def main():
cars = pd.read_hdf('data/processed/datasets.hdf', 'cars')
carsX = cars.drop('Class', 1).copy().values
carsY = cars['Class'].copy().values
madelon = pd.read_hdf('data/processed/datasets.hdf', 'madelon')
madelonX = madelon.drop('Class', 1).copy().values
madelonY = madelon['Class'].copy().values
cars_trgX, cars_tstX, cars_trgY, cars_tstY = ms.train_test_split(
carsX, carsY, test_size=0.3, random_state=0, stratify=carsY)
madelon_trgX, madelon_tstX, madelon_trgY, madelon_tstY = ms.train_test_split(
madelonX, madelonY, test_size=0.3, random_state=0, stratify=madelonY)
d = carsX.shape[1]
hiddens_cars = [(h, ) * l for l in [1, 2, 3] for h in [d, d // 2, d * 2]]
alphas = [10**-x for x in np.arange(1, 9.01, 1 / 2)]
d = madelonX.shape[1]
hiddens_madelon = [(h, ) * l for l in [1, 2, 3]
for h in [d, d // 2, d * 2]]
pipeM = Pipeline([
('Scale', StandardScaler()),
('Cull1', SelectFromModel(RandomForestClassifier(),
threshold='median')),
('Cull2', SelectFromModel(RandomForestClassifier(),
threshold='median')),
('Cull3', SelectFromModel(RandomForestClassifier(),
threshold='median')),
('Cull4', SelectFromModel(RandomForestClassifier(),
threshold='median')), ('KNN', knnC())
])
pipeA = Pipeline([('Scale', StandardScaler()), ('KNN', knnC())])
params_madelon = {
'KNN__metric': ['manhattan', 'euclidean', 'chebyshev'],
'KNN__n_neighbors': np.arange(1, 51, 3),
'KNN__weights': ['uniform', 'distance']
}
params_cars = {
'KNN__metric': ['manhattan', 'euclidean', 'chebyshev'],
'KNN__n_neighbors': np.arange(1, 51, 3),
'KNN__weights': ['uniform', 'distance']
}
madelon_clf = basicResults(pipeM, madelon_trgX, madelon_trgY, madelon_tstX,
madelon_tstY, params_madelon, 'KNN', 'madelon')
cars_clf = basicResults(pipeA, cars_trgX, cars_trgY, cars_tstX, cars_tstY,
params_cars, 'KNN', 'cars')
#madelon_final_params={'KNN__n_neighbors': 43, 'KNN__weights': 'uniform', 'KNN__p': 1}
#cars_final_params={'KNN__n_neighbors': 142, 'KNN__p': 1, 'KNN__weights': 'uniform'}
madelon_final_params = madelon_clf.best_params_
cars_final_params = cars_clf.best_params_
pipeM.set_params(**madelon_final_params)
makeTimingCurve(madelonX, madelonY, pipeM, 'KNN', 'madelon')
pipeA.set_params(**cars_final_params)
makeTimingCurve(carsX, carsY, pipeA, 'KNN', 'cars')
d = adultX.shape[1]
hiddens_adult = [(h,)*l for l in [1,2,3] for h in [d,d//2,d*2]]
d = wineX.shape[1]
hiddens_wine = [(h,)*l for l in [1,2,3] for h in [d,d//2,d*2]]
adult_trnX, adult_tstX, adult_trnY, adult_tstY = ms.train_test_split(adultX, adultY, test_size=0.3, random_state=0,stratify=adultY)
wine_trnX, wine_tstX, wine_trnY, wine_tstY = ms.train_test_split(wineX, wineY, test_size=0.3, random_state=0,stratify=wineY)
N_adult = adult_trnX.shape[0]
N_wine = wine_trnX.shape[0]
pipeA = Pipeline([('Scale',StandardScaler()),
('KNN',knnC())])
pipeW = Pipeline([('Scale',StandardScaler()),
('KNN',knnC())])
params_wine= {'KNN__metric':['manhattan','euclidean','chebyshev'],'KNN__n_neighbors':np.arange(1,51,5),'KNN__weights':['uniform','distance']}
params_adult= {'KNN__metric':['manhattan','euclidean','chebyshev'],'KNN__n_neighbors':np.arange(1,51,5),'KNN__weights':['uniform','distance']}
#
start = time.time()
wine_clf = basicResults(pipeW,wine_trnX,wine_trnY,wine_tstX,wine_tstY,params_wine,'KNN','wine')
end = time.time()
print(end-start)
start = time.time()
adult_clf = basicResults(pipeA,adult_trnX,adult_trnY,adult_tstX,adult_tstY,params_adult,'KNN','adult')
cancer_trgX, cancer_tstX, cancer_trgY, cancer_tstY = ms.train_test_split(
cancerX, cancerY, test_size=0.25, random_state=0, stratify=cancerY)
d = adultX.shape[1]
hiddens_adult = [(h, ) * l for l in [1, 2, 3] for h in [d, d // 2, d * 2]]
d = cancerX.shape[1]
hiddens_cancer = [(h, ) * l for l in [1, 2, 3] for h in [d, d // 2, d * 2]]
alphas = [10**-x for x in np.arange(1, 9.01, 1 / 2)]
pipeM = Pipeline([
('Scale', StandardScaler()),
('Cull1', SelectFromModel(RandomForestClassifier(), threshold='median')),
('Cull2', SelectFromModel(RandomForestClassifier(), threshold='median')),
('Cull3', SelectFromModel(RandomForestClassifier(), threshold='median')),
('Cull4', SelectFromModel(RandomForestClassifier(), threshold='median')),
('KNN', knnC())
])
pipeA = Pipeline([('Scale', StandardScaler()), ('KNN', knnC())])
params_adult = {
'KNN__metric': ['manhattan', 'euclidean', 'chebyshev'],
'KNN__n_neighbors': np.arange(1, 51, 3),
'KNN__weights': ['uniform', 'distance']
}
params_cancer = {
'KNN__metric': ['manhattan', 'euclidean', 'chebyshev'],
'KNN__n_neighbors': np.arange(1, 51, 3),
'KNN__weights': ['uniform', 'distance']
}
def main():
adult = pd.read_csv('data/adult_parsed.csv')
# plt.figure(figsize=(15,12))
# cor_map = adult.corr()
# sns.heatmap(cor_map, annot=True, fmt='.3f', cmap='YlGnBu')
# plt.show()
adult['net_capital'] = adult['capital-gain']-adult['capital-loss']
adult = adult.drop(["fnlwgt","capital-gain","capital-loss","workclass"],axis=1)
adult['income']=adult['income'].map({'<=50K': 0, '>50K': 1})
adult['gender'] = adult['gender'].map({'Male': 0, 'Female': 1}).astype(int)
adult['race'] = adult['race'].map({'Black': 0, 'Asian-Pac-Islander': 1, 'Other': 2, 'White': 3,
'Amer-Indian-Eskimo': 4}).astype(int)
adult['marital-status'] = adult['marital-status'].map({'Never-married':0,'Widowed':1,'Divorced':2, 'Separated':3,
'Married-spouse-absent':4, 'Married-civ-spouse':5, 'Married-AF-spouse':6})
adult['education'] = adult['education'].map({'Preschool':0,'1st-4th':1,'5th-6th':2, '7th-8th':3,
'9th':4, '10th':5, '11th':6, '12th':7, 'Prof-school':8,
'HS-grad':9, 'Some-college':10, 'Assoc-voc':11, 'Assoc-acdm':12,
'Bachelors':13, 'Masters':14, 'Doctorate':15})
adult['occupation'] = adult['occupation'].map({'Priv-house-serv':0,'Protective-serv':1,'Handlers-cleaners':2, 'Machine-op-inspct':3,
'Adm-clerical':4, 'Farming-fishing':5, 'Transport-moving':6, 'Craft-repair':7, 'Other-service':8,
'Tech-support':9, 'Sales':10, 'Exec-managerial':11, 'Prof-specialty':12, 'Armed-Forces':13 })
adult['native-country'] = adult['native-country'].map({'?':-1,'Puerto-Rico':0,'Haiti':1,'Cuba':2, 'Iran':3,
'Honduras':4, 'Jamaica':5, 'Vietnam':6, 'Mexico':7, 'Dominican-Republic':8,
'Laos':9, 'Ecuador':10, 'El-Salvador':11, 'Cambodia':12, 'Columbia':13,
'Guatemala':14, 'South':15, 'India':16, 'Nicaragua':17, 'Yugoslavia':18,
'Philippines':19, 'Thailand':20, 'Trinadad&Tobago':21, 'Peru':22, 'Poland':23,
'China':24, 'Hungary':25, 'Greece':26, 'Taiwan':27, 'Italy':28, 'Portugal':29,
'France':30, 'Hong':31, 'England':32, 'Scotland':33, 'Ireland':34,
'Holand-Netherlands':35, 'Canada':36, 'Germany':37, 'Japan':38,
'Outlying-US(Guam-USVI-etc)':39, 'United-States':40
})
adult['relationship'] = adult['relationship'].map({'Unmarried':0,'Other-relative':1, 'Not-in-family':2,
'Wife':3, 'Husband':4,'Own-child':5})
adult = pd.get_dummies(adult)
adult_income_X = adult.drop('income',1).copy().values
adult_income_Y = adult['income'].copy().values
# wine_data = pd.read_csv('data/wine-red-white-merge.csv')
# wineX = wine_data.drop('quality',1).copy().values
# wineY = wine_data['quality'].copy().values
adult_income_trgX, adult_income_tstX, adult_income_trgY, adult_income_tstY = ms.train_test_split(adult_income_X, adult_income_Y, test_size=0.3, random_state=0,stratify=adult_income_Y)
# wine_trgX, wine_tstX, wine_trgY, wine_tstY = ms.train_test_split(wineX, wineY, test_size=0.3, random_state=0,stratify=wineY)
d = adult_income_X.shape[1]
hiddens_adult_income = [(h,)*l for l in [1,2,3] for h in [d,d//2,d*2]]
alphas = [10**-x for x in np.arange(1,9.01,1/2)]
# d = wineX.shape[1]
# hiddens_wine = [(h,)*l for l in [1,2,3] for h in [d,d//2,d*2]]
pipeM = Pipeline([('Scale',StandardScaler()),
('Cull1',SelectFromModel(RandomForestClassifier(),threshold='median')),
('Cull2',SelectFromModel(RandomForestClassifier(),threshold='median')),
('Cull3',SelectFromModel(RandomForestClassifier(),threshold='median')),
('Cull4',SelectFromModel(RandomForestClassifier(),threshold='median')),
('KNN',knnC())])
pipeA = Pipeline([('Scale',StandardScaler()),
('KNN',knnC())])
params_adult_income= {'KNN__metric':['manhattan','euclidean','chebyshev'],'KNN__n_neighbors':np.arange(1,51,3),'KNN__weights':['uniform','distance']}
# params_wine= {'KNN__metric':['manhattan','euclidean','chebyshev'],'KNN__n_neighbors':np.arange(1,51,3),'KNN__weights':['uniform','distance']}
adult_income_clf = basicResults(pipeA,adult_income_trgX,adult_income_trgY,adult_income_tstX,adult_income_tstY,params_adult_income,'KNN','adult_income')
# wine_clf = basicResults(pipeM,wine_trgX,wine_trgY,wine_tstX,wine_tstY,params_wine,'KNN','wine')
# wine_final_params={'KNN__n_neighbors': 43, 'KNN__weights': 'uniform', 'KNN__p': 1}
#adult_income_final_params={'KNN__n_neighbors': 142, 'KNN__p': 1, 'KNN__weights': 'uniform'}
# wine_final_params=wine_clf.best_params_
adult_income_final_params=adult_income_clf.best_params_
# pipeM.set_params(**wine_final_params)
# makeTimingCurve(wineX,wineY,pipeM,'KNN','wine')
pipeA.set_params(**adult_income_final_params)
makeTimingCurve(adult_income_X,adult_income_Y,pipeA,'KNN','adult_income')
import pandas as pd
from helpers import basicResults, makeTimingCurve
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from data_loader import load_data
dataset = "mushrooms" # SET DATASET TO USE. "mushrooms" or "credit_cards"
data_x, data_y = load_data(dataset)
data_train_x, data_test_x, data_train_y, data_test_y = ms.train_test_split(
data_x, data_y, test_size=0.3, random_state=0, stratify=data_y)
pipeM = Pipeline([ #('Scale',StandardScaler()),
# ('Cull1', SelectFromModel(RandomForestClassifier(), threshold='median')),
('KNN', knnC())
])
params = {
'KNN__metric': ['manhattan', 'euclidean', 'chebyshev'],
'KNN__n_neighbors': np.arange(1, 40, 5),
'KNN__weights': ['uniform', 'distance']
}
data_clf = basicResults(pipeM, data_train_x, data_train_y, data_test_x,
data_test_y, params, 'KNN', dataset)
data_final_params = data_clf.best_params_
pipeM.set_params(**data_final_params)
makeTimingCurve(data_x, data_y, pipeM, 'KNN', dataset)
