from os.path import dirname, join

from bokeh.models.widgets import Slider, Select, TextInput, MultiSelect, Dropdown, Button

import numpy as np

import pandas as pd

# import pandas.io.sql as psql

from bokeh.layouts import layout, widgetbox

from bokeh.plotting import figure

from sklearn.svm import SVC

from bokeh.layouts import layout, widgetbox

from bokeh.models import ColumnDataSource, HoverTool, Div, LabelSet

from bokeh.io import curdoc

from sklearn.model_selection import train_test_split

from sklearn.metrics import classification_report,confusion_matrix

from sklearn.metrics import accuracy_score

from bokeh.transform import factor_cmap

from bokeh.palettes import Spectral6

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import chi2

from sklearn.feature_selection import SelectFromModel

from sklearn.ensemble import RandomForestClassifier

from sklearn.feature_selection import RFE

from bokeh.io import show, output_file

from numpy.random import random

from sklearn.utils import resample

from sklearn.neural_network import MLPClassifier

from sklearn.preprocessing import StandardScaler

from sklearn import tree

from bokeh.layouts import column, row

# from bokeh.charts import Bar

axis_map = {

}

desc = Div(text="""<h1>An Interactive Explorer for Churn Data</h1>

<br/>""")

df = pd.read_csv('C:\\Users\\rajat\\Desktop\\Master project\\churn.csv',names=['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o'])

div = Div(text="""Your <a href="https://en.wikipedia.org/wiki/HTML">HTML</a>-supported text is initialized with the <b>text</b> argument. The

remaining div arguments are <b>width</b> and <b>height</b>. For this example, those values

are <i>200</i> and <i>100</i> respectively.""",

width=200, height=100)

#columns = ['luxury_car_user','avg_dist','city_Astapor',"city_KingsLanding",'phone_Android','phone_iPhone']

df=df[1:]

y=df.o

x=df.drop('o',axis=1)

x_train_original,x_test_original,y_train_original,y_test_original=train_test_split(x,y,test_size=0.25)

#For standardizing data

clf = MLPClassifier(hidden_layer_sizes=(30,30,9),max_iter=1000)

# rfe = RFE(clf, 5)

clf.fit(x_train_original,y_train_original)

predictions=clf.predict(x_test_original)

print("Accuracy =", accuracy_score(y_test_original,predictions))

print(np.unique(predictions))

tn, fp, fn, tp = confusion_matrix(y_test_original,predictions).ravel()

fruits = ['True Positive', 'False Positive', 'False Negative', 'True Negative']

source = ColumnDataSource(data=dict(bins=fruits, counts=[1, 10, 20, 30]))

#fruits = [tp, fp, tn, fn]

# counts = [tp, fp, tn, fn]

source.data = dict(fruits=fruits, counts=[tp, fp, fn, tn])

p = figure(tools='crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,lasso_select',x_range=fruits, plot_height=450, title="Counts")

p.vbar(x='fruits', top='counts', width=0.9, source=source, legend="fruits",

line_color='white',fill_color=factor_cmap('fruits', palette=Spectral6, factors=fruits))

labels = LabelSet(x='fruits', y='counts', text='counts', level='glyph',

x_offset=-15, y_offset=0, source=source, render_mode='canvas')

p.add_layout(labels)

p.title.text = "Model Accuracy %f" % accuracy_score(y_test_original,predictions)

tsize = Slider(title="Test Size", start=0.05, end=0.50, value=0.2, step=0.05)

layer = Slider(title="Layer 1", start=10, end=50, value=10, step=10)

layer1 = Slider(title="Layer 2", start=10, end=50, value=10, step=10)

layer2 = Slider(title="Layer 3", start=10, end=50, value=10, step=10)

layer3 = Slider(title="Layer 4", start=10, end=50, value=10, step=10)

maxiter = Slider(title="Iterations", start=100, end=3000, value=100, step=100)

button = Button(label="Submit Parameters", button_type="success")

button2 = Button(label="Play_Maximum_Iterations", button_type="success")

multi_select = MultiSelect(title="Features to drop:", value=[],options=[('a','avg_dist'),('b','avg_rating_by_driver'),('c','avg_rating_of_driver'),('d','avg_surge'),('e','surge_pct'),('f','trips_in_first_30_days'),('g','luxury_car_user'),('h','weekday_pct'),('i','city_Astapor'),('k','city_Winterfell'),('l','phone_Android'),('m','phone_iPhone'),('n','phone_no_phone')])

p.add_tools(HoverTool(tooltips = [

("Count", "@counts"),

]))

def update_points():

p.title.text = "Model Accuracy %f" % accuracy_score(y_test_original,predictions)

button.on_click(update_points)

layer = Slider(title="Layer 1", start=10, end=50, value=10, step=10)

layer1 = Slider(title="Layer 2", start=10, end=50, value=10, step=10)

layer2 = Slider(title="Layer 3", start=10, end=50, value=10, step=10)

layer3 = Slider(title="Layer 4", start=10, end=50, value=10, step=10)

maxiter = Slider(title="Iterations", start=100, end=3000, value=100, step=100)

def animate_update1():

tsize.value = 0.05

def animatelayer1():

curdoc().remove_periodic_callback(animate_update)

def animate_update():

input.value = 10

def animatelayer2():

curdoc().remove_periodic_callback(animate_update2)

def animate_update2():

layer1.value = 10

def animatelayer3():

curdoc().remove_periodic_callback(animate_update3)

def animate_update3():

layer2.value = 10

def animatelayer4():

curdoc().remove_periodic_callback(animate_update4)

def animate_update4():

input.value = 10

def animate2():

curdoc().remove_periodic_callback(animate_update1)

def animate_Iter():

input.value = 100

def animateIter():

curdoc().remove_periodic_callback(animate_Iter)

button1 = Button(label='Play_Layer1')

button1.on_click(animatelayer1)

button3 = Button(label='Play_Layer2')

button3.on_click(animatelayer2)

button4 = Button(label='Play_Layer3')

button4.on_click(animatelayer3)

button5 = Button(label='Play_Layer4')

button5.on_click(animatelayer4)

buttonIter = Button(label='Play_Iteration')

buttonIter.on_click(animateIter)

button2= Button(label='Play_Test')

button2.on_click(animate2)

controls = [tsize,layer,layer1,layer2,layer3,maxiter,button1, button3,button4,button5,button2]

sizing_mode = 'fixed' # 'scale_width' also looks nice with this example

inputs = widgetbox(*controls, sizing_mode=sizing_mode)

# layout = layout([

# [desc,p],

# [tsize,layer,layer1, button1, ],

# [layer3,maxiter,button3,button4],

# [button5,multi_select,button2],

# [button]

# ])

layout = layout([

[desc,multi_select,button],

[inputs, p],

], sizing_mode=sizing_mode)

output_file("panning.html")

#update() # initial load of the data

curdoc().add_root(layout)

curdoc().title = "Churn"