forked from EricSchles/datascience_book
-
Notifications
You must be signed in to change notification settings - Fork 0
/
automl_keras.py
65 lines (60 loc) · 2.57 KB
/
automl_keras.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
from sklearn import ensemble
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn import metrics
from sklearn import model_selection
import random
from functools import partial
from collections import defaultdict
import itertools
def baseline_model(neurons, layers):
# create model
model = Sequential()
model.add(Dense(neurons, input_dim=10))
for _ in range(layers):
model.add(Dense(neurons))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def automl_basic(X_train, X_test, y_train, y_test, baseline, min_neurons, max_neurons, max_layers, num_runs = 3):
accuracy_scores = defaultdict(list)
for layers_neurons in itertools.product(range(max_layers), range(min_neurons, max_neurons)):
layers = layers_neurons[0]
neurons = layers_neurons[1]
print("Number of hidden layers", layers)
for i in range(num_runs):
deep_broad_model = partial(baseline, neurons, layers)
estimator = KerasClassifier(build_fn=deep_broad_model, epochs=100, batch_size=5, verbose=0)
estimator.fit(X_train, y_train)
y_pred = estimator.predict(X_test)
accuracy_scores[layers_neurons].append(metrics.accuracy_score(y_test, y_pred))
return accuracy_scores
def choose_best_model(accuracy_scores, min_neurons, max_neurons, max_layers):
best_acc = 0
best_layers = 0
best_neurons = 0
for layers_neurons in itertools.product(range(max_layers), range(min_neurons, max_neurons)):
cur_acc = np.mean(accuracy_scores[layers_neurons])
if cur_acc > best_acc:
best_acc = cur_acc
best_layers = layers_neurons[0]
best_neurons = layers_neurons[1]
return best_acc, best_layers, best_neurons
random.seed(1)
df = pd.read_csv("housepricedata.csv")
X = df.values[:,0:10]
y = df.values[:,10]
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
min_neurons = 8
max_neurons = 13
max_layers = 10
accuracy_scores = automl_basic(X_train, X_test, y_train, y_test, baseline_model, min_neurons, max_neurons, max_layers, num_runs=2)
best_acc, best_layers, best_neurons = choose_best_model(accuracy_scores, min_neurons, max_neurons, max_layers)
print("Optimal number of hidden layers", best_layers)
print("Optimal number of neurons per layer", best_neurons)
print("Optimal accuracy", best_acc)