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import warnings
warnings.filterwarnings('ignore')
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%matplotlib inline
%pylab inline
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import pandas as pd
print(pd.__version__)
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df = pd.read_csv('./insurance-customers-300.csv', sep=';')
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y=df['group']
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df.drop('group', axis='columns', inplace=True)
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X = df.as_matrix()
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df.describe()
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from scipy.stats import norm
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age_threshold = 0.01
ages = np.linspace(17, 90, 100)
def is_young(age):
return norm.pdf(age, loc=20, scale=3)
def good_age(age):
return norm.pdf(age, loc=45, scale=5)
def is_old(age):
return norm.pdf(age, loc=90, scale=8)
plt.plot(ages, is_young(ages), 'r', lw=2, alpha=0.5)
plt.plot(ages, good_age(ages), 'g', lw=2, alpha=0.5)
plt.plot(ages, is_old(ages), 'r', lw=2, alpha=0.5)
plt.ylabel('PDF')
plt.xlabel('Age')
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kms = np.linspace(0, 100, 100)
kms_threshold = 0.005
def no_practice(km):
return norm.pdf(km, loc=1, scale=3)
def much_driving(km):
return norm.pdf(km, loc=100, scale=20)
def sweet_spot(km):
return norm.pdf(km, loc=20, scale=5)
plt.plot(kms, no_practice(kms), 'r', lw=2, alpha=0.5)
plt.plot(kms, much_driving(kms), 'r', lw=2, alpha=0.5)
plt.plot(kms, sweet_spot(kms), 'g', lw=2, alpha=0.5)
plt.ylabel('PDF')
plt.xlabel('thousand km per year')
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kmhs = np.linspace(90, 250, 100)
kmhs_threshold = 0.002
def too_fast(kmh):
return norm.pdf(kmh, loc=250, scale=30)
plt.plot(kmhs, too_fast(kmhs), 'r', lw=2, alpha=0.5)
plt.ylabel('PDF')
plt.xlabel('km/h')
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# ignore this, it is just technical code
# should come from a lib, consider it to appear magically
# http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
cmap_print = ListedColormap(['#AA8888', '#004000', '#FFFFDD'])
cmap_bold = ListedColormap(['#AA4444', '#006000', '#AAAA00'])
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#FFFFDD'])
font_size=25
def meshGrid(x_data, y_data):
h = 1 # step size in the mesh
x_min, x_max = x_data.min() - 1, x_data.max() + 1
y_min, y_max = y_data.min() - 1, y_data.max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
return (xx,yy)
def plotPrediction(clf, x_data, y_data, x_label, y_label, colors, title="", mesh=True, fname=None):
xx,yy = meshGrid(x_data, y_data)
plt.figure(figsize=(20,10))
if clf and mesh:
Z = clf.predict(np.c_[yy.ravel(), xx.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
if fname:
plt.scatter(x_data, y_data, c=colors, cmap=cmap_print, s=200, marker='o', edgecolors='k')
else:
plt.scatter(x_data, y_data, c=colors, cmap=cmap_bold, s=80, marker='o', edgecolors='k')
plt.xlabel(x_label, fontsize=font_size)
plt.ylabel(y_label, fontsize=font_size)
plt.title(title, fontsize=font_size)
if fname:
plt.savefig(fname)
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# 0: red
# 1: green
# 2: yellow
class ClassifierBase:
def predict(self, X):
return np.array([ self.predict_single(x) for x in X])
def score(self, X, y):
n = len(y)
correct = 0
predictions = self.predict(X)
for prediction, ground_truth in zip(predictions, y):
if prediction == ground_truth:
correct = correct + 1
return correct / n
age_factor = 1
kmhs_factor = 10
kms_factor = 0.5
def scoring(x):
speed, age, km_per_year = x
pos_score = good_age(age) * age_factor + sweet_spot(km_per_year) * kms_factor
neg_score = (is_young(age) * age_factor + is_old(age) * age_factor
+ too_fast(speed) * kmhs_factor
+ no_practice(km_per_year) * kms_factor + much_driving(km_per_year) * kms_factor)
return pos_score - neg_score
score_threshold = 0.01
def predict_for_score(x):
score = scoring(x)
if abs(score) < score_threshold:
return 2
if score < 0:
return 0
return 1
class ScoringStatsClassifier(ClassifierBase):
def predict_single(self, x):
try:
speed, age, km_per_year = x
except:
speed, age = x
km_per_year = 40
return predict_for_score([speed, age, km_per_year])
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clf = ScoringStatsClassifier()
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clf.score(X, y)
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plotPrediction(clf, X[:, 1], X[:, 0],
'Age', 'Max Speed', y,
title="Max Speed vs Age (Scoring Stats Model)")
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