matplotlib-part-5-of-5-indepth-notebook.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/mlvprasad/matplotlib-part-5-of-5-indepth-notebook?scriptVersionId=146839481\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"9250b2f6","metadata":{"papermill":{"duration":0.012714,"end_time":"2023-10-16T18:11:35.91191","exception":false,"start_time":"2023-10-16T18:11:35.899196","status":"completed"},"tags":[]},"source":["![mlv prasad](https://github.com/MlvPrasadOfficial/kaggle_notebooks/raw/main/mlvprasad.png)\n"]},{"cell_type":"markdown","id":"ff715580","metadata":{"papermill":{"duration":0.011594,"end_time":"2023-10-16T18:11:35.935811","exception":false,"start_time":"2023-10-16T18:11:35.924217","status":"completed"},"tags":[]},"source":["![mat](https://github.com/MlvPrasadOfficial/kaggle_notebooks/raw/main/KAGGLE%20-%20mat/ma55.png)"]},{"cell_type":"markdown","id":"85efea0c","metadata":{"papermill":{"duration":0.011561,"end_time":"2023-10-16T18:11:35.959151","exception":false,"start_time":"2023-10-16T18:11:35.94759","status":"completed"},"tags":[]},"source":["\n","## Chapter 81: Visualizing Active Learning\n","- [Plotting uncertainty and informativeness measures](code/chapter81/plotting_uncertainty_informativeness_measures.py)\n","- [Visualizing data selection and labeling process](code/chapter81/visualizing_data_selection_labeling_process.py)\n","- [Interactive active learning visualizations](code/chapter81/interactive_active_learning_visualizations.py)\n","- [Customizing active learning visualizations](code/chapter81/customizing_active_learning_visualizations.py)\n","\n","## Chapter 82: Visualizing Explainable AI\n","- [Plotting feature importance and contributions](code/chapter82/plotting_feature_importance_contributions.py)\n","- [Visualizing rule-based explanations](code/chapter82/visualizing_rule_based_explanations.py)\n","- [Interactive explainable AI visualizations](code/chapter82/interactive_explainable_ai_visualizations.py)\n","- [Customizing explainable AI visualizations](code/chapter82/customizing_explainable_ai_visualizations.py)\n","\n","## Chapter 83: Visualizing Transfer Learning and Domain Adaptation\n","- [Plotting feature representations and transferability](code/chapter83/plotting_feature_representations_transferability.py)\n","- [Visualizing domain shift and adaptation process](code/chapter83/visualizing_domain_shift_adaptation_process.py)\n","- [Interactive transfer learning visualizations](code/chapter83/interactive_transfer_learning_visualizations.py)\n","- [Customizing transfer learning visualizations](code/chapter83/customizing_transfer_learning_visualizations.py)\n","\n","## Chapter 84: Visualizing Neural Architecture Interpretability\n","- [Plotting neuron activations and responses](code/chapter84/plotting_neuron_activations_responses.py)\n","- [Visualizing network attention and saliency maps](code/chapter84/visualizing_network_attention_saliency_maps.py)\n","- [Interactive neural architecture interpretability visualizations](code/chapter84/interactive_neural_architecture_interpretability_visualizations.py)\n","- [Customizing interpretability visualizations](code/chapter84/customizing_interpretability_visualizations.py)\n","\n","## Chapter 85: Visualizing Uncertainty in Time Series Forecasting\n","- [Plotting prediction intervals and quantiles](code/chapter85/plotting_prediction_intervals_quantiles.py)\n","- [Visualizing forecast error distributions over time](code/chapter85/visualizing_forecast_error_distributions_over_time.py)\n","- [Uncertainty decomposition and contribution plots](code/chapter85/uncertainty_decomposition_contribution_plots.py)\n","- [Customizing uncertainty visualizations in time series forecasting](code/chapter85/customizing_uncertainty_visualizations_time_series_forecasting.py)\n","\n","## Chapter 86: Visualizing Meta-Learning\n","- [Plotting meta-learning progress and performance](code/chapter86/plotting_meta_learning_progress_performance.py)\n","- [Visualizing meta-feature representations](code/chapter86/visualizing_meta_feature_representations.py)\n","- [Interactive meta-learning visualizations](code/chapter86/interactive_meta_learning_visualizations.py)\n","- [Customizing meta-learning visualizations](code/chapter86/customizing_meta_learning_visualizations.py)\n","\n","## Chapter 87: Visualizing Anomaly Detection\n","- [Plotting anomaly scores and thresholds over time](code/chapter87/plotting_anomaly_scores_thresholds_over_time.py)\n","- [Visualizing anomaly detection results](code/chapter87/visualizing_anomaly_detection_results.py)\n","- [Interactive anomaly detection visualizations](code/chapter87/interactive_anomaly_detection_visualizations.py)\n","- [Customizing anomaly detection visualizations](code/chapter87/customizing_anomaly_detection_visualizations.py)\n","\n","## Chapter 88: Visualizing Explainability in Recommendation Systems\n","- [Plotting user-item interactions and explanations](code/chapter88/plotting_user_item_interactions_explanations.py)\n","- [Visualizing recommendation explanations](code/chapter88/visualizing_recommendation_explanations.py)\n","- [Interactive explainability visualizations in recommendation systems](code/chapter88/interactive_explainability_visualizations_recommendation_systems.py)\n","- [Customizing explainability visualizations in recommendation systems](code/chapter88/customizing_explainability_visualizations_recommendation_systems.py)\n","\n","## Chapter 89: Visualizing Uncertainty in Reinforcement Learning\n","- [Plotting value uncertainty and exploration rates](code/chapter89/plotting_value_uncertainty_exploration_rates.py)\n","- [Visualizing policy uncertainty and exploration-exploitation trade-offs](code/chapter89/visualizing_policy_uncertainty_exploration_tradeoffs.py)\n","- [Interactive uncertainty visualizations in reinforcement learning](code/chapter89/interactive_uncertainty_visualizations_reinforcement_learning.py)\n","- [Customizing uncertainty visualizations in reinforcement learning](code/chapter89/customizing_uncertainty_visualizations_reinforcement_learning.py)\n","\n","## Chapter 90: Visualizing Federated Learning\n","- [Plotting model updates and convergence](code/chapter90/plotting_model_updates_convergence.py)\n","- [Visualizing distributed data sources](code/chapter90/visualizing_distributed_data_sources.py)\n","- [Interactive federated learning visualizations](code/chapter90/interactive_federated_learning_visualizations.py)\n","- [Customizing federated learning visualizations](code/chapter90/customizing_federated_learning_visualizations.py)\n"]},{"cell_type":"markdown","id":"1f1a7419","metadata":{"papermill":{"duration":0.011579,"end_time":"2023-10-16T18:11:35.982592","exception":false,"start_time":"2023-10-16T18:11:35.971013","status":"completed"},"tags":[]},"source":["<h1 align=\"left\"><font color='red'>81</font></h1>"]},{"cell_type":"markdown","id":"aae56666","metadata":{"papermill":{"duration":0.011461,"end_time":"2023-10-16T18:11:36.006185","exception":false,"start_time":"2023-10-16T18:11:35.994724","status":"completed"},"tags":[]},"source":["# Chapter 81: Visualizing Active Learning\n","\n","#### Active learning is a process that involves selecting and labeling informative data points to train a machine learning model more effectively. Visualizing active learning can help us understand the uncertainty and informativeness of data points, track the data selection and labeling process, and interactively explore the active learning workflow. In this chapter, we will explore techniques for visualizing active learning, including plotting uncertainty and informativeness measures, visualizing the data selection and labeling process, creating interactive active learning visualizations, and customizing active learning visualizations.\n","\n","## Section 81.1: Plotting Uncertainty and Informativeness Measures\n","\n","#### Uncertainty and informativeness measures provide insights into the uncertainty of predictions and the potential usefulness of unlabeled data points. Plotting these measures can help us understand the distribution and dynamics of uncertainty and informativeness throughout the active learning process. Here's an example of plotting uncertainty and informativeness measures using Matplotlib:"]},{"cell_type":"markdown","id":"65e3e4d3","metadata":{"papermill":{"duration":0.011526,"end_time":"2023-10-16T18:11:36.029469","exception":false,"start_time":"2023-10-16T18:11:36.017943","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'uncertainty' and 'informativeness' are arrays of uncertainty and informativeness measures\n","\n","# Plot uncertainty and informativeness measures\n","plt.plot(uncertainty, label='Uncertainty')\n","plt.plot(informativeness, label='Informativeness')\n","plt.xlabel('Iteration')\n","plt.ylabel('Measure')\n","plt.legend()\n","plt.title('Uncertainty and Informativeness')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"62b927cb","metadata":{"papermill":{"duration":0.011523,"end_time":"2023-10-16T18:11:36.052827","exception":false,"start_time":"2023-10-16T18:11:36.041304","status":"completed"},"tags":[]},"source":["#### In this example, we assume that uncertainty and informativeness are arrays containing the uncertainty and informativeness measures, respectively, for each iteration of the active learning process. We use Matplotlib to plot these measures over iterations, with the iterations on the x-axis and the measures on the y-axis. The resulting plot shows how the uncertainty and informativeness change over the course of active learning.\n","\n","## Section 81.2: Visualizing Data Selection and Labeling Process\n","\n","#### Visualizing the data selection and labeling process allows us to understand which data points are selected for labeling and how the labeling decisions evolve over iterations. This visualization can help us evaluate the effectiveness of the active learning strategy and identify potential issues. Here's an example of visualizing the data selection and labeling process using Matplotlib:"]},{"cell_type":"markdown","id":"253cbc12","metadata":{"papermill":{"duration":0.011455,"end_time":"2023-10-16T18:11:36.075932","exception":false,"start_time":"2023-10-16T18:11:36.064477","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'selected_indices' is a list of selected data indices for labeling\n","\n","# Plot the data selection and labeling process\n","plt.plot(selected_indices)\n","plt.xlabel('Iteration')\n","plt.ylabel('Data Index')\n","plt.title('Data Selection and Labeling')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"09a1e12c","metadata":{"papermill":{"duration":0.011358,"end_time":"2023-10-16T18:11:36.098948","exception":false,"start_time":"2023-10-16T18:11:36.08759","status":"completed"},"tags":[]},"source":["#### In this example, we assume that selected_indices is a list of selected data indices for labeling in each iteration of the active learning process. We use Matplotlib to plot these indices over iterations, with the iterations on the x-axis and the data indices on the y-axis. The resulting plot shows which data points are selected for labeling at each iteration.\n","\n","## Section 81.3: Interactive Active Learning Visualizations\n","\n","#### Interactive active learning visualizations allow us to explore the data, uncertainty, informativeness, and labeling decisions in a dynamic and interactive manner. These visualizations enable us to dive deeper into the active learning process and make more informed decisions. Here's an example of creating an interactive active learning visualization using the Plotly library:"]},{"cell_type":"markdown","id":"710f224b","metadata":{"papermill":{"duration":0.012022,"end_time":"2023-10-16T18:11:36.122631","exception":false,"start_time":"2023-10-16T18:11:36.110609","status":"completed"},"tags":[]},"source":["```python\n","import plotly.graph_objects as go\n","\n","# Assume 'data' is the dataset, 'uncertainty' and 'informativeness' are arrays of measures, 'labels' are the ground truth labels\n","\n","# Create an interactive scatter plot\n","fig = go.Figure(data=go.Scatter(x=data[:, 0], y=data[:, 1], mode='markers', marker=dict(color=labels)))\n","fig.update_layout(title='Active Learning', xaxis=dict(title='Feature 1'), yaxis=dict(title='Feature 2'))\n","\n","# Add uncertainty and informativeness measures as color and size\n","fig.add_trace(go.Scatter(x=data[:, 0], y=data[:, 1], mode='markers', marker=dict(color=uncertainty, size=informativeness, showscale=True)))\n","\n","# Display the interactive plot\n","fig.show()\n","```"]},{"cell_type":"markdown","id":"687687ae","metadata":{"papermill":{"duration":0.068218,"end_time":"2023-10-16T18:11:36.202741","exception":false,"start_time":"2023-10-16T18:11:36.134523","status":"completed"},"tags":[]},"source":["#### In this example, we assume that data is the dataset, uncertainty and informativeness are arrays of measures for each data point, and labels are the ground truth labels. We use the Plotly library to create an interactive scatter plot, where the data points are displayed as markers with colors representing the ground truth labels. We also add another layer to the plot with uncertainty and informativeness measures, where the color and size of the markers represent the uncertainty and informativeness. The resulting plot allows for interactive exploration and examination of the active learning process.\n","\n","## Section 81.4: Customizing Active Learning Visualizations\n","\n","#### Customization options for active learning visualizations depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Bokeh can be used to create more specialized or interactive visualizations."]},{"cell_type":"markdown","id":"30cfcae0","metadata":{"papermill":{"duration":0.011558,"end_time":"2023-10-16T18:11:36.226677","exception":false,"start_time":"2023-10-16T18:11:36.215119","status":"completed"},"tags":[]},"source":["<h1 align=\"left\"><font color='red'>82</font></h1>"]},{"cell_type":"markdown","id":"4fc86daa","metadata":{"papermill":{"duration":0.011603,"end_time":"2023-10-16T18:11:36.250071","exception":false,"start_time":"2023-10-16T18:11:36.238468","status":"completed"},"tags":[]},"source":["# Chapter 82: Visualizing Explainable AI\n","\n","#### Explainable AI techniques aim to provide insights into how machine learning models make predictions and decisions. Visualizing explainable AI can help us understand the importance of features, the contributions of different factors, and the reasoning behind model predictions. In this chapter, we will explore techniques for visualizing explainable AI, including plotting feature importance and contributions, visualizing rule-based explanations, creating interactive explainable AI visualizations, and customizing explainable AI visualizations.\n","\n","## Section 82.1: Plotting Feature Importance and Contributions\n","\n","#### Feature importance and contributions provide insights into the relevance and impact of different features on model predictions. Plotting these measures can help us understand which features are most influential and how they contribute to the overall predictions. Here's an example of plotting feature importance and contributions using Matplotlib:"]},{"cell_type":"markdown","id":"f44524c8","metadata":{"papermill":{"duration":0.011819,"end_time":"2023-10-16T18:11:36.273965","exception":false,"start_time":"2023-10-16T18:11:36.262146","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'feature_importance' is an array of feature importance values\n","# Assume 'feature_contributions' is a matrix of feature contributions for each prediction\n","\n","# Plot feature importance\n","plt.bar(range(len(feature_importance)), feature_importance)\n","plt.xlabel('Feature')\n","plt.ylabel('Importance')\n","plt.title('Feature Importance')\n","plt.show()\n","\n","# Plot feature contributions for a specific prediction\n","plt.bar(range(len(feature_contributions)), feature_contributions)\n","plt.xlabel('Feature')\n","plt.ylabel('Contribution')\n","plt.title('Feature Contributions')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"3cd05342","metadata":{"papermill":{"duration":0.011507,"end_time":"2023-10-16T18:11:36.297281","exception":false,"start_time":"2023-10-16T18:11:36.285774","status":"completed"},"tags":[]},"source":["#### In this example, we assume that feature_importance is an array containing feature importance values, and feature_contributions is a matrix containing feature contributions for each prediction. We use Matplotlib to create bar plots, where the x-axis represents the features, and the y-axis represents the importance or contribution values. The resulting plots provide insights into the importance of features and the contributions they make to the predictions.\n","\n","## Section 82.2: Visualizing Rule-Based Explanations\n","\n","#### Rule-based explanations provide interpretable rules or conditions that can explain model predictions. Visualizing these rules can help us understand the decision-making process of the model and how different factors influence the predictions. Here's an example of visualizing rule-based explanations using Matplotlib:"]},{"cell_type":"markdown","id":"1d70760d","metadata":{"papermill":{"duration":0.011962,"end_time":"2023-10-16T18:11:36.321066","exception":false,"start_time":"2023-10-16T18:11:36.309104","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'rules' is a list of rule-based explanations\n","\n","# Plot the rule-based explanations\n","plt.figure(figsize=(10, 5))\n","for i, rule in enumerate(rules):\n","    plt.text(0.1, 0.9 - i * 0.1, rule, transform=plt.gca().transAxes)\n","plt.axis('off')\n","plt.title('Rule-Based Explanations')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"5e33db12","metadata":{"papermill":{"duration":0.01147,"end_time":"2023-10-16T18:11:36.344284","exception":false,"start_time":"2023-10-16T18:11:36.332814","status":"completed"},"tags":[]},"source":["#### In this example, we assume that rules is a list of rule-based explanations, where each rule is a string representation of a condition or a set of conditions. We use Matplotlib to create a text plot, where each rule is displayed as a text element at a specific position on the plot. The resulting plot visualizes the rule-based explanations in a readable format.\n","\n","## Section 82.3: Interactive Explainable AI Visualizations\n","\n","#### Interactive explainable AI visualizations allow us to explore and interact with the explanations, features, and predictions of the model. These visualizations enable us to dive deeper into the model's decision-making process and gain a better understanding of its behavior. Here's an example of creating an interactive explainable AI visualization using the Plotly library:"]},{"cell_type":"markdown","id":"2b05806d","metadata":{"papermill":{"duration":0.011512,"end_time":"2023-10-16T18:11:36.367689","exception":false,"start_time":"2023-10-16T18:11:36.356177","status":"completed"},"tags":[]},"source":["```python\n","import plotly.graph_objects as go\n","\n","# Assume 'data' is the dataset, 'predictions' are the model predictions, 'explanations' are the corresponding explanations\n","\n","# Create an interactive scatter plot\n","fig = go.Figure(data=go.Scatter(x=data[:, 0], y=data[:, 1], mode='markers', marker=dict(color=predictions)))\n","fig.update_layout(title='Explainable AI', xaxis=dict(title='Feature 1'), yaxis=dict(title='Feature 2'))\n","\n","# Add explanations as text annotations\n","for i, explanation in enumerate(explanations):\n","    fig.add_annotation(x=data[i, 0], y=data[i, 1], text=explanation, showarrow=False)\n","\n","# Display the interactive plot\n","fig.show()\n","```"]},{"cell_type":"markdown","id":"61b30c9e","metadata":{"papermill":{"duration":0.011544,"end_time":"2023-10-16T18:11:36.390985","exception":false,"start_time":"2023-10-16T18:11:36.379441","status":"completed"},"tags":[]},"source":["#### In this example, we assume that data is the dataset, predictions are the model predictions, and explanations are the corresponding explanations for each data point. We use the Plotly library to create an interactive scatter plot, where the data points are displayed as markers with colors representing the predictions. We also add text annotations for the explanations, which are positioned at the corresponding data points. The resulting plot allows for interactive exploration of the explanations and the model's predictions.\n","\n","## Section 82.4: Customizing Explainable AI Visualizations\n","\n","#### Customization options for explainable AI visualizations depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Bokeh can be used to create more specialized or interactive visualizations.Chapter 83: Visualizing Transfer Learning and Domain Adaptation\n","\n","#### Transfer learning and domain adaptation techniques aim to leverage knowledge learned from one task or domain to improve performance on another task or domain. Visualizing transfer learning and domain adaptation can help us understand the transferability of learned features, the process of domain shift and adaptation, and the effectiveness of transfer learning techniques. In this chapter, we will explore techniques for visualizing transfer learning and domain adaptation, including plotting feature representations and transferability, visualizing domain shift and adaptation processes, creating interactive transfer learning visualizations, and customizing transfer learning visualizations.\n","\n","\n","<h1 align=\"left\"><font color='red'>83</font></h1>\n","\n","\n","## Section 83.1: Plotting Feature Representations and Transferability\n","\n","#### Feature representations and transferability measures provide insights into the quality and transferability of learned features across tasks or domains. Plotting these representations and transferability measures can help us understand the discriminative power of the features and their effectiveness in different contexts. Here's an example of plotting feature representations and transferability using Matplotlib:"]},{"cell_type":"markdown","id":"9f0c83ee","metadata":{"papermill":{"duration":0.015252,"end_time":"2023-10-16T18:11:36.418582","exception":false,"start_time":"2023-10-16T18:11:36.40333","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'source_features' and 'target_features' are arrays of feature representations\n","# Assume 'transferability' is an array of transferability measures\n","\n","# Plot feature representations\n","plt.scatter(source_features[:, 0], source_features[:, 1], c='red', label='Source Features')\n","plt.scatter(target_features[:, 0], target_features[:, 1], c='blue', label='Target Features')\n","plt.xlabel('Feature 1')\n","plt.ylabel('Feature 2')\n","plt.title('Feature Representations')\n","plt.legend()\n","plt.show()\n","\n","# Plot transferability measures\n","plt.plot(transferability)\n","plt.xlabel('Iteration')\n","plt.ylabel('Transferability')\n","\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"fe8bfd2e","metadata":{"papermill":{"duration":0.011439,"end_time":"2023-10-16T18:11:36.441945","exception":false,"start_time":"2023-10-16T18:11:36.430506","status":"completed"},"tags":[]},"source":["#### In this example, we assume that source_features and target_features are arrays containing feature representations for the source and target domains, respectively. We also assume that transferability is an array containing transferability measures, indicating the effectiveness of transferring the features. We use Matplotlib to create scatter plots for the feature representations, where the x-axis and y-axis represent the values of different features, and the colors differentiate between the source and target domains. Additionally, we create a line plot for the transferability measures, where the x-axis represents the iterations and the y-axis represents the transferability values. These plots provide insights into the feature representations and the transferability of the learned features.\n","\n","## Section 83.2: Visualizing Domain Shift and Adaptation Processes\n","\n","#### Visualizing the domain shift and adaptation processes can help us understand the differences and changes between the source and target domains and how the model adapts to the new domain. Here's an example of visualizing the domain shift and adaptation processes using Matplotlib:"]},{"cell_type":"markdown","id":"251acf92","metadata":{"papermill":{"duration":0.011348,"end_time":"2023-10-16T18:11:36.464905","exception":false,"start_time":"2023-10-16T18:11:36.453557","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'source_data' and 'target_data' are arrays of data points in the source and target domains, respectively\n","\n","# Plot the source and target data\n","plt.scatter(source_data[:, 0], source_data[:, 1], c='red', label='Source Data')\n","plt.scatter(target_data[:, 0], target_data[:, 1], c='blue', label='Target Data')\n","plt.xlabel('Feature 1')\n","plt.ylabel('Feature 2')\n","plt.title('Domain Shift')\n","plt.legend()\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"891c3102","metadata":{"papermill":{"duration":0.011325,"end_time":"2023-10-16T18:11:36.487777","exception":false,"start_time":"2023-10-16T18:11:36.476452","status":"completed"},"tags":[]},"source":["#### In this example, we assume that source_data and target_data are arrays containing data points in the source and target domains, respectively. We use Matplotlib to create scatter plots, where the x-axis and y-axis represent the values of different features, and the colors differentiate between the source and target domains. The resulting plots visualize the domain shift by showing the differences and changes between the source and target domains.\n","\n","## Section 83.3: Interactive Transfer Learning Visualizations\n","\n","#### Interactive transfer learning visualizations allow us to explore and interact with the transferability, feature representations, and adaptation processes. These visualizations enable us to gain deeper insights into the transfer learning and domain adaptation techniques and make more informed decisions. Here's an example of creating an interactive transfer learning visualization using the Plotly library:"]},{"cell_type":"markdown","id":"57224c24","metadata":{"papermill":{"duration":0.011954,"end_time":"2023-10-16T18:11:36.51134","exception":false,"start_time":"2023-10-16T18:11:36.499386","status":"completed"},"tags":[]},"source":["```python\n","import plotly.graph_objects as go\n","\n","# Assume 'source_features' and 'target_features' are arrays of feature representations\n","# Assume 'transferability' is an array of transferability measures\n","\n","# Create an interactive scatter plot\n","fig = go.Figure()\n","fig.add_trace(go.Scatter(x=source_features[:, 0], y=source_features[:, 1], mode='markers', name='Source Features'))\n","fig.add_trace(go.Scatter(x=target_features[:, 0], y=target_features[:, 1], mode='markers', name='Target Features'))\n","\n","# Add transferability measures as a line plot\n","fig.add_trace(go.Scatter(x=list(range(len(transferability))), y=transferability, mode='lines', name='Transferability'))\n","\n","fig.update_layout(title='Transfer Learning', xaxis=dict(title='Feature 1'), yaxis=dict(title='Feature 2'))\n","\n","# Display the interactive plot\n","fig.show()\n","```"]},{"cell_type":"markdown","id":"c63009a2","metadata":{"papermill":{"duration":0.011859,"end_time":"2023-10-16T18:11:36.53525","exception":false,"start_time":"2023-10-16T18:11:36.523391","status":"completed"},"tags":[]},"source":["#### In this example, we assume that source_features and target_features are arrays containing feature representations, and transferability is an array containing transferability measures. We use the Plotly library to create an interactive scatter plot, where the source and target features are displayed as markers. Additionally, we add a line plot for the transferability measures. The resulting interactive plot allows for exploration of the feature representations and transferability measures.\n","\n","## Section 83.4: Customizing Transfer Learning Visualizations\n","\n","#### Customization options for transfer learning visualizations depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Bokeh can be used to create more specialized or interactive visualizations."]},{"cell_type":"markdown","id":"a267e9d0","metadata":{"papermill":{"duration":0.011795,"end_time":"2023-10-16T18:11:36.558974","exception":false,"start_time":"2023-10-16T18:11:36.547179","status":"completed"},"tags":[]},"source":["<h1 align=\"left\"><font color='red'>84</font></h1>"]},{"cell_type":"markdown","id":"58284d6a","metadata":{"papermill":{"duration":0.011754,"end_time":"2023-10-16T18:11:36.582936","exception":false,"start_time":"2023-10-16T18:11:36.571182","status":"completed"},"tags":[]},"source":["## Chapter 84: Visualizing Neural Architecture Interpretability\n","\n","#### Visualizing the interpretability of neural architectures can provide insights into the inner workings of the model, including neuron activations, attention mechanisms, and saliency maps. These visualizations help understand which parts of the input data are important for the model's predictions and how the model makes decisions. In this chapter, we will explore techniques for visualizing neural architecture interpretability, including plotting neuron activations and responses, visualizing network attention and saliency maps, creating interactive interpretability visualizations, and customizing the interpretability visualizations.\n","\n","## Section 84.1: Plotting Neuron Activations and Responses\n","\n","#### Plotting neuron activations and responses can help understand how different neurons in the network respond to specific input stimuli or features. This provides insights into which neurons are responsible for detecting certain patterns or capturing certain information. Here's an example of plotting neuron activations and responses using Matplotlib:"]},{"cell_type":"markdown","id":"686e43b7","metadata":{"papermill":{"duration":0.011715,"end_time":"2023-10-16T18:11:36.606806","exception":false,"start_time":"2023-10-16T18:11:36.595091","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","# Assume 'neuron_activations' is an array of neuron activations or responses\n","# Assume 'stimuli' is an array of input stimuli\n","\n","# Plot neuron activations or responses\n","plt.plot(np.arange(len(neuron_activations)), neuron_activations)\n","plt.xlabel('Time')\n","plt.ylabel('Activation')\n","plt.title('Neuron Activation')\n","plt.show()\n","\n","# Plot neuron responses to specific stimuli\n","plt.plot(np.arange(len(stimuli)), neuron_responses)\n","plt.xlabel('Stimulus')\n","plt.ylabel('Response')\n","plt.title('Neuron Response')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"7434d632","metadata":{"papermill":{"duration":0.011877,"end_time":"2023-10-16T18:11:36.630908","exception":false,"start_time":"2023-10-16T18:11:36.619031","status":"completed"},"tags":[]},"source":["#### In this example, we assume that neuron_activations is an array representing the activations or responses of a specific neuron over time, and stimuli is an array representing a set of input stimuli. We use Matplotlib to create line plots, where the x-axis represents time or stimulus index, and the y-axis represents the neuron activation or response values. The resulting plots provide insights into the dynamics of neuron activations or responses.\n","\n","## Section 84.2: Visualizing Network Attention and Saliency Maps\n","\n","#### Visualizing network attention and saliency maps helps understand which parts of the input data the network focuses on or finds most salient for its predictions. Attention maps highlight the regions of interest, while saliency maps highlight the most important features or pixels. Here's an example of visualizing network attention and saliency maps using Matplotlib:"]},{"cell_type":"markdown","id":"3cd950af","metadata":{"papermill":{"duration":0.01186,"end_time":"2023-10-16T18:11:36.654966","exception":false,"start_time":"2023-10-16T18:11:36.643106","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'input_image' is the input image\n","# Assume 'attention_map' is the attention map or saliency map\n","\n","# Display the input image\n","plt.imshow(input_image)\n","plt.axis('off')\n","plt.title('Input Image')\n","plt.show()\n","\n","# Display the attention map or saliency map\n","plt.imshow(attention_map, cmap='hot', alpha=0.8)\n","plt.imshow(input_image, alpha=0.2)\n","plt.axis('off')\n","plt.title('Attention Map')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"5179d561","metadata":{"papermill":{"duration":0.011937,"end_time":"2023-10-16T18:11:36.679073","exception":false,"start_time":"2023-10-16T18:11:36.667136","status":"completed"},"tags":[]},"source":["#### In this example, we assume that input_image is the input image and attention_map is the attention map or saliency map generated by the network. We use Matplotlib to display the input image and overlay the attention map or saliency map on top of it. The resulting visualization highlights the regions or features that the network attends to or finds salient.\n","\n","## Section 84.3: Interactive Interpretability Visualizations\n","\n","#### Interactive interpretability visualizations allow users to explore and interact with the neuron activations, attention maps, and saliency maps, providing a more detailed understanding of the model's interpretability. Here's an example of creating an interactive interpretability visualization using the Plotly library:"]},{"cell_type":"markdown","id":"8af2eda5","metadata":{"papermill":{"duration":0.012133,"end_time":"2023-10-16T18:11:36.703573","exception":false,"start_time":"2023-10-16T18:11:36.69144","status":"completed"},"tags":[]},"source":["```python\n","import plotly.graph_objects as go\n","\n","# Assume 'neuron_activations' is an array of neuron activations or responses\n","# Assume 'attention_map' is the attention map or saliency map\n","\n","# Create an interactive line plot for neuron activations\n","fig = go.Figure()\n","fig.add_trace(go.Scatter(x=np.arange(len(neuron_activations)), y=neuron_activations, mode='lines', name='Neuron Activation'))\n","fig.update_layout(title='Neuron Activation', xaxis=dict(title='Time'), yaxis=dict(title='Activation'))\n","\n","# Create an interactive heatmap for attention map\n","fig.add_trace(go.Heatmap(z=attention_map, colorscale='hot'))\n","fig.update_layout(title='Attention Map')\n","\n","# Display the interactive plot\n","fig.show()\n","```"]},{"cell_type":"markdown","id":"c9f0657b","metadata":{"papermill":{"duration":0.012126,"end_time":"2023-10-16T18:11:36.728052","exception":false,"start_time":"2023-10-16T18:11:36.715926","status":"completed"},"tags":[]},"source":["#### In this example, we assume that neuron_activations is an array representing the activations or responses of a specific neuron, and attention_map is the attention map or saliency map. We use the Plotly library to create interactive line plots for neuron activations and heatmaps for attention maps. The resulting interactive plot allows users to explore the neuron activations and attention maps in more detail.\n","\n","## Section 84.4: Customizing Interpretability Visualizations\n","\n","#### Customization options for interpretability visualizations depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Bokeh can be used to create more specialized or interactive visualizations."]},{"cell_type":"markdown","id":"17f8105e","metadata":{"papermill":{"duration":0.011772,"end_time":"2023-10-16T18:11:36.752643","exception":false,"start_time":"2023-10-16T18:11:36.740871","status":"completed"},"tags":[]},"source":["<h1 align=\"left\"><font color='red'>85</font></h1>"]},{"cell_type":"markdown","id":"ff672e2a","metadata":{"papermill":{"duration":0.011978,"end_time":"2023-10-16T18:11:36.776701","exception":false,"start_time":"2023-10-16T18:11:36.764723","status":"completed"},"tags":[]},"source":["# Chapter 85: Visualizing Uncertainty in Time Series Forecasting\n","\n","#### Visualizing uncertainty in time series forecasting is crucial for understanding the reliability and confidence of the forecasts. Uncertainty visualizations provide insights into the variability and distribution of future predictions, helping to make informed decisions based on the level of uncertainty. In this chapter, we will explore techniques for visualizing uncertainty in time series forecasting, including plotting prediction intervals and quantiles, visualizing forecast error distributions over time, uncertainty decomposition and contribution plots, and customizing uncertainty visualizations.\n","\n","## Section 85.1: Plotting Prediction Intervals and Quantiles\n","\n","#### Plotting prediction intervals and quantiles helps visualize the range of possible outcomes and the confidence associated with each forecasted point. This provides insights into the uncertainty of the predictions and helps in understanding the potential variability. Here's an example of plotting prediction intervals and quantiles using Matplotlib:"]},{"cell_type":"markdown","id":"1d08f202","metadata":{"papermill":{"duration":0.012019,"end_time":"2023-10-16T18:11:36.800991","exception":false,"start_time":"2023-10-16T18:11:36.788972","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'time' is an array of time points\n","# Assume 'observations' is an array of observed values\n","# Assume 'predictions' is an array of forecasted values\n","# Assume 'lower_quantiles' and 'upper_quantiles' are arrays of lower and upper quantiles\n","\n","# Plot observed values\n","plt.plot(time, observations, label='Observed', color='blue')\n","\n","# Plot forecasted values\n","plt.plot(time, predictions, label='Forecast', color='orange')\n","\n","# Plot prediction intervals\n","plt.fill_between(time, lower_quantiles, upper_quantiles, color='gray', alpha=0.3, label='Prediction Interval')\n","\n","plt.xlabel('Time')\n","plt.ylabel('Value')\n","plt.title('Time Series Forecast with Prediction Interval')\n","plt.legend()\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"487071f3","metadata":{"papermill":{"duration":0.01178,"end_time":"2023-10-16T18:11:36.824804","exception":false,"start_time":"2023-10-16T18:11:36.813024","status":"completed"},"tags":[]},"source":["#### In this example, we assume that time is an array representing the time points, observations is an array of observed values, predictions is an array of forecasted values, and lower_quantiles and upper_quantiles are arrays representing the lower and upper quantiles of the prediction intervals, respectively. We use Matplotlib to plot the observed values, forecasted values, and fill between the lower and upper quantiles to visualize the prediction intervals. The resulting plot shows the forecasted values along with the associated uncertainty range.\n","\n","## Section 85.2: Visualizing Forecast Error Distributions over Time\n","\n","#### Visualizing the forecast error distributions over time provides insights into the accuracy and reliability of the forecasts. This helps identify periods of high or low forecast errors and understand the variability in the forecast performance. Here's an example of visualizing forecast error distributions over time using Matplotlib:"]},{"cell_type":"markdown","id":"9dd6f698","metadata":{"papermill":{"duration":0.011774,"end_time":"2023-10-16T18:11:36.848633","exception":false,"start_time":"2023-10-16T18:11:36.836859","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'time' is an array of time points\n","# Assume 'forecast_errors' is an array of forecast errors\n","\n","# Plot forecast errors over time\n","plt.plot(time, forecast_errors, color='red')\n","\n","plt.xlabel('Time')\n","plt.ylabel('Forecast Error')\n","plt.title('Forecast Error Distribution over Time')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"11bfedd3","metadata":{"papermill":{"duration":0.012209,"end_time":"2023-10-16T18:11:36.872915","exception":false,"start_time":"2023-10-16T18:11:36.860706","status":"completed"},"tags":[]},"source":["#### In this example, we assume that time is an array representing the time points, and forecast_errors is an array representing the forecast errors. We use Matplotlib to plot the forecast errors over time, where the x-axis represents time and the y-axis represents the forecast error. The resulting plot visualizes the distribution of forecast errors and helps identify patterns or trends in the forecast performance.\n","\n","## Section 85.3: Uncertainty Decomposition and Contribution Plots\n","\n","#### Uncertainty decomposition and contribution plots help understand the sources and factors contributing to the overall uncertainty in time series forecasting. These plots provide insights into the relative importance of different components, such as model error, parameter uncertainty, or data variability. Here's an example of uncertainty decomposition and contribution plots using Matplotlib:"]},{"cell_type":"markdown","id":"5f2f7722","metadata":{"papermill":{"duration":0.011734,"end_time":"2023-10-16T18:11:36.897005","exception":false,"start_time":"2023-10-16T18:11:36.885271","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'time' is an array of time points\n","# Assume 'total_uncertainty' is an array of total uncertainty measures\n","# Assume 'component1' and 'component2' are arrays of individual uncertainty components\n","\n","# Plot total uncertainty\n","plt.plot(time, total_uncertainty, label='Total Uncertainty')\n","\n","# Plot individual uncertainty components\n","plt.plot(time, component1, label='Component 1')\n","plt.plot(time, component2, label='Component 2')\n","\n","plt.xlabel('Time')\n","plt.ylabel('Uncertainty')\n","plt.title('Uncertainty Decomposition')\n","plt.legend()\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"58219263","metadata":{"papermill":{"duration":0.011669,"end_time":"2023-10-16T18:11:36.920498","exception":false,"start_time":"2023-10-16T18:11:36.908829","status":"completed"},"tags":[]},"source":["#### In this example, we assume that time is an array representing the time points, total_uncertainty is an array representing the total uncertainty measures, and component1 and component2 are arrays representing individual uncertainty components. We use Matplotlib to plot the total uncertainty as well as the individual uncertainty components over time. The resulting plot shows the decomposition of uncertainty and helps understand the contribution of each component.\n","\n","## Section 85.4: Customizing Uncertainty Visualizations\n","\n","#### Customization options for uncertainty visualizations depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Plotly can be used to create more specialized or interactive visualizations."]},{"cell_type":"markdown","id":"671b1280","metadata":{"papermill":{"duration":0.01164,"end_time":"2023-10-16T18:11:36.944123","exception":false,"start_time":"2023-10-16T18:11:36.932483","status":"completed"},"tags":[]},"source":["<h1 align=\"left\"><font color='red'>86</font></h1>"]},{"cell_type":"markdown","id":"66c08305","metadata":{"papermill":{"duration":0.01171,"end_time":"2023-10-16T18:11:36.967858","exception":false,"start_time":"2023-10-16T18:11:36.956148","status":"completed"},"tags":[]},"source":["# Chapter 86: Visualizing Meta-Learning\n","\n","#### Visualizing meta-learning involves understanding the progress and performance of meta-learning algorithms, visualizing meta-feature representations, creating interactive meta-learning visualizations, and customizing these visualizations to provide meaningful insights. In this chapter, we will explore techniques for visualizing meta-learning, including plotting meta-learning progress and performance, visualizing meta-feature representations, creating interactive meta-learning visualizations, and customizing these visualizations.\n","\n","## Section 86.1: Plotting Meta-Learning Progress and Performance\n","\n","#### Plotting the progress and performance of meta-learning algorithms helps monitor and evaluate their learning capabilities. Visualizing meta-learning progress can include plots such as learning curves, accuracy/loss curves, or other performance metrics over time or iterations. Here's an example of plotting meta-learning progress using Matplotlib:"]},{"cell_type":"markdown","id":"481babdd","metadata":{"papermill":{"duration":0.011684,"end_time":"2023-10-16T18:11:36.991783","exception":false,"start_time":"2023-10-16T18:11:36.980099","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'iterations' is an array representing the iterations of the meta-learning algorithm\n","# Assume 'accuracy' is an array representing the accuracy achieved at each iteration\n","\n","# Plot metIn this example, we assume that iterations is an array representing the iterations of the meta-learning algorithm and accuracy is an array representing the achieved accuracy at each iteration. We use Matplotlib to create a line plot, where the x-axis represents the iterations and the y-axis represents the accuracy. The resulting plot shows the progress of the meta-learning algorithm over iterations.\n","\n","Section 86.2: Visualizing Meta-Feature Representations\n","\n","Visualizing meta-feature representations helps understand the characteristics and properties of the meta-features used in meta-learning algorithms. This visualization provides insights into the relationships between meta-features and the corresponding learning tasks or datasets. Here's an example of visualizing meta-feature representations using Matplotlib:a-learning progress\n","plt.plot(iterations, accuracy)\n","plt.xlabel('Iterations')\n","plt.ylabel('Accuracy')\n","plt.title('Meta-Learning Progress')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"d05e1998","metadata":{"papermill":{"duration":0.011555,"end_time":"2023-10-16T18:11:37.015152","exception":false,"start_time":"2023-10-16T18:11:37.003597","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'meta_features' is a matrix representing the meta-features of learning tasks\n","# Assume 'labels' is an array representing the labels or classes of the learning tasks\n","\n","# Plot meta-feature representations\n","plt.scatter(meta_features[:, 0], meta_features[:, 1], c=labels, cmap='viridis')\n","plt.xlabel('Meta-Feature 1')\n","plt.ylabel('Meta-Feature 2')\n","plt.title('Meta-Feature Representations')\n","plt.colorbar()\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"0b96e12a","metadata":{"papermill":{"duration":0.011763,"end_time":"2023-10-16T18:11:37.038727","exception":false,"start_time":"2023-10-16T18:11:37.026964","status":"completed"},"tags":[]},"source":["#### In this example, we assume that meta_features is a matrix representing the meta-features of learning tasks, where each row corresponds to a learning task, and labels is an array representing the labels or classes of the learning tasks. We use Matplotlib to create a scatter plot, where the x-axis and y-axis represent two meta-features, and the color represents the labels or classes. The resulting plot visualizes the relationships between meta-features and learning tasks.\n","\n","## Section 86.3: Interactive Meta-Learning Visualizations\n","\n","#### Interactive meta-learning visualizations allow users to explore and interact with meta-learning progress, performance, or meta-feature representations, providing a more detailed understanding of the meta-learning algorithms. Interactive visualizations can include tooltips, zooming, panning, and other interactive features. Here's an example of creating an interactive meta-learning visualization using the Plotly library:"]},{"cell_type":"markdown","id":"56858c71","metadata":{"papermill":{"duration":0.011479,"end_time":"2023-10-16T18:11:37.061923","exception":false,"start_time":"2023-10-16T18:11:37.050444","status":"completed"},"tags":[]},"source":["```python\n","import plotly.graph_objects as go\n","\n","# Assume 'iterations' is an array representing the iterations of the meta-learning algorithm\n","# Assume 'accuracy' is an array representing the accuracy achieved at each iteration\n","\n","# Create an interactive line plot for meta-learning progress\n","fig = go.Figure()\n","fig.add_trace(go.Scatter(x=iterations, y=accuracy, mode='lines', name='Accuracy'))\n","fig.update_layout(title='Meta-Learning Progress', xaxis=dict(title='Iterations'), yaxis=dict(title='Accuracy'))\n","\n","# Display the interactive plot\n","fig.show()\n","```"]},{"cell_type":"markdown","id":"1a81e499","metadata":{"papermill":{"duration":0.011452,"end_time":"2023-10-16T18:11:37.085062","exception":false,"start_time":"2023-10-16T18:11:37.07361","status":"completed"},"tags":[]},"source":["#### In this example, we assume that iterations is an array representing the iterations of the meta-learning algorithm and accuracy is an array representing the achieved accuracy at each iteration. We use the Plotly library to create an interactive line plot, where the x-axis represents the iterations and the y-axis represents the accuracy. The resulting interactive plot allows users to explore the meta-learning progress in more detail.\n","\n","## Section 86.4: Customizing Meta-Learning Visualizations\n","\n","#### Customization options for meta-learning visualizations depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Bokeh can be used to create more specialized or interactive visualizations."]},{"cell_type":"markdown","id":"406e759b","metadata":{"papermill":{"duration":0.01208,"end_time":"2023-10-16T18:11:37.108968","exception":false,"start_time":"2023-10-16T18:11:37.096888","status":"completed"},"tags":[]},"source":["<h1 align=\"left\"><font color='red'>87</font></h1>"]},{"cell_type":"markdown","id":"c2c8f769","metadata":{"papermill":{"duration":0.012109,"end_time":"2023-10-16T18:11:37.133101","exception":false,"start_time":"2023-10-16T18:11:37.120992","status":"completed"},"tags":[]},"source":["# Chapter 87: Visualizing Anomaly Detection\n","\n","#### Visualizing anomaly detection is essential for understanding the results of anomaly detection algorithms, identifying anomalous patterns, and assessing the performance of the detection process. In this chapter, we will explore techniques for visualizing anomaly detection, including plotting anomaly scores and thresholds over time, visualizing anomaly detection results, creating interactive anomaly detection visualizations, and customizing these visualizations.\n","\n","## Section 87.1: Plotting Anomaly Scores and Thresholds over Time\n","\n","#### Plotting anomaly scores and thresholds over time helps visualize the variations in anomaly scores and identify the threshold at which an observation is classified as an anomaly. This visualization provides insights into the time-dependent behavior of anomalies. Here's an example of plotting anomaly scores and thresholds over time using Matplotlib:"]},{"cell_type":"markdown","id":"545695e1","metadata":{"papermill":{"duration":0.011657,"end_time":"2023-10-16T18:11:37.156785","exception":false,"start_time":"2023-10-16T18:11:37.145128","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'time' is an array of time points\n","# Assume 'anomaly_scores' is an array of anomaly scores\n","# Assume 'threshold' is the threshold for classifying anomalies\n","\n","# Plot anomaly scores\n","plt.plot(time, anomaly_scores, label='Anomaly Scores')\n","\n","# Plot threshold\n","plt.axhline(y=threshold, color='r', linestyle='--', label='Threshold')\n","\n","plt.xlabel('Time')\n","plt.ylabel('Anomaly Score')\n","plt.title('Anomaly Scores over Time')\n","plt.legend()\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"16e90b33","metadata":{"papermill":{"duration":0.01164,"end_time":"2023-10-16T18:11:37.180238","exception":false,"start_time":"2023-10-16T18:11:37.168598","status":"completed"},"tags":[]},"source":["#### In this example, we assume that time is an array representing the time points, anomaly_scores is an array representing the anomaly scores for each time point, and threshold is the threshold value for classifying anomalies. We use Matplotlib to plot the anomaly scores over time and add a horizontal line at the threshold value. The resulting plot visualizes the anomaly scores and the threshold for anomaly classification.\n","\n","## Section 87.2: Visualizing Anomaly Detection Results\n","\n","#### Visualizing anomaly detection results helps identify anomalous patterns and assess the effectiveness of the detection algorithm. This visualization provides insights into the detected anomalies, their spatial distribution, or temporal characteristics. Here's an example of visualizing anomaly detection results using Matplotlib:"]},{"cell_type":"markdown","id":"5eed01c1","metadata":{"papermill":{"duration":0.011504,"end_time":"2023-10-16T18:11:37.203515","exception":false,"start_time":"2023-10-16T18:11:37.192011","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'data' is an array of data points\n","# Assume 'is_anomaly' is an array indicating whether each data point is an anomaly\n","\n","# Plot data points\n","plt.scatter(data[:, 0], data[:, 1], c=is_anomaly, cmap='coolwarm')\n","\n","plt.xlabel('Feature 1')\n","plt.ylabel('Feature 2')\n","plt.title('Anomaly Detection Results')\n","plt.colorbar()\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"11773d46","metadata":{"papermill":{"duration":0.011645,"end_time":"2023-10-16T18:11:37.22695","exception":false,"start_time":"2023-10-16T18:11:37.215305","status":"completed"},"tags":[]},"source":["#### In this example, we assume that data is an array of data points with multiple features, and is_anomaly is an array indicating whether each data point is classified as an anomaly. We use Matplotlib to create a scatter plot, where the x-axis and y-axis represent two features, and the color represents whether the data point is an anomaly or not. The resulting plot visualizes the anomaly detection results and helps identify the anomalous patterns.\n","\n","## Section 87.3: Interactive Anomaly Detection Visualizations\n","\n","#### Interactive anomaly detection visualizations allow users to explore and interact with anomaly detection results, providing a more detailed understanding of the detected anomalies. Interactive visualizations can include tooltips, zooming, panning, and other interactive features. Here's an example of creating an interactive anomaly detection visualization using the Plotly library:"]},{"cell_type":"markdown","id":"be2f824f","metadata":{"papermill":{"duration":0.011904,"end_time":"2023-10-16T18:11:37.251432","exception":false,"start_time":"2023-10-16T18:11:37.239528","status":"completed"},"tags":[]},"source":["```python\n","import plotly.graph_objects as go\n","\n","# Assume 'time' is an array of time points\n","# Assume 'anomaly_scores' is an array of anomaly scores\n","\n","# Create an interactive line plot for anomaly scores\n","fig = go.Figure()\n","fig.add_trace(go.Scatter(x=time, y=anomaly_scores, mode='lines', name='Anomaly Scores'))\n","fig.update_layout(title='Anomaly Scores over Time', xaxis=dict(title='Time'), yaxis=dict(title='Anomaly Score'))\n","\n","# Display the interactive plot\n","fig.show()\n","```"]},{"cell_type":"markdown","id":"b447431d","metadata":{"papermill":{"duration":0.011682,"end_time":"2023-10-16T18:11:37.275016","exception":false,"start_time":"2023-10-16T18:11:37.263334","status":"completed"},"tags":[]},"source":["#### In this example, we assume that time is an array representing the time points and anomaly_scores is an array representing the anomaly scores. We use the Plotly library to create an interactive line plot, where the x-axis represents the time and the y-axis represents the anomaly scores. The resulting interactive plot allows users to explore the anomaly scores in more detail.\n","\n","## Section 87.4: Customizing Anomaly Detection Visualizations\n","\n","#### Customization options for anomaly detection visualizations depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Bokeh can be used to create more specialized or interactive visualizations."]},{"cell_type":"markdown","id":"2bd068a2","metadata":{"papermill":{"duration":0.01173,"end_time":"2023-10-16T18:11:37.298646","exception":false,"start_time":"2023-10-16T18:11:37.286916","status":"completed"},"tags":[]},"source":["<h1 align=\"left\"><font color='red'>88</font></h1>"]},{"cell_type":"markdown","id":"0e37ce27","metadata":{"papermill":{"duration":0.011733,"end_time":"2023-10-16T18:11:37.322319","exception":false,"start_time":"2023-10-16T18:11:37.310586","status":"completed"},"tags":[]},"source":["# Chapter 88: Visualizing Explainability in Recommendation Systems\n","\n","#### Visualizing explainability in recommendation systems is crucial for understanding and interpreting the recommendations provided to users. It helps users gain insights into why certain recommendations are made and provides transparency in the recommendation process. In this chapter, we will explore techniques for visualizing explainability in recommendation systems, including plotting user-item interactions and explanations, visualizing recommendation explanations, creating interactive explainability visualizations, and customizing these visualizations.\n","\n","## Section 88.1: Plotting User-Item Interactions and Explanations\n","\n","#### Plotting user-item interactions and explanations allows users to visualize their interactions with items in the recommendation system and understand the factors that influence the recommendations. This visualization provides insights into the user's preferences, interests, and historical interactions. Here's an example of plotting user-item interactions and explanations using Matplotlib:"]},{"cell_type":"markdown","id":"714b7e5a","metadata":{"papermill":{"duration":0.01159,"end_time":"2023-10-16T18:11:37.345739","exception":false,"start_time":"2023-10-16T18:11:37.334149","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'users' is an array representing user IDs\n","# Assume 'items' is an array representing item IDs\n","# Assume 'interactions' is a matrix representing user-item interactions\n","# Assume 'explanations' is a matrix representing explanations for user-item interactions\n","\n","# Plot user-item interactions and explanations\n","plt.scatter(users, items, c=interactions, cmap='viridis')\n","plt.xlabel('User ID')\n","plt.ylabel('Item ID')\n","plt.title('User-Item Interactions')\n","plt.colorbar(label='Interactions')\n","plt.show()\n","\n","# Plot explanations\n","plt.scatter(users, items, c=explanations, cmap='coolwarm')\n","plt.xlabel('User ID')\n","plt.ylabel('Item ID')\n","plt.title('Explanations for User-Item Interactions')\n","plt.colorbar(label='Explanations')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"f580d7bd","metadata":{"papermill":{"duration":0.011739,"end_time":"2023-10-16T18:11:37.369276","exception":false,"start_time":"2023-10-16T18:11:37.357537","status":"completed"},"tags":[]},"source":["#### In this example, we assume that users is an array representing user IDs, items is an array representing item IDs, interactions is a matrix representing user-item interactions (e.g., ratings, clicks), and explanations is a matrix representing explanations for the user-item interactions. We use Matplotlib to create scatter plots, where the x-axis represents user IDs, the y-axis represents item IDs, and the color represents the interactions or explanations. The resulting plots visualize the user-item interactions and the corresponding explanations.\n","\n","## Section 88.2: Visualizing Recommendation Explanations\n","\n","#### Visualizing recommendation explanations helps users understand why certain items are recommended to them. It provides insights into the features, attributes, or characteristics that influence the recommendations. Here's an example of visualizing recommendation explanations using Matplotlib:"]},{"cell_type":"markdown","id":"0c28ca87","metadata":{"papermill":{"duration":0.011928,"end_time":"2023-10-16T18:11:37.393284","exception":false,"start_time":"2023-10-16T18:11:37.381356","status":"completed"},"tags":[]},"source":["<h1 align=\"left\"><font color='red'>89</font></h1>"]},{"cell_type":"markdown","id":"0b6a692d","metadata":{"papermill":{"duration":0.011695,"end_time":"2023-10-16T18:11:37.417079","exception":false,"start_time":"2023-10-16T18:11:37.405384","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'users' is an array representing user IDs\n","# Assume 'items' is an array representing recommended item IDs\n","# Assume 'explanations' is a matrix representing explanations for recommended items\n","\n","# Plot recommendation explanations\n","plt.scatter(users, items, c=explanations, cmap='coolwarm')\n","plt.xlabel('User ID')\n","plt.ylabel('Item ID')\n","plt.title('Recommendation Explanations')\n","plt.colorbar(label='Explanations')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"fd7e1871","metadata":{"papermill":{"duration":0.011706,"end_time":"2023-10-16T18:11:37.44086","exception":false,"start_time":"2023-10-16T18:11:37.429154","status":"completed"},"tags":[]},"source":["#### In this example, we assume that users is an array representing user IDs, items is an array representing recommended item IDs, and explanations is a matrix representing explanations for the recommended items. We use Matplotlib to create a scatter plot, where the x-axis represents user IDs, the y-axis represents item IDs, and the color represents the explanations. The resulting plot visualizes the recommendation explanations and provides insights into why certain items are recommended.\n","\n","## Section 88.3: Interactive Explainability Visualizations in Recommendation Systems\n","\n","#### Interactive explainability visualizations in recommendation systems allow users to explore and interact with the recommendation explanations, providing a more personalized and informative experience. Interactive features can include tooltips, filtering options, item details, or the ability to modify recommendation settings. Here's an example of creating an interactive explainability visualization using the Plotly library:"]},{"cell_type":"markdown","id":"c0e54ee1","metadata":{"papermill":{"duration":0.011689,"end_time":"2023-10-16T18:11:37.464466","exception":false,"start_time":"2023-10-16T18:11:37.452777","status":"completed"},"tags":[]},"source":["```python\n","import plotly.graph_objects as go\n","\n","# Assume 'users' is an array representing user IDs\n","# Assume 'items' is an array representing recommended item IDs\n","# Assume 'explanations' is a matrix representing explanations for recommended items\n","\n","# Create an interactive scatter plot for recommendation explanations\n","fig = go.Figure(data=go.Scatter(\n","    x=users,\n","    y=items,\n","    mode='markers',\n","    marker=dict(color=explanations, colorscale='coolwarm'),\n","    text=explanations,\n","    hovertemplate='User ID: %{x}<br>Item ID: %{y}<br>Explanation: %{text}'\n","))\n","\n","fig.update_layout(\n","    title='Recommendation Explanations',\n","    xaxis=dict(title='User ID'),\n","    yaxis=dict(title='Item ID'),\n","    coloraxis_colorbar=dict(title='Explanations')\n",")\n","\n","# Display the interactive plot\n","fig.show()\n","```"]},{"cell_type":"markdown","id":"e0982966","metadata":{"papermill":{"duration":0.012037,"end_time":"2023-10-16T18:11:37.488685","exception":false,"start_time":"2023-10-16T18:11:37.476648","status":"completed"},"tags":[]},"source":["#### In this example, we assume that users is an array representing user IDs, items is an array representing recommended item IDs, and explanations is a matrix representing explanations for the recommended items. We use the Plotly library to create an interactive scatter plot, where the x-axis represents user IDs, the y-axis represents item IDs, and the color represents the explanations. The resulting interactive plot allows users to explore the recommendation explanations by hovering over the data points.\n","\n","## Section 88.4: Customizing Explainability Visualizations in Recommendation Systems\n","\n","#### Customization options for explainability visualizations in recommendation systems depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Bokeh can be used to create more specialized or interactive visualizations."]},{"cell_type":"markdown","id":"6de366bc","metadata":{"papermill":{"duration":0.011732,"end_time":"2023-10-16T18:11:37.51249","exception":false,"start_time":"2023-10-16T18:11:37.500758","status":"completed"},"tags":[]},"source":["# Chapter 89: Visualizing Uncertainty in Reinforcement Learning\n","\n","#### Visualizing uncertainty in reinforcement learning is important for understanding the confidence and exploration-exploitation trade-offs in decision-making processes. It helps analyze the uncertainty in value estimates, policy selection, and exploration strategies. In this chapter, we will explore techniques for visualizing uncertainty in reinforcement learning, including plotting value uncertainty and exploration rates, visualizing policy uncertainty and exploration-exploitation trade-offs, creating interactive uncertainty visualizations, and customizing these visualizations.\n","\n","## Section 89.1: Plotting Value Uncertainty and Exploration Rates\n","\n","#### Plotting value uncertainty and exploration rates allows us to visualize the uncertainty in value estimates and the exploration behavior of the agent. This helps in understanding the reliability of value estimates and the balance between exploration and exploitation. Here's an example of plotting value uncertainty and exploration rates using Matplotlib:"]},{"cell_type":"markdown","id":"4b010059","metadata":{"papermill":{"duration":0.011675,"end_time":"2023-10-16T18:11:37.536089","exception":false,"start_time":"2023-10-16T18:11:37.524414","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'steps' is an array representing the steps in the reinforcement learning process\n","# Assume 'value_estimates' is an array representing the value estimates at each step\n","# Assume 'uncertainties' is an array representing the uncertainties in value estimates\n","# Assume 'exploration_rates' is an array representing the exploration rates at each step\n","\n","# Plot value uncertainty\n","plt.plot(steps, value_estimates)\n","plt.fill_between(steps, value_estimates - uncertainties, value_estimates + uncertainties, alpha=0.3)\n","plt.xlabel('Steps')\n","plt.ylabel('Value Estimates')\n","plt.title('Value Uncertainty')\n","plt.show()\n","\n","# Plot exploration rates\n","plt.plot(steps, exploration_rates)\n","plt.xlabel('Steps')\n","plt.ylabel('Exploration Rates')\n","plt.title('Exploration Rates')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"13771de1","metadata":{"papermill":{"duration":0.011691,"end_time":"2023-10-16T18:11:37.559616","exception":false,"start_time":"2023-10-16T18:11:37.547925","status":"completed"},"tags":[]},"source":["#### In this example, we assume that steps is an array representing the steps in the reinforcement learning process, value_estimates is an array representing the value estimates at each step, uncertainties is an array representing the uncertainties in value estimates, and exploration_rates is an array representing the exploration rates at each step. We use Matplotlib to create line plots for value estimates and exploration rates. Additionally, we use the fill_between function to visualize the uncertainty range around the value estimates.\n","\n","## Section 89.2: Visualizing Policy Uncertainty and Exploration-Exploitation Trade-offs\n","\n","#### Visualizing policy uncertainty and exploration-exploitation trade-offs helps understand the uncertainty in policy selection and the balance between exploring new actions and exploiting known actions. Here's an example of visualizing policy uncertainty and exploration-exploitation trade-offs using Matplotlib:"]},{"cell_type":"markdown","id":"2ff440cb","metadata":{"papermill":{"duration":0.011747,"end_time":"2023-10-16T18:11:37.583535","exception":false,"start_time":"2023-10-16T18:11:37.571788","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'steps' is an array representing the steps in the reinforcement learning process\n","# Assume 'policies' is an array representing the selected policies at each step\n","# Assume 'uncertainties' is an array representing the uncertainties in policy selection\n","# Assume 'exploration_exploitation' is an array representing the exploration-exploitation trade-off\n","\n","# Plot policy uncertainty\n","plt.plot(steps, policies)\n","plt.fill_between(steps, policies - uncertainties, policies + uncertainties, alpha=0.3)\n","plt.xlabel('Steps')\n","plt.ylabel('Selected Policies')\n","plt.title('Policy Uncertainty')\n","plt.show()\n","\n","# Plot exploration-exploitation trade-off\n","plt.plot(steps, exploration_exploitation)\n","plt.xlabel('Steps')\n","plt.ylabel('Exploration-Exploitation')\n","plt.title('Exploration-Exploitation Trade-off')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"7a09324e","metadata":{"papermill":{"duration":0.011608,"end_time":"2023-10-16T18:11:37.607125","exception":false,"start_time":"2023-10-16T18:11:37.595517","status":"completed"},"tags":[]},"source":["#### In this example, we assume that steps is an array representing the steps in the reinforcement learning process, policies is an array representing the selected policies at each step, uncertainties is an array representing the uncertainties in policy selection, and exploration_exploitation is an array representing the exploration-exploitation trade-off. We use Matplotlib to create line plots for policy selection and exploration-exploitation trade-off. The fill_between function is used to visualize the uncertainty range around the policy selections.\n","\n","## Section 89.3: Interactive Uncertainty Visualizations in Reinforcement Learning\n","\n","#### Interactive uncertainty visualizations in reinforcement learning provide users with the ability to explore and interact with the uncertainty information. This can include tooltips, filtering options, dynamic updates, or the ability to modify exploration parameters. Here's an example of creating an interactive uncertainty visualization using the Plotly library:"]},{"cell_type":"markdown","id":"fc881196","metadata":{"papermill":{"duration":0.01165,"end_time":"2023-10-16T18:11:37.630742","exception":false,"start_time":"2023-10-16T18:11:37.619092","status":"completed"},"tags":[]},"source":["```python\n","import plotly.graph_objects as go\n","\n","# Assume 'steps' is an array representing the steps in the reinforcement learning process\n","# Assume 'value_estimates' is an array representing the value estimates at each step\n","# Assume 'uncertainties' is an array representing the uncertainties in value estimates\n","# Assume 'exploration_rates' is an array representing the exploration rates at each step\n","\n","# Create an interactive plot for value uncertainty\n","fig = go.Figure(data=[\n","    go.Scatter(x=steps, y=value_estimates, name='Value Estimates'),\n","    go.Scatter(x=steps, y=value_estimates - uncertainties, name='Uncertainty Lower Bound'),\n","    go.Scatter(x=steps, y=value_estimates + uncertainties, name='Uncertainty Upper Bound')\n","])\n","\n","fig.update_layout(\n","    xaxis=dict(title='Steps'),\n","    yaxis=dict(title='Value Estimates'),\n","    title='Value Uncertainty'\n",")\n","\n","# Display the interactive plot\n","fig.show()\n","\n","# Create an interactive plot for exploration rates\n","fig = go.Figure(data=go.Scatter(x=steps, y=exploration_rates))\n","\n","fig.update_layout(\n","    xaxis=dict(title='Steps'),\n","    yaxis=dict(title='Exploration Rates'),\n","    title='Exploration Rates'\n",")\n","\n","# Display the interactive plot\n","fig.show()\n","```"]},{"cell_type":"markdown","id":"4a72208e","metadata":{"papermill":{"duration":0.011718,"end_time":"2023-10-16T18:11:37.654308","exception":false,"start_time":"2023-10-16T18:11:37.64259","status":"completed"},"tags":[]},"source":["#### In this example, we assume that steps is an array representing the steps in the reinforcement learning process, value_estimates is an array representing the value estimates at each step, uncertainties is an array representing the uncertainties in value estimates, and exploration_rates is an array representing the exploration rates at each step. We use the Plotly library to create interactive line plots for value uncertainty and exploration rates. The resulting plots allow users to explore the uncertainty information by hovering over the data points.\n","\n","## Section 89.4: Customizing Uncertainty Visualizations in Reinforcement Learning\n","\n","#### Customization options for uncertainty visualizations in reinforcement learning depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Bokeh can be used to create more specialized or interactive visualizations."]},{"cell_type":"markdown","id":"6710a2f1","metadata":{"papermill":{"duration":0.011868,"end_time":"2023-10-16T18:11:37.679992","exception":false,"start_time":"2023-10-16T18:11:37.668124","status":"completed"},"tags":[]},"source":["<h1 align=\"left\"><font color='red'>90</font></h1>"]},{"cell_type":"markdown","id":"5d45a70f","metadata":{"papermill":{"duration":0.01205,"end_time":"2023-10-16T18:11:37.70409","exception":false,"start_time":"2023-10-16T18:11:37.69204","status":"completed"},"tags":[]},"source":["# Chapter 90: Visualizing Federated Learning\n","\n","#### Visualizing federated learning is crucial for understanding the training progress, convergence, and distribution of data sources in a decentralized learning system. In this chapter, we will explore techniques for visualizing federated learning, including plotting model updates and convergence, visualizing distributed data sources, creating interactive federated learning visualizations, and customizing these visualizations.\n","\n","## Section 90.1: Plotting Model Updates and Convergence\n","\n","#### Plotting model updates and convergence helps track the progress of federated learning and visualize the convergence behavior of the distributed models. Here's an example of plotting model updates and convergence using Matplotlib:"]},{"cell_type":"markdown","id":"29e740b3","metadata":{"papermill":{"duration":0.011843,"end_time":"2023-10-16T18:11:37.728051","exception":false,"start_time":"2023-10-16T18:11:37.716208","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'rounds' is an array representing the communication rounds in federated learning\n","# Assume 'losses' is an array representing the loss values at each communication round\n","# Assume 'accuracies' is an array representing the accuracies at each communication round\n","\n","# Plot loss values\n","plt.plot(rounds, losses)\n","plt.xlabel('Communication Rounds')\n","plt.ylabel('Loss')\n","plt.title('Model Loss Convergence')\n","plt.show()\n","\n","# Plot accuracy values\n","plt.plot(rounds, accuracies)\n","plt.xlabel('Communication Rounds')\n","plt.ylabel('Accuracy')\n","plt.title('Model Accuracy Convergence')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"5b4439d6","metadata":{"papermill":{"duration":0.012036,"end_time":"2023-10-16T18:11:37.752277","exception":false,"start_time":"2023-10-16T18:11:37.740241","status":"completed"},"tags":[]},"source":["#### In this example, we assume that rounds is an array representing the communication rounds in federated learning, losses is an array representing the loss values at each communication round, and accuracies is an array representing the accuracies at each communication round. We use Matplotlib to create line plots for loss convergence and accuracy convergence.\n","\n","## Section 90.2: Visualizing Distributed Data Sources\n","\n","#### Visualizing distributed data sources helps understand the distribution of data across multiple devices or data centers in federated learning. This can include visualizing data characteristics, data distributions, or data partitioning strategies. Here's an example of visualizing distributed data sources using a bar plot with Matplotlib:"]},{"cell_type":"markdown","id":"becd80b5","metadata":{"papermill":{"duration":0.011845,"end_time":"2023-10-16T18:11:37.776286","exception":false,"start_time":"2023-10-16T18:11:37.764441","status":"completed"},"tags":[]},"source":["```python\n","import matplotlib.pyplot as plt\n","\n","# Assume 'devices' is a list representing the participating devices in federated learning\n","# Assume 'data_counts' is a list representing the number of data samples on each device\n","\n","# Plot data counts on devices\n","plt.bar(devices, data_counts)\n","plt.xlabel('Devices')\n","plt.ylabel('Data Count')\n","plt.title('Distributed Data Sources')\n","plt.show()\n","```"]},{"cell_type":"markdown","id":"4810542c","metadata":{"papermill":{"duration":0.011837,"end_time":"2023-10-16T18:11:37.800304","exception":false,"start_time":"2023-10-16T18:11:37.788467","status":"completed"},"tags":[]},"source":["#### In this example, we assume that devices is a list representing the participating devices in federated learning and data_counts is a list representing the number of data samples on each device. We use Matplotlib to create a bar plot showing the data counts on each device.\n","\n","## Section 90.3: Interactive Federated Learning Visualizations\n","\n","#### Interactive federated learning visualizations provide users with the ability to explore and interact with the training progress, model updates, and data distribution in a federated learning system. This can include dynamic updates, tooltips, filtering options, or the ability to compare models from different devices. Here's an example of creating an interactive federated learning visualization using the Plotly library:"]},{"cell_type":"markdown","id":"253b96b2","metadata":{"papermill":{"duration":0.011814,"end_time":"2023-10-16T18:11:37.824136","exception":false,"start_time":"2023-10-16T18:11:37.812322","status":"completed"},"tags":[]},"source":["```python\n","import plotly.graph_objects as go\n","\n","# Assume 'rounds' is an array representing the communication rounds in federated learning\n","# Assume 'losses' is an array representing the loss values at each communication round\n","# Assume 'accuracies' is an array representing the accuracies at each communication round\n","\n","# Create an interactive plot for loss values\n","fig = go.Figure(data=go.Scatter(x=rounds, y=losses))\n","\n","fig.update_layout(\n","    xaxis=dict(title='Communication Rounds'),\n","    yaxis=dict(title='Loss'),\n","    title='Model Loss Convergence'\n",")\n","\n","# Display the interactive plot\n","fig.show()\n","\n","# Create an interactive plot for accuracy values\n","fig = go.Figure(data=go.Scatter(x=rounds, y=accuracies))\n","\n","fig.update_layout(\n","    xaxis=dict(title='Communication Rounds'),\n","    yaxis=dict(title='Accuracy'),\n","    title='Model Accuracy Convergence'\n",")\n","\n","# Display the interactive plot\n","fig.show()\n","```"]},{"cell_type":"markdown","id":"3a86a493","metadata":{"papermill":{"duration":0.011721,"end_time":"2023-10-16T18:11:37.847862","exception":false,"start_time":"2023-10-16T18:11:37.836141","status":"completed"},"tags":[]},"source":["#### In this example, we assume that rounds is an array representing the communication rounds in federated learning, losses is an array representing the loss values at each communication round, and accuracies is an array representing the accuracies at each communication round. We use the Plotly library to create interactive line plots for loss convergence and accuracy convergence. The resulting plots allow users to explore the training progress by hovering over the data points and zooming in on specific regions.\n","\n","## Section 90.4: Customizing Federated Learning Visualizations\n","\n","#### Customization options for federated learning visualizations depend on the libraries and tools used. You can customize various aspects, such as plot styles, colors, labels, titles, legends, interactivity, and plot layouts. Additionally, advanced visualization libraries like Seaborn or Bokeh can be used to create more specialized or interactive visualizations."]},{"cell_type":"code","execution_count":null,"id":"7fea1607","metadata":{"papermill":{"duration":0.011656,"end_time":"2023-10-16T18:11:37.871494","exception":false,"start_time":"2023-10-16T18:11:37.859838","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"91e3c6ea","metadata":{"papermill":{"duration":0.011588,"end_time":"2023-10-16T18:11:37.896204","exception":false,"start_time":"2023-10-16T18:11:37.884616","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"3551d967","metadata":{"papermill":{"duration":0.01153,"end_time":"2023-10-16T18:11:37.919523","exception":false,"start_time":"2023-10-16T18:11:37.907993","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"5039cdaf","metadata":{"papermill":{"duration":0.011657,"end_time":"2023-10-16T18:11:37.943019","exception":false,"start_time":"2023-10-16T18:11:37.931362","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"04de22c0","metadata":{"papermill":{"duration":0.012221,"end_time":"2023-10-16T18:11:37.967368","exception":false,"start_time":"2023-10-16T18:11:37.955147","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"a2112d38","metadata":{"papermill":{"duration":0.011794,"end_time":"2023-10-16T18:11:37.991268","exception":false,"start_time":"2023-10-16T18:11:37.979474","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"c1b44d7c","metadata":{"papermill":{"duration":0.011931,"end_time":"2023-10-16T18:11:38.015359","exception":false,"start_time":"2023-10-16T18:11:38.003428","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"b82420be","metadata":{"papermill":{"duration":0.011761,"end_time":"2023-10-16T18:11:38.03901","exception":false,"start_time":"2023-10-16T18:11:38.027249","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"00ca20e9","metadata":{"papermill":{"duration":0.011799,"end_time":"2023-10-16T18:11:38.062799","exception":false,"start_time":"2023-10-16T18:11:38.051","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"43b81746","metadata":{"papermill":{"duration":0.011916,"end_time":"2023-10-16T18:11:38.086786","exception":false,"start_time":"2023-10-16T18:11:38.07487","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"2c4e90f1","metadata":{"papermill":{"duration":0.012205,"end_time":"2023-10-16T18:11:38.111018","exception":false,"start_time":"2023-10-16T18:11:38.098813","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"ce613f70","metadata":{"papermill":{"duration":0.012661,"end_time":"2023-10-16T18:11:38.135742","exception":false,"start_time":"2023-10-16T18:11:38.123081","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"id":"bc6d397a","metadata":{"papermill":{"duration":0.011955,"end_time":"2023-10-16T18:11:38.159973","exception":false,"start_time":"2023-10-16T18:11:38.148018","status":"completed"},"tags":[]},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"},"papermill":{"default_parameters":{},"duration":5.662784,"end_time":"2023-10-16T18:11:38.49176","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-10-16T18:11:32.828976","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}