plot_curve

pymc_marketing.plot.plot_curve(curve, non_grid_names, n_samples=10, hdi_probs=None, random_seed=None, subplot_kwargs=None, sample_kwargs=None, hdi_kwargs=None, axes=None, same_axes=False, colors=None, legend=None, sel_to_string=None)

Plot HDI with samples of the curve across coords.

Parameters:

curvexr.DataArray

Curve to plot

non_grid_namesstr | set[str]

The names to exclude from the grid. HDI and samples both have defaults of hdi and chain, draw, respectively

n_samplesint, optional

Number of samples

hdi_probsfloat | list[float], optional

HDI probabilities. Defaults to None which uses arviz default for stats.ci_prob which is 94%

random_seednp.random.Generator, optional

Random number generator. Defaults to None which uses np.random.default_rng()

subplot_kwargsdict, optional

Additional kwargs to while creating the fig and axes

sample_kwargsdict, optional

Kwargs for the plot_samples() function

hdi_kwargsdict, optional

Kwargs for the plot_hdi() function

same_axesbool

If all of the plots are on the same axis

colorsIterable[str], optional

Colors for the plots

legendbool, optional

If to include a legend. Defaults to True if same_axes

sel_to_stringCallable[[Selection], str], optional

Function to convert selection to a string. Defaults to “, “.join(f”{key}={value}” for key, value in sel.items())

Returns:

tuple[plt.Figure, npt.NDArray[plt.Axes]]

Figure and the axes

Examples

Plot prior for arbitrary Deterministic in PyMC model

import numpy as np
import pandas as pd

import pymc as pm

import matplotlib.pyplot as plt

from pymc_marketing.plot import plot_curve

seed = sum(map(ord, "Arbitrary curve"))
rng = np.random.default_rng(seed)

dates = pd.date_range("2024-01-01", periods=52, freq="W")

coords = {"date": dates, "product": ["A", "B"]}
with pm.Model(coords=coords) as model:
    data = pm.Normal(
        "data",
        mu=[-0.5, 0.5],
        sigma=1,
        dims=("date", "product"),
    )
    cumsum = pm.Deterministic(
        "cumsum",
        data.cumsum(axis=0),
        dims=("date", "product"),
    )
    idata = pm.sample_prior_predictive(random_seed=rng)

curve = idata.prior["cumsum"]

fig, axes = plot_curve(
    curve,
    "date",
    subplot_kwargs={"figsize": (15, 5)},
    random_seed=rng,
)
plt.show()

(Source code, png, hires.png, pdf)

Choose the HDI intervals and number of samples

fig, axes = plot_curve(
    curve,
    "date",
    n_samples=3,
    hdi_probs=[0.5, 0.95],
    random_seed=rng,
)
fig.suptitle("Same data but fewer lines and more HDIs")
plt.show()

(Source code)

Plot same curve on same axes with custom colors

colors = ["red", "blue"]
fig, axes = plot_curve(
    curve,
    "date",
    same_axes=True,
    colors=colors,
    random_seed=rng,
)
axes[0].set(title="Same data but on same axes and custom colors")
plt.show()

(Source code)