Module minder_utils.visualisation.visual_intrinsic_selector
Expand source code
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from minder_utils.configurations import config
sns.set()
def visualise(importance, datatype):
    plt.clf()
    df = pd.DataFrame(importance)
    df = df.melt(var_name='Sensor', value_name='Importance')
    map_dict = dict(zip(np.arange(len(config[datatype]['sensors'])),
                        config[datatype]['sensors']))
    df.Sensor = df.Sensor.map(map_dict)
    sns.boxplot(x='Sensor', y='Importance', data=df)
    plt.title(datatype)
    plt.xticks(rotation=90)
    plt.tight_layout()
    plt.savefig('./results/visual/{}.png'.format(datatype))Functions
- def visualise(importance, datatype)
- 
Expand source codedef visualise(importance, datatype): plt.clf() df = pd.DataFrame(importance) df = df.melt(var_name='Sensor', value_name='Importance') map_dict = dict(zip(np.arange(len(config[datatype]['sensors'])), config[datatype]['sensors'])) df.Sensor = df.Sensor.map(map_dict) sns.boxplot(x='Sensor', y='Importance', data=df) plt.title(datatype) plt.xticks(rotation=90) plt.tight_layout() plt.savefig('./results/visual/{}.png'.format(datatype))