[MRG] Reliability curves for calibration of predict_proba

This PR adds the reliability_curve metric to metrics.ranking.py. Reliability diagrams allow checking if the predicted probabilities of a binary classifier are well calibrated. The PR also contains an example comparing how well the predicted probabilities of different classifiers are calibrated. A notebook giving the same example can be found under http://jmetzen.github.io/2014-08-16/reliability-diagram.html

For some backgrounds on reliability-diagrams, please refer to the paper "Predicting Good Probabilities with Supervised Learning"
I suspect this algorithm is equivalent to:
```python
    bin_width = 1.0 / bins
    # TODO: check boundary cases
    binned = np.searchsorted(np.linspace(0, 1.0, bins), y_score)
    bin_sums = np.bincount(binned, weights=y_score, minlength=bins)
    bin_positives = np.bincount(binned, weights=y_true, minlength=bins)
    bin_total = np.bincount(binned, minlength=bins)
    bin_total[bin_counts == 0] = np.nan
    y_score_bin_mean = bin_sums / bin_total
    empirical_prob_pos = bin_positives / bin_total
```