使用房价预测数据搭建回归模型
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
print(module.__name__, module.__version__)
2.6.2 sys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0) matplotlib 3.3.4 numpy 1.19.5 pandas 1.1.5 sklearn 0.24.2 tensorflow 2.6.2 keras.api._v2.keras 2.6.0
from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() print(housing.DESCR) print(housing.data.shape) print(housing.target.shape)
.. _california_housing_dataset:
California Housing dataset
--------------------------
**Data Set Characteristics:**
:Number of Instances: 20640
:Number of Attributes: 8 numeric, predictive attributes and the target
:Attribute Information:
- MedInc median income in block
- HouseAge median house age in block
- AveRooms average number of rooms
- AveBedrms average number of bedrooms
- Population block population
- AveOccup average house occupancy
- Latitude house block latitude
- Longitude house block longitude
:Missing Attribute Values: None
This dataset was obtained from the StatLib repository.
http://lib.stat.cmu.edu/datasets/
The target variable is the median house value for California districts.
This dataset was derived from the 1990 U.S. census, using one row per census
block group. A block group is the smallest geographical unit for which the U.S.
Census Bureau publishes sample data (a block group typically has a population
of 600 to 3,000 people).
It can be downloaded/loaded using the
:func:`sklearn.datasets.fetch_california_housing` function.
.. topic:: References
- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
Statistics and Probability Letters, 33 (1997) 291-297
(20640, 8)
(20640,)
import pprint pprint.pprint(housing.data[0:5]) pprint.pprint(housing.target[0:5])
array([[ 8.32520000e+00, 4.10000000e+01, 6.98412698e+00,
1.02380952e+00, 3.22000000e+02, 2.55555556e+00,
3.78800000e+01, -1.22230000e+02],
[ 8.30140000e+00, 2.10000000e+01, 6.23813708e+00,
9.71880492e-01, 2.40100000e+03, 2.10984183e+00,
3.78600000e+01, -1.22220000e+02],
[ 7.25740000e+00, 5.20000000e+01, 8.28813559e+00,
1.07344633e+00, 4.96000000e+02, 2.80225989e+00,
3.78500000e+01, -1.22240000e+02],
[ 5.64310000e+00, 5.20000000e+01, 5.81735160e+00,
1.07305936e+00, 5.58000000e+02, 2.54794521e+00,
3.78500000e+01, -1.22250000e+02],
[ 3.84620000e+00, 5.20000000e+01, 6.28185328e+00,
1.08108108e+00, 5.65000000e+02, 2.18146718e+00,
3.78500000e+01, -1.22250000e+02]])
array([4.526, 3.585, 3.521, 3.413, 3.422])
from sklearn.model_selection import train_test_split
# random_state是随机种子，保证代码的可复现性
# train、test的比例默认是3：7
x_train_all, x_test, y_train_all, y_test = train_test_split(
housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(
x_train_all, y_train_all, random_state = 11)
print(x_train.shape, y_train.shape)
print(x_valid.shape, y_valid.shape)
print(x_test.shape, y_test.shape)
(11610, 8) (11610,) (3870, 8) (3870,) (5160, 8) (5160,)
from sklearn.preprocessing import StandardScaler # 数据归一化 scaler = StandardScaler() x_train_scaled = scaler.fit_transform(x_train) x_valid_scaled = scaler.transform(x_valid) x_test_scaled = scaler.transform(x_test)
model = keras.models.Sequential([
keras.layers.Dense(30, activation='relu',
input_shape=x_train.shape[1:]),
keras.layers.Dense(1),
])
model.summary()
model.compile(loss="mean_squared_error", optimizer="sgd")
# EarlyStopping 早停止，patience当连续多少个epochs时验证集精度不再变好终止训练
callbacks = [keras.callbacks.EarlyStopping(
patience=5, min_delta=1e-3)]
Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_2 (Dense) (None, 30) 270 _________________________________________________________________ dense_3 (Dense) (None, 1) 31 ================================================================= Total params: 301 Trainable params: 301 Non-trainable params: 0 _________________________________________________________________
history = model.fit(x_train_scaled, y_train,
validation_data = (x_valid_scaled, y_valid),
epochs = 100,
callbacks = callbacks)
Epoch 1/100 363/363 [==============================] - 1s 1ms/step - loss: 0.8832 - val_loss: 0.6537 Epoch 2/100 363/363 [==============================] - 0s 1ms/step - loss: 0.5799 - val_loss: 0.4851 Epoch 3/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4440 - val_loss: 0.4484 Epoch 4/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4655 - val_loss: 0.4704 Epoch 5/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4152 - val_loss: 0.4224 Epoch 6/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4242 - val_loss: 0.4259 Epoch 7/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3993 - val_loss: 0.4197 Epoch 8/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3942 - val_loss: 0.4121 Epoch 9/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4017 - val_loss: 0.3942 Epoch 10/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3867 - val_loss: 0.3934 Epoch 11/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3774 - val_loss: 0.3887 Epoch 12/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3731 - val_loss: 0.3806 Epoch 13/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3667 - val_loss: 0.3751 Epoch 14/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3886 - val_loss: 0.3786 Epoch 15/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3609 - val_loss: 0.3691 Epoch 16/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3627 - val_loss: 0.3672 Epoch 17/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3686 - val_loss: 0.3693 Epoch 18/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3533 - val_loss: 0.3657 Epoch 19/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3583 - val_loss: 0.4049 Epoch 20/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3684 Epoch 21/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3543 Epoch 22/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3519 Epoch 23/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3505 Epoch 24/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3623 Epoch 25/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3856 Epoch 26/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3461 Epoch 27/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3554 Epoch 28/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3486 Epoch 29/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3369 - val_loss: 0.3491 Epoch 30/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3355 - val_loss: 0.3454 Epoch 31/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3335 - val_loss: 0.3582
def plot_learning_curves(history):
pd.DataFrame(history.history).plot(figsize=(8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1)
plt.show()
plot_learning_curves(history)
model.evaluate(x_test_scaled, y_test, verbose=0)
0.35621199011802673
