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"""Abstract representation of tables and their relations."""
# ------------------------------------------------------------------------------
from dataclasses import dataclass, fields
from inspect import cleandoc
from textwrap import indent
from typing import List, Optional, Sequence, Tuple, TypeVar, Union
from getml.utilities.formatting import _SignatureFormatter
from .helpers import _check_join_key, _handle_on, _handle_ts
from .relationship import many_to_many
from .roles_obj import Roles
# ------------------------------------------------------------------------------
T = TypeVar("T")
Pair = Union[List[T], Tuple[T], T]
# ------------------------------------------------------------------------------
[docs]class Placeholder:
"""Abstract representation of tables and their relations.
This class is an abstract representation of the
:class:`~getml.DataFrame` or :class:`~getml.data.View`.
However, it does not contain any actual data.
You might also want to refer to :class:`~getml.data.DataModel`.
Args:
name (str):
The name used for this placeholder. This name will appear
in the generated SQL code.
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, name, roles=None):
self.name = name
if roles is None:
self._roles = Roles()
elif isinstance(roles, dict):
self._roles = Roles(**roles)
else:
self._roles = roles
self.joins = []
self.parent = None
def __dir__(self):
attrs = dir(type(self)) + list(self.__dict__.keys())
attrs.extend(col for col in self.columns if col.isidentifier())
return attrs
def __getattr__(self, key):
try:
return self[key]
except KeyError:
super().__getattribute__(key)
def __getitem__(self, key):
if key in vars(self)["_roles"].columns:
return key
else:
raise KeyError(
f"No column with with name {key!r} on the Placeholder's signature."
)
def _getml_deserialize(self):
cmd = {"name_": self.name, "roles_": self.roles.to_dict()}
cmd["allow_lagged_targets_"] = [join.lagged_targets for join in self.joins]
cmd["horizon_"] = [join.horizon or 0.0 for join in self.joins]
cmd["join_keys_used_"] = [_handle_on(join.on)[0] for join in self.joins]
cmd["joined_tables_"] = [join.right._getml_deserialize() for join in self.joins]
cmd["memory_"] = [join.memory or 0.0 for join in self.joins]
cmd["other_join_keys_used_"] = [_handle_on(join.on)[1] for join in self.joins]
cmd["other_time_stamps_used_"] = [
_handle_ts(join.time_stamps)[1] for join in self.joins
]
cmd["relationship_"] = [join.relationship for join in self.joins]
cmd["time_stamps_used_"] = [
_handle_ts(join.time_stamps)[0] for join in self.joins
]
cmd["upper_time_stamps_used_"] = [
join.upper_time_stamp or "" for join in self.joins
]
return cmd
def __repr__(self):
template = cleandoc(
"""
{name}:
columns:
{cols}
"""
)
if self.joins:
template += "\n\n" + cleandoc(
"""
joins:
{joins}
"""
)
def format_on(on):
template = "({left.name}.{on[0]}, {join.right.name}.{on[1]})"
if isinstance(on, list) and all(isinstance(key, tuple) for key in on):
formatted = "\n- " + "\n- ".join(
template.format(on=keys, left=self, join=join) for keys in on
)
return indent(formatted, " " * 2)
return template.format(on=on, left=self, join=join)
cols = [
f"- {col}: {role}" for col, role in zip(self.columns, self.roles.to_list())
]
if len(cols) > 5:
cols = cols[:5] + ["- ..."]
joins = []
for join in self.joins:
for param, value in vars(join).items():
if param == "right":
joins.append(f"- right: {join.right.name!r}")
continue
if value is not None:
if param == "on":
joins.append(f" on: {format_on(value)}")
elif param == "time_stamps":
joins.append(
f" {param}: ({self.name}.{value[0]}, {join.right.name}.{value[1]})"
)
else:
joins.append(f" {param}: {value!r}")
joins = indent("\n".join(joins), " " * 2)
cols = indent("\n".join(cols), " " * 2)
return template.format(name=self.name, cols=cols, joins=joins)
def _ipython_key_completions_(self):
return self.columns
@property
def children(self):
return set([self]) ^ set(self.to_list())
[docs] def join(
self,
right,
on=None,
time_stamps=None,
relationship=many_to_many,
memory=None,
horizon=None,
lagged_targets=False,
upper_time_stamp=None,
):
"""
Joins another to placeholder to this placeholder.
Args:
right (:class:`~getml.data.Placeholder`):
The placeholder you would like to join.
on (None, string, Tuple[str] or List[Union[str, Tuple[str]]]):
The join keys to use. If none is passed, then everything
will be joined to everything else.
time_stamps (string or Tuple[str]):
The time stamps used to limit the join.
relationship (str):
The relationship between the two tables. Must be from
:mod:`~getml.data.relationship`.
memory (float):
The difference between the time stamps until data is 'forgotten'.
Limiting your joins using memory can significantly speed up
training time. Also refer to :mod:`~getml.data.time`.
horizon (float):
The prediction horizon to apply to this join.
Also refer to :mod:`~getml.data.time`.
lagged_targets (bool):
Whether you want to allow lagged targets. If this is set to True,
you must also pass a positive, non-zero *horizon*.
upper_time_stamp (str):
Name of a time stamp in *right* that serves as an upper limit
on the join.
"""
if not isinstance(right, type(self)):
msg = (
"'right' must be a getml.data.Placeholder. "
+ "You can create a placeholder by calling .to_placeholder() "
+ "on DataFrames or Views."
)
raise TypeError(msg)
if self in right.to_list():
raise ValueError(
"Cicular references to other placeholders are not allowed."
)
if isinstance(on, str):
on = (on, on)
if isinstance(time_stamps, str):
time_stamps = (time_stamps, time_stamps)
if isinstance(on, list) and all(isinstance(key, tuple) for key in on):
keys = list(zip(*on))
else:
keys = on
for i, ph in enumerate([self, right]):
if ph.roles.join_key and on:
not_a_join_key = _check_join_key(keys[i], ph.roles.join_key)
if not_a_join_key:
raise ValueError(f"Not a join key: {not_a_join_key}.")
if ph.roles.time_stamp and time_stamps:
if time_stamps[i] not in ph.roles.time_stamp:
raise ValueError(f"Not a time stamp: {time_stamps[i]}.")
if lagged_targets and horizon in (0.0, None):
raise ValueError(
"If you allow lagged targets, then you must also set a "
+ "horizon > 0.0. This is to avoid 'easter eggs'."
)
if horizon not in (0.0, None) and time_stamps is None:
raise ValueError(
"Setting 'horizon' (i.e. a relative look-back window) "
+ "requires a 'time_stamp'."
)
if memory not in (0.0, None) and time_stamps is None:
raise ValueError(
"Setting 'memory' (i.e. a relative look-back window) "
+ "requires a 'time_stamp'."
)
join = Join(
right=right,
on=on,
time_stamps=time_stamps,
relationship=relationship,
memory=memory,
horizon=horizon,
lagged_targets=lagged_targets,
upper_time_stamp=upper_time_stamp,
)
if any(join == existing for existing in self.joins):
raise ValueError(
"A join with the following set of parameters already exists on "
f"the placeholder {self.name!r}:"
f"\n\n{join}\n\n"
"Redundant joins are not allowed."
)
self.joins.append(join)
right.parent = self
@property
def population(self):
if self.parent is None:
return self
return self.parent.population
@property
def roles(self):
return self._roles
@roles.setter
def roles(self, roles):
if not isinstance(roles, (Roles, dict)):
raise TypeError("'roles' must be a dict or getml.data.Roles")
if isinstance(roles, dict):
self._roles = Roles(**roles)
else:
self._roles = roles
[docs] def to_list(self):
"""
Returns a list of this placeholder and all of its descendants.
"""
return [self] + [ph for join in self.joins for ph in join.right.to_list()]
[docs] def to_dict(self):
"""
Expresses this placeholder and all of its descendants as a dictionary.
"""
phs = {}
for ph in self.to_list():
key = ph.name
if ph.children:
i = 2
while key in phs:
key = f"{ph.name}{i}"
i += 1
phs[key] = ph
return phs
@property
def columns(self):
return self.roles.columns
# ------------------------------------------------------------------------------
@dataclass
class Join:
right: Placeholder
on: Optional[ # pytype: disable=invalid-annotation
Union[Pair, Sequence[Pair]] # pytype: disable=invalid-annotation
] = None # pytype: disable=invalid-annotation
time_stamps: Optional[ # pytype: disable=invalid-annotation
Union[Pair, Sequence[Pair]] # pytype: disable=invalid-annotation
] = None # pytype: disable=invalid-annotation
upper_time_stamp: str = ""
relationship: Optional[str] = many_to_many
memory: Optional[float] = None
horizon: Optional[float] = None
lagged_targets: Optional[bool] = None
def __eq__(self, other):
return vars(self) == vars(other)
def __getitem__(self, key):
try:
return getattr(self, key)
except TypeError:
raise KeyError(key)
def __iter__(self):
yield from vars(self)
def __len__(self):
return len(fields(self))
def __repr__(self):
sig = _SignatureFormatter(self)
sig.data["right"] = self.right.name
return sig._format()