etudes.datasets package

Submodules

etudes.datasets.base module

Datasets module.

etudes.datasets.base.coal_mining_disasters_load_data(base_dir='../datasets/')

Coal mining disasters dataset.

Examples

from etudes.datasets import coal_mining_disasters_load_data

X, y = coal_mining_disasters_load_data()

fig, ax = plt.subplots()

ax.vlines(X.squeeze(), ymin=0, ymax=y, linewidth=0.5, alpha=0.8)

ax.set_xlabel("days")
ax.set_ylabel("incidents")

plt.show()

(Source code, png, hires.png, pdf)

etudes.datasets.base.get_sequence_path(sequence_num, base_dir='../datasets')

etudes.datasets.base.load_bee_dance_dataframe(base_dir='../datasets')

etudes.datasets.base.mauna_loa_load_dataframe(base_dir='../datasets/')

Mauna Loa dataset.

Examples

import seaborn as sns
from etudes.datasets import mauna_loa_load_dataframe

data = mauna_loa_load_dataframe()

g = sns.relplot(x='date', y='average', kind="line",
                data=data, height=5, aspect=1.5, alpha=0.8)
g.set_ylabels(r"average $\mathrm{CO}_2$ (ppm)")

(Source code, png, hires.png, pdf)

etudes.datasets.base.read_fcnet_data(f, max_configs=None, num_seeds=4)

etudes.datasets.base.read_sequence(sequence_num, base_dir='../datasets')

etudes.datasets.base.read_sequence_column(sequence_num, col_name, base_dir='../datasets')

etudes.datasets.decorators module

etudes.datasets.decorators.binarize(positive_label=3, negative_label=5)

MNIST binary classification.

Examples

import tensorflow as tf

from etudes.datasets import binarize
from etudes.plotting import plot_image_grid

@binarize(positive_label=2, negative_label=7)
def binary_mnist_load_data():
    return tf.keras.datasets.mnist.load_data()

(X_train, Y_train), (X_test, Y_test) = binary_mnist_load_data()

num_train, img_rows, img_cols = X_train.shape
num_test, img_rows, img_cols = X_test.shape

fig, (ax1, ax2) = plt.subplots(ncols=2)

plot_image_grid(ax1, X_train[Y_train == 0],
                shape=(img_rows, img_cols), nrows=10, cmap="cividis")

plot_image_grid(ax2, X_train[Y_train == 1],
                shape=(img_rows, img_cols), nrows=10, cmap="cividis")

plt.show()

(Source code)

etudes.datasets.networks module

etudes.datasets.networks.load_cora(data_home='datasets/legacy/cora')

etudes.datasets.networks.load_dataset(name, data_home='datasets')

etudes.datasets.networks.load_pickle(name, ext, data_home='datasets', encoding='latin1')

etudes.datasets.networks.load_test_indices(name, data_home='datasets')

etudes.datasets.synthetic module

etudes.datasets.synthetic.make_classification_dataset(X_pos, X_neg, shuffle=False, dtype='float64', random_state=None)

etudes.datasets.synthetic.make_density_ratio_estimation_dataset(p=None, q=None)

etudes.datasets.synthetic.make_regression_dataset(latent_fn=<function synthetic_sinusoidal>)

Make synthetic dataset.

Examples

Test

from etudes.datasets import synthetic_sinusoidal, make_regression_dataset

num_train = 64 # nbr training points in synthetic dataset
num_index_points = 256
num_features = 1
observation_noise_variance = 1e-1

f = synthetic_sinusoidal
X_pred = np.linspace(-0.6, 0.6, num_index_points).reshape(-1, num_features)

load_data = make_regression_dataset(f)
X_train, Y_train = load_data(num_train, num_features,
                             observation_noise_variance,
                             x_min=-0.5, x_max=0.5)

fig, ax = plt.subplots()

ax.plot(X_pred, f(X_pred), label="true")
ax.scatter(X_train, Y_train, marker='x', color='k',
            label="noisy observations")

ax.legend()

ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')

plt.show()

(Source code)

etudes.datasets.synthetic.synthetic_sinusoidal(x)

Module contents

etudes.datasets.make_regression_dataset(latent_fn=<function synthetic_sinusoidal>)

Make synthetic dataset.

Examples

Test

from etudes.datasets import synthetic_sinusoidal, make_regression_dataset

num_train = 64 # nbr training points in synthetic dataset
num_index_points = 256
num_features = 1
observation_noise_variance = 1e-1

f = synthetic_sinusoidal
X_pred = np.linspace(-0.6, 0.6, num_index_points).reshape(-1, num_features)

load_data = make_regression_dataset(f)
X_train, Y_train = load_data(num_train, num_features,
                             observation_noise_variance,
                             x_min=-0.5, x_max=0.5)

fig, ax = plt.subplots()

ax.plot(X_pred, f(X_pred), label="true")
ax.scatter(X_train, Y_train, marker='x', color='k',
            label="noisy observations")

ax.legend()

ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')

plt.show()

(Source code)

etudes.datasets.make_classification_dataset(X_pos, X_neg, shuffle=False, dtype='float64', random_state=None)

etudes.datasets.mauna_loa_load_dataframe(base_dir='../datasets/')

Mauna Loa dataset.

Examples

import seaborn as sns
from etudes.datasets import mauna_loa_load_dataframe

data = mauna_loa_load_dataframe()

g = sns.relplot(x='date', y='average', kind="line",
                data=data, height=5, aspect=1.5, alpha=0.8)
g.set_ylabels(r"average $\mathrm{CO}_2$ (ppm)")

(Source code, png, hires.png, pdf)

etudes.datasets.coal_mining_disasters_load_data(base_dir='../datasets/')

Coal mining disasters dataset.

Examples

from etudes.datasets import coal_mining_disasters_load_data

X, y = coal_mining_disasters_load_data()

fig, ax = plt.subplots()

ax.vlines(X.squeeze(), ymin=0, ymax=y, linewidth=0.5, alpha=0.8)

ax.set_xlabel("days")
ax.set_ylabel("incidents")

plt.show()

(Source code, png, hires.png, pdf)

etudes.datasets.load_bee_dance_dataframe(base_dir='../datasets')

etudes.datasets.binary_mnist_load_data()

etudes.datasets.read_fcnet_data(f, max_configs=None, num_seeds=4)