INSTRUCTION
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| RESPONSE
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Creates new H2OWord2vecEstimator based on an external model.: param external: H2OFrame with an external model: return: H2OWord2vecEstimator instance representing the external model
|
def from_external(external=H2OFrame):
"""
Creates new H2OWord2vecEstimator based on an external model.
:param external: H2OFrame with an external model
:return: H2OWord2vecEstimator instance representing the external model
"""
w2v_model = H2OWord2vecEstimator(pre_trained=external)
w2v_model.train()
return w2v_model
|
Determines vec_size for a pre - trained model after basic model verification.
|
def _determine_vec_size(self):
"""
Determines vec_size for a pre-trained model after basic model verification.
"""
first_column = self.pre_trained.types[self.pre_trained.columns[0]]
if first_column != 'string':
raise H2OValueError("First column of given pre_trained model %s is required to be a String",
self.pre_trained.frame_id)
if list(self.pre_trained.types.values()).count('string') > 1:
raise H2OValueError("There are multiple columns in given pre_trained model %s with a String type.",
self.pre_trained.frame_id)
self.vec_size = self.pre_trained.dim[1] - 1;
|
Mean absolute error regression loss.
|
def h2o_mean_absolute_error(y_actual, y_predicted, weights=None):
"""
Mean absolute error regression loss.
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:returns: mean absolute error loss (best is 0.0).
"""
ModelBase._check_targets(y_actual, y_predicted)
return _colmean((y_predicted - y_actual).abs())
|
Mean squared error regression loss
|
def h2o_mean_squared_error(y_actual, y_predicted, weights=None):
"""
Mean squared error regression loss
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:returns: mean squared error loss (best is 0.0).
"""
ModelBase._check_targets(y_actual, y_predicted)
return _colmean((y_predicted - y_actual) ** 2)
|
Median absolute error regression loss
|
def h2o_median_absolute_error(y_actual, y_predicted):
"""
Median absolute error regression loss
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:returns: median absolute error loss (best is 0.0)
"""
ModelBase._check_targets(y_actual, y_predicted)
return (y_predicted - y_actual).abs().median()
|
Explained variance regression score function.
|
def h2o_explained_variance_score(y_actual, y_predicted, weights=None):
"""
Explained variance regression score function.
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:returns: the explained variance score.
"""
ModelBase._check_targets(y_actual, y_predicted)
_, numerator = _mean_var(y_actual - y_predicted, weights)
_, denominator = _mean_var(y_actual, weights)
if denominator == 0.0:
return 1. if numerator == 0 else 0. # 0/0 => 1, otherwise, 0
return 1 - numerator / denominator
|
R - squared ( coefficient of determination ) regression score function
|
def h2o_r2_score(y_actual, y_predicted, weights=1.):
"""
R-squared (coefficient of determination) regression score function
:param y_actual: H2OFrame of actual response.
:param y_predicted: H2OFrame of predicted response.
:param weights: (Optional) sample weights
:returns: R-squared (best is 1.0, lower is worse).
"""
ModelBase._check_targets(y_actual, y_predicted)
numerator = (weights * (y_actual - y_predicted) ** 2).sum()
denominator = (weights * (y_actual - _colmean(y_actual)) ** 2).sum()
if denominator == 0.0:
return 1. if numerator == 0. else 0. # 0/0 => 1, else 0
return 1 - numerator / denominator
|
Plots training set ( and validation set if available ) scoring history for an H2ORegressionModel. The timestep and metric arguments are restricted to what is available in its scoring history.
|
def plot(self, timestep="AUTO", metric="AUTO", **kwargs):
"""
Plots training set (and validation set if available) scoring history for an H2ORegressionModel. The timestep
and metric arguments are restricted to what is available in its scoring history.
:param timestep: A unit of measurement for the x-axis.
:param metric: A unit of measurement for the y-axis.
:returns: A scoring history plot.
"""
if self._model_json["algo"] in ("deeplearning", "deepwater", "xgboost", "drf", "gbm"):
if metric == "AUTO":
metric = "rmse"
elif metric not in ("rmse", "deviance", "mae"):
raise ValueError("metric for H2ORegressionModel must be one of: AUTO, rmse, deviance, or mae")
self._plot(timestep=timestep, metric=metric, **kwargs)
|
Assert that the argument has the specified type.
|
def assert_is_type(var, *types, **kwargs):
"""
Assert that the argument has the specified type.
This function is used to check that the type of the argument is correct, otherwises it raises an H2OTypeError.
See more details in the module's help.
:param var: variable to check
:param types: the expected types
:param kwargs:
message: override the error message
skip_frames: how many local frames to skip when printing out the error.
:raises H2OTypeError: if the argument is not of the desired type.
"""
assert types, "The list of expected types was not provided"
expected_type = types[0] if len(types) == 1 else U(*types)
if _check_type(var, expected_type): return
# Type check failed => Create a nice error message
assert set(kwargs).issubset({"message", "skip_frames"}), "Unexpected keyword arguments: %r" % kwargs
message = kwargs.get("message", None)
skip_frames = kwargs.get("skip_frames", 1)
args = _retrieve_assert_arguments()
vname = args[0]
etn = _get_type_name(expected_type, dump=", ".join(args[1:]))
vtn = _get_type_name(type(var))
raise H2OTypeError(var_name=vname, var_value=var, var_type_name=vtn, exp_type_name=etn, message=message,
skip_frames=skip_frames)
|
Assert that string variable matches the provided regular expression.
|
def assert_matches(v, regex):
"""
Assert that string variable matches the provided regular expression.
:param v: variable to check.
:param regex: regular expression to check against (can be either a string, or compiled regexp).
"""
m = re.match(regex, v)
if m is None:
vn = _retrieve_assert_arguments()[0]
message = "Argument `{var}` (= {val!r}) did not match /{regex}/".format(var=vn, regex=regex, val=v)
raise H2OValueError(message, var_name=vn, skip_frames=1)
return m
|
Assert that variable satisfies the provided condition.
|
def assert_satisfies(v, cond, message=None):
"""
Assert that variable satisfies the provided condition.
:param v: variable to check. Its value is only used for error reporting.
:param bool cond: condition that must be satisfied. Should be somehow related to the variable ``v``.
:param message: message string to use instead of the default.
"""
if not cond:
vname, vexpr = _retrieve_assert_arguments()
if not message:
message = "Argument `{var}` (= {val!r}) does not satisfy the condition {expr}" \
.format(var=vname, val=v, expr=vexpr)
raise H2OValueError(message=message, var_name=vname, skip_frames=1)
|
Magic variable name retrieval.
|
def _retrieve_assert_arguments():
"""
Magic variable name retrieval.
This function is designed as a helper for assert_is_type() function. Typically such assertion is used like this::
assert_is_type(num_threads, int)
If the variable `num_threads` turns out to be non-integer, we would like to raise an exception such as
H2OTypeError("`num_threads` is expected to be integer, but got <str>")
and in order to compose an error message like that, we need to know that the variables that was passed to
assert_is_type() carries a name "num_threads". Naturally, the variable itself knows nothing about that.
This is where this function comes in: we walk up the stack trace until the first frame outside of this
file, find the original line that called the assert_is_type() function, and extract the variable name from
that line. This is slightly fragile, in particular we assume that only one assert_is_type statement can be per line,
or that this statement does not spill over multiple lines, etc.
"""
try:
raise RuntimeError("Catch me!")
except RuntimeError:
# Walk up the stacktrace until we are outside of this file
tb = sys.exc_info()[2]
assert tb.tb_frame.f_code.co_name == "_retrieve_assert_arguments"
this_filename = tb.tb_frame.f_code.co_filename
fr = tb.tb_frame
while fr is not None and fr.f_code.co_filename == this_filename:
fr = fr.f_back
# Read the source file and tokenize it, extracting the expressions.
try:
with io.open(fr.f_code.co_filename, "r", encoding="utf-8") as f:
# Skip initial lines that are irrelevant
for i in range(fr.f_lineno - 1): next(f)
# Create tokenizer
g = tokenize.generate_tokens(f.readline)
step = 0
args_tokens = []
level = 0
for ttt in g:
if step == 0:
if ttt[0] != tokenize.NAME: continue
if not ttt[1].startswith("assert_"): continue
step = 1
elif step == 1:
assert ttt[0] == tokenize.OP and ttt[1] == "("
args_tokens.append([])
step = 2
elif step == 2:
if level == 0 and ttt[0] == tokenize.OP and ttt[1] == ",":
args_tokens.append([])
elif level == 0 and ttt[0] == tokenize.OP and ttt[1] == ")":
break
else:
if ttt[0] == tokenize.OP and ttt[1] in "([{": level += 1
if ttt[0] == tokenize.OP and ttt[1] in ")]}": level -= 1
assert level >= 0, "Parse error: parentheses level became negative"
args_tokens[-1].append(ttt)
args = [tokenize.untokenize(at).strip().replace("\n", " ") for at in args_tokens]
return args
except IOError:
return "arg",
|
Return True if the variable is of the specified type and False otherwise.
|
def _check_type(var, vtype):
"""
Return True if the variable is of the specified type, and False otherwise.
:param var: variable to check
:param vtype: expected variable's type
"""
if vtype is None:
return var is None
if isinstance(vtype, _primitive_type):
return var == vtype
if vtype is str:
return isinstance(var, _str_type)
if vtype is int:
return isinstance(var, _int_type)
if vtype is numeric:
return isinstance(var, _num_type)
if isinstance(vtype, MagicType):
return vtype.check(var)
if isinstance(vtype, type):
# ``vtype`` is a name of the class, or a built-in type such as "list", "tuple", etc
return isinstance(var, vtype)
if isinstance(vtype, list):
# ``vtype`` is a list literal
elem_type = U(*vtype)
return isinstance(var, list) and all(_check_type(item, elem_type) for item in var)
if isinstance(vtype, set):
# ``vtype`` is a set literal
elem_type = U(*vtype)
return isinstance(var, set) and all(_check_type(item, elem_type) for item in var)
if isinstance(vtype, tuple):
# ``vtype`` is a tuple literal
return (isinstance(var, tuple) and len(vtype) == len(var) and
all(_check_type(var[i], vtype[i]) for i in range(len(vtype))))
if isinstance(vtype, dict):
# ``vtype`` is a dict literal
ttkv = U(*viewitems(vtype))
return isinstance(var, dict) and all(_check_type(kv, ttkv) for kv in viewitems(var))
if isinstance(vtype, (FunctionType, BuiltinFunctionType)):
return vtype(var)
raise RuntimeError("Ivalid type %r in _check_type()" % vtype)
|
Return the name of the provided type.
|
def _get_type_name(vtype, dump=None):
"""
Return the name of the provided type.
_get_type_name(int) == "integer"
_get_type_name(str) == "string"
_get_type_name(tuple) == "tuple"
_get_type_name(Exception) == "Exception"
_get_type_name(U(int, float, bool)) == "integer|float|bool"
_get_type_name(U(H2OFrame, None)) == "?H2OFrame"
"""
if vtype is None:
return "None"
if vtype is str:
return "string"
if vtype is int:
return "integer"
if vtype is numeric:
return "numeric"
if is_type(vtype, str):
return '"%s"' % repr(vtype)[1:-1]
if is_type(vtype, int):
return str(vtype)
if isinstance(vtype, MagicType):
return vtype.name(dump)
if isinstance(vtype, type):
return vtype.__name__
if isinstance(vtype, list):
return "list(%s)" % _get_type_name(U(*vtype), dump)
if isinstance(vtype, set):
return "set(%s)" % _get_type_name(U(*vtype), dump)
if isinstance(vtype, tuple):
return "(%s)" % ", ".join(_get_type_name(item, dump) for item in vtype)
if isinstance(vtype, dict):
return "dict(%s)" % ", ".join("%s: %s" % (_get_type_name(tk, dump), _get_type_name(tv, dump))
for tk, tv in viewitems(vtype))
if isinstance(vtype, (FunctionType, BuiltinFunctionType)):
if vtype.__name__ == "<lambda>":
return _get_lambda_source_code(vtype, dump)
else:
return vtype.__name__
raise RuntimeError("Unexpected `vtype`: %r" % vtype)
|
Attempt to find the source code of the lambda_fn within the string src.
|
def _get_lambda_source_code(lambda_fn, src):
"""Attempt to find the source code of the ``lambda_fn`` within the string ``src``."""
def gen_lambdas():
def gen():
yield src + "\n"
g = gen()
step = 0
tokens = []
for tok in tokenize.generate_tokens(getattr(g, "next", getattr(g, "__next__", None))):
if step == 0:
if tok[0] == tokenize.NAME and tok[1] == "lambda":
step = 1
tokens = [tok]
level = 0
elif step == 1:
if tok[0] == tokenize.NAME:
tokens.append(tok)
step = 2
else:
step = 0
elif step == 2:
if tok[0] == tokenize.OP and tok[1] == ":":
tokens.append(tok)
step = 3
else:
step = 0
elif step == 3:
if level == 0 and (tok[0] == tokenize.OP and tok[1] in ",)" or tok[0] == tokenize.ENDMARKER):
yield tokenize.untokenize(tokens).strip()
step = 0
else:
tokens.append(tok)
if tok[0] == tokenize.OP:
if tok[1] in "[({": level += 1
if tok[1] in "])}": level -= 1
assert not tokens
actual_code = lambda_fn.__code__.co_code
for lambda_src in gen_lambdas():
try:
fn = eval(lambda_src, globals(), locals())
if fn.__code__.co_code == actual_code:
return lambda_src.split(":", 1)[1].strip()
except Exception:
pass
return "<lambda>"
|
Return string representing the name of this type.
|
def name(self, src=None):
"""Return string representing the name of this type."""
res = [_get_type_name(tt, src) for tt in self._types]
if len(res) == 2 and "None" in res:
res.remove("None")
return "?" + res[0]
else:
return " | ".join(res)
|
Return True if the variable matches this type and False otherwise.
|
def check(self, var):
"""Return True if the variable matches this type, and False otherwise."""
return all(_check_type(var, tt) for tt in self._types)
|
Return string representing the name of this type.
|
def name(self, src=None):
"""Return string representing the name of this type."""
return " & ".join(_get_type_name(tt, src) for tt in self._types)
|
Return True if the variable does not match any of the types and False otherwise.
|
def check(self, var):
"""Return True if the variable does not match any of the types, and False otherwise."""
return not any(_check_type(var, tt) for tt in self._types)
|
Return string representing the name of this type.
|
def name(self, src=None):
"""Return string representing the name of this type."""
if len(self._types) > 1:
return "!(%s)" % str("|".join(_get_type_name(tt, src) for tt in self._types))
else:
return "!" + _get_type_name(self._types[0], src)
|
Return True if the variable matches this type and False otherwise.
|
def check(self, var):
"""Return True if the variable matches this type, and False otherwise."""
return isinstance(var, tuple) and all(_check_type(t, self._element_type) for t in var)
|
Return True if the variable matches this type and False otherwise.
|
def check(self, var):
"""Return True if the variable matches this type, and False otherwise."""
if not isinstance(var, dict): return False
if any(key not in self._types for key in var): return False
for key, ktype in viewitems(self._types):
val = var.get(key, None)
if not _check_type(val, ktype):
return False
return True
|
Return string representing the name of this type.
|
def name(self, src=None):
"""Return string representing the name of this type."""
return "{%s}" % ", ".join("%s: %s" % (key, _get_type_name(ktype, src))
for key, ktype in viewitems(self._types))
|
Return True if the variable matches the specified type.
|
def check(self, var):
"""Return True if the variable matches the specified type."""
return (isinstance(var, _int_type) and
(self._lower_bound is None or var >= self._lower_bound) and
(self._upper_bound is None or var <= self._upper_bound))
|
Return string representing the name of this type.
|
def name(self, src=None):
"""Return string representing the name of this type."""
if self._upper_bound is None and self._lower_bound is None: return "int"
if self._upper_bound is None:
if self._lower_bound == 1: return "int>0"
return "int≥%d" % self._lower_bound
if self._lower_bound is None:
return "int≤%d" % self._upper_bound
return "int[%d…%d]" % (self._lower_bound, self._upper_bound)
|
Return True if the variable matches the specified type.
|
def check(self, var):
"""Return True if the variable matches the specified type."""
return (isinstance(var, _num_type) and
(self._lower_bound is None or var >= self._lower_bound) and
(self._upper_bound is None or var <= self._upper_bound))
|
Return True if the variable matches this type and False otherwise.
|
def check(self, var):
"""Return True if the variable matches this type, and False otherwise."""
if self._class is None: self._init()
return self._class and self._checker(var, self._class)
|
Check whether the provided value is a valid enum constant.
|
def check(self, var):
"""Check whether the provided value is a valid enum constant."""
if not isinstance(var, _str_type): return False
return _enum_mangle(var) in self._consts
|
Retrieve the config as a dictionary of key - value pairs.
|
def get_config():
"""Retrieve the config as a dictionary of key-value pairs."""
self = H2OConfigReader._get_instance()
if not self._config_loaded:
self._read_config()
return self._config
|
Find and parse config file storing all variables in self. _config.
|
def _read_config(self):
"""Find and parse config file, storing all variables in ``self._config``."""
self._config_loaded = True
conf = []
for f in self._candidate_log_files():
if os.path.isfile(f):
self._logger.info("Reading config file %s" % f)
section_rx = re.compile(r"^\[(\w+)\]$")
keyvalue_rx = re.compile(r"^(\w+:)?([\w.]+)\s*=(.*)$")
with io.open(f, "rt", encoding="utf-8") as config_file:
section_name = None
for lineno, line in enumerate(config_file):
line = line.strip()
if line == "" or line.startswith("#"): continue
m1 = section_rx.match(line)
if m1:
section_name = m1.group(1)
continue
m2 = keyvalue_rx.match(line)
if m2:
lng = m2.group(1)
key = m2.group(2)
val = m2.group(3).strip()
if lng and lng.lower() != "py:": continue
if section_name:
key = section_name + "." + key
if key in H2OConfigReader._allowed_config_keys:
conf.append((key, val))
else:
self._logger.error("Key %s is not a valid config key" % key)
continue
self._logger.error("Syntax error in config file line %d: %s" % (lineno, line))
self._config = dict(conf)
return
|
Return possible locations for the. h2oconfig file one at a time.
|
def _candidate_log_files():
"""Return possible locations for the .h2oconfig file, one at a time."""
# Search for .h2oconfig in the current directory and all parent directories
relpath = ".h2oconfig"
prevpath = None
while True:
abspath = os.path.abspath(relpath)
if abspath == prevpath: break
prevpath = abspath
relpath = "../" + relpath
yield abspath
# Also check if .h2oconfig exists in the user's directory
yield os.path.expanduser("~/.h2oconfig")
|
Start the progress bar and return only when the progress reaches 100%.
|
def execute(self, progress_fn, print_verbose_info=None):
"""
Start the progress bar, and return only when the progress reaches 100%.
:param progress_fn: the executor function (or a generator). This function should take no arguments
and return either a single number -- the current progress level, or a tuple (progress level, delay),
where delay is the time interval for when the progress should be checked again. This function may at
any point raise the ``StopIteration(message)`` exception, which will interrupt the progress bar,
display the ``message`` in red font, and then re-raise the exception.
:raises StopIteration: if the job is interrupted. The reason for interruption is provided in the exception's
message. The message will say "cancelled" if the job was interrupted by the user by pressing Ctrl+C.
"""
assert_is_type(progress_fn, FunctionType, GeneratorType, MethodType)
if isinstance(progress_fn, GeneratorType):
# Convert generator to a regular function
progress_fn = (lambda g: lambda: next(g))(progress_fn)
# Initialize the execution context
self._next_poll_time = 0
self._t0 = time.time()
self._x0 = 0
self._v0 = 0.01 # corresponds to 100s completion time
self._ve = 0.01
progress = 0
status = None # Status message in case the job gets interrupted.
try:
while True:
# We attempt to synchronize all helper functions, ensuring that each of them has the same idea
# for what the current time moment is. Otherwise we could have some corner cases when one method
# says that something must happen right now, while the other already sees that moment in the past.
now = time.time()
# Query the progress level, but only if it's time already
if self._next_poll_time <= now:
res = progress_fn() # may raise StopIteration
assert_is_type(res, (numeric, numeric), numeric)
if not isinstance(res, tuple):
res = (res, -1)
# Progress querying could have taken some time, so update the current time moment
now = time.time()
self._store_model_progress(res, now)
self._recalculate_model_parameters(now)
# Render the widget regardless of whether it's too early or not
progress = min(self._compute_progress_at_time(now)[0], 1)
if progress == 1 and self._get_real_progress() >= 1:
# Do not exit until both the model and the actual progress reach 100% mark.
break
result = self._widget.render(progress)
assert_is_type(result, RenderResult)
time0 = result.next_time
time1 = self._get_time_at_progress(result.next_progress)
next_render_time = min(time0, time1)
self._draw(result.rendered)
# Wait until the next rendering/querying cycle
wait_time = min(next_render_time, self._next_poll_time) - now
if wait_time > 0:
time.sleep(wait_time)
if print_verbose_info is not None:
print_verbose_info(progress)
except KeyboardInterrupt:
# If the user presses Ctrl+C, we interrupt the progress bar.
status = "cancelled"
except StopIteration as e:
# If the generator raises StopIteration before reaching 100%, then the progress display will
# reamin incomplete.
status = str(e)
# Do one final rendering before we exit
result = self._widget.render(progress=progress, status=status)
self._draw(result.rendered, final=True)
if status == "cancelled":
# Re-raise the exception, to inform the upstream caller that something unexpected happened.
raise StopIteration(status)
|
Save the current model progress into self. _progress_data and update self. _next_poll_time.
|
def _store_model_progress(self, res, now):
"""
Save the current model progress into ``self._progress_data``, and update ``self._next_poll_time``.
:param res: tuple (progress level, poll delay).
:param now: current timestamp.
"""
raw_progress, delay = res
raw_progress = clamp(raw_progress, 0, self._maxval)
self._progress_data.append((now, raw_progress))
if delay < 0:
# calculation of ``_guess_next_poll_interval()`` should be done only *after* we pushed the fresh data to
# ``self._progress_data``.
delay = self._guess_next_poll_interval()
self._next_poll_time = now + clamp(delay, self.MIN_PROGRESS_CHECK_INTERVAL, self.MAX_PROGRESS_CHECK_INTERVAL)
|
Compute t0 x0 v0 ve.
|
def _recalculate_model_parameters(self, now):
"""Compute t0, x0, v0, ve."""
time_until_end = self._estimate_progress_completion_time(now) - now
assert time_until_end >= 0, "Estimated progress completion cannot be in the past."
x_real = self._get_real_progress()
if x_real == 1:
t0, x0, v0, ve = now, 1, 0, 0
else:
x0, v0 = self._compute_progress_at_time(now)
t0 = now
if x0 >= 1:
# On rare occasion, the model's progress may have reached 100% by ``now``. This can happen if
# (1) the progress is close to 100% initially and has high speed, (2) on the previous call we
# estimated that the process completion time will be right after the next poll time, and (3)
# the polling itself took so much time that the process effectively "overshoot".
# If this happens, then we adjust x0, v0 to the previous valid data checkpoint.
t0, x0, v0 = self._t0, self._x0, self._v0
time_until_end += now - t0
z = self.BETA * time_until_end
max_speed = (1 - x_real**2) / self.FINISH_DELAY
ve = v0 + (self.BETA * (1 - x0) - v0 * z) / (z - 1 + math.exp(-z))
if ve < 0:
# Current speed is too high -- reduce v0 (violate non-smoothness of speed)
v0 = self.BETA * (1 - x0) / (1 - math.exp(-z))
ve = 0
if ve > max_speed:
# Current speed is too low: finish later, but do not allow ``ve`` to be higher than ``max_speed``
ve = max_speed
self._t0, self._x0, self._v0, self._ve = t0, x0, v0, ve
|
Estimate the moment when the underlying process is expected to reach completion.
|
def _estimate_progress_completion_time(self, now):
"""
Estimate the moment when the underlying process is expected to reach completion.
This function should only return future times. Also this function is not allowed to return time moments less
than self._next_poll_time if the actual progress is below 100% (this is because we won't know that the
process have finished until we poll the external progress function).
"""
assert self._next_poll_time >= now
tlast, wlast = self._progress_data[-1]
# If reached 100%, make sure that we finish as soon as possible, but maybe not immediately
if wlast == self._maxval:
current_completion_time = (1 - self._x0) / self._v0 + self._t0
return clamp(current_completion_time, now, now + self.FINISH_DELAY)
# Calculate the approximate speed of the raw progress based on recent data
tacc, wacc = 0, 0
factor = self.GAMMA
for t, x in self._progress_data[-2::-1]:
tacc += factor * (tlast - t)
wacc += factor * (wlast - x)
factor *= self.GAMMA
if factor < 1e-2: break
# If there was no progress at all, then just assume it's 5 minutes from now
if wacc == 0: return now + 300
# Estimate the completion time assuming linear progress
t_estimate = tlast + tacc * (self._maxval - wlast) / wacc
# Adjust the estimate if it looks like it may happen too soon
if t_estimate <= self._next_poll_time:
t_estimate = self._next_poll_time + self.FINISH_DELAY
return t_estimate
|
Determine when to query the progress status next.
|
def _guess_next_poll_interval(self):
"""
Determine when to query the progress status next.
This function is used if the external progress function did not return time interval for when it should be
queried next.
"""
time_elapsed = self._progress_data[-1][0] - self._progress_data[0][0]
real_progress = self._get_real_progress()
return min(0.2 * time_elapsed, 0.5 + (1 - real_progress)**0.5)
|
Calculate the modelled progress state for the given time moment.
|
def _compute_progress_at_time(self, t):
"""
Calculate the modelled progress state for the given time moment.
:returns: tuple (x, v) of the progress level and progress speed.
"""
t0, x0, v0, ve = self._t0, self._x0, self._v0, self._ve
z = (v0 - ve) * math.exp(-self.BETA * (t - t0))
vt = ve + z
xt = clamp(x0 + ve * (t - t0) + (v0 - ve - z) / self.BETA, 0, 1)
return xt, vt
|
Return the projected time when progress level x_target will be reached.
|
def _get_time_at_progress(self, x_target):
"""
Return the projected time when progress level `x_target` will be reached.
Since the underlying progress model is nonlinear, we need to do use Newton method to find a numerical solution
to the equation x(t) = x_target.
"""
t, x, v = self._t0, self._x0, self._v0
# The convergence should be achieved in just few iterations, however in unlikely situation that it doesn't
# we don't want to loop forever...
for _ in range(20):
if v == 0: return 1e20
# make time prediction assuming the progress will continue at a linear speed ``v``
t += (x_target - x) / v
# calculate the actual progress at that time
x, v = self._compute_progress_at_time(t)
# iterate until convergence
if abs(x - x_target) < 1e-3: return t
return time.time() + 100
|
Print the rendered string to the stdout.
|
def _draw(self, txt, final=False):
"""Print the rendered string to the stdout."""
if not self._file_mode:
# If the user presses Ctrl+C this ensures we still start writing from the beginning of the line
sys.stdout.write("\r")
sys.stdout.write(txt)
if final and not isinstance(self._widget, _HiddenWidget):
sys.stdout.write("\n")
else:
if not self._file_mode:
sys.stdout.write("\r")
sys.stdout.flush()
|
Render the widget.
|
def render(self, progress, width=None, status=None):
"""Render the widget."""
results = [widget.render(progress, width=self._widget_lengths[i], status=status)
for i, widget in enumerate(self._widgets)]
if self._file_mode:
res = ""
for i, result in enumerate(results):
res += result.rendered
if result.length < self._widget_lengths[i] and progress < 1: break
res += " " if i < len(results) - 1 else ""
rendered_str = res[len(self._rendered):]
self._rendered = res
else:
rendered_str = " ".join(r.rendered for r in results)
if self._to_render:
rendered_str = self._to_render + rendered_str
self._to_render = None
next_progress = min(r.next_progress for r in results)
next_time = min(r.next_time for r in results)
return RenderResult(rendered_str, next_progress=next_progress, next_time=next_time)
|
Initial rendering stage done in order to compute widths of all widgets.
|
def _compute_widget_sizes(self):
"""Initial rendering stage, done in order to compute widths of all widgets."""
wl = [0] * len(self._widgets)
flex_count = 0
# First render all non-flexible widgets
for i, widget in enumerate(self._widgets):
if isinstance(widget, ProgressBarFlexibleWidget):
flex_count += 1
else:
wl[i] = widget.render(1).length
remaining_width = self._width - sum(wl)
remaining_width -= len(self._widgets) - 1 # account for 1-space interval between widgets
if remaining_width < 10 * flex_count:
if self._file_mode:
remaining_width = 10 * flex_count
else:
# The window is too small to accomodate the widget: try to split it into several lines, otherwise
# switch to "file mode". If we don't do this, then rendering the widget will cause it to wrap, and
# then when we use \r to go to the beginning of the line, only part of the widget will be overwritten,
# which means we'll have many (possibly hundreds) of progress bar lines in the end.
widget0 = self._widgets[0]
if isinstance(widget0, PBWString) and remaining_width + widget0.render(0).length >= 10 * flex_count:
remaining_width += widget0.render(0).length + 1
self._to_render = widget0.render(0).rendered + "\n"
self._widgets = self._widgets[1:]
if remaining_width < 10 * flex_count:
self._file_mode = True
remaining_width = 10 * flex_count
remaining_width = max(remaining_width, 10 * flex_count) # Ensure at least 10 chars per flexible widget
for i, widget in enumerate(self._widgets):
if isinstance(widget, ProgressBarFlexibleWidget):
target_length = int(remaining_width / flex_count)
result = widget.render(1, target_length)
wl[i] = result.length
remaining_width -= result.length
flex_count -= 1
return wl
|
Find current STDOUT s width in characters.
|
def _get_terminal_size():
"""Find current STDOUT's width, in characters."""
# If output is not terminal but a regular file, assume 100 chars width
if not sys.stdout.isatty():
return 80
# Otherwise, first try getting the dimensions from shell command `stty`:
try:
import subprocess
ret = subprocess.check_output(["stty", "size"]).strip().split(" ")
if len(ret) == 2:
return int(ret[1])
except:
pass
# Otherwise try using ioctl
try:
from termios import TIOCGWINSZ
from fcntl import ioctl
from struct import unpack
res = unpack("hh", ioctl(sys.stdout, TIOCGWINSZ, b"1234"))
return int(res[1])
except:
pass
# Finally check the COLUMNS environment variable
return int(os.environ.get("COLUMNS", 80))
|
Render the widget.
|
def render(self, progress, width=None, status=None):
"""Render the widget."""
if width <= 3: return RenderResult()
bar_width = width - 2 # Total width minus the bar ends
n_chars = int(progress * bar_width + 0.001)
endf, endl = self._bar_ends
if self._file_mode:
out = endf
out += self._bar_symbols[-1] * n_chars
out += endl if progress == 1 else ""
if status:
out += " (%s)" % status
next_progress = (n_chars + 1) / bar_width
rendered_len = len(out)
else:
frac_chars = int((progress * bar_width - n_chars) * len(self._bar_symbols))
out = endf
out += self._bar_symbols[-1] * n_chars
out += self._bar_symbols[frac_chars - 1] if frac_chars > 0 else ""
rendered_len = len(out)
if status:
out += colorama.Fore.RED + " (" + status + ")" + colorama.Style.RESET_ALL
rendered_len += 3 + len(status)
out += " " * (width - 1 - rendered_len)
out += endl
next_progress = (n_chars + (frac_chars + 1) / len(self._bar_symbols)) / bar_width
rendered_len += max(0, width - 1 - rendered_len) + 1
return RenderResult(rendered=out, length=rendered_len, next_progress=next_progress)
|
Inform the widget about the encoding of the underlying character stream.
|
def set_encoding(self, encoding):
"""Inform the widget about the encoding of the underlying character stream."""
self._bar_ends = "[]"
self._bar_symbols = "#"
if not encoding: return
s1 = "\u258F\u258E\u258D\u258C\u258B\u258A\u2589\u2588"
s2 = "\u258C\u2588"
s3 = "\u2588"
if self._file_mode:
s1 = s2 = None
assert len(s3) == 1
for s in (s1, s2, s3):
if s is None: continue
try:
s.encode(encoding)
self._bar_ends = "||"
self._bar_symbols = s
return
except UnicodeEncodeError:
pass
except LookupError:
print("Warning: unknown encoding %s" % encoding)
|
Render the widget.
|
def render(self, progress, width=None, status=None):
"""Render the widget."""
current_pct = int(progress * 100 + 0.1)
return RenderResult(rendered="%3d%%" % current_pct, next_progress=(current_pct + 1) / 100)
|
This function will generate an H2OFrame of two columns. One column will be float and the other will be long.: param sizeMat: integer denoting size of bounds: param lowBound: lower bound: param uppderBound:: param trueRandom:: param numRep: number of times to repeat arrays in order to generate duplicated rows: param numZeros:: param numNans:: param numInfs:: return:
|
def genDataFrame(sizeMat, lowBound, uppderBound, numRep, numZeros, numNans, numInfs):
'''
This function will generate an H2OFrame of two columns. One column will be float and the other will
be long.
:param sizeMat: integer denoting size of bounds
:param lowBound: lower bound
:param uppderBound:
:param trueRandom:
:param numRep: number of times to repeat arrays in order to generate duplicated rows
:param numZeros:
:param numNans:
:param numInfs:
:return:
'''
if (numNans > 0):
floatA = [float('NaN')]*numNans
intA = [float('NaN')]*numNans
if (numInfs > 0):
floatA.extend([float('inf')]*numInfs)
intA.extend([float('inf')]*numInfs)
floatA.extend([-1.0*float('inf')]*numInfs)
intA.extend([-1*float('inf')]*numInfs)
# first generate the zeros into floatA and intA. Multiply them with +/-1 to mess them up a little
for index in range(numZeros):
floatA.append(0.0*randint(-1,1))
intA.append(0*randint(-1,1))
# next generate +/- integers or various magnitude
for rad in sizeMat:
tempInt = pow(2,rad)
tempIntN = pow(2,rad+1)
intA.append(tempInt)
intA.append(-1*tempInt)
randInt = randint(tempInt, tempIntN) # randomly generated integer, add both +/- values
intA.append(randInt)
intA.append(-1*randInt)
intA.append(randint(tempInt, tempIntN)) # randomly generated integer
intA.append(-1*(randint(tempInt, tempIntN))) # randomly generated negative integer
floatA.append(tempInt*1.0)
floatA.append(-1.0*tempInt)
tempD = uniform(tempInt, tempIntN)
floatA.append(tempD)
floatA.append(-1.0*tempD)
floatA.append(uniform(tempInt, tempIntN))
floatA.append(-1.0*uniform(tempInt, tempIntN))
|
Returns encoding map as an object that maps column_name - > frame_with_encoding_map_for_this_column_name
|
def fit(self, frame = None):
"""
Returns encoding map as an object that maps 'column_name' -> 'frame_with_encoding_map_for_this_column_name'
:param frame frame: An H2OFrame object with which to create the target encoding map
"""
self._teColumns = list(map(lambda i: frame.names[i], self._teColumns)) if all(isinstance(n, int) for n in self._teColumns) else self._teColumns
self._responseColumnName = frame.names[self._responseColumnName] if isinstance(self._responseColumnName, int) else self._responseColumnName
self._foldColumnName = frame.names[self._foldColumnName] if isinstance(self._foldColumnName, int) else self._foldColumnName
self._encodingMap = ExprNode("target.encoder.fit", frame, self._teColumns, self._responseColumnName,
self._foldColumnName)._eager_map_frame()
return self._encodingMap
|
Apply transformation to te_columns based on the encoding maps generated during TargetEncoder. fit () call. You must not pass encodings manually from. fit () method because they are being stored internally after. fit () had been called.
|
def transform(self, frame=None, holdout_type=None, noise=-1, seed=-1):
"""
Apply transformation to `te_columns` based on the encoding maps generated during `TargetEncoder.fit()` call.
You must not pass encodings manually from `.fit()` method because they are being stored internally
after `.fit()' had been called.
:param frame frame: to which frame we are applying target encoding transformations.
:param str holdout_type: Supported options:
1) "kfold" - encodings for a fold are generated based on out-of-fold data.
2) "loo" - leave one out. Current row's response value is subtracted from the pre-calculated per-level frequencies.
3) "none" - we do not holdout anything. Using whole frame for training
:param float noise: the amount of random noise added to the target encoding. This helps prevent overfitting. Defaults to 0.01 * range of y.
:param int seed: a random seed used to generate draws from the uniform distribution for random noise. Defaults to -1.
"""
assert_is_type(holdout_type, "kfold", "loo", "none")
# We need to make sure that frames are being sent in the same order
assert self._encodingMap.map_keys['string'] == self._teColumns
encodingMapKeys = self._encodingMap.map_keys['string']
encodingMapFramesKeys = list(map(lambda x: x['key']['name'], self._encodingMap.frames))
return H2OFrame._expr(expr=ExprNode("target.encoder.transform", encodingMapKeys, encodingMapFramesKeys, frame, self._teColumns, holdout_type,
self._responseColumnName, self._foldColumnName,
self._blending, self._inflectionPoint, self._smoothing,
noise, seed))
|
For an H2O Enum column we perform one - hot - encoding here and add one more column missing ( NA ) to it.
|
def generatePandaEnumCols(pandaFtrain, cname, nrows, domainL):
"""
For an H2O Enum column, we perform one-hot-encoding here and add one more column, "missing(NA)" to it.
:param pandaFtrain: panda frame derived from H2OFrame
:param cname: column name of enum col
:param nrows: number of rows of enum col
:return: panda frame with enum col encoded correctly for native XGBoost
"""
import numpy as np
import pandas as pd
cmissingNames=[cname+".missing(NA)"]
tempnp = np.zeros((nrows,1), dtype=np.int)
# check for nan and assign it correct value
colVals = pandaFtrain[cname]
for ind in range(nrows):
try:
if not(colVals[ind] in domainL):
tempnp[ind]=1
except ValueError:
pass
zeroFrame = pd.DataFrame(tempnp)
zeroFrame.columns=cmissingNames
temp = pd.get_dummies(pandaFtrain[cname], prefix=cname, drop_first=False)
tempNames = list(temp) # get column names
colLength = len(tempNames)
newNames = ['a']*colLength
for ind in range(0,colLength):
newNames[ind]=cname+"_"+domainL[ind]
ftemp = temp[newNames]
ctemp = pd.concat([ftemp, zeroFrame], axis=1)
return ctemp
|
Retrieve an existing H2OFrame from the H2O cluster using the frame s id.
|
def get_frame(frame_id, rows=10, rows_offset=0, cols=-1, full_cols=-1, cols_offset=0, light=False):
"""
Retrieve an existing H2OFrame from the H2O cluster using the frame's id.
:param str frame_id: id of the frame to retrieve
:param int rows: number of rows to fetch for preview (10 by default)
:param int rows_offset: offset to fetch rows from (0 by default)
:param int cols: number of columns to fetch (all by default)
:param full_cols: number of columns to fetch together with backed data
:param int cols_offset: offset to fetch rows from (0 by default)
:param bool light: wether to use light frame endpoint or not
:returns: an existing H2OFrame with the id provided; or None if such frame doesn't exist.
"""
fr = H2OFrame()
fr._ex._cache._id = frame_id
try:
fr._ex._cache.fill(rows=rows, rows_offset=rows_offset, cols=cols, full_cols=full_cols, cols_offset=cols_offset, light=light)
except EnvironmentError:
return None
return fr
|
Reload frame information from the backend H2O server.
|
def refresh(self):
"""Reload frame information from the backend H2O server."""
self._ex._cache.flush()
self._frame(fill_cache=True)
|
The list of column names ( List [ str ] ).
|
def names(self):
"""The list of column names (List[str])."""
if not self._ex._cache.names_valid():
self._ex._cache.flush()
self._frame(fill_cache=True)
return list(self._ex._cache.names)
|
Number of rows in the dataframe ( int ).
|
def nrows(self):
"""Number of rows in the dataframe (int)."""
if not self._ex._cache.nrows_valid():
self._ex._cache.flush()
self._frame(fill_cache=True)
return self._ex._cache.nrows
|
Number of columns in the dataframe ( int ).
|
def ncols(self):
"""Number of columns in the dataframe (int)."""
if not self._ex._cache.ncols_valid():
self._ex._cache.flush()
self._frame(fill_cache=True)
return self._ex._cache.ncols
|
The dictionary of column name/ type pairs.
|
def types(self):
"""The dictionary of column name/type pairs."""
if not self._ex._cache.types_valid():
self._ex._cache.flush()
self._frame(fill_cache=True)
return dict(self._ex._cache.types)
|
The type for the given column.
|
def type(self, col):
"""
The type for the given column.
:param col: either a name, or an index of the column to look up
:returns: type of the column, one of: ``str``, ``int``, ``real``, ``enum``, ``time``, ``bool``.
:raises H2OValueError: if such column does not exist in the frame.
"""
assert_is_type(col, int, str)
if not self._ex._cache.types_valid() or not self._ex._cache.names_valid():
self._ex._cache.flush()
self._frame(fill_cache=True)
types = self._ex._cache.types
if is_type(col, str):
if col in types:
return types[col]
else:
names = self._ex._cache.names
if -len(names) <= col < len(names):
return types[names[col]]
raise H2OValueError("Column '%r' does not exist in the frame" % col)
|
Extract columns of the specified type from the frame.
|
def columns_by_type(self, coltype="numeric"):
"""
Extract columns of the specified type from the frame.
:param str coltype: A character string indicating which column type to filter by. This must be
one of the following:
- ``"numeric"`` - Numeric, but not categorical or time
- ``"categorical"`` - Integer, with a categorical/factor String mapping
- ``"string"`` - String column
- ``"time"`` - Long msec since the Unix Epoch - with a variety of display/parse options
- ``"uuid"`` - UUID
- ``"bad"`` - No none-NA rows (triple negative! all NAs or zero rows)
:returns: list of indices of columns that have the requested type
"""
assert_is_type(coltype, "numeric", "categorical", "string", "time", "uuid", "bad")
assert_is_type(self, H2OFrame)
return ExprNode("columnsByType", self, coltype)._eager_scalar()
|
Used by the H2OFrame. __repr__ method to print or display a snippet of the data frame.
|
def show(self, use_pandas=False, rows=10, cols=200):
"""
Used by the H2OFrame.__repr__ method to print or display a snippet of the data frame.
If called from IPython, displays an html'ized result. Else prints a tabulate'd result.
"""
if self._ex is None:
print("This H2OFrame has been removed.")
return
if not self._has_content():
print("This H2OFrame is empty and not initialized.")
return
if self.nrows == 0:
print("This H2OFrame is empty.")
return
if not self._ex._cache.is_valid(): self._frame()._ex._cache.fill()
if H2ODisplay._in_ipy():
import IPython.display
if use_pandas and can_use_pandas():
IPython.display.display(self.head(rows=rows, cols=cols).as_data_frame(fill_cache=True))
else:
IPython.display.display_html(self._ex._cache._tabulate("html", False), raw=True)
else:
if use_pandas and can_use_pandas():
print(self.head(rows=rows, cols=cols).as_data_frame(True)) # no keyword fill_cache
else:
s = self.__unicode__()
stk = traceback.extract_stack()
if "IPython" in stk[-3][0]:
s = "\n%s" % s
try:
print(s)
except UnicodeEncodeError:
print(s.encode("ascii", "replace"))
|
Display summary information about the frame.
|
def summary(self, return_data=False):
"""
Display summary information about the frame.
Summary includes min/mean/max/sigma and other rollup data.
:param bool return_data: Return a dictionary of the summary output
"""
if not self._has_content():
print("This H2OFrame is empty and not initialized.")
return self._ex._cache._data;
if not self._ex._cache.is_valid(): self._frame()._ex._cache.fill()
if not return_data:
if self.nrows == 0:
print("This H2OFrame is empty.")
elif H2ODisplay._in_ipy():
import IPython.display
IPython.display.display_html(self._ex._cache._tabulate("html", True), raw=True)
else:
print(self._ex._cache._tabulate("simple", True))
else:
return self._ex._cache._data
|
Generate an in - depth description of this H2OFrame.
|
def describe(self, chunk_summary=False):
"""
Generate an in-depth description of this H2OFrame.
This will print to the console the dimensions of the frame; names/types/summary statistics for each column;
and finally first ten rows of the frame.
:param bool chunk_summary: Retrieve the chunk summary along with the distribution summary
"""
if self._has_content():
res = h2o.api("GET /3/Frames/%s" % self.frame_id, data={"row_count": 10})["frames"][0]
self._ex._cache._fill_data(res)
print("Rows:{}".format(self.nrow))
print("Cols:{}".format(self.ncol))
#The chunk & distribution summaries are not cached, so must be pulled if chunk_summary=True.
if chunk_summary:
res["chunk_summary"].show()
res["distribution_summary"].show()
print("\n")
self.summary()
|
Return the first rows and cols of the frame as a new H2OFrame.
|
def head(self, rows=10, cols=200):
"""
Return the first ``rows`` and ``cols`` of the frame as a new H2OFrame.
:param int rows: maximum number of rows to return
:param int cols: maximum number of columns to return
:returns: a new H2OFrame cut from the top left corner of the current frame, and having dimensions at
most ``rows`` x ``cols``.
"""
assert_is_type(rows, int)
assert_is_type(cols, int)
nrows = min(self.nrows, rows)
ncols = min(self.ncols, cols)
newdt = self[:nrows, :ncols]
return newdt._frame(rows=nrows, cols=cols, fill_cache=True)
|
Multiply this frame viewed as a matrix by another matrix.
|
def mult(self, matrix):
"""
Multiply this frame, viewed as a matrix, by another matrix.
:param matrix: another frame that you want to multiply the current frame by; must be compatible with the
current frame (i.e. its number of rows must be the same as number of columns in the current frame).
:returns: new H2OFrame, which is the result of multiplying the current frame by ``matrix``.
"""
if self.ncols != matrix.nrows:
raise H2OValueError("Matrix is not compatible for multiplication with the current frame")
return H2OFrame._expr(expr=ExprNode("x", self, matrix))
|
Create a time column from individual components.
|
def moment(year=None, month=None, day=None, hour=None, minute=None, second=None, msec=None, date=None, time=None):
"""
Create a time column from individual components.
Each parameter should be either an integer, or a single-column H2OFrame
containing the corresponding time parts for each row.
The "date" part of the timestamp can be specified using either the tuple ``(year, month, day)``, or an
explicit ``date`` parameter. The "time" part of the timestamp is optional, but can be specified either via
the ``time`` parameter, or via the ``(hour, minute, second, msec)`` tuple.
:param year: the year part of the constructed date
:param month: the month part of the constructed date
:param day: the day-of-the-month part of the constructed date
:param hour: the hours part of the constructed date
:param minute: the minutes part of the constructed date
:param second: the seconds part of the constructed date
:param msec: the milliseconds part of the constructed date
:param date date: construct the timestamp from the Python's native ``datetime.date`` (or ``datetime.datetime``)
object. If the object passed is of type ``date``, then you can specify the time part using either the
``time`` argument, or ``hour`` ... ``msec`` arguments (but not both). If the object passed is of type
``datetime``, then no other arguments can be provided.
:param time time: construct the timestamp from this Python's native ``datetime.time`` object. This argument
cannot be used alone, it should be supplemented with either ``date`` argument, or ``year`` ... ``day``
tuple.
:returns: H2OFrame with one column containing the date constructed from the provided arguments.
"""
assert_is_type(date, None, datetime.date, numpy_datetime, pandas_timestamp)
assert_is_type(time, None, datetime.time)
assert_is_type(year, None, int, H2OFrame)
assert_is_type(month, None, int, H2OFrame)
assert_is_type(day, None, int, H2OFrame)
assert_is_type(hour, None, int, H2OFrame)
assert_is_type(minute, None, int, H2OFrame)
assert_is_type(second, None, int, H2OFrame)
assert_is_type(msec, None, int, H2OFrame)
if time is not None:
if hour is not None or minute is not None or second is not None or msec is not None:
raise H2OValueError("Arguments hour, minute, second, msec cannot be used together with `time`.")
hour = time.hour
minute = time.minute
second = time.second
msec = time.microsecond // 1000
if date is not None:
if is_type(date, pandas_timestamp):
date = date.to_pydatetime()
if is_type(date, numpy_datetime):
date = date.astype("M8[ms]").astype("O")
if year is not None or month is not None or day is not None:
raise H2OValueError("Arguments year, month and day cannot be used together with `date`.")
year = date.year
month = date.month
day = date.day
if isinstance(date, datetime.datetime):
if time is not None:
raise H2OValueError("Argument `time` cannot be used together with `date` of datetime type.")
if hour is not None or minute is not None or second is not None or msec is not None:
raise H2OValueError("Arguments hour, minute, second, msec cannot be used together with `date` "
"of datetime type.")
hour = date.hour
minute = date.minute
second = date.second
msec = date.microsecond // 1000
if year is None or month is None or day is None:
raise H2OValueError("Either arguments (`year`, `month` and `day`) or the `date` are required.")
if hour is None: hour = 0
if minute is None: minute = 0
if second is None: second = 0
if msec is None: msec = 0
local_vars = locals()
res_nrows = None
for n in ["year", "month", "day", "hour", "minute", "second", "msec"]:
x = local_vars[n]
if isinstance(x, H2OFrame):
if x.ncols != 1:
raise H2OValueError("Argument `%s` is a frame with more than 1 column" % n)
if x.type(0) not in {"int", "real"}:
raise H2OValueError("Column `%s` is not numeric (type = %s)" % (n, x.type(0)))
if res_nrows is None:
res_nrows = x.nrows
if x.nrows == 0 or x.nrows != res_nrows:
raise H2OValueError("Incompatible column `%s` having %d rows" % (n, x.nrows))
if res_nrows is None:
res_nrows = 1
res = H2OFrame._expr(ExprNode("moment", year, month, day, hour, minute, second, msec))
res._ex._cache._names = ["name"]
res._ex._cache._types = {"name": "time"}
res._ex._cache._nrows = res_nrows
res._ex._cache._ncols = 1
return res
|
Get the factor levels.
|
def levels(self):
"""
Get the factor levels.
:returns: A list of lists, one list per column, of levels.
"""
lol = H2OFrame._expr(expr=ExprNode("levels", self)).as_data_frame(False)
lol.pop(0) # Remove column headers
lol = list(zip(*lol))
return [[ll for ll in l if ll != ''] for l in lol]
|
Get the number of factor levels for each categorical column.
|
def nlevels(self):
"""
Get the number of factor levels for each categorical column.
:returns: A list of the number of levels per column.
"""
levels = self.levels()
return [len(l) for l in levels] if levels else 0
|
A method to set all column values to one of the levels.
|
def set_level(self, level):
"""
A method to set all column values to one of the levels.
:param str level: The level at which the column will be set (a string)
:returns: H2OFrame with entries set to the desired level.
"""
return H2OFrame._expr(expr=ExprNode("setLevel", self, level), cache=self._ex._cache)
|
Replace the levels of a categorical column.
|
def set_levels(self, levels):
"""
Replace the levels of a categorical column.
New levels must be aligned with the old domain. This call has copy-on-write semantics.
:param List[str] levels: A list of strings specifying the new levels. The number of new
levels must match the number of old levels.
:returns: A single-column H2OFrame with the desired levels.
"""
assert_is_type(levels, [str])
return H2OFrame._expr(expr=ExprNode("setDomain", self, False, levels), cache=self._ex._cache)
|
Change names of columns in the frame.
|
def rename(self, columns=None):
"""
Change names of columns in the frame.
Dict key is an index or name of the column whose name is to be set.
Dict value is the new name of the column.
:param columns: dict-like transformations to apply to the column names
"""
assert_is_type(columns, None, dict)
new_names = self.names
ncols = self.ncols
for col, name in columns.items():
col_index = None
if is_type(col, int) and (-ncols <= col < ncols):
col_index = (col + ncols) % ncols # handle negative indices
elif is_type(col, str) and col in self.names:
col_index = self.names.index(col) # lookup the name
if col_index is not None:
new_names[col_index] = name
return self.set_names(new_names)
|
Change names of all columns in the frame.
|
def set_names(self, names):
"""
Change names of all columns in the frame.
:param List[str] names: The list of new names for every column in the frame.
"""
assert_is_type(names, [str])
assert_satisfies(names, len(names) == self.ncol)
self._ex = ExprNode("colnames=", self, range(self.ncol), names) # Update-in-place, but still lazy
return self
|
Set a new name for a column.
|
def set_name(self, col=None, name=None):
"""
Set a new name for a column.
:param col: index or name of the column whose name is to be set; may be skipped for 1-column frames
:param name: the new name of the column
"""
assert_is_type(col, None, int, str)
assert_is_type(name, str)
ncols = self.ncols
col_index = None
if is_type(col, int):
if not(-ncols <= col < ncols):
raise H2OValueError("Index %d is out of bounds for a frame with %d columns" % (col, ncols))
col_index = (col + ncols) % ncols # handle negative indices
elif is_type(col, str):
if col not in self.names:
raise H2OValueError("Column %s doesn't exist in the frame." % col)
col_index = self.names.index(col) # lookup the name
else:
assert col is None
if ncols != 1:
raise H2OValueError("The frame has %d columns; please specify which one to rename" % ncols)
col_index = 0
if name != self.names[col_index] and name in self.types:
raise H2OValueError("Column '%s' already exists in the frame" % name)
oldname = self.names[col_index]
old_cache = self._ex._cache
self._ex = ExprNode("colnames=", self, col_index, name) # Update-in-place, but still lazy
self._ex._cache.fill_from(old_cache)
if self.names is None:
self._frame()._ex._cache.fill()
else:
self._ex._cache._names = self.names[:col_index] + [name] + self.names[col_index + 1:]
self._ex._cache._types[name] = self._ex._cache._types.pop(oldname)
return
|
Compute cumulative sum over rows/ columns of the frame.
|
def cumsum(self, axis=0):
"""
Compute cumulative sum over rows / columns of the frame.
:param int axis: 0 for column-wise, 1 for row-wise
:returns: new H2OFrame with cumulative sums of the original frame.
"""
return H2OFrame._expr(expr=ExprNode("cumsum", self, axis), cache=self._ex._cache)
|
Test whether elements of an H2OFrame are contained in the item.
|
def isin(self, item):
"""
Test whether elements of an H2OFrame are contained in the ``item``.
:param items: An item or a list of items to compare the H2OFrame against.
:returns: An H2OFrame of 0s and 1s showing whether each element in the original H2OFrame is contained in item.
"""
if is_type(item, list, tuple, set):
if self.ncols == 1 and (self.type(0) == 'str' or self.type(0) == 'enum'):
return self.match(item)
else:
return functools.reduce(H2OFrame.__or__, (self == i for i in item))
else:
return self == item
|
Build a fold assignments column for cross - validation.
|
def modulo_kfold_column(self, n_folds=3):
"""
Build a fold assignments column for cross-validation.
Rows are assigned a fold according to the current row number modulo ``n_folds``.
:param int n_folds: An integer specifying the number of validation sets to split the training data into.
:returns: A single-column H2OFrame with the fold assignments.
"""
return H2OFrame._expr(expr=ExprNode("modulo_kfold_column", self, n_folds))._frame()
|
Build a fold assignment column with the constraint that each fold has the same class distribution as the fold column.
|
def stratified_kfold_column(self, n_folds=3, seed=-1):
"""
Build a fold assignment column with the constraint that each fold has the same class
distribution as the fold column.
:param int n_folds: The number of folds to build.
:param int seed: A seed for the random number generator.
:returns: A single column H2OFrame with the fold assignments.
"""
return H2OFrame._expr(
expr=ExprNode("stratified_kfold_column", self, n_folds, seed))._frame()
|
Compactly display the internal structure of an H2OFrame.
|
def structure(self):
"""Compactly display the internal structure of an H2OFrame."""
df = self.as_data_frame(use_pandas=False)
cn = df.pop(0)
nr = self.nrow
nc = self.ncol
width = max([len(c) for c in cn])
isfactor = self.isfactor()
numlevels = self.nlevels()
lvls = self.levels()
print("H2OFrame: '{}' \nDimensions: {} obs. of {} variables".format(self.frame_id, nr, nc))
for i in range(nc):
print("$ {} {}: ".format(cn[i], ' ' * (width - max(0, len(cn[i])))), end=' ')
if isfactor[i]:
nl = numlevels[i]
print("Factor w/ {} level(s) {} ".format(nl, '"' + '","'.join(lvls[i]) + '"'), end='\n')
else:
print("num {}".format(" ".join(it[0] if it else "nan" for it in h2o.as_list(self[:10, i], False)[1:])))
|
Obtain the dataset as a python - local object.
|
def as_data_frame(self, use_pandas=True, header=True):
"""
Obtain the dataset as a python-local object.
:param bool use_pandas: If True (default) then return the H2OFrame as a pandas DataFrame (requires that the
``pandas`` library was installed). If False, then return the contents of the H2OFrame as plain nested
list, in a row-wise order.
:param bool header: If True (default), then column names will be appended as the first row in list
:returns: A python object (a list of lists of strings, each list is a row, if use_pandas=False, otherwise
a pandas DataFrame) containing this H2OFrame instance's data.
"""
if can_use_pandas() and use_pandas:
import pandas
return pandas.read_csv(StringIO(self.get_frame_data()), low_memory=False, skip_blank_lines=False)
from h2o.utils.csv.readers import reader
frame = [row for row in reader(StringIO(self.get_frame_data()))]
if not header:
frame.pop(0)
return frame
|
Drop a single column or row or a set of columns or rows from a H2OFrame.
|
def drop(self, index, axis=1):
"""
Drop a single column or row or a set of columns or rows from a H2OFrame.
Dropping a column or row is not in-place.
Indices of rows and columns are zero-based.
:param index: A list of column indices, column names, or row indices to drop; or
a string to drop a single column by name; or an int to drop a single column by index.
:param int axis: If 1 (default), then drop columns; if 0 then drop rows.
:returns: a new H2OFrame with the respective dropped columns or rows. The original H2OFrame remains
unchanged.
"""
if axis == 1:
if not isinstance(index, list):
#If input is a string, i.e., "C1":
if is_type(index, str):
#Check if index is an actual column(s) in the frame
if index not in self.names:
raise H2OValueError("Column(s) selected to drop are not in original frame: %r" % index)
index = self.names.index(index)
#If input is an int indicating a column index, i.e., 3:
elif is_type(index, int):
#Check if index is an actual column index in the frame
if index > self.ncol:
raise H2OValueError("Column index selected to drop is not part of the frame: %r" % index)
if index < 0:
raise H2OValueError("Column index selected to drop is not positive: %r" % index)
fr = H2OFrame._expr(expr=ExprNode("cols", self, -(index + 1)), cache=self._ex._cache)
fr._ex._cache.ncols -= 1
fr._ex._cache.names = self.names[:index] + self.names[index + 1:]
fr._ex._cache.types = {name: self.types[name] for name in fr._ex._cache.names}
return fr
elif isinstance(index, list):
#If input is an int array indicating a column index, i.e., [3] or [1,2,3]:
if is_type(index, [int]):
if max(index) > self.ncol:
raise H2OValueError("Column index selected to drop is not part of the frame: %r" % index)
if min(index) < 0:
raise H2OValueError("Column index selected to drop is not positive: %r" % index)
for i in range(len(index)):
index[i] = -(index[i] + 1)
#If index is a string array, i.e., ["C1", "C2"]
elif is_type(index, [str]):
#Check if index is an actual column(s) in the frame
if not set(index).issubset(self.names):
raise H2OValueError("Column(s) selected to drop are not in original frame: %r" % index)
for i in range(len(index)):
index[i] = -(self.names.index(index[i]) + 1)
fr = H2OFrame._expr(expr=ExprNode("cols", self, index), cache=self._ex._cache)
fr._ex._cache.ncols -= len(index)
fr._ex._cache.names = [i for i in self.names
if self.names.index(i) not in list(map(lambda x: abs(x) - 1, index))]
fr._ex._cache.types = {name: fr.types[name] for name in fr._ex._cache.names}
else:
raise ValueError("Invalid column index types. Must either be a list of all int indexes, "
"a string list of all column names, a single int index, or"
"a single string for dropping columns.")
return fr
elif axis == 0:
if is_type(index, [int]):
#Check if index is an actual column index in the frame
if max(index) > self.nrow:
raise H2OValueError("Row index selected to drop is not part of the frame: %r" % index)
if min(index) < 0:
raise H2OValueError("Row index selected to drop is not positive: %r" % index)
index = [-(x + 1) for x in index]
fr = H2OFrame._expr(expr=ExprNode("rows", self, index), cache=self._ex._cache)
fr._ex._cache.nrows -= len(index)
else:
raise ValueError("Invalid row indexes. Must be a list of int row indexes to drop from the H2OFrame.")
return fr
|
Pop a column from the H2OFrame at index i.
|
def pop(self, i):
"""
Pop a column from the H2OFrame at index i.
:param i: The index (int) or name (str) of the column to pop.
:returns: an H2OFrame containing the column dropped from the current frame; the current frame is modified
in-place and loses the column.
"""
if is_type(i, str): i = self.names.index(i)
col = H2OFrame._expr(expr=ExprNode("cols", self, i))
old_cache = self._ex._cache
self._ex = ExprNode("cols", self, -(i + 1))
self._ex._cache.ncols -= 1
self._ex._cache.names = old_cache.names[:i] + old_cache.names[i + 1:]
self._ex._cache.types = {name: old_cache.types[name] for name in self._ex._cache.names}
self._ex._cache._data = None
col._ex._cache.ncols = 1
col._ex._cache.names = [old_cache.names[i]]
return col
|
Compute quantiles.
|
def quantile(self, prob=None, combine_method="interpolate", weights_column=None):
"""
Compute quantiles.
:param List[float] prob: list of probabilities for which quantiles should be computed.
:param str combine_method: for even samples this setting determines how to combine quantiles. This can be
one of ``"interpolate"``, ``"average"``, ``"low"``, ``"high"``.
:param weights_column: optional weights for each row. If not given, all rows are assumed to have equal
importance. This parameter can be either the name of column containing the observation weights in
this frame, or a single-column separate H2OFrame of observation weights.
:returns: a new H2OFrame containing the quantiles and probabilities.
"""
if len(self) == 0: return self
if prob is None: prob = [0.01, 0.1, 0.25, 0.333, 0.5, 0.667, 0.75, 0.9, 0.99]
if weights_column is None:
weights_column = "_"
else:
assert_is_type(weights_column, str, I(H2OFrame, lambda wc: wc.ncol == 1 and wc.nrow == self.nrow))
if isinstance(weights_column, H2OFrame):
merged = self.cbind(weights_column)
weights_column = merged.names[-1]
return H2OFrame._expr(expr=ExprNode("quantile", merged, prob, combine_method, weights_column))
return H2OFrame._expr(expr=ExprNode("quantile", self, prob, combine_method, weights_column))
|
Append multiple H2OFrames to this frame column - wise or row - wise.
|
def concat(self, frames, axis=1):
"""
Append multiple H2OFrames to this frame, column-wise or row-wise.
:param List[H2OFrame] frames: list of frames that should be appended to the current frame.
:param int axis: if 1 then append column-wise (default), if 0 then append row-wise.
:returns: an H2OFrame of the combined datasets.
"""
if len(frames) == 0:
raise ValueError("Input list of frames is empty! Nothing to concat.")
if axis == 1:
df = self.cbind(frames)
else:
df = self.rbind(frames)
return df
|
Append data to this frame column - wise.
|
def cbind(self, data):
"""
Append data to this frame column-wise.
:param H2OFrame data: append columns of frame ``data`` to the current frame. You can also cbind a number,
in which case it will get converted into a constant column.
:returns: new H2OFrame with all frames in ``data`` appended column-wise.
"""
assert_is_type(data, H2OFrame, numeric, [H2OFrame, numeric])
frames = [data] if not isinstance(data, list) else data
new_cols = list(self.columns)
new_types = dict(self.types)
for frame in frames:
if isinstance(frame, H2OFrame):
if frame.nrow != self.nrow:
raise H2OValueError("Cannot bind a dataframe with %d rows to a data frame with %d rows: "
"the number of rows should match" % (frame.nrow, self.nrow))
new_cols += frame.columns
new_types.update(frame.types)
else:
new_cols += [None]
unique_cols = set(new_cols)
fr = H2OFrame._expr(expr=ExprNode("cbind", self, *frames), cache=self._ex._cache)
fr._ex._cache.ncols = len(new_cols)
if len(new_cols) == len(unique_cols) and None not in unique_cols:
fr._ex._cache.names = new_cols
fr._ex._cache.types = new_types
else:
# Invalidate names and types since they contain duplicate / unknown names, and the server will choose those.
fr._ex._cache.names = None
fr._ex._cache.types = None
return fr
|
Append data to this frame row - wise.
|
def rbind(self, data):
"""
Append data to this frame row-wise.
:param data: an H2OFrame or a list of H2OFrame's to be combined with current frame row-wise.
:returns: this H2OFrame with all frames in data appended row-wise.
"""
assert_is_type(data, H2OFrame, [H2OFrame])
frames = [data] if not isinstance(data, list) else data
for frame in frames:
if frame.ncol != self.ncol:
raise H2OValueError("Cannot row-bind a dataframe with %d columns to a data frame with %d columns: "
"the columns must match" % (frame.ncol, self.ncol))
if frame.columns != self.columns or frame.types != self.types:
raise H2OValueError("Column names and types must match for rbind() to work")
fr = H2OFrame._expr(expr=ExprNode("rbind", self, *frames), cache=self._ex._cache)
fr._ex._cache.nrows = self.nrow + sum(frame.nrow for frame in frames)
return fr
|
Split a frame into distinct subsets of size determined by the given ratios.
|
def split_frame(self, ratios=None, destination_frames=None, seed=None):
"""
Split a frame into distinct subsets of size determined by the given ratios.
The number of subsets is always 1 more than the number of ratios given. Note that
this does not give an exact split. H2O is designed to be efficient on big data
using a probabilistic splitting method rather than an exact split. For example
when specifying a split of 0.75/0.25, H2O will produce a test/train split with
an expected value of 0.75/0.25 rather than exactly 0.75/0.25. On small datasets,
the sizes of the resulting splits will deviate from the expected value more than
on big data, where they will be very close to exact.
:param List[float] ratios: The fractions of rows for each split.
:param List[str] destination_frames: The names of the split frames.
:param int seed: seed for the random number generator
:returns: A list of H2OFrames
"""
assert_is_type(ratios, [numeric], None)
assert_is_type(destination_frames, [str], None)
assert_is_type(seed, int, None)
if ratios is None:
ratios = [0.75]
if not ratios:
raise ValueError("Ratios array may not be empty")
if destination_frames is not None:
if len(ratios) + 1 != len(destination_frames):
raise ValueError("The number of provided destination_frames must be one more "
"than the number of provided ratios")
num_slices = len(ratios) + 1
boundaries = []
last_boundary = 0
i = 0
while i < num_slices - 1:
ratio = ratios[i]
if ratio < 0:
raise ValueError("Ratio must be greater than 0")
boundary = last_boundary + ratio
if boundary >= 1.0:
raise ValueError("Ratios must add up to less than 1.0")
boundaries.append(boundary)
last_boundary = boundary
i += 1
splits = []
tmp_runif = self.runif(seed)
tmp_runif.frame_id = "%s_splitter" % _py_tmp_key(h2o.connection().session_id)
i = 0
while i < num_slices:
if i == 0:
# lower_boundary is 0.0
upper_boundary = boundaries[i]
tmp_slice = self[(tmp_runif <= upper_boundary), :]
elif i == num_slices - 1:
lower_boundary = boundaries[i - 1]
# upper_boundary is 1.0
tmp_slice = self[(tmp_runif > lower_boundary), :]
else:
lower_boundary = boundaries[i - 1]
upper_boundary = boundaries[i]
tmp_slice = self[((tmp_runif > lower_boundary) & (tmp_runif <= upper_boundary)), :]
if destination_frames is None:
splits.append(tmp_slice)
else:
destination_frame_id = destination_frames[i]
tmp_slice.frame_id = destination_frame_id
splits.append(tmp_slice)
i += 1
del tmp_runif
return splits
|
Return a new GroupBy object using this frame and the desired grouping columns.
|
def group_by(self, by):
"""
Return a new ``GroupBy`` object using this frame and the desired grouping columns.
The returned groups are sorted by the natural group-by column sort.
:param by: The columns to group on (either a single column name, or a list of column names, or
a list of column indices).
"""
assert_is_type(by, str, int, [str, int])
return GroupBy(self, by)
|
Return a new Frame that is sorted by column ( s ) in ascending order. A fully distributed and parallel sort. However the original frame can contain String columns but sorting cannot be done on String columns. Default sorting direction is ascending.
|
def sort(self, by, ascending=[]):
"""
Return a new Frame that is sorted by column(s) in ascending order. A fully distributed and parallel sort.
However, the original frame can contain String columns but sorting cannot be done on String columns.
Default sorting direction is ascending.
:param by: The column to sort by (either a single column name, or a list of column names, or
a list of column indices)
:param ascending: Boolean array to denote sorting direction for each sorting column. True for ascending
sort and False for descending sort.
:return: a new sorted Frame
"""
assert_is_type(by, str, int, [str, int])
if type(by) != list: by = [by]
if type(ascending) != list: ascending = [ascending] # convert to list
ascendingI=[1]*len(by) # intitalize sorting direction to ascending by default
for c in by:
if self.type(c) not in ["enum","time","int","real","string"]:
raise H2OValueError("Sort by column: " + str(c) + " not of enum, time, int, real, or string type")
if len(ascending)>0: # user did not specify sort direction, assume all columns ascending
assert len(ascending)==len(by), "Sorting direction must be specified for each sorted column."
for index in range(len(by)):
ascendingI[index]=1 if ascending[index] else -1
return H2OFrame._expr(expr=ExprNode("sort",self,by,ascendingI))
|
Return a new Frame that fills NA along a given axis and along a given direction with a maximum fill length
|
def fillna(self,method="forward",axis=0,maxlen=1):
"""
Return a new Frame that fills NA along a given axis and along a given direction with a maximum fill length
:param method: ``"forward"`` or ``"backward"``
:param axis: 0 for columnar-wise or 1 for row-wise fill
:param maxlen: Max number of consecutive NA's to fill
:return:
"""
assert_is_type(axis, 0, 1)
assert_is_type(method,str)
assert_is_type(maxlen, int)
return H2OFrame._expr(expr=ExprNode("h2o.fillna",self,method,axis,maxlen))
|
Impute missing values into the frame modifying it in - place.
|
def impute(self, column=-1, method="mean", combine_method="interpolate", by=None, group_by_frame=None, values=None):
"""
Impute missing values into the frame, modifying it in-place.
:param int column: Index of the column to impute, or -1 to impute the entire frame.
:param str method: The method of imputation: ``"mean"``, ``"median"``, or ``"mode"``.
:param str combine_method: When the method is ``"median"``, this setting dictates how to combine quantiles
for even samples. One of ``"interpolate"``, ``"average"``, ``"low"``, ``"high"``.
:param by: The list of columns to group on.
:param H2OFrame group_by_frame: Impute the values with this pre-computed grouped frame.
:param List values: The list of impute values, one per column. None indicates to skip the column.
:returns: A list of values used in the imputation or the group-by result used in imputation.
"""
if is_type(column, str): column = self.names.index(column)
if is_type(by, str): by = self.names.index(by)
if values is None:
values = "_"
else:
assert len(values) == len(self.columns), "Length of values does not match length of columns"
# convert string values to categorical num values
values2 = []
for i in range(0,len(values)):
if self.type(i) == "enum":
try:
values2.append(self.levels()[i].index(values[i]))
except:
raise H2OValueError("Impute value of: " + values[i] + " not found in existing levels of"
" column: " + self.col_names[i])
else:
values2.append(values[i])
values = values2
if group_by_frame is None: group_by_frame = "_"
# This code below is needed to ensure the frame (self) exists on the server. Without it, self._ex._cache.fill()
# fails with an assertion that ._id is None.
# This code should be removed / reworked once we have a more consistent strategy of dealing with frames.
self._ex._eager_frame()
if by is not None or group_by_frame is not "_":
res = H2OFrame._expr(
expr=ExprNode("h2o.impute", self, column, method, combine_method, by, group_by_frame, values))._frame()
else:
res = ExprNode("h2o.impute", self, column, method, combine_method, by, group_by_frame,
values)._eager_scalar()
self._ex._cache.flush()
self._ex._cache.fill(10)
return res
|
Merge two datasets based on common column names. We do not support all_x = True and all_y = True. Only one can be True or none is True. The default merge method is auto and it will default to the radix method. The radix method will return the correct merge result regardless of duplicated rows in the right frame. In addition the radix method can perform merge even if you have string columns in your frames. If there are duplicated rows in your rite frame they will not be included if you use the hash method. The hash method cannot perform merge if you have string columns in your left frame. Hence we consider the radix method superior to the hash method and is the default method to use.
|
def merge(self, other, all_x=False, all_y=False, by_x=None, by_y=None, method="auto"):
"""
Merge two datasets based on common column names. We do not support all_x=True and all_y=True.
Only one can be True or none is True. The default merge method is auto and it will default to the
radix method. The radix method will return the correct merge result regardless of duplicated rows
in the right frame. In addition, the radix method can perform merge even if you have string columns
in your frames. If there are duplicated rows in your rite frame, they will not be included if you use
the hash method. The hash method cannot perform merge if you have string columns in your left frame.
Hence, we consider the radix method superior to the hash method and is the default method to use.
:param H2OFrame other: The frame to merge to the current one. By default, must have at least one column in common with
this frame, and all columns in common are used as the merge key. If you want to use only a subset of the
columns in common, rename the other columns so the columns are unique in the merged result.
:param bool all_x: If True, include all rows from the left/self frame
:param bool all_y: If True, include all rows from the right/other frame
:param by_x: list of columns in the current frame to use as a merge key.
:param by_y: list of columns in the ``other`` frame to use as a merge key. Should have the same number of
columns as in the ``by_x`` list.
:param method: string representing the merge method, one of auto(default), radix or hash.
:returns: New H2OFrame with the result of merging the current frame with the ``other`` frame.
"""
if by_x is None and by_y is None:
common_names = list(set(self.names) & set(other.names))
if not common_names:
raise H2OValueError("No columns in common to merge on!")
if by_x is None:
by_x = [self.names.index(c) for c in common_names]
else:
by_x = _getValidCols(by_x,self)
if by_y is None:
by_y = [other.names.index(c) for c in common_names]
else:
by_y = _getValidCols(by_y,other)
return H2OFrame._expr(expr=ExprNode("merge", self, other, all_x, all_y, by_x, by_y, method))
|
Reorder levels of an H2O factor for one single column of a H2O frame
|
def relevel(self, y):
"""
Reorder levels of an H2O factor for one single column of a H2O frame
The levels of a factor are reordered such that the reference level is at level 0, all remaining levels are
moved down as needed.
:param str y: The reference level
:returns: New reordered factor column
"""
return H2OFrame._expr(expr=ExprNode("relevel", self, quote(y)))
|
Insert missing values into the current frame modifying it in - place.
|
def insert_missing_values(self, fraction=0.1, seed=None):
"""
Insert missing values into the current frame, modifying it in-place.
Randomly replaces a user-specified fraction of entries in a H2O dataset with missing
values.
:param float fraction: A number between 0 and 1 indicating the fraction of entries to replace with missing.
:param int seed: The seed for the random number generator used to determine which values to make missing.
:returns: the original H2OFrame with missing values inserted.
"""
kwargs = {}
kwargs['dataset'] = self.frame_id # Eager; forces eval now for following REST call
kwargs['fraction'] = fraction
if seed is not None: kwargs['seed'] = seed
job = {}
job['job'] = h2o.api("POST /3/MissingInserter", data=kwargs)
H2OJob(job, job_type=("Insert Missing Values")).poll()
self._ex._cache.flush()
return self
|
Compute the frame s sum by - column ( or by - row ).
|
def sum(self, skipna=True, axis=0, **kwargs):
"""
Compute the frame's sum by-column (or by-row).
:param bool skipna: If True (default), then NAs are ignored during the computation. Otherwise presence
of NAs renders the entire result NA.
:param int axis: Direction of sum computation. If 0 (default), then sum is computed columnwise, and the result
is a frame with 1 row and number of columns as in the original frame. If 1, then sum is computed rowwise
and the result is a frame with 1 column (called "sum"), and number of rows equal to the number of rows
in the original frame. For row or column sums, the ``return_frame`` parameter must be True.
:param bool return_frame: A boolean parameter that indicates whether to return an H2O frame or one single aggregated value. Default is False.
:returns: either an aggregated value with sum of values per-column (old semantic); or an H2OFrame containing sum of values
per-column/per-row in the original frame (new semantic). The new semantic is triggered by either
providing the ``return_frame=True`` parameter, or having the ``general.allow_breaking_changed`` config
option turned on.
"""
assert_is_type(skipna, bool)
assert_is_type(axis, 0, 1)
# Deprecated since 2016-10-14,
if "na_rm" in kwargs:
warnings.warn("Parameter na_rm is deprecated; use skipna instead", category=DeprecationWarning)
na_rm = kwargs.pop("na_rm")
assert_is_type(na_rm, bool)
skipna = na_rm # don't assign to skipna directly, to help with error reporting
# Determine whether to return a frame or a list
return_frame = get_config_value("general.allow_breaking_changes", False)
if "return_frame" in kwargs:
return_frame = kwargs.pop("return_frame")
assert_is_type(return_frame, bool)
if kwargs:
raise H2OValueError("Unknown parameters %r" % list(kwargs))
if return_frame:
return H2OFrame._expr(ExprNode("sumaxis", self, skipna, axis))
else:
return ExprNode("sumNA" if skipna else "sum", self)._eager_scalar()
|
Compute the variance - covariance matrix of one or two H2OFrames.
|
def var(self, y=None, na_rm=False, use=None):
"""
Compute the variance-covariance matrix of one or two H2OFrames.
:param H2OFrame y: If this parameter is given, then a covariance matrix between the columns of the target
frame and the columns of ``y`` is computed. If this parameter is not provided then the covariance matrix
of the target frame is returned. If target frame has just a single column, then return the scalar variance
instead of the matrix. Single rows are treated as single columns.
:param str use: A string indicating how to handle missing values. This could be one of the following:
- ``"everything"``: outputs NaNs whenever one of its contributing observations is missing
- ``"all.obs"``: presence of missing observations will throw an error
- ``"complete.obs"``: discards missing values along with all observations in their rows so that only
complete observations are used
:param bool na_rm: an alternative to ``use``: when this is True then default value for ``use`` is
``"everything"``; and if False then default ``use`` is ``"complete.obs"``. This parameter has no effect
if ``use`` is given explicitly.
:returns: An H2OFrame of the covariance matrix of the columns of this frame (if ``y`` is not given),
or with the columns of ``y`` (if ``y`` is given). However when this frame and ``y`` are both single rows
or single columns, then the variance is returned as a scalar.
"""
symmetric = False
if y is None:
y = self
symmetric = True
if use is None: use = "complete.obs" if na_rm else "everything"
if self.nrow == 1 or (self.ncol == 1 and y.ncol == 1):
return ExprNode("var", self, y, use, symmetric)._eager_scalar()
return H2OFrame._expr(expr=ExprNode("var", self, y, use, symmetric))._frame()
|
Compute the correlation matrix of one or two H2OFrames.
|
def cor(self, y=None, na_rm=False, use=None):
"""
Compute the correlation matrix of one or two H2OFrames.
:param H2OFrame y: If this parameter is provided, then compute correlation between the columns of ``y``
and the columns of the current frame. If this parameter is not given, then just compute the correlation
matrix for the columns of the current frame.
:param str use: A string indicating how to handle missing values. This could be one of the following:
- ``"everything"``: outputs NaNs whenever one of its contributing observations is missing
- ``"all.obs"``: presence of missing observations will throw an error
- ``"complete.obs"``: discards missing values along with all observations in their rows so that only
complete observations are used
:param bool na_rm: an alternative to ``use``: when this is True then default value for ``use`` is
``"everything"``; and if False then default ``use`` is ``"complete.obs"``. This parameter has no effect
if ``use`` is given explicitly.
:returns: An H2OFrame of the correlation matrix of the columns of this frame (if ``y`` is not given),
or with the columns of ``y`` (if ``y`` is given). However when this frame and ``y`` are both single rows
or single columns, then the correlation is returned as a scalar.
"""
assert_is_type(y, H2OFrame, None)
assert_is_type(na_rm, bool)
assert_is_type(use, None, "everything", "all.obs", "complete.obs")
if y is None:
y = self
if use is None: use = "complete.obs" if na_rm else "everything"
if self.nrow == 1 or (self.ncol == 1 and y.ncol == 1): return ExprNode("cor", self, y, use)._eager_scalar()
return H2OFrame._expr(expr=ExprNode("cor", self, y, use))._frame()
|
Compute a pairwise distance measure between all rows of two numeric H2OFrames.
|
def distance(self, y, measure=None):
"""
Compute a pairwise distance measure between all rows of two numeric H2OFrames.
:param H2OFrame y: Frame containing queries (small)
:param str use: A string indicating what distance measure to use. Must be one of:
- ``"l1"``: Absolute distance (L1-norm, >=0)
- ``"l2"``: Euclidean distance (L2-norm, >=0)
- ``"cosine"``: Cosine similarity (-1...1)
- ``"cosine_sq"``: Squared Cosine similarity (0...1)
:examples:
>>>
>>> iris_h2o = h2o.import_file(path=pyunit_utils.locate("smalldata/iris/iris.csv"))
>>> references = iris_h2o[10:150,0:4
>>> queries = iris_h2o[0:10,0:4]
>>> A = references.distance(queries, "l1")
>>> B = references.distance(queries, "l2")
>>> C = references.distance(queries, "cosine")
>>> D = references.distance(queries, "cosine_sq")
>>> E = queries.distance(references, "l1")
>>> (E.transpose() == A).all()
:returns: An H2OFrame of the matrix containing pairwise distance / similarity between the
rows of this frame (N x p) and ``y`` (M x p), with dimensions (N x M).
"""
assert_is_type(y, H2OFrame)
if measure is None: measure = "l2"
return H2OFrame._expr(expr=ExprNode("distance", self, y, measure))._frame()
|
Compute element - wise string distances between two H2OFrames. Both frames need to have the same shape and only contain string/ factor columns.
|
def strdistance(self, y, measure=None, compare_empty=True):
"""
Compute element-wise string distances between two H2OFrames. Both frames need to have the same
shape and only contain string/factor columns.
:param H2OFrame y: A comparison frame.
:param str measure: A string identifier indicating what string distance measure to use. Must be one of:
- ``"lv"``: Levenshtein distance
- ``"lcs"``: Longest common substring distance
- ``"qgram"``: q-gram distance
- ``"jaccard"``: Jaccard distance between q-gram profiles
- ``"jw"``: Jaro, or Jaro-Winker distance
- ``"soundex"``: Distance based on soundex encoding
:param compare_empty if set to FALSE, empty strings will be handled as NaNs
:examples:
>>>
>>> x = h2o.H2OFrame.from_python(['Martha', 'Dwayne', 'Dixon'], column_types=['factor'])
>>> y = h2o.H2OFrame.from_python(['Marhta', 'Duane', 'Dicksonx'], column_types=['string'])
>>> x.strdistance(y, measure="jw")
:returns: An H2OFrame of the matrix containing element-wise distance between the
strings of this frame and ``y``. The returned frame has the same shape as the input frames.
"""
assert_is_type(y, H2OFrame)
assert_is_type(measure, Enum('lv', 'lcs', 'qgram', 'jaccard', 'jw', 'soundex'))
assert_is_type(compare_empty, bool)
return H2OFrame._expr(expr=ExprNode("strDistance", self, y, measure, compare_empty))._frame()
|
Convert columns in the current frame to categoricals.
|
def asfactor(self):
"""
Convert columns in the current frame to categoricals.
:returns: new H2OFrame with columns of the "enum" type.
"""
for colname in self.names:
t = self.types[colname]
if t not in {"bool", "int", "string", "enum"}:
raise H2OValueError("Only 'int' or 'string' are allowed for "
"asfactor(), got %s:%s " % (colname, t))
fr = H2OFrame._expr(expr=ExprNode("as.factor", self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {name: "enum" for name in self.types}
else:
raise H2OTypeError("Types are not available in result")
return fr
|
Return the list of levels for an enum ( categorical ) column.
|
def categories(self):
"""
Return the list of levels for an enum (categorical) column.
This function can only be applied to single-column categorical frame.
"""
if self.ncols != 1:
raise H2OValueError("This operation only applies to a single factor column")
if self.types[self.names[0]] != "enum":
raise H2OValueError("Input is not a factor. This operation only applies to a single factor column")
return self.levels()[0]
|
Split the strings in the target column on the given regular expression pattern.
|
def strsplit(self, pattern):
"""
Split the strings in the target column on the given regular expression pattern.
:param str pattern: The split pattern.
:returns: H2OFrame containing columns of the split strings.
"""
fr = H2OFrame._expr(expr=ExprNode("strsplit", self, pattern))
fr._ex._cache.nrows = self.nrow
return fr
|
Tokenize String
|
def tokenize(self, split):
"""
Tokenize String
tokenize() is similar to strsplit(), the difference between them is that tokenize() will store the tokenized
text into a single column making it easier for additional processing (filtering stop words, word2vec algo, ...).
:param str split The regular expression to split on.
@return An H2OFrame with a single column representing the tokenized Strings. Original rows of the input DF are separated by NA.
"""
fr = H2OFrame._expr(expr=ExprNode("tokenize", self, split))
return fr
|
For each string in the frame count the occurrences of the provided pattern. If countmathces is applied to a frame all columns of the frame must be type string otherwise the returned frame will contain errors.
|
def countmatches(self, pattern):
"""
For each string in the frame, count the occurrences of the provided pattern. If countmathces is applied to
a frame, all columns of the frame must be type string, otherwise, the returned frame will contain errors.
The pattern here is a plain string, not a regular expression. We will search for the occurrences of the
pattern as a substring in element of the frame. This function is applicable to frames containing only
string or categorical columns.
:param str pattern: The pattern to count matches on in each string. This can also be a list of strings,
in which case all of them will be searched for.
:returns: numeric H2OFrame with the same shape as the original, containing counts of matches of the
pattern for each cell in the original frame.
"""
assert_is_type(pattern, str, [str])
fr = H2OFrame._expr(expr=ExprNode("countmatches", self, pattern))
fr._ex._cache.nrows = self.nrow
fr._ex._cache.ncols = self.ncol
return fr
|
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