Concrete strength

by: Chris Cave

Feyn version: 2.1+

Last updated: 27/09/2021

Importing the dataset

Here we use the QLattice to predict the compressive strength of concrete based on the ingredients that have been used to make it. You can find this dataset and further descriptions of the features on UCI Machine Learning Repository.

import pandas as pd
import feyn
import numpy as np

from sklearn.model_selection import train_test_split

data = pd.read_csv("../data/concrete.csv")

data.head()

	cement	blast_furnace_slag	fly_ash	water	superplasticizer	coarse_aggregate	fine_aggregate	age	concrete_compressive_strength
0	540.0	0.0	0.0	162.0	2.5	1040.0	676.0	28	79.99
1	540.0	0.0	0.0	162.0	2.5	1055.0	676.0	28	61.89
2	332.5	142.5	0.0	228.0	0.0	932.0	594.0	270	40.27
3	332.5	142.5	0.0	228.0	0.0	932.0	594.0	365	41.05
4	198.6	132.4	0.0	192.0	0.0	978.4	825.5	360	44.30

Connect to the QLattice

random_state = 42

train, test = train_test_split(data, test_size=0.4, random_state=random_state)
ql = feyn.connect_qlattice()
ql.reset(random_state)

output_name = "concrete_compressive_strength"

Use auto_run to obtain models

models = ql.auto_run(train, output_name=output_name)

best = models[0]

Summary plot to evaluate performance

best.plot(train, test)

Training Metrics

Test