92d352eb861df852fbfc25bae2ca3215fbfcf45d,examples/example_pipeline.py,,,#,21

Before Change

axes[0].scatter(x[n_train:], test[:preds.shape[0]], alpha=0.4, marker="x")
axes[0].fill_between(x[n_train:], conf_int[:, 0], conf_int[:, 1],
                     alpha=0.1, color="b")
axes[0].set_title("Actual test samples vs. forecasts")
axes[0].set_xlim((0, data.shape[0]))

// We can also call `update` directly on the pipeline object, which will update
// the intermittent transformers, where necessary:

After Change

axes[0].scatter(x0, in_sample_preds, alpha=0.4, marker="x")
axes[0].fill_between(x0, in_sample_confint[:, 0], in_sample_confint[:, 1],
                     alpha=0.1, color="b")
axes[0].set_title("Actual train samples vs. in-sample predictions")
axes[0].set_xlim((0, x0.shape[0]))

// Visualize actual + predicted
x1 = np.arange(n_train + preds.shape[0])

Instances