1，数据集下载
!pip install -q tf-nightly # Requires tf 1.13
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import tensorflow as tf
tf.enable_eager_execution()
tf.logging.set_verbosity(tf.logging.ERROR)
tf.set_random_seed(123)
# Load dataset.
dftrain = pd.read_csv('https://storage.googleapis.com/tfbt/titanic_train.csv')
dfeval = pd.read_csv('https://storage.googleapis.com/tfbt/titanic_eval.csv')
y_train = dftrain.pop('survived')
y_eval = dfeval.pop('survived')
2，一系列数据检查
这一条特别
dftrain.sex.value_counts().plot(kind='barh');
(dftrain['class']
.value_counts()
.plot(kind='barh'));
3，Create feature columns and input functions，特征列和输入函数
3.1，one-hot-encoding, normalization, and bucketization
3.2，数字型和分类型
fc = tf.feature_column
CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',
'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']
def one_hot_cat_column(feature_name, vocab):
return fc.indicator_column(
fc.categorical_column_with_vocabulary_list(feature_name,
vocab))
feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
# Need to one-hot encode categorical features.
vocabulary = dftrain[feature_name].unique()
print(feature_name,vocabulary)
feature_columns.append(one_hot_cat_column(feature_name, vocabulary))
print(feature_columns,"
")
for feature_name in NUMERIC_COLUMNS:
feature_columns.append(fc.numeric_column(feature_name,
dtype=tf.float32))
3.3，view all of the feature column transformations//第0条example
fc.input_layer(dict(example), feature_columns).numpy()
3.4，create the input functions创建输入函数
# Use entire batch since this is such a small dataset.
NUM_EXAMPLES = len(y_train)
def make_input_fn(X, y, n_epochs=None, shuffle=True):
def input_fn():
dataset = tf.data.Dataset.from_tensor_slices((dict(X), y))
if shuffle:
dataset = dataset.shuffle(NUM_EXAMPLES)
# For training, cycle thru dataset as many times as need (n_epochs=None).
dataset = dataset.repeat(n_epochs)
# In memory training doesn't use batching.
dataset = dataset.batch(NUM_EXAMPLES)
return dataset
return input_fn
# Training and evaluation input functions.
train_input_fn = make_input_fn(dftrain, y_train)
eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)
4，Train and evaluate the model训练和评估
4.1，first train a linear classifier (logistic regression model)，训练一个二分类模型
linear_est = tf.estimator.LinearClassifier(feature_columns)
# Train model.
linear_est.train(train_input_fn, max_steps=100)
# Evaluation.
results = linear_est.evaluate(eval_input_fn)
print('Accuracy : ', results['accuracy'])
print('Dummy model: ', results['accuracy_baseline'])
4.2，train a Boosted Trees model
# Since data fits into memory, use entire dataset per layer. It will be faster.
# Above one batch is defined as the entire dataset.
n_batches = 1
est = tf.estimator.BoostedTreesClassifier(feature_columns,n_batches_per_layer=n_batches)
//est = tf.estimator.BoostedTreesClassifier(feature_columns,n_batches_per_layer=n_batches,n_trees=300)
# The model will stop training once the specified number of trees is built, not
# based on the number of steps.
est.train(train_input_fn, max_steps=100)
# Eval.
results = est.evaluate(eval_input_fn)
print('Accuracy : ', results['accuracy'])
print('Dummy model: ', results['accuracy_baseline'])
4.3，内存模式
def make_inmemory_train_input_fn(X, y):
def input_fn():
return dict(X), y
return input_fn
train_input_fn = make_inmemory_train_input_fn(dftrain, y_train)
eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)
est = tf.contrib.estimator.boosted_trees_classifier_train_in_memory(
train_input_fn,
feature_columns)
print(est.evaluate(eval_input_fn)['accuracy'])
5，预测
pred_dicts = list(est.predict(eval_input_fn))
probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])
probs.plot(kind='hist', bins=20, title='predicted probabilities');
6，看看roc曲线
from sklearn.metrics import roc_curve
from matplotlib import pyplot as plt
fpr, tpr, _ = roc_curve(y_eval, probs)
plt.plot(fpr, tpr)
plt.title('ROC curve')
plt.xlabel('false positive rate')
plt.ylabel('true positive rate')
plt.xlim(0,)
plt.ylim(0,);