def visualize(df, data, n_data, dataset_name): visualize_scores( df, score_names=["rmse"], is_higher_score_better=[False], # err_param_name="std", # err_param_name="magnitude", err_param_name="prob_break", title= f"Prediction scores for {dataset_name} dataset (n={n_data}) with added error" ) visualize_time_series_prediction( df, data, score_name="rmse", is_higher_score_better=False, # err_param_name="std", # err_param_name="magnitude", err_param_name="prob_break", model_name="LSTM", err_train_column="err_train", test_pred_column="test_pred", title= f"Predictions for {dataset_name} dataset (n={n_data}) with added error" ) plt.show()
def visualize(df, dataset_name, label_names, test_data, use_interactive_mode): visualize_scores( df, score_names=["test_mean_accuracy", "train_mean_accuracy"], is_higher_score_better=[True, True], err_param_name="p", title=f"{dataset_name} classification scores with added error" ) visualize_best_model_params( df, "MultinomialNB", model_params=["alpha"], score_names=["test_mean_accuracy"], is_higher_score_better=[True], err_param_name="p", title=f"Best parameters for {dataset_name} classification", y_log=True ) visualize_best_model_params( df, "LinearSVC", model_params=["C"], score_names=["test_mean_accuracy"], is_higher_score_better=[True], err_param_name="p", title=f"Best parameters for {dataset_name} classification", y_log=True ) visualize_classes( df, label_names, err_param_name="p", reduced_data_column="reduced_test_data", labels_column="test_labels", cmap="tab20", title=f"{dataset_name} test set (n={len(test_data)}) true classes with added error" ) if use_interactive_mode: def on_click(element, label, predicted_label): print(label, " predicted as ", predicted_label, ":", sep="") print(element, end="\n\n") else: on_click = None visualize_confusion_matrices( df, label_names, score_name="test_mean_accuracy", is_higher_score_better=True, err_param_name="p", labels_col="test_labels", predictions_col="predicted_test_labels", on_click=on_click ) plt.show()
def visualize(df): # visualize_scores(df, ["mAP-50"], [True], "std", "Object detection with Gaussian noise", x_log=False) # visualize_scores(df, ["mAP-50"], [True], "std", "Object detection with Gaussian blur", x_log=False) # visualize_scores(df, ["mAP-50"], [True], "snowflake_probability", "Object detection with snow filter", x_log=True) # visualize_scores(df, ["mAP-50"], [True], "probability", "Object detection with rain filter", x_log=True) # visualize_scores(df, ["mAP-50"], [True], "probability", "Object detection with added stains", x_log=True) # visualize_scores(df, ["mAP-50"], [True], "quality", "Object detection with JPEG compression", x_log=False) visualize_scores(df, ["mAP-50"], [True], "k", "Object detection with reduced resolution", x_log=False) # visualize_scores(df, ["mAP-50"], [True], "rat", "Object detection with added brightness", x_log=False) plt.show()
def visualize(df, label_names, dataset_name, data, use_interactive_mode): visualize_scores( df, score_names=["AMI", "ARI"], is_higher_score_better=[True, True], # err_param_name="std", # err_param_name="probability", err_param_name="max_angle", # title=f"{dataset_name} clustering scores with added gaussian noise", # title=f"{dataset_name} clustering scores with missing pixels", title=f"{dataset_name} clustering scores with rotation", ) visualize_best_model_params( df, model_name="HDBSCAN", model_params=["min_cluster_size", "min_samples"], score_names=["AMI", "ARI"], is_higher_score_better=[True, True], # err_param_name="std", # err_param_name="probability", err_param_name="max_angle", title=f"Best parameters for {dataset_name} clustering") visualize_classes( df, label_names, # err_param_name="std", # err_param_name="probability", err_param_name="max_angle", reduced_data_column="reduced_data", labels_column="labels", cmap="tab10", # title=f"{dataset_name} (n={data.shape[0]}) classes with added gaussian noise" # title=f"{dataset_name} (n={data.shape[0]}) classes with missing pixels" title=f"{dataset_name} (n={data.shape[0]}) classes with rotation") if use_interactive_mode: def on_click(original, modified): # reshape data original = original.reshape((28, 28)) modified = modified.reshape((28, 28)) # create a figure and draw the images fg, axs = plt.subplots(1, 2) axs[0].matshow(original, cmap='gray_r') axs[0].axis('off') axs[1].matshow(modified, cmap='gray_r') axs[1].axis('off') fg.show() # Remember to enable runner's interactive mode visualize_interactive_plot(df, "max_angle", data, "tab10", "reduced_data", on_click) plt.show()
def visualize(df): # Visualize mean squared error for all used standard deviations visualize_scores(df=df, score_names=["MSE"], is_higher_score_better=[False], err_param_name="std", title="Mean squared error") visualize_best_model_params(df=df, model_name="Predictor #1", model_params=["weight"], score_names=["MSE"], is_higher_score_better=[False], err_param_name="std", title=f"Best model params") plt.show()