class LassoLarsCVImpl():
def __init__(self, fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=3, max_n_alphas=1000, n_jobs=None, eps=2.220446049250313e-16, copy_X=True, positive=False):
self._hyperparams = {
'fit_intercept': fit_intercept,
'verbose': verbose,
'max_iter': max_iter,
'normalize': normalize,
'precompute': precompute,
'cv': cv,
'max_n_alphas': max_n_alphas,
'n_jobs': n_jobs,
'eps': eps,
'copy_X': copy_X,
'positive': positive}
self._wrapped_model = SKLModel(**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,
fit_intercept=True,
verbose=False,
max_iter=500,
normalize=True,
precompute='auto',
cv=3,
max_n_alphas=1000,
n_jobs=None,
eps=2.220446049250313e-16,
copy_X=True,
positive=False):
self._hyperparams = {
'fit_intercept': fit_intercept,
'verbose': verbose,
'max_iter': max_iter,
'normalize': normalize,
'precompute': precompute,
'cv': cv,
'max_n_alphas': max_n_alphas,
'n_jobs': n_jobs,
'eps': eps,
'copy_X': copy_X,
'positive': positive
}
self._wrapped_model = Op(**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
def connectWidgets(self):
# LassoLARS
ll = LassoLars()
self.alpha_text.setText(str(ll.alpha))
self.fit_interceptCheckBox.setChecked(ll.fit_intercept)
self.verboseCheckBox.setChecked(ll.verbose)
self.normalizeCheckBox.setChecked(ll.normalize)
self.setComboBox(self.precomputeComboBox,
['True', 'False', 'auto', 'array-like'])
self.defaultComboItem(self.precomputeComboBox, ll.precompute)
self.max_iterSpinBox.setValue(ll.max_iter)
self.copy_XCheckBox.setChecked(ll.copy_X)
self.fit_pathCheckBox.setChecked(ll.fit_path)
self.positiveCheckBox.setChecked(ll.positive)
# LassoLarsCV
llcv = LassoLarsCV()
self.max_n_alphasSpinBox.setValue(llcv.max_n_alphas)
self.n_jobsSpinBox.setValue(llcv.n_jobs)
# LassoLarsIC
llic = LassoLarsIC()
self.cvSpinBox.setValue(3)
self.setComboBox(self.criterionComboBox, ['aic', 'bic'])
self.defaultComboItem(self.criterionComboBox, llic.criterion)
def function(self):
model = self.modelComboBox.currentIndex()
if model == 0:
params = {
'alpha': self.alpha_text.text(),
'fit_intercept': self.fit_interceptCheckBox.isChecked(),
'verbose': self.fit_interceptCheckBox.isChecked(),
'normalize': self.normalizeCheckBox.isChecked(),
'precompute': self.precomputeComboBox.currentText(),
'max_iter': self.max_iterSpinBox.value(),
'copy_X': self.copy_XCheckBox.isChecked(),
'fit_path': self.fit_pathCheckBox.isChecked(),
'positive': self.positiveCheckBox.isChecked(),
'model': model
}
params_check = dict(params)
params_check.pop('model')
return params, self.getChangedValues(params_check, LassoLars())
elif model == 1:
params = {
'fit_intercept': self.fit_interceptCheckBox.isChecked(),
'verbose': self.fit_interceptCheckBox.isChecked(),
'max_iter': self.max_iterSpinBox.value(),
'normalize': self.normalizeCheckBox.isChecked(),
'precompute': self.precomputeComboBox.currentText(),
'cv': self.cvSpinBox.value(),
'max_n_alphas': self.max_n_alphasSpinBox.value(),
'n_jobs': self.n_jobsSpinBox.value(),
'copy_X': self.copy_XCheckBox.isChecked(),
'positive': self.positiveCheckBox.isChecked(),
'model': model
}
params_check = dict(params)
params_check.pop('model')
return params, self.getChangedValues(params_check, LassoLarsCV())
elif model == 2:
params = {
'criterion': self.criterionComboBox.currentText(),
'fit_intercept': self.fit_interceptCheckBox.isChecked(),
'verbose': self.fit_interceptCheckBox.isChecked(),
'normalize': self.normalizeCheckBox.isChecked(),
'precompute': self.precomputeComboBox.currentText(),
'max_iter': self.max_iterSpinBox.value(),
'copy_X': self.copy_XCheckBox.isChecked(),
'positive': self.positiveCheckBox.isChecked(),
'model': model
}
params_check = dict(params)
params_check.pop('model')
return params, self.getChangedValues(params_check, LassoLarsIC())
else:
params = {}
print("Error")
def set_learning_method(config, X_train, y_train):
"""
Instantiates the sklearn's class corresponding to the value set in the
configuration file for running the learning method.
TODO: use reflection to instantiate the classes
@param config: configuration object
@return: an estimator with fit() and predict() methods
"""
estimator = None
learning_cfg = config.get("learning", None)
if learning_cfg:
p = learning_cfg.get("parameters", None)
o = learning_cfg.get("optimize", None)
scorers = \
set_scorer_functions(learning_cfg.get("scorer", ['mae', 'rmse']))
method_name = learning_cfg.get("method", None)
if method_name == "SVR":
if o:
tune_params = set_optimization_params(o)
estimator = optimize_model(SVR(), X_train,
y_train, tune_params, scorers,
o.get("cv", 5),
o.get("verbose", True),
o.get("n_jobs", 1))
elif p:
estimator = SVR(C=p.get("C", 10),
epsilon=p.get('epsilon', 0.01),
kernel=p.get('kernel', 'rbf'),
degree=p.get('degree', 3),
gamma=p.get('gamma', 0.0034),
tol=p.get('tol', 1e-3),
verbose=False)
else:
estimator = SVR()
elif method_name == "RandomForestRegressor":
if o:
tune_params = set_optimization_params(o)
print tune_params
estimator = optimize_model(RandomForestRegressor(), X_train,
y_train, tune_params, scorers,
o.get("cv", 5),
o.get("verbose", True),
o.get("n_jobs", 1))
elif p:
estimator = RandomForestRegressor(
n_estimators=p.get("n_estimators", 100),
criterion=p.get("criterion", 'mse'),
n_jobs=p.get("n_jobs", -1),
random_state=p.get("random_state", 0),
max_features=p.get("max_features", 'auto'))
elif method_name == "SVC":
if o:
tune_params = set_optimization_params(o)
estimator = optimize_model(SVC(), X_train,
y_train, tune_params, scorers,
o.get('cv', 5),
o.get('verbose', True),
o.get('n_jobs', 1))
elif p:
estimator = SVC(C=p.get('C', 1.0),
kernel=p.get('kernel', 'rbf'),
degree=p.get('degree', 3),
gamma=p.get('gamma', 0.0),
coef0=p.get('coef0', 0.0),
tol=p.get('tol', 1e-3),
verbose=p.get('verbose', False))
else:
estimator = SVC()
elif method_name == "LassoCV":
if p:
estimator = LassoCV(eps=p.get('eps', 1e-3),
n_alphas=p.get('n_alphas', 100),
normalize=p.get('normalize', False),
precompute=p.get('precompute', 'auto'),
max_iter=p.get('max_iter', 1000),
tol=p.get('tol', 1e-4),
cv=p.get('cv', 10),
verbose=False)
else:
estimator = LassoCV()
elif method_name == "LassoLars":
if o:
tune_params = set_optimization_params(o)
estimator = optimize_model(LassoLars(), X_train,
y_train, tune_params, scorers,
o.get("cv", 5),
o.get("verbose", True),
o.get("n_jobs", 1))
if p:
estimator = LassoLars(alpha=p.get('alpha', 1.0),
fit_intercept=p.get(
'fit_intercept', True),
verbose=p.get('verbose', False),
normalize=p.get('normalize', True),
max_iter=p.get('max_iter', 500),
fit_path=p.get('fit_path', True))
else:
estimator = LassoLars()
elif method_name == "LassoLarsCV":
if p:
estimator = LassoLarsCV(max_iter=p.get('max_iter', 500),
normalize=p.get('normalize', True),
max_n_alphas=p.get(
'max_n_alphas', 1000),
n_jobs=p.get('n_jobs', 1),
cv=p.get('cv', 10),
verbose=False)
else:
estimator = LassoLarsCV()
return estimator, scorers
def get_lasso_feature_scores(x, y, mode=CLASSIFICATION, scaling=0.5,
sample_fraction=0.75, n_resampling=200,
random_state=None):
'''
Calculate features scores using a randomized lasso (regression) or
randomized logistic regression (classification). This is also known as
stability selection.
see http://scikit-learn.org/stable/modules/feature_selection.html for
details.
Parameters
----------
x : structured array
y : 1D nd.array
mode : {CLASSIFICATION, REGRESSION}
scaling : float, optional
scaling parameter, should be between 0 and 1
sample_fraction : float, optional
the fraction of samples to used in each randomized
dataset
n_resmpling : int, optional
the number of times the model is trained on a random subset
of the data
random_state : int, optional
if it is an int, it specifies the seed to use, defaults to
None.
Returns
-------
pandas DataFrame
sorted in descending order of tuples with uncertainty and feature
scores
'''
uncs = recfunctions.get_names(x.dtype)
x = _prepare_experiments(x)
if mode==CLASSIFICATION:
lfs = RandomizedLogisticRegression(scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x,y)
elif mode==REGRESSION:
# we use LassoLarsCV to determine alpha see
# http://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_recovery.html
lars_cv = LassoLarsCV(cv=6).fit(x, y,)
alphas = np.linspace(lars_cv.alphas_[0], .1 * lars_cv.alphas_[0], 6)
# fit the randomized lasso
lfs = RandomizedLasso(alpha=alphas,scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x, y)
else:
raise ValueError('{} invalid value for mode'.format(mode))
importances = lfs.scores_
importances = zip(uncs, importances)
importances = list(importances)
importances.sort(key=itemgetter(1), reverse=True)
importances = pd.DataFrame(importances)
return importances
'KMeans':KMeans(), 'KNeighborsClassifier':KNeighborsClassifier(), 'KNeighborsRegressor':KNeighborsRegressor(), 'KernelCenterer':KernelCenterer(), 'KernelDensity':KernelDensity(), 'KernelPCA':KernelPCA(), 'KernelRidge':KernelRidge(), 'LSHForest':LSHForest(), 'LabelPropagation':LabelPropagation(), 'LabelSpreading':LabelSpreading(), 'Lars':Lars(), 'LarsCV':LarsCV(), 'Lasso':Lasso(), 'LassoCV':LassoCV(), 'LassoLars':LassoLars(), 'LassoLarsCV':LassoLarsCV(), 'LassoLarsIC':LassoLarsIC(), 'LatentDirichletAllocation':LatentDirichletAllocation(), 'LedoitWolf':LedoitWolf(), 'LinearDiscriminantAnalysis':LinearDiscriminantAnalysis(), 'LinearRegression':LinearRegression(), 'LinearSVC':LinearSVC(), 'LinearSVR':LinearSVR(), 'LocallyLinearEmbedding':LocallyLinearEmbedding(), 'LogisticRegression':LogisticRegression(), 'LogisticRegressionCV':LogisticRegressionCV(), 'MDS':MDS(), 'MLPClassifier':MLPClassifier(), 'MLPRegressor':MLPRegressor(), 'MaxAbsScaler':MaxAbsScaler(), 'MeanShift':MeanShift(),
# Apply Some Featuring
poly_reg = PolynomialFeatures(degree=1)
# Transform into numpy object
x_train = poly_reg.fit_transform(X_train)
X_test = poly_reg.fit_transform(X_test)
y_test = np.array(y_test.ix[:, 0])
y_train = np.array(y_train.ix[:, 0])
# Build model with good params
model = LassoLarsCV(copy_X=True,
cv=None,
eps=2.2204460492503131e-16,
fit_intercept=True,
max_iter=500,
max_n_alphas=1000,
n_jobs=1,
normalize=True,
positive=False,
precompute='auto',
verbose=False)
# Fit the model
model.fit(x_train, y_train)
# Predict
y_pred = model.predict(X_test)
# Scoring
if regression:
print('Score on test set:', mean_absolute_error(y_test, y_pred))
def get_lasso_feature_scores(results,
classify,
scaling=0.5,
sample_fraction=0.75,
n_resampling=200,
random_state=None):
'''
Calculate features scores using a randomized lasso (regression) or
randomized logistic regression (classification). This is also known as
stability selection.
see http://scikit-learn.org/stable/modules/feature_selection.html for
details.
Parameters
----------
results : tuple
classify : callable or str
a classify function or variable analogous to PRIM
scaling : float, optional
scaling parameter, should be between 0 and 1
sample_fraction : float, optional
the fraction of samples to used in each randomized
dataset
n_resmpling : int, optional
the number of times the model is trained on a random subset
of the data
random_state : int, optional
if it is an int, it specifies the seed to use, defaults to
None.
Returns
-------
list of tuples
sorted in descending order of tuples with uncertainty and feature
scores
'''
experiments, outcomes = results
uncs = recfunctions.get_names(experiments.dtype)
x = _prepare_experiments(experiments)
y, categorical = _prepare_outcomes(outcomes, classify)
if categorical:
lfs = RandomizedLogisticRegression(scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x, y)
else:
# we use LassoLarsCV to determine alpha see
# http://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_recovery.html
lars_cv = LassoLarsCV(cv=6).fit(
x,
y,
)
alphas = np.linspace(lars_cv.alphas_[0], .1 * lars_cv.alphas_[0], 6)
# fit the randomized lasso
lfs = RandomizedLasso(alpha=alphas,
scaling=scaling,
sample_fraction=sample_fraction,
n_resampling=n_resampling,
random_state=random_state)
lfs.fit(x, y)
importances = lfs.scores_
importances = zip(uncs, importances)
importances = list(importances)
importances.sort(key=itemgetter(1), reverse=True)
return importances
