diff --git a/deepof/data.py b/deepof/data.py
index a0721256785720bfd372ad5447cbbdf993295d71..6c0f8642400736cb91d662e2012f33a2f088467d 100644
--- a/deepof/data.py
+++ b/deepof/data.py
@@ -17,13 +17,11 @@ Contains methods for generating training and test sets ready for model training.
import os
import warnings
from collections import defaultdict
-from multiprocessing import cpu_count
from typing import Dict, List, Tuple, Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
-import seaborn as sns
import tensorflow as tf
from joblib import delayed, Parallel, parallel_backend
from pkg_resources import resource_filename
@@ -913,7 +911,6 @@ class coordinates:
save_weights: bool = True,
reg_cat_clusters: bool = False,
reg_cluster_variance: bool = False,
- entropy_samples: int = 10000,
entropy_knn: int = 100,
input_type: str = False,
run: int = 0,
@@ -979,7 +976,6 @@ class coordinates:
save_weights=save_weights,
reg_cat_clusters=reg_cat_clusters,
reg_cluster_variance=reg_cluster_variance,
- entropy_samples=entropy_samples,
entropy_knn=entropy_knn,
input_type=input_type,
run=run,
diff --git a/deepof/model_utils.py b/deepof/model_utils.py
index 5667c0e89a31216828f5f546246a83322cef91da..82c5e3e7fbaf3a7cc195a7acda93a0784e0e34f0 100644
--- a/deepof/model_utils.py
+++ b/deepof/model_utils.py
@@ -8,16 +8,13 @@ Functions and general utilities for the deepof tensorflow models. See documentat
"""
-from itertools import combinations
+from functools import partial
from typing import Any, Tuple
import matplotlib.pyplot as plt
-import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
-from functools import partial
from tensorflow.keras import backend as K
-from tensorflow.keras.constraints import Constraint
from tensorflow.keras.layers import Layer
tfd = tfp.distributions
diff --git a/deepof/models.py b/deepof/models.py
index 2c27d987fdd9a7293f2cce920febf89c82a78bfb..8e08820c037f6e8466c4407f1eb801d88544c8db 100644
--- a/deepof/models.py
+++ b/deepof/models.py
@@ -8,18 +8,16 @@ deep autoencoder models for unsupervised pose detection
"""
-from typing import Any, Dict, Tuple
+from typing import Dict, Tuple
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras import Input, Model, Sequential
from tensorflow.keras.activations import softplus
-from tensorflow.keras.constraints import UnitNorm
from tensorflow.keras.initializers import he_uniform, random_uniform
from tensorflow.keras.layers import BatchNormalization, Bidirectional
from tensorflow.keras.layers import Dense, Dropout, GRU
-from tensorflow.keras.layers import RepeatVector, Reshape, TimeDistributed
-from tensorflow.keras.losses import Huber
+from tensorflow.keras.layers import RepeatVector, Reshape
from tensorflow.keras.optimizers import Nadam
import deepof.model_utils
@@ -28,6 +26,7 @@ tfb = tfp.bijectors
tfd = tfp.distributions
tfpl = tfp.layers
+
# noinspection PyDefaultArgument
class GMVAE:
""" Gaussian Mixture Variational Autoencoder for pose motif elucidation. """
@@ -204,8 +203,8 @@ class GMVAE:
for _ in range(self.dense_layers_per_branch)
]
Model_E4 = []
- for l in seq_E:
- Model_E4.append(l)
+ for layer in seq_E:
+ Model_E4.append(layer)
Model_E4.append(BatchNormalization())
# Decoder layers
@@ -219,8 +218,8 @@ class GMVAE:
for _ in range(self.dense_layers_per_branch)
]
Model_D1 = []
- for l in seq_D:
- Model_D1.append(l)
+ for layer in seq_D:
+ Model_D1.append(layer)
Model_D1.append(BatchNormalization())
Model_D2 = Dense(
@@ -501,7 +500,7 @@ class GMVAE:
x_decoded_var = tf.keras.activations.softplus(
Dense(tfpl.IndependentNormal.params_size(input_shape[2:]) // 2)(generator)
)
- x_decoded_var = tf.keras.layers.Lambda(lambda x: 1e-3 + x)(x_decoded_var)
+ x_decoded_var = tf.keras.layers.Lambda(lambda v: 1e-3 + v)(x_decoded_var)
x_decoded = tf.keras.layers.concatenate(
[x_decoded_mean, x_decoded_var], axis=-1
)
@@ -549,7 +548,7 @@ class GMVAE:
predictor
)
)
- x_predicted_var = tf.keras.layers.Lambda(lambda x: 1e-3 + x)(
+ x_predicted_var = tf.keras.layers.Lambda(lambda v: 1e-3 + v)(
x_predicted_var
)
x_decoded = tf.keras.layers.concatenate(
diff --git a/deepof/pose_utils.py b/deepof/pose_utils.py
index b8c2c8e890557987dbe870a91eae721e81f80dd0..9201dbd7d963fa0a064686342bb2d481cb549742 100644
--- a/deepof/pose_utils.py
+++ b/deepof/pose_utils.py
@@ -19,7 +19,6 @@ import numpy as np
import pandas as pd
import regex as re
import seaborn as sns
-import tensorflow as tf
from scipy import stats
import deepof.utils
@@ -205,7 +204,7 @@ def sniff_object(
tol_speed: float,
nose: str,
centered_data: bool = False,
- object: str = "arena",
+ s_object: str = "arena",
animal_id: str = "",
):
"""Returns True if the specified mouse is sniffing an object
@@ -232,7 +231,7 @@ def sniff_object(
if animal_id != "":
animal_id += "_"
- if object == "arena":
+ if s_object == "arena":
if arena_type == "circular":
center = np.zeros(2) if centered_data else np.array(arena[0])
axes = arena[1]
@@ -256,7 +255,7 @@ def sniff_object(
)
nosing = nosing_min & (~nosing_max)
- elif object == "partner":
+ elif s_object == "partner":
raise NotImplementedError
else:
@@ -425,6 +424,7 @@ def following_path(
< distance_dframe[tuple(sorted([follower + "_Nose", followed + "_Nose"]))]
)
+ # noinspection PyArgumentList
follow = np.all(
np.array([(dist_df.min(axis=1) < tol), right_orient1, right_orient2]),
axis=0,
@@ -778,7 +778,7 @@ def rule_based_tagging(
params["climb_tol"],
params["huddle_speed"],
_id + undercond + "Nose",
- object="arena",
+ s_object="arena",
animal_id=_id,
)
)
diff --git a/deepof/train_model.py b/deepof/train_model.py
index eb4d872eb9185a3e798b10f38da395bfcbf455dc..056c680a8f981954b0742c9febd29216eff180bf 100644
--- a/deepof/train_model.py
+++ b/deepof/train_model.py
@@ -11,6 +11,7 @@ usage: python -m examples.model_training -h
import argparse
import os
+
import deepof.data
import deepof.train_utils
import deepof.utils
@@ -422,13 +423,12 @@ else:
loss=loss,
loss_warmup=kl_wu,
warmup_mode=kl_annealing_mode,
- X_val=(X_val if X_val.shape != (0,) else None),
input_type=input_type,
cp=False,
- entropy_samples=entropy_samples,
entropy_knn=entropy_knn,
logparam=logparam,
outpath=output_path,
+ overlap_loss=overlap_loss,
run=run,
)
diff --git a/deepof/train_utils.py b/deepof/train_utils.py
index bb2d79992f06877d9e4f112df7aa59ed0530224e..72cf9dddf03205f925d40cba291c10871523ea78 100644
--- a/deepof/train_utils.py
+++ b/deepof/train_utils.py
@@ -17,7 +17,6 @@ import numpy as np
import tensorflow as tf
from kerastuner import BayesianOptimization, Hyperband, Objective
from kerastuner_tensorboard_logger import TensorBoardLogger
-from sklearn.metrics import roc_auc_score
from tensorboard.plugins.hparams import api as hp
import deepof.hypermodels
@@ -75,12 +74,10 @@ def get_callbacks(
loss: str,
loss_warmup: int = 0,
warmup_mode: str = "none",
- X_val: np.array = None,
input_type: str = False,
cp: bool = False,
reg_cat_clusters: bool = False,
reg_cluster_variance: bool = False,
- entropy_samples: int = 15000,
entropy_knn: int = 100,
logparam: dict = None,
outpath: str = ".",
@@ -294,7 +291,6 @@ def autoencoder_fitting(
save_weights: bool,
reg_cat_clusters: bool,
reg_cluster_variance: bool,
- entropy_samples: int,
entropy_knn: int,
input_type: str,
run: int = 0,
@@ -314,13 +310,6 @@ def autoencoder_fitting(
tf.data.experimental.AutoShardPolicy.DATA
)
- # Generate validation dataset for callback usage
- X_val_dataset = (
- tf.data.Dataset.from_tensor_slices(X_val)
- .with_options(options)
- .batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
- )
-
# Defines what to log on tensorboard (useful for trying out different models)
logparam = {
"encoding": encoding_size,
@@ -342,11 +331,9 @@ def autoencoder_fitting(
overlap_loss=overlap_loss,
warmup_mode=kl_annealing_mode,
input_type=input_type,
- X_val=(X_val_dataset if X_val.shape != (0,) else None),
cp=save_checkpoints,
reg_cat_clusters=reg_cat_clusters,
reg_cluster_variance=reg_cluster_variance,
- entropy_samples=entropy_samples,
entropy_knn=entropy_knn,
logparam=logparam,
outpath=output_path,
@@ -613,9 +600,8 @@ def tune_search(
.batch(batch_size, drop_remainder=True)
.shuffle(buffer_size=X_train.shape[0])
)
- val_dataset = (
- tf.data.Dataset.from_tensor_slices((Xvals, tuple(yvals)))
- .batch(batch_size, drop_remainder=True)
+ val_dataset = tf.data.Dataset.from_tensor_slices((Xvals, tuple(yvals))).batch(
+ batch_size, drop_remainder=True
)
# Convert data to tf.data.Dataset objects
diff --git a/tests/test_train_utils.py b/tests/test_train_utils.py
index 8438aaea3b70b3eac010b37140e5d8215e3749d9..c8e282b261b3d3adca8a7eced1a616c2daed08d5 100644
--- a/tests/test_train_utils.py
+++ b/tests/test_train_utils.py
@@ -8,17 +8,19 @@ Testing module for deepof.train_utils
"""
-from hypothesis import given, reproduce_failure
+import os
+
+import numpy as np
+import tensorflow as tf
from hypothesis import HealthCheck
+from hypothesis import given
from hypothesis import settings
from hypothesis import strategies as st
from hypothesis.extra.numpy import arrays
+
import deepof.data
import deepof.model_utils
import deepof.train_utils
-import numpy as np
-import os
-import tensorflow as tf
def test_load_treatments():
@@ -93,7 +95,6 @@ def test_autoencoder_fitting(
phenotype_prediction,
rule_based_prediction,
):
-
X_train = np.random.uniform(-1, 1, [20, 5, 6])
y_train = np.round(np.random.uniform(0, 1, [20, 1]))