234 lines
7.1 KiB
Plaintext
234 lines
7.1 KiB
Plaintext
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{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.9"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.7.9 64-bit ('cscw_2021_sponsor': conda)",
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"metadata": {
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"interpreter": {
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"hash": "ffeddcc79d80f7c9c5ec51de4979e749e22c27f4b977e5c0d8b1b28de2095a12"
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}
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 用来找到特殊的用户案例\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import math"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 按照commit变化情况找到历史上增加，被赞助后减少的例子，使用一次函数拟合\n",
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"df = pd.read_csv('data_4_filtered_users_first_sponsored.csv')\n",
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"logins = df.login.unique()\n",
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"df_time = pd.read_csv('selected_sponsor_firstSponsored_logins.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"149\n258\n591\n155\n240\n279\n4\n"
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]
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}
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],
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"source": [
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"# 1. sponsor有积极作用: 1.1 一直递减，斜率减缓; 1.2 一直递增，斜率增加; 1.3 先递减，后递增\n",
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"# 2. sponsor有消极作用: 2.1 一直递减，斜率增加; 1.2 一直递增，斜率降低; 1.3 先递增，后递减\n",
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"# 3. sponsor没有作用: 斜率没有变化\n",
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"first_1 = {} # value为斜率的差值绝对值\n",
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"first_2 = {}\n",
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"first_3 = {}\n",
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"second_1 = {}\n",
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"second_2 = {}\n",
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"second_3 = {}\n",
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"third = {}\n",
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"for login in logins:\n",
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" rows = df.loc[df.login==login, ['index', 'commit_num']]\n",
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" rows = rows.set_index('index')\n",
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" x1 = [-6, -5, -4, -3, -2, -1]\n",
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" x2 = [1, 2, 3, 4, 5, 6]\n",
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" y1 = []\n",
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" y2 = []\n",
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" for x in x1:\n",
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" y1.append(rows.iloc[x]['commit_num'])\n",
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" x1 = [x+7 for x in x1]\n",
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" for x in x2:\n",
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" y2.append(rows.iloc[x]['commit_num'])\n",
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" z1 = np.polyfit(x1, y1, 1)\n",
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" p1 = np.poly1d(z1)\n",
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" z2 = np.polyfit(x2, y2, 1)\n",
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" p2 = np.poly1d(z2)\n",
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"\n",
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" if z1[0] <= 0 and z2[0] <= 0 and z2[0] > z1[0]:\n",
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" v = abs(z2[0] - z1[0])\n",
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" first_1[login] = v\n",
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" elif z1[0] >= 0 and z2[0] >= 0 and z2[0] > z1[0]:\n",
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" v = abs(z2[0] - z1[0])\n",
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" first_2[login] = v\n",
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" elif z1[0] <= 0 and z2[0] >=0 and z2[0] > z1[0]:\n",
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" v = abs(z2[0] - z1[0])\n",
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" first_3[login] = v\n",
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" elif z1[0] <=0 and z2[0] <= 0 and z2[0] < z1[0]:\n",
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" v = abs(z1[0] - z2[0])\n",
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" second_1[login] = v \n",
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" elif z1[0] >=0 and z2[0] >= 0 and z2[0] < z1[0]:\n",
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" v = abs(z1[0] - z2[0])\n",
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" second_2[login] = v \n",
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" elif z1[0] >=0 and z2[0] <= 0 and z2[0] < z1[0]:\n",
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" v = abs(z1[0] - z2[0])\n",
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" second_3[login] = v\n",
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" elif z2[0] == z1[0]:\n",
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" v = abs(z1[0] - z2[0])\n",
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" third[login] = v\n",
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"print(len(first_1))\n",
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"print(len(first_2))\n",
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"print(len(first_3))\n",
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"print(len(second_1))\n",
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"print(len(second_2))\n",
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"print(len(second_3))\n",
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"print(len(third))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 将first second按照斜率差值排序\n",
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"first_1 = sorted(first_1.items(), key=lambda x: (-x[1], x[0]))\n",
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"first_2 = sorted(first_2.items(), key=lambda x: (-x[1], x[0]))\n",
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"first_3 = sorted(first_3.items(), key=lambda x: (-x[1], x[0]))\n",
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"second_1 = sorted(second_1.items(), key=lambda x: (-x[1], x[0]))\n",
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"second_2 = sorted(second_2.items(), key=lambda x: (-x[1], x[0]))\n",
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"second_3 = sorted(second_3.items(), key=lambda x: (-x[1], x[0]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 76,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 拿到top 10 in each set\n",
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"n = 10\n",
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"first_1_logins = [(item[0], df_time.loc[df_time.login==item[0], 'first_sponsored_at'].values[0]) for item in first_1][:n]\n",
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"first_2_logins = [(item[0], df_time.loc[df_time.login==item[0], 'first_sponsored_at'].values[0]) for item in first_2][:n]\n",
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"first_3_logins = [(item[0], df_time.loc[df_time.login==item[0], 'first_sponsored_at'].values[0]) for item in first_3][:n]\n",
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"second_1_logins = [(item[0], df_time.loc[df_time.login==item[0], 'first_sponsored_at'].values[0]) for item in second_1][:n]\n",
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"second_2_logins = [(item[0], df_time.loc[df_time.login==item[0], 'first_sponsored_at'].values[0]) for item in second_2][:n]\n",
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"second_3_logins = [(item[0], df_time.loc[df_time.login==item[0], 'first_sponsored_at'].values[0]) for item in second_3][:n]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"[('b4b4r07',\n",
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" 144 2019-10-25\n",
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" Name: first_sponsored_at, dtype: object),\n",
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" ('Harmon758',\n",
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" 551 2019-11-01\n",
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" Name: first_sponsored_at, dtype: object),\n",
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" ('swannodette',\n",
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" 1429 2020-01-31\n",
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" Name: first_sponsored_at, dtype: object),\n",
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" ('nikitavoloboev',\n",
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" 1064 2020-03-21\n",
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" Name: first_sponsored_at, dtype: object),\n",
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" ('astrojuanlu',\n",
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" 120 2019-11-20\n",
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" Name: first_sponsored_at, dtype: object),\n",
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" ('wopian',\n",
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" 1612 2020-06-20\n",
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" Name: first_sponsored_at, dtype: object),\n",
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" ('JonnyHaystack',\n",
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" 693 2020-02-09\n",
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" Name: first_sponsored_at, dtype: object),\n",
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" ('octref',\n",
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" 1101 2020-06-12\n",
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" Name: first_sponsored_at, dtype: object),\n",
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" ('groue',\n",
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" 533 2020-01-27\n",
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" Name: first_sponsored_at, dtype: object),\n",
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" ('r9y9',\n",
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" 1212 2020-06-28\n",
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" Name: first_sponsored_at, dtype: object)]"
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]
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},
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"metadata": {},
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"execution_count": 63
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}
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],
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"source": [
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"first_1_logins\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'2019-10-25'"
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]
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},
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"metadata": {},
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"execution_count": 74
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}
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],
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"source": [
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"df_time.loc[df_time.login=='b4b4r07', 'first_sponsored_at'].values[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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]
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}
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