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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()
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