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README.md

README.md

ParaText

ParaText is a C++ library to read text files in parallel on multi-core machines. The alpha release includes a CSV reader and Python bindings. The library itself has no dependencies other than the standard library.

Depedencies

ParaText has the following dependencies:

  • a fully C++11-compliant C++ compiler (gcc 4.8 or above, clang 3.4 or above)
  • SWIG 2.0.7 or above (Python 2 bindings)
  • SWIG 3.0.8 or above (Python 3 bindings)
  • Python 2.7 or 3.5
  • setuptools
  • numpy

Pandas is required only if using ParaText to read CSV files into Pandas. The SWIG available from Ubuntu 14.04 does not work with Python 3.

Anaconda packages the latest version of SWIG that works properly with Python 3. You can install it as follows:

conda install swig

Building Python

First, go into the python directory:

   cd python/

Then run setup.py:

   python setup.py build install

Use the --prefix option if you prefer to install ParaText to a different location:

   cd python/
   python setup.py build install --prefix=/my/prefix/dir

Using ParaText in Python

First, import the paratext Python package.

   import paratext

Loading into Pandas

A CSV file can be loaded into Pandas in just one line of code using the load_csv_to_pandas function.

df = paratext.load_csv_to_pandas("hepatitis.csv")

The data frame looks something like this:

In [1]: print df.head()
   AGE     SEX STEROID ANTIVIRALS FATIGUE MALAISE ANOREXIA LIVER_BIG  \
0   30    male      no         no      no      no       no        no   
1   50  female      no         no     yes      no       no        no   
2   78  female     yes         no     yes      no       no       yes   
3   31  female     nan        yes      no      no       no       yes   
4   34  female     yes         no      no      no       no       yes   

  LIVER_FIRM SPLEEN_PALPABLE SPIDERS ASCITES VARICES  BILIRUBIN  \
0         no              no      no      no      no        1.0   
1         no              no      no      no      no        0.9   
2         no              no      no      no      no        0.7   
3         no              no      no      no      no        0.7   
4         no              no      no      no      no        1.0   

   ALK_PHOSPHATE  SGOT  ALBUMIN  PROTIME HISTOLOGY Class  
0             85    18      4.0      NaN        no  LIVE  
1            135    42      3.5      NaN        no  LIVE  
2             96    32      4.0      NaN        no  LIVE  
3             46    52      4.0       80        no  LIVE  
4            NaN   200      4.0      NaN        no  LIVE

Loading into Dictionaries (more memory-efficient)

A Python dictionary of arrays is preferable over a DataFrame if the memory budget is very tight. The load_csv_to_dict loads a CSV file, storing the columns as a dictionary of arrays.

  dict_frame, levels = paratext.load_csv_to_dict(filename)

It returns a two element tuple. The first dict_frame is a Python dictionary that maps column names to column data. The second levels is also a Python dictionary keyed by column name. It contains a list of level strings for each categorical column.

The following code visits the columns. For each column, it prints its name, the first 5 values of its data, and the categorical levels (None if not categorical).

  for key in dict_frame.keys():
      print key, repr(dict_frame[key][0:5]), levels.get(key, None)

This gives the following output:

PROTIME array([ nan,  nan,  nan,  80.,  nan], dtype=float32) None
LIVER_BIG array([0, 0, 1, 1, 1], dtype=uint8) ['no' 'yes' 'nan']
ALBUMIN array([ 4. ,  3.5,  4. ,  4. ,  4. ], dtype=float32) None
ALK_PHOSPHATE array([  85.,  135.,   96.,   46.,   nan], dtype=float32) None
ANTIVIRALS array([0, 0, 0, 1, 0], dtype=uint8) ['no' 'yes']
HISTOLOGY array([0, 0, 0, 0, 0], dtype=uint8) ['no' 'yes']
BILIRUBIN array([ 1.,  0.89999998,  0.69999999,  0.69999999, 1. ], dtype=float32) None
AGE array([30, 50, 78, 31, 34], dtype=uint8) None
SEX array([0, 1, 1, 1, 1], dtype=uint8) ['male' 'female']
STEROID array([0, 0, 1, 2, 1], dtype=uint8) ['no' 'yes' 'nan']
SGOT array([  18.,   42.,   32.,   52.,  200.], dtype=float32) None
MALAISE array([0, 0, 0, 0, 0], dtype=uint8) ['no' 'yes' 'nan']
FATIGUE array([0, 1, 1, 0, 0], dtype=uint8) ['no' 'yes' 'nan']
SPIDERS array([0, 0, 0, 0, 0], dtype=uint8) ['no' 'yes' 'nan']
VARICES array([0, 0, 0, 0, 0], dtype=uint8) ['no' 'nan' 'yes']
LIVER_FIRM array([0, 0, 0, 0, 0], dtype=uint8) ['no' 'yes' 'nan']
SPLEEN_PALPABLE array([0, 0, 0, 0, 0], dtype=uint8) ['no' 'yes' 'nan']
ASCITES array([0, 0, 0, 0, 0], dtype=uint8) ['no' 'yes' 'nan']
Class array([0, 0, 0, 0, 0], dtype=uint8) ['LIVE' 'DIE']
ANOREXIA array([0, 0, 0, 0, 0], dtype=uint8) ['no' 'yes' 'nan']

All categorical columns in this data set have 3 or fewer levels so they are all uint8. A string representation uses at least 8 times as much space, but it can also be less computationally efficient. An integer representation is ideal for learning on categorical columns. Integer comparisons over contiguous integer buffers are pretty cheap compared to exhaustive string comparisons on (potentially) discontiguous string values. This makes a big difference for combinatorial learning algorithms.

Handling Multi-Line Fields

ParaText supports reading CSV files with multi-line fields in parallel. This feature must be explicitly activated as it requires extra overhead to adjust the boundaries of the chunks processed by the workers.

df = paratext.load_csv_to_pandas("messy.csv", allow_quoted_newlines=True)

Header Detection

ParaText detects the presence of a header. This can be turned off with no_header=True.

Column Typing

This library distinguishes between a column's data type and its semantics. The semantics defines how to interpret a column (e.g. numeric vs. categorical). and the data type (uint8, int64, float, etc.) is the type for encoding column values.

Three semantic types are supported:

  • num: numeric data.

  • cat: categorical data.

  • text: large strings like e-mails and text documents.

ParaText supports (u)int(8|16|32|64)|float|double|string data types.

Parameters

Most CSV loading functions in ParaText have the following parameters:

  • cat_names: A list of column names to force as categorical regardless of the inferred type.

  • text_names: A list of column names that should be treated as rich text regardless of its inferred type.

  • num_names: A list of column names that should be treated as numeric regardless of its inferred type.

  • num_threads: The number of parser threads to spawn. The default is the number of cores.

  • allow_quoted_newlines: Allows multi-line text fields. This is turned off by default.

  • no_header: Do not auto-detect the presence of a header. Assume the first line is data. This is turned off by default.

  • max_level_name_length: If a field's length exceeds this value, the entire column is treated as text rather than categorical. The default is unlimited.

  • max_levels: The maximum number of levels of a categorical column. The default is unlimited.

  • number_only: Whether it can be safely assumed the columns only contain numbers. The default is unlimited.

  • block_size: The number of bytes to read at a time in each worker thread. The default is unlimited.

Escape Characters

ParaText supports backslash escape characters:

* `\t': tab

* `\n': newline

* `\r': carriage return

* `\v': vertical tab

* `\0': null terminator (0x00)

* `\b': backspace

* '\xnn': an 8-bit character represented with a 2 digit hexidecimal number.

* '\unnnn': a Unicode code point represented as 4-digit hexidecimal number.

* '\Unnnnnnnn': a Unicode code point represented as 8-digit hexiecimal number.

Writing CSV

ParaText does not yet support parallel CSV writing. However, it bundles a CSV writer that can be used to write DataFrames with arbitrary string and byte buffer data in a lossless fashion.

If a character in a Python string, unicode, or bytes object could be treated as non-data when parsed (e.g. a doublequote or escape character), it is escaped. Moreover, any character that is outside the desired encoding is also escaped. This enables, for example, the lossless writing of non-UTF-8 to a UTF-8 file.

For example, to restrict the encoding to 7-bit printable ASCII, pass out_encoding='printable_ascii'

   import paratext.serial
   df = pandas.DataFrame({"X": [b"\xff\\\n \" oh my!"]})
   paratext.serial.save_frame("lossless.csv", df, allow_quoted_newlines=True, out_encoding='printable_ascii', dos=False)

This results in a file:

"X"
"\xff\\
 \" oh my!"

Instead, pass out_encoding='utf-8' to save_frame.

   import paratext.serial
   df = pandas.DataFrame({"X": [b"\xff\\\n \" oh my!"],"Y": ["\U0001F600"]})
   paratext.serial.save_frame("lossless2.csv", df, allow_quoted_newlines=True, out_encoding='utf-8', dos=False)

Now, the file only escapes cells in the DataFrame with non-UTF8 data. All other UTF8 characters are preserved.

"X","Y"
"\xff\\
 \" oh my!","<U+1F600>"

Other Notes

ParaText is a work-in-progress. There are a few unimplemented features that may prevent it from working on all CSV files. We note them below.

  1. There is no way to supply type hints (e.g. uint64 or float) of a column. Only the interpretation of a column (numeric, categorical, or text) can be forced.

  2. DateTime will be supported in a future release.

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