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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 benchmarking for the basic document and efficient document
Step1:  Plot the Basic and Efficient data first
Basic
Step2:  do a linear regression for the 2 lines and evaluate the r-squared value
Step3:  both r-squared look quite nice
plot the resulting lines. I'm ignoring the cross-validation here
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Python Code:
data = pd.read_csv('../benchMarkingResult.txt',
                   header=None,
                   sep='\t',
                   names=('iteration',
                          'basic_result',
                          'efficient_result'))
Explanation: benchmarking for the basic document and efficient document
End of explanation
x = data.iteration
y1 = data.basic_result
y2 = data.efficient_result
fig, ax = plt.subplots(figsize=(10,10))
ax.scatter(x, y1, s=15)
ax.scatter(x, y2, s=15, c='g')
Explanation: Plot the Basic and Efficient data first
Basic
End of explanation
d1 = {'x':x, 'y_basic':y1}
d2 = {'x':x, 'y_efficient':y2}
mod1 = smf.ols(formula='y_basic ~ x - 1',
               data = d1).fit()
print mod1.summary()
mod2 = smf.ols(formula='y_efficient ~ x - 1', data=d2).fit()
mod2.summary()
Explanation: do a linear regression for the 2 lines and evaluate the r-squared value
End of explanation
y_est_basic = mod1.predict(x)
y_est_effi = mod2.predict(x)
fig, ax = plt.subplots(figsize=(10,10))
ax.scatter(x, y1, s=15)
ax.scatter(x, y2, s=15, c='g')
ax.plot(x, y_est_basic)
ax.plot(x, y_est_effi)
ax.legend(scatterpoints=1)
Explanation: both r-squared look quite nice
plot the resulting lines. I'm ignoring the cross-validation here
End of explanation
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