| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from sklearn import metrics | |
| from datasets import load_dataset | |
| import histos | |
| dataset = load_dataset("cmpatino/optimal_observables", "train") | |
| dataset_df = dataset["train"].to_pandas() | |
| dataset_df["target"] = dataset_df["target"].map({0: "spin-OFF", 1: "spin-ON"}) | |
| def get_roc_auc_scores(pos_samples, neg_samples): | |
| y_score = np.concatenate([pos_samples, neg_samples], axis=0) | |
| if pos_samples.mean() >= neg_samples.mean(): | |
| y_true = np.concatenate( | |
| [np.ones_like(pos_samples), np.zeros_like(neg_samples)], axis=0 | |
| ) | |
| roc_auc_score = metrics.roc_auc_score(y_true, y_score) | |
| else: | |
| y_true = np.concatenate( | |
| [np.zeros_like(pos_samples), np.ones_like(neg_samples)], axis=0 | |
| ) | |
| roc_auc_score = metrics.roc_auc_score(y_true, y_score) | |
| return roc_auc_score | |
| def get_plot(features, n_bins): | |
| plotting_df = dataset_df.copy() | |
| if len(features) == 1: | |
| fig, ax = plt.subplots() | |
| pos_samples = plotting_df[plotting_df["target"] == "spin-ON"][features[0]] | |
| neg_samples = plotting_df[plotting_df["target"] == "spin-OFF"][features[0]] | |
| roc_auc_score = get_roc_auc_scores(pos_samples, neg_samples) | |
| values = [ | |
| pos_samples, | |
| neg_samples, | |
| ] | |
| labels = ["spin-ON", "spin-OFF"] | |
| fig = histos.ratio_hist( | |
| processes_q=values, | |
| hist_labels=labels, | |
| reference_label=labels[1], | |
| n_bins=n_bins, | |
| hist_range=None, | |
| title=f"{features[0]} (ROC AUC: {roc_auc_score:.3f})", | |
| ) | |
| return fig | |
| if len(features) == 2: | |
| fig, ax = plt.subplots(ncols=2, figsize=(12, 6)) | |
| pos_samples = plotting_df[plotting_df["target"] == "spin-ON"][features] | |
| neg_samples = plotting_df[plotting_df["target"] == "spin-OFF"][features] | |
| x_lims = ( | |
| min(pos_samples[features[0]].min(), neg_samples[features[0]].min()), | |
| max(pos_samples[features[0]].max(), neg_samples[features[0]].max()), | |
| ) | |
| y_lims = ( | |
| min(pos_samples[features[1]].min(), neg_samples[features[1]].min()), | |
| max(pos_samples[features[1]].max(), neg_samples[features[1]].max()), | |
| ) | |
| ranges = (x_lims, y_lims) | |
| sns.histplot( | |
| pos_samples, | |
| x=features[0], | |
| y=features[1], | |
| bins=n_bins, | |
| ax=ax[0], | |
| color="C0", | |
| binrange=ranges, | |
| ) | |
| sns.histplot( | |
| neg_samples, | |
| x=features[0], | |
| y=features[1], | |
| bins=n_bins, | |
| ax=ax[1], | |
| color="C1", | |
| binrange=ranges, | |
| ) | |
| ax[0].set_title("spin-ON") | |
| ax[1].set_title("spin-OFF") | |
| return fig | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Plots"): | |
| with gr.Column(): | |
| with gr.Row(): | |
| features = gr.Dropdown( | |
| choices=dataset_df.columns.to_list(), | |
| label="Feature", | |
| value="m_tt", | |
| multiselect=True, | |
| ) | |
| n_bins = gr.Slider( | |
| label="Number of Bins for Histogram", | |
| value=10, | |
| minimum=10, | |
| maximum=100, | |
| step=10, | |
| ) | |
| feature_plot = gr.Plot(label="Feature's Plot") | |
| with gr.Tab("ROC-AUC Table"): | |
| roc_auc_values = [] | |
| for feature in dataset_df.columns.to_list(): | |
| if feature in ["target", "reco_weight"]: | |
| continue | |
| pos_samples = dataset_df[dataset_df["target"] == "spin-ON"][feature] | |
| neg_samples = dataset_df[dataset_df["target"] == "spin-OFF"][feature] | |
| roc_auc_score = get_roc_auc_scores(pos_samples, neg_samples) | |
| roc_auc_values.append([feature, roc_auc_score]) | |
| roc_auc_table = gr.Dataframe( | |
| label="ROC-AUC Table", headers=["Feature", "ROC-AUC"], value=roc_auc_values | |
| ) | |
| features.change( | |
| get_plot, | |
| [features, n_bins], | |
| feature_plot, | |
| queue=False, | |
| ) | |
| n_bins.change( | |
| get_plot, | |
| [features, n_bins], | |
| feature_plot, | |
| queue=False, | |
| ) | |
| demo.load( | |
| get_plot, | |
| [features, n_bins], | |
| feature_plot, | |
| queue=False, | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |