pyFUME
pyFUME is a Python package for automatic Fuzzy Models Estimation from data [1]. pyFUME contains functions to estimate the antecedent sets and the consequent parameters of a Takagi-Sugeno fuzzy model directly from data. This information is then used to create an executable fuzzy model using the Simpful library. pyFUME also provides facilities for the evaluation of performance. For more information about pyFUME's functionalities, please check the online documentation.
Usage
For the following example, we use the Concrete Compressive Strength data set [2] as can be found in the UCI repository. The code in Example 1 is simple and easy to use, making it ideal to use for practitioners who wish to use the default settings or only wish to use few non-default settings using additional input arguments (Example 2). Users that wish to deviate from the default settings can use the code as shown in Example 3.
Example 1
from pyfume import pyFUME
# Set the path to the data and choose the number of clusters
path='./Concrete_data.csv'
nc=3
# Generate the Takagi-Sugeno FIS
FIS = pyFUME(datapath=path, nr_clus=nc)
# Calculate and print the accuracy of the generated model
MAE=FIS.calculate_error(method="MAE")
print ("The estimated error of the developed model is:", MAE)
## Use the FIS to predict the compressive strength of a new concrete sample
# Extract the model from the FIS object
model=FIS.get_model()
# Set the values for each variable
model.set_variable('Cement', 300.0)
model.set_variable('BlastFurnaceSlag', 50.0)
model.set_variable('FlyAsh', 0.0)
model.set_variable('Water', 175.0)
model.set_variable('Superplasticizer',0.7)
model.set_variable('CoarseAggregate', 900.0)
model.set_variable('FineAggregate', 600.0)
model.set_variable('Age', 45.0)
# Perform inference and print predicted value
print(model.Sugeno_inference(['OUTPUT']))
Example 2
from pyfume import pyFUME
# Set the path to the data and choose the number of clusters
path='./Concrete_data.csv'
nc=3
# Generate the Takagi-Sugeno FIS
FIS = pyFUME(datapath=path, nr_clus=nc, normalization='minmax', feature_selection=True)
# Calculate and print the accuracy of the generated model
MAE=FIS.calculate_error(method="MAE")
print ("The estimated error of the developed model is:", MAE)
## Use the FIS to predict the compressive strength of a new concrete sample
# Extract the model from the FIS object
model=FIS.get_model()
# Set the values for each variable
model.set_variable('Cement', 300.0)
model.set_variable('BlastFurnaceSlag', 50.0)
model.set_variable('FlyAsh', 0.0)
model.set_variable('Water', 175.0)
model.set_variable('Superplasticizer',0.7)
model.set_variable('CoarseAggregate', 900.0)
model.set_variable('FineAggregate', 600.0)
model.set_variable('Age', 45.0)
# Perform inference and print predicted value
print(model.Sugeno_inference(['OUTPUT']))
Example 3
from pyfume import *
# Set the path to the data and choose the number of clusters
path='./Concrete_data.csv'
nr_clus=3
# Load and normalize the data using min-max normalization
dl=DataLoader(path,normalize='minmax')
variable_names=dl.variable_names
dataX=dl.dataX
dataY=dl.dataY
# Split the data using the hold-out method in a training (default: 75%)
# and test set (default: 25%).
ds = DataSplitter()
x_train, y_train, x_test, y_test = ds.holdout(dataX=dl.dataX, dataY=dl.dataY)
# Select features relevant to the problem
fs=FeatureSelector(dataX=x_train, dataY=y_train, nr_clus=nr_clus, variable_names=variable_names)
selected_feature_indices, variable_names=fs.wrapper()
# Adapt the training and test input data after feature selection
x_train = x_train[:, selected_feature_indices]
x_test = x_test[:, selected_feature_indices]
# Cluster the training data (in input-output space) using FCM with default settings
cl = Clusterer(x_train=x_train, y_train=y_train, nr_clus=nr_clus)
cluster_centers, partition_matrix, _ = cl.cluster(method="fcm")
# Estimate the membership funtions of the system (default: mf_shape = gaussian)
ae = AntecedentEstimator(x_train=x_train, partition_matrix=partition_matrix)
antecedent_parameters = ae.determineMF()
# Calculate the firing strength of each rule for each data instance
fsc=FireStrengthCalculator(antecedent_parameters=antecedent_parameters, nr_clus=nr_clus, variable_names=variable_names)
firing_strengths = fsc.calculate_fire_strength(data=x_train)
# Estimate the parameters of the consequent functions
ce = ConsequentEstimator(x_train=x_train, y_train=y_train, firing_strengths=firing_strengths)
consequent_parameters = ce.suglms()
# Build a first-order Takagi-Sugeno model using Simpful. Specify the optional
# 'extreme_values' argument to specify the universe of discourse of the input
# variables if you which to use Simpful's membership function plot functionalities.
simpbuilder = SugenoFISBuilder(antecedent_sets=antecedent_parameters, consequent_parameters=consequent_parameters, variable_names=variable_names)
model = simpbuilder.get_model()
# Calculate the mean squared error (MSE) of the model using the test data set
test=SugenoFISTester(model=model, test_data=x_test, variable_names=variable_names, golden_standard=y_test)
MAE = test.calculate_MAE()
print('The mean absolute error of the created model is', MAE)
Installation
pip install pyfume
Further information
If you need further information, please write an e-mail to Caro Fuchs: c.e.m.fuchs(at)tue.nl.
References
[1] Fuchs, C., Spolaor, S., Nobile, M. S., & Kaymak, U. (2020) "pyFUME: a Python package for fuzzy model estimation". In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE.
[2] I-Cheng Yeh, "Modeling of strength of high performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998). http://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file pyFUME-0.1.22.tar.gz.
File metadata
- Download URL: pyFUME-0.1.22.tar.gz
- Upload date:
- Size: 28.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/3.2.0 pkginfo/1.7.0 requests/2.25.1 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.7.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c1c8b1ef951e3b165fdc6a632b52d1bce55825bf3a5688306da64c207bc53bc3
|
|
| MD5 |
54ed279b22b2a8f78dde1b6dfde0b671
|
|
| BLAKE2b-256 |
50eee11c1edc6be4e4a969a1ce5278a54d04df2de64c7581f99364c328f6219c
|
File details
Details for the file pyFUME-0.1.22-py3-none-any.whl.
File metadata
- Download URL: pyFUME-0.1.22-py3-none-any.whl
- Upload date:
- Size: 54.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/3.2.0 pkginfo/1.7.0 requests/2.25.1 setuptools/50.3.2 requests-toolbelt/0.9.1 tqdm/4.55.1 CPython/3.7.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
b078de95873048c4aeeaa426a4c2986eb4a710f0082a5928e1d09fce7d3edf4d
|
|
| MD5 |
47af0a8abb80a4adf6b843927b5f1743
|
|
| BLAKE2b-256 |
10c7b5d21914839bc715c68b9640f4b68b55d96806fc103c2ecff6272104a9f3
|
