Python MLPRegressor.score方法代碼示例

# 需要導入模塊: from sklearn.neural_network import MLPRegressor [as 別名]
# 或者: from sklearn.neural_network.MLPRegressor import score [as 別名]
def test_lbfgs_regression():
    # Test lbfgs on the boston dataset, a regression problems.
    X = Xboston
    y = yboston
    for activation in ACTIVATION_TYPES:
        mlp = MLPRegressor(solver='lbfgs', hidden_layer_sizes=50,
                           max_iter=150, shuffle=True, random_state=1,
                           activation=activation)
        mlp.fit(X, y)
        if activation == 'identity':
            assert_greater(mlp.score(X, y), 0.84)
        else:
            # Non linear models perform much better than linear bottleneck:
            assert_greater(mlp.score(X, y), 0.95)

# 需要導入模塊: from sklearn.neural_network import MLPRegressor [as 別名]
# 或者: from sklearn.neural_network.MLPRegressor import score [as 別名]
def test_multioutput_regression():
    # Test that multi-output regression works as expected
    X, y = make_regression(n_samples=200, n_targets=5)
    mlp = MLPRegressor(solver='lbfgs', hidden_layer_sizes=50, max_iter=200,
                       random_state=1)
    mlp.fit(X, y)
    assert_greater(mlp.score(X, y), 0.9)

# 需要導入模塊: from sklearn.neural_network import MLPRegressor [as 別名]
# 或者: from sklearn.neural_network.MLPRegressor import score [as 別名]
def test_lbfgs_regression():
    # Test lbfgs on the boston dataset, a regression problems."""
    X = Xboston
    y = yboston
    for activation in ACTIVATION_TYPES:
        mlp = MLPRegressor(algorithm='l-bfgs', hidden_layer_sizes=50,
                           max_iter=150, shuffle=True, random_state=1,
                           activation=activation)
        mlp.fit(X, y)
        assert_greater(mlp.score(X, y), 0.95)

# 需要導入模塊: from sklearn.neural_network import MLPRegressor [as 別名]
# 或者: from sklearn.neural_network.MLPRegressor import score [as 別名]
def test_partial_fit_regression():
    # Test partial_fit on regression.
    # `partial_fit` should yield the same results as 'fit' for regression.
    X = Xboston
    y = yboston

    for momentum in [0, .9]:
        mlp = MLPRegressor(solver='sgd', max_iter=100, activation='relu',
                           random_state=1, learning_rate_init=0.01,
                           batch_size=X.shape[0], momentum=momentum)
        with warnings.catch_warnings(record=True):
            # catch convergence warning
            mlp.fit(X, y)
        pred1 = mlp.predict(X)
        mlp = MLPRegressor(solver='sgd', activation='relu',
                           learning_rate_init=0.01, random_state=1,
                           batch_size=X.shape[0], momentum=momentum)
        for i in range(100):
            mlp.partial_fit(X, y)

        pred2 = mlp.predict(X)
        assert_almost_equal(pred1, pred2, decimal=2)
        score = mlp.score(X, y)
        assert_greater(score, 0.75)

# 需要導入模塊: from sklearn.neural_network import MLPRegressor [as 別名]
# 或者: from sklearn.neural_network.MLPRegressor import score [as 別名]
#Example  with a Regressor using the scikit-learn library
# example for the XOr gate
from sklearn.neural_network import MLPRegressor 

X = [[0., 0.],[0., 1.], [1., 0.], [1., 1.]] # each one of the entries 00 01 10 11
y = [0, 1, 1, 0] # outputs for each one of the entries

# check http://scikit-learn.org/dev/modules/generated/sklearn.neural_network.MLPRegressor.html#sklearn.neural_network.MLPRegressor
#for more details
reg = MLPRegressor(hidden_layer_sizes=(5),activation='tanh', algorithm='sgd', alpha=0.001, learning_rate='constant',
                   max_iter=10000, random_state=None, verbose=False, warm_start=False, momentum=0.8, tol=10e-8, shuffle=False)

reg.fit(X,y)

outp =  reg.predict([[0., 0.],[0., 1.], [1., 0.], [1., 1.]])

print'Results:'
print '0 0 0:', outp[0]
print '0 1 1:', outp[1]
print '1 0 1:', outp[2]
print '1 1 0:', outp[0]
print'Score:', reg.score(X, y)

# 需要導入模塊: from sklearn.neural_network import MLPRegressor [as 別名]
# 或者: from sklearn.neural_network.MLPRegressor import score [as 別名]
plt.rc('font', **font)

fig, axes = plt.subplots(nrows=1, ncols=1)
axes.set_title("Data: " + file)
axes.set_ylabel('Normalized distant count')
axes.set_xlabel('Distance ($\AA$)')

axes.hist(y_train, 150, color='blue',normed=True, label='plot',linewidth=2,alpha=1.0)
plt.show()
"""

# Fit model
clf.fit(X_train, y_train)

# Compute and print r^2 score
print(clf.score(X_test, y_test))

# Store predicted energies
Ecmp = clf.predict(X_test)

Ecmp = gt.hatokcal * (Ecmp)
Eact = gt.hatokcal * (y_test)

# Compute RMSE in kcal/mol
rmse = gt.calculaterootmeansqrerror(Ecmp, Eact)

# End timer
_t1e = tm.time()
print("Computation complete. Time: " + "{:.4f}".format((_t1e - _t1b)) + "s")

# Output model information

# 需要導入模塊: from sklearn.neural_network import MLPRegressor [as 別名]
# 或者: from sklearn.neural_network.MLPRegressor import score [as 別名]

#.........這裏部分代碼省略.........
                self.df.iloc[:, k] = v

    def preprocess(self, removeColumnsWithMissingValues = False):
        print("DEB Preprocessing")
        m = self.df.as_matrix()

        # it is possible to encode enumerable features and to remove missing values
        with open('enumerable_columns.txt') as f:  # e.g., self.enumerable_columns = [0, 5, 8]
            self.enumerable_columns = f.read()
            if self.enumerable_columns.__contains__(','):
                self.enumerable_columns = list(map(int, self.enumerable_columns.split(',')))
            else:
                self.enumerable_columns = [int(self.enumerable_columns)]
            print("enumerable columns are: " + str(self.enumerable_columns))
        le = preprocessing.LabelEncoder()
        for col in self.enumerable_columns:
            # if the column is enumerable
            self.df[self.header[col]] = le.fit_transform(self.df[self.header[col]])  #  A -> 0, B -> 1, ...

        #  remove cols with missing values (NaN), even though you risk to reduce too much the dataset
        if removeColumnsWithMissingValues:
            for i in range(0, m.shape[1]):
                if True in m[:, i]:
                    self.df = numpy.delete(self.df, 0, i)  # delete column


############## MPL architecture #######################
    def createTrainingAndTestSet(self):
        print("DEB Create Training set. Using formula 80-20%")
        self.trainSet, self.testSet = train_test_split(self.df, test_size=0.20)

    # hearth of the algorithm!
    def createTSnew(self):
        print("DEB Create TS new")
        for i in range(0, self.trainSet.shape[0]):
            for j in range(0, self.repeatSometimes):
                # choose small random subset of features X_hat
                X_hat = [int(self.trainSet.shape[1] * random.random()) for i in range(0, self.dim_random_subset)]
                # insert into TSnew the sample: (x1...X_hat = 0 ... xk ; x1...xk)
                row = numpy.copy(self.trainSet.as_matrix()[i, :])
                for feature in X_hat:  # here you set the random features to 0. X_hat represents the indices of such features
                    row[feature] = 0
                self.TSnew_X = self.TSnew_X.append(pandas.DataFrame(row.reshape(-1, len(row))))  # append row to TSnew_X
                copy = numpy.copy(self.trainSet.as_matrix()[i, :])
                self.TSnew_Y = self.TSnew_Y.append(pandas.DataFrame(copy.reshape(-1, len(copy))))  # Y = x1...xk

############## Train & Predict ########################
    def train(self):
        print("DEB Training with TSnew")
        self.MLP = MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
                                 beta_2=0.999, early_stopping=False, epsilon=1e-08,
                                 hidden_layer_sizes=len(self.TSnew_Y.columns), learning_rate='constant',
                                 learning_rate_init=0.001, max_iter=200, momentum=0.9,
                                 nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
                                 solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,
                                 warm_start=False)
        self.MLP.fit(self.TSnew_X, self.TSnew_Y)

    def predict(self):
        print("DEB Test")

        testSetNew_X = pandas.DataFrame()
        testSetNew_Y = pandas.DataFrame()

        # preparing the test set - here you do the same as in function createTSnew:
        if not os.path.isfile('testSetNew_X{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset)):
            for i in range(0, self.testSet.shape[0]):
                # choose small random subset of features X_hat
                X_hat = [int(self.testSet.shape[1] * random.random()) for i in range(0, self.dim_random_subset)]
                # insert into TSnew the sample: (x1...X_hat = 0 ... xk ; x1...xk)
                row = numpy.copy(self.testSet.as_matrix()[i, :])
                for feature in X_hat:  # here you set the random features to 0. X_hat represents the indices of such features
                    row[feature] = 0
                testSetNew_X = testSetNew_X.append(pandas.DataFrame(row.reshape(-1, len(row))))
                copy = numpy.copy(self.testSet.as_matrix()[i, :])
                testSetNew_Y = testSetNew_Y.append(pandas.DataFrame(copy.reshape(-1, len(copy))))  # Y = x1...xk
            testSetNew_Y.to_csv('testSetNew_X{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))
            testSetNew_Y.to_csv('testSetNew_Y{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))
        else:  # if the needed DataFrames have already been calculated, simply load them from disk
            self.trainSet = self.trainSet.from_csv('testSetNew_X{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))
            self.trainSet = self.trainSet.from_csv('testSetNew_Y{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))

        # predictions
        self.MLP.predict(testSetNew_X)
        print("Score of method (repetitions={}, subset={}): {}%".format(self.repeatSometimes, self.dim_random_subset, self.MLP.score(testSetNew_X, testSetNew_Y) * 100))

########################## Helper functions ####################
    def writeCSV(self):
        print("DEB WriteCSV")
        self.trainSet.to_csv('trainSet{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))
        self.testSet.to_csv('testSet{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))
        self.TSnew_X.to_csv('TSnew_X{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))
        self.TSnew_Y.to_csv('TSnew_Y{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))

    def readCSV(self):
        print("DEB ReadCSV")
        self.trainSet = self.trainSet.from_csv('trainSet{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))
        self.testSet = self.testSet.from_csv('testSet{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))
        self.TSnew_X = self.TSnew_X.from_csv('TSnew_X{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))
        self.TSnew_Y = self.TSnew_Y.from_csv('TSnew_Y{}-{}.csv'.format(self.repeatSometimes, self.dim_random_subset))

# 需要導入模塊: from sklearn.neural_network import MLPRegressor [as 別名]
# 或者: from sklearn.neural_network.MLPRegressor import score [as 別名]
Y_tr = pheno[:1000,1:]   #slicing pheno
#Y_va = pheno[201:250,:]
Y_te = pheno[1001:,1:]

diabetes_X_train = X_tr
diabetes_X_test = X_te
diabetes_y_train = Y_tr
diabetes_y_test = Y_te

reg = MLPRegressor(hidden_layer_sizes=(1, ),algorithm='l-bfgs')
reg.fit(X_tr,Y_tr)

scores = cross_val_score(reg,geno[:,1:],pheno[:,1:],cv=10)

#Result_Y = np.zeros((249,1), dtype='float64')
Result_Y = reg.predict(X_te)
#Yte = np.array(Y_te, dtype=np.float64) 
r_row,p_score = pearsonr(Result_Y,Y_te)

# The mean square error
print("Residual sum of squares: %.2f"
      % np.mean((reg.predict(diabetes_X_test) - diabetes_y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % reg.score(diabetes_X_test, diabetes_y_test))
print(Result_Y)
print(scores)
print(Result_Y.shape)
print(r_row)
print(p_score)

# 需要導入模塊: from sklearn.neural_network import MLPRegressor [as 別名]
# 或者: from sklearn.neural_network.MLPRegressor import score [as 別名]
from __future__ import print_function, division
from future.utils import iteritems
from builtins import range, input
# Note: you may need to update your version of future
# sudo pip install -U future


import numpy as np
from sklearn.neural_network import MLPRegressor
from util import getKaggleMNIST



# get data
X, _, Xt, _ = getKaggleMNIST()

# create the model and train it
model = MLPRegressor()
model.fit(X, X)

# test the model
print("Train R^2:", model.score(X, X))
print("Test R^2:", model.score(Xt, Xt))

Xhat = model.predict(X)
mse = ((Xhat - X)**2).mean()
print("Train MSE:", mse)

Xhat = model.predict(Xt)
mse = ((Xhat - Xt)**2).mean()
print("Test MSE:", mse)