Source code for deephyper.analysis.hpo

"""Visualization tools for Hyperparameter Optimization.
from typing import Tuple
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

[docs]def filter_failed_objectives(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]: """Filter out lines from the DataFrame with failed objectives. Args: df (pd.DataFrame): the results of a Hyperparameter Search. Returns: Tuple[pd.DataFrame, pd.DataFrame]: ``df_without_failures, df_with_failures`` the first are results of a Hyperparameter Search without failed objectives and the second are results of Hyperparameter search with failed objectives. """ # Single-Objective if "objective" in df.columns: if pd.api.types.is_string_dtype(df.objective): mask = df.objective.str.startswith("F") df_with_failures = df[mask] df_without_failures = df[~mask] df_without_failures.loc[ :, "objective" ] = df_without_failures.objective.astype(float) else: df_without_failures = df df_with_failures = df[np.zeros(len(df), dtype=bool)] # Multi-Objective elif "objective_0" in df.columns: objcol = list(df.filter(regex=r"^objective_\d+$").columns) mask = np.zeros(len(df), dtype=bool) for col in objcol: if pd.api.types.is_string_dtype(df[col]): mask = mask | df[col].str.startswith("F") df_with_failures = df[mask] df_without_failures = df[~mask] df_without_failures.loc[:, objcol] = df_without_failures[objcol].astype(float) else: raise ValueError( "The DataFrame does not contain neither a column named 'objective' nor columns named 'objective_<int>'." ) return df_without_failures, df_with_failures
[docs]def parameters_at_max( df: pd.DataFrame, column: str = "objective" ) -> Tuple[dict, float]: """Return the parameters at the maximum of the objective function. Args: df (pd.DataFrame): the results of a Hyperparameter Search. column (str, optional): the column to use for the maximization. Defaults to ``"objective"``. Returns: Tuple[dict, float]: the parameters at the maximum of the ``column`` and its corresponding value. """ df, _ = filter_failed_objectives(df) idx = df[column].argmax() value = df.iloc[idx][column] config = df.iloc[idx].to_dict() config = {k[2:]: v for k, v in config.items() if k.startswith("p:")} return config, value
[docs]def plot_search_trajectory_single_objective_hpo( results, show_failures: bool = True, ax=None, **kwargs ): """Plot the search trajectory of a Single-Objective Hyperparameter Search. Args: results (pd.DataFrame): the results of a Hyperparameter Search. show_failures (bool, optional): whether to show the failed objectives. Defaults to ``True``. ax (matplotlib.pyplot.axes): the axes to use for the plot. Returns: (matplotlib.pyplot.figure, matplotlib.pyplot.axes): the figure and axes of the plot. """ if results.objective.dtype != np.float64: x = np.arange(len(results)) mask_failed = np.where(results.objective.str.startswith("F"))[0] mask_success = np.where(~results.objective.str.startswith("F"))[0] x_success, x_failed = x[mask_success], x[mask_failed] y_success = results.objective[mask_success].astype(float) y_min, y_max = y_success.min(), y_success.max() y_min = y_min - 0.05 * (y_max - y_min) y_max = y_max - 0.05 * (y_max - y_min) scatter_kwargs = dict(marker="o", s=10, c="skyblue") scatter_kwargs.update(kwargs) fig = plt.gcf() if fig is None: fig = plt.figure() if ax is None: ax = fig.gca() ax.plot(x_success, y_success.cummax()) ax.scatter(x_success, y_success, **scatter_kwargs, label="Successes") if show_failures: ax.scatter( x_failed, np.full_like(x_failed, y_min), marker="v", color="red", label="Failures", ) ax.set_xlabel("Evaluations") ax.set_ylabel("Objective") ax.legend() ax.grid(True) # ax.set_ylim(y_min, y_max) ax.set_xlim(x.min(), x.max()) return fig, ax