Python get_dir Examples

Example #1

File: linalg.py Project: charanpald/tyre-hug

import numpy
import timeit
import matplotlib.pyplot as plt
import sys
from tyrehug.settings import DATA_DIR, get_dir

numpy.random.seed(21)
num_repeats = 20
min_size = 500
max_size = 5001
step = 500

data_dir = get_dir(DATA_DIR)

def benchmark_numpy():
    times = []
    print("Benchmarking Numpy " + str(numpy.__version__))

    for i in range(min_size, max_size, step):
        print(i)
        global A, B
        A = numpy.random.rand(i, i).astype(numpy.float32)
        B = numpy.random.rand(i, i).astype(numpy.float32)

        current_times = [i]

        timer = timeit.Timer("numpy.dot(A, B)", "import numpy; from __main__ import A, B")
        current_times.append(numpy.min(timer.repeat(num_repeats, 1)))

        timer = timeit.Timer("numpy.exp(A)", "import numpy; from __main__ import A")
        current_times.append(numpy.min(timer.repeat(num_repeats, 1)))

Example #2

File: mlbenchmark.py Project: charanpald/tyre-hug

import numpy
import os
import sklearn.datasets as datasets
import sklearn.svm as svm
import sklearn.cross_validation as cross_validation
import sklearn.metrics as metrics
import sklearn.preprocessing as preprocessing
from tyrehug.settings import DATA_DIR, get_dir

learner = svm.SVC(kernel='linear', C=1)
data_dir = get_dir(os.path.join(DATA_DIR, "mlbenchmark"))
print(data_dir)

def benchmark(learner, dataset, filename):
    num_folds = 5
    num_metrics = 4
    scores = numpy.zeros((num_folds, num_metrics))

    X = preprocessing.scale(dataset.data)
    y = dataset.target

    num_labels = numpy.unique(y).shape[0]
    if num_labels == 2:
        average = "binary"
    else:
        average="weighted"

    if not os.path.exists(filename):
        for i, (train_inds, test_inds) in enumerate(cross_validation.StratifiedKFold(y, num_folds)):
            X_train, y_train = X[train_inds, :], y[train_inds]
            X_test, y_test = X[test_inds, :], y[test_inds]

Example #3

File: linalg.py Project: santoshchapaneri/tyre-hug

import numpy
import timeit
import matplotlib.pyplot as plt
import sys
from tyrehug.settings import DATA_DIR, get_dir

numpy.random.seed(21)
num_repeats = 20
min_size = 500
max_size = 5001
step = 500

data_dir = get_dir(DATA_DIR)


def benchmark_numpy():
    times = []
    print("Benchmarking Numpy " + str(numpy.__version__))

    for i in range(min_size, max_size, step):
        print(i)
        global A, B
        A = numpy.random.rand(i, i).astype(numpy.float32)
        B = numpy.random.rand(i, i).astype(numpy.float32)

        current_times = [i]

        timer = timeit.Timer("numpy.dot(A, B)",
                             "import numpy; from __main__ import A, B")
        current_times.append(numpy.min(timer.repeat(num_repeats, 1)))