Comment code sections and change to pd.concatinate method.
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@ -291,15 +291,15 @@ plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, reca
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best_sensor_group = sensor_groups_importance_scores[0][0] # take the highest rated sensor group
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best_sensor_group = sensor_groups_importance_scores[0][0] # take the highest rated sensor group
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best_sensor_features = [col for col in model_input if col.startswith(best_sensor_group)]
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best_sensor_features = [col for col in model_input if col.startswith(best_sensor_group)]
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best_sensor_features_scores = find_sensor_group_features_importance(model_input, best_sensor_features)
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# best_sensor_features_scores = find_sensor_group_features_importance(model_input, best_sensor_features)
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xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(best_sensor_features_scores)
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# xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(best_sensor_features_scores)
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# %% [markdown]
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# %% [markdown]
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# ### Visualize best sensor's F1 and recall scores
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# ### Visualize best sensor's F1 and recall scores
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print(best_sensor_features_scores)
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# print(best_sensor_features_scores)
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plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
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# plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
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title="Best sensor addition it's features with F1 and recall scores")
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# title="Best sensor addition it's features with F1 and recall scores")
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# %%
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# %%
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# This section iterates over all sensor groups and investigates sequential feature importance feature-by-feature
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# This section iterates over all sensor groups and investigates sequential feature importance feature-by-feature
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@ -311,8 +311,8 @@ for i, sensor_group in enumerate(sensor_groups_importance_scores):
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current_sensor_features = [col for col in model_input if col.startswith(sensor_group[0])]
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current_sensor_features = [col for col in model_input if col.startswith(sensor_group[0])]
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current_sensor_features_scores = find_sensor_group_features_importance(model_input, current_sensor_features)
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current_sensor_features_scores = find_sensor_group_features_importance(model_input, current_sensor_features)
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xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(current_sensor_features_scores)
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xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(current_sensor_features_scores)
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feature_sequence = feature_sequence.append(pd.DataFrame({"sensor_name":sensor_group[0], "feature_sequence": [xs], "recall": [y_recall],
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feature_sequence = pd.concatinate([feature_sequence, pd.DataFrame({"sensor_name":sensor_group[0], "feature_sequence": [xs], "recall": [y_recall],
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"f1_score": [y_fscore], "recall_std": [recall_std], "f1_std": [fscore_std]}))
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"f1_score": [y_fscore], "recall_std": [recall_std], "f1_std": [fscore_std]})])
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plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
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plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
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title=f"Sequential addition of features for {sensor_group[0]} and its F1, and recall scores")
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title=f"Sequential addition of features for {sensor_group[0]} and its F1, and recall scores")
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