def data_sampling(data,num_of_samples):
random = check_random_state(seed=None)
n_samples, n_features = data.shape
if(num_of_samples>n_samples):
indexes = np.concatenate((sample_without_replacement(n_samples, n_samples, random_state=random), random.randint(0, n_samples, num_of_samples-n_samples)), axis=None)
else:
indexes = sample_without_replacement(n_samples, num_of_samples, random_state=random)
return data.loc[indexes]
def mutation(self, pm):
n_mutations_in_features = self.random_state.binomial(self.genome_features.shape[0], pm)
n_mutations_in_samples = self.random_state.binomial(self.genome_samples.shape[0], pm)
mutated_samples = sample_without_replacement(
self.genome_samples.shape[0], n_mutations_in_samples, random_state=self.random_state
)
mutated_features = sample_without_replacement(
self.genome_features.shape[0], n_mutations_in_features, random_state=self.random_state
)
self.genome_samples[mutated_samples] = ~self.genome_samples[mutated_samples]
self.genome_features[mutated_features] = ~self.genome_features[mutated_features]
def data_sampling(data, replacement,num_of_samples):
random = check_random_state(seed=None)
n_samples, n_features = data.shape
if replacement:
indexes = random.randint(0, n_samples, num_of_samples)
else:
if(num_of_samples>n_samples):
indexes = sample_without_replacement(n_samples, n_samples, random_state=random)
else:
indexes = sample_without_replacement(n_samples, num_of_samples, random_state=random)
return data.loc[indexes]
def generate_indices(random_state, bootstrap, n_population, n_samples):
""" Draw randomly sampled indices. Internal use only.
See sklearn/ensemble/bagging.py
Parameters
----------
random_state : RandomState
A random number generator instance to define the state of the random
permutations generator.
bootstrap : bool
Specifies whether to bootstrap indice generation
n_population : int
Specifies the population size when generating indices
n_samples : int
Specifies number of samples to draw
Returns
-------
indices : numpy array, shape (n_samples,)
randomly drawn indices
"""
# Draw sample indices
if bootstrap:
indices = random_state.randint(0, n_population, n_samples)
else:
indices = sample_without_replacement(n_population, n_samples,
random_state=random_state)
return indices
def __init__(self, n_samples, n_features, ps, pf, random_state):
self.random_state = random_state
self.genome_features = np.zeros((n_features,), dtype=np.bool8)
self.genome_samples = np.zeros((n_samples,), dtype=np.bool8)
n_pick_samples = np.floor(n_samples * ps)
n_pick_features = np.floor(n_features * pf)
picked_samples = sample_without_replacement(n_samples, n_pick_samples, random_state=random_state)
picked_features = sample_without_replacement(n_features, n_pick_features, random_state=random_state)
self.genome_samples[picked_samples] = True
self.genome_features[picked_features] = True
self.cache_est_weight = 1
self.cache_contribution = 0
self.cache_predictions = None
def check_sample_int_distribution(sample_without_replacement):
# This test is heavily inspired from test_random.py of python-core.
#
# For the entire allowable range of 0 <= k <= N, validate that
# sample generates all possible permutations
n_population = 10
# a large number of trials prevents false negatives without slowing normal
# case
n_trials = 10000
for n_samples in range(n_population):
# Counting the number of combinations is not as good as counting the
# the number of permutations. However, it works with sampling algorithm
# that does not provide a random permutation of the subset of integer.
n_expected = combinations(n_population, n_samples, exact=True)
output = {}
for i in range(n_trials):
output[frozenset(sample_without_replacement(n_population,
n_samples))] = None
if len(output) == n_expected:
break
else:
raise AssertionError(
"number of combinations != number of expected (%s != %s)" %
(len(output), n_expected))
def run_test(X, Y, A, B, Sigma=None, proj=None, n_combinations=50000):
X, Y, A, B = normalize_list([X, Y, A, B], Sigma=Sigma, proj=proj)
if Sigma is not None:
A = np.matmul(A, Sigma)
B = np.matmul(B, Sigma)
base_statistics = statistics(X, Y, A, B)
union_XY = np.vstack((X, Y))
xy_size = union_XY.shape[0]
x_size = X.shape[0]
count = 0
all_idx = set(range(xy_size))
if comb(xy_size, x_size) > n_combinations:
for _ in range(n_combinations):
group_1_idx = sample_without_replacement(xy_size, x_size)
group_2_idx = list(all_idx.difference(group_1_idx))
sample_stat = statistics(union_XY[group_1_idx],
union_XY[group_2_idx], A, B)
count += sample_stat > base_statistics
else:
for group_1_idx in combinations(range(xy_size), x_size):
group_2_idx = list(all_idx.difference(group_1_idx))
sample_stat = statistics(union_XY[list(group_1_idx)],
union_XY[group_2_idx], A, B)
count += sample_stat > base_statistics
p_val = count / n_combinations
effect_val = effect_size(X, Y, A, B)
print('P-val is %f; effect size is %f' % (p_val, effect_val))
return p_val, effect_val
def check_sample_int(sample_without_replacement):
# This test is heavily inspired from test_random.py of python-core.
#
# For the entire allowable range of 0 <= k <= N, validate that
# the sample is of the correct length and contains only unique items
n_population = 100
for n_samples in range(n_population + 1):
s = sample_without_replacement(n_population, n_samples)
assert len(s) == n_samples
unique = np.unique(s)
assert np.size(unique) == n_samples
assert np.all(unique < n_population)
# test edge case n_population == n_samples == 0
assert np.size(sample_without_replacement(0, 0)) == 0
def check_sample_int_distribution(sample_without_replacement):
# This test is heavily inspired from test_random.py of python-core.
#
# For the entire allowable range of 0 <= k <= N, validate that
# sample generates all possible permutations
n_population = 10
# a large number of trials prevents false negatives without slowing normal
# case
n_trials = 10000
for n_samples in range(n_population):
# Counting the number of combinations is not as good as counting the
# the number of permutations. However, it works with sampling algorithm
# that does not provide a random permutation of the subset of integer.
n_expected = comb(n_population, n_samples, exact=True)
output = {}
for i in range(n_trials):
output[frozenset(
sample_without_replacement(n_population, n_samples))] = None
if len(output) == n_expected:
break
else:
raise AssertionError(
"number of combinations != number of expected (%s != %s)" %
(len(output), n_expected))
def sample_without_replacement_method(n_population,
n_samples,
random_state=None):
return sample_without_replacement(n_population,
n_samples,
method=m,
random_state=random_state)
def generate_indices(random_state, bootstrap, n_population, n_samples):
""" Draw randomly sampled indices. Internal use only.
See sklearn/ensemble/bagging.py
Parameters
----------
random_state : RandomState
A random number generator instance to define the state of the random
permutations generator.
bootstrap : bool
Specifies whether to bootstrap indice generation
n_population : int
Specifies the population size when generating indices
n_samples : int
Specifies number of samples to draw
Returns
-------
indices : numpy array, shape (n_samples,)
randomly drawn indices
"""
# Draw sample indices
if bootstrap:
indices = random_state.randint(0, n_population, n_samples)
else:
indices = sample_without_replacement(n_population,
n_samples,
random_state=random_state)
return indices
def check_sample_int(sample_without_replacement):
# This test is heavily inspired from test_random.py of python-core.
#
# For the entire allowable range of 0 <= k <= N, validate that
# the sample is of the correct length and contains only unique items
n_population = 100
for n_samples in range(n_population + 1):
s = sample_without_replacement(n_population, n_samples)
assert_equal(len(s), n_samples)
unique = np.unique(s)
assert_equal(np.size(unique), n_samples)
assert_true(np.all(unique < n_population))
# test edge case n_population == n_samples == 0
assert_equal(np.size(sample_without_replacement(0, 0)), 0)
def __iter__(self):
# check if all distributions are given as lists
# in this case we want to sample without replacement
all_lists = np.all([not hasattr(v, "rvs")
for v in self.param_distributions.values()])
rnd = check_random_state(self.random_state)
if all_lists:
# look up sampled parameter settings in parameter grid
param_grid = ParameterGrid(self.param_distributions)
grid_size = len(param_grid)
if grid_size < self.n_iter:
raise ValueError(
"The total space of parameters %d is smaller "
"than n_iter=%d." % (grid_size, self.n_iter)
+ " For exhaustive searches, use GridSearchCV.")
for i in sample_without_replacement(grid_size, self.n_iter,
random_state=rnd):
yield param_grid[i]
else:
# Always sort the keys of a dictionary, for reproducibility
items = sorted(self.param_distributions.items())
for _ in six.moves.range(self.n_iter):
params = dict()
for k, v in items:
if hasattr(v, "rvs"):
if sp_version < (0, 16):
params[k] = v.rvs()
else:
params[k] = v.rvs(random_state=rnd)
else:
params[k] = v[rnd.randint(len(v))]
yield params
def function4(percent, targetX): #this function make noisy to given percentage
percent = percent/100
sample = sample_without_replacement(len(targetX),percent*len(targetX))
change = random.sample(range(0,64),10)
for i in change:
targetX[sample][i] = abs(targetX[sample][i]-16)
return targetX
def _generate_indices(random_state, bootstrap, n_population, n_samples):
if bootstrap:
indices = random_state.randint(0, n_population, n_samples)
else:
indices = sample_without_replacement(n_population,
n_samples,
random_state=random_state)
return indices
def fit(self, X, y=None):
X = check_array(X)
n_samples, n_features = X.shape
random_state = check_random_state(self.random_state)
self.components = sample_without_replacement(
n_features, self.n_components, random_state=random_state)
return self
def check_edge_case_of_sample_int(sample_without_replacement):
# n_population < n_sample
assert_raises(ValueError, sample_without_replacement, 0, 1)
assert_raises(ValueError, sample_without_replacement, 1, 2)
# n_population == n_samples
assert_equal(sample_without_replacement(0, 0).shape, (0, ))
assert_equal(sample_without_replacement(1, 1).shape, (1, ))
# n_population >= n_samples
assert_equal(sample_without_replacement(5, 0).shape, (0, ))
assert_equal(sample_without_replacement(5, 1).shape, (1, ))
# n_population < 0 or n_samples < 0
assert_raises(ValueError, sample_without_replacement, -1, 5)
assert_raises(ValueError, sample_without_replacement, 5, -1)
def check_edge_case_of_sample_int(sample_without_replacement):
# n_poluation < n_sample
assert_raises(ValueError, sample_without_replacement, 0, 1)
assert_raises(ValueError, sample_without_replacement, 1, 2)
# n_population == n_samples
assert_equal(sample_without_replacement(0, 0).shape, (0, ))
assert_equal(sample_without_replacement(1, 1).shape, (1, ))
# n_population >= n_samples
assert_equal(sample_without_replacement(5, 0).shape, (0, ))
assert_equal(sample_without_replacement(5, 1).shape, (1, ))
# n_population < 0 or n_samples < 0
assert_raises(ValueError, sample_without_replacement, -1, 5)
assert_raises(ValueError, sample_without_replacement, 5, -1)
def _minify_dataset(ratings_df: pd.DataFrame,
random_state: int) -> pd.DataFrame:
users_count = len(ratings_df['user_id'].unique())
samples = 200 if 200 < users_count else users_count / 2
users_subset = set(
sample_without_replacement(users_count,
samples,
random_state=random_state))
return ratings_df[ratings_df['user_id'].isin(users_subset)]
def get_random_gene_df(gene_df, n_genes, label_col="type"):
labels = gene_df.loc[:, label_col]
unlab_df = gene_df.drop(label_col, axis=1)
index_set = sample_without_replacement(gene_df.shape[1], n_genes)
gene_arr_all = gene_df.columns
gene_arr_rand = gene_arr_all[index_set]
gene_df_rand = gene_df[gene_arr_rand]
gene_df_rand["type"] = labels
return gene_df_rand
def subsampled_hadamard_matrix(n_components, n_features, random_state=None):
"""Sub-sampled hadamard matrix to have shape n_components and n_features
A hadamard matrix of shape at (least n_components, n_features) is
subsampled without replacement.
Parameters
----------
n_components : int,
Dimensionality of the target projection space.
n_features : int,
Dimensionality of the original source space.
random_state : int, RandomState instance or None (default=None)
Control the pseudo random number generator used to generate the
matrix at fit time.
Returns
-------
components : numpy array of shape [n_components, n_features]
The generated random matrix.
"""
if n_components <= 0:
raise ValueError("n_components must be strictly positive, got %d" %
n_components)
if n_features <= 0:
raise ValueError("n_features must be strictly positive, got %d" %
n_components)
random_state = check_random_state(random_state)
n_hadmard_size = max(2**np.ceil(np.log2(x))
for x in (n_components, n_features))
row = sample_without_replacement(n_hadmard_size,
n_components,
random_state=random_state)
col = sample_without_replacement(n_hadmard_size,
n_features,
random_state=random_state)
hadamard_matrix = sp_hadamard(n_hadmard_size, dtype=np.float)[row][:, col]
hadamard_matrix *= 1 / np.sqrt(n_components)
return hadamard_matrix
def observer(state):
"""Observe simulation state with uncertainty -> approximate state."""
# First average over cells to get non-spatial distribution
ncells = int(np.round(len(state) / 15, 0))
state_by_cell = np.reshape(state, (int(ncells), 15))
# Species proportions
species_by_cell = np.array([
np.sum(state_by_cell[:, 0:3], axis=1),
np.sum(state_by_cell[:, 3:6], axis=1),
np.sum(state_by_cell[:, 6:9], axis=1),
np.sum(state_by_cell[:, 9:12], axis=1),
np.sum(state_by_cell[:, 12:14], axis=1), state_by_cell[:, 14]
]).T
# Add empty space to be sampled also
space = np.array(1.0 - np.sum(state_by_cell, axis=1)).reshape((400, 1))
cell_props = np.append(species_by_cell, space, axis=1)
# Create population of hosts and sample appropriately
population = np.array(pop_size * cell_props)
obs_states = []
for i in range(ncells):
sample = sample_without_replacement(pop_size, n_samples)
bins = np.append([0.0], np.cumsum(population[i]))
observed_species = np.histogram(sample, bins)[0]
observed_state = np.zeros(15)
# Tanoak
for j in range(4):
idcs = ((3 * j), (3 * j + 3))
inf_probs = state_by_cell[i, idcs[0]:idcs[1]] / np.sum(
state_by_cell[i, idcs[0]:idcs[1]])
inf_sample = np.random.choice(3,
observed_species[j],
p=inf_probs)
observed_state[idcs[0]:idcs[1]] = np.histogram(
inf_sample, range(4))[0]
# Bay
inf_probs = state_by_cell[i, 12:14] / np.sum(state_by_cell[i,
12:14])
inf_sample = np.random.choice(2, observed_species[4], p=inf_probs)
observed_state[12:14] = np.histogram(inf_sample, range(3))[0]
# Redwood
observed_state[14] = observed_species[5]
obs_states.append(observed_state)
obs_states = np.array(obs_states)
obs_state = (np.sum(obs_states, axis=0) / (n_samples * ncells))
return obs_state
def bootstrap_generator(n_bootstrap_iterations, sample_fraction, X, random_state=None):
"""Generates bootstrap samples from dataset."""
if random_state is not None:
np.random.seed(random_state)
random.seed(random_state)
n_samples = len(X)
n_subsamples = np.floor(sample_fraction * n_samples).astype(int)
for _ in range(n_bootstrap_iterations):
subsample = sample_without_replacement(n_samples, n_subsamples)
yield subsample
def subsampled_hadamard_matrix(n_components, n_features, random_state=None):
"""Sub-sampled hadamard matrix to have shape n_components and n_features
A hadamard matrix of shape at (least n_components, n_features) is
subsampled without replacement.
Parameters
----------
n_components : int,
Dimensionality of the target projection space.
n_features : int,
Dimensionality of the original source space.
random_state : int, RandomState instance or None (default=None)
Control the pseudo random number generator used to generate the
matrix at fit time.
Returns
-------
components : numpy array of shape [n_components, n_features]
The generated random matrix.
"""
if n_components <= 0:
raise ValueError("n_components must be strictly positive, got %d" %
n_components)
if n_features <= 0:
raise ValueError("n_features must be strictly positive, got %d" %
n_components)
random_state = check_random_state(random_state)
n_hadmard_size = max(2 ** np.ceil(np.log2(x))
for x in (n_components, n_features))
row = sample_without_replacement(n_hadmard_size, n_components,
random_state=random_state)
col = sample_without_replacement(n_hadmard_size, n_features,
random_state=random_state)
hadamard_matrix = sp_hadamard(n_hadmard_size, dtype=np.float)[row][:, col]
hadamard_matrix *= 1 / np.sqrt(n_components)
return hadamard_matrix
def decoderaccuracy_wtih_numcells(self, x, y, iterations, task,
classifier_type):
numcells = np.size(x, 1)
percsamples = [1, 5, 10, 20, 50, 80, 100]
numsamples = [np.int(numcells * (p / 100)) for p in percsamples]
numcells_dataframe = pd.DataFrame(
columns=['SampleSize', 'Split', 'R2', 'rho', 'score', 'errorprob'])
k = KFold(n_splits=numcell_kfold_splits,
random_state=None,
shuffle=False)
for n, ns in enumerate(numsamples):
print(f'Fitting on %d neurons' % ns)
for i in np.arange(iterations):
cells = sample_without_replacement(numcells, ns)
x_resample = x[:, cells]
count_cv = 1
# Also do k-fold validation for these iterations
for train_index, test_index in k.split(x_resample):
# print(f'Validation %d' % count_cv)
# Split data
x_rs_train, x_rs_test = x_resample[
train_index], x_resample[test_index]
y_rs_train, y_rs_test = y[train_index], y[test_index]
nbpfmodel = self.fit_SVM(x_rs_train,
y_rs_train,
classifier_type=classifier_type)
scores, prediction, probability = self.validate_model(
classifier_type=classifier_type,
model=nbpfmodel,
x_test=x_rs_test,
y_test=y_rs_test,
task=task,
plotflag=plot_numcells)
backend.clear_session()
R2 = CommonFunctions.get_R2(y_actual=y_rs_test,
y_predicted=prediction)
rho = CommonFunctions.get_R2(y_actual=y_rs_test,
y_predicted=prediction)
numcells_dataframe = numcells_dataframe.append(
{
'SampleSize': f'%d%%' % percsamples[n],
'Split': count_cv,
'R2': R2,
'rho': rho,
'score': scores,
'errorprob': probability
},
ignore_index=True)
count_cv += 1
return numcells_dataframe
def _generate_ts_indices(random_state, bootstrap, n_population, block_size):
"""Draw randomly sampled indices."""
# Draw sample indices
if bootstrap:
indices = mb_bootstrap_indicies(n_population, block_size)
else:
# FIXME: block bootstrap without replacement
indices = sample_without_replacement(
n_population, n_samples, random_state=random_state
)
return indices
def data_pseudo_labeling(unlabeled_dataset, neigh, keywords):
from sklearn.utils.random import sample_without_replacement
index_pseudo_data = sample_without_replacement(len(unlabeled_dataset),
seudo_labeled_data_size)
pseudo_data = unlabeled_dataset.iloc[sorted(index_pseudo_data)]
pseudo_predict = neigh.predict(pseudo_data[keywords])
pseudo_data.insert(0, 'Class', pseudo_predict)
selected_columns = keywords.insert(0, 'Patent_Number')
selected_columns = selected_columns.insert(0, 'Class')
pseudo_labeled_data = pseudo_data.filter(selected_columns)
return pseudo_labeled_data
def _generate_random_features(random_state, bootstrap, n_population,
n_samples):
"""Draw randomly sampled indices."""
# Draw sample indices
if bootstrap:
indices = random_state.randint(0, n_population, n_samples)
else:
indices = sample_without_replacement(n_population,
n_samples,
random_state=random_state)
return indices
def reportAccuracyVersusSparsityOfInput(df, normalized_df, model, labeled_tags,
train_index, test_index, percent):
N_train = train_index.shape[0]
train_subset_index = sample_without_replacement(N_train,
N_train * percent / 100)
subset_train_index = train_index[train_subset_index]
test_index_updated = set(train_index).union(set(test_index)).difference(
set(subset_train_index))
accuracy, c_matrix, ra_score = testClassification(df, normalized_df, model,
labeled_tags,
list(subset_train_index),
list(test_index_updated))
return accuracy, ra_score
def _generate_hypercube(samples, dimensions, rng):
"""Returns distinct binary samples of length dimensions
"""
if dimensions > 30:
return np.hstack([
rng.randint(2, size=(samples, dimensions - 30)),
_generate_hypercube(samples, 30, rng)
])
out = sample_without_replacement(2**dimensions, samples,
random_state=rng).astype(dtype='>u4',
copy=False)
out = np.unpackbits(out.view('>u1')).reshape((-1, 32))[:, -dimensions:]
return out
def _generate_indices(random_state, bootstrap, n_population, n_samples):
"""Draw randomly sampled indices. Internal use only.
See sklearn/ensemble/bagging.py
"""
# Draw sample indices
if bootstrap:
indices = random_state.randint(0, n_population, n_samples)
else:
indices = sample_without_replacement(n_population,
n_samples,
random_state=random_state)
return indices
def bootstrap_generator(n_bootstrap_iterations,
sample_fraction,
X,
random_state=None):
"""Generates bootstrap samples from dataset."""
n_samples = len(X)
n_subsamples = np.floor(sample_fraction * n_samples).astype(int)
subsamples = []
for _ in range(n_bootstrap_iterations):
subsample = sample_without_replacement(n_samples,
n_subsamples,
random_state=None)
subsamples.append(subsample)
return subsamples
def test_sample_without_replacement_algorithms():
methods = ("auto", "tracking_selection", "reservoir_sampling", "pool")
for m in methods:
sample_without_replacement_method = \
lambda n_population, n_samples, random_state=None: \
sample_without_replacement(n_population,
n_samples,
method=m,
random_state=random_state)
check_edge_case_of_sample_int(sample_without_replacement_method)
check_sample_int(sample_without_replacement_method)
check_sample_int_distribution(sample_without_replacement_method)
def subsampled_identity_matrix(n_components,
n_features,
random_state=None,
with_replacement=True):
"""Sub-sampled identity matrix to have shape n_components and n_features
Parameters
----------
n_components : int,
Dimensionality of the target projection space.
n_features : int,
Dimensionality of the original source space.
random_state : int, RandomState instance or None (default=None)
Control the pseudo random number generator used to generate the
matrix at fit time.
with_replacement : bool,
Whether or not drawing components with replacements.
Returns
-------
components : numpy array of shape [n_components, n_features]
The generated random matrix.
"""
if n_components <= 0:
raise ValueError("n_components must be strictly positive, got %d" %
n_components)
if n_features <= 0:
raise ValueError("n_features must be strictly positive, got %d" %
n_components)
rng = check_random_state(random_state)
components = sparse.dia_matrix((np.ones(n_features), [0]),
shape=(n_features, n_features)).tocsr()
if with_replacement:
mask = rng.randint(n_features, size=(n_components, ))
else:
mask = sample_without_replacement(n_features,
n_components,
random_state=rng)
components = components[mask]
return components * np.sqrt(1.0 * n_features / n_components)
def get_all_indices(self,
n_samples=None,
max_samples=None,
random_state=None):
"""Get the indices on which to evaluate the fitness of a program.
Parameters
----------
n_samples : int
The number of samples.
max_samples : int
The maximum number of samples to use.
random_state : RandomState instance
The random number generator.
Returns
-------
indices : array-like, shape = [n_samples]
The in-sample indices.
not_indices : array-like, shape = [n_samples]
The out-of-sample indices.
"""
if self._indices_state is None and random_state is None:
raise ValueError('The program has not been evaluated for fitness '
'yet, indices not available.')
if n_samples is not None and self._n_samples is None:
self._n_samples = n_samples
if max_samples is not None and self._max_samples is None:
self._max_samples = max_samples
if random_state is not None and self._indices_state is None:
self._indices_state = random_state.get_state()
indices_state = check_random_state(None)
indices_state.set_state(self._indices_state)
not_indices = sample_without_replacement(self._n_samples,
self._n_samples -
self._max_samples,
random_state=indices_state)
sample_counts = np.bincount(not_indices, minlength=self._n_samples)
indices = np.where(sample_counts == 0)[0]
return indices, not_indices
def make_train_validation_test_triplets_list(triplet_file, random_seed=None):
random.seed(random_seed)
triplets = np.loadtxt(triplet_file)
# sample part of the triplets
n_triplets = len(triplets)
triplets = triplets[sample_without_replacement(n_population=n_triplets,
n_samples=40000)]
train_triplets_file = "./train_triplets_list.txt"
validation_triplets_file = "./validation_triplets_list.txt"
test_triplets_file = "./test_triplets_list.txt"
if os.path.exists(train_triplets_file) and os.path.exists(
validation_triplets_file) and os.path.exists(test_triplets_file):
triplets_train = np.loadtxt(train_triplets_file)
triplets_validation = np.loadtxt(validation_triplets_file)
triplets_test = np.loadtxt(test_triplets_file)
else:
train_images = random.sample(range(0, 5000),
3600) #list(range(0, 3800))
triplets_train = [
t for t in triplets
if (t[0] in train_images and t[1] in train_images
and t[2] in train_images)
]
triplets_vt = [
t for t in triplets
if (t[0] not in train_images and t[1] not in train_images
and t[2] not in train_images)
]
triplets_validation, triplets_test = train_test_split(triplets_vt,
train_size=0.5)
np.savetxt(train_triplets_file, triplets_train)
np.savetxt(validation_triplets_file, triplets_validation)
np.savetxt(test_triplets_file, triplets_test)
print("Train dataset size: %d" % (len(triplets_train)))
print("Validation dataset size: %d" % (len(triplets_validation)))
print("Test dataset size: %d" % (len(triplets_test)))
return triplets_train, triplets_validation, triplets_test
def latin_hypercube_sampling(bounds, pop):
"""Latin Hypercube sampling to generate more uniformly distributed differential evolution initial parameters values.
Parameters
----------
bounds : np.array
Bounds to generate parameters within, should be of shape (nb of parameters, 2)
pop : int
Number of sets of inital parameters to generate
"""
ranges = np.linspace(bounds[:, 0], bounds[:, 1], pop + 1).T
ranges = np.array([ranges[:, :-1], ranges[:, 1:]]).T
cs = np.random.uniform(low=ranges[:, :, 0], high=ranges[:, :, 1])
a = sample_without_replacement(pop**len(bounds), pop)
a = np.array(np.unravel_index(a, [pop] * len(bounds)))
return np.array([cs[a[i], i] for i in range(len(bounds))]).T
def subsampled_identity_matrix(n_components, n_features, random_state=None,
with_replacement=True):
"""Sub-sampled identity matrix to have shape n_components and n_features
Parameters
----------
n_components : int,
Dimensionality of the target projection space.
n_features : int,
Dimensionality of the original source space.
random_state : int, RandomState instance or None (default=None)
Control the pseudo random number generator used to generate the
matrix at fit time.
with_replacement : bool,
Whether or not drawing components with replacements.
Returns
-------
components : numpy array of shape [n_components, n_features]
The generated random matrix.
"""
if n_components <= 0:
raise ValueError("n_components must be strictly positive, got %d" %
n_components)
if n_features <= 0:
raise ValueError("n_features must be strictly positive, got %d" %
n_components)
rng = check_random_state(random_state)
components = sparse.dia_matrix((np.ones(n_features), [0]),
shape=(n_features, n_features)).tocsr()
if with_replacement:
mask = rng.randint(n_features, size=(n_components,))
else:
mask = sample_without_replacement(n_features, n_components,
random_state=rng)
components = components[mask]
return components * np.sqrt(1.0 * n_features / n_components)
def get_all_indices(self, n_samples=None, max_samples=None,
random_state=None):
"""Get the indices on which to evaluate the fitness of a program.
Parameters
----------
n_samples : int
The number of samples.
max_samples : int
The maximum number of samples to use.
random_state : RandomState instance
The random number generator.
Returns
-------
indices : array-like, shape = [n_samples]
The in-sample indices.
not_indices : array-like, shape = [n_samples]
The out-of-sample indices.
"""
if self._indices_state is None and random_state is None:
raise ValueError('The program has not been evaluated for fitness '
'yet, indices not available.')
if n_samples is not None and self._n_samples is None:
self._n_samples = n_samples
if max_samples is not None and self._max_samples is None:
self._max_samples = max_samples
if random_state is not None and self._indices_state is None:
self._indices_state = random_state.get_state()
indices_state = check_random_state(None)
indices_state.set_state(self._indices_state)
not_indices = sample_without_replacement(
self._n_samples,
self._n_samples - self._max_samples,
random_state=indices_state)
sample_counts = np.bincount(not_indices, minlength=self._n_samples)
indices = np.where(sample_counts == 0)[0]
return indices, not_indices
def fit(self, X, y, random_state=None):
"""
Train ENOLS on the given training set.
Parameters
----------
X: an input array of shape (n_sample, n_features)
y: an array of shape (n_sample,) containing the classes for the input examples
Return
------
self: the fitted model
"""
# use random instead of np.random to sample random numbers below
random = check_random_state(random_state)
estimators = [('lr', LinearRegression())]
if isinstance(self.sample_size, int):
self.sample_size = 'reservoir_sampling'
# add all the trained OLS models to this list
self.estimators_lr, self.estimators_TSR, self.estimators_enols = [], [], []
for i in range(self.n_estimators):
samples = sample_without_replacement(n_population=random.choice([50, 100]),
n_samples=random.choice([10, 20]),
random_state=random_state, method=self.sample_size)
X_train, y_train = [], []
for i in samples:
X_train.append(X[i]), y_train.append(y[i])
reg = LinearRegression()
reg.fit(np.array(X_train), np.array(y_train))
tsr = TheilSenRegressor()
tsr.fit(np.array(X_train), np.array(y_train))
enol = StackingRegressor(estimators=estimators, final_estimator=LinearRegression())
enol.fit(np.array(X_train), np.array(y_train))
self.estimators_lr.append(reg), self.estimators_TSR.append(tsr), self.estimators_enols.append(enol)
return self
def lesinn(self, x_train, to_query):
ensemble_size = 50
subsample_size = int(.01 * x_train.shape[0])
scores = np.zeros([to_query.shape[0], 1])
seeds = self.Trainer.rng.randint(MAX_INT, size=ensemble_size)
for i in range(0, ensemble_size):
rs = np.random.RandomState(seeds[i])
sid = sample_without_replacement(n_population=x_train.shape[0],
n_samples=subsample_size,
random_state=rs)
subsample = x_train[sid]
kdt = KDTree(subsample, metric='euclidean')
dists, indices = kdt.query(to_query, k=self.n_neighbors)
#import pdb; pdb.set_trace()
dists = np.mean(dists, axis=1)[:, np.newaxis]
scores += dists
scores = scores / ensemble_size
return scores
def equalise_laps_with_numlaps_innorew(Imgobj, X, Y, Tasklabel):
stoplicklap = Imgobj.Parsed_Behavior['lick_stop'].item()
numlaps_afterlickstops = Imgobj.Parsed_Behavior['numlaps'].item(
)['Task2'] - stoplicklap
print('Number of laps being chosen', numlaps_afterlickstops)
numlaps_currenttask = Imgobj.Parsed_Behavior['numlaps'].item(
)[Tasklabel] - 3
samplelaps = sample_without_replacement(numlaps_currenttask,
numlaps_afterlickstops)
lapframes = \
[scipy.io.loadmat(os.path.join(Imgobj.FolderName, 'Behavior', p))['E'].T for p in Imgobj.PlaceFieldData if
Tasklabel in p][0]
print(samplelaps)
X_eq = X[np.where(lapframes == samplelaps)[0], :]
Y_eq = Y[np.where(lapframes == samplelaps)[0]]
return X_eq, Y_eq
def _generate_hypercube(samples, dimensions, rng):
"""Returns distinct binary samples of length dimensions
"""
if not has_sklearn():
raise RuntimeError("Scikit-learn is needed to run \
make_classification.")
from sklearn.utils.random import sample_without_replacement
if dimensions > 30:
return np.hstack([np.random.randint(2, size=(samples,
dimensions - 30)),
_generate_hypercube(samples, 30, rng)])
random_state = int(rng.randint(dimensions))
out = sample_without_replacement(2 ** dimensions, samples,
random_state=random_state).astype(
dtype='>u4', copy=False)
out = np.unpackbits(out.view('>u1')).reshape((-1, 32))[:, -dimensions:]
return out
def _aom_moa_helper(mode, scores, n_buckets, method, bootstrap_estimators,
random_state):
"""Internal helper function for Average of Maximum (AOM) and
Maximum of Average (MOA). See :cite:`aggarwal2015theoretical` for details.
First dividing estimators into subgroups, take the maximum/average score
as the subgroup score. Finally, take the average/maximum of all subgroup
outlier scores.
Parameters
----------
mode : str
Define the operation model, either "AOM" or "MOA".
scores : numpy array of shape (n_samples, n_estimators)
The score matrix outputted from various estimators.
n_buckets : int, optional (default=5)
The number of subgroups to build.
method : str, optional (default='static')
{'static', 'dynamic'}, if 'dynamic', build subgroups
randomly with dynamic bucket size.
bootstrap_estimators : bool, optional (default=False)
Whether estimators are drawn with replacement.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the
random number generator; If RandomState instance, random_state is
the random number generator; If None, the random number generator
is the RandomState instance used by `np.random`.
Returns
-------
combined_scores : Numpy array of shape (n_samples,)
The combined outlier scores.
"""
if mode != 'AOM' and mode != 'MOA':
raise NotImplementedError(
'{mode} is not implemented'.format(mode=mode))
scores = check_array(scores)
# TODO: add one more parameter for max number of estimators
# use random_state instead
# for now it is fixed at n_estimators/2
n_estimators = scores.shape[1]
check_parameter(n_buckets, 2, n_estimators, param_name='n_buckets')
scores_buckets = np.zeros([scores.shape[0], n_buckets])
if method == 'static':
n_estimators_per_bucket = int(n_estimators / n_buckets)
if n_estimators % n_buckets != 0:
raise ValueError('n_estimators / n_buckets has a remainder. Not '
'allowed in static mode.')
if not bootstrap_estimators:
# shuffle the estimator order
shuffled_list = shuffle(list(range(0, n_estimators, 1)),
random_state=random_state)
head = 0
for i in range(0, n_estimators, n_estimators_per_bucket):
tail = i + n_estimators_per_bucket
batch_ind = int(i / n_estimators_per_bucket)
if mode == 'AOM':
scores_buckets[:, batch_ind] = np.max(
scores[:, shuffled_list[head:tail]], axis=1)
else:
scores_buckets[:, batch_ind] = np.mean(
scores[:, shuffled_list[head:tail]], axis=1)
# increment index
head = head + n_estimators_per_bucket
# noinspection PyUnusedLocal
else:
for i in range(n_buckets):
ind = sample_without_replacement(n_estimators,
n_estimators_per_bucket,
random_state=random_state)
if mode == 'AOM':
scores_buckets[:, i] = np.max(scores[:, ind], axis=1)
else:
scores_buckets[:, i] = np.mean(scores[:, ind], axis=1)
elif method == 'dynamic': # random bucket size
for i in range(n_buckets):
# the number of estimators in a bucket should be 2 - n/2
max_estimator_per_bucket = RandomState(seed=random_state).randint(
2, int(n_estimators / 2))
ind = sample_without_replacement(n_estimators,
max_estimator_per_bucket,
random_state=random_state)
if mode == 'AOM':
scores_buckets[:, i] = np.max(scores[:, ind], axis=1)
else:
scores_buckets[:, i] = np.mean(scores[:, ind], axis=1)
else:
raise NotImplementedError(
'{method} is not implemented'.format(method=method))
if mode == 'AOM':
return np.mean(scores_buckets, axis=1)
else:
return np.max(scores_buckets, axis=1)
# sample(n_population, n_sample)
#
sampling_algorithm = {}
###########################################################################
# Set Python core input
sampling_algorithm["python-core-sample"] = \
lambda n_population, n_sample: \
random.sample(xrange(n_population), n_sample)
###########################################################################
# Set custom automatic method selection
sampling_algorithm["custom-auto"] = \
lambda n_population, n_samples, random_state=None: \
sample_without_replacement(n_population,
n_samples,
method="auto",
random_state=random_state)
###########################################################################
# Set custom tracking based method
sampling_algorithm["custom-tracking-selection"] = \
lambda n_population, n_samples, random_state=None: \
sample_without_replacement(n_population,
n_samples,
method="tracking_selection",
random_state=random_state)
###########################################################################
# Set custom reservoir based method
sampling_algorithm["custom-reservoir-sampling"] = \
lambda n_population, n_samples, random_state=None: \
def _parallel_build_estimators(n_estimators, ensemble, X, y, cost_mat,
seeds, verbose):
"""Private function used to build a batch of estimators within a job."""
# Retrieve settings
n_samples, n_features = X.shape
max_samples = ensemble.max_samples
max_features = ensemble.max_features
if (not isinstance(max_samples, (numbers.Integral, np.integer)) and
(0.0 < max_samples <= 1.0)):
max_samples = int(max_samples * n_samples)
if (not isinstance(max_features, (numbers.Integral, np.integer)) and
(0.0 < max_features <= 1.0)):
max_features = int(max_features * n_features)
bootstrap = ensemble.bootstrap
bootstrap_features = ensemble.bootstrap_features
# Build estimators
estimators = []
estimators_samples = []
estimators_features = []
for i in range(n_estimators):
if verbose > 1:
print("building estimator %d of %d" % (i + 1, n_estimators))
random_state = check_random_state(seeds[i])
seed = check_random_state(random_state.randint(MAX_INT))
estimator = ensemble._make_estimator(append=False)
try: # Not all estimator accept a random_state
estimator.set_params(random_state=seed)
except ValueError:
pass
# Draw features
if bootstrap_features:
features = random_state.randint(0, n_features, max_features)
else:
features = sample_without_replacement(n_features,
max_features,
random_state=random_state)
# Draw samples, using a mask, and then fit
if bootstrap:
indices = random_state.randint(0, n_samples, max_samples)
else:
indices = sample_without_replacement(n_samples,
max_samples,
random_state=random_state)
sample_counts = np.bincount(indices, minlength=n_samples)
estimator.fit((X[indices])[:, features], y[indices], cost_mat[indices, :])
samples = sample_counts > 0.
estimators.append(estimator)
estimators_samples.append(samples)
estimators_features.append(features)
return estimators, estimators_samples, estimators_features
def _parallel_build_ranking_estimators(n_estimators, ensemble, X, y, Q, sample_weight, seeds, verbose):
"""Private function used to build a batch of estimators within a job.
Now it supports queries and querywise sampling.
It also breaks the PEP8 line length constraint now"""
# Retrieve settings
n_samples, n_features = X.shape
max_samples = ensemble.max_samples
max_features = ensemble.max_features
uQueries = np.unique(Q)
sample_whole_queries = False
if hasattr(ensemble, "sample_whole_queries"):
sample_whole_queries = ensemble.sample_whole_queries
if not isinstance(max_samples, (numbers.Integral, np.integer)) and (0.0 < max_samples <= 1.0):
if sample_whole_queries:
max_samples = int(max_samples * len(uQueries))
else:
max_samples = int(max_samples * n_samples)
if not isinstance(max_features, (numbers.Integral, np.integer)) and (0.0 < max_features <= 1.0):
max_features = int(max_features * n_features)
bootstrap = ensemble.bootstrap
bootstrap_features = ensemble.bootstrap_features
support_sample_weight = has_fit_parameter(ensemble.base_estimator_, "sample_weight")
# Build estimators
estimators = []
estimators_samples = []
estimators_features = []
for i in range(n_estimators):
if verbose > 1:
print("building estimator %d of %d" % (i + 1, n_estimators))
random_state = check_random_state(seeds[i])
seed = check_random_state(random_state.randint(MAX_INT))
estimator = ensemble._make_estimator(append=False)
try: # Not all estimator accept a random_state
estimator.set_params(random_state=seed)
except ValueError:
pass
# Draw features
if bootstrap_features:
features = random_state.randint(0, n_features, max_features)
else:
features = sample_without_replacement(n_features, max_features, random_state=random_state)
# Draw samples, using sample weights, and then fit
if support_sample_weight:
if sample_weight is None:
curr_sample_weight = np.ones((n_samples,))
else:
curr_sample_weight = sample_weight.copy()
if bootstrap:
if sample_whole_queries:
Qindices = uQueries[random_state.randint(0, len(uQueries), max_samples)]
Qindices.sort()
indices = reduce(np.append, [np.where(Q == i) for i in Qindices])
else:
indices = random_state.randint(0, n_samples, max_samples)
sample_counts = bincount(indices, minlength=n_samples)
curr_sample_weight *= sample_counts
else:
if sample_whole_queries:
notQindices = uQueries[random_state.randint(0, len(uQueries), len(uQueries) - max_samples)]
notQindices.sort()
not_indices = reduce(np.append, [np.where(Q == i) for i in Qindices])
else:
not_indices = sample_without_replacement(
n_samples, n_samples - max_samples, random_state=random_state
)
curr_sample_weight[not_indices] = 0
estimator.fit(X[:, features], y, Q=Q, sample_weight=curr_sample_weight)
samples = curr_sample_weight > 0.0
# Draw samples, using a mask, and then fit
else:
if bootstrap:
if sample_whole_queries:
Qindices = uQueries[random_state.randint(0, len(uQueries), max_samples)]
Qindices.sort()
indices = reduce(np.append, [np.where(Q == i) for i in Qindices])
else:
indices = random_state.randint(0, n_samples, max_samples)
else:
if sample_whole_queries:
Qindices = uQueries[
sample_without_replacement(len(uQueries), max_samples, random_state=random_state)
]
Qindices.sort()
indices = reduce(np.append, [np.where(Q == i) for i in Qindices])
else:
indices = sample_without_replacement(n_samples, max_samples, random_state=random_state)
sample_counts = bincount(indices, minlength=n_samples)
estimator.fit((X[indices])[:, features], y[indices], Q=Q[indices])
samples = sample_counts > 0.0
estimators.append(estimator)
estimators_samples.append(samples)
estimators_features.append(features)
return estimators, estimators_samples, estimators_features
def _parallel_build_estimators(n_estimators, ensemble, all_X, all_y, sample_weight,
seeds, verbose):
"""Private function used to build a batch of estimators within a job."""
positives = np.where(all_y == 1)[0]
unlabeled = np.where(all_y == 0)[0]
X_positives = all_X[positives]
X_unlabeled = all_X[unlabeled]
y_positives = all_y[positives]
y_unlabeled = all_y[unlabeled]
# Retrieve settings
n_samples, n_features = X_unlabeled.shape
max_samples = ensemble.max_samples
max_features = ensemble.max_features
if (not isinstance(max_samples, (numbers.Integral, np.integer)) and
(0.0 < max_samples <= 1.0)):
max_samples = int(max_samples * n_samples)
if (not isinstance(max_features, (numbers.Integral, np.integer)) and
(0.0 < max_features <= 1.0)):
max_features = int(max_features * n_features)
bootstrap = ensemble.bootstrap
bootstrap_features = ensemble.bootstrap_features
#can't currently support sample weights
if sample_weight is not None:
raise ValueError("Can't currently support sample weight with PUBagging")
support_sample_weight = False
#support_sample_weight = has_fit_parameter(ensemble.base_estimator_,
# "sample_weight")
#if not support_sample_weight and sample_weight is not None:
# raise ValueError("The base estimator doesn't support sample weight")
# Build estimators
estimators = []
estimators_samples = []
estimators_features = []
for i in range(n_estimators):
if verbose > 1:
print("building estimator %d of %d" % (i + 1, n_estimators))
random_state = check_random_state(seeds[i])
seed = check_random_state(random_state.randint(MAX_INT))
estimator = ensemble._make_estimator(append=False)
try: # Not all estimator accept a random_state
estimator.set_params(random_state=seed)
except ValueError:
pass
# Draw features
if bootstrap_features:
features = random_state.randint(0, n_features, max_features)
else:
features = sample_without_replacement(n_features,
max_features,
random_state=random_state)
# Draw samples, using sample weights, and then fit
if support_sample_weight:
if sample_weight is None:
curr_sample_weight = np.ones((n_samples,))
else:
curr_sample_weight = sample_weight.copy()
if bootstrap:
indices = random_state.randint(0, n_samples, max_samples)
sample_counts = bincount(indices, minlength=n_samples)
curr_sample_weight *= sample_counts
else:
not_indices = sample_without_replacement(
n_samples,
n_samples - max_samples,
random_state=random_state)
curr_sample_weight[not_indices] = 0
estimator.fit(all_X[:, features], all_y, sample_weight=curr_sample_weight)
samples = curr_sample_weight > 0.
# Draw samples, using a mask, and then fit
else:
if bootstrap:
indices = random_state.randint(0, n_samples, max_samples)
else:
indices = sample_without_replacement(n_samples,
max_samples,
random_state=random_state)
sample_counts = bincount(indices, minlength=n_samples)
new_X=np.vstack((X_positives, X_unlabeled[indices]))
new_y=np.concatenate((y_positives, y_unlabeled[indices]))
estimator.fit(new_X[:, features], new_y)
samples = sample_counts > 0.
estimators.append(estimator)
estimators_samples.append(samples)
estimators_features.append(features)
return estimators, estimators_samples, estimators_features
def sparse_random_matrix(n_components, n_features, density='auto',
random_state=None):
"""Generalized Achlioptas random sparse matrix for random projection
Setting density to 1 / 3 will yield the original matrix by Dimitris
Achlioptas while setting a lower value will yield the generalization
by Ping Li et al.
If we note :math:`s = 1 / density`, the components of the random matrix are
drawn from:
- -sqrt(s) / sqrt(n_components) with probability 1 / 2s
- 0 with probability 1 - 1 / s
- +sqrt(s) / sqrt(n_components) with probability 1 / 2s
Parameters
----------
n_components : int,
Dimensionality of the target projection space.
n_features : int,
Dimensionality of the original source space.
density : float in range ]0, 1/3], optional
Ratio of non-zero component in the random projection matrix.
By default the value is set to the minimum density as recommended
by Ping Li et al.: 1 / sqrt(n_features)
Use density = 1 / 3.0 if you want to reproduce the results from
Achlioptas, 2001.
random_state : integer, RandomState instance or None (default)
Control the pseudo random number generator used to generate the
matrix at fit time.
Returns
-------
components: numpy array or CSR matrix with shape [n_components, n_features]
The generated Gaussian random matrix.
See Also
--------
gaussian_random_matrix
References
----------
.. [1] Ping Li, T. Hastie and K. W. Church, 2006,
"Very Sparse Random Projections".
http://www.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf
.. [2] D. Achlioptas, 2001, "Database-friendly random projections",
http://www.cs.ucsc.edu/~optas/papers/jl.pdf
"""
_check_input_size(n_components, n_features)
density = _check_density(density, n_features)
rng = check_random_state(random_state)
if density == 1:
# skip index generation if totally dense
components = rng.binomial(1, 0.5, (n_components, n_features)) * 2 - 1
return 1 / np.sqrt(n_components) * components
else:
# Generate location of non zero elements
indices = []
offset = 0
indptr = [offset]
for i in xrange(n_components):
# find the indices of the non-zero components for row i
n_nonzero_i = rng.binomial(n_features, density)
indices_i = sample_without_replacement(n_features, n_nonzero_i,
random_state=rng)
indices.append(indices_i)
offset += n_nonzero_i
indptr.append(offset)
indices = np.concatenate(indices)
# Among non zero components the probability of the sign is 50%/50%
data = rng.binomial(1, 0.5, size=np.size(indices)) * 2 - 1
# build the CSR structure by concatenating the rows
components = sp.csr_matrix((data, indices, indptr),
shape=(n_components, n_features))
return np.sqrt(1 / density) / np.sqrt(n_components) * components
def sample_without_replacement_method(n_population, n_samples,
random_state=None):
return sample_without_replacement(n_population, n_samples,
method=m,
random_state=random_state)
def _spark_build_estimators(n_estimators, ensemble, X, y, sample_weight,
seeds, verbose):
"""Private function used to build a batch of estimators within a job."""
print "building estimators"
# Retrieve settings
X = X.value
y = y.value
ensemble = ensemble
sample_weight = sample_weight.value
n_samples, n_features = X.shape
max_samples = ensemble.max_samples
max_features = ensemble.max_features
if (not isinstance(max_samples, (numbers.Integral, np.integer)) and
(0.0 < max_samples <= 1.0)):
max_samples = int(max_samples * n_samples)
if (not isinstance(max_features, (numbers.Integral, np.integer)) and
(0.0 < max_features <= 1.0)):
max_features = int(max_features * n_features)
bootstrap = ensemble.bootstrap
bootstrap_features = ensemble.bootstrap_features
support_sample_weight = has_fit_parameter(ensemble.base_estimator_,
"sample_weight")
# Build estimators
estimators = []
estimators_samples = []
estimators_features = []
for i in range(n_estimators):
if verbose > 1:
print("building estimator %d of %d" % (i + 1, n_estimators))
random_state = check_random_state(seeds[i])
seed = check_random_state(random_state.randint(MAX_INT))
estimator = ensemble._make_estimator(append=False)
try: # Not all estimator accept a random_state
estimator.set_params(random_state=seed)
except ValueError:
pass
# Draw features
if bootstrap_features:
features = random_state.randint(0, n_features, max_features)
else:
features = sample_without_replacement(n_features,
max_features,
random_state=random_state)
# Draw samples, using sample weights, and then fit
if support_sample_weight:
if sample_weight is None:
curr_sample_weight = np.ones((n_samples,))
else:
curr_sample_weight = sample_weight.copy()
if bootstrap:
indices = random_state.randint(0, n_samples, max_samples)
sample_counts = bincount(indices, minlength=n_samples)
curr_sample_weight *= sample_counts
else:
not_indices = sample_without_replacement(
n_samples,
n_samples - max_samples,
random_state=random_state)
curr_sample_weight[not_indices] = 0
estimator.fit(X[:, features], y, sample_weight=curr_sample_weight)
samples = curr_sample_weight > 0.
# Draw samples, using a mask, and then fit
else:
if bootstrap:
indices = random_state.randint(0, n_samples, max_samples)
else:
indices = sample_without_replacement(n_samples,
max_samples,
random_state=random_state)
sample_counts = bincount(indices, minlength=n_samples)
estimator.fit((X[indices])[:, features], y[indices])
samples = sample_counts > 0.
estimators.append(estimator)
estimators_samples.append(samples)
estimators_features.append(features)
return estimators, estimators_samples, estimators_features
def _generator_fitted_estimators(n_estimators, ensemble, X, y, sample_weight, seeds, verbose):
"""Private function used to build an iterator of estimators."""
# Modified from sklearn.ensemble.bagging._parallel_build_estimators
# Retrieve settings
n_samples, n_features = X.shape
max_samples = ensemble.max_samples
max_features = ensemble.max_features
if not isinstance(max_samples, (numbers.Integral, np.integer)) and (0.0 < max_samples <= 1.0):
max_samples = int(max_samples * n_samples)
if not isinstance(max_features, (numbers.Integral, np.integer)) and (0.0 < max_features <= 1.0):
max_features = int(max_features * n_features)
bootstrap = ensemble.bootstrap
bootstrap_features = ensemble.bootstrap_features
support_sample_weight = has_fit_parameter(ensemble.base_estimator_, "sample_weight")
# Build estimators
for i in range(n_estimators):
if verbose > 1:
print("building estimator %d of %d" % (i + 1, n_estimators))
random_state = check_random_state(seeds[i])
seed = check_random_state(random_state.randint(MAX_INT))
estimator = ensemble._make_estimator(append=False)
try: # Not all estimator accept a random_state
estimator.set_params(random_state=seed)
except ValueError:
pass
# Draw features
if bootstrap_features:
features = random_state.randint(0, n_features, max_features)
else:
features = sample_without_replacement(n_features, max_features, random_state=random_state)
# Draw samples, using sample weights, and then fit
if support_sample_weight:
if sample_weight is None:
curr_sample_weight = np.ones((n_samples,))
else:
curr_sample_weight = sample_weight.copy()
if bootstrap:
indices = random_state.randint(0, n_samples, max_samples)
sample_counts = np.bincount(indices, minlength=n_samples)
curr_sample_weight *= sample_counts
else:
not_indices = sample_without_replacement(n_samples, n_samples - max_samples, random_state=random_state)
curr_sample_weight[not_indices] = 0
estimator.fit(X[:, features], y, sample_weight=curr_sample_weight)
samples = curr_sample_weight > 0.0
# Draw samples, using a mask, and then fit
else:
if bootstrap:
indices = random_state.randint(0, n_samples, max_samples)
else:
indices = sample_without_replacement(n_samples, max_samples, random_state=random_state)
sample_counts = np.bincount(indices, minlength=n_samples)
estimator.fit((X[indices])[:, features], y[indices])
samples = sample_counts > 0.0
yield estimator, samples, features