Commit 39be1caa authored by lucas_miranda's avatar lucas_miranda
Browse files

Replaced for loop with vectorised mapping on ClusterOverlap regularization layer

parent dae6a811
Pipeline #102635 passed with stages
in 18 minutes and 44 seconds
......@@ -495,65 +495,67 @@ class ClusterOverlap(Layer):
config.update({"samples": self.samples})
return config
def call(self, inputs, **kwargs):
def call(self, inputs, training=None, **kwargs):
"""Updates Layer's call method"""
encodings, categorical = inputs[0], inputs[1]
hard_groups = tf.math.argmax(categorical, axis=1)
max_groups = tf.reduce_max(categorical, axis=1)
if training:
get_local_neighbourhood_entropy = partial(
get_neighbourhood_entropy,
tensor=encodings,
clusters=hard_groups,
k=self.k,
)
hard_groups = tf.math.argmax(categorical, axis=1)
max_groups = tf.reduce_max(categorical, axis=1)
purity_vector = tf.map_fn(
get_local_neighbourhood_entropy,
tf.constant(list(range(self.batch_size))),
dtype=tf.dtypes.float32,
)
get_local_neighbourhood_entropy = partial(
get_neighbourhood_entropy,
tensor=encodings,
clusters=hard_groups,
k=self.k,
)
### CANDIDATE FOR REMOVAL. EXPLORE HOW USEFUL THIS REALLY IS ###
neighbourhood_entropy = purity_vector * max_groups
number_of_clusters = tf.cast(
tf.shape(
tf.unique(
tf.reshape(
tf.gather(
tf.cast(hard_groups, tf.dtypes.float32),
tf.where(max_groups >= self.min_confidence),
batch_dims=0,
purity_vector = tf.map_fn(
get_local_neighbourhood_entropy,
tf.constant(list(range(self.batch_size))),
dtype=tf.dtypes.float32,
)
### CANDIDATE FOR REMOVAL. EXPLORE HOW USEFUL THIS REALLY IS ###
neighbourhood_entropy = purity_vector * max_groups
number_of_clusters = tf.cast(
tf.shape(
tf.unique(
tf.reshape(
tf.gather(
tf.cast(hard_groups, tf.dtypes.float32),
tf.where(max_groups >= self.min_confidence),
batch_dims=0,
),
[-1],
),
[-1],
),
)[0],
)[0],
)[0],
tf.dtypes.float32,
)
tf.dtypes.float32,
)
self.add_metric(
number_of_clusters,
name="number_of_populated_clusters",
)
self.add_metric(
number_of_clusters,
name="number_of_populated_clusters",
)
self.add_metric(
max_groups,
aggregation="mean",
name="average_confidence_in_selected_cluster",
)
self.add_metric(
max_groups,
aggregation="mean",
name="average_confidence_in_selected_cluster",
)
self.add_metric(
neighbourhood_entropy, aggregation="mean", name="neighbourhood_entropy"
)
self.add_metric(
neighbourhood_entropy, aggregation="mean", name="neighbourhood_entropy"
)
if self.loss_weight:
# minimize local entropy
self.add_loss(self.loss_weight * tf.reduce_mean(neighbourhood_entropy))
# maximize number of clusters
# self.add_loss(-self.loss_weight * tf.reduce_mean(number_of_clusters))
if self.loss_weight:
# minimize local entropy
self.add_loss(self.loss_weight * tf.reduce_mean(neighbourhood_entropy))
# maximize number of clusters
# self.add_loss(-self.loss_weight * tf.reduce_mean(number_of_clusters))
return encodings
%% Cell type:code id: tags:
 
``` python
%load_ext autoreload
%autoreload 2
```
 
%% Cell type:code id: tags:
 
``` python
import warnings
 
warnings.filterwarnings("ignore")
```
 
%% Cell type:markdown id: tags:
 
# deepOF model evaluation
 
%% Cell type:markdown id: tags:
 
Given a dataset and a trained model, this notebook allows the user to
 
* Load and inspect the different models (encoder, decoder, grouper, gmvaep)
* Visualize reconstruction quality for a given model
* Visualize a static latent space
* Visualize trajectories on the latent space for a given video
* sample from the latent space distributions and generate video clips showcasing generated data
 
%% Cell type:code id: tags:
 
``` python
import os
 
os.chdir(os.path.dirname("../"))
```
 
%% Cell type:code id: tags:
 
``` python
import deepof.data
import deepof.utils
import numpy as np
import pandas as pd
import re
import tensorflow as tf
from collections import Counter
from sklearn.preprocessing import StandardScaler
 
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import umap
 
from ipywidgets import interactive, interact, HBox, Layout, VBox
from IPython import display
from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt
import seaborn as sns
 
from ipywidgets import interact
```
 
%% Cell type:markdown id: tags:
 
### 1. Define and run project
 
%% Cell type:code id: tags:
 
``` python
path = os.path.join("..", "..", "Desktop", "deepof-data", "deepof_single_topview")
trained_network = os.path.join("..", "..", "Desktop", "deepof_trained_weights_280521", "var_annealing")
exclude_bodyparts = tuple([""])
window_size = 22
batch_size = 32
```
 
%% Cell type:code id: tags:
 
``` python
%%time
proj = deepof.data.project(
path=path, smooth_alpha=0.999, exclude_bodyparts=exclude_bodyparts, arena_dims=[380],
)
```
 
%% Output
 
CPU times: user 41.9 s, sys: 3.25 s, total: 45.2 s
Wall time: 37.5 s
 
%% Cell type:code id: tags:
 
``` python
%%time
proj = proj.run(verbose=True)
print(proj)
```
 
%% Output
 
Loading trajectories...
Smoothing trajectories...
Interpolating outliers...
Iterative imputation of ocluded bodyparts...
Computing distances...
Computing angles...
Done!
deepof analysis of 166 videos
CPU times: user 9min 14s, sys: 11.8 s, total: 9min 26s
Wall time: 2min 3s
 
%% Cell type:markdown id: tags:
 
### 2. Load pretrained deepof model
 
%% Cell type:code id: tags:
 
``` python
coords = proj.get_coords(center="Center", align="Spine_1", align_inplace=True)
data_prep = coords.preprocess(test_videos=0, window_step=1, window_size=window_size, shuffle=False)[
0
]
```
 
%% Cell type:code id: tags:
 
``` python
[i for i in os.listdir(trained_network) if i.endswith("h5")]
```
 
%% Output
 
['GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=2_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=2_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=2_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
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'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5']
 
%% Cell type:code id: tags:
 
``` python
deepof_weights = [i for i in os.listdir(trained_network) if i.endswith("h5")][1]
deepof_weights
```
 
%% Output
 
'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5'
 
%% Cell type:code id: tags:
 
``` python
# Set model parameters
encoding = int(re.findall("encoding=(\d+)_", deepof_weights)[0])
k = int(re.findall("k=(\d+)_", deepof_weights)[0])
loss = re.findall("loss=(.+?)_", deepof_weights)[0]
NextSeqPred = float(re.findall("NextSeqPred=(.+?)_", deepof_weights)[0])
PhenoPred = float(re.findall("PhenoPred=(.+?)_", deepof_weights)[0])
RuleBasedPred = float(re.findall("RuleBasedPred=(.+?)_", deepof_weights)[0])
```
 
%% Cell type:code id: tags:
 
``` python
(
encoder,
decoder,
grouper,
gmvaep,
prior,
posterior,
) = deepof.models.GMVAE(
loss=loss,
number_of_components=k,
compile_model=True,
batch_size=batch_size,
encoding=encoding,
next_sequence_prediction=NextSeqPred,
phenotype_prediction=PhenoPred,
rule_based_prediction=RuleBasedPred,
).build(
data_prep.shape
)
gmvaep.load_weights(os.path.join(trained_network, deepof_weights))
```
 
%% Output
Tensor("cluster_overlap_44/Shape:0", shape=(2,), dtype=int32)
%% Cell type:code id: tags:
 
``` python
gmvaep.predict(data_prep[:2048])
```
 
%% Output
 
array([[[-2.20274441e-02, -7.49340594e-01, -1.00606358e+00, ...,
5.97163141e-01, 7.87078515e-02, 3.10142875e-01],
[-1.86636951e-02, -7.35167682e-01, -1.04070246e+00, ...,
5.36489308e-01, 6.65854439e-02, 2.50531852e-01],
[-1.82860009e-02, -7.65824318e-01, -1.08549905e+00, ...,
5.57325482e-01, 3.26592624e-02, 2.32314825e-01],
Tensor("SEQ_2_SEQ_GMVAE/cluster_overlap_44/Shape:0", shape=(2,), dtype=int32)
array([[[-2.33654119e-02, -7.18961537e-01, -1.04058743e+00, ...,
6.04326367e-01, -8.63653272e-02, 3.18874061e-01],
[-2.12193187e-02, -7.04667985e-01, -1.08008337e+00, ...,
5.50029635e-01, -9.52750891e-02, 2.61153668e-01],
[-2.03663222e-02, -7.36600041e-01, -1.12794685e+00, ...,
5.78937709e-01, -1.28708437e-01, 2.42814869e-01],
...,
[-6.10359153e-03, -7.89098859e-01, -1.30699337e+00, ...,
6.69562757e-01, -4.62342173e-01, 3.77381265e-01],
[-4.65778168e-03, -8.11704040e-01, -1.28605306e+00, ...,
7.13757634e-01, -4.67065245e-01, 4.34089184e-01],
[-6.71512820e-03, -8.53176653e-01, -1.29082215e+00, ...,
7.71026969e-01, -5.02031207e-01, 4.69406664e-01]],
[-9.44020599e-03, -7.42647946e-01, -1.30498624e+00, ...,
6.58422947e-01, -5.47523737e-01, 3.99996579e-01],
[-7.29610212e-03, -7.65350342e-01, -1.27779627e+00, ...,
6.95867777e-01, -5.41299820e-01, 4.53622282e-01],
[-9.27564688e-03, -8.18071365e-01, -1.28233838e+00, ...,
7.52779126e-01, -5.64222336e-01, 4.90554869e-01]],
[[-2.22376399e-02, -7.70553827e-01, -9.43590641e-01, ...,
5.78278065e-01, 2.24873498e-01, 3.14347565e-01],
[-1.82316527e-02, -7.55171597e-01, -9.75605845e-01, ...,
5.09953022e-01, 2.11454228e-01, 2.45110869e-01],
[-1.79231763e-02, -7.78624117e-01, -1.02039325e+00, ...,
5.19503474e-01, 1.74318790e-01, 2.36997604e-01],
[[-2.22253725e-02, -8.41416478e-01, -1.12681007e+00, ...,
6.94921017e-01, -3.09668630e-01, 4.00050104e-01],
[-2.30817143e-02, -8.39012742e-01, -1.17822659e+00, ...,
6.44349039e-01, -3.31067830e-01, 3.31252694e-01],
[-2.34456696e-02, -8.76340389e-01, -1.22851527e+00, ...,
6.79111958e-01, -3.67583066e-01, 3.47556919e-01],
...,
[-3.98765504e-03, -7.79918849e-01, -1.20278263e+00, ...,
5.98293304e-01, -2.54329056e-01, 3.15500557e-01],
[-3.29605211e-03, -8.03981423e-01, -1.18459630e+00, ...,
6.46142304e-01, -2.72717953e-01, 3.54292005e-01],
[-4.49736090e-03, -8.39645684e-01, -1.18976438e+00, ...,
7.12202549e-01, -3.01250070e-01, 3.82732034e-01]],
[-1.15935905e-02, -8.33023310e-01, -1.16872275e+00, ...,
6.26452863e-01, -3.46652746e-01, 3.66459996e-01],
[-7.99606554e-03, -8.50840628e-01, -1.14563727e+00, ...,
6.63835168e-01, -3.26457262e-01, 3.89723182e-01],
[-8.63283966e-03, -9.02453482e-01, -1.13931096e+00, ...,
7.31720626e-01, -3.24028820e-01, 4.03006196e-01]],
[[-2.36156955e-02, -7.91016340e-01, -1.11879790e+00, ...,
7.13154435e-01, -3.65756422e-01, 4.06932175e-01],
[-2.54947953e-02, -7.88331568e-01, -1.17535889e+00, ...,
6.70483768e-01, -3.85737628e-01, 3.32547605e-01],
[-2.56239660e-02, -8.24208558e-01, -1.23989546e+00, ...,
7.14474797e-01, -4.28516060e-01, 3.39852810e-01],
[[-2.67037526e-02, -7.14372754e-01, -1.01690137e+00, ...,
5.67512035e-01, -7.96585530e-02, 2.87923396e-01],
[-2.66271308e-02, -7.22530246e-01, -1.08599091e+00, ...,
5.39661050e-01, -1.06031761e-01, 2.33209401e-01],
[-2.72963755e-02, -7.53687024e-01, -1.14411652e+00, ...,
5.67179024e-01, -1.51002675e-01, 2.36247778e-01],
...,
[-1.38379335e-02, -7.91984320e-01, -1.10805249e+00, ...,
6.95663571e-01, -3.35209459e-01, 3.72855306e-01],
[-8.09174031e-03, -8.20150554e-01, -1.07291603e+00, ...,
7.31382430e-01, -3.07114869e-01, 3.89692128e-01],
[-7.56193697e-03, -8.81913245e-01, -1.08008969e+00, ...,
7.96405435e-01, -2.96295941e-01, 4.00389731e-01]],
[-9.20080859e-03, -7.82252252e-01, -9.62321341e-01, ...,
6.10508740e-01, -6.60327971e-02, 2.83200055e-01],
[-3.12909577e-03, -7.93930829e-01, -9.27874446e-01, ...,
6.45921230e-01, -4.59667295e-02, 2.94262946e-01],
[-2.58489698e-03, -8.41321766e-01, -9.23104703e-01, ...,
7.15546906e-01, -3.20333168e-02, 2.93513715e-01]],
...,
[[ 1.27419364e-03, -1.70991755e+00, -7.36668050e-01, ...,
1.14400041e+00, 1.80199802e-01, 4.03101444e-01],
[ 9.55599174e-03, -1.77840245e+00, -7.31509030e-01, ...,
1.10214877e+00, 1.92172885e-01, 3.62863183e-01],
[ 6.99162669e-03, -1.84455645e+00, -6.68665648e-01, ...,
1.12744784e+00, 1.78093016e-01, 3.79255176e-01],
[[ 1.11031905e-03, -1.75031507e+00, -8.52379441e-01, ...,
1.21988916e+00, 1.03995144e-01, 4.59409773e-01],
[ 8.80013034e-03, -1.83671558e+00, -8.69083345e-01, ...,
1.19457781e+00, 1.23950966e-01, 4.14475203e-01],
[ 6.59639947e-03, -1.91086435e+00, -8.22446942e-01, ...,
1.22027707e+00, 1.20491169e-01, 4.16057706e-01],
...,
[ 1.46682467e-02, -1.67971981e+00, -5.29618204e-01, ...,
1.07131672e+00, 2.70632327e-01, 3.96659851e-01],
[ 1.90188847e-02, -1.64219010e+00, -5.86603642e-01, ...,
1.06212115e+00, 2.97838628e-01, 4.20529246e-01],
[ 1.77764259e-02, -1.60712445e+00, -5.87549746e-01, ...,
1.08539033e+00, 3.32792282e-01, 4.31078553e-01]],
[ 1.49972383e-02, -1.74408889e+00, -5.97882569e-01, ...,
1.21293759e+00, 4.05578047e-01, 5.18048167e-01],
[ 1.90434121e-02, -1.69634783e+00, -6.43523514e-01, ...,
1.19492638e+00, 4.35988188e-01, 5.23940802e-01],
[ 1.63217653e-02, -1.64555275e+00, -6.33343041e-01, ...,
1.19980395e+00, 4.42245930e-01, 5.15006006e-01]],
[[ 5.71209192e-03, -1.57354665e+00, -8.53239119e-01, ...,
1.10862482e+00, 1.20560825e-01, 4.82771069e-01],
[ 1.34701636e-02, -1.63361990e+00, -8.83263171e-01, ...,
1.09039259e+00, 1.39529616e-01, 4.46821988e-01],
[ 1.27676763e-02, -1.69738317e+00, -8.67947996e-01, ...,
1.11015606e+00, 1.38534516e-01, 4.43767011e-01],
[[ 2.67705321e-03, -1.70834327e+00, -7.96462834e-01, ...,
1.17186177e+00, 1.32418081e-01, 4.49965090e-01],
[ 1.02437194e-02, -1.78479028e+00, -8.03244650e-01, ...,