# Datetime Partitioned Projects¶

If your dataset is modeling events taking place over time, datetime partitioning may be appropriate. Datetime partitioning ensures that when partitioning the dataset for training and validation, rows are ordered according to the value of the date partition feature.

## Setting Up a Datetime Partitioned Project¶

After creating a project and before setting the target, create a DatetimePartitioningSpecification to define how the project should be partitioned. By passing the specification into DatetimePartitioning.generate, the full partitioning can be previewed before finalizing the partitioning. After verifying that the partitioning is correct for the project dataset, pass the specification into Project.set_target via the partitioning_method argument. Once modeling begins, the project can be used as normal.

The following code block shows the basic workflow for creating datetime partitioned projects.

import datarobot as dr

project = dr.Project.create('some_data.csv')
spec = dr.DatetimePartitioningSpecification('my_date_column')
# can customize the spec as needed

partitioning_preview = dr.DatetimePartitioning.generate(project.id, spec)
# the preview generated is based on the project's data

print(partitioning_preview.to_dataframe())
# hmm ... I want more backtests
spec.number_of_backtests = 5
partitioning_preview = dr.DatetimePartitioning.generate(project.id, spec)
print(partitioning_preview.to_dataframe())
# looks good

project.set_target('target_column', partitioning_method=spec)

# I can retrieve the partitioning settings after the target has been set too
partitioning = dr.DatetimePartitioning.get(project.id)


### Configuring Backtests¶

Backtests are configurable using one of two methods:

Method 1:

• index (int): The index from zero of this backtest.
• gap_duration (str): A duration string such as those returned by the partitioning_methods.construct_duration_string helper method. This represents the gap between training and validation scoring data for this backtest.
• validation_start_date (datetime.datetime): Represents the start date of the validation scoring data for this backtest.
• validation_duration (str): A duration string such as those returned by the partitioning_methods.construct_duration_string helper method. This represents the desired duration of the validation scoring data for this backtest.
import datarobot as dr

partitioning_spec = dr.DatetimePartitioningSpecification(
backtests=[
# modify the first backtest using option 1
dr.BacktestSpecification(
index=0,
gap_duration=dr.partitioning_methods.construct_duration_string(),
validation_start_date=datetime(year=2010, month=1, day=1),
validation_duration=dr.partitioning_methods.construct_duration_string(years=1),
)
],
# other partitioning settings...
)


Method 2 (New in version v2.20):

• validation_start_date (datetime.datetime): Represents the start date of the validation scoring data for this backtest.
• validation_end_date (datetime.datetime): Represents the end date of the validation scoring data for this backtest.
• primary_training_start_date (datetime.datetime): Represents the desired start date of the training partition for this backtest.
• primary_training_end_date (datetime.datetime): Represents the desired end date of the training partition for this backtest.
import datarobot as dr

partitioning_spec = dr.DatetimePartitioningSpecification(
backtests=[
# modify the first backtest using option 2
dr.BacktestSpecification(
index=0,
primary_training_start_date=datetime(year=2005, month=1, day=1),
primary_training_end_date=datetime(year=2010, month=1, day=1),
validation_start_date=datetime(year=2010, month=1, day=1),
validation_end_date=datetime(year=2011, month=1, day=1),
)
],
# other partitioning settings...
)


Note that Method 2 allows you to directly configure the start and end dates of each partition, including the training partition. The gap partition is calculated as the time between primary_training_end_date and validation_start_date. Using the same date for both primary_training_end_date and validation_start_date will result in no gap being created.

After configuring backtests, you can set use_project_settings to True in calls to Model.train_datetime. This will create models that are trained and validated using your custom backtest training partition start and end dates.

## Modeling with a Datetime Partitioned Project¶

While Model objects can still be used to interact with the project, DatetimeModel objects, which are only retrievable from datetime partitioned projects, provide more information including which date ranges and how many rows are used in training and scoring the model as well as scores and statuses for individual backtests.

The autopilot workflow is the same as for other projects, but to manually train a model, Project.train_datetime and Model.train_datetime should be used in the place of Project.train and Model.train. To create frozen models, Model.request_frozen_datetime_model should be used in place of DatetimeModel.request_frozen_datetime_model. Unlike other projects, to trigger computation of scores for all backtests use DatetimeModel.score_backtests instead of using the scoring_type argument in the train methods.

## Accuracy Over Time Plots¶

For datetime partitioned model you can retrieve the Accuracy over Time plot. To do so use DatetimeModel.get_accuracy_over_time_plot. You can also retrieve the detailed metadata using DatetimeModel.get_accuracy_over_time_plots_metadata, and the preview plot using DatetimeModel.get_accuracy_over_time_plot_preview.

## Dates, Datetimes, and Durations¶

When specifying a date or datetime for datetime partitioning, the client expects to receive and will return a datetime. Timezones may be specified, and will be assumed to be UTC if left unspecified. All dates returned from DataRobot are in UTC with a timezone specified.

Datetimes may include a time, or specify only a date; however, they may have a non-zero time component only if the partition column included a time component in its date format. If the partition column included only dates like “24/03/2015”, then the time component of any datetimes, if present, must be zero.

When date ranges are specified with a start and an end date, the end date is exclusive, so only dates earlier than the end date are included, but the start date is inclusive, so dates equal to or later than the start date are included. If the start and end date are the same, then no dates are included in the range.

Durations are specified using a subset of ISO8601. Durations will be of the form PnYnMnDTnHnMnS where each “n” may be replaced with an integer value. Within the duration string,

• nY represents the number of years
• the nM following the “P” represents the number of months
• nD represents the number of days
• nH represents the number of hours
• the nM following the “T” represents the number of minutes
• nS represents the number of seconds

and “P” is used to indicate that the string represents a period and “T” indicates the beginning of the time component of the string. Any section with a value of 0 may be excluded. As with datetimes, if the partition column did not include a time component in its date format, the time component of any duration must be either unspecified or consist only of zeros.

Example Durations:

• “P3Y6M” (three years, six months)
• “P1Y0M0DT0H0M0S” (one year)
• “P1Y5DT10H” (one year, 5 days, 10 hours)

datarobot.helpers.partitioning_methods.construct_duration_string is a helper method that can be used to construct appropriate duration strings.