fuzzy-rules-generator/density_tree.ipynb at 4ddb6e77a967d19008b6dce42cb8519865a3b34e

In [1]:

import pickle
import pandas as pd
from sklearn import tree

model = pickle.load(open("data/dtree.model.sav", "rb"))
features = (
    pd.read_csv("data/density_train.csv", sep=";", decimal=",")
    .drop(["Density"], axis=1)
    .columns.values.tolist()
)

rules = tree.export_text(model, feature_names=features)
print(rules)

|--- Al2O3 <= 0.18
|   |--- TiO2 <= 0.18
|   |   |--- T <= 32.50
|   |   |   |--- TiO2 <= 0.03
|   |   |   |   |--- Al2O3 <= 0.03
|   |   |   |   |   |--- T <= 22.50
|   |   |   |   |   |   |--- value: [1.06]
|   |   |   |   |   |--- T >  22.50
|   |   |   |   |   |   |--- value: [1.06]
|   |   |   |   |--- Al2O3 >  0.03
|   |   |   |   |   |--- value: [1.09]
|   |   |   |--- TiO2 >  0.03
|   |   |   |   |--- T <= 27.50
|   |   |   |   |   |--- T <= 22.50
|   |   |   |   |   |   |--- value: [1.09]
|   |   |   |   |   |--- T >  22.50
|   |   |   |   |   |   |--- value: [1.09]
|   |   |   |   |--- T >  27.50
|   |   |   |   |   |--- value: [1.08]
|   |   |--- T >  32.50
|   |   |   |--- TiO2 <= 0.03
|   |   |   |   |--- Al2O3 <= 0.03
|   |   |   |   |   |--- T <= 55.00
|   |   |   |   |   |   |--- T <= 47.50
|   |   |   |   |   |   |   |--- value: [1.05]
|   |   |   |   |   |   |--- T >  47.50
|   |   |   |   |   |   |   |--- value: [1.04]
|   |   |   |   |   |--- T >  55.00
|   |   |   |   |   |   |--- T <= 62.50
|   |   |   |   |   |   |   |--- value: [1.04]
|   |   |   |   |   |   |--- T >  62.50
|   |   |   |   |   |   |   |--- value: [1.03]
|   |   |   |   |--- Al2O3 >  0.03
|   |   |   |   |   |--- T <= 60.00
|   |   |   |   |   |   |--- T <= 52.50
|   |   |   |   |   |   |   |--- value: [1.07]
|   |   |   |   |   |   |--- T >  52.50
|   |   |   |   |   |   |   |--- value: [1.06]
|   |   |   |   |   |--- T >  60.00
|   |   |   |   |   |   |--- T <= 67.50
|   |   |   |   |   |   |   |--- value: [1.06]
|   |   |   |   |   |   |--- T >  67.50
|   |   |   |   |   |   |   |--- value: [1.05]
|   |   |   |--- TiO2 >  0.03
|   |   |   |   |--- T <= 50.00
|   |   |   |   |   |--- T <= 37.50
|   |   |   |   |   |   |--- value: [1.08]
|   |   |   |   |   |--- T >  37.50
|   |   |   |   |   |   |--- value: [1.08]
|   |   |   |   |--- T >  50.00
|   |   |   |   |   |--- T <= 67.50
|   |   |   |   |   |   |--- T <= 62.50
|   |   |   |   |   |   |   |--- value: [1.06]
|   |   |   |   |   |   |--- T >  62.50
|   |   |   |   |   |   |   |--- value: [1.06]
|   |   |   |   |   |--- T >  67.50
|   |   |   |   |   |   |--- value: [1.06]
|   |--- TiO2 >  0.18
|   |   |--- T <= 40.00
|   |   |   |--- T <= 30.00
|   |   |   |   |--- value: [1.22]
|   |   |   |--- T >  30.00
|   |   |   |   |--- value: [1.21]
|   |   |--- T >  40.00
|   |   |   |--- T <= 60.00
|   |   |   |   |--- T <= 52.50
|   |   |   |   |   |--- T <= 47.50
|   |   |   |   |   |   |--- value: [1.20]
|   |   |   |   |   |--- T >  47.50
|   |   |   |   |   |   |--- value: [1.19]
|   |   |   |   |--- T >  52.50
|   |   |   |   |   |--- value: [1.19]
|   |   |   |--- T >  60.00
|   |   |   |   |--- value: [1.18]
|--- Al2O3 >  0.18
|   |--- T <= 35.00
|   |   |--- T <= 22.50
|   |   |   |--- value: [1.19]
|   |   |--- T >  22.50
|   |   |   |--- T <= 27.50
|   |   |   |   |--- value: [1.18]
|   |   |   |--- T >  27.50
|   |   |   |   |--- value: [1.18]
|   |--- T >  35.00
|   |   |--- T <= 52.50
|   |   |   |--- T <= 42.50
|   |   |   |   |--- value: [1.17]
|   |   |   |--- T >  42.50
|   |   |   |   |--- T <= 47.50
|   |   |   |   |   |--- value: [1.17]
|   |   |   |   |--- T >  47.50
|   |   |   |   |   |--- value: [1.16]
|   |   |--- T >  52.50
|   |   |   |--- T <= 65.00
|   |   |   |   |--- T <= 57.50
|   |   |   |   |   |--- value: [1.16]
|   |   |   |   |--- T >  57.50
|   |   |   |   |   |--- value: [1.15]
|   |   |   |--- T >  65.00
|   |   |   |   |--- value: [1.14]

In [2]:

from src.rules import get_rules


rules = get_rules(model, features)
display(len(rules))
rules

Out[2]:

[if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T > 55.0) and (T > 62.5) -> 1.033,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T > 55.0) and (T <= 62.5) -> 1.038,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T <= 55.0) and (T > 47.5) -> 1.045,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T <= 55.0) and (T <= 47.5) -> 1.051,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T > 60.0) and (T > 67.5) -> 1.053,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T > 50.0) and (T > 67.5) -> 1.056,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T > 60.0) and (T <= 67.5) -> 1.057,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T > 22.5) -> 1.06,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T > 50.0) and (T <= 67.5) and (T > 62.5) -> 1.06,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 <= 0.025) and (Al2O3 <= 0.025) and (T <= 22.5) -> 1.062,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T > 50.0) and (T <= 67.5) and (T <= 62.5) -> 1.064,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T <= 60.0) and (T > 52.5) -> 1.064,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) and (T <= 60.0) and (T <= 52.5) -> 1.069,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T <= 50.0) and (T > 37.5) -> 1.078,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (TiO2 > 0.025) and (T <= 50.0) and (T <= 37.5) -> 1.081,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 > 0.025) and (T > 27.5) -> 1.084,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 <= 0.025) and (Al2O3 > 0.025) -> 1.088,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 > 0.025) and (T <= 27.5) and (T > 22.5) -> 1.088,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (TiO2 > 0.025) and (T <= 27.5) and (T <= 22.5) -> 1.091,
 if (Al2O3 > 0.175) and (T > 35.0) and (T > 52.5) and (T > 65.0) -> 1.144,
 if (Al2O3 > 0.175) and (T > 35.0) and (T > 52.5) and (T <= 65.0) and (T > 57.5) -> 1.152,
 if (Al2O3 > 0.175) and (T > 35.0) and (T > 52.5) and (T <= 65.0) and (T <= 57.5) -> 1.157,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) and (T > 42.5) and (T > 47.5) -> 1.161,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) and (T > 42.5) and (T <= 47.5) -> 1.166,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) and (T <= 42.5) -> 1.17,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T > 60.0) -> 1.178,
 if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) and (T > 27.5) -> 1.179,
 if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) and (T <= 27.5) -> 1.184,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) and (T > 52.5) -> 1.187,
 if (Al2O3 > 0.175) and (T <= 35.0) and (T <= 22.5) -> 1.189,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) and (T <= 52.5) and (T > 47.5) -> 1.193,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) and (T <= 52.5) and (T <= 47.5) -> 1.198,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T <= 30.0) -> 1.219]

In [3]:

from src.rules import normalise_rules


rules = normalise_rules(rules)
display(len(rules))
rules

Out[3]:

[if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.045,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.051,
 if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056,
 if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 67.5) -> 1.057,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (T > 22.5) -> 1.06,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.06,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.062,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.064,
 if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.064,
 if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.069,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.078,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.081,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 27.5) -> 1.084,
 if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091,
 if (Al2O3 > 0.175) and (T > 35.0) -> 1.144,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.152,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.157,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.161,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.166,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.17,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) -> 1.178,
 if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.179,
 if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.184,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.187,
 if (Al2O3 > 0.175) and (T <= 35.0) -> 1.189,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.193,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.198,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) -> 1.219]

In [4]:

from src.rules import delete_same_rules


rules = delete_same_rules(rules)
display(len(rules))
rules

Out[4]:

[if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.048,
 if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056,
 if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 67.5) -> 1.057,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (T > 22.5) -> 1.06,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.062,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.062,
 if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.067,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.079,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 27.5) -> 1.084,
 if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088,
 if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091,
 if (Al2O3 > 0.175) and (T > 35.0) -> 1.144,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.155,
 if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.166,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) -> 1.178,
 if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.182,
 if (Al2O3 > 0.175) and (T <= 35.0) -> 1.189,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.193,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208,
 if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) -> 1.219]

In [5]:

from src.rules import get_features, vectorize_rules

features = get_features(rules, ["T"])
print(features)

df_rules = vectorize_rules(rules, features)
df_rules.head(5)

['(Al2O3 <= 0.175)', '(Al2O3 > 0.025)', '(Al2O3 > 0.175)', '(TiO2 <= 0.175)', '(TiO2 > 0.025)', '(TiO2 > 0.175)']

Out[5]:

	(Al2O3 <= 0.175)	(Al2O3 > 0.025)	(Al2O3 > 0.175)	(TiO2 <= 0.175)	(TiO2 > 0.025)	(TiO2 > 0.175)	consequent
rule
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033	1	0	0	1	0	0	1.0333299999999999
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038	1	0	0	1	0	0	1.03826
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.048	1	0	0	1	0	0	1.0478999999999998
if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053	1	1	0	1	0	0	1.05291
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056	1	0	0	1	1	0	1.05601

In [6]:

from src.cluster_helper import draw_best_clusters_plot, get_best_clusters_num

random_state = 9

X = df_rules.copy()
X = X.drop(["consequent"], axis=1)

clusters_score = get_best_clusters_num(X, random_state)
display(clusters_score)

draw_best_clusters_plot(clusters_score)

clusters_num = sorted(clusters_score.items(), key=lambda x: x[1], reverse=True)[0][0]
display(f"The best clusters count is {clusters_num}")

c:\Users\user\Projects\python\fuzzy\.venv\Lib\site-packages\sklearn\base.py:1473: ConvergenceWarning: Number of distinct clusters (5) found smaller than n_clusters (6). Possibly due to duplicate points in X.
  return fit_method(estimator, *args, **kwargs)

{2: 0.5483575964237912,
 3: 0.602943554055511,
 4: 0.8221763769597347,
 5: 1.0,
 6: 1.0}

'The best clusters count is 5'

In [7]:

from sklearn import cluster

from src.cluster_helper import print_cluster_result

kmeans = cluster.KMeans(n_clusters=clusters_num, random_state=random_state)
kmeans.fit(X)

print_cluster_result(X, clusters_num, kmeans.labels_)

Кластер 1 (5):
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) -> 1.033;
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 62.5) -> 1.038;
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 55.0) -> 1.048;
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) and (T > 22.5) -> 1.06;
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.062
--------
Кластер 2 (5):
if (Al2O3 > 0.175) and (T > 35.0) -> 1.144;
if (Al2O3 > 0.175) and (T > 35.0) and (T <= 65.0) -> 1.155;
if (Al2O3 > 0.175) and (T > 35.0) and (T <= 52.5) -> 1.166;
if (Al2O3 > 0.175) and (T <= 35.0) and (T > 22.5) -> 1.182;
if (Al2O3 > 0.175) and (T <= 35.0) -> 1.189
--------
Кластер 3 (6):
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) -> 1.056;
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 67.5) -> 1.062;
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T > 32.5) and (T <= 50.0) -> 1.079;
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 27.5) -> 1.084;
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) and (T > 22.5) -> 1.088;
if (Al2O3 <= 0.175) and (TiO2 <= 0.175) and (TiO2 > 0.025) and (T <= 32.5) -> 1.091
--------
Кластер 4 (4):
if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) -> 1.178;
if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T > 40.0) and (T <= 60.0) -> 1.193;
if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) and (T > 30.0) -> 1.208;
if (Al2O3 <= 0.175) and (TiO2 > 0.175) and (T <= 40.0) -> 1.219
--------
Кластер 5 (4):
if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) -> 1.053;
if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 67.5) -> 1.057;
if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T > 32.5) and (T <= 60.0) -> 1.067;
if (Al2O3 <= 0.175) and (Al2O3 > 0.025) and (TiO2 <= 0.175) and (T <= 32.5) -> 1.088
--------

In [8]:

density_train = pd.read_csv("data/density_train.csv", sep=";", decimal=",")
density_test = pd.read_csv("data/density_test.csv", sep=";", decimal=",")

display(density_train.head(3))
display(density_test.head(3))

	T	Density
0	20	1.06250
1	25	1.05979
2	35	1.05404

	T	Al2O3	Density
0	30	0.00	1.05696
1	55	0.00	1.04158
2	25	0.05	1.08438

In [9]:

from src.rules import simplify_and_group_rules

clustered_rules = simplify_and_group_rules(density_train, rules, clusters_num, kmeans.labels_)
clustered_rules

Out[9]:

[[if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 70) -> 1.033,
  if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 47.5) -> 1.038,
  if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 43.75) -> 1.048,
  if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 27.5) -> 1.06,
  if (Al2O3 = 0.0) and (TiO2 = 0.0) and (T = 20) -> 1.062],
 [if (Al2O3 = 0.3) and (T = 70) -> 1.144,
  if (Al2O3 = 0.3) and (T = 50.0) -> 1.155,
  if (Al2O3 = 0.3) and (T = 43.75) -> 1.166,
  if (Al2O3 = 0.3) and (T = 28.75) -> 1.182,
  if (Al2O3 = 0.3) and (T = 20) -> 1.189],
 [if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 70) -> 1.056,
  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 50.0) -> 1.062,
  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 41.25) -> 1.079,
  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 30.0) -> 1.084,
  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 27.5) -> 1.088,
  if (Al2O3 = 0.0) and (TiO2 = 0.1) and (T = 20) -> 1.091],
 [if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 70) -> 1.178,
  if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 50.0) -> 1.193,
  if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 35.0) -> 1.208,
  if (Al2O3 = 0.0) and (TiO2 = 0.3) and (T = 20) -> 1.219],
 [if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 70) -> 1.053,
  if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 50.0) -> 1.057,
  if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 46.25) -> 1.067,
  if (Al2O3 = 0.1) and (TiO2 = 0.0) and (T = 20) -> 1.088]]

In [10]:

import numpy as np
from skfuzzy import control as ctrl
import skfuzzy as fuzz

temp = ctrl.Antecedent(density_train["T"].sort_values().unique(), "temp")
al = ctrl.Antecedent(np.arange(0, 0.3, 0.005), "al")
ti = ctrl.Antecedent(np.arange(0, 0.3, 0.005), "ti")
density = ctrl.Consequent(np.arange(1.03, 1.22, 0.00001), "density")

temp.automf(3, variable_type="quant")
temp.view()
al.automf(3, variable_type="quant")
al.view()
ti.automf(3, variable_type="quant")
ti.view()
density.automf(5, variable_type="quant")
density.view()

c:\Users\user\Projects\python\fuzzy\.venv\Lib\site-packages\skfuzzy\control\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
  fig.show()

In [11]:

from src.rules import  get_fuzzy_rules

fuzzy_variables = {"Al2O3": al, "TiO2": ti, "T": temp, "consequent": density}
fuzzy_rules = get_fuzzy_rules(clustered_rules, fuzzy_variables)

fuzzy_cntrl = ctrl.ControlSystem(fuzzy_rules)

sim = ctrl.ControlSystemSimulation(fuzzy_cntrl, lenient=False)

display(len(fuzzy_rules))
fuzzy_rules

Out[11]:

[IF (al[low] AND ti[low]) AND temp[high] THEN density[lower]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[low] AND ti[low]) AND temp[average] THEN density[lower]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[low] AND ti[low]) AND temp[low] THEN density[low]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF al[high] AND temp[high] THEN density[average]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF al[high] AND temp[average] THEN density[high]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF al[high] AND temp[low] THEN density[high]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[low] AND ti[average]) AND temp[high] THEN density[low]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[low] AND ti[average]) AND temp[average] THEN density[low]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[low] AND ti[average]) AND temp[low] THEN density[low]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[low] AND ti[high]) AND temp[high] THEN density[high]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[low] AND ti[high]) AND temp[average] THEN density[higher]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[low] AND ti[high]) AND temp[low] THEN density[higher]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[average] AND ti[low]) AND temp[high] THEN density[lower]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[average] AND ti[low]) AND temp[average] THEN density[low]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax,
 IF (al[average] AND ti[low]) AND temp[low] THEN density[low]
 	AND aggregation function : fmin
 	OR aggregation function  : fmax]

In [12]:

sim.input["temp"] = 25
sim.input["al"] = 0.0
sim.input["ti"] = 0.0
sim.compute()
sim.print_state()
display(sim.output["density"])
density.view(sim=sim)

=============
 Antecedents 
=============
Antecedent: al                      = 0.0
  - low                             : 1.0
  - average                         : 0.0
  - high                            : 0.0
Antecedent: ti                      = 0.0
  - low                             : 1.0
  - average                         : 0.0
  - high                            : 0.0
Antecedent: temp                    = 25
  - low                             : 0.8
  - average                         : 0.2
  - high                            : 0.0

=======
 Rules 
=======
RULE #0:
  IF (al[low] AND ti[low]) AND temp[high] THEN density[lower]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[low]                                                : 1.0
  - ti[low]                                                : 1.0
  - temp[high]                                             : 0.0
                      (al[low] AND ti[low]) AND temp[high] = 0.0
  Activation (THEN-clause):
                                            density[lower] : 0.0

RULE #1:
  IF (al[low] AND ti[low]) AND temp[average] THEN density[lower]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[low]                                                : 1.0
  - ti[low]                                                : 1.0
  - temp[average]                                          : 0.2
                   (al[low] AND ti[low]) AND temp[average] = 0.2
  Activation (THEN-clause):
                                            density[lower] : 0.2

RULE #2:
  IF (al[low] AND ti[low]) AND temp[low] THEN density[low]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[low]                                                : 1.0
  - ti[low]                                                : 1.0
  - temp[low]                                              : 0.8
                       (al[low] AND ti[low]) AND temp[low] = 0.8
  Activation (THEN-clause):
                                              density[low] : 0.8

RULE #3:
  IF al[high] AND temp[high] THEN density[average]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[high]                                               : 0.0
  - temp[high]                                             : 0.0
                                   al[high] AND temp[high] = 0.0
  Activation (THEN-clause):
                                          density[average] : 0.0

RULE #4:
  IF al[high] AND temp[average] THEN density[high]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[high]                                               : 0.0
  - temp[average]                                          : 0.2
                                al[high] AND temp[average] = 0.0
  Activation (THEN-clause):
                                             density[high] : 0.0

RULE #5:
  IF al[high] AND temp[low] THEN density[high]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[high]                                               : 0.0
  - temp[low]                                              : 0.8
                                    al[high] AND temp[low] = 0.0
  Activation (THEN-clause):
                                             density[high] : 0.0

RULE #6:
  IF (al[low] AND ti[average]) AND temp[high] THEN density[low]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[low]                                                : 1.0
  - ti[average]                                            : 0.0
  - temp[high]                                             : 0.0
                  (al[low] AND ti[average]) AND temp[high] = 0.0
  Activation (THEN-clause):
                                              density[low] : 0.0

RULE #7:
  IF (al[low] AND ti[average]) AND temp[average] THEN density[low]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[low]                                                : 1.0
  - ti[average]                                            : 0.0
  - temp[average]                                          : 0.2
               (al[low] AND ti[average]) AND temp[average] = 0.0
  Activation (THEN-clause):
                                              density[low] : 0.0

RULE #8:
  IF (al[low] AND ti[average]) AND temp[low] THEN density[low]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[low]                                                : 1.0
  - ti[average]                                            : 0.0
  - temp[low]                                              : 0.8
                   (al[low] AND ti[average]) AND temp[low] = 0.0
  Activation (THEN-clause):
                                              density[low] : 0.0

RULE #9:
  IF (al[low] AND ti[high]) AND temp[high] THEN density[high]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[low]                                                : 1.0
  - ti[high]                                               : 0.0
  - temp[high]                                             : 0.0
                     (al[low] AND ti[high]) AND temp[high] = 0.0
  Activation (THEN-clause):
                                             density[high] : 0.0

RULE #10:
  IF (al[low] AND ti[high]) AND temp[average] THEN density[higher]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[low]                                                : 1.0
  - ti[high]                                               : 0.0
  - temp[average]                                          : 0.2
                  (al[low] AND ti[high]) AND temp[average] = 0.0
  Activation (THEN-clause):
                                           density[higher] : 0.0

RULE #11:
  IF (al[low] AND ti[high]) AND temp[low] THEN density[higher]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[low]                                                : 1.0
  - ti[high]                                               : 0.0
  - temp[low]                                              : 0.8
                      (al[low] AND ti[high]) AND temp[low] = 0.0
  Activation (THEN-clause):
                                           density[higher] : 0.0

RULE #12:
  IF (al[average] AND ti[low]) AND temp[high] THEN density[lower]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[average]                                            : 0.0
  - ti[low]                                                : 1.0
  - temp[high]                                             : 0.0
                  (al[average] AND ti[low]) AND temp[high] = 0.0
  Activation (THEN-clause):
                                            density[lower] : 0.0

RULE #13:
  IF (al[average] AND ti[low]) AND temp[average] THEN density[low]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[average]                                            : 0.0
  - ti[low]                                                : 1.0
  - temp[average]                                          : 0.2
               (al[average] AND ti[low]) AND temp[average] = 0.0
  Activation (THEN-clause):
                                              density[low] : 0.0

RULE #14:
  IF (al[average] AND ti[low]) AND temp[low] THEN density[low]
	AND aggregation function : fmin
	OR aggregation function  : fmax

  Aggregation (IF-clause):
  - al[average]                                            : 0.0
  - ti[low]                                                : 1.0
  - temp[low]                                              : 0.8
                   (al[average] AND ti[low]) AND temp[low] = 0.0
  Activation (THEN-clause):
                                              density[low] : 0.0


==============================
 Intermediaries and Conquests 
==============================
Consequent: density                  = 1.076592785948375
  lower:
    Accumulate using accumulation_max : 0.2
  low:
    Accumulate using accumulation_max : 0.8
  average:
    Accumulate using accumulation_max : 0.0
  high:
    Accumulate using accumulation_max : 0.0
  higher:
    Accumulate using accumulation_max : 0.0

np.float64(1.076592785948375)

In [22]:

from sklearn import metrics
import math


def fuzzy_pred(row):
    sim.input["temp"] = row["T"]
    sim.input["al"] = row["Al2O3"]
    sim.input["ti"] = row["TiO2"]
    sim.compute()
    return sim.output["density"]


def rmse(row):
    return math.sqrt(metrics.mean_squared_error([row["Real"]], [row["Inferred"]]))

result_train = density_train.copy()
result_train["Real"] = result_train["Density"]
result_train["Inferred"] = result_train.apply(fuzzy_pred, axis=1)
result_train["RMSE"] = result_train.apply(rmse, axis=1)
result_train.head(15)

Out[22]:

	T	Al2O3	Density	Real	Inferred	RMSE
0	20	0.00	1.06250	1.06250	1.077498	0.014998
1	25	0.00	1.05979	1.05979	1.076593	0.016803
2	35	0.00	1.05404	1.05404	1.069156	0.015116
3	40	0.00	1.05103	1.05103	1.061106	0.010076
4	45	0.00	1.04794	1.04794	1.045833	0.002107
5	50	0.00	1.04477	1.04477	1.046360	0.001590
6	60	0.00	1.03826	1.03826	1.047642	0.009382
7	65	0.00	1.03484	1.03484	1.046360	0.011520
8	70	0.00	1.03182	1.03182	1.045833	0.014013
9	20	0.05	1.08755	1.08755	1.077498	0.010052
10	45	0.05	1.07105	1.07105	1.067145	0.003905
11	50	0.05	1.06760	1.06760	1.067145	0.000455
12	55	0.05	1.06409	1.06409	1.067988	0.003898
13	65	0.05	1.05691	1.05691	1.062538	0.005628
14	70	0.05	1.05291	1.05291	1.047191	0.005719

In [26]:

result_test = density_test.copy()
result_test["Real"] = result_test["Density"]
result_test["Inferred"] = result_test.apply(fuzzy_pred, axis=1)
result_test["RMSE"] = result_test.apply(rmse, axis=1)
# result_test["RMSE"] = result_test["RMSE"].apply(lambda x: "{:,.4f}".format(x))
result_test = result_test.round({"RMSE": 3})
result_test

Out[26]:

	T	Al2O3	TiO2	Density	Real	Inferred	RMSE
0	30	0.00	0.00	1.05696	1.05696	1.073918	0.017
1	55	0.00	0.00	1.04158	1.04158	1.047642	0.006
2	25	0.05	0.00	1.08438	1.08438	1.076518	0.008
3	30	0.05	0.00	1.08112	1.08112	1.073918	0.007
4	35	0.05	0.00	1.07781	1.07781	1.069156	0.009
5	40	0.05	0.00	1.07446	1.07446	1.067145	0.007
6	60	0.05	0.00	1.06053	1.06053	1.067988	0.007
7	35	0.30	0.00	1.17459	1.17459	1.172492	0.002
8	65	0.30	0.00	1.14812	1.14812	1.136460	0.012
9	45	0.00	0.05	1.07424	1.07424	1.067145	0.007
10	50	0.00	0.05	1.07075	1.07075	1.067145	0.004
11	55	0.00	0.05	1.06721	1.06721	1.067988	0.001
12	20	0.00	0.30	1.22417	1.22417	1.204157	0.020
13	30	0.00	0.30	1.21310	1.21310	1.202348	0.011
14	40	0.00	0.30	1.20265	1.20265	1.203630	0.001
15	60	0.00	0.30	1.18265	1.18265	1.176072	0.007
16	70	0.00	0.30	1.17261	1.17261	1.172492	0.000

In [25]:

rmetrics = {}
rmetrics["RMSE_train"] = math.sqrt(
    metrics.mean_squared_error(result_train["Real"], result_train["Inferred"])
)
rmetrics["RMSE_test"] = math.sqrt(
    metrics.mean_squared_error(result_test["Real"], result_test["Inferred"])
)
rmetrics["RMAE_test"] = math.sqrt(
    metrics.mean_absolute_error(result_test["Real"], result_test["Inferred"])
)
rmetrics["R2_test"] = metrics.r2_score(result_test["Real"], result_test["Inferred"])

rmetrics

Out[25]:

{'RMSE_train': 0.009765373953597112,
 'RMSE_test': 0.009031443610368107,
 'RMAE_test': 0.08581225574298121,
 'R2_test': 0.978451748357252}

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