99a4c21f6e
287 KiB
287 KiB
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
34
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
34
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
24
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) 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 (7). Possibly due to duplicate points in X. return fit_method(estimator, *args, **kwargs) 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 (8). Possibly due to duplicate points in X. return fit_method(estimator, *args, **kwargs) 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 (9). Possibly due to duplicate points in X. return fit_method(estimator, *args, **kwargs) 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 (10). 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,
7: 1.0,
8: 1.0,
9: 1.0,
10: 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 | Al2O3 | TiO2 | Density | |
|---|---|---|---|---|
| 0 | 20 | 0.0 | 0.0 | 1.06250 |
| 1 | 25 | 0.0 | 0.0 | 1.05979 |
| 2 | 35 | 0.0 | 0.0 | 1.05404 |
| T | Al2O3 | TiO2 | Density | |
|---|---|---|---|---|
| 0 | 30 | 0.00 | 0.0 | 1.05696 |
| 1 | 55 | 0.00 | 0.0 | 1.04158 |
| 2 | 25 | 0.05 | 0.0 | 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
15
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"] = 20
sim.input["al"] = 0.0
sim.input["ti"] = 0.0
sim.compute()
sim.print_state()
display(sim.output["density"])
=============
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 = 20
- low : 1.0
- average : 0.0
- 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] : 0.0
(al[low] AND ti[low]) AND temp[average] = 0.0
Activation (THEN-clause):
density[lower] : 0.0
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] : 1.0
(al[low] AND ti[low]) AND temp[low] = 1.0
Activation (THEN-clause):
density[low] : 1.0
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] : 0.0
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] : 1.0
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] : 0.0
(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] : 1.0
(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] : 0.0
(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] : 1.0
(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] : 0.0
(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] : 1.0
(al[average] AND ti[low]) AND temp[low] = 0.0
Activation (THEN-clause):
density[low] : 0.0
==============================
Intermediaries and Conquests
==============================
Consequent: density = 1.0774975002635008
lower:
Accumulate using accumulation_max : 0.0
low:
Accumulate using accumulation_max : 1.0
average:
Accumulate using accumulation_max : 0.0
high:
Accumulate using accumulation_max : 0.0
higher:
Accumulate using accumulation_max : 0.0
np.float64(1.0774975002635008)
In [13]:
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"]
result_train = density_train.copy()
result_train["DensityPred"] = result_train.apply(fuzzy_pred, axis=1)
result_train.head(15)
Out[13]:
| T | Al2O3 | TiO2 | Density | DensityPred | |
|---|---|---|---|---|---|
| 0 | 20 | 0.00 | 0.0 | 1.06250 | 1.077498 |
| 1 | 25 | 0.00 | 0.0 | 1.05979 | 1.076593 |
| 2 | 35 | 0.00 | 0.0 | 1.05404 | 1.069156 |
| 3 | 40 | 0.00 | 0.0 | 1.05103 | 1.061106 |
| 4 | 45 | 0.00 | 0.0 | 1.04794 | 1.045833 |
| 5 | 50 | 0.00 | 0.0 | 1.04477 | 1.046360 |
| 6 | 60 | 0.00 | 0.0 | 1.03826 | 1.047642 |
| 7 | 65 | 0.00 | 0.0 | 1.03484 | 1.046360 |
| 8 | 70 | 0.00 | 0.0 | 1.03182 | 1.045833 |
| 9 | 20 | 0.05 | 0.0 | 1.08755 | 1.077498 |
| 10 | 45 | 0.05 | 0.0 | 1.07105 | 1.067145 |
| 11 | 50 | 0.05 | 0.0 | 1.06760 | 1.067145 |
| 12 | 55 | 0.05 | 0.0 | 1.06409 | 1.067988 |
| 13 | 65 | 0.05 | 0.0 | 1.05691 | 1.062538 |
| 14 | 70 | 0.05 | 0.0 | 1.05291 | 1.047191 |
In [14]:
result_test = density_test.copy()
result_test["DensityPred"] = result_test.apply(fuzzy_pred, axis=1)
result_test
Out[14]:
| T | Al2O3 | TiO2 | Density | DensityPred | |
|---|---|---|---|---|---|
| 0 | 30 | 0.00 | 0.00 | 1.05696 | 1.073918 |
| 1 | 55 | 0.00 | 0.00 | 1.04158 | 1.047642 |
| 2 | 25 | 0.05 | 0.00 | 1.08438 | 1.076518 |
| 3 | 30 | 0.05 | 0.00 | 1.08112 | 1.073918 |
| 4 | 35 | 0.05 | 0.00 | 1.07781 | 1.069156 |
| 5 | 40 | 0.05 | 0.00 | 1.07446 | 1.067145 |
| 6 | 60 | 0.05 | 0.00 | 1.06053 | 1.067988 |
| 7 | 35 | 0.30 | 0.00 | 1.17459 | 1.172492 |
| 8 | 65 | 0.30 | 0.00 | 1.14812 | 1.136460 |
| 9 | 45 | 0.00 | 0.05 | 1.07424 | 1.067145 |
| 10 | 50 | 0.00 | 0.05 | 1.07075 | 1.067145 |
| 11 | 55 | 0.00 | 0.05 | 1.06721 | 1.067988 |
| 12 | 20 | 0.00 | 0.30 | 1.22417 | 1.204157 |
| 13 | 30 | 0.00 | 0.30 | 1.21310 | 1.202348 |
| 14 | 40 | 0.00 | 0.30 | 1.20265 | 1.203630 |
| 15 | 60 | 0.00 | 0.30 | 1.18265 | 1.176072 |
| 16 | 70 | 0.00 | 0.30 | 1.17261 | 1.172492 |
In [15]:
import math
from sklearn import metrics
rmetrics = {}
rmetrics["RMSE_train"] = math.sqrt(
metrics.mean_squared_error(result_train["Density"], result_train["DensityPred"])
)
rmetrics["RMSE_test"] = math.sqrt(
metrics.mean_squared_error(result_test["Density"], result_test["DensityPred"])
)
rmetrics["RMAE_test"] = math.sqrt(
metrics.mean_absolute_error(result_test["Density"], result_test["DensityPred"])
)
rmetrics["R2_test"] = metrics.r2_score(
result_test["Density"], result_test["DensityPred"]
)
rmetrics
Out[15]:
{'RMSE_train': 0.009765373953597112,
'RMSE_test': 0.009031443610368107,
'RMAE_test': 0.08581225574298121,
'R2_test': 0.978451748357252}