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#!/home/jziebart/python/Python-2.7.15/python
import os
import sys
#sys.path.append('/home/jziebart/.local/bin')
#sys.path.append('/home/jziebart/.local/lib')
import plotly
import plotly.graph_objs as go
import csv
import string
netID = sys.argv[-1]
outfile = netID+"ts_plotly.html"
filename=netID+"ts_output.txt"
f=open(filename,"r")
#Read the first line to get the variable name
line=f.readline()
line = map(string.strip,line.strip().split(" "))
varName = line[-1]
print varName
#Read type file to determine if it is continuous or discrete
typefile = netID+"type.txt"
tf=open(typefile,"r")
line=tf.readline()
varnames = map(string.strip,line.strip().split("\t"))
print varnames
line=tf.readline()
vartypes = map(string.strip,line.strip().split("\t"))
varindex = varnames.index(varName)
cd_type = int(vartypes[varindex])
if cd_type == 1:
#Make scatterplot for continuous_data
#Read introductory lines from file
for i in range(6):
line = f.readline()
#Read the data
x = []
y = []
line = f.readline()
while line:
line = map(string.strip,line.strip().split("\t"))
if line[1] != 'NA':
x.append(float(line[1]))
y.append(float(line[2]))
line=f.readline()
data = [go.Scatter(x=x,y=y,mode='markers')]
layout = go.Layout(
xaxis=dict(
autorange=True,
title='Actual values',
titlefont=dict(
family='Arial, sans-serif',
size=18,
color='black'
),
),
yaxis=dict(
autorange=True,
title='Predicted values',
titlefont=dict(
family='Arial, sans-serif',
size=18,
color='black'
),
)
)
fig = go.Figure(data=data, layout = layout)
plotly.offline.plot(fig,filename=outfile)
else:
#Make bar chart for discrete data
#Read introductory lines from file
for i in range(5):
line = f.readline()
#Get names of states
line = map(string.strip,line.strip().split("\t"))
states = line[2:]
#Read the data
actual = []
predicted = []
line = f.readline()
while line:
line = map(string.strip,line.strip().split("\t"))
if line[1] != 'NA':
actual.append(line[1])
predict_x = line[2:]
predict_x = [float(x) for x in predict_x]
max_value = max(predict_x)
max_index = predict_x.index(max_value)
predicted.append(states[max_index])
#check if multiple states are equally likely to be predicted states
#I am not going to count these as being predicted here
max_items = [x for x in predict_x if (abs(x-max_value) < 0.000001)]
if len(max_items) > 1:
predicted.pop()
actual.pop()
line=f.readline()
#Go through states and get number of true positives, false positives, and false negatives
tp_all = []
fp_all = []
fn_all = []
for state in states:
tp = 0
fp = 0
fn = 0
for i in range(len(actual)):
actual_i = actual[i]
predicted_i = predicted[i]
if actual_i == state:
if predicted_i == state:
tp = tp + 1
else:
fn = fn + 1
elif predicted_i == state:
fp = fp + 1
tp_all.append(tp)
fn_all.append(fn)
fp_all.append(fp)
print tp_all
print fn_all
print fp_all
trace1 = go.Bar(x=states,y=tp_all,name="True Positives")
trace2 = go.Bar(x=states,y=fn_all,name="False Negatives")
trace3 = go.Bar(x=states,y=fp_all,name="False Positives")
data = [trace1,trace2,trace3]
layout = go.Layout(
barmode='group',
xaxis=dict(
autorange=True,
title='State',
titlefont=dict(
family='Arial, sans-serif',
size=18,
color='black'
),
),
yaxis=dict(
autorange=True,
title='Number of cases',
titlefont=dict(
family='Arial, sans-serif',
size=18,
color='black'
),
)
)
fig = go.Figure(data=data, layout = layout)
plotly.offline.plot(fig,filename=outfile)
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