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KnnSim.py
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from collections import OrderedDict
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import DataFrame
from pandas import Series
import plotnine as gg
import sklearn as sk
from sklearn.neighbors import KNeighborsClassifier
plt.ion()
import SimData
x2_train = SimData.simulate2Group(n = 100,
p = 2,
effect = [1.25] * 2)
knnFit = KNeighborsClassifier(n_neighbors=3)
knnFit.fit(np.array(x2_train['x']), np.array(x2_train['y']))
knnResub = Series(knnFit.predict(x2_train['x']),
index = x2_train['y'].index)
np.sum(np.diag(pd.crosstab(knnResub, x2_train['y'])))
x2_test = SimData.simulate2Group(n = 100,
p = 2,
effect = [1.25] * 2)
knnTest = Series(knnFit.predict(x2_test['x']),
index = x2_test['y'].index)
np.sum(np.diag(pd.crosstab(knnTest, x2_test['y'])))
def expandGrid(od):
cartProd = list(itertools.product(*od.values()))
return DataFrame(cartProd, columns=od.keys())
parVals = OrderedDict()
parVals['n'] = [100]
parVals['p'] = [2, 5, 10, 25, 100, 500]
parVals['k'] = [3, 5, 10, 25]
parGrid = expandGrid(parVals)
parGrid['effect'] = 2.5
parGrid['effect'] = parGrid['effect'] / np.sqrt(parGrid['p'])
def knnSimulate(param):
trainSet = SimData.simulate2Group(
n = int(param['n']),
p = int(param['p']),
effect = [param['effect']] * int(param['p'])
)
knnFit = KNeighborsClassifier(n_neighbors=int(param['k']))
knnFit.fit(np.array(trainSet['x']), np.array(trainSet['y']))
testSet = SimData.simulate2Group(
n = int(param['n']),
p = int(param['p']),
effect = [param['effect']] * int(param['p'])
)
out = OrderedDict()
out['p'] = int(param['p'])
out['k'] = int(param['k'])
out['train'] = trainSet
out['test'] = testSet
out['resubPreds'] = knnFit.predict(trainSet['x'])
out['resubProbs'] = knnFit.predict_proba(trainSet['x'])
out['testPreds'] = knnFit.predict(testSet['x'])
out['testProbs'] = knnFit.predict_proba(testSet['x'])
out['resubTable'] = pd.crosstab(
Series(out['resubPreds'], index=trainSet['y'].index),
trainSet['y']
)
out['resubAccuracy'] = (np.sum(np.diag(out['resubTable'])) /
(1.0 * np.sum(np.sum(out['resubTable']))))
out['testTable'] = pd.crosstab(
Series(out['testPreds'], index=testSet['y'].index),
testSet['y']
)
out['testAccuracy'] = (np.sum(np.diag(out['testTable'])) /
(1.0 * np.sum(np.sum(out['testTable']))))
return out
knnResults = [knnSimulate(parGrid.iloc[i])
for i in range(parGrid.shape[0])]
repeatedKnnResults = []
for r in range(10):
repeatedKnnResults.extend(knnSimulate(parGrid.iloc[i])
for i in range(parGrid.shape[0]))
knnResultsSimplified = DataFrame([(x['p'],
x['k'],
x['resubAccuracy'],
x['testAccuracy'])
for x in repeatedKnnResults],
columns = ['p',
'k',
'resubAccuracy',
'testAccuracy'])
ggdata = pd.concat(
[DataFrame({'p' : knnResultsSimplified.p,
'k' : knnResultsSimplified.k.apply(int),
'type' : 'resub',
'Accuracy' : knnResultsSimplified.resubAccuracy}),
DataFrame({'p' : knnResultsSimplified.p,
'k' : knnResultsSimplified.k.apply(int),
'type' : 'test',
'Accuracy' : knnResultsSimplified.testAccuracy})],
axis = 0
)
plt.close()
ggo = gg.ggplot(ggdata, gg.aes(x='p', y='Accuracy',
color='type', group='type', linetype='type'))
ggo += gg.facet_wrap('~ k')
ggo += gg.scale_x_log10()
ggo += gg.geom_point(alpha=0.6)
ggo += gg.stat_smooth()
ggo += gg.theme_bw()
print(ggo)