def get_regressor(x,
y,
n_estimators=1500,
pCut=0.05,
n_tries=5,
verbose=False):
if verbose:
sys.stderr.write('getting regressor\n')
clfs = []
oob_scores = []
for i in range(n_tries):
if verbose:
sys.stderr.write('%d.' % i)
clf = ExtraTreesRegressor(n_estimators=n_estimators,
oob_score=True,
bootstrap=True,
max_features='sqrt',
n_jobs=1,
random_state=i).fit(x, y)
clfs.append(clf)
oob_scores.append(clf.oob_score_)
clf = clfs[np.argmax(oob_scores)]
clf.feature_importances = pd.Series(clf.feature_importances_,
index=x.columns)
return clf, oob_scores
def get_regressor(x, y, n_estimators=1500, n_tries=5, verbose=False):
"""Calculate an ExtraTreesRegressor on predictor and target variables
Parameters
----------
x : numpy.array
Predictor vector
y : numpy.array
Target vector
n_estimators : int, optional
Number of estimators to use
n_tries : int, optional
Number of attempts to calculate regression
verbose : bool, optional
If True, output progress statements
Returns
-------
classifier : sklearn.ensemble.ExtraTreesRegressor
The classifier with the highest out of bag scores of all the
attempted "tries"
oob_scores : numpy.array
Out of bag scores of the classifier
"""
if verbose:
sys.stderr.write('Getting regressor\n')
clfs = []
oob_scores = []
for i in range(n_tries):
if verbose:
sys.stderr.write('%d.' % i)
clf = ExtraTreesRegressor(n_estimators=n_estimators,
oob_score=True,
bootstrap=True,
max_features='sqrt',
n_jobs=1,
random_state=i).fit(x, y)
clfs.append(clf)
oob_scores.append(clf.oob_score_)
clf = clfs[np.argmax(oob_scores)]
clf.feature_importances = pd.Series(clf.feature_importances_,
index=x.columns)
return clf, oob_scores
def get_regressor(x, y, n_estimators=1500, n_tries=5,
verbose=False):
"""Calculate an ExtraTreesRegressor on predictor and target variables
Parameters
----------
x : numpy.array
Predictor vector
y : numpy.array
Target vector
n_estimators : int, optional
Number of estimators to use
n_tries : int, optional
Number of attempts to calculate regression
verbose : bool, optional
If True, output progress statements
Returns
-------
classifier : sklearn.ensemble.ExtraTreesRegressor
The classifier with the highest out of bag scores of all the
attempted "tries"
oob_scores : numpy.array
Out of bag scores of the classifier
"""
if verbose:
sys.stderr.write('Getting regressor\n')
clfs = []
oob_scores = []
for i in range(n_tries):
if verbose:
sys.stderr.write('%d.' % i)
clf = ExtraTreesRegressor(n_estimators=n_estimators, oob_score=True,
bootstrap=True, max_features='sqrt',
n_jobs=1, random_state=i).fit(x, y)
clfs.append(clf)
oob_scores.append(clf.oob_score_)
clf = clfs[np.argmax(oob_scores)]
clf.feature_importances = pd.Series(clf.feature_importances_,
index=x.columns)
return clf, oob_scores
def get_regressor(x, y, n_estimators=1500, pCut=0.05, n_tries=5,
verbose=False):
if verbose:
sys.stderr.write('getting regressor\n')
clfs = []
oob_scores = []
for i in range(n_tries):
if verbose:
sys.stderr.write('%d.' % i)
clf = ExtraTreesRegressor(n_estimators=n_estimators, oob_score=True,
bootstrap=True, max_features='sqrt',
n_jobs=1, random_state=i).fit(x, y)
clfs.append(clf)
oob_scores.append(clf.oob_score_)
clf = clfs[np.argmax(oob_scores)]
clf.feature_importances = pd.Series(clf.feature_importances_,
index=x.columns)
return clf, oob_scores
