import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegressionCV
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.utils import np_utils
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
# https://github.com/fastforwardlabs/keras-hello-world/blob/master/kerashelloworld.ipynb
def one_hot_encode_object_array(arr):
uniques, ids = np.unique(arr, return_inverse=True)
return np_utils.to_categorical(ids, len(uniques))
def iris_sklearn():
iris = sns.load_dataset("iris")
X = iris.values[:, :4]
y = iris.values[:, 4]
train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.5, random_state=0)
print(train_X.shape, test_X.shape, train_y.shape, test_y.shape)
lr = LogisticRegressionCV()
lr.fit(train_X, train_y)
print("Accuracy = {:.2f}".format(lr.score(test_X, test_y)))
def iris_keras():
iris = sns.load_dataset("iris")
X = iris.values[:, :4]
y = iris.values[:, 4]
train_X, test_X, train_y, test_y = train_test_split(X, y, train_size=0.5, random_state=0)
print(train_X.shape, test_X.shape, train_y.shape, test_y.shape)
train_y_ohe = one_hot_encode_object_array(train_y)
test_y_ohe = one_hot_encode_object_array(test_y)
model = Sequential()
model.add(Dense(16, input_shape=(4, )))
model.add(Activation('sigmoid'))
model.add(Dense(3))
model.add(Activation('softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_X, train_y_ohe, nb_epoch=100, batch_size=1, verbose=0)
loss, accuracy = model.evaluate(test_X, test_y_ohe, verbose=0)
print("Accuracy = {:.2f}".format(accuracy))
if __name__ == "__main__":
# iris_sklearn()
iris_keras()