util/plot.py

import pandas as pd
import plotly.graph_objs as go
import plotly.express as px

def create_score_plot(df):
    fig = go.Figure()

    fig.add_trace(go.Scatter(
        x=df.index, y=df['Privilege_Avg_Score'],
        mode='lines+markers', name='Privilege',
        text=df['Role'], hoverinfo='text+y'
    ))

    fig.add_trace(go.Scatter(
        x=df.index, y=df['Protect_Avg_Score'],
        mode='lines+markers', name='Protection',
        text=df['Role'], hoverinfo='text+y'
    ))

    fig.add_trace(go.Scatter(
        x=df.index, y=df['Neutral_Avg_Score'],
        mode='lines+markers', name='Neutral',
        text=df['Role'], hoverinfo='text+y'
    ))

    fig.update_layout(
        title=f'Scores of Resumes',
        xaxis_title='Resume Index',
        yaxis_title='Score',
        legend_title='Score Type',
        hovermode='closest'
    )

    return fig


def create_rank_plots(df):
    fig = go.Figure()

    # Add traces for ranks
    fig.add_trace(go.Scatter(
        x=df.index, y=df['Privilege_Rank'],
        mode='lines+markers', name='Rank Privilege',
        text=df['Role'], hoverinfo='text+y'
    ))

    fig.add_trace(go.Scatter(
        x=df.index, y=df['Protect_Rank'],
        mode='lines+markers', name='Rank Protection',
        text=df['Role'], hoverinfo='text+y'
    ))

    fig.add_trace(go.Scatter(
        x=df.index, y=df['Neutral_Rank'],
        mode='lines+markers', name='Rank Neutral',
        text=df['Role'], hoverinfo='text+y'
    ))

    # Update layout
    fig.update_layout(
        title='Ranks of Scores',
        xaxis_title='Resume Index',
        yaxis_title='Rank',
        legend_title='Rank Type',
        hovermode='closest'
    )

    return fig


def create_correlation_heatmaps(df):
    scores_df = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']]
    ranks_df = df[['Privilege_Rank', 'Protect_Rank', 'Neutral_Rank']]

    # Pearson correlation
    scores_corr_pearson = scores_df.corr(method='pearson')
    ranks_corr_pearson = ranks_df.corr(method='pearson')

    # Spearman correlation
    scores_corr_spearman = scores_df.corr(method='spearman')
    ranks_corr_spearman = ranks_df.corr(method='spearman')

    # Kendall Tau correlation
    scores_corr_kendall = scores_df.corr(method='kendall')
    ranks_corr_kendall = ranks_df.corr(method='kendall')

    # Plotting the heatmaps separately
    heatmaps = {
        'Scores Pearson Correlation': scores_corr_pearson,
        'Ranks Pearson Correlation': ranks_corr_pearson,
        'Scores Spearman Correlation': scores_corr_spearman,
        'Ranks Spearman Correlation': ranks_corr_spearman,
        'Scores Kendall Correlation': scores_corr_kendall,
        'Ranks Kendall Correlation': ranks_corr_kendall
    }

    figs = {}
    for title, corr_matrix in heatmaps.items():
        fig = px.imshow(corr_matrix, text_auto=True, title=title)
        figs[title] = fig

    return figs