Creating Custom Line Plots With Weights & Biases

Method: wandb.plot.line()

data = [[x, y] for (x, y) in zip(x_values, y_values)]
table = wandb.Table(data=data, columns = ["x", "y"])
wandb.log({"my_custom_plot_id" : wandb.plot.line(table,
                                 "x", "y", title="Custom Y vs X Line Plot")})

Basic Usage Example

data = [[x, y] for (x, y) in zip(recall_micro, precision_micro)]
table = wandb.Table(data=data, columns = ["recall_micro", "precision_micro"])
wandb.log({"my_lineplot_id" : wandb.plot.line(table, "recall_micro", 
           "precision_micro", stroke=None, title="Average Precision")})

Create a data object: collect your points as a 2D list/array, where each row is a point and each column is a dimension. This line plot assumes two dimensions / two columns, but you could pass in more data and customize the plot further if you wish. You could also use the optional stroke= argument to wandb.plot.line() to pass in a third field corresponding to the line stroke type (solid, dash, dot...) or color.
Pass data to a wandb.Table() object in which you name the columns in order so you can refer to them later
Pass the table object and the same x and y column names in order to wandb.plot.line() with an optional title, which will create your custom plot under the key my_lineplot_id. To visualize multiple runs on the same plot, keep this plot key constant. Note that the table itself will also be logged in the "Media" section of your workspace, under my_lineplot_id_table.

Customized Usage

rename the axis titles for clarity: add "title" : "Your Title" to the x and y fields under encoding
change the color spectrum to reflect the increase in training data/epochs as the curves go from purple to yellow:

        "color": {
          "type": "nominal",
          "field" : "name",
          "scale" : {"scheme" : "plasma"}
        }

P.S. Computing Average Precision for Multi-Class Models

a model's predicted scores (val_predictions) on a set of examples
the corresponding ground truth labels (ground_truth) for those examples

from sklearn.metrics import precision_recall_curve
from sklearn.preprocessing import label_binarize

# generate binary correctness labels across classes
binary_ground_truth = label_binarize(ground_truth,
                                         classes=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

# compute a PR curve with sklearn like you normally would
precision_micro, recall_micro, _ = precision_recall_curve(binary_ground_truth.ravel(),
                                                 val_predictions.ravel())

# now you can log these values to a custom chart!