features_factory

A small python package that helps dealing with Machine Learning features (using pandas).

Installing the package

Latest available code:

pip install features_factory

Specific version:

pip install features_factory==X.Y.Z

Quickstart

• Verify the input column country_code in a pandas DataFrame df:

feat = CountryCodeInputFeature('Country Code')
input_error = feat.verify_input(df)
input_error.has_missing_columns(), input_error.has_columns_with_nan(), input_error.has_columns_with_wrong_format()

• Map a column into another through a lambda function:

original_feat = StringInputFeature('Full Name')  # declare a feature corresponding to the column
feat = OneComponentFeature('First Name', original_feat, lambda x: x.split(' ')[0])
enriched_df = feat.insert_into(df)

• Map two columns into one using a lambda function:

original_feat1 = StringInputFeature('First Name')
original_feat2 = StringInputFeature('Second Name')
feat = TwoComponentFeature('Full Name', original_feat1, original_feat2, lambda r: r[0] + ' ' + r[1])
new_df = feat.insert_into(df)

• Create a stack of features:

original_feat1 = StringInputFeature('First Name')
original_feat2 = StringInputFeature('Second Name')
feat = TwoComponentFeature('Full Name', original_feat1, original_feat2, lambda r: r[0] + ' ' + r[1])
stack = Stack([original_feat1, original_feat2, feat])

• Use a stack like a list:

stack.add(feat)

stack.remove(feat)

print('Number of features in this stack = ', len(stack))

for feat in stack:
    print(feat.name())

feat = stack[1]

stack = stack1 + stack2
stack += stack3

• A stack automatically ignores duplicates:

stack = Stack([feat1, feat2])
stack.add([feat1])
len(stack) == 2

• Handy stack functionalities:

original_feat1 = StringInputFeature('First Name')
original_feat2 = StringInputFeature('Second Name')
feat = TwoComponentFeature('Full Name', original_feat1, original_feat2, lambda r: r[0] + ' ' + r[1])
stack = Stack([feat])
stack = stack.with_dependencies()
stack.names() == ['Full Name', 'First Name', 'Second Name']

original_feat1 = StringInputFeature('First Name')
original_feat2 = StringInputFeature('Second Name')
feat = TwoComponentFeature('Full Name', original_feat1, original_feat2, lambda r: r[0] + ' ' + r[1])
stack = Stack([feat1, feat2, feat])
stack = stack.only_inputs()
stack.names() == ['First Name', 'Second Name']

• Verify multiple input columns:

stack = Stack([feat1, feat2, feat3])
input_error = stack.verify(df).get_input_data_error()

• Create a stack of features, verify the input data, the feature dependencies, and insert the feature in the df:

# input features
distance = FloatInputFeature('Distance [m]')
duration = IntInputFeature('Duration [s]')
runner_first_name = StringInputFeature('Runner First Name')
runner_last_name = StringInputFeature('Runner Last Name')
runner_age = IntInputFeature('Runner Age')
# derived features
speed = TwoComponentFeature('Average Speed [km/h]', distance, duration,
                     lambda r: 3.6*r[0]/r[1])
full_name = TwoComponentFeature('Full Name', runner_first_name, runner_last_name,
                         lambda r: r[0] + ' ' + r[1])
full_name_with_age = TwoComponentFeature('Full Name With Age', full_name, runner_age,
                                  lambda r: r[0] + ' (age {})'.format(r[1]))
# final feature
summary = TwoComponentFeature('Summary', full_name_with_age, speed,
                       lambda r: 'The runner {} run with and average speed of {} km/h'.format(r[0], r[1]))
# create a stack
stack = Stack([summary]).with_dependencies()
# look for errors
stack_error = stack.verify(df)
# populate the df with all the features
if stack_error.is_empty():
    new_df = stack.insert_into(df)

• Are you working with a moltitude of features and you need to apply the same operation to them? Check out the StackFactory class. E.g.

int1 = IntInputFeature('int1')
int2 = IntInputFeature('int2')
float1 = FloatInputFeature('float1')

names = ['2 x int1', '2 x int2', '2 x float1']
dependencies = [int1, int2, float1]
args = [{'name': name, 'dependency': feat, 'map_function': lambda x: 2*x}
        for name, feat in zip(names, dependencies)]
stack = StackFactory.clones(OneComponentFeature, args)

df = pd.DataFrame({int1.name(): [3, 5, 7], int2.name(): [15, 20, 50], float1.name(): [2.2, 0.1, 5.5]})
df = stack.with_dependencies().insert_into(df)
print(df)
#    int1  int2  float1  2 x float1  2 x int1  2 x int2
# 0     3    15     2.2         4.4         6        30
# 1     5    20     0.1         0.2        10        40
# 2     7    50     5.5        11.0        14       100

Pre-Built Features

Input Features:

• BoolInputFeature: boolean
• IntInputFeature: integer
• FloatInputFeature: floating point
• DateTimeInputFeature: datetime
• DateInputFeature: date
• StringInputFeature: string
• StringTimestampInputFeature: string encoding a timestamp readable via pandas.to_datetime, or according a specific format
• CountryCodeInputFeature: two-digit country code (e.g. DE, IT, FR, ES)

One-Component Features

• OneComponentFeature: define a new feature starting from another one, simply by specifying a lambda function
• RenamedFeature: rename a feature column
• DateTimeFromStringFeature: extract the datetime from a string which encodes a timestamp
• DateFromStringFeature: extract the date from a string which encodes a timestamp
• MonthFromDateFeature: extract the month from a date-like object
• WeekdayFromDateFeature: extract the weekday from a date-like object (0=Monday, 6=Sunday)

Two-Component Features

• TwoComponentFeature: define a new feature starting from two others, simply by specifying a lambda function
• DurationFeature: given a start datetime and an end datetime, compute the duration

Multi-Component Features:

• MultiComponentFeature: define a new feature starting from multiple other ones, simply by specifying a lambda function

Composed Features:

• MeanValueForKeyFeature: given a column with keys and one with values, aggregate the values according to the keys and compute their averages. Finally assign the averages to the new column, according to the keys.

External Data Source Features:

• ValueFromExternalDfFeature: merge with an external dataframe on some columns and one resulting column.

Why You Should Use This Library

• data verification is a rather painful and tricky task. This library can help in many ways:

  • make you think about it
  • let you use some checks that others already used that can help you identify issues, like missing columns, presence of NaN, and wrong data format
  • how many times did it happen that you check the data and they seem ok, but then you modify them somehow, don't check them again (because what should have changed?) but something goes wrong? With this library you build a stack that let you make this verification in a very simple manner, avoiding these situations.

• often features are built one on top of another creating a rather complicated tree of dependencies that can be annoying to manage manually. This library lets you define the features structure, and then take care of everything for you.

• think for a moment about how many times people wrote again and again the same verification code for a feature, or the code to generate one. And how many times stupid mistakes led to a big waste of time? The idea of this open source library is to avoid this.

• using this library will force you to a separation of concepts. Using it, your code will look cleaner.

Developers should know

Create a virtual environment and activate it

python -m venv venv
source venv/bin/activate

Install the development packages

pip install -e .[dev]

and use pre-commit to make sure that your code is blackified automatically (used the black package):

pre-commit install

Run tests:

pip install -e .[test]
coverage run -m unittest discover tests
coverage report

Build documentation (see more details here):

pip install -e .[doc]
mkdocs build

or use

mkdocs serve

if you prefer a live, self-refreshing, documentation.