# Copyright 2022 The SQLNet Company GmbH
#
# This file is licensed under the Elastic License 2.0 (ELv2).
# Refer to the LICENSE.txt file in the root of the repository
# for details.
#
"""
A container for the placeholders.
"""
from copy import deepcopy
from inspect import cleandoc
from getml.utilities.formatting import _Formatter
from .diagram import _Diagram
from .helpers import _is_typed_list
from .placeholder import Placeholder
from .staging import _make_staging_overview
[docs]class DataModel:
"""Abstract representation of the relationship between tables.
You might also want to refer to :class:`~getml.data.Placeholder`.
Args:
population (:class:`~getml.data.Placeholder`):
The placeholder representing the population table,
which defines the
`statistical population <https://en.wikipedia.org/wiki/Statistical_population>`_
and contains the targets.
Examples:
This example will construct a data model in which the
'population_table' depends on the 'peripheral_table' via
the 'join_key' column. In addition, only those rows in
'peripheral_table' for which 'time_stamp' is smaller or
equal to the 'time_stamp' in 'population_table' are considered:
.. code-block:: python
dm = getml.data.DataModel(
population_table.to_placeholder("POPULATION")
)
dm.add(peripheral_table.to_placeholder("PERIPHERAL"))
dm.POPULATION.join(
dm.PERIPHERAL,
on="join_key",
time_stamps="time_stamp"
)
If you want to add more than one peripheral table, you can
use :func:`~getml.data.to_placeholder`:
.. code-block:: python
dm = getml.data.DataModel(
population_table.to_placeholder("POPULATION")
)
dm.add(
getml.data.to_placeholder(
PERIPHERAL1=peripheral_table_1,
PERIPHERAL2=peripheral_table_2,
)
)
If the relationship between two tables is many-to-one or one-to-one
you should clearly say so:
.. code-block:: python
dm.POPULATION.join(
dm.PERIPHERAL,
on="join_key",
time_stamps="time_stamp",
relationship=getml.data.relationship.many_to_one,
)
Please also refer to :mod:`~getml.data.relationship`.
If the join keys or time stamps are named differently in the two
different tables, use a tuple:
.. code-block:: python
dm.POPULATION.join(
dm.PERIPHERAL,
on=("join_key", "other_join_key"),
time_stamps=("time_stamp", "other_time_stamp"),
)
You can join over more than one join key:
.. code-block:: python
dm.POPULATION.join(
dm.PERIPHERAL,
on=["join_key1", "join_key2", ("join_key3", "other_join_key3")],
time_stamps="time_stamp",
)
You can also limit the scope of your joins using *memory*. This
can significantly speed up training time. For instance, if you
only want to consider data from the last seven days, you could
do something like this:
.. code-block:: python
dm.POPULATION.join(
dm.PERIPHERAL,
on="join_key",
time_stamps="time_stamp",
memory=getml.data.time.days(7),
)
In some use cases, particularly those involving time series, it
might be a good idea to use targets from the past. You can activate
this using *lagged_targets*. But if you do that, you must
also define a prediction *horizon*. For instance, if you want to
predict data for the next hour, using data from the last seven days,
you could do this:
.. code-block:: python
dm.POPULATION.join(
dm.PERIPHERAL,
on="join_key",
time_stamps="time_stamp",
lagged_targets=True,
horizon=getml.data.time.hours(1),
memory=getml.data.time.days(7),
)
Please also refer to :mod:`~getml.data.time`.
If the join involves many matches, it might be a good idea to set the
relationship to :const:`~getml.data.relationship.propositionalization`.
This forces the pipeline to always use a propositionalization
algorithm for this join, which can significantly speed things up.
.. code-block:: python
dm.POPULATION.join(
dm.PERIPHERAL,
on="join_key",
time_stamps="time_stamp",
relationship=getml.data.relationship.propositionalization,
)
Please also refer to :mod:`~getml.data.relationship`.
In some cases, it is necessary to have more than one placeholder
on the same table. This is necessary to create more complicated
data models. In this case, you can do something like this:
.. code-block:: python
dm.add(
getml.data.to_placeholder(
PERIPHERAL=[peripheral_table]*2,
)
)
# We can now access our two placeholders like this:
placeholder1 = dm.PERIPHERAL[0]
placeholder2 = dm.PERIPHERAL[1]
If you want to check out a real-world example where this
is necessary, refer to the
`CORA notebook <https://nbviewer.getml.com/github/getml/getml-demo/blob/master/cora.ipynb>`_.
"""
def __init__(self, population):
if isinstance(population, str):
population = Placeholder(population)
if not isinstance(population, Placeholder):
raise TypeError(
"'population' must be a getml.data.Placeholder or a str, got "
+ type(population).__name__
+ "."
)
self.population = population
self.peripheral = {}
def _add(self, placeholder):
if placeholder.name in self.peripheral:
try:
self.peripheral[placeholder.name].append(placeholder)
except AttributeError:
self.peripheral[placeholder.name] = [
self.peripheral[placeholder.name],
placeholder,
]
else:
self.peripheral.update({placeholder.name: placeholder})
def __dir__(self):
attrs = dir(type(self)) + list(vars(self))
attrs.extend(self.names)
return attrs
def __getattr__(self, key):
try:
return self[key]
except KeyError:
super().__getattribute__(key)
def __getitem__(self, key):
population = vars(self)["population"]
peripheral = vars(self)["peripheral"]
phs = {
"population": population,
population.name: population,
**peripheral,
}
return phs[key]
def _getml_deserialize(self):
def deserialize(elem):
return (
[e._getml_deserialize() for e in elem]
if isinstance(elem, list)
else elem._getml_deserialize()
)
cmd = self.population._getml_deserialize()
cmd["peripheral_"] = {k: deserialize(v) for (k, v) in self.peripheral.items()}
return cmd
def __iter__(self):
yield from [self.population.name] + ["population"] + list(self.peripheral)
def __repr__(self):
return "\n\n".join(repr(ph) for ph in self.population.to_list())
def _make_diagram(self):
return _Diagram(self.population).to_html()
def _make_staging(self):
headers = [["data frames", "staging table"]]
rows = _make_staging_overview(self.population)
staging_table = _Formatter(headers=headers, rows=rows)._render_html()
return staging_table
def _repr_html_(self):
output = cleandoc(
f"""
<div style='margin-top: 15px; margin-bottom: 5px;'>
<div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>
{self._make_diagram()}
</div>
<div style='margin-top: 15px;'>
<div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>
{self._make_staging()}
</div>
"""
)
return output
[docs] def add(self, *placeholders):
"""
Adds peripheral placeholders to the data model.
Args:
placeholders (:class:`~getml.data.Placeholder`):
The placeholder or placeholders you would like to add.
"""
def to_list(elem):
return elem if isinstance(elem, list) else [elem]
# We want to be 100% sure that all handles are unique,
# so we need deepcopy.
placeholders_dc = [
deepcopy(ph) for elem in placeholders for ph in to_list(elem)
]
if not _is_typed_list(placeholders_dc, Placeholder):
raise TypeError(
"'placeholders' must consist of getml.data.Placeholders "
+ "or lists thereof."
)
for placeholder in placeholders_dc:
self._add(placeholder)
@property
def names(self):
"""
A list of the names of all tables contained in the DataModel.
"""
return [name for name in self]