getml.data.Placeholder
Abstract representation of tables and their relations.
This class is an abstract representation of the DataFrame
or View
. However, it does not contain any actual data.
You might also want to refer to DataModel
.
ATTRIBUTE | DESCRIPTION |
---|---|
name | The name used for this placeholder. This name will appear in the generated SQL code. TYPE: |
roles | The roles of the columns in this placeholder. If you pass a dictionary, the keys must be the column names and the values must be lists of roles. If you pass a TYPE: |
Example
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:
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"
)
to_placeholder
: dm = getml.data.DataModel(
population_table.to_placeholder("POPULATION")
)
dm.add(
getml.data.to_placeholder(
PERIPHERAL1=peripheral_table_1,
PERIPHERAL2=peripheral_table_2,
)
)
dm.POPULATION.join(
dm.PERIPHERAL,
on="join_key",
time_stamps="time_stamp",
relationship=getml.data.relationship.many_to_one,
)
relationship
. If the join keys or time stamps are named differently in the two different tables, use a tuple:
dm.POPULATION.join(
dm.PERIPHERAL,
on=("join_key", "other_join_key"),
time_stamps=("time_stamp", "other_time_stamp"),
)
dm.POPULATION.join(
dm.PERIPHERAL,
on=["join_key1", "join_key2", ("join_key3", "other_join_key3")],
time_stamps="time_stamp",
)
dm.POPULATION.join(
dm.PERIPHERAL,
on="join_key",
time_stamps="time_stamp",
memory=getml.data.time.days(7),
)
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),
)
time
. If the join involves many matches, it might be a good idea to set the relationship to propositionalization
. This forces the pipeline to always use a propositionalization algorithm for this join, which can significantly speed things up.
dm.POPULATION.join(
dm.PERIPHERAL,
on="join_key",
time_stamps="time_stamp",
relationship=getml.data.relationship.propositionalization,
)
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:
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]
Source code in getml/data/placeholder.py
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|
join
join(
right: Placeholder,
on: OnType = None,
time_stamps: TimeStampsType = None,
relationship: str = many_to_many,
memory: Optional[float] = None,
horizon: Optional[float] = None,
lagged_targets: bool = False,
upper_time_stamp: Optional[str] = None,
)
Joins another to placeholder to this placeholder.
PARAMETER | DESCRIPTION |
---|---|
right | The placeholder you would like to join. TYPE: |
on | The join keys to use. If none is passed, then everything will be joined to everything else. TYPE: |
time_stamps | The time stamps used to limit the join. TYPE: |
relationship | The relationship between the two tables. Must be from TYPE: |
memory | The difference between the time stamps until data is 'forgotten'. Limiting your joins using memory can significantly speed up training time. Also refer to |
horizon | The prediction horizon to apply to this join. Also refer to |
lagged_targets | Whether you want to allow lagged targets. If this is set to True, you must also pass a positive, non-zero horizon. TYPE: |
upper_time_stamp | Name of a time stamp in right that serves as an upper limit on the join. |
Source code in getml/data/placeholder.py
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|
to_list
to_list()
Returns a list of this placeholder and all of its descendants.
Source code in getml/data/placeholder.py
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|
to_dict
to_dict()
Expresses this placeholder and all of its descendants as a dictionary.
Source code in getml/data/placeholder.py
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|