app.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
from shiny import render
from shiny.express import input, output, ui
from utils import (
    filter_and_select,
    plot_2d_comparison,
    plot_color_square,
    wens_method_heatmap,
    plot_fcgr,
    plot_persistence_homology,
    plot_distrobutions
)
import os
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)


############################################################# Virus Dataset ########################################################
#ds = load_dataset('Hack90/virus_tiny')
df = pd.read_parquet('virus_ds.parquet')
virus = df['Organism_Name'].unique()
virus = {v: v for v in virus}
df_new = pd.read_parquet("virus.parquet", columns= ['organism_name'])
df_new = df_new.groupby('organism_name').apply(lambda x: x.head(100) if len(x) > 10 else None).reset_index(drop=True)
filter_species = df_new.organism_name.value_counts().reset_index()[df_new.organism_name.value_counts().reset_index()['count'] > 40 ]['organism_name'][1:].tolist()

df_old = pd.read_parquet("virus.parquet", columns =['seq', 'organism_name'])
MASTER_DF = df_old[df_old['organism_name'].isin(filter_species)].copy()
del df_new
del df_old
virus_new = {v: v for v in filter_species}
loss_typesss = pd.read_csv("training_data_5.csv")['loss_type'].unique().tolist()
model_typesss = pd.read_csv("training_data_5.csv")['model_type'].unique().tolist()
param_typesss = pd.read_csv("training_data_5.csv")['param_type'].unique().tolist()

############################################################# Filter and Select ########################################################
def filter_and_select(group):
    if len(group) >= 3:
        return group.head(3)
    
############################################################# UI #################################################################

ui.page_opts(fillable=True)

with ui.navset_card_tab(id="tab"):
    with ui.nav_panel("Viral Macrostructure"):
        ui.panel_title("Do viruses have underlying structure?")
        with ui.layout_columns():
            with ui.card():
                ui.input_selectize("virus_selector", "Select your viruses:", virus, multiple=True, selected=None)
            with ui.card():
                ui.input_selectize(
                    "plot_type_macro",
                    "Select your method:",
                    ["Chaos Game Representation", "2D Line", "ColorSquare", "Persistant Homology", "Wens Method"],
                    multiple=False,
                    selected=None,
                )

        @render.plot()
        def plot_macro():
            df = pd.read_parquet("virus_ds.parquet")
            df = df[df["Organism_Name"].isin(input.virus_selector())]
            grouped = df.groupby("Organism_Name")["Sequence"].apply(list)

            plot_type = input.plot_type_macro()
            if plot_type == "2D Line":
                return plot_2d_comparison(grouped, grouped.index)
            elif plot_type == "ColorSquare":
                filtered_df = df.groupby("Organism_Name").apply(filter_and_select).reset_index(drop=True)
                return plot_color_square(filtered_df["Sequence"], filtered_df["Organism_Name"].unique())
            elif plot_type == "Wens Method":
                return wens_method_heatmap(df, df["Organism_Name"].unique())
            elif plot_type == "Chaos Game Representation":
                filtered_df = df.groupby("Organism_Name").apply(filter_and_select).reset_index(drop=True)
                return plot_fcgr(filtered_df["Sequence"], df["Organism_Name"].unique())
            elif plot_type == "Persistant Homology":
                filtered_df = df.groupby("Organism_Name").apply(filter_and_select).reset_index(drop=True)
                return plot_persistence_homology(filtered_df["Sequence"], filtered_df["Organism_Name"])
    
    with ui.nav_panel("Viral Genome Distributions"):
        ui.panel_title("How does sequence distribution vary across sequence length?")
        with ui.layout_columns():
            with ui.card():
                ui.input_selectize("virus_selector_1", "Select your viruses:", virus_new, multiple=True, selected=None)
            with ui.card():
                ui.input_slider(
                    "basepair","Select basepair",0, 10000, 15
                )

        @render.plot()
        def plot_distro():
            df = MASTER_DF[MASTER_DF["organism_name"].isin(input.virus_selector_1())].copy()
            grouped = df.groupby("organism_name")["seq"].apply(list)
            return plot_distrobutions(grouped, grouped.index, input.basepair())

    with ui.nav_panel("Viral Microstructure"):
        ui.panel_title("Kmer Distribution")
        with ui.layout_columns():
            with ui.card():
                ui.input_slider("kmer", "kmer", 0, 10, 4)
                ui.input_slider("top_k", "top:", 0, 1000, 15)
                ui.input_selectize("plot_type", "Select metric:", ["percentage", "count"], multiple=False, selected=None)

        @render.plot()
        def plot_micro():
            df = pd.read_csv("kmers.csv")
            k = input.kmer()
            top_k = input.top_k()
            plot_type = input.plot_type()

            if k > 0:
                df = df[df["k"] == k].head(top_k)
                fig, ax = plt.subplots()
                if plot_type == "count":
                    ax.bar(df["kmer"], df["count"])
                    ax.set_ylabel("Count")
                elif plot_type == "percentage":
                    ax.bar(df["kmer"], df["percent"] * 100)
                    ax.set_ylabel("Percentage")
                ax.set_title(f"Most common {k}-mers")
                ax.set_xlabel("K-mer")
                ax.set_xticklabels(df["kmer"], rotation=90)
                return fig

    with ui.nav_panel("Viral Model Training"):
        ui.panel_title("Does context size matter for a nucleotide model?")

        def plot_loss_rates(df, model_type):
            x = np.linspace(0, 1, 1000)
            loss_rates = []
            labels = ["32", "64", "128", "256", "512", "1024"]
            df = df.drop(columns=["Step"])
            for col in df.columns:
                y = df[col].dropna().astype("float", errors="ignore").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 {model_type} parameter model across context windows")
            ax.set_xlabel("Training steps")
            ax.set_ylabel("Loss rate")
            return fig

        @render.plot()
        def plot_context_size_scaling():
            df = pd.read_csv("14m.csv")
            fig = plot_loss_rates(df, "14M")
            if fig:
                return fig

    with ui.nav_panel("Model loss analysis"):
        ui.panel_title("Paper stuff")
        with ui.card():
            ui.input_selectize(
                "param_type",
                "Select Param Type:",
                param_typesss,
                multiple=True,
                
            )
            ui.input_selectize(
                "model_type",
                "Select Model Type:",
                model_typesss,
                multiple=True,
                selected=["pythia", "denseformer"],
            )
            ui.input_selectize(
                "loss_type",
                "Select Loss Type:",
                loss_typesss,
                multiple=True,
                selected=["compliment", "cross_entropy", "headless"],
            )

        def plot_loss_rates_model(df, param_types, loss_types, model_types):
            x = np.linspace(0, 1, 1000)
            loss_rates = []
            labels = []
            for param_type in param_types:
                for loss_type in loss_types:
                    for model_type in model_types:
                        y = df[
                            (df["param_type"] == float(param_type))
                            & (df["loss_type"] == loss_type)
                            & (df["model_type"] == model_type)
                        ]["loss_interp"].values
                        if len(y) > 0:
                            f = interp1d(np.linspace(0, 1, len(y)), y)
                            loss_rates.append(f(x))
                            labels.append(f"{param_type}_{loss_type}_{model_type}")
            fig, ax = plt.subplots()
            for i, loss_rate in enumerate(loss_rates):
                ax.plot(x, loss_rate, label=labels[i])
            ax.legend()
            ax.set_xlabel("Training steps")
            ax.set_ylabel("Loss rate")
            return fig

        @render.plot()
        def plot_model_scaling():
            df = pd.read_csv("training_data_5.csv")
            df = df[df["epoch_interp"] > 0.035]
            fig = plot_loss_rates_model(
                df, input.param_type(), input.loss_type(), input.model_type()
            )
            if fig:
                return fig

    with ui.nav_panel("Scaling Laws"):
        ui.panel_title("Params & Losses")
        with ui.card():
            ui.input_selectize(
                "model_type_scale",
                "Select Model Type:",
                model_typesss,
                multiple=True,
                selected=["evo", "denseformer"],
            )
            ui.input_selectize(
                "loss_type_scale",
                "Select Loss Type:",
                loss_typesss,
                multiple=True,
                selected=["cross_entropy"],
            )

        def plot_loss_rates_model_scale(df, loss_type, model_types):
            df = df[df["loss_type"] == loss_type[0]]
            params = []
            loss_rates = []
            labels = []
            for model_type in model_types:
                df_new = df[df["model_type"] == model_type]
                losses = []
                params_model = []
                for paramy in df_new["num_params"].unique():
                    loss = df_new[df_new["num_params"] == paramy]["loss_interp"].min()
                    par = int(paramy)
                    losses.append(loss)
                    params_model.append(par)
                df_reorder = pd.DataFrame({"loss": losses, "params": params_model})
                df_reorder = df_reorder.sort_values(by="params")
                loss_rates.append(df_reorder["loss"].to_list())
                params.append(df_reorder["params"].to_list())
                labels.append(model_type)
            fig, ax = plt.subplots()
            for i, loss_rate in enumerate(loss_rates):
                ax.plot(params[i], loss_rate, label=labels[i])
            ax.legend()
            ax.set_xlabel("Params")
            ax.set_ylabel("Loss")
            return fig

        @render.plot()
        def plot_big_boy_model():
            df = pd.read_csv("training_data_5.csv")
            fig = plot_loss_rates_model_scale(
                df, input.loss_type_scale(), input.model_type_scale()
            )
            if fig:
                return fig
    with ui.nav_panel("Logits View"):
        ui.panel_title("Logits et all")
        with ui.card():
            ui.input_selectize(
                "model_bigness",
                "Select Model size:",
                ["14", "31", "70", "160", "410"],
                multiple=True,
                selected=["70", "160"],
            )
            ui.input_selectize(
                "loss_loss_loss",
                "Select Loss Type:",
                ["compliment", "cross_entropy", "headless", "2d_representation_GaussianPlusCE", "2d_representation_MSEPlusCE"],
                multiple=True,
                selected=["cross_entropy"],
            )
            ui.input_selectize(
                "logits_select",
                "Select logits:",
                ["1", "2", "3", "4", "5", "6", "7", "8"],
                multiple=True,
                selected=["6"],
            )
        
        def plot_logits_representation(model_bigness, loss_type, logits):
            num_rows = 2  # Number of rows in the subplot grid
            num_cols = len(logits)  # Number of columns based on the number of selected logits
            fig, axs = plt.subplots(num_rows, num_cols, figsize=(20, 10))
           # axs = axs.flatten()  # Flatten axs to handle 1D indexing
        
            for size in model_bigness:
                for loss in loss_type:
                    file_name = f"virus_pythia_{size}_1024_{loss}_logit_cumsums.npy"
                    if os.path.exists(file_name):
                        data = np.load(file_name, allow_pickle=True).item()
                        for k, logit in enumerate(logits):
                            if len(logits) == 1:
                                logit_index = int(logit) - 1
                                axs[0].plot(data['lm_logits_y_cumsum'][0, :, logit_index], label=f'Generated_{loss}_{size}')
                                axs[0].plot(data['shift_labels_y_cumsum'][0, :, logit_index], label=f'Expected_{loss}_{size}')
                                axs[0].set_title(f'Logit: {logit}- Single')
                                axs[0].legend()
                                axs[1].plot(data['lm_logits_y_full_cumsum'][0, :, logit_index], label=f'Generated_{loss}_{size}')
                                axs[1].plot(data['shift_labels_y_full_cumsum'][0, :, logit_index], label=f'Expected_{loss}_{size}')
                                axs[1].set_title(f'Logit: {logit} - Full')
                                axs[1].legend()
                    else:
                        print(f"File not found: {file_name}")
        
            for k in range(len(logits), num_cols):
                fig.delaxes(axs[k])  # Remove any extra subplots if fewer logits are selected
        
            plt.tight_layout()
            return fig

        @render.plot()
        def plot_logits_representation_ui():
            fig = plot_logits_representation(
                input.model_bigness(), input.loss_loss_loss(), input.logits_select()
            )
            if fig:
                return fig