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Python2018/labs07/sklearn.ipynb
2018-07-17 14:46:56 +00:00

25 KiB

Analiza danych w Pythonie: sklearn

Tomasz Dwojak

3 czerwca 2018

  • Pierwsza część: pandas
  • Druga część: sklearn

Przypomnienie z UMZ

  • przygotowanie i czyszczenie danych
  • wybór i trening modelu
  • tuning
  • ewaluacja
import sklearn
import pandas as pd
import numpy as np
data = pd.read_csv("./gapminder.csv", index_col=0)
data.head()
female_BMI male_BMI gdp population under5mortality life_expectancy fertility
Afghanistan 21.07402 20.62058 1311.0 26528741.0 110.4 52.8 6.20
Albania 25.65726 26.44657 8644.0 2968026.0 17.9 76.8 1.76
Algeria 26.36841 24.59620 12314.0 34811059.0 29.5 75.5 2.73
Angola 23.48431 22.25083 7103.0 19842251.0 192.0 56.7 6.43
Antigua and Barbuda 27.50545 25.76602 25736.0 85350.0 10.9 75.5 2.16
y = data['life_expectancy']
X = data.drop('life_expectancy', axis=1)
from sklearn.model_selection import train_test_split
train_X, test_X, train_y, test_y = \
    train_test_split(X, y, test_size=0.2, random_state=123, shuffle=True)
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X,y)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
predicted = model.predict(test_X)
predicted[:5]
array([67.56279809, 76.25840076, 50.21126326, 59.21303855, 72.06348723])
from sklearn.metrics import mean_squared_error
rmse = np.sqrt(mean_squared_error(predicted, test_y))
print("RMSE:", rmse)
RMSE: 3.5179543848147863
 r2 = model.score(test_X, test_y)
0.795295000468209

API

  • model
  • fix
  • predict
for p in zip(train_X.columns, model.coef_):
    print("{}: {:.3}".format(p[0], p[1]))
female_BMI: -1.18
male_BMI: 1.46
gdp: 5.11e-05
population: 7.21e-10
under5mortality: -0.159
fertility: 0.421
model2 = LinearRegression()
model2.fit(train_X['male_BMI'].reshape(-1, 1), train_y)
/usr/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead
  
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
model2.intercept_
0.5852413468462743
from matplotlib import pyplot as plt
%matplotlib inline

plt.scatter(train_X['male_BMI'], train_y,color='g')
plt.plot(train_X['male_BMI'], model2.predict(train_X['male_BMI'].reshape(-1, 1)),color='k')

plt.show()
/usr/lib/python3.6/site-packages/ipykernel_launcher.py:5: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead
  """