Converter¶

Multiple Aspect Trajectory Tools Framework

MAT-data: Data Preprocessing for Multiple Aspect Trajectory Data Mining

The present application offers a tool, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. It integrates into a unique platform the fragmented approaches available for multiple aspects trajectories and in general for multidimensional sequence classification into a unique web-based and python library system. Offers both movelets visualization and classification methods.

@author: Tarlis Portela

matdata.converter.any2ts(data_path, folder, file, cols=None, tid_col='tid', class_col='label', opLabel='Converting TS')[source]¶

Converts data from various formats (CSV, Parquet, etc.) to a time series format.

Parameters:¶

data_pathstr: The directory path where the data files are located.
folderstr: The folder containing the data file to be converted.
filestr: The name of the data file to be converted.
colslist of str, optional: A list of column names to be included in the time series data.
tid_colstr, optional (default=’tid’): The name of the column to be used as the trajectory identifier.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
opLabelstr, optional (default=’Converting TS’): A label describing the operation, useful for logging or display purposes.

Returns:¶

pandas.DataFrame: A DataFrame containing the time series data, with trajectory identifier, class label, and specified columns.

matdata.converter.csv2df(url, class_col='label', tid_col='tid', missing='?')[source]¶

Converts a CSV file from a given URL into a pandas DataFrame.

Parameters:¶

urlstr: The URL pointing to the CSV file to be read.
class_colstr, optional (default=’label’): Unused, kept for standard.
tid_colstr, optional (default=’tid’): Unused, kept for standard.
missingstr, optional (default=’?’): The placeholder for missing values in the CSV file.

Returns:¶

pandas.DataFrame: A DataFrame containing the data from the CSV file, with missing values handled as specified and columns renamed if necessary.

matdata.converter.df2csv(df, data_path, file='train', tid_col='tid', class_col='label', select_cols=None, opLabel='Writing CSV')[source]¶

Writes a pandas DataFrame to a CSV file.

Parameters:¶

dfpandas.DataFrame: The DataFrame to be written to the CSV file.
data_pathstr: The directory path where the Parquet file will be saved.
filestr, optional (default=’train’): The base name of the CSV file (without extension).
tid_colstr, optional (default=’tid’): The name of the column to be used as the trajectory identifier.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
select_colslist of str, optional: A list of column names to be included in the CSV file. If None, all columns are included.
opLabelstr, optional (default=’Writing PARQUET’): A label describing the operation, useful for logging or display purposes.

Returns:¶

pandas.DataFrame: The input DataFrame

matdata.converter.df2mat(df, folder, file, cols=None, mat_cols=None, desc_cols=None, label_columns=None, other_dsattrs=None, tid_col='tid', class_col='label', opLabel='Converting MAT')[source]¶

Converts a pandas DataFrame to a Multiple Aspect Trajectory .mat file and saves it to the specified folder.

Parameters:¶

dfpandas.DataFrame: The DataFrame to be converted to a .mat file.
folderstr: The directory where the .mat file will be saved.
filestr: The base name of the .mat file (without extension).
colslist of str, optional: A list of column names from the DataFrame to include in the .mat file. If None, all columns are included.
mat_colslist of str, optional: A list of column names representing the trajectory attibutes. If None, no columns are used.
desc_colslist of str, optional: A dict of column descriptors to be included as descriptive metadata.
label_columnslist of str, optional: A list of column names that can be treated as labels in the .mat file.
other_dsattrsdict, optional: A dictionary of additional dataset attributes to be included in the .mat file.
tid_colstr, optional (default=’tid’): The name of the column to be used as the trajectory identifier.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
opLabelstr, optional (default=’Converting MAT’): A label describing the operation, useful for logging or display purposes.

Returns:¶

None

matdata.converter.df2parquet(df, data_path, file='train', tid_col='tid', class_col='label', select_cols=None, opLabel='Writing Parquet')[source]¶

Writes a pandas DataFrame to a Parquet file.

Parameters:¶

dfpandas.DataFrame: The DataFrame to be written to the Parquet file.
data_pathstr: The directory path where the Parquet file will be saved.
filestr, optional (default=’train’): The base name of the Parquet file (without extension).
tid_colstr, optional (default=’tid’): The name of the column to be used as the trajectory identifier.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
select_colslist of str, optional: A list of column names to be included in the Parquet file. If None, all columns are included.
opLabelstr, optional (default=’Writing PARQUET’): A label describing the operation, useful for logging or display purposes.

Returns:¶

pandas.DataFrame: The input DataFrame

matdata.converter.df2zip(df, data_path, file, tid_col='tid', class_col='label', select_cols=None, opLabel='Writing MAT')[source]¶

Writes a pandas DataFrame to a CSV file and compresses it into a ZIP archive.

Parameters:¶

dfpandas.DataFrame: The DataFrame to be written to the CSV file and then compressed into a ZIP archive.
data_pathstr: The directory path where the ZIP archive will be saved.
filestr: The base name of the CSV file (without extension) to be compressed into the ZIP archive.
tid_colstr, optional (default=’tid’): The name of the column to be used as the trajectory identifier.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
select_colslist of str, optional: A list of column names to be included in the CSV file. If None, all columns are included.
opLabelstr, optional (default=’Writing ZIP’): A label describing the operation, useful for logging or display purposes.

Returns:¶

pandas.DataFrame: The input DataFrame

matdata.converter.mat2df(url, class_col='label', tid_col='tid', missing='?')[source]¶

Converts a MATLAB .mat file from a given URL into a pandas DataFrame.

Parameters:¶

urlstr: The URL pointing to the .mat file to be read.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
tid_colstr, optional (default=’tid’): The name of the column to be used as the unique trajectory identifier.
missingstr, optional (default=’?’): The placeholder for missing values in the dataset.

Returns:¶

pandas.DataFrame: A DataFrame containing the data from the .mat file, with missing values handled as specified and columns renamed if necessary.

Raises:¶

Exception: Not Implemented.

matdata.converter.parquet2df(url, class_col='label', tid_col='tid', missing='?')[source]¶

Converts a Parquet file from a given URL into a pandas DataFrame.

Parameters:¶

urlstr: The URL pointing to the Parquet file to be read.
class_colstr, optional (default=’label’): Unused, kept for standard.
tid_colstr, optional (default=’tid’): Unused, kept for standard.
missingstr, optional (default=’?’): The placeholder for missing values in the dataset.

Returns:¶

pandas.DataFrame: A DataFrame containing the data from the Parquet file, with missing values handled as specified and columns renamed if necessary.

matdata.converter.ts2df(url, class_col='label', tid_col='tid', missing='?')[source]¶

Converts a time series file from a given URL into a pandas DataFrame.

Parameters:¶

urlstr: The URL pointing to the time series file to be read.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
tid_colstr, optional (default=’tid’): The name of the column to be used as the unique trajectory identifier.
missingstr, optional (default=’?’): The placeholder for missing values in the dataset.

Returns:¶

pandas.DataFrame: A DataFrame containing the data from the time series file, with missing values handled as specified and columns renamed if necessary.

matdata.converter.xes2df(url, class_col='label', tid_col='tid', opLabel='Converting XES', save=False, start_tid=1)[source]¶

Converts an XES (eXtensible Event Stream) file from a given URL into a pandas DataFrame.

Parameters:¶

urlstr: The URL pointing to the XES file to be read.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
tid_colstr, optional (default=’tid’): The name of the column to be used as the trajectory identifier.
opLabelstr, optional (default=’Converting XES’): A label describing the operation, useful for logging or display purposes.
savebool, optional (default=False): A flag indicating whether to save the DataFrame to a file after conversion.
start_tidint, optional (default=1): The starting value for trajectory identifiers as tid_col values need to be generated.

Returns:¶

pandas.DataFrame: A DataFrame containing the data from the XES file, with columns renamed if necessary.

matdata.converter.zip2arf(folder, file, cols, tid_col='tid', class_col='label', missing='?', opLabel='Reading CSV')[source]¶

Extracts a CSV file from a ZIP archive and converts it into an ARFF (Attribute-Relation File Format) file.

Parameters:¶

folderstr: The directory path where the ZIP archive is located.
filestr: The name of the ZIP archive file (with or without extension).
colslist of str: A list of column names to be included in the ARFF file.
tid_colstr, optional (default=’tid’): The name of the column to be used as the trajectory identifier.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
missingstr, optional (default=’?’): The placeholder for missing values in the CSV file.
opLabelstr, optional (default=’Reading CSV’): A label describing the operation, useful for logging or display purposes.

Returns:¶

pandas.DataFrame: A DataFrame containing the data from the extracted ZIP file, with missing values handled as specified and columns renamed if necessary.

matdata.converter.zip2csv(folder, file, cols, class_col='label', tid_col='tid', missing='?')[source]¶

Extracts and compile Trajectory CSV files from a ZIP archive and converts it into a pandas DataFrame.

Parameters:¶

folderstr: The directory path where the ZIP archive is located, and destination to the CSV resulting file.
filestr: The name of the ZIP archive file (with or without extension).
colslist of str: A list of column names to be included in the DataFrame.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
tid_colstr, optional (default=’tid’): The name of the column to be used as the trajectory identifier.
missingstr, optional (default=’?’): The placeholder for missing values in the CSV file.

Returns:¶

pandas.DataFrame: A DataFrame containing the data from the extracted CSV file, with missing values handled as specified and columns renamed if necessary.

matdata.converter.zip2df(url, class_col='label', tid_col='tid', missing='?', opLabel='Reading ZIP')[source]¶

Extracts and converts a CSV trajectory file from a ZIP archive located at a given URL into a pandas DataFrame.

Parameters:¶

urlstr: The URL pointing to the ZIP archive containing the CSV file to be read.
class_colstr, optional (default=’label’): The name of the column to be treated as the class/label column.
tid_colstr, optional (default=’tid’): The name of the column to be used as the unique trajectory identifier.
missingstr, optional (default=’?’): The placeholder for missing values in the CSV file.
opLabelstr, optional (default=’Reading ZIP’): A label describing the operation, for logging purposes.

Returns:¶

pandas.DataFrame: A DataFrame containing the data from the extracted CSV file, with missing values handled as specified and columns renamed if necessary.

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