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from shiny import render | |
from shiny.express import input, ui | |
import pandas as pd | |
from pathlib import Path | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import pandas as pd | |
from scipy.interpolate import interp1d | |
import numpy as np | |
ui.page_opts(fillable=True) | |
ui.panel_title("Does context size matter for dna models?") | |
with ui.card(): | |
ui.input_selectize( | |
"plot_type", | |
"Select your model size:", | |
["14M", "31M", "70M", "160M", "410M"], | |
multiple=False, | |
) | |
def plot_loss_rates(df, type): | |
# interplot each column to be same number of points | |
x = np.linspace(0, 1, 1000) | |
loss_rates = [] | |
labels = ['32', '64', '128', '256', '512', '1024'] | |
#drop the column step | |
df = df.drop(columns=['Step']) | |
for col in df.columns: | |
y = df[col].dropna().astype('float', errors = 'ignore').dropna().values | |
f = interp1d(np.linspace(0, 1, len(y)), y) | |
loss_rates.append(f(x)) | |
fig, ax = plt.subplots() | |
for i, loss_rate in enumerate(loss_rates): | |
ax.plot(x, loss_rate, label=labels[i]) | |
ax.legend() | |
ax.set_title(f'Loss rates for a {type} parameter model') | |
ax.set_xlabel('Training steps') | |
ax.set_ylabel('Loss rate') | |
return fig | |
def plot(): | |
fig = None | |
if input.plot_type() == "14M": | |
df = pd.read_csv('14m.csv') | |
fig = plot_loss_rates(df, '14M') | |
return fig | |