使用带有 statsmodels 的 OLS 模型预测值

Question

I calculated a model using OLS (multiple linear regression). I divided my data to train and test (half each), and then I would like to predict values for the 2nd half of the labels.

model = OLS(labels[:half], data[:half])
predictions = model.predict(data[half:])

The problem is that I get and error: File "/usr/local/lib/python2.7/dist-packages/statsmodels-0.5.0-py2.7-linux-i686.egg/statsmodels/regression/linear_model.py", line 281, in predict return np.dot(exog, params) ValueError: matrices are not aligned

I have the following array shapes: data.shape: (426, 215) labels.shape: (426,)

If I transpose the input to model.predict, I do get a result but with a shape of (426,213), so I suppose its wrong as well (I expect one vector of 213 numbers as label predictions):

model.predict(data[half:].T)

Any idea how to get it to work?

Answer 1

For statsmodels >=0.4, if I remember correctly

model.predict doesn't know about the parameters, and requires them in the call see http://statsmodels.sourceforge.net/stable/generated/statsmodels.regression.linear_model.OLS.predict.html

What should work in your case is to fit the model and then use the predict method of the results instance.

model = OLS(labels[:half], data[:half])
results = model.fit()
predictions = results.predict(data[half:])

or shorter

results = OLS(labels[:half], data[:half]).fit()
predictions = results.predict(data[half:])

http://statsmodels.sourceforge.net/stable/generated/statsmodels.regression.linear_model.RegressionResults.predict.html with missing docstring

Note: this has been changed in the development version (backwards compatible), that can take advantage of "formula" information in predict http://statsmodels.sourceforge.net/devel/generated/statsmodels.regression.linear_model.RegressionResults.predict.html

Answer 2

You can also call get_prediction method of the Results object to get the prediction together with its error estimate and confidence intervals. Example:

import numpy as np
import statsmodels.api as sm

X = np.array([0, 1, 2, 3])
y = np.array([1, 2, 3.5, 4])
X = sm.add_constant(X)
model = sm.OLS(y, X)
results = model.fit()

predict:

# Predict at x=2.5
X_test = np.array([1, 2.5])  # "1" refers to the intercept term
results.get_prediction(X_test).summary_frame(alpha=0.05)  # alpha = significance level for confidence interval

gives:

    mean    mean_se mean_ci_lower   mean_ci_upper   obs_ci_lower    obs_ci_upper
0   3.675   0.198431    2.821219    4.528781    2.142416    5.207584

where mean_ci refers to the confidence interval and obs_ci refers to the prediction interval .