Frequently Asked Questions

Here we are attempting to answer some commonly asked questions that appear on Github, and Stack Overflow.

import featuretools as ft
import pandas as pd
import numpy as np
import woodwork as ww

EntitySet

How do I get a list of column names and types in an EntitySet?

After you create your EntitySet, you may wish to view the column names. An EntitySet contains multiple DataFrames, one for each table in the EntitySet.

es = ft.demo.load_mock_customer(return_entityset=True)
es

Entityset: transactions
  DataFrames:
    transactions [Rows: 500, Columns: 6]
    products [Rows: 5, Columns: 3]
    sessions [Rows: 35, Columns: 5]
    customers [Rows: 5, Columns: 5]
  Relationships:
    transactions.product_id -> products.product_id
    transactions.session_id -> sessions.session_id
    sessions.customer_id -> customers.customer_id

If you want to view the underlying Dataframe, you can do the following:

es['transactions'].head()

	transaction_id	session_id	transaction_time	product_id	amount	_ft_last_time
298	298	1	2014-01-01 00:00:00	5	127.64	2014-01-01 00:00:00
2	2	1	2014-01-01 00:01:05	2	109.48	2014-01-01 00:01:05
308	308	1	2014-01-01 00:02:10	3	95.06	2014-01-01 00:02:10
116	116	1	2014-01-01 00:03:15	4	78.92	2014-01-01 00:03:15
371	371	1	2014-01-01 00:04:20	3	31.54	2014-01-01 00:04:20

If you want view the columns and types for the “transactions” DataFrame, you can do the following:

es['transactions'].ww

	Physical Type	Logical Type	Semantic Tag(s)
Column
transaction_id	int64	Integer	['index']
session_id	int64	Integer	['numeric', 'foreign_key']
transaction_time	datetime64[ns]	Datetime	['time_index']
product_id	category	Categorical	['category', 'foreign_key']
amount	float64	Double	['numeric']
_ft_last_time	datetime64[ns]	Datetime	['last_time_index']

What is the difference between copy_columns and additional_columns?

The function normalize_dataframe creates a new DataFrame and a relationship from unique values of an existing DataFrame. It takes 2 similar arguments:

• additional_columns removes columns from the base DataFrame and moves them to the new DataFrame.

• copy_columns keeps the given columns in the base DataFrame, but also copies them to the new DataFrame.

data = ft.demo.load_mock_customer()
transactions_df = data["transactions"].merge(data["sessions"]).merge(data["customers"])
products_df = data["products"]

es = ft.EntitySet(id="customer_data")
es = es.add_dataframe(dataframe_name="transactions",
           dataframe=transactions_df,
           index="transaction_id",
           time_index="transaction_time")

es = es.add_dataframe(dataframe_name="products",
           dataframe=products_df,
           index="product_id")

es = es.add_relationship("products", "product_id", "transactions", "product_id")

Before we normalize to create a new DataFrame, let’s look at the base DataFrame

es['transactions'].head()

	transaction_id	session_id	transaction_time	product_id	amount	customer_id	device	session_start	zip_code	join_date	birthday
298	298	1	2014-01-01 00:00:00	5	127.64	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18
2	2	1	2014-01-01 00:01:05	2	109.48	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18
308	308	1	2014-01-01 00:02:10	3	95.06	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18
116	116	1	2014-01-01 00:03:15	4	78.92	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18
371	371	1	2014-01-01 00:04:20	3	31.54	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18

Notice the columns session_id, session_start, join_date, device, customer_id, and zip_code.

es = es.normalize_dataframe(base_dataframe_name="transactions",
                         new_dataframe_name="sessions",
                         index="session_id",
                         make_time_index="session_start",
                         additional_columns=["join_date"],
                         copy_columns=["device", "customer_id", "zip_code","session_start"])

Above, we normalized the columns to create a new DataFrame.

• For additional_columns, the following column ['join_date] will be removed from the transactions DataFrame, and moved to the new sessions DataFrame.

• For copy_columns, the following columns ['device', 'customer_id', 'zip_code','session_start'] will be copied from the transactions DataFrame to the new sessions DataFrame.

Let’s see this in the actual EntitySet.

es['transactions'].head()

	transaction_id	session_id	transaction_time	product_id	amount	customer_id	device	session_start	zip_code	birthday
298	298	1	2014-01-01 00:00:00	5	127.64	2	desktop	2014-01-01	13244	1986-08-18
2	2	1	2014-01-01 00:01:05	2	109.48	2	desktop	2014-01-01	13244	1986-08-18
308	308	1	2014-01-01 00:02:10	3	95.06	2	desktop	2014-01-01	13244	1986-08-18
116	116	1	2014-01-01 00:03:15	4	78.92	2	desktop	2014-01-01	13244	1986-08-18
371	371	1	2014-01-01 00:04:20	3	31.54	2	desktop	2014-01-01	13244	1986-08-18

Notice above how ['device', 'customer_id', 'zip_code','session_start'] are still in the transactions DataFrame, while ['join_date'] is not. But, they have all been moved to the sessions DataFrame, as seen below.

es['sessions'].head()

	session_id	join_date	device	customer_id	zip_code	session_start
1	1	2012-04-15 23:31:04	desktop	2	13244	2014-01-01 00:00:00
2	2	2010-07-17 05:27:50	mobile	5	60091	2014-01-01 00:17:20
3	3	2011-04-08 20:08:14	mobile	4	60091	2014-01-01 00:28:10
4	4	2011-04-17 10:48:33	mobile	1	60091	2014-01-01 00:44:25
5	5	2011-04-08 20:08:14	mobile	4	60091	2014-01-01 01:11:30

Why did my columns get new semantic tags?

During the creation of your EntitySet, you might be wondering why the semantic tags in your columns change.

data = ft.demo.load_mock_customer()
transactions_df = data["transactions"].merge(data["sessions"]).merge(data["customers"])
products_df = data["products"]

es = ft.EntitySet(id="customer_data")
es = es.add_dataframe(dataframe_name="transactions",
           dataframe=transactions_df,
           index="transaction_id",
           time_index="transaction_time")
es.plot()

If a column contains semantic tags, they will appear on the right side of a semicolon in the plot above. Notice how session_id and session_start do not have any semantic tags currently associated to them.

Now, let’s normalize the transactions DataFrame to create a new DataFrame.

es = es.normalize_dataframe(base_dataframe_name="transactions",
                         new_dataframe_name="sessions",
                         index="session_id",
                         make_time_index="session_start",
                         additional_columns=["session_start"])
es.plot()

The session_id now has the sematic tag foreign_key in the transactions DataFrame, and index in the new DataFrame, sessions. This is the case because when we normalize the DataFrame, we create a new relationship between the transactions and sessions. There is a one to many relationship between the parent DataFrame, sessions, and child DataFrame, transactions.

Therefore, session_id has the semantic tag foreign_key in transactions because it represents an index in another DataFrame. There would be a similar effect if we added another DataFrame using add_dataframe and add_relationship.

In addition, when we created the new DataFrame, we set session_start as the time_index. This added the semantic tag time_index to the session_start column in the new sessions DataFrame because it now represents a time_index.

How do I update a column’s description or metadata?

You can directly update the description or metadata attributes of the column schema. However, you must specifically use the column schema returned by DataFrame.ww.columns['col_name'], not DataFrame.ww['col_name'].ww.schema. The column schema from DataFrame.ww.columns['col_name'] is still associated with the EntitySet and propagates any attribute updates, whereas the other does not. As an example, this is how you can update a column’s description or metadata:

column_schema = df.ww.columns['col_name']
column_schema.description = 'my description'
column_schema.metadata.update(key='value')

How do I combine two or more interesting values?

You might want to create features that are conditioned on multiple values before they are calculated. This would require the use of interesting_values. However, since we are trying to create the feature with multiple conditions, we will need to modify the Dataframe before we create the EntitySet.

Let’s look at how you might accomplish this.

First, let’s create our Dataframes.

data = ft.demo.load_mock_customer()
transactions_df = data["transactions"].merge(data["sessions"]).merge(data["customers"])
products_df = data["products"]

transactions_df.head()

	transaction_id	session_id	transaction_time	product_id	amount	customer_id	device	session_start	zip_code	join_date	birthday
0	298	1	2014-01-01 00:00:00	5	127.64	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18
1	2	1	2014-01-01 00:01:05	2	109.48	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18
2	308	1	2014-01-01 00:02:10	3	95.06	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18
3	116	1	2014-01-01 00:03:15	4	78.92	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18
4	371	1	2014-01-01 00:04:20	3	31.54	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18

products_df.head()

	product_id	brand
0	1	B
1	2	B
2	3	B
3	4	B
4	5	A

Now, let’s modify our transactions Dataframe to create the additional column that represents multiple conditions for our feature.

transactions_df['product_id_device'] = transactions_df['product_id'].astype(str) + ' and ' + transactions_df['device']

Here, we created a new column called product_id_device, which just combines the product_id column, and the device column.

Now let’s create our EntitySet.

es = ft.EntitySet(id="customer_data")
es = es.add_dataframe(dataframe_name="transactions",
           dataframe=transactions_df,
           index="transaction_id",
           time_index="transaction_time",
           logical_types={"product_id": ww.logical_types.Categorical,
               "product_id_device": ww.logical_types.Categorical,
               "zip_code": ww.logical_types.PostalCode})

es = es.add_dataframe(dataframe_name="products",
           dataframe=products_df,
           index="product_id")

es = es.normalize_dataframe(base_dataframe_name="transactions",
                         new_dataframe_name="sessions",
                         index="session_id",
                         additional_columns=["device", "product_id_device", "customer_id"])

es = es.normalize_dataframe(base_dataframe_name="sessions",
                         new_dataframe_name="customers",
                         index="customer_id")
es

Entityset: customer_data
  DataFrames:
    transactions [Rows: 500, Columns: 9]
    products [Rows: 5, Columns: 2]
    sessions [Rows: 35, Columns: 5]
    customers [Rows: 5, Columns: 2]
  Relationships:
    transactions.session_id -> sessions.session_id
    sessions.customer_id -> customers.customer_id

Now, we are ready to add our interesting values.

First, let’s view our options for what the interesting values could be.

interesting_values = transactions_df['product_id_device'].unique().tolist()
interesting_values

['5 and desktop',
 '2 and desktop',
 '3 and desktop',
 '4 and desktop',
 '1 and desktop',
 '4 and mobile',
 '5 and mobile',
 '1 and mobile',
 '3 and mobile',
 '2 and mobile',
 '4 and tablet',
 '3 and tablet',
 '2 and tablet',
 '1 and tablet',
 '5 and tablet']

If you wanted to, you could pick a subset of these, and the where features created would only use those conditions. In our example, we will use all the possible interesting values.

Here, we set all of these values as our interesting values for this specific DataFrame and column. If we wanted to, we could make interesting values in the same way for more than one column, but we will just stick with this one for this example.

values = {'product_id_device': interesting_values}
es.add_interesting_values(dataframe_name='sessions', values=values)

Now we can run DFS.

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      agg_primitives=["count"],
                                      where_primitives=["count"],
                                      trans_primitives=[])
feature_matrix.head()

	COUNT(sessions)	COUNT(transactions)	COUNT(sessions WHERE product_id_device = 2 and desktop)	COUNT(sessions WHERE product_id_device = 1 and tablet)	COUNT(sessions WHERE product_id_device = 3 and tablet)	COUNT(sessions WHERE product_id_device = 4 and mobile)	COUNT(sessions WHERE product_id_device = 1 and mobile)	COUNT(sessions WHERE product_id_device = 3 and desktop)	COUNT(sessions WHERE product_id_device = 5 and mobile)	COUNT(sessions WHERE product_id_device = 4 and desktop)	...	COUNT(transactions WHERE sessions.product_id_device = 1 and mobile)	COUNT(transactions WHERE sessions.product_id_device = 2 and mobile)	COUNT(transactions WHERE sessions.product_id_device = 3 and mobile)	COUNT(transactions WHERE sessions.product_id_device = 4 and mobile)	COUNT(transactions WHERE sessions.product_id_device = 1 and desktop)	COUNT(transactions WHERE sessions.product_id_device = 3 and tablet)	COUNT(transactions WHERE sessions.product_id_device = 5 and desktop)	COUNT(transactions WHERE sessions.product_id_device = 5 and tablet)	COUNT(transactions WHERE sessions.product_id_device = 3 and desktop)	COUNT(transactions WHERE sessions.product_id_device = 2 and desktop)
customer_id
2	7	93	0	1	0	1	0	0	0	1	...	0	13	0	18	8	0	16	13	0	0
5	6	79	0	0	0	1	1	0	0	1	...	18	0	8	10	0	0	15	0	0	0
4	8	109	1	0	0	0	1	0	0	1	...	15	23	15	0	0	0	10	18	0	10
1	8	126	1	0	1	3	0	0	0	0	...	0	0	0	56	0	16	12	0	0	15
3	6	93	0	0	0	0	0	0	0	0	...	0	0	16	0	33	0	29	0	0	0

5 rows × 32 columns

To better understand the where clause features, let’s examine one of those features. The feature COUNT(sessions WHERE product_id_device = 5 and tablet), tells us how many sessions the customer purchased product_id 5 while on a tablet. Notice how the feature depends on multiple conditions (product_id = 5 & device = tablet).

feature_matrix[["COUNT(sessions WHERE product_id_device = 5 and tablet)"]]

	COUNT(sessions WHERE product_id_device = 5 and tablet)
customer_id
2	1
5	0
4	1
1	0
3	0

Can I create an EntitySet using Dask or Spark dataframes? (BETA)

Support for Dask EntitySets and Spark EntitySets is still in Beta - if you encounter any errors using either of these approaches, please let us know by creating a new issue on Github.

Yes! Featuretools supports creating an EntitySet from Dask dataframes or from Spark dataframes. You can simply follow the same process you would when creating an EntitySet from pandas dataframes.

There are some limitations to be aware of when using Dask or Spark dataframes. When creating a DataFrame, type inference can significantly slow down the runtime compared to pandas DataFrames, so users are encouraged to specify logical types for all columns during creation. Also, other quality checks are not performed, such as checking for unique index values. An EntitySet must be created entirely of one type of DataFrame (Dask, Spark, or pandas) - you cannot mix pandas DataFrames, Dask DataFrames, and Spark DataFrames with each other in the same EntitySet.

For more information on creating an EntitySet from Dask dataframes or from Spark dataframes, see the Using Dask EntitySets and the Using Spark EntitySets guides.

DFS

Why is DFS not creating aggregation features?

You may have created your EntitySet, and then applied DFS to create features. However, you may be puzzled as to why no aggregation features were created.

• This is most likely because you have a single DataFrame in your EntitySet, and DFS is not capable of creating aggregation features with fewer than 2 DataFrames. Featuretools looks for a relationship, and aggregates based on that relationship.

Let’s look at a simple example.

data = ft.demo.load_mock_customer()
transactions_df = data["transactions"].merge(data["sessions"]).merge(data["customers"])

es = ft.EntitySet(id="customer_data")
es = es.add_dataframe(dataframe_name="transactions",
           dataframe=transactions_df,
           index="transaction_id")
es

Entityset: customer_data
  DataFrames:
    transactions [Rows: 500, Columns: 11]
  Relationships:
    No relationships

Notice how we only have 1 DataFrame in our EntitySet. If we try to create aggregation features on this EntitySet, it will not be possible because DFS needs 2 DataFrames to generate aggregation features.

feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name="transactions")
feature_defs

/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/v1.11.0/lib/python3.8/site-packages/featuretools/synthesis/deep_feature_synthesis.py:161: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created
  warnings.warn(

[<Feature: session_id>,
 <Feature: product_id>,
 <Feature: amount>,
 <Feature: customer_id>,
 <Feature: device>,
 <Feature: zip_code>,
 <Feature: DAY(birthday)>,
 <Feature: DAY(join_date)>,
 <Feature: DAY(session_start)>,
 <Feature: DAY(transaction_time)>,
 <Feature: MONTH(birthday)>,
 <Feature: MONTH(join_date)>,
 <Feature: MONTH(session_start)>,
 <Feature: MONTH(transaction_time)>,
 <Feature: WEEKDAY(birthday)>,
 <Feature: WEEKDAY(join_date)>,
 <Feature: WEEKDAY(session_start)>,
 <Feature: WEEKDAY(transaction_time)>,
 <Feature: YEAR(birthday)>,
 <Feature: YEAR(join_date)>,
 <Feature: YEAR(session_start)>,
 <Feature: YEAR(transaction_time)>]

None of the above features are aggregation features. To fix this issue, you can add another DataFrame to your EntitySet.

Solution #1 - You can add new DataFrame if you have additional data.

products_df = data["products"]
es = es.add_dataframe(dataframe_name="products",
           dataframe=products_df,
           index="product_id")
es

Entityset: customer_data
  DataFrames:
    transactions [Rows: 500, Columns: 11]
    products [Rows: 5, Columns: 2]
  Relationships:
    No relationships

Notice how we now have an additional DataFrame in our EntitySet, called products.

Solution #2 - You can normalize an existing DataFrame.

es = es.normalize_dataframe(base_dataframe_name="transactions",
                         new_dataframe_name="sessions",
                         index="session_id",
                         make_time_index="session_start",
                         additional_columns=["device", "customer_id", "zip_code", "join_date"],
                         copy_columns=["session_start"])
es

Entityset: customer_data
  DataFrames:
    transactions [Rows: 500, Columns: 7]
    products [Rows: 5, Columns: 2]
    sessions [Rows: 35, Columns: 6]
  Relationships:
    transactions.session_id -> sessions.session_id

Notice how we now have an additional DataFrame in our EntitySet, called sessions. Here, the normalization created a relationship between transactions and sessions. However, we could have specified a relationship between transactions and products if we had only used Solution #1.

Now, we can generate aggregation features.

feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name="transactions")
feature_defs[:-10]

[<Feature: session_id>,
 <Feature: product_id>,
 <Feature: amount>,
 <Feature: DAY(birthday)>,
 <Feature: DAY(session_start)>,
 <Feature: DAY(transaction_time)>,
 <Feature: MONTH(birthday)>,
 <Feature: MONTH(session_start)>,
 <Feature: MONTH(transaction_time)>,
 <Feature: WEEKDAY(birthday)>,
 <Feature: WEEKDAY(session_start)>,
 <Feature: WEEKDAY(transaction_time)>,
 <Feature: YEAR(birthday)>,
 <Feature: YEAR(session_start)>,
 <Feature: YEAR(transaction_time)>,
 <Feature: sessions.device>,
 <Feature: sessions.customer_id>,
 <Feature: sessions.zip_code>,
 <Feature: sessions.COUNT(transactions)>,
 <Feature: sessions.MAX(transactions.amount)>,
 <Feature: sessions.MEAN(transactions.amount)>,
 <Feature: sessions.MIN(transactions.amount)>,
 <Feature: sessions.MODE(transactions.product_id)>,
 <Feature: sessions.NUM_UNIQUE(transactions.product_id)>,
 <Feature: sessions.SKEW(transactions.amount)>]

A few of the aggregation features are:

• <Feature: sessions.MAX(transactions.amount)>

• <Feature: sessions.SKEW(transactions.amount)>

• <Feature: sessions.MIN(transactions.amount)>

• <Feature: sessions.MEAN(transactions.amount)>

• <Feature: sessions.COUNT(transactions)>

How do I speed up the runtime of DFS?

One issue you may encounter while running ft.dfs is slow performance. While Featuretools has generally optimal default settings for calculating features, you may want to speed up performance when you are calculating on a large number of features.

One quick way to speed up performance is by adjusting the n_jobs settings of ft.dfs or ft.calculate_feature_matrix.

# setting n_jobs to -1 will use all cores

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      n_jobs=-1)


feature_matrix, feature_defs = ft.calculate_feature_matrix(entityset=es,
                                                           features=feature_defs,
                                                           n_jobs=-1)

For more ways to speed up performance, please visit:

• Improving Computational Performance

How do I include only certain features when running DFS?

When using DFS to generate features, you may wish to include only certain features. There are multiple ways that you do this:

• Use ignore_columns to specify columns in a DataFrame that should not be used to create features. It is a dictionary mapping dataframe names to a list of column names to ignore.

• Use drop_contains to drop features that contain any of the strings listed in this parameter.

• Use drop_exact to drop features that exactly match any of the strings listed in this parameter.

Here is an example of using all three parameters:

es = ft.demo.load_mock_customer(return_entityset=True)

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      ignore_columns={
                                           "transactions": ["amount"],
                                           "customers": ["age", "gender", "birthday"]
                                       }, # ignore these columns
                                      drop_contains=["customers.SUM("], # drop features that contain these strings
                                      drop_exact=["STD(transactions.quanity)"]) # drop features that exactly match

How do I specify primitives on a per column or per DataFrame basis?

When using DFS to generate features, you may wish to use only certain features or DataFrames for specific primitives. This can be done through the primitive_options parameter. The primitive_options parameter is a dictionary that maps a primitive or a tuple of primitives to a dictionary containing options for the primitive(s). A primitive or tuple of primitives can also be mapped to a list of option dictionaries if the primitive(s) takes multiple inputs. The primitive keys can be the string names of the primitive, the primitive class, or specific instances of the primitive. Each dictionary supplies options for their respective input column. There are multiple ways to control how primitives get applied through these options:

• Use ignore_dataframes to specify DataFrames that should not be used to create features for that primitive. It is a list of DataFrame names to ignore.

• Use include_dataframes to specify the only DataFrames to be included to create features for that primitive. It is a list of DataFrame names to include.

• Use ignore_columns to specify columns in a DataFrame that should not be used to create features for that primitive. It is a dictionary mapping a DataFrame name to a list of column names to ignore.

• Use include_columns to specify the only columns in a DataFrame that should be used to create features for that primitive. It is a dictionary mapping a DataFrame name to a list of column names to include.

You can also use primitive_options to specify which DataFrames or columns you wish to use as groupbys for groupby transformation primitives:

• Use ignore_groupby_dataframes to specify DataFrames that should not be used to get groupbys for that primitive. It is a list of DataFrame names to ignore.

• Use include_groupby_dataframes to specify the only DataFrames that should be used to get groupbys for that primitive. It is a list of DataFrame names to include.

• Use ignore_groupby_columns to specify columns in a DataFrame that should not be used as groupbys for that primitive. It is a dictionary mapping a DataFrame name to a list of column names to ignore.

• Use include_groupby_columns to specify the only columns in a DataFrame that should be used as groupbys for that primitive. It is a dictionary mapping a DataFrame name to a list of column names to include.

Here is an example of using some of these options:

es = ft.demo.load_mock_customer(return_entityset=True)

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      primitive_options={"mode": {"ignore_dataframes": ["sessions"],
                                                                  "ignore_columns": {"products": ["brand"],
                                                                                        "transactions": ["product_id"]}},
                                                         # For mode, ignore the "sessions" DataFrame and only include "brands" in the
                                                         # "products" dataframe and "product_id" in the "transactions" DataFrame
                                                         ("count", "mean"): {"include_dataframes": ["sessions", "transactions"]}
                                                         # For count and mean, only include the dataframes "sessions" and "transactions"
                                                         })

Note that if options are given for a specific instance of a primitive and for the primitive generally (either by string name or class), the instances with their own options will not use the generic options. For example, in this case:

special_mean = Mean()
options = {
    special_mean: {'include_dataframes': ['customers']},
    'mean': {'include_dataframes': ['sessions']}

the primitive special_mean will not use the DataFrame sessions because it’s options have it only include customers. Every other instance of the Mean primitive will use the 'mean' options.

For more examples of specifying options for DFS, please visit:

• Specifying Primitive Options

If I didn’t specify the cutoff_time, what date will be used for the feature calculations?

The cutoff time will be set to the current time using cutoff_time = datetime.now().

How do I select a certain amount of past data when calculating features?

You may encounter a situation when you wish to make prediction using only a certain amount of historical data. You can accomplish this using the training_window parameter in ft.dfs. When you use the training_window, Featuretools will use the historical data between the cutoff_time and cutoff_time - training_window.

In order to make the calculation, Featuretools will check the time in the time_index column of the target_dataframe.

es = ft.demo.load_mock_customer(return_entityset=True)
es['customers'].ww.time_index

'join_date'

Our target_dataframe has a time_index, which is needed for the training_window calculation. Here, we are creating a cutoff time DataFrame so that we can have a unique training window for each customer.

cutoff_times = pd.DataFrame()
cutoff_times['customer_id'] = [1, 2, 3, 1]
cutoff_times['time'] = pd.to_datetime(['2014-1-1 04:00', '2014-1-1 05:00', '2014-1-1 06:00', '2014-1-1 08:00'])
cutoff_times['label'] = [True, True, False, True]

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      cutoff_time=cutoff_times,
                                      cutoff_time_in_index=True,
                                      training_window="1 hour")
feature_matrix.head()

		zip_code	COUNT(sessions)	MODE(sessions.device)	NUM_UNIQUE(sessions.device)	COUNT(transactions)	MAX(transactions.amount)	MEAN(transactions.amount)	MIN(transactions.amount)	MODE(transactions.product_id)	NUM_UNIQUE(transactions.product_id)	...	STD(sessions.SUM(transactions.amount))	SUM(sessions.MAX(transactions.amount))	SUM(sessions.MEAN(transactions.amount))	SUM(sessions.MIN(transactions.amount))	SUM(sessions.NUM_UNIQUE(transactions.product_id))	SUM(sessions.SKEW(transactions.amount))	SUM(sessions.STD(transactions.amount))	MODE(transactions.sessions.device)	NUM_UNIQUE(transactions.sessions.device)	label
customer_id	time
1	2014-01-01 04:00:00	60091	1	tablet	1	12	139.09	85.469167	6.78	4	5	...	NaN	139.09	85.469167	6.78	5.0	-0.830975	39.825249	tablet	1	True
2	2014-01-01 05:00:00	13244	1	tablet	1	13	118.85	77.304615	21.82	1	5	...	NaN	118.85	77.304615	21.82	5.0	-0.314918	33.725036	tablet	1	True
3	2014-01-01 06:00:00	13244	2	desktop	1	12	128.26	81.747500	20.06	3	5	...	563.882303	220.02	172.597273	111.82	6.0	-0.289466	35.704680	desktop	1	False
1	2014-01-01 08:00:00	60091	1	mobile	1	16	126.11	88.755625	11.62	4	5	...	NaN	126.11	88.755625	11.62	5.0	-1.038434	32.324534	mobile	1	True

4 rows × 76 columns

Above, we ran DFS with training_window argument of 1 hour to create features that only used customer data collected in the last hour (from the cutoff time we provided).

How do I apply DFS to a single table?

You can run DFS on a single table. Featuretools will be able to generate features for your data, but only transform features.

For example:

transactions_df = ft.demo.load_mock_customer(return_single_table=True)

es = ft.EntitySet(id="customer_data")
es = es.add_dataframe(dataframe_name="transactions",
           dataframe=transactions_df,
           index="transaction_id",
           time_index="transaction_time")

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="transactions",
                                      trans_primitives=['time_since', 'day', 'is_weekend',
                                                        'cum_min', 'minute', 'weekday',
                                                        'percentile', 'year', 'week',
                                                        'cum_mean'])

/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/v1.11.0/lib/python3.8/site-packages/featuretools/synthesis/deep_feature_synthesis.py:161: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created
  warnings.warn(

Before we examine the output, let’s look at our original single table.

transactions_df.head()

	transaction_id	session_id	transaction_time	product_id	amount	customer_id	device	session_start	zip_code	join_date	birthday	brand
298	298	1	2014-01-01 00:00:00	5	127.64	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18	A
2	2	1	2014-01-01 00:01:05	2	109.48	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18	B
308	308	1	2014-01-01 00:02:10	3	95.06	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18	B
116	116	1	2014-01-01 00:03:15	4	78.92	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18	B
371	371	1	2014-01-01 00:04:20	3	31.54	2	desktop	2014-01-01	13244	2012-04-15 23:31:04	1986-08-18	B

Now we can look at the transformations that Featuretools was able to apply to this single DataFrame to create feature matrix.

feature_matrix.head()

	session_id	product_id	amount	customer_id	device	zip_code	brand	CUM_MEAN(amount)	CUM_MEAN(customer_id)	CUM_MEAN(session_id)	...	WEEK(session_start)	WEEK(transaction_time)	WEEKDAY(birthday)	WEEKDAY(join_date)	WEEKDAY(session_start)	WEEKDAY(transaction_time)	YEAR(birthday)	YEAR(join_date)	YEAR(session_start)	YEAR(transaction_time)
transaction_id
298	1	5	127.64	2	desktop	13244	A	127.640000	2.0	1.0	...	1	1	0	6	2	2	1986	2012	2014	2014
2	1	2	109.48	2	desktop	13244	B	118.560000	2.0	1.0	...	1	1	0	6	2	2	1986	2012	2014	2014
308	1	3	95.06	2	desktop	13244	B	110.726667	2.0	1.0	...	1	1	0	6	2	2	1986	2012	2014	2014
116	1	4	78.92	2	desktop	13244	B	102.775000	2.0	1.0	...	1	1	0	6	2	2	1986	2012	2014	2014
371	1	3	31.54	2	desktop	13244	B	88.528000	2.0	1.0	...	1	1	0	6	2	2	1986	2012	2014	2014

5 rows × 44 columns

How do I prevent label leakage with DFS?

One concern you might have with using DFS is about label leakage. You want to make sure that labels in your data aren’t used incorrectly to create features and the feature matrix.

Featuretools is particularly focused on helping users avoid label leakage.

There are two ways to prevent label leakage depending on if your data has timestamps or not.

1. Data without timestamps

In the case where you do not have timestamps, you can create one EntitySet using only the training data and then run ft.dfs. This will create a feature matrix using only the training data, but also return a list of feature definitions. Next, you can create an EntitySet using the test data and recalculate the same features by calling ft.calculate_feature_matrix with the list of feature definitions from before.

Here is what that flow would look like:

First, let’s create our training data.

train_data = pd.DataFrame({"customer_id": [1, 2, 3, 4, 5],
                           "age": [40, 50, 10, 20, 30],
                           "gender": ["m", "f", "m", "f", "f"],
                           "signup_date": pd.date_range('2014-01-01 01:41:50', periods=5, freq='25min'),
                           "labels": [True, False, True, False, True]})
train_data.head()

	customer_id	age	gender	signup_date	labels
0	1	40	m	2014-01-01 01:41:50	True
1	2	50	f	2014-01-01 02:06:50	False
2	3	10	m	2014-01-01 02:31:50	True
3	4	20	f	2014-01-01 02:56:50	False
4	5	30	f	2014-01-01 03:21:50	True

Now, we can create an entityset for our training data.

es_train_data = ft.EntitySet(id="customer_train_data")
es_train_data = es_train_data.add_dataframe(dataframe_name="customers",
                                                    dataframe=train_data,
                                                    index="customer_id")
es_train_data

Entityset: customer_train_data
  DataFrames:
    customers [Rows: 5, Columns: 5]
  Relationships:
    No relationships

Next, we are ready to create our features, and feature matrix for the training data. We don’t want Featuretools to use the labels column to build new features, so we will use the ignore_columns option to exclude it. This would also remove the labels column from the feature matrix, so we will tell DFS to include it as a seed feature.

labels_feature = ft.Feature(es_train_data['customers'].ww['labels'])
feature_matrix_train, feature_defs = ft.dfs(entityset=es_train_data,
                                            target_dataframe_name="customers",
                                            ignore_columns={"customers": ["labels"]},
                                            seed_features=[labels_feature])
feature_matrix_train

/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/v1.11.0/lib/python3.8/site-packages/featuretools/synthesis/deep_feature_synthesis.py:161: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created
  warnings.warn(

	age	labels	DAY(signup_date)	MONTH(signup_date)	WEEKDAY(signup_date)	YEAR(signup_date)
customer_id
1	40	True	1	1	2	2014
2	50	False	1	1	2	2014
3	10	True	1	1	2	2014
4	20	False	1	1	2	2014
5	30	True	1	1	2	2014

We will also encode our feature matrix to make machine learning compatible features.

feature_matrix_train_enc, features_enc = ft.encode_features(feature_matrix_train, feature_defs)
feature_matrix_train_enc.head()

	age	labels	DAY(signup_date) = 1	DAY(signup_date) is unknown	MONTH(signup_date) = 1	MONTH(signup_date) is unknown	WEEKDAY(signup_date) = 2	WEEKDAY(signup_date) is unknown	YEAR(signup_date) = 2014	YEAR(signup_date) is unknown
customer_id
1	40	True	True	False	True	False	True	False	True	False
2	50	False	True	False	True	False	True	False	True	False
3	10	True	True	False	True	False	True	False	True	False
4	20	False	True	False	True	False	True	False	True	False
5	30	True	True	False	True	False	True	False	True	False

Notice how the whole feature matrix only includes numeric and boolean values now.

Now we can use the feature definitions to calculate our feature matrix for the test data, and avoid label leakage.

test_train = pd.DataFrame({"customer_id": [6, 7, 8, 9, 10],
                           "age": [20, 25, 55, 22, 35],
                           "gender": ["f", "m", "m", "m", "m"],
                           "signup_date": pd.date_range('2014-01-01 01:41:50', periods=5, freq='25min'),
                           "labels": [True, False, False, True, True]})

es_test_data = ft.EntitySet(id="customer_test_data")
es_test_data = es_test_data.add_dataframe(dataframe_name="customers",
                                                  dataframe=test_train,
                                                  index="customer_id",
                                                  time_index="signup_date")

# Use the feature definitions from earlier
feature_matrix_enc_test = ft.calculate_feature_matrix(features=features_enc,
                                                      entityset=es_test_data)

feature_matrix_enc_test.head()

	age	labels	DAY(signup_date) = 1	DAY(signup_date) is unknown	MONTH(signup_date) = 1	MONTH(signup_date) is unknown	WEEKDAY(signup_date) = 2	WEEKDAY(signup_date) is unknown	YEAR(signup_date) = 2014	YEAR(signup_date) is unknown
customer_id
6	20	True	True	False	True	False	True	False	True	False
7	25	False	True	False	True	False	True	False	True	False
8	55	False	True	False	True	False	True	False	True	False
9	22	True	True	False	True	False	True	False	True	False
10	35	True	True	False	True	False	True	False	True	False

Check out the Modeling section for an example of using the encoded matrix with sklearn.

2. Data with timestamps

If your data has timestamps, the best way to prevent label leakage is to use a list of cutoff times, which specify the last point in time data is allowed to be used for each row in the resulting feature matrix. To use cutoff times, you need to set a time index for each time sensitive DataFrame in your entity set.

Tip: Even if your data doesn’t have time stamps, you could add a column with dummy timestamps that can be used by Featuretools as time index.

When you call ft.dfs, you can provide a DataFrame of cutoff times like this:

cutoff_times = pd.DataFrame({"customer_id": [1, 2, 3, 4, 5],
                             "time": pd.date_range('2014-01-01 01:41:50', periods=5, freq='25min')})
cutoff_times.head()

	customer_id	time
0	1	2014-01-01 01:41:50
1	2	2014-01-01 02:06:50
2	3	2014-01-01 02:31:50
3	4	2014-01-01 02:56:50
4	5	2014-01-01 03:21:50

train_test_data = pd.DataFrame({"customer_id": [1, 2, 3, 4, 5],
                                "age": [20, 25, 55, 22, 35],
                                "gender": ["f", "m", "m", "m", "m"],
                                "signup_date": pd.date_range('2010-01-01 01:41:50', periods=5, freq='25min')})

es_train_test_data = ft.EntitySet(id="customer_train_test_data")
es_train_test_data = es_train_test_data.add_dataframe(dataframe_name="customers",
                                                              dataframe=train_test_data,
                                                              index="customer_id",
                                                              time_index="signup_date")

feature_matrix_train_test, features = ft.dfs(entityset=es_train_test_data,
                                             target_dataframe_name="customers",
                                             cutoff_time=cutoff_times,
                                             cutoff_time_in_index=True)
feature_matrix_train_test.head()

/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/v1.11.0/lib/python3.8/site-packages/featuretools/synthesis/deep_feature_synthesis.py:161: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created
  warnings.warn(

		age	DAY(signup_date)	MONTH(signup_date)	WEEKDAY(signup_date)	YEAR(signup_date)
customer_id	time
1	2014-01-01 01:41:50	20	1	1	4	2010
2	2014-01-01 02:06:50	25	1	1	4	2010
3	2014-01-01 02:31:50	55	1	1	4	2010
4	2014-01-01 02:56:50	22	1	1	4	2010
5	2014-01-01 03:21:50	35	1	1	4	2010

Above, we have created a feature matrix that uses cutoff times to avoid label leakage. We could also encode this feature matrix using ft.encode_features.

What is the difference between passing a primitive object versus a string to DFS?

There are 2 ways to pass primitives to DFS: the primitive object, or a string of the primitive name.

We will use the Transform primitive called TimeSincePrevious to illustrate the differences.

First, let’s use the string of primitive name.

es = ft.demo.load_mock_customer(return_entityset=True)

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      agg_primitives=[],
                                      trans_primitives=["time_since_previous"])
feature_matrix

	zip_code	TIME_SINCE_PREVIOUS(join_date)
customer_id
5	60091	NaN
4	60091	22948824.0
1	60091	744019.0
3	13244	10212841.0
2	13244	21282510.0

Now, let’s use the primitive object.

from featuretools.primitives import TimeSincePrevious

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      agg_primitives=[],
                                      trans_primitives=[TimeSincePrevious])
feature_matrix

	zip_code	TIME_SINCE_PREVIOUS(join_date)
customer_id
5	60091	NaN
4	60091	22948824.0
1	60091	744019.0
3	13244	10212841.0
2	13244	21282510.0

As we can see above, the feature matrix is the same.

However, if we need to modify controllable parameters in the primitive, we should use the primitive object. For instance, let’s make TimeSincePrevious return units of hours (the default is in seconds).

from featuretools.primitives import TimeSincePrevious

time_since_previous_in_hours = TimeSincePrevious(unit='hours')

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      agg_primitives=[],
                                      trans_primitives=[time_since_previous_in_hours])
feature_matrix

	zip_code	TIME_SINCE_PREVIOUS(join_date, unit=hours)
customer_id
5	60091	NaN
4	60091	6374.673333
1	60091	206.671944
3	13244	2836.900278
2	13244	5911.808333

Features

How can I select features based on some attributes (a specific string, an explicit primitive type, a return type, a given depth)?

You may wish to select a subset of your features based on some attributes.

Let’s say you wanted to select features that had the string amount in its name. You can check for this by using the get_name function on the feature definitions.

es = ft.demo.load_mock_customer(return_entityset=True)

feature_defs = ft.dfs(entityset=es,
                      target_dataframe_name="customers",
                      features_only=True)

features_with_amount = []
for x in feature_defs:
    if 'amount' in x.get_name():
        features_with_amount.append(x)
features_with_amount[0:5]

[<Feature: MAX(transactions.amount)>,
 <Feature: MEAN(transactions.amount)>,
 <Feature: MIN(transactions.amount)>,
 <Feature: SKEW(transactions.amount)>,
 <Feature: STD(transactions.amount)>]

You might also want to only select features that are aggregation features.

from featuretools import AggregationFeature

features_only_aggregations = []
for x in feature_defs:
    if type(x) == AggregationFeature:
        features_only_aggregations.append(x)
features_only_aggregations[0:5]

[<Feature: COUNT(sessions)>,
 <Feature: MODE(sessions.device)>,
 <Feature: NUM_UNIQUE(sessions.device)>,
 <Feature: COUNT(transactions)>,
 <Feature: MAX(transactions.amount)>]

Also, you might only want to select features that are calculated at a certain depth. You can do this by using the get_depth function.

features_only_depth_2 = []
for x in feature_defs:
    if x.get_depth() == 2:
        features_only_depth_2.append(x)
features_only_depth_2[0:5]

[<Feature: MAX(sessions.COUNT(transactions))>,
 <Feature: MAX(sessions.MEAN(transactions.amount))>,
 <Feature: MAX(sessions.MIN(transactions.amount))>,
 <Feature: MAX(sessions.NUM_UNIQUE(transactions.product_id))>,
 <Feature: MAX(sessions.SKEW(transactions.amount))>]

Finally, you might only want features that return a certain type. You can do this by using the column_schema attribute. For more information on working with column schemas, take a look at Transitioning from Variables to Woodwork.

features_only_numeric = []
for x in feature_defs:
    if 'numeric' in x.column_schema.semantic_tags:
        features_only_numeric.append(x)
features_only_numeric[0:5]

[<Feature: COUNT(sessions)>,
 <Feature: NUM_UNIQUE(sessions.device)>,
 <Feature: COUNT(transactions)>,
 <Feature: MAX(transactions.amount)>,
 <Feature: MEAN(transactions.amount)>]

Once you have your specific feature list, you can use ft.calculate_feature_matrix to generate a feature matrix for only those features.

For our example, let’s use the features with only the string amount in its name.

feature_matrix = ft.calculate_feature_matrix(entityset=es,
                                             features=features_with_amount) # change to your specific feature list
feature_matrix.head()

	MAX(transactions.amount)	MEAN(transactions.amount)	MIN(transactions.amount)	SKEW(transactions.amount)	STD(transactions.amount)	SUM(transactions.amount)	MAX(sessions.MEAN(transactions.amount))	MAX(sessions.MIN(transactions.amount))	MAX(sessions.SKEW(transactions.amount))	MAX(sessions.STD(transactions.amount))	...	STD(sessions.MAX(transactions.amount))	STD(sessions.MEAN(transactions.amount))	STD(sessions.MIN(transactions.amount))	STD(sessions.SKEW(transactions.amount))	STD(sessions.SUM(transactions.amount))	SUM(sessions.MAX(transactions.amount))	SUM(sessions.MEAN(transactions.amount))	SUM(sessions.MIN(transactions.amount))	SUM(sessions.SKEW(transactions.amount))	SUM(sessions.STD(transactions.amount))
customer_id
5	149.02	80.375443	7.55	-0.025941	44.095630	6349.66	94.481667	20.65	0.602209	51.149250	...	7.928001	11.007471	4.961414	0.415426	402.775486	839.76	472.231119	86.49	0.014384	259.873954
4	149.95	80.070459	5.73	-0.036348	45.068765	8727.68	110.450000	54.83	0.382868	54.293903	...	3.514421	13.027258	16.960575	0.387884	235.992478	1157.99	649.657515	131.51	0.002764	356.125829
1	139.43	71.631905	5.81	0.019698	40.442059	9025.62	88.755625	26.36	0.640252	46.905665	...	7.322191	13.759314	6.954507	0.589386	279.510713	1057.97	582.193117	78.59	-0.476122	312.745952
3	149.15	67.060430	5.89	0.418230	43.683296	6236.62	82.109444	20.06	0.854976	50.110120	...	10.724241	11.174282	5.424407	0.429374	219.021420	847.63	405.237462	66.21	2.286086	257.299895
2	146.81	77.422366	8.73	0.098259	37.705178	7200.28	96.581000	56.46	0.755711	47.935920	...	17.221593	11.477071	15.874374	0.509798	251.609234	931.63	548.905851	154.60	-0.277640	258.700528

5 rows × 37 columns

Above, notice how all the column names for our feature matrix contain the string amount.

How do I create where features?

Sometimes, you might want to create features that are conditioned on a second value before it is calculated. This extra filter is called a “where clause”. You can create these features using the using the interesting_values of a column.

If you have categorical columns in your EntitySet, you can use add_interesting_values. This function will find interesting values for your categorical columns, which can then be used to generate “where” clauses.

First, let’s create our EntitySet.

es = ft.demo.load_mock_customer(return_entityset=True)
es

Entityset: transactions
  DataFrames:
    transactions [Rows: 500, Columns: 6]
    products [Rows: 5, Columns: 3]
    sessions [Rows: 35, Columns: 5]
    customers [Rows: 5, Columns: 5]
  Relationships:
    transactions.product_id -> products.product_id
    transactions.session_id -> sessions.session_id
    sessions.customer_id -> customers.customer_id

Now we can add the interesting values for the categorical column.

es.add_interesting_values()

Now we can run DFS with the where_primitives argument to define which primitives to apply with where clauses. In this case, let’s use the primitive count. For this to work, the primitive count must be present in both agg_primitives and where_primitives.

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      agg_primitives=["count"],
                                      where_primitives=["count"],
                                      trans_primitives=[])
feature_matrix.head()

	zip_code	COUNT(sessions)	COUNT(transactions)	COUNT(sessions WHERE device = desktop)	COUNT(sessions WHERE device = mobile)	COUNT(sessions WHERE device = tablet)	COUNT(sessions WHERE customers.zip_code = 60091)	COUNT(sessions WHERE customers.zip_code = 13244)	COUNT(transactions WHERE sessions.device = desktop)	COUNT(transactions WHERE sessions.device = mobile)	COUNT(transactions WHERE sessions.device = tablet)
customer_id
5	60091	6	79	2	3	1	6	0	29	36	14
4	60091	8	109	3	4	1	8	0	38	53	18
1	60091	8	126	2	3	3	8	0	27	56	43
3	13244	6	93	4	1	1	0	6	62	16	15
2	13244	7	93	3	2	2	0	7	34	31	28

We have now created some useful features. One example of a useful feature is the COUNT(sessions WHERE device = tablet). This feature tells us how many sessions a customer completed on a tablet.

feature_matrix[["COUNT(sessions WHERE device = tablet)"]]

	COUNT(sessions WHERE device = tablet)
customer_id
5	1
4	1
1	3
3	1
2	2

Primitives

What is the difference between the primitive types (Transform, GroupBy Transform, & Aggregation)?

You might curious to know the difference between the primitive groups. Let’s review the differences between transform, groupby transform, and aggregation primitives.

First, let’s create a simple EntitySet.

import pandas as pd
import featuretools as ft

df = pd.DataFrame({
    "id": [1, 2, 3, 4, 5, 6],
    "time_index": pd.date_range("1/1/2019", periods=6, freq="D"),
    "group": ["a", "a", "a", "a", "a", "a"],
    "val": [5, 1, 10, 20, 6, 23],
})
es = ft.EntitySet()
es = es.add_dataframe(dataframe_name="observations",
                              dataframe=df,
                              index="id",
                              time_index="time_index")

es = es.normalize_dataframe(base_dataframe_name="observations",
                         new_dataframe_name="groups",
                         index="group")

es.plot()

After calling normalize_dataframe, the column “group” has the semantic tag “foreign_key” because it identifies another DataFrame. Alternatively, it could be set using the semantic_tags parameter when we first call es.add_dataframe().

Transform Primitive

The cum_sum primitive calculates the running sum in list of numbers.

from featuretools.primitives import CumSum

cum_sum = CumSum()
cum_sum([1, 2, 3, 4, 5]).tolist()

[1, 3, 6, 10, 15]

If we apply it using the trans_primitives argument it will calculate it over the entire observations DataFrame like this:

feature_matrix, feature_defs = ft.dfs(target_dataframe_name="observations",
                                      entityset=es,
                                      agg_primitives=[],
                                      trans_primitives=["cum_sum"],
                                      groupby_trans_primitives=[])

feature_matrix

	group	val	CUM_SUM(val)
id
1	a	5	5.0
2	a	1	6.0
3	a	10	16.0
4	a	20	36.0
5	a	6	42.0
6	a	23	65.0

Groupby Transform Primitive

If we apply it using groupby_trans_primitives, then DFS will first group by any foreign key columns before applying the transform primitive. As a result, we get the cumulative sum by group.

feature_matrix, feature_defs = ft.dfs(target_dataframe_name="observations",
                                      entityset=es,
                                      agg_primitives=[],
                                      trans_primitives=[],
                                      groupby_trans_primitives=["cum_sum"])

feature_matrix

	group	val	CUM_SUM(val) by group
id
1	a	5	5.0
2	a	1	6.0
3	a	10	16.0
4	a	20	36.0
5	a	6	42.0
6	a	23	65.0

Aggregation Primitive

Finally, there is also the aggregation primitive “sum”. If we use sum, it will calculate the sum for the group at the cutoff time for each row. Because we didn’t specify a cutoff time it will use all the data for each group for each row.

feature_matrix, feature_defs = ft.dfs(target_dataframe_name="observations",
                                      entityset=es,
                                      agg_primitives=["sum"],
                                      trans_primitives=[],
                                      cutoff_time_in_index=True,
                                      groupby_trans_primitives=[])

feature_matrix

		group	val	groups.SUM(observations.val)
id	time
1	2022-06-30 23:09:53.744626	a	5	65.0
2	2022-06-30 23:09:53.744626	a	1	65.0
3	2022-06-30 23:09:53.744626	a	10	65.0
4	2022-06-30 23:09:53.744626	a	20	65.0
5	2022-06-30 23:09:53.744626	a	6	65.0
6	2022-06-30 23:09:53.744626	a	23	65.0

If we set the cutoff time of each row to be the time index, then use sum as an aggregation primitive, the result is the same as cum_sum. (Though the order is different in the displayed dataframe).

cutoff_time = df[["id", "time_index"]]
cutoff_time

	id	time_index
1	1	2019-01-01
2	2	2019-01-02
3	3	2019-01-03
4	4	2019-01-04
5	5	2019-01-05
6	6	2019-01-06

feature_matrix, feature_defs = ft.dfs(target_dataframe_name="observations",
                                      entityset=es,
                                      agg_primitives=["sum"],
                                      trans_primitives=[],
                                      groupby_trans_primitives=[],
                                      cutoff_time_in_index=True,
                                      cutoff_time=cutoff_time)

feature_matrix

		group	val	groups.SUM(observations.val)
id	time
1	2019-01-01	a	5	5.0
2	2019-01-02	a	1	6.0
3	2019-01-03	a	10	16.0
4	2019-01-04	a	20	36.0
5	2019-01-05	a	6	42.0
6	2019-01-06	a	23	65.0

How do I get a list of all Aggregation and Transform primitives?

You can do featuretools.list_primitives() to get all the primitive in Featuretools. It will return a DataFrame with the names, type, and description of the primitives, and if the primitive can be used with entitysets created from Dask dataframes. You can also visit primitives.featurelabs.com to obtain a list of all available primitives.

df_primitives = ft.list_primitives()
df_primitives.head()

	name	type	dask_compatible	spark_compatible	description	valid_inputs	return_type
0	n_most_common	aggregation	False	False	Determines the `n` most common elements.	<ColumnSchema (Semantic Tags = ['category'])>	None
1	num_unique	aggregation	True	True	Determines the number of distinct values, igno...	<ColumnSchema (Semantic Tags = ['category'])>	<ColumnSchema (Logical Type = IntegerNullable)...
2	min	aggregation	True	True	Calculates the smallest value, ignoring `NaN` ...	<ColumnSchema (Semantic Tags = ['numeric'])>	<ColumnSchema (Semantic Tags = ['numeric'])>
3	time_since_last	aggregation	False	False	Calculates the time elapsed since the last dat...	<ColumnSchema (Logical Type = Datetime) (Seman...	<ColumnSchema (Logical Type = Double) (Semanti...
4	trend	aggregation	False	False	Calculates the trend of a column over time.	<ColumnSchema (Logical Type = Datetime) (Seman...	<ColumnSchema (Semantic Tags = ['numeric'])>

df_primitives.tail()

	name	type	dask_compatible	spark_compatible	description	valid_inputs	return_type
123	greater_than_equal_to	transform	True	True	Determines if values in one list are greater t...	<ColumnSchema (Logical Type = Ordinal: None)>,...	<ColumnSchema (Logical Type = BooleanNullable)>
124	weekday	transform	True	True	Determines the day of the week from a datetime.	<ColumnSchema (Logical Type = Datetime)>	<ColumnSchema (Logical Type = Ordinal: [0, 1, ...
125	equal_scalar	transform	True	True	Determines if values in a list are equal to a ...	<ColumnSchema>	<ColumnSchema (Logical Type = BooleanNullable)>
126	median_word_length	transform	False	False	Determines the median word length.	<ColumnSchema (Logical Type = NaturalLanguage)>	<ColumnSchema (Logical Type = Double) (Semanti...
127	diversity_score	transform	False	False	Calculates the overall complexity of the text ...	<ColumnSchema (Logical Type = NaturalLanguage)>	<ColumnSchema (Logical Type = Double) (Semanti...

What primitives can I use when creating a feature matrix from a Dask EntitySet? (BETA)

Support for Dask EntitySets is still in Beta - if you encounter any errors using this approach, please let us know by creating a new issue on Github.

When creating a feature matrix from a Dask EntitySet, only certain primitives can be used. Computation of certain features is quite expensive in a distributed environment, and as a result only a subset of Featuretools primitives are currently supported when using a Dask EntitySet.

The table returned by featuretools.list_primitives() will contain a column labeled dask_compatible. Any primitive that has a value of True in this column can be used safely when computing a feature matrix from a Dask EntitySet.

How do I change the units for a TimeSince primitive?

There are a few primitives in Featuretools that make some time-based calculation. These include TimeSince, TimeSincePrevious, TimeSinceLast, TimeSinceFirst.

You can change the units from the default seconds to any valid time unit, by doing the following:

from featuretools.primitives import TimeSince, TimeSincePrevious, TimeSinceLast, TimeSinceFirst

time_since = TimeSince(unit="minutes")
time_since_previous = TimeSincePrevious(unit="hours")
time_since_last = TimeSinceLast(unit="days")
time_since_first = TimeSinceFirst(unit="years")

es = ft.demo.load_mock_customer(return_entityset=True)

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      agg_primitives=[time_since_last, time_since_first],
                                      trans_primitives=[time_since, time_since_previous])

Above, we changed the units to the following: - minutes for TimeSince - hours for TimeSincePrevious - days for TimeSinceLast - years for TimeSinceFirst.

Now we can see that our feature matrix contains multiple features where the units for the TimeSince primitives are changed.

feature_matrix.head()

	zip_code	TIME_SINCE_FIRST(sessions.session_start, unit=years)	TIME_SINCE_LAST(sessions.session_start, unit=days)	TIME_SINCE_FIRST(transactions.transaction_time, unit=years)	TIME_SINCE_LAST(transactions.transaction_time, unit=days)	TIME_SINCE(birthday, unit=minutes)	TIME_SINCE(join_date, unit=minutes)	TIME_SINCE_PREVIOUS(join_date, unit=hours)	TIME_SINCE_FIRST(transactions.sessions.session_start, unit=years)	TIME_SINCE_LAST(transactions.sessions.session_start, unit=days)
customer_id
5	60091	8.500163	3102.630429	8.500163	3102.625163	1.994683e+07	6.288102e+06	NaN	8.500163	3102.630429
4	60091	8.500143	3102.741771	8.500143	3102.735001	8.350510e+06	5.905622e+06	6374.673333	8.500143	3102.741771
1	60091	8.500112	3102.666540	8.500112	3102.655255	1.470235e+07	5.893221e+06	206.671944	8.500112	3102.666540
3	13244	8.500007	3102.601088	8.500007	3102.589804	9.787630e+06	5.723007e+06	2836.900278	8.500007	3102.601088
2	13244	8.500196	3102.624410	8.500196	3102.615382	1.886539e+07	5.368299e+06	5911.808333	8.500196	3102.624410

There are now features where time unit is different from the default of seconds, such as TIME_SINCE_LAST(sessions.session_start, unit=days), and TIME_SINCE_FIRST(sessions.session_start, unit=years).

Modeling

How does my train & test data work with Featuretools and sklearn’s train_test_split?

You might be wondering how to properly use your train & test data with Featuretools, and sklearn’s train_test_split. There are a few things you must do to ensure accuracy with this workflow.

Let’s imagine we have a Dataframes for our train data, with the labels.

train_data = pd.DataFrame({"customer_id": [1, 2, 3, 4, 5],
                           "age": [20, 25, 55, 22, 35],
                           "gender": ["f", "m", "m", "m", "m"],
                           "signup_date": pd.date_range('2010-01-01 01:41:50', periods=5, freq='25min'),
                           "labels": [False, True, True, False, False]})
train_data.head()

	customer_id	age	gender	signup_date	labels
0	1	20	f	2010-01-01 01:41:50	False
1	2	25	m	2010-01-01 02:06:50	True
2	3	55	m	2010-01-01 02:31:50	True
3	4	22	m	2010-01-01 02:56:50	False
4	5	35	m	2010-01-01 03:21:50	False

Now we can create our EntitySet for the train data, and create our features. To prevent label leakage, we will use cutoff times (see earlier question).

es_train_data = ft.EntitySet(id="customer_data")
es_train_data = es_train_data.add_dataframe(dataframe_name="customers",
                                                    dataframe=train_data,
                                                    index="customer_id")

cutoff_times = pd.DataFrame({"customer_id": [1, 2, 3, 4, 5],
                             "time": pd.date_range('2014-01-01 01:41:50', periods=5, freq='25min')})

feature_matrix_train, features = ft.dfs(entityset=es_train_data,
                                        target_dataframe_name="customers",
                                        cutoff_time=cutoff_times,
                                        cutoff_time_in_index=True)
feature_matrix_train.head()

/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/v1.11.0/lib/python3.8/site-packages/featuretools/synthesis/deep_feature_synthesis.py:161: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created
  warnings.warn(

		age	labels	DAY(signup_date)	MONTH(signup_date)	WEEKDAY(signup_date)	YEAR(signup_date)
customer_id	time
1	2014-01-01 01:41:50	20	False	1	1	4	2010
2	2014-01-01 02:06:50	25	True	1	1	4	2010
3	2014-01-01 02:31:50	55	True	1	1	4	2010
4	2014-01-01 02:56:50	22	False	1	1	4	2010
5	2014-01-01 03:21:50	35	False	1	1	4	2010

We will also encode our feature matrix to compatible for machine learning algorithms.

feature_matrix_train_enc, feature_enc = ft.encode_features(feature_matrix_train, features)
feature_matrix_train_enc.head()

		age	labels	DAY(signup_date) = 1	DAY(signup_date) is unknown	MONTH(signup_date) = 1	MONTH(signup_date) is unknown	WEEKDAY(signup_date) = 4	WEEKDAY(signup_date) is unknown	YEAR(signup_date) = 2010	YEAR(signup_date) is unknown
customer_id	time
1	2014-01-01 01:41:50	20	False	True	False	True	False	True	False	True	False
2	2014-01-01 02:06:50	25	True	True	False	True	False	True	False	True	False
3	2014-01-01 02:31:50	55	True	True	False	True	False	True	False	True	False
4	2014-01-01 02:56:50	22	False	True	False	True	False	True	False	True	False
5	2014-01-01 03:21:50	35	False	True	False	True	False	True	False	True	False

from sklearn.model_selection import train_test_split

X = feature_matrix_train_enc.drop(['labels'], axis=1)
y = feature_matrix_train_enc['labels']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Now you can use the encoded feature matrix with sklearn’s train_test_split. This will allow you to train your model, and tune your parameters.

How are categorical columns encoded when splitting training and testing data?

You might be wondering what happens when categorical columns are encoded with your training and testing data. You might be curious to know what happens if the train data has a categorical column that is not present in the testing data.

Let’s explore a simple example to see what happens during the encoding process.

train_data = pd.DataFrame({
    "customer_id": [1, 2, 3, 4, 5],
    "product_purchased": ["coke zero", "car", "toothpaste", "coke zero", "car"],
})
es_train = ft.EntitySet(id="customer_data")
es_train = es_train.add_dataframe(
    dataframe_name="customers",
    dataframe=train_data,
    index="customer_id",
    logical_types={'product_purchased': ww.logical_types.Categorical},
)
feature_matrix_train, features = ft.dfs(entityset=es_train, target_dataframe_name='customers')
feature_matrix_train

/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-featuretools/envs/v1.11.0/lib/python3.8/site-packages/featuretools/synthesis/deep_feature_synthesis.py:161: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created
  warnings.warn(

	product_purchased
customer_id
1	coke zero
2	car
3	toothpaste
4	coke zero
5	car

We will use ft.encode_features to properly encode the product_purchased column.

feature_matrix_train_encoded, features_encoded = ft.encode_features(feature_matrix_train,
                                                                    features)
feature_matrix_train_encoded.head()

	product_purchased = coke zero	product_purchased = car	product_purchased = toothpaste	product_purchased is unknown
customer_id
1	True	False	False	False
2	False	True	False	False
3	False	False	True	False
4	True	False	False	False
5	False	True	False	False

Now lets imagine we have some test data that has doesn’t have one of the categorical values (toothpaste). Also, the test data has a value that wasn’t present in the train data (water).

test_data = pd.DataFrame({"customer_id": [6, 7, 8, 9, 10],
                          "product_purchased": ["coke zero", "car", "coke zero", "coke zero", "water"]})

es_test = ft.EntitySet(id="customer_data")
es_test = es_test.add_dataframe(dataframe_name="customers",
                                        dataframe=test_data,
                                        index="customer_id")

feature_matrix_test = ft.calculate_feature_matrix(entityset=es_test,
                                                  features=features_encoded)
feature_matrix_test.head()

	product_purchased = coke zero	product_purchased = car	product_purchased = toothpaste	product_purchased is unknown
customer_id
6	True	False	False	False
7	False	True	False	False
8	True	False	False	False
9	True	False	False	False
10	False	False	False	True

As seen above, we were able to successfully handle the encoding, and deal with the following complications: - toothpaste was present in the training data but not present in the testing data - water was present in the test data but not present in the training data.

Errors & Warnings

Why am I getting this error ‘Index is not unique on dataframe’?

You may be trying to create your EntitySet, and run into this error.

IndexError: Index column must be unique

This is because each dataframe in your EntitySet needs a unique index.

Let’s look at a simple example.

product_df = pd.DataFrame({'id': [1, 2, 3, 4, 4],
                           'rating': [3.5, 4.0, 4.5, 1.5, 5.0]})
product_df

	id	rating
0	1	3.5
1	2	4.0
2	3	4.5
3	4	1.5
4	4	5.0

Notice how the id column has a duplicate index of 4. If you try to add this dataframe to the EntitySet, you will run into the following error.

es = ft.EntitySet(id="product_data")
es = es.add_dataframe(dataframe_name="products",
                      dataframe=product_df,
                      index="id")

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-78-854fbaf207f8> in <module>
      1 es = ft.EntitySet(id="product_data")
----> 2 es = es.add_dataframe(dataframe_name="products",
      3                       dataframe=product_df,
      4                       index="id")

~/Code/featuretools/featuretools/entityset/entityset.py in add_dataframe(self, dataframe, dataframe_name, index, logical_types, semantic_tags, make_index, time_index, secondary_time_index, already_sorted)
    625             index_was_created, index, dataframe = _get_or_create_index(index, make_index, dataframe)
    626
--> 627             dataframe.ww.init(name=dataframe_name,
    628                               index=index,
    629                               time_index=time_index,

/usr/local/Caskroom/miniconda/base/envs/featuretools/lib/python3.8/site-packages/woodwork/table_accessor.py in init(self, index, time_index, logical_types, already_sorted, schema, validate, use_standard_tags, **kwargs)
     94         """
     95         if validate:
---> 96             _validate_accessor_params(self._dataframe, index, time_index, logical_types, schema, use_standard_tags)
     97         if schema is not None:
     98             self._schema = schema

/usr/local/Caskroom/miniconda/base/envs/featuretools/lib/python3.8/site-packages/woodwork/table_accessor.py in _validate_accessor_params(dataframe, index, time_index, logical_types, schema, use_standard_tags)
    877         # We ignore these parameters if a schema is passed
    878         if index is not None:
--> 879             _check_index(dataframe, index)
    880         if logical_types:
    881             _check_logical_types(dataframe.columns, logical_types)

/usr/local/Caskroom/miniconda/base/envs/featuretools/lib/python3.8/site-packages/woodwork/table_accessor.py in _check_index(dataframe, index)
    903         # User specifies an index that is in the dataframe but not unique
    904         # Does not check for Dask as Dask does not support is_unique
--> 905         raise IndexError('Index column must be unique')
    906
    907

IndexError: Index column must be unique

To fix the above error, you can do one of the following solutions:

Solution #1 - You can create a unique index on your Dataframe.

product_df = pd.DataFrame({'id': [1, 2, 3, 4, 5],
                           'rating': [3.5, 4.0, 4.5, 1.5, 5.0]})
product_df

	id	rating
0	1	3.5
1	2	4.0
2	3	4.5
3	4	1.5
4	5	5.0

Notice how we now have a unique index column called id.

es = es.add_dataframe(dataframe_name="products",
                      dataframe=product_df,
                      index="id")
es

Entityset: transactions
  DataFrames:
    transactions [Rows: 500, Columns: 6]
    products [Rows: 5, Columns: 2]
    sessions [Rows: 35, Columns: 5]
    customers [Rows: 5, Columns: 5]
  Relationships:
    transactions.product_id -> products.product_id
    transactions.session_id -> sessions.session_id
    sessions.customer_id -> customers.customer_id

As seen above, we can now create our DataFrame for our EntitySet without an error by creating a unique index in our Dataframe.

Solution #2 - Set make_index to True in your call to add_dataframe to create a new index on that data - make_index creates a unique index for each row by just looking at what number the row is, in relation to all the other rows.

product_df = pd.DataFrame({'id': [1, 2, 3, 4, 4],
                           'rating': [3.5, 4.0, 4.5, 1.5, 5.0]})

es = ft.EntitySet(id="product_data")
es = es.add_dataframe(dataframe_name="products",
                      dataframe=product_df,
                      index="product_id",
                      make_index=True)

es['products']

	product_id	id	rating
0	0	1	3.5
1	1	2	4.0
2	2	3	4.5
3	3	4	1.5
4	4	4	5.0

As seen above, we created our dataframe for our EntitySet without an error using the make_index argument.

Why am I getting the following warning ‘Using training_window but last_time_index is not set’?

If you are using a training window, and you haven’t set a last_time_index for your dataframe, you will get this warning. The training window attribute in Featuretools limits the amount of past data that can be used while calculating a particular feature vector.

You can add the last_time_index to all dataframes automatically by calling your_entityset.add_last_time_indexes() after you create your EntitySet. This will remove the warning.

es = ft.demo.load_mock_customer(return_entityset=True)
es.add_last_time_indexes()

Now we can run DFS without getting the warning.

cutoff_times = pd.DataFrame()
cutoff_times['customer_id'] = [1, 2, 3, 1]
cutoff_times['time'] = pd.to_datetime(['2014-1-1 04:00', '2014-1-1 05:00', '2014-1-1 06:00', '2014-1-1 08:00'])
cutoff_times['label'] = [True, True, False, True]

feature_matrix, feature_defs = ft.dfs(entityset=es,
                                      target_dataframe_name="customers",
                                      cutoff_time=cutoff_times,
                                      cutoff_time_in_index=True,
                                      training_window="1 hour")

last_time_index vs. time_index

• The time_index is when the instance was first known.

• The last_time_index is when the instance appears for the last time.

• For example, a customer’s session has multiple transactions which can happen at different points in time. If we are trying to count the number of sessions a user has in a given time period, we often want to count all the sessions that had any transaction during the training window. To accomplish this, we need to not only know when a session starts (time_index), but also when it ends (last_time_index). The last time that an instance appears in the data is stored as the last_time_index of a dataframe.

• Once the last_time_index has been set, Featuretools will check to see if the last_time_index is after the start of the training window. That, combined with the cutoff time, allows DFS to discover which data is relevant for a given training window.

Why am I getting errors with Featuretools on Google Colab?

Google Colab, by default, has Featuretools 0.4.1 installed. You may run into issues following our newest guides, or latest documentation while using an older version of Featuretools. Therefore, we suggest you upgrade to the latest featuretools version by doing the following in your notebook in Google Colab:

!pip install -U featuretools

You may need to Restart the runtime by doing Runtime -> Restart Runtime. You can check latest Featuretools version by doing following:

import featuretools as ft
print(ft.__version__)

You should see a version greater than 0.4.1

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