Sometimes you need to generate multiple column-level Tests or Metrics. To simplify this, you can use metric generator helper functions.
Pre-requisites:
Imports
Use the following code to generate toy data for this guide.
import pandas as pd
import numpy as np
from evidently import Dataset
from evidently import DataDefinition
np.random.seed(42)
data = {
"Age": np.random.randint(18, 60, size=30),
"Salary": np.random.randint(30000, 120000, size=30),
"Department": np.random.choice(["HR", "IT", "Finance", "Marketing", "Operations"], size=30),
"YearsExperience": np.random.randint(1, 15, size=30),
"EducationLevel": np.random.choice(["High School", "Bachelor", "Master", "PhD"], size=30)
}
dummy_df = pd.DataFrame(data)
eval_data_1 = Dataset.from_pandas(
pd.DataFrame(dummy_df.iloc[:15]),
data_definition=DataDefinition()
)
eval_data_2 = Dataset.from_pandas(
pd.DataFrame(dummy_df.iloc[15:]),
data_definition=DataDefinition()
)
Imports
from evidently import Report
from evidently.metrics import *
from evidently.generators import ColumnMetricGenerator
Metric generators
Example 1. Apply the selected metric (ValueDrift) to all columns in the dataset.
report = Report([
ColumnMetricGenerator(ValueDrift)
])
my_eval = report.run(eval_data_1, eval_data_2)
my_eval
Example 2. Apply the selected metric (ValueDrift) to the listed columns in the dataset. Use metric_kwargs to pass any applicable metric parameters.
report = Report([
ColumnMetricGenerator(ValueDrift,
columns=["EducationLevel", "Salary"],
metric_kwargs={"method":"psi"}), # metric parameters
])
my_eval = report.run(eval_data_1, eval_data_2)
my_eval
Example 3. Apply the selected metric (ValueDrift) only to the categorical (cat) columns in the dataset.
report = Report([
ColumnMetricGenerator(UniqueValueCount,
column_types='cat'), #apply to categorical columns only
])
my_eval = report.run(eval_data_1, eval_data_2)
my_eval
Available:
num - numericalcat - categoricalall - all
Test generators
You can use the same approach to generate Tests. Use metric_kwargs to pass test conditions.
Example. Generate the same Test for all the columns in the dataset. It will use defaults if you do not specify the test condition.
from evidently.future.tests import *
report = Report([
ColumnMetricGenerator(MinValue,
column_types='num',
metric_kwargs={"tests":[gt(0)]}),
])
my_eval = report.run(eval_data_1, eval_data_2)
my_eval
This will apply the minimum value test to all numerical columns in the dataset and check that they are above 0.
