trying to annotate new DLC-ed files with prototypes from model

[kipkeller]

In order to label new videos with the discovered prototypes, I am trying to do this:
Recommended Approach: Train a LISBET Classifier on Prototypes

Prepare a Labeled Dataset with Prototype Annotations:
Using the example Python snippet to patch the MY NEW DLC-labeled dataset (in directory: A1Suppression_0-100) with prototype labels (that are in directory: prototypes), I have tried to adapt the suggested python code:

import numpy as np
import pandas as pd
import xarray as xr

from lisbet.datasets import dump_records, load_records

def extract_labels(csv_path):
df = pd.read_csv(csv_path, index_col=0)

# Rows that already have at least one positive label
covered = df.eq(1).any(axis=1)

# Create / update the fallback class
df["Other"] = (~covered).astype(int)

# Keep only the first 1 in every row
first_mask = df.eq(1).cumsum(axis=1).eq(1)

# Apply the mask – everything that isn’t the first 1 becomes 0
df &= first_mask

return df.values

def patch_dataset():
records = load_records(
data_format="DLC",
data_path="A1suppression_0-100",
data_scale="None",
data_filter="train",
)["main_records"]

patched_records = []
for key, data in records:
    posetracks = data["posetracks"].unstack("features")

    labels = extract_labels(f"prototypes\{key}\machineAnnotation_hmmbest_6_32.csv")

    assert labels.shape[0] == posetracks.sizes["time"]

    # Convert to xarray Dataset
    annotations = xr.Dataset(
        data_vars=dict(
            label=(
                ["time", "behaviors", "annotators"],
                labels[..., np.newaxis],
            )
        ),
        coords=dict(
            time=posetracks.time,
            behaviors=[f"motif_{motif_id}" for motif_id in range(labels.shape[1])],
            annotators=["LISBET"],
        ),
        attrs=dict(
            source_software=posetracks.source_software,
            ds_type="annotations",
            fps=posetracks.fps,
            time_unit=posetracks.time_unit,
        ),
    )

    patched_record = (
        key,
        {"posetracks": posetracks, "annotations": annotations},
    )

    patched_records.append(patched_record)

dump_records("datasets\proto_A1suppression_0-100", patched_records)

if name == "main":
patch_dataset()

The first error that comes up is: cannot import name 'dump_records' from 'lisbet.datasets'
But I am sure more will follow - because I am not confident I have modified the Python code correctly

Once this is accomplished, I think the next steps are:

to train the classifier:

betman train_model ^
--run_id=proto_classifier ^
--data_format=DLC ^
--data_scale="1x1" ^
--data_filter=train ^
--learning_rate=1e-4 ^
--epochs=10 ^
--load_backbone_weights=models\A1supp01-embedder\weights\weights_last.pt ^
--freeze_backbone_weights ^
--save_history ^
-v ^
dataset\proto_A1supp

and, finally, annotate the new data:

betman annotate_behavior ^
--data_format=DLC ^
--data_scale="None" ^
--data_filter=test ^
-v ^
A1suppression\task1_classic_classification ^
models\proto_classifier\model_config.yml ^
models\proto_classifier\weights\weights_last.pt

There is a lot here I don't understand. Any help would be appreciated.