Machine learning for animal behavior analysis
Project description
BehaveML
Machine learning for animal behavior.
Interprets pose-tracking files (currently only from DLC), behavior annotations (currently only from BORIS) to train a behavior classifier.
Installation
pip install behaveml
Can install optional extras with:
pip install numpy, cython
pip install behaveml[all]
This includes matplotlib, keras, and Linderman lab's state-space model package, ssm. Note that installing ssm requires cython and numpy for the build, so must be already present in the environment.
Quickstart
Import
from glob import glob
from behaveml import VideosetDataFrame, clone_metadata
from behaveml import compute_dl_probability_features, compute_mars_features
Gather the DLC and BORIS tracking and annotation files
tracking_files = glob('./tests/data/dlc/*.csv')
boris_files = glob('./tests/data/boris/*.csv')
Setup some parameters
frame_width = 20 # (float) length of entire horizontal shot
frame_width_units = 'in' # (str) units frame_width is given in
fps = 30 # (int) frames per second
resolution = (1200, 1600) # (tuple) HxW in pixels
Create a parameter object and video dataset
metadata = clone_metadata(tracking_files,
label_files = boris_files,
frame_width = frame_width,
fps = fps,
frame_width_units = frame_width_units,
resolution = resolution)
animal_renamer = {'adult': 'resident', 'juvenile':'intruder'}
dataset = VideosetDataFrame(metadata, animal_renamer=animal_renamer)
Now create features on this dataset
dataset.add_features(compute_dl_probability_features,
featureset_name = '1dcnn',
add_to_features = True)
dataset.add_features(compute_mars_features,
featureset_name = 'MARS',
add_to_features = True)
Now access a features table, labels, and groups for learning with dataset.features, dataset.labels, dataset.groups
.
For example:
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import accuracy_score
model = RandomForestClassifier()
predictions = cross_val_predict(model, dataset.features, dataset.labels, dataset.groups)
score = accuracy_score(dataset.labels, predictions)
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