From fbe192b89c28684a1498b3141bfbf7f61680c89b Mon Sep 17 00:00:00 2001 From: Atalya Alon Date: Sat, 6 Apr 2019 23:02:51 +0300 Subject: [PATCH] winning team 1st place - normalize accidents --- .../normalize_accidents.ipynb | 1122 +++++++++++++++++ .../normalize_accidents_presentation.pptx | Bin 0 -> 7209307 bytes 2 files changed, 1122 insertions(+) create mode 100644 DataHackathon2019/normalize_accidents/normalize_accidents.ipynb create mode 100644 DataHackathon2019/normalize_accidents/normalize_accidents_presentation.pptx diff --git a/DataHackathon2019/normalize_accidents/normalize_accidents.ipynb b/DataHackathon2019/normalize_accidents/normalize_accidents.ipynb new file mode 100644 index 0000000..59f22b3 --- /dev/null +++ b/DataHackathon2019/normalize_accidents/normalize_accidents.ipynb @@ -0,0 +1,1122 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Normalize accidents (good version)", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "metadata": { + "id": "_680G_RPjqq7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Setup" + ] + }, + { + "metadata": { + "id": "SOzjx5P_7WqG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Link to data in Drive:\n", + "https://drive.google.com/drive/folders/1eVA-xVbFa1VDfJ1q_Iu52vFUdx1CsTFx?usp=sharing" + ] + }, + { + "metadata": { + "id": "vIbbKrnz1TEe", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "pd.set_option('display.max_columns', 500)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yVhXWElPGm2s", + "colab_type": "code", + "outputId": "b58eede9-3e30-4760-d914-5d81890bc1a7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/gdrive')\n", + "\n", + "#os.listdir('/content/gdrive/My Drive/')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "v0qBDcqrj3ar", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Read and organize data" + ] + }, + { + "metadata": { + "id": "ezogxRLDXa-J", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "markers_path = '/content/gdrive/My Drive/anyway_tables_csv_updated/markers_hebrew.csv'\n", + "markers_df = pd.read_csv(markers_path)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wfmVXX04Fqkx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "tabmef = pd.read_csv('/content/gdrive/My Drive/anyway_tables_csv_updated/Traffic/2017/תקליטור/h20171092tabmef.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mKHy8NJbPfZi", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "markers_df = pd.read_csv(markers_path)\n", + "markers_df_selected = markers_df.sort_values(by=['road1'])\n", + "wanted_fields = ['provider_and_id','road1', 'km', 'accident_severity_hebrew', 'location_accuracy', 'multi_lane_hebrew']\n", + "markers_df_selected = markers_df_selected[wanted_fields] #.query('location_accuracy==1')\n", + "clean_markers = markers_df_selected.dropna(subset=['road1','km'])\n", + "\n", + "tabmef = pd.read_csv('/content/gdrive/My Drive/anyway_tables_csv_updated/Traffic/2017/תקליטור/h20171092tabmef.csv')\n", + "tabmef = tabmef.drop_duplicates(['kvish','keta','hodesh','taarich','shaa']) # spurious duplicate entries\n", + "traffic_by_keta = tabmef.groupby(['kvish','keta','maslul'])['nefah'].mean().reset_index().groupby(['kvish','keta'])['nefah'].sum().reset_index()\n", + "\n", + "traffic_keys = pd.read_csv('/content/gdrive/My Drive/anyway_tables_csv_updated/Traffic/2017/תקליטור/roads_non_urban_no_unicode_real_2.csv', encoding='utf-8')\n", + "merged_traffic_info = traffic_keys.merge(traffic_by_keta, left_on=['road', 'keta'], right_on=['kvish', 'keta'])\n", + "import sqlite3\n", + "connection = sqlite3.connect(':memory:')\n", + "merged_traffic_info.to_sql('traffic', connection)\n", + "clean_markers.to_sql('markers', connection)\n", + "accidents_and_traffic = pd.read_sql_query(\"\"\"\n", + "SELECT *\n", + "FROM markers left outer join traffic\n", + "on (markers.road1 = traffic.road\n", + "AND traffic.km_from * 10 <= markers.km and traffic.km_to * 10 >= markers.km\n", + "AND location_accuracy=1)\n", + "\"\"\", connection)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "2LgsQMjmut1F", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "print(markers_df_selected['road1'].notnull().value_counts())\n", + "print(len(clean_markers))\n", + "print(clean_markers['road1'].isin(traffic_keys['road'].unique()).value_counts())\n", + "print(accidents_and_traffic['km_from'].notnull().value_counts())\n", + "save_columns = ['provider_and_id','road1','km','km_from','km_to','keta','nefah']\n", + "accidents_and_traffic[save_columns].to_csv('/content/gdrive/My Drive/anyway_tables_csv_updated/2017-accidents_and_traffic.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9_pUHYn2yQq-", + "colab_type": "code", + "outputId": "9bab72aa-09cb-4860-95c0-5b3cd7f615a5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "# stats about which segments we have traffic data for\n", + "traffic_keys['keta_length'] = traffic_keys['km_to']-traffic_keys['km_from']\n", + "traffic_keys.groupby('road')['keta_length'].sum() / traffic_keys.groupby('road')['km_to'].max()\n", + "traffic_keys.groupby('road')['keta_length'].sum().sum() / traffic_keys.groupby('road')['km_to'].max().sum()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.5156099190734279" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "E5bztHctj9PC", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Normalize accidents by traffic" + ] + }, + { + "metadata": { + "id": "4oBojcYKjleZ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "accidents_and_traffic_merged = accidents_and_traffic.merge(markers_df[['provider_and_id','accident_severity']],on='provider_and_id')\n", + "accidents_and_traffic_merged['n_accidents'] = 1\n", + "accidents_and_traffic_merged['n_accidents_serious'] = (accidents_and_traffic_merged['accident_severity']<=2).astype(int)\n", + "accidents_and_traffic_merged['n_accidents_fatal'] = (accidents_and_traffic_merged['accident_severity']==1).astype(int)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "u1uYRusJ739O", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def get_risk(accidents_and_traffic_merged, cost_col='n_accidents', plotting=False):\n", + " by_keta = accidents_and_traffic_merged.dropna(subset=['km_to']).groupby(['road1','keta','km_from','km_to', 'from_intersection', 'to_intersection'])['nefah'].mean().to_frame()\n", + " by_keta['cost'] = accidents_and_traffic_merged.dropna(subset=['km_to']).groupby(['road1','keta','km_from','km_to', 'from_intersection', 'to_intersection'])[cost_col].sum()\n", + " by_keta['multi_lane_hebrew'] = accidents_and_traffic_merged.dropna(subset=['km_to']).groupby(['road1','keta','km_from','km_to', 'from_intersection', 'to_intersection'])['multi_lane_hebrew'].agg(lambda x:x.value_counts().index[0] if len(x.value_counts().index) else None)\n", + " by_keta.reset_index(inplace=True)\n", + " by_keta['keta_length'] = by_keta['km_to'] - by_keta['km_from']\n", + " by_keta['risk_per_traffic_km'] = by_keta.eval('cost / nefah / keta_length')\n", + " by_keta['risk_per_traffic'] = by_keta.eval('cost / nefah')\n", + " by_keta.sort_values('risk_per_traffic')[::-1]\n", + " by_keta['log_nefah'] = np.log1p(by_keta['nefah'])\n", + " by_keta['log_cost'] = np.log1p(by_keta['cost'])\n", + " by_keta['log_nefah_km'] = np.log1p(by_keta['nefah']*by_keta['keta_length'])\n", + " if plotting:\n", + " by_keta.plot.scatter('log_nefah_km','log_cost')\n", + " plt.title(cost_col)\n", + " plt.show()\n", + " #plt.savefig('/content/gdrive/My Drive/traffic_results/{}_graph1'.format(cost_col))\n", + " sns.lmplot('log_nefah_km','log_cost',data=by_keta)\n", + " plt.title(cost_col)\n", + " plt.show()\n", + " #plt.savefig('/content/gdrive/My Drive/traffic_results/{}_graph2'.format(cost_col))\n", + " print(by_keta[['nefah','log_nefah','log_nefah_km','cost','log_cost']].corr())\n", + " #by_keta[['nefah','log_nefah','log_nefah_km','cost','log_cost']].corr().to_csv('/content/gdrive/My Drive/traffic_results/{}_corrcoefs.csv'.format(cost_col))\n", + " by_keta.sort_values('risk_per_traffic_km')[::-1]\n", + " years_in_accident_data = 2018-2008+1 # markers_df['accident_year'].nunique()\n", + " hours_in_year = 24*365\n", + " by_keta['risk_per_km'] = by_keta['risk_per_traffic_km']/(years_in_accident_data*hours_in_year)\n", + " # markers_df['accident_year'].value_counts()\n", + " by_keta.sort_values('risk_per_km',ascending=False,inplace=True)\n", + " by_keta['rank_cost'] = by_keta['cost'].rank(ascending=False)\n", + " by_keta['rank_risk_per_km'] = by_keta['risk_per_km'].rank(ascending=False)\n", + " if plotting:\n", + " plt.figure()\n", + " sns.distplot(by_keta['risk_per_traffic_km'])\n", + " #plt.title(cost_col)\n", + " plt.show()\n", + " #plt.savefig('/content/gdrive/My Drive/traffic_results/{}_graph3'.format(cost_col))\n", + " return by_keta\n", + "\n", + "\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Kn5HqKx9jgxb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "by_keta_accidents = get_risk(accidents_and_traffic_merged,'n_accidents', True)\n", + "by_keta_serious = get_risk(accidents_and_traffic_merged,'n_accidents_serious', False)\n", + "by_keta_fatal = get_risk(accidents_and_traffic_merged,'n_accidents_fatal', False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "YIGQi1NKM84J", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "" + ] + }, + { + "metadata": { + "id": "FsZZYlIjkHe2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Check if dangerous intersections have Mifrada" + ] + }, + { + "metadata": { + "id": "c96NwoDVnE3A", + "colab_type": "code", + "outputId": "15029e76-b1ab-4a5b-d753-a75e9749fc5a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1906 + } + }, + "cell_type": "code", + "source": [ + "by_keta_accidents.sort_values('risk_per_km',ascending=False)[['road1', 'keta', 'from_intersection', 'to_intersection', 'cost','multi_lane_hebrew','risk_per_traffic_km']].rename(columns={'cost': 'num_accidents'})" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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road1ketafrom_intersectionto_intersectionnum_accidentsmulti_lane_hebrewrisk_per_traffic_km
83.020.0צומת טוביהצומת מלאכי (קסטינה)169מיפרדה בנויה ללא גדר בטיחות0.490009
14470.090.0מחלף סומךצומת אבליים486מיפרדה בנויה ללא גדר בטיחות0.443245
63.010.0צומת אבא הללצומת הודיה300מיפרדה בנויה ללא גדר בטיחות0.286870
12765.030.0צומת חדרה (מזרח)צומת אלון (שמורת אלון)291מיפרדה עם גדר בטיחות0.285086
194.030.0צומת ברכיהצומת אבא הלל290מיפרדה בנויה ללא גדר בטיחות0.262901
164.010.0צומת חבל עזהצומת יד מרדכי119מיפרדה בנויה ללא גדר בטיחות0.259723
12965.050.0צומת חנהצומת מנשה229מיפרדה עם גדר בטיחות0.254538
14670.0110.0צומת יבורצומת אחיהוד602מיפרדה בנויה ללא גדר בטיחות0.250208
16985.026.0צומת כרמיאל מערבצומת כרמיאל410מיפרדה עם גדר בטיחות0.249607
7238.020.0צומת האלהכניסה לרמת בית שמש (דרום)534מיפרדה בנויה ללא גדר בטיחות0.239625
285461.040.0צומת יהוד (מערב)כניסה ליהוד116מיפרדה בנויה ללא גדר בטיחות0.215499
267443.010.0צומת גינתוןצומת בן שמן-מצבה212מיפרדה בנויה ללא גדר בטיחות0.210603
284.0200.0צומת חרושתצומת נחל חדרה200מיפרדה בנויה ללא גדר בטיחות0.204717
257431.06.0מחלף מבוא איילוןמחלף קריית ראשון358מיפרדה עם גדר בטיחות0.199060
8040.072.0מחלף גדרהכניסה לגדרה304מיפרדה עם גדר בטיחות0.194054
6835.030.0צומת פלוגותכניסה לקריית גת141מיפרדה בנויה ללא גדר בטיחות0.193638
184.022.0צומת אשקלוןצומת ברכיה200מיפרדה בנויה ללא גדר בטיחות0.191351
241402.030.0צומת כפר סבאצומת רעננה65מיפרדה בנויה ללא גדר בטיחות0.186784
14170.048.0צומת יקנעםצומת התשבי231מיפרדה בנויה ללא גדר בטיחות0.186010
268443.020.0צומת בן שמן-מצבהכניסה למבוא מודיעים500מיפרדה בנויה ללא גדר בטיחות0.172246
250412.050.0צומת בית דגןמחלף שפירים254מיפרדה בנויה ללא גדר בטיחות0.164369
8240.090.0צומת תל נוףצומת ביל\"ו811מיפרדה בנויה ללא גדר בטיחות0.160063
344.0290.0צומת שמרתצומת נהרייה1100מיפרדה בנויה ללא גדר בטיחות0.157180
374.0310.0צומת בצתצומת53מיפרדה עם גדר בטיחות0.153819
304.0218.0צומת אור עקיבאצומת בנימינה177מיפרדה בנויה ללא גדר בטיחות0.148548
334.0282.0צומת עכו (מזרח)צומת שמרת596מיפרדה בנויה ללא גדר בטיחות0.146604
12865.040.0צומת אלון (שמורת אלון)צומת חנה256מיפרדה בנויה ללא גדר בטיחות0.143903
4718944.010.0צומת אלקושצומת בירנית33None0.132827
294.0210.0צומת נחל חדרהצומת אור עקיבא446מיפרדה עם גדר בטיחות0.130024
11155.020.0צומת נווה ימין (מערב)צומת כפר סבא (מזרח)89מיפרדה בנויה ללא גדר בטיחות0.129990
........................
4205611.010.0מחלף גשר השלוםכניסה לבית יהושע (צפון)13None0.005558
3733613.010.0צומת זרחיה-שפירכניסה לשפיר1None0.005442
3582410.010.0צומת מעוןצומת כיסופים4None0.005368
4245700.010.0צומת בית יצחקצומת כפר חיים9מיפרדה מסומנת בצבע0.005309
4467021.010.0צומת עופרכניסה לכרם מהר\"ל2None0.005242
4487079.010.0צומת בית שאן-נווה איתןצומת תל אשתורי2None0.005186
297562.010.0צומת קדימהכניסה לצורן29מיפרדה בנויה ללא גדר בטיחות0.005171
4336502.010.0צומת מנשהצומת כרכור11מיפרדה בנויה ללא גדר בטיחות0.005125
215240.010.0צומת חוליתכניסה למחסום סופה1None0.004949
4497110.010.0מחלף עתליתכניסה לנווה ים7מיפרדה בנויה ללא גדר בטיחות0.004350
265431.070.0מחלף ענבהכניסה למודיעין-מכבים-רעות64מיפרדה עם גדר בטיחות0.004327
4396667.010.0צומת תל שובהכניסה לבית שאן רח שאול המלך3מיפרדה בנויה ללא גדר בטיחות0.004324
4648077.010.0צומת חוקוקכניסה לרמות לבנים1None0.004061
10845.0810.0צומת מחנה עופרצומת עטרות28מיפרדה בנויה ללא גדר בטיחות0.003871
3934111.010.0צומת יבנה (צפון)כניסה למרכז מחקר16None0.003731
20298.010.0צומת צמחצומת מעגן3None0.003651
4698911.010.0צומת עבדוןכניסה למנות2None0.003470
3823877.010.0כניסה לאורהכניסה ליד קנדי8None0.003464
239402.010.0צומת עדניםכניסה לירקונה18מיפרדה בנויה ללא גדר בטיחות0.003456
4125112.010.0צומת לנווה ירקכניסה לנווה ירק1None0.003377
4195533.010.0צומת צור נתןצומת2None0.003300
242404.0810.0צומת עטרותמחלף יגאל20מיפרדה עם גדר בטיחות0.002843
4416688.010.0צומת מעוז חייםכניסה לכפר רופין1None0.002322
4668611.010.0צומת רגבהכניסה לרגבה (צפון)1None0.002240
4135443.010.0צומת נווה ימין (מערב)צומת נווה ימין2None0.002224
4436734.010.0כניסה ליעלכניסה לאביטל1None0.002115
3783835.010.0צומת צרעהכניסה לאכסניה(גבעת שמש)2None0.001936
3773712.010.0צומת לאמוניםכניסה לגבעתי1None0.001902
4477078.010.0יישוב בית שאן תל סהרוןצומת בית שאן (מערב)1None0.001543
4064503.010.0מחלף בן גוריוןכניסה לנתב\"ג3מיפרדה בנויה ללא גדר בטיחות0.001239
\n", + "

477 rows × 7 columns

\n", + "
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גדר בטיחות 0.193638 \n", + "18 200 מיפרדה בנויה ללא גדר בטיחות 0.191351 \n", + "241 65 מיפרדה בנויה ללא גדר בטיחות 0.186784 \n", + "141 231 מיפרדה בנויה ללא גדר בטיחות 0.186010 \n", + "268 500 מיפרדה בנויה ללא גדר בטיחות 0.172246 \n", + "250 254 מיפרדה בנויה ללא גדר בטיחות 0.164369 \n", + "82 811 מיפרדה בנויה ללא גדר בטיחות 0.160063 \n", + "34 1100 מיפרדה בנויה ללא גדר בטיחות 0.157180 \n", + "37 53 מיפרדה עם גדר בטיחות 0.153819 \n", + "30 177 מיפרדה בנויה ללא גדר בטיחות 0.148548 \n", + "33 596 מיפרדה בנויה ללא גדר בטיחות 0.146604 \n", + "128 256 מיפרדה בנויה ללא גדר בטיחות 0.143903 \n", + "471 33 None 0.132827 \n", + "29 446 מיפרדה עם גדר בטיחות 0.130024 \n", + "111 89 מיפרדה בנויה ללא גדר בטיחות 0.129990 \n", + ".. ... ... ... \n", + "420 13 None 0.005558 \n", + "373 1 None 0.005442 \n", + "358 4 None 0.005368 \n", + "424 9 מיפרדה מסומנת בצבע 0.005309 \n", + "446 2 None 0.005242 \n", + "448 2 None 0.005186 \n", + "297 29 מיפרדה בנויה ללא גדר בטיחות 0.005171 \n", + "433 11 מיפרדה בנויה 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