Python SGDRegressor.fit_transform示例

示例#1

文件： main.py 项目： PsychoGeek13/ml

def SDGRegressionExample():
    import numpy as np
    from sklearn.datasets import load_boston
    from sklearn.linear_model import SGDRegressor
    from sklearn.cross_validation import cross_val_score
    from sklearn.preprocessing import StandardScaler
    from sklearn.cross_validation import train_test_split
    data = load_boston()
    X_train, X_test, y_train, y_test = train_test_split(data.data,data.target)
    X_scaler = StandardScaler()
    y_scaler = StandardScaler()
    X_train = X_scaler.fit_transform(X_train)
    y_train = y_scaler.fit_transform(y_train)
    X_test = X_scaler.transform(X_test)
    y_test = y_scaler.transform(y_test)
    regressor = SGDRegressor(loss='squared_loss')
    scores = cross_val_score(regressor, X_train, y_train, cv=5)
    print 'Cross validation r-squared scores:', scores
    print 'Average cross validation r-squared score:', np.mean(scores)
    regressor.fit_transform(X_train, y_train)
    print 'Test set r-squared score', regressor.score(X_test, y_test)

示例#2

def SGDDemo():
  import numpy as np
  from sklearn.datasets import load_boston
  from sklearn.linear_model import SGDRegressor
  from sklearn.cross_validation import cross_val_score
  from sklearn.preprocessing import StandardScaler
  from sklearn.cross_validation import train_test_split

  data = load_boston()
  X_train,X_test,y_train,y_test = train_test_split(data.data,data.target)

  X_scaler = StandardScaler()
  y_scaler = StandardScaler()
  X_train = X_scaler.fit_transform(X_train)
  y_train = y_scaler.fit_transform(y_train)
  X_test = X_scaler.transform(X_test)
  y_test = y_scaler.transform(y_test)

  regressor = SGDRegressor(loss='squared_loss')
  scores = cross_val_score(regressor,X_train,y_train,cv=5)
  print "Cross validation r-sqr ",np.mean(scores)
  regressor.fit_transform(X_train,y_train)
  print "TEST score :",regressor.score(X_test,y_test)

示例#3

文件： boston_house.py 项目： HadXu/MachineLearning

X_train,X_test,y_train,y_test =train_test_split(data.data,data.target)


X_scaler = StandardScaler()
y_scaler = StandardScaler()
X_train = X_scaler.fit_transform(X_train)
y_train = y_scaler.fit_transform(y_train)
X_test = X_scaler.transform(X_test)
y_test = y_scaler.transform(y_test)

regressor = SGDRegressor(loss='squared_loss')
score = cross_val_score(regressor, X_train, y_train, cv=5)
print score
print np.mean(score)

regressor.fit_transform(X_train, y_train)

print regressor.score(X_test, y_test)

示例#4

import numpy as np
from sklearn.datasets import load_boston
from sklearn.linear_model import SGDRegressor
from sklearn.cross_validation import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split

data = load_boston()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target)

X_scaler = StandardScaler()
y_scaler = StandardScaler()

X_train = X_scaler.fit_transform(X_train)
y_train = y_scaler.fit_transform(y_train)
X_test = X_scaler.transform(X_test)
y_test = y_scaler.transform(y_test)

regressor = SGDRegressor(loss='squared_loss')
scores = cross_val_score(regressor, X_train, y_train, cv=5)

print 'Cross validation r-squared scores:', scores
print 'Average cross validation r-squared score:', np.mean(scores)

regressor.fit_transform(X_train, y_train)
print 'Test set r-squared score', regressor.score(X_test, y_test)

示例#5

import numpy as np
from sklearn.datasets import load_boston
from sklearn.linear_model import SGDRegressor
from sklearn.cross_validation import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split

data = load_boston()
# print data
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target)
X_scaler = StandardScaler()
y_scaler = StandardScaler()
# print X_train
X_train = X_scaler.fit_transform(X_train)
y_train = y_scaler.fit_transform(y_train)
# print X_train
X_test = X_scaler.fit_transform(X_test)
y_test = y_scaler.fit_transform(y_test)

regressor = SGDRegressor(loss='squared_loss')
scores = cross_val_score(regressor, X_train, y_train, cv=5)
print X_train.shape
print "CV ", scores
print regressor.fit_transform(X_train, y_train).shape
print "Test r-ss", regressor.score(X_test, y_test)

示例#6

文件： boston.py 项目： sb123456789sb/Practical-MachineLearning

    X_train, X_test, Y_train, Y_test = train_test_split(X, Y)

    X_scaler = StandardScaler()
    Y_scaler = StandardScaler()

    X_train = X_scaler.fit_transform(X_train)
    Y_train = Y_scaler.fit_transform(Y_train)
    X_test = X_scaler.transform(X_test)
    Y_test = Y_scaler.transform(Y_test)

    print X_train[0:5]

    print len(X_train)
    print Y_test

    clf = SGDRegressor(loss="squared_loss")
    scores = cross_val_score(clf, X_train, Y_train, cv=5)
    print scores
    print np.mean(scores)

    clf.fit_transform(X_train, Y_train)

    pred = clf.predict(X_test)

    print clf.score(X_test, Y_test)

    # correlation(X_train,Y_train)
    # feature_selection(X_train,Y_train)
    scatter_plot(X_train, Y_train)

示例#7

文件： boston.py 项目： ayush1997/Practical-MachineLearning

    Y_scaler = StandardScaler()

    X_train = X_scaler.fit_transform(X_train)
    Y_train = Y_scaler.fit_transform(Y_train)
    X_test = X_scaler.transform(X_test)
    Y_test = Y_scaler.transform(Y_test)

    print X_train[0:5]


    print len(X_train)
    print Y_test

    clf =SGDRegressor(loss="squared_loss")
    scores = cross_val_score(clf,X_train,Y_train,cv=5)
    print scores
    print np.mean(scores)

    clf.fit_transform(X_train,Y_train)

    pred  = clf.predict(X_test)

    print  clf.score(X_test,Y_test)




    # correlation(X_train,Y_train)
    # feature_selection(X_train,Y_train)
    scatter_plot(X_train,Y_train)