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experiment4.py
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import mlrose_hiive
import numpy as np
import pandas as pd
from mlrose_hiive import QueensGenerator, MaxKColorGenerator, TSPGenerator, FlipFlopGenerator, KnapsackGenerator,ContinuousPeaksGenerator
from mlrose_hiive import SARunner, GARunner, NNGSRunner, MIMICRunner, RHCRunner
# # import itertools as it
# import seaborn as sns
import matplotlib.pyplot as plt
# from matplotlib.ticker import StrMethodFormatter
from imblearn.over_sampling import RandomOverSampler
# from sklearn import preprocessing
# from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
# # from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV, KFold
# from sklearn.tree import DecisionTreeClassifier
# from sklearn.neural_network import MLPClassifier
# from sklearn.ensemble import AdaBoostClassifier
# from sklearn.ensemble import GradientBoostingClassifier
# from sklearn import svm
# # from sklearn.neighbors import NearestNeighbors
# from sklearn.neighbors import KNeighborsClassifier
#
# from sklearn.model_selection import cross_validate
# from sklearn.model_selection import learning_curve
# from sklearn.model_selection import validation_curve
#
from sklearn.metrics import accuracy_score
# from sklearn.metrics import recall_score
# from sklearn.metrics import log_loss
# from sklearn.metrics import confusion_matrix
#
# from warnings import simplefilter
# from sklearn.exceptions import ConvergenceWarning
import time as tm
# # from sklearn import metrics
from os import path
def nn():
# Age year nodes Survival
cancer_df = pd.read_csv(path.join('data','haberman.data'))
# cancer_df.rename(columns={'Class':'Class_category'}, inplace=True)
dataset = 2
cancer_df.columns = ['Age', 'Year', 'Nodes','survival']
# y = cancer_df.iloc[:, -1]
# Negative class/Long survival :0 , Positive class/short life :1
cancer_df.survival.replace((1, 2), (0, 1), inplace=True)
y = cancer_df['survival']
X = cancer_df.drop(['survival'], axis=1)
# define oversampling strategy
oversample = RandomOverSampler(sampling_strategy='minority')
# fit and apply the transform
X_over, y_over = oversample.fit_resample(X, y)
X = X_over
y = y_over
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=3240)
################################################
# BackProp
################################################
# learner = MLPClassifier(hidden_layer_sizes=(30,5), learning_rate_init=0.000001, max_iter=300)
# grid_search_parameters = ({
# 'max_iters': [1, 2, 4, 8, 16, 32, 64, 128], # nn params
# 'learning_rate': [0.001, 0.002, 0.003], # nn params
# 'schedule': [ArithDecay(1), ArithDecay(100), ArithDecay(1000)] # sa params
# })
grid_search_parameters = ({
'max_iters': [300], # nn params
'learning_rate': [0.000001], # nn params
'activation': [mlrose_hiive.relu], # nn params
# 'restarts': [1], # rhc params
})
nnr = NNGSRunner(x_train=X_train,
y_train=y_train,
x_test=X_test,
y_test=y_test,
experiment_name='nn_gd',
algorithm=mlrose_hiive.algorithms.gd.gradient_descent,
# algorithm=mlrose_hiive.algorithms.sa.simulated_annealing,
grid_search_parameters=grid_search_parameters,
iteration_list=4 ** np.arange(7),
# iteration_list=[1, 10, 50, 100, 250, 500, 1000, 2500, 5000, 10000],
hidden_layer_sizes=[[30,5]],
# hidden_layer_sizes=[[44,44]],
bias=True,
early_stopping=False,
clip_max=1e+10,
max_attempts=10,
n_jobs=2, cv=4,
generate_curves=True,
seed=631298)
results = nnr.run() # GridSearchCV instance returned
# def __init__(self, x_train, y_train, x_test, y_test, experiment_name, seed, iteration_list, algorithm,
# grid_search_parameters, grid_search_scorer_method=skmt.balanced_accuracy_score,
# bias=True, early_stopping=True, clip_max=1e+10, activation=None,
# max_attempts=500, n_jobs=1, cv=5, generate_curves=True, output_directory=None,
# **kwargs):
################################################
# 1 RHC
################################################
grid_search_parameters = ({
'max_iters': [300], # nn params
'learning_rate': [0.000001], # nn params
'activation': [mlrose_hiive.relu], # nn params
# 'restarts': [1], # rhc params
})
nnr = NNGSRunner(x_train=X_train,
y_train=y_train,
x_test=X_test,
y_test=y_test,
experiment_name='nn_gd',
algorithm=mlrose_hiive.algorithms.rhc.random_hill_climb,
# algorithm=mlrose_hiive.algorithms.sa.simulated_annealing,
grid_search_parameters=grid_search_parameters,
iteration_list=4 ** np.arange(7),
# iteration_list=[1, 10, 50, 100, 250, 500, 1000, 2500, 5000, 10000],
hidden_layer_sizes=[[30,5]],
# hidden_layer_sizes=[[44,44]],
bias=True,
early_stopping=False,
clip_max=1e+10,
max_attempts=10,
n_jobs=2, cv=4,
generate_curves=True,
seed=631298)
results = nnr.run() # GridSearchCV instance returned
################################################
# 2 SA
################################################
grid_search_parameters = ({
'max_iters': [300], # nn params
'learning_rate': [0.000001], # nn params
'activation': [mlrose_hiive.relu], # nn params
# 'restarts': [1], # rhc params
})
nnr = NNGSRunner(x_train=X_train,
y_train=y_train,
x_test=X_test,
y_test=y_test,
experiment_name='nn_gd',
algorithm=mlrose_hiive.algorithms.sa.simulated_annealing,
grid_search_parameters=grid_search_parameters,
iteration_list=4 ** np.arange(7),
# iteration_list=[1, 10, 50, 100, 250, 500, 1000, 2500, 5000, 10000],
hidden_layer_sizes=[[30,5]],
# hidden_layer_sizes=[[44,44]],
bias=True,
early_stopping=False,
clip_max=1e+10,
max_attempts=10,
n_jobs=2, cv=4,
generate_curves=True,
seed=631298)
results = nnr.run() # GridSearchCV instance returned
################################################
# 3 GA
################################################
grid_search_parameters = ({
'max_iters': [300], # nn params
'learning_rate': [0.000001], # nn params
'activation': [mlrose_hiive.relu], # nn params
# 'restarts': [1], # rhc params
})
nnr = NNGSRunner(x_train=X_train,
y_train=y_train,
x_test=X_test,
y_test=y_test,
experiment_name='nn_gd',
algorithm=mlrose_hiive.algorithms.ga.genetic_alg,
grid_search_parameters=grid_search_parameters,
iteration_list=4 ** np.arange(7),
# iteration_list=[1, 10, 50, 100, 250, 500, 1000, 2500, 5000, 10000],
hidden_layer_sizes=[[30, 5]],
# hidden_layer_sizes=[[44,44]],
bias=True,
early_stopping=False,
clip_max=1e+10,
max_attempts=10,
n_jobs=2, cv=4,
generate_curves=True,
seed=631298)
results = nnr.run() # GridSearchCV instance returned
def main():
nn()
if __name__ == "__main__":
main()