Package: pspso
Welcome to pspso's documentation!Overview and InstallationOverviewpspsois a python library for selecting machine learning algorithms
parameters. The first version supports two single algorithms:
Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). It
supports two ensembles: Extreme Gradient Boosting (XGBoost) and Gradient
Boosting Decision Trees (GBDT).Two types of machine learning tasks are supported by pspso:Regression.Binary classification.Three scores are supported in the first version of pspso:Regression:Root Mean Square Error (RMSE)Binary Classication:Area under the Curve (AUC) of the Receiver Operating
Characteristic (ROC)AccuracyInstallationUse the package managerpipto install
pspso.pip install pspsoUsageMLP Example (Binary Classification)pspsois used to select the machine learning algorithms parameters.
Below is an example for using the pspso to select the parameters of the
MLP. pspso handles the MLP random weights intialization issue that may
cause losing the best solution in consecutive iterations.The following example demonstrates the selection process of the MLP
parameters. A variable namedparamswas not given by the user. Hence,
the default search space of the MLP is loaded. This search space
contains five parameters:params = {"optimizer": ["RMSprop", "adam", "sgd",'adamax','nadam','adadelta'] ,
      "learning_rate":  [0.1,0.3,2],
      "neurons": [1,40,0],
      "hiddenactivation": ['relu','sigmoid','tanh'],
      "activation":['relu','sigmoid','tanh']}The task and the score were defined asbinary classificationandaucrespectively. Then, the PSO was used to select the parameters of the
MLP. Results are provided back to the user through theprint_results()function.from sklearn.preprocessing import MinMaxScaler
from pspso import pspso
from sklearn import datasets
from sklearn.model_selection import train_test_split

breastcancer = datasets.load_breast_cancer()
data=breastcancer.data#get the breast cancer dataset input features
target=breastcancer.target# target
X_train, X_test, Y_train, Y_test = train_test_split(data, target,test_size=0.1,random_state=42,stratify=target)
normalize = MinMaxScaler(feature_range=(0,1))#normalize input features 
X_train=normalize.fit_transform(X_train)
X_test=normalize.transform(X_test)
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train,test_size=0.15,random_state=42,stratify=Y_train)
p=pspso(estimator='mlp',task='binary classification', score='auc')
pos,cost,duration,model,optimizer=p.fitpspso(X_train,Y_train,X_val,Y_val)
p.print_results()#print the results
testscore=pspso.predict(p.model,p.estimator,p.task,p.score, X_test, Y_test)
print(1-testscore)In this example, four parameters were examined: optimizer,
learning_rate, hiddenactivation, and activation. The number of neurons
in the hidden layer was kept as default.Output:Estimator: mlp
Task: binary classification
Selection type: PSO
Number of attempts:50
Total number of combinations: 45360
Parameters:
{'optimizer': 'nadam', 'learning_rate': 0.29, 'neurons': 4, 'hiddenactivation': 'sigmoid', 'activation': 'sigmoid'}
Global best position: [3.8997699  0.28725911 4.21218138 1.41200923 0.84643591]
Global best cost: 0.0
Time taken to find the set of parameters: 160.3374378681183
Number of particles: 5
Number of iterations: 10
0.9867724867724867XGBoost Example (Binary Classification)from sklearn.preprocessing import MinMaxScaler
from pspso import pspso
from sklearn import datasets
from sklearn.model_selection import train_test_split

breastcancer = datasets.load_breast_cancer()
data=breastcancer.data#get the breast cancer dataset input features
target=breastcancer.target# target
X_train, X_test, Y_train, Y_test = train_test_split(data, target,test_size=0.1,random_state=42,stratify=target)
normalize = MinMaxScaler(feature_range=(0,1))#normalize input features 
X_train=normalize.fit_transform(X_train)
X_test=normalize.transform(X_test)
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train,test_size=0.15,random_state=42,stratify=Y_train)

params = {
        "learning_rate":  [0.01,0.2,2],
        "max_depth": [1,10,0],
        "n_estimators": [2,200,0],
        "subsample": [0.7,1,1]}
p=pspso(estimator='xgboost',params=params,task='binary classification', score='auc')
pos,cost,duration,model,optimizer=p.fitpspso(X_train,Y_train,X_val,Y_val)
p.print_results()#print the results
testscore=pspso.predict(p.model,p.estimator,p.task,p.score, X_test, Y_test)
print(1-testscore)XGBoost Example (Regression)The XGBoost is an implementation of boosting decision trees. Five
parameters were utilized for selection: objective, learning rate,
maximum depth, number of estimators, and subsample. Three categorical
values were selected for the objective parameter. The learning rate
parameter values range between0.01and0.2with2decimal point,
maximum depth ranges between1and10with0decimal points(1,2,3,4,5,6,7,8,9,10), etc. The task and score are selected as
regression and RMSE respectively. The number of particles and number of
iterations can be left as default values if needed. Then, a pspso
instance is created. By applying the fitpspso function, the selection
process is applied. Finally, results are printed back to the user. The
best model, best parameters, score, time, and other details will be
saved in the created instance for the user to check.from sklearn.preprocessing import MinMaxScaler
from pspso import pspso
from sklearn import datasets
from sklearn.model_selection import train_test_split

boston_data = datasets.load_boston()
data=boston_data.data
target=boston_data.target

X_train, X_test, Y_train, Y_test = train_test_split(data, target,test_size=0.1,random_state=42)
normalize = MinMaxScaler(feature_range=(0,1))#normalize input features
normalizetarget = MinMaxScaler(feature_range=(0,1))#normalize target

X_train=normalize.fit_transform(X_train)
X_test=normalize.transform(X_test)
Y_train=normalizetarget.fit_transform(Y_train.reshape(-1,1))
Y_test=normalizetarget.transform(Y_test.reshape(-1,1))

X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train,test_size=0.25,random_state=42)
params = {
        "objective":['reg:tweedie',"reg:linear","reg:gamma"],
        "learning_rate":  [0.01,0.2,2],
        "max_depth": [1,10,0],
        "n_estimators": [2,200,0],
        "subsample": [0.7,1,1]}
p=pspso(estimator='xgboost',params=params,task='regression', score='rmse')
pos,cost,duration,model,optimizer=p.fitpspso(X_train,Y_train,X_val,Y_val)
p.print_results()#print the results
testscore=pspso.predict(p.model,p.estimator,p.task,p.score, X_test, Y_test)
print(testscore)User InputThe user is required to select the type of the algorithm ('mlp', 'svm',
'xgboost', 'gbdt'); the task type ('binary
classification','regression'), score ('rmse', 'acc', or 'auc'). The user
can keep the parameters variable empty, where a default set of
parameters and ranges is loaded for each algorithm.from pspso import pspso
task='binary classification'
score='auc'
p=pspso.pspso('xgboost',None,task,score)Pspso allows the user to provide a range of parameters for exploration.
The parameters vary between each algorithm. Any parameter supported by
the Scikit-Learn API for GBDT and XGBoost can be added to the selection
process. A set of parameters that contains five XGBoost parameters is
shown below. The parameters are encoded in JSON object that consists ofkey,valuepairs:params = {"objective":['reg:tweedie',"reg:linear","reg:gamma"],
        "learning_rate":  [0.01,0.2,2],
        "max_depth": [1,10,0],
        "n_estimators": [2,200,0],
        "subsample": [0.7,1,1]}The key can be any parameter belonging to to the algorithm under
investigation. The value is a list. Pspso will check the type of the
first element in the list, which will determine if the values of the
parameter are categorical or numerical.Categorical ParametersIf the parameter values arecategorical, string values are expected to
be found in the list, as shown inobjectiveparameter. The values in
the list will be automatically mapped into a list of integers, where
each integer represents a value in the original list. The order of the
values inside the list affect the position of the value in the search
space.Numerical ParametersIf the parameter is numerical, a list of three elements [lb,ub, rv] is
expected to be found:lb: repesents the lowest value in the search spaceub: represents the maximum value in the search spacerv: represents the number of decimal points the parameter values
are rounded to before being added for training the algorithmFor e.g if you want pspso to select n_estimators, add the following
list[2,200,0]. By that, the lowest n_estimators will be2, the
highest to be examined is200, and each possible value is rounded to
an integer value (0decimal points).Other parametersThe user is given the chance to handle some of the default parameters
such as the number of epochs in the MLP. Although this parameter can be
optimized, but its not encouraged. The user can modify this by changing
a pspso class instance. For e.g., to change the number of epochs from
default to 10 in MLP training:from pspso import pspso
task='binary classification'
score='auc'
p=pspso.pspso('mlp',None,task,score)# in case of empty set of params (None) default search space is loaded
p.defaultparams['epochs']=10The verbosity can be modified for any algorithm, which allows showing
details of the training process:from pspso import pspso
task='binary classification'
score='auc'
p=pspso.pspso('mlp',None,task,score)
p.verbosity=1Early stopping rounds can alos be modified, the user can set a value
different to the default value:from pspso import pspso
task='binary classification'
score='auc'
p=pspso.pspso('xgboost',None,task,score)
p.early_stopping=10Other parameters such that n_jobs in XGBoost can also be modified
before the start of the selection process.ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.We are working towards adding the cross validation support that will
take the training data and number of folds, then split the records and
train each fold. Finally, the average performance is retuned to the
user.We are also working on adding multi-class classification and data
oversampling techniques.Steps for adding another machine learning algorithmThe main reason behind the development of this package is to facilitate
the use of the algorithms with a minimum amount of code required, in a
way like AutoML. However, the steps to add an algorithm are followed:Add a condition in theget_default_search_space()function to
include the new algorithm default search space parameters with
upper/lower boundsAdd a default search space based on the algorithm and the task
(binary classification or regression) to theget_default_params()functionCreate a function forward_prop_algorithm_namethat accepts
parameters (similar to forward_prop_gbdt,forward_prop_svm) and
returns two variables: the model and fitness valueAdd a condition in the function __f__ to forward the task to the
function created in step 3Add a condition in the function __predict__ to allow building
the model using the function created in step 3LicenseCopyright (c) [2020] [Ali Haidar]Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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