This ⚡ Lightning component ⚡ was generated automatically with:
lightning init component evidently_data_analysisIt takes some time for the output to be generated and visible in the browser. Try and refresh a couple of times in the browser to see the output.
After doing a train-test split of our dataset which is to be used for modelling or to compare the data distributions of training data vs incoming data in production(test data), this component helps analyse the data trends and any drifts detected in the train and test sets. In simple words, it gives us a visual analysis to verify if our train and test sets have somewhat similar distributions or not and if they vary then by what degree.
This component uses Evidently AI to facilitate this data drift detection which is one of the crucial steps in the whole ML development lifecycle.
This is made primarily for tabular dataset analysis and currently supported tasks are classification or regression.
This must be done before jumping into modeling and the app can be extended to include the training along with the model prediction analysis too (Evidently AI helps for model prediction analysis as well)
Stop looking at bland numbers which make limited sense, give the visualization a shot.
Dataframes with the following characteristics - numeric, textual and categorical columns with no missing values (an exception will be raised is data is missing). No time-series or date time supported as of now. If Date-time columns are preprocessed to make them discrete/categorical values, they shall be considered. Any suggestions to improve the input possibilities are accepted.
First, install evidently_data_analysis (warning: this component has not been officially approved on the lightning gallery):
lightning install component https://github.com/Nachimak28/evidently-data-analysisIf the above does not work, manually setup the environment:
git clone https://github.com/Nachimak28/evidently-data-analysis
cd evidently-data-analysis
conda create --yes --name evidently python=3.8
conda activate evidently
python -m pip install -r requirements.txt
python -m pip install lightning
python -m lightning run app app.py
python -m lightning run app app.py --cloudOnce the app is installed, use it in an app:
Example #1 - Passing data during initialization
import pandas as pd
from evidently_data_analysis import EvidentlyDataAnalysis
from lightning.app.frontend.web import StaticWebFrontend
import lightning as L
class LitApp(L.LightningFlow):
def __init__(self, train_dataframe_path, test_dataframe_path, target_column_name, task_type) -> None:
super().__init__()
self.train_dataframe_path = train_dataframe_path
self.test_dataframe_path = test_dataframe_path
self.target_column_name = target_column_name
self.task_type = task_type
self.evidently_data_analysis = EvidentlyDataAnalysis(
train_dataframe_path=self.train_dataframe_path,
test_dataframe_path=self.test_dataframe_path,
target_column_name=self.target_column_name,
task_type=self.task_type
)
def run(self):
self.evidently_data_analysis.run()
def configure_layout(self):
tab_1 = {'name': 'Data report', 'content': self.evidently_data_analysis}
return tab_1
if __name__ == "__main__":
# classification use case
app = L.LightningApp(LitApp(
train_dataframe_path='resources/ba_cancer_train.csv',
test_dataframe_path='resources/ba_cancer_test.csv',
target_column_name='target',
task_type='classification'
))Example #2
# Note: This is not a complete example but is a demo of providing data to the component during the execution of the run method of some other component instead of providing data during the initialization
# provide payload arguments of the train and test dataframes transferred from another component
# some other component's run method:
class LitApp(L.LightningFlow):
def __init__(self):
self.other_component = OtherComponent()
self.evidently_data_anaylysis = EvidentlyDataAnalysis() #default initialization
def run(self):
self.other_component.run()
self.evidently_data_analysis.task_type = 'classification'
self.evidently_data_analysis.target_column_name = 'target'
# other_component has two attributes which are of type lightning.app.storage.payload.Payload
self.evidently_data_analysis.run(
train_df=self.other_component.train_df,
test_df=self.other_component.test_df
)
# some other work components running
def configure_layout(self):
tabs = []
tab_1 = {'name': 'Data report', 'content': self.evidently_data_analysis}
# tab_2 = ... some other frontend
# tab_3 = ... some other frontend
tabs.append(tab_1)
return tabs
if __name__ == "__main__":
app = L.LightningApp(LitApp())
- Write relevant tests
- Integrate more use cases supported by EvidentlyAI (only 2 use cases - classification and regression supported and they're covered in this component)
- Test in lightning cloud