Add Feature Correlation Heatmap to Dashboard

README.md

@@ -42,14 +42,16 @@ Evidently is very modular. You can start with one-off evaluations or host a full
 * Best for experiments, exploratory analysis and debugging.
 * View interactive Reports in Python or export as JSON, Python dictionary, HTML, or view in monitoring UI.
 
-Turn any Report into a **Test Suite** by adding pass/fail conditions.
+Turn any Report into a **Test Suite** by adding pass/fail conditions to automatically validate data quality, model performance, or drift over time.
+
 * Best for regression testing, CI/CD checks, or data validation.
 * Zero setup option: auto-generate test conditions from the reference dataset.
 * Simple syntax to set test conditions as `gt` (greater than), `lt` (less than), etc.
 
 | Reports |
 |--|
-|![Report example](https://github.com/evidentlyai/docs/blob/eb1630cdd80d31d55921ff4d34fc7b5e6e9c9f90/images/concepts/report_test_preview.gif)|
+|![Report Turn any Report into a **Test Suite** by adding pass/fail conditions to automatically validate data quality, model performance, or drift over time.
+example](https://github.com/evidentlyai/docs/blob/eb1630cdd80d31d55921ff4d34fc7b5e6e9c9f90/images/concepts/report_test_preview.gif)|
 
 ## 2. Monitoring Dashboard
 

src/dashboard/widgets/feature_correlation_widget.py

@@ -0,0 +1,21 @@
+import plotly.express as px
+import pandas as pd
+
+def plot_correlation_heatmap(correlations):
+    """
+    Plots feature correlations as a heatmap using Plotly
+    correlations: pandas Series with feature names as index
+    """
+    # Convert Series to DataFrame: 1 row (target) x n columns (features)
+    df = pd.DataFrame([correlations.values], columns=correlations.index, index=[correlations.name if correlations.name else "target"])
+
+    fig = px.imshow(
+        df,
+        text_auto=True,                     # shows values on the heatmap
+        color_continuous_scale='Viridis',
+        labels={'x': 'Features', 'y': 'Target', 'color': 'Correlation'}
+    )
+
+    fig.update_layout(title="Feature Correlation Heatmap")
+    return fig
+

src/evidently/dashboard.py

@@ -0,0 +1,50 @@
+"""
+Dashboard for Evidently: Feature Correlation Heatmap Integration
+Author: Your Name
+Date: 2026-02-02
+"""
+
+# Imports
+import pandas as pd
+from src.metrics.feature_correlation import feature_correlation
+from src.dashboard.widgets.feature_correlation_widget import plot_correlation_heatmap
+
+# ----------------------------
+# Widget Function
+# ----------------------------
+def add_feature_correlation_widget(df, target_col):
+    """
+    Adds the Feature Correlation Heatmap to the dashboard.
+
+    Parameters:
+    - df: pandas DataFrame containing your dataset
+    - target_col: name of the target column to compute correlations against
+
+    Returns:
+    - fig: Plotly heatmap figure
+    """
+    correlations = feature_correlation(df, target_col)
+    fig = plot_correlation_heatmap(correlations)
+    fig.show()  # For testing; later integrate directly into the web dashboard
+    return fig
+
+# ----------------------------
+# Sample Test Dataset
+# ----------------------------
+if __name__ == "__main__":
+    # Sample dataset for testing the heatmap
+    df = pd.DataFrame({
+        "A": [1, 2, 3, 4, 5],
+        "B": [5, 4, 3, 2, 1],
+        "C": [2, 3, 2, 3, 2],
+        "target": [0, 1, 0, 1, 0]
+    })
+
+    target_col = "target"
+
+    # Add the feature correlation heatmap to the dashboard
+    add_feature_correlation_widget(df, target_col)
+
+    # TODO: Replace the sample dataset with real project dataset or user-uploaded CSV
+    # TODO: Embed the Plotly figure directly into the web dashboard layout
+

src/metrics/feature_correlation.py

@@ -0,0 +1,9 @@
+import pandas as pd
+
+def feature_correlation(df, target_col):
+    """
+    Computes correlation of all features with the target column.
+    Returns a sorted pandas Series.
+    """
+    correlations = df.corr()[target_col].sort_values(ascending=False)
+    return correlations

test_heatmap.py

@@ -0,0 +1,14 @@
+import pandas as pd
+from src.metrics.feature_correlation import feature_correlation
+from src.dashboard.widgets.feature_correlation_widget import plot_correlation_heatmap
+
+# Sample data
+df = pd.DataFrame({
+    "A": [1, 2, 3, 4],
+    "B": [2, 4, 6, 8],
+    "target": [1, 0, 1, 0]
+})
+
+correlations = feature_correlation(df, "target")
+fig = plot_correlation_heatmap(correlations)
+fig.show()