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Update ModelBuilder to show regression lines #538

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Merged
merged 4 commits into from
Apr 6, 2023
Merged

Update ModelBuilder to show regression lines #538

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3a00c7a
Update ModelBuilder to show regression lines
BerylKanali Mar 31, 2023
c3445c2
Update to show correct regression lines
BerylKanali Apr 6, 2023
fdd7fd6
Pre-commit rerun
BerylKanali Apr 6, 2023
e7bfaa1
Delete linear_model_v1.nc
BerylKanali Apr 6, 2023
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Binary file added examples/howto/linear_model_v1.nc
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344 changes: 35 additions & 309 deletions examples/howto/model_builder.ipynb
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20 changes: 11 additions & 9 deletions examples/howto/model_builder.myst.md
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,9 @@ jupytext:
format_name: myst
format_version: 0.13
kernelspec:
display_name: pymc5
display_name: pymc-dev
language: python
name: pymc5
name: python3
---

# Using ModelBuilder class for deploying PyMC models
Expand Down Expand Up @@ -37,6 +37,7 @@ import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
import xarray as xr

from numpy.random import RandomState

Expand Down Expand Up @@ -269,14 +270,15 @@ After using the `predict()`, we can plot our data and see graphically how satisf

```{code-cell} ipython3
fig, ax = plt.subplots(figsize=(7, 7))
ax.plot(
x_pred,
pred_mean["y"],
"x",
label="predict",
)
posterior = az.extract(idata, num_samples=20)
x_plot = xr.DataArray(np.linspace(1, 2, 100))
y_plot = posterior["b"] * x_plot + posterior["a"]
Line2 = ax.plot(x_plot, y_plot.transpose(), color="C1")
Line1 = ax.plot(x_pred, pred_mean["y"], "x")
ax.set(title="Posterior predictive regression lines", xlabel="x", ylabel="y")
plt.legend(loc=0);
ax.legend(
handles=[Line1[0], Line2[0]], labels=["predicted average", "inferred regression line"], loc=0
);
```

```{code-cell} ipython3
Expand Down