# -*- coding: utf-8 -*-
# import numpy no numpy cuz windoz
import collections, csv, itertools, os, re, tempfile, urllib2, sys, urllib,imp,copy, tabulate
import h2o
from expr import ExprNode
from astfun import _bytecode_decompile_lambda
from group_by import GroupBy
# TODO: Automatically convert column names into Frame properties!
[docs]class H2OFrame(object):
def __init__(self, python_obj=None):
self._ex = ExprNode()
self._ex._children=None
if python_obj is not None:
self._upload_python_object(python_obj)
@property
def columns(self):
"""
Returns
-------
A list of column names.
"""
return self.names
@columns.setter
[docs] def columns(self, value):
"""
Set the column names of this H2OFrame.
Parameters
----------
value : list
"""
self.set_names(value)
@property
def col_names(self):
"""
Returns
-------
A list of column names.
"""
return self.names
@col_names.setter
[docs] def col_names(self, value):
"""
Set the column names of this H2OFrame.
Parameters
----------
value : list
"""
self.set_names(value)
@property
def names(self):
"""Retrieve the column names (one name per H2OVec) for this H2OFrame.
Returns
-------
A str list of column names
"""
if not self._ex._cache.names_valid():
self._ex._cache.flush()
self._frame()._ex._cache.fill()
return self._ex._cache.names
@names.setter
[docs] def names(self,value):
"""
Set the column names of this H2OFrame.
Parameters
----------
value : list
"""
self.set_names(value)
@property
[docs] def nrow(self):
"""
Returns
-------
The number of rows in the H2OFrame.
"""
if not self._ex._cache.nrows_valid():
self._ex._cache.flush()
self._frame()
return self._ex._cache.nrows
@property
[docs] def ncol(self):
"""
Returns
-------
The number of columns in the H2OFrame.
"""
if not self._ex._cache.ncols_valid():
self._ex._cache.flush()
self._frame()
return self._ex._cache.ncols
@property
[docs] def dim(self):
"""
Returns
-------
The number of rows and columns in the H2OFrame as a list [rows, cols].
"""
return [self.nrow, self.ncol]
@property
[docs] def shape(self):
"""
Returns
-------
A tuple (nrow, ncol)
"""
return self.nrow, self.ncol
@property
[docs] def types(self):
"""
Returns
-------
A dictionary of column_name-type pairs.
"""
if not self._ex._cache.types_valid():
self._ex._cache.flush()
self._frame()._ex._cache.fill()
return self._ex._cache.types
@property
def frame_id(self):
"""
Returns
-------
Get the name of this frame.
"""
return self._frame()._ex._cache._id
@frame_id.setter
[docs] def frame_id(self, value):
if self._ex._cache._id is None:
h2o.assign(self, value)
else:
oldname = self.frame_id
self._ex._cache._id = value
h2o.rapids("(rename \"{}\" \"{}\")".format(oldname, value))
@staticmethod
def _expr(expr,cache=None):
fr = H2OFrame()
fr._ex = expr
if cache is not None:
fr._ex._cache.fill_from(cache)
return fr
@staticmethod
[docs] def get_frame(frame_id):
"""Create an H2OFrame mapped to an existing id in the cluster.
Returns
-------
H2OFrame that points to a pre-existing big data H2OFrame in the cluster
"""
fr = H2OFrame()
fr._ex._cache._id = frame_id
fr._ex._cache.fill()
return fr
def _import_parse(self, path, destination_frame, header, separator, column_names, column_types, na_strings):
rawkey = h2o.lazy_import(path)
self._parse(rawkey,destination_frame, header, separator, column_names, column_types, na_strings)
return self
def _upload_parse(self, path, destination_frame, header, sep, column_names, column_types, na_strings):
fui = {"file": os.path.abspath(path)}
rawkey = h2o.H2OConnection.post_json(url_suffix="PostFile", file_upload_info=fui)["destination_frame"]
self._parse(rawkey,destination_frame, header, sep, column_names, column_types, na_strings)
return self
def _upload_python_object(self, python_obj, destination_frame="", header=(-1, 0, 1), separator="", column_names=None, column_types=None, na_strings=None):
# [] and () cases -- folded together since H2OFrame is mutable
if isinstance(python_obj, (list, tuple)): col_header, data_to_write = _handle_python_lists(python_obj)
# {} and collections.OrderedDict cases
elif isinstance(python_obj, (dict, collections.OrderedDict)): col_header, data_to_write = _handle_python_dicts(python_obj)
# handle a numpy.ndarray
# elif isinstance(python_obj, numpy.ndarray):
#
# header, data_to_write = H2OFrame._handle_numpy_array(python_obj)
else: raise ValueError("`python_obj` must be a tuple, list, dict, collections.OrderedDict. Got: " + str(type(python_obj)))
if col_header is None or data_to_write is None: raise ValueError("No data to write")
#
## write python data to file and upload
#
# create a temporary file that will be written to
tmp_handle,tmp_path = tempfile.mkstemp(suffix=".csv")
tmp_file = os.fdopen(tmp_handle,'wb')
# create a new csv writer object thingy
csv_writer = csv.DictWriter(tmp_file, fieldnames=col_header, restval=None, dialect="excel", extrasaction="ignore", delimiter=",")
#csv_writer.writeheader() # write the header
if column_names is None: column_names = col_header
csv_writer.writerows(data_to_write) # write the data
tmp_file.close() # close the streams
self._upload_parse(tmp_path, destination_frame, header, separator, column_names, column_types, na_strings) # actually upload the data to H2O
os.remove(tmp_path) # delete the tmp file
@staticmethod
[docs] def from_python(python_obj, destination_frame="", header=(-1, 0, 1), separator="", column_names=None, column_types=None, na_strings=None):
"""
Properly handle native python data types. For a discussion of the rules and
permissible data types please refer to the main documentation for H2OFrame.
Parameters
----------
python_obj : tuple, list, dict, collections.OrderedDict
If a nested list/tuple, then each nested collection is a column.
destination_frame : str, optional
The unique hex key assigned to the imported file. If none is given, a key will
automatically be generated.
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 new H2OFrame instance.
Examples
--------
>>> l = [[1,2,3,4,5], [99,123,51233,321]]
>>> l = H2OFrame(l)
>>> l
"""
fr = H2OFrame()
fr._upload_python_object(python_obj, destination_frame, header, separator, column_names, column_types, na_strings)
return fr
def _parse(self, rawkey, destination_frame="", header=None, separator=None, column_names=None, column_types=None, na_strings=None):
setup = h2o.parse_setup(rawkey, destination_frame, header, separator, column_names, column_types, na_strings)
return self._parse_raw(setup)
def _parse_raw(self, setup):
# Parse parameters (None values provided by setup)
p = { "destination_frame" : None,
"parse_type" : None,
"separator" : None,
"single_quotes" : None,
"check_header" : None,
"number_columns" : None,
"chunk_size" : None,
"delete_on_done" : True,
"blocking" : False,
"column_types" : None,
}
if setup["column_names"]: p["column_names"] = None
if setup["na_strings"]: p["na_strings"] = None
p.update({k: v for k, v in setup.iteritems() if k in p})
# Extract only 'name' from each src in the array of srcs
p['source_frames'] = [h2o._quoted(src['name']) for src in setup['source_frames']]
h2o.H2OJob(h2o.H2OConnection.post_json(url_suffix="Parse", **p), "Parse").poll()
# Need to return a Frame here for nearly all callers
# ... but job stats returns only a dest_key, requiring another REST call to get nrow/ncol
self._ex._cache._id = p["destination_frame"]
self._ex._cache.fill()
[docs] def filter_na_cols(self, frac=0.2):
"""Filter columns with proportion of NAs >= frac.
Parameters
----------
frac : float
Fraction of NAs in the column.
Returns
-------
A list of column indices
"""
return ExprNode("filterNACols", self, frac)._eager_scalar()
[docs] def type(self,name):
"""
Returns
-------
The type for a named column
"""
return self.types[name]
def __iter__(self):
return (self[i] for i in range(self.ncol))
def __str__(self):
if sys.gettrace() is None:
row_string = ' rows x ' if self.nrow > 1 else ' row x '
column_string = ' columns]' if self.ncol > 1 else ' column]'
return self._frame()._ex._cache._tabulate("simple",False).encode("utf-8", errors="ignore") + '\n\n[' + str(self.nrow) \
+ row_string + str(self.ncol) + column_string
return ""
def __len__(self):
return self.nrow
def __repr__(self):
if sys.gettrace() is None:
self.show()
return ""
[docs] def show(self, use_pandas=False):
"""
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 not self._ex._cache.is_valid(): self._frame()._ex._cache.fill()
if h2o.H2ODisplay._in_ipy():
import IPython.display
if use_pandas and h2o.can_use_pandas():
IPython.display.display(self.head().as_data_frame(True))
else:
IPython.display.display_html(self._ex._cache._tabulate("html",False),raw=True)
else:
if use_pandas and h2o.can_use_pandas():
print self.head().as_data_frame(True)
else:
print self
[docs] def summary(self):
"""Summary: show(), plus includes min/mean/max/sigma and other rollup data"""
if not self._ex._cache.is_valid(): self._frame()._ex._cache.fill()
if h2o.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))
[docs] def describe(self):
"""Generate an in-depth description of this H2OFrame. Everything in summary(), plus
the data layout.
"""
# Force a fetch of 10 rows; the chunk & distribution summaries are not
# cached, so must be pulled. While we're at it, go ahead and fill in
# the default caches if they are not already filled in
res = h2o.H2OConnection.get_json("Frames/"+urllib.quote(self.frame_id)+"?row_count="+str(10))["frames"][0]
self._ex._cache._fill_data(res)
print "Rows:{:,}".format(self.nrow), "Cols:{:,}".format(self.ncol)
res["chunk_summary"].show()
res["distribution_summary"].show()
print("\n")
self.summary()
def _frame(self): self._ex._eager_frame(); return self
[docs] def head(self,rows=10,cols=200):
"""Analogous to Rs `head` call on a data.frame.
Parameters
----------
rows : int, default=10
Number of rows starting from the topmost
cols : int, default=200
Number of columns starting from the leftmost
Returns
-------
An H2OFrame.
"""
nrows = min(self.nrow, rows)
ncols = min(self.ncol, cols)
return self[:nrows,:ncols]
[docs] def tail(self, rows=10, cols=200):
"""Analogous to Rs `tail` call on a data.frame.
Parameters
----------
rows : int, default=10
Number of rows starting from the bottommost
cols: int, default=200
Number of columns starting from the leftmost
Returns
-------
An H2OFrame.
"""
nrows = min(self.nrow, rows)
ncols = min(self.ncol, cols)
start_idx = self.nrow - nrows
return self[start_idx:start_idx+nrows,:ncols]
[docs] def logical_negation(self): return H2OFrame._expr(expr=ExprNode("not", self), cache=self._ex._cache)
# ops
def __add__ (self, i): return H2OFrame._expr(expr=ExprNode("+", self,i), cache=self._ex._cache)
def __sub__ (self, i): return H2OFrame._expr(expr=ExprNode("-", self,i), cache=self._ex._cache)
def __mul__ (self, i): return H2OFrame._expr(expr=ExprNode("*", self,i), cache=self._ex._cache)
def __div__ (self, i): return H2OFrame._expr(expr=ExprNode("/", self,i), cache=self._ex._cache)
def __floordiv__(self, i): return H2OFrame._expr(expr=ExprNode("intDiv",self,i), cache=self._ex._cache)
def __mod__ (self, i): return H2OFrame._expr(expr=ExprNode("mod", self,i), cache=self._ex._cache)
def __or__ (self, i): return H2OFrame._expr(expr=ExprNode("|", self,i), cache=self._ex._cache)
def __and__ (self, i): return H2OFrame._expr(expr=ExprNode("&", self,i), cache=self._ex._cache)
def __ge__ (self, i): return H2OFrame._expr(expr=ExprNode(">=", self,i), cache=self._ex._cache)
def __gt__ (self, i): return H2OFrame._expr(expr=ExprNode(">", self,i), cache=self._ex._cache)
def __le__ (self, i): return H2OFrame._expr(expr=ExprNode("<=", self,i), cache=self._ex._cache)
def __lt__ (self, i): return H2OFrame._expr(expr=ExprNode("<", self,i), cache=self._ex._cache)
def __eq__ (self, i): return H2OFrame._expr(expr=ExprNode("==", self,i), cache=self._ex._cache)
def __ne__ (self, i): return H2OFrame._expr(expr=ExprNode("!=", self,i), cache=self._ex._cache)
def __pow__ (self, i): return H2OFrame._expr(expr=ExprNode("^", self,i), cache=self._ex._cache)
# rops
def __rmod__(self, i): return H2OFrame._expr(expr=ExprNode("mod",i,self), cache=self._ex._cache)
def __radd__(self, i): return self.__add__(i)
def __rsub__(self, i): return H2OFrame._expr(expr=ExprNode("-",i, self), cache=self._ex._cache)
def __rand__(self, i): return self.__and__(i)
def __ror__ (self, i): return self.__or__ (i)
def __rdiv__(self, i): return H2OFrame._expr(expr=ExprNode("/",i, self), cache=self._ex._cache)
def __rfloordiv__(self, i): return H2OFrame._expr(expr=ExprNode("intDiv",i,self), cache=self._ex._cache)
def __rmul__(self, i): return self.__mul__(i)
def __rpow__(self, i): return H2OFrame._expr(expr=ExprNode("^",i, self), cache=self._ex._cache)
# unops
def __abs__ (self): return H2OFrame._expr(expr=ExprNode("abs",self), cache=self._ex._cache)
def __contains__(self, i): return all([(t==self).any() for t in i]) if _is_list(i) else (i==self).any()
def __invert__(self): return H2OFrame._expr(expr=ExprNode("!!", self), cache=self._ex._cache)
[docs] def mult(self, matrix):
"""Perform matrix multiplication.
Parameters
----------
matrix : H2OFrame
The right-hand-side matrix
Returns
-------
H2OFrame result of the matrix multiplication
"""
return H2OFrame._expr(expr=ExprNode("x", self, matrix))
[docs] def cos(self) : return H2OFrame._expr(expr=ExprNode("cos", self), cache=self._ex._cache)
[docs] def sin(self) : return H2OFrame._expr(expr=ExprNode("sin", self), cache=self._ex._cache)
[docs] def tan(self) : return H2OFrame._expr(expr=ExprNode("tan", self), cache=self._ex._cache)
[docs] def acos(self) : return H2OFrame._expr(expr=ExprNode("acos", self), cache=self._ex._cache)
[docs] def asin(self) : return H2OFrame._expr(expr=ExprNode("asin", self), cache=self._ex._cache)
[docs] def atan(self) : return H2OFrame._expr(expr=ExprNode("atan", self), cache=self._ex._cache)
[docs] def cosh(self) : return H2OFrame._expr(expr=ExprNode("cosh", self), cache=self._ex._cache)
[docs] def sinh(self) : return H2OFrame._expr(expr=ExprNode("sinh", self), cache=self._ex._cache)
[docs] def tanh(self) : return H2OFrame._expr(expr=ExprNode("tanh", self), cache=self._ex._cache)
[docs] def acosh(self) : return H2OFrame._expr(expr=ExprNode("acosh", self), cache=self._ex._cache)
[docs] def asinh(self) : return H2OFrame._expr(expr=ExprNode("asinh", self), cache=self._ex._cache)
[docs] def atanh(self) : return H2OFrame._expr(expr=ExprNode("atanh", self), cache=self._ex._cache)
[docs] def cospi(self) : return H2OFrame._expr(expr=ExprNode("cospi", self), cache=self._ex._cache)
[docs] def sinpi(self) : return H2OFrame._expr(expr=ExprNode("sinpi", self), cache=self._ex._cache)
[docs] def tanpi(self) : return H2OFrame._expr(expr=ExprNode("tanpi", self), cache=self._ex._cache)
[docs] def abs(self) : return H2OFrame._expr(expr=ExprNode("abs", self), cache=self._ex._cache)
[docs] def sign(self) : return H2OFrame._expr(expr=ExprNode("sign", self), cache=self._ex._cache)
[docs] def sqrt(self) : return H2OFrame._expr(expr=ExprNode("sqrt", self), cache=self._ex._cache)
[docs] def trunc(self) : return H2OFrame._expr(expr=ExprNode("trunc", self), cache=self._ex._cache)
[docs] def ceil(self) : return H2OFrame._expr(expr=ExprNode("ceiling", self), cache=self._ex._cache)
[docs] def floor(self) : return H2OFrame._expr(expr=ExprNode("floor", self), cache=self._ex._cache)
[docs] def log(self) : return H2OFrame._expr(expr=ExprNode("log", self), cache=self._ex._cache)
[docs] def log10(self) : return H2OFrame._expr(expr=ExprNode("log10", self), cache=self._ex._cache)
[docs] def log1p(self) : return H2OFrame._expr(expr=ExprNode("log1p", self), cache=self._ex._cache)
[docs] def log2(self) : return H2OFrame._expr(expr=ExprNode("log2", self), cache=self._ex._cache)
[docs] def exp(self) : return H2OFrame._expr(expr=ExprNode("exp", self), cache=self._ex._cache)
[docs] def expm1(self) : return H2OFrame._expr(expr=ExprNode("expm1", self), cache=self._ex._cache)
[docs] def gamma(self) : return H2OFrame._expr(expr=ExprNode("gamma", self), cache=self._ex._cache)
[docs] def lgamma(self) : return H2OFrame._expr(expr=ExprNode("lgamma", self), cache=self._ex._cache)
[docs] def digamma(self) : return H2OFrame._expr(expr=ExprNode("digamma", self), cache=self._ex._cache)
[docs] def trigamma(self): return H2OFrame._expr(expr=ExprNode("trigamma", self), cache=self._ex._cache)
@staticmethod
[docs] def mktime(year=1970,month=0,day=0,hour=0,minute=0,second=0,msec=0):
"""All units are zero-based (including months and days).
Missing year is 1970.
Parameters
----------
year : int, H2OFrame
month: int, H2OFrame
day : int, H2OFrame
hour : int, H2OFrame
minute : int, H2OFrame
second : int, H2OFrame
msec : int, H2OFrame
Returns
-------
H2OFrame of one column containing the date in millis since the epoch.
"""
return ExprNode("mktime", year,month,day,hour,minute,second,msec)._eager_frame()
[docs] def unique(self):
"""Extract the unique values in the column.
Returns
-------
H2OFrame of just the unique values in the column.
"""
return H2OFrame._expr(expr=ExprNode("unique", self))
[docs] def levels(self):
"""Get the factor levels.
Returns
-------
A dictionary of column_name:column_levels pairs.
"""
# TODO
lol = H2OFrame._expr(expr=ExprNode("levels", self)).as_data_frame(False)
for l in lol: l.pop(0) # Remove column headers
return lol
[docs] def nlevels(self):
"""Get the number of factor levels for this frame.
Returns
-------
A dictionary of column_name:number_levels pairs.
"""
levels = self.levels()
return [len(l) for l in levels]
[docs] def set_level(self, level):
"""A method to set all column values to one of the levels.
Parameters
----------
level : str
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)
[docs] def set_levels(self, levels):
"""Works on a single categorical column.
New domains must be aligned with the old domains. This call has copy-on-write semantics.
Parameters
----------
levels : list
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.
"""
return H2OFrame._expr(expr=ExprNode("setDomain",self,levels), cache=self._ex._cache)
[docs] def set_names(self,names):
"""Change all of this H2OFrame instance's column names.
Parameters
----------
names : list
A list of strings equal to the number of columns in the H2OFrame.
"""
self._ex = ExprNode("colnames=",self, range(self.ncol), names) # Update-in-place, but still lazy
return self
[docs] def set_name(self,col=None,name=None):
"""Set the name of the column at the specified index.
Parameters
----------
col : int, str
Index of the column whose name is to be set; may be skipped for 1-column frames
name : str
The new name of the column to set
Returns
-------
Returns self.
"""
if isinstance(col, basestring): col = self.names.index(col) # lookup the name
if not isinstance(col, int) and self.ncol > 1: raise ValueError("`col` must be an index. Got: " + str(col))
if self.ncol == 1: col = 0
old_cache = self._ex._cache
self._ex = ExprNode("colnames=",self,col,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] + [name] + self.names[col+1:]
[docs] def as_date(self, format):
"""Return the column with all elements converted to millis since the epoch.
Parameters
----------
format : str
A datetime format string (e.g. "YYYY-mm-dd")
Returns
-------
An H2OFrame instance.
"""
fr = H2OFrame._expr(expr=ExprNode("as.Date",self,format), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {k:"int" for k in self._ex._cache.types.keys()}
return fr
[docs] def cumsum(self):
"""
Returns
-------
The cumulative sum over the column.
"""
return H2OFrame._expr(expr=ExprNode("cumsum",self), cache=self._ex._cache)
[docs] def cumprod(self):
"""
Returns
-------
The cumulative product over the column.
"""
return H2OFrame._expr(expr=ExprNode("cumprod",self), cache=self._ex._cache)
[docs] def cummin(self):
"""
Returns
-------
The cumulative min over the column.
"""
return H2OFrame._expr(expr=ExprNode("cummin",self), cache=self._ex._cache)
[docs] def cummax(self):
"""
Returns
-------
The cumulative max over the column.
"""
return H2OFrame._expr(expr=ExprNode("cummax",self), cache=self._ex._cache)
[docs] def prod(self,na_rm=False):
"""
Parameters
----------
na_rm : bool, default=False
True or False to remove NAs from computation.
Returns
-------
The product of the column.
"""
return ExprNode("prod.na" if na_rm else "prod",self)._eager_scalar()
[docs] def any(self):
"""
Returns
-------
True if any element is True or NA in the column.
"""
return bool(ExprNode("any",self)._eager_scalar())
[docs] def any_na_rm(self):
"""
Returns
-------
True if any element is True in the column.
"""
return bool(ExprNode("any.na",self)._eager_scalar())
[docs] def all(self):
"""
Returns
-------
True if every element is True or NA in the column.
"""
return bool(ExprNode("all",self)._eager_scalar())
[docs] def isnumeric(self):
"""
Returns
-------
True if the column is numeric, otherwise return False
"""
if self._ex._cache.types_valid():
return str(self._ex._cache.types.values()[0]) in ["numeric", "int", "real"]
return bool(ExprNode("is.numeric",self)._eager_scalar())
[docs] def isstring(self):
"""
Returns
-------
True if the column is a string column, otherwise False (same as ischaracter)
"""
return bool(ExprNode("is.character",self)._eager_scalar())
[docs] def ischaracter(self):
"""
Returns
-------
True if the column is a character column, otherwise False (same as isstring)
"""
return self.isstring()
[docs] def kfold_column(self, n_folds=3, seed=-1):
"""Build a fold assignments column for cross-validation. This call will produce a
column having the same data layout as the calling object.
Parameters
----------
n_folds : int
An integer specifying the number of validation sets to split the training data
into.
seed : int, optional
Seed for random numbers as fold IDs are randomly assigned.
Returns
-------
A single column H2OFrame with the fold assignments.
"""
return H2OFrame._expr(expr=ExprNode("kfold_column",self,n_folds,seed))._frame() # want this to be eager!
[docs] 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.
Parameters
----------
n_folds : int
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() # want this to be eager!
[docs] 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.
Parameters
----------
n_folds: int
The number of folds to build.
seed: int
A random seed.
Returns
-------
A single column H2OFrame with the fold assignments.
"""
return H2OFrame._expr(expr=ExprNode("stratified_kfold_column",self,n_folds,seed))._frame() # want this to be eager!
[docs] def structure(self):
"""Similar to R's str method: Compactly Display the Structure of this H2OFrame."""
df = self.as_data_frame(use_pandas=False)
nr = self.nrow
nc = self.ncol
cn = df.pop(0)
width = max([len(c) for c in cn])
isfactor = [c.isfactor() for c in self]
numlevels = [self.nlevels(i) for i in range(nc)]
lvls = self.levels()
print "self._newExpr '{}': \t {} obs. of {} variables(s)".format(self.frame_id,nr,nc)
for i in range(nc):
print "$ {} {}: ".format(cn[i], ' '*(width-max(0,len(cn[i])))),
if isfactor[i]:
nl = numlevels[i]
print "Factor w/ {} level(s) {},..: ".format(nl, '"' + '","'.join(zip(*lvls)[i]) + '"'),
print " ".join(it[0] for it in h2o.as_list(self[:10,i].match(list(zip(*lvls)[i])), False)[1:]),
print "..."
else:
print "num {} ...".format(" ".join(it[0] for it in h2o.as_list(self[:10,i], False)[1:]))
[docs] def as_data_frame(self, use_pandas=False):
"""Obtain the dataset as a python-local object.
Parameters
----------
use_pandas : bool, default=False
A flag specifying whether or not to return a pandas DataFrame.
Returns
-------
A local 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.
"""
url = 'http://' + h2o.H2OConnection.ip() + ':' + str(h2o.H2OConnection.port()) + "/3/DownloadDataset?frame_id=" + urllib.quote(self.frame_id) + "&hex_string=false"
response = urllib2.urlopen(url)
if h2o.can_use_pandas() and use_pandas:
import pandas
df = pandas.read_csv(response,low_memory=False)
time_cols = []
category_cols = []
if self.types is not None:
for col_name in self.names:
xtype = self.type(col_name)
if xtype.lower() == 'time': time_cols.append(col_name)
elif xtype.lower() == 'enum': category_cols.append(col_name)
#change Time to pandas datetime
if time_cols:
#hacky way to get the utc offset
from datetime import datetime
sample_timestamp = 1380610868
utc_offset = 1000 * ((datetime.utcfromtimestamp(sample_timestamp) - datetime.fromtimestamp(sample_timestamp)).total_seconds())
try:
df[time_cols] = (df[time_cols] - utc_offset).astype('datetime64[ms]')
except pandas.tslib.OutOfBoundsDatetime:
pass
#change Enum to pandas category
for cat_col in category_cols: #for loop is required
df[cat_col] = df[cat_col].astype('category')
return df
else:
cr = csv.reader(response)
t_col_list = [[''] if row == [] else row for row in cr]
return [list(x) for x in zip(*t_col_list)]
[docs] def flatten(self):
return ExprNode("flatten",self)._eager_scalar()
def __getitem__(self, item):
"""Frame slicing. Supports R-like row and column slicing.
Parameters
----------
item : tuple, list, string, int
If a tuple, then this indicates both row and column selection. The tuple
must be exactly length 2.
If a list, then this indicates column selection.
If a int, the this indicates a single column to be retrieved at the index.
If a string, then slice on the column with this name.
Returns
-------
An instance of H2OFrame.
Examples
--------
fr[2] # All rows, column 2
fr[-2] # All rows, 2nd column from end
fr[:,-1] # All rows, last column
fr[0:5,:] # first 5 rows, all columns
fr[fr[0] > 1, :] # all rows greater than 1 in the first column, all columns
fr[[1,5,6]] # columns 1, 5, and 6
fr[0:50, [1,2,3]] # first 50 rows, columns 1,2, and 3
"""
# Select columns based on a string, a list of strings, an int or a slice.
# Note that the python column selector handles the case of negative
# selections, or out-of-range selections - without having to compute
# self._ncols in the front-end - which would force eager evaluation just to
# range check in the front-end.
new_ncols=-1
new_nrows=-1
new_names=None
new_types=None
fr=None
flatten=False
if isinstance(item, (basestring,list,int,slice)):
new_ncols,new_names,new_types,item = self._compute_ncol_update(item)
new_nrows = self.nrow
fr = H2OFrame._expr(expr=ExprNode("cols_py",self,item))
elif isinstance(item, (ExprNode, H2OFrame)):
new_ncols = self.ncol
new_names = self.names
new_types = self.types
new_nrows = -1 # have a "big" predicate column -- update cache later on...
fr = H2OFrame._expr(expr=ExprNode("rows",self,item))
elif isinstance(item, tuple):
rows, cols = item
allrows = allcols = False
if isinstance(cols, slice): allcols = all([a is None for a in [cols.start,cols.step,cols.stop]])
if isinstance(rows, slice): allrows = all([a is None for a in [rows.start,rows.step,rows.stop]])
if allrows and allcols: return self # fr[:,:] -> all rows and columns.. return self
if allrows:
new_ncols,new_names,new_types,cols = self._compute_ncol_update(cols)
new_nrows = self.nrow
fr = H2OFrame._expr(expr=ExprNode("cols_py",self,cols)) # fr[:,cols] -> really just a column slice
if allcols:
new_ncols = self.ncol
new_names = self.names
new_types = self.types
new_nrows,rows = self._compute_nrow_update(rows)
fr = H2OFrame._expr(expr=ExprNode("rows",self,rows)) # fr[rows,:] -> really just a row slices
if not allrows and not allcols:
new_ncols,new_names,new_types,cols = self._compute_ncol_update(cols)
new_nrows,rows = self._compute_nrow_update(rows)
fr = H2OFrame._expr(expr=ExprNode("rows", ExprNode("cols_py",self,cols),rows))
flatten = isinstance(rows, int) and isinstance(cols, (basestring,int))
else:
raise ValueError("Unexpected __getitem__ selector: "+str(type(item))+" "+str(item.__class__))
assert fr is not None
# Pythonic: if the row & col selector turn into ints (or a single col
# name), then extract the single element out of the Frame. Otherwise
# return a Frame, EVEN IF the selectors are e.g. slices-of-1-value.
if flatten:
return fr.flatten()
fr._ex._cache.ncols = new_ncols
fr._ex._cache.nrows = new_nrows
fr._ex._cache.names = new_names
fr._ex._cache.types = new_types
return fr
def _compute_ncol_update(self, item): # computes new ncol, names, and types
try :
new_ncols=-1
if isinstance(item, list):
new_ncols = len(item)
if _is_str_list(item):
new_types = {k:self.types[k] for k in item}
new_names = item
else:
new_names = [self.names[i] for i in item]
new_types = {name:self.types[name] for name in new_names}
elif isinstance(item, slice):
start = 0 if item.start is None else item.start
end = min(self.ncol, self.ncol if item.stop is None else item.stop)
if end < 0:
end = self.ncol+end
if item.start is not None or item.stop is not None:
new_ncols = end - start
range_list = range(start,end)
new_names = [self.names[i] for i in range_list]
new_types = {name:self.types[name] for name in new_names}
item = slice(start,end)
elif isinstance(item, (basestring,int)):
new_ncols = 1
if isinstance(item, basestring):
new_names = [item]
new_types = None if item not in self.types else {item:self.types[item]}
else:
new_names = [self.names[item]]
new_types = {new_names[0]:self.types[new_names[0]]}
else:
raise ValueError("Unexpected type: " + str(type(item)))
return [new_ncols,new_names,new_types,item]
except:
return [-1,None,None,item]
def _compute_nrow_update(self, item):
try:
new_nrows=-1
if isinstance(item, list):
new_nrows = len(item)
elif isinstance(item, slice):
start = 0 if item.start is None else item.start
end = self.nrow if item.stop is None else item.stop
if end < 0:
end = self.nrow+end
if item.start is not None or item.stop is not None:
new_nrows = end - start
item = slice(start,end)
elif isinstance(item, H2OFrame):
new_nrows=-1
else:
new_nrows=1
return [new_nrows,item]
except:
return [-1,item]
def __setitem__(self, b, c):
"""Replace or update column(s) in an H2OFrame.
Parameters
----------
b : int, str
A 0-based index or a column name.
c :
The value replacing 'b'
Returns
-------
Returns this H2OFrame.
"""
col_expr=None
row_expr=None
colname=None # When set, we are doing an append
if isinstance(b, basestring): # String column name, could be new or old
if b in self.names:
col_expr = self.names.index(b) # Old, update
else:
col_expr = self.ncol
colname = b # New, append
elif isinstance(b, int): col_expr = b # Column by number
elif isinstance(b, tuple): # Both row and col specifiers
row_expr = b[0]
col_expr = b[1]
if isinstance(col_expr, basestring): # Col by name
if col_expr not in self.names: # Append
colname = col_expr
col_expr = self.ncol
elif isinstance(col_expr, slice): # Col by slice
if col_expr.start is None and col_expr.stop is None:
col_expr = slice(0,self.ncol) # Slice of all
elif isinstance(b, ExprNode): row_expr = b # Row slicing
elif isinstance(b, list): col_expr = b
src = float("nan") if c is None else c
src_in_self = self.is_src_in_self(src)
old_cache = self._ex._cache
if colname is None:
self._ex = ExprNode(":=",self,src,col_expr,row_expr)
self._ex._cache.fill_from(old_cache)
if isinstance(src, H2OFrame) and \
src._ex._cache.types_valid() and \
self._ex._cache.types_valid():
self._ex._cache._types.update(src._ex._cache.types)
else:
self._ex._cache.types = None
else:
self._ex = ExprNode("append",self,src,colname)
self._ex._cache.fill_from(old_cache)
self._ex._cache.names = self.names + [colname]
if not self._ex._cache.types_valid() or \
not isinstance(src, H2OFrame) or \
not src._ex._cache.types_valid():
self._ex._cache.types = None
else:
self._ex._cache._types[colname] = src._ex._cache.types.values()[0]
if isinstance(src, H2OFrame) and src_in_self:
src._ex=None # wipe out to keep ref counts correct
self._frame() # setitem is eager
[docs] def is_src_in_self(self,src):
# src._ex._children[0]._children[0] is self._ex
if isinstance(src, H2OFrame):
if self._ex is src._ex:
return True
else:
if src._ex._children is not None:
for ch in src._ex._children:
if self.is_src_in_self(ch): return True
elif isinstance(src, ExprNode):
if self._ex is src: return True
else:
if src._children is not None:
for ch in src._children:
if self.is_src_in_self(ch): return True
return False
def __int__(self):
if self.ncol != 1 or self.nrow != 1: raise ValueError("Not a 1x1 Frame")
return int(self.flatten())
def __float__(self):
if self.ncol != 1 or self.nrow != 1: raise ValueError("Not a 1x1 Frame")
return float(self.flatten())
[docs] def drop(self, i):
"""Drop a column from the current H2OFrame.
Parameters
----------
i : str, int
The column to be dropped
Returns
-------
H2OFrame with the column at index i dropped. Returns a new H2OFrame.
"""
if isinstance(i, basestring): i = self.names.index(i)
fr = H2OFrame._expr(expr=ExprNode("cols", self,-(i+1)), cache=self._ex._cache)
fr._ex._cache.ncols -= 1
fr._ex._cache.names = self.names[:i] + self.names[i+1:]
fr._ex._cache.types = {name:self.types[name] for name in fr._ex._cache.names}
return fr
[docs] def pop(self,i):
"""Pop a column from the H2OFrame at index i
Parameters
----------
i : int, str
The index or name of the column to pop.
Returns
-------
The column dropped from the frame; the frame is side-effected to lose the column
"""
if isinstance(i, basestring): 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}
col._ex._cache.ncols = 1
col._ex._cache.names = [old_cache.names[i]]
return col
[docs] def quantile(self, prob=None, combine_method="interpolate"):
"""Compute quantiles.
Parameters
----------
prob : list, default=[0.01,0.1,0.25,0.333,0.5,0.667,0.75,0.9,0.99]
A list of probabilities of any length.
combine_method : str, default="interpolate"
For even samples, how to combine quantiles.
Should be one of ["interpolate", "average", "low", "hi"]
Returns
-------
A new H2OFrame containing the quantiles and probabilities.
"""
if len(self) == 0: return self
if not prob: prob=[0.01,0.1,0.25,0.333,0.5,0.667,0.75,0.9,0.99]
return H2OFrame._expr(expr=ExprNode("quantile",self,prob,combine_method))
[docs] def cbind(self,data):
"""Append data to this H2OFrame column-wise.
Parameters
----------
data : H2OFrame
H2OFrame to be column bound to the right of this H2OFrame.
Returns
-------
H2OFrame of the combined datasets.
"""
fr = H2OFrame._expr(expr=ExprNode("cbind", self, data), cache=self._ex._cache)
fr._ex._cache.ncols = self.ncol + data.ncol
fr._ex._cache.names = None # invalidate for possibly duplicate names
fr._ex._cache.types = None # invalidate for possibly duplicate names
return fr
[docs] def rbind(self, data):
"""Combine H2O Datasets by rows.
Takes a sequence of H2O data sets and combines them by rows.
Parameters
----------
data : H2OFrame
Returns
-------
Returns this H2OFrame with data appended row-wise.
"""
if not isinstance(data, H2OFrame): raise ValueError("`data` must be an H2OFrame, but got {0}".format(type(data)))
fr = H2OFrame._expr(expr=ExprNode("rbind", self, data), cache=self._ex._cache)
fr._ex._cache.nrows=self.nrow + data.nrow
return fr
[docs] 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.
Parameters
----------
ratios : list
The fraction of rows for each split.
destination_frames : list
The names of the split frames.
seed : int
Used for selecting which H2OFrame a row will belong to.
Returns
-------
A list of H2OFrame instances
"""
if ratios is None:
ratios = [0.75]
if len(ratios) < 1:
raise ValueError("Ratios must have length of at least 1")
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)
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
return splits
[docs] def ddply(self,cols,fun):
"""Unimplemented"""
raise ValueError("unimpl")
[docs] def group_by(self,by):
"""Returns a new GroupBy object using this frame and the desired grouping columns.
The returned groups are sorted by the natural group-by column sort.
Parameters
----------
by : list
The columns to group on.
Returns
-------
A new GroupBy object.
"""
return GroupBy(self,by)
[docs] def impute(self,column,method="mean",combine_method="interpolate",by=None):
"""Impute a column in this H2OFrame
Parameters
----------
column : int, str
The column to impute
method: str, default="mean"
How to compute the imputation value.
combine_method: str, default="interpolate"
For even samples and method="median", how to combine quantiles.
by : list
Columns to group-by for computing imputation value per groups of columns.
Returns
-------
An H2OFrame with the desired column's NAs filled with imputed values.
Note that the returned Frame is in conceptually a new Frame, but due
to back-end optimizations is frequently not actually a copy.
"""
if isinstance(column, basestring): column = self.names.index(column)
if isinstance(by, basestring): by = self.names.index(by)
return H2OFrame._expr(expr=ExprNode("h2o.impute", self, column, method, combine_method, by), cache=self._ex._cache)
[docs] def merge(self, other, allLeft=True, allRite=False):
"""Merge two datasets based on common column names
Parameters
----------
other: H2OFrame
Other dataset to merge. Must have at least one column in common with self,
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.
allLeft: bool, default=True
If True, include all rows from the left/self frame
allRite: bool, default=True
If True, include all rows from the right/other frame
Returns
-------
Original self frame enhanced with merged columns and rows
"""
return H2OFrame._expr(expr=ExprNode("merge", self, other, allLeft, allRite))
[docs] def insert_missing_values(self, fraction=0.1, seed=None):
"""Inserting Missing Values into an H2OFrame.
*This is primarily used for testing*.
Randomly replaces a user-specified fraction of entries in a H2O dataset with missing
values.
WARNING: This will modify the original dataset. Unless this is intended, this
function should only be called on a subset of the original.
Parameters
----------
fraction : float
A number between 0 and 1 indicating the fraction of entries to replace with missing.
seed : int
A random number used to select which entries to replace with missing values.
Returns
-------
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.H2OConnection.post_json("MissingInserter", **kwargs)
h2o.H2OJob(job, job_type=("Insert Missing Values")).poll()
return self
[docs] def min(self):
"""
Returns
-------
The minimum value of all frame entries
"""
return ExprNode("min", self)._eager_scalar()
[docs] def max(self):
"""
Returns
-------
The maximum value of all frame entries
"""
return ExprNode("max", self)._eager_scalar()
[docs] def sum(self, na_rm=False):
"""
Returns
-------
The sum of all frame entries
"""
return ExprNode("sumNA" if na_rm else "sum", self)._eager_scalar()
[docs] def mean(self,na_rm=False):
"""Compute the mean.
Parameters
----------
na_rm: bool, default=False
If True, then remove NAs from the computation.
Returns
-------
A list containing the mean for each column (NaN for non-numeric columns).
"""
return ExprNode("mean", self, na_rm)._eager_scalar()
[docs] def var(self,y=None,use="everything"):
"""Compute the variance, or co-variance matrix.
Parameters
----------
y : H2OFrame, default=None
If y is None, then the variance is computed for self. If self has more than one
column, then the covariance matrix is returned.
If y is not None, then the covariance between self and y is computed (self and y
must therefore both be single columns).
use : str
One of "everything", "complete.obs", or "all.obs"
Returns
-------
The covariance matrix of the columns in this H2OFrame if y is given, or a eagerly
computed scalar if y is not given.
"""
if y is None: y = self
if self.nrow==1 or (self.ncol==1 and y.ncol==1): return ExprNode("var",self,y,use)._eager_scalar()
return H2OFrame._expr(expr=ExprNode("var",self,y,use))._frame()
[docs] def sd(self, na_rm=False):
"""Compute the standard deviation.
Parameters
----------
na_rm : bool, default=False
Remove NAs from the computation.
Returns
-------
A list containing the standard deviation for each column (NaN for non-numeric
columns).
"""
return ExprNode("sd", self, na_rm)._eager_scalar()
[docs] def asfactor(self):
"""
Returns
-------
H2Oframe of one column converted to a factor.
"""
fr = H2OFrame._expr(expr=ExprNode("as.factor",self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {fr._ex._cache.types.keys()[0]:"enum"}
return fr
[docs] def isfactor(self):
#TODO: list for fr.ncol > 1 ?
"""Test if the selection is a factor column.
Returns
-------
True if the column is categorical; otherwise False. For String columns, the result is
False.
"""
if self._ex._cache.types_valid():
return str(self._ex._cache.types.values()[0]) == "enum"
return bool(ExprNode("is.factor", self)._eager_scalar())
[docs] def anyfactor(self):
"""Test if H2OFrame has any factor columns.
Returns
-------
True if there are any categorical columns; False otherwise.
"""
return bool(ExprNode("any.factor", self)._eager_scalar())
[docs] def transpose(self):
"""Transpose rows and columns of H2OFrame.
Returns
-------
The transpose of the input frame.
"""
return H2OFrame._expr(expr=ExprNode("t", self))
[docs] def strsplit(self, pattern):
"""Split the strings in the target column on the given pattern
Parameters
----------
pattern : str
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
[docs] def countmatches(self, pattern):
"""For each string in the column, count the occurrences of pattern.
Parameters
----------
pattern : str
The pattern to count matches on in each string.
Returns
-------
A single-column H2OFrame containing the counts for the per-row occurrences of
pattern in the input column.
"""
fr = H2OFrame._expr(expr=ExprNode("countmatches", self, pattern))
fr._ex._cache.nrows = self.nrow
fr._ex._cache.ncols = self.ncol
return fr
[docs] def trim(self):
"""Trim white space on the left and right of strings in a single-column H2OFrame.
Returns
-------
H2OFrame with trimmed strings.
"""
fr = H2OFrame._expr(expr=ExprNode("trim", self))
fr._ex._cache.nrows = self.nrow
fr._ex._cache.ncol = self.ncol
return fr
[docs] def nchar(self):
"""Count the number of characters in each string of single-column H2OFrame.
Returns
-------
A single-column H2OFrame containing the per-row character count.
"""
return H2OFrame._expr(expr=ExprNode("length", self))
[docs] def table(self, data2=None):
"""Compute the counts of values appearing in a column, or co-occurence counts between
two columns.
Parameters
----------
data2 : H2OFrame
Default is None, can be an optional single column to aggregate counts by.
Returns
-------
H2OFrame of the counts at each combination of factor levels
"""
return H2OFrame._expr(expr=ExprNode("table",self,data2)) if data2 is not None else H2OFrame._expr(expr=ExprNode("table",self))
[docs] def hist(self, breaks="Sturges", plot=True, **kwargs):
"""Compute a histogram over a numeric column.
Parameters
----------
breaks: str, int, list
Can be one of "Sturges", "Rice", "sqrt", "Doane", "FD", "Scott."
Can be a single number for the number of breaks.
Can be a list containing sthe split points, e.g., [-50,213.2123,9324834]
If breaks is "FD", the MAD is used over the IQR in computing bin width.
plot : bool, default=True
If True, then a plot is generated
Returns
-------
If plot is False, return H2OFrame with these columns: breaks, counts, mids_true,
mids, and density; otherwise produce the plot.
"""
frame = H2OFrame._expr(expr=ExprNode("hist", self, breaks))._frame()
total = frame["counts"].sum(True)
densities = [(frame[i,"counts"]/total)*(1/(frame[i,"breaks"]-frame[i-1,"breaks"])) for i in range(1,frame["counts"].nrow)]
densities.insert(0,0)
densities_frame = H2OFrame(densities)
densities_frame.set_names(["density"])
frame = frame.cbind(densities_frame)
if plot:
try:
imp.find_module('matplotlib')
import matplotlib
if 'server' in kwargs.keys() and kwargs['server']: matplotlib.use('Agg', warn=False)
import matplotlib.pyplot as plt
except ImportError:
print "matplotlib is required to make the histogram plot. Set `plot` to False, if a plot is not desired."
return
lower = float(frame[0,"breaks"])
clist = h2o.as_list(frame["counts"], use_pandas=False)
clist = zip(*clist)
clist.pop(0)
clist.pop(0)
mlist = h2o.as_list(frame["mids"], use_pandas=False)
mlist = zip(*mlist)
mlist.pop(0)
mlist.pop(0)
counts = [float(c[0]) for c in clist]
counts.insert(0,0)
mids = [float(m[0]) for m in mlist]
mids.insert(0,lower)
plt.xlabel(self.names[0])
plt.ylabel('Frequency')
plt.title('Histogram of {0}'.format(self.names[0]))
plt.bar(mids, counts)
if not ('server' in kwargs.keys() and kwargs['server']): plt.show()
else: return frame
[docs] def sub(self, pattern, replacement, ignore_case=False):
"""Substitute the first occurrence of pattern in a string with replacement.
Parameters
----------
pattern : str
A regular expression.
replacement : str
A replacement string.
ignore_case : bool
If True then pattern will match against upper and lower case.
Returns
-------
H2OFrame
"""
return H2OFrame._expr(expr=ExprNode("replacefirst", self, pattern, replacement, ignore_case))
[docs] def gsub(self, pattern, replacement, ignore_case=False):
"""Globally substitute occurrences of pattern in a string with replacement.
Parameters
----------
pattern : str
A regular expression.
replacement : str
A replacement string.
ignore_case : bool
If True then pattern will match against upper and lower case.
Returns
-------
H2OFrame
"""
return H2OFrame._expr(expr=ExprNode("replaceall", self, pattern, replacement, ignore_case))
[docs] def interaction(self, 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
----------
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, optional
A string indicating the destination key.
Returns
-------
H2OFrame
"""
return h2o.interaction(data=self, factors=factors, pairwise=pairwise, max_factors=max_factors,
min_occurrence=min_occurrence, destination_frame=destination_frame)
[docs] def toupper(self):
"""Translate characters from lower to upper case for a particular column
Returns
-------
H2OFrame
"""
return H2OFrame._expr(expr=ExprNode("toupper", self), cache=self._ex._cache)
[docs] def tolower(self):
"""Translate characters from upper to lower case for a particular column
Returns
-------
H2OFrame
"""
return H2OFrame._expr(expr=ExprNode("tolower", self), cache=self._ex._cache)
[docs] def rep_len(self, length_out):
"""Replicates the values in `data` in the H2O backend
Parameters
----------
length_out : int
Number of columns of the resulting H2OFrame
Returns
-------
H2OFrame
"""
return H2OFrame._expr(expr=ExprNode("rep_len", self, length_out))
[docs] def scale(self, center=True, scale=True):
"""Centers and/or scales the columns of the self._newExpr
Parameters
----------
center : bool, list
If True, then demean the data by the mean. If False, no shifting is done.
If a list, then shift each column by the given amount in the list.
scale : bool, list
If True, then scale the data by the column standard deviation. If False, no scaling
is done.
If a list, then scale each column by the given amount in the list.
Returns
-------
H2OFrame
"""
return H2OFrame._expr(expr=ExprNode("scale", self, center, scale), cache=self._ex._cache)
[docs] def signif(self, digits=6):
"""Round doubles/floats to the given number of significant digits.
Parameters
----------
digits : int, default=6
Number of significant digits to round doubles/floats.
Returns
-------
H2OFrame
"""
return H2OFrame._expr(expr=ExprNode("signif", self, digits), cache=self._ex._cache)
[docs] def round(self, digits=0):
"""Round doubles/floats to the given number of digits.
Parameters
----------
digits : int
Returns
-------
H2OFrame
"""
return H2OFrame._expr(expr=ExprNode("round", self, digits), cache=self._ex._cache)
[docs] def asnumeric(self):
"""All factor columns converted to numeric.
Returns
-------
H2OFrame
"""
fr = H2OFrame._expr(expr=ExprNode("as.numeric", self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {k:"real" for k in fr._ex._cache.types.keys()}
return fr
[docs] def ascharacter(self):
"""All columns converted to String columns
Returns
-------
H2OFrame
"""
fr = H2OFrame._expr(expr=ExprNode("as.character", self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {k:"string" for k in fr._ex._cache.types.keys()}
return fr
[docs] def na_omit(self):
"""Remove rows with NAs from the H2OFrame.
Returns
-------
H2OFrame
"""
fr = H2OFrame._expr(expr=ExprNode("na.omit", self), cache=self._ex._cache)
fr._ex._cache.nrows=-1
return fr
[docs] def isna(self):
"""For each row in a column, determine if it is NA or not.
Returns
-------
Single-column H2OFrame of 1s and 0s. 1 means the value was NA.
"""
fr = H2OFrame._expr(expr=ExprNode("is.na", self))
fr._ex._cache.nrows = self._ex._cache.nrows
return fr
[docs] def year(self):
"""
Returns
-------
Year column from a msec-since-Epoch column
"""
fr = H2OFrame._expr(expr=ExprNode("year", self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {k:"int" for k in self._ex._cache.types.keys()}
return fr
[docs] def month(self):
"""
Returns
-------
Month column from a msec-since-Epoch column
"""
fr = H2OFrame._expr(expr=ExprNode("month", self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {k:"int" for k in self._ex._cache.types.keys()}
return fr
[docs] def week(self):
"""
Returns
-------
Week column from a msec-since-Epoch column
"""
fr = H2OFrame._expr(expr=ExprNode("week", self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {k:"int" for k in self._ex._cache.types.keys()}
return fr
[docs] def day(self):
"""
Returns
-------
Day column from a msec-since-Epoch column
"""
fr = H2OFrame._expr(expr=ExprNode("day", self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {k:"int" for k in self._ex._cache.types.keys()}
return fr
[docs] def dayOfWeek(self):
"""
Returns
-------
Day-of-Week column from a msec-since-Epoch column
"""
fr = H2OFrame._expr(expr=ExprNode("dayOfWeek", self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {k:"int" for k in self._ex._cache.types.keys()}
return fr
[docs] def hour(self):
"""
Returns
-------
Hour-of-Day column from a msec-since-Epoch column
"""
fr = H2OFrame._expr(expr=ExprNode("hour", self), cache=self._ex._cache)
if fr._ex._cache.types_valid():
fr._ex._cache.types = {k:"int" for k in self._ex._cache.types.keys()}
return fr
[docs] def runif(self, seed=None):
"""Generate a column of random numbers drawn from a uniform distribution [0,1) and
having the same data layout as the calling H2OFrame instance.
Parameters
----------
seed : int, optional
A random seed. If None, then one will be generated.
Returns
-------
Single-column H2OFrame filled with doubles sampled uniformly from [0,1).
"""
fr = H2OFrame._expr(expr=ExprNode("h2o.runif", self, -1 if seed is None else seed))
fr._ex._cache.ncols=1
fr._ex._cache.nrows=self.nrow
return fr
[docs] def stratified_split(self,test_frac=0.2,seed=-1):
"""Construct a column that can be used to perform a random stratified split.
Parameters
----------
test_frac : float, default=0.2
The fraction of rows that will belong to the "test".
seed : int
For seeding the random splitting.
Returns
-------
A categorical column of two levels "train" and "test".
Examples
--------
>>> my_stratified_split = my_frame["response"].stratified_split(test_frac=0.3,seed=12349453)
>>> train = my_frame[my_stratified_split=="train"]
>>> test = my_frame[my_stratified_split=="test"]
# check the distributions among the initial frame, and the train/test frames match
>>> my_frame["response"].table()["Count"] / my_frame["response"].table()["Count"].sum()
>>> train["response"].table()["Count"] / train["response"].table()["Count"].sum()
>>> test["response"].table()["Count"] / test["response"].table()["Count"].sum()
"""
return H2OFrame._expr(expr=ExprNode('h2o.random_stratified_split', self, test_frac, seed))
[docs] def match(self, table, nomatch=0):
"""
Makes a vector of the positions of (first) matches of its first argument in its second.
:param table:
:param nomatch:
:return: H2OFrame of one boolean column
"""
return H2OFrame._expr(expr=ExprNode("match", self, table, nomatch, None))
[docs] def cut(self, breaks, labels=None, include_lowest=False, right=True, dig_lab=3):
"""Cut a numeric vector into factor "buckets". Similar to R's cut method.
Parameters
----------
breaks : list
The cut points in the numeric vector (must span the range of the col.)
labels: list
Factor labels, defaults to set notation of intervals defined by breaks.
include_lowest : bool
By default, cuts are defined as (lo,hi]. If True, get [lo,hi].
right : bool
Include the high value: (lo,hi]. If False, get (lo,hi).
dig_lab: int
Number of digits following the decimal point to consider.
Returns
-------
Single-column H2OFrame of categorical data.
"""
fr = H2OFrame._expr(expr=ExprNode("cut",self,breaks,labels,include_lowest,right,dig_lab), cache=self._ex._cache)
fr._ex._cache.ncols = 1
fr._ex._cache.nrows = self.nrow
fr._ex._cache.types = {k:"enum" for k in self.names}
return fr
[docs] def which(self):
"""Equivalent to [ index for index,value in enumerate(self) if value ]
Returns
-------
Single-column H2OFrame filled with 0-based indices for which the elements are not
zero.
"""
return H2OFrame._expr(expr=ExprNode("which",self))
[docs] def ifelse(self,yes,no):
"""Equivalent to [y if t else n for t,y,n in zip(self,yes,no)]
Based on the booleans in the test vector, the output has the values of the
yes and no vectors interleaved (or merged together). All Frames must have
the same row count. Single column frames are broadened to match wider
Frames. Scalars are allowed, and are also broadened to match wider frames.
Parameters
----------
test : H2OFrame (self)
Frame of values treated as booleans; may be a single column
yes : H2OFrame
Frame to use if [test] is true ; may be a scalar or single column
no : H2OFrame
Frame to use if [test] is false; may be a scalar or single column
Returns
-------
H2OFrame of the merged yes/no Frames/scalars according to the test input frame.
"""
return H2OFrame._expr(expr=ExprNode("ifelse",self,yes,no))
[docs] def apply(self, fun=None, axis=0):
"""Apply a lambda expression to an H2OFrame.
Parameters
----------
fun: lambda
A lambda expression to be applied per row or per column
axis: int
0: apply to each column; 1: apply to each row
Returns
-------
H2OFrame
"""
if axis not in [0,1]:
raise ValueError("margin must be either 0 (cols) or 1 (rows).")
if fun is None:
raise ValueError("No function to apply.")
if isinstance(fun, type(lambda:0)) and fun.__name__ == (lambda:0).__name__: # have lambda
res = _bytecode_decompile_lambda(fun.func_code)
return H2OFrame._expr(expr=ExprNode("apply",self, 1+(axis==0),*res))
else:
raise ValueError("unimpl: not a lambda")
@staticmethod
[docs] def temp_ctr():
global _id_ctr
return _id_ctr
# private static methods
_id_ctr = 0
def _py_tmp_key():
global _id_ctr
_id_ctr=_id_ctr+1
return "py_" + str(_id_ctr)
def _gen_header(cols): return ["C" + str(c) for c in range(1, cols + 1, 1)]
def _check_lists_of_lists(python_obj):
# all items in the list must be a list too
lol_all = all(isinstance(l, (tuple, list)) for l in python_obj)
# All items in the list must be a list!
if not lol_all:
raise ValueError("`python_obj` is a mixture of nested lists and other types.")
# in fact, we must have a list of flat lists!
for l in python_obj:
if any(isinstance(ll, (tuple, list)) for ll in l):
raise ValueError("`python_obj` is not a list of flat lists!")
def _handle_python_lists(python_obj):
cols = len(python_obj) # cols will be len(python_obj) if not a list of lists
lol = _is_list_of_lists(python_obj) # do we have a list of lists: [[...], ..., [...]] ?
if lol:
_check_lists_of_lists(python_obj) # must be a list of flat lists, raise ValueError if not
else:
cols=1
python_obj=[python_obj]
# create the header
header = _gen_header(cols)
# shape up the data for csv.DictWriter
rows = map(list, itertools.izip_longest(*python_obj))
data_to_write = [dict(zip(header,row)) for row in rows]
return header, data_to_write
def _is_list(l) : return isinstance(l, (tuple, list))
def _is_str_list(l): return isinstance(l, (tuple, list)) and all([isinstance(i,basestring) for i in l])
def _is_num_list(l): return isinstance(l, (tuple, list)) and all([isinstance(i,(float,int)) for i in l])
def _is_list_of_lists(o): return any(isinstance(l, (list, tuple)) for l in o)
def _handle_numpy_array(python_obj): return _handle_python_lists(python_obj=python_obj.tolist())
def _handle_pandas_data_frame(python_obj): return _handle_numpy_array(python_obj=python_obj.as_matrix())
def _handle_python_dicts(python_obj):
header = python_obj.keys()
is_valid = all([re.match(r'^[a-zA-Z_][a-zA-Z0-9_.]*$', col) for col in header]) # is this a valid header?
if not is_valid:
raise ValueError("Did not get a valid set of column names! Must match the regular expression: ^[a-zA-Z_][a-zA-Z0-9_.]*$ ")
for k in python_obj: # check that each value entry is a flat list/tuple
v = python_obj[k]
if isinstance(v, (tuple, list)): # if value is a tuple/list, then it must be flat
if _is_list_of_lists(v):
raise ValueError("Values in the dictionary must be flattened!")
rows = map(list, itertools.izip_longest(*python_obj.values()))
data_to_write = [dict(zip(header, row)) for row in rows]
return header, data_to_write