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app_old.py
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#!/usr/bin/env python
# coding: utf-8
import string
import re, json
import datetime
import numpy as np
import pandas as pd
# import dash_bio as dashbio
import dash_table
from dash import no_update
from dash.dependencies import Input, Output, ALL, State
import dash_html_components as html
import dash_core_components as dcc
from jupyter_dash import JupyterDash
import plotly.express as px
import dash
import plotly.graph_objects as go
import plotly.tools as tls
from collections import Counter
# In[2]:
df=pd.read_pickle("cleaned_df.pickle")
# In[3]:
with open("Neighborhoods.geojson") as f:
neigh_geojson = json.load(f)
with open("Police_Districts.geojson") as f1:
dist_geojson = json.load(f1)
# In[4]:
# df['Datetime']=df.CrimeDate+' '+df.CrimeTime
# # In[5]:
# df['Datetime']=pd.to_datetime(df.Datetime)
# df['Year']=df.Datetime.dt.year
# df['Month']=df.Datetime.dt.month
# df['MonthName']=df.Datetime.dt.month_name()
# df['Day']=df.Datetime.dt.day
# df['Quarter']=df.Datetime.dt.quarter
# df['Date']=df.Datetime.dt.date
# df['Hour']=df.Datetime.dt.hour
# df['Minute']=df.Datetime.dt.minute
# df['Period']=((df.Datetime.dt.hour% 24 + 4) // 4).map({1: 'Late Night',
# 2: 'Early Morning',
# 3: 'Morning',
# 4: 'Noon',
# 5: 'Evening',
# 6: 'Night'})
# df['DayofWeek']=df.Datetime.dt.weekday
# df['WeekDay']=df.Datetime.dt.weekday.apply(lambda x: 'Weekend' if ((x==5)|(x==6)) else "Weekday")
neig=pd.read_pickle('neis.pickle')
dist=pd.read_pickle('dist.pickle')
def intro():
"""
:return: A Div containing dashboard title & descriptions.
"""
return html.Div(
id="description-card",
children=[
html.H3("Baltimore Victim-based Crime Data"),
intro_tabone(),
intro_tabtwo()
# dcc.Tabs(id='tabs-example', value='tab-1', children=[
# dcc.Tab(label='Introduction', value='tab-1'),
# dcc.Tab(label='Control Board', value='tab-2')]),
# html.Div(id='tabs-example-content'),
# html.H3("Welcome to the Clinical Analytics Dashboard"),
# html.Blockquote(
# id="intro",
# children=["Rejection hurts. Yes but yet, I've met nobody who has not been rejected.\
# It is a part of life and instead of drowning in the sorrow and somberness of being rejected, we can make it fun and try to try to analyze it.",
# ]
# ),
# html.Div(children=[
# 'This is the Dashboard analyzing the time-sapce pattern of the crime data in Baltimore for the job talk of Research Data Scientist.']),
# html.Br(),
# html.H4('Instructions'),
# html.Div(children=[
# html.P("This dashboard enables multiple ways for you to interact with the plots. Every plot can be zoomed and selected, along with hovering tooltips. Despite these basics, it also supports the following interactions:"),
# html.Li(
# 'Subsetting the dataset by selecting the time range, the weekdays, and the hours.'),
# html.Li(
# 'Showing specific data entries by clicking on the Heatmap.'),
# html.Div('Other interaction options are detailed in the corresponding part')])
# html.Li(
# 'Change the Solver of the Temporal Process and the number of days to prdict.'),
# html.Li('Change the metric to rank the important terms, and the number of topics to model.')])
]
)
# In[14]:
def intro_tabone():
return html.Div(children=[
html.Blockquote("Welcome to the Clinical Analytics Dashboard"),
html.P("This dashboard is the visual deliverale for the Research Data Scientist at National Police Foundation.")
])
def intro_tabtwo():
return html.Div(
id="control-card",
children=[
html.Div(children=[
html.Div(className='columns', children=[html.Strong("Select Granularity of Spatial Attributes"),
dcc.Checklist(
id='space_grand',
options=[
{'label': 'District', 'value': 'New_District'},
{'label': 'Neighborhood', 'value': 'Neighborhood_New'},
{'label': 'Street', 'value': 'Location'}
],
value=['Neighborhood_New']
),
html.Br(),
html.Strong("Select Granularity of Temporal Attributes"),
dcc.Dropdown(
id='time_grand',
options=[
{'label': 'Month', 'value': 'month'},
{'label': 'Day', 'value': 'day'},
{'label': 'Day of Week', 'value': 'dayofweek'},
{'label': 'Weekday or Weekend',
'value': 'weekday'},
{'label': 'Hour', 'value': 'hour'},
{'label': 'Minute', 'value': 'minute'},
{'label': 'Time of Day', 'value': 'period'},
{'label':'Quarter','value':'quarter'}
],
value=['month', 'dayofweek', 'period'],
multi=True
),
html.Br(),
html.Strong("Select the Type of Crime(S)"),
dcc.Dropdown(
id='crime_type',
options=[{'label':i.title(),'value':i}for i in df.Description.unique()],
value=['SHOOTING'],
multi=True
)])
])
]
)
def year_slider():
return html.Div(children=[
html.Strong("Select the Time Range for Analysis"),
html.Div(style={"width": "100%", "height": "100%",
"display": "inline-block", "position": "relative"},
className='columns', children=[dcc.RangeSlider(
id='year_slider',
updatemode='mouseup', # don't let it update till mouse released
min=1963,
max=2020,
step=None,
marks={2016: {'label':'2016',"style": {"transform": "rotate(45deg)"}},
2018: {'label':'2018',"style": {"transform": "rotate(45deg)"}},
2020: {'label':'2020',"style": {"transform": "rotate(45deg)"}},
2015: {'label':'2015',"style": {"transform": "rotate(45deg)"}},
2017: {'label':'2017',"style": {"transform": "rotate(45deg)"}},
2019: {'label':'2019',"style": {"transform": "rotate(45deg)"}},
2014: {'label':'2014',"style": {"transform": "rotate(45deg)"}},
2011: {'label':'2011',"style": {"transform": "rotate(45deg)"}},
2009: {'label':'2009',"style": {"transform": "rotate(45deg)"}},
1975: {'label':'1975',"style": {"transform": "rotate(45deg)"}},
2012: {'label':'2012',"style": {"transform": "rotate(45deg)"}},
2013: {'label':'2013',"style": {"transform": "rotate(45deg)"}},
1980: {'label':'1980',"style": {"transform": "rotate(45deg)"}},
1973: {'label':'1973',"style": {"transform": "rotate(45deg)"}},
2008: {'label':'2008',"style": {"transform": "rotate(45deg)"}},
1978: {'label':'1978',"style": {"transform": "rotate(45deg)"}},
1988: {'label':'1988',"style": {"transform": "rotate(45deg)"}},
1977: {'label':'1977',"style": {"transform": "rotate(45deg)"}},
1995: {'label':'1995',"style": {"transform": "rotate(45deg)"}},
2001: {'label':'2001',"style": {"transform": "rotate(45deg)"}},
2000: {'label':'2000',"style": {"transform": "rotate(45deg)"}},
2007: {'label':'2007',"style": {"transform": "rotate(45deg)"}},
1969: {'label':'1969',"style": {"transform": "rotate(45deg)"}},
1982: {'label':'1982',"style": {"transform": "rotate(45deg)"}},
1963: {'label':'1963',"style": {"transform": "rotate(45deg)"}},
1981: {'label': '1981',"style": {"transform": "rotate(45deg)"}},
1999: {'label': '1999',"style": {"transform": "rotate(45deg)"}},
2003: {'label':'2003',"style": {"transform": "rotate(45deg)"}},
2010: {'label':'2010',"style": {"transform": "rotate(45deg)"}},
2004: {'label':'2004',"style": {"transform": "rotate(45deg)"}},
1998: {'label':'1998',"style": {"transform": "rotate(45deg)"}},
1993: {'label':'1993',"style": {"transform": "rotate(45deg)"}},
1985: {'label': '1985',"style": {"transform": "rotate(45deg)"}},
2006: {'label':'2006',"style": {"transform": "rotate(45deg)"}}},
value=[2014, 2020],
# vertical=True,
# verticalHeight=600
)])])
# In[18]:
slider_map={'{"index":1,"type":"slider"}':'Month',
'{"index":2,"type":"slider"}':'Day',
'{"index":3,"type":"slider"}':'DayofWeek',
'{"index":4,"type":"slider"}':'WeekDay',
'{"index":5,"type":"slider"}':'Quarter',
'{"index":6,"type":"slider"}':'Hour',
'{"index":7,"type":"slider"}':'Period'}
def make_sliders(time):
sliders = []
for t in time:
if t == 'month':
sliders.append(dcc.Slider(
id={'type':'slider','index':1},
updatemode='mouseup', # don't let it update till mouse released
min=1,
max=12,
marks={1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',
7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'},
step=1,
value=1
))
elif t == 'day':
sliders.append(dcc.Slider(
id={'type':'slider','index':2},
updatemode='mouseup', # don't let it update till mouse released
min=1,
max=31,
marks={i: str(i) for i in range(1, 32)},
step=1,
value=1
))
elif t == 'dayofweek':
sliders.append(dcc.Slider(
id={'type':'slider','index':3},
updatemode='mouseup', # don't let it update till mouse released
min=0,
max=6,
marks={0: "Monday", 1: 'Tue', 2: 'Wed',
3: 'Thur', 4: 'Fri', 5: 'Sat', 6: 'Sun'},
step=1,
value=0
))
elif t == 'weekday':
sliders.append(dcc.Slider(
id={'type':'slider','index':4},
updatemode='mouseup', # don't let it update till mouse released
min=1,
max=2,
marks={1: 'Weekdays', 2: 'Weekends'},
step=1,
value=1
))
elif t == 'quarter':
sliders.append(dcc.Slider(
id={'type':'slider','index':5},
updatemode='mouseup', # don't let it update till mouse released
min=1,
max=4,
marks={1: 'Q1', 2: 'Q2', 3: 'Q3', 4: 'Q4'},
step=1,
value=1
))
elif t == 'hour':
sliders.append(dcc.Slider(
id={'type':'slider','index':6},
updatemode='mouseup', # don't let it update till mouse released
min=0,
max=23,
marks={0: '12 AM', 1: '1 AM', 2: '2 AM', 3: '3 AM', 4: '4 AM', 5: '5 AM', 6: '6 AM', 7: '7 AM',
8: '8 AM', 9: '9 AM', 10: '10 AM', 11: '11 AM', 12: '12 PM', 13: '1 PM', 14: '2 PM', 15: '3 PM',
16: '4 PM', 17: '5 PM', 18: '6 PM', 19: '7 PM', 20: '8 PM', 21: '9 PM', 22: '10 PM', 23: '11 PM'},
step=1,
value=1
))
elif t == 'period':
sliders.append(dcc.Slider(
id={'type':'slider','index':7},
updatemode='mouseup', # don't let it update till mouse released
min=1,
max=6,
marks={1: 'Late Night',
2: 'Early Morning',
3: 'Morning',
4: 'Noon',
5: 'Evening',
6: 'Night'},
step=1,
value=1
))
return sliders
# In[20]:
def para_html():
return html.Div(id='para', className="columns pretty_container", children=[
html.Div(style={"display": "inline-flex"}, children=[html.H5('Parallel Coordinates of the Flow of Rej. Emails.'),
# html.Div(children=[
# html.I(
# className="fas fa-question-circle fa-lg", id="target"),
# dbc.Tooltip("How do we read this? Bascially, each ploly line represents a combination of day/time patterns, and the color indicates the frequency of this pattern.\
# Each variable in the data set is represented by a column of rectangles, \
# where each rectangle corresponds to a discrete value taken on by that variable.\
# The relative heights of the rectangles reflect the relative\
# frequency of occurrence of the corresponding value. ", target="target",
# style={"max-width": "400px", "padding": ".25rem .5rem", "color": "#fff", "text-align": "center", "background-color": "#000", "border-radius": ".25rem"}),
# ],
# className="p-5 text-muted"
# )
]),
html.Div("Let's highlight the most prominent streamline of the email flow. \
It could come in handy when we observed some trends in the data. \
This plot will update automatically with the Date/Day/Hour you selected in the Control widgets. ☝️",
style={'margin': "10px"}),
html.Div(dcc.Input(
id="dtrue", type="number",
debounce=True, placeholder="Debounce True",value=98
)),
html.Div(id='quant', children=[]),
dcc.Loading(dcc.Graph(id='paco'))])
def subset_df(crime, space, year, time):
#filter crime
new_df=df[df.Description.isin(crime)]
#filter year
new_df=new_df[(new_df.Year>=year[0])&(new_df.Year<=year[-1])]
grouping_attr=space+list(time.keys())
grouping_attr.extend(['Description','Total Incidents'])
subdf=new_df.loc[:,grouping_attr]
return subdf
def make_paco(df, quant):
col=list(df.columns)
col.remove('Total Incidents')
col_group=col
newdf=df.groupby(col_group).count().reset_index().sort_values('Total Incidents')
num=newdf['Total Incidents'].quantile(quant)
newdf=newdf[newdf['Total Incidents']>num]
fig=px.parallel_categories(newdf, dimensions=col, color='Total Incidents')
return fig, fig.data[0]['dimensions']
def pattern_html():
return html.Div(id='temperal pro', className='columns pretty_container', children=[
html.Div(style={"display": "inline-flex"}, children=[
html.H5("Maximal Frequent Pattern Mining",
style={'margin-left': '10px'}),
html.Div(children=[
html.I(
className="fas fa-question-circle fa-lg", id="target2"),
# dbc.Tooltip("How can we interact with this? 1) Changing the kernel of the model to see the other kind of results; \n 2)\
# Selecting how many days you want to use to predict future email events;\n 3)\
# Showing the actual predicted result by hover on the dots in the line chart.", target="target2",
# style={"max-width": "400px", "padding": ".25rem .5rem", "color": "#fff", "text-align": "center", "background-color": "#000", "border-radius": ".25rem"}),
],
className="p-5 text-muted"
)]),
html.P(
children="A Temporal Process is a kind of random process whose realization consists of discrete events \
localized in time. Compared with \
traditional Time-Seris, each data entry was allocated in different time interval. The scattering nature of receiving\
an email fits better with a Temporal Process Analysis. \n \
A very popular kind of termporal process is the Hawkes process, which could be consider\
as an 'auto-regression' type of process. Here I used the Hawkes Process to simulate the events.\
You can select the Kernal and the days to forecast below.👇",
style={'margin': '10px'}
),
html.Div(
id="select model",
style={"text-align": "center"},
className="six columns", children=[html.Strong('Filter the Patterns'),
dcc.Checklist(
id='len_picker',
options=[
{'label': 'Two',
'value': 2},
{'label': 'Three',
'value': 3},
{'label': 'Four',
'value': 4},
{'label': 'Five',
'value': 5},
{'label': 'Six',
'value': 6},
],
value=[2,3,4,5,6],
labelStyle={'display': 'inline-block'}
)
]),
html.Div(
id="select_bum",
style={"text-align": "center"},
className="six columns", children=[html.Strong('Select Number of Patterns to Show'),
dcc.Slider(
id='numslider',
min=1,
max=100,
step=1,
value=15,
updatemode='drag'
)
]),
html.Br(),
html.Br(),
html.Div(className='columns', children=dcc.Loading(
dcc.Graph(id='pattern_bar'))
)
]
)
from mlxtend.frequent_patterns import fpmax
def pattern(df, crime):
if len(crime)==1:
df=df.drop(columns=['Total Incidents','Description'])
rules=fpmax(pd.get_dummies(df.astype('category')), min_support=0.001, max_len=6,use_colnames=True)
else:
df=df.drop(columns=['Total Incidents'])
rules=fpmax(pd.get_dummies(df.astype('category')), min_support=0.001, max_len=6,use_colnames=True)
rules['len']=rules.itemsets.apply(len)
rules['itemsets']=rules.itemsets.apply(list)
rules.sort_values('support', ascending=False)
rules=rules.sort_values('support', ascending=False).reset_index()
return rules
def filter_df(newdf,times):
for t in times.items():
if t[0]=='Weekday':
real_val={1:'Weekday',2:'Weekend'}[t[1]]
newdf=newdf[newdf[t[0]]==real_val]
elif t[0]=='Period':
real_val={1: 'Late Night',
2: 'Early Morning',
3: 'Morning',
4: 'Noon',
5: 'Evening',
6: 'Night'}[t[1]]
newdf=newdf[newdf[t[0]]==real_val]
else:
newdf=newdf[newdf[t[0]]==t[1]]
print(newdf)
return newdf
def season_html():
return html.Div(id='season', className='columns pretty_container', children=[
html.Div(style={"display": "inline-flex"}, children=[
html.H5("Seasonality Mining",
style={'margin-left': '10px'}),
html.Div(children=[
html.I(
className="fas fa-question-circle fa-lg"),
# dbc.Tooltip("How can we interact with this? 1) Changing the kernel of the model to see the other kind of results; \n 2)\
# Selecting how many days you want to use to predict future email events;\n 3)\
# Showing the actual predicted result by hover on the dots in the line chart.", target="target2",
# style={"max-width": "400px", "padding": ".25rem .5rem", "color": "#fff", "text-align": "center", "background-color": "#000", "border-radius": ".25rem"}),
],
className="p-5 text-muted"
)]),
html.P(
children="A Temporal Process is a kind of random process whose realization consists of discrete events \
localized in time. Compared with \
traditional Time-Seris, each data entry was allocated in different time interval. The scattering nature of receiving\
an email fits better with a Temporal Process Analysis. \n \
A very popular kind of termporal process is the Hawkes process, which could be consider\
as an 'auto-regression' type of process. Here I used the Hawkes Process to simulate the events.\
You can select the Kernal and the days to forecast below.👇",
style={'margin': '10px'}
),
html.Div(children=[
dcc.Tabs(id='stabsexample', value='stab1', children=[
dcc.Tab(label='Fourier', value='stab1'),
dcc.Tab(label='SARIMA', value='stab2'),
dcc.Tab(label='TABTS', value='stab3')]),
html.Div(id='stabcontent')])
]
)
def stab1():
return html.Div(children=[
html.Div(children=[dcc.Dropdown(
id='scrime',
options=[{'label': i.title(), 'value': i}
for i in df.Description.unique()],
value='SHOOTING'
),
dcc.RadioItems(
options=[
{'label': 'Construct Hourly Time Series', 'value': 'hourly'},
{'label': 'Construct Daily Time Series', 'value': 'daily'},
{'label': 'Construct Monthly Time Series', 'value': 'monthly'}
],
value='hourly',
id='speriod'
)]),
html.Div(className='six columns', children=[
dcc.Graph(id='stab1_line')]),
html.Div(className='six columns',id='stab1_df', children=[
dash_table.DataTable(
id='stab1_table',page_size= 15)])
])
def ftt_result(crime, period):
sdf=df[(df.Year>=2014)&(df.Year)<=2020]
sdf=df[df.Description==crime]
if period=='hourly':
signal=sdf.resample('H', on='Datetime').sum()['Total Incidents']
fft_outputft, power, freq, peaks, peak_freq, peak_power= get_fftt(signal)
output = pd.DataFrame()
output['index'] = peaks
output['freq (1/hour)'] = peak_freq
output['amplitude'] = peak_power
output['period (hour)'] = 1 / peak_freq
# output['fft'] = fft_output[peaks]
output['peak']='peak'
output = output.sort_values('amplitude', ascending=False)
fft_df=pd.DataFrame()
fft_df['freq']=freq
fft_df['power']=power
f=px.line(fft_df, x='freq',y='power', title='All Frequencies and Their Amplitute')
f.add_trace(px.scatter(output, x='freq (1/hour)', y='amplitude', color='peak',template='ggplot2').data[0])
f.update_layout(margin={"l":0,"b":0},legend=dict(
orientation="h"))
ftt_table=dash_table.DataTable(
id='stab1_table',
columns=[{"name": i, "id": i} for i in output.columns],
data=output.to_dict('records'))
elif period=='daily':
signal=sdf.resample('D', on='Datetime').sum()['Total Incidents']
fft_outputft, power, freq, peaks, peak_freq, peak_power= get_fftt(signal)
output = pd.DataFrame()
output['index'] = peaks
output['freq (1/day)'] = peak_freq
output['amplitude'] = peak_power
output['period (day)'] = 1 / peak_freq
# output['fft'] = fft_output[peaks]
output['peak']='peak'
output = output.sort_values('amplitude', ascending=False)
fft_df=pd.DataFrame()
fft_df['freq']=freq
fft_df['power']=power
f=px.line(fft_df, x='freq',y='power', title='All Frequencies and Their Amplitute')
f.add_trace(px.scatter(output, x='freq (1/day)', y='amplitude', color='peak',template='ggplot2').data[0])
f.update_layout(margin={"l":0,"b":0},legend=dict(
orientation="h"))
ftt_table=dash_table.DataTable(
id='stab1_table',
columns=[{"name": i, "id": i} for i in output.columns],
data=output.to_dict('records'))
else:
signal=sdf.resample('M', on='Datetime').sum()['Total Incidents']
fft_outputft, power, freq, peaks, peak_freq, peak_power= get_fftt(signal)
output = pd.DataFrame()
output['index'] = peaks
output['freq (1/month)'] = peak_freq
output['amplitude'] = peak_power
output['period (month)'] = 1 / peak_freq
# output['fft'] = fft_output[peaks]
output['peak']='peak'
output = output.sort_values('amplitude', ascending=False)
fft_df=pd.DataFrame()
fft_df['freq']=freq
fft_df['power']=power
f=px.line(fft_df, x='freq',y='power', title='All Frequencies and Their Amplitute')
f.add_trace(px.scatter(output, x='freq (1/month)', y='amplitude', color='peak',template='ggplot2').data[0])
f.update_layout(margin={"l":0,"b":0},legend=dict(
orientation="h"))
return f, output
# In[232]:
from scipy import fft
from scipy import signal as sig
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math
def get_fftt(signal):
fft_output = fft.fft(signal)
power = np.abs(fft_output)
freq = fft.fftfreq(len(signal))
mask = freq >= 0
freq = freq[mask]
power = power[mask]
peaks = sig.find_peaks(power[freq >=0], prominence=200)[0]
# peaks = power[freq >=0]
peak_freq = freq[peaks]
peak_power = power[peaks]
return fft_output, power, freq, peaks, peak_freq, peak_power
def sarima_html():
return html.Div(children=[
html.Div(children=[dcc.Dropdown(
id='sacrime',
options=[{'label': i.title(), 'value': i}
for i in df.Description.unique()],
value='SHOOTING'
),
dcc.RadioItems(
options=[
{'label': 'Construct Daily Time Series', 'value': 'daily'},
{'label': 'Construct Monthly Time Series', 'value': 'monthly'},
{'label': 'Construct Quarterly Time Series', 'value': 'quarterly'}
],
value='quarterly',
id='saperiod'
)]),
html.Div(className='six columns', children=[
dcc.Graph(id='stab2_decom')]),
html.Div(className='six columns', children=[
dash_table.DataTable(
id='stab2_par',page_size= 15)]),
html.Div(className='columns', children=[
dcc.Graph(id='pred')]),
html.Div(className='six columns', children=[
dash_table.DataTable(
id='seco',page_size= 15)]),
html.Div(className='six columns', children=[
dash_table.DataTable(
id='noseco',page_size= 15)])
])
# In[388]:
def sarima_result(crime, period):
sdf=df[(df.Year>=2014)&(df.Year)<=2020]
sdf=df[df.Description==crime]
if period=='monthly':
signal=sdf.resample('M', on='Datetime').sum()['Total Incidents']
decom_fig=decom(signal)
pra_df,se_co,nose_co,result_df=srima_fit(signal,12)
elif period=='daily':
signal=sdf.resample('D', on='Datetime').sum()['Total Incidents']
decom_fig=decom(signal)
pra_df,se_co,nose_co,result_df=srima_fit(signal,365)
else:
signal=sdf.resample('Q', on='Datetime').sum()['Total Incidents']
decom_fig=decom(signal)
pra_df,se_co,nose_co,result_df=srima_fit(signal,4)
return decom_fig, pra_df, se_co, nose_co, result_df
# In[381]:
from statsmodels.tsa.seasonal import seasonal_decompose
def decom(signal):
result = seasonal_decompose(signal)
fig = result.plot()
n_fig=tls.mpl_to_plotly(fig)
return n_fig
# In[385]:
import pmdarima as pm
def srima_fit(signal, m):
seasonal_model = pm.auto_arima(signal, start_p=1, start_q=1,
max_p=3, max_q=3,m=m,
start_P=0, seasonal=True,
d=1, D=1, trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
non_seasonal_model = pm.auto_arima(signal, start_p=1, start_q=1,
max_p=3, max_q=3,
start_P=0, seasonal=False,
d=1, D=1, trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
se_pra=seasonal_model.get_params()
se_pra['aic']=seasonal_model.aic()
nose_pra=non_seasonal_model.get_params()
nose_pra['aic']=non_seasonal_model.aic()
pra_df=pd.DataFrame([se_pra,nose_pra])
se_co=pd.read_html(seasonal_model.summary().tables[1].as_html(), header=0, index_col=0)[0]
nose_co=pd.read_html(non_seasonal_model.summary().tables[1].as_html(), header=0, index_col=0)[0]
train=signal[:int(0.8*(len(signal)))]
test=signal[int(0.8*(len(signal))):]
seasonal_model.fit(train)
se_forecast = seasonal_model.predict(n_periods=len(test))
non_seasonal_model.fit(train)
nose_forecast = non_seasonal_model.predict(n_periods=len(test))
future_forecast1 = pd.DataFrame(se_forecast,index = test.index,columns=['SesonalPrediction'])
future_forecast2 = pd.DataFrame(nose_forecast,index = test.index,columns=['NonseaPrediction'])
result_df=pd.concat([test,future_forecast1,future_forecast2],axis=1)
return pra_df,se_co,nose_co,result_df
# In[413]:
from fbprophet import Prophet
def pro(crime,period):
sdf=df[(df.Year>=2014)&(df.Year)<=2020]
sdf=df[df.Description==crime]
signal=sdf.resample('D', on='Datetime').sum()['Total Incidents']
if 'weekly' in period:
m = Prophet(weekly_seasonality=True)
else:
m = Prophet(weekly_seasonality=False)
if 'monthly' in period:
m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
if 'quarterly' in period:
m.add_seasonality(name='quarterly', period=91.3125, fourier_order=10)
forecast = m.fit(df).predict(future)
fig = m.plot_components(forecast)
return tls.mpl_to_plotly(fig)
# In[414]:
def pro_html():
return html.Div(children=[
html.Div(children=[dcc.Dropdown(
id='pcrime',
options=[{'label': i.title(), 'value': i}
for i in df.Description.unique()],
value='SHOOTING'
),
dcc.Dropdown(
options=[
{'label': 'Add Daily Seasonality', 'value': 'daily'},
{'label': 'Add Monthly Seasonality', 'value': 'monthly'},
{'label': 'Add Quarterly Seasonality', 'value': 'quarterly'}
],
value=['quarterly'],
id='pperiod',
multi=True
)]),
html.Div(className='columns', children=[
dcc.Graph(id='pro')])
])
# In[416]:
# %tb
# app = dash.Dash(__name__, external_stylesheets=["https://codepen.io/chriddyp/pen/bWLwgP.css",
# "https://dash-gallery.plotly.host/dash-oil-and-gas/assets/styles.css?m=1590087908.0"])
app = dash.Dash(__name__, external_stylesheets=["https://codepen.io/chriddyp/pen/bWLwgP.css",
"https://dash-gallery.plotly.host/dash-oil-and-gas/assets/styles.css?m=1590087908.0",
"https://use.fontawesome.com/releases/v5.10.2/css/all.css"])
server = app.server
app.config.suppress_callback_exceptions = True
app.layout = html.Div(
id="app-container",
children=[html.Div(id="left-column ",
style={},
className="four columns pretty_container",
children=intro()
),
html.Div(
id="right-column",
className="eight columns",
style={},
children=[html.Div(
[
html.Div(
[html.H6(),
html.P("No. of Days Selected"),
html.Strong(id="days_selected")],
id="days",
className="mini_container",
),
html.Div(
[html.H6(), html.P(
"No. of Letters Received in the Period"),
html.Strong(id="total")],
className="mini_container",
),
html.Div(
[html.H6(), html.P(
"Peak Day and Hour"),
html.Strong(id="peak_date")],
className="mini_container",
),
html.Div(
[html.H6(id="pn"), html.P(
"Rej Letters Peak Volume"),
html.Strong(id="peak_num")],
className="mini_container",
),
],
id="info-container",
className="row container-display",
),
year_slider(),
html.Div(
className='mini_container',
children=[
dcc.Loading(dcc.Graph(id='map'))]),
html.Div(id='sliders'),
html.Div(id='test')
]),
para_html(),
dcc.Store(id='pacodata'),
dcc.Store(id='clickpaco'),
html.Div(id='test_click'),
pattern_html(),
season_html()
])
@app.callback(Output('stabcontent', 'children'),
[Input('stabsexample', 'value')])
def render_content(tab):
# return tab
if tab == 'stab1':
return stab1()
elif tab=='stab2':
return sarima_html()
else:
return pro_html()
@app.callback([
Output('pro', 'figure')