import numpy as np
from clustpy.data._utils import _get_download_dir, _download_file
import os
import zipfile
def _load_timeseries_classification_data(dataset_name: str, subset: str, labels_minus_one: bool, file_type: str,
last_column_are_labels: bool, downloads_path: str) -> (
np.ndarray, np.ndarray):
"""
Helper function to load timeseries data from www.timeseriesclassification.com.
Parameters
----------
dataset_name : str
name of the data set
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data
labels_minus_one : bool
Convert labels from 1,... to 0,...
file_type : str
file type within the zip file. Currently supported are "txt" and "ts". Is usually "txt"
last_column_are_labels : bool
specifies if the last column contains the labels. If false labels should be contained in the first column
downloads_path : str
path to the directory where the data is stored. If input was None this will be equal to
'[USER]/Downloads/clustpy_datafiles'
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array, the labels numpy array
"""
subset = subset.lower()
assert subset in ["all", "train",
"test"], "subset must match 'all', 'train' or 'test'. Your input {0}".format(subset)
directory = _get_download_dir(downloads_path) + "/" + dataset_name + "/"
filename = directory + dataset_name + ".zip"
if not os.path.isfile(filename):
if not os.path.isdir(directory):
os.mkdir(directory)
_download_file("http://www.timeseriesclassification.com/Downloads/" + dataset_name + ".zip",
filename)
# Unpack zipfile
with zipfile.ZipFile(filename, 'r') as zipf:
zipf.extractall(directory)
# Load data and labels
if subset == "all" or subset == "train":
# Normally we have txt files
if file_type == "txt":
dataset = np.genfromtxt(directory + dataset_name + "_TRAIN.txt")
elif file_type == "ts":
# Ts files must be changed first
with open(directory + dataset_name + "_TRAIN.ts", "rb") as f:
clean_lines = (line.replace(b":", b",").replace(b"@", b"#") for line in f)
dataset = np.genfromtxt(clean_lines, delimiter=",", comments="#")
# Are labels in first or last column?
if last_column_are_labels:
data = dataset[:, :-1]
labels = dataset[:, -1]
else:
data = dataset[:, 1:]
labels = dataset[:, 0]
if subset == "all" or subset == "test":
# Normally we have txt files
if file_type == "txt":
test_dataset = np.genfromtxt(directory + dataset_name + "_TEST.txt")
elif file_type == "ts":
# Ts files must be changed first
with open(directory + dataset_name + "_TEST.ts", "rb") as f:
clean_lines = (line.replace(b":", b",").replace(b"@", b"#") for line in f)
test_dataset = np.genfromtxt(clean_lines, delimiter=",", comments="#")
# Are labels in first or last column?
if last_column_are_labels:
if subset == "all":
data = np.r_[data, test_dataset[:, :-1]]
labels = np.r_[labels, test_dataset[:, -1]]
else:
data = test_dataset[:, :-1]
labels = test_dataset[:, -1]
else:
if subset == "all":
data = np.r_[data, test_dataset[:, 1:]]
labels = np.r_[labels, test_dataset[:, 0]]
else:
data = test_dataset[:, 1:]
labels = test_dataset[:, 0]
# Convert labels to int32 format
labels = labels.astype(np.int32)
if labels_minus_one:
# Convert labels from 1,... to 0,...
labels -= 1
return data, labels
[docs]def load_motestrain(subset: str = "all", downloads_path: str = None) -> (np.ndarray, np.ndarray):
"""
Load the motestrain data set. It consists of 1272 samples belonging to one of 2 classes.
The data set is composed of 20 training and 1252 test samples.
N=1272, d=84, k=2.
Parameters
----------
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data (default: 'all')
downloads_path : str
path to the directory where the data is stored (default: None -> [USER]/Downloads/clustpy_datafiles)
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array (1272 x 84), the labels numpy array (1272)
References
-------
http://www.timeseriesclassification.com/description.php?Dataset=MoteStrain
"""
data, labels = _load_timeseries_classification_data("MoteStrain", subset, True, "txt", False, downloads_path)
return data, labels
[docs]def load_proximal_phalanx_outline(subset: str = "all", downloads_path: str = None) -> (np.ndarray, np.ndarray):
"""
Load the proximal phalanx outline data set. It consists of 876 samples belonging to one of 2 classes.
The data set is composed of 600 training and 276 test samples.
N=876, d=80, k=2.
Parameters
----------
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data (default: 'all')
downloads_path : str
path to the directory where the data is stored (default: None -> [USER]/Downloads/clustpy_datafiles)
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array (876 x 80), the labels numpy array (876)
References
-------
http://www.timeseriesclassification.com/description.php?Dataset=ProximalPhalanxOutlineCorrect
"""
data, labels = _load_timeseries_classification_data("DistalPhalanxOutlineCorrect", subset, False, "txt", False,
downloads_path)
return data, labels
[docs]def load_diatom_size_reduction(subset: str = "all", downloads_path: str = None) -> (np.ndarray, np.ndarray):
"""
Load the diatom size reduction data set. It consists of 322 samples belonging to one of 4 classes.
The data set is composed of 16 training and 306 test samples.
N=322, d=345, k=4.
Parameters
----------
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data (default: 'all')
downloads_path : str
path to the directory where the data is stored (default: None -> [USER]/Downloads/clustpy_datafiles)
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array (322 x 345), the labels numpy array (322)
References
-------
http://www.timeseriesclassification.com/description.php?Dataset=DiatomSizeReduction
"""
data, labels = _load_timeseries_classification_data("DiatomSizeReduction", subset, True, "txt", False,
downloads_path)
return data, labels
[docs]def load_symbols(subset: str = "all", downloads_path: str = None) -> (np.ndarray, np.ndarray):
"""
Load the symbols data set. It consists of 1020 samples belonging to one of 6 classes.
The data set is composed of 25 training and 995 test samples.
N=1020, d=398, k=6.
Parameters
----------
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data (default: 'all')
downloads_path : str
path to the directory where the data is stored (default: None -> [USER]/Downloads/clustpy_datafiles)
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array (1020 x 398), the labels numpy array (1020)
References
-------
http://www.timeseriesclassification.com/description.php?Dataset=Symbols
"""
data, labels = _load_timeseries_classification_data("Symbols", subset, True, "txt", False, downloads_path)
return data, labels
[docs]def load_olive_oil(subset: str = "all", downloads_path: str = None) -> (np.ndarray, np.ndarray):
"""
Load the OliveOil data set. It consists of 60 samples belonging to one of 4 classes.
The data set is composed of 30 training and 30 test samples.
N=60, d=570, k=4.
Parameters
----------
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data (default: 'all')
downloads_path : str
path to the directory where the data is stored (default: None -> [USER]/Downloads/clustpy_datafiles)
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array (60 x 570), the labels numpy array (60)
References
-------
http://www.timeseriesclassification.com/description.php?Dataset=OliveOil
"""
data, labels = _load_timeseries_classification_data("OliveOil", subset, True, "txt", False, downloads_path)
return data, labels
[docs]def load_plane(subset: str = "all", downloads_path: str = None) -> (np.ndarray, np.ndarray):
"""
Load the plane data set. It consists of 210 samples belonging to one of 7 classes.
The data set is composed of 105 training and 105 test samples.
N=210, d=144, k=7.
Parameters
----------
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data (default: 'all')
downloads_path : str
path to the directory where the data is stored (default: None -> [USER]/Downloads/clustpy_datafiles)
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array (210 x 144), the labels numpy array (210)
References
-------
http://www.timeseriesclassification.com/description.php?Dataset=Plane
"""
data, labels = _load_timeseries_classification_data("Plane", subset, True, "txt", False, downloads_path)
return data, labels
[docs]def load_sony_aibo_robot_surface(subset: str = "all", downloads_path: str = None) -> (np.ndarray, np.ndarray):
"""
Load the Sony AIBO Robot Surface 1 data set. It consists of 621 samples belonging to one of 2 classes.
The data set is composed of 20 training and 601 test samples.
N=621, d=70, k=2.
Parameters
----------
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data (default: 'all')
downloads_path : str
path to the directory where the data is stored (default: None -> [USER]/Downloads/clustpy_datafiles)
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array (621 x 70), the labels numpy array (621)
References
-------
http://www.timeseriesclassification.com/description.php?Dataset=SonyAIBORobotSurface1
"""
data, labels = _load_timeseries_classification_data("SonyAIBORobotSurface1", subset, True, "txt", False,
downloads_path)
return data, labels
[docs]def load_two_patterns(subset: str = "all", downloads_path: str = None) -> (np.ndarray, np.ndarray):
"""
Load the two patterns data set. It consists of 5000 samples belonging to one of 4 classes.
The data set is composed of 1000 training and 4000 test samples.
N=5000, d=128, k=4.
Parameters
----------
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data (default: 'all')
downloads_path : str
path to the directory where the data is stored (default: None -> [USER]/Downloads/clustpy_datafiles)
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array (5000 x 128), the labels numpy array (5000)
References
-------
http://www.timeseriesclassification.com/description.php?Dataset=TwoPatterns
"""
data, labels = _load_timeseries_classification_data("TwoPatterns", subset, True, "txt", False, downloads_path)
return data, labels
[docs]def load_lsst(subset: str = "all", downloads_path: str = None) -> (np.ndarray, np.ndarray):
"""
Load the LSST data set. It consists of 4925 samples belonging to one of 14 classes.
The data set is composed of 2459 training and 2466 test samples.
N=4925, d=216, k=14.
Parameters
----------
subset : str
can be 'all', 'test' or 'train'. 'all' combines test and train data (default: 'all')
downloads_path : str
path to the directory where the data is stored (default: None -> [USER]/Downloads/clustpy_datafiles)
Returns
-------
data, labels : (np.ndarray, np.ndarray)
the data numpy array (4925 x 216), the labels numpy array (4925)
References
-------
http://www.timeseriesclassification.com/description.php?Dataset=LSST
"""
data, labels = _load_timeseries_classification_data("LSST", subset, True, "ts", True, downloads_path)
# Current labels are: 5, 14, 15, 41, 51, 52, ... -> change to: 0, 1, 2, 3, 4, ...
for i, l in enumerate(np.unique(labels)):
labels[labels == l] = i
return data, labels