datasets.py

import numpy as np
import pandas as pd
from scipy import signal
from sklearn.datasets import load_boston
from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures
from .make_blobs import make_blobs


def make_forge():
    # a carefully hand-designed dataset lol
    X, y = make_blobs(centers=2, random_state=4, n_samples=30)
    y[np.array([7, 27])] = 0
    mask = np.ones(len(X), dtype=np.bool)
    mask[np.array([0, 1, 5, 26])] = 0
    X, y = X[mask], y[mask]
    return X, y


def make_wave(n_samples=100):
    rnd = np.random.RandomState(42)
    x = rnd.uniform(-3, 3, size=n_samples)
    y_no_noise = (np.sin(4 * x) + x)
    y = (y_no_noise + rnd.normal(size=len(x))) / 2
    return x.reshape(-1, 1), y


def load_extended_boston():
    boston = load_boston()
    X = boston.data

    X = MinMaxScaler().fit_transform(boston.data)
    X = PolynomialFeatures(degree=2, include_bias=False).fit_transform(X)
    return X, boston.target


def load_citibike():
    data_mine = pd.read_csv("data/citibike.csv")
    data_mine['one'] = 1
    data_mine['starttime'] = pd.to_datetime(data_mine.starttime)
    data_starttime = data_mine.set_index("starttime")
    data_resampled = data_starttime.resample("3h", how="sum").fillna(0)
    return data_resampled.one


def make_signals():
    # fix a random state seed
    rng = np.random.RandomState(42)
    n_samples = 2000
    time = np.linspace(0, 8, n_samples)
    # create three signals
    s1 = np.sin(2 * time)  # Signal 1 : sinusoidal signal
    s2 = np.sign(np.sin(3 * time))  # Signal 2 : square signal
    s3 = signal.sawtooth(2 * np.pi * time)  # Signal 3: saw tooth signal

    # concatenate the signals, add noise
    S = np.c_[s1, s2, s3]
    S += 0.2 * rng.normal(size=S.shape)

    S /= S.std(axis=0)  # Standardize data
    S -= S.min()
    return S