Metrics - weights

Example to show how to use the SigmoidA class to compute weights.

Todo

The SigmoidA class name stands for Sigmoid Approximation. Probably this example should include external references, a better explanation of the sigmoid function and how the SigmoidA (approximation) is computed and how to use it.

# Libraries
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import statsmodels.robust.norms as norms

# pyAMR
from pyamr.metrics.weights import SigmoidA

# Matplotlib options
mpl.rc('legend', fontsize=6)
mpl.rc('xtick', labelsize=6)
mpl.rc('ytick', labelsize=6)
mpl.rc('font', size=7)

# Set pandas configuration.
pd.set_option('display.max_colwidth', 14)
pd.set_option('display.width', 150)
pd.set_option('display.precision', 4)

# Constants
length = 100

# Create time-series.
x = np.linspace(0,100,100)
f = np.concatenate((np.random.rand(35)*50+50,
                  np.random.rand(30)*50+100,
                  np.random.rand(35)*50+150))

# Weight Functions
w_functions = [
    SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0),
    SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0, percentiles=[10,90]),
    SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0, percentiles=[25,75]),
    SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0, percentiles=[40,50]),
    SigmoidA(r=200, g=0.5, offset=0.0, scale=1.0, thresholds=[15,85])
]

# Create figure
fig, axes = plt.subplots(1,3, figsize=(10,4))

# Plot frequencies
axes[1].bar(x, f, color='gray', alpha=0.7, label='Frequency')

# Plot weights
for i,W in enumerate(w_functions):
    axes[0].plot(x, W.weights(x), label=W._identifier(short=True))
    axes[2].plot(x, W.weights(f), marker='o', alpha=0.5,
      markeredgecolor='k', markeredgewidth=0.5, markersize=4,
      linewidth=0.00, label=W._identifier(short=True))

# Titles
axes[0].set_title('Sigmoid Function')
axes[1].set_title('Frequencies')
axes[2].set_title('Weights')

# Legends
axes[0].legend()
axes[1].legend()
axes[2].legend()

# Tight layout
plt.tight_layout()

# Show
plt.show()