plots refactoring started
This commit is contained in:
Lorenzo Volpi
parent
4a06c83256
commit
b8e43c02f2
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@ -1,7 +1,7 @@
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from quacc.plot.plot import (
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from quacc.legacy.plot.plot import (
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get_backend,
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plot_delta,
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plot_diagonal,
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plot_shift,
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plot_fit_scores,
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plot_shift,
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)
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@ -1,78 +1,49 @@
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from collections import defaultdict
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from pathlib import Path
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import os
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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.evaluation.estimators import CE, _renames
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from quacc.plot.base import BasePlot
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class PlotCfg:
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def __init__(self, mode, lwidth, font=None, legend=None, template="seaborn"):
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self.mode = mode
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self.lwidth = lwidth
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self.legend = {} if legend is None else legend
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self.font = {} if font is None else font
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self.template = template
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web_cfg = PlotCfg("lines+markers", 2)
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png_cfg_old = PlotCfg(
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"lines",
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5,
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legend=dict(
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orientation="h",
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yanchor="bottom",
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xanchor="right",
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y=1.02,
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x=1,
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font=dict(size=24),
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),
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font=dict(size=24),
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# template="ggplot2",
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)
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png_cfg = PlotCfg(
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"lines",
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5,
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legend=dict(
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font=dict(
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family="DejaVu Sans",
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size=24,
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),
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),
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font=dict(size=24),
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# template="ggplot2",
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)
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_cfg = png_cfg
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class PlotlyPlot(BasePlot):
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__themes = defaultdict(
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lambda: {
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"template": _cfg.template,
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MODE = "lines"
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L_WIDTH = 5
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LEGEND = {
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"font": {
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"family": "DejaVu Sans",
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"size": 24,
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}
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}
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FONT = {"size": 24}
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TEMPLATE = "ggplot2"
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def _save_or_return(fig, basedir, dataset_name, measure_name, plot_type):
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if basedir is not None:
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plotsubdir = dataset_name
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os.path.join(basedir, "plots", measure_name, plotsubdir, plot_type + ".svg")
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return fig
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def _update_layout(fig, title, x_label, y_label, **kwargs):
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fig.update_layout(
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xaxis_title=x_label,
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yaxis_title=y_label,
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template=TEMPLATE,
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font=FONT,
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legend=LEGEND,
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**kwargs,
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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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self.rename = True
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def hex_to_rgb(self, hex: str, t: float | None = None):
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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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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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@ -86,156 +57,45 @@ class PlotlyPlot(BasePlot):
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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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font=_cfg.font,
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legend=_cfg.legend,
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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 _get_ref_limits(true_accs: np.ndarray, estim_accs: dict[str, np.ndarray]):
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"""get lmits of reference line"""
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def rename_plots(
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self,
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columns,
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):
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if not self.rename:
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return columns
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new_columns = []
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for c in columns:
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nc = c
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for old, new in _renames.items():
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if c.startswith(old):
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nc = new + c[len(old) :]
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new_columns.append(nc)
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return np.array(new_columns)
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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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if isinstance(base_prevs[0], float):
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base_prevs = np.around([(1 - bp, bp) for bp in base_prevs], decimals=4)
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x = [str(tuple(bp)) for bp in base_prevs]
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named_data = {c: d for c, d in zip(columns, data)}
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r_columns = {c: r for c, r in zip(columns, self.rename_plots(columns))}
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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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columns = np.array(CE.name.sort(columns))
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for name in columns:
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delta = named_data[name]
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r_name = r_columns[name]
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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=_cfg.mode,
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name=r_name,
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line=dict(color=self.hex_to_rgb(color), width=_cfg.lwidth),
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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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fixed_lim=False,
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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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if fixed_lim:
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_lims = np.array([[0.0, 1.0], [0.0, 1.0]])
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else:
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_edges = (
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np.min([np.min(x), np.min(data)]),
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np.max([np.max(x), np.max(data)]),
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np.min([np.min(true_accs), np.min(estim_accs)]),
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np.max([np.max(true_accs), np.max(estim_accs)]),
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)
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_lims = np.array([[_edges[0], _edges[1]], [_edges[0], _edges[1]]])
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named_data = {c: d for c, d in zip(columns, data)}
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r_columns = {c: r for c, r in zip(columns, self.rename_plots(columns))}
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columns = np.array(CE.name.sort(columns))
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for name in columns:
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val = named_data[name]
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r_name = r_columns[name]
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def plot_diagonal(
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method_names,
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true_accs,
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estim_accs,
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*,
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measure_name="vanilla_accuracy",
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dataset_name=None,
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basedir=None,
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) -> go.Figure:
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fig = go.Figure()
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x = true_accs
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line_colors = _get_colors(len(method_names))
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_lims = _get_ref_limits(true_accs, estim_accs)
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for name, estim in zip(method_names, estim_accs):
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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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slope, interc = np.polyfit(x, estim, 1)
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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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y=estim,
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customdata=np.stack((estim - x,), axis=-1),
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mode="markers",
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name=r_name,
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marker=dict(color=self.hex_to_rgb(color, t=0.5)),
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name=name,
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marker=dict(color=_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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# showlegend=False,
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),
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# go.Scatter(
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# x=[x[-1]],
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# y=[val[-1]],
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# mode="markers",
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# marker=dict(color=self.hex_to_rgb(color), size=8),
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# name=r_name,
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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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@ -245,12 +105,14 @@ class PlotlyPlot(BasePlot):
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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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line=dict(color=_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(
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_update_layout(
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fig,
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x_label=f"True {measure_name}",
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y_label=f"Estimated {measure_name}",
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autosize=False,
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width=1300,
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height=1000,
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@ -258,73 +120,98 @@ class PlotlyPlot(BasePlot):
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yaxis_scaleratio=1.0,
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yaxis_range=[-0.1, 1.1],
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)
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return fig
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return _save_or_return(fig, basedir, dataset_name, measure_name, "diagonal")
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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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def plot_delta(
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method_names: list[str],
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prevs: np.ndarray,
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acc_errs: np.ndarray,
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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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stdevs: np.ndarray | None = None,
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prev_name="Test",
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measure_name="Vanilla Accuracy",
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dataset_name=None,
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basedir=None,
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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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x = shift_prevs
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line_colors = self.get_colors(len(columns))
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named_data = {c: d for c, d in zip(columns, data)}
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r_columns = {c: r for c, r in zip(columns, self.rename_plots(columns))}
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columns = np.array(CE.name.sort(columns))
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for name in columns:
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delta = named_data[name]
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r_name = r_columns[name]
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col_idx = (columns == name).nonzero()[0][0]
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x = [str(bp) for bp in prevs]
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line_colors = _get_colors(len(method_names))
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if stdevs is None:
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stdevs = [None] * len(method_names)
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for name, delta, stdev in zip(method_names, acc_errs, stdevs):
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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=MODE,
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name=name,
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line=dict(color=_hex_to_rgb(color), width=L_WIDTH),
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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 stdev is not None:
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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=name,
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fill="toself",
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fillcolor=_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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_update_layout(
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fig,
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x_label=f"{prev_name} Prevalence",
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y_label=f"Prediction Error for {measure_name}",
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)
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return _save_or_return(
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fig, basedir, dataset_name, measure_name, "delta" if stdevs is None else "stdev"
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)
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def plot_shift(
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method_names: list[str],
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prevs: np.ndarray,
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acc_errs: np.ndarray,
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*,
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counts: np.ndarray | None = None,
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measure_name="Vanilla Accuracy",
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dataset_name=None,
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basedir=None,
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) -> go.Figure:
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fig = go.Figure()
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x = prevs
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line_colors = _get_colors(len(method_names))
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if counts is None:
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counts = [None] * len(method_names)
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for name, delta, count in zip(method_names, acc_errs, counts):
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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=_cfg.mode,
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name=r_name,
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line=dict(color=self.hex_to_rgb(color), width=_cfg.lwidth),
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customdata=np.stack((count,), axis=-1),
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mode=MODE,
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name=name,
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line=dict(color=_hex_to_rgb(color), width=L_WIDTH),
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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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if count 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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def plot_fit_scores(
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self,
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train_prevs,
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scores,
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*,
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pos_class=1,
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title="default",
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x_label="prev.",
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y_label="position",
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legend=True,
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) -> go.Figure:
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fig = go.Figure()
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# x = train_prevs
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x = [str(tuple(bp)) for bp in train_prevs]
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fig.add_trace(
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go.Scatter(
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x=x,
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y=scores,
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mode="lines+markers",
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showlegend=False,
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),
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_update_layout(
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fig,
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x_label="Amount of Prior Probability Shift",
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y_label=f"Prediction Error for {measure_name}",
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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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return _save_or_return(fig, basedir, dataset_name, measure_name, "shift")
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