Intelegentny_Pszczelarz/.venv/Lib/site-packages/sklearn/datasets/_species_distributions.py

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"""
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/details/3038/0>`_ ,
the Brown-throated Sloth.
- `"Microryzomys minutus"
<http://www.iucnredlist.org/details/13408/0>`_ ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.
References
----------
`"Maximum entropy modeling of species geographic distributions"
<http://rob.schapire.net/papers/ecolmod.pdf>`_ S. J. Phillips,
R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006.
Notes
-----
For an example of using this dataset, see
:ref:`examples/applications/plot_species_distribution_modeling.py
<sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.
"""
# Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD 3 clause
from io import BytesIO
from os import makedirs, remove
from os.path import exists
import logging
import numpy as np
import joblib
from . import get_data_home
from ._base import _fetch_remote
from ._base import RemoteFileMetadata
from ..utils import Bunch
from ._base import _pkl_filepath
# The original data can be found at:
# https://biodiversityinformatics.amnh.org/open_source/maxent/samples.zip
SAMPLES = RemoteFileMetadata(
filename="samples.zip",
url="https://ndownloader.figshare.com/files/5976075",
checksum="abb07ad284ac50d9e6d20f1c4211e0fd3c098f7f85955e89d321ee8efe37ac28",
)
# The original data can be found at:
# https://biodiversityinformatics.amnh.org/open_source/maxent/coverages.zip
COVERAGES = RemoteFileMetadata(
filename="coverages.zip",
url="https://ndownloader.figshare.com/files/5976078",
checksum="4d862674d72e79d6cee77e63b98651ec7926043ba7d39dcb31329cf3f6073807",
)
DATA_ARCHIVE_NAME = "species_coverage.pkz"
logger = logging.getLogger(__name__)
def _load_coverage(F, header_length=6, dtype=np.int16):
"""Load a coverage file from an open file object.
This will return a numpy array of the given dtype
"""
header = [F.readline() for _ in range(header_length)]
make_tuple = lambda t: (t.split()[0], float(t.split()[1]))
header = dict([make_tuple(line) for line in header])
M = np.loadtxt(F, dtype=dtype)
nodata = int(header[b"NODATA_value"])
if nodata != -9999:
M[nodata] = -9999
return M
def _load_csv(F):
"""Load csv file.
Parameters
----------
F : file object
CSV file open in byte mode.
Returns
-------
rec : np.ndarray
record array representing the data
"""
names = F.readline().decode("ascii").strip().split(",")
rec = np.loadtxt(F, skiprows=0, delimiter=",", dtype="a22,f4,f4")
rec.dtype.names = names
return rec
def construct_grids(batch):
"""Construct the map grid from the batch object
Parameters
----------
batch : Batch object
The object returned by :func:`fetch_species_distributions`
Returns
-------
(xgrid, ygrid) : 1-D arrays
The grid corresponding to the values in batch.coverages
"""
# x,y coordinates for corner cells
xmin = batch.x_left_lower_corner + batch.grid_size
xmax = xmin + (batch.Nx * batch.grid_size)
ymin = batch.y_left_lower_corner + batch.grid_size
ymax = ymin + (batch.Ny * batch.grid_size)
# x coordinates of the grid cells
xgrid = np.arange(xmin, xmax, batch.grid_size)
# y coordinates of the grid cells
ygrid = np.arange(ymin, ymax, batch.grid_size)
return (xgrid, ygrid)
def fetch_species_distributions(*, data_home=None, download_if_missing=True):
"""Loader for species distribution dataset from Phillips et. al. (2006).
Read more in the :ref:`User Guide <datasets>`.
Parameters
----------
data_home : str, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
download_if_missing : bool, default=True
If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.
Returns
-------
data : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
coverages : array, shape = [14, 1592, 1212]
These represent the 14 features measured
at each point of the map grid.
The latitude/longitude values for the grid are discussed below.
Missing data is represented by the value -9999.
train : record array, shape = (1624,)
The training points for the data. Each point has three fields:
- train['species'] is the species name
- train['dd long'] is the longitude, in degrees
- train['dd lat'] is the latitude, in degrees
test : record array, shape = (620,)
The test points for the data. Same format as the training data.
Nx, Ny : integers
The number of longitudes (x) and latitudes (y) in the grid
x_left_lower_corner, y_left_lower_corner : floats
The (x,y) position of the lower-left corner, in degrees
grid_size : float
The spacing between points of the grid, in degrees
Notes
-----
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/details/3038/0>`_ ,
the Brown-throated Sloth.
- `"Microryzomys minutus"
<http://www.iucnredlist.org/details/13408/0>`_ ,
also known as the Forest Small Rice Rat, a rodent that lives in Peru,
Colombia, Ecuador, Peru, and Venezuela.
- For an example of using this dataset with scikit-learn, see
:ref:`examples/applications/plot_species_distribution_modeling.py
<sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py>`.
References
----------
* `"Maximum entropy modeling of species geographic distributions"
<http://rob.schapire.net/papers/ecolmod.pdf>`_
S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,
190:231-259, 2006.
"""
data_home = get_data_home(data_home)
if not exists(data_home):
makedirs(data_home)
# Define parameters for the data files. These should not be changed
# unless the data model changes. They will be saved in the npz file
# with the downloaded data.
extra_params = dict(
x_left_lower_corner=-94.8,
Nx=1212,
y_left_lower_corner=-56.05,
Ny=1592,
grid_size=0.05,
)
dtype = np.int16
archive_path = _pkl_filepath(data_home, DATA_ARCHIVE_NAME)
if not exists(archive_path):
if not download_if_missing:
raise IOError("Data not found and `download_if_missing` is False")
logger.info("Downloading species data from %s to %s" % (SAMPLES.url, data_home))
samples_path = _fetch_remote(SAMPLES, dirname=data_home)
with np.load(samples_path) as X: # samples.zip is a valid npz
for f in X.files:
fhandle = BytesIO(X[f])
if "train" in f:
train = _load_csv(fhandle)
if "test" in f:
test = _load_csv(fhandle)
remove(samples_path)
logger.info(
"Downloading coverage data from %s to %s" % (COVERAGES.url, data_home)
)
coverages_path = _fetch_remote(COVERAGES, dirname=data_home)
with np.load(coverages_path) as X: # coverages.zip is a valid npz
coverages = []
for f in X.files:
fhandle = BytesIO(X[f])
logger.debug(" - converting {}".format(f))
coverages.append(_load_coverage(fhandle))
coverages = np.asarray(coverages, dtype=dtype)
remove(coverages_path)
bunch = Bunch(coverages=coverages, test=test, train=train, **extra_params)
joblib.dump(bunch, archive_path, compress=9)
else:
bunch = joblib.load(archive_path)
return bunch