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Merge pull request #70 from BoostV/patch-wip-plots
Generate single plots
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,204 @@ | ||
| """Plotting functions.""" | ||
| from scipy.stats import norm | ||
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
| from matplotlib.ticker import MaxNLocator, FuncFormatter | ||
| from ProcessOptimizer.space import ( | ||
| Categorical | ||
| ) | ||
| from ProcessOptimizer import expected_minimum | ||
| from ProcessOptimizer.plots import ( | ||
| partial, | ||
| dependence, | ||
| _cat_format, | ||
| _map_categories | ||
| ) | ||
|
|
||
| def plot_brownie_bee( | ||
| result, | ||
| n_points=40, | ||
| n_samples=250, | ||
| size=2, | ||
| max_quality=5, | ||
| ): | ||
| """Single factor dependence plot of the model intended for use with the | ||
| Brownie Bee user interface. | ||
| Each plot shows how quality depends on the dimension `i` when all other | ||
| factor values are locked to those of the expected minimum. A vertical line | ||
| indicates the location of the expected minimum for each factor. | ||
| Parameters | ||
| ---------- | ||
| * `result` [`OptimizeResult`] | ||
| The result for which to create the plots. | ||
| * `n_points` [int, default=40] | ||
| Number of points at which to evaluate the partial dependence | ||
| along each dimension. | ||
| * `n_samples` [int, default=250] | ||
| Number of random samples to use for averaging the model function | ||
| at each of the `n_points`. | ||
| * `size` [float, default=2] | ||
| Height (in inches) of each returned figure. | ||
| * `max_quality` [int, default=5] | ||
| The maximal quality obtainable in the setup of Brownie Bee. Quality is | ||
| assumed to be measured on a scale from 0 to this number, and the y-axis | ||
| of each plot is scaled to reflect this. | ||
| Returns | ||
| ------- | ||
| * `plot_list`: [`Figures`]: | ||
| A list of individual matplotlib figure handles, one for each dimension | ||
| present in 'result' and a last one representing a histogram of samples | ||
| drawn at the expected minimum. | ||
| """ | ||
| # Here we define the value to highlight in each dimension. These | ||
| # same values will be used for evaluating the plots when calculating | ||
| # dependence. (Unless partial dependence is to be used instead). | ||
|
|
||
| space = result.space | ||
| # Check if we have any categorical dimensions, as this influences the plots | ||
| is_cat = [isinstance(dim, Categorical) for dim in space.dimensions] | ||
| # Identify the location of the expected minimum, and its mean and std | ||
| x_eval, [res_mean, res_std] = expected_minimum( | ||
| result, | ||
| n_random_starts=20, | ||
| random_state=None, | ||
| return_std=True, | ||
| ) | ||
|
|
||
| rvs_transformed = space.transform(space.rvs(n_samples=n_samples)) | ||
| _, minimum, _ = _map_categories(space, result.x_iters, x_eval) | ||
|
|
||
| # Gather all data relevant for plotting | ||
| plots_data = [] | ||
| for i in range(space.n_dims): | ||
| row = [] | ||
| xi, yi, stddevs = dependence( | ||
| space, | ||
| result.models[-1], | ||
| i, | ||
| j=None, | ||
| sample_points=rvs_transformed, | ||
| n_points=n_points, | ||
| x_eval=x_eval, | ||
| ) | ||
| row.append({"xi": xi, "yi": yi, "std": stddevs}) | ||
|
|
||
| plots_data.append(row) | ||
|
|
||
| # Create the list to store figure handles | ||
| figure_list = [] | ||
|
|
||
| # Build all the plots in the figure | ||
| for n in range(space.n_dims): | ||
| # Prepare a figure | ||
| fig, ax_ = plt.subplots( | ||
| figsize=(size, size), | ||
| dpi=200, | ||
| ) | ||
| # Set the padding | ||
| fig.subplots_adjust( | ||
| left=0.18, right=0.95, bottom=0.2, top=0.95, hspace=0.0, wspace=0.0 | ||
| ) | ||
|
|
||
| # Get data to plot in this subplot | ||
| xi = plots_data[n][0]["xi"] | ||
| yi = plots_data[n][0]["yi"] | ||
| stddevs = plots_data[n][0]["std"] | ||
|
|
||
| # Set y-axis limits | ||
| ax_.set_ylim(0, max_quality) | ||
|
|
||
| # Enter here when we plot a categoric factor | ||
| if is_cat[n]: | ||
| # Expand the x-axis for this factor so we can see the first | ||
| # and the last category | ||
| ax_.set_xlim(np.min(xi)-0.2, np.max(xi)+0.2) | ||
|
|
||
| # Highlight the expected minimum | ||
| ax_.axvline(minimum[n], linestyle="--", color="k", lw=1) | ||
| # Create one uniformly colored bar for each category. | ||
| # Edgecolor ensures we can see the bar when plotting | ||
| # at best obeservation, as stddev is often tiny there | ||
| ax_.bar( | ||
| xi, | ||
| 2*1.96*stddevs, | ||
| width=0.2, | ||
| bottom=(-yi-1.96*stddevs), | ||
| alpha=0.5, | ||
| color="green", | ||
| edgecolor="green", | ||
| ) | ||
|
|
||
| # For non-categoric factors | ||
| else: | ||
| ax_.set_xlim(np.min(xi), np.max(xi)) | ||
| # Highlight the expected minimum | ||
| ax_.axvline(minimum[n], linestyle="--", color="k", lw=1) | ||
| # Show the uncertainty | ||
| ax_.fill_between( | ||
| xi, | ||
| y1=-(yi - 1.96*stddevs), | ||
| y2=-(yi + 1.96*stddevs), | ||
| alpha=0.5, | ||
| color="green", | ||
| edgecolor="green", | ||
| linewidth=0.0, | ||
| ) | ||
|
|
||
| # Fix formatting of the y-axis with ticks from 0 to our max quality | ||
| ax_.yaxis.set_major_locator(MaxNLocator(5, integer=True)) | ||
| ax_.tick_params(axis="y", direction="inout") | ||
| # Fix formatting of the x-axis | ||
| [labl.set_rotation(45) for labl in ax_.get_xticklabels()] | ||
|
|
||
| if space.dimensions[n].prior == "log-uniform": | ||
| ax_.set_xscale("log") | ||
| else: | ||
| ax_.xaxis.set_major_locator( | ||
| MaxNLocator(6, prune="both", integer=is_cat[n]) | ||
| ) | ||
| if is_cat[n]: | ||
| # Axes for categorical dimensions are really integers; | ||
| # we have to label them with the category names | ||
| ax_.xaxis.set_major_formatter( | ||
| FuncFormatter( | ||
| partial(_cat_format, space.dimensions[n]) | ||
| ) | ||
| ) | ||
|
|
||
| # Add the figure to the output list | ||
| figure_list.append(fig) | ||
|
|
||
| # Prepare a figure for a histogram of expected quality | ||
| fig, ax_ = plt.subplots( | ||
| figsize=(size, size), | ||
| dpi=200, | ||
| ) | ||
| # Set the padding | ||
| fig.subplots_adjust( | ||
| left=0.05, right=0.95, bottom=0.2, top=0.95, hspace=0.0, wspace=0.0 | ||
| ) | ||
| # Plot out to 3 standard deviations from the mean but not outside the scale | ||
| xi = np.linspace( | ||
| max(-res_mean-3*res_std, 0), | ||
| min(-res_mean+3*res_std, max_quality), | ||
| 100, | ||
| ) | ||
| # Create histogram y-values | ||
| yi = norm.pdf(xi, -res_mean, res_std) | ||
| # Build the plot | ||
| ax_.fill_between( | ||
| xi, | ||
| y1=np.zeros((len(xi),)), | ||
| y2=yi, | ||
| alpha=0.5, | ||
| color="blue", | ||
| edgecolor="blue", | ||
| linewidth=0.0, | ||
| ) | ||
| # Cosmetics | ||
| ax_.get_yaxis().set_visible(False) | ||
|
|
||
| # Add the figure to the output list | ||
| figure_list.append(fig) | ||
|
|
||
| return figure_list |