522 lines
12 KiB
Markdown
522 lines
12 KiB
Markdown
---
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name: data-visualizer
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description: |
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Automated data visualization for EDA, model performance, and business reporting. Activates for "visualize data", "create plots", "EDA", "exploratory analysis", "confusion matrix", "ROC curve", "feature distribution", "correlation heatmap", "plot results", "dashboard". Generates publication-quality visualizations integrated with SpecWeave increments.
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---
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# Data Visualizer
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## Overview
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Automated visualization generation for exploratory data analysis, model performance reporting, and stakeholder communication. Creates publication-quality plots, interactive dashboards, and business-friendly reports—all integrated with SpecWeave's increment workflow.
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## Visualization Categories
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### 1. Exploratory Data Analysis (EDA)
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**Automated EDA Report**:
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```python
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from specweave import EDAVisualizer
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visualizer = EDAVisualizer(increment="0042")
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# Generates comprehensive EDA report
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report = visualizer.generate_eda_report(df)
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# Creates:
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# - Dataset overview (rows, columns, memory, missing values)
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# - Numerical feature distributions (histograms + KDE)
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# - Categorical feature counts (bar charts)
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# - Correlation heatmap
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# - Missing value pattern
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# - Outlier detection plots
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# - Feature relationships (pairplot for top features)
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```
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**Individual EDA Plots**:
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```python
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# Distribution plots
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visualizer.plot_distribution(
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data=df['age'],
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title="Age Distribution",
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bins=30
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)
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# Correlation heatmap
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visualizer.plot_correlation_heatmap(
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data=df[numerical_columns],
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method='pearson' # or 'spearman', 'kendall'
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)
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# Missing value patterns
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visualizer.plot_missing_values(df)
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# Outlier detection (boxplots)
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visualizer.plot_outliers(df[numerical_columns])
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```
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### 2. Model Performance Visualizations
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**Classification Performance**:
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```python
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from specweave import ClassificationVisualizer
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viz = ClassificationVisualizer(increment="0042")
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# Confusion matrix
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viz.plot_confusion_matrix(
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y_true=y_test,
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y_pred=y_pred,
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classes=['Negative', 'Positive']
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)
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# ROC curve
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viz.plot_roc_curve(
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y_true=y_test,
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y_proba=y_proba
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)
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# Precision-Recall curve
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viz.plot_precision_recall_curve(
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y_true=y_test,
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y_proba=y_proba
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)
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# Learning curves (train vs val)
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viz.plot_learning_curve(
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train_scores=train_scores,
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val_scores=val_scores
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)
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# Calibration curve (are probabilities well-calibrated?)
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viz.plot_calibration_curve(
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y_true=y_test,
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y_proba=y_proba
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)
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```
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**Regression Performance**:
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```python
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from specweave import RegressionVisualizer
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viz = RegressionVisualizer(increment="0042")
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# Predicted vs Actual
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viz.plot_predictions(
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y_true=y_test,
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y_pred=y_pred
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)
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# Residual plot
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viz.plot_residuals(
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y_true=y_test,
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y_pred=y_pred
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)
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# Residual distribution (should be normal)
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viz.plot_residual_distribution(
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residuals=y_test - y_pred
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)
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# Error by feature value
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viz.plot_error_analysis(
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y_true=y_test,
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y_pred=y_pred,
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features=X_test
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)
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```
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### 3. Feature Analysis Visualizations
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**Feature Importance**:
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```python
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from specweave import FeatureVisualizer
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viz = FeatureVisualizer(increment="0042")
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# Feature importance (bar chart)
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viz.plot_feature_importance(
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feature_names=feature_names,
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importances=model.feature_importances_,
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top_n=20
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)
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# SHAP summary plot
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viz.plot_shap_summary(
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shap_values=shap_values,
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features=X_test
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)
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# Partial dependence plots
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viz.plot_partial_dependence(
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model=model,
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features=['age', 'income'],
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X=X_train
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)
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# Feature interaction
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viz.plot_feature_interaction(
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model=model,
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features=('age', 'income'),
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X=X_train
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)
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```
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### 4. Time Series Visualizations
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**Time Series Plots**:
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```python
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from specweave import TimeSeriesVisualizer
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viz = TimeSeriesVisualizer(increment="0042")
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# Time series with trend
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viz.plot_timeseries(
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data=sales_data,
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show_trend=True
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)
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# Seasonal decomposition
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viz.plot_seasonal_decomposition(
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data=sales_data,
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period=12 # Monthly seasonality
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)
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# Autocorrelation (ACF, PACF)
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viz.plot_autocorrelation(data=sales_data)
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# Forecast with confidence intervals
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viz.plot_forecast(
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actual=test_data,
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forecast=forecast,
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confidence_intervals=(0.80, 0.95)
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)
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```
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### 5. Model Comparison Visualizations
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**Compare Multiple Models**:
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```python
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from specweave import ModelComparisonVisualizer
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viz = ModelComparisonVisualizer(increment="0042")
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# Compare metrics across models
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viz.plot_model_comparison(
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models=['Baseline', 'XGBoost', 'LightGBM', 'Neural Net'],
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metrics={
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'accuracy': [0.65, 0.87, 0.86, 0.85],
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'roc_auc': [0.70, 0.92, 0.91, 0.90],
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'training_time': [1, 45, 32, 320]
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}
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)
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# ROC curves for multiple models
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viz.plot_roc_curves_comparison(
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models_predictions={
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'XGBoost': (y_test, y_proba_xgb),
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'LightGBM': (y_test, y_proba_lgbm),
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'Neural Net': (y_test, y_proba_nn)
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}
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)
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```
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## Interactive Visualizations
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**Plotly Integration**:
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```python
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from specweave import InteractiveVisualizer
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viz = InteractiveVisualizer(increment="0042")
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# Interactive scatter plot (zoom, pan, hover)
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viz.plot_interactive_scatter(
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x=X_test[:, 0],
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y=X_test[:, 1],
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colors=y_pred,
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hover_data=df[['id', 'amount', 'merchant']]
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)
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# Interactive confusion matrix (click for details)
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viz.plot_interactive_confusion_matrix(
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y_true=y_test,
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y_pred=y_pred
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)
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# Interactive feature importance (sortable, filterable)
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viz.plot_interactive_feature_importance(
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feature_names=feature_names,
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importances=importances
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)
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```
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## Business Reporting
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**Automated ML Report**:
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```python
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from specweave import MLReportGenerator
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generator = MLReportGenerator(increment="0042")
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# Generate executive summary report
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report = generator.generate_report(
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model=model,
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test_data=(X_test, y_test),
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business_metrics={
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'false_positive_cost': 5,
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'false_negative_cost': 500
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}
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)
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# Creates:
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# - Executive summary (1 page, non-technical)
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# - Key metrics (accuracy, precision, recall)
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# - Business impact ($$ saved, ROI)
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# - Model performance visualizations
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# - Recommendations
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# - Technical appendix
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```
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**Report Output** (HTML/PDF):
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```markdown
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# Fraud Detection Model - Executive Summary
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## Key Results
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- **Accuracy**: 87% (target: >85%) ✅
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- **Fraud Detection Rate**: 62% (catching 310 frauds/day)
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- **False Positive Rate**: 38% (190 false alarms/day)
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## Business Impact
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- **Fraud Prevented**: $155,000/day
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- **Review Cost**: $950/day (190 transactions × $5)
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- **Net Benefit**: $154,050/day ✅
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- **Annual Savings**: $56.2M
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## Model Performance
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[Confusion Matrix Visualization]
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[ROC Curve]
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[Feature Importance]
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## Recommendations
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1. ✅ Deploy to production immediately
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2. Monitor fraud patterns weekly
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3. Retrain model monthly with new data
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```
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## Dashboard Creation
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**Real-Time Dashboard**:
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```python
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from specweave import DashboardCreator
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creator = DashboardCreator(increment="0042")
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# Create Grafana/Plotly dashboard
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dashboard = creator.create_dashboard(
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title="Model Performance Dashboard",
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panels=[
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{'type': 'metric', 'query': 'prediction_latency_p95'},
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{'type': 'metric', 'query': 'predictions_per_second'},
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{'type': 'timeseries', 'query': 'accuracy_over_time'},
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{'type': 'timeseries', 'query': 'error_rate'},
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{'type': 'heatmap', 'query': 'prediction_distribution'},
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{'type': 'table', 'query': 'recent_anomalies'}
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]
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)
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# Exports to Grafana JSON or Plotly Dash app
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dashboard.export(format='grafana')
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```
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## Visualization Best Practices
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### 1. Publication-Quality Plots
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```python
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# Set consistent styling
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visualizer.set_style(
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style='seaborn', # Or 'ggplot', 'fivethirtyeight'
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context='paper', # Or 'notebook', 'talk', 'poster'
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palette='colorblind' # Accessible colors
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)
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# High-resolution exports
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visualizer.save_figure(
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filename='model_performance.png',
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dpi=300, # Publication quality
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bbox_inches='tight'
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)
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```
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### 2. Accessible Visualizations
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```python
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# Colorblind-friendly palettes
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visualizer.use_colorblind_palette()
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# Add alt text for accessibility
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visualizer.add_alt_text(
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plot=fig,
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description="Confusion matrix showing 87% accuracy"
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)
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# High contrast for presentations
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visualizer.set_high_contrast_mode()
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```
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### 3. Annotation and Context
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```python
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# Add reference lines
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viz.add_reference_line(
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y=0.85, # Target accuracy
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label='Target',
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color='red',
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linestyle='--'
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)
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# Add annotations
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viz.annotate_point(
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x=optimal_threshold,
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y=optimal_f1,
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text='Optimal threshold: 0.47'
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)
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```
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## Integration with SpecWeave
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### Automated Visualization in Increments
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```python
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# All visualizations auto-saved to increment folder
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visualizer = EDAVisualizer(increment="0042")
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# Creates:
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# .specweave/increments/0042-fraud-detection/
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# ├── visualizations/
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# │ ├── eda/
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# │ │ ├── distributions.png
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# │ │ ├── correlation_heatmap.png
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# │ │ └── missing_values.png
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# │ ├── model_performance/
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# │ │ ├── confusion_matrix.png
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# │ │ ├── roc_curve.png
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# │ │ ├── precision_recall.png
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# │ │ └── learning_curves.png
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# │ ├── feature_analysis/
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# │ │ ├── feature_importance.png
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# │ │ ├── shap_summary.png
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# │ │ └── partial_dependence/
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# │ └── reports/
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# │ ├── executive_summary.html
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# │ └── technical_report.pdf
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```
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### Living Docs Integration
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```bash
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/specweave:sync-docs update
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```
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Updates:
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```markdown
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<!-- .specweave/docs/internal/architecture/ml-model-performance.md -->
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## Fraud Detection Model Performance (Increment 0042)
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### Model Accuracy
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### Key Metrics
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- Accuracy: 87%
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- Precision: 85%
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- Recall: 62%
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- ROC AUC: 0.92
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### Feature Importance
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Top 5 features:
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1. amount_vs_user_average (0.18)
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2. days_since_last_purchase (0.12)
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3. merchant_risk_score (0.10)
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4. velocity_24h (0.08)
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5. location_distance_from_home (0.07)
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```
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## Commands
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```bash
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# Generate EDA report
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/ml:visualize-eda 0042
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# Generate model performance report
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/ml:visualize-performance 0042
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# Create interactive dashboard
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/ml:create-dashboard 0042
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# Export all visualizations
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/ml:export-visualizations 0042 --format png,pdf,html
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```
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## Advanced Features
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### 1. Automated Report Generation
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```python
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# Generate full increment report with all visualizations
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generator = IncrementReportGenerator(increment="0042")
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report = generator.generate_full_report()
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# Includes:
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# - EDA visualizations
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# - Experiment comparisons
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# - Best model performance
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# - Feature importance
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# - Business impact
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# - Deployment readiness
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```
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### 2. Custom Visualization Templates
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```python
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# Create reusable templates
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template = VisualizationTemplate(name="fraud_analysis")
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template.add_panel("confusion_matrix")
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template.add_panel("roc_curve")
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template.add_panel("top_fraud_features")
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template.add_panel("fraud_trends_over_time")
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# Apply to any increment
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template.apply(increment="0042")
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```
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### 3. Version Control for Visualizations
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```python
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# Track visualization changes across model versions
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viz_tracker = VisualizationTracker(increment="0042")
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# Compare model v1 vs v2 visualizations
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viz_tracker.compare_versions(
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version_1="model-v1",
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version_2="model-v2"
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)
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# Shows: Confusion matrix improved, ROC curve comparison, etc.
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```
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## Summary
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Data visualization is critical for:
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- ✅ Exploratory data analysis (understand data before modeling)
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- ✅ Model performance communication (stakeholder buy-in)
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- ✅ Feature analysis (understand what drives predictions)
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- ✅ Business reporting (translate metrics to impact)
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- ✅ Model debugging (identify issues visually)
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This skill automates visualization generation, ensuring all ML work is visual, accessible, and business-friendly within SpecWeave's increment workflow.
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