def _keyify_if_h2oframe(item):
if isinstance(item, H2OFrame):
return item.frame_id
elif isinstance(item, list) and all(i is None or isinstance(i, H2OFrame) for i in item):
return [quoted(i) if i is None else quoted(i.frame_id) for i in item]
else:
return item
def _keyify_if_h2oframe(item):
if isinstance(item, H2OFrame):
return item.frame_id
elif isinstance(item, list) and all(i is None or isinstance(i, H2OFrame) for i in item):
return [quoted(i) if i is None else quoted(i.frame_id) for i in item]
else:
return item
def parse_raw(setup, id=None, first_line_is_header=(-1, 0, 1)):
"""Used in conjunction with lazy_import and parse_setup in order to make alterations
before parsing.
Parameters
----------
setup : dict
Result of h2o.parse_setup
id : str, optional
An id for the frame.
first_line_is_header : int, optional
-1,0,1 if the first line is to be used as the header
Returns
-------
H2OFrame
"""
if id: setup["destination_frame"] = quoted(id).replace("%", ".").replace("&", ".")
if first_line_is_header != (-1, 0, 1):
if first_line_is_header not in (-1, 0, 1): raise ValueError("first_line_is_header should be -1, 0, or 1")
setup["check_header"] = first_line_is_header
fr = H2OFrame()
fr._parse_raw(setup)
return fr
def _keyify(item):
if isinstance(item, Keyed):
return item.key
elif isinstance(item, list) and any(isinstance(i, Keyed) for i in item):
return [quoted(H2OEstimator._keyify(i)) for i in item]
else:
return item
def _model_build(self, x, y, tframe, vframe, kwargs):
kwargs['training_frame'] = tframe
if vframe is not None: kwargs["validation_frame"] = vframe
if is_type(y, int): y = tframe.names[y]
if y is not None: kwargs['response_column'] = y
if not is_type(x, list, tuple): x = [x]
if is_type(x[0], int):
x = [tframe.names[i] for i in x]
offset = kwargs["offset_column"]
folds = kwargs["fold_column"]
weights = kwargs["weights_column"]
ignored_columns = list(set(tframe.names) - set(x + [y, offset, folds, weights]))
kwargs["ignored_columns"] = None if not ignored_columns else [quoted(col) for col in ignored_columns]
kwargs = dict([(k, kwargs[k].frame_id if isinstance(kwargs[k], H2OFrame) else kwargs[k]) for k in kwargs if
kwargs[k] is not None]) # gruesome one-liner
algo = self.model._compute_algo() # unique to grid search
if self.grid_id is not None: kwargs["grid_id"] = self.grid_id
rest_ver = kwargs.pop("_rest_version") if "_rest_version" in kwargs else None
grid = H2OJob(h2o.api("POST /99/Grid/%s" % algo, data=kwargs), job_type=(algo + " Grid Build"))
if self._future:
self._job = grid
return
grid.poll()
grid_json = h2o.api("GET /99/Grids/%s" % (grid.dest_key))
failure_messages_stacks = ""
error_index = 0
if len(grid_json["failure_details"]) > 0:
print("Errors/Warnings building gridsearch model\n")
# will raise error if no grid model is returned, store error messages here
for error_message in grid_json["failure_details"]:
if isinstance(grid_json["failed_params"][error_index], dict):
for h_name in grid_json['hyper_names']:
print("Hyper-parameter: {0}, {1}".format(h_name,
grid_json['failed_params'][error_index][h_name]))
if len(grid_json["failure_stack_traces"]) > error_index:
print("failure_details: {0}\nfailure_stack_traces: "
"{1}\n".format(error_message, grid_json['failure_stack_traces'][error_index]))
failure_messages_stacks += error_message+'\n'
error_index += 1
self.models = [h2o.get_model(key['name']) for key in grid_json['model_ids']]
# get first model returned in list of models from grid search to get model class (binomial, multinomial, etc)
# sometimes no model is returned due to bad parameter values provided by the user.
if len(grid_json['model_ids']) > 0:
first_model_json = h2o.api("GET /%d/Models/%s" %
(rest_ver or 3, grid_json['model_ids'][0]['name']))['models'][0]
self._resolve_grid(grid.dest_key, grid_json, first_model_json)
else:
if len(failure_messages_stacks)>0:
raise ValueError(failure_messages_stacks)
else:
raise ValueError("Gridsearch returns no model due to bad parameter values or other reasons....")
def _model_build(self, x, y, tframe, vframe, kwargs):
kwargs['training_frame'] = tframe
if vframe is not None: kwargs["validation_frame"] = vframe
if is_type(y, int): y = tframe.names[y]
if y is not None: kwargs['response_column'] = y
if not is_type(x, list, tuple): x = [x]
if is_type(x[0], int):
x = [tframe.names[i] for i in x]
offset = kwargs["offset_column"]
folds = kwargs["fold_column"]
weights = kwargs["weights_column"]
ignored_columns = list(set(tframe.names) - set(x + [y, offset, folds, weights]))
kwargs["ignored_columns"] = None if not ignored_columns else [quoted(col) for col in ignored_columns]
kwargs = dict([(k, kwargs[k].frame_id if isinstance(kwargs[k], H2OFrame) else kwargs[k]) for k in kwargs if
kwargs[k] is not None]) # gruesome one-liner
algo = self.model._compute_algo() # unique to grid search
if self.grid_id is not None: kwargs["grid_id"] = self.grid_id
rest_ver = kwargs.pop("_rest_version") if "_rest_version" in kwargs else None
grid = H2OJob(h2o.api("POST /99/Grid/%s" % algo, data=kwargs), job_type=(algo + " Grid Build"))
if self._future:
self._job = grid
return
grid.poll()
grid_json = h2o.api("GET /99/Grids/%s" % (grid.dest_key))
failure_messages_stacks = ""
error_index = 0
if len(grid_json["failure_details"]) > 0:
print("Errors/Warnings building gridsearch model\n")
# will raise error if no grid model is returned, store error messages here
for error_message in grid_json["failure_details"]:
if isinstance(grid_json["failed_params"][error_index], dict):
for h_name in grid_json['hyper_names']:
print("Hyper-parameter: {0}, {1}".format(h_name,
grid_json['failed_params'][error_index][h_name]))
if len(grid_json["failure_stack_traces"]) > error_index:
print("failure_details: {0}\nfailure_stack_traces: "
"{1}\n".format(error_message, grid_json['failure_stack_traces'][error_index]))
failure_messages_stacks += error_message+'\n'
error_index += 1
self.models = [h2o.get_model(key['name']) for key in grid_json['model_ids']]
# get first model returned in list of models from grid search to get model class (binomial, multinomial, etc)
# sometimes no model is returned due to bad parameter values provided by the user.
if len(grid_json['model_ids']) > 0:
first_model_json = h2o.api("GET /%d/Models/%s" %
(rest_ver or 3, grid_json['model_ids'][0]['name']))['models'][0]
self._resolve_grid(grid.dest_key, grid_json, first_model_json)
else:
if len(failure_messages_stacks)>0:
raise ValueError(failure_messages_stacks)
else:
raise ValueError("Gridsearch returns no model due to bad parameter values or other reasons....")
def fit(self, fr, **fit_params):
res = []
for step in self.steps:
res.append(step[1].to_rest(step[0]))
res = "[" + ",".join([quoted(r.replace('"', "'")) for r in res]) + "]"
j = h2o.api("POST /99/Assembly", data={"steps": res, "frame": fr.frame_id})
self.id = j["assembly"]["name"]
return H2OFrame.get_frame(j["result"]["name"])
def _model_build(self, x, y, tframe, vframe, kwargs):
kwargs["training_frame"] = tframe
if vframe is not None: kwargs["validation_frame"] = vframe
if is_type(y, int): y = tframe.names[y]
if y is not None: kwargs["response_column"] = y
if not isinstance(x, (list, tuple)): x = [x]
if is_type(x[0], int):
x = [tframe.names[i] for i in x]
offset = kwargs["offset_column"]
folds = kwargs["fold_column"]
weights = kwargs["weights_column"]
ignored_columns = list(
set(tframe.names) - set(x + [y, offset, folds, weights]))
kwargs["ignored_columns"] = None if ignored_columns == [] else [
quoted(col) for col in ignored_columns
]
kwargs["interactions"] = (None if "interactions" not in kwargs
or kwargs["interactions"] is None else [
quoted(col)
for col in kwargs["interactions"]
])
kwargs = {
k: H2OEstimator._keyify_if_h2oframe(kwargs[k])
for k in kwargs
}
rest_ver = kwargs.pop(
"_rest_version") if "_rest_version" in kwargs else 3
model = H2OJob(h2o.api("POST /%d/ModelBuilders/%s" %
(rest_ver, self.algo),
data=kwargs),
job_type=(self.algo + " Model Build"))
if self._future:
self._job = model
return
model.poll()
model_json = h2o.api("GET /%d/Models/%s" %
(rest_ver, model.dest_key))["models"][0]
self._resolve_model(model.dest_key, model_json)
def fit(self, fr, **fit_params):
res = []
for step in self.steps:
res.append(step[1].to_rest(step[0]))
res = "[" + ",".join([quoted(r.replace('"', "'")) for r in res]) + "]"
j = h2o.api("POST /99/Assembly",
data={
"steps": res,
"frame": fr.frame_id
})
self.id = j["assembly"]["name"]
return H2OFrame.get_frame(j["result"]["name"])
def fit(self, fr):
assert_is_type(fr, H2OFrame)
steps = "[%s]" % ",".join(
quoted(step[1].to_rest(step[0]).replace('"', "'"))
for step in self.steps)
j = h2o.api("POST /99/Assembly",
data={
"steps": steps,
"frame": fr.frame_id
})
self.id = j["assembly"]["name"]
return H2OFrame.get_frame(j["result"]["name"])
def fit(self, fr):
"""
To perform the munging operations on a frame specified in steps on the frame fr.
:param fr: H2OFrame where munging operations are to be performed on.
:return: H2OFrame after munging operations are completed.
"""
assert_is_type(fr, H2OFrame)
steps = "[%s]" % ",".join(quoted(step[1].to_rest(step[0]).replace('"', "'")) for step in self.steps)
j = h2o.api("POST /99/Assembly", data={"steps": steps, "frame": fr.frame_id})
self.id = j["assembly"]["name"]
return H2OFrame.get_frame(j["result"]["name"])
def fit(self, fr):
"""
To perform the munging operations on a frame specified in steps on the frame fr.
:param fr: H2OFrame where munging operations are to be performed on.
:return: H2OFrame after munging operations are completed.
"""
assert_is_type(fr, H2OFrame)
steps = "[%s]" % ",".join(
quoted(step[1].to_rest(step[0]).replace('"', "'"))
for step in self.steps)
j = h2o.api("POST /99/Assembly",
data={
"steps": steps,
"frame": fr.frame_id
})
self.id = j["assembly"]["name"]
return H2OFrame.get_frame(j["result"]["name"])
def interaction(data, factors, pairwise, max_factors, min_occurrence, destination_frame=None):
"""
Categorical Interaction Feature Creation in H2O.
Creates a frame in H2O with n-th order interaction features between categorical columns, as specified by
the user.
Parameters
----------
data : H2OFrame
the H2OFrame that holds the target categorical columns.
factors : list
factors Factor columns (either indices or column names).
pairwise : bool
Whether to create pairwise interactions between factors (otherwise create one
higher-order interaction). Only applicable if there are 3 or more factors.
max_factors : int
Max. number of factor levels in pair-wise interaction terms (if enforced, one extra
catch-all factor will be made)
min_occurrence : int
Min. occurrence threshold for factor levels in pair-wise interaction terms
destination_frame : str
A string indicating the destination key. If empty, this will be auto-generated by H2O.
Returns
-------
H2OFrame
"""
factors = [data.names[n] if is_int(n) else n for n in factors]
parms = {"dest": py_tmp_key(append=h2oconn.session_id) if destination_frame is None else destination_frame,
"source_frame": data.frame_id,
"factor_columns": [quoted(f) for f in factors],
"pairwise": pairwise,
"max_factors": max_factors,
"min_occurrence": min_occurrence,
}
H2OJob(api("POST /3/Interaction", data=parms), "Interactions").poll()
return get_frame(parms["dest"])
def fit(self, fr):
"""
To perform the munging operations on a frame specified in steps on the frame fr.
:param fr: H2OFrame where munging operations are to be performed on.
:return: H2OFrame after munging operations are completed.
:examples:
>>> iris = h2o.load_dataset("iris")
>>> assembly = H2OAssembly(steps=[("col_select",
... H2OColSelect(["Sepal.Length",
... "Petal.Length", "Species"])),
... ("cos_Sepal.Length",
... H2OColOp(op=H2OFrame.cos,
... col="Sepal.Length",
... inplace=True)),
... ("str_cnt_Species",
... H2OColOp(op=H2OFrame.countmatches,
... col="Species",
... inplace=False,
... pattern="s"))])
>>> fit = assembly.fit(iris)
>>> fit
"""
assert_is_type(fr, H2OFrame)
steps = "[%s]" % ",".join(
quoted(step[1].to_rest(step[0]).replace('"', "'"))
for step in self.steps)
j = h2o.api("POST /99/Assembly",
data={
"steps": steps,
"frame": fr.frame_id
})
self.id = j["assembly"]["name"]
return H2OFrame.get_frame(j["result"]["name"])
def extend_parms(parms):
if blending_frame is not None:
parms['blending_frame'] = blending_frame
if self.metalearner_fold_column is not None:
parms['ignored_columns'].remove(
quoted(self.metalearner_fold_column))
def train(self, x=None, y=None, training_frame=None, offset_column=None, fold_column=None,
weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None,
model_id=None):
"""
Train the H2O model.
:param x: A list of column names or indices indicating the predictor columns.
:param y: An index or a column name indicating the response column.
:param H2OFrame training_frame: The H2OFrame having the columns indicated by x and y (as well as any
additional columns specified by fold, offset, and weights).
:param offset_column: The name or index of the column in training_frame that holds the offsets.
:param fold_column: The name or index of the column in training_frame that holds the per-row fold
assignments.
:param weights_column: The name or index of the column in training_frame that holds the per-row weights.
:param validation_frame: H2OFrame with validation data to be scored on while training.
:param float max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
"""
assert_is_type(training_frame, H2OFrame)
assert_is_type(validation_frame, None, H2OFrame)
assert_is_type(y, None, int, str)
assert_is_type(x, None, int, str, [str, int], {str, int})
assert_is_type(ignored_columns, None, [str, int], {str, int})
assert_is_type(offset_column, None, int, str)
assert_is_type(fold_column, None, int, str)
assert_is_type(weights_column, None, int, str)
assert_is_type(max_runtime_secs, None, numeric)
assert_is_type(model_id, None, str)
algo = self.algo
parms = self._parms.copy()
if "__class__" in parms: # FIXME: hackt for PY3
del parms["__class__"]
is_auto_encoder = bool(parms.get("autoencoder"))
is_supervised = not(is_auto_encoder or algo in {"pca", "svd", "kmeans", "glrm", "word2vec"})
ncols = training_frame.ncols
names = training_frame.names
if is_supervised:
if y is None: y = "response"
if is_type(y, int):
if not (-ncols <= y < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % y)
y = names[y]
else:
if y not in names:
raise H2OValueError("Column %s does not exist in the training frame" % y)
self._estimator_type = "classifier" if training_frame.types[y] == "enum" else "regressor"
elif y is not None:
raise H2OValueError("y should not be provided for an unsupervised model")
assert_is_type(y, str, None)
ignored_columns_set = set()
if ignored_columns is not None:
if x is not None:
raise H2OValueError("Properties x and ignored_columns cannot be specified simultaneously")
for ic in ignored_columns:
if is_type(ic, int):
if not (-ncols <= ic < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % ic)
ignored_columns_set.add(names[ic])
else:
if ic not in names:
raise H2OValueError("Column %s not in the training frame" % ic)
ignored_columns_set.add(ic)
if x is None:
xset = set(names) - {y} - ignored_columns_set
else:
xset = set()
if is_type(x, int, str): x = [x]
for xi in x:
if is_type(xi, int):
if not (-ncols <= xi < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % xi)
xset.add(names[xi])
else:
if xi not in names:
raise H2OValueError("Column %s not in the training frame" % xi)
xset.add(xi)
x = list(xset)
parms["offset_column"] = offset_column
parms["fold_column"] = fold_column
parms["weights_column"] = weights_column
parms["max_runtime_secs"] = max_runtime_secs
# Overwrites the model_id parameter only if model_id is passed
if model_id is not None:
parms["model_id"] = model_id
# Step 2
is_auto_encoder = "autoencoder" in parms and parms["autoencoder"]
is_unsupervised = is_auto_encoder or self.algo in {"pca", "svd", "kmeans", "glrm", "word2vec"}
if is_auto_encoder and y is not None: raise ValueError("y should not be specified for autoencoder.")
if not is_unsupervised and y is None: raise ValueError("Missing response")
# Step 3
parms["training_frame"] = training_frame
if validation_frame is not None: parms["validation_frame"] = validation_frame
if is_type(y, int): y = training_frame.names[y]
if y is not None: parms["response_column"] = y
if not isinstance(x, (list, tuple)): x = [x]
if is_type(x[0], int):
x = [training_frame.names[i] for i in x]
offset = parms["offset_column"]
folds = parms["fold_column"]
weights = parms["weights_column"]
ignored_columns = list(set(training_frame.names) - set(x + [y, offset, folds, weights]))
parms["ignored_columns"] = None if ignored_columns == [] else [quoted(col) for col in ignored_columns]
parms["interactions"] = (None if "interactions" not in parms or parms["interactions"] is None else
[quoted(col) for col in parms["interactions"]])
parms = {k: H2OEstimator._keyify_if_h2oframe(parms[k]) for k in parms}
rest_ver = parms.pop("_rest_version") if "_rest_version" in parms else 3
model = H2OJob(h2o.api("POST /%d/ModelBuilders/%s" % (rest_ver, self.algo), data=parms),
job_type=(self.algo + " Model Build"))
if self._future:
self._job = model
self._rest_version = rest_ver
return
model.poll()
model_json = h2o.api("GET /%d/Models/%s" % (rest_ver, model.dest_key))["models"][0]
self._resolve_model(model.dest_key, model_json)
def parse_setup(raw_frames, destination_frame="", header=(-1, 0, 1), separator="", column_names=None,
column_types=None, na_strings=None):
"""During parse setup, the H2O cluster will make several guesses about the attributes of
the data. This method allows a user to perform corrective measures by updating the
returning dictionary from this method. This dictionary is then fed into `parse_raw` to
produce the H2OFrame instance.
Parameters
----------
raw_frames : H2OFrame
A collection of imported file frames
destination_frame : str, optional
The unique hex key assigned to the imported file. If none is given, a key will
automatically be generated.
parse : bool, optional
A logical value indicating whether the file should be parsed after import.
header : int, optional
-1 means the first line is data, 0 means guess, 1 means first line is header.
sep : str, optional
The field separator character. Values on each line of the file are separated by this
character. If sep = "", the parser will automatically detect the separator.
col_names : list, optional
A list of column names for the file.
col_types : list or dict, optional
A list of types or a dictionary of column names to types to specify whether columns
should be forced to a certain type upon import parsing. If a list, the types for
elements that are None will be guessed. The possible types a column may have are:
"unknown" - this will force the column to be parsed as all NA
"uuid" - the values in the column must be true UUID or will be parsed as NA
"string" - force the column to be parsed as a string
"numeric" - force the column to be parsed as numeric. H2O will handle the
compression of the numeric data in the optimal manner.
"enum" - force the column to be parsed as a categorical column.
"time" - force the column to be parsed as a time column. H2O will attempt to
parse the following list of date time formats
date - "yyyy-MM-dd", "yyyy MM dd", "dd-MMM-yy", "dd MMM yy"
time - "HH:mm:ss", "HH:mm:ss:SSS", "HH:mm:ss:SSSnnnnnn", "HH.mm.ss" "HH.mm.ss.SSS",
"HH.mm.ss.SSSnnnnnn"
Times can also contain "AM" or "PM".
na_strings : list or dict, optional
A list of strings, or a list of lists of strings (one list per column), or a
dictionary of column names to strings which are to be interpreted as missing values.
Returns
-------
A dictionary is returned containing all of the guesses made by the H2O back end.
"""
# The H2O backend only accepts things that are quoted
if is_str(raw_frames): raw_frames = [raw_frames]
# temporary dictionary just to pass the following information to the parser: header, separator
kwargs = {}
# set header
if header != (-1, 0, 1):
if header not in (-1, 0, 1): raise ValueError("header should be -1, 0, or 1")
kwargs["check_header"] = header
# set separator
if separator:
if not is_str(separator) or len(separator) != 1:
raise ValueError("separator should be a single character string; got %r" % separator)
kwargs["separator"] = ord(separator)
kwargs["source_frames"] = [quoted(id) for id in raw_frames]
j = api("POST /3/ParseSetup", data=kwargs)
if "warnings" in j and j["warnings"]:
for w in j['warnings']:
warnings.warn(w)
# TODO: really should be url encoding...
if destination_frame: j["destination_frame"] = destination_frame.replace("%", ".").replace("&", ".")
if column_names is not None:
if not isinstance(column_names, list): raise ValueError("col_names should be a list")
if len(column_names) != len(j["column_types"]): raise ValueError(
"length of col_names should be equal to the number of columns")
j["column_names"] = column_names
if column_types is not None:
if isinstance(column_types, dict):
# overwrite dictionary to ordered list of column types. if user didn't specify column type for all names,
# use type provided by backend
if j["column_names"] is None: # no colnames discovered! (C1, C2, ...)
j["column_names"] = gen_header(j["number_columns"])
if not set(column_types.keys()).issubset(set(j["column_names"])): raise ValueError(
"names specified in col_types is not a subset of the column names")
idx = 0
column_types_list = []
for name in j["column_names"]:
if name in column_types:
column_types_list.append(column_types[name])
else:
column_types_list.append(j["column_types"][idx])
idx += 1
column_types = column_types_list
elif isinstance(column_types, list):
if len(column_types) != len(j["column_types"]): raise ValueError(
"length of col_types should be equal to the number of columns")
column_types = [column_types[i] if column_types[i] else j["column_types"][i] for i in
range(len(column_types))]
else: # not dictionary or list
raise ValueError("col_types should be a list of types or a dictionary of column names to types")
j["column_types"] = column_types
if na_strings is not None:
if isinstance(na_strings, dict):
# overwrite dictionary to ordered list of lists of na_strings
if not j["column_names"]: raise ValueError("column names should be specified")
if not set(na_strings.keys()).issubset(set(j["column_names"])): raise ValueError(
"names specified in na_strings is not a subset of the column names")
j["na_strings"] = [[] for _ in range(len(j["column_names"]))]
for name, na in na_strings.items():
idx = j["column_names"].index(name)
if is_str(na): na = [na]
for n in na: j["na_strings"][idx].append(quoted(n))
elif is_list_of_lists(na_strings):
if len(na_strings) != len(j["column_types"]): raise ValueError(
"length of na_strings should be equal to the number of columns")
j["na_strings"] = [[quoted(na) for na in col] if col is not None else [] for col in na_strings]
elif isinstance(na_strings, list):
j["na_strings"] = [[quoted(na) for na in na_strings]] * len(j["column_types"])
else: # not a dictionary or list
raise ValueError(
"na_strings should be a list, a list of lists (one list per column), or a dictionary of column "
"names to strings which are to be interpreted as missing values")
# quote column names and column types also when not specified by user
if j["column_names"]: j["column_names"] = list(map(quoted, j["column_names"]))
j["column_types"] = list(map(quoted, j["column_types"]))
return j
def train(self, x=None, y=None, training_frame=None, offset_column=None, fold_column=None,
weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None,
model_id=None, verbose=False):
"""
Train the H2O model.
:param x: A list of column names or indices indicating the predictor columns.
:param y: An index or a column name indicating the response column.
:param H2OFrame training_frame: The H2OFrame having the columns indicated by x and y (as well as any
additional columns specified by fold, offset, and weights).
:param offset_column: The name or index of the column in training_frame that holds the offsets.
:param fold_column: The name or index of the column in training_frame that holds the per-row fold
assignments.
:param weights_column: The name or index of the column in training_frame that holds the per-row weights.
:param validation_frame: H2OFrame with validation data to be scored on while training.
:param float max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
:param bool verbose: Print scoring history to stdout. Defaults to False.
"""
assert_is_type(training_frame, None, H2OFrame)
assert_is_type(validation_frame, None, H2OFrame)
assert_is_type(y, None, int, str)
assert_is_type(x, None, int, str, [str, int], {str, int})
assert_is_type(ignored_columns, None, [str, int], {str, int})
assert_is_type(offset_column, None, int, str)
assert_is_type(fold_column, None, int, str)
assert_is_type(weights_column, None, int, str)
assert_is_type(max_runtime_secs, None, numeric)
assert_is_type(model_id, None, str)
assert_is_type(verbose, bool)
if self._requires_training_frame() and training_frame is None:
raise H2OValueError("Training frame required for %s algorithm, but none was given.", self.algo)
training_frame_exists = training_frame is None
if training_frame_exists:
self._verify_training_frame_params(offset_column, fold_column, weights_column, validation_frame)
algo = self.algo
if verbose and algo not in ["drf", "gbm", "deeplearning", "xgboost"]:
raise H2OValueError("Verbose should only be set to True for drf, gbm, deeplearning, and xgboost models")
parms = self._parms.copy()
if "__class__" in parms: # FIXME: hackt for PY3
del parms["__class__"]
is_auto_encoder = bool(parms.get("autoencoder"))
is_supervised = not(is_auto_encoder or algo in {"aggregator", "pca", "svd", "kmeans", "glrm", "word2vec"})
if not training_frame_exists:
names = training_frame.names
ncols = training_frame.ncols
if is_supervised:
if y is None: y = "response"
if is_type(y, int):
if not (-ncols <= y < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % y)
y = names[y]
else:
if y not in names:
raise H2OValueError("Column %s does not exist in the training frame" % y)
self._estimator_type = "classifier" if training_frame.types[y] == "enum" else "regressor"
else:
# If `y` is provided for an unsupervised model we'll simply ignore
# it. This way an unsupervised model can be used as a step in
# sklearn's pipeline.
y = None
if not training_frame_exists:
assert_is_type(y, str, None)
ignored_columns_set = set()
if ignored_columns is not None:
if x is not None:
raise H2OValueError("Properties x and ignored_columns cannot be specified simultaneously")
for ic in ignored_columns:
if is_type(ic, int):
if not (-ncols <= ic < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % ic)
ignored_columns_set.add(names[ic])
else:
if ic not in names:
raise H2OValueError("Column %s not in the training frame" % ic)
ignored_columns_set.add(ic)
if x is None:
xset = set(names) - {y} - ignored_columns_set
else:
xset = set()
if is_type(x, int, str): x = [x]
for xi in x:
if is_type(xi, int):
if not (-ncols <= xi < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % xi)
xset.add(names[xi])
else:
if xi not in names:
raise H2OValueError("Column %s not in the training frame" % xi)
xset.add(xi)
x = list(xset)
parms["offset_column"] = offset_column
parms["fold_column"] = fold_column
parms["weights_column"] = weights_column
parms["max_runtime_secs"] = max_runtime_secs
# Overwrites the model_id parameter only if model_id is passed
if model_id is not None:
parms["model_id"] = model_id
# Step 2
is_auto_encoder = "autoencoder" in parms and parms["autoencoder"]
is_unsupervised = is_auto_encoder or self.algo in {"aggregator", "pca", "svd", "kmeans", "glrm", "word2vec"}
if is_auto_encoder and y is not None: raise ValueError("y should not be specified for autoencoder.")
if not is_unsupervised and y is None: raise ValueError("Missing response")
# Step 3
if not training_frame_exists:
parms["training_frame"] = training_frame
offset = parms["offset_column"]
folds = parms["fold_column"]
weights = parms["weights_column"]
if validation_frame is not None: parms["validation_frame"] = validation_frame
if is_type(y, int): y = training_frame.names[y]
if y is not None: parms["response_column"] = y
if not isinstance(x, (list, tuple)): x = [x]
if is_type(x[0], int):
x = [training_frame.names[i] for i in x]
if not training_frame_exists:
ignored_columns = list(set(training_frame.names) - set(x + [y, offset, folds, weights]))
parms["ignored_columns"] = None if ignored_columns == [] else [quoted(col) for col in ignored_columns]
parms["interactions"] = (None if "interactions" not in parms or parms["interactions"] is None else
[quoted(col) for col in parms["interactions"]])
parms["interaction_pairs"] = (None if "interaction_pairs" not in parms or parms["interaction_pairs"] is None else
[tuple(map(quoted, ip)) for ip in parms["interaction_pairs"]])
parms = {k: H2OEstimator._keyify_if_h2oframe(parms[k]) for k in parms}
rest_ver = parms.pop("_rest_version") if "_rest_version" in parms else 3
model_builder_json = h2o.api("POST /%d/ModelBuilders/%s" % (rest_ver, self.algo), data=parms)
model = H2OJob(model_builder_json, job_type=(self.algo + " Model Build"))
if self._future:
self._job = model
self._rest_version = rest_ver
return
model.poll(verbose_model_scoring_history=verbose)
model_json = h2o.api("GET /%d/Models/%s" % (rest_ver, model.dest_key))["models"][0]
self._resolve_model(model.dest_key, model_json)
def _train(self, x=None, y=None, training_frame=None, offset_column=None, fold_column=None,
weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None,
model_id=None, verbose=False, extend_parms_fn=None):
has_default_training_frame = hasattr(self, 'training_frame') and self.training_frame is not None
training_frame = H2OFrame._validate(training_frame, 'training_frame',
required=self._requires_training_frame() and not has_default_training_frame)
validation_frame = H2OFrame._validate(validation_frame, 'validation_frame')
assert_is_type(y, None, int, str)
assert_is_type(x, None, int, str, [str, int], {str, int})
assert_is_type(ignored_columns, None, [str, int], {str, int})
assert_is_type(offset_column, None, int, str)
assert_is_type(fold_column, None, int, str)
assert_is_type(weights_column, None, int, str)
assert_is_type(max_runtime_secs, None, numeric)
assert_is_type(model_id, None, str)
assert_is_type(verbose, bool)
assert_is_type(extend_parms_fn, None, FunctionType)
override_default_training_frame = training_frame is not None
if not override_default_training_frame:
self._verify_training_frame_params(offset_column, fold_column, weights_column, validation_frame)
training_frame = self.training_frame if has_default_training_frame else None
algo = self.algo
if verbose and algo not in ["drf", "gbm", "deeplearning", "xgboost"]:
raise H2OValueError("Verbose should only be set to True for drf, gbm, deeplearning, and xgboost models")
parms = self._parms.copy()
if algo=="pca" and "k" not in parms.keys():
parms["k"] = 1
if "__class__" in parms: # FIXME: hackt for PY3
del parms["__class__"]
is_auto_encoder = bool(parms.get("autoencoder"))
is_supervised = not(is_auto_encoder or algo in {"aggregator", "pca", "svd", "kmeans", "glrm", "word2vec", "isolationforest", "generic"})
names = training_frame.names if training_frame is not None else []
ncols = training_frame.ncols if training_frame is not None else 0
types = training_frame.types if training_frame is not None else {}
if is_supervised:
if y is None: y = "response"
if is_type(y, int):
if not (-ncols <= y < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % y)
y = names[y]
else:
if y not in names:
raise H2OValueError("Column %s does not exist in the training frame" % y)
self._estimator_type = "classifier" if types[y] == "enum" else "regressor"
else:
# If `y` is provided for an unsupervised model we'll simply ignore
# it. This way an unsupervised model can be used as a step in
# sklearn's pipeline.
y = None
if override_default_training_frame:
assert_is_type(y, str, None)
ignored_columns_set = set()
if ignored_columns is None and "ignored_columns" in parms:
ignored_columns = parms['ignored_columns']
if ignored_columns is not None:
if x is not None:
raise H2OValueError("Properties x and ignored_columns cannot be specified simultaneously")
for ic in ignored_columns:
if is_type(ic, int):
if not (-ncols <= ic < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % ic)
ignored_columns_set.add(names[ic])
else:
if ic not in names:
raise H2OValueError("Column %s not in the training frame" % ic)
ignored_columns_set.add(ic)
if x is None:
xset = set(names) - {y} - ignored_columns_set
else:
xset = set()
if is_type(x, int, str): x = [x]
for xi in x:
if is_type(xi, int):
if not (-ncols <= xi < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % xi)
xset.add(names[xi])
else:
if xi not in names:
raise H2OValueError("Column %s not in the training frame" % xi)
xset.add(xi)
x = list(xset)
self._check_and_save_parm(parms, "offset_column", offset_column)
self._check_and_save_parm(parms, "weights_column", weights_column)
self._check_and_save_parm(parms, "fold_column", fold_column)
if max_runtime_secs is not None: parms["max_runtime_secs"] = max_runtime_secs
# Overwrites the model_id parameter only if model_id is passed
if model_id is not None:
parms["model_id"] = model_id
# Step 2
is_auto_encoder = "autoencoder" in parms and parms["autoencoder"]
is_unsupervised = is_auto_encoder or self.algo in {"aggregator", "pca", "svd", "kmeans", "glrm", "word2vec", "isolationforest"}
if is_auto_encoder and y is not None:
raise ValueError("y should not be specified for autoencoder.")
if not is_unsupervised and y is None and self.algo not in ["generic"]:
raise ValueError("Missing response")
# Step 3
if override_default_training_frame:
parms["training_frame"] = training_frame
offset = parms["offset_column"]
folds = parms["fold_column"]
weights = parms["weights_column"]
if validation_frame is not None:
parms["validation_frame"] = validation_frame
if is_type(y, int):
y = names[y]
if y is not None:
parms["response_column"] = y
if not isinstance(x, (list, tuple)):
x = [x]
if is_type(x[0], int):
x = [names[i] for i in x]
if override_default_training_frame:
ignored_columns = list(set(names) - set(x + [y, offset, folds, weights] + self._additional_used_columns(parms)))
parms["ignored_columns"] = None if ignored_columns == [] else [quoted(col) for col in ignored_columns]
parms["interactions"] = (None if "interactions" not in parms or parms["interactions"] is None
else [quoted(col) for col in parms["interactions"]])
parms["interaction_pairs"] = (None if "interaction_pairs" not in parms or parms["interaction_pairs"] is None
else [tuple(map(quoted, ip)) for ip in parms["interaction_pairs"]])
# internal hook allowing subclasses to extend train parms
if extend_parms_fn is not None:
extend_parms_fn(parms)
parms = {k: H2OEstimator._keyify_if_h2oframe(parms[k]) for k in parms}
if ("stopping_metric" in parms.keys()) and ("r2" in parms["stopping_metric"]):
raise H2OValueError("r2 cannot be used as an early stopping_metric yet. Check this JIRA https://0xdata.atlassian.net/browse/PUBDEV-5381 for progress.")
rest_ver = parms.pop("_rest_version") if "_rest_version" in parms else 3
model_builder_json = h2o.api("POST /%d/ModelBuilders/%s" % (rest_ver, self.algo), data=parms)
model = H2OJob(model_builder_json, job_type=(self.algo + " Model Build"))
if self._future:
self._job = model
self._rest_version = rest_ver
return
model.poll(poll_updates=self._print_model_scoring_history if verbose else None)
model_json = h2o.api("GET /%d/Models/%s" % (rest_ver, model.dest_key))["models"][0]
self._resolve_model(model.dest_key, model_json)
def train(self, x=None, y=None, training_frame=None, offset_column=None, fold_column=None,
weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None,
model_id=None, verbose=False):
"""
Train the H2O model.
:param x: A list of column names or indices indicating the predictor columns.
:param y: An index or a column name indicating the response column.
:param H2OFrame training_frame: The H2OFrame having the columns indicated by x and y (as well as any
additional columns specified by fold, offset, and weights).
:param offset_column: The name or index of the column in training_frame that holds the offsets.
:param fold_column: The name or index of the column in training_frame that holds the per-row fold
assignments.
:param weights_column: The name or index of the column in training_frame that holds the per-row weights.
:param validation_frame: H2OFrame with validation data to be scored on while training.
:param float max_runtime_secs: Maximum allowed runtime in seconds for model training. Use 0 to disable.
:param bool verbose: Print scoring history to stdout. Defaults to False.
"""
assert_is_type(training_frame, None, H2OFrame)
assert_is_type(validation_frame, None, H2OFrame)
assert_is_type(y, None, int, str)
assert_is_type(x, None, int, str, [str, int], {str, int})
assert_is_type(ignored_columns, None, [str, int], {str, int})
assert_is_type(offset_column, None, int, str)
assert_is_type(fold_column, None, int, str)
assert_is_type(weights_column, None, int, str)
assert_is_type(max_runtime_secs, None, numeric)
assert_is_type(model_id, None, str)
assert_is_type(verbose, bool)
if self._requires_training_frame() and training_frame is None:
raise H2OValueError("Training frame required for %s algorithm, but none was given.", self.algo)
training_frame_exists = training_frame is None
if training_frame_exists:
self._verify_training_frame_params(offset_column, fold_column, weights_column, validation_frame)
algo = self.algo
if verbose and algo not in ["drf", "gbm", "deeplearning", "xgboost"]:
raise H2OValueError("Verbose should only be set to True for drf, gbm, deeplearning, and xgboost models")
parms = self._parms.copy()
if "__class__" in parms: # FIXME: hackt for PY3
del parms["__class__"]
is_auto_encoder = bool(parms.get("autoencoder"))
is_supervised = not(is_auto_encoder or algo in {"aggregator", "pca", "svd", "kmeans", "glrm", "word2vec"})
if not training_frame_exists:
names = training_frame.names
ncols = training_frame.ncols
if is_supervised:
if y is None: y = "response"
if is_type(y, int):
if not (-ncols <= y < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % y)
y = names[y]
else:
if y not in names:
raise H2OValueError("Column %s does not exist in the training frame" % y)
self._estimator_type = "classifier" if training_frame.types[y] == "enum" else "regressor"
else:
# If `y` is provided for an unsupervised model we'll simply ignore
# it. This way an unsupervised model can be used as a step in
# sklearn's pipeline.
y = None
if not training_frame_exists:
assert_is_type(y, str, None)
ignored_columns_set = set()
if ignored_columns is not None:
if x is not None:
raise H2OValueError("Properties x and ignored_columns cannot be specified simultaneously")
for ic in ignored_columns:
if is_type(ic, int):
if not (-ncols <= ic < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % ic)
ignored_columns_set.add(names[ic])
else:
if ic not in names:
raise H2OValueError("Column %s not in the training frame" % ic)
ignored_columns_set.add(ic)
if x is None:
xset = set(names) - {y} - ignored_columns_set
else:
xset = set()
if is_type(x, int, str): x = [x]
for xi in x:
if is_type(xi, int):
if not (-ncols <= xi < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % xi)
xset.add(names[xi])
else:
if xi not in names:
raise H2OValueError("Column %s not in the training frame" % xi)
xset.add(xi)
x = list(xset)
parms["offset_column"] = offset_column
parms["fold_column"] = fold_column
parms["weights_column"] = weights_column
if max_runtime_secs is not None: parms["max_runtime_secs"] = max_runtime_secs
# Overwrites the model_id parameter only if model_id is passed
if model_id is not None:
parms["model_id"] = model_id
# Step 2
is_auto_encoder = "autoencoder" in parms and parms["autoencoder"]
is_unsupervised = is_auto_encoder or self.algo in {"aggregator", "pca", "svd", "kmeans", "glrm", "word2vec"}
if is_auto_encoder and y is not None: raise ValueError("y should not be specified for autoencoder.")
if not is_unsupervised and y is None: raise ValueError("Missing response")
# Step 3
if not training_frame_exists:
parms["training_frame"] = training_frame
offset = parms["offset_column"]
folds = parms["fold_column"]
weights = parms["weights_column"]
if validation_frame is not None: parms["validation_frame"] = validation_frame
if is_type(y, int): y = training_frame.names[y]
if y is not None: parms["response_column"] = y
if not isinstance(x, (list, tuple)): x = [x]
if is_type(x[0], int):
x = [training_frame.names[i] for i in x]
if not training_frame_exists:
ignored_columns = list(set(training_frame.names) - set(x + [y, offset, folds, weights]))
parms["ignored_columns"] = None if ignored_columns == [] else [quoted(col) for col in ignored_columns]
parms["interactions"] = (None if "interactions" not in parms or parms["interactions"] is None else
[quoted(col) for col in parms["interactions"]])
parms["interaction_pairs"] = (None if "interaction_pairs" not in parms or parms["interaction_pairs"] is None else
[tuple(map(quoted, ip)) for ip in parms["interaction_pairs"]])
parms = {k: H2OEstimator._keyify_if_h2oframe(parms[k]) for k in parms}
if ("stopping_metric" in parms.keys()) and ("r2" in parms["stopping_metric"]):
raise H2OValueError("r2 cannot be used as an early stopping_metric yet. Check this JIRA https://0xdata.atlassian.net/browse/PUBDEV-5381 for progress.")
rest_ver = parms.pop("_rest_version") if "_rest_version" in parms else 3
model_builder_json = h2o.api("POST /%d/ModelBuilders/%s" % (rest_ver, self.algo), data=parms)
model = H2OJob(model_builder_json, job_type=(self.algo + " Model Build"))
if self._future:
self._job = model
self._rest_version = rest_ver
return
model.poll(verbose_model_scoring_history=verbose)
model_json = h2o.api("GET /%d/Models/%s" % (rest_ver, model.dest_key))["models"][0]
self._resolve_model(model.dest_key, model_json)
def _train(self, x=None, y=None, training_frame=None, offset_column=None, fold_column=None,
weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None,
model_id=None, verbose=False, extend_parms_fn=None):
assert_is_type(training_frame, None, H2OFrame)
assert_is_type(validation_frame, None, H2OFrame)
assert_is_type(y, None, int, str)
assert_is_type(x, None, int, str, [str, int], {str, int})
assert_is_type(ignored_columns, None, [str, int], {str, int})
assert_is_type(offset_column, None, int, str)
assert_is_type(fold_column, None, int, str)
assert_is_type(weights_column, None, int, str)
assert_is_type(max_runtime_secs, None, numeric)
assert_is_type(model_id, None, str)
assert_is_type(verbose, bool)
assert_is_type(extend_parms_fn, None, FunctionType)
if self._requires_training_frame() and training_frame is None:
raise H2OValueError("Training frame required for %s algorithm, but none was given." % self.algo)
training_frame_exists = training_frame is None
if training_frame_exists:
self._verify_training_frame_params(offset_column, fold_column, weights_column, validation_frame)
algo = self.algo
if verbose and algo not in ["drf", "gbm", "deeplearning", "xgboost"]:
raise H2OValueError("Verbose should only be set to True for drf, gbm, deeplearning, and xgboost models")
parms = self._parms.copy()
if "__class__" in parms: # FIXME: hackt for PY3
del parms["__class__"]
is_auto_encoder = bool(parms.get("autoencoder"))
is_supervised = not(is_auto_encoder or algo in {"aggregator", "pca", "svd", "kmeans", "glrm", "word2vec", "isolationforest", "generic"})
if not training_frame_exists:
names = training_frame.names
ncols = training_frame.ncols
if is_supervised:
if y is None: y = "response"
if is_type(y, int):
if not (-ncols <= y < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % y)
y = names[y]
else:
if y not in names:
raise H2OValueError("Column %s does not exist in the training frame" % y)
self._estimator_type = "classifier" if training_frame.types[y] == "enum" else "regressor"
else:
# If `y` is provided for an unsupervised model we'll simply ignore
# it. This way an unsupervised model can be used as a step in
# sklearn's pipeline.
y = None
if not training_frame_exists:
assert_is_type(y, str, None)
ignored_columns_set = set()
if ignored_columns is None and "ignored_columns" in parms:
ignored_columns = parms['ignored_columns']
if ignored_columns is not None:
if x is not None:
raise H2OValueError("Properties x and ignored_columns cannot be specified simultaneously")
for ic in ignored_columns:
if is_type(ic, int):
if not (-ncols <= ic < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % ic)
ignored_columns_set.add(names[ic])
else:
if ic not in names:
raise H2OValueError("Column %s not in the training frame" % ic)
ignored_columns_set.add(ic)
if x is None:
xset = set(names) - {y} - ignored_columns_set
else:
xset = set()
if is_type(x, int, str): x = [x]
for xi in x:
if is_type(xi, int):
if not (-ncols <= xi < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % xi)
xset.add(names[xi])
else:
if xi not in names:
raise H2OValueError("Column %s not in the training frame" % xi)
xset.add(xi)
x = list(xset)
self._check_and_save_parm(parms, "offset_column", offset_column)
self._check_and_save_parm(parms, "weights_column", weights_column)
self._check_and_save_parm(parms, "fold_column", fold_column)
if max_runtime_secs is not None: parms["max_runtime_secs"] = max_runtime_secs
# Overwrites the model_id parameter only if model_id is passed
if model_id is not None:
parms["model_id"] = model_id
# Step 2
is_auto_encoder = "autoencoder" in parms and parms["autoencoder"]
is_unsupervised = is_auto_encoder or self.algo in {"aggregator", "pca", "svd", "kmeans", "glrm", "word2vec", "isolationforest"}
if is_auto_encoder and y is not None: raise ValueError("y should not be specified for autoencoder.")
if not is_unsupervised and y is None and self.algo not in ["generic"]: raise ValueError("Missing response")
# Step 3
if not training_frame_exists:
parms["training_frame"] = training_frame
offset = parms["offset_column"]
folds = parms["fold_column"]
weights = parms["weights_column"]
if validation_frame is not None: parms["validation_frame"] = validation_frame
if is_type(y, int): y = training_frame.names[y]
if y is not None: parms["response_column"] = y
if not isinstance(x, (list, tuple)): x = [x]
if is_type(x[0], int):
x = [training_frame.names[i] for i in x]
if not training_frame_exists:
ignored_columns = list(set(training_frame.names) - set(x + [y, offset, folds, weights]))
parms["ignored_columns"] = None if ignored_columns == [] else [quoted(col) for col in ignored_columns]
parms["interactions"] = (None if "interactions" not in parms or parms["interactions"] is None else
[quoted(col) for col in parms["interactions"]])
parms["interaction_pairs"] = (None if "interaction_pairs" not in parms or parms["interaction_pairs"] is None else
[tuple(map(quoted, ip)) for ip in parms["interaction_pairs"]])
# internal hook allowing subclasses to extend train parms
if extend_parms_fn is not None:
extend_parms_fn(parms)
parms = {k: H2OEstimator._keyify_if_h2oframe(parms[k]) for k in parms}
if ("stopping_metric" in parms.keys()) and ("r2" in parms["stopping_metric"]):
raise H2OValueError("r2 cannot be used as an early stopping_metric yet. Check this JIRA https://0xdata.atlassian.net/browse/PUBDEV-5381 for progress.")
rest_ver = parms.pop("_rest_version") if "_rest_version" in parms else 3
model_builder_json = h2o.api("POST /%d/ModelBuilders/%s" % (rest_ver, self.algo), data=parms)
model = H2OJob(model_builder_json, job_type=(self.algo + " Model Build"))
if self._future:
self._job = model
self._rest_version = rest_ver
return
model.poll(verbose_model_scoring_history=verbose)
model_json = h2o.api("GET /%d/Models/%s" % (rest_ver, model.dest_key))["models"][0]
self._resolve_model(model.dest_key, model_json)
def _make_parms(self, x=None, y=None, training_frame=None, offset_column=None, fold_column=None,
weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None,
model_id=None, verbose=False, extend_parms_fn=None):
has_default_training_frame = hasattr(self, 'training_frame') and self.training_frame is not None
training_frame = H2OFrame._validate(training_frame, 'training_frame',
required=self._options_.get('requires_training_frame', True) and not has_default_training_frame)
validation_frame = H2OFrame._validate(validation_frame, 'validation_frame')
assert_is_type(y, None, int, str)
assert_is_type(x, None, int, str, [str, int], {str, int})
assert_is_type(ignored_columns, None, [str, int], {str, int})
assert_is_type(offset_column, None, int, str)
assert_is_type(fold_column, None, int, str)
assert_is_type(weights_column, None, int, str)
assert_is_type(max_runtime_secs, None, numeric)
assert_is_type(model_id, None, str)
assert_is_type(verbose, bool)
assert_is_type(extend_parms_fn, None, FunctionType)
override_default_training_frame = training_frame is not None
if not override_default_training_frame:
self._verify_training_frame_params(offset_column, fold_column, weights_column, validation_frame)
training_frame = self.training_frame if has_default_training_frame else None
if verbose and not self._options_.get('verbose', False):
raise H2OValueError("Verbose mode is not available for %s" % self.__class__.__name__)
parms = self._parms.copy()
names = training_frame.names if training_frame is not None else []
ncols = training_frame.ncols if training_frame is not None else 0
types = training_frame.types if training_frame is not None else {}
if self.supervised_learning:
if y is None: y = "response"
if is_type(y, int):
if not (-ncols <= y < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % y)
y = names[y]
else:
if y not in names:
raise H2OValueError("Column %s does not exist in the training frame" % y)
self._estimator_type = "classifier" if types[y] == "enum" else "regressor"
else:
# If `y` is provided for an unsupervised model we'll simply ignore
# it. This way an unsupervised model can be used as a step in
# sklearn's pipeline.
y = None
self._estimator_type = "unsupervised"
if override_default_training_frame:
assert_is_type(y, str, None)
ignored_columns_set = set()
if ignored_columns is None and "ignored_columns" in parms:
ignored_columns = parms['ignored_columns']
if ignored_columns is not None:
if x is not None:
raise H2OValueError("Properties x and ignored_columns cannot be specified simultaneously")
for ic in ignored_columns:
if is_type(ic, int):
if not (-ncols <= ic < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % ic)
ignored_columns_set.add(names[ic])
else:
if ic not in names:
raise H2OValueError("Column %s not in the training frame" % ic)
ignored_columns_set.add(ic)
if x is None:
xset = set(names) - {y} - ignored_columns_set
else:
xset = set()
if is_type(x, int, str): x = [x]
for xi in x:
if is_type(xi, int):
if not (-ncols <= xi < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % xi)
xset.add(names[xi])
else:
if xi not in names:
raise H2OValueError("Column %s not in the training frame" % xi)
xset.add(xi)
x = list(xset)
self._check_and_save_parm(parms, "offset_column", offset_column)
self._check_and_save_parm(parms, "weights_column", weights_column)
self._check_and_save_parm(parms, "fold_column", fold_column)
if max_runtime_secs is not None: parms["max_runtime_secs"] = max_runtime_secs
# Overwrites the model_id parameter only if model_id is passed
if model_id is not None:
parms["model_id"] = model_id
if override_default_training_frame:
parms["training_frame"] = training_frame
offset = parms["offset_column"]
folds = parms["fold_column"]
weights = parms["weights_column"]
if validation_frame is not None:
parms["validation_frame"] = validation_frame
if is_type(y, int):
y = names[y]
if y is not None:
parms["response_column"] = y
if not isinstance(x, (list, tuple)):
x = [x]
if len(x) > 0 and is_type(x[0], int):
x = [names[i] for i in x]
if override_default_training_frame:
ignored_columns = list(set(names) - set(x + [y, offset, folds, weights]))
parms["ignored_columns"] = None if ignored_columns == [] else [quoted(col) for col in ignored_columns]
parms["interactions"] = (None if "interactions" not in parms or parms["interactions"] is None
else [quoted(col) for col in parms["interactions"]])
parms["interaction_pairs"] = (None if "interaction_pairs" not in parms or parms["interaction_pairs"] is None
else [tuple(map(quoted, ip)) for ip in parms["interaction_pairs"]])
# internal hook allowing subclasses to extend train parms
if extend_parms_fn is not None:
extend_parms_fn(parms)
parms = {k: H2OEstimator._keyify(v) for k, v in parms.items()}
if "r2" in (parms.get('stopping_metric') or []):
raise H2OValueError("r2 cannot be used as an early stopping_metric yet. Check this JIRA https://0xdata.atlassian.net/browse/PUBDEV-5381 for progress.")
return parms
def train(self, x=None, y=None, training_frame=None, offset_column=None, fold_column=None,
weights_column=None, validation_frame=None, max_runtime_secs=None, ignored_columns=None):
"""
Train the H2O model.
Parameters
----------
x : list, None
A list of column names or indices indicating the predictor columns.
y :
An index or a column name indicating the response column.
training_frame : H2OFrame
The H2OFrame having the columns indicated by x and y (as well as any
additional columns specified by fold, offset, and weights).
offset_column : str, optional
The name or index of the column in training_frame that holds the offsets.
fold_column : str, optional
The name or index of the column in training_frame that holds the per-row fold
assignments.
weights_column : str, optional
The name or index of the column in training_frame that holds the per-row weights.
validation_frame : H2OFrame, optional
H2OFrame with validation data to be scored on while training.
max_runtime_secs : float
Maximum allowed runtime in seconds for model training. Use 0 to disable.
"""
assert_is_type(training_frame, H2OFrame)
assert_is_type(validation_frame, None, H2OFrame)
assert_is_type(y, None, int, str)
assert_is_type(x, None, int, str, [str, int], {str, int})
assert_is_type(ignored_columns, None, [str, int], {str, int})
assert_is_type(offset_column, None, int, str)
assert_is_type(fold_column, None, int, str)
assert_is_type(weights_column, None, int, str)
assert_is_type(max_runtime_secs, None, numeric)
algo = self.algo
parms = self._parms.copy()
if "__class__" in parms: # FIXME: hackt for PY3
del parms["__class__"]
is_auto_encoder = bool(parms.get("autoencoder"))
is_supervised = not(is_auto_encoder or algo in {"pca", "svd", "kmeans", "glrm", "word2vec"})
ncols = training_frame.ncols
names = training_frame.names
if is_supervised:
if y is None: y = "response"
if is_type(y, int):
if not (-ncols <= y < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % y)
y = names[y]
else:
if y not in names:
raise H2OValueError("Column %s does not exist in the training frame" % y)
self._estimator_type = "classifier" if training_frame.types[y] == "enum" else "regressor"
elif y is not None:
raise H2OValueError("y should not be provided for an unsupervised model")
assert_is_type(y, str, None)
ignored_columns_set = set()
if ignored_columns is not None:
if x is not None:
raise H2OValueError("Properties x and ignored_columns cannot be specified simultaneously")
for ic in ignored_columns:
if is_type(ic, int):
if not (-ncols <= ic < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % ic)
ignored_columns_set.add(names[ic])
else:
if ic not in names:
raise H2OValueError("Column %s not in the training frame" % ic)
ignored_columns_set.add(ic)
if x is None:
xset = set(names) - {y} - ignored_columns_set
else:
xset = set()
if is_type(x, int, str): x = [x]
for xi in x:
if is_type(xi, int):
if not (-ncols <= xi < ncols):
raise H2OValueError("Column %d does not exist in the training frame" % xi)
xset.add(names[xi])
else:
if xi not in names:
raise H2OValueError("Column %s not in the training frame" % xi)
xset.add(xi)
x = list(xset)
parms["offset_column"] = offset_column
parms["fold_column"] = fold_column
parms["weights_column"] = weights_column
parms["max_runtime_secs"] = max_runtime_secs
# Step 2
is_auto_encoder = "autoencoder" in parms and parms["autoencoder"]
is_unsupervised = is_auto_encoder or self.algo in {"pca", "svd", "kmeans", "glrm", "word2vec"}
if is_auto_encoder and y is not None: raise ValueError("y should not be specified for autoencoder.")
if not is_unsupervised and y is None: raise ValueError("Missing response")
# Step 3
parms["training_frame"] = training_frame
if validation_frame is not None: parms["validation_frame"] = validation_frame
if is_type(y, int): y = training_frame.names[y]
if y is not None: parms["response_column"] = y
if not isinstance(x, (list, tuple)): x = [x]
if is_type(x[0], int):
x = [training_frame.names[i] for i in x]
offset = parms["offset_column"]
folds = parms["fold_column"]
weights = parms["weights_column"]
ignored_columns = list(set(training_frame.names) - set(x + [y, offset, folds, weights]))
parms["ignored_columns"] = None if ignored_columns == [] else [quoted(col) for col in ignored_columns]
parms["interactions"] = (None if "interactions" not in parms or parms["interactions"] is None else
[quoted(col) for col in parms["interactions"]])
parms = {k: H2OEstimator._keyify_if_h2oframe(parms[k]) for k in parms}
rest_ver = parms.pop("_rest_version") if "_rest_version" in parms else 3
model = H2OJob(h2o.api("POST /%d/ModelBuilders/%s" % (rest_ver, self.algo), data=parms),
job_type=(self.algo + " Model Build"))
if self._future:
self._job = model
self._rest_version = rest_ver
return
model.poll()
model_json = h2o.api("GET /%d/Models/%s" % (rest_ver, model.dest_key))["models"][0]
self._resolve_model(model.dest_key, model_json)
