def test_auto_init(n_samples, n_features, n_classes, n_components):
# Test that auto choose the init as expected with every configuration
# of order of n_samples, n_features, n_classes and n_components.
rng = np.random.RandomState(42)
nca_base = NeighborhoodComponentsAnalysis(init='auto',
n_components=n_components,
max_iter=1,
random_state=rng)
if n_classes >= n_samples:
pass
# n_classes > n_samples is impossible, and n_classes == n_samples
# throws an error from lda but is an absurd case
else:
X = rng.randn(n_samples, n_features)
y = np.tile(range(n_classes), n_samples // n_classes + 1)[:n_samples]
if n_components > n_features:
# this would return a ValueError, which is already tested in
# test_params_validation
pass
else:
nca = clone(nca_base)
nca.fit(X, y)
if n_components <= min(n_classes - 1, n_features):
nca_other = clone(nca_base).set_params(init='lda')
elif n_components < min(n_features, n_samples):
nca_other = clone(nca_base).set_params(init='pca')
else:
nca_other = clone(nca_base).set_params(init='identity')
nca_other.fit(X, y)
assert_array_almost_equal(nca.components_, nca_other.components_)
def calibrate_probs(labels, weights, probs, logistic=False, random_state=11, threshold=0., return_calibrator=False, symmetrize=False):
"""
Calibrate output to probabilities using 2-folding to calibrate all data
:param probs: probabilities, numpy.array of shape [n_samples]
:param labels: numpy.array of shape [n_samples] with labels
:param weights: numpy.array of shape [n_samples]
:param threshold: float, to set labels 0/1
:param logistic: bool, use logistic or isotonic regression
:param symmetrize: bool, do symmetric calibration, ex. for B+, B-
:return: calibrated probabilities
"""
labels = (labels > threshold) * 1
ind = numpy.arange(len(probs))
ind_1, ind_2 = train_test_split(ind, random_state=random_state, train_size=0.5)
calibrator = LogisticRegression(C=100) if logistic else IsotonicRegression(y_min=0, y_max=1, out_of_bounds='clip')
est_calib_1, est_calib_2 = clone(calibrator), clone(calibrator)
probs_1 = probs[ind_1]
probs_2 = probs[ind_2]
if logistic:
probs_1 = numpy.clip(probs_1, 0.001, 0.999)
probs_2 = numpy.clip(probs_2, 0.001, 0.999)
probs_1 = logit(probs_1)[:, numpy.newaxis]
probs_2 = logit(probs_2)[:, numpy.newaxis]
if symmetrize:
est_calib_1.fit(numpy.r_[probs_1, 1-probs_1],
numpy.r_[labels[ind_1] > 0, labels[ind_1] <= 0])
est_calib_2.fit(numpy.r_[probs_2, 1-probs_2],
numpy.r_[labels[ind_2] > 0, labels[ind_2] <= 0])
else:
est_calib_1.fit(probs_1, labels[ind_1])
est_calib_2.fit(probs_2, labels[ind_2])
else:
if symmetrize:
est_calib_1.fit(numpy.r_[probs_1, 1-probs_1],
numpy.r_[labels[ind_1] > 0, labels[ind_1] <= 0],
numpy.r_[weights[ind_1], weights[ind_1]])
est_calib_2.fit(numpy.r_[probs_2, 1-probs_2],
numpy.r_[labels[ind_2] > 0, labels[ind_2] <= 0],
numpy.r_[weights[ind_2], weights[ind_2]])
else:
est_calib_1.fit(probs_1, labels[ind_1], weights[ind_1])
est_calib_2.fit(probs_2, labels[ind_2], weights[ind_2])
calibrated_probs = numpy.zeros(len(probs))
if logistic:
calibrated_probs[ind_1] = est_calib_2.predict_proba(probs_1)[:, 1]
calibrated_probs[ind_2] = est_calib_1.predict_proba(probs_2)[:, 1]
else:
calibrated_probs[ind_1] = est_calib_2.transform(probs_1)
calibrated_probs[ind_2] = est_calib_1.transform(probs_2)
if return_calibrator:
return calibrated_probs, (est_calib_1, est_calib_2)
else:
return calibrated_probs
def __init__(
self,
estimator=LinearSVC(),
masker=NiftiMasker(),
labelizer=LabelEncoder(),
reporter=Reporter(),
estimated_name="coef_",
):
self.estimator = clone(estimator)
self.masker = clone(masker)
self.labelizer = clone(labelizer)
self.reporter = reporter
self.estimated_name = estimated_name
def classification_metrics(self, X, y, n_iter=10, test_size=0.25, random_state=0):
"""
returns the roc auc of the classifier binary only., and the portion of correct predictions via CV
@param y: all non-zero will be set to 1
@param n_iter, test_size: StratifiedShuffleSplit parameters
@param random_state: random state used for StratifiedShuffleSplit
@return: roc, accuracy, accuracy_zero, accuracy_one
"""
roc = 0
accuracy = 0
accuracy_zero = 0 # portion of zeros correctly predicted
accuracy_one = 0 # portion of ones correctly predicted
y = np.array([0 if d == 0 else 1 for d in y])
prePipe = clone(self.common_preprocessing_pipe)
pipeToUse = clone(self.classifier_pipe)
cvObj = StratifiedShuffleSplit(y, n_iter=n_iter, test_size=test_size, random_state=random_state)
for trainInds, testInds in cvObj: # all cv data
trainX = X[trainInds]
trainY = y[trainInds]
testX = X[testInds]
testY = y[testInds]
trainX = prePipe.fit_transform(trainX)
testX = prePipe.transform(testX)
pipeToUse.fit(trainX, trainY)
y_scores = pipeToUse.predict_proba(testX)
y_pred = pipeToUse.predict(testX)
temp = next((i for i in range(len(testY)) if y_pred[i] == 1), None)
roc += roc_auc_score(testY, y_scores[:, 1])
accuracy += sum(y_pred == testY) * 1.0 / len(testY)
accuracy_zero += 1.0 * sum(np.logical_and(y_pred == testY, testY == 0)) / sum(testY == 0)
accuracy_one += 1.0 * sum(np.logical_and(y_pred == testY, testY == 1)) / sum(testY == 1)
roc /= n_iter
accuracy_zero /= n_iter
accuracy_one /= n_iter
accuracy /= n_iter
print ">>> The classifier has roc = %0.3f, zero-accuracy = %0.3f, " "one-accuracy = %0.3f, overall accuracy = %0.3f." % (
roc,
accuracy_zero,
accuracy_one,
accuracy,
)
return roc, accuracy, accuracy_zero, accuracy_one
def _fit_stage(self, X, y, rmTolerance):
"""
fits one stage of gradient boosting
@param X:
@param y:
@param rmTolerance: tolerance for 1D optimization
@return: nothing
"""
residuals = self.lossFunction.negative_gradient(y, self._currentPrediction)
trainX, trainY, _, _ = splitTrainTest(X, residuals, 1-self.subsample) # stochastic boosting. train only on a portion of the data
if len(np.unique(trainY))==1:
hm = MajorityPredictor().fit(trainY)
else:
cvObj = KFold(n=len(trainX), n_folds=self.cvNumFolds, indices=False, shuffle=True, random_state=self.randomState)
# find the h that best mimics the negative gradient
if self.n_jobs > 1: # parallel
n_jobs = max(1, self.n_jobs/len(self.learners), self.cvNumFolds)
# n_jobs = 1
pool = MyPool(processes=self.n_jobs, initializer=gbjjInit, initargs=(trainX, trainY, self.lossFunction, n_jobs, cvObj))
temp = pool.map_async(gbjjInnerLoop, self.learners)
temp.wait()
h_res = temp.get()
pool.close()
pool.join()
else: # single thread
h_res = []
for learner in self.learners:
if self.verbosity >= 2:
print 'Fitting learner:', learner
l = clone(learner)
scores = jjcross_val_score(l, trainX, trainY, score_func=self.lossFunction, n_jobs=1, cv=cvObj)
h_res.append(scores.mean())
hm = clone(self.learners[np.argsort(h_res)[0]])
if self.verbosity>=1:
print "The best classifier is", hm.__class__
# find rm
hm.fit(trainX, trainY)
hmx = hm.predict(X)
rm = minimize_scalar(lambda r: self.lossFunction(y, self._currentPrediction + r*hmx), tol=rmTolerance).x
# append estimator and weight
self._estimators.append((hm, rm))
def fit(self, X, y, sample_weight=None):
assert isinstance(self.base_estimators, dict), 'Estimators should be passed in a dictionary'
assert len(X) == len(y), 'the lengths are different'
assert sample_weight is None or len(sample_weight) == len(y), 'the lengths are different'
if sample_weight is None:
sample_weight = numpy.ones(len(y))
assert self.feature_name in X.columns, 'there is no feature %s' % self.feature_name
self.columns_order = X.columns
column = numpy.array(X[self.feature_name])
self.column_values = list(set(column))
self.stayed_columns = dict() # value -> list of columns
self.common_features = dict() # (value_from, value_to) -> list of columns
self.classifiers = dict() # (value_from, value_to, classifier_name) -> classifier
self.final_classifiers = dict() # (value, classifier_name) -> classifier
rows_dict = dict() # (value) -> boolean list of rows
self.final_columns_orders = dict() # (value) -> list of features
for value in self.column_values:
rows = numpy.array(X[self.feature_name] == value)
rows_dict[value] = rows
x_part = X.loc[rows, :]
cols = pandas.notnull(x_part).all()
self.stayed_columns[value] = cols[cols==True].keys()
for value_to, rows_to in rows_dict.items():
columns_to = self.stayed_columns[value_to]
new_features = pandas.DataFrame()
for value_from, rows_from in rows_dict.items():
if value_from == value_to:
continue
common_columns = list(set(self.stayed_columns[value_from]).union(set(self.stayed_columns[value_to])))
common_columns.remove(self.feature_name)
self.common_features[value_from, value_to] = common_columns
for name, estimator in self.base_estimators.items():
rows_from = rows_dict[value_from]
new_classifier = sklearn.clone(estimator)\
.fit(X.loc[rows_from, common_columns], y[rows_from], sample_weight=sample_weight[rows_from])
self.classifiers[value_from, value_to, name] = new_classifier
new_feature = new_classifier.predict_proba(X.loc[rows_to, common_columns])[:, 1]
new_features[str(value_from) + "_" + name] = new_feature
X_to_part = X.loc[rows_to, columns_to]
new_features = new_features.set_index(X_to_part.index)
X_to_part = pandas.concat([X_to_part, new_features], axis=1)
final_classifier = sklearn.clone(self.final_estimator)
final_classifier.fit(X_to_part, y[rows_to], sample_weight=sample_weight[rows_to])
self.final_columns_orders[value_to] = X_to_part.columns
self.final_classifiers[value_to] = final_classifier
return self
def fit(self, original, target, original_weight=None, target_weight=None):
"""
Prepare reweighting formula by training a sequence of trees.
:param original: values from original distribution, array-like of shape [n_samples, n_features]
:param target: values from target distribution, array-like of shape [n_samples, n_features]
:param original_weight: weights for samples of original distributions
:param target_weight: weights for samples of original distributions
:return: self
"""
original, original_weight = self._normalize_input(original, original_weight, normalize=False)
target, target_weight = self._normalize_input(target, target_weight, normalize=False)
folds_original = self._get_folds_column(len(original))
folds_target = self._get_folds_column(len(target))
for _ in range(self.n_folds):
self.reweighters.append(clone(self.base_reweighter))
original = numpy.array(original)
target = numpy.array(target)
for i in range(self.n_folds):
self.reweighters[i].fit(original[folds_original != i, :], target[folds_target != i, :],
original_weight=original_weight[folds_original != i],
target_weight=target_weight[folds_target != i])
self.train_length = len(original)
return self
def train(self,num_examples,deltas=list(range(1,6)),use_transformations=False,use_weights=True,verbosity=0):
'''Train ensemble of classifiers using newly generated data for every
member of the ensemble.
'''
time_start = time.time()
self.classifiers = dict()
for delta in deltas:
self.classifiers[delta] = [] # list for ensemble of classifiers
for i in range(self.n_estimators):
# base classifier
clf = clone(self.base_classifier)
if use_weights:
train_x,train_y,train_w = self.make_weighted_training_data(create_examples(num_examples=num_examples,deltas=[delta]),use_transformations=use_transformations)
if verbosity>1:
print ('delta={0}, #{1}: training with {2} weighted examples'.format(delta,i,len(train_x)))
# fit
clf.fit(train_x,train_y,sample_weight=train_w)
else:
train_x,train_y = self.make_training_data(create_examples(num_examples=num_examples,deltas=[delta]),use_transformations=use_transformations)
if verbosity>1:
print ('delta={0}, #{1}: training with {2} examples'.format(delta,i,len(train_x)))
clf.fit(train_x,train_y)
self.classifiers[delta].append(clf)
time_end = time.time()
if verbosity>0:
print('training completed in {0:.1f} seconds'.format(time_end-time_start))
def train(self, examples, use_transformations=False,use_weights=True):
time_start = time.time()
self.classifiers = dict()
deltas = set([e.delta for e in examples])
for delta in deltas:
# create classifier with same params as base classifier
clf = clone(self.base_classifier)
if use_weights:
# weighted training data for current delta
# training data for current delta
(train_x,train_y,train_w) = self.make_weighted_training_data([e for e in examples if e.delta==delta],use_transformations=use_transformations)
print ('delta={0}, training with {1} weighted examples'.format(delta,len(train_x)))
# fit
clf.fit(train_x,train_y,train_w)
else:
# training data for current delta
(train_x,train_y) = self.make_training_data([e for e in examples if e.delta==delta],use_transformations=use_transformations)
print ('delta={0}, training with {1} examples'.format(delta,len(train_x)))
# fit
clf.fit(train_x,train_y)
# store
self.classifiers[delta] = clf
time_end = time.time()
print('training completed in {0} seconds'.format(time_end-time_start))
def test_no_attributes_set_in_init(estimator, preprocessor):
"""Check setting during init. Adapted from scikit-learn."""
estimator = clone(estimator)
estimator.set_params(preprocessor=preprocessor)
if hasattr(type(estimator).__init__, "deprecated_original"):
return
init_params = _get_args(type(estimator).__init__)
parents_init_params = [param for params_parent in
(_get_args(parent) for parent in
type(estimator).__mro__)
for param in params_parent]
# Test for no setting apart from parameters during init
invalid_attr = (set(vars(estimator)) - set(init_params) -
set(parents_init_params))
assert not invalid_attr, \
("Estimator %s should not set any attribute apart"
" from parameters during init. Found attributes %s."
% (type(estimator).__name__, sorted(invalid_attr)))
# Ensure that each parameter is set in init
invalid_attr = (set(init_params) - set(vars(estimator)) -
set(["self"]))
assert not invalid_attr, \
("Estimator %s should store all parameters"
" as an attribute during init. Did not find "
"attributes %s." % (type(estimator).__name__, sorted(invalid_attr)))
def test_various_scoring_on_tuples_learners(estimator, build_dataset,
with_preprocessor):
"""Tests that scikit-learn's scoring returns something finite,
for other scoring than default scoring. (List of scikit-learn's scores can be
found in sklearn.metrics.scorer). For each type of output (predict,
predict_proba, decision_function), we test a bunch of scores.
We only test on pairs learners because quadruplets don't have a y argument.
"""
input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)
estimator = clone(estimator)
estimator.set_params(preprocessor=preprocessor)
set_random_state(estimator)
# scores that need a predict function: every tuples learner should have a
# predict function (whether the pair is of positive samples or negative
# samples)
for scoring in ['accuracy', 'f1']:
check_score_is_finite(scoring, estimator, input_data, labels)
# scores that need a predict_proba:
if hasattr(estimator, "predict_proba"):
for scoring in ['neg_log_loss', 'brier_score']:
check_score_is_finite(scoring, estimator, input_data, labels)
# scores that need a decision_function: every tuples learner should have a
# decision function (the metric between points)
for scoring in ['roc_auc', 'average_precision', 'precision', 'recall']:
check_score_is_finite(scoring, estimator, input_data, labels)
def best_pipelines_by_algo(grid, algo_tag = 'regressor__algorithm', bigger_is_better=True):
results = []
if bigger_is_better:
best_mean = -np.inf
def is_better(x, y): return x > y
else:
best_mean = np.inf
def is_better(x, y): return x < y
best_params_by_algo = {}
for params, mean, std in grid.grid_scores_:
# print(params)
algo_type = type(params[algo_tag])
if algo_type not in best_params_by_algo:
best_params_by_algo[algo_type] = (mean, params)
else:
best_mean = best_params_by_algo[algo_type][0]
if is_better(mean, best_mean):
best_params_by_algo[algo_type] = (mean, params)
for algo, (mean, params) in best_params_by_algo.iteritems():
new_estimator = clone(grid.estimator)
new_estimator.set_params(**params)
results.append(new_estimator)
return results
def test_get_metric_works_does_not_raise(estimator, build_dataset):
"""Tests that the metric returned by get_metric does not raise errors (or
warnings) similarly to the distance functions in scipy.spatial.distance"""
input_data, labels, _, X = build_dataset()
model = clone(estimator)
set_random_state(model)
model.fit(input_data, labels)
metric = model.get_metric()
list_test_get_metric_doesnt_raise = [(X[0], X[1]),
(X[0].tolist(), X[1].tolist()),
(X[0][None], X[1][None])]
for u, v in list_test_get_metric_doesnt_raise:
with pytest.warns(None) as record:
metric(u, v)
assert len(record) == 0
# Test that the scalar case works
model.transformer_ = np.array([3.1])
metric = model.get_metric()
for u, v in [(5, 6.7), ([5], [6.7]), ([[5]], [[6.7]])]:
with pytest.warns(None) as record:
metric(u, v)
assert len(record) == 0
def param_search(estimator, param_dict, n_iter=None, seed=None):
"""
Generator for cloned copies of `estimator` set with parameters
as specified by `param_dict`. `param_dict` can contain either lists
of parameter values (grid search) or a scipy distribution function
to be sampled from. If distributions, you must specify `n_iter`.
Parameters:
___________
estimator:
sklearn-like estimator
param_dict:
dict of parameter name: values, where values can be an iterable
or a distribution function
n_iter:
number of draws to take from parameter distributions
"""
if n_iter is None:
param_iter = ParameterGrid(param_dict)
else:
param_iter = ParameterSampler(param_dict,
n_iter,
random_state=seed)
estimators = []
for params in param_iter:
new_estimator = sklearn.clone(estimator)
new_estimator.set_params(**params)
estimators.append(new_estimator)
return estimators
def _fit_best_estimator(self, X, y, sample_weight=None):
# Training classifier once again
self.best_estimator_ = sklearn.clone(self.base_estimator).set_params(**self.generator.best_params_)
if sample_weight is None:
self.best_estimator_.fit(X, y)
else:
self.best_estimator_.fit(X, y, sample_weight=sample_weight)
def _fit(self, X, y, num_workers=1, verbose=False):
t = time.time()
param_grid = [{
'C': [.001, .1, 10, 100],
}]
base_clf = sklearn.linear_model.LogisticRegression(
fit_intercept=False, class_weight='auto',
dual=False, penalty='l2')
kfold = sklearn.cross_validation.StratifiedKFold(y, 3)
param_iterator = sklearn.grid_search.ParameterGrid(param_grid)
out = joblib.Parallel(n_jobs=num_workers, pre_dispatch=num_workers * 2)(
joblib.delayed(fit_grid_point)(
X[train], y[train], X[test], y[test], base_clf, clf_params)
for clf_params in param_iterator for train, test in kfold
)
df = pandas.DataFrame(out, columns=['setting', 'score', 'entropy'])
dfg = df.groupby('setting').mean()
if verbose:
print(dfg)
dfg = dfg.sort(['score', 'entropy'], ascending=[0, 1])
best_params = json.loads(dfg.index[0])
best_score, best_entropy = dfg.ix[0].values
print('Best at {}: {:.2f} | {:.2f} and took {:.2f} s'.format(best_params, best_score, best_entropy, time.time() - t))
clf = sklearn.clone(base_clf)
clf.set_params(**best_params)
clf.fit(X, y)
return clf, best_score, best_entropy
def make_per_customer_submission(
dataset_name, estimator, x_transformation=identity, y_transformation=identity, include_variables=None
):
"""BROKEN FOR NOW!"""
with open(j(DATA_DIR, dataset_name, "per-customer-train.pickle"), "rb") as f:
dv, train_customers, train_y, train_x, train_weights = pickle.load(f)
choose_columnns = lambda x: x[
:, np.array(get_feature_indexi(dv.get_feature_names(), include_variables), dtype="int")
]
x_all_transforms = lambda x: x_transformation(choose_columnns(x))
model = clone(estimator)
try:
model.fit(x_all_transforms(train_x), y_transformation(train_y), sample_weight=train_weights)
except TypeError:
print("%s doesn't support `sample_weight`. Ignoring it." % str(model))
model.fit(x_all_transforms(train_x), y_transformation(train_y))
with open(j(DATA_DIR, dataset_name, "per-customer-test.pickle"), "rb") as f:
_, test_customers, test_y, test_x, test_weights = pickle.load(f)
with open(j("submissions", "%s.csv" % datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")), "w") as f:
f.write("customer_ID,plan\n")
for c, ps in zip(test_customers, model.predict(x_all_transforms(test_x))):
f.write("%s,%s\n" % (c, "".join(str(pp) for pp in ps)))
def fit(self, X, feature):
try:
feature = int(feature)
except Exception:
self.logger("feature should be int")
raise TypeError("feature should be int")
X = X.view(np.ndarray)
self.input_col_count = X.shape[1]
self.feature = feature
my_X = Misc.exclude_cols(X, self.feature)
my_y = X[:, self.feature]
y_mean = np.mean(my_y)
y_std = np.std(my_y)
# ref: http://www.sciencedirect.com/science/article/pii/S0893608004002102
self._learner.C = max(abs(y_mean + 3 * y_std), abs(y_mean - 3 * y_std))
cvs = cv.KFold(len(X), 10, shuffle=True)
output_errors = np.empty(0)
for train, test in cvs:
tmp_l = sklearn.clone(self._learner)
tmp_l.fit(my_X[train, :], X[train, self.feature])
output_errors = np.hstack((output_errors, tmp_l.predict(my_X[test, :]) - X[test, self.feature]))
self.error_std = np.std(output_errors)
self.error_mean = np.mean(output_errors)
self._learner.fit(my_X, X[:, self.feature])
return self
def fit(self, X, y, sample_weight=None):
label = self.uniform_label
self.uniform_label = numpy.array([label]) if isinstance(label, numbers.Number) else numpy.array(label)
sample_weight = check_sample_weight(y, sample_weight=sample_weight).copy()
assert numpy.all(numpy.in1d(y, [0, 1])), 'only two-class classification is supported by now'
y = column_or_1d(y)
y_signed = 2 * y - 1
X = pandas.DataFrame(X)
knn_indices = computeKnnIndicesOfSameClass(self.uniform_variables, X, y, self.n_neighbours)
# for those events with non-uniform label we repeat it's own index several times
for label in [0, 1]:
if label not in self.uniform_label:
knn_indices[y == label, :] = numpy.arange(len(y))[y == label][:, numpy.newaxis]
X = self.get_train_vars(X)
cumulative_score = numpy.zeros(len(X))
self.estimators = []
for stage in range(self.n_estimators):
classifier = sklearn.clone(self.base_estimator)
classifier.fit(X, y, sample_weight=sample_weight)
score = self.learning_rate * self.compute_score(classifier, X=X)
cumulative_score += score
sample_weight *= numpy.exp(- y_signed * numpy.take(score, knn_indices).mean(axis=1))
sample_weight = self.normalize_weights(y=y, sample_weight=sample_weight)
self.estimators.append(classifier)
def lambda_choice(penalty, lambdas, n_folds, K, y, n_iter=10000, verbose=0, n_jobs=-1):
estimator = fista.Fista(penalty=penalty, n_iter=n_iter)
infos = Parallel(n_jobs=n_jobs, verbose=verbose)(
delayed(_sub_info)(clone(estimator), K, y, K, y, lambda_)
for lambda_ in lambdas)
return infos
def fit_models(self, X, Y, bands=None):
""" Fit timeseries models for `bands` within `Y` for a given `X`
Args:
X (np.ndarray): design matrix (number of observations x number of
features)
Y (np.ndarray): independent variable matrix (number of series x
number of observations) observation in the X design matrix
bands (iterable): Subset of bands of `Y` to fit. If None are
provided, fit all bands in Y
Returns:
np.ndarray: fitted model objects
"""
if bands is None:
bands = np.arange(self.n_series)
models = []
for b in bands:
y = Y.take(b, axis=0)
model = sklearn.clone(self.lm).fit(X, y) # TODO: no clone?
# Add in RMSE calculation # TODO: numba?
model.rmse = ((y - model.predict(X)) ** 2).mean(axis=0) ** 0.5
# Add intercept to intercept term of design matrix
model.coef = model.coef_.copy()
model.coef[0] += model.intercept_
models.append(model)
return np.array(models)
def run(self, directory="datasets/"):
loader = ArffLoader("{}/{}.arff".format(directory, self.dataset))
inputs, labels = loader.get_dataset()
n_features = inputs.shape[1]
if self.subset_size >= n_features:
return None
if self.normalize:
preprocessing.normalize(inputs, copy=False)
results = {
"experiment": self,
"scores": {scorer_name: numpy.zeros(self.n_runs) for scorer_name, _ in self.scorers},
"score_times": {scorer_name: numpy.zeros(self.n_runs) for scorer_name, _ in self.scorers},
"errors": {classifier_name: numpy.zeros(self.n_runs) for classifier_name, _ in self.classifiers},
"classifier_times": {classifier_name: numpy.zeros(self.n_runs) for classifier_name, _ in self.classifiers}
}
for run in range(self.n_runs):
numpy.random.seed(run)
indices = numpy.random.choice(n_features, size=self.subset_size, replace=False)
inputs_subset = inputs[:, indices].copy()
for scorer_name, scorer in self.scorers:
score, t = self._execute_score_run(run, scorer, inputs_subset, labels)
results["scores"][scorer_name][run] = score
results["score_times"][scorer_name][run] = t
for classifier_name, classifier in self.classifiers:
error, t = self._execute_classifier_run(run, sklearn.clone(classifier), inputs_subset, labels)
results["errors"][classifier_name][run] = error
results["classifier_times"][classifier_name][run] = t
return results
def score(parms):
e = sklearn.clone(learner)
pp = dict(parms)
pp.pop("id")
try:
e.set_params(**pp)
e.fit(*trainSet, maxIters=maxIters)
if visualParams is not None:
imgPathBase = os.path.join(imageDestFolder, "{}".format(store.params["id"]))
# Write some images!
e.visualize(visualParams, path=imgPathBase + ".png")
e.visualize(visualParams, path=imgPathBase + "_example.png", inputs=testSet[0][0])
if scoreModel is None:
return dict(score=e.score(*testSet))
else:
return scoreModel(e, testSet)
except:
sys.stderr.write("Error for {}:\n{}\n".format(parms, traceback.format_exc()))
if e.UNSUPERVISED:
score = 1.0
else:
score = -1.0
e = None
def estimate_classifier(params_dict, base_estimator, X, y, folds, fold_checks,
score_function, sample_weight=None, label=1,
scorer_needs_x=False, catch_exceptions=True):
"""This function is needed to train classifier with some parameters on the cluster."""
try:
k_folder = StratifiedKFold(y=y, n_folds=folds, shuffle=True)
score = 0.
for train_indices, test_indices in islice(k_folder, fold_checks):
trainX, trainY = X.irow(train_indices), y[train_indices]
testX, testY = X.irow(test_indices), y[test_indices]
estimator = sklearn.clone(base_estimator).set_params(**params_dict)
train_options = {}
test_options = {}
if sample_weight is not None:
train_weights, test_weights = \
sample_weight[train_indices], sample_weight[test_indices]
train_options['sample_weight'] = train_weights
test_options['sample_weight'] = test_weights
if scorer_needs_x:
test_options['X'] = testX
estimator.fit(trainX, trainY, **train_options)
proba = estimator.predict_proba(testX)
score += score_function(testY, proba[:, label], **test_options)
return score / fold_checks
except Exception as e:
# If there was some exception on the node, it will be returned
if catch_exceptions:
return e
else:
raise
def train(self, n_experts, instances, labels):
experts = []
self.weigher_sampler.train(instances)
for centroid in self.centroid_picker.pick(instances, labels, n_experts):
expert = LocalExpert(sklearn.clone(self.base_estimator), self.weigher_sampler)
expert.train(instances, labels, centroid)
experts.append(expert)
return experts
def __init__(self, test_indices=None,
lm=sklearn.linear_model.Lasso(alpha=20),
**kwargs):
self.test_indices = np.asarray(test_indices)
self.lm = sklearn.clone(lm)
self.n_record = 0
self.record = []
def single_learning_rate(mf, learning_rate, X_tr, X_te):
mf = clone(mf)
mf.set_params(learning_rate=learning_rate, verbose=5)
cb = Callback(X_tr, X_te)
mf.set_params(callback=cb)
mf.fit(X_tr)
return dict(time=cb.times,
rmse=cb.rmse)
def __call__(self, base_estimator, params, X, y, sample_weight=None):
cl = clone(base_estimator)
cl.set_params(**params)
if sample_weight is not None:
cl.fit(X, y, sample_weight)
else:
cl.fit(X, y)
return roc_auc_score(self.testY, cl.predict_proba(self.testX)[:, 1])
def custom(base_estimator, params, X, y, sample_weight=None):
cl = clone(base_estimator)
cl.set_params(**params)
if sample_weight is not None:
cl.fit(X, y, sample_weight)
else:
cl.fit(X, y)
return roc_auc_score(labels, cl.predict_proba(test)[:, 1])
def fit_grid_point(X, y, X_val, y_val, base_clf, clf_params):
clf = sklearn.clone(base_clf)
clf.set_params(**clf_params)
clf.fit(X, y)
proba = clf.predict_proba(X_val)
score = sklearn.metrics.accuracy_score(proba.argmax(1), y_val)
entropy = (scipy.stats.distributions.entropy(proba.T) / np.log(proba.shape[1])).mean()
return json.dumps(clf_params), score, entropy
def test_get_metric_equivalent_to_explicit_mahalanobis(estimator,
build_dataset):
"""Tests that using the get_metric method of mahalanobis metric learners is
equivalent to explicitely calling scipy's mahalanobis metric
"""
rng = np.random.RandomState(42)
input_data, labels, _, X = build_dataset()
model = clone(estimator)
set_random_state(model)
model.fit(*remove_y(estimator, input_data, labels))
metric = model.get_metric()
n_features = X.shape[1]
a, b = (rng.randn(n_features), rng.randn(n_features))
expected_dist = mahalanobis(a[None],
b[None],
VI=model.get_mahalanobis_matrix())
assert_allclose(metric(a, b), expected_dist, rtol=1e-13)
def test_score_pairs_dim(estimator, build_dataset):
# scoring of 3D arrays should return 1D array (several tuples),
# and scoring of 2D arrays (one tuple) should return an error (like
# scikit-learn's error when scoring 1D arrays)
input_data, labels, _, X = build_dataset()
model = clone(estimator)
set_random_state(model)
model.fit(input_data, labels)
tuples = np.array(list(product(X, X)))
assert model.score_pairs(tuples).shape == (tuples.shape[0], )
context = make_context(estimator)
msg = ("3D array of formed tuples expected{}. Found 2D array "
"instead:\ninput={}. Reshape your data and/or use a preprocessor.\n"
.format(context, tuples[1]))
with pytest.raises(ValueError) as raised_error:
model.score_pairs(tuples[1])
assert str(raised_error.value) == msg
def test_data_valid(model, data, target):
is_log = 0
if is_log == 1:
target = np.log(target)
test_model = clone(model)
X_train, X_test, y_train, y_test = train_test_split(data,
target,
test_size=0.33,
random_state=1)
test_model.fit(X_train, y_train)
y_predict = test_model.predict(X_test)
if (is_log == 1):
rmse(y_test, y_predict)
else:
rmse_log(y_test, y_predict)
def crossval_fm_scores(numbered_params, fm, vecs, labels, groups, n_params):
"""Calculates the mean avg precision score for a given classifier via CV.
Used at training time to find the MAP for the given classifier and params
via cross validation.
"""
assert isinstance(fm, FusionModel)
params_number, params = numbered_params
logger.debug(
'%s: param %d/%d, starting CV for:\nparams: %s',
fm.name, params_number, n_params, params)
clf = clone(fm.clf)
clf.set_params(**params)
n_splits = config.FUSION_PARAM_TUNING_CV_KFOLD
splits = GroupKFold(n_splits).split(vecs, labels, groups=groups)
map_ = parallel_map if fm.parallelize_cv else map
single_split_func = partial(
crossval_fm_score_single_split,
fm=fm,
clf=clf,
params=params,
params_number=params_number,
n_params=n_params,
n_splits=n_splits,
vecs=vecs,
labels=labels,
groups=groups,
)
cv_results = map_(single_split_func, enumerate(splits, 1))
training_times, all_avg_precs = zip(*cv_results)
all_avg_precs = list(itertools.chain.from_iterable(all_avg_precs))
t = sum(training_times, timedelta())
# if params_number % 10 == 0:
# logger.info('%s: completed crossval of param %d', name, params_number)
mean_avg_prec = np.mean(all_avg_precs)
std_avg_prec = np.std(all_avg_precs)
logger.info(
'%s: param %d/%d:\n'
'params: %s\n'
'CV results: score (MAP): %.3f (std: %.3f), training time: %s',
fm.name, params_number, n_params, params, mean_avg_prec, std_avg_prec, t
)
return mean_avg_prec, std_avg_prec
def fit(self, X, y=None):
"""
Fit the model using X, y as training data. Will also learn the groups that exist within the dataset.
:param X: array-like, shape=(n_columns, n_samples,) training data.
:param y: array-like, shape=(n_samples,) training data.
:return: Returns an instance of self.
"""
X, y = self.__prepare_input_data(X, y)
if self.shrinkage is not None:
self.__set_shrinkage_function()
self.group_colnames_ = [str(_) for _ in as_list(self.groups)]
if self.value_columns is not None:
self.value_colnames_ = [str(_) for _ in as_list(self.value_columns)]
else:
self.value_colnames_ = [_ for _ in X.columns if _ not in self.group_colnames_]
self.__validate(X, y)
# List of all hierarchical subsets of columns
self.group_colnames_hierarchical_ = expanding_list(self.group_colnames_, list)
self.fallback_ = None
if self.shrinkage is None and self.use_global_model:
subset_x = X[self.value_colnames_]
self.fallback_ = clone(self.estimator).fit(subset_x, y)
if self.shrinkage is not None:
self.estimators_ = {}
for level_colnames in self.group_colnames_hierarchical_:
self.estimators_.update(
self.__fit_grouped_estimator(X, y, self.value_colnames_, level_colnames)
)
else:
self.estimators_ = self.__fit_grouped_estimator(X, y, self.value_colnames_, self.group_colnames_)
self.groups_ = as_list(self.estimators_.keys())
if self.shrinkage is not None:
self.shrinkage_factors_ = self.__get_shrinkage_factor(X)
return self
def test_validate_calibration_params_invalid_parameters_error_before__fit(
estimator, build_dataset):
"""For all pairs metric learners (which currently all have a _fit method),
make sure that calibration parameters are validated before fitting"""
estimator = clone(estimator)
input_data, labels, _, _ = build_dataset()
def breaking_fun(**args): # a function that fails so that we will miss
# the calibration at the end and therefore the right error message from
# validating params should be thrown before
raise RuntimeError('Game over.')
estimator._fit = breaking_fun
expected_msg = ('Strategy can either be "accuracy", "f_beta" or '
'"max_tpr" or "max_tnr". Got "weird" instead.')
with pytest.raises(ValueError) as raised_error:
estimator.fit(input_data, labels, calibration_params={'strategy': 'weird'})
assert str(raised_error.value) == expected_msg
def test_pipeline_consistency(estimator, build_dataset, with_preprocessor):
# Adapted from scikit learn
# check that make_pipeline(est) gives same score as est
# we do this test on all except quadruplets (since they don't have a y
# in fit):
if estimator.__class__.__name__ not in [
e.__class__.__name__ for (e, _) in quadruplets_learners
]:
input_data, y, preprocessor, _ = build_dataset(with_preprocessor)
def make_random_state(estimator, in_pipeline):
rs = {}
name_estimator = estimator.__class__.__name__
if name_estimator[-11:] == '_Supervised':
name_param = 'random_state'
if in_pipeline:
name_param = name_estimator.lower() + '__' + name_param
rs[name_param] = check_random_state(0)
return rs
estimator = clone(estimator)
estimator.set_params(preprocessor=preprocessor)
pipeline = make_pipeline(estimator)
estimator.fit(*remove_y_quadruplets(estimator, input_data, y),
**make_random_state(estimator, False))
pipeline.fit(*remove_y_quadruplets(estimator, input_data, y),
**make_random_state(estimator, True))
if hasattr(estimator, 'score'):
result = estimator.score(
*remove_y_quadruplets(estimator, input_data, y))
result_pipe = pipeline.score(
*remove_y_quadruplets(estimator, input_data, y))
assert_allclose_dense_sparse(result, result_pipe)
if hasattr(estimator, 'predict'):
result = estimator.predict(input_data)
result_pipe = pipeline.predict(input_data)
assert_allclose_dense_sparse(result, result_pipe)
if issubclass(estimator.__class__, TransformerMixin):
if hasattr(estimator, 'transform'):
result = estimator.transform(input_data)
result_pipe = pipeline.transform(input_data)
assert_allclose_dense_sparse(result, result_pipe)
def fit(self, X, y, sample_weight=None):
"""
Train the classifier, will train several base classifiers on overlapping
subsets of training dataset.
:param X: pandas.DataFrame of shape [n_samples, n_features]
:param y: labels of events - array-like of shape [n_samples]
:param sample_weight: weight of events,
array-like of shape [n_samples] or None if all weights are equal
"""
if hasattr(self.base_estimator, 'features'):
assert self.base_estimator.features is None, 'Base estimator must have None features! ' \
'Use features parameter in Folding to fix it'
X, y, sample_weight = check_inputs(X,
y,
sample_weight=sample_weight,
allow_none_weights=True)
X = self._get_features(X)
self._set_classes(y)
folds_column = self._get_folds_column(len(X))
for _ in range(self.n_folds):
self.estimators.append(clone(self.base_estimator))
if sample_weight is None:
weights_iterator = (None for _ in range(self.n_folds))
else:
weights_iterator = (sample_weight[folds_column != index]
for index in range(self.n_folds))
result = utils.map_on_cluster(self.ipc_profile, train_estimator,
range(len(self.estimators)),
self.estimators,
(X.iloc[folds_column != index, :].copy()
for index in range(self.n_folds)),
(y[folds_column != index]
for index in range(self.n_folds)),
weights_iterator)
for status, data in result:
if status == 'success':
name, classifier, spent_time = data
self.estimators[name] = classifier
else:
print('Problem while training on the node, report:\n', data)
return self
def setup_dummy_YATSM(X, Y, dates, i_breaks):
""" Setup a dummy YATSM model
Args:
X (np.ndarray): n x p features
Y (np.ndarray): n_series x n independent data
dates (np.ndarray): n dates
i_breaks (iterable): indices of ``dates`` representing break dates
(can be zero or nonzero, but len(i_breaks) is len(yatsm.record))
Returns:
YATSM model
"""
n = dates.size
yatsm = YATSM()
yatsm.X, yatsm.Y, yatsm.dates = X, Y, dates
yatsm.n_coef, yatsm.n_series = X.shape[1], Y.shape[0]
yatsm.models = np.array(
[sklearn.clone(yatsm.estimator) for i in range(yatsm.n_series)])
yatsm.test_indices = np.arange(yatsm.n_series)
n_models = len(i_breaks)
yatsm.record = np.hstack([yatsm.record_template] * n_models)
def populate_record(yatsm, i_rec, i_start, i_end, i_break):
yatsm.record[i_rec]['start'] = yatsm.dates[i_start]
yatsm.record[i_rec]['end'] = yatsm.dates[i_end]
yatsm.record[i_rec]['break'] = (yatsm.dates[i_break]
if i_break else i_break)
yatsm.fit_models(X[i_start:i_end, :], Y[:, i_start:i_end])
for i, m in enumerate(yatsm.models):
yatsm.record[i_rec]['coef'][:, i] = m.coef
yatsm.record[i_rec]['rmse'][i] = m.rmse
return yatsm
i_start = 0
i_end = i_breaks[0] - 1 if i_breaks[0] else n - 1
i_break = i_breaks[0]
yatsm = populate_record(yatsm, 0, i_start, i_end, i_break)
for idx, i_break in enumerate(i_breaks[1:]):
i_start = i_breaks[idx] + 1
i_end = i_break - 1 if i_break else n - 1
yatsm = populate_record(yatsm, idx + 1, i_start, i_end, i_break)
return yatsm
def __init__(self,
test_indices=None,
estimator={
'object': sklearn.linear_model.Lasso(alpha=20),
'fit': {}
},
**kwargs):
self.test_indices = np.asarray(test_indices)
self.estimator = sklearn.clone(estimator['object'])
self.estimator_fit = estimator.get('fit', {})
self.models = [] # leave empty, fill in during `fit`
self.n_record = 0
self.record = []
self.n_series, self.n_features = 0, 0
self.px = kwargs.get('px', 0)
self.py = kwargs.get('py', 0)
def _train(train_data: DataFrame, time_series_predictor: Any,
clusterer: Clustering) -> dict:
models = dict()
train_data = clusterer.cluster_data(train_data)
for cluster in range(clusterer.n_clusters):
cluster_train_df = train_data[cluster]
if not cluster_train_df.empty:
time_series_predictor.fit(cluster_train_df)
models[cluster] = time_series_predictor
time_series_predictor = clone(time_series_predictor, safe=False)
return {
ModelType.CLUSTERER.value: clusterer,
ModelType.TIME_SERIES_PREDICTOR.value: models
}
def _setparams_clustering(method, masker, n_clusters, crop=False):
"""Setting the parameters of the clustering method
method : sklearn clustering-like or Random Projections
masker : NiftiMasker
n_clusters : int
crop: bool, only for slic
"""
method = clone(method)
if hasattr(method, 'n_clusters'):
method.set_params(**{'masker': masker, 'n_clusters': n_clusters})
if hasattr(method, 'crop'):
method.set_params(crop=crop)
else:
method.set_params(n_components=n_clusters)
return method
def fit(self, X, y, **kwargs):
"""Fit the estimator.
If `prefit` is set to `True` then the base estimator is kept as is.
Otherwise it is fitted from the provided arguments.
"""
if self.estimator is None:
raise ValueError(BASE_ESTIMATOR_NONE_ERROR_MESSAGE)
if not self.prefit:
self.estimator_ = clone(self.estimator).fit(X, y, **kwargs)
else:
try:
check_is_fitted(self.estimator)
except NotFittedError:
warn(BASE_ESTIMATOR_NOT_FITTED_WARNING.format(type(self).__name__))
self.estimator_ = self.estimator
return self
def extra_trees(X,y,n_est):
'''
INPUT: Dataframe with features (X), target variable dataframe (y), number of estimators (parameter)
OUTPUT: Score of ExtaTrees model
'''
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
ext = ExtraTreesRegressor(n_estimators=n_est)
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std
clf = clone(ext)
clf = ext.fit(X_train, y_train)
scores = ext.score(X_test, y_test)
return 'ExtraTrees Score: '+str(scores), dict(zip(cols2, clf.feature_importances_))
def get_deep_copy(self):
"""
This creates an untrained copy of the model structure
"""
if self.mtype == mtype.SKL:
return clone(self.model)
elif self.mtype == mtype.XG:
if isinstance(self.model, XGBRegressor):
return XGBRegressor(**self.model.get_params())
if isinstance(self.model, XGBClassifier):
return XGBClassifier(**self.model.get_params())
elif self.mtype == mtype.LGBM:
# TODO
pass
elif self.mtype == mtype.KERAS:
# TODO Consider whether ref is to KerasNN wrapper or the model itself. Probably latter.
#return KerasNN(self.model.neuronList, self.model.dropout, self.model.epochs)
pass
def randomForests(X, y, X_test):
predictions = []
n_estimators = 200
models = [
DecisionTreeClassifier(max_depth=None),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(DecisionTreeClassifier(max_depth=None),
n_estimators=n_estimators)
]
for model in models:
clf = clone(model)
clf = model.fit(X, y)
prediction = clf.predict(X_test)
predictions.append(prediction)
return (predictions)
def train_predict(self, X_train, y_train, X_predict):
# ~~~~~~~~~~~~~~~~~~ Summary ~~~~~~~~~~~~~~~~~~~~
# This function will train on the entire training dataset and will then create predictions on the Kaggle test set.
# This function is mean primarily for the purpose of submission.
#
# ~~~~~~~~~~~~~~~~ Parameters ~~~~~~~~~~~~~~~~~~~
# Input:
# - X_train: Full train dataset to train on
# - y_train: Full y train to train on
# - X_predict: Full test dataset (without labels)
# Output:
# - y_pred: predictions on X
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
instance = clone(self.model)
instance.fit(X_train, y_train)
y_pred = instance.predict(X_predict)
return y_pred
def __init__(self,
n_trees=500,
min_leaf_size=10, max_depth=10,
subsample_ratio=0.7,
bootstrap=False,
lambda_reg=0.01,
propensity_model=LogisticRegression(penalty='l1', solver='saga',
multi_class='auto'), # saga solver supports l1
model_Y=WeightedLassoCVWrapper(cv=3),
propensity_model_final=None,
model_Y_final=None,
categories='auto',
n_jobs=-1,
random_state=None):
# Copy and/or define models
self.propensity_model = clone(propensity_model, safe=False)
self.model_Y = clone(model_Y, safe=False)
self.propensity_model_final = clone(propensity_model_final, safe=False)
self.model_Y_final = clone(model_Y_final, safe=False)
if self.propensity_model_final is None:
self.propensity_model_final = clone(self.propensity_model, safe=False)
if self.model_Y_final is None:
self.model_Y_final = clone(self.model_Y, safe=False)
# Nuisance estimators shall be defined during fitting because they need to know the number of distinct
# treatments
nuisance_estimator = None
second_stage_nuisance_estimator = None
# Define parameter estimators
parameter_estimator = DiscreteTreatmentOrthoForest.parameter_estimator_func
second_stage_parameter_estimator =\
DiscreteTreatmentOrthoForest.second_stage_parameter_estimator_gen(lambda_reg)
# Define moment and mean gradient estimator
moment_and_mean_gradient_estimator =\
DiscreteTreatmentOrthoForest.moment_and_mean_gradient_estimator_func
if categories != 'auto':
categories = [categories] # OneHotEncoder expects a 2D array with features per column
self._one_hot_encoder = OneHotEncoder(categories=categories, sparse=False, drop='first')
super(DiscreteTreatmentOrthoForest, self).__init__(
nuisance_estimator,
second_stage_nuisance_estimator,
parameter_estimator,
second_stage_parameter_estimator,
moment_and_mean_gradient_estimator,
n_trees=n_trees,
min_leaf_size=min_leaf_size,
max_depth=max_depth,
subsample_ratio=subsample_ratio,
bootstrap=bootstrap,
n_jobs=n_jobs,
random_state=random_state)
def test_predict_monotonous(estimator, build_dataset, with_preprocessor):
"""Test that there is a threshold distance separating points labeled as
similar and points labeled as dissimilar """
input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)
estimator = clone(estimator)
estimator.set_params(preprocessor=preprocessor)
set_random_state(estimator)
pairs_train, pairs_test, y_train, y_test = train_test_split(
input_data, labels)
estimator.fit(pairs_train, y_train)
distances = estimator.score_pairs(pairs_test)
predictions = estimator.predict(pairs_test)
min_dissimilar = np.min(distances[predictions == -1])
max_similar = np.max(distances[predictions == 1])
assert max_similar <= min_dissimilar
separator = np.mean([min_dissimilar, max_similar])
assert (predictions[distances > separator] == -1).all()
assert (predictions[distances < separator] == 1).all()
def adapt_ubm(ubm, X, adapt_params='m', adapt_iter=10):
# clone UBM (n_components, covariance type, etc...)
gmm = sklearn.clone(ubm)
# initialize with UBM precomputed weights, means and covariance matrices
gmm.n_init = 1
gmm.init_params = ''
gmm.weights_ = ubm.weights_
gmm.means_ = ubm.means_
gmm.covars_ = ubm.covars_
# adapt only some parameters
gmm.params = adapt_params
gmm.n_iter = adapt_iter
gmm.fit(X)
return gmm
def __init__(self,
forecaster,
refit_interval=0,
refit_window_size=None,
refit_window_lag=0):
self.forecaster = forecaster
self.forecaster_ = clone(forecaster)
self.refit_interval = refit_interval
self.refit_window_size = refit_window_size
self.refit_window_lag = refit_window_lag
super(UpdateRefitsEvery, self).__init__()
self.clone_tags(forecaster)
# fit must be executed to fit the wrapped estimator and remember the cutoff
self.set_tags(fit_is_empty=False)
def learn_curve(model, train_x, train_label, cv=3, scoring='neg_log_loss'):
model_c = clone(model)
N, train_score, test_score = learning_curve(model_c,
train_x,
train_label,
cv=cv,
train_sizes=np.linspace(
0.5, 1, 5),
scoring=scoring)
plt.figure(figsize=(7, 4))
plt.title('{}'.format(type(model).__name__))
plt.plot(N, np.mean(train_score, 1), color='blue', label='training score')
plt.plot(N, np.mean(test_score, 1), color='red', label='validation score')
plt.xlabel('training sample')
plt.ylabel(scoring)
plt.legend(loc=0)
plt.show()
def _transform(self, X, y=None):
"""Transform X and return a transformed version.
private _transform containing core logic, called from transform
Parameters
----------
X : Series or Panel of mtype X_inner_mtype
if X_inner_mtype is list, _transform must support all types in it
Data to be transformed
y : Series or Panel of mtype y_inner_mtype, default=None
Additional data, e.g., labels for transformation
Returns
-------
transformed version of X
"""
return clone(self.transformer).fit_transform(X=X, y=y)
def test_dont_overwrite_parameters(estimator, build_dataset,
with_preprocessor):
# Adapted from scikit-learn
# check that fit method only changes or sets private attributes
input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)
estimator = clone(estimator)
estimator.set_params(preprocessor=preprocessor)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
dict_before_fit = estimator.__dict__.copy()
estimator.fit(*remove_y_quadruplets(estimator, input_data, labels))
dict_after_fit = estimator.__dict__
public_keys_after_fit = [
key for key in dict_after_fit.keys() if is_public_parameter(key)
]
attrs_added_by_fit = [
key for key in public_keys_after_fit
if key not in dict_before_fit.keys()
]
# check that fit doesn't add any public attribute
assert not attrs_added_by_fit, (
"Estimator adds public attribute(s) during"
" the fit method."
" Estimators are only allowed to add private "
"attributes"
" either started with _ or ended"
" with _ but %s added" % ', '.join(attrs_added_by_fit))
# check that fit doesn't change any public attribute
attrs_changed_by_fit = [
key for key in public_keys_after_fit
if (dict_before_fit[key] is not dict_after_fit[key])
]
assert not attrs_changed_by_fit, (
"Estimator changes public attribute(s) during"
" the fit method. Estimators are only allowed"
" to change attributes started"
" or ended with _, but"
" %s changed" % ', '.join(attrs_changed_by_fit))
def __init__(self,
controls_model,
treated_model,
cate_controls_model=None,
cate_treated_model=None,
propensity_model=LogisticRegression(),
propensity_func=None):
self.controls_model = clone(controls_model, safe=False)
self.treated_model = clone(treated_model, safe=False)
self.cate_controls_model = clone(cate_controls_model, safe=False)
self.cate_treated_model = clone(cate_treated_model, safe=False)
if self.cate_controls_model is None:
self.cate_controls_model = clone(self.controls_model, safe=False)
if self.cate_treated_model is None:
self.cate_treated_model = clone(self.treated_model, safe=False)
self.propensity_func = clone(propensity_func, safe=False)
self.propensity_model = clone(propensity_model, safe=False)
self.has_propensity_func = self.propensity_func is not None
def test_cross_validation_manual_vs_scikit(estimator, build_dataset,
with_preprocessor):
"""Tests that if we make a manual cross-validation, the result will be the
same as scikit-learn's cross-validation (some code for generating the
folds is taken from scikit-learn).
"""
if any(hasattr(estimator, method) for method in ["predict", "score"]):
input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)
estimator = clone(estimator)
estimator.set_params(preprocessor=preprocessor)
set_random_state(estimator)
n_splits = 3
kfold = KFold(shuffle=False, n_splits=n_splits)
n_samples = input_data.shape[0]
fold_sizes = (n_samples // n_splits) * np.ones(n_splits, dtype=np.int)
fold_sizes[:n_samples % n_splits] += 1
current = 0
scores, predictions = [], np.zeros(input_data.shape[0])
for fold_size in fold_sizes:
start, stop = current, current + fold_size
current = stop
test_slice = slice(start, stop)
train_mask = np.ones(input_data.shape[0], bool)
train_mask[test_slice] = False
y_train, y_test = labels[train_mask], labels[test_slice]
estimator.fit(*remove_y_quadruplets(
estimator, input_data[train_mask], y_train))
if hasattr(estimator, "score"):
scores.append(
estimator.score(*remove_y_quadruplets(
estimator, input_data[test_slice], y_test)))
if hasattr(estimator, "predict"):
predictions[test_slice] = estimator.predict(
input_data[test_slice])
if hasattr(estimator, "score"):
assert all(scores == cross_val_score(
estimator,
*remove_y_quadruplets(estimator, input_data, labels),
cv=kfold))
if hasattr(estimator, "predict"):
assert all(predictions == cross_val_predict(
estimator,
*remove_y_quadruplets(estimator, input_data, labels),
cv=kfold))
def fit(self, X, y=None):
"""
Fit the model using X, y as training data. Will also learn the groups that exist within the dataset.
:param X: array-like, shape=(n_columns, n_samples,) training data.
:param y: array-like, shape=(n_samples,) training data.
:return: Returns an instance of self.
"""
X_group, X_value = _split_groups_and_values(X,
self.groups,
min_value_cols=0,
check_X=self.check_X,
**self._check_kwargs)
X_group = self.__add_shrinkage_column(X_group)
if y is not None:
y = check_array(y, ensure_2d=False)
if self.shrinkage is not None:
self.__set_shrinkage_function()
# List of all hierarchical subsets of columns
self.group_colnames_hierarchical_ = expanding_list(
X_group.columns, list)
self.fallback_ = None
if self.shrinkage is None and self.use_global_model:
self.fallback_ = clone(self.estimator).fit(X_value, y)
if self.shrinkage is not None:
self.estimators_ = self.__fit_shrinkage_groups(X_group, X_value, y)
else:
self.estimators_ = self.__fit_grouped_estimator(
X_group, X_value, y)
self.groups_ = as_list(self.estimators_.keys())
if self.shrinkage is not None:
self.shrinkage_factors_ = self.__get_shrinkage_factor(X_group)
return self
def stacking(model, train_data, train_target, test_data,test_target,n_fold):
"""
:param model: 模型算法
:param train_data: 训练集(不含带预测的目标特征)
:param train_target: 需要预测的目标特征
:param test_data: 测试集
:param n_fold: 交叉验证的折数
:return:
"""
train_data = pd.DataFrame(train_data)
train_target = pd.DataFrame(train_target)
test_data = pd.DataFrame(test_data)
skf = StratifiedKFold(n_splits=n_fold,shuffle=True) # StratifiedKFold 默认分层采样
train_pred = np.zeros((train_data.shape[0], 1), int) # 存储训练集预测结果
test_pred = np.zeros((test_data.shape[0], 1), int) # 存储测试集预测结果 行数：len(test_data) ,列数：1列
for skf_index, (train_index, val_index) in enumerate(skf.split(train_data, train_target)):
print('第 ', skf_index + 1, ' 折交叉验证开始... ')
# 训练集划分
new_model = clone(model)
# print('pre-model',model)
# print('new-model',new_model)
x_train, x_val = train_data.iloc[train_index], train_data.iloc[val_index]
y_train, y_val = train_target.iloc[train_index], train_target.iloc[val_index]
# 模型构建
y_train = np.ravel(y_train) # 向量转成数组
new_model.fit(X=x_train, y=y_train)
# 模型预测
accs = accuracy_score(y_val, new_model.predict(x_val))
print('第 ', skf_index + 1, ' 折交叉验证 : accuracy ： ', accs)
# 训练集预测结果
val_pred = new_model.predict(x_val)
for i in range(len(val_index)):
train_pred[val_index[i]] = val_pred[i]
# 保存测试集预测结果
print('第 ', skf_index + 1, ' 折accuracy ： ', accuracy_score(test_target, new_model.predict(test_data)))
test_pred = np.column_stack((test_pred, new_model.predict(test_data))) # 将矩阵按列合并
test_pred_mean = np.mean(test_pred, axis=1) # 按行计算均值(会出现小数)
test_pred_mean = pd.DataFrame(test_pred_mean) # 转成DataFrame
test_pred_mean = test_pred_mean.apply(lambda x: round(x)) # 小数需要四舍五入成整数
test_set = np.ravel(test_pred_mean)
return test_set.reshape(test_set.shape[0],1), np.array(train_pred)
def sWeights_to_proba(x,
sWeights,
model,
use_cross_val=False,
cv_params={
"cv": 4,
"n_jobs": 1
}):
if model.get_params()['loss_function'] != 'ConstrainedRegression':
raise ValueError(
"Smart training requires catboost with ConstrainedRegression loss")
if use_cross_val:
raw_predictions = sklearn.model_selection.cross_val_predict(
model, x, sWeights, **cv_params)
else:
model_ = sklearn.clone(model)
model_.fit(x, sWeights)
raw_predictions = model_.predict(x)
return expit(raw_predictions)
def test_accuracy_toy_example(estimator, build_dataset):
"""Test that the default scoring for triplets (accuracy) works on some
toy example"""
triplets, _, _, X = build_dataset(with_preprocessor=False)
estimator = clone(estimator)
set_random_state(estimator)
estimator.fit(triplets)
# We take the two first points and we build 4 regularly spaced points on the
# line they define, so that it's easy to build triplets of different
# similarities.
X_test = X[0] + np.arange(4)[:, np.newaxis] * (X[0] - X[1]) / 4
triplets_test = np.array([[X_test[0], X_test[2], X_test[1]],
[X_test[1], X_test[3], X_test[0]],
[X_test[1], X_test[2], X_test[3]],
[X_test[3], X_test[0], X_test[2]]])
# we force the transformation to be identity so that we control what it does
estimator.components_ = np.eye(X.shape[1])
assert estimator.score(triplets_test) == 0.25
