code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def _get_selection_columns(cls, op: DataFrameGroupByAgg) -> Union[None, List]:
"""
Get groupby selection columns from op parameters.
If this returns None, it means all columns are required.
Parameters
----------
op
Returns
-------
"""
if ... |
Get groupby selection columns from op parameters.
If this returns None, it means all columns are required.
Parameters
----------
op
Returns
-------
| _get_selection_columns | python | mars-project/mars | mars/dataframe/groupby/nunique.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/groupby/nunique.py | Apache-2.0 |
def groupby_sample(
groupby,
n: Optional[int] = None,
frac: Optional[float] = None,
replace: bool = False,
weights: Union[Sequence, pd.Series, None] = None,
random_state: Optional[np.random.RandomState] = None,
errors: str = "ignore",
):
"""
Return a random sample of items from each ... |
Return a random sample of items from each group.
You can use `random_state` for reproducibility.
Parameters
----------
n : int, optional
Number of items to return for each group. Cannot be used with
`frac` and must be no larger than the smallest group unless
`replace` is T... | groupby_sample | python | mars-project/mars | mars/dataframe/groupby/sample.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/groupby/sample.py | Apache-2.0 |
def groupby_transform(
groupby,
f,
*args,
dtypes=None,
dtype=None,
name=None,
index=None,
output_types=None,
skip_infer=False,
**kwargs,
):
"""
Call function producing a like-indexed DataFrame on each group and
return a DataFrame having the same indexes as the origina... |
Call function producing a like-indexed DataFrame on each group and
return a DataFrame having the same indexes as the original object
filled with the transformed values
Parameters
----------
f : function
Function to apply to each group.
dtypes : Series, default None
Specify... | groupby_transform | python | mars-project/mars | mars/dataframe/groupby/transform.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/groupby/transform.py | Apache-2.0 |
def _tree_getitem(cls, op):
"""
DataFrame doesn't store the index value except RangeIndex or specify `store=True` in `parse_index`,
So we build a tree structure to avoid too much dependence for getitem node.
"""
out_series = op.outputs[0]
combine_size = options.combine_si... |
DataFrame doesn't store the index value except RangeIndex or specify `store=True` in `parse_index`,
So we build a tree structure to avoid too much dependence for getitem node.
| _tree_getitem | python | mars-project/mars | mars/dataframe/indexing/getitem.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/indexing/getitem.py | Apache-2.0 |
def df_insert(df, loc, column, value, allow_duplicates=False):
"""
Insert column into DataFrame at specified location.
Raises a ValueError if `column` is already contained in the DataFrame,
unless `allow_duplicates` is set to True.
Parameters
----------
loc : int
Insertion index. M... |
Insert column into DataFrame at specified location.
Raises a ValueError if `column` is already contained in the DataFrame,
unless `allow_duplicates` is set to True.
Parameters
----------
loc : int
Insertion index. Must verify 0 <= loc <= len(columns).
column : str, number, or hash... | df_insert | python | mars-project/mars | mars/dataframe/indexing/insert.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/indexing/insert.py | Apache-2.0 |
def df_rename(
df,
mapper=None,
index=None,
columns=None,
axis="index",
copy=True,
inplace=False,
level=None,
errors="ignore",
):
"""
Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra ... |
Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra labels listed don't throw an
error.
Parameters
----------
mapper : dict-like or function
Dict-like or functions transformations to apply to
... | df_rename | python | mars-project/mars | mars/dataframe/indexing/rename.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/indexing/rename.py | Apache-2.0 |
def series_rename(
series,
index=None,
*,
axis="index",
copy=True,
inplace=False,
level=None,
errors="ignore"
):
"""
Alter Series index labels or name.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra label... |
Alter Series index labels or name.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra labels listed don't throw an
error.
Alternatively, change ``Series.name`` with a scalar value.
Parameters
----------
axis : {0 or "inde... | series_rename | python | mars-project/mars | mars/dataframe/indexing/rename.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/indexing/rename.py | Apache-2.0 |
def index_rename(index, name, inplace=False):
"""
Alter Index or MultiIndex name.
Able to set new names without level. Defaults to returning new index.
Length of names must match number of levels in MultiIndex.
Parameters
----------
name : label or list of labels
Name(s) to set.
... |
Alter Index or MultiIndex name.
Able to set new names without level. Defaults to returning new index.
Length of names must match number of levels in MultiIndex.
Parameters
----------
name : label or list of labels
Name(s) to set.
inplace : bool, default False
Modifies the ... | index_rename | python | mars-project/mars | mars/dataframe/indexing/rename.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/indexing/rename.py | Apache-2.0 |
def index_set_names(index, names, level=None, inplace=False):
"""
Set Index or MultiIndex name.
Able to set new names partially and by level.
Parameters
----------
names : label or list of label
Name(s) to set.
level : int, label or list of int or label, optional
If the ind... |
Set Index or MultiIndex name.
Able to set new names partially and by level.
Parameters
----------
names : label or list of label
Name(s) to set.
level : int, label or list of int or label, optional
If the index is a MultiIndex, level(s) to set (None for all
levels). Ot... | index_set_names | python | mars-project/mars | mars/dataframe/indexing/rename.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/indexing/rename.py | Apache-2.0 |
def rename_axis(
df_or_series,
mapper=None,
index=None,
columns=None,
axis=0,
copy=True,
inplace=False,
):
"""
Set the name of the axis for the index or columns.
Parameters
----------
mapper : scalar, list-like, optional
Value to set the axis name attribute.
... |
Set the name of the axis for the index or columns.
Parameters
----------
mapper : scalar, list-like, optional
Value to set the axis name attribute.
index, columns : scalar, list-like, dict-like or function, optional
A scalar, list-like, dict-like or functions transformations to
... | rename_axis | python | mars-project/mars | mars/dataframe/indexing/rename_axis.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/indexing/rename_axis.py | Apache-2.0 |
def calc_columns_index(column_name, df):
"""
Calculate the chunk index on the axis 1 according to the selected column.
:param column_name: selected column name
:param df: input tiled DataFrame
:return: chunk index on the columns axis
"""
column_nsplits = df.nsplits[1]
# if has duplicate ... |
Calculate the chunk index on the axis 1 according to the selected column.
:param column_name: selected column name
:param df: input tiled DataFrame
:return: chunk index on the columns axis
| calc_columns_index | python | mars-project/mars | mars/dataframe/indexing/utils.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/indexing/utils.py | Apache-2.0 |
def convert_labels_into_positions(pandas_index, labels):
"""
Convert labels into positions
:param pandas_index: pandas Index
:param labels: labels
:return: positions
"""
result = []
for label in labels:
loc = pandas_index.get_loc(label)
if isinstance(loc, (int, np.intege... |
Convert labels into positions
:param pandas_index: pandas Index
:param labels: labels
:return: positions
| convert_labels_into_positions | python | mars-project/mars | mars/dataframe/indexing/utils.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/indexing/utils.py | Apache-2.0 |
def merge(
df: Union[DataFrame, Series],
right: Union[DataFrame, Series],
how: str = "inner",
on: str = None,
left_on: str = None,
right_on: str = None,
left_index: bool = False,
right_index: bool = False,
sort: bool = False,
suffixes: Tuple[Optional[str], Optional[str]] = ("_x",... |
Merge DataFrame or named Series objects with a database-style join.
A named Series object is treated as a DataFrame with a single named column.
The join is done on columns or indexes. If joining columns on
columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes
on indexes o... | merge | python | mars-project/mars | mars/dataframe/merge/merge.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/merge/merge.py | Apache-2.0 |
def join(
df: Union[DataFrame, Series],
other: Union[DataFrame, Series],
on: str = None,
how: str = "left",
lsuffix: str = "",
rsuffix: str = "",
sort: bool = False,
method: str = None,
auto_merge: str = "both",
auto_merge_threshold: int = 8,
bloom_filter: Union[bool, Dict] =... |
Join columns of another DataFrame.
Join columns with `other` DataFrame either on index or on a key
column. Efficiently join multiple DataFrame objects by index at once by
passing a list.
Parameters
----------
other : DataFrame, Series, or list of DataFrame
Index should be similar ... | join | python | mars-project/mars | mars/dataframe/merge/merge.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/merge/merge.py | Apache-2.0 |
def df_dropna(
df, axis=0, how=no_default, thresh=no_default, subset=None, inplace=False
):
"""
Remove missing values.
See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.
Parameters
----------
axis : {0 or 'index'... |
Remove missing values.
See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine if rows or columns which contain missing values are
... | df_dropna | python | mars-project/mars | mars/dataframe/missing/dropna.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/missing/dropna.py | Apache-2.0 |
def series_dropna(series, axis=0, inplace=False, how=None):
"""
Return a new Series with missing values removed.
See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.
Parameters
----------
axis : {0 or 'index'}, default... |
Return a new Series with missing values removed.
See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.
Parameters
----------
axis : {0 or 'index'}, default 0
There is only one axis to drop values from.
inplace ... | series_dropna | python | mars-project/mars | mars/dataframe/missing/dropna.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/missing/dropna.py | Apache-2.0 |
def index_dropna(index, how="any"):
"""
Return Index without NA/NaN values.
Parameters
----------
how : {'any', 'all'}, default 'any'
If the Index is a MultiIndex, drop the value when any or all levels
are NaN.
Returns
-------
Index
"""
use_inf_as_na = options.d... |
Return Index without NA/NaN values.
Parameters
----------
how : {'any', 'all'}, default 'any'
If the Index is a MultiIndex, drop the value when any or all levels
are NaN.
Returns
-------
Index
| index_dropna | python | mars-project/mars | mars/dataframe/missing/dropna.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/missing/dropna.py | Apache-2.0 |
def ffill(df, axis=None, inplace=False, limit=None, downcast=None):
"""
Synonym for :meth:`DataFrame.fillna` with ``method='ffill'``.
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
"""
return fillna(
df, method="ffill", axis=ax... |
Synonym for :meth:`DataFrame.fillna` with ``method='ffill'``.
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
| ffill | python | mars-project/mars | mars/dataframe/missing/fillna.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/missing/fillna.py | Apache-2.0 |
def bfill(df, axis=None, inplace=False, limit=None, downcast=None):
"""
Synonym for :meth:`DataFrame.fillna` with ``method='bfill'``.
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
"""
return fillna(
df, method="bfill", axis=ax... |
Synonym for :meth:`DataFrame.fillna` with ``method='bfill'``.
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
| bfill | python | mars-project/mars | mars/dataframe/missing/fillna.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/missing/fillna.py | Apache-2.0 |
def index_fillna(index, value=None, downcast=None):
"""
Fill NA/NaN values with the specified value.
Parameters
----------
value : scalar
Scalar value to use to fill holes (e.g. 0).
This value cannot be a list-likes.
downcast : dict, default is None
A dict of item->dtype... |
Fill NA/NaN values with the specified value.
Parameters
----------
value : scalar
Scalar value to use to fill holes (e.g. 0).
This value cannot be a list-likes.
downcast : dict, default is None
A dict of item->dtype of what to downcast if possible,
or the string 'in... | index_fillna | python | mars-project/mars | mars/dataframe/missing/fillna.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/missing/fillna.py | Apache-2.0 |
def _generate_function_str(self, out_tileable):
"""
Generate python code from tileable DAG
"""
from ...tensor.arithmetic.core import TensorBinOp, TensorUnaryOp
from ...tensor.base import TensorWhere
from ...tensor.datasource import Scalar
from ..arithmetic.core im... |
Generate python code from tileable DAG
| _generate_function_str | python | mars-project/mars | mars/dataframe/reduction/core.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/reduction/core.py | Apache-2.0 |
def nunique_dataframe(df, axis=0, dropna=True, combine_size=None):
"""
Count distinct observations over requested axis.
Return Series with number of distinct observations. Can ignore NaN
values.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use.... |
Count distinct observations over requested axis.
Return Series with number of distinct observations. Can ignore NaN
values.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
column-wise.
dr... | nunique_dataframe | python | mars-project/mars | mars/dataframe/reduction/nunique.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/reduction/nunique.py | Apache-2.0 |
def nunique_series(series, dropna=True, combine_size=None):
"""
Return number of unique elements in the object.
Excludes NA values by default.
Parameters
----------
dropna : bool, default True
Don't include NaN in the count.
combine_size : int, optional
The number of chunks... |
Return number of unique elements in the object.
Excludes NA values by default.
Parameters
----------
dropna : bool, default True
Don't include NaN in the count.
combine_size : int, optional
The number of chunks to combine.
Returns
-------
int
See Also
---... | nunique_series | python | mars-project/mars | mars/dataframe/reduction/nunique.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/reduction/nunique.py | Apache-2.0 |
def df_corrwith(df, other, axis=0, drop=False, method="pearson"):
"""
Compute pairwise correlation.
Pairwise correlation is computed between rows or columns of
DataFrame with rows or columns of Series or DataFrame. DataFrames
are first aligned along both axes before computing the
correlations.
... |
Compute pairwise correlation.
Pairwise correlation is computed between rows or columns of
DataFrame with rows or columns of Series or DataFrame. DataFrames
are first aligned along both axes before computing the
correlations.
Parameters
----------
other : DataFrame, Series
Obje... | df_corrwith | python | mars-project/mars | mars/dataframe/statistics/corr.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/statistics/corr.py | Apache-2.0 |
def quantile_series(series, q=0.5, interpolation="linear"):
"""
Return value at the given quantile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
0 <= q <= 1, the quantile(s) to compute.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
... |
Return value at the given quantile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
0 <= q <= 1, the quantile(s) to compute.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
This optional parameter specifies the interpolation method to... | quantile_series | python | mars-project/mars | mars/dataframe/statistics/quantile.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/statistics/quantile.py | Apache-2.0 |
def quantile_dataframe(df, q=0.5, axis=0, numeric_only=True, interpolation="linear"):
"""
Return values at the given quantile over requested axis.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Value between 0 <= q <= 1, the quantile(s) to compute.
axis : {0, ... |
Return values at the given quantile over requested axis.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Value between 0 <= q <= 1, the quantile(s) to compute.
axis : {0, 1, 'index', 'columns'} (default 0)
Equals 0 or 'index' for row-wise, 1 or 'columns' f... | quantile_dataframe | python | mars-project/mars | mars/dataframe/statistics/quantile.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/statistics/quantile.py | Apache-2.0 |
def ewm(
obj,
com=None,
span=None,
halflife=None,
alpha=None,
min_periods=0,
adjust=True,
ignore_na=False,
axis=0,
):
r"""
Provide exponential weighted functions.
Parameters
----------
com : float, optional
Specify decay in terms of center of mass,
... |
Provide exponential weighted functions.
Parameters
----------
com : float, optional
Specify decay in terms of center of mass,
:math:`\alpha = 1 / (1 + com),\text{ for } com \geq 0`.
span : float, optional
Specify decay in terms of span,
:math:`\alpha = 2 / (span + 1... | ewm | python | mars-project/mars | mars/dataframe/window/ewm/core.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/window/ewm/core.py | Apache-2.0 |
def expanding(obj, min_periods=1, center=False, axis=0):
"""
Provide expanding transformations.
Parameters
----------
min_periods : int, default 1
Minimum number of observations in window required to have a value
(otherwise result is NA).
center : bool, default False
Set the labels ... |
Provide expanding transformations.
Parameters
----------
min_periods : int, default 1
Minimum number of observations in window required to have a value
(otherwise result is NA).
center : bool, default False
Set the labels at the center of the window.
axis : int or str, default 0
... | expanding | python | mars-project/mars | mars/dataframe/window/expanding/core.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/window/expanding/core.py | Apache-2.0 |
def rolling(
obj,
window,
min_periods=None,
center=False,
win_type=None,
on=None,
axis=0,
closed=None,
):
"""
Provide rolling window calculations.
Parameters
----------
window : int, or offset
Size of the moving window. This is the number of observations used... |
Provide rolling window calculations.
Parameters
----------
window : int, or offset
Size of the moving window. This is the number of observations used for
calculating the statistic. Each window will be a fixed size.
If its an offset then this will be the time period of each wind... | rolling | python | mars-project/mars | mars/dataframe/window/rolling/core.py | https://github.com/mars-project/mars/blob/master/mars/dataframe/window/rolling/core.py | Apache-2.0 |
def _merge_config(full_config: Dict, config: Dict) -> Dict:
"""
Merge the config to full_config, the config support flatten key, e.g.
config={
'scheduling.autoscale.enabled': True,
'scheduling.autoscale.scheduler_check_interval': 1,
'scheduling.autoscale.scheduler_backlog_timeout': ... |
Merge the config to full_config, the config support flatten key, e.g.
config={
'scheduling.autoscale.enabled': True,
'scheduling.autoscale.scheduler_check_interval': 1,
'scheduling.autoscale.scheduler_backlog_timeout': 1,
'scheduling.autoscale.worker_idle_timeout': 10,
... | _merge_config | python | mars-project/mars | mars/deploy/utils.py | https://github.com/mars-project/mars/blob/master/mars/deploy/utils.py | Apache-2.0 |
async def wait_all_supervisors_ready(endpoint):
"""
Wait till all containers are ready
"""
from ..services.cluster import ClusterAPI
cluster_api = None
while True:
try:
cluster_api = await ClusterAPI.create(endpoint)
break
except: # noqa: E722 # pylint... |
Wait till all containers are ready
| wait_all_supervisors_ready | python | mars-project/mars | mars/deploy/utils.py | https://github.com/mars-project/mars/blob/master/mars/deploy/utils.py | Apache-2.0 |
def new_cluster(
kube_api_client=None,
image=None,
supervisor_num=1,
supervisor_cpu=None,
supervisor_mem=None,
worker_num=1,
worker_cpu=None,
worker_mem=None,
worker_spill_paths=None,
worker_cache_mem=None,
min_worker_num=None,
web_num=1,
web_cpu=None,
web_mem=Non... |
:param kube_api_client: Kubernetes API client, can be created with ``new_client_from_config``
:param image: Docker image to use, ``marsproject/mars:<mars version>`` by default
:param supervisor_num: Number of supervisors in the cluster, 1 by default
:param supervisor_cpu: Number of CPUs for every super... | new_cluster | python | mars-project/mars | mars/deploy/kubernetes/client.py | https://github.com/mars-project/mars/blob/master/mars/deploy/kubernetes/client.py | Apache-2.0 |
async def wait_all_supervisors_ready(self):
"""
Wait till all containers are ready
"""
await wait_all_supervisors_ready(self.args.endpoint) |
Wait till all containers are ready
| wait_all_supervisors_ready | python | mars-project/mars | mars/deploy/kubernetes/core.py | https://github.com/mars-project/mars/blob/master/mars/deploy/kubernetes/core.py | Apache-2.0 |
def new_ray_session(
address: str = None,
session_id: str = None,
backend: str = "mars",
default: bool = True,
**new_cluster_kwargs,
) -> AbstractSession:
"""
Parameters
----------
address: str
mars web server address.
session_id: str
session id. If not specified... |
Parameters
----------
address: str
mars web server address.
session_id: str
session id. If not specified, will be generated automatically.
backend: str
The executor backend. Available values are "mars" and "ray", default is "mars".
default: bool
whether set the ... | new_ray_session | python | mars-project/mars | mars/deploy/oscar/ray.py | https://github.com/mars-project/mars/blob/master/mars/deploy/oscar/ray.py | Apache-2.0 |
async def wait_all_supervisors_ready(self):
"""
Wait till all containers are ready, both in yarn and in Cluster Service
"""
await wait_all_supervisors_ready(self.args.endpoint) |
Wait till all containers are ready, both in yarn and in Cluster Service
| wait_all_supervisors_ready | python | mars-project/mars | mars/deploy/yarn/core.py | https://github.com/mars-project/mars/blob/master/mars/deploy/yarn/core.py | Apache-2.0 |
def score(self, X, y, sample_weight=None, session=None, run_kwargs=None):
"""
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be c... |
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
each label set be correctly predicted.
Parameters
----------
X : array-like of ... | score | python | mars-project/mars | mars/learn/base.py | https://github.com/mars-project/mars/blob/master/mars/learn/base.py | Apache-2.0 |
def score(self, X, y, sample_weight=None):
"""Return the coefficient of determination :math:`R^2` of the
prediction.
The coefficient :math:`R^2` is defined as :math:`(1 - \\frac{u}{v})`,
where :math:`u` is the residual sum of squares ``((y_true - y_pred)
** 2).sum()`` and :math:... | Return the coefficient of determination :math:`R^2` of the
prediction.
The coefficient :math:`R^2` is defined as :math:`(1 - \frac{u}{v})`,
where :math:`u` is the residual sum of squares ``((y_true - y_pred)
** 2).sum()`` and :math:`v` is the total sum of squares ``((y_true -
y_... | score | python | mars-project/mars | mars/learn/base.py | https://github.com/mars-project/mars/blob/master/mars/learn/base.py | Apache-2.0 |
def _validate_data(
self, X, y=None, reset=True, validate_separately=False, **check_params
):
"""Validate input data and set or check the `n_features_in_` attribute.
Parameters
----------
X : {array-like, sparse matrix, dataframe} of shape \
(n_samples, n_fea... | Validate input data and set or check the `n_features_in_` attribute.
Parameters
----------
X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
The input samples.
y : array-like of shape (n_samples,), default=None
The target... | _validate_data | python | mars-project/mars | mars/learn/base.py | https://github.com/mars-project/mars/blob/master/mars/learn/base.py | Apache-2.0 |
def _check_method(self, method):
"""
Check if self.estimator has 'method'.
Raises
------
AttributeError
"""
estimator = self.estimator
if not hasattr(estimator, method):
msg = "The wrapped estimator '{}' does not have a '{}' method.".format(
... |
Check if self.estimator has 'method'.
Raises
------
AttributeError
| _check_method | python | mars-project/mars | mars/learn/wrappers.py | https://github.com/mars-project/mars/blob/master/mars/learn/wrappers.py | Apache-2.0 |
def transform(self, X):
"""
Transform block or partition-wise for Mars inputs.
For Mars inputs, a Mars tensor is returned. For other
inputs (NumPy array, pandas dataframe, scipy sparse matrix), the
regular return value is returned.
If the underlying estimator does not h... |
Transform block or partition-wise for Mars inputs.
For Mars inputs, a Mars tensor is returned. For other
inputs (NumPy array, pandas dataframe, scipy sparse matrix), the
regular return value is returned.
If the underlying estimator does not have a ``transform`` method, then
... | transform | python | mars-project/mars | mars/learn/wrappers.py | https://github.com/mars-project/mars/blob/master/mars/learn/wrappers.py | Apache-2.0 |
def score(self, X, y):
"""
Returns the score on the given data.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Input data, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, s... |
Returns the score on the given data.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Input data, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, ... | score | python | mars-project/mars | mars/learn/wrappers.py | https://github.com/mars-project/mars/blob/master/mars/learn/wrappers.py | Apache-2.0 |
def predict(self, X, execute=True):
"""
Predict for X.
For Mars inputs, a Mars tensor is returned. For other
inputs (NumPy array, pandas dataframe, scipy sparse matrix), the
regular return value is returned.
Parameters
----------
X : array-like
... |
Predict for X.
For Mars inputs, a Mars tensor is returned. For other
inputs (NumPy array, pandas dataframe, scipy sparse matrix), the
regular return value is returned.
Parameters
----------
X : array-like
Returns
-------
y : array-like
... | predict | python | mars-project/mars | mars/learn/wrappers.py | https://github.com/mars-project/mars/blob/master/mars/learn/wrappers.py | Apache-2.0 |
def predict_proba(self, X, execute=True):
"""
Probability estimates.
For Mars inputs, a Mars tensor is returned. For other
inputs (NumPy array, pandas dataframe, scipy sparse matrix), the
regular return value is returned.
If the underlying estimator does not have a ``pr... |
Probability estimates.
For Mars inputs, a Mars tensor is returned. For other
inputs (NumPy array, pandas dataframe, scipy sparse matrix), the
regular return value is returned.
If the underlying estimator does not have a ``predict_proba``
method, then an ``AttributeErro... | predict_proba | python | mars-project/mars | mars/learn/wrappers.py | https://github.com/mars-project/mars/blob/master/mars/learn/wrappers.py | Apache-2.0 |
def predict_log_proba(self, X, execute=True):
"""
Log of probability estimates.
For Mars inputs, a Mars tensor is returned. For other
inputs (NumPy array, pandas dataframe, scipy sparse matrix), the
regular return value is returned.
If the underlying estimator does not ... |
Log of probability estimates.
For Mars inputs, a Mars tensor is returned. For other
inputs (NumPy array, pandas dataframe, scipy sparse matrix), the
regular return value is returned.
If the underlying estimator does not have a ``predict_proba``
method, then an ``Attrib... | predict_log_proba | python | mars-project/mars | mars/learn/wrappers.py | https://github.com/mars-project/mars/blob/master/mars/learn/wrappers.py | Apache-2.0 |
def _validate_center_shape(X, n_centers, centers):
"""Check if centers is compatible with X and n_centers"""
if len(centers) != n_centers:
raise ValueError(
"The shape of the initial centers (%s) "
"does not match the number of clusters %i" % (centers.shape, n_centers)
)
... | Check if centers is compatible with X and n_centers | _validate_center_shape | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def _tolerance(X, tol):
"""Return a tolerance which is independent of the dataset"""
variances = mt.var(X, axis=0)
return mt.mean(variances) * tol | Return a tolerance which is independent of the dataset | _tolerance | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def _check_normalize_sample_weight(sample_weight, X):
"""Set sample_weight if None, and check for correct dtype"""
sample_weight_was_none = sample_weight is None
sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype)
if not sample_weight_was_none:
# normalize the weights to sum ... | Set sample_weight if None, and check for correct dtype | _check_normalize_sample_weight | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def k_means(
X,
n_clusters,
sample_weight=None,
init="k-means||",
n_init=10,
max_iter=300,
verbose=False,
tol=1e-4,
random_state=None,
copy_x=True,
algorithm="auto",
oversampling_factor=2,
init_iter=5,
return_n_iter=False,
):
"""K-means clustering algorithm.
... | K-means clustering algorithm.
Parameters
----------
X : Tensor, shape (n_samples, n_features)
The observations to cluster. It must be noted that the data
will be converted to C ordering, which will cause a memory copy
if the given data is not C-contiguous.
n_clusters : int
... | k_means | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def _labels_inertia(
X, sample_weight, x_squared_norms, centers, session=None, run_kwargs=None
):
"""E step of the K-means EM algorithm.
Compute the labels and the inertia of the given samples and centers.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
... | E step of the K-means EM algorithm.
Compute the labels and the inertia of the given samples and centers.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples to assign to the labels. If sparse matrix, must be in
CSR format.
sampl... | _labels_inertia | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def _init_centroids(
X,
n_clusters=8,
init="k-means++",
random_state=None,
x_squared_norms=None,
init_size=None,
oversampling_factor=2,
init_iter=5,
):
"""Compute the initial centroids
Parameters
----------
X : Tensor of shape (n_samples, n_features)
The input s... | Compute the initial centroids
Parameters
----------
X : Tensor of shape (n_samples, n_features)
The input samples.
n_clusters : int, default=8
number of centroids.
init : {'k-means++', 'k-means||', 'random', tensor, callable}, default="k-means++"
Method for initialization... | _init_centroids | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def fit(self, X, y=None, sample_weight=None, session=None, run_kwargs=None):
"""Compute k-means clustering.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training instances to cluster. It must be noted that the data
will b... | Compute k-means clustering.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training instances to cluster. It must be noted that the data
will be converted to C ordering, which will cause a memory
copy if the given data ... | fit | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def fit_predict(self, X, y=None, sample_weight=None, session=None, run_kwargs=None):
"""Compute cluster centers and predict cluster index for each sample.
Convenience method; equivalent to calling fit(X) followed by
predict(X).
Parameters
----------
X : {array-like, spa... | Compute cluster centers and predict cluster index for each sample.
Convenience method; equivalent to calling fit(X) followed by
predict(X).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to transform.
y : Ign... | fit_predict | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def fit_transform(
self, X, y=None, sample_weight=None, session=None, run_kwargs=None
):
"""Compute clustering and transform X to cluster-distance space.
Equivalent to fit(X).transform(X), but more efficiently implemented.
Parameters
----------
X : {array-like, spar... | Compute clustering and transform X to cluster-distance space.
Equivalent to fit(X).transform(X), but more efficiently implemented.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data to transform.
y : Ignored
... | fit_transform | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def transform(self, X, session=None, run_kwargs=None):
"""Transform X to a cluster-distance space.
In the new space, each dimension is the distance to the cluster
centers. Note that even if X is sparse, the array returned by
`transform` will typically be dense.
Parameters
... | Transform X to a cluster-distance space.
In the new space, each dimension is the distance to the cluster
centers. Note that even if X is sparse, the array returned by
`transform` will typically be dense.
Parameters
----------
X : {array-like, sparse matrix} of shape (n... | transform | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def _transform(self, X, session=None, run_kwargs=None):
"""guts of transform method; no input validation"""
return euclidean_distances(X, self.cluster_centers_).execute(
session=session, **(run_kwargs or dict())
) | guts of transform method; no input validation | _transform | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def predict(self, X, sample_weight=None, session=None, run_kwargs=None):
"""Predict the closest cluster each sample in X belongs to.
In the vector quantization literature, `cluster_centers_` is called
the code book and each value returned by `predict` is the index of
the closest code in... | Predict the closest cluster each sample in X belongs to.
In the vector quantization literature, `cluster_centers_` is called
the code book and each value returned by `predict` is the index of
the closest code in the code book.
Parameters
----------
X : {array-like, spar... | predict | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def score(self, X, y=None, sample_weight=None, session=None, run_kwargs=None):
"""Opposite of the value of X on the K-means objective.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data.
y : Ignored
Not used, ... | Opposite of the value of X on the K-means objective.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
New data.
y : Ignored
Not used, present here for API consistency by convention.
sample_weight : array-like of sha... | score | python | mars-project/mars | mars/learn/cluster/_kmeans.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_kmeans.py | Apache-2.0 |
def _k_init(X, n_clusters, x_squared_norms, random_state, n_local_trials=None):
"""Init n_clusters seeds according to k-means++
Parameters
----------
X : array or sparse matrix, shape (n_samples, n_features)
The data to pick seeds for. To avoid memory copy, the input data
should be doub... | Init n_clusters seeds according to k-means++
Parameters
----------
X : array or sparse matrix, shape (n_samples, n_features)
The data to pick seeds for. To avoid memory copy, the input data
should be double precision (dtype=np.float64).
n_clusters : integer
The number of seeds ... | _k_init | python | mars-project/mars | mars/learn/cluster/_k_means_init.py | https://github.com/mars-project/mars/blob/master/mars/learn/cluster/_k_means_init.py | Apache-2.0 |
def pick_workers(workers, size):
"""
Pick workers from a list.
This method will try to pick workers as balanced as it can.
1. If size <= len(workers), randomly pick workers from the list.
2. If size > len(workers), just select all workers in a random order,
then see the rest size, if it's s... |
Pick workers from a list.
This method will try to pick workers as balanced as it can.
1. If size <= len(workers), randomly pick workers from the list.
2. If size > len(workers), just select all workers in a random order,
then see the rest size, if it's still more than the workers size,
... | pick_workers | python | mars-project/mars | mars/learn/contrib/utils.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/utils.py | Apache-2.0 |
def run_pytorch_script(
script: Union[bytes, str, BinaryIO, TextIO],
n_workers: int,
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, Any] = None,
port: ... |
Run PyTorch script in Mars cluster.
Parameters
----------
script: str or file-like object
Script to run
n_workers : int
Number of PyTorch workers
data : dict
Variable name to data.
gpu : bool
Run PyTorch script on GPU
command_argv : list
Extra co... | run_pytorch_script | python | mars-project/mars | mars/learn/contrib/pytorch/run_script.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/pytorch/run_script.py | Apache-2.0 |
def to_tf(self) -> "tf.data.Dataset":
"""Get TF Dataset.
convert into a tensorflow.data.Dataset
"""
def make_generator(): # pragma: no cover
if not self._executed:
self._execute()
self._executed = True
for i in range(len(self._t... | Get TF Dataset.
convert into a tensorflow.data.Dataset
| to_tf | python | mars-project/mars | mars/learn/contrib/tensorflow/dataset.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/tensorflow/dataset.py | Apache-2.0 |
def gen_tensorflow_dataset(
tensors,
output_shapes: Tuple[int, ...] = None,
output_types: Tuple[np.dtype, ...] = None,
fetch_kwargs=None,
):
"""
convert mars data type to tf.data.Dataset. Note this is based tensorflow 2.0
For example
-----------
>>> # convert a tensor to tf.data.Data... |
convert mars data type to tf.data.Dataset. Note this is based tensorflow 2.0
For example
-----------
>>> # convert a tensor to tf.data.Dataset.
>>> data = mt.tensor([[1, 2], [3, 4]])
>>> dataset = gen_tensorflow_dataset(data)
>>> list(dataset.as_numpy_iterator())
[array([1, 2]), array([... | gen_tensorflow_dataset | python | mars-project/mars | mars/learn/contrib/tensorflow/dataset.py | https://github.com/mars-project/mars/blob/master/mars/learn/contrib/tensorflow/dataset.py | Apache-2.0 |
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 test_make_classification_informative_features(setup):
"""Test the construction of informative features in make_classification
Also tests `n_clusters_per_class`, `n_classes`, `hypercube` and
fully-specified `weights`.
"""
# Create very separate clusters; check that vertices are unique and
# ... | Test the construction of informative features in make_classification
Also tests `n_clusters_per_class`, `n_classes`, `hypercube` and
fully-specified `weights`.
| test_make_classification_informative_features | python | mars-project/mars | mars/learn/datasets/tests/test_samples_generator.py | https://github.com/mars-project/mars/blob/master/mars/learn/datasets/tests/test_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 _infer_dimension(spectrum, n_samples):
"""Infers the dimension of a dataset with a given spectrum.
The returned value will be in [1, n_features - 1].
"""
xp = get_array_module(spectrum, nosparse=True)
ll = xp.empty_like(spectrum)
ll[0] = -np.inf # we don't want to return n_components = 0
... | Infers the dimension of a dataset with a given spectrum.
The returned value will be in [1, n_features - 1].
| _infer_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(self, X, session=None, run=True, run_kwargs=None):
"""Dispatch to the right submethod depending on the chosen solver."""
# Raise an error for sparse input.
# This is more informative than the generic one raised by check_array.
if (hasattr(X, "issparse") and X.issparse()) or iss... | Dispatch to the right submethod depending on the chosen solver. | _fit | 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 |
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