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)
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Expand source code
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))