!python --version

Python 3.10.5

#!pip install ipywidgets
#!jupyter labextension install @jupyter-widgets/jupyterlab-manager

!jupyter labextension list

JupyterLab v4.2.6
/home/rajaraman/miniconda3/envs/ml3105/share/jupyter/labextensions
        jupyterlab_pygments v0.3.0 enabled OK (python, jupyterlab_pygments)
        jupyterlab-plotly v5.24.1 enabled  X
        @jupyter-notebook/lab-extension v7.2.2 enabled OK
        @jupyter-widgets/jupyterlab-manager v3.1.11 enabled OK (python, jupyterlab_widgets)


   The following extensions may be outdated or specify dependencies that are incompatible with the current version of jupyterlab:
        jupyterlab-plotly

   If you are a user, check if an update is available for these packages.
   If you are a developer, re-run with `--verbose` flag for more details.

import pandas as pd
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import MixedNulls, TrainTestFeatureDrift

# Extend the train and test datasets with more rows
train_data = pd.DataFrame({
    'age': [25, 35, 45, 30, 50, 60, 70, 80, 90, 100],
    'salary': [50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000, 140000],
    'city': ['New York', 'Los Angeles', 'New York', 'Chicago', 'San Francisco',
             'Boston', 'Austin', 'Seattle', 'Dallas', 'Denver']
})

test_data = pd.DataFrame({
    'age': [30, 40, 50, 60, 70, 80, 90, 100, 110, 120],
    'salary': [55000, 65000, 75000, 85000, 95000, 105000, 115000, 125000, 135000, 145000],
    'city': ['Chicago', 'Los Angeles', 'San Francisco', 'New York', 'Boston',
             'Austin', 'Seattle', 'Dallas', 'Denver', 'Houston']
})

# Define categorical features
categorical_features = ['city']

# Create Deepchecks Dataset instances
train_dataset = Dataset(train_data, cat_features=categorical_features)
test_dataset = Dataset(test_data, cat_features=categorical_features)

# Run checks using Dataset
null_check = MixedNulls().run(train_dataset)
print(null_check)

Mixed Nulls: {'n_samples': 10, 'columns': {'age': {}, 'salary': {}, 'city': {}}, 'feature_importance': age       NaN
salary    NaN
city      NaN
dtype: object}

from deepchecks.tabular.checks import FeatureDrift

# Run FeatureDrift check
drift_check = FeatureDrift().run(train_dataset, test_dataset)
drift_check.show()

VBox(children=(HTML(value='<h4><b>Feature Drift</b></h4>'), HTML(value='<p>    Calculate drift between train d…

# from deepchecks.plot.plot import plot_feature_drift

# # Assuming `drift_check` contains results
# plot_feature_drift(drift_check.value)

# Get the drift results as a dictionary
drift_results = drift_check.value

# Print results for each feature
for feature, metrics in drift_results.items():
    print(f"Feature: {feature}")
    for metric, value in metrics.items():
        print(f"  {metric}: {value}")

Feature: age
  Drift score: 0.20000000000000007
  Method: Kolmogorov-Smirnov
  Importance: None
Feature: salary
  Drift score: 0.10000000000000009
  Method: Kolmogorov-Smirnov
  Importance: None
Feature: city
  Drift score: 0.0
  Method: Cramer's V
  Importance: None

# Convert drift results to a DataFrame
drift_results_df = pd.DataFrame.from_dict(drift_check.value, orient='index')
print(drift_results_df)

        Drift score              Method Importance
age             0.2  Kolmogorov-Smirnov       None
salary          0.1  Kolmogorov-Smirnov       None
city            0.0          Cramer's V       None

import matplotlib.pyplot as plt

# Extract drift scores
features = list(drift_results.keys())
scores = [metrics['Drift score'] for metrics in drift_results.values()]

# Plot
plt.figure(figsize=(10, 6))
plt.bar(features, scores)
plt.xlabel('Features')
plt.ylabel('Drift Score')
plt.title('Feature Drift Scores')
plt.xticks(rotation=45)
plt.show()

# !pip install ipywidgets

#!jupyter nbextension enable --py widgetsnbextension
#jupyter labextension install @jupyter-widgets/jupyterlab-manager

drift_check = FeatureDrift().run(train_dataset, test_dataset)
drift_check.show()

VBox(children=(HTML(value='<h4><b>Feature Drift</b></h4>'), HTML(value='<p>    Calculate drift between train d…

from IPython.display import Image, display

# Path to your PNG file
image_path = 'newplot.png'

# Display the image
display(Image(filename=image_path))

# !pip install deepchecks

import pandas as pd
from deepchecks.tabular.checks import MixedNulls, TrainTestFeatureDrift
# from deepchecks import Suite

!pip show pandas | grep "Version"

Version: 2.1.4

from deepchecks import Suite

import deepchecks
print(deepchecks.__version__)

0.18.1

# Sample data
train_data = pd.DataFrame({
    'age': [25, 35, 45, None, 50 …

!python --version

Python 3.10.5

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from deepchecks.tabular import Dataset
from deepchecks.tabular.suites import full_suite

# Load the Titanic dataset
url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv …

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from deepchecks import Dataset
from deepchecks.checks import TrainTestFeatureDrift, ConfusionMatrixReport
from deepchecks.suites import full_suite

# Generate a synthetic dataset
from sklearn.datasets import make_classification

# Generate a dataset with 2 classes
X, y …