code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def run_tensorflow_script(
script: Union[bytes, str, BinaryIO, TextIO],
n_workers: int,
n_ps: int = 0,
data: Dict[str, TileableType] = None,
gpu: Optional[bool] = None,
command_argv: List[str] = None,
retry_when_fail: bool = False,
session: SessionType = None,
run_kwargs: Dict[str, A... |
Run TensorFlow script in Mars cluster.
Parameters
----------
script: str or file-like object
Script to run
n_workers : int
Number of TensorFlow workers.
n_ps : int
Number of TensorFlow PS workers.
data : dict
Variable name to data.
gpu : bool
Run... | run_tensorflow_script | python | mars-project/mars | mars/learn/contrib/tensorflow/run_script.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/tensorflow/run_script.py | Apache-2.0 |
def fit(
self,
X,
y,
sample_weights=None,
eval_set=None,
sample_weight_eval_set=None,
**kw,
):
"""
Fit the regressor.
Parameters
----------
X : array_like
... |
Fit the regressor.
Parameters
----------
X : array_like
Feature matrix
y : array_like
Labels
sample_weight : array_like
instance weights
eval_set : list, optional
A list o... | fit | python | mars-project/mars | mars/learn/contrib/xgboost/core.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/core.py | Apache-2.0 |
def wrap_evaluation_matrices(
missing: float,
X: Any,
y: Any,
sample_weight: Optional[Any],
base_margin: Optional[Any],
eval_set: Optional[List[Tuple[Any, Any]]],
sample_weight_eval_set: Optional[List[Any]],
base_margin_eval_set: Optional[List[Any]],
... | Convert array_like evaluation matrices into DMatrix. Perform validation on the way. | wrap_evaluation_matrices | python | mars-project/mars | mars/learn/contrib/xgboost/core.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/core.py | Apache-2.0 |
def print(self, use_logger: bool) -> None:
"""Execute the print command from worker."""
msg = self.sock.recvstr()
# On dask we use print to avoid setting global verbosity.
if use_logger:
logger.info(msg.strip())
else:
print(msg.strip(), flush=True) | Execute the print command from worker. | print | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def decide_rank(self, job_map: Dict[str, int]) -> int:
"""Get the rank of current entry."""
if self.rank >= 0:
return self.rank
if self.jobid != "NULL" and self.jobid in job_map:
return job_map[self.jobid]
return -1 | Get the rank of current entry. | decide_rank | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def assign_rank(
self,
rank: int,
wait_conn: Dict[int, "WorkerEntry"],
tree_map: _TreeMap,
parent_map: Dict[int, int],
ring_map: _RingMap,
) -> List[int]:
"""Assign the rank for current entry."""
self.rank = rank
nnset = set(tree_map[rank])
... | Assign the rank for current entry. | assign_rank | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def __init__(
self, host_ip: str, n_workers: int, port: int = 0, use_logger: bool = False
) -> None:
"""A Python implementation of RABIT tracker.
Parameters
..........
use_logger:
Use logging.info for tracker print command. When set to False, Python print
... | A Python implementation of RABIT tracker.
Parameters
..........
use_logger:
Use logging.info for tracker print command. When set to False, Python print
function is used instead.
| __init__ | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def find_share_ring(
self, tree_map: _TreeMap, parent_map: Dict[int, int], rank: int
) -> List[int]:
"""
get a ring structure that tends to share nodes with the tree
return a list starting from rank
"""
nset = set(tree_map[rank])
cset = nset - set([parent_map[... |
get a ring structure that tends to share nodes with the tree
return a list starting from rank
| find_share_ring | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def get_ring(self, tree_map: _TreeMap, parent_map: Dict[int, int]) -> _RingMap:
"""
get a ring connection used to recover local data
"""
assert parent_map[0] == -1
rlst = self.find_share_ring(tree_map, parent_map, 0)
assert len(rlst) == len(tree_map)
ring_map: _Ri... |
get a ring connection used to recover local data
| get_ring | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def get_link_map(self, n_workers: int) -> Tuple[_TreeMap, Dict[int, int], _RingMap]:
"""
get the link map, this is a bit hacky, call for better algorithm
to place similar nodes together
"""
tree_map, parent_map = self._get_tree(n_workers)
ring_map = self.get_ring(tree_map... |
get the link map, this is a bit hacky, call for better algorithm
to place similar nodes together
| get_link_map | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def accept_workers(self, n_workers: int) -> None:
"""Wait for all workers to connect to the tracker."""
# set of nodes that finishes the job
shutdown: Dict[int, WorkerEntry] = {}
# set of nodes that is waiting for connections
wait_conn: Dict[int, WorkerEntry] = {}
# maps ... | Wait for all workers to connect to the tracker. | accept_workers | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def start(self, n_workers: int) -> None:
"""Start the tracker, it will wait for `n_workers` to connect."""
def run() -> None:
self.accept_workers(n_workers)
self.thread = Thread(target=run, args=(), daemon=True)
self.thread.start() | Start the tracker, it will wait for `n_workers` to connect. | start | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def get_host_ip(host_ip: Optional[str] = None) -> str:
"""Get the IP address of current host. If `host_ip` is not none then it will be
returned as it's
"""
if host_ip is None or host_ip == "auto":
host_ip = "ip"
if host_ip == "dns":
host_ip = socket.getfqdn()
elif host_ip == "i... | Get the IP address of current host. If `host_ip` is not none then it will be
returned as it's
| get_host_ip | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def start_rabit_tracker(args: argparse.Namespace) -> None:
"""Standalone function to start rabit tracker.
Parameters
----------
args: arguments to start the rabit tracker.
"""
envs = {"DMLC_NUM_WORKER": args.num_workers, "DMLC_NUM_SERVER": args.num_servers}
rabit = RabitTracker(
host... | Standalone function to start rabit tracker.
Parameters
----------
args: arguments to start the rabit tracker.
| start_rabit_tracker | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def main() -> None:
"""Main function if tracker is executed in standalone mode."""
parser = argparse.ArgumentParser(description="Rabit Tracker start.")
parser.add_argument(
"--num-workers",
required=True,
type=int,
help="Number of worker process to be launched.",
)
pa... | Main function if tracker is executed in standalone mode. | main | python | mars-project/mars | mars/learn/contrib/xgboost/tracker.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/tracker.py | Apache-2.0 |
def train(params, dtrain, evals=(), **kwargs):
"""
Train XGBoost model in Mars manner.
Parameters
----------
Parameters are the same as `xgboost.train`.
Returns
-------
results: Booster
"""
evals_result = kwargs.pop("evals_result", dict())
session = kwargs.pop("session", N... |
Train XGBoost model in Mars manner.
Parameters
----------
Parameters are the same as `xgboost.train`.
Returns
-------
results: Booster
| train | python | mars-project/mars | mars/learn/contrib/xgboost/train.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/xgboost/train.py | Apache-2.0 |
def make_classification(
n_samples=100,
n_features=20,
n_informative=2,
n_redundant=2,
n_repeated=0,
n_classes=2,
n_clusters_per_class=2,
weights=None,
flip_y=0.01,
class_sep=1.0,
hypercube=True,
shift=0.0,
scale=1.0,
shuffle=True,
random_state=None,
):
""... | Generate a random n-class classification problem.
This initially creates clusters of points normally distributed (std=1)
about vertices of an ``n_informative``-dimensional hypercube with sides of
length ``2*class_sep`` and assigns an equal number of clusters to each
class. It introduces interdependence... | make_classification | python | mars-project/mars | mars/learn/datasets/samples_generator.py | https://github.com/mars-project/mars/blob/master/mars/learn/datasets/samples_generator.py | Apache-2.0 |
def make_regression(
n_samples=100,
n_features=100,
*,
n_informative=10,
n_targets=1,
bias=0.0,
effective_rank=None,
tail_strength=0.5,
noise=0.0,
shuffle=True,
coef=False,
random_state=None,
):
"""Generate a random regression problem.
The input set can either be... | Generate a random regression problem.
The input set can either be well conditioned (by default) or have a low
rank-fat tail singular profile. See :func:`make_low_rank_matrix` for
more details.
The output is generated by applying a (potentially biased) random linear
regression model with `n_informa... | make_regression | python | mars-project/mars | mars/learn/datasets/samples_generator.py | https://github.com/mars-project/mars/blob/master/mars/learn/datasets/samples_generator.py | Apache-2.0 |
def make_blobs(
n_samples=100,
n_features=2,
centers=None,
cluster_std=1.0,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=None,
):
"""Generate isotropic Gaussian blobs for clustering.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n... | Generate isotropic Gaussian blobs for clustering.
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int or array-like, optional (default=100)
If int, it is the total number of points equally divided among
clusters.
If array-like, each ele... | make_blobs | python | mars-project/mars | mars/learn/datasets/samples_generator.py | https://github.com/mars-project/mars/blob/master/mars/learn/datasets/samples_generator.py | Apache-2.0 |
def make_low_rank_matrix(
n_samples=100,
n_features=100,
effective_rank=10,
tail_strength=0.5,
random_state=None,
chunk_size=None,
):
"""Generate a mostly low rank matrix with bell-shaped singular values
Most of the variance can be explained by a bell-shaped curve of width
effective... | Generate a mostly low rank matrix with bell-shaped singular values
Most of the variance can be explained by a bell-shaped curve of width
effective_rank: the low rank part of the singular values profile is::
(1 - tail_strength) * exp(-1.0 * (i / effective_rank) ** 2)
The remaining singular values'... | make_low_rank_matrix | python | mars-project/mars | mars/learn/datasets/samples_generator.py | https://github.com/mars-project/mars/blob/master/mars/learn/datasets/samples_generator.py | Apache-2.0 |
def get_covariance(self, session=None):
"""Compute data covariance with the generative model.
``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``
where S**2 contains the explained variances, and sigma2 contains the
noise variances.
Returns
-------
... | Compute data covariance with the generative model.
``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``
where S**2 contains the explained variances, and sigma2 contains the
noise variances.
Returns
-------
cov : Tensor, shape=(n_features, n_features)
... | get_covariance | python | mars-project/mars | mars/learn/decomposition/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_base.py | Apache-2.0 |
def get_precision(self, session=None):
"""Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with
the matrix inversion lemma for efficiency.
Returns
-------
precision : Tensor, shape=(n_features, n_features)
... | Compute data precision matrix with the generative model.
Equals the inverse of the covariance but computed with
the matrix inversion lemma for efficiency.
Returns
-------
precision : Tensor, shape=(n_features, n_features)
Estimated precision of data.
| get_precision | python | mars-project/mars | mars/learn/decomposition/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_base.py | Apache-2.0 |
def fit(X, y=None, session=None, run_kwargs=None):
"""Placeholder for fit. Subclasses should implement this method!
Fit the model with X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
... | Placeholder for fit. Subclasses should implement this method!
Fit the model with X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples and
n_features is the number of features.
Ret... | fit | python | mars-project/mars | mars/learn/decomposition/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_base.py | Apache-2.0 |
def transform(self, X, session=None):
"""Apply dimensionality reduction to X.
X is projected on the first principal components previously extracted
from a training set.
Parameters
----------
X : array-like, shape (n_samples, n_features)
New data, where n_sam... | Apply dimensionality reduction to X.
X is projected on the first principal components previously extracted
from a training set.
Parameters
----------
X : array-like, shape (n_samples, n_features)
New data, where n_samples is the number of samples
and n_f... | transform | python | mars-project/mars | mars/learn/decomposition/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_base.py | Apache-2.0 |
def inverse_transform(self, X, session=None):
"""Transform data back to its original space.
In other words, return an input X_original whose transform would be X.
Parameters
----------
X : array-like, shape (n_samples, n_components)
New data, where n_samples is the ... | Transform data back to its original space.
In other words, return an input X_original whose transform would be X.
Parameters
----------
X : array-like, shape (n_samples, n_components)
New data, where n_samples is the number of samples
and n_components is the num... | inverse_transform | python | mars-project/mars | mars/learn/decomposition/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_base.py | Apache-2.0 |
def _assess_dimension(spectrum, rank, n_samples):
"""Compute the log-likelihood of a rank ``rank`` dataset.
The dataset is assumed to be embedded in gaussian noise of shape(n,
dimf) having spectrum ``spectrum``.
Parameters
----------
spectrum : array of shape (n_features)
Data spectrum... | Compute the log-likelihood of a rank ``rank`` dataset.
The dataset is assumed to be embedded in gaussian noise of shape(n,
dimf) having spectrum ``spectrum``.
Parameters
----------
spectrum : array of shape (n_features)
Data spectrum.
rank : int
Tested rank value. It should be ... | _assess_dimension | python | mars-project/mars | mars/learn/decomposition/_pca.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_pca.py | Apache-2.0 |
def fit_transform(self, X, y=None, session=None):
"""Fit the model with X and apply the dimensionality reduction on X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is th... | Fit the model with X and apply the dimensionality reduction on X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
y : Ignored
Returns
... | fit_transform | python | mars-project/mars | mars/learn/decomposition/_pca.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_pca.py | Apache-2.0 |
def _fit_full(self, X, n_components, session=None, run_kwargs=None):
"""Fit the model by computing full SVD on X"""
n_samples, n_features = X.shape
if n_components == "mle":
if n_samples < n_features:
raise ValueError(
"n_components='mle' is only ... | Fit the model by computing full SVD on X | _fit_full | python | mars-project/mars | mars/learn/decomposition/_pca.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_pca.py | Apache-2.0 |
def _fit_truncated(self, X, n_components, svd_solver):
"""Fit the model by computing truncated SVD (by ARPACK or randomized)
on X
"""
n_samples, n_features = X.shape
if isinstance(n_components, str):
raise ValueError(
"n_components=%r cannot be a stri... | Fit the model by computing truncated SVD (by ARPACK or randomized)
on X
| _fit_truncated | python | mars-project/mars | mars/learn/decomposition/_pca.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_pca.py | Apache-2.0 |
def score_samples(self, X, session=None):
"""Return the log-likelihood of each sample.
See. "Pattern Recognition and Machine Learning"
by C. Bishop, 12.2.1 p. 574
or http://www.miketipping.com/papers/met-mppca.pdf
Parameters
----------
X : tensor, shape(n_sample... | Return the log-likelihood of each sample.
See. "Pattern Recognition and Machine Learning"
by C. Bishop, 12.2.1 p. 574
or http://www.miketipping.com/papers/met-mppca.pdf
Parameters
----------
X : tensor, shape(n_samples, n_features)
The data.
Returns... | score_samples | python | mars-project/mars | mars/learn/decomposition/_pca.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_pca.py | Apache-2.0 |
def score(self, X, y=None, session=None):
"""Return the average log-likelihood of all samples.
See. "Pattern Recognition and Machine Learning"
by C. Bishop, 12.2.1 p. 574
or http://www.miketipping.com/papers/met-mppca.pdf
Parameters
----------
X : tensor, shape(... | Return the average log-likelihood of all samples.
See. "Pattern Recognition and Machine Learning"
by C. Bishop, 12.2.1 p. 574
or http://www.miketipping.com/papers/met-mppca.pdf
Parameters
----------
X : tensor, shape(n_samples, n_features)
The data.
... | score | python | mars-project/mars | mars/learn/decomposition/_pca.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_pca.py | Apache-2.0 |
def fit_transform(self, X, y=None, session=None):
"""Fit LSI model to X and perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
session : session to run
y : Ignored
... | Fit LSI model to X and perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data.
session : session to run
y : Ignored
Returns
-------
X_new : array, shape (n_sa... | fit_transform | python | mars-project/mars | mars/learn/decomposition/_truncated_svd.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_truncated_svd.py | Apache-2.0 |
def transform(self, X, session=None):
"""Perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
New data.
session : session to run
Returns
-------
X_new : array, shape (n_sa... | Perform dimensionality reduction on X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
New data.
session : session to run
Returns
-------
X_new : array, shape (n_samples, n_components)
Reduced version ... | transform | python | mars-project/mars | mars/learn/decomposition/_truncated_svd.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_truncated_svd.py | Apache-2.0 |
def inverse_transform(self, X, session=None):
"""Transform X back to its original space.
Returns an array X_original whose transform would be X.
Parameters
----------
X : array-like, shape (n_samples, n_components)
New data.
session : session to run
... | Transform X back to its original space.
Returns an array X_original whose transform would be X.
Parameters
----------
X : array-like, shape (n_samples, n_components)
New data.
session : session to run
Returns
-------
X_original : array, shap... | inverse_transform | python | mars-project/mars | mars/learn/decomposition/_truncated_svd.py | https://github.com/mars-project/mars/blob/master/mars/learn/decomposition/_truncated_svd.py | Apache-2.0 |
def _make_estimator(estimator, random_state=None):
"""Make and configure a copy of the `base_estimator_` attribute.
Warning: This method should be used to properly instantiate new
sub-estimators.
"""
estimator = clone_estimator(estimator)
if random_state is not None:
_set_random_states(... | Make and configure a copy of the `base_estimator_` attribute.
Warning: This method should be used to properly instantiate new
sub-estimators.
| _make_estimator | python | mars-project/mars | mars/learn/ensemble/_bagging.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_bagging.py | Apache-2.0 |
def fit(self, X, y=None, sample_weight=None, session=None, run_kwargs=None):
"""
Build a Bagging ensemble of estimators from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sp... |
Build a Bagging ensemble of estimators from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.... | fit | python | mars-project/mars | mars/learn/ensemble/_bagging.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_bagging.py | Apache-2.0 |
def predict(self, X, session=None, run_kwargs=None):
"""
Predict class for X.
The predicted class of an input sample is computed as the class with
the highest mean predicted probability. If base estimators do not
implement a ``predict_proba`` method, then it resorts to voting.
... |
Predict class for X.
The predicted class of an input sample is computed as the class with
the highest mean predicted probability. If base estimators do not
implement a ``predict_proba`` method, then it resorts to voting.
Parameters
----------
X : {array-like, s... | predict | python | mars-project/mars | mars/learn/ensemble/_bagging.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_bagging.py | Apache-2.0 |
def predict_proba(self, X, session=None, run_kwargs=None):
"""
Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implemen... |
Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
method, then it resorts to voting and th... | predict_proba | python | mars-project/mars | mars/learn/ensemble/_bagging.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_bagging.py | Apache-2.0 |
def predict_log_proba(self, X, session=None, run_kwargs=None):
"""
Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the base
estimators in the ensemble.
Pa... |
Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the base
estimators in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of sha... | predict_log_proba | python | mars-project/mars | mars/learn/ensemble/_bagging.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_bagging.py | Apache-2.0 |
def decision_function(self, X, session=None, run_kwargs=None):
"""
Average of the decision functions of the base classifiers.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accept... |
Average of the decision functions of the base classifiers.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
... | decision_function | python | mars-project/mars | mars/learn/ensemble/_bagging.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_bagging.py | Apache-2.0 |
def predict(self, X, session=None, run_kwargs=None):
"""
Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the estimators in the ensemble.
Parameters
----------
X : {array-... |
Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the estimators in the ensemble.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
... | predict | python | mars-project/mars | mars/learn/ensemble/_bagging.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_bagging.py | Apache-2.0 |
def fit(
self, X, y=None, sample_weight=None, session=None, run_kwargs=None
) -> "IsolationForest":
"""
Fit estimator.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for m... |
Fit estimator.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csc_matrix`` for maximum effici... | fit | python | mars-project/mars | mars/learn/ensemble/_iforest.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_iforest.py | Apache-2.0 |
def predict(self, X, session=None, run_kwargs=None):
"""
Predict if a particular sample is an outlier or not.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``d... |
Predict if a particular sample is an outlier or not.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
... | predict | python | mars-project/mars | mars/learn/ensemble/_iforest.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_iforest.py | Apache-2.0 |
def decision_function(self, X, session=None, run_kwargs=None):
"""
Average anomaly score of X of the base classifiers.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree ... |
Average anomaly score of X of the base classifiers.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth
of the leaf containing this observation, which is equ... | decision_function | python | mars-project/mars | mars/learn/ensemble/_iforest.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_iforest.py | Apache-2.0 |
def score_samples(self, X, session=None, run_kwargs=None):
"""
Opposite of the anomaly score defined in the original paper.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a ... |
Opposite of the anomaly score defined in the original paper.
The anomaly score of an input sample is computed as
the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth
of the leaf containing this observation, whi... | score_samples | python | mars-project/mars | mars/learn/ensemble/_iforest.py | https://github.com/mars-project/mars/blob/master/mars/learn/ensemble/_iforest.py | Apache-2.0 |
def fit(self, X, y):
"""
Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of f... |
Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like... | fit | python | mars-project/mars | mars/learn/glm/_logistic.py | https://github.com/mars-project/mars/blob/master/mars/learn/glm/_logistic.py | Apache-2.0 |
def predict_proba(self, X):
"""
Probability estimates.
The returned estimates for all classes are ordered by the
label of classes.
For a multi_class problem, if multi_class is set to be "multinomial"
the softmax function is used to find the predicted probability of
... |
Probability estimates.
The returned estimates for all classes are ordered by the
label of classes.
For a multi_class problem, if multi_class is set to be "multinomial"
the softmax function is used to find the predicted probability of
each class.
Else use a one-... | predict_proba | python | mars-project/mars | mars/learn/glm/_logistic.py | https://github.com/mars-project/mars/blob/master/mars/learn/glm/_logistic.py | Apache-2.0 |
def _preprocess_data(
X,
y,
fit_intercept,
normalize=False,
copy=True,
sample_weight=None,
return_mean=False,
check_input=True,
):
"""Center and scale data.
Centers data to have mean zero along axis 0. If fit_intercept=False or if
the X is a sparse matrix, no centering is do... | Center and scale data.
Centers data to have mean zero along axis 0. If fit_intercept=False or if
the X is a sparse matrix, no centering is done, but normalization can still
be applied. The function returns the statistics necessary to reconstruct
the input data, which are X_offset, y_offset, X_scale, su... | _preprocess_data | python | mars-project/mars | mars/learn/linear_model/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/linear_model/_base.py | Apache-2.0 |
def _rescale_data(X, y, sample_weight):
"""Rescale data sample-wise by square root of sample_weight.
For many linear models, this enables easy support for sample_weight.
Returns
-------
X_rescaled : {array-like, sparse matrix}
y_rescaled : {array-like, sparse matrix}
"""
n_samples = X... | Rescale data sample-wise by square root of sample_weight.
For many linear models, this enables easy support for sample_weight.
Returns
-------
X_rescaled : {array-like, sparse matrix}
y_rescaled : {array-like, sparse matrix}
| _rescale_data | python | mars-project/mars | mars/learn/linear_model/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/linear_model/_base.py | Apache-2.0 |
def fit(self, X, y, sample_weight=None):
"""
Fit linear model.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values. Wil... |
Fit linear model.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target values. Will be cast to X's dtype if necessary.
sample... | fit | python | mars-project/mars | mars/learn/linear_model/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/linear_model/_base.py | Apache-2.0 |
def decision_function(self, X):
"""
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed
distance of that sample to the hyperplane.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_featu... |
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed
distance of that sample to the hyperplane.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Samples.
Returns
... | decision_function | python | mars-project/mars | mars/learn/linear_model/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/linear_model/_base.py | Apache-2.0 |
def predict(self, X):
"""
Predict class labels for samples in X.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Samples.
Returns
-------
C : array, shape [n_samples]
Predicted class label per ... |
Predict class labels for samples in X.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
Samples.
Returns
-------
C : array, shape [n_samples]
Predicted class label per sample.
| predict | python | mars-project/mars | mars/learn/linear_model/_base.py | https://github.com/mars-project/mars/blob/master/mars/learn/linear_model/_base.py | Apache-2.0 |
def _check_targets(y_true, y_pred):
"""Check that y_true and y_pred belong to the same classification task
This converts multiclass or binary types to a common shape, and raises a
ValueError for a mix of multilabel and multiclass targets, a mix of
multilabel formats, for the presence of continuous-valu... | Check that y_true and y_pred belong to the same classification task
This converts multiclass or binary types to a common shape, and raises a
ValueError for a mix of multilabel and multiclass targets, a mix of
multilabel formats, for the presence of continuous-valued or multioutput
targets, or for targe... | _check_targets | python | mars-project/mars | mars/learn/metrics/_check_targets.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_check_targets.py | Apache-2.0 |
def accuracy_score(
y_true, y_pred, normalize=True, sample_weight=None, session=None, run_kwargs=None
):
"""Accuracy classification score.
In multilabel classification, this function computes subset accuracy:
the set of labels predicted for a sample must *exactly* match the
corresponding set of lab... | Accuracy classification score.
In multilabel classification, this function computes subset accuracy:
the set of labels predicted for a sample must *exactly* match the
corresponding set of labels in y_true.
Read more in the :ref:`User Guide <accuracy_score>`.
Parameters
----------
y_true :... | accuracy_score | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def log_loss(
y_true, y_pred, *, eps=1e-15, normalize=True, sample_weight=None, labels=None
):
r"""Log loss, aka logistic loss or cross-entropy loss.
This is the loss function used in (multinomial) logistic regression
and extensions of it such as neural networks, defined as the negative
log-likelih... | Log loss, aka logistic loss or cross-entropy loss.
This is the loss function used in (multinomial) logistic regression
and extensions of it such as neural networks, defined as the negative
log-likelihood of a logistic model that returns ``y_pred`` probabilities
for its training data ``y_true``.
The... | log_loss | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def multilabel_confusion_matrix(
y_true,
y_pred,
*,
sample_weight=None,
labels=None,
samplewise=False,
session=None,
run_kwargs=None
):
"""
Compute a confusion matrix for each class or sample.
Compute class-wise (default) or sample-wise (samplewise=True) multilabel
confu... |
Compute a confusion matrix for each class or sample.
Compute class-wise (default) or sample-wise (samplewise=True) multilabel
confusion matrix to evaluate the accuracy of a classification, and output
confusion matrices for each class or sample.
In multilabel confusion matrix :math:`MCM`, the coun... | multilabel_confusion_matrix | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def _prf_divide(
numerator, denominator, metric, modifier, average, warn_for, zero_division="warn"
): # pragma: no cover
"""Performs division and handles divide-by-zero.
On zero-division, sets the corresponding result elements equal to
0 or 1 (according to ``zero_division``). Plus, if
``zero_divis... | Performs division and handles divide-by-zero.
On zero-division, sets the corresponding result elements equal to
0 or 1 (according to ``zero_division``). Plus, if
``zero_division != "warn"`` raises a warning.
The metric, modifier and average arguments are used only for determining
an appropriate wa... | _prf_divide | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def _check_set_wise_labels(
y_true, y_pred, average, labels, pos_label, session=None, run_kwargs=None
): # pragma: no cover
"""Validation associated with set-wise metrics
Returns identified labels
"""
exec_kwargs = dict(session=session, **(run_kwargs or dict()))
average_options = (None, "micro... | Validation associated with set-wise metrics
Returns identified labels
| _check_set_wise_labels | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def precision_recall_fscore_support(
y_true,
y_pred,
*,
beta=1.0,
labels=None,
pos_label=1,
average=None,
warn_for=("precision", "recall", "f-score"),
sample_weight=None,
zero_division="warn",
session=None,
run_kwargs=None
):
"""Compute precision, recall, F-measure an... | Compute precision, recall, F-measure and support for each class
The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
true positives and ``fp`` the number of false positives. The precision is
intuitively the ability of the classifier not to label as positive a sample
that is negat... | precision_recall_fscore_support | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def precision_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
zero_division="warn"
):
"""Compute the precision
The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
true positives and ``fp`` the number of false ... | Compute the precision
The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
true positives and ``fp`` the number of false positives. The precision is
intuitively the ability of the classifier not to label as positive a sample
that is negative.
The best value is 1 and the wors... | precision_score | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def recall_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
zero_division="warn"
):
"""Compute the recall
The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
true positives and ``fn`` the number of false negatives... | Compute the recall
The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
true positives and ``fn`` the number of false negatives. The recall is
intuitively the ability of the classifier to find all the positive samples.
The best value is 1 and the worst value is 0.
Read more in... | recall_score | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def f1_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
zero_division="warn"
):
"""Compute the F1 score, also known as balanced F-score or F-measure
The F1 score can be interpreted as a weighted average of the precision and
recall, wh... | Compute the F1 score, also known as balanced F-score or F-measure
The F1 score can be interpreted as a weighted average of the precision and
recall, where an F1 score reaches its best value at 1 and worst score at 0.
The relative contribution of precision and recall to the F1 score are
equal. The formu... | f1_score | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def fbeta_score(
y_true,
y_pred,
*,
beta,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
zero_division="warn"
):
"""Compute the F-beta score
The F-beta score is the weighted harmonic mean of precision and recall,
reaching its optimal value at 1 and its w... | Compute the F-beta score
The F-beta score is the weighted harmonic mean of precision and recall,
reaching its optimal value at 1 and its worst value at 0.
The `beta` parameter determines the weight of recall in the combined
score. ``beta < 1`` lends more weight to precision, while ``beta > 1``
fav... | fbeta_score | python | mars-project/mars | mars/learn/metrics/_classification.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_classification.py | Apache-2.0 |
def auc(x, y, session=None, run_kwargs=None):
"""Compute Area Under the Curve (AUC) using the trapezoidal rule
This is a general function, given points on a curve. For computing the
area under the ROC-curve, see :func:`roc_auc_score`. For an alternative
way to summarize a precision-recall curve, see
... | Compute Area Under the Curve (AUC) using the trapezoidal rule
This is a general function, given points on a curve. For computing the
area under the ROC-curve, see :func:`roc_auc_score`. For an alternative
way to summarize a precision-recall curve, see
:func:`average_precision_score`.
Parameters
... | auc | python | mars-project/mars | mars/learn/metrics/_ranking.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_ranking.py | Apache-2.0 |
def _binary_clf_curve(
y_true, y_score, pos_label=None, sample_weight=None, session=None, run_kwargs=None
):
"""Calculate true and false positives per binary classification threshold.
Parameters
----------
y_true : tensor, shape = [n_samples]
True targets of binary classification
y_sco... | Calculate true and false positives per binary classification threshold.
Parameters
----------
y_true : tensor, shape = [n_samples]
True targets of binary classification
y_score : tensor, shape = [n_samples]
Estimated probabilities or decision function
pos_label : int or str, defau... | _binary_clf_curve | python | mars-project/mars | mars/learn/metrics/_ranking.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_ranking.py | Apache-2.0 |
def roc_auc_score(
y_true,
y_score,
*,
average="macro",
sample_weight=None,
max_fpr=None,
multi_class="raise",
labels=None,
session=None,
run_kwargs=None,
):
"""
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
from prediction scores.
Note... |
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
from prediction scores.
Note: this implementation can be used with binary, multiclass and
multilabel classification, but some restrictions apply (see Parameters).
Read more in the :ref:`User Guide <roc_metrics>`.
Parame... | roc_auc_score | python | mars-project/mars | mars/learn/metrics/_ranking.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_ranking.py | Apache-2.0 |
def roc_curve(
y_true,
y_score,
pos_label=None,
sample_weight=None,
drop_intermediate=True,
session=None,
run_kwargs=None,
):
"""Compute Receiver operating characteristic (ROC)
Note: this implementation is restricted to the binary classification task.
Read more in the :ref:`Use... | Compute Receiver operating characteristic (ROC)
Note: this implementation is restricted to the binary classification task.
Read more in the :ref:`User Guide <roc_metrics>`.
Parameters
----------
y_true : tensor, shape = [n_samples]
True binary labels. If labels are not either {-1, 1} or ... | roc_curve | python | mars-project/mars | mars/learn/metrics/_ranking.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_ranking.py | Apache-2.0 |
def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric"):
"""Check that y_true and y_pred belong to the same regression task.
Parameters
----------
y_true : array-like
y_pred : array-like
multioutput : array-like or string in ['raw_values', uniform_average',
'variance_weig... | Check that y_true and y_pred belong to the same regression task.
Parameters
----------
y_true : array-like
y_pred : array-like
multioutput : array-like or string in ['raw_values', uniform_average',
'variance_weighted'] or None
None is accepted due to backward compatibility of r2_s... | _check_reg_targets | python | mars-project/mars | mars/learn/metrics/_regresssion.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_regresssion.py | Apache-2.0 |
def r2_score(
y_true,
y_pred,
*,
sample_weight=None,
multioutput="uniform_average",
session=None,
run_kwargs=None
):
""":math:`R^2` (coefficient of determination) regression score function.
Best possible score is 1.0 and it can be negative (because the
model can be arbitrarily w... | :math:`R^2` (coefficient of determination) regression score function.
Best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a :math:`R^2` score of 0.0.
... | r2_score | python | mars-project/mars | mars/learn/metrics/_regresssion.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_regresssion.py | Apache-2.0 |
def get_scorer(score_func: Union[str, Callable], **kwargs) -> Callable:
"""
Get a scorer from string
Parameters
----------
score_func : str | callable
scoring method as string. If callable it is returned as is.
Returns
-------
scorer : callable
The scorer.
"""
i... |
Get a scorer from string
Parameters
----------
score_func : str | callable
scoring method as string. If callable it is returned as is.
Returns
-------
scorer : callable
The scorer.
| get_scorer | python | mars-project/mars | mars/learn/metrics/_scorer.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/_scorer.py | Apache-2.0 |
def _return_float_dtype(X, Y):
"""
1. If dtype of X and Y is float32, then dtype float32 is returned.
2. Else dtype float is returned.
"""
X = astensor(X)
if Y is None:
Y_dtype = X.dtype
else:
Y = astensor(Y)
Y_dtype = Y.dtype... |
1. If dtype of X and Y is float32, then dtype float32 is returned.
2. Else dtype float is returned.
| _return_float_dtype | python | mars-project/mars | mars/learn/metrics/pairwise/core.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/pairwise/core.py | Apache-2.0 |
def cosine_similarity(X, Y=None, dense_output=True):
"""Compute cosine similarity between samples in X and Y.
Cosine similarity, or the cosine kernel, computes similarity as the
normalized dot product of X and Y:
K(X, Y) = <X, Y> / (||X||*||Y||)
On L2-normalized data, this function is equival... | Compute cosine similarity between samples in X and Y.
Cosine similarity, or the cosine kernel, computes similarity as the
normalized dot product of X and Y:
K(X, Y) = <X, Y> / (||X||*||Y||)
On L2-normalized data, this function is equivalent to linear_kernel.
Read more in the :ref:`User Guide... | cosine_similarity | python | mars-project/mars | mars/learn/metrics/pairwise/cosine.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/pairwise/cosine.py | Apache-2.0 |
def cosine_distances(X, Y=None):
"""Compute cosine distance between samples in X and Y.
Cosine distance is defined as 1.0 minus the cosine similarity.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array_like, sparse matrix
with shape (n_samples_X, n_features)... | Compute cosine distance between samples in X and Y.
Cosine distance is defined as 1.0 minus the cosine similarity.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array_like, sparse matrix
with shape (n_samples_X, n_features).
Y : array_like, sparse matrix (op... | cosine_distances | python | mars-project/mars | mars/learn/metrics/pairwise/cosine.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/pairwise/cosine.py | Apache-2.0 |
def haversine_distances(X, Y=None):
"""Compute the Haversine distance between samples in X and Y
The Haversine (or great circle) distance is the angular distance between
two points on the surface of a sphere. The first distance of each point is
assumed to be the latitude, the second is the longitude, g... | Compute the Haversine distance between samples in X and Y
The Haversine (or great circle) distance is the angular distance between
two points on the surface of a sphere. The first distance of each point is
assumed to be the latitude, the second is the longitude, given in radians.
The dimension of the d... | haversine_distances | python | mars-project/mars | mars/learn/metrics/pairwise/haversine.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/pairwise/haversine.py | Apache-2.0 |
def manhattan_distances(X, Y=None, sum_over_features=True):
""" Compute the L1 distances between the vectors in X and Y.
With sum_over_features equal to False it returns the componentwise
distances.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array_like
... | Compute the L1 distances between the vectors in X and Y.
With sum_over_features equal to False it returns the componentwise
distances.
Read more in the :ref:`User Guide <metrics>`.
Parameters
----------
X : array_like
A tensor with shape (n_samples_X, n_features).
Y : array_like... | manhattan_distances | python | mars-project/mars | mars/learn/metrics/pairwise/manhattan.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/pairwise/manhattan.py | Apache-2.0 |
def _precompute_metric_params(X, Y, xp, metric=None, **kwds): # pragma: no cover
"""Precompute data-derived metric parameters if not provided"""
if metric == "seuclidean" and "V" not in kwds:
if X is Y:
V = xp.var(X, axis=0, ddof=1)
else:
V = xp.var(xp.vstack([X, Y]), ax... | Precompute data-derived metric parameters if not provided | _precompute_metric_params | python | mars-project/mars | mars/learn/metrics/pairwise/pairwise_distances_topk.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/pairwise/pairwise_distances_topk.py | Apache-2.0 |
def _check_chunk_size(reduced, chunk_size): # pragma: no cover
"""Checks chunk is a sequence of expected size or a tuple of same"""
if reduced is None:
return
is_tuple = isinstance(reduced, tuple)
if not is_tuple:
reduced = (reduced,)
if any(isinstance(r, tuple) or not hasattr(r, "_... | Checks chunk is a sequence of expected size or a tuple of same | _check_chunk_size | python | mars-project/mars | mars/learn/metrics/pairwise/pairwise_distances_topk.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/pairwise/pairwise_distances_topk.py | Apache-2.0 |
def _topk_reduce_func(cls, dist, start, topk, xp, metric):
"""Reduce a chunk of distances to topk
Parameters
----------
dist : array of shape (n_samples_chunk, n_samples)
start : int
The index in X which the first row of dist corresponds to.
topk : int
... | Reduce a chunk of distances to topk
Parameters
----------
dist : array of shape (n_samples_chunk, n_samples)
start : int
The index in X which the first row of dist corresponds to.
topk : int
Returns
-------
dist : array of shape (n_samples_ch... | _topk_reduce_func | python | mars-project/mars | mars/learn/metrics/pairwise/pairwise_distances_topk.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/pairwise/pairwise_distances_topk.py | Apache-2.0 |
def rbf_kernel(X, Y=None, gamma=None):
"""
Compute the rbf (gaussian) kernel between X and Y::
K(x, y) = exp(-gamma ||x-y||^2)
for each pair of rows x in X and y in Y.
Read more in the :ref:`User Guide <rbf_kernel>`.
Parameters
----------
X : tensor of shape (n_samples_X, n_featu... |
Compute the rbf (gaussian) kernel between X and Y::
K(x, y) = exp(-gamma ||x-y||^2)
for each pair of rows x in X and y in Y.
Read more in the :ref:`User Guide <rbf_kernel>`.
Parameters
----------
X : tensor of shape (n_samples_X, n_features)
Y : tensor of shape (n_samples_Y, n_... | rbf_kernel | python | mars-project/mars | mars/learn/metrics/pairwise/rbf_kernel.py | https://github.com/mars-project/mars/blob/master/mars/learn/metrics/pairwise/rbf_kernel.py | Apache-2.0 |
def train_test_split(*arrays, **options):
"""Split arrays or matrices into random train and test subsets
Parameters
----------
*arrays : sequence of indexables with same length / shape[0]
Allowed inputs are lists, numpy arrays, scipy-sparse
matrices or pandas dataframes.
test_size ... | Split arrays or matrices into random train and test subsets
Parameters
----------
*arrays : sequence of indexables with same length / shape[0]
Allowed inputs are lists, numpy arrays, scipy-sparse
matrices or pandas dataframes.
test_size : float, int or None, optional (default=None)
... | train_test_split | python | mars-project/mars | mars/learn/model_selection/_split.py | https://github.com/mars-project/mars/blob/master/mars/learn/model_selection/_split.py | Apache-2.0 |
def _validate_shuffle_split(n_samples, test_size, train_size, default_test_size=None):
"""
Validation helper to check if the test/test sizes are meaningful wrt to the
size of the data (n_samples)
"""
if test_size is None and train_size is None:
test_size = default_test_size
test_size_ty... |
Validation helper to check if the test/test sizes are meaningful wrt to the
size of the data (n_samples)
| _validate_shuffle_split | python | mars-project/mars | mars/learn/model_selection/_split.py | https://github.com/mars-project/mars/blob/master/mars/learn/model_selection/_split.py | Apache-2.0 |
def split(self, X, y=None, groups=None): # pragma: no cover
"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features... | Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
y : array-like of shape (n_samples,... | split | python | mars-project/mars | mars/learn/model_selection/_split.py | https://github.com/mars-project/mars/blob/master/mars/learn/model_selection/_split.py | Apache-2.0 |
def split(self, X, y=None, groups=None):
"""Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of fe... | Generate indices to split data into training and test set.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples
and n_features is the number of features.
y : array-like of shape (n_samples,... | split | python | mars-project/mars | mars/learn/model_selection/_split.py | https://github.com/mars-project/mars/blob/master/mars/learn/model_selection/_split.py | Apache-2.0 |
def kneighbors(
self,
X=None,
n_neighbors=None,
return_distance=True,
session=None,
run_kwargs=None,
**kw,
):
"""Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Parameters
----... | Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each point.
Parameters
----------
X : array-like, shape (n_query, n_features), or (n_query, n_indexed) if metric == 'precomputed'
The query point or points.
If ... | kneighbors | python | mars-project/mars | mars/learn/neighbors/base.py | https://github.com/mars-project/mars/blob/master/mars/learn/neighbors/base.py | Apache-2.0 |
def kneighbors_graph(
self,
X=None,
n_neighbors=None,
mode="connectivity",
session=None,
run_kwargs=None,
):
"""Computes the (weighted) graph of k-Neighbors for points in X
Parameters
----------
X : array-like, shape (n_query, n_featur... | Computes the (weighted) graph of k-Neighbors for points in X
Parameters
----------
X : array-like, shape (n_query, n_features), or (n_query, n_indexed) if metric == 'precomputed'
The query point or points.
If not provided, neighbors of each indexed point ... | kneighbors_graph | python | mars-project/mars | mars/learn/neighbors/base.py | https://github.com/mars-project/mars/blob/master/mars/learn/neighbors/base.py | Apache-2.0 |
def _tile_chunks(cls, op, in_tensor, faiss_index, n_sample):
"""
If the distribution on each chunk is the same,
refer to:
https://github.com/facebookresearch/faiss/wiki/FAQ#how-can-i-distribute-index-building-on-several-machines
1. train an IndexIVF* on a representative sample o... |
If the distribution on each chunk is the same,
refer to:
https://github.com/facebookresearch/faiss/wiki/FAQ#how-can-i-distribute-index-building-on-several-machines
1. train an IndexIVF* on a representative sample of the data, store it.
2. for each node, load the trained index, ... | _tile_chunks | python | mars-project/mars | mars/learn/neighbors/_faiss.py | https://github.com/mars-project/mars/blob/master/mars/learn/neighbors/_faiss.py | Apache-2.0 |
def _gen_index_string_and_sample_count(
shape, n_sample, accuracy, memory_require, gpu=None, **kw
):
"""
Generate index string and sample count according to guidance of faiss:
https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
"""
size, dim = shape
memory_require = ... |
Generate index string and sample count according to guidance of faiss:
https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
| _gen_index_string_and_sample_count | python | mars-project/mars | mars/learn/neighbors/_faiss.py | https://github.com/mars-project/mars/blob/master/mars/learn/neighbors/_faiss.py | Apache-2.0 |
def normalize(X, norm="l2", axis=1, copy=True, return_norm=False):
"""
Scale input vectors individually to unit norm (vector length).
Parameters
----------
X : {array-like, sparse matrix}, shape [n_samples, n_features]
The data to normalize, element by element.
scipy.sparse matrices... |
Scale input vectors individually to unit norm (vector length).
Parameters
----------
X : {array-like, sparse matrix}, shape [n_samples, n_features]
The data to normalize, element by element.
scipy.sparse matrices should be in CSR format to avoid an
un-necessary copy.
norm ... | normalize | python | mars-project/mars | mars/learn/preprocessing/normalize.py | https://github.com/mars-project/mars/blob/master/mars/learn/preprocessing/normalize.py | Apache-2.0 |
def _handle_zeros_in_scale(scale, copy=True):
"""Makes sure that whenever scale is zero, we handle it correctly.
This happens in most scalers when we have constant features.
"""
# if we are fitting on 1D arrays, scale might be a scalar
if np.isscalar(scale): # pragma: no cover
if scale ==... | Makes sure that whenever scale is zero, we handle it correctly.
This happens in most scalers when we have constant features.
| _handle_zeros_in_scale | python | mars-project/mars | mars/learn/preprocessing/_data.py | https://github.com/mars-project/mars/blob/master/mars/learn/preprocessing/_data.py | Apache-2.0 |
def inverse_transform(self, X, session=None, run_kwargs=None):
"""Undo the scaling of X according to feature_range.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data that will be transformed. It cannot be sparse.
Returns
------... | Undo the scaling of X according to feature_range.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Input data that will be transformed. It cannot be sparse.
Returns
-------
Xt : ndarray of shape (n_samples, n_features)
Transf... | inverse_transform | python | mars-project/mars | mars/learn/preprocessing/_data.py | https://github.com/mars-project/mars/blob/master/mars/learn/preprocessing/_data.py | Apache-2.0 |
def minmax_scale(
X, feature_range=(0, 1), *, axis=0, copy=True, session=None, run_kwargs=None
):
"""Transform features by scaling each feature to a given range.
This estimator scales and translates each feature individually such
that it is in the given range on the training set, i.e. between
zero ... | Transform features by scaling each feature to a given range.
This estimator scales and translates each feature individually such
that it is in the given range on the training set, i.e. between
zero and one.
The transformation is given by (when ``axis=0``)::
X_std = (X - X.min(axis=0)) / (X.ma... | minmax_scale | python | mars-project/mars | mars/learn/preprocessing/_data.py | https://github.com/mars-project/mars/blob/master/mars/learn/preprocessing/_data.py | Apache-2.0 |
def inverse_transform(self, y, session=None, run_kwargs=None):
"""Transform labels back to original encoding.
Parameters
----------
y : ndarray of shape (n_samples,)
Target values.
Returns
-------
y : ndarray of shape (n_samples,)
Origina... | Transform labels back to original encoding.
Parameters
----------
y : ndarray of shape (n_samples,)
Target values.
Returns
-------
y : ndarray of shape (n_samples,)
Original encoding.
| inverse_transform | python | mars-project/mars | mars/learn/preprocessing/_label.py | https://github.com/mars-project/mars/blob/master/mars/learn/preprocessing/_label.py | Apache-2.0 |
def fit(self, y, session=None, run_kwargs=None):
"""Fit label binarizer.
Parameters
----------
y : ndarray of shape (n_samples,) or (n_samples, n_classes)
Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification.
Re... | Fit label binarizer.
Parameters
----------
y : ndarray of shape (n_samples,) or (n_samples, n_classes)
Target values. The 2-d matrix should only contain 0 and 1,
represents multilabel classification.
Returns
-------
self : returns an instance of ... | fit | python | mars-project/mars | mars/learn/preprocessing/_label.py | https://github.com/mars-project/mars/blob/master/mars/learn/preprocessing/_label.py | Apache-2.0 |
def fit_transform(self, y, session=None, run_kwargs=None):
"""Fit label binarizer and transform multi-class labels to binary
labels.
The output of transform is sometimes referred to as
the 1-of-K coding scheme.
Parameters
----------
y : {ndarray, sparse matrix} ... | Fit label binarizer and transform multi-class labels to binary
labels.
The output of transform is sometimes referred to as
the 1-of-K coding scheme.
Parameters
----------
y : {ndarray, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)
... | fit_transform | python | mars-project/mars | mars/learn/preprocessing/_label.py | https://github.com/mars-project/mars/blob/master/mars/learn/preprocessing/_label.py | Apache-2.0 |
def inverse_transform(self, Y, threshold=None):
"""Transform binary labels back to multi-class labels.
Parameters
----------
Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
Target values. All sparse matrices are converted to CSR before
inverse transf... | Transform binary labels back to multi-class labels.
Parameters
----------
Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
Target values. All sparse matrices are converted to CSR before
inverse transformation.
threshold : float, default=None
... | inverse_transform | python | mars-project/mars | mars/learn/preprocessing/_label.py | https://github.com/mars-project/mars/blob/master/mars/learn/preprocessing/_label.py | Apache-2.0 |
def label_binarize(
y, *, classes, neg_label=0, pos_label=1, sparse_output=False, execute=True
):
"""Binarize labels in a one-vs-all fashion.
Several regression and binary classification algorithms are
available in scikit-learn. A simple way to extend these algorithms
to the multi-class classificat... | Binarize labels in a one-vs-all fashion.
Several regression and binary classification algorithms are
available in scikit-learn. A simple way to extend these algorithms
to the multi-class classification case is to use the so-called
one-vs-all scheme.
This function makes it possible to compute this ... | label_binarize | python | mars-project/mars | mars/learn/preprocessing/_label.py | https://github.com/mars-project/mars/blob/master/mars/learn/preprocessing/_label.py | Apache-2.0 |
def predict(self, X, session=None, run_kwargs=None):
"""Performs inductive inference across the model.
Parameters
----------
X : array_like, shape = [n_samples, n_features]
Returns
-------
y : array_like, shape = [n_samples]
Predictions for input dat... | Performs inductive inference across the model.
Parameters
----------
X : array_like, shape = [n_samples, n_features]
Returns
-------
y : array_like, shape = [n_samples]
Predictions for input data
| predict | python | mars-project/mars | mars/learn/semi_supervised/_label_propagation.py | https://github.com/mars-project/mars/blob/master/mars/learn/semi_supervised/_label_propagation.py | Apache-2.0 |
def predict_proba(self, X, session=None, run_kwargs=None):
"""Predict probability for each possible outcome.
Compute the probability estimates for each single sample in X
and each possible outcome seen during training (categorical
distribution).
Parameters
----------
... | Predict probability for each possible outcome.
Compute the probability estimates for each single sample in X
and each possible outcome seen during training (categorical
distribution).
Parameters
----------
X : array_like, shape = [n_samples, n_features]
Returns... | predict_proba | python | mars-project/mars | mars/learn/semi_supervised/_label_propagation.py | https://github.com/mars-project/mars/blob/master/mars/learn/semi_supervised/_label_propagation.py | Apache-2.0 |
def fit(self, X, y, session=None, run_kwargs=None):
"""Fit a semi-supervised label propagation model based
All the input data is provided matrix X (labeled and unlabeled)
and corresponding label matrix y with a dedicated marker value for
unlabeled samples.
Parameters
--... | Fit a semi-supervised label propagation model based
All the input data is provided matrix X (labeled and unlabeled)
and corresponding label matrix y with a dedicated marker value for
unlabeled samples.
Parameters
----------
X : array-like of shape (n_samples, n_features... | fit | python | mars-project/mars | mars/learn/semi_supervised/_label_propagation.py | https://github.com/mars-project/mars/blob/master/mars/learn/semi_supervised/_label_propagation.py | Apache-2.0 |
def get_chunk_n_rows(row_bytes, max_n_rows=None, working_memory=None):
"""Calculates how many rows can be processed within working_memory
Parameters
----------
row_bytes : int
The expected number of bytes of memory that will be consumed
during the processing of each row.
max_n_rows ... | Calculates how many rows can be processed within working_memory
Parameters
----------
row_bytes : int
The expected number of bytes of memory that will be consumed
during the processing of each row.
max_n_rows : int, optional
The maximum return value.
working_memory : int or ... | get_chunk_n_rows | python | mars-project/mars | mars/learn/utils/core.py | https://github.com/mars-project/mars/blob/master/mars/learn/utils/core.py | Apache-2.0 |
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