482 lines
13 KiB
Python
482 lines
13 KiB
Python
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from __future__ import annotations
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import random
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from typing import TYPE_CHECKING
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from matplotlib import patches
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import matplotlib.lines as mlines
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import numpy as np
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from pandas.core.dtypes.missing import notna
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from pandas.io.formats.printing import pprint_thing
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from pandas.plotting._matplotlib.style import get_standard_colors
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from pandas.plotting._matplotlib.tools import (
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create_subplots,
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do_adjust_figure,
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maybe_adjust_figure,
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set_ticks_props,
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)
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if TYPE_CHECKING:
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from collections.abc import Hashable
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from matplotlib.axes import Axes
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from matplotlib.figure import Figure
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from pandas import (
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DataFrame,
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Index,
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Series,
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)
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def scatter_matrix(
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frame: DataFrame,
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alpha: float = 0.5,
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figsize: tuple[float, float] | None = None,
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ax=None,
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grid: bool = False,
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diagonal: str = "hist",
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marker: str = ".",
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density_kwds=None,
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hist_kwds=None,
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range_padding: float = 0.05,
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**kwds,
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):
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df = frame._get_numeric_data()
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n = df.columns.size
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naxes = n * n
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fig, axes = create_subplots(naxes=naxes, figsize=figsize, ax=ax, squeeze=False)
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# no gaps between subplots
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maybe_adjust_figure(fig, wspace=0, hspace=0)
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mask = notna(df)
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marker = _get_marker_compat(marker)
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hist_kwds = hist_kwds or {}
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density_kwds = density_kwds or {}
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# GH 14855
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kwds.setdefault("edgecolors", "none")
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boundaries_list = []
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for a in df.columns:
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values = df[a].values[mask[a].values]
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rmin_, rmax_ = np.min(values), np.max(values)
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rdelta_ext = (rmax_ - rmin_) * range_padding / 2
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boundaries_list.append((rmin_ - rdelta_ext, rmax_ + rdelta_ext))
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for i, a in enumerate(df.columns):
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for j, b in enumerate(df.columns):
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ax = axes[i, j]
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if i == j:
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values = df[a].values[mask[a].values]
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# Deal with the diagonal by drawing a histogram there.
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if diagonal == "hist":
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ax.hist(values, **hist_kwds)
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elif diagonal in ("kde", "density"):
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from scipy.stats import gaussian_kde
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y = values
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gkde = gaussian_kde(y)
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ind = np.linspace(y.min(), y.max(), 1000)
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ax.plot(ind, gkde.evaluate(ind), **density_kwds)
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ax.set_xlim(boundaries_list[i])
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else:
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common = (mask[a] & mask[b]).values
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ax.scatter(
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df[b][common], df[a][common], marker=marker, alpha=alpha, **kwds
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)
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ax.set_xlim(boundaries_list[j])
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ax.set_ylim(boundaries_list[i])
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ax.set_xlabel(b)
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ax.set_ylabel(a)
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if j != 0:
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ax.yaxis.set_visible(False)
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if i != n - 1:
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ax.xaxis.set_visible(False)
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if len(df.columns) > 1:
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lim1 = boundaries_list[0]
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locs = axes[0][1].yaxis.get_majorticklocs()
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locs = locs[(lim1[0] <= locs) & (locs <= lim1[1])]
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adj = (locs - lim1[0]) / (lim1[1] - lim1[0])
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lim0 = axes[0][0].get_ylim()
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adj = adj * (lim0[1] - lim0[0]) + lim0[0]
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axes[0][0].yaxis.set_ticks(adj)
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if np.all(locs == locs.astype(int)):
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# if all ticks are int
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locs = locs.astype(int)
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axes[0][0].yaxis.set_ticklabels(locs)
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set_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
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return axes
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def _get_marker_compat(marker):
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if marker not in mlines.lineMarkers:
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return "o"
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return marker
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def radviz(
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frame: DataFrame,
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class_column,
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ax: Axes | None = None,
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color=None,
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colormap=None,
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**kwds,
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) -> Axes:
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import matplotlib.pyplot as plt
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def normalize(series):
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a = min(series)
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b = max(series)
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return (series - a) / (b - a)
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n = len(frame)
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classes = frame[class_column].drop_duplicates()
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class_col = frame[class_column]
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df = frame.drop(class_column, axis=1).apply(normalize)
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if ax is None:
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ax = plt.gca()
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ax.set_xlim(-1, 1)
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ax.set_ylim(-1, 1)
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to_plot: dict[Hashable, list[list]] = {}
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colors = get_standard_colors(
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num_colors=len(classes), colormap=colormap, color_type="random", color=color
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)
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for kls in classes:
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to_plot[kls] = [[], []]
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m = len(frame.columns) - 1
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s = np.array(
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[(np.cos(t), np.sin(t)) for t in [2 * np.pi * (i / m) for i in range(m)]]
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)
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for i in range(n):
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row = df.iloc[i].values
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row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1)
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y = (s * row_).sum(axis=0) / row.sum()
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kls = class_col.iat[i]
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to_plot[kls][0].append(y[0])
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to_plot[kls][1].append(y[1])
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for i, kls in enumerate(classes):
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ax.scatter(
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to_plot[kls][0],
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to_plot[kls][1],
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color=colors[i],
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label=pprint_thing(kls),
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**kwds,
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)
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ax.legend()
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ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor="none"))
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for xy, name in zip(s, df.columns):
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ax.add_patch(patches.Circle(xy, radius=0.025, facecolor="gray"))
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if xy[0] < 0.0 and xy[1] < 0.0:
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ax.text(
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xy[0] - 0.025, xy[1] - 0.025, name, ha="right", va="top", size="small"
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)
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elif xy[0] < 0.0 <= xy[1]:
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ax.text(
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xy[0] - 0.025,
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xy[1] + 0.025,
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name,
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ha="right",
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va="bottom",
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size="small",
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)
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elif xy[1] < 0.0 <= xy[0]:
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ax.text(
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xy[0] + 0.025, xy[1] - 0.025, name, ha="left", va="top", size="small"
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)
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elif xy[0] >= 0.0 and xy[1] >= 0.0:
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ax.text(
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xy[0] + 0.025, xy[1] + 0.025, name, ha="left", va="bottom", size="small"
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)
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ax.axis("equal")
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return ax
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def andrews_curves(
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frame: DataFrame,
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class_column,
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ax: Axes | None = None,
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samples: int = 200,
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color=None,
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colormap=None,
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**kwds,
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) -> Axes:
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import matplotlib.pyplot as plt
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def function(amplitudes):
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def f(t):
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x1 = amplitudes[0]
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result = x1 / np.sqrt(2.0)
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# Take the rest of the coefficients and resize them
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# appropriately. Take a copy of amplitudes as otherwise numpy
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# deletes the element from amplitudes itself.
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coeffs = np.delete(np.copy(amplitudes), 0)
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coeffs = np.resize(coeffs, (int((coeffs.size + 1) / 2), 2))
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# Generate the harmonics and arguments for the sin and cos
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# functions.
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harmonics = np.arange(0, coeffs.shape[0]) + 1
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trig_args = np.outer(harmonics, t)
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result += np.sum(
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coeffs[:, 0, np.newaxis] * np.sin(trig_args)
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+ coeffs[:, 1, np.newaxis] * np.cos(trig_args),
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axis=0,
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)
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return result
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return f
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n = len(frame)
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class_col = frame[class_column]
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classes = frame[class_column].drop_duplicates()
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df = frame.drop(class_column, axis=1)
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t = np.linspace(-np.pi, np.pi, samples)
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used_legends: set[str] = set()
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color_values = get_standard_colors(
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num_colors=len(classes), colormap=colormap, color_type="random", color=color
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)
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colors = dict(zip(classes, color_values))
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if ax is None:
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ax = plt.gca()
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ax.set_xlim(-np.pi, np.pi)
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for i in range(n):
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row = df.iloc[i].values
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f = function(row)
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y = f(t)
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kls = class_col.iat[i]
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label = pprint_thing(kls)
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if label not in used_legends:
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used_legends.add(label)
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ax.plot(t, y, color=colors[kls], label=label, **kwds)
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else:
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ax.plot(t, y, color=colors[kls], **kwds)
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ax.legend(loc="upper right")
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ax.grid()
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return ax
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def bootstrap_plot(
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series: Series,
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fig: Figure | None = None,
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size: int = 50,
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samples: int = 500,
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**kwds,
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) -> Figure:
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import matplotlib.pyplot as plt
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# TODO: is the failure mentioned below still relevant?
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# random.sample(ndarray, int) fails on python 3.3, sigh
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data = list(series.values)
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samplings = [random.sample(data, size) for _ in range(samples)]
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means = np.array([np.mean(sampling) for sampling in samplings])
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medians = np.array([np.median(sampling) for sampling in samplings])
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midranges = np.array(
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[(min(sampling) + max(sampling)) * 0.5 for sampling in samplings]
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)
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if fig is None:
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fig = plt.figure()
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x = list(range(samples))
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axes = []
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ax1 = fig.add_subplot(2, 3, 1)
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ax1.set_xlabel("Sample")
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axes.append(ax1)
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ax1.plot(x, means, **kwds)
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ax2 = fig.add_subplot(2, 3, 2)
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ax2.set_xlabel("Sample")
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axes.append(ax2)
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ax2.plot(x, medians, **kwds)
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ax3 = fig.add_subplot(2, 3, 3)
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ax3.set_xlabel("Sample")
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axes.append(ax3)
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ax3.plot(x, midranges, **kwds)
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ax4 = fig.add_subplot(2, 3, 4)
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ax4.set_xlabel("Mean")
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axes.append(ax4)
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ax4.hist(means, **kwds)
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ax5 = fig.add_subplot(2, 3, 5)
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ax5.set_xlabel("Median")
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axes.append(ax5)
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ax5.hist(medians, **kwds)
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ax6 = fig.add_subplot(2, 3, 6)
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ax6.set_xlabel("Midrange")
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axes.append(ax6)
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ax6.hist(midranges, **kwds)
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for axis in axes:
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plt.setp(axis.get_xticklabels(), fontsize=8)
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plt.setp(axis.get_yticklabels(), fontsize=8)
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if do_adjust_figure(fig):
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plt.tight_layout()
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return fig
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def parallel_coordinates(
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frame: DataFrame,
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class_column,
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cols=None,
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ax: Axes | None = None,
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color=None,
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use_columns: bool = False,
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xticks=None,
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colormap=None,
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axvlines: bool = True,
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axvlines_kwds=None,
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sort_labels: bool = False,
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**kwds,
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) -> Axes:
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import matplotlib.pyplot as plt
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if axvlines_kwds is None:
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axvlines_kwds = {"linewidth": 1, "color": "black"}
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n = len(frame)
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classes = frame[class_column].drop_duplicates()
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class_col = frame[class_column]
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if cols is None:
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df = frame.drop(class_column, axis=1)
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else:
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df = frame[cols]
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used_legends: set[str] = set()
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ncols = len(df.columns)
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|
|
|
||
|
|
# determine values to use for xticks
|
||
|
|
x: list[int] | Index
|
||
|
|
if use_columns is True:
|
||
|
|
if not np.all(np.isreal(list(df.columns))):
|
||
|
|
raise ValueError("Columns must be numeric to be used as xticks")
|
||
|
|
x = df.columns
|
||
|
|
elif xticks is not None:
|
||
|
|
if not np.all(np.isreal(xticks)):
|
||
|
|
raise ValueError("xticks specified must be numeric")
|
||
|
|
if len(xticks) != ncols:
|
||
|
|
raise ValueError("Length of xticks must match number of columns")
|
||
|
|
x = xticks
|
||
|
|
else:
|
||
|
|
x = list(range(ncols))
|
||
|
|
|
||
|
|
if ax is None:
|
||
|
|
ax = plt.gca()
|
||
|
|
|
||
|
|
color_values = get_standard_colors(
|
||
|
|
num_colors=len(classes), colormap=colormap, color_type="random", color=color
|
||
|
|
)
|
||
|
|
|
||
|
|
if sort_labels:
|
||
|
|
classes = sorted(classes)
|
||
|
|
color_values = sorted(color_values)
|
||
|
|
colors = dict(zip(classes, color_values))
|
||
|
|
|
||
|
|
for i in range(n):
|
||
|
|
y = df.iloc[i].values
|
||
|
|
kls = class_col.iat[i]
|
||
|
|
label = pprint_thing(kls)
|
||
|
|
if label not in used_legends:
|
||
|
|
used_legends.add(label)
|
||
|
|
ax.plot(x, y, color=colors[kls], label=label, **kwds)
|
||
|
|
else:
|
||
|
|
ax.plot(x, y, color=colors[kls], **kwds)
|
||
|
|
|
||
|
|
if axvlines:
|
||
|
|
for i in x:
|
||
|
|
ax.axvline(i, **axvlines_kwds)
|
||
|
|
|
||
|
|
ax.set_xticks(x)
|
||
|
|
ax.set_xticklabels(df.columns)
|
||
|
|
ax.set_xlim(x[0], x[-1])
|
||
|
|
ax.legend(loc="upper right")
|
||
|
|
ax.grid()
|
||
|
|
return ax
|
||
|
|
|
||
|
|
|
||
|
|
def lag_plot(series: Series, lag: int = 1, ax: Axes | None = None, **kwds) -> Axes:
|
||
|
|
# workaround because `c='b'` is hardcoded in matplotlib's scatter method
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
|
||
|
|
kwds.setdefault("c", plt.rcParams["patch.facecolor"])
|
||
|
|
|
||
|
|
data = series.values
|
||
|
|
y1 = data[:-lag]
|
||
|
|
y2 = data[lag:]
|
||
|
|
if ax is None:
|
||
|
|
ax = plt.gca()
|
||
|
|
ax.set_xlabel("y(t)")
|
||
|
|
ax.set_ylabel(f"y(t + {lag})")
|
||
|
|
ax.scatter(y1, y2, **kwds)
|
||
|
|
return ax
|
||
|
|
|
||
|
|
|
||
|
|
def autocorrelation_plot(series: Series, ax: Axes | None = None, **kwds) -> Axes:
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
|
||
|
|
n = len(series)
|
||
|
|
data = np.asarray(series)
|
||
|
|
if ax is None:
|
||
|
|
ax = plt.gca()
|
||
|
|
ax.set_xlim(1, n)
|
||
|
|
ax.set_ylim(-1.0, 1.0)
|
||
|
|
mean = np.mean(data)
|
||
|
|
c0 = np.sum((data - mean) ** 2) / n
|
||
|
|
|
||
|
|
def r(h):
|
||
|
|
return ((data[: n - h] - mean) * (data[h:] - mean)).sum() / n / c0
|
||
|
|
|
||
|
|
x = np.arange(n) + 1
|
||
|
|
y = [r(loc) for loc in x]
|
||
|
|
z95 = 1.959963984540054
|
||
|
|
z99 = 2.5758293035489004
|
||
|
|
ax.axhline(y=z99 / np.sqrt(n), linestyle="--", color="grey")
|
||
|
|
ax.axhline(y=z95 / np.sqrt(n), color="grey")
|
||
|
|
ax.axhline(y=0.0, color="black")
|
||
|
|
ax.axhline(y=-z95 / np.sqrt(n), color="grey")
|
||
|
|
ax.axhline(y=-z99 / np.sqrt(n), linestyle="--", color="grey")
|
||
|
|
ax.set_xlabel("Lag")
|
||
|
|
ax.set_ylabel("Autocorrelation")
|
||
|
|
ax.plot(x, y, **kwds)
|
||
|
|
if "label" in kwds:
|
||
|
|
ax.legend()
|
||
|
|
ax.grid()
|
||
|
|
return ax
|
||
|
|
|
||
|
|
|
||
|
|
def unpack_single_str_list(keys):
|
||
|
|
# GH 42795
|
||
|
|
if isinstance(keys, list) and len(keys) == 1:
|
||
|
|
keys = keys[0]
|
||
|
|
return keys
|