import pandas as pd
import numpy as np
from prediction import predictions
def compute_r_squared(data, predictions):
# Write a function that, given two input numpy arrays, 'data', and 'predictions,'
# returns the coefficient of determination, R^2, for the model that produced
# predictions.
#
# Numpy has a couple of functions -- np.mean() and np.sum() --
# that you might find useful, but you don't have to use them.
# YOUR CODE GOES HERE
SST = ((data-np.mean(data))**2).sum()
SSReg = ((predictions-data)**2).sum()
r_squared = 1 - SSReg/SST
return r_squared
if __name__ == "__main__":
input_filename = "turnstile_data_master_with_weather.csv"
turnstile_master = pd.read_csv(input_filename)
predicted_values = predictions(turnstile_master)
r_squared = compute_r_squared(turnstile_master['ENTRIESn_hourly'], predicted_values)
print r_squared
def compute_r_squared(data, predictions):
# Write a function that, given two input numpy arrays, 'data', and 'predictions,'
# returns the coefficient of determination, R^2, for the model that produced
# predictions.
#
# Numpy has a couple of functions -- np.mean() and np.sum() --
# that you might find useful, but you don't have to use them.
# YOUR CODE GOES HERE
up = np.square(np.subtract(data, predictions)).sum()
down = np.square(
np.subtract(data, np.dot(np.mean(data),
np.ones(np.array(data).shape)))).sum()
#r_squared = 1 - np.square(np.subtract(data,predictions)).sum()/np.square(data-np.mean(data).sum()
r_squared = 1 - up / down
#####NEATER ANSWER BY GRADER. BUT I DON'T USE IT. JUST PUT IT HERE AS PART OF MY OWN DOCUMENTATION
#_mean = np.mean(data)
#_a = np.sum(np.square(data - predictions))
#_b = np.sum(np.square(data - _mean))
#r_squared = 1.0 - (_a / _b)
return r_squared
if __name__ == "__main__":
input_filename = "turnstile_data_master_with_weather.csv"
turnstile_master = pd.read_csv(input_filename)
predictions = predictions(turnstile_master)
r_squared = compute_r_squared(turnstile_master['ENTRIESn_hourly'],
predictions)
print r_squared
import pandas as pd
import numpy as np
from prediction import predictions
def compute_r_squared(data, predictions):
# Write a function that, given two input numpy arrays, 'data', and 'predictions,'
# returns the coefficient of determination, R^2, for the model that produced
# predictions.
#
# Numpy has a couple of functions -- np.mean() and np.sum() --
# that you might find useful, but you don't have to use them.
# YOUR CODE GOES HERE
return r_squared
if __name__ == "__main__":
input_filename = "turnstile_data_master_with_weather.csv"
turnstile_master = pd.read_csv(input_filename)
predicted_values = predictions(turnstile_master)
r_squared = compute_r_squared(turnstile_master['ENTRIESn_hourly'], predicted_values)
print r_squared
from prediction import predictions
import pandas as pd
import numpy as np
def compute_r_squared(data, predictions):
# Write a function that, given two input numpy arrays, 'data', and 'predictions,'
# returns the coefficient of determination, R^2, for the model that produced
# predictions.
#
# Numpy has a couple of functions -- np.mean() and np.sum() --
# that you might find useful, but you don't have to use them.
# YOUR CODE GOES HERE
# this code doesnt run here. but it does on the quiz.
r_squared = 1 - (np.power((data - predictions), 2)).sum() / (np.power((data - np.mean(data)), 2)).sum()
return r_squared
if __name__ == "__main__":
input_filename = "turnstile_data_master_with_weather.csv"
turnstile_master = pd.read_csv(input_filename)
predictions = predictions(turnstile_master)
r_squared = compute_r_squared(turnstile_master['ENTRIESn_hourly'], predictions)
print r_squared
