def __init__(self, context, inputData, inputMask=None, inputState=None):
"""Create a DataTable from a type-context, input data,
possible input masks, and possible input states.
For maximum flexibility, very few assumptions are made about
the format of C{inputData}. It need only have a structure
that is equivalent to a dictionary mapping strings (field
names) to lists of values (data columns). Numpy
U{record arrays<http://docs.scipy.org/doc/numpy/user/basics.rec.html>},
U{NpzFiles <http://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html>},
and U{Pandas data frames<http://pandas.pydata.org/>}
effectively present their data in this format because::
inputData[fieldName]
yields a column of values. Regardless of the input type,
these values are then interpreted by the C{context} to set
their PMML type.
The length of the resulting DataTable is equal to the length
of the shortest DataColumn. Generally, one should use
equal-length arrays to build a DataTable.
@type context: PmmlBinding, FieldType, string, dict, or None
@param context: If a rooted PmmlBinding, use the PMML's DataDictionary to interpret C{inputData}. If a FieldType, use that FieldType to interpret all fields. If a string, use that dataType (e.g. "integer", "dateDaysSince[1960]") to interpret all fields. If a dictionary from field names to FieldTypes or dataType strings, use them on a per-field basis. Otherwise, assume a FieldType from the Numpy C{dtype}. The last option only works if all C{inputData} columns are Numpy arrays.
@type inputData: any dict-like mapping from strings to lists
@param inputData: Maps field names (strings) to columns of data (lists or Numpy arrays) that are interpreted by C{context}.
@type inputMask: dict-like mapping from strings to lists of bool, or None
@param inputMask: If None, missing data are identified by C{NaN} values in the C{inputData} (Pandas convention). Otherwise, C{NaN} or a True value in the corresponding {inputMask} would label a data item as MISSING.
@type inputState: DataTableState or None
@param inputState: Initial state of the DataTable. To continue a previous calculation, use the C{dataTable.state} from the previous calculation.
@raise TypeError: If the C{inputData} columns are not Numpy arrays and a C{context} is not given, this method raises an error.
"""
if isinstance(context, PmmlBinding) and len(context.xpath("ancestor-or-self::pmml:PMML")) != 0:
# get types from PMML
dataColumns = OrderedDict()
for fieldName, fieldDefinition in context.fieldContext().items():
fieldType = FieldType(fieldDefinition)
try:
dataField = inputData[fieldName]
except KeyError:
dataField = None
else:
try:
maskField = inputMask[fieldName]
except (KeyError, TypeError):
maskField = None
if dataField is not None:
dataColumns[fieldName] = fieldType.toDataColumn(dataField, maskField)
else:
if not isinstance(context, dict):
context = dict((x, context) for x in inputData)
if all(isinstance(x, FieldType) for x in context.values()):
# FieldTypes provided explicitly
dataColumns = OrderedDict()
for fieldName in sorted(context.keys()):
data = inputData[fieldName]
if inputMask is None:
mask = None
else:
mask = inputMask[fieldName]
dataColumns[fieldName] = context[fieldName].toDataColumn(data, mask)
elif all(isinstance(x, basestring) for x in context.values()):
# FieldTypes provided by dataType name
dataColumns = OrderedDict()
for fieldName in sorted(context.keys()):
data = inputData[fieldName]
if inputMask is None:
mask = None
else:
mask = inputMask[fieldName]
if context[fieldName] == "string":
fieldType = FakeFieldType(context[fieldName], "categorical")
else:
fieldType = FakeFieldType(context[fieldName], "continuous")
dataColumns[fieldName] = fieldType.toDataColumn(data, mask)
elif all(isinstance(inputData[x], NP.ndarray) for x in inputData.keys()):
# FieldTypes provided by NumPy types
dataColumns = OrderedDict()
for fieldName in sorted(context.keys()):
data = inputData[fieldName]
if inputMask is None:
mask = None
else:
mask = inputMask[fieldName]
if data.dtype in (NP.object, NP.object0, NP.object_, NP.str, NP.str_, NP.string0, NP.string_) or re.match("\|S[0-9]+", str(data.dtype)) is not None:
fieldType = FakeFieldType("string", "categorical")
elif data.dtype in (NP.int, NP.int0, NP.int8, NP.int16, NP.int32, NP.int64, NP.int_, NP.integer):
fieldType = FakeFieldType("integer", "continuous")
elif data.dtype in (NP.float, NP.__getattr__("float16", noneIfMissing=True), NP.float32):
fieldType = FakeFieldType("float", "continuous")
elif data.dtype in (NP.float64, NP.float128, NP.float_, NP.double):
fieldType = FakeFieldType("double", "continuous")
elif data.dtype in (NP.bool, NP.bool8, NP.bool_):
fieldType = FakeFieldType("boolean", "continuous")
else:
raise TypeError("Unrecognized NumPy dtype: %r" % data.dtype)
dataColumns[fieldName] = fieldType.toDataColumn(data, mask)
else:
raise TypeError("Context must be PMML (anchored by a <PMML> ancestor), a dictionary of FieldType objects, dataType strings, or inputData must consist entirely of NumPy arrays")
self._configure(dataColumns, inputState)
def __init__(self, context, inputData, inputMask=None, inputState=None):
"""Create a DataTable from a type-context, input data,
possible input masks, and possible input states.
For maximum flexibility, very few assumptions are made about
the format of C{inputData}. It need only have a structure
that is equivalent to a dictionary mapping strings (field
names) to lists of values (data columns). Numpy
U{record arrays<http://docs.scipy.org/doc/numpy/user/basics.rec.html>},
U{NpzFiles <http://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html>},
and U{Pandas data frames<http://pandas.pydata.org/>}
effectively present their data in this format because::
inputData[fieldName]
yields a column of values. Regardless of the input type,
these values are then interpreted by the C{context} to set
their PMML type.
The length of the resulting DataTable is equal to the length
of the shortest DataColumn. Generally, one should use
equal-length arrays to build a DataTable.
@type context: PmmlBinding, FieldType, string, dict, or None
@param context: If a rooted PmmlBinding, use the PMML's DataDictionary to interpret C{inputData}. If a FieldType, use that FieldType to interpret all fields. If a string, use that dataType (e.g. "integer", "dateDaysSince[1960]") to interpret all fields. If a dictionary from field names to FieldTypes or dataType strings, use them on a per-field basis. Otherwise, assume a FieldType from the Numpy C{dtype}. The last option only works if all C{inputData} columns are Numpy arrays.
@type inputData: any dict-like mapping from strings to lists
@param inputData: Maps field names (strings) to columns of data (lists or Numpy arrays) that are interpreted by C{context}.
@type inputMask: dict-like mapping from strings to lists of bool, or None
@param inputMask: If None, missing data are identified by C{NaN} values in the C{inputData} (Pandas convention). Otherwise, C{NaN} or a True value in the corresponding {inputMask} would label a data item as MISSING.
@type inputState: DataTableState or None
@param inputState: Initial state of the DataTable. To continue a previous calculation, use the C{dataTable.state} from the previous calculation.
@raise TypeError: If the C{inputData} columns are not Numpy arrays and a C{context} is not given, this method raises an error.
"""
if isinstance(context, PmmlBinding) and len(
context.xpath("ancestor-or-self::pmml:PMML")) != 0:
# get types from PMML
dataColumns = OrderedDict()
for fieldName, fieldDefinition in context.fieldContext().items():
fieldType = FieldType(fieldDefinition)
try:
dataField = inputData[fieldName]
except KeyError:
dataField = None
else:
try:
maskField = inputMask[fieldName]
except (KeyError, TypeError):
maskField = None
if dataField is not None:
dataColumns[fieldName] = fieldType.toDataColumn(
dataField, maskField)
else:
if not isinstance(context, dict):
context = dict((x, context) for x in inputData)
if all(isinstance(x, FieldType) for x in context.values()):
# FieldTypes provided explicitly
dataColumns = OrderedDict()
for fieldName in sorted(context.keys()):
data = inputData[fieldName]
if inputMask is None:
mask = None
else:
mask = inputMask[fieldName]
dataColumns[fieldName] = context[fieldName].toDataColumn(
data, mask)
elif all(isinstance(x, basestring) for x in context.values()):
# FieldTypes provided by dataType name
dataColumns = OrderedDict()
for fieldName in sorted(context.keys()):
data = inputData[fieldName]
if inputMask is None:
mask = None
else:
mask = inputMask[fieldName]
if context[fieldName] == "string":
fieldType = FakeFieldType(context[fieldName],
"categorical")
else:
fieldType = FakeFieldType(context[fieldName],
"continuous")
dataColumns[fieldName] = fieldType.toDataColumn(data, mask)
elif all(
isinstance(inputData[x], NP.ndarray)
for x in inputData.keys()):
# FieldTypes provided by NumPy types
dataColumns = OrderedDict()
for fieldName in sorted(context.keys()):
data = inputData[fieldName]
if inputMask is None:
mask = None
else:
mask = inputMask[fieldName]
if data.dtype in (NP.object, NP.object0, NP.object_,
NP.str, NP.str_, NP.string0,
NP.string_) or re.match(
"\|S[0-9]+", str(
data.dtype)) is not None:
fieldType = FakeFieldType("string", "categorical")
elif data.dtype in (NP.int, NP.int0, NP.int8, NP.int16,
NP.int32, NP.int64, NP.int_,
NP.integer):
fieldType = FakeFieldType("integer", "continuous")
elif data.dtype in (NP.float,
NP.__getattr__("float16",
noneIfMissing=True),
NP.float32):
fieldType = FakeFieldType("float", "continuous")
elif data.dtype in (NP.float64, NP.float128, NP.float_,
NP.double):
fieldType = FakeFieldType("double", "continuous")
elif data.dtype in (NP.bool, NP.bool8, NP.bool_):
fieldType = FakeFieldType("boolean", "continuous")
else:
raise TypeError("Unrecognized NumPy dtype: %r" %
data.dtype)
dataColumns[fieldName] = fieldType.toDataColumn(data, mask)
else:
raise TypeError(
"Context must be PMML (anchored by a <PMML> ancestor), a dictionary of FieldType objects, dataType strings, or inputData must consist entirely of NumPy arrays"
)
self._configure(dataColumns, inputState)
