def reportPerformance(self, X, y):
y_pred = self.reg.predict(X)
print "Explained variance: {0:.5f}".format(
metrics.explained_variance_score(y, y_pred)), "\n"
print "Mean abs error: {0:.5f}".format(
metrics.mean_absolute_error(y, y_pred)), "\n"
print "Mean sqrt error: {0:.5f}".format(
metrics.mean_squared_error(y, y_pred)), "\n"
print "R2 score: {0:.5f}".format(metrics.r2_score(y, y_pred)), "\n"
def measure_performance(est, X, y):
y_pred = est.predict(X)
print "Explained variance: {0:.5f}".format(
metrics.explained_variance_score(y, y_pred)), "\n"
print "Mean abs error: {0:.5f}".format(
metrics.mean_absolute_error(y, y_pred)), "\n"
print "Mean sqrt error: {0:.5f}".format(
metrics.mean_squared_error(y, y_pred)), "\n"
print "R2 score: {0:.5f}".format(metrics.r2_score(y, y_pred)), "\n"
def score(self, X, y):
"""Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the
regression sum of squares ((y - y_pred) ** 2).sum() and v is the
residual sum of squares ((y_true - y_true.mean()) ** 2).sum().
Best possible score is 1.0, lower values are worse.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training set.
y : array-like, shape = [n_samples]
Returns
-------
z : float
"""
return r2_score(y, self.predict(X))
prediction_.extend(prediction)
verbose('----------\n')
verbose("Evaluation")
if opts.mode in ['age', 'gender']:
from sklearn.metrics.metrics import precision_score, recall_score, confusion_matrix, classification_report, accuracy_score, f1_score
# Calculando desempeño
print('Accuracy :', accuracy_score(y_, prediction_))
print('Precision :', precision_score(y_, prediction_))
print('Recall :', recall_score(y_, prediction_))
print('F-score :', f1_score(y_, prediction_))
print('\nClasification report:\n',
classification_report(y_, prediction_))
print('\nConfussion matrix :\n', confusion_matrix(y_, prediction_))
else:
from sklearn.metrics.metrics import mean_absolute_error, mean_squared_error, r2_score
print('Mean Abs Error :', mean_absolute_error(y_, prediction_))
print('Mean Sqr Error :', mean_squared_error(y_, prediction_))
print('R2 Error :', r2_score(y_, prediction_))
#plots:
#import matplotlib.pyplot as plt
#confusion_matrix_plot = confusion_matrix(y_test, prediction)
#plt.title('matriz de confusion')
#plt.colorbar()
#plt.xlabel()
#plt.xlabel('categoria de verdad')
#plt.ylabel('categoria predecida')
#plt.show()
def measure_performance(est, X, y ):
y_pred=est.predict(X)
print "Explained variance: {0:.5f}".format(metrics.explained_variance_score(y,y_pred)),"\n"
print "Mean abs error: {0:.5f}".format(metrics.mean_absolute_error(y,y_pred)),"\n"
print "Mean sqrt error: {0:.5f}".format(metrics.mean_squared_error(y,y_pred)),"\n"
print "R2 score: {0:.5f}".format(metrics.r2_score(y,y_pred)),"\n"
def reportPerformance( self, X, y ):
y_pred=self.reg.predict(X)
print "Explained variance: {0:.5f}".format(metrics.explained_variance_score(y,y_pred)),"\n"
print "Mean abs error: {0:.5f}".format(metrics.mean_absolute_error(y,y_pred)),"\n"
print "Mean sqrt error: {0:.5f}".format(metrics.mean_squared_error(y,y_pred)),"\n"
print "R2 score: {0:.5f}".format(metrics.r2_score(y,y_pred)),"\n"
print X_train[123, :]
'''
norm1 = np.linalg.norm(y_train)
if norm1 != 0:
y_train, y_test = y_train/norm1, y_test/norm1
print norm1
'''
print y_train.shape
model = SVR(C=1.0, gamma=1.0)
model = LinearRegression()
lasso = Lasso(alpha=0.1).fit(X_train, y_train)
enet = ElasticNet(alpha=0.1, l1_ratio=0.7).fit(X_train, y_train)
y_pred = lasso.predict(X_test)
print "MSE", mean_squared_error(y_test, y_pred)
m = np.mean(y_test)
print "MSE (Mean)", mean_squared_error(y_test, m * np.ones(len(y_test)))
print "r^2 on test data", r2_score(y_test, y_pred)
plt.plot(enet.coef_, label='Elastic net coefficients')
plt.plot(lasso.coef_, label='Lasso coefficients')
plt.legend(loc='best')
plt.title("Lasso R^2: %f, Elastic Net R^2: %f" % (r2_score(
y_test, lasso.predict(X_test)), r2_score(y_test, enet.predict(X_test))))
plt.show()
verbose('----------\n')
verbose("Evaluation")
if opts.mode in ['age','gender']:
from sklearn.metrics.metrics import precision_score, recall_score, confusion_matrix, classification_report, accuracy_score, f1_score
# Calculando desempeño
print( 'Accuracy :', accuracy_score(y_, prediction_))
print( 'Precision :', precision_score(y_, prediction_))
print( 'Recall :', recall_score(y_, prediction_))
print( 'F-score :', f1_score(y_, prediction_))
print( '\nClasification report:\n', classification_report(y_,
prediction_))
print( '\nConfussion matrix :\n',confusion_matrix(y_, prediction_))
else:
from sklearn.metrics.metrics import mean_absolute_error, mean_squared_error,r2_score
print( 'Mean Abs Error :', mean_absolute_error(y_, prediction_))
print( 'Mean Sqr Error :', mean_squared_error(y_, prediction_))
print( 'R2 Error :', r2_score(y_, prediction_))
#plots:
#import matplotlib.pyplot as plt
#confusion_matrix_plot = confusion_matrix(y_test, prediction)
#plt.title('matriz de confusion')
#plt.colorbar()
#plt.xlabel()
#plt.xlabel('categoria de verdad')
#plt.ylabel('categoria predecida')
#plt.show()
'''
norm1 = np.linalg.norm(y_train)
if norm1 != 0:
y_train, y_test = y_train/norm1, y_test/norm1
print norm1
'''
print y_train.shape
model = SVR(C=1.0, gamma=1.0)
model = LinearRegression()
lasso = Lasso(alpha=0.1).fit(X_train, y_train)
enet = ElasticNet(alpha=0.1, l1_ratio=0.7).fit(X_train, y_train)
y_pred = lasso.predict(X_test)
print "MSE", mean_squared_error(y_test, y_pred)
m = np.mean(y_test)
print "MSE (Mean)",mean_squared_error(y_test, m*np.ones(len(y_test)))
print "r^2 on test data", r2_score(y_test, y_pred)
plt.plot(enet.coef_, label='Elastic net coefficients')
plt.plot(lasso.coef_, label='Lasso coefficients')
plt.legend(loc='best')
plt.title("Lasso R^2: %f, Elastic Net R^2: %f"
% (r2_score(y_test, lasso.predict(X_test)), r2_score(y_test, enet.predict(X_test))))
plt.show()
