NuSVC':NuSVC(), 'NuSVR':NuSVR(), 'Nystroem':Nystroem(), 'OAS':OAS(), 'OneClassSVM':OneClassSVM(), 'OrthogonalMatchingPursuit':OrthogonalMatchingPursuit(), 'OrthogonalMatchingPursuitCV':OrthogonalMatchingPursuitCV(), 'PCA':PCA(), 'PLSCanonical':PLSCanonical(), 'PLSRegression':PLSRegression(), 'PLSSVD':PLSSVD(), 'PassiveAggressiveClassifier':PassiveAggressiveClassifier(), 'PassiveAggressiveRegressor':PassiveAggressiveRegressor(), 'Perceptron':Perceptron(), 'ProjectedGradientNMF':ProjectedGradientNMF(), 'QuadraticDiscriminantAnalysis':QuadraticDiscriminantAnalysis(), 'RANSACRegressor':RANSACRegressor(),
dataset = read_csv('air_pollution.csv')
examDf = DataFrame(dataset)
new_examDf = DataFrame(examDf.drop('data',axis=1))
X_train,X_test,y_train,y_test = (new_examDf.iloc[:220,:7],new_examDf.iloc[220:-1,:7],new_examDf.AQI[1:221],new_examDf.AQI[221:])
X_train = np.array(X_train)
X_test = np.array(X_test)
y_test = np.array(y_test)
y_train = np.array(y_train)
clf = NuSVR(nu=0.5, C=1.0, kernel='linear', degree=3, gamma='auto')
clf.fit(X_train,y_train)
y_pred = clf.predict(X_test)
RMSE = sqrt(mean_squared_error(y_test,y_pred))
print ('RMSE: %.3f' % RMSE)
MAE = mean_absolute_error(y_test,y_pred)
print('MAE: %.3f' % MAE)
e = (abs((y_test - y_pred)/y_test))
print('mBA：%.3f' % np.mean(e))
plt.figure()
plt.plot(range(len(y_pred)),y_pred,'b',label="predict")
plt.plot(range(len(y_pred)),y_test,'r',label="test")
plt.legend(loc="upper right")
plt.xlabel("predict——test")
from scipy.stats.stats import pearsonr
import numpy as np
from sklearn.svm.classes import NuSVR
from sklearn.datasets.svmlight_format import load_svmlight_file
if __name__ == '__main__':
trainfile = './data/svm_train.txt'
problem = svm_read_problem(trainfile)
rank_model = svm_train(problem[0][:-100], problem[1][:-100],
'-s 4 -h 0 -m 1000')
predicted_f, _, _ = svm_predict(
np.ones(100).tolist(), problem[1][-100:], rank_model)
scores_rank_test = problem[0][-100:]
print(("Pearson correlation for fold = %f" %
pearsonr(scores_rank_test, predicted_f)[0]))
svr = NuSVR()
lingfeat, y = load_svmlight_file(trainfile)
svr.fit(lingfeat[:-100], y[:-100])
y_pred = svr.predict(lingfeat[-100:])
print(("Pearson correlation for fold = %f" %
pearsonr(scores_rank_test, y_pred)[0]))