# Copyright 2021 The SQLNet Company GmbH
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# DEALINGS IN THE SOFTWARE.
"""
For keeping and versioning data.
"""
from datetime import datetime
from inspect import cleandoc
import getml.communication as comm
from getml.data.columns import StringColumn, StringColumnView, from_value
from getml.log import logger
from getml.utilities.formatting import _Formatter
from .data_frame import DataFrame
from .helpers import _is_typed_dict, _make_id
from .helpers2 import (
_deep_copy,
_get_last_change,
_make_subsets_from_kwargs,
_make_subsets_from_split,
)
from .subset import Subset
from .view import View
[docs]class Container:
"""
A container holds the actual data in the form of a :class:`~getml.DataFrame` or a :class:`~getml.data.View`.
The purpose of a container is twofold:
- Assigning concrete data to an abstract :class:`~getml.data.DataModel`.
- Storing data and allowing you to reproduce previous results.
Args:
population (:class:`~getml.DataFrame` or :class:`~getml.data.View`, optional):
The population table defines the
`statistical population <https://en.wikipedia.org/wiki/Statistical_population>`_
of the machine learning problem and contains the target variables.
peripheral (dict, optional):
The peripheral tables are joined onto *population* or other
peripheral tables. Note that you can also pass them using
:meth:`~getml.data.Container.add`.
split (:class:`~getml.data.columns.StringColumn` or :class:`~getml.data.columns.StringColumnView`, optional):
Contains information on how you want to split *population* into
different :class:`~getml.data.Subset` s.
Also refer to :mod:`~getml.data.split`.
deep_copy (bool, optional):
Whether you want to create deep copies or your tables.
train (:class:`~getml.DataFrame` or :class:`~getml.data.View`, optional):
The population table used in the *train*
:class:`~getml.data.Subset`.
You can either pass *population* and *split* or you can pass
the subsets separately using *train*, *validation*, *test*
and *kwargs*.
validation (:class:`~getml.DataFrame` or :class:`~getml.data.View`, optional):
The population table used in the *validation*
:class:`~getml.data.Subset`.
You can either pass *population* and *split* or you can pass
the subsets separately using *train*, *validation*, *test*
and *kwargs*.
test (:class:`~getml.DataFrame` or :class:`~getml.data.View`, optional):
The population table used in the *test*
:class:`~getml.data.Subset`.
You can either pass *population* and *split* or you can pass
the subsets separately using *train*, *validation*, *test*
and *kwargs*.
kwargs (:class:`~getml.DataFrame` or :class:`~getml.data.View`, optional):
The population table used in :class:`~getml.data.Subset` s
other than the predefined *train*, *validation* and *test* subsets.
You can call these subsets anything you want to and can access them
just like *train*, *validation* and *test*.
You can either pass *population* and *split* or you can pass
the subsets separately using *train*, *validation*, *test*
and *kwargs*.
Example:
.. code-block:: python
# Pass the subset.
container = getml.data.Container(my_subset=my_data_frame)
# You can access the subset just like train,
# validation or test
my_pipeline.fit(container.my_subset)
Examples:
A :class:`~getml.data.DataModel` only contains abstract data. When we
fit a pipeline, we need to assign concrete data.
Note that this example is taken from the
`loans notebook <https://nbviewer.getml.com/github/getml/getml-demo/blob/master/loans.ipynb>`_.
.. code-block:: python
# The abstract data model is constructed
# using the DataModel class. A data model
# does not contain any actual data. It just
# defines the abstract relational structure.
dm = getml.data.DataModel(
population_train.to_placeholder("population")
)
dm.add(getml.data.to_placeholder(
meta=meta,
order=order,
trans=trans)
)
dm.population.join(
dm.trans,
on="account_id",
time_stamps=("date_loan", "date")
)
dm.population.join(
dm.order,
on="account_id",
)
dm.population.join(
dm.meta,
on="account_id",
)
# We now have abstract placeholders on something
# called "population", "meta", "order" and "trans".
# But how do we assign concrete data? By using
# a container.
container = getml.data.Container(
train=population_train,
test=population_test
)
# meta, order and trans are either
# DataFrames or Views. Their aliases need
# to match the names of the placeholders in the
# data model.
container.add(
meta=meta,
order=order,
trans=trans
)
# Freezing makes the container immutable.
# This is not required, but often a good idea.
container.freeze()
# When we call 'train', the container
# will return the train set and the
# peripheral tables.
my_pipeline.fit(container.train)
# Same for 'test'
my_pipeline.score(container.test)
If you don't already have a train and test set,
you can use a function from the
:mod:`~getml.data.split` module.
.. code-block:: python
split = getml.data.split.random(
train=0.8, test=0.2)
container = getml.data.Container(
population=population_all,
split=split,
)
# The remaining code is the same as in
# the example above. In particular,
# container.train and container.test
# work just like above.
Containers can also be used for storage and reproducing your
results.
A recommended pattern is to assign 'baseline roles' to your data frames
and then using a :class:`~getml.data.View` to tweak them:
.. code-block:: python
# Assign baseline roles
data_frame.set_role(["jk"], getml.data.roles.join_key)
data_frame.set_role(["col1", "col2"], getml.data.roles.categorical)
data_frame.set_role(["col3", "col4"], getml.data.roles.numerical)
data_frame.set_role(["col5"], getml.data.roles.target)
# Make the data frame immutable, so in-place operations are
# no longer possible.
data_frame.freeze()
# Save the data frame.
data_frame.save()
# I suspect that col1 leads to overfitting, so I will drop it.
view = data_frame.drop(["col1"])
# Insert the view into a container.
container = getml.data.Container(...)
container.add(some_alias=view)
container.save()
The advantage of using such a pattern is that it enables you to
always completely retrace your entire pipeline without creating
deep copies of the data frames whenever you have made a small
change like the one in our example. Note that the pipeline will
record which container you have used.
"""
def __init__(
self,
population=None,
peripheral=None,
split=None,
deep_copy=False,
train=None,
validation=None,
test=None,
**kwargs,
):
if population is not None and not isinstance(population, (DataFrame, View)):
raise TypeError(
"'population' must be a getml.DataFrame or a getml.data.View, got "
+ type(population).__name__
+ "."
)
if peripheral is not None and not _is_typed_dict(
peripheral, str, [DataFrame, View]
):
raise TypeError(
"'peripheral' must be a dict "
+ "of getml.DataFrames or getml.data.Views."
)
if split is not None and not isinstance(
split, (StringColumn, StringColumnView)
):
raise TypeError(
"'split' must be StringColumn or a StringColumnView, got "
+ type(split).__name__
+ "."
)
if not isinstance(deep_copy, bool):
raise TypeError(
"'deep_copy' must be a bool, got " + type(split).__name__ + "."
)
exclusive = (population is not None) ^ (
len(_make_subsets_from_kwargs(train, validation, test, **kwargs)) != 0
)
if not exclusive:
raise ValueError(
"'population' and 'train', 'validation', 'test' as well as "
+ "other subsets signified by kwargs are mutually exclusive. "
+ "You have to pass "
+ "either 'population' or some subsets, but you cannot pass both."
)
if population is None and split is not None:
raise ValueError(
"'split's are used for splitting population DataFrames."
"Hence, if you supply 'split', you also have to supply "
"a population."
)
if population is not None and split is None:
logger.warning(
"You have passed a population table without passing 'split'. "
"You can access the entire set to pass to your pipeline "
"using the .full attribute."
)
split = from_value("full")
self._id = _make_id()
self._population = population
self._peripheral = peripheral or {}
self._split = split
self._deep_copy = deep_copy
self._subsets = (
_make_subsets_from_split(population, split)
if split is not None
else _make_subsets_from_kwargs(train, validation, test, **kwargs)
)
if split is None and not _is_typed_dict(self._subsets, str, [DataFrame, View]):
raise TypeError(
"'train', 'validation', 'test' and all other subsets must be either a "
"getml.DataFrame or a getml.data.View."
)
if deep_copy:
self._population = _deep_copy(self._population, self._id)
self._peripheral = {
k: _deep_copy(v, self._id) for (k, v) in self._peripheral.items()
}
self._subsets = {
k: _deep_copy(v, self._id) for (k, v) in self._subsets.items()
}
self._last_change = _get_last_change(
self._population, self._peripheral, self._subsets
)
self._frozen_time = None
def __dir__(self):
attrs = dir(type(self)) + self._ipython_key_completion()
attrs = [x for x in attrs if x.isidentifier()]
return attrs
def __getattr__(self, key):
try:
return self[key]
except KeyError:
super().__getattribute__(key)
def __getitem__(self, key):
if "_" + key in self.__dict__:
return self.__dict__["_" + key]
if key in self.__dict__["_subsets"]:
if self.__dict__["_deep_copy"] and self._frozen_time is None:
raise ValueError(
cleandoc(
f"""
If you set deep_copy=True, you must call
{type(self).__name__}.freeze() before you can extract data. The
idea of deep_copy is to ensure that you can always retrace and
reproduce your results. That is why the container
needs to be immutable before it can be used.
"""
)
)
last_change = _get_last_change(
self.__dict__["_population"],
self.__dict__["_peripheral"],
self.__dict__["_subsets"],
)
if self.__dict__["_last_change"] != last_change:
logger.warning(
cleandoc(
f"""
Some of the data frames added to the {type(self).__name__} have
been modified after they were added. This might lead to
unexpected results. To avoid these sort of problems, you can set
deep_copy=True when creating the {type(self).__name__}.
"""
)
)
return Subset(
container_id=self.__dict__["_id"],
population=self.__dict__["_subsets"][key].with_name(key),
peripheral=self.__dict__["_peripheral"],
)
if key in self.__dict__["_peripheral"]:
return self.__dict__["_peripheral"][key]
raise KeyError(f"{type(self).__name__} holds no data with name {key!r}.")
def __repr__(self):
pop, perph = self._format()
template = cleandoc(
"""
population
{pop}
peripheral
{perph}
"""
)
return template.format(pop=pop._render_string(), perph=perph._render_string())
def _repr_html_(self):
pop, perph = self._format()
template = cleandoc(
"""
<div style='float: left; margin-right: 50px;'>
<h4>population</h4>
{pop}
</div>
<div style='float: left;'>
<h4>peripheral</h4>
{perph}
</div>
"""
)
return template.format(pop=pop._render_html(), perph=perph._render_html())
def _format(self):
headers_pop = [["subset", "name", "rows", "type"]]
rows_pop = [
[key, subset.name, subset.nrows(), type(subset).__name__]
for key, subset in self.subsets.items() # pytype: disable=attribute-error
]
headers_perph = [["name", "rows", "type"]]
rows_perph = [
[perph.name, perph.nrows(), type(perph).__name__]
for perph in self.peripheral.values() # pytype: disable=attribute-error
]
names = [
perph.name
for perph in self.peripheral.values() # pytype: disable=attribute-error
]
aliases = list(self.peripheral.keys()) # pytype: disable=attribute-error
if any(alias not in names for alias in aliases):
headers_perph[0].insert(0, "alias")
for alias, row in zip(aliases, rows_perph):
row.insert(0, alias)
return _Formatter(headers=headers_pop, rows=rows_pop), _Formatter(
headers=headers_perph, rows=rows_perph
)
def _getml_deserialize(self):
cmd = {k[1:] + "_": v for (k, v) in self.__dict__.items() if v is not None}
if self._population is not None:
cmd["population_"] = self._population._getml_deserialize()
if self._split is not None:
cmd[
"split_"
] = self._split._getml_deserialize() # pytype: disable=attribute-error
cmd["peripheral_"] = {
k: v._getml_deserialize() for (k, v) in self._peripheral.items()
}
cmd["subsets_"] = {
k: v._getml_deserialize() for (k, v) in self._subsets.items()
}
return cmd
def _ipython_key_completion(self):
attrs = [v[1:] for v in list(vars(self))]
attrs.extend(self._peripheral)
if not self._deep_copy or self._frozen_time is not None:
attrs.extend(self._subsets)
return attrs
def __setattr__(self, name, value):
if not name or name[0] != "_":
raise ValueError("Attempting a write operation on read-only data.")
vars(self)[name] = value
[docs] def add(self, *args, **kwargs):
"""
Adds new peripheral data frames or views.
"""
wrong_type = [item for item in args if not isinstance(item, (DataFrame, View))]
if wrong_type:
raise TypeError(
"All unnamed arguments must be getml.DataFrames or getml.data.Views."
)
wrong_type = [
k for (k, v) in kwargs.items() if not isinstance(v, (DataFrame, View))
]
if wrong_type:
raise TypeError(
"You must pass getml.DataFrames or getml.data.Views, "
f"but the following arguments were neither: {wrong_type!r}."
)
kwargs = {**{item.name: item for item in args}, **kwargs}
if self._frozen_time is not None:
raise ValueError(
f"You cannot add data frames after the {type(self).__name__} has been frozen."
)
if self._deep_copy:
kwargs = {k: _deep_copy(v, self._id) for (k, v) in kwargs.items()}
self._peripheral = {**self._peripheral, **kwargs}
self._last_change = _get_last_change(
self._population, self._peripheral, self._subsets
)
[docs] def freeze(self):
"""
Freezes the container, so that changes are no longer possible.
This is required before you can extract data when deep_copy=True. The idea of
deep_copy is to ensure that you can always retrace and reproduce your results.
That is why the container needs to be immutable before it can be
used.
"""
self.sync()
self._frozen_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
[docs] def save(self):
"""
Saves the Container to disk.
"""
cmd = dict()
cmd["type_"] = "DataContainer.save"
cmd["name_"] = self._id
cmd["container_"] = self._getml_deserialize()
comm.send(cmd)
[docs] def sync(self):
"""
Synchronizes the last change with the data to avoid warnings that the data
has been changed.
This is only a problem when deep_copy=False.
"""
if self._frozen_time is not None:
raise ValueError(f"{type(self).__name__} has already been frozen.")
self._last_change = _get_last_change(
self._population, self._peripheral, self._subsets
)