"""
=======================
MNIST dataset benchmark
=======================

Benchmark on the MNIST dataset.  The dataset comprises 70,000 samples
and 784 features. Here, we consider the task of predicting
10 classes -  digits from 0 to 9 from their raw images. By contrast to the
covertype dataset, the feature space is homogenous.

Example of output :

    [..]
    Classification performance:
    ===========================
    Classifier               train-time   test-time   error-rat
    ------------------------------------------------------------
    Nystroem-SVM                105.07s       0.91s       0.0227
    ExtraTrees                   48.20s       1.22s       0.0288
    RandomForest                 47.17s       1.21s       0.0304
    SampledRBF-SVM              140.45s       0.84s       0.0486
    CART                         22.84s       0.16s       0.1214
    dummy                         0.01s       0.02s       0.8973

"""
from __future__ import division, print_function

# Author: Issam H. Laradji
#         Arnaud Joly <arnaud.v.joly@gmail.com>
# License: BSD 3 clause

import os
from time import time
import argparse
import numpy as np

from sklearn.datasets import fetch_mldata
from sklearn.datasets import get_data_home
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.dummy import DummyClassifier
from sklearn.externals.joblib import Memory
from sklearn.kernel_approximation import Nystroem
from sklearn.kernel_approximation import RBFSampler
from sklearn.metrics import zero_one_loss
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils import check_array

# Memoize the data extraction and memory map the resulting
# train / test splits in readonly mode
memory = Memory(os.path.join(get_data_home(), 'mnist_benchmark_data'),
                mmap_mode='r')


@memory.cache
def load_data(dtype=np.float32, order='F'):
    """Load the data, then cache and memmap the train/test split"""
    ######################################################################
    ## Load dataset
    print("Loading dataset...")
    data = fetch_mldata('MNIST original')
    X = check_array(data['data'], dtype=dtype, order=order)
    y = data["target"]

    # Normalize features
    X = X / 255

    ## Create train-test split (as [Joachims, 2006])
    print("Creating train-test split...")
    n_train = 60000
    X_train = X[:n_train]
    y_train = y[:n_train]
    X_test = X[n_train:]
    y_test = y[n_train:]

    return X_train, X_test, y_train, y_test


ESTIMATORS = {
    "dummy": DummyClassifier(),
    'CART': DecisionTreeClassifier(),
    'ExtraTrees': ExtraTreesClassifier(n_estimators=100),
    'RandomForest': RandomForestClassifier(n_estimators=100),
    'Nystroem-SVM':
    make_pipeline(Nystroem(gamma=0.015, n_components=1000), LinearSVC(C=100)),
    'SampledRBF-SVM':
    make_pipeline(RBFSampler(gamma=0.015, n_components=1000), LinearSVC(C=100))
}


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--classifiers', nargs="+",
                        choices=ESTIMATORS, type=str,
                        default=['ExtraTrees', 'Nystroem-SVM'],
                        help="list of classifiers to benchmark.")
    parser.add_argument('--n-jobs', nargs="?", default=1, type=int,
                        help="Number of concurrently running workers for "
                             "models that support parallelism.")
    parser.add_argument('--order', nargs="?", default="C", type=str,
                        choices=["F", "C"],
                        help="Allow to choose between fortran and C ordered "
                             "data")
    parser.add_argument('--random-seed', nargs="?", default=0, type=int,
                        help="Common seed used by random number generator.")
    args = vars(parser.parse_args())

    print(__doc__)

    X_train, X_test, y_train, y_test = load_data(order=args["order"])

    print("")
    print("Dataset statistics:")
    print("===================")
    print("%s %d" % ("number of features:".ljust(25), X_train.shape[1]))
    print("%s %d" % ("number of classes:".ljust(25), np.unique(y_train).size))
    print("%s %s" % ("data type:".ljust(25), X_train.dtype))
    print("%s %d (size=%dMB)" % ("number of train samples:".ljust(25),
                                 X_train.shape[0], int(X_train.nbytes / 1e6)))
    print("%s %d (size=%dMB)" % ("number of test samples:".ljust(25),
                                  X_test.shape[0], int(X_test.nbytes / 1e6)))

    print()
    print("Training Classifiers")
    print("====================")
    error, train_time, test_time = {}, {}, {}
    for name in sorted(args["classifiers"]):
        print("Training %s ... " % name, end="")
        estimator = ESTIMATORS[name]
        estimator_params = estimator.get_params()

        estimator.set_params(**{p: args["random_seed"]
                                for p in estimator_params
                                if p.endswith("random_state")})

        if "n_jobs" in estimator_params:
            estimator.set_params(n_jobs=args["n_jobs"])

        time_start = time()
        estimator.fit(X_train, y_train)
        train_time[name] = time() - time_start

        time_start = time()
        y_pred = estimator.predict(X_test)
        test_time[name] = time() - time_start

        error[name] = zero_one_loss(y_test, y_pred)

        print("done")

    print()
    print("Classification performance:")
    print("===========================")
    print("{0: <24} {1: >10} {2: >11} {3: >12}"
          "".format("Classifier  ", "train-time", "test-time", "error-rate"))
    print("-" * 60)
    for name in sorted(args["classifiers"], key=error.get):

        print("{0: <23} {1: >10.2f}s {2: >10.2f}s {3: >12.4f}"
              "".format(name, train_time[name], test_time[name], error[name]))

    print()
