my_utils.py

import umap
import altair as alt

from numba.core.errors import NumbaDeprecationWarning, NumbaPendingDeprecationWarning
import warnings

warnings.simplefilter('ignore', category=NumbaDeprecationWarning)
warnings.simplefilter('ignore', category=NumbaPendingDeprecationWarning)


def umap_plot(text, emb):

    cols = list(text.columns)
    # UMAP reduces the dimensions from 1024 to 2 dimensions that we can plot
    reducer = umap.UMAP(n_neighbors=2)
    umap_embeds = reducer.fit_transform(emb)
    # Prepare the data to plot and interactive visualization
    # using Altair
    #df_explore = pd.DataFrame(data={'text': qa['text']})
    #print(df_explore)
    
    #df_explore = pd.DataFrame(data={'text': qa_df[0]})
    df_explore = text.copy()
    df_explore['x'] = umap_embeds[:,0]
    df_explore['y'] = umap_embeds[:,1]
    
    # Plot
    chart = alt.Chart(df_explore).mark_circle(size=60).encode(
        x=#'x',
        alt.X('x',
            scale=alt.Scale(zero=False)
        ),
        y=
        alt.Y('y',
            scale=alt.Scale(zero=False)
        ),
        tooltip=cols
        #tooltip=['text']
    ).properties(
        width=700,
        height=400
    )
    return chart

def umap_plot_big(text, emb):

    cols = list(text.columns)
    # UMAP reduces the dimensions from 1024 to 2 dimensions that we can plot
    reducer = umap.UMAP(n_neighbors=100)
    umap_embeds = reducer.fit_transform(emb)
    # Prepare the data to plot and interactive visualization
    # using Altair
    #df_explore = pd.DataFrame(data={'text': qa['text']})
    #print(df_explore)
    
    #df_explore = pd.DataFrame(data={'text': qa_df[0]})
    df_explore = text.copy()
    df_explore['x'] = umap_embeds[:,0]
    df_explore['y'] = umap_embeds[:,1]
    
    # Plot
    chart = alt.Chart(df_explore).mark_circle(size=60).encode(
        x=#'x',
        alt.X('x',
            scale=alt.Scale(zero=False)
        ),
        y=
        alt.Y('y',
            scale=alt.Scale(zero=False)
        ),
        tooltip=cols
        #tooltip=['text']
    ).properties(
        width=700,
        height=400
    )
    return chart

def umap_plot_old(sentences, emb):
    # UMAP reduces the dimensions from 1024 to 2 dimensions that we can plot
    reducer = umap.UMAP(n_neighbors=2)
    umap_embeds = reducer.fit_transform(emb)
    # Prepare the data to plot and interactive visualization
    # using Altair
    #df_explore = pd.DataFrame(data={'text': qa['text']})
    #print(df_explore)
    
    #df_explore = pd.DataFrame(data={'text': qa_df[0]})
    df_explore = sentences
    df_explore['x'] = umap_embeds[:,0]
    df_explore['y'] = umap_embeds[:,1]
    
    # Plot
    chart = alt.Chart(df_explore).mark_circle(size=60).encode(
        x=#'x',
        alt.X('x',
            scale=alt.Scale(zero=False)
        ),
        y=
        alt.Y('y',
            scale=alt.Scale(zero=False)
        ),
        tooltip=['text']
    ).properties(
        width=700,
        height=400
    )
    return chart