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Baselines.py
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Baselines.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Oct 26 09:45:59 2018
@author: Tahereh
"""
import pandas as pd
import numpy as np
import csv
import os
import math
import graphviz
import pydotplus
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn import tree
from sklearn.metrics import accuracy_score
from sklearn.externals.six import StringIO
from IPython.display import Image
def mergeBaselines():
appended_data = []
path = 'ModelData/baselines/'
for filename in os.listdir(path):
print(filename)
df = pd.read_csv(path+filename, sep=",")
appended_data.append(df)
appended_data = pd.concat(appended_data, axis=1)
appended_data.to_csv('ModelData/Final_Baseline.csv', sep=',')
print('finish writing into csv..')
def meanLoadofYear():
df = pd.read_csv('ModelData/clean/oneYear17-18_clean.csv', sep=",")
train_size = int(df.shape[0]*0.75)
df_test = df.iloc[train_size+1:df.shape[0]]
df_test['_Date_'] = df_test['_Date_'].astype(str)
df_test = df_test.loc[((df_test['_Date_'].str[:2] <= '12') | (df_test['_Date_'].str.len() == 5)) & (df_test['_Date_'].str[:] != '0')]
avgload = df_test['load'].mean()
print(avgload) #9.44
return avgload
def createBaselineSet2(df, route , stop):
new_df = PrepareDataset(df)
groupby_df = new_df.groupby(["Rout","Uniqu", "Hour", "Minute"])[["load"]].mean().reset_index()
groupby_df.columns = ['Rout', 'Uniqu', 'Hour', 'Minute', 'MeanLoad']
path = '../2-CSV/ModelData/baseline/' + str(route) + '_' + str(stop) + '_baseline2' + '.csv'
groupby_df.to_csv(path, sep=',')
def FinalBaselinePerQuarterWithDirection(df):
if df is None:
df = pd.read_csv('ModelData/clean/oneYear17-18_clean.csv', sep=",")
print('csv is read!')
#clean----------
df = df[(df['Rout'] != 0) & (df['Rout'] != 998) & (df['Uniqu'] != '00009999') & (df['Uniqu'] != '00009998') & (df['Uniqu'] != '00009997') & (df['Uniqu'] != '00009996') & (df['Uniqu'] != '00009995')]
"""
df['duplicate_flag'] = df.duplicated(subset=['_Date_','TripID', 'Uniqu'], keep=False)
df = df.loc[df['duplicate_flag'] == False]
"""
print('dataset is clean!')
#---------------------
#df = PrepareDataset(df)
#--------------------------
groupby_df = df.groupby(["Rout",'Uniqu', "Hour", "Minute", "Direction"])[["load"]].mean().reset_index()
groupby_df.columns = ['Rout', 'Uniqu','Hour', 'Minute', "Direction", 'MeanLoad']
groupby_df_A = df.groupby(["Rout", 'Uniqu', "Hour", "Minute", "Direction"])[["LoadFactor"]].count().reset_index()
#groupby_df_A.add_suffix('_count').reset_index()
groupby_df_A.columns = ['Rout', 'Uniqu', 'Hour', 'Minute', "Direction", 'ALL']
#print('groupby_All_size = ' , groupby_df_A.shape[0])
# with 5 categories for fullness-----------------------
#Crushed
groupby_df_B = df[(df['LoadFactor'] >= 1.4)].groupby(["Rout", 'Uniqu', "Hour", "Minute", "Direction"])[["LoadFactor"]].count().reset_index()
groupby_df_B.columns = ['Rout', 'Uniqu', 'Hour','Minute', "Direction", 'Crushed']
# Many people need to stand
groupby_df_C = df[(df['LoadFactor'] >= 1.1) & (df['LoadFactor'] < 1.4)].groupby(["Rout", 'Uniqu',"Hour", "Minute", "Direction"])[["LoadFactor"]].count().reset_index()
groupby_df_C.columns = ['Rout', 'Uniqu', 'Hour','Minute' , "Direction", 'ManyPeopleStand']
# few people need to stand
groupby_df_D = df[(df['LoadFactor'] >= 0.8) & (df['LoadFactor'] < 1.1)].groupby(["Rout",'Uniqu', "Hour", "Minute", "Direction"])[["LoadFactor"]].count().reset_index()
groupby_df_D.columns = ['Rout', 'Uniqu', 'Hour','Minute', "Direction", 'FewPeopleStand']
# Few empty seats
groupby_df_E = df[(df['LoadFactor'] >= 0.5) & (df['LoadFactor'] < 0.8)].groupby(["Rout",'Uniqu', "Hour", "Minute", "Direction"])[["LoadFactor"]].count().reset_index()
groupby_df_E.columns = ['Rout','Uniqu', 'Hour','Minute', "Direction", 'FewEmptySeats']
#Many empty seats
groupby_df_F = df[(df['LoadFactor'] < 0.5)].groupby(["Rout", 'Uniqu', "Hour", "Minute", "Direction"])[["LoadFactor"]].count().reset_index()
groupby_df_F.columns = ['Rout', 'Uniqu', 'Hour','Minute', "Direction", 'ManyEmptySeats']
groupby_df_merge1 = pd.merge(groupby_df_A , groupby_df_B , on=["Rout",'Uniqu', "Hour","Minute", "Direction"], how='outer')
groupby_df_merge2 = pd.merge(groupby_df_merge1 , groupby_df_C , on=["Rout",'Uniqu', "Hour","Minute", "Direction"], how='outer')
groupby_df_merge3 = pd.merge(groupby_df_merge2 , groupby_df_D , on=["Rout",'Uniqu', "Hour","Minute", "Direction"], how='outer')
groupby_df_merge4 = pd.merge(groupby_df_merge3 , groupby_df_E , on=["Rout",'Uniqu', "Hour","Minute", "Direction"], how='outer')
groupby_df_merge5 = pd.merge(groupby_df_merge4 , groupby_df_F , on=["Rout",'Uniqu', "Hour","Minute", "Direction"], how='outer')
groupby_df_merge = pd.merge(groupby_df_merge5 , groupby_df , on=["Rout", 'Uniqu', "Hour","Minute", "Direction"], how='outer')
groupby_df_merge = groupby_df_merge.fillna(0)
groupby_df_merge['Crushed'] = round(groupby_df_merge[['Crushed']].div(groupby_df_merge.ALL, axis=0)*100,2)
groupby_df_merge['ManyPeopleStand'] = round(groupby_df_merge[['ManyPeopleStand']].div(groupby_df_merge.ALL, axis=0)*100,2)
groupby_df_merge['FewPeopleStand'] = round(groupby_df_merge[['FewPeopleStand']].div(groupby_df_merge.ALL, axis=0)*100, 2)
groupby_df_merge['FewEmptySeats'] = round(groupby_df_merge[['FewEmptySeats']].div(groupby_df_merge.ALL, axis=0)*100, 2)
groupby_df_merge['ManyEmptySeats'] = round(groupby_df_merge[['ManyEmptySeats']].div(groupby_df_merge.ALL, axis=0)*100, 2)
groupby_df_merge['Category'] = 1
groupby_df_merge.loc[((groupby_df_merge['Crushed'] >= groupby_df_merge['ManyPeopleStand']) & (groupby_df_merge['Crushed'] >= groupby_df_merge['FewPeopleStand']) & (groupby_df_merge['Crushed'] >= groupby_df_merge['FewEmptySeats']) & (groupby_df_merge['Crushed'] >= groupby_df_merge['ManyEmptySeats'])), 'Category'] = 5
groupby_df_merge.loc[((groupby_df_merge['ManyPeopleStand'] >= groupby_df_merge['Crushed']) & (groupby_df_merge['ManyPeopleStand'] >= groupby_df_merge['FewPeopleStand']) & (groupby_df_merge['ManyPeopleStand'] >= groupby_df_merge['FewEmptySeats']) & (groupby_df_merge['ManyPeopleStand'] >= groupby_df_merge['ManyEmptySeats'])), 'Category'] = 4
groupby_df_merge.loc[((groupby_df_merge['FewPeopleStand'] >= groupby_df_merge['Crushed']) & (groupby_df_merge['FewPeopleStand'] >= groupby_df_merge['ManyPeopleStand'])& (groupby_df_merge['FewPeopleStand'] >= groupby_df_merge['FewEmptySeats']) & (groupby_df_merge['FewPeopleStand'] >= groupby_df_merge['ManyEmptySeats'])), 'Category'] = 3
groupby_df_merge.loc[((groupby_df_merge['FewEmptySeats'] >= groupby_df_merge['Crushed']) & (groupby_df_merge['FewEmptySeats'] >= groupby_df_merge['ManyPeopleStand'])& (groupby_df_merge['FewEmptySeats'] >= groupby_df_merge['FewPeopleStand']) & (groupby_df_merge['FewEmptySeats'] >= groupby_df_merge['ManyEmptySeats'])), 'Category'] = 2
groupby_df_merge.loc[((groupby_df_merge['ManyEmptySeats'] >= groupby_df_merge['Crushed']) & (groupby_df_merge['ManyEmptySeats'] >= groupby_df_merge['ManyPeopleStand'])& (groupby_df_merge['ManyEmptySeats'] >= groupby_df_merge['FewPeopleStand']) & (groupby_df_merge['ManyEmptySeats'] >= groupby_df_merge['FewEmptySeats'])), 'Category'] = 1
print('Merge is done!')
#groupby_df_merge.to_csv('ModelData/baselines/final_Baseline_with_new_categories.csv', sep=',')
groupby_df_merge.to_csv('ModelData/baselines/final_Baseline18-19_5category_15min_80_percent_trainset_with_direction_stop.csv', sep=',', index=False)
print('output csv is done!')
return groupby_df_merge
def createDecisionTree():
#df = pd.read_csv('ModelData/baselines/final_Baseline_15min_for_Decisiontree.csv', sep=",")
df = pd.read_csv('../2-CSV/ModelData/baseline/final_Baseline_15min_for_Decisiontree.csv', sep=",")
y = df.Category
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.20, random_state=None)
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train[['Hour', 'Minute']], y_train)
features = clf.feature_importances_
score = clf.score(X=X_test[['Hour', 'Minute']], y=y_test) #Returns the mean accuracy on the given test data and labels.
print('score=', round(score,2))
#predicted = clf.predict(X_test)
#---------------
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data,
filled=True, rounded=True,
special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
Image(graph.create_png())
#--------- RULES
trainX = X_train[['Hour', 'Minute']]
tree_to_code(clf, trainX.columns.values)
def tree_to_code(tree, feature_names):
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
print ("def tree({}):".format(", ".join(feature_names)))
def recurse(node, depth):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
print( "{}if {} <= {}:".format(indent, name, threshold))
recurse(tree_.children_left[node], depth + 1)
print ("{}else: # if {} > {}".format(indent, name, threshold))
recurse(tree_.children_right[node], depth + 1)
else:
print ("{}return {}".format(indent, tree_.value[node]))
recurse(0, 1)
def trainModelToCompareWithBaseline():
########## couldnot nou use this function
#df = PrepareDataset()
#--------------
train_size = int(df.shape[0]*0.75)
df_train = df.iloc[1: train_size]
df_test = df.iloc[train_size+1:df.shape[0]]
#--------------
cols=['Hour', 'Minute']
from sklearn import linear_model
trainX=df_train[cols]
trainy=df_train['LoadFactor']
model = linear_model.LinearRegression()
model.fit(trainX, trainy)
print('Coefficients: \n', model.coef_)
#--------------
#groupby???
#--------------
#test
import statistics as st
import math
df_baseline = df = pd.read_csv('ModelData/baselines/final_Baseline_15min_colors_percent_final.csv', sep=",")
diff = df_test['LoadFactor'].values - df_baseline['LoadFactor'].values
rmse = math.sqrt(st.mean((diff)**2))
print("RMSE= " , rmse , '\n')
def OverloadPercentagePerRoute():
df = pd.read_csv('ModelData/clean/oneYear17-18_clean.csv', sep=",")
print('csv is read!')
#clean----------
df = df[(df['Rout'] != 0) & (df['Rout'] != 998) & (df['Uniqu'] != '00009999') & (df['Uniqu'] != '00009998') & (df['Uniqu'] != '00009997') & (df['Uniqu'] != '00009996') & (df['Uniqu'] != '00009995')]
df['duplicate_flag'] = df.duplicated(subset=['_Date_','TripID', 'Uniqu'], keep=False)
df = df.loc[df['duplicate_flag'] == False]
print('dataset is clean!')
#-----------
df['Full'] = df['Full'].astype(int)
df['load'] = df['load'].astype(int)
df['LoadFactor'] = df[['load']].div(df.Full , axis=0)
df['LoadFactor'] = df['LoadFactor'].astype(float)
print('LoadFactor is added!')
groupby_df_A = df.groupby(["Rout"])[["LoadFactor"]].count().reset_index()
groupby_df_A.columns = ['Rout', 'ALL']
groupby_df_B = df[(df['LoadFactor'] > 1)].groupby(["Rout"])[["LoadFactor"]].count().reset_index()
groupby_df_B.columns = ['Rout', 'Overload']
groupby_df_merge = pd.merge(groupby_df_A , groupby_df_B , on=["Rout"], how='outer')
groupby_df_merge = groupby_df_merge.fillna(0)
# number of all trips for all routes during the year: 89,812,851
TotalTrips = groupby_df_merge['ALL'].sum()
#print(TotalTrips)
#TotalLF = groupby_df_merge['Overload'].sum()
groupby_df_merge['Importance'] = round(groupby_df_merge[['ALL']].div(TotalTrips, axis="index"),2)
groupby_df_merge['Importance*Overload'] = groupby_df_merge[['Overload']].multiply(groupby_df_merge['Importance'], axis="index")
#groupby_df_merge['Final'] = round(groupby_df_merge[['Overload']].div(groupby_df_merge.ALL, axis="index")*100,2)
groupby_df_merge.sort_values(["Importance*Overload"], inplace=True, ascending=False)
print('Merge is done!')
groupby_df_merge.to_csv('ModelData/baselines/Overload_Freq_Per_Route.csv', sep=',')
print('output csv is done!')
def OverloadPercentagePerStop():
df = pd.read_csv('ModelData/clean/oneYear17-18_clean.csv', sep=",")
print('csv is read!')
#clean----------
df = df[(df['Rout'] != 0) & (df['Rout'] != 998) & (df['Uniqu'] != '00009999') & (df['Uniqu'] != '00009998') & (df['Uniqu'] != '00009997') & (df['Uniqu'] != '00009996') & (df['Uniqu'] != '00009995')]
df['duplicate_flag'] = df.duplicated(subset=['_Date_','TripID', 'Uniqu'], keep=False)
df = df.loc[df['duplicate_flag'] == False]
print('dataset is clean!')
#-----------
df['Full'] = df['Full'].astype(int)
df['load'] = df['load'].astype(int)
df['LoadFactor'] = df[['load']].div(df.Full , axis=0)
df['LoadFactor'] = df['LoadFactor'].astype(float)
print('LoadFactor is added!')
groupby_df_A = df.groupby(["Uniqu"])[["LoadFactor"]].count().reset_index()
groupby_df_A.columns = ['Uniqu', 'ALL']
groupby_df_B = df[(df['LoadFactor'] > 1)].groupby(["Uniqu"])[["LoadFactor"]].count().reset_index()
groupby_df_B.columns = ['Uniqu', 'Overload']
groupby_df_merge = pd.merge(groupby_df_A , groupby_df_B , on=["Uniqu"], how='outer')
groupby_df_merge = groupby_df_merge.fillna(0)
# number of all trips for all routes during the year: 89812851
TotalTrips = groupby_df_merge['ALL'].sum()
#TotalLF = groupby_df_merge['Overload'].sum()
groupby_df_merge['Importance'] = round(groupby_df_merge[['ALL']].div(TotalTrips, axis="index"),5)
groupby_df_merge['Importance*Overload'] = groupby_df_merge[['Overload']].multiply(groupby_df_merge['Importance'], axis="index")
#groupby_df_merge['Final'] = round(groupby_df_merge[['Overload']].div(groupby_df_merge.ALL, axis="index")*100,2)
groupby_df_merge.sort_values(["Importance*Overload"], inplace=True, ascending=False)
print('Merge is done!')
groupby_df_merge.to_csv('ModelData/baselines/Overload_Freq_Per_Stop.csv', sep=',')
print('output csv is done!')
def OverloadPercentagePerTime():
df = pd.read_csv('ModelData/clean/oneYear17-18_clean.csv', sep=",")
print('csv is read!')
#clean----------
df = df[(df['Rout'] != 0) & (df['Rout'] != 998) & (df['Uniqu'] != '00009999') & (df['Uniqu'] != '00009998') & (df['Uniqu'] != '00009997') & (df['Uniqu'] != '00009996') & (df['Uniqu'] != '00009995')]
df['duplicate_flag'] = df.duplicated(subset=['_Date_','TripID', 'Uniqu'], keep=False)
df = df.loc[df['duplicate_flag'] == False]
print('dataset is clean!')
#-----------
df['Hour'] = 0
df.loc[((df['Arrive'] >= 50000) & (df['Arrive'] < 60000)), 'Hour'] = 5
df.loc[ (df.Arrive >= 60000) & (df.Arrive < 70000) , 'Hour'] = 6
df.loc[ (df.Arrive >= 70000) & (df.Arrive < 80000) , 'Hour'] = 7
df.loc[ (df.Arrive >= 80000) & (df.Arrive < 90000) , 'Hour'] = 8
df.loc[ (df.Arrive >= 90000) & (df.Arrive < 100000) , 'Hour'] = 9
df.loc[ (df.Arrive >= 100000) & (df.Arrive < 110000) , 'Hour'] = 10
df.loc[ (df.Arrive >= 110000) & (df.Arrive < 120000) , 'Hour'] = 11
df.loc[ (df.Arrive >= 120000) & (df.Arrive < 130000) , 'Hour'] = 12
df.loc[ (df.Arrive >= 130000) & (df.Arrive < 140000) , 'Hour'] = 13
df.loc[ (df.Arrive >= 140000) & (df.Arrive < 150000) , 'Hour'] = 14
df.loc[ (df.Arrive >= 150000) & (df.Arrive < 160000) , 'Hour'] = 15
df.loc[ (df.Arrive >= 160000) & (df.Arrive < 170000) , 'Hour'] = 16
df.loc[ (df.Arrive >= 170000) & (df.Arrive < 180000) , 'Hour'] = 17
df.loc[ (df.Arrive >= 180000) & (df.Arrive < 190000) , 'Hour'] = 18
df.loc[ (df.Arrive >= 190000) & (df.Arrive < 200000) , 'Hour'] = 19
df.loc[ (df.Arrive >= 200000) & (df.Arrive < 210000) , 'Hour'] = 20
df.loc[ (df.Arrive >= 210000) & (df.Arrive < 220000) , 'Hour'] = 21
df.loc[ (df.Arrive >= 220000) & (df.Arrive < 230000) , 'Hour'] = 22
df.loc[ (df.Arrive >= 230000) & (df.Arrive < 240000) , 'Hour'] = 23
df.loc[ (df.Arrive >= 240000) & (df.Arrive < 250000) , 'Hour'] = 0
df.loc[ (df.Arrive >= 250000) & (df.Arrive < 260000) , 'Hour'] = 1
df.loc[ (df.Arrive >= 260000) & (df.Arrive < 270000) , 'Hour'] = 2
df.loc[ (df.Arrive >= 30000) & (df.Arrive < 40000) , 'Hour'] = 3
df.loc[ (df.Arrive >= 40000) & (df.Arrive < 50000) , 'Hour'] = 4
print('Hour is added!')
##########
##seperating minute from time
df['Min'] = 0
df['Arrive'] = df['Arrive'].astype(str)
df['Min'] = np.where(((df['Hour'] >= 3) & (df['Hour'] <= 9)), df.Arrive.str[1:3] , df.Arrive.str[2:4])
print('Min is added!')
df['Minute'] = 0
df['Min'] = df['Min'].astype(int)
df.loc[((df['Min'] >= 00) & (df['Min'] < 15)), 'Minute'] = 0
df.loc[((df['Min'] >= 15) & (df['Min'] < 30)), 'Minute'] = 15
df.loc[((df['Min'] >= 30) & (df['Min'] < 45)), 'Minute'] = 30
df.loc[((df['Min'] >= 45) & (df['Min'] <= 59)), 'Minute'] = 45
print('Minute is added!')
df['Full'] = df['Full'].astype(int)
df['load'] = df['load'].astype(int)
df['LoadFactor'] = df[['load']].div(df.Full , axis=0)
df['LoadFactor'] = df['LoadFactor'].astype(float)
print('LoadFactor is added!')
groupby_df_A = df.groupby(["Hour", "Minute"])[["LoadFactor"]].count().reset_index()
groupby_df_A.columns = ["Hour", "Minute", 'ALL']
groupby_df_B = df[(df['LoadFactor'] > 1)].groupby(["Hour", "Minute"])[["LoadFactor"]].count().reset_index()
groupby_df_B.columns = ["Hour", "Minute", 'Overload']
groupby_df_merge = pd.merge(groupby_df_A , groupby_df_B , on=["Hour", "Minute"], how='outer')
groupby_df_merge = groupby_df_merge.fillna(0)
# number of all trips for all routes during the year: 89812851
TotalTrips = groupby_df_merge['ALL'].sum()
groupby_df_merge['Importance'] = round(groupby_df_merge[['ALL']].div(TotalTrips, axis="index"),2)
groupby_df_merge['Importance*Overload'] = groupby_df_merge[['Overload']].multiply(groupby_df_merge['Importance'], axis="index")
#groupby_df_merge['Final'] = round(groupby_df_merge[['Overload']].div(groupby_df_merge.ALL, axis="index")*100,2)
groupby_df_merge.sort_values(["Importance*Overload"], inplace=True, ascending=False)
print('Merge is done!')
groupby_df_merge.to_csv('ModelData/baselines/Overload_Freq_Per_Time.csv', sep=',')
print('output csv is done!')
def Barchart():
df = pd.read_csv('../2-CSV/ModelData/baseline/Overload_Freq_Per_Route.csv', sep=",")
fig, ax = plt.subplots(figsize = (10,4))
df['Rout'] = df['Rout'].astype(str)
x = df['Rout']
y = df['Overload']
ax.bar(x , y, color='blue')
plt.xticks(np.arange(len(x)+1), x ,rotation=90)
#plt.yticks(np.arange(len(y)+1), y)
ax.set_xlabel("Route", fontsize=12)
ax.set_ylabel("Overload", fontsize=12)
#ax.set_xticklabels(df['Rout'], rotation = 'vertical', ha="right" , fontsize=6)
plt.savefig("../2-CSV/ModelData/baseline/Overload_Freq_Per_Route.png")
def NumberofStopsPerRouteDirection():
df_stopsperroute = pd.read_csv('../2-CSV/ModelData/numberofStopsPerRoute.csv', sep=",")
bins = [0,10, 20, 30, 40, 50,60,70,80,90,100,110, 120,130,140,150, 160, 170,180]
group_names = ['0-10', '11-20', '21-30', '31-40', '41-50', '51-60', '61-70', '71-80', '81-90', '91-100', '101-110', '111-120', '121-130', '131-140', '141-150', '151-160', '161-170', '171-180']
df_stopsperroute['Cat_in'] = pd.cut(df_stopsperroute['inbound'], bins=bins, labels=group_names)
df_stopsperroute['Cat_out'] = pd.cut(df_stopsperroute['outbound'], bins=bins, labels=group_names)
#print(df_stopsperroute.head(10))
print(pd.value_counts(df_stopsperroute['Cat_in']) , '\n')
print(pd.value_counts(df_stopsperroute['Cat_out']))
def PartitionRoutesInBaselineBothDirections():
import operator
df = pd.read_csv('../2-CSV/ModelData/4-baseline/final_Baseline_with_new_categories.csv', sep=",")
df_routeMapping = pd.read_csv('../2-CSV/ModelData/RouteMappings.csv', sep=",")
df_stops = pd.read_csv('../2-CSV/ModelData/allStopsperRoute_weekdays_with_direction.csv', sep=",")
ManyEmptySeats_in = {} #{route_i, #green}
FewEmptySeats_in = {}
FewPeopleStand_in = {}
ManyPeopleStand_in = {}
Crushed_in = {}
ManyEmptySeats_out = {}
FewEmptySeats_out = {}
FewPeopleStand_out = {}
ManyPeopleStand_out = {}
Crushed_out = {}
for route in df_routeMapping['route_id']:
route_fullname = df_routeMapping.loc[df_routeMapping['route_id'] == route, 'route_fullname'].iloc[0]
route_name = df_routeMapping.loc[df_routeMapping['route_id'] == route, 'route_name'].iloc[0]
stop_count_inbound = df_stops[(df_stops['route_id'] == route_fullname) & (df_stops['direction_id'] == 1)].shape[0]
stop_count_outbound = df_stops[(df_stops['route_id'] == route_fullname)& (df_stops['direction_id'] == 0)].shape[0]
#records_total = df[df['Rout'] == route].shape[0]
#print(route_name , stop_count, records_total)
if (stop_count_inbound != 0):
ManyEmptySeats_in[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 1)].shape[0])/stop_count_inbound, 3)
FewEmptySeats_in[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 2)].shape[0])/stop_count_inbound, 3)
FewPeopleStand_in[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 3)].shape[0])/stop_count_inbound, 3)
ManyPeopleStand_in[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 4)].shape[0])/stop_count_inbound, 3)
Crushed_in[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 5)].shape[0])/stop_count_inbound, 3)
if (stop_count_outbound != 0):
ManyEmptySeats_out[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 1)].shape[0])/stop_count_outbound, 3)
FewEmptySeats_out[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 2)].shape[0])/stop_count_outbound, 3)
FewPeopleStand_out[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 3)].shape[0])/stop_count_outbound, 3)
ManyPeopleStand_out[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 4)].shape[0])/stop_count_outbound, 3)
Crushed_out[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 5)].shape[0])/stop_count_outbound, 3)
sorted_ManyEmptySeats_in = sorted(ManyEmptySeats_in.items(), key=operator.itemgetter(1), reverse=True)
sorted_FewEmptySeats_in = sorted(FewEmptySeats_in.items(), key=operator.itemgetter(1), reverse=True)
sorted_FewPeopleStand_in = sorted(FewPeopleStand_in.items(), key=operator.itemgetter(1), reverse=True)
sorted_ManyPeopleStand_in = sorted(ManyPeopleStand_in.items(), key=operator.itemgetter(1), reverse=True)
sorted_Crushed_in = sorted(Crushed_in.items(), key=operator.itemgetter(1), reverse=True)
sorted_ManyEmptySeats_out = sorted(ManyEmptySeats_out.items(), key=operator.itemgetter(1), reverse=True)
sorted_FewEmptySeats_out = sorted(FewEmptySeats_out.items(), key=operator.itemgetter(1), reverse=True)
sorted_FewPeopleStand_out = sorted(FewPeopleStand_out.items(), key=operator.itemgetter(1), reverse=True)
sorted_ManyPeopleStand_out = sorted(ManyPeopleStand_out.items(), key=operator.itemgetter(1), reverse=True)
sorted_Crushed_out = sorted(Crushed_out.items(), key=operator.itemgetter(1), reverse=True)
print('inbound list: \n', sorted_ManyEmptySeats_in, '\n\n', sorted_FewEmptySeats_in, '\n\n', sorted_FewPeopleStand_in, '\n\n', sorted_ManyPeopleStand_in , '\n\n', sorted_Crushed_in)
print('outbound list: \n', sorted_ManyEmptySeats_out, '\n\n', sorted_FewEmptySeats_out, '\n\n', sorted_FewPeopleStand_out, '\n\n', sorted_ManyPeopleStand_out , '\n\n', sorted_Crushed_out)
#sort dictionaries
def PartitionRoutesInBaseline():
import operator
df = pd.read_csv('../2-CSV/ModelData/4-baseline/final_Baseline_with_new_categories.csv', sep=",")
df_routeMapping = pd.read_csv('../2-CSV/ModelData/RouteMappings.csv', sep=",")
df_stops = pd.read_csv('../2-CSV/ModelData/allStopsperRoute_weekdays_with_direction.csv', sep=",")
ManyEmptySeats = {} #{route_i, #green}
FewEmptySeats = {}
FewPeopleStand = {}
ManyPeopleStand = {}
Crushed = {}
for route in df_routeMapping['route_id']:
route_fullname = df_routeMapping.loc[df_routeMapping['route_id'] == route, 'route_fullname'].iloc[0]
route_name = df_routeMapping.loc[df_routeMapping['route_id'] == route, 'route_name'].iloc[0]
stop_count = df_stops[(df_stops['route_id'] == route_fullname)].shape[0]
#records_total = df[df['Rout'] == route].shape[0]
#print(route_name , stop_count, records_total)
if (stop_count != 0):
ManyEmptySeats[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 1)].shape[0])/stop_count, 3)
FewEmptySeats[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 2)].shape[0])/stop_count, 3)
FewPeopleStand[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 3)].shape[0])/stop_count, 3)
ManyPeopleStand[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 4)].shape[0])/stop_count, 3)
Crushed[route_name] = round((df[(df['Rout'] == route) & (df['Category'] == 5)].shape[0])/stop_count, 3)
sorted_ManyEmptySeats = sorted(ManyEmptySeats.items(), key=operator.itemgetter(1), reverse=True)
sorted_FewEmptySeats = sorted(FewEmptySeats.items(), key=operator.itemgetter(1), reverse=True)
sorted_FewPeopleStand = sorted(FewPeopleStand.items(), key=operator.itemgetter(1), reverse=True)
sorted_ManyPeopleStand = sorted(ManyPeopleStand.items(), key=operator.itemgetter(1), reverse=True)
sorted_Crushed = sorted(Crushed.items(), key=operator.itemgetter(1), reverse=True)
print(sorted_ManyEmptySeats, '\n\n', sorted_FewEmptySeats, '\n\n', sorted_FewPeopleStand, '\n\n', sorted_ManyPeopleStand , '\n\n', sorted_Crushed)
#sort dictionaries
def Compute15minBaselineRMSE():
#comapre baseline15-min with original data records
df = pd.read_csv('ModelData/clean/oneYear17-18_clean_20_percent_testset.csv', sep=",")
baseline = pd.read_csv('ModelData/baselines/final_Baseline_15min_80_percent_trainset.csv', sep=",")
#--------------------------
#routes = [612,714, 903, 281, 444, 613]
route = 56
stop = 'E25085'
df = df[(df['Rout'] == route) & (df['Uniqu'] == stop)]
RMSE = 0
diff = 0
trueCount = 0
rows = baseline[(baseline['Rout'] == route) & (baseline['Uniqu'] == stop)]
for row in rows[:-1].itertuples():
hour = rows.loc[row.Index, 'Hour']
minute = rows.loc[row.Index, 'Minute']
baselineLoad = rows.loc[row.Index, 'MeanLoad']
df_match = df[(df['Hour'] == hour) & (df['Minute'] == minute)]
trueloads = df_match['load']
if len(trueloads) != 0:
trueCount = trueCount + len(trueloads)
for trueload in trueloads:
diff = diff + (trueload - baselineLoad)**2
RMSE = math.sqrt(diff / trueCount)
print('Baseline RMSE for ', route , 'is ', round(RMSE,2))
def CreateBaselineTrainSet():
#comapre baseline15-min with original data records
# first part: create baseline train set
df = pd.read_csv('ModelData/NewData/csv/clean/oneYear18-19_clean.csv', sep=",")
print('csv is read!')
#--------------------------
df = df[(df['Rout'] != 0) & (df['Rout'] != 998) & (df['Uniqu'] != '00009999') & (df['Uniqu'] != '00009998') & (df['Uniqu'] != '00009997') & (df['Uniqu'] != '00009996') & (df['Uniqu'] != '00009995')]
#---------------------
#df = PrepareDataset(df)
#--------------------
y = df.load
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.20, random_state=None)
#train_size = int(df.shape[0]*0.8)
#df_test = df.iloc[train_size+1:df.shape[0]]
df = X_test
df.to_csv('ModelData/NewData/csv/clean/oneYear18-19_clean_20_percent_testset.csv', sep=',')
#--------------------------
FinalBaselinePerQuarterWithDirection(X_train)
############################################
def Compute15minBaselineLogLoss():
#--------------------------
df = pd.read_csv('ModelData/clean/oneYear17-18_clean_20_percent_testset.csv', sep=",")
baseline = pd.read_csv('ModelData/baselines/final_Baseline_5category_15min_80_percent_trainset.csv', sep=",")
#--------------------------
#--------------------------
route = 281
stop = 'P02430'
df = df[(df['Rout'] == route) & (df['Uniqu'] == stop)]
logloss = 0
sumlog = 0
trueCount = 0
target = 0
predict_prob = 0
matchcount = 0
accuracy = 0
rows = baseline[(baseline['Rout'] == route) & (baseline['Uniqu'] == stop)]
for row in rows[:-1].itertuples():
hour = rows.loc[row.Index, 'Hour']
minute = rows.loc[row.Index, 'Minute']
PredictedCategory = rows.loc[row.Index, 'Category']
df_match = df[(df['Hour'] == hour) & (df['Minute'] == minute)]
trueCategories = df_match['load_category']
if len(trueCategories) != 0:
trueCount = trueCount + len(trueCategories)
for trueCategory in trueCategories:
predict_prob = list(trueCategories).count(trueCategory) / len(trueCategories)
sumlog+= - np.log(predict_prob)
# cross entropy loss = log loss (-target {1 if true label=predicted label , 0 otherwise} * log (predict_prob))
if (trueCategory == PredictedCategory):
matchcount = matchcount + 1
logloss = sumlog / trueCount
accuracy = matchcount / trueCount
print('Baseline Log Loss for ', route , 'at ', stop , ' is ', round(logloss,3))
print('Baseline Accuracy for ', route , 'at ', stop , ' is ', round(accuracy,3)) #accuracy = F1 micro
def ComputeBaselineLogLossForOneRouteAllStops():
df = pd.read_csv('ModelData/clean/oneYear17-18_clean_20_percent_testset.csv', sep=",")
baseline = pd.read_csv('ModelData/baselines/final_Baseline_5category_15min_80_percent_trainset.csv', sep=",")
route = 612
df = df[(df['Rout'] == route)]
logloss = 0
sumlog = 0
trueCount = 0
predict_prob = 0
matchcount = 0
accuracy = 0
rows = baseline[(baseline['Rout'] == route)]
for row in rows[:-1].itertuples():
hour = rows.loc[row.Index, 'Hour']
minute = rows.loc[row.Index, 'Minute']
PredictedCategory = rows.loc[row.Index, 'Category']
df_match = df[(df['Hour'] == hour) & (df['Minute'] == minute)]
trueCategories = df_match['load_category']
if len(trueCategories) != 0:
trueCount = trueCount + len(trueCategories)
for trueCategory in trueCategories:
predict_prob = list(trueCategories).count(trueCategory) / len(trueCategories)
sumlog+= - np.log(predict_prob)
# cross entropy loss = log loss (-target {1 if true label=predicted label , 0 otherwise} * log (predict_prob))
if (trueCategory == PredictedCategory):
matchcount = matchcount + 1
logloss = sumlog / trueCount
accuracy = matchcount / trueCount
print('Baseline Log Loss for ', route , ' is ', round(logloss,3))
print('Baseline Accuracy for ', route , ' is ', round(accuracy,3)) #accuracy = F1 micro
def ComputeBaselineLogLossForOneRouteAllStopsWithDirection(df, baseline, route, direction):
new_df = df[(df['Rout'] == route) & (df['Direction'] == direction)]
logloss = 0
sumlog = 0
trueCount = 0
predict_prob = 0
matchcount = 0
accuracy = 0
rows = baseline[(baseline['Rout'] == route) & (baseline['Direction'] == direction)]
for row in rows[:-1].itertuples():
hour = rows.loc[row.Index, 'Hour']
minute = rows.loc[row.Index, 'Minute']
PredictedCategory = rows.loc[row.Index, 'Category']
df_match = new_df[(new_df['Hour'] == hour) & (new_df['Minute'] == minute)]
trueCategories = df_match['load_category']
if len(trueCategories) != 0:
trueCount = trueCount + len(trueCategories)
for trueCategory in trueCategories:
predict_prob = list(trueCategories).count(trueCategory) / len(trueCategories)
sumlog+= - np.log(predict_prob)
# cross entropy loss = log loss (-target {1 if true label=predicted label , 0 otherwise} * log (predict_prob))
if (trueCategory == PredictedCategory):
matchcount = matchcount + 1
logloss = sumlog / trueCount
accuracy = matchcount / trueCount
print('Baseline Log Loss for ', route , ' ' , direction , ' is ', round(logloss,2))
print('Baseline F1 score for ', route , ' ' , direction , ' is ', round(accuracy,2)) #accuracy = F1 micro
import csv
with open('ModelData/selected_routes_baseline_logloss_f1.csv','a') as file:
writer = csv.writer(file)
writer.writerow([route, direction , round(logloss,2), round(accuracy,2)])
def ComputeBaselineRMSEForOneRouteAllStopsWithDirection(df, baseline, route, direction):
new_df = df[(df['Rout'] == route) & (df['Direction'] == direction)]
RMSE = 0
diff = 0
trueCount = 0
rows = baseline[(baseline['Rout'] == route) & (baseline['Direction'] == direction)]
for row in rows[:-1].itertuples():
hour = rows.loc[row.Index, 'Hour']
minute = rows.loc[row.Index, 'Minute']
baselineLoad = rows.loc[row.Index, 'MeanLoad'] #load1 instead of mean load
df_match = new_df[(new_df['Hour'] == hour) & (new_df['Minute'] == minute)]
trueloads = df_match['load']
if len(trueloads) != 0:
trueCount = trueCount + len(trueloads)
for trueload in trueloads:
diff = diff + (trueload - baselineLoad)**2
RMSE = math.sqrt(diff / trueCount)
print('Baseline RMSE for ', route , ' ' , direction , 'is ', round(RMSE,2))
import csv
with open('ModelData/selected_routes_baseline_rmse.csv','a') as file:
writer = csv.writer(file)
writer.writerow([route, direction , round(RMSE,2)])
def ComputeBaselineRMSEForOneRouteWithStopWithDirection(df, baseline, route, direction):
new_df = df[(df['Rout'] == route) & (df['Direction'] == direction)]
rows = baseline[(baseline['Rout'] == route) & (baseline['Direction'] == direction)]
stops = new_df['Uniqu'].unique()
stop_size = len(stops)
print(route, direction, stop_size)
sumRMSE = 0
for stop in stops:
true_df = new_df[new_df['Uniqu'] == stop]
predict_df = rows[(rows['Uniqu'] == stop)]
RMSE = 0
diff = 0
trueCount = 0
for row in predict_df[:-1].itertuples():
hour = predict_df.loc[row.Index, 'Hour']
minute = predict_df.loc[row.Index, 'Minute']
baselineLoad = predict_df.loc[row.Index, 'MeanLoad'] #load1 instead of mean load
df_match = true_df[(true_df['Hour'] == hour) & (true_df['Minute'] == minute)]
trueloads = df_match['load']
if len(trueloads) != 0:
trueCount = trueCount + len(trueloads)
for trueload in trueloads:
diff = diff + (trueload - baselineLoad)**2
if trueCount != 0:
RMSE = math.sqrt(diff / trueCount)
sumRMSE = sumRMSE + RMSE
print('Baseline RMSE for ', route , ' ' , direction , 'is ', round(sumRMSE/stop_size,2))
import csv
with open('ModelData/selected_routes_baseline18-19_RMSE_avg_stops.csv','a') as file:
writer = csv.writer(file)
writer.writerow([route, direction , round(sumRMSE/stop_size,2)])
def ComputeBaselineLogLossForOneRouteWithStopWithDirection(df, baseline, route, direction):
new_df = df[(df['Rout'] == route) & (df['Direction'] == direction)]
rows = baseline[(baseline['Rout'] == route) & (baseline['Direction'] == direction)]
stops = new_df['Uniqu'].unique()
stop_size = len(stops)
print(route, ' ', direction, ' ', stop_size)
sumlogloss = 0
sumacc = 0
for stop in stops:
true_df = new_df[new_df['Uniqu'] == stop]
predict_df = rows[(rows['Uniqu'] == stop)]
logloss = 0
sumlog = 0
trueCount = 0
predict_prob = 0
matchcount = 0
accuracy = 0
for row in predict_df[:-1].itertuples():
hour = predict_df.loc[row.Index, 'Hour']
minute = predict_df.loc[row.Index, 'Minute']
PredictedCategory = predict_df.loc[row.Index, 'Category']
df_match = true_df[(true_df['Hour'] == hour) & (true_df['Minute'] == minute)]
trueCategories = df_match['load_category']
if len(trueCategories) != 0:
trueCount = trueCount + len(trueCategories)
for trueCategory in trueCategories:
predict_prob = list(trueCategories).count(trueCategory) / len(trueCategories)
sumlog+= - np.log(predict_prob)
# cross entropy loss = log loss (-target {1 if true label=predicted label , 0 otherwise} * log (predict_prob))
if (trueCategory == PredictedCategory):
matchcount = matchcount + 1
if trueCount != 0:
logloss = sumlog / trueCount
accuracy = matchcount / trueCount
sumlogloss = sumlogloss + logloss
sumacc = sumacc + accuracy
else:
print('trueCount for ', stop, 'is zero')
print('Baseline Log Loss for ', route , ' ' , direction , ' is ', round(sumlogloss/stop_size,2))
print('Baseline F1 score for ', route , ' ' , direction , ' is ', round(sumacc/stop_size,2)) #accuracy = F1 micro
with open('ModelData/selected_routes_baseline18-19_logloss_f1_avg_stops.csv','a') as file:
writer = csv.writer(file)
writer.writerow([route, direction , round(sumlogloss/stop_size,2), round(sumacc/stop_size,2)])
def ComputeBaselineLogLossForOneStopAllRoutes():
df = pd.read_csv('ModelData/clean/oneYear17-18_clean_20_percent_testset.csv', sep=",")
baseline = pd.read_csv('ModelData/baselines/final_Baseline_5category_15min_80_percent_trainset.csv', sep=",")
stop = 'E01000'
df = df[(df['Uniqu'] == stop)]
logloss = 0
sumlog = 0
trueCount = 0
predict_prob = 0
matchcount = 0
accuracy = 0
rows = baseline[(baseline['Uniqu'] == stop)]
for row in rows[:-1].itertuples():
hour = rows.loc[row.Index, 'Hour']
minute = rows.loc[row.Index, 'Minute']
PredictedCategory = rows.loc[row.Index, 'Category']
df_match = df[(df['Hour'] == hour) & (df['Minute'] == minute)]
trueCategories = df_match['load_category']
if len(trueCategories) != 0:
trueCount = trueCount + len(trueCategories)
for trueCategory in trueCategories:
predict_prob = list(trueCategories).count(trueCategory) / len(trueCategories)
sumlog+= - np.log(predict_prob)
# cross entropy loss = log loss (-target {1 if true label=predicted label , 0 otherwise} * log (predict_prob))
if (trueCategory == PredictedCategory):
matchcount = matchcount + 1
logloss = sumlog / trueCount
accuracy = matchcount / trueCount
print('Baseline Log Loss for ', stop , ' is ', round(logloss,2))
print('Baseline Accuracy for ', stop , ' is ', round(accuracy,2)) #accuracy = F1 micro
def preparebaseline(df):
df['load1'] = np.where((df.ID == 0) , 0 , df['load'].shift(1))
#df['load2'] = np.where(((df.ID == 0) | (df.ID == 1)) , 0 , df['load'].shift(2))
#df['load3'] = np.where(((df.ID == 0) | (df.ID == 1) | (df.ID == 2)), 0 , df['load'].shift(3))
#df['load4'] = np.where(((df.ID == 0) | (df.ID == 1) | (df.ID == 2) | (df.ID == 3)), 0 , df['load'].shift(4))
df['load5'] = np.where(((df.ID == 0) | (df.ID == 1) | (df.ID == 2) | (df.ID == 3) | (df.ID == 4)), 0 , df['load'].shift(5))
#df['load6'] = np.where(((df.ID == 0) | (df.ID == 1) | (df.ID == 2) | (df.ID == 3) | (df.ID == 4) | (df.ID == 5)), 0 , df['load'].shift(6))
#df['load7'] = np.where(((df.ID == 0) | (df.ID == 1) | (df.ID == 2) | (df.ID == 3) | (df.ID == 4) | (df.ID == 5)| (df.ID == 6)), 0 , df['load'].shift(7))
#df['load8'] = np.where(((df.ID == 0) | (df.ID == 1) | (df.ID == 2) | (df.ID == 3) | (df.ID == 4) | (df.ID == 5) | (df.ID == 6)| (df.ID == 7)), 0 , df['load'].shift(8))
#df['load9'] = np.where(((df.ID == 0) | (df.ID == 1) | (df.ID == 2) | (df.ID == 3) | (df.ID == 4) | (df.ID == 5) | (df.ID == 6) | (df.ID == 7) | (df.ID == 8)), 0 , df['load'].shift(9))
df['load10'] = np.where(((df.ID == 0) | (df.ID == 1) | (df.ID == 2) | (df.ID == 3) | (df.ID == 4) | (df.ID == 5) | (df.ID == 6) | (df.ID == 7) | (df.ID == 8) | (df.ID == 9)), 0 , df['load'].shift(10))
df = df.dropna()
df = df[~df.isin([np.nan, np.inf, -np.inf]).any(1)]
df['Full'] = df['Full'].astype(int)
df['load10'] = df['load10'].astype(int)
df['LoadFactor'] = df[['load10']].div(df.Full , axis=0) #------------- load1 <= load
df['LoadFactor'] = df['LoadFactor'].astype(float)
#--------------
# isFull is used in Logistic Regression
df['isFull'] = np.where((df.LoadFactor > 0.75), 1, 0)
#----------------
df['load_category'] = 1
df.loc[(df['LoadFactor'] < 0.5), 'load_category'] = 1
df.loc[((df['LoadFactor'] < 0.8) & (df['LoadFactor'] >= 0.5)), 'load_category'] = 2
df.loc[((df['LoadFactor'] < 1.1) & (df['LoadFactor'] >= 0.8)), 'load_category'] = 3
df.loc[((df['LoadFactor'] < 1.4) & (df['LoadFactor'] >= 1.1)), 'load_category'] = 4
df.loc[(df['LoadFactor'] >= 1.4), 'load_category'] = 5
return df
def trainANDtestSets(df):
y = df.load
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.20, random_state=200)
return X_train, X_test
def baselinebyPooling(df_train, df_test, index):
sumlog = 0
trueCount = 0
predict_prob = 0
matchcount = 0
stops = []
stop = df_train.loc[index, 'Uniqu']
if stop not in stops:
stops.append(stop)
PredictedCategory = df_train.loc[index, 'load_category']
df_match = df_test[(df_test['Uniqu'] == stop)]
trueCategories = df_match['load_category']
if len(trueCategories) != 0:
trueCount = trueCount + len(trueCategories)
for trueCategory in trueCategories:
predict_prob = list(trueCategories).count(trueCategory) / len(trueCategories)
sumlog+= - np.log(predict_prob)
# cross entropy loss = log loss (-target {1 if true label=predicted label , 0 otherwise} * log (predict_prob))
if (trueCategory == PredictedCategory):
matchcount = matchcount + 1
return sumlog, trueCount, matchcount
def baselineOnlyLoad():
path = 'ModelData/routes-allstops/'
for filename in os.listdir(path):
if filename != 'desktop.ini':
print('filename= ', filename)
df = pd.read_csv(path+filename, sep=",")
route = filename.split('.')[0]
logloss = 0
sumlog = 0
trueCount = 0
predict_prob = 0
matchcount = 0
accuracy = 0
stops = []
new_df = preparebaseline(df)
new_df = new_df.dropna()
df_train, df_test = trainANDtestSets(new_df)
for row in df_train[:-1].itertuples():
stop = df_train.loc[row.Index, 'Uniqu']
if stop not in stops: #there is no big difference between the results if I dont ignore the stops that have been already seen but this way is much faster.
stops.append(stop)
PredictedCategory = df_train.loc[row.Index, 'load_category']
#df_match = df_test[(df_test['Uniqu'] == stop) & (df_test['Year'] == year) & (df_test['Month'] == month) & (df_test['Day'] == day) & (df_test['Hour'] == hour) & (df_test['Minute'] == minute)]
df_match = df_test[(df_test['Uniqu'] == stop)]
trueCategories = df_match['load_category']
if len(trueCategories) != 0:
trueCount = trueCount + len(trueCategories)
for trueCategory in trueCategories:
predict_prob = list(trueCategories).count(trueCategory) / len(trueCategories)
sumlog+= - np.log(predict_prob)
# cross entropy loss = log loss (-target {1 if true label=predicted label , 0 otherwise} * log (predict_prob))
if (trueCategory == PredictedCategory):
matchcount = matchcount + 1
logloss = sumlog / trueCount
accuracy = matchcount / trueCount
print('Baseline Log Loss for ', route , ' is ', round(logloss,2))
print('Baseline F1 score for ', route , ' is ', round(accuracy,2)) #accuracy = F1 micro
import csv
with open('ModelData/selected_routes_baseline_logloss_f1_only_10th_Pload_2.csv','a') as file:
writer = csv.writer(file)
writer.writerow([route, round(logloss,2), round(accuracy,2)])
def aggregateCategoriesBaseline_faster():
df_5min = pd.read_csv('../2-CSV/ModelData/4-baseline/test_baseline_5min.csv', sep=",")
df_15min = pd.read_csv('../2-CSV/ModelData/4-baseline/test_baseline_15min.csv', sep=",")
routes = pd.read_csv('../2-CSV/ModelData/4-baseline/RouteMappings.csv',sep=",")
df_agg = pd.DataFrame(columns=['Rout', 'Uniqu', 'Hour', 'Minute', 'ALL', 'Crushed', 'ManyPeopleStand', 'FewPeopleStand'
, 'FewEmptySeats', 'ManyEmptySeats', 'MeanLoad', 'Category'])
for route in routes['route_id']:
new_df = df_5min[(df_5min['Rout'] == route)]
stops = new_df['Uniqu'].unique()
minutes = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55]
for stop in stops:
for hour in range(0,24):
for minute in minutes:
new_df_h = new_df[(new_df['Uniqu'] == stop) & (new_df['Hour'] == hour)]
if len(new_df_h) != 0:
new_df_m = new_df_h[(new_df_h['Minute'] == minute)]
if len(new_df_m) != 0:
count = new_df_m['ALL'].iloc[0]
#print(route, stop, hour, minute , count)
if (count < 10):
if (minute >= 0) & (minute < 15):
minute_15min = 0
elif (minute >= 15) & (minute < 30):
minute_15min = 15
elif (minute >= 30) & (minute < 45):
minute_15min = 30
elif (minute >= 45) & (minute <= 59):
minute_15min = 45
related_row_15min = df_15min[(df_15min['Rout'] == route) & (df_15min['Uniqu'] == stop) & (df_15min['Hour'] == hour) & (df_15min['Minute'] == minute_15min)]
if len(related_row_15min) != 0:
count_15min = related_row_15min['ALL'].iloc[0]
if count_15min >= 10: #if count_15min < 10 => dont report
crushed_15min = related_row_15min['Crushed'].iloc[0]
manyPeopleStand_15min = related_row_15min['ManyPeopleStand'].iloc[0]
fewPeopleStand_15min = related_row_15min['FewPeopleStand'].iloc[0]
fewEmptySeats_15min = related_row_15min['FewEmptySeats'].iloc[0]
manyEmptySeats_15min = related_row_15min['ManyEmptySeats'].iloc[0]
meanLoad_15min = related_row_15min['MeanLoad'].iloc[0]
category_15min = related_row_15min['Category'].iloc[0]
#-------------------
if minute_15min == 0:
related_rows_5min = new_df_h[(new_df_h['Minute'] >= 0) & (new_df_h['Minute'] < 15)]
elif minute_15min == 15:
related_rows_5min = new_df_h[(new_df_h['Minute'] >= 15) & (new_df_h['Minute'] < 30)]