Category Archives: visualization

Use python for image generation

Here are some suggestions to visualize your results with python.
The idea is mainly to put your data in a pandas dataframe and then use pandas methods to plot it.

Bar plots

Here's the simple one with one variable:

vals = {'24 layers':9.37, '6 layers teached':9.94, '6 layers':10.20, 'human':10.34}
df_plot = pd.DataFrame(vals, index=[0])
ax = df_plot.plot(kind='bar')
ax.set_ylim(8, 12)
ax.set_title('error in MAE')

Here's an example for a barplot with two variables and three features:

vals_arou  = [3.2, 3.6]
vals_val  = [-1.2, -0.4]
vals_dom  = [2.6, 3.2]
cols = ['orig','scrambled']
plot = pd.DataFrame(columns = cols)
plot.loc['arousal'] = vals_arou
plot.loc['valence'] = vals_val
plot.loc['dominance'] = vals_dom
ax = plot.plot(kind='bar', rot=0)
ax.set_ylim(-1.8, 3.7)
# this displays the actual values
for container in ax.containers:

Stacked barplots

Here's an example using seaborn package for stacked barplots:
For a pandas dataframe with columns age in years and db for two database names:

import seaborn as sns
f = plt.figure(figsize=(7,5))
ax = f.add_subplot(1,1,1)
sns.histplot(data=df, ax = ax, stat="count", multiple="stack",
             x="duration", kde=False,
             element="bars", legend=True)
ax.set_title("Age distriubution")

Box plots

Here's a code comparing two box plots with data dots

import seaborn as sns
import pandas as pd
n = [0.375, 0.389, 0.38, 0.346, 0.373, 0.335, 0.337, 0.363, 0.338, 0.339]
e = [0.433 0.451, 0.462, 0.464, 0.455, 0.456, 0.464, 0.461 0.457, 0.456]
data = pd.DataFrame({'simple':n, 'with soft labels':e})
sns.boxplot(data = data)
sns.swarmplot(data=data, color='.25', size=1)

Confusion matrix

We can simply use the audplot package

from audplot import confusion_matrix

truth = [0, 1, 1, 1, 2, 2, 2] * 1000
prediction = [0, 1, 2, 2, 0, 0, 2] * 1000
confusion_matrix(truth, prediction)

Pie plot

Here is an example for a pie plot

import pandas as pd

import pandas as pd
plot_df = 
    pd.DataFrame({'cases':[461, 85, 250]}, 
    index=['unknown', 'Corona positive', 
    'Corona negative'])
plot_df.plot(kind='pie', y='cases', autopct='%.2f')

looks like this:


import matplotlib.pyplot as plt
# assuming you have two dataframes with a speaker column, you could plot the histogram of samples per speaker like this 
test = df_test.speaker.value_counts()[df_test.speaker.value_counts()>0]
train = df_train.speaker.value_counts()[df_train.speaker.value_counts()>0]

plt.hist([train, test], bins = np.linspace(0, 500, 100), label=['train', 'test'])
plt.legend(loc='upper right')
# better use EPS for publication as it's vector graphics (and scales)

How to use Latex for your project documentation

Using a documentation system that separates content and presentation has many advantages, the biggest one probably flexibility.
I vote for latex and since there is now a company that offers free latex environment, you don't have to set it up yourself (you still can, but it might be tedious).

I've set up a sample project that you should be able to copy and use as a start here:

Overleaf sample project

Make a t-SNE plot

This post shows you how to generate a t-distributed stochastic neighbor embedding (t-SNE) plot with the opensmile features extracted from emodb data (which is explained in more detail in a previous blog post).

A t-SNE plot is a very useful visualization, as it condenses your feature space into two dimensions (so it can be plotted) and then uses colors to represent the class membership. This means, if you can identify clusters of same colored dots in your data cloud, the features are able to separate the classes.

We need the following imports:

import audformat
from sklearn.manifold import TSNE
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
import opensmile

First, you download and prepare emodb:

# get and unpack the berlin Emodb emotional database
!wget -c
!mv download
# preapare the dataframe
db = audformat.Database.load('./emodb')
root = './emodb/'
db.map_files(lambda x: os.path.join(root, x))
df = db.tables['emotion'].df

Then, you extract the geMAPS features:

smile = opensmile.Smile(
feats_df = smile.process_files(df.index)

And finally, you generate the t-SNE plot with the sklearn library like this:

# Plot a TSNE
def plotTsne(feats, labels, perplexity=30, learning_rate=200):
    model = TSNE(n_components=2, random_state=0, perplexity=perplexity, learning_rate=learning_rate)
    tsne_data = model.fit_transform(feats)
    tsne_data_labs = np.vstack((tsne_data.T, labels)).T
    tsne_df = pd.DataFrame(data=tsne_data_labs, columns=('Dim_1', 'Dim_2', 'label'))
    sns.FacetGrid(tsne_df, hue='label', size=6).map(plt.scatter, 'Dim_1', 'Dim_2').add_legend()
plotTsne(feats_df, df['emotion'], 30, 200)

It seems that these features are useful to distinguish at least the category anger from the rest.

You might want to fiddle around with the two main parameters of the algorithm: perplexity and learning-rate.