| %matplotlib inline | |
| from urllib.request import urlopen | |
| import numpy as np | |
| import cv2 | |
| from matplotlib import pyplot as plt | |
| from albumentations import ( | |
| HorizontalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90, | |
| Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue, |
| id | firstname | lastname | billingid | product | price | birth | new_date | years_between |
| 10 | james | maxwell | 875 | taco | 3 | 1923/03/12 | 12-03-1923 | 95.4355 |
| 11 | isaac | newton | 992 | pasta | 9 | 1999/02/15 | 15-02-1999 | 19.5108 |
| 12 | emmy | noether | 234 | pasta | 9 | 1993/12/08 | 08-12-1993 | 24.6962 |
| id | firstName | lastName | billingId | product | price | birth | dummyCol |
| 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
| 2 | André | Ampère | 423 | piza | 8 | 1950/07/08 | gonna |
| 3 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990/07/09 | give |
| import numpy as np | |
| from sklearn.datasets import make_classification | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from skorch import NeuralNetClassifier | |
| X, y = make_classification(1000, 20, n_informative=10, random_state=0) | |
| X = X.astype(np.float32) |
| from tpot import TPOTClassifier | |
| from sklearn.datasets import load_digits | |
| from sklearn.model_selection import train_test_split | |
| # load and split dataset | |
| digitsdigits == load_digitsload_di () | |
| X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, | |
| train_size=0.75, test_size=0.25) | |
| # Fit the TPOT classifier |
| from tpot import TPOTClassifier | |
| from sklearn.datasets import load_iris | |
| from sklearn.model_selection import train_test_split | |
| # Load iris dataset | |
| iris = load_iris() | |
| # Split the data | |
| X_trainX_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, |
| from keras.datasets import mnist | |
| from autokeras.classifier import ImageClassifier | |
| if __name__ == '__main__': | |
| (x_train, y_train), (x_test, y_test) = mnist.load_data() | |
| x_train = x_train.reshape(x_train.shape + (1,)) | |
| x_test = x_test.reshape(x_test.shape + (1,)) | |
| clf = ImageClassifier(verbose=True, augment=False) | |
| clf.fit(x_train, y_train, time_limit=12 * 60 * 60) |