技术标签: 机器学习实战
确保matplotlib工作和写函数来保存图片
保证python2和python3的可以使用
from __future__ import division, print_function, unicode_literals
import numpy as np
import os
# 确保notebook运行时结果稳定
np.random.seed(42)
# 修改默认属性
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
# 设置储存图片路径
PROJECT_ROOT_DIR = "."
CHAPETER_ID = "end_to_end_project"
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID)
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
# 忽略没用的警告
import warnings
warnings.filterwarnings(action="ignore", message="^internal gelsd")
import os
import tarfile
from six.moves import urllib
DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/"
HOUSING_PATH = os.path.join("datasets", "housing")
HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz"
def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH)
os.makedirs(housing_path, exist_ok=True) # 创建目录
tgz_path = os.path.join(housing_path, "housing.tgz")
urllib.request.urlretrieve(housing_url, tgz_path) # 从远程获取文件放入指定文件
housing_tgz = tarfile.open(tgz_path)
housing_tgz.extractall(path=housing_path) # 解压文件到当前目录
housing_tgz.close()
fetch_housing_data()
import pandas as pd
def load_housing_data(housing_path=HOUSING_PATH):
csv_path = os.path.join(housing_path, "housing.csv")
return pd.read_csv(csv_path)
housing = load_housing_data()
housing.head()
housing.info()
housing["ocean_proximity"].value_counts()
housing.describe()
%matplotlib inline
import matplotlib.pyplot as plt
housing.hist(bins=50, figsize=(20,15))
save_fig("attribute_histogram_plots")
plt.show()
# 使每次运行时输出的结果保持一致
np.random.seed(42)
import numpy as np
def split_train_test(data, test_ratio):
shuffled_indices = np.random.permutation(len(data))
test_set_size = int(len(data) * test_ratio)
test_indices = shuffled_indices[:test_set_size]
train_indices = shuffled_indices[test_set_size:]
return data.iloc[train_indices], data.iloc[test_indices]
train_set, test_set = split_train_test(housing, 0.2)
print(len(train_set), "train +", len(test_set), "test")
from zlib import crc32
def test_set_check(identifier, test_ratio):
return crc32(np.int64(identifier)) & 0xffffffff < test_ratio * 2**32
def split_train_test_by_id(data, test_ratio, id_column):
ids = data[id_column]
in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))
return data.loc[~in_test_set], data.loc[in_test_set]
import hashlib
def test_set_check(identifier, test_ratio, hash=hashlib.md5):
return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio
def test_set_check(identifier, test_ratio, hash=hashlib.md5):
return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio
housing_with_id = housing.reset_index() # adds an `index` column
train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, "index")
housing_with_id["id"] = housing["longitude"] * 1000 + housing["latitude"]
train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, "id")
test_set.head()
from sklearn.model_selection import train_test_split
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
test_set.head()
housing["median_income"].hist()
# 将数据分成五个区间,并且标签
housing["income_cat"] = pd.cut(housing["median_income"],bins=[0., 1.5, 3.0, 4.5, 6., np.inf],labels=[1, 2, 3, 4, 5])
housing["income_cat"].value_counts()
using["income_cat"].hist()
# 交叉验证
from sklearn.model_selection import StratifiedShuffleSplit
for train_index, test_index in split.split(housing, housing["income_cat"]):
strat_train_set = housing.loc[train_index]
strat_test_set = housing.loc[test_index]
strat_test_set["income_cat"].value_counts() / len(strat_test_set)
housing["income_cat"].value_counts() / len(housing)
def income_cat_proportions(data):
return data["income_cat"].value_counts() / len(data)
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
compare_props = pd.DataFrame({
"Overall": income_cat_proportions(housing),
"Stratified": income_cat_proportions(strat_test_set),
"Random": income_cat_proportions(test_set),
}).sort_index()
compare_props["Rand. %error"] = 100 * compare_props["Random"] / compare_props["Overall"] - 100
compare_props["Strat. %error"] = 100 * compare_props["Stratified"] / compare_props["Overall"] - 100
compare_props
for set_ in (strat_train_set, strat_test_set):
set_.drop("income_cat", axis=1, inplace=True)
housing = strat_train_set.copy()
housing.plot(kind="scatter", x="longitude", y="latitude")
save_fig("bad_visualization_plot")
housing.plot(kind="scatter", x="longitude", y="latitude", alpha=0.1)
save_fig("better_visualization_plot")
housing.plot(kind="scatter", x="longitude", y="latitude", alpha=0.4, s=housing["population"]/100, label="population", figsize=(10,7), c="median_house_value", cmap=plt.get_cmap("jet"), colorbar=True, sharex=False)
plt.legend()
save_fig("housing_prices_scatterplot")
import matplotlib.image as mpimg
california_img = mpimg.imread(PROJECT_ROOT_DIR + '/images/end_to_end_project/california.png')
ax = housing.plot(kind="scatter", x="longitude", y="latitude", figsize=(10,7), s=housing['population']/100, label="Population", c="median_house_value", cmap=plt.get_cmap("jet"), colorbar=False, alpha=0.4,)
plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5, cmap=plt.get_cmap("jet"))
plt.ylabel("Latitude", fontsize=14)
plt.xlabel("Longitude", fontsize=14)
prices = housing["median_house_value"]
tick_values = np.linspace(prices.min(), prices.max(), 11)
cbar = plt.colorbar()
cbar.ax.set_yticklabels(["$%dk"%(round(v/1000)) for v in tick_values], fontsize=14)
cbar.set_label('Median House Value', fontsize=16)
plt.legend(fontsize=16)
save_fig("california_housing_prices_plot")
plt.show()
corr_matrix = housing.corr()
corr_matrix["median_house_value"].sort_values(ascending=False)
from pandas.plotting import scatter_matrix
attributes = ["median_house_value", "median_income", "total_rooms", "housing_median_age"]
scatter_matrix(housing[attributes], figsize=(12, 8))
save_fig("scatter_matrix_plot")
housing.plot(kind="scatter", x="median_income", y="median_house_value",alpha=0.1)
plt.axis([0, 16, 0, 550000])
save_fig("income_vs_house_value_scatterplot")
housing["rooms_per_household"] = housing["total_rooms"]/housing["households"]
housing["bedrooms_per_room"] = housing["total_bedrooms"]/housing["total_rooms"]
housing["population_per_household"]=housing["population"]/housing["households"]
corr_matrix = housing.corr()
corr_matrix["median_house_value"].sort_values(ascending=False)
housing.plot(kind="scatter", x="rooms_per_household", y="median_house_value", alpha=0.2)
plt.axis([0, 5, 0, 520000])
plt.show()
housing.describe()
housing = strat_train_set.drop("median_house_value", axis=1) # 舍弃训练集中的标记
housing_labels = strat_train_set["median_house_value"].copy()
sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()
sample_incomplete_rows
sample_incomplete_rows.dropna(subset=["total_bedrooms"]) # option 1
sample_incomplete_rows.drop("total_bedrooms", axis=1) # option 2
median = housing["total_bedrooms"].median()
sample_incomplete_rows["total_bedrooms"].fillna(median, inplace=True) # option 3
sample_incomplete_rows
try:
from sklearn.impute import SimpleImputer # Scikit-Learn 0.20+
except ImportError:
from sklearn.preprocessing import Imputer as SimpleImputer
imputer = SimpleImputer(strategy="median")
## 要先移除文本属性的值,再插值
housing_num = housing.drop('ocean_proximity', axis=1)
# alternatively: housing_num = housing.select_dtypes(include=[np.number])
# 计算每一列的中间值
imputer.fit(housing_num)
# 显示计算的每一列的中间值
imputer.statistics_
X = imputer.transform(housing_num)
housing_tr = pd.DataFrame(X, columns=housing_num.columns, index=housing.index)
housing_tr.loc[sample_incomplete_rows.index.values]
imputer.strategy
housing_tr = pd.DataFrame(X, columns=housing_num.columns, index=housing_num.index)
housing_tr.head()
housing_cat = housing[['ocean_proximity']]
housing_cat.head(10)
try:
from sklearn.preprocessing import OrdinalEncoder
except ImportError:
from future_encoders import OrdinalEncoder # Scikit-Learn < 0.20
ordinal_encoder = OrdinalEncoder()
housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)
housing_cat_encoded[:10]
ordinal_encoder.categories_
try:
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import OneHotEncoder
except ImportError:
from future_encoders import OneHotEncoder
cat_encoder = OneHotEncoder()
housing_cat_1hot = cat_encoder.fit_transform(housing_cat)
housing_cat_1hot
housing_cat_1hot.toarray()
cat_encoder = OneHotEncoder(sparse=False)
housing_cat_1hot = cat_encoder.fit_transform(housing_cat)
housing_cat_1hot
cat_encoder.categories_
housing.columns
from sklearn.base import BaseEstimator, TransformerMixin
# 获得指定列名的下标
rooms_ix, bedrooms_ix, population_ix, household_ix = [list(housing.columns).index(col) for col in ("total_rooms", "total_bedrooms", "population", "households")]
class CombinedAttributesAdder(BaseEstimator, TransformerMixin):
# 为原数据集添加新的columns,表征新的属性
def __init__(self, add_bedrooms_per_room = True): # no *args or **kwargs
self.add_bedrooms_per_room = add_bedrooms_per_room
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
rooms_per_household = X[:, rooms_ix] / X[:, household_ix]
population_per_household = X[:, population_ix] / X[:, household_ix]
if self.add_bedrooms_per_room:
bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]
return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]
else:
return np.c_[X, rooms_per_household, population_per_household]
attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)
housing_extra_attribs = attr_adder.transform(housing.values)
# 另一种方式构建添加新属性的函数
from sklearn.preprocessing import FunctionTransformer
def add_extra_features(X, add_bedrooms_per_room=True):
rooms_per_household = X[:, rooms_ix] / X[:, household_ix]
population_per_household = X[:, population_ix] / X[:, household_ix]
if add_bedrooms_per_room:
bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]
return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]
else:
return np.c_[X, rooms_per_household, population_per_household]
attr_adder = FunctionTransformer(add_extra_features, validate=False,kw_args={
"add_bedrooms_per_room": False})
housing_extra_attribs = attr_adder.fit_transform(housing.values)
housing_extra_attribs = pd.DataFrame(housing_extra_attribs,columns=list(housing.columns)+["rooms_per_household", "population_per_household"], index=housing.index)
housing_extra_attribs.head()
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
# 用来预处理数值属性的pipeline
num_pipeline = Pipeline([('imputer', SimpleImputer(strategy="median")),
('attribs_adder', FunctionTransformer(add_extra_features, validate=False)),
('std_scaler', StandardScaler()),
])
housing_num_tr = num_pipeline.fit_transform(housing_num)
housing_num_tr
try:
from sklearn.compose import ColumnTransformer
except ImportError:
from future_encoders import ColumnTransformer # Scikit-Learn < 0.20
num_attribs = list(housing_num)
cat_attribs = ["ocean_proximity"]
full_pipeline = ColumnTransformer([
("num", num_pipeline, num_attribs),
("cat", OneHotEncoder(), cat_attribs),
])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
housing_prepared.shape
# 另一种构建处理类别和数值属性的pipeline方式
from sklearn.base import BaseEstimator, TransformerMixin
class OldDataFrameSelector(BaseEstimator, TransformerMixin):
def __init__(self, attribute_names):
self.attribute_names = attribute_names
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.attribute_names].values
num_attribs = list(housing_num)
cat_attribs = ["ocean_proximity"]
old_num_pipeline = Pipeline([
('selector', OldDataFrameSelector(num_attribs)),
('imputer', SimpleImputer(strategy="median")),
('attribs_adder', FunctionTransformer(add_extra_features, validate=False)),
('std_scaler', StandardScaler()),
])
old_cat_pipeline = Pipeline([
('selector', OldDataFrameSelector(cat_attribs)),
('cat_encoder', OneHotEncoder(sparse=False)),
])
from sklearn.pipeline import FeatureUnion
old_full_pipeline = FeatureUnion(transformer_list=[
("num_pipeline", old_num_pipeline),
("cat_pipeline", old_cat_pipeline),
])
old_housing_prepared = old_full_pipeline.fit_transform(housing)
old_housing_prepared
np.allclose(housing_prepared, old_housing_prepared)
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(housing_prepared, housing_labels)
some_data = housing.iloc[:5]
some_labels = housing_labels.iloc[:5]
some_data_prepared = full_pipeline.transform(some_data)
print("Predictions:", lin_reg.predict(some_data_prepared))
print("Labels:", list(some_labels))
some_data_prepared
from sklearn.metrics import mean_squared_error
housing_predictions = lin_reg.predict(housing_prepared)
lin_mse = mean_squared_error(housing_labels, housing_predictions)
lin_rmse = np.sqrt(lin_mse)
lin_rmse
from sklearn.metrics import mean_absolute_error
lin_mae = mean_absolute_error(housing_labels, housing_predictions)
lin_mae
from sklearn.tree import DecisionTreeRegressor
tree_reg = DecisionTreeRegressor(random_state=42)
tree_reg.fit(housing_prepared, housing_labels)
housing_predictions = tree_reg.predict(housing_prepared)
tree_mse = mean_squared_error(housing_labels, housing_predictions)
tree_rmse = np.sqrt(tree_mse)
tree_rmse
from sklearn.model_selection import cross_val_score
scores = cross_val_score(tree_reg, housing_prepared, housing_labels, scoring="neg_mean_squared_error", cv=10)
tree_rmse_scores = np.sqrt(-scores)
def display_scores(scores):
print("Scores:", scores)
print("Mean:", scores.mean())
print("Standard deviation:", scores.std())
display_scores(tree_rmse_scores)
lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring="neg_mean_squared_error", cv=10)
lin_rmse_scores = np.sqrt(-lin_scores)
display_scores(lin_rmse_scores)
from sklearn.ensemble import RandomForestRegressor
forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)
forest_reg.fit(housing_prepared, housing_labels)
housing_predictions = forest_reg.predict(housing_prepared)
forest_mse = mean_squared_error(housing_labels, housing_predictions)
forest_rmse = np.sqrt(forest_mse)
forest_rmse
from sklearn.model_selection import cross_val_score
forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels, scoring="neg_mean_squared_error", cv=10)
forest_rmse_scores = np.sqrt(-forest_scores)
display_scores(forest_rmse_scores)
scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring="neg_mean_squared_error", cv=10)
pd.Series(np.sqrt(-scores)).describe()
from sklearn.svm import SVR
svm_reg = SVR(kernel="linear")
svm_reg.fit(housing_prepared, housing_labels)
housing_predictions = svm_reg.predict(housing_prepared)
svm_mse = mean_squared_error(housing_labels, housing_predictions)
svm_rmse = np.sqrt(svm_mse)
svm_rmse
from sklearn.model_selection import GridSearchCV
param_grid = [
# 尝试12(3x4)组超参数组合
{
'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},
# 设置bootstap为否,尝试6(2x3)组超参数组合
{
'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},
]
forest_reg = RandomForestRegressor(random_state=42)
# 总共训练(12+6)x 5=90轮
grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring='neg_mean_squared_error', return_train_score=True)
grid_search.best_params_
grid_search.best_estimator_
cvres = grid_search.cv_results_
for mean_score, params in zip(cvres["mean_test_score"], cvres["params"]):
print(np.sqrt(-mean_score), params)
pd.DataFrame(grid_search.cv_results_)
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import randint
param_distribs = {
'n_estimators': randint(low=1, high=200),
'max_features': randint(low=1, high=8),
}
forest_reg = RandomForestRegressor(random_state=42)
rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs, n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)
rnd_search.fit(housing_prepared, housing_labels)
cvres = rnd_search.cv_results_
for mean_score, params in zip(cvres["mean_test_score"], cvres["params"]):
print(np.sqrt(-mean_score), params)
feature_importances = grid_search.best_estimator_.feature_importances_
feature_importances
extra_attribs = ["rooms_per_hhold", "pop_per_hhold", "bedrooms_per_room"]
cat_encoder = full_pipeline.named_transformers_["cat"]
cat_one_hot_attribs = list(cat_encoder.categories_[0])
attributes = num_attribs + extra_attribs + cat_one_hot_attribs
sorted(zip(feature_importances, attributes), reverse=True)
final_model = grid_search.best_estimator_
X_test = strat_test_set.drop("median_house_value", axis=1)
y_test = strat_test_set["median_house_value"].copy()
X_test_prepared = full_pipeline.transform(X_test)
final_predictions = final_model.predict(X_test_prepared)
final_mse = mean_squared_error(y_test, final_predictions)
final_rmse = np.sqrt(final_mse)
final_rmse
from scipy import stats
confidence = 0.95
squared_errors = (final_predictions - y_test) ** 2
mean = squared_errors.mean()
m = len(squared_errors)
np.sqrt(stats.t.interval(confidence, m - 1,
loc=np.mean(squared_errors),
scale=stats.sem(squared_errors)))
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传统上用户登陆状态会以 Session 的形式保存在服务器上,而 Session ID 则保存在前端的 Cookie 中;而使用 JWT 以后,用户的认证信息将会以 Token 的形式保存在前端,服务器不需要保存任何的用户状态,这也就是为什么 JWT 被称为无状态登陆的原因,无状态登陆最大的优势就是完美支持分布式部署,可以使用一个 Token 发送给不同的服务器,而所有的服务器都会返回同样的结果。有状态和无状态最大的区别就是服务端会不会保存客户端的信息。
文章浏览阅读784次。发表于10小时前| 2674次阅读| 来源TechCrunch| 19 条评论| 作者Jon EvansiOSAndroid应用开发产品编程语言JavaObjective-C摘要:即便Android市场份额已经超过80%,对于开发者来说,使用哪一个平台做开发仍然很难选择。本文从开发环境、配置、UX设计、语言、API、网络、分享、碎片化、发布等九个方面把Android和iOS_ios 开发角度
搜索引擎的发展历史可以追溯到20世纪90年代初,随着互联网的快速发展和信息量的急剧增加,人们开始感受到了获取和管理信息的挑战。这些阶段展示了搜索引擎在技术和商业模式上的不断演进,以满足用户对信息获取的不断增长的需求。
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文章浏览阅读5.7w次,点赞50次,收藏276次。FRP搭建内网穿透1.概述:frp可以通过有公网IP的的服务器将内网的主机暴露给互联网,从而实现通过外网能直接访问到内网主机;frp有服务端和客户端,服务端需要装在有公网ip的服务器上,客户端装在内网主机上。2.简单的图解:3.准备工作:1.一个域名(www.test.xyz)2.一台有公网IP的服务器(阿里云、腾讯云等都行)3.一台内网主机4.下载frp,选择适合的版本下载解压如下:我这里服务器端和客户端都放在了/usr/local/frp/目录下4.执行命令# 服务器端给执_locyanfrp
文章浏览阅读687次。题目:http://acm.hust.edu.cn/vjudge/contest/view.action?cid=93745#problem/A题意:给出r*c的01矩阵,可以翻转格子使得0表成1,1变成0,求出最小的步数使得每一行中1的个数相等,每一列中1的个数相等。思路:网络流。容量可以保证每一行和每一列的1的个数相等,费用可以算出最小步数。行向列建边,如果该格子是_uva12534
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文章浏览阅读1.6k次。测试算法的性能 很多时候我们需要对算法的性能进行测试,最简单的方式是看算法在特定的数据集上的执行时间,简单的测试算法性能的函数实现见testSort()。【思想】:用clock_t计算某排序算法所需的时间,(endTime - startTime)/ CLOCKS_PER_SEC来表示执行了多少秒。【关于宏CLOCKS_PER_SEC】:以下摘自百度百科,“CLOCKS_PE_算法性能测试
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