How to get befenit from Python (IDP) acceleration?

Hello,

My python script is not being accelerated at all.

I have installed Intel Distribution for Python (IDP) using l_python2_pu3_2017.3.053 obtained at Intel® Distribution for Python* . I ran "./install_gui.sh" and I got no error messages.  Then I have loaded Intel environment by typing "source /opt/intel/intelpyhon3/bin/activate". I call "python test.py" and the script runs with Intel Python. My problem is that I cannot see any improvement when using IDP, compared to standard python from Ubuntu. There must be something wrong with my environment.

This is the information about my system:

Hardware:  Intel® Core™ i7 4500U@1.8 GHz processor, 64 bits, 8GB RAM

Software:  Ubuntu 16.04 LTS operating system, Intel® distribution for Python* 2.7.13, standard Python* 2.7.12, Scikit-learn 0.19.0

My code is simple and uses scikit-learn library. Since scikit-learn used scipy and numpy that are supported by Intel, I had supposed its performance would be improved too. But it is not what happened. My code is simple:

#########################

import numpy as np

import pandas as pd

from sklearn.cross_validation import train_test_split

from sklearn import svm

import time

import sys

input_file = "drill39mil.csv"

# Read data

mydata = pd.read_csv(input_file, header = 0, delimiter = ",")

# Break data into train and test dataset

train_mydata, test_mydata = train_test_split(mydata, test_size = 0.2)

# Provided your csv has header row, and the label column is named "classe"

train_data_target = train_mydata["classe"]

test_data_target = test_mydata["classe"]

# Select all but last column as data, which is the classification class

train_data = train_mydata.iloc[:,:-1]

test_data = test_mydata.iloc[:,:-1]

start = time.time()

#######       Classifier

clf = svm.SVC()

#Perform training

clf.fit(train_data, train_data_target)

# Make class predictions for all observations

Z = clf.predict(test_data)

# Compare predicted class labels with actual class labels

accuracy=clf.score(test_data,test_data_target)

print ("Predicted model accuracy: "+ str(accuracy))

end = time.time()

print("Time (s):" + str(end - start))

print (sys.version)

#######################

The outputs are when running Intel Python are:

Predicted model accuracy: 0.543351131452

Time (s):628.276842833

2.7.13 |Intel Corporation| (default, Apr 27 2017, 15:33:46)

[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]

And when running standard Python from Ubuntu are:

Predicted model accuracy: 0.550597508263

Time (s):589.650998831

2.7.12 (default, Nov 19 2016, 06:48:10)

[GCC 5.4.0 20160609]

As you see, Intel Python is slower that standard Python. Can anyone give me a tip of what is going wrong?

Thanks in advance,

Flávio Mello