Spaces:
Sleeping
Sleeping
File size: 5,303 Bytes
b81628e 6a39113 3f722df 6a39113 0736615 6a39113 0736615 6a39113 897eeff 6a39113 897eeff 6a39113 897eeff 6a39113 897eeff 6a39113 897eeff 6a39113 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 |
import evaluate
import json
import sys
from pathlib import Path
import gradio as gr
import numpy as np
import pandas as pd
import ast
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
plt.rcParams["figure.dpi"] = 300
plt.switch_backend(
"agg"
) # ; https://stackoverflow.com/questions/14694408/runtimeerror-main-thread-is-not-in-main-loop
def default_plot():
fig = plt.figure()
ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((3, 1), (2, 0))
ranged = np.linspace(0, 1, 10)
ax1.plot(
ranged,
ranged,
color="darkgreen",
ls="dotted",
label="Perfect",
)
# Bin differences
ax1.set_ylabel("Conditional Expectation")
ax1.set_ylim([0, 1.05])
ax1.set_title("Reliability Diagram")
ax1.set_xlim([-0.05, 1.05]) # respective to bin range
# Bin frequencies
ax2.set_xlabel("Confidence")
ax2.set_ylabel("Count")
ax2.set_xlim([-0.05, 1.05]) # respective to bin range
return fig, ax1, ax2
def reliability_plot(results):
# DEV: might still need to write tests in case of equal mass binning
# DEV: nicer would be to plot like a polygon
# see: https://github.com/markus93/fit-on-the-test/blob/main/Experiments_Synthetic/binnings.py
fig, ax1, ax2 = default_plot()
# Bin differences
bins_with_left_edge = np.insert(results["y_bar"], 0, 0, axis=0)
bins_with_right_edge = np.insert(results["y_bar"], -1, 1.0, axis=0)
bins_with_leftright_edge = np.insert(bins_with_left_edge, -1, 1.0, axis=0)
weights = np.nan_to_num(results["p_bar"], copy=True, nan=0)
# NOTE: the histogram API is strange
_, _, patches = ax1.hist(
bins_with_left_edge,
weights=weights,
bins=bins_with_leftright_edge,
)
for b in range(len(patches)):
perfect = bins_with_right_edge[b] # if b != n_bins else
empirical = weights[b] # patches[b]._height
bin_color = (
"limegreen"
if perfect == empirical
else "dodgerblue"
if empirical < perfect
else "orangered"
)
patches[b].set_facecolor(bin_color) # color based on over/underconfidence
ax1handles = [
mpatches.Patch(color="orangered", label="Overconfident"),
mpatches.Patch(color="limegreen", label="Perfect", linestyle="dotted"),
mpatches.Patch(color="dodgerblue", label="Underconfident"),
]
# Bin frequencies
anindices = np.where(~np.isnan(results["p_bar"]))[0]
bin_freqs = np.zeros(len(results["p_bar"]))
bin_freqs[anindices] = results["bin_freq"]
ax2.hist(
bins_with_left_edge, weights=bin_freqs, color="midnightblue", bins=bins_with_leftright_edge
)
acc_plt = ax2.axvline(x=results["accuracy"], ls="solid", lw=3, c="black", label="Accuracy")
conf_plt = ax2.axvline(
x=results["p_bar_cont"], ls="dotted", lw=3, c="#444", label="Avg. confidence"
)
ax1.legend(loc="lower right", handles=ax1handles)
ax2.legend(handles=[acc_plt, conf_plt])
ax1.set_xticks(bins_with_left_edge)
ax2.set_xticks(bins_with_left_edge)
plt.tight_layout()
return fig
def compute_and_plot(data, n_bins, bin_range, scheme, proxy, p):
# DEV: check on invalid datatypes with better warnings
if isinstance(data, pd.DataFrame):
data.dropna(inplace=True)
predictions = [
ast.literal_eval(prediction) if not isinstance(prediction, list) else prediction
for prediction in data["predictions"]
]
references = [reference for reference in data["references"]]
results = metric._compute(
predictions,
references,
n_bins=n_bins,
scheme=scheme,
proxy=proxy,
p=p,
detail=True,
)
plot = reliability_plot(results)
return results["ECE"], plot
sliders = [
gr.Slider(0, 100, value=10, label="n_bins"),
gr.Slider(
0, 100, value=None, label="bin_range", visible=False
), # DEV: need to have a double slider
gr.Dropdown(choices=["equal-range", "equal-mass"], value="equal-range", label="scheme"),
gr.Dropdown(choices=["upper-edge", "center"], value="upper-edge", label="proxy"),
gr.Dropdown(choices=[1, 2, np.inf], value=1, label="p"),
]
slider_defaults = [slider.value for slider in sliders]
# example data
component = gr.inputs.Dataframe(
headers=["predictions", "references"], col_count=2, datatype="number", type="pandas"
)
component.value = [
[[0.6, 0.2, 0.2], 0],
[[0.7, 0.1, 0.2], 2],
[[0, 0.95, 0.05], 1],
]
sample_data = [[component] + slider_defaults]
local_path = Path(sys.path[0])
metric = evaluate.load("jordyvl/ece")
outputs = [gr.outputs.Textbox(label="ECE"), gr.Plot(label="Reliability diagram")]
# outputs[1].value = default_plot().__dict__ #DEV: Does not work in gradio; needs to be JSON encoded
iface = gr.Interface(
fn=compute_and_plot,
inputs=[component] + sliders,
outputs=outputs,
description=metric.info.description,
article=evaluate.utils.parse_readme(local_path / "README.md"),
title=f"Metric: {metric.name}",
# examples=sample_data; #DEV: ValueError: Examples argument must either be a directory or a nested list, where each sublist represents a set of inputs.
).launch()
|