#!/usr/bin/env python
# coding: utf-8

# # Particle Swarm Optimization

# # Installation

# In[1]:


get_ipython().system('pip install zoofs')


# # Load Breast cancer dataset 

# In[2]:


from sklearn.datasets import load_breast_cancer
import pandas as pd
data = load_breast_cancer()
X_train=pd.DataFrame(data['data'],columns=data['feature_names'])
y_train=pd.Series(data['target'])


# # Importing Zoofs Algo

# In[3]:


from zoofs import ParticleSwarmOptimization


# # Setting up Objective function and fitting algo

# In[4]:


from sklearn.metrics import log_loss
# define your own objective function, make sure the function receives four parameters,
#  fit your model and return the objective value ! 
def objective_function_topass(model,X_train, y_train, X_valid, y_valid):      
    model.fit(X_train,y_train)  
    P=log_loss(y_valid,model.predict_proba(X_valid))
    return P
    
# create object of algorithm
algo_object=ParticleSwarmOptimization(objective_function_topass,n_iteration=25,
                                       population_size=20,minimize=True)
import lightgbm as lgb
lgb_model = lgb.LGBMClassifier()                                       
# fit the algorithm
algo_object.fit(lgb_model,X_train, y_train, X_train, y_train,verbose=True)
#plot your results


# # Plotting the Results

# In[5]:


algo_object.plot_history()