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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import os |
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import sys |
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import time |
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import numpy as np |
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import pandas as pd |
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import streamlit as st |
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import plotly.express as px |
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import matplotlib.pyplot as plt |
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color_scale = px.colors.sequential.Jet |
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def parallel_coordinates_plotly(*args, plotly_width=1200, plotly_height=540, **kwargs): |
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fig = px.parallel_coordinates(*args, **kwargs, color_continuous_scale=color_scale) |
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fig.update_layout(autosize=False, width=plotly_width, height=plotly_height) |
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return fig |
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def filter_data(filter, df, columns): |
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if len(df) > 1: |
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for column in columns: |
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if column not in list(filter["parameter"]): |
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continue |
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filter_ = filter[filter["parameter"] == column] |
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lower, upper = ( |
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filter_["lower bound"].values[0], |
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filter_["upper bound"].values[0], |
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) |
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col_data = df[column] |
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if lower == "lower": |
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lower = np.min(col_data) |
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else: |
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lower = float(lower) |
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if upper == "upper": |
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upper = np.max(col_data) |
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else: |
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upper = float(upper) |
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df = df[(df[column] >= lower) & (df[column] <= upper)] |
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return df |
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def main(): |
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try: |
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st.set_page_config(page_title="Hyperactive Progress Board", layout="wide") |
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except: |
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pass |
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search_ids = sys.argv[1:] |
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search_id_dict = {} |
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for search_id in search_ids: |
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search_id_dict[search_id] = {} |
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progress_data_path = "./progress_data_" + search_id + ".csv~" |
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filter_path = "./filter_" + search_id + ".csv" |
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if os.path.isfile(progress_data_path): |
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search_id_dict[search_id]["progress_data"] = pd.read_csv(progress_data_path) |
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if os.path.isfile(filter_path): |
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search_id_dict[search_id]["filter"] = pd.read_csv(filter_path) |
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for search_id in search_id_dict.keys(): |
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progress_data = search_id_dict[search_id]["progress_data"] |
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filter = search_id_dict[search_id]["filter"] |
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st.title(search_id) |
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st.components.v1.html( |
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"""<hr style="height:1px;border:none;color:#333;background-color:#333;" /> """, |
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height=10, |
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) |
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col1, col2 = st.beta_columns([1, 2]) |
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progress_data_f = progress_data[ |
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~progress_data.isin([np.nan, np.inf, -np.inf]).any(1) |
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] |
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nth_iter = progress_data_f["nth_iter"] |
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score_best = progress_data_f["score_best"] |
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nth_process = list(progress_data_f["nth_process"]) |
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if np.all(nth_process == nth_process[0]): |
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fig, ax = plt.subplots() |
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plt.plot(nth_iter, score_best) |
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col1.pyplot(fig) |
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else: |
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fig, ax = plt.subplots() |
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ax.set_xlabel("nth iteration") |
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ax.set_ylabel("score") |
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for i in np.unique(nth_process): |
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nth_iter_p = nth_iter[nth_process == i] |
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score_best_p = score_best[nth_process == i] |
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plt.plot(nth_iter_p, score_best_p, label=str(i) + ". process") |
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plt.legend() |
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col1.pyplot(fig) |
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progress_data_f.drop( |
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["nth_iter", "score_best", "nth_process"], axis=1, inplace=True |
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) |
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prog_data_columns = list(progress_data_f.columns) |
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progress_data_f = filter_data(filter, progress_data_f, prog_data_columns) |
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# remove score |
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prog_data_columns.remove("score") |
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fig = parallel_coordinates_plotly( |
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progress_data_f, dimensions=prog_data_columns, color="score" |
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) |
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col2.plotly_chart(fig) |
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for _ in range(3): |
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st.write(" ") |
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time.sleep(1) |
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st.experimental_rerun() |
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if __name__ == "__main__": |
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main() |
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SimonBlanke /
Hyperactive
