Commit 1ebd5f15 authored by lucas_miranda's avatar lucas_miranda
Browse files

Increased default dimensionality of latent space

parent 69196db8
...@@ -133,9 +133,9 @@ class GMVAE: ...@@ -133,9 +133,9 @@ class GMVAE:
defaults = { defaults = {
"bidirectional_merge": "concat", "bidirectional_merge": "concat",
"clipvalue": 1.0, "clipvalue": 0.75,
"dense_activation": "relu", "dense_activation": "relu",
"dense_layers_per_branch": 3, "dense_layers_per_branch": 1,
"dropout_rate": 0.1, "dropout_rate": 0.1,
"learning_rate": 1e-4, "learning_rate": 1e-4,
"units_conv": 64, "units_conv": 64,
......
...@@ -19,11 +19,11 @@ warmup_epochs = [15] ...@@ -19,11 +19,11 @@ warmup_epochs = [15]
warmup_mode = ["sigmoid"] warmup_mode = ["sigmoid"]
losses = ["ELBO"] # , "MMD", "ELBO+MMD"] losses = ["ELBO"] # , "MMD", "ELBO+MMD"]
overlap_loss = [0.1, 0.2, 0.5, 0.75, 1.] overlap_loss = [0.1, 0.2, 0.5, 0.75, 1.]
encodings = [32] # [2, 4, 6, 8, 10, 12, 14, 16] encodings = [16] # [2, 4, 6, 8, 10, 12, 14, 16]
cluster_numbers = [15] # [1, 5, 10, 15, 20, 25] cluster_numbers = [15] # [1, 5, 10, 15, 20, 25]
latent_reg = ["variance"] # ["none", "categorical", "variance", "categorical+variance"] latent_reg = ["variance"] # ["none", "categorical", "variance", "categorical+variance"]
entropy_knn = [10] entropy_knn = [10]
next_sequence_pred_weights = [0.15] next_sequence_pred_weights = [0.0]
phenotype_pred_weights = [0.0] phenotype_pred_weights = [0.0]
rule_based_pred_weights = [0.0] rule_based_pred_weights = [0.0]
window_lengths = [22] # range(11,56,11) window_lengths = [22] # range(11,56,11)
......
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
%load_ext autoreload %load_ext autoreload
%autoreload 2 %autoreload 2
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
import warnings import warnings
   
warnings.filterwarnings("ignore") warnings.filterwarnings("ignore")
``` ```
   
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
   
# deepOF model evaluation # deepOF model evaluation
   
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
   
Given a dataset and a trained model, this notebook allows the user to Given a dataset and a trained model, this notebook allows the user to
   
* Load and inspect the different models (encoder, decoder, grouper, gmvaep) * Load and inspect the different models (encoder, decoder, grouper, gmvaep)
* Visualize reconstruction quality for a given model * Visualize reconstruction quality for a given model
* Visualize a static latent space * Visualize a static latent space
* Visualize trajectories on the latent space for a given video * Visualize trajectories on the latent space for a given video
* sample from the latent space distributions and generate video clips showcasing generated data * sample from the latent space distributions and generate video clips showcasing generated data
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
import os import os
   
os.chdir(os.path.dirname("../")) os.chdir(os.path.dirname("../"))
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
import deepof.data import deepof.data
import deepof.utils import deepof.utils
import numpy as np import numpy as np
import pandas as pd import pandas as pd
import re import re
import tensorflow as tf import tensorflow as tf
from collections import Counter from collections import Counter
from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import StandardScaler
   
from sklearn.manifold import TSNE from sklearn.manifold import TSNE
from sklearn.decomposition import PCA from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import umap import umap
   
from ipywidgets import interactive, interact, HBox, Layout, VBox from ipywidgets import interactive, interact, HBox, Layout, VBox
from IPython import display from IPython import display
from matplotlib.animation import FuncAnimation from matplotlib.animation import FuncAnimation
from mpl_toolkits.mplot3d import Axes3D from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import seaborn as sns import seaborn as sns
import tqdm.notebook as tqdm import tqdm.notebook as tqdm
   
from ipywidgets import interact from ipywidgets import interact
``` ```
   
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
   
### 1. Define and run project ### 1. Define and run project
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
path = os.path.join("..", "..", "Desktop", "deepof-data", "deepof_single_topview") path = os.path.join("..", "..", "Desktop", "deepof-data", "deepof_single_topview")
trained_network = os.path.join("..", "..", "Desktop", "deepof_trained_weights_280521", "var_annealing") trained_network = os.path.join("..", "..", "Desktop", "deepof_trained_weights_280521", "var_annealing")
exclude_bodyparts = tuple([""]) exclude_bodyparts = tuple([""])
window_size = 22 window_size = 22
batch_size = 32 batch_size = 32
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
%%time %%time
proj = deepof.data.project( proj = deepof.data.project(
path=path, smooth_alpha=0.999, exclude_bodyparts=exclude_bodyparts, arena_dims=[380], path=path, smooth_alpha=0.999, exclude_bodyparts=exclude_bodyparts, arena_dims=[380],
) )
``` ```
   
%% Output %% Output
   
CPU times: user 47.3 s, sys: 3.22 s, total: 50.5 s CPU times: user 47.3 s, sys: 3.22 s, total: 50.5 s
Wall time: 42.8 s Wall time: 42.8 s
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
%%time %%time
proj = proj.run(verbose=True) proj = proj.run(verbose=True)
print(proj) print(proj)
``` ```
   
%% Output %% Output
   
Loading trajectories... Loading trajectories...
Smoothing trajectories... Smoothing trajectories...
Interpolating outliers... Interpolating outliers...
Iterative imputation of ocluded bodyparts... Iterative imputation of ocluded bodyparts...
Computing distances... Computing distances...
Computing angles... Computing angles...
Done! Done!
deepof analysis of 166 videos deepof analysis of 166 videos
CPU times: user 16min 22s, sys: 26.6 s, total: 16min 49s CPU times: user 16min 22s, sys: 26.6 s, total: 16min 49s
Wall time: 3min 28s Wall time: 3min 28s
   
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
   
### 2. Load pretrained deepof model ### 2. Load pretrained deepof model
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
coords = proj.get_coords(center="Center", align="Spine_1", align_inplace=True) 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)[ data_prep = coords.preprocess(test_videos=0, window_step=1, window_size=window_size, shuffle=False)[
0 0
] ]
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
[i for i in os.listdir(trained_network) if i.endswith("h5")] [i for i in os.listdir(trained_network) if i.endswith("h5")]
``` ```
   
%% Output %% 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=0.15_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=0.0_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=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=0.15_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=0.0_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=0.15_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=0.0_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=0.15_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=0.15_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=0.15_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=0.0_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=0.0_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=0.15_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=0.0_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=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=0.15_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=0.15_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=0.0_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=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=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=0.15_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=0.0_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=0.15_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=0.15_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=0.15_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=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=0.0_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',
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'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=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=0.0_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=0.0_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'] '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: %% Cell type:code id: tags:
   
``` python ``` python
deepof_weights = [i for i in os.listdir(trained_network) if i.endswith("h5")][1] deepof_weights = [i for i in os.listdir(trained_network) if i.endswith("h5")][1]
deepof_weights deepof_weights
``` ```
   
%% Output %% 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' '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: %% Cell type:code id: tags:
   
``` python ``` python
# Set model parameters # Set model parameters
encoding = int(re.findall("encoding=(\d+)_", deepof_weights)[0]) encoding = int(re.findall("encoding=(\d+)_", deepof_weights)[0])
k = int(re.findall("k=(\d+)_", deepof_weights)[0]) k = int(re.findall("k=(\d+)_", deepof_weights)[0])
loss = re.findall("loss=(.+?)_", deepof_weights)[0] loss = re.findall("loss=(.+?)_", deepof_weights)[0]
NextSeqPred = float(re.findall("NextSeqPred=(.+?)_", deepof_weights)[0]) NextSeqPred = float(re.findall("NextSeqPred=(.+?)_", deepof_weights)[0])
PhenoPred = float(re.findall("PhenoPred=(.+?)_", deepof_weights)[0]) PhenoPred = float(re.findall("PhenoPred=(.+?)_", deepof_weights)[0])
RuleBasedPred = float(re.findall("RuleBasedPred=(.+?)_", deepof_weights)[0]) RuleBasedPred = float(re.findall("RuleBasedPred=(.+?)_", deepof_weights)[0])
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
( (
encoder, encoder,
decoder, decoder,
grouper, grouper,
gmvaep, gmvaep,
prior, prior,
posterior, posterior,
) = deepof.models.GMVAE( ) = deepof.models.GMVAE(
loss=loss, loss=loss,
number_of_components=k, number_of_components=k,
compile_model=True, compile_model=True,
batch_size=batch_size, batch_size=batch_size,
encoding=encoding, encoding=encoding,
next_sequence_prediction=NextSeqPred, next_sequence_prediction=0.1,
phenotype_prediction=PhenoPred, phenotype_prediction=PhenoPred,
rule_based_prediction=RuleBasedPred, rule_based_prediction=RuleBasedPred,
).build( ).build(
data_prep.shape data_prep.shape
) )
#gmvaep.load_weights(os.path.join(trained_network, deepof_weights)) #gmvaep.load_weights(os.path.join(trained_network, deepof_weights))
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
# Uncomment to see model summaries # Uncomment to see model summaries
# encoder.summary() # encoder.summary()
# decoder.summary() # decoder.summary()
# grouper.summary() # grouper.summary()
gmvaep.summary() gmvaep.summary()
``` ```
   
%% Output
Model: "SEQ_2_SEQ_GMVAE"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_15 (InputLayer) [(None, 22, 26)] 0
__________________________________________________________________________________________________
conv1d_24 (Conv1D) (None, 11, 64) 8384 input_15[0][0]
__________________________________________________________________________________________________
batch_normalization_94 (BatchNo (None, 11, 64) 256 conv1d_24[0][0]
__________________________________________________________________________________________________
bidirectional_48 (Bidirectional (None, 11, 256) 148992 batch_normalization_94[0][0]
__________________________________________________________________________________________________
batch_normalization_95 (BatchNo (None, 11, 256) 1024 bidirectional_48[0][0]
__________________________________________________________________________________________________
bidirectional_49 (Bidirectional (None, 128) 123648 batch_normalization_95[0][0]
__________________________________________________________________________________________________
batch_normalization_96 (BatchNo (None, 128) 512 bidirectional_49[0][0]
__________________________________________________________________________________________________
dense_88 (Dense) (None, 64) 8256 batch_normalization_96[0][0]
__________________________________________________________________________________________________
batch_normalization_97 (BatchNo (None, 64) 256 dense_88[0][0]
__________________________________________________________________________________________________
dropout_8 (Dropout) (None, 64) 0 batch_normalization_97[0][0]
__________________________________________________________________________________________________
sequential_14 (Sequential) (None, 32) 2208 dropout_8[0][0]
__________________________________________________________________________________________________
cluster_means (Dense) (None, 90) 2970 sequential_14[0][0]
__________________________________________________________________________________________________
cluster_variances (Dense) (None, 90) 2970 sequential_14[0][0]
__________________________________________________________________________________________________
concatenate_14 (Concatenate) (None, 180) 0 cluster_means[0][0]
cluster_variances[0][0]
__________________________________________________________________________________________________
cluster_assignment (Dense) (None, 15) 495 sequential_14[0][0]
__________________________________________________________________________________________________
reshape_8 (Reshape) (None, 12, 15) 0 concatenate_14[0][0]
__________________________________________________________________________________________________
encoding_distribution (Distribu multiple 0 cluster_assignment[0][0]
reshape_8[0][0]
__________________________________________________________________________________________________
kl_divergence_layer_6 (KLDiverg multiple 181 encoding_distribution[0][0]
__________________________________________________________________________________________________
latent_distribution (Lambda) multiple 0 kl_divergence_layer_6[0][0]
__________________________________________________________________________________________________
dense_97 (Dense) (None, 32) 224 latent_distribution[0][0]
__________________________________________________________________________________________________
batch_normalization_102 (BatchN (None, 32) 128 dense_97[0][0]
__________________________________________________________________________________________________
dense_92 (Dense) (None, 64) 2112 batch_normalization_102[0][0]
__________________________________________________________________________________________________
batch_normalization_103 (BatchN (None, 64) 256 dense_92[0][0]
__________________________________________________________________________________________________
repeat_vector_9 (RepeatVector) (None, 22, 64) 0 batch_normalization_103[0][0]
__________________________________________________________________________________________________
bidirectional_52 (Bidirectional (None, 22, 256) 148992 repeat_vector_9[0][0]
__________________________________________________________________________________________________
batch_normalization_104 (BatchN (None, 22, 256) 1024 bidirectional_52[0][0]
__________________________________________________________________________________________________
bidirectional_53 (Bidirectional (None, 22, 256) 296448 batch_normalization_104[0][0]
__________________________________________________________________________________________________
batch_normalization_105 (BatchN (None, 22, 256) 1024 bidirectional_53[0][0]
__________________________________________________________________________________________________
conv1d_26 (Conv1D) (None, 22, 64) 81984 batch_normalization_105[0][0]
__________________________________________________________________________________________________
dense_99 (Dense) (None, 22, 26) 1690 conv1d_26[0][0]
__________________________________________________________________________________________________
tf.math.softplus_7 (TFOpLambda) (None, 22, 26) 0 dense_99[0][0]
__________________________________________________________________________________________________
dense_98 (Dense) (None, 22, 26) 1690 conv1d_26[0][0]
__________________________________________________________________________________________________
lambda_7 (Lambda) (None, 22, 26) 0 tf.math.softplus_7[0][0]
__________________________________________________________________________________________________
concatenate_16 (Concatenate) (None, 22, 52) 0 dense_98[0][0]
lambda_7[0][0]
__________________________________________________________________________________________________
vae_reconstruction (Functional) multiple 337940 latent_distribution[0][0]
__________________________________________________________________________________________________
vae_prediction (IndependentNorm multiple 0 concatenate_16[0][0]
==================================================================================================
Total params: 1,173,664
Trainable params: 1,170,271
Non-trainable params: 3,393
__________________________________________________________________________________________________
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
# Uncomment to plot model structure # Uncomment to plot model structure
def plot_model(model, name): def plot_model(model, name):
tf.keras.utils.plot_model( tf.keras.utils.plot_model(
model, model,
to_file=os.path.join( to_file=os.path.join(
path, path,
"deepof_{}_{}.png".format(name, datetime.now().strftime("%Y%m%d-%H%M%S")), "deepof_{}_{}.png".format(name, datetime.now().strftime("%Y%m%d-%H%M%S")),
), ),
show_shapes=True, show_shapes=True,
show_dtype=False, show_dtype=False,
show_layer_names=True, show_layer_names=True,
rankdir="TB", rankdir="TB",
expand_nested=True, expand_nested=True,
dpi=200, dpi=200,
) )
   
   
# plot_model(encoder, "encoder") # plot_model(encoder, "encoder")
# plot_model(decoder, "decoder") # plot_model(decoder, "decoder")
# plot_model(grouper, "grouper") # plot_model(grouper, "grouper")
# plot_model(gmvaep, "gmvaep") # plot_model(gmvaep, "gmvaep")
``` ```
   
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
   
### 3. Visualize priors ### 3. Visualize priors
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
import tensorflow_probability as tfp import tensorflow_probability as tfp
   
tfb = tfp.bijectors tfb = tfp.bijectors
tfd = tfp.distributions tfd = tfp.distributions
tfpl = tfp.layers tfpl = tfp.layers
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
def get_prior(number_of_components, encoding, init): def get_prior(number_of_components, encoding, init):
   
prior = tfd.MixtureSameFamily( prior = tfd.MixtureSameFamily(
mixture_distribution=tfd.categorical.Categorical( mixture_distribution=tfd.categorical.Categorical(
probs=tf.ones(number_of_components) / number_of_components probs=tf.ones(number_of_components) / number_of_components
), ),
components_distribution=tfd.MultivariateNormalDiag( components_distribution=tfd.MultivariateNormalDiag(
loc=tf.Variable( loc=tf.Variable(
init([number_of_components, encoding],), init([number_of_components, encoding],),
name="prior_means", name="prior_means",
), ),
scale_diag=tfp.util.TransformedVariable( scale_diag=tfp.util.TransformedVariable(
tf.ones([number_of_components, encoding]) / number_of_components, tf.ones([number_of_components, encoding]) / number_of_components,
tfb.Softplus(), tfb.Softplus(),
name="prior_scales", name="prior_scales",
), ),
), ),
) )
   
return prior return prior
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
def sample_and_plot(prior, samples, ax, label): def sample_and_plot(prior, samples, ax, label):
"""Sample from the prior and plot with colours corresponding to different clusters""" """Sample from the prior and plot with colours corresponding to different clusters"""
   
samples = prior.sample(samples) samples = prior.sample(samples)
means = prior.components_distribution.mean() means = prior.components_distribution.mean()
samples = tf.concat([samples, means], axis=0) samples = tf.concat([samples, means], axis=0)
pca = PCA(n_components=2) pca = PCA(n_components=2)
prior = pca.fit_transform(samples) prior = pca.fit_transform(samples)
   
samples = prior[:-number_of_components, :] samples = prior[:-number_of_components, :]
means = prior[-number_of_components:, :] means = prior[-number_of_components:, :]
   
ax.scatter(prior[:,0], prior[:,1]) ax.scatter(prior[:,0], prior[:,1])
ax.scatter(means[:,0], means[:,1], label=label) ax.scatter(means[:,0], means[:,1], label=label)
ax.set_xlabel("PC1") ax.set_xlabel("PC1")
ax.set_ylabel("PC2") ax.set_ylabel("PC2")
ax.legend() ax.legend()
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
number_of_components = 15 number_of_components = 15
encoding = 6 encoding = 6
   
initializers = [ initializers = [
tf.keras.initializers.Constant(), tf.keras.initializers.Constant(),
tf.keras.initializers.GlorotNormal(), tf.keras.initializers.GlorotNormal(),
tf.keras.initializers.GlorotUniform(), tf.keras.initializers.GlorotUniform(),
tf.keras.initializers.HeNormal(), tf.keras.initializers.HeNormal(),
tf.keras.initializers.HeUniform(), tf.keras.initializers.HeUniform(),
tf.keras.initializers.LecunNormal(), tf.keras.initializers.LecunNormal(),
tf.keras.initializers.LecunUniform(), tf.keras.initializers.LecunUniform(),
tf.keras.initializers.Orthogonal(), tf.keras.initializers.Orthogonal(),
tf.keras.initializers.RandomNormal(), tf.keras.initializers.RandomNormal(),
tf.keras.initializers.RandomUniform(), tf.keras.initializers.RandomUniform(),
tf.keras.initializers.TruncatedNormal(), tf.keras.initializers.TruncatedNormal(),
tf.keras.initializers.VarianceScaling(), tf.keras.initializers.VarianceScaling(),
] ]
   
fig, ax = plt.subplots(4, 3, figsize=(10, 15), sharex=True, sharey=True) fig, ax = plt.subplots(4, 3, figsize=(10, 15), sharex=True, sharey=True)
ax = [item for sublist in ax for item in sublist] ax = [item for sublist in ax for item in sublist]
   
for i, x in enumerate(ax): for i, x in enumerate(ax):
prior = get_prior(number_of_components, encoding, initializers[i]) prior = get_prior(number_of_components, encoding, initializers[i])
sample_and_plot( sample_and_plot(
prior, prior,
1000, 1000,
x, x,
label=re.findall("initializers_v2.(.*?) ", str(initializers[i]))[0], label=re.findall("initializers_v2.(.*?) ", str(initializers[i]))[0],
) )
   
fig.tight_layout(rect=[0.0, 0.0, 1.0, 0.97]) fig.tight_layout(rect=[0.0, 0.0, 1.0, 0.97])
plt.suptitle("effect of initialization on deepOF prior") plt.suptitle("effect of initialization on deepOF prior")
   
plt.show() plt.show()
``` ```
   
%% Output %% Output
   
--------------------------------------------------------------------------- ---------------------------------------------------------------------------
NameError Traceback (most recent call last) NameError Traceback (most recent call last)
<ipython-input-7-8a0b102aafa0> in <module> <ipython-input-7-8a0b102aafa0> in <module>
3 3
4 initializers = [ 4 initializers = [
----> 5 tf.keras.initializers.Constant(), ----> 5 tf.keras.initializers.Constant(),
6 tf.keras.initializers.GlorotNormal(), 6 tf.keras.initializers.GlorotNormal(),
7 tf.keras.initializers.GlorotUniform(), 7 tf.keras.initializers.GlorotUniform(),
NameError: name 'tf' is not defined NameError: name 'tf' is not defined
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
from scipy.spatial.distance import pdist from scipy.spatial.distance import pdist
   
   
def quantify_separation(init, samples): def quantify_separation(init, samples):
   
distances = [] distances = []
   
for i in range(samples): for i in range(samples):
means = get_prior( means = get_prior(
number_of_components, encoding, init number_of_components, encoding, init
).components_distribution.mean() ).components_distribution.mean()
mean_dist = np.mean(pdist(means)) mean_dist = np.mean(pdist(means))
distances.append(mean_dist) distances.append(mean_dist)
   
return ( return (
np.mean(distances), np.mean(distances),
np.min(distances), np.min(distances),
np.max(distances), np.max(distances),
1.96 * np.std(distances), 1.96 * np.std(distances),
) )
   
   
prior_init_eval_dict = {} prior_init_eval_dict = {}
for init in tqdm.tqdm(initializers): for init in tqdm.tqdm(initializers):
prior_init_eval_dict[ prior_init_eval_dict[
re.findall("initializers_v2.(.*?) ", str(init))[0] re.findall("initializers_v2.(.*?) ", str(init))[0]
] = quantify_separation(init, 100) ] = quantify_separation(init, 100)
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
prior_init_eval = pd.DataFrame(prior_init_eval_dict).T prior_init_eval = pd.DataFrame(prior_init_eval_dict).T
prior_init_eval.rename(columns={0:"mean", 1:"min", 2:"max", 3:"CI95"}, inplace=True) prior_init_eval.rename(columns={0:"mean", 1:"min", 2:"max", 3:"CI95"}, inplace=True)
prior_init_eval.sort_values("mean", ascending=False) prior_init_eval.sort_values("mean", ascending=False)
``` ```
   
%% Output %% Output
   
--------------------------------------------------------------------------- ---------------------------------------------------------------------------
NameError Traceback (most recent call last) NameError Traceback (most recent call last)
<ipython-input-8-9346bb65520c> in <module> <ipython-input-8-9346bb65520c> in <module>
----> 1 prior_init_eval = pd.DataFrame(prior_init_eval_dict).T ----> 1 prior_init_eval = pd.DataFrame(prior_init_eval_dict).T
2 prior_init_eval.rename(columns={0:"mean", 1:"min", 2:"max", 3:"CI95"}, inplace=True) 2 prior_init_eval.rename(columns={0:"mean", 1:"min", 2:"max", 3:"CI95"}, inplace=True)
3 prior_init_eval.sort_values("mean", ascending=False) 3 prior_init_eval.sort_values("mean", ascending=False)
NameError: name 'pd' is not defined NameError: name 'pd' is not defined
   
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
   
### 4. Evaluate reconstruction (to be incorporated into deepof.evaluate) ### 4. Evaluate reconstruction (to be incorporated into deepof.evaluate)
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
# Auxiliary animation functions # Auxiliary animation functions
   
   
def plot_mouse_graph(instant_x, instant_y, instant_rec_x, instant_rec_y, ax, edges): def plot_mouse_graph(instant_x, instant_y, instant_rec_x, instant_rec_y, ax, edges):
"""Generates a graph plot of the mouse""" """Generates a graph plot of the mouse"""
plots = [] plots = []
rec_plots = [] rec_plots = []
for edge in edges: for edge in edges:
(temp_plot,) = ax.plot( (temp_plot,) = ax.plot(
[float(instant_x[edge[0]]), float(instant_x[edge[1]])], [float(instant_x[edge[0]]), float(instant_x[edge[1]])],
[float(instant_y[edge[0]]), float(instant_y[edge[1]])], [float(instant_y[edge[0]]), float(instant_y[edge[1]])],
color="#006699", color="#006699",
linewidth=2.0, linewidth=2.0,
) )
(temp_rec_plot,) = ax.plot( (temp_rec_plot,) = ax.plot(
[float(instant_rec_x[edge[0]]), float(instant_rec_x[edge[1]])], [float(instant_rec_x[edge[0]]), float(instant_rec_x[edge[1]])],
[float(instant_rec_y[edge[0]]), float(instant_rec_y[edge[1]])], [float(instant_rec_y[edge[0]]), float(instant_rec_y[edge[1]])],
color="red", color="red",
linewidth=2.0, linewidth=2.0,
) )
plots.append(temp_plot) plots.append(temp_plot)
rec_plots.append(temp_rec_plot) rec_plots.append(temp_rec_plot)
return plots, rec_plots return plots, rec_plots
   
   
def update_mouse_graph(x, y, rec_x, rec_y, plots, rec_plots, edges): def update_mouse_graph(x, y, rec_x, rec_y, plots, rec_plots, edges):
"""Updates the graph plot to enable animation""" """Updates the graph plot to enable animation"""
   
for plot, edge in zip(plots, edges): for plot, edge in zip(plots, edges):
plot.set_data( plot.set_data(
[float(x[edge[0]]), float(x[edge[1]])], [float(x[edge[0]]), float(x[edge[1]])],
[float(y[edge[0]]), float(y[edge[1]])], [float(y[edge[0]]), float(y[edge[1]])],
) )
for plot, edge in zip(rec_plots, edges): for plot, edge in zip(rec_plots, edges):
plot.set_data( plot.set_data(
[float(rec_x[edge[0]]), float(rec_x[edge[1]])], [float(rec_x[edge[0]]), float(rec_x[edge[1]])],
[float(rec_y[edge[0]]), float(rec_y[edge[1]])], [float(rec_y[edge[0]]), float(rec_y[edge[1]])],
) )
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
# Display a video with the original data superimposed with the reconstructions # Display a video with the original data superimposed with the reconstructions
   
coords = proj.get_coords(center="Center", align="Spine_1", align_inplace=True) coords = proj.get_coords(center="Center", align="Spine_1", align_inplace=True)
random_exp = np.random.choice(list(coords.keys()), 1)[0] random_exp = np.random.choice(list(coords.keys()), 1)[0]
print(random_exp) print(random_exp)
   
   
def animate_mice_across_time(random_exp): def animate_mice_across_time(random_exp):
   
# Define canvas # Define canvas
fig, ax = plt.subplots(1, 1, figsize=(10, 10)) fig, ax = plt.subplots(1, 1, figsize=(10, 10))
   
# Retrieve body graph # Retrieve body graph
edges = deepof.utils.connect_mouse_topview() edges = deepof.utils.connect_mouse_topview()
   
for bpart in exclude_bodyparts: for bpart in exclude_bodyparts:
if bpart: if bpart:
edges.remove_node(bpart) edges.remove_node(bpart)
   
for limb in ["Left_fhip", "Right_fhip", "Left_bhip", "Right_bhip"]: for limb in ["Left_fhip", "Right_fhip", "Left_bhip", "Right_bhip"]:
edges.remove_edge("Center", limb) edges.remove_edge("Center", limb)
if ("Tail_base", limb) in edges.edges(): if ("Tail_base", limb) in edges.edges():
edges.remove_edge("Tail_base", limb) edges.remove_edge("Tail_base", limb)
   
edges = edges.edges() edges = edges.edges()
   
# Compute observed and predicted data to plot # Compute observed and predicted data to plot
data = coords[random_exp] data = coords[random_exp]
coords_rec = coords.filter_videos([random_exp]) coords_rec = coords.filter_videos([random_exp])
data_prep = coords_rec.preprocess( data_prep = coords_rec.preprocess(
test_videos=0, window_step=1, window_size=window_size, shuffle=False test_videos=0, window_step=1, window_size=window_size, shuffle=False
)[0][:512] )[0][:512]
   
data_rec = gmvaep.predict(data_prep) data_rec = gmvaep.predict(data_prep)
try: try:
data_rec = pd.DataFrame(coords_rec._scaler.inverse_transform(data_rec[:, 6, :])) data_rec = pd.DataFrame(coords_rec._scaler.inverse_transform(data_rec[:, 6, :]))
except TypeError: except TypeError:
data_rec = data_rec[0] data_rec = data_rec[0]
data_rec = pd.DataFrame(coords_rec._scaler.inverse_transform(data_rec[:, 6, :])) data_rec = pd.DataFrame(coords_rec._scaler.inverse_transform(data_rec[:, 6, :]))
   
data_rec.columns = data.columns data_rec.columns = data.columns
data = pd.DataFrame(coords_rec._scaler.inverse_transform(data_prep[:, 6, :])) data = pd.DataFrame(coords_rec._scaler.inverse_transform(data_prep[:, 6, :]))
data.columns = data_rec.columns data.columns = data_rec.columns
   
# Add Central coordinate, lost during alignment # Add Central coordinate, lost during alignment
data["Center", "x"] = 0 data["Center", "x"] = 0
data["Center", "y"] = 0 data["Center", "y"] = 0
data_rec["Center", "x"] = 0 data_rec["Center", "x"] = 0
data_rec["Center", "y"] = 0 data_rec["Center", "y"] = 0
   
# Plot! # Plot!
init_x = data.xs("x", level=1, axis=1, drop_level=False).iloc[0, :] init_x = data.xs("x", level=1, axis=1, drop_level=False).iloc[0, :]
init_y = data.xs("y", level=1, axis=1, drop_level=False).iloc[0, :] init_y = data.xs("y", level=1, axis=1, drop_level=False).iloc[0, :]
init_rec_x = data_rec.xs("x", level=1, axis=1, drop_level=False).iloc[0, :] init_rec_x = data_rec.xs("x", level=1, axis=1, drop_level=False).iloc[0, :]
init_rec_y = data_rec.xs("y", level=1, axis=1, drop_level=False).iloc[0, :] init_rec_y = data_rec.xs("y", level=1, axis=1, drop_level=False).iloc[0, :]
   
plots, rec_plots = plot_mouse_graph( plots, rec_plots = plot_mouse_graph(
init_x, init_y, init_rec_x, init_rec_y, ax, edges init_x, init_y, init_rec_x, init_rec_y, ax, edges
) )
scatter = ax.scatter( scatter = ax.scatter(
x=np.array(init_x), y=np.array(init_y), color="#006699", label="Original" x=np.array(init_x), y=np.array(init_y), color="#006699", label="Original"
) )
rec_scatter = ax.scatter( rec_scatter = ax.scatter(
x=np.array(init_rec_x), x=np.array(init_rec_x),
y=np.array(init_rec_y), y=np.array(init_rec_y),
color="red", color="red",
label="Reconstruction", label="Reconstruction",
) )
   
# Update data in main plot # Update data in main plot
def animation_frame(i): def animation_frame(i):
# Update scatter plot # Update scatter plot
x = data.xs("x", level=1, axis=1, drop_level=False).iloc[i, :] x = data.xs("x", level=1, axis=1, drop_level=False).iloc[i, :]
y = data.xs("y", level=1, axis=1, drop_level=False).iloc[i, :] y = data.xs("y", level=1, axis=1, drop_level=False).iloc[i, :]
rec_x = data_rec.xs("x", level=1, axis=1, drop_level=False).iloc[i, :] rec_x = data_rec.xs("x", level=1, axis=1, drop_level=False).iloc[i, :]
rec_y = data_rec.xs("y", level=1, axis=1, drop_level=False).iloc[i, :] rec_y = data_rec.xs("y", level=1, axis=1, drop_level=False).iloc[i, :]
   
scatter.set_offsets(np.c_[np.array(x), np.array(y)]) scatter.set_offsets(np.c_[np.array(x), np.array(y)])
rec_scatter.set_offsets(np.c_[np.array(rec_x), np.array(rec_y)]) rec_scatter.set_offsets(np.c_[np.array(rec_x), np.array(rec_y)])
update_mouse_graph(x, y, rec_x, rec_y, plots, rec_plots, edges) update_mouse_graph(x, y, rec_x, rec_y, plots, rec_plots, edges)
   
return scatter return scatter
   
animation = FuncAnimation(fig, func=animation_frame, frames=250, interval=50,) animation = FuncAnimation(fig, func=animation_frame, frames=250, interval=50,)
   
ax.set_title("Original versus reconstructed data") ax.set_title("Original versus reconstructed data")
ax.set_ylim(-100, 60) ax.set_ylim(-100, 60)
ax.set_xlim(-60, 60) ax.set_xlim(-60, 60)
ax.set_xlabel("x") ax.set_xlabel("x")
ax.set_ylabel("y") ax.set_ylabel("y")
plt.legend() plt.legend()
   
video = animation.to_html5_video() video = animation.to_html5_video()
html = display.HTML(video) html = display.HTML(video)
display.display(html) display.display(html)
plt.close() plt.close()
   
   
animate_mice_across_time(random_exp) animate_mice_across_time(random_exp)
``` ```
   
%% Output %% Output
   
Test 11_s11 Test 11_s11
   
   
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
   
### 5. Evaluate latent space (to be incorporated into deepof.evaluate) ### 5. Evaluate latent space (to be incorporated into deepof.evaluate)
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
# Get encodings and groupings for the same random video as above # Get encodings and groupings for the same random video as above
data_prep = coords.preprocess( data_prep = coords.preprocess(
test_videos=0, window_step=1, window_size=window_size, shuffle=True test_videos=0, window_step=1, window_size=window_size, shuffle=True
)[0][:10000] )[0][:10000]
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
encodings = encoder.predict(data_prep) encodings = encoder.predict(data_prep)
groupings = grouper.predict(data_prep) groupings = grouper.predict(data_prep)
hard_groups = np.argmax(groupings, axis=1) hard_groups = np.argmax(groupings, axis=1)
``` ```
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
@interact(minimum_confidence=(0.0, 1.0, 0.01)) @interact(minimum_confidence=(0.0, 1.0, 0.01))
def plot_cluster_population(minimum_confidence): def plot_cluster_population(minimum_confidence):
plt.figure(figsize=(12, 8)) plt.figure(figsize=(12, 8))
   
groups = hard_groups[np.max(groupings, axis=1) > minimum_confidence].flatten() groups = hard_groups[np.max(groupings, axis=1) > minimum_confidence].flatten()
groups = np.concatenate([groups, np.arange(groupings.shape[1])]) groups = np.concatenate([groups, np.arange(groupings.shape[1])])
sns.countplot(groups) sns.countplot(groups)
plt.xlabel("Cluster") plt.xlabel("Cluster")
plt.title("Training instances per cluster") plt.title("Training instances per cluster")
plt.ylim(0, hard_groups.shape[0] * 1.1) plt.ylim(0, hard_groups.shape[0] * 1.1)
plt.show() plt.show()
``` ```
   
%% Output %% Output
   
   
%% Cell type:markdown id: tags: %% Cell type:markdown id: tags:
   
The slider in the figure above lets you set the minimum confidence the model may yield when assigning a training instance to a cluster in order to be visualized. The slider in the figure above lets you set the minimum confidence the model may yield when assigning a training instance to a cluster in order to be visualized.
   
%% Cell type:code id: tags: %% Cell type:code id: tags:
   
``` python ``` python
# Plot real data in the latent space # Plot real data in the latent space
   
samples = np.random.choice(range(encodings.shape[0]), 10000) samples = np.random.choice(range(encodings.shape[0]), 10000)
sample_enc = encodings[samples, :] sample_enc = encodings[samples, :]
sample_grp = groupings[samples, :] sample_grp = groupings[samples, :]
sample_hgr = hard_groups[samples] sample_hgr = hard_groups[samples]
k = sample_grp.shape[1] k = sample_grp.shape[1]
   
umap_reducer = umap.UMAP(n_components=2) umap_reducer = umap.UMAP(n_components=2)
pca_reducer = PCA(n_components=2) pca_reducer = PCA(n_components=2)
tsne_reducer = TSNE(n_components=2) tsne_reducer = TSNE(n_components=2)
lda_reducer = LinearDiscriminantAnalysis(n_components=2) lda_reducer = LinearDiscriminantAnalysis(n_components=2)
   
umap_enc = umap_reducer.fit_transform(sample_enc) umap_enc = umap_reducer.fit_transform(sample_enc)
pca_enc = pca_reducer.fit_transform(sample_enc) pca_enc = pca_reducer.fit_transform(sample_enc)
tsne_enc = tsne_reducer.fit_transform(sample_enc) tsne_enc = tsne_reducer.fit_transform(sample_enc)
try: try:
lda_enc = lda_reducer.fit_transform(sample_enc, sample_hgr) lda_enc = lda_reducer.fit_transform(sample_enc, sample_hgr)
except ValueError: except ValueError:
warnings.warn( warnings.warn(
"Only one class found. Can't use LDA", DeprecationWarning, stacklevel=2