plotly plot backend added
This commit is contained in:
Lorenzo Volpi
parent
f0bfb2e039
commit
c670f48b5b
265
quacc/plot.py
265
quacc/plot.py
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@ -1,265 +0,0 @@
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from pathlib import Path
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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from cycler import cycler
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from quacc import utils
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matplotlib.use("agg")
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def _get_markers(n: int):
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ls = "ovx+sDph*^1234X><.Pd"
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if n > len(ls):
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ls = ls * (n / len(ls) + 1)
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return list(ls)[:n]
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def plot_delta(
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base_prevs,
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columns,
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data,
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*,
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stdevs=None,
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pos_class=1,
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metric="acc",
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name="default",
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train_prev=None,
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legend=True,
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avg=None,
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return_fig=False,
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base_path=None,
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) -> Path:
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_base_title = "delta_stdev" if stdevs is not None else "delta"
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if train_prev is not None:
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t_prev_pos = int(round(train_prev[pos_class] * 100))
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title = f"{_base_title}_{name}_{t_prev_pos}_{metric}"
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else:
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title = f"{_base_title}_{name}_avg_{avg}_{metric}"
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if base_path is None:
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base_path = utils.get_quacc_home() / "plots"
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fig, ax = plt.subplots()
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ax.set_aspect("auto")
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ax.grid()
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NUM_COLORS = len(data)
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cm = plt.get_cmap("tab10")
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if NUM_COLORS > 10:
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cm = plt.get_cmap("tab20")
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cy = cycler(color=[cm(i) for i in range(NUM_COLORS)])
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base_prevs = base_prevs[:, pos_class]
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for method, deltas, _cy in zip(columns, data, cy):
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ax.plot(
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base_prevs,
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deltas,
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label=method,
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color=_cy["color"],
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linestyle="-",
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marker="o",
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markersize=3,
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zorder=2,
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)
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if stdevs is not None:
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_col_idx = np.where(columns == method)[0]
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stdev = stdevs[_col_idx].flatten()
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nn_idx = np.intersect1d(
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np.where(deltas != np.nan)[0],
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np.where(stdev != np.nan)[0],
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)
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_bps, _ds, _st = base_prevs[nn_idx], deltas[nn_idx], stdev[nn_idx]
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ax.fill_between(
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_bps,
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_ds - _st,
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_ds + _st,
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color=_cy["color"],
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alpha=0.25,
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)
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x_label = "test" if avg is None or avg == "train" else "train"
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ax.set(
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xlabel=f"{x_label} prevalence",
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ylabel=metric,
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title=title,
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)
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if legend:
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ax.legend(loc="center left", bbox_to_anchor=(1, 0.5))
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if return_fig:
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return fig
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output_path = base_path / f"{title}.png"
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fig.savefig(output_path, bbox_inches="tight")
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return output_path
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def plot_diagonal(
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reference,
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columns,
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data,
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*,
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pos_class=1,
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metric="acc",
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name="default",
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train_prev=None,
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legend=True,
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return_fig=False,
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base_path=None,
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):
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if train_prev is not None:
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t_prev_pos = int(round(train_prev[pos_class] * 100))
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title = f"diagonal_{name}_{t_prev_pos}_{metric}"
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else:
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title = f"diagonal_{name}_{metric}"
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if base_path is None:
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base_path = utils.get_quacc_home() / "plots"
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fig, ax = plt.subplots()
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ax.set_aspect("auto")
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ax.grid()
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ax.set_aspect("equal")
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NUM_COLORS = len(data)
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cm = plt.get_cmap("tab10")
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if NUM_COLORS > 10:
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cm = plt.get_cmap("tab20")
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cy = cycler(
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color=[cm(i) for i in range(NUM_COLORS)],
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marker=_get_markers(NUM_COLORS),
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)
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reference = np.array(reference)
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x_ticks = np.unique(reference)
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x_ticks.sort()
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for deltas, _cy in zip(data, cy):
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ax.plot(
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reference,
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deltas,
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color=_cy["color"],
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linestyle="None",
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marker=_cy["marker"],
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markersize=3,
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zorder=2,
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alpha=0.25,
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)
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# ensure limits are equal for both axes
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_alims = np.stack(((ax.get_xlim(), ax.get_ylim())), axis=-1)
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_lims = np.array([f(ls) for f, ls in zip([np.min, np.max], _alims)])
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ax.set(xlim=tuple(_lims), ylim=tuple(_lims))
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for method, deltas, _cy in zip(columns, data, cy):
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slope, interc = np.polyfit(reference, deltas, 1)
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y_lr = np.array([slope * x + interc for x in _lims])
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ax.plot(
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_lims,
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y_lr,
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label=method,
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color=_cy["color"],
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linestyle="-",
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markersize="0",
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zorder=1,
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)
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# plot reference line
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ax.plot(
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_lims,
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_lims,
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color="black",
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linestyle="--",
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markersize=0,
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zorder=1,
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)
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ax.set(xlabel=f"true {metric}", ylabel=f"estim. {metric}", title=title)
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if legend:
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ax.legend(loc="center left", bbox_to_anchor=(1, 0.5))
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if return_fig:
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return fig
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output_path = base_path / f"{title}.png"
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fig.savefig(output_path, bbox_inches="tight")
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return output_path
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def plot_shift(
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shift_prevs,
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columns,
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data,
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*,
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counts=None,
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pos_class=1,
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metric="acc",
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name="default",
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train_prev=None,
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legend=True,
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return_fig=False,
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base_path=None,
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) -> Path:
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if train_prev is not None:
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t_prev_pos = int(round(train_prev[pos_class] * 100))
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title = f"shift_{name}_{t_prev_pos}_{metric}"
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else:
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title = f"shift_{name}_avg_{metric}"
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if base_path is None:
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base_path = utils.get_quacc_home() / "plots"
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fig, ax = plt.subplots()
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ax.set_aspect("auto")
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ax.grid()
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NUM_COLORS = len(data)
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cm = plt.get_cmap("tab10")
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if NUM_COLORS > 10:
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cm = plt.get_cmap("tab20")
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cy = cycler(color=[cm(i) for i in range(NUM_COLORS)])
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shift_prevs = shift_prevs[:, pos_class]
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for method, shifts, _cy in zip(columns, data, cy):
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ax.plot(
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shift_prevs,
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shifts,
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label=method,
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color=_cy["color"],
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linestyle="-",
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marker="o",
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markersize=3,
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zorder=2,
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)
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if counts is not None:
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_col_idx = np.where(columns == method)[0]
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count = counts[_col_idx].flatten()
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for prev, shift, cnt in zip(shift_prevs, shifts, count):
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label = f"{cnt}"
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plt.annotate(
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label,
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(prev, shift),
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textcoords="offset points",
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xytext=(0, 10),
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ha="center",
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color=_cy["color"],
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fontsize=12.0,
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)
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ax.set(xlabel="dataset shift", ylabel=metric, title=title)
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if legend:
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ax.legend(loc="center left", bbox_to_anchor=(1, 0.5))
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if return_fig:
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return fig
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output_path = base_path / f"{title}.png"
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fig.savefig(output_path, bbox_inches="tight")
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return output_path
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@ -0,0 +1,201 @@
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from collections import defaultdict
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from pathlib import Path
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import numpy as np
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import plotly
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import plotly.graph_objects as go
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from quacc.plot.base import BasePlot
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class PlotlyPlot(BasePlot):
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__themes = defaultdict(
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lambda: {
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"template": "seaborn",
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}
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)
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__themes = __themes | {
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"dark": {
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"template": "plotly_dark",
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},
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}
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def __init__(self, theme=None):
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self.theme = PlotlyPlot.__themes[theme]
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def hex_to_rgb(self, hex: str, t: float | None = None):
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hex = hex.lstrip("#")
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rgb = [int(hex[i : i + 2], 16) for i in [0, 2, 4]]
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if t is not None:
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rgb.append(t)
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return f"{'rgb' if t is None else 'rgba'}{str(tuple(rgb))}"
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def get_colors(self, num):
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match num:
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case v if v > 10:
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__colors = plotly.colors.qualitative.Light24
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case _:
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__colors = plotly.colors.qualitative.Plotly
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def __generator(cs):
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while True:
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for c in cs:
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yield c
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return __generator(__colors)
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def update_layout(self, fig, title, x_label, y_label):
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fig.update_layout(
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title=title,
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xaxis_title=x_label,
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yaxis_title=y_label,
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template=self.theme["template"],
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)
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def save_fig(self, fig, base_path, title) -> Path:
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return None
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def plot_delta(
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self,
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base_prevs,
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columns,
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data,
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*,
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stdevs=None,
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pos_class=1,
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title="default",
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x_label="prevs.",
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y_label="error",
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legend=True,
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) -> go.Figure:
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fig = go.Figure()
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x = base_prevs[:, pos_class]
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line_colors = self.get_colors(len(columns))
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for name, delta in zip(columns, data):
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color = next(line_colors)
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_line = [
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go.Scatter(
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x=x,
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y=delta,
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mode="lines+markers",
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name=name,
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line=dict(color=self.hex_to_rgb(color)),
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hovertemplate="prev.: %{x}<br>error: %{y:,.4f}",
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)
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]
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_error = []
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if stdevs is not None:
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_col_idx = np.where(columns == name)[0]
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stdev = stdevs[_col_idx].flatten()
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_error = [
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go.Scatter(
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x=np.concatenate([x, x[::-1]]),
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y=np.concatenate([delta - stdev, (delta + stdev)[::-1]]),
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name=int(_col_idx[0]),
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fill="toself",
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fillcolor=self.hex_to_rgb(color, t=0.2),
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line=dict(color="rgba(255, 255, 255, 0)"),
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hoverinfo="skip",
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showlegend=False,
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)
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]
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fig.add_traces(_line + _error)
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self.update_layout(fig, title, x_label, y_label)
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return fig
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def plot_diagonal(
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self,
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reference,
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columns,
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data,
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*,
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pos_class=1,
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title="default",
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x_label="true",
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y_label="estim.",
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legend=True,
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) -> go.Figure:
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fig = go.Figure()
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x = reference
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line_colors = self.get_colors(len(columns))
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_edges = (np.min([np.min(x), np.min(data)]), np.max([np.max(x), np.max(data)]))
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_lims = np.array([[_edges[0], _edges[1]], [_edges[0], _edges[1]]])
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for name, val in zip(columns, data):
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color = next(line_colors)
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slope, interc = np.polyfit(x, val, 1)
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y_lr = np.array([slope * _x + interc for _x in _lims[0]])
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fig.add_traces(
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[
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go.Scatter(
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x=x,
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y=val,
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customdata=np.stack((val - x,), axis=-1),
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mode="markers",
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name=name,
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line=dict(color=self.hex_to_rgb(color, t=0.5)),
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hovertemplate="true acc: %{x:,.4f}<br>estim. acc: %{y:,.4f}<br>acc err.: %{customdata[0]:,.4f}",
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),
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go.Scatter(
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x=_lims[0],
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y=y_lr,
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mode="lines",
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name=name,
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line=dict(color=self.hex_to_rgb(color), width=3),
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showlegend=False,
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),
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]
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)
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fig.add_trace(
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go.Scatter(
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x=_lims[0],
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y=_lims[1],
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mode="lines",
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name="reference",
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showlegend=False,
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line=dict(color=self.hex_to_rgb("#000000"), dash="dash"),
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)
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)
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self.update_layout(fig, title, x_label, y_label)
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fig.update_layout(yaxis_scaleanchor="x", yaxis_scaleratio=1.0)
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return fig
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def plot_shift(
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self,
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shift_prevs,
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columns,
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data,
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*,
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counts=None,
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pos_class=1,
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title="default",
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x_label="true",
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y_label="estim.",
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legend=True,
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) -> go.Figure:
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fig = go.Figure()
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x = shift_prevs[:, pos_class]
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line_colors = self.get_colors(len(columns))
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for name, delta in zip(columns, data):
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col_idx = (columns == name).nonzero()[0][0]
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color = next(line_colors)
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fig.add_trace(
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go.Scatter(
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x=x,
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y=delta,
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customdata=np.stack((counts[col_idx],), axis=-1),
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mode="lines+markers",
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name=name,
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line=dict(color=self.hex_to_rgb(color)),
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hovertemplate="shift: %{x}<br>error: %{y}"
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+ "<br>count: %{customdata[0]}"
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if counts is not None
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else "",
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)
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)
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self.update_layout(fig, title, x_label, y_label)
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return fig
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