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# plot decision tree
from numpy import loadtxt
from xgboost import XGBClassifier
from xgboost import plot_tree
import matplotlib.pyplot as plt
# load data
dataset = loadtxt('./data/pima-indians-diabetes.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
y = dataset[:,8]
# fit model no training data
model = XGBClassifier()
model.fit(X, y)
# plot single tree
plot_tree(model)
plt.show()
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import matplotlib
matplotlib.use('nbagg')
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import stats
import pandas as pd
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from matplotlib.pylab import *
(y,x) = mgrid[-2:2.1:0.2, -2:2.1:0.2]
z = x * exp(-x ** 2 - y ** 2)
(dy,dx) = gradient(z)
quiver(x,y,dx,dy,z)
hold(True)
contour(x,y,z,10)
show()
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%matplotlib inline
from scipy.stats import norm, rayleigh
import pandas as pd
a = rayleigh.rvs(loc=5,scale=2,size=1000)+1
b = rayleigh.rvs(loc=5,scale=2,size=1000)
c = rayleigh.rvs(loc=5,scale=2,size=1000)-1
data = pd.DataFrame({"a":a,"b":b,"c":c},columns=["a","b","c"])
data.plot(kind="hist",stacked=True,bins=30,figsize=(8,4))
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%matplotlib inline
import matplotlib.pyplot as plt
A = [5,30,45,22]
B = [5,25,50,20]
C = [5,25,50,20]
X = range(4)
plt.bar(X,A,color='b')
plt.bar(X,B,color='r',bottom = A)
#plt.bar(X,C,color='y',bottom = A+B)
plt.show()
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import numpy as np
import random as rn
rn.random()
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import re
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y = [1,2,3,4,5]
y = np.array(y)
print('mean is',y.mean())
y.dot(y)
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%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
X,y = make_classification(n_samples=100, n_features=2,n_informative=2,
n_redundant=0,n_clusters_per_class=1,class_sep=1.0,
random_state=1001,n_classes=3)
plt.scatter(X[:,0],X[:,1],marker='o',c=y,linewidth=0,edgecolor=None)
plt.show()
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print(X[:4,:])
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import numpy as np
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X,y)
h=0.02
x_min, x_max = X[:,0].min() -.5, X[:,0].max() + 0.5
y_min, y_max = X[:,1].min() -.5, X[:,1].max() + 0.5
xx,yy = np.meshgrid(np.arange(x_min,x_max,h),np.arange(y_min,y_max,h))
Z = clf.predict(np.c_[xx.ravel(),yy.ravel()])
Z = Z.reshape(xx.shape)
print(Z[0,0])
plt.pcolormesh(xx,yy,Z,cmap=plt.cm.autumn)
plt.scatter(X[:,0],X[:,1],marker='o',c=y,linewidth=0,edgecolor=None)
plt.show()
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%timeit Z = clf.predict_proba(np.c_[xx.ravel(),yy.ravel()])
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print(Z)
print(Z.shape)
print(Z[0])
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