Superset and Jupyter notebooks on AWS as Service

Jupyter Notebook (In EC2 Instance):

The following script is written to run jupyter notebook as a server inside the AWS EC2 instance which you can access from your desktop/laptop if EC2 instance is accessible from your machine:

  • $ conda activate python37
  • $ jupyter notebook –generate-config
    • This will create the jupyter_notebook_config.py configuration file inside your working folder i.e. /home/<username>/.jupyter/
  • $ jupyter notebook password  
    • You can set the password here
  • $ vi /home/centos/.jupyter/jupyter_notebook_config.py
    • Edit the following 2 lines 
    •   c.NotebookApp.ip = ‘0.0.0.0’
    •   c.NotebookApp.port = 8888
  • $ jupyter notebook

Apache Superset (In EC2 Instance):

That’s all.

@avkashchauhan

Installing Apache Superset into CentOS 7 with Python 3.7

Following are the starter commands to install superset:

  • $ python –version
    • Python 3.7.5
  • $ pip install superset

Possible Errors:

You might be hitting any or all of the following error(s):

Running setup.py install for python-geohash … error
ERROR: Command errored out with exit status 1:

building ‘_geohash’ extension
……
unable to execute ‘gcc’: No such file or directory
error: command ‘gcc’ failed with exit status 1

gcc: error trying to exec ‘cc1plus’: execvp: No such file or directory
error: command ‘gcc’ failed with exit status 1

Look for:

  • $ gcc –version <= You must have gcc installed
  • $ locate cc1plus <= You must have cc1plus install

Install the required libraries and tools:

If any of the above components are missing, you need to install a few required libraries:

  • $ sudo yum install mlocate <= For locate command
  • $ sudo updatedb  <= Update for mlocate
  • $ sudo yum install gcc <=For gcc if you don’t have
  • $ sudo yum install gcc-c++  <== For cc1plus if you dont have

Verify the following again:

  • $ gcc –version
  • $ locate cc1plus
    • /usr/libexec/gcc/x86_64-redhat-linux/4.8.2/cc1plus

Note:

  • If you could locate cc1plus properly however still getting the error, try the following
    • sudo ln -s /usr/libexec/gcc/x86_64-redhat-linux/4.8.2/cc1plus /usr/local/bin/
  • Try installing again

Final Installation:

Now you can install  superset as below:

  • $ pip install superset
    • Python 3.7.5
      Flask 1.1.1
      Werkzeug 0.16.0
  • $ superset db upgrade
  • $ export FLASK_APP=superset
  • $ flask fab create-admin
    • Recognized Database Authentications.
      Admin User admin created.
  • $ superset init
  • $ superset run -p 8080 –with-threads –reload –debugger

 

That’s all.

@avkashchauhan

Conda Python 3.5 and OpenCV 3 with Matplotlib and QT5 backend

As title suggests lets get to work:

Create the Conda Environment with Python 3.5

$ conda create -n python35 python=35
$ conda activate python35

Inside the conda environment we need to install pyqt5, pyside, pyobj-core, pyobjc-framework-cocoa packages:

Installing QT5 required packages inside Conda:

$ conda install -c dsdale24 pyqt5
$ conda install -c conda-forge pyside
## Note: I couldn;t find these with conda on conda-forge so used pip
$ pip install pyobjc-core
$ pip install pyobjc-framework-cocoa

Verifying Python 3.5:

$ python

Python 3.5.4 |Anaconda, Inc.| (default, Feb 19 2018, 11:51:41)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>

Checking backend used by matplotlib:

import matplotlib
matplotlib.get_backend()

If you see ‘MacOSX‘ means it is using MacOSX backend and we need to change it to qt as below:

Changing matplotlib backend to use QT5:

matplotlib.use('qt5agg')
matplotlib.get_backend()

This will result as qt5agg backend to be used with CV2.

Sample code to show image using OpenCV3

Trying a sample OpenCV3 code to show image:

import cv2
image = cv2.imread("/work/src/github/aiprojects/avkash_cv/matrix.png")
import matplotlib.pyplot as plt
plt.figure()
plt.imshow(image)
plt.show()

This is how image rendered with QT5 backend:

matrix

That’s it, enjoy!!

@avkashchauhan

 

 

 

 

 

 

Calculating AUC and GINI model metrics for logistic classification

For logistics classification problem we use AUC metrics to check the model performance. The higher is better however any value above 80% is considered good and over 90% means the model is behaving great.

AUC is an abbreviation for Area Under the Curve. It is used in classification analysis in order to determine which of the used models predicts the classes best. An example of its application are ROC curves. Here, the true positive rates are plotted against false positive rates. You can learn more about AUC in this QUORA discussion.

We will also look for GINI metric which you can learn from wiki.  In this example we will learn how AUC and GINI model metric is calculated using True Positive Results (TPR) and False Positive Results (FPR) values from a given test dataset.

You can get the full working Jupyter Notebook here from my Github.

Lets build a logistic classification model in H2O using the prostate data set:

Preparation of H2O environment and dataset:

## Importing required libraries
import h2o
import sys
import pandas as pd
from h2o.estimators.gbm import H2OGradientBoostingEstimator

## Starting H2O machine learning cluster
h2o.init()

## Importing dataset
local_url = "https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate/prostate.csv"
df = h2o.import_file(local_url)

## defining feaures and response column
y = "CAPSULE"
feature_names = df.col_names
feature_names.remove(y)

## setting our response column to catagorical so our model classify the problem
df[y] = df[y].asfactor()

Now we will be splitting the dataset into 3 sets for training, validation and test:

df_train, df_valid, df_test = df.split_frame(ratios=[0.8,0.1])
print(df_train.shape)
print(df_valid.shape)
print(df_test.shape)

Setting  H2O GBM Estimator and building GBM Model:

prostate_gbm = H2OGradientBoostingEstimator(model_id = "prostate_gbm",
 ntrees=500,
 learn_rate=0.001,
 max_depth=10,
 score_each_iteration=True)

## Building H2O GBM Model:
prostate_gbm.train(x = feature_names, y = y, training_frame=df_train, validation_frame=df_valid)

## Understand the H2O GBM Model
prostate_gbm

Generating model performance with training, validation & test datasets:

train_performance = prostate_gbm.model_performance(df_train)
valid_performance = prostate_gbm.model_performance(df_valid)
test_performance = prostate_gbm.model_performance(df_test)

Let’s take a look at the AUC metrics provided by Model performance:

print(train_performance.auc())
print(valid_performance.auc())
print(test_performance.auc())
print(prostate_gbm.auc())

Let’s take a look at the GINI metrics provided by Model performance:

print(train_performance.gini())
print(valid_performance.gini())
print(test_performance.gini())
print(prostate_gbm.gini())

Let generate the predictions using test dataset:

predictions = prostate_gbm.predict(df_test)
## Here we will get the probability for the 'p1' values from the prediction frame:
predict_probability = predictions['p1']

Now we will import required scikit-learn libraries to generate AUC manually:

from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import random

Lets get the real response results from the test data frame:

actual = df_test[y].as_data_frame()
actual_list = actual['CAPSULE'].tolist()
print(actual_list)

Now lets get the results probabilities from the prediction frame:

predictions_temp = predict_probability_x['p1'].as_data_frame()
predictions_list = predictions_temp['p1'].tolist()
print(predictions_list)

Calculating False Positive Rate and True Positive Rate:

Lets calculate TPR, FPR and Threshold metrics from the predictions and original data frame
– False Positive Rate (fpr)
– True Positive Rate (tpr)
– Threashold

fpr, tpr, thresholds = roc_curve(actual_list, predictions_list)
roc_auc = auc(fpr, tpr)
print(roc_auc)
print(test_performance.auc())

Note: Above you will see that our calculated ROC values is exactly same as given by model performance for test dataset. 

Lets plot the AUC Curve using matplotlib:

plt.title('ROC (Receiver Operating Characteristic)')
plt.plot(fpr, tpr, 'b',
label='AUC = %0.4f'% roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.2])
plt.ylim([-0.1,1.2])
plt.ylabel('True Positive Rate (TPR)')
plt.xlabel('False Positive Rate (FPR)')
plt.show()

Screen Shot 2017-10-19 at 10.30.21 PM

This is how GINI metric is calculated from AUC:

GINI = (2 * roc_auc) - 1
print(GINI)
print(test_performance.gini())

Note: Above you will see that our calculated GINI values is exactly same as given by model performance for test dataset.

Thats it, enjoy!!


					

How R2 error is calculated in Generalized Linear Model

What is R2 (R^2 i.e. R-Squared)?

R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. … 100% indicates that the model explains all the variability of the response data around its mean. (From here)

You can get the full working jupyter notebook for this article from here directly from my Github.

Even when this article explains how R^2 error is calculated for an H2O GLM (Generalized Linear Model) however same math is use for any other statistical model. So you can use this function anywhere you would want to apply.

Lets build an H2O GLM Model first:

import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator

h2o.init()

local_url = "https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate.csv"
df = h2o.import_file(local_url)

y = "CAPSULE"
feature_names = df.col_names
feature_names.remove(y)

df_train, df_valid, df_test = df.split_frame(ratios=[0.8,0.1])
print(df_train.shape)
print(df_valid.shape)
print(df_test.shape)

prostate_glm = H2OGeneralizedLinearEstimator(model_id = "prostate_glm")

prostate_glm.train(x = feature_names, y = y, training_frame=df_train, validation_frame=df_valid)
prostate_glm

Now calculate Model Performance based on training, validation and test data:

train_performance = prostate_glm.model_performance(df_train)
valid_performance = prostate_glm.model_performance(df_valid)
test_performance = prostate_glm.model_performance(df_test)

Now lets check the default R^2 metrics for training, validation and test data:

print(train_performance.r2())
print(valid_performance.r2())
print(test_performance.r2())
print(prostate_glm.r2())

Now lets get the prediction for the test data which we kept separate:

predictions = prostate_glm.predict(df_test)

Here is the math which is use to calculate the R2 metric for the test data set:

SSE = ((predictions-df_test[y])**2).sum()
y_hat = df_test[y].mean()
SST = ((df_test[y]-y_hat[0])**2).sum()
1-SSE/SST

Now lets get model performance for given test data as below:

print(test_performance.r2())

Above we can see that both values, one give by model performance for test data and the other we calculated are same.

Thats it, enjoy!!

 

 

 

 

Saving H2O model object as text locally

Sometimes you may want to store the H2O model object as text to local file system. In this example I will show you how you can save H2O model object to local disk as simple text content. You can get full working jupyter notebook for this example here from my Github.

Based on my experience the following example works fine with python 2.7.12 and python 3.4. I also found that the H2O model object tables were not saved to text file from jupyter notebook however when I ran the same code form command line into python shell, all the content was written perfectly.

Lets build an H2O GBM model using the public PROSTATE dataset (The following script is full working script which will generate the GBM binomial model):

import h2o
h2o.init()

local_url = "https://raw.githubusercontent.com/h2oai/sparkling-water/master/examples/smalldata/prostate.csv"
df = h2o.import_file(local_url)

y = "CAPSULE"
feature_names = df.col_names
feature_names.remove(y) 
df[y] = df[y].asfactor()

df_train, df_valid = df.split_frame(ratios=[0.9])
print(df_train.shape)
print(df_valid.shape)

prostate_gbm = H2OGradientBoostingEstimator(model_id = "prostate_gbm",
 ntrees=1000,
 learn_rate=0.5,
 max_depth=20,
 stopping_tolerance=0.001,
 stopping_rounds=2,
 score_each_iteration=True)

prostate_gbm.train(x = feature_names, y = y, training_frame=df_train, validation_frame=df_valid)
prostate_gbm

Now we will save the model details to the disk as below:

old_target = sys.stdout
f = open('/Users/avkashchauhan/Downloads/model_output.txt', 'w')
sys.stdout = f

Lets see the content of the local file we have just created in the above step (It is empty):

!cat /Users/avkashchauhan/Downloads/model_output.txt

Now we will launch the following commands which will fill the standard output buffer with the model details as text:

print("Model summary>>> model_object.show()")
prostate_gbm.show()

Now we will push the standard output buffer to the text file which is created locally:

sys.stdout = old_target

Now we will check back the local file contents and this time you will see that the output of above command is written into the file:

!cat /Users/avkashchauhan/Downloads/model_output.txt

You will see the command output stored into the local text file as below:

Model summary>>> model_object.show()
Model Details
=============
H2OGradientBoostingEstimator :  Gradient Boosting Machine
Model Key:  prostate_gbm


ModelMetricsBinomial: gbm
** Reported on train data. **

MSE: 0.036289343297
RMSE: 0.190497620187
LogLoss: 0.170007804527
Mean Per-Class Error: 0.0160045361428
AUC: 0.998865964296
Gini: 0.997731928592
Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.487417363665: 
Maximum Metrics: Maximum metrics at their respective thresholds

Gains/Lift Table: Avg response rate: 40.36 %



ModelMetricsBinomial: gbm
** Reported on validation data. **

MSE: 0.161786079676
RMSE: 0.402226403505
LogLoss: 0.483923658542
Mean Per-Class Error: 0.174208144796
AUC: 0.871040723982
Gini: 0.742081447964
Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.205076283533: 
Maximum Metrics: Maximum metrics at their respective thresholds

Gains/Lift Table: Avg response rate: 39.53 %


Scoring History: 
Variable Importances:

Note: If you are thinking what “!” sign does here, so it is used here to run a linux shell command (in this case “cat”  is the linux command) inside jupyter cell.

Thats it, enjoy!!

 

Launching H2O cluster on different port in pysparkling

In this example we will launch H2O machine learning cluster using pysparkling package. You can visit my github and this article to learn more about the code execution explained in this article.

For you would need to  install pysparkling in python 2.7 setup as below:

> pip install -U h2o_pysparkling_2.1

Now we can launch the pysparkling Shell as below:

SPARK_HOME=/Users/avkashchauhan/tools/spark-2.1.0-bin-hadoop2.6

Launch pysparkling shell:

~/tools/sw2/sparkling-water-2.1.14 $ bin/pysparkling

Python Code Script Launch the H2O cluster in pysparkling:

## Importing Libraries
from pysparkling import *
import h2o

## Setting H2O Conf Object
h2oConf = H2OConf(sc)
h2oConf

## Setting H2O Conf for different port
h2oConf.set_client_port_base(54300)
h2oConf.set_node_base_port(54300)

## Gett H2O Conf Object to see the configuration
h2oConf

## Launching H2O Cluster
hc = H2OContext.getOrCreate(spark, h2oConf)

## Getting H2O Cluster status
h2o.cluster_status()

Now If you verify the Sparkling Water configuration you will see that the H2O is running on the given IP and port 54300 as configured:

Sparkling Water configuration:
  backend cluster mode : internal
  workers              : None
  cloudName            : Not set yet, it will be set automatically before starting H2OContext.
  flatfile             : true
  clientBasePort       : 54300
  nodeBasePort         : 54300
  cloudTimeout         : 60000
  h2oNodeLog           : INFO
  h2oClientLog         : WARN
  nthreads             : -1
  drddMulFactor        : 10

Thats it, enjoy!!