数据科学 1 体会(推荐算法)

数据产生：

网站：用户每次点击
手机：位置和速度
智能手表、手环：心率、行动、饮食、睡眠
智能汽车：驾驶习惯
智能家居：生活习惯

数据科学家

从混乱数据中理出价值的人

寻找关键联系人

根据⽤户⽹络关系数据识别关键联系人

用户列表

users = [
 { "id": 0, "name": "Hero" },
 { "id": 1, "name": "Dunn" },
 { "id": 2, "name": "Sue" },
 { "id": 3, "name": "Chi" },
 { "id": 4, "name": "Thor" },
 { "id": 5, "name": "Clive" },
 { "id": 6, "name": "Hicks" },
 { "id": 7, "name": "Devin" },
 { "id": 8, "name": "Kate" },
 { "id": 9, "name": "Klein" },
 { "id": 10, "name": "Jen" }
]

用户好友关系

friendships = [(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 4),
 (4, 5), (5, 6), (5, 7), (6, 8), (7, 8), (8, 9)]

为每个用户创建朋友列表

for user in users:
    user["friends"] = []

填充好友数据

for i, j in friendships:
    users[i]["friends"].append(users[j])
    users[j]["friends"].append(users[i]) 

问题： 平均朋友联系数是多少？
答： 全部联系数除以用户个数

def number_of_friends(user):
    return len(user["friends"])

total_connections = sum(number_of_friends(user) for user in users) # 24

num_users = len(users)
avg_connections = total_connections / num_users # 2.4

按朋友数多少排序

# 取(user_id, number_of_friends)
num_friends_by_id = [(user['id'], number_of_friends(user)) for user in users]
sorted(num_friends_by_id, key=lambda item: item[1], reverse=True)

案例: 你可能知道的人

找朋友的人

def friends_of_friend_ids_bad(user):
    return [foaf["id"]
        for friend in user["friends"]
        for foaf in friend["friends"]] 

查找共同的朋友

from collections import Counter # not loaded by default

def not_the_same(user, other_user):
    return user["id"] != other_user["id"]

def not_friends(user, other_user):
    return all(not_the_same(friend, other_user)
        for friend in user["friends"])

def friends_of_friend_ids(user):
    return Counter(foaf["id"]
        for friend in user["friends"]
        for foaf in friend["friends"]
        if not_the_same(user, foaf)
        and not_friends(user, foaf))

print(friends_of_friend_ids(users[3])) 

找共同兴趣的人

interests = [
 (0, "Hadoop"), (0, "Big Data"), (0, "HBase"), (0, "Java"),
 (0, "Spark"), (0, "Storm"), (0, "Cassandra"),
 (1, "NoSQL"), (1, "MongoDB"), (1, "Cassandra"), (1, "HBase"),
 (1, "Postgres"), (2, "Python"), (2, "scikit-learn"), (2, "scipy"),
 (2, "numpy"), (2, "statsmodels"), (2, "pandas"), (3, "R"), (3, "Python"),
 (3, "statistics"), (3, "regression"), (3, "probability"),
 (4, "machine learning"), (4, "regression"), (4, "decision trees"),
 (4, "libsvm"), (5, "Python"), (5, "R"), (5, "Java"), (5, "C++"),
 (5, "Haskell"), (5, "programming languages"), (6, "statistics"),
 (6, "probability"), (6, "mathematics"), (6, "theory"),
 (7, "machine learning"), (7, "scikit-learn"), (7, "Mahout"),
 (7, "neural networks"), (8, "neural networks"), (8, "deep learning"),
 (8, "Big Data"), (8, "artificial intelligence"), (9, "Hadoop"),
 (9, "Java"), (9, "MapReduce"), (9, "Big Data")
]

def data_scientists_who_like(target_interest):
 return [user_id
 for user_id, user_interest in interests
 if user_interest == target_interest]

每次搜索都要遍历列表，性能差，建立一个字典

from collections import defaultdict

user_ids_by_interest = defaultdict(list)

for user_id, interest in interests:
    user_ids_by_interest[interest].append(user_id)

interests_by_user_id = defaultdict(list)
for user_id, interest in interests:
    interests_by_user_id[user_id].append(interest)

找与指定用户爱好最多相似的用户

def most_common_interests_with(user_id):
    return Counter(interested_user_id
        for interest in interests_by_user_id[user_id]
        for interested_user_id in user_ids_by_interest[interest]
        if interested_user_id != user_id)

案例：工资与工作年限

salaries_and_tenures = [(83000, 8.7), (88000, 8.1),
 (48000, 0.7), (76000, 6),
 (69000, 6.5), (76000, 7.5),
 (60000, 2.5), (83000, 10),
 (48000, 1.9), (63000, 4.2)]

绘图

def make_chart_salaries_by_tenure():
    tenures = [tenure for salary, tenure in salaries_and_tenures]
    salaries = [salary for salary, tenure in salaries_and_tenures]  
    plt.scatter(tenures, salaries)
    plt.xlabel("Years Experience")
    plt.ylabel("Salary")
    plt.show()

make_chart_salaries_by_tenure

按工作作年线算平均收入

salary_by_tenure = defaultdict(list)

for salary, tenure in salaries_and_tenures:
    salary_by_tenure[tenure].append(salary)

average_salary_by_tenure = {
    tenure : sum(salaries) / len(salaries)
    for tenure, salaries in salary_by_tenure.items()
}

分组后计算

def tenure_bucket(tenure):
    if tenure < 2: return "less than two"
    elif tenure < 5: return "between two and five"
    else: return "more than five"

salary_by_tenure_bucket = defaultdict(list)

for salary, tenure in salaries_and_tenures:
    bucket = tenure_bucket(tenure)
    salary_by_tenure_bucket[bucket].append(salary)

average_salary_by_bucket = {
    tenure_bucket : sum(salaries) / len(salaries)
    for tenure_bucket, salaries in salary_by_tenure_bucket.items()
}

案例：兴趣主题

words_and_counts = Counter(word for user, interest in interests for word in interest.lower().split())

for word, count in words_and_counts.most_common():
    if count > 1:
    print(word, count)