Deploy Productionalise Azure Machine Learning Algorithm Using Python

ByBernard Adabankah July 24, 2018

This is a script to consume Azure Machine Learning Model which you build using Azure ML Studio.

So basically this is a Request-Response Service (RRS) according to Microsoft.

This was a Boosted Decision Tree Algorithm build with Census Adult Data which is available as one of the Sample DataSet in Azure ML.

You can adapt the code to suit your need as this is a guide.

For large file batch predictions, you might have to use the Batch Execution Service (BES) instead.

The sample data associated with this example can be downloaded from here

Hope this helps.

Please ask your questions in the comment box below

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Python

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Bydatapandasadmin August 26, 2016October 23, 2016

This is some quick notes about graphing or plotting graphs with Matplotlib in Python. Credits to sentdex. You can check him out on Youtube.

import matplotlib.pyplot as plt

%matplotlib inline

# simple syntax is matplotlib plots as plt.plot(x, y)

plt.plot([1,2,3], [5,6,7])

plt.show()

In [4]:

# plotting with variables and basic labelling of our plot

x = [1,2,3]

y = [5,6,7]

##adding a legend to the plot

#add another line vectors first

x2 = [4,6,8]

y2= [10,14,18]

plt.plot(x, y, label='First Line') # you have to give the plots a label before you can call the legends on them

plt.plot(x2, y2, label='Second Line')

plt.xlabel('Plot number')

plt.ylabel('Important var')

plt.title('Plotting\nNice Graph') #\nGives you a sub title

plt.legend() #call the legend

plt.show()

In [10]:

#plotting barcharts and histograms

x3 = [2,4,6,8,10]

y3 = [3,5,7,9,11]

#second set of bars

x4 = [1,3,5,7,9]

y4 = [2,6,6,12,4]

#plot barchart

plt.bar(x3, y3, label='Bar1', color='r')

plt.bar(x4,y4, label='Bar2', color ='c')

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Bar Chart')

plt.legend()

plt.show()

In [14]:

#plotting histogram.

population_dist = [24,45,5,6,42,65,34,75,75,34,75,7,24,26,87,98,98,67,86, 65,63, 87,98,76,53,35]

#ids = [x for x in range(len(population_dist))] for bar chart plotting

#plot barchart

#plt.bar(ids, population_dist, label='hist')

#histogram works with bins so we will have to create bin 'containers' for our distribution

bins = [10,20,30,40,50,60,70,80,90,100]

plt.hist(population_dist, bins, histtype='bar', rwidth=0.5)

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Histogram')

plt.legend()

plt.show()

In [20]:

## scatter plot.

#basic skeletion

x = [1,2,3,4,5,6,7,8,9]

y = [4,5,8,3,6,9,5,4,8]

plt.scatter(x,y, label='ScatterShow', color='c', marker='^' , s=60) # s = size of the marker

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Scatter')

plt.legend()

plt.show()

In [23]:

#stacked plot

days = [1, 2, 3, 4, 5]

sleeping = [6,8,7,3,8]

eating = [6,9,7,3,6]

working = [9,8,5,7,8]

#working around legends for stackplots

plt.plot([],[], color='red', label='eating', linewidth=2)

plt.plot([],[], color='blue', label='sleeping', linewidth=2)

plt.plot([],[], color='yellow', label='working', linewidth=2)

plt.stackplot(days, eating, sleeping, working, color=['red','blue','yellow'])

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Stacked Plot')

plt.legend()

plt.show()

In [30]:

#pie charts

days = [1, 2, 3, 4, 5]

sleeping = [6,8,7,3,8]

eating = [6,9,7,3,6]

working = [9,8,5,7,8]

slices = [2,4,6,8,1]

colors = ['red','blue','green','brown','pink']

activities = ['sleeping','eating','working','dancing','travelling']

plt.pie(slices, labels=activities, colors=colors, startangle=90, shadow=True, explode=(0,0.1,0,0,0), autopct='%1.1f%%')

Out[30]:

([<matplotlib.patches.Wedge at 0x814e588>,

<matplotlib.patches.Wedge at 0x7fb7080>,

<matplotlib.patches.Wedge at 0x96f9ba8>,

<matplotlib.patches.Wedge at 0x82853c8>,

<matplotlib.patches.Wedge at 0x785a080>],

[<matplotlib.text.Text at 0x82ac4a8>,

<matplotlib.text.Text at 0x96f9438>,

<matplotlib.text.Text at 0x8026390>,

<matplotlib.text.Text at 0x94ac400>,

<matplotlib.text.Text at 0x785ada0>],

[<matplotlib.text.Text at 0x7fb7fd0>,

<matplotlib.text.Text at 0x96f97b8>,

<matplotlib.text.Text at 0x8285f98>,

<matplotlib.text.Text at 0x94aca58>,

<matplotlib.text.Text at 0x65bf860>])

In [48]:

#modifying the matplotlib

#plotting barchart.

x3 = [2,4,6,8,10]

y3 = [3,5,7,9,11]

#second set of bars

x4 = [1,3,5,7,9]

y4 = [2,6,6,12,4]

fig = plt.figure(facecolor='#f0f0f0') #set facecolour or background colour which will be maintained once we save our graph

ax1 = plt.subplot2grid((1,1),(0,0))

#tilt the xlabels

for labels in ax1.xaxis.get_ticklabels():

labels.set_rotation(45)

#plot barchart

ax1.bar(x3, y3, label='Bar1', color='r')

ax1.bar(x4,y4, label='Bar2', color ='c')

ax1.grid(True) #add a grid

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Bar Chart')

plt.legend()

#customise the legend

leg = ax1.legend(loc=2,ncol=6, prop={'size':10})

leg.get_frame().set_alpha(0.4)

##define spacing around the ticks and labels

plt.subplots_adjust(left = 0.09, bottom=0.20, right=0.94, top= 0.93, wspace = 0.2, hspace = 0)

plt.show()

#lets save the graph

fig.savefig('matplotlib.png', facecolor=fig.get_facecolor())

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Coursera | Data Science | Python

Exploring Statistical Analysis in the Context of Correlation – Testing a Potential Moderator
Bydatapandasadmin January 24, 2016October 23, 2016

Exploring Statistical Analysis in the Context of Correlation Chosen Dataset I will be working with Data from the Gapminder dataset. This happens to be the same dataset I worked with under the Data Management and Visualization course assignments. As elaborated and discussed in under the Data Management and Visualization course assignments, I have chosen to focus on the country,…

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Examining The Structure of A Data Science Project
ByBernard Adabankah August 16, 2018August 27, 2018

It is really ideal that you define the structure of your Data Science project before beginning the project. The Data Science Project can take a couple of structures, however this is a high level guide which can help you structure and remain focused with your Data Science project Structure Credit: JHU @Coursera. Also this…

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Test a Multiple – Multivariate Regression Model
Bydatapandasadmin February 20, 2016October 23, 2016

OVERVIEW My research work deals with Ghana, a country from the Gapminder dataset. What I found in my multiple regression analysis. Discussion of the results for the associations between all of my explanatory variables and my response variable The primary quantitative explanatory variable in my regression analysis is the Income Per Person (incomeperperson) and…

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Python

Python Bokeh plotting Data Exploration Visualization And Pivot Tables Analysis

Bydatapandasadmin October 4, 2016October 23, 2016

This is a quick walk through Bokeh data exploration and visualization and also python pivot_tables (credit to pbpython on the pivot_tables).Visualization Dashboard In [49]:

import sqlalchemy

import pyodbc

from pandas import DataFrame

from bokeh.plotting import figure, output_file, show

import pandas as pd

In [50]:

df = DataFrame

## connect to SqlServer Database and get information.

try:

import urllib

params = urllib.quote_plus("DRIVER={SQL Server Native Client 11.0};SERVER=.\MSSQLSERVER_ENT;DATABASE=Antrak;Trusted_Connection=yes;")

engine = sqlalchemy.create_engine('mssql+pyodbc:///?odbc_connect=%s"' % params)

resoverall = engine.execute("SELECT top 10 * FROM FlightBookingsMain")

df = df(resoverall.fetchall())

df.columns = resoverall.keys()

except (RuntimeError, TypeError, NameError):

print('Error in Conneccting')

print(RuntimeError, TypeError, NameError)

finally:

print("connected")

connected

In [51]:

1 2	#lets look at the head of the dataframe df.head()

Out[51]: First Name Last Name Sex Phone Fax Email Address Booking Date Departure Date Arrival Date Address1 … Number of Passengers Total Cost Currency…

Python

Sending Email Using Python Smtplib Automate Email Sending Alerts
Bydatapandasadmin September 3, 2016October 23, 2016

When you have a script running and you have logs set , you might also want to send yourself or colleagues, email alerts in case things go wrong or simply to let you know that your scripts ran to completion without any problems. In this short notes, we take a look at sending email…

Read More Sending Email Using Python Smtplib Automate Email Sending Alerts

Deploy Productionalise Azure Machine Learning Algorithm Using Python

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Exploring Statistical Analysis in the Context of Correlation – Testing a Potential Moderator

Examining The Structure of A Data Science Project

Test a Multiple – Multivariate Regression Model

Python Bokeh plotting Data Exploration Visualization And Pivot Tables Analysis

Sending Email Using Python Smtplib Automate Email Sending Alerts

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