<gh_stars>1-10
#!/usr/bin/env python

"""
This is a module for generating datasets
"""

import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn.datasets


def anscombes_quartet():
    df = pd.read_fwf('data/anscombe.txt')  # np.loadtxt("anscombe.txt")
    return df


def get_iris_dataset():
    from sklearn.datasets import load_iris
    iris = load_iris(return_X_y=False)
    X = pd.DataFrame(iris.data, columns=iris.feature_names)
    y = pd.Series(iris.target).replace(
        to_replace=[0, 1, 2], value=iris.target_names)
    y = pd.DataFrame({'target': y})
    df = pd.concat((X, y), axis=1)
    return df, iris.DESCR


def get_mnist_dataset():
    from sklearn.datasets import load_digits
    mnist = load_digits(n_class=10, return_X_y=False)
    X = pd.DataFrame(mnist.data)
    y = pd.DataFrame({'y': mnist.target})
    df = pd.concat((X, y), axis=1)
    return df, mnist.DESCR


def get_wine_dataset():
    from sklearn.datasets import load_wine

    wine = load_wine(return_X_y=False)
    X = pd.DataFrame(wine.data, columns=wine.feature_names)
    y = pd.Series(wine.target).replace(
        to_replace=[0, 1, 2], value=wine.target_names)
    y = pd.DataFrame({'class': y})
    df = pd.concat((X, y), axis=1)
    return df, wine.DESCR


def load_sk_dataset(dataset_name: str, return_X_y: bool):
    if dataset_name == 'boston':
        return sklearn.datasets.load_boston(return_X_y=return_X_y)
    elif dataset_name == 'breast_cancer':
        return sklearn.datasets.load_breast_cancer(return_X_y=return_X_y)
    elif dataset_name == 'diabetes':
        return sklearn.datasets.load_diabetes(return_X_y=return_X_y)
    elif dataset_name == 'digits':
        return sklearn.datasets.load_digits(return_X_y=return_X_y)
    elif dataset_name == 'iris':
        return sklearn.datasets.load_iris(return_X_y=return_X_y)
    elif dataset_name == 'linnerud':
        return sklearn.datasets.load_linnerud(return_X_y=return_X_y)
    elif dataset_name == 'wine':
        return sklearn.datasets.load_wine(return_X_y=return_X_y)
    else:
        return None


def load_sns_dataset(dataset_name: str):
    """
    Retrieves a Seaborn dataset with a name specified in
    https://github.com/mwaskom/seaborn-data

    Examples: anscombe, diamonds, iris, mpg, tips, titanic
    """
    datasets = sns.get_dataset_names()
    if dataset_name not in datasets:
        return None
    return sns.load_dataset(dataset_name)


def gen_sk_dataset(dataset_name: str, **kwargs):
    if dataset_name == 'moons':
        X, y = sklearn.datasets.make_moons(**kwargs)
    elif dataset_name == 'circles':
        X, y = sklearn.datasets.make_circles(**kwargs)
    else:
        X, y = sklearn.datasets.make_blobs(**kwargs)

    df = pd.concat((pd.DataFrame(X), pd.DataFrame(y)), axis=1)
    df.columns=['x1', 'x2', 'y']
    return df


def gen_gaussian_2D(n=100, dx=1., dy=1., x0=0., y0=0., theta=0., seed=12345):
    x = x0 + dx*np.random.normal(size=n)
    y = y0 + dy*np.random.normal(size=n)
    X0 = np.vstack((x, y)).T

    # Rotate points using a rotation matrix
    theta_rad = np.radians(theta) 
    rot = np.array([[ np.cos(theta_rad), -np.sin(theta_rad) ],
                    [ np.sin(theta_rad),  np.cos(theta_rad) ]])
    return X0 @ rot.T


def generated_dataset_example():
    blobs = gen_sk_dataset('blobs', n_samples=200)
    moons = gen_sk_dataset('moons', n_samples=200, noise=0.1)
    circles = gen_sk_dataset('circles', n_samples=200, factor=0.5, noise=0.1)
    fig, ax = plt.subplots(1, 3, figsize=(15,4))
    for i, df in enumerate([blobs, moons, circles]):
        df.plot.scatter(x='x1', y='x2', c='y', ax=ax[i])


def plot_anscombe(data):
    fig = plt.figure(figsize=(10, 7))
    for i in range(4):
        i2 = i*2
        x = data.iloc[:, i2]
        y = data.iloc[:, i2+1]
        ax = fig.add_subplot(2, 2, i+1)
        ax.scatter(x, y)
        ax.plot(x, np.poly1d(np.polyfit(x, y, 1))(x), 'r')
        if (i == 0 or i == 2):
            ax.set_ylabel('y')
        if (i == 2 or i == 3):
            ax.set_xlabel('x')
    plt.savefig('anscombe.png')


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('action')
    args = parser.parse_args()

    if args.action == 'anscombe':
        aq = anscombes_quartet()
        plot_anscombe(aq)

#!/usr/bin/env python

"""
This is a module for generating datasets
"""

import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn.datasets


def anscombes_quartet():
    df = pd.read_fwf('data/anscombe.txt')  # np.loadtxt("anscombe.txt")
    return df


def get_iris_dataset():
    from sklearn.datasets import load_iris
    iris = load_iris(return_X_y=False)
    X = pd.DataFrame(iris.data, columns=iris.feature_names)
    y = pd.Series(iris.target).replace(
        to_replace=[0, 1, 2], value=iris.target_names)
    y = pd.DataFrame({'target': y})
    df = pd.concat((X, y), axis=1)
    return df, iris.DESCR


def get_mnist_dataset():
    from sklearn.datasets import load_digits
    mnist = load_digits(n_class=10, return_X_y=False)
    X = pd.DataFrame(mnist.data)
    y = pd.DataFrame({'y': mnist.target})
    df = pd.concat((X, y), axis=1)
    return df, mnist.DESCR


def get_wine_dataset():
    from sklearn.datasets import load_wine

    wine = load_wine(return_X_y=False)
    X = pd.DataFrame(wine.data, columns=wine.feature_names)
    y = pd.Series(wine.target).replace(
        to_replace=[0, 1, 2], value=wine.target_names)
    y = pd.DataFrame({'class': y})
    df = pd.concat((X, y), axis=1)
    return df, wine.DESCR


def load_sk_dataset(dataset_name: str, return_X_y: bool):
    if dataset_name == 'boston':
        return sklearn.datasets.load_boston(return_X_y=return_X_y)
    elif dataset_name == 'breast_cancer':
        return sklearn.datasets.load_breast_cancer(return_X_y=return_X_y)
    elif dataset_name == 'diabetes':
        return sklearn.datasets.load_diabetes(return_X_y=return_X_y)
    elif dataset_name == 'digits':
        return sklearn.datasets.load_digits(return_X_y=return_X_y)
    elif dataset_name == 'iris':
        return sklearn.datasets.load_iris(return_X_y=return_X_y)
    elif dataset_name == 'linnerud':
        return sklearn.datasets.load_linnerud(return_X_y=return_X_y)
    elif dataset_name == 'wine':
        return sklearn.datasets.load_wine(return_X_y=return_X_y)
    else:
        return None


def load_sns_dataset(dataset_name: str):
    """
    Retrieves a Seaborn dataset with a name specified in
    https://github.com/mwaskom/seaborn-data

    Examples: anscombe, diamonds, iris, mpg, tips, titanic
    """
    datasets = sns.get_dataset_names()
    if dataset_name not in datasets:
        return None
    return sns.load_dataset(dataset_name)


def gen_sk_dataset(dataset_name: str, **kwargs):
    if dataset_name == 'moons':
        X, y = sklearn.datasets.make_moons(**kwargs)
    elif dataset_name == 'circles':
        X, y = sklearn.datasets.make_circles(**kwargs)
    else:
        X, y = sklearn.datasets.make_blobs(**kwargs)

    df = pd.concat((pd.DataFrame(X), pd.DataFrame(y)), axis=1)
    df.columns=['x1', 'x2', 'y']
    return df


def gen_gaussian_2D(n=100, dx=1., dy=1., x0=0., y0=0., theta=0., seed=12345):
    x = x0 + dx*np.random.normal(size=n)
    y = y0 + dy*np.random.normal(size=n)
    X0 = np.vstack((x, y)).T

    # Rotate points using a rotation matrix
    theta_rad = np.radians(theta) 
    rot = np.array([[ np.cos(theta_rad), -np.sin(theta_rad) ],
                    [ np.sin(theta_rad),  np.cos(theta_rad) ]])
    return X0 @ rot.T


def generated_dataset_example():
    blobs = gen_sk_dataset('blobs', n_samples=200)
    moons = gen_sk_dataset('moons', n_samples=200, noise=0.1)
    circles = gen_sk_dataset('circles', n_samples=200, factor=0.5, noise=0.1)
    fig, ax = plt.subplots(1, 3, figsize=(15,4))
    for i, df in enumerate([blobs, moons, circles]):
        df.plot.scatter(x='x1', y='x2', c='y', ax=ax[i])


def plot_anscombe(data):
    fig = plt.figure(figsize=(10, 7))
    for i in range(4):
        i2 = i*2
        x = data.iloc[:, i2]
        y = data.iloc[:, i2+1]
        ax = fig.add_subplot(2, 2, i+1)
        ax.scatter(x, y)
        ax.plot(x, np.poly1d(np.polyfit(x, y, 1))(x), 'r')
        if (i == 0 or i == 2):
            ax.set_ylabel('y')
        if (i == 2 or i == 3):
            ax.set_xlabel('x')
    plt.savefig('anscombe.png')


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('action')
    args = parser.parse_args()

    if args.action == 'anscombe':
        aq = anscombes_quartet()
        plot_anscombe(aq)
#!/usr/bin/env python This is a module for generating datasets # np.loadtxt("anscombe.txt") Retrieves a Seaborn dataset with a name specified in     https://github.com/mwaskom/seaborn-data      Examples: anscombe, diamonds, iris, mpg, tips, titanic # Rotate points using a rotation matrix