# -*- coding: utf-8 -*-
# License: BSD-3-Clause
# Author: LKouadio
"""
Base IO code for managing all the datasets
Created on Thu Oct 13 14:26:47 2022
"""
import os
import csv
import shutil
import numpy as np
import pandas as pd
from pathlib import Path
from importlib import resources
from collections import namedtuple
from ..utils.box import Boxspace
from ..utils.coreutils import _is_readable
from ..utils.funcutils import random_state_validator , is_iterable
DMODULE = "watex.datasets.data" ; DESCR = "watex.datasets.descr"
# create a namedtuple for remote data and url
RemoteMetadata = namedtuple("RemoteMetadata", ["file", "url", "checksum"])
RemoteDataURL ='https://raw.githubusercontent.com/WEgeophysics/watex/master/watex/datasets/data/'
[docs]
def get_data(data =None) -> str:
if data is None:
data = os.environ.get("WATEX_DATA", os.path.join("~", "watex_data"))
data = os.path.expanduser(data)
os.makedirs(data, exist_ok=True)
return data
get_data.__doc__ ="""\
Get the data from home directory and return watex data directory
By default the data directory is set to a folder named 'watex_data' in the
user home folder. Alternatively, it can be set by the 'WATEX_DATA' environment
variable or programmatically by giving an explicit folder path. The '~'
symbol is expanded to the user home folder.
If the folder does not already exist, it is automatically created.
Parameters
----------
data : str, default=None
The path to watex data directory. If `None`, the default path
is `~/watex_data`.
Returns
-------
data: str
The path to watex data directory.
"""
[docs]
def remove_data(data=None): #clear
"""Delete all the content of the data home cache.
Parameters
----------
data : str, default=None
The path to watex data directory. If `None`, the default path
is `~/watex_data`.
"""
data = get_data(data)
shutil.rmtree(data)
def _to_dataframe(data, tnames=None , feature_names =None, target =None ):
""" Validate that data is readable by pandas rearder and parse the data.
then separate data from training to target. Be sure that the target
must be included to the dataframe columns
:param data: str, or path-like object
data file to read or dataframe
:param tnames: list of str
name of target that might be a column of the target frame.
:param feature_names: List of features to selects. Preferably
should be include in the dataframe columns
:params target: Ndarray or array-like,
A target for supervised learning . Can be ndarray -for muliclass
output and array-like for singular label
:returns:
dataframe combined and X, y (X= features frames, y = target frame)
"""
# if a ndarray is given ,then convert to dataframe
# by adding feature names.
d0, y = None , None
if isinstance (data, np.ndarray ):
d0 = pd.DataFrame(data = data, columns = feature_names)
else :
# read with pandas config parsers including the target
df = _is_readable(data)
# if tnames are given convert the array
# of target to a target frame
if (
d0 is not None
and tnames is not None
and target is not None
) :
if not is_iterable(tnames):
tnames = [tnames]
target = pd.DataFrame(data =target , columns =tnames )
# if target is not None:
df = pd.concat ([d0, target], axis =1 )# stack to columns
X = df[feature_names ]
if tnames is not None :
y = df [tnames[0]] if len(tnames)==1 else df [tnames]
return df, X, y
[docs]
def csv_data_loader(
data_file,*, data_module=DMODULE, descr_file=None, descr_module=DESCR,
include_headline =False,
):
feature_names= None # expect to extract features as the head columns
cat_feature_exist = False # for homogeneous feature control
#with resources.files(data_module).joinpath(
# data_file).open('r', encoding='utf-8') as csv_file: #python >3.8
with resources.open_text(data_module, data_file,
encoding='utf-8') as csv_file: # Python 3.8
data_file = csv.reader(csv_file)
temp = next(data_file)
n_samples = int(temp[0]) ; n_features = int(temp[1])
# remove empty if exist in the list
tnames = np.array(list(filter(None,temp[2:])))
# to prevent an error change the datatype to string
data = np.empty((n_samples, n_features)).astype('<U99')
target = np.empty((n_samples,), dtype=float)
if include_headline:
# remove target expected to be located to the last columns
feature_names = list(next(data_file)[:-1])
#XXX TODO: move the target[i] to try exception if target is str
for i, ir in enumerate(data_file):
try :
data[i] = np.asarray(ir[:-1], dtype=float)
except ValueError:
data[i] = np.asarray(ir[:-1], dtype ='<U99' ) # dont convert anything
cat_feature_exist = True # mean cat feature exists
target[i] = np.asarray(ir[-1], dtype=float)
if not cat_feature_exist:
# reconvert the datatype to float
data = data.astype (float)
# reconvert target if problem is classification rather than regression
try :
target =target.astype(int )
except : pass
if descr_file is None:
return data, target, tnames, feature_names
else:
assert descr_module is not None
descr = description_loader(descr_module=descr_module, descr_file=descr_file)
return data, target, tnames, feature_names, descr
csv_data_loader.__doc__="""\
Loads `data_file` from `data_module with `importlib.resources`.
Parameters
----------
data_file: str
Name of csv file to be loaded from `data_module/data_file`.
For example `'bagoue.csv'`.
data_module : str or module, default='watex.datasets.data'
Module where data lives. The default is `'watex.datasets.data'`.
descr_file_name : str, default=None
Name of rst file to be loaded from `descr_module/descr_file`.
For example `'bagoue.rst'`. See also :func:`description_loader`.
If not None, also returns the corresponding description of
the dataset.
descr_module : str or module, default='watex.datasets.descr'
Module where `descr_file` lives. See also :func:`description_loader`.
The default is `'watex.datasets.descr'`.
Returns
-------
data : ndarray of shape (n_samples, n_features)
A 2D array with each row representing one sample and each column
representing the features of a given sample.
target : ndarry of shape (n_samples,)
A 1D array holding target variables for all the samples in `data`.
For example target[0] is the target variable for data[0].
target_names : ndarry of shape (n_samples,)
A 1D array containing the names of the classifications. For example
target_names[0] is the name of the target[0] class.
descr : str, optional
Description of the dataset (the content of `descr_file_name`).
Only returned if `descr_file` is not None.
"""
[docs]
def description_loader(descr_file, *, descr_module=DESCR, encoding ='utf8'):
# fdescr=resources.files(descr_module).joinpath(descr_file).read_text(
# encoding=encoding)
fdescr = resources.read_text(descr_module, descr_file, encoding= 'utf8')
return fdescr
description_loader.__doc__ ="""\
Load `descr_file` from `descr_module` with `importlib.resources`.
Parameters
----------
descr_file_name : str, default=None
Name of rst file to be loaded from `descr_module/descr_file`.
For example `'bagoue.rst'`. See also :func:`description_loader`.
If not None, also returns the corresponding description of
the dataset.
descr_module : str or module, default='watex.datasets.descr'
Module where `descr_file` lives. See also :func:`description_loader`.
The default is `'watex.datasets.descr'`.
Returns
-------
fdescr : str
Content of `descr_file_name`.
"""
[docs]
def text_files_loader(
container_path,*,description=None,categories=None,
load_content=True,shuffle=True,encoding=None,decode_error="strict",
random_state=42, allowed_extensions=None,
):
target = []
target_names = []
filenames = []
folders = [
f for f in sorted(os.listdir(container_path)) if os.path.isdir(
os.path.join(container_path, f))
]
if categories is not None:
folders = [f for f in folders if f in categories]
if allowed_extensions is not None:
allowed_extensions = frozenset(allowed_extensions)
for label, folder in enumerate(folders):
target_names.append(folder)
folder_path = os.path.join(container_path, folder)
files = sorted(os.listdir(folder_path))
if allowed_extensions is not None:
documents = [
os.path.join(folder_path, file)
for file in files
if os.path.splitext(file)[1] in allowed_extensions
]
else:
documents = [os.path.join(folder_path, file) for file in files]
target.extend(len(documents) * [label])
filenames.extend(documents)
# convert to array for fancy indexing
filenames = np.array(filenames)
target = np.array(target)
if shuffle:
random_state = random_state_validator(random_state)
indices = np.arange(filenames.shape[0])
random_state.shuffle(indices)
filenames = filenames[indices]
target = target[indices]
if load_content:
data = []
for filename in filenames:
data.append(Path(filename).read_bytes())
if encoding is not None:
data = [d.decode(encoding, decode_error) for d in data]
return Boxspace(
data=data,
filenames=filenames,
target_names=target_names,
target=target,
DESCR=description,
)
return Boxspace(
filenames=filenames, target_names=target_names, target=target,
DESCR=description
)
text_files_loader.__doc__ ="""\
Load text files with categories as subfolder names.
Individual samples are assumed to be files stored a two levels folder
structure such as the following::
container_folder/
category_1_folder/
file1.txt
file2.txt
...
file30.txt
category_2_folder/
file31.txt
file32.txt
...
The folder names are used as supervised signal label names. The individual
file names are not important.
In addition, if load_content is false it does not try to load the files in memory.
If you set load_content=True, you should also specify the encoding of the
text using the 'encoding' parameter. For many modern text files, 'utf-8'
will be the correct encoding. If you want files with a specific file extension
(e.g. `.txt`) then you can pass a list of those file extensions to
`allowed_extensions`.
Parameters
----------
container_path : str
Path to the main folder holding one subfolder per category.
description : str, default=None
A paragraph describing the characteristic of the dataset: its source,
reference, etc.
categories : list of str, default=None
If None (default), load all the categories. If not None, list of
category names to load (other categories ignored).
load_content : bool, default=True
Whether to load or not the content of the different files. If true a
'data' attribute containing the text information is present in the data
structure returned. If not, a filenames attribute gives the path to the
files.
shuffle : bool, default=True
Whether or not to shuffle the data: might be important for models that
make the assumption that the samples are independent and identically
distributed (i.i.d.), such as stochastic gradient descent.
encoding : str, default=None
If None, do not try to decode the content of the files (e.g. for images
or other non-text content). If not None, encoding to use to decode text
files to Unicode if load_content is True.
decode_error : {'strict', 'ignore', 'replace'}, default='strict'
Instruction on what to do if a byte sequence is given to analyze that
contains characters not of the given `encoding`. Passed as keyword
argument 'errors' to bytes.decode.
random_state : int, RandomState instance or None, default=42
Determines random number generation for dataset shuffling. Pass an int
for reproducible output across multiple function calls.
allowed_extensions : list of str, default=None
List of desired file extensions to filter the files to be loaded.
Returns
-------
data : :class:`~watex.utils.Boxspace`
Dictionary-like object, with the following attributes.
data : list of str
Only present when `load_content=True`.
The raw text data to learn.
target : ndarray
The target labels (integer index).
target_names : list
The names of target classes.
DESCR : str
The full description of the dataset.
filenames: ndarray
The filenames holding the dataset.
"""
