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import streamlit as st |
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import torch |
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from normflows import nflow |
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import numpy as np |
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import seaborn as sns |
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import pandas as pd |
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uploaded_file = st.file_uploader("Choose original dataset") |
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col1,col2,col3 = st.columns(3) |
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bw = col1.number_input('Scale',value=3.05) |
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wd = col2.number_input('Weight Decay',value=0.0002) |
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iters = col3.number_input('Iterations',value=400) |
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def compute(dim): |
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api = nflow(dim=dim,latent=16,dataset=uploaded_file) |
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api.compile(optim=torch.optim.ASGD,bw=bw,lr=0.0001,wd=wd) |
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my_bar = st.progress(0) |
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for idx in api.train(iters=iters): |
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my_bar.progress(idx[0]/iters) |
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my_bar.progress(100) |
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samples = np.array(api.model.sample( |
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torch.tensor(api.scaled).float()).detach()) |
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g = sns.jointplot(x=samples[:, 0], y=samples[:, 1], kind='kde',cmap=sns.color_palette("Blues", as_cmap=True),fill=True,label='Gaussian KDE',levels=1000) |
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w = sns.scatterplot(x=api.scaled[:,0],y=api.scaled[:,1],ax=g.ax_joint,c='orange',marker='+',s=100,label='Real') |
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st.pyplot(w.get_figure()) |
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def random_normal_samples(n, dim=3): |
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return torch.zeros(n, dim).normal_(mean=0, std=1) |
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samples = np.array(api.model.sample(torch.tensor(random_normal_samples(1000,api.scaled.shape[-1])).float()).detach()) |
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return api.scaler.inverse_transform(samples) |
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if uploaded_file is not None: |
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dims = len(uploaded_file.getvalue().decode("utf-8").split('\n')[0].split(','))-1 |
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samples=compute(dims) |
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st.download_button('Download generated CSV', pd.DataFrame(samples).to_csv(), 'text/csv') |
