tool.py

import gc
import os
import sys
import time
import pickle
import datetime
import numpy as np
import pandas as pd
from tqdm import tqdm
import multiprocessing
from functools import partial
from dateutil.parser import parse
from lightgbm import LGBMClassifier
from collections import defaultdict
from datetime import date, timedelta
from contextlib import contextmanager
from joblib import dump, load, Parallel, delayed
from sklearn.linear_model import LinearRegression
from sklearn.decomposition import NMF, PCA, TruncatedSVD
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation as LDA



# 求解rmse的均值和标准差
def get_ave_std(c1,c2,f1,f2):
    '''
    :param c1: 提交的常数1
    :param c2: 提交的常数2
    :param f1: 得分1
    :param f2: 得分2
    :return: 均值和标准差
    '''
    f1 = f1**2; f2 = f2**2;
    a = 2; b = 2*(c1+c2); c = c1**2+c2**2-(f1-f2);
    ave = (f1 - f2 + c2 ** 2 - c1 ** 2) / 2 / (c2 - c1)
    std = (f1 - (c1 - ave) ** 2) ** 0.5
    return ave,std

# 求解rmse的均值
def get_sub_ave_std(c1,c2,f1,f2,n1,n2):
    '''
    :param c1: 提交1的常数
    :param c2: 提交2有差异的部分的常数
    :param f1: 提交1的分数
    :param f2: 提交2的分数
    :param n1: 提交总个数
    :param n2: 提交2有差异部分的个数
    :return: 提交2有差异部分的均值
    '''
    result = ((c1+c2)-((f1**2-f2**2)*n1/n2/(c1-c2)))/2
    return result


# 抽样函数
def make_sample(n,n_sub=2,seed=None):
    import random
    if seed is not None:
        random.seed(seed)
    if type(n) is int:
        l = list(range(n))
        s = int(n / n_sub)
    else:
        l = list(n)
        s = int(len(n) / n_sub)
    random.shuffle(l)
    result = []
    for i in range(n_sub):
        if i == n_sub:
            result.append(l[i*s:])
        else:
            result.append(l[i*s: (i+1)*s])
    return result

# 统计list的value_counts
def value_counts(l):
    s = set(l)
    d = dict([(x,0) for x in s])
    for i in l:
        d[i] += 1
    result = pd.Series(d)
    result.sort_values(ascending=False,inplace=True)
    return result

# 分类特征转化率
def analyse(data,name,label='label'):
    result = data.groupby(name)[label].agg({'count':'count',
                                              'sum':'sum'})
    result['rate'] = result['sum']/result['count']
    return result

# 连续特征转化率，等距分隔
def analyse2(data,name='id',label='label', factor=10):
    grouping = pd.cut(data[name],factor)
    rate = data.groupby(grouping)[label].agg({'sum':'sum',
                                              'count':'count'})
    rate['rate'] = rate['sum']/rate['count']
    return rate

# 连续特征转化率，等数分隔
def analyse3(data,name='id',label='label', factor=10):
    grouping = pd.qcut(data[name],factor)
    rate = data.groupby(grouping)[label].agg({'sum':'sum',
                                              'count':'count'})
    rate['rate'] = rate['sum']/rate['count']
    return rate

# 分组标准化
def grp_standard(data,key,names,replace=False):
    for name in names:
        new_name = name + '_' + key + '_' + 'standardize' if replace else name
        mean_std = data.groupby(key, as_index=False)[name].agg({'mean': 'mean',
                                                               'std': 'std'})
        data = data.merge(mean_std, on=key, how='left')
        data[new_name] = ((data[name]-data['mean'])/data['std']).fillna(0).astype(np.float32)
        data[new_name] = data[new_name].replace(-np.inf, 0).fillna(0)
        data.drop(['mean','std'],axis=1,inplace=True)
    return data

# 分组归一化
def grp_normalize(data,key,names,start=0,replace=False):
    for name in names:
        new_name = name + '_' + key + '_' + 'normalize' if replace else name
        max_min = data.groupby(key,as_index=False)[name].agg({'max':'max',
                                                              'min':'min'})
        data = data.merge(max_min, on=key, how='left')
        data[new_name] = (data[name]-data['min'])/(data['max']-data['min'])
        data[new_name] = data[new_name].replace(-np.inf, start).fillna(start).astype(np.float32)
        data.drop(['max','min'],axis=1,inplace=True)
    return data

# 分组排序
def grp_rank(data,key,names,ascending=True):
    for name in names:
        data.sort_values([key, name], inplace=True, ascending=ascending)
        data['rank'] = range(data.shape[0])
        min_rank = data.groupby(key, as_index=False)['rank'].agg({'min_rank': 'min'})
        data = pd.merge(data, min_rank, on=key, how='left')
        data['rank'] = data['rank'] - data['min_rank']
        data[names] = data['rank']
    data.drop(['rank'],axis=1,inplace=True)
    return data

# 合并节约内存
def concat(L):
    result = None
    for l in L:
        if result is None:
            result = l
        else:
            result[l.columns.tolist()] = l
    return result

# 分组排序函数
def group_rank(data, key, values, ascending=True):
    if type(key)==list:
        data_temp = data[key + [values]].copy()
        data_temp.sort_values(key + [values], inplace=True, ascending=ascending)
        data_temp['rank'] = range(data_temp.shape[0])
        min_rank = data_temp.groupby(key,as_index=False)['rank'].agg({'min_rank':'min'})
        index = data_temp.index
        data_temp = data_temp.merge(min_rank,on=key,how='left')
        data_temp.index = index
    else:
        data_temp = data[[key,values]].copy()
        data_temp.sort_values(key + [values], inplace=True, ascending=ascending)
        data_temp['rank'] = range(data_temp.shape[0])
        data_temp['min_rank'] = data_temp[key].map(data_temp.groupby(key)['rank'].min())
    data_temp['rank'] = data_temp['rank'] - data_temp['min_rank']
    return data_temp['rank']

def nunique(x):
    return len(set(x))


# 前后时间差的函数：
def group_diff_time(data,key,value,n):
    data_temp = data[key+[value]].copy()
    shift_value = data_temp.groupby(key)[value].shift(n)
    data_temp['shift_value'] = data_temp[value] - shift_value
    return data_temp['shift_value']



# smape
def smape(y_true,y_pred):
    y_true = np.array(y_true)
    y_pred = np.array(y_pred)
    y_diff = np.abs(y_true-y_pred)
    y_sum = y_true+y_pred
    return np.mean(y_diff/y_sum)*2


# groupby 直接拼接
def groupby(data,stat,key,value,func):
    key = key if type(key)==list else [key]
    data_temp = data[key].copy()
    feat = stat.groupby(key,as_index=False)[value].agg({'feat':func})
    data_temp = data_temp.merge(feat,on=key,how='left')
    return data_temp['feat']



# 计算关系紧密程度指数
def tfidf2(df,key1,key2):
    key = key1 + key2
    tfidf2 = '_'.join(key) + '_tfidf2'
    df1 = df.groupby(key,as_index=False)[key[0]].agg({'key_count': 'size'})
    df2 = df1.groupby(key1,as_index=False)['count'].agg({'key1_count': 'sum'})
    df3 = df1.groupby(key2, as_index=False)['count'].agg({'key2_count': 'sum'})
    df1 = df1.merge(df2,on=key1,how='left').merge(df3,on=key2,how='left')
    df1[tfidf2] = df1['key_count'] / df['key2_count'] / df['key1_count']


# 相差的日期数
def diff_of_days(day1, day2):
    days = (parse(day1[:10]) - parse(day2[:10])).days
    return days

# 相差的分钟数
def diff_of_minutes(time1,time2):
    minutes = (parse(time1) - parse(time2)).total_seconds()//60
    return abs(minutes)

# 相差的小时数
def diff_of_hours(time1,time2):
    hours = (parse(time1) - parse(time2)).total_seconds()//3600
    return abs(hours)

# 日期的加减
def date_add_days(start_date, days):
    end_date = parse(start_date[:10]) + timedelta(days=days)
    end_date = end_date.strftime('%Y-%m-%d')
    return end_date

# 日期的加减
def date_add_hours(start_date, hours):
    end_date = parse(start_date) + timedelta(hours=hours)
    end_date = end_date.strftime('%Y-%m-%d %H:%M:%S')
    return end_date

# 获取某个类型里面第n次的值
def get_last_values(data, stat, key, sort_value, value, shift, sort=None):
    key = key if type(key)==list else [key]
    if sort == 'ascending':
        stat_temp = stat.sort_values(sort_value, ascending=True)
    elif sort == 'descending':
        stat_temp = stat.sort_values(sort_value, ascending=False)
    else:
        stat_temp = stat.copy()
    stat_temp['value'] = stat_temp.groupby(key)[value].shift(shift)
    stat_temp.drop_duplicates(key,keep='last',inplace=True)
    data_temp = data[key].copy()
    data_temp = data_temp.merge(stat_temp,on=key,how='left')
    return data_temp['value']

# 获取某个类型里面第n次的值
def get_first_values(data, stat, key, sort_value, value, shift, sort=None):
    key = key if type(key)==list else [key]
    if sort == 'ascending':
        stat_temp = stat.sort_values(sort_value, ascending=True)
    elif sort == 'descending':
        stat_temp = stat.sort_values(sort_value, ascending=False)
    else:
        stat_temp = stat.copy()
    stat_temp['value'] = stat_temp.groupby(key)[value].shift(-shift)
    stat_temp.drop_duplicates(key,keep='first',inplace=True)
    data_temp = data[key].copy()
    data_temp = data_temp.merge(stat_temp,on=key,how='left')
    return data_temp['value']



# 压缩数据
def compress(data):
    size = sys.getsizeof(data)/2**20
    def intcp(series):
        ma = max(series)
        mi = min(series)
        if (ma<128) & (mi>=-128):
            return 'int8'
        elif (ma<32768) & (mi>=-32768):
            return 'int16'
        elif (ma<2147483648) & (mi>=-2147483648):
            return 'int32'
        else:
            return None
    def floatcp(series):
        ma = max(series)
        mi = min(series)
        if (ma<32770) & (mi>-32770):
            return 'float16'
        elif (ma<2147483600) & (mi>-2147483600):
            return 'float32'
        else:
            return None

    for c in data.columns:
        ctype = None
        dtypes = data[c].dtypes
        if dtypes == np.int64:
            ctype = intcp(data[c])
        if dtypes == np.int32:
            ctype = intcp(data[c])
        if dtypes == np.int16:
            ctype = intcp(data[c])
        if dtypes == np.float64:
            ctype = floatcp(data[c])
        if dtypes == np.float32:
            ctype = floatcp(data[c])
        if ctype is None:
            continue
        try:

            data[c] = data[c].astype(ctype)
            print('{}   convet to {},     done!   {}'.format(dtypes,ctype,c))
        except:
            print('特征{}的类型为：{}，转化出线问题！！！'.format(c,dtypes))
    print('原始数据大小为： {}M'.format(round(size, 2)))
    print('新数据大小为：  {}M'.format(round(sys.getsizeof(data) / 2 ** 20,2)))
    return data




def trend(y):
    try:
        x = np.arange(0, len(y)).reshape(-1, 1)
        lr = LinearRegression()
        lr.fit(x, y)
        trend = lr.coef_[0]
    except:
        trend = np.nan
    return trend


@contextmanager
def timer(title):
    t0 = time.time()
    yield
    print("{} - done in {:.0f}s".format(title, time.time() - t0))


def jiangwei(stat,data, id, feature):
    print('lda ...')
    mapping = {}
    for sample in stat[[id, feature]].values:
        mapping.setdefault(sample[0], []).append(str(sample[1]))
    ids = list(mapping.keys())
    sentences = [' '.join(mapping[cate_]) for cate_ in ids]
    stat_sentences_matrix = CountVectorizer(token_pattern='(?u)\\b\\w+\\b', min_df=2).fit_transform(sentences)
    mapping = {}
    for sample in data[[id, feature]].values:
        mapping.setdefault(sample[0], []).append(str(sample[1]))
    ids = list(mapping.keys())
    sentences = [' '.join(mapping[cate_]) for cate_ in ids]
    data_sentences_matrix = CountVectorizer(token_pattern='(?u)\\b\\w+\\b', min_df=2).fit_transform(sentences)

    lda = LDA(n_components=5,
              learning_method='online',
              batch_size=1000,
              n_jobs=40,
              random_state=520)
    lda.fit(stat_sentences_matrix)
    lda_matrix = lda.transform(data_sentences_matrix)
    lda_matrix = pd.DataFrame(lda_matrix,columns=['lda_{}_{}'.format(feature, i) for i in range(5)]).astype('float16')

    nmf = NMF(n_components=5,
              random_state=520,
              beta_loss='kullback-leibler',
              solver='mu',
              max_iter=1000,
              alpha=.1,
              l1_ratio=.5)
    nmf.fit(stat_sentences_matrix)
    nmf_matrix = nmf.transform(stat_sentences_matrix)
    nmf_matrix = pd.DataFrame(nmf_matrix,columns=['nmf_{}_{}'.format(feature, i) for i in range(5)]).astype('float16')

    pca = TruncatedSVD(5)
    pca.fit(stat_sentences_matrix)
    pca_matrix = pca.transform(stat_sentences_matrix)
    pca_matrix = pd.DataFrame(pca_matrix,
                                   columns=["%s_%s_svd_action" % ('user_sku', i) for i in range(5)]).astype('float32')

    matrix = concat([lda_matrix,nmf_matrix,pca_matrix])
    matrix[id] = ids
    return matrix