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Enhancement to predict function

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s45157 2018-01-21 19:11:37 +01:00
parent e20007b2df
commit d4ba49aa16
1 changed files with 13 additions and 29 deletions
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labs06/task02.py
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@ -39,33 +39,18 @@ def find_13(dane):
def find_best_flats(dane): def find_best_flats(dane):
return dane.loc[(dane['Borough'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)] return dane.loc[(dane['Borough'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
def predict(dane, col_name): def predict(dane, rooms, sqrMeters):
from sklearn import linear_model from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score import numpy as np
d_X = pd.DataFrame(dane[col_name]) data = dane
d_X_train = d_X[4000:] df = pd.DataFrame(data, columns=np.array(['Rooms','SqrMeters']))
d_X_test = d_X[:4000] target = pd.DataFrame(data, columns=["Expected"])
d_y = pd.DataFrame(dane['Expected']) X = df
d_y_train = d_y[4000:] y = target["Expected"]
d_y_test = d_y[:4000] lm = linear_model.LinearRegression()
regr = linear_model.LinearRegression() model = lm.fit(X, y)
regr.fit(d_X_train, d_y_train) inData = pd.DataFrame.from_records([(rooms, sqrMeters)], columns=['Rooms', 'SqrMeters'])
y_pred = regr.predict(d_X_test) return lm.predict(inData)[0]
print('MODEL(%s): pred_y = %f * x + %f' % (col_name, regr.coef_[0], regr.intercept_) )
print('Mean squared error: %.2f' % mean_squared_error(d_y_test, y_pred))
import matplotlib.pyplot as plt
plt.clf()
dataLine, = plt.plot(d_X_test, d_y_test, 'ro', label='collected data')
predLine, = plt.plot(d_X_test, y_pred, color='blue', linestyle='--', linewidth = 2, label='predictions')
ax = plt.gca().add_artist(plt.legend(handles=[dataLine], loc=1))
plt.legend(handles=[predLine], loc=4)
plt.xticks(())
plt.yticks(())
plt.xlabel(col_name)
plt.ylabel('Price')
plt.show()
def main(): def main():
dane = wczytaj_dane() dane = wczytaj_dane()
@ -87,8 +72,7 @@ def main():
print('"Najlepsze" mieszkania: ') print('"Najlepsze" mieszkania: ')
print(find_best_flats(dane)) print(find_best_flats(dane))
predict(dane, 'Rooms') print('Predicted price(actual 146000): ', predict(dane,1,31.21))
predict(dane, 'SqrMeters')
if __name__ == "__main__": if __name__ == "__main__":