Source code for h2o.utils.shared_utils

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
#
# Copyright 2016 H2O.ai;  Apache License Version 2.0 (see LICENSE for details)
#
"""Shared utilities used by various classes, all placed here to avoid circular imports.

This file INTENTIONALLY has NO module dependencies!

TODO: clean up this file that turned into a waste bin over the years:
- split this into more specific modules.
- utility modules should have a specific name to limit the scope of the garbage we put in (like waste sorting).
- utility modules should if possible be placed under appropriate parent module 
  (e.g. model/mojo related utility functions should go under h2o.model)
- utility functions used ONLY in tests should go to test utilities! no reason to export those to end users!
- same for model_utils.py nearby
"""
from .compatibility import *  # NOQA

import csv
import contextlib
import io
import itertools
import os
import re
import shutil
import string
import subprocess
import sys
import tempfile
import zipfile


try:
    from contextlib import AbstractContextManager
except ImportError:
    import abc
    
    class AbstractContextManager(metaclass=abc.ABCMeta):
        @classmethod
        def __subclasshook__(cls, C):
            if cls is AbstractContextManager:
                return all(any(m in SC.__dict__ for SC in C.__mro__) for m in ("__enter__", "__exit__"))
            return NotImplemented


from h2o.backend.server import H2OLocalServer
from h2o.exceptions import H2OValueError
from h2o.utils.typechecks import assert_is_type, is_type, numeric
from h2o.utils.threading import local_env

_id_ctr = 0

# The set of characters allowed in frame IDs. Since frame ids are used within REST API urls, they may
# only contain characters allowed within the "segment" part of the URL (see RFC 3986). Additionally, we
# forbid all characters that are declared as "illegal" in Key.java.
_id_allowed_characters = set("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-._~")

__all__ = ('mojo_predict_csv', 'mojo_predict_pandas')


class List(list):
    """a list accepting attributes"""
    pass


class LookupSeq(tuple):
    """
    An immutable sequence implementation (actually a tuple) optimized for fast lookups.
    Some code needs both random/indexed access on large lists and do many lookups `elem in my_list` (e.g. in a loop),
    it is recommended to use this class in that case to avoid forgetting to build or use a set every time we need a lookup. 
    
    Note that this list is read-only as we don't want to have to synchronize the backed set used for the lookups.
    """
    def __new__(cls, seq=()):
        """need to implement  __new__ to be able to extend tuple (not necessary for list)"""
        return super(LookupSeq, cls).__new__(cls, seq)
    
    def __init__(self, seq=()):
        self.__set = frozenset(self)  # lookup functions backed by a set
        
    def __contains__(self, item):
        return item in self.__set
    
    def set(self):
        """
        use this for arithmetic operations on the elements to avoid confusion.
        We still want this to behave like a list for the most part, 
        and this is slightly faster than building a set from the list itself.
        """
        return self.__set


def _py_tmp_key(append):
    global _id_ctr
    _id_ctr += 1
    return "py_" + str(_id_ctr) + append


def check_frame_id(frame_id):
    check_id(frame_id, "H2OFrame")


def check_id(id, type):
    """Check that the provided id is valid in Rapids language."""
    if id is None:
        return
    if id.strip() == "":
        raise H2OValueError("%s id cannot be an empty string: %r" % (type, id))
    for i, ch in enumerate(id):
        # '$' character has special meaning at the beginning of the string; and prohibited anywhere else
        if ch == "$" and i == 0: continue
        if ch not in _id_allowed_characters:
            raise H2OValueError("Character '%s' is illegal in %s id: %s" % (ch, type, id))
    if re.match(r"-?[0-9]", id):
        raise H2OValueError("%s id cannot start with a number: %s" % (type, id))


def temp_ctr():
    return _id_ctr


def is_module_available(mod):
    if local_env(mod+"_disabled"): # fast track if module is explicitly disabled
        return False
    if mod in sys.modules and sys.modules[mod] is not None:  # fast track + safer in unusual environments 
        return True
        
    import importlib.util
    return importlib.util.find_spec(mod) is not None

def can_use_pandas():
    return is_module_available('pandas')

def can_use_datatable():
    return is_module_available('datatable') and sys.version_info.major == 3 and sys.version_info.minor <= 9

def can_use_polars():
    return is_module_available('polars') and sys.version_info.major == 3 and sys.version_info.minor > 9

def can_use_pyarrow():
    if can_use_pandas() and sys.version_info.minor > 9:
        import pandas
        return is_module_available('pyarrow') and sys.version_info.major == 3 and sys.version_info.minor > 9 and \
           sys.version_info.major == 3 and float(pandas.__version__[0]) >= 1
    else:
        return False

def can_use_numpy():
    return is_module_available('numpy')

_url_safe_chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_.~"
_url_chars_map = [chr(i) if chr(i) in _url_safe_chars else "%%%02X" % i for i in range(256)]


def url_encode(s):
    # Note: type cast str(s) will not be needed once all code is made compatible
    return "".join(_url_chars_map[c] for c in bytes_iterator(s))

def quote(s):
    return url_encode(s)

def clamp(x, xmin, xmax):
    """Return the value of x, clamped from below by `xmin` and from above by `xmax`."""
    return max(xmin, min(x, xmax))

def _gen_header(cols):
    return ["C" + str(c) for c in range(1, cols + 1, 1)]

def stringify_dict(d):
    return stringify_list(["{'key': %s, 'value': %s}" % (_quoted(k), v) for k, v in d.items()])


def stringify_dict_as_map(d):
    return "{%s}" % ",".join(["%s: %s" % (_quoted(k), stringify_object(v, stringify_dict_as_map)) for k, v in d.items()])


def stringify_list(arr):
    return "[%s]" % ",".join(stringify_list(item) if isinstance(item, list) else _str(item)
                             for item in arr)


def stringify_object(o, dict_function=stringify_dict):
    if isinstance(o, dict):
        return dict_function(o)
    elif isinstance(o, list):
        return stringify_list(o)
    else:
        return _str(o)


def _str(item):
    return _str_tuple(item) if isinstance(item, tuple) else str(item)


def _str_tuple(t):
    return "{%s}" % ",".join(["%s: %s" % (ti[0], _str(ti[1])) for ti in zip(list(string.ascii_lowercase), t)])


def _is_list(l):
    return isinstance(l, (tuple, list))


def _is_str_list(l):
    return is_type(l, [str])


def _is_num_list(l):
    return is_type(l, [numeric])


def _is_list_of_lists(o):
    return any(isinstance(l, (tuple, list)) for l in o)

def _is_fr(o):
    return o.__class__.__name__ == "H2OFrame"  # hack to avoid circular imports


def _quoted(key):
    if key is None: return "\"\""
    # mimic behavior in R to replace "%" and "&" characters, which break the call to /Parse, with "."
    # key = key.replace("%", ".")
    # key = key.replace("&", ".")
    is_quoted = len(re.findall(r'\"(.+?)\"', key)) != 0
    key = key if is_quoted else '"' + key + '"'
    return key


def _locate(path):
    """Search for a relative path and turn it into an absolute path.
    This is handy when hunting for data files to be passed into h2o and used by import file.
    Note: This function is for unit testing purposes only.

    Parameters
    ----------
    path : str
      Path to search for

    :return: Absolute path if it is found.  None otherwise.
    """

    tmp_dir = os.path.realpath(os.getcwd())
    possible_result = os.path.join(tmp_dir, path)
    while True:
        if os.path.exists(possible_result):
            return possible_result

        next_tmp_dir = os.path.dirname(tmp_dir)
        if next_tmp_dir == tmp_dir:
            raise ValueError("File not found: " + path)

        tmp_dir = next_tmp_dir
        possible_result = os.path.join(tmp_dir, path)


def _colmean(column):
    """Return the mean of a single-column frame."""
    assert column.ncols == 1
    return column.mean(return_frame=True).flatten()


def get_human_readable_bytes(size):
    """
    Convert given number of bytes into a human readable representation, i.e. add prefix such as kb, Mb, Gb,
    etc. The `size` argument must be a non-negative integer.

    :param size: integer representing byte size of something
    :return: string representation of the size, in human-readable form
    """
    if size == 0: return "0"
    if size is None: return ""
    assert_is_type(size, int)
    assert size >= 0, "`size` cannot be negative, got %d" % size
    suffixes = "PTGMk"
    maxl = len(suffixes)
    for i in range(maxl + 1):
        shift = (maxl - i) * 10
        if size >> shift == 0: continue
        ndigits = 0
        for nd in [3, 2, 1]:
            if size >> (shift + 12 - nd * 3) == 0:
                ndigits = nd
                break
        if ndigits == 0 or size == (size >> shift) << shift:
            rounded_val = str(size >> shift)
        else:
            rounded_val = "%.*f" % (ndigits, size / (1 << shift))
        return "%s %sb" % (rounded_val, suffixes[i] if i < maxl else "")


def get_human_readable_time(time_ms):
    """
    Convert given duration in milliseconds into a human-readable representation, i.e. hours, minutes, seconds,
    etc. More specifically, the returned string may look like following:
        1 day 3 hours 12 mins
        3 days 0 hours 0 mins
        8 hours 12 mins
        34 mins 02 secs
        13 secs
        541 ms
    In particular, the following rules are applied:
        * milliseconds are printed only if the duration is less than a second;
        * seconds are printed only if the duration is less than an hour;
        * for durations greater than 1 hour we print days, hours and minutes keeping zeros in the middle (i.e. we
          return "4 days 0 hours 12 mins" instead of "4 days 12 mins").

    :param time_ms: duration, as a number of elapsed milliseconds.
    :return: human-readable string representation of the provided duration.
    """
    millis = time_ms % 1000
    secs = (time_ms // 1000) % 60
    mins = (time_ms // 60000) % 60
    hours = (time_ms // 3600000) % 24
    days = (time_ms // 86400000)

    res = ""
    if days > 1:
        res += "%d days" % days
    elif days == 1:
        res += "1 day"

    if hours > 1 or (hours == 0 and res):
        res += " %d hours" % hours
    elif hours == 1:
        res += " 1 hour"

    if mins > 1 or (mins == 0 and res):
        res += " %d mins" % mins
    elif mins == 1:
        res += " 1 min"

    if days == 0 and hours == 0:
        res += " %02d secs" % secs
    if not res:
        res = " %d ms" % millis

    return res.strip()


def normalize_slice(s, total):
    """
    Return a "canonical" version of slice ``s``.

    :param slice s: the original slice expression
    :param total int: total number of elements in the collection sliced by ``s``
    :return slice: a slice equivalent to ``s`` but not containing any negative indices or Nones.
    """
    newstart = 0 if s.start is None else max(0, s.start + total) if s.start < 0 else min(s.start, total)
    newstop = total if s.stop is None else max(0, s.stop + total) if s.stop < 0 else min(s.stop, total)
    newstep = 1 if s.step is None else s.step
    return slice(newstart, newstop, newstep)


def slice_is_normalized(s):
    """Return True if slice ``s`` in "normalized" form."""
    return (s.start is not None and s.stop is not None and s.step is not None and s.start <= s.stop)


gen_header = _gen_header
py_tmp_key = _py_tmp_key
locate = _locate
quoted = _quoted
is_list = _is_list
is_fr = _is_fr
is_list_of_lists = _is_list_of_lists
is_num_list = _is_num_list
is_str_list = _is_str_list

gen_model_file_name = "h2o-genmodel.jar"
h2o_predictor_class = "hex.genmodel.tools.PredictCsv"


[docs]def mojo_predict_pandas(dataframe, mojo_zip_path, genmodel_jar_path=None, classpath=None, java_options=None, verbose=False, setInvNumNA=False, predict_contributions=False, predict_calibrated=False): """ MOJO scoring function to take a Pandas frame and use MOJO model as zip file to score. :param dataframe: Pandas frame to score. :param mojo_zip_path: Path to MOJO zip downloaded from H2O. :param genmodel_jar_path: Optional, path to genmodel jar file. If None (default) then the h2o-genmodel.jar in the same folder as the MOJO zip will be used. :param classpath: Optional, specifies custom user defined classpath which will be used when scoring. If None (default) then the default classpath for this MOJO model will be used. :param java_options: Optional, custom user defined options for Java. By default ``-Xmx4g`` is used. :param verbose: Optional, if True, then additional debug information will be printed. False by default. :param predict_contributions: if True, then return prediction contributions instead of regular predictions (only for tree-based models). :param predict_calibrated: if true, then return calibrated probabilities in addition to the predicted probabilities. :return: Pandas frame with predictions """ tmp_dir = tempfile.mkdtemp() try: if not can_use_pandas(): raise RuntimeError('Cannot import pandas') import pandas assert_is_type(dataframe, pandas.DataFrame) input_csv_path = os.path.join(tmp_dir, 'input.csv') prediction_csv_path = os.path.join(tmp_dir, 'prediction.csv') dataframe.to_csv(input_csv_path) mojo_predict_csv(input_csv_path=input_csv_path, mojo_zip_path=mojo_zip_path, output_csv_path=prediction_csv_path, genmodel_jar_path=genmodel_jar_path, classpath=classpath, java_options=java_options, verbose=verbose, setInvNumNA=setInvNumNA, predict_contributions=predict_contributions, predict_calibrated=predict_calibrated) return pandas.read_csv(prediction_csv_path) finally: shutil.rmtree(tmp_dir)
[docs]def mojo_predict_csv(input_csv_path, mojo_zip_path, output_csv_path=None, genmodel_jar_path=None, classpath=None, java_options=None, verbose=False, setInvNumNA=False, predict_contributions=False, predict_calibrated=False, extra_cmd_args=None): """ MOJO scoring function to take a CSV file and use MOJO model as zip file to score. :param input_csv_path: Path to input CSV file. :param mojo_zip_path: Path to MOJO zip downloaded from H2O. :param output_csv_path: Optional, name of the output CSV file with computed predictions. If None (default), then predictions will be saved as prediction.csv in the same folder as the MOJO zip. :param genmodel_jar_path: Optional, path to genmodel jar file. If None (default) then the h2o-genmodel.jar in the same folder as the MOJO zip will be used. :param classpath: Optional, specifies custom user defined classpath which will be used when scoring. If None (default) then the default classpath for this MOJO model will be used. :param java_options: Optional, custom user defined options for Java. By default ``-Xmx4g -XX:ReservedCodeCacheSize=256m`` is used. :param verbose: Optional, if True, then additional debug information will be printed. False by default. :param predict_contributions: if True, then return prediction contributions instead of regular predictions (only for tree-based models). :param predict_calibrated: if true, then return calibrated probabilities in addition to the predicted probabilities. :param extra_cmd_args: Optional, a list of additional arguments to append to genmodel.jar's command line. :return: List of computed predictions """ default_java_options = '-Xmx4g -XX:ReservedCodeCacheSize=256m' prediction_output_file = 'prediction.csv' # Checking java java = H2OLocalServer._find_java() H2OLocalServer._check_java(java=java, verbose=verbose) # Ensure input_csv exists if verbose: print("input_csv:\t%s" % input_csv_path) if not os.path.isfile(input_csv_path): raise RuntimeError("Input csv cannot be found at %s" % input_csv_path) # Ensure mojo_zip exists mojo_zip_path = os.path.abspath(mojo_zip_path) if verbose: print("mojo_zip:\t%s" % mojo_zip_path) if not os.path.isfile(mojo_zip_path): raise RuntimeError("MOJO zip cannot be found at %s" % mojo_zip_path) parent_dir = os.path.dirname(mojo_zip_path) # Set output_csv if necessary if output_csv_path is None: output_csv_path = os.path.join(parent_dir, prediction_output_file) # Set path to h2o-genmodel.jar if necessary and check it's valid if genmodel_jar_path is None: genmodel_jar_path = os.path.join(parent_dir, gen_model_file_name) if verbose: print("genmodel_jar:\t%s" % genmodel_jar_path) if not os.path.isfile(genmodel_jar_path): raise RuntimeError("Genmodel jar cannot be found at %s" % genmodel_jar_path) if verbose and output_csv_path is not None: print("output_csv:\t%s" % output_csv_path) # Set classpath if necessary if classpath is None: classpath = genmodel_jar_path if verbose: print("classpath:\t%s" % classpath) # Set java_options if necessary if java_options is None: java_options = default_java_options if verbose: print("java_options:\t%s" % java_options) # Construct command to invoke java cmd = [java] for option in java_options.split(' '): cmd += [option] cmd += ["-cp", classpath, h2o_predictor_class, "--mojo", mojo_zip_path, "--input", input_csv_path, '--output', output_csv_path, '--decimal'] if setInvNumNA: cmd.append('--setConvertInvalidNum') if predict_contributions: cmd.append('--predictContributions') if predict_calibrated: cmd.append('--predictCalibrated') if extra_cmd_args: cmd += extra_cmd_args if verbose: cmd_str = " ".join(cmd) print("java cmd:\t%s" % cmd_str) # invoke the command subprocess.check_call(cmd, shell=False) # load predictions in form of a dict with open(output_csv_path) as csv_file: result = list(csv.DictReader(csv_file)) return result
class InMemoryZipArch(object): def __init__(self, file_name = None, compression = zipfile.ZIP_DEFLATED): self._data = io.BytesIO() self._arch = zipfile.ZipFile(self._data, "w", compression, False) self._file_name = file_name def append(self, filename_in_zip, file_contents): self._arch.writestr(filename_in_zip, file_contents) return self def write_to_file(self, filename): # Mark the files as having been created on Windows so that # Unix permissions are not inferred as 0000 for zfile in self._arch.filelist: zfile.create_system = 0 self._arch.close() with open(filename, 'wb') as f: f.write(self._data.getvalue()) def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): if self._file_name is None: return self.write_to_file(self._file_name) @contextlib.contextmanager def as_resource(o): if isinstance(o, AbstractContextManager): with o as res: yield res else: yield o