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# SPDX-FileCopyrightText: 2025 This source file is part of the OpenTSLM open-source project.
#
# SPDX-License-Identifier: MIT
import numpy as np
import pandas as pd
from tqdm import tqdm
import requests
import io
import zipfile
import os
from tqdm import tqdm
name = "DataLoader"
regression_datasets = [
# "AustraliaRainfall",
# "HouseholdPowerConsumption1",
# "HouseholdPowerConsumption2",
# "BeijingPM25Quality",
# "BeijingPM10Quality",
# "Covid3Month",
# "LiveFuelMoistureContent",
# "FloodModeling1",
# "FloodModeling2",
# "FloodModeling3",
# "AppliancesEnergy",
# "BenzeneConcentration",
# "NewsHeadlineSentiment",
# "NewsTitleSentiment",
"BIDMC32RR",
"BIDMC32HR",
"BIDMC32SpO2",
"IEEEPPG",
"PPGDalia",
]
# The following code is adapted from the python package sktime to read .ts file.
class TsFileParseException(Exception):
"""
Should be raised when parsing a .ts file and the format is incorrect.
"""
pass
def download_and_extract_monash_ucr(destination="monash_datasets"):
url = (
"https://zenodo.org/record/3902651/files/Monash_UEA_UCR_Regression_Archive.zip"
)
# make sure the destination folder exists
os.makedirs(destination, exist_ok=True)
# start the download, but stream it so we can show progress
print(f"Downloading Monash/UEA/UCR datasets from Zenodo to ‘{destination}’…")
response = requests.get(url, stream=True)
total_size = int(response.headers.get("content-length", 0))
chunk_size = 1024
buffer = io.BytesIO()
with tqdm(
total=total_size, unit="iB", unit_scale=True, desc="Downloading", ncols=80
) as bar:
for chunk in response.iter_content(chunk_size=chunk_size):
if chunk: # filter out keep-alive chunks
buffer.write(chunk)
bar.update(len(chunk))
# rewind to beginning of buffer
buffer.seek(0)
# now extract, showing progress per-file
with zipfile.ZipFile(buffer) as z:
members = z.infolist()
with tqdm(total=len(members), desc="Extracting", ncols=80) as bar:
for member in members:
z.extract(member, destination)
bar.update(1)
print("Download and extraction complete.")
def load_from_tsfile_to_dataframe(
full_file_path_and_name,
return_separate_X_and_y=True,
replace_missing_vals_with="NaN",
):
"""Loads data from a .ts file into a Pandas DataFrame.
Parameters
----------
full_file_path_and_name: str
The full pathname of the .ts file to read.
return_separate_X_and_y: bool
true if X and Y values should be returned as separate Data Frames (X) and a numpy array (y), false otherwise.
This is only relevant for data that
replace_missing_vals_with: str
The value that missing values in the text file should be replaced with prior to parsing.
Returns
-------
DataFrame, ndarray
If return_separate_X_and_y then a tuple containing a DataFrame and a numpy array containing the relevant time-series and corresponding class values.
DataFrame
If not return_separate_X_and_y then a single DataFrame containing all time-series and (if relevant) a column "class_vals" the associated class values.
"""
# Initialize flags and variables used when parsing the file
metadata_started = False
data_started = False
has_problem_name_tag = False
has_timestamps_tag = False
has_univariate_tag = False
has_class_labels_tag = False
has_target_labels_tag = False
has_data_tag = False
previous_timestamp_was_float = None
previous_timestamp_was_int = None
previous_timestamp_was_timestamp = None
num_dimensions = None
is_first_case = True
instance_list = []
class_val_list = []
line_num = 0
with open(full_file_path_and_name, "r", encoding="utf-8", errors="replace") as file:
for line in tqdm(file):
# Strip white space from start/end of line and change to lowercase for use below
line = line.strip().lower()
# Empty lines are valid at any point in a file
if line:
# Check if this line contains metadata
# Please note that even though metadata is stored in this function it is not currently published externally
if line.startswith("@problemname"):
# Check that the data has not started
if data_started:
raise TsFileParseException("metadata must come before data")
# Check that the associated value is valid
tokens = line.split(" ")
token_len = len(tokens)
if token_len == 1:
raise TsFileParseException(
"problemname tag requires an associated value"
)
problem_name = line[len("@problemname") + 1 :]
has_problem_name_tag = True
metadata_started = True
elif line.startswith("@timestamps"):
# Check that the data has not started
if data_started:
raise TsFileParseException("metadata must come before data")
# Check that the associated value is valid
tokens = line.split(" ")
token_len = len(tokens)
if token_len != 2:
raise TsFileParseException(
"timestamps tag requires an associated Boolean value"
)
elif tokens[1] == "true":
timestamps = True
elif tokens[1] == "false":
timestamps = False
else:
raise TsFileParseException("invalid timestamps value")
has_timestamps_tag = True
metadata_started = True
elif line.startswith("@univariate"):
# Check that the data has not started
if data_started:
raise TsFileParseException("metadata must come before data")
# Check that the associated value is valid
tokens = line.split(" ")
token_len = len(tokens)
if token_len != 2:
raise TsFileParseException(
"univariate tag requires an associated Boolean value"
)
elif tokens[1] == "true":
univariate = True
elif tokens[1] == "false":
univariate = False
else:
raise TsFileParseException("invalid univariate value")
has_univariate_tag = True
metadata_started = True
elif line.startswith("@classlabel"):
# Check that the data has not started
if data_started:
raise TsFileParseException("metadata must come before data")
# Check that the associated value is valid
tokens = line.split(" ")
token_len = len(tokens)
if token_len == 1:
raise TsFileParseException(
"classlabel tag requires an associated Boolean value"
)
if tokens[1] == "true":
class_labels = True
elif tokens[1] == "false":
class_labels = False
else:
raise TsFileParseException("invalid classLabel value")
# Check if we have any associated class values
if token_len == 2 and class_labels:
raise TsFileParseException(
"if the classlabel tag is true then class values must be supplied"
)
has_class_labels_tag = True
class_label_list = [token.strip() for token in tokens[2:]]
metadata_started = True
elif line.startswith("@targetlabel"):
# Check that the data has not started
if data_started:
raise TsFileParseException("metadata must come before data")
# Check that the associated value is valid
tokens = line.split(" ")
token_len = len(tokens)
if token_len == 1:
raise TsFileParseException(
"targetlabel tag requires an associated Boolean value"
)
if tokens[1] == "true":
target_labels = True
elif tokens[1] == "false":
target_labels = False
else:
raise TsFileParseException("invalid targetLabel value")
has_target_labels_tag = True
class_val_list = []
metadata_started = True
# Check if this line contains the start of data
elif line.startswith("@data"):
if line != "@data":
raise TsFileParseException(
"data tag should not have an associated value"
)
if data_started and not metadata_started:
raise TsFileParseException("metadata must come before data")
else:
has_data_tag = True
data_started = True
# If the 'data tag has been found then metadata has been parsed and data can be loaded
elif data_started:
# Check that a full set of metadata has been provided
incomplete_regression_meta_data = (
not has_problem_name_tag
or not has_timestamps_tag
or not has_univariate_tag
or not has_target_labels_tag
or not has_data_tag
)
incomplete_classification_meta_data = (
not has_problem_name_tag
or not has_timestamps_tag
or not has_univariate_tag
or not has_class_labels_tag
or not has_data_tag
)
if (
incomplete_regression_meta_data
and incomplete_classification_meta_data
):
raise TsFileParseException(
"a full set of metadata has not been provided before the data"
)
# Replace any missing values with the value specified
line = line.replace("?", replace_missing_vals_with)
# Check if we dealing with data that has timestamps
if timestamps:
# We're dealing with timestamps so cannot just split line on ':' as timestamps may contain one
has_another_value = False
has_another_dimension = False
timestamps_for_dimension = []
values_for_dimension = []
this_line_num_dimensions = 0
line_len = len(line)
char_num = 0
while char_num < line_len:
# Move through any spaces
while char_num < line_len and str.isspace(line[char_num]):
char_num += 1
# See if there is any more data to read in or if we should validate that read thus far
if char_num < line_len:
# See if we have an empty dimension (i.e. no values)
if line[char_num] == ":":
if len(instance_list) < (
this_line_num_dimensions + 1
):
instance_list.append([])
instance_list[this_line_num_dimensions].append(
pd.Series()
)
this_line_num_dimensions += 1
has_another_value = False
has_another_dimension = True
timestamps_for_dimension = []
values_for_dimension = []
char_num += 1
else:
# Check if we have reached a class label
if line[char_num] != "(" and target_labels:
class_val = line[char_num:].strip()
# if class_val not in class_val_list:
# raise TsFileParseException(
# "the class value '" + class_val + "' on line " + str(
# line_num + 1) + " is not valid")
class_val_list.append(float(class_val))
char_num = line_len
has_another_value = False
has_another_dimension = False
timestamps_for_dimension = []
values_for_dimension = []
else:
# Read in the data contained within the next tuple
if line[char_num] != "(" and not target_labels:
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " does not start with a '('"
)
char_num += 1
tuple_data = ""
while (
char_num < line_len
and line[char_num] != ")"
):
tuple_data += line[char_num]
char_num += 1
if (
char_num >= line_len
or line[char_num] != ")"
):
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " does not end with a ')'"
)
# Read in any spaces immediately after the current tuple
char_num += 1
while char_num < line_len and str.isspace(
line[char_num]
):
char_num += 1
# Check if there is another value or dimension to process after this tuple
if char_num >= line_len:
has_another_value = False
has_another_dimension = False
elif line[char_num] == ",":
has_another_value = True
has_another_dimension = False
elif line[char_num] == ":":
has_another_value = False
has_another_dimension = True
char_num += 1
# Get the numeric value for the tuple by reading from the end of the tuple data backwards to the last comma
last_comma_index = tuple_data.rfind(",")
if last_comma_index == -1:
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " contains a tuple that has no comma inside of it"
)
try:
value = tuple_data[last_comma_index + 1 :]
value = float(value)
except ValueError:
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " contains a tuple that does not have a valid numeric value"
)
# Check the type of timestamp that we have
timestamp = tuple_data[0:last_comma_index]
try:
timestamp = int(timestamp)
timestamp_is_int = True
timestamp_is_timestamp = False
except ValueError:
timestamp_is_int = False
if not timestamp_is_int:
try:
timestamp = float(timestamp)
timestamp_is_float = True
timestamp_is_timestamp = False
except ValueError:
timestamp_is_float = False
if (
not timestamp_is_int
and not timestamp_is_float
):
try:
timestamp = timestamp.strip()
timestamp_is_timestamp = True
except ValueError:
timestamp_is_timestamp = False
# Make sure that the timestamps in the file (not just this dimension or case) are consistent
if (
not timestamp_is_timestamp
and not timestamp_is_int
and not timestamp_is_float
):
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " contains a tuple that has an invalid timestamp '"
+ timestamp
+ "'"
)
if (
previous_timestamp_was_float is not None
and previous_timestamp_was_float
and not timestamp_is_float
):
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " contains tuples where the timestamp format is inconsistent"
)
if (
previous_timestamp_was_int is not None
and previous_timestamp_was_int
and not timestamp_is_int
):
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " contains tuples where the timestamp format is inconsistent"
)
if (
previous_timestamp_was_timestamp is not None
and previous_timestamp_was_timestamp
and not timestamp_is_timestamp
):
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " contains tuples where the timestamp format is inconsistent"
)
# Store the values
timestamps_for_dimension += [timestamp]
values_for_dimension += [value]
# If this was our first tuple then we store the type of timestamp we had
if (
previous_timestamp_was_timestamp is None
and timestamp_is_timestamp
):
previous_timestamp_was_timestamp = True
previous_timestamp_was_int = False
previous_timestamp_was_float = False
if (
previous_timestamp_was_int is None
and timestamp_is_int
):
previous_timestamp_was_timestamp = False
previous_timestamp_was_int = True
previous_timestamp_was_float = False
if (
previous_timestamp_was_float is None
and timestamp_is_float
):
previous_timestamp_was_timestamp = False
previous_timestamp_was_int = False
previous_timestamp_was_float = True
# See if we should add the data for this dimension
if not has_another_value:
if len(instance_list) < (
this_line_num_dimensions + 1
):
instance_list.append([])
if timestamp_is_timestamp:
timestamps_for_dimension = (
pd.DatetimeIndex(
timestamps_for_dimension
)
)
instance_list[
this_line_num_dimensions
].append(
pd.Series(
index=timestamps_for_dimension,
data=values_for_dimension,
)
)
this_line_num_dimensions += 1
timestamps_for_dimension = []
values_for_dimension = []
elif has_another_value:
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " ends with a ',' that is not followed by another tuple"
)
elif has_another_dimension and target_labels:
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " ends with a ':' while it should list a class value"
)
elif has_another_dimension and not target_labels:
if len(instance_list) < (this_line_num_dimensions + 1):
instance_list.append([])
instance_list[this_line_num_dimensions].append(
pd.Series(dtype=np.float32)
)
this_line_num_dimensions += 1
num_dimensions = this_line_num_dimensions
# If this is the 1st line of data we have seen then note the dimensions
if not has_another_value and not has_another_dimension:
if num_dimensions is None:
num_dimensions = this_line_num_dimensions
if num_dimensions != this_line_num_dimensions:
raise TsFileParseException(
"line "
+ str(line_num + 1)
+ " does not have the same number of dimensions as the previous line of data"
)
# Check that we are not expecting some more data, and if not, store that processed above
if has_another_value:
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " ends with a ',' that is not followed by another tuple"
)
elif has_another_dimension and target_labels:
raise TsFileParseException(
"dimension "
+ str(this_line_num_dimensions + 1)
+ " on line "
+ str(line_num + 1)
+ " ends with a ':' while it should list a class value"
)
elif has_another_dimension and not target_labels:
if len(instance_list) < (this_line_num_dimensions + 1):
instance_list.append([])
instance_list[this_line_num_dimensions].append(pd.Series())
this_line_num_dimensions += 1
num_dimensions = this_line_num_dimensions
# If this is the 1st line of data we have seen then note the dimensions
if (
not has_another_value
and num_dimensions != this_line_num_dimensions
):
raise TsFileParseException(
"line "
+ str(line_num + 1)
+ " does not have the same number of dimensions as the previous line of data"
)
# Check if we should have class values, and if so that they are contained in those listed in the metadata
if target_labels and len(class_val_list) == 0:
raise TsFileParseException(
"the cases have no associated class values"
)
else:
dimensions = line.split(":")
# If first row then note the number of dimensions (that must be the same for all cases)
if is_first_case:
num_dimensions = len(dimensions)
if target_labels:
num_dimensions -= 1
for dim in range(0, num_dimensions):
instance_list.append([])
is_first_case = False
# See how many dimensions that the case whose data in represented in this line has
this_line_num_dimensions = len(dimensions)
if target_labels:
this_line_num_dimensions -= 1
# All dimensions should be included for all series, even if they are empty
if this_line_num_dimensions != num_dimensions:
raise TsFileParseException(
"inconsistent number of dimensions. Expecting "
+ str(num_dimensions)
+ " but have read "
+ str(this_line_num_dimensions)
)
# Process the data for each dimension
for dim in range(0, num_dimensions):
dimension = dimensions[dim].strip()
if dimension:
data_series = dimension.split(",")
data_series = [float(i) for i in data_series]
instance_list[dim].append(pd.Series(data_series))
else:
instance_list[dim].append(pd.Series())
if target_labels:
class_val_list.append(
float(dimensions[num_dimensions].strip())
)
line_num += 1
# Check that the file was not empty
if line_num:
# Check that the file contained both metadata and data
complete_regression_meta_data = (
has_problem_name_tag
and has_timestamps_tag
and has_univariate_tag
and has_target_labels_tag
and has_data_tag
)
complete_classification_meta_data = (
has_problem_name_tag
and has_timestamps_tag
and has_univariate_tag
and has_class_labels_tag
and has_data_tag
)
if (
metadata_started
and not complete_regression_meta_data
and not complete_classification_meta_data
):
raise TsFileParseException("metadata incomplete")
elif metadata_started and not data_started:
raise TsFileParseException("file contained metadata but no data")
elif metadata_started and data_started and len(instance_list) == 0:
raise TsFileParseException("file contained metadata but no data")
# Create a DataFrame from the data parsed above
data = pd.DataFrame(dtype=np.float32)
for dim in range(0, num_dimensions):
data["dim_" + str(dim)] = instance_list[dim]
# Check if we should return any associated class labels separately
if target_labels:
if return_separate_X_and_y:
return data, np.asarray(class_val_list)
else:
data["class_vals"] = pd.Series(class_val_list)
return data
else:
return data
else:
raise TsFileParseException("empty file")
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