"""Benchmarks of Lasso regularization path computation using Lars and CD

The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function

from collections import defaultdict
import gc
import sys
from time import time

import numpy as np

from sklearn.linear_model import lars_path
from sklearn.linear_model import lasso_path
from sklearn.datasets.samples_generator import make_regression


def compute_bench(samples_range, features_range):

    it = 0

    results = defaultdict(lambda: [])

    max_it = len(samples_range) * len(features_range)
    for n_samples in samples_range:
        for n_features in features_range:
            it += 1
            print('====================')
            print('Iteration %03d of %03d' % (it, max_it))
            print('====================')
            dataset_kwargs = {
                'n_samples': n_samples,
                'n_features': n_features,
                'n_informative': n_features / 10,
                'effective_rank': min(n_samples, n_features) / 10,
                #'effective_rank': None,
                'bias': 0.0,
            }
            print("n_samples: %d" % n_samples)
            print("n_features: %d" % n_features)
            X, y = make_regression(**dataset_kwargs)

            gc.collect()
            print("benchmarking lars_path (with Gram):", end='')
            sys.stdout.flush()
            tstart = time()
            G = np.dot(X.T, X)  # precomputed Gram matrix
            Xy = np.dot(X.T, y)
            lars_path(X, y, Xy=Xy, Gram=G, method='lasso')
            delta = time() - tstart
            print("%0.3fs" % delta)
            results['lars_path (with Gram)'].append(delta)

            gc.collect()
            print("benchmarking lars_path (without Gram):", end='')
            sys.stdout.flush()
            tstart = time()
            lars_path(X, y, method='lasso')
            delta = time() - tstart
            print("%0.3fs" % delta)
            results['lars_path (without Gram)'].append(delta)

            gc.collect()
            print("benchmarking lasso_path (with Gram):", end='')
            sys.stdout.flush()
            tstart = time()
            lasso_path(X, y, precompute=True)
            delta = time() - tstart
            print("%0.3fs" % delta)
            results['lasso_path (with Gram)'].append(delta)

            gc.collect()
            print("benchmarking lasso_path (without Gram):", end='')
            sys.stdout.flush()
            tstart = time()
            lasso_path(X, y, precompute=False)
            delta = time() - tstart
            print("%0.3fs" % delta)
            results['lasso_path (without Gram)'].append(delta)

    return results


if __name__ == '__main__':
    from mpl_toolkits.mplot3d import axes3d  # register the 3d projection
    import matplotlib.pyplot as plt

    samples_range = np.linspace(10, 2000, 5).astype(np.int)
    features_range = np.linspace(10, 2000, 5).astype(np.int)
    results = compute_bench(samples_range, features_range)

    max_time = max(max(t) for t in results.values())

    fig = plt.figure('scikit-learn Lasso path benchmark results')
    i = 1
    for c, (label, timings) in zip('bcry', sorted(results.items())):
        ax = fig.add_subplot(2, 2, i, projection='3d')
        X, Y = np.meshgrid(samples_range, features_range)
        Z = np.asarray(timings).reshape(samples_range.shape[0],
                                        features_range.shape[0])

        # plot the actual surface
        ax.plot_surface(X, Y, Z.T, cstride=1, rstride=1, color=c, alpha=0.8)

        # dummy point plot to stick the legend to since surface plot do not
        # support legends (yet?)
        # ax.plot([1], [1], [1], color=c, label=label)

        ax.set_xlabel('n_samples')
        ax.set_ylabel('n_features')
        ax.set_zlabel('Time (s)')
        ax.set_zlim3d(0.0, max_time * 1.1)
        ax.set_title(label)
        # ax.legend()
        i += 1
    plt.show()
