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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 numbers |
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import numpy as np |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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import plotly.express as px |
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try: |
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from progress_io import ProgressIO |
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except: |
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from .progress_io import ProgressIO |
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pd.options.mode.chained_assignment = "raise" |
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color_scale = px.colors.sequential.Jet |
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class StreamlitBackend: |
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def __init__(self, search_ids): |
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self.search_ids = search_ids |
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self.search_id_dict = {} |
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_io_ = ProgressIO("./") |
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for search_id in search_ids: |
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self.search_id_dict[search_id] = {} |
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self.search_id_dict[search_id]["prog_d"] = _io_.load_progress(search_id) |
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self.search_id_dict[search_id]["filt_f"] = _io_.load_filter(search_id) |
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def get_progress_data(self, search_id): |
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progress_data = self.search_id_dict[search_id]["prog_d"] |
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if progress_data is None: |
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return |
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return progress_data[~progress_data.isin([np.nan, np.inf, -np.inf]).any(1)] |
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def pyplot(self, progress_data): |
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if progress_data is None or len(progress_data) <= 1: |
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return None |
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nth_iter = progress_data["nth_iter"] |
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score_best = progress_data["score_best"] |
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nth_process = list(progress_data["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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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("best 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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return fig |
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def filter_data(self, df, filter_df): |
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prog_data_columns = list(df.columns) |
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if len(df) > 1: |
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for column in prog_data_columns: |
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if column not in list(filter_df["parameter"]): |
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continue |
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filter_ = filter_df[filter_df["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 isinstance(lower, numbers.Number): |
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lower = float(lower) |
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else: |
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lower = np.min(col_data) |
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if isinstance(upper, numbers.Number): |
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upper = float(upper) |
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else: |
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upper = np.max(col_data) |
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df = df[(df[column] >= lower) & (df[column] <= upper)] |
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return df |
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def plotly(self, progress_data, search_id): |
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if progress_data is None or len(progress_data) <= 1: |
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return None |
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filter_df = self.search_id_dict[search_id]["filt_f"] |
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progress_data = progress_data.drop( |
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["nth_iter", "score_best", "nth_process", "best"], axis=1 |
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) |
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if filter_df is not None: |
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progress_data = self.filter_data(progress_data, filter_df) |
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# remove score |
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prog_data_columns = list(progress_data.columns) |
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prog_data_columns.remove("score") |
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fig = px.parallel_coordinates( |
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progress_data, |
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dimensions=prog_data_columns, |
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color="score", |
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color_continuous_scale=color_scale, |
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) |
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fig.update_layout(autosize=False, width=1200, height=540) |
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return fig |
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def create_plots(self, search_id): |
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progress_data = self.get_progress_data(search_id) |
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pyplot_fig = self.pyplot(progress_data) |
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plotly_fig = self.plotly(progress_data, search_id) |
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return pyplot_fig, plotly_fig |
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def create_info(self, search_id): |
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progress_data = self.get_progress_data(search_id) |
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if progress_data is None or len(progress_data) <= 1: |
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return None |
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progress_data_best = progress_data[progress_data["best"] == 1] |
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progress_data_best = progress_data_best.drop( |
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["nth_iter", "score_best", "nth_process", "best"], axis=1 |
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) |
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progress_data_best = progress_data_best.sort_values("score") |
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last_best = progress_data_best.tail(5) |
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last_best = last_best.rename( |
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columns={ |
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"score": "best 5 scores", |
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} |
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) |
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return last_best |
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SimonBlanke /
Hyperactive
