CS109A Introduction to Data Science

%matplotlib inline
import sys
import numpy as np
import pylab as pl
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import matplotlib
import sklearn as sk


from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier 
import sklearn.metrics as met

df_heart = pd.read_csv('Heart.csv')
df_heart.head()

data_x = df_heart.MaxHR
data_y = df_heart.AHD.map(lambda x: 0 if x=='No' else 1)


knn1 = KNeighborsClassifier(n_neighbors=1)
knn5 = KNeighborsClassifier(n_neighbors=5)
knn10 = KNeighborsClassifier(n_neighbors=10)
knn50 = KNeighborsClassifier(n_neighbors=50)

knn1.fit(data_x.values.reshape(-1,1), data_y);
knn5.fit(data_x.values.reshape(-1,1), data_y);
knn10.fit(data_x.values.reshape(-1,1), data_y);
knn50.fit(data_x.values.reshape(-1,1), data_y);


fig = plt.figure()
fig.patch.set_alpha(0.0)
plt.xkcd(scale=0.1, length=0.0)
plt.gcf().subplots_adjust(bottom=0.20, left = 0.16, right=0.86)


x=np.linspace(np.min(data_x),np.max(data_x))
yhat1 = knn1.predict(x.reshape(-1,1))
yhat5 = knn5.predict(x.reshape(-1,1))
yhat10 = knn10.predict(x.reshape(-1,1))
yhat50 = knn50.predict(x.reshape(-1,1))

plt.plot(data_x, data_y, 'o' ,alpha=0.1, label='Data')
plt.plot(x,yhat1, label='knn1')
plt.plot(x,yhat5, label='knn5')
plt.plot(x,yhat10, label='knn10')
plt.plot(x,yhat50, label='knn50')

plt.legend()

plt.xlabel("MaxHR")
plt.ylabel("Heart disease (AHD)")

plt.show()

phat1 = knn1.predict_proba(x.reshape(-1,1))[:,1]
phat5 = knn5.predict_proba(x.reshape(-1,1))[:,1]
phat10 = knn10.predict_proba(x.reshape(-1,1))[:,1]
phat50 = knn50.predict_proba(x.reshape(-1,1))[:,1]

plt.plot(data_x, data_y, 'o' ,alpha=0.1, label='Data')
plt.plot(x,phat1, label='knn1')
plt.plot(x,phat5, label='knn5')
plt.plot(x,phat10, label='knn10')
plt.plot(x,phat50, label='knn50')

plt.legend()

plt.xlabel("MaxHR")
plt.ylabel("Heart disease (AHD)")

plt.show()

#two predictors

knn1 = KNeighborsClassifier(n_neighbors=1)
knn5 = KNeighborsClassifier(n_neighbors=5)
knn10 = KNeighborsClassifier(n_neighbors=10)
knn50 = KNeighborsClassifier(n_neighbors=50)

data_x = df_heart[['MaxHR','RestBP']]
data_y = df_heart.AHD.map(lambda x: 0 if x=='No' else 1)

knn1.fit(data_x, data_y);
knn5.fit(data_x, data_y);
knn10.fit(data_x, data_y);
knn50.fit(data_x, data_y);

print(knn1.score(data_x, data_y))
print(knn5.score(data_x, data_y))
print(knn10.score(data_x, data_y))
print(knn50.score(data_x, data_y))

# Don't forget to split into train and test 
# (or better yet, use cross-validation) 
# to determine what k is actually best!

# There are some missing values to begin with
print(df_heart.shape)
print(df_heart.dropna().shape)

import numpy.random as random
random.seed(109)
n = df_heart["MaxHR"].size

# create 20 missing completely at random observations
miss = random.choice(n,20)

heart_mcar = pd.read_csv('Heart.csv')
heart_mcar.loc[miss,"MaxHR"] = np.nan
print(heart_mcar["MaxHR"][miss].head())
print(heart_mcar.dropna().shape)

# create 20 missing at random observations 
miss = random.binomial(1,0.1+0.2*df_heart["Sex"],n)

heart_mar = pd.read_csv('Heart.csv')
heart_mar.loc[miss==1,"MaxHR"]=np.nan
print(heart_mar.loc[miss==1,"MaxHR"].head())
print(heart_mar.dropna().shape)

# create 20 missing not at random observations 
miss = random.binomial(1,0.1*(df_heart["MaxHR"]>df_heart["MaxHR"].mean()),n)

heart_mnar = pd.read_csv('Heart.csv')
heart_mnar.loc[miss==1,"MaxHR"]=np.nan
print(heart_mnar.loc[miss==1,"MaxHR"].head())
print(heart_mnar.dropna().shape)

# sklearn is not happy when you give it missing values
knn50 = KNeighborsClassifier(n_neighbors=50)

data_x = heart_mcar[['MaxHR','RestBP']]
data_y = df_heart.AHD.map(lambda x: 0 if x=='No' else 1)

knn50.fit(data_x, data_y);

# So let's just fill in the mean to make it happy

data_x = data_x.fillna(data_x.mean())

knn50.fit(data_x, data_y);