Python Scripts

Sometimes we might need convert categorical feature into multiple binary features. Such situation emerged while I was implementing decision tree with independent categorical variable using python sklearn.tree for the post Building Decision Trees in Python – Handling Categorical Data and it turned out that a text independent variable is not supported.

One of solution would be binary encoding, also called one-hot-encoding when we might code [‘red’,’green’,’blue’] with 3 columns, one for each category, having 1 when the category match and 0 otherwise. [1]

Here we implement the python code that makes such binary encoding. The script looks at text data column and add numerical columns with values 0 or 1 to the original data. If category word exists in the column then it will be 1 in the column for this category, otherwise 0.

The list of categories is initialized in the beginning of the script. Additionally we initialize data source file, number of column with text data, and number of first empty column on right side. The script will add columns on right side starting from first empty column.

The next step in the script is to navigate through each row and do binary conversion and update data.

Below is some example of added binary columns to data input .

Below is full source code.

# -*- coding: utf-8 -*-

import pandas as pd

words = ["adwords", "adsense","mortgage","money","loan"]
data = pd.read_csv('adwords_data5.csv', sep= ',' , header = 0)


total_rows = len(data.index)


y_text_column_index=7
y_column_index=16





for index, w in enumerate(words):
  data[w] = 0   
  col_index=data.columns.get_loc(w)
  
  for x in range (total_rows):
      if w in data.iloc[x,y_text_column_index] :
           data.iloc[x,y_column_index+index]=1
      else :
           data.iloc[x,y_column_index+index]=0  


print (data)

References
1. strings as features in decision tree/random forest
2. Building Decision Trees in Python
3. Building Decision Trees in Python – Handling Categorical Data

In the post Building Decision Trees in Python we looked at the decision tree with numerical continuous dependent variable. This type of decision trees can be called also regression tree.

But what if we need to use categorical dependent variable? It is still possible to create decision tree and in this post we will look how to create decision tree if dependent variable is categorical data. In this case the decision tree is called classification tree. Classification trees, as the name implies are used to separate the dataset into classes belonging to the response variable. [4] Classification is a typical problem that can be found in such fields as machine learning, predictive analytics, data mining.

Getting Data
For simplicity we will use the same dataset as before but will convert numerical target variable into categorical variable as we want build python code for decision tree with categorical dependent variable.
To convert dependent variable into categorical we use the following simple rule:
if CPC < 22 then CPC = "Low"
else CPC = “High”

For independent variables we will use “keyword” and “number of words” fields.
Keyword (usually it is several words) is a categorical variable. In general it is not the problem as in either case (regression or classification tree), the predictors or independent variables may be categorical or numeric. It is the target variable that determines the type of decision tree needed.[4]

However sklearn.tree (at least the version that is used here) does not support categorical independent variable. See discussion and suggestions on stack overflow [5]. To use independent categorical variable, we will code the categorical feature into multiple binary features. For example, we might code [‘red’,’green’,’blue’] with 3 columns, one for each category, having 1 when the category match and 0 otherwise. This is called one-hot-encoding, binary encoding, one-of-k-encoding or whatever. [5]

Below are shown few rows from the table data. We added 2 columns for categories “insurance”, “Adsense”. We actually have more categories and therefore we added more columns but this is not shown in the table.

For small dataset such conversion can be done manually. But we also created python script in the post Converting Categorical Text Variable into Binary Variables for this specific task. [10]

Keyword	Number of words	Insurance	Adsense	CTR	Cost	Cost (categorical)
car insurance premium	3	1	0	0.012	20	Low
AdSense data	2	0	1	0.025	1061	High

Building the Code
Now we need to build the code. The call for decision tree is looking like this:

clf_gini = DecisionTreeClassifier(criterion = “gini”, random_state = 100,
max_depth=8, min_samples_leaf=4)

we use here criterion Gini index for splitting data.
In the call to export_graphviz we specify class names:

export_graphviz(tree, out_file=f, feature_names=feature_names, filled=True, rounded=True, class_names=[“Low”, “High”] )

The rest of the code is the same as in previous post for regression tree.

Here is the python computer code:

# -*- coding: utf-8 -*-


import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
import subprocess

from sklearn.tree import  export_graphviz


def visualize_tree(tree, feature_names):
    
    with open("dt.dot", 'w') as f:
        
        export_graphviz(tree, out_file=f, feature_names=feature_names,  filled=True, rounded=True, class_names=["Low", "High"] )

    command = ["C:\\Program Files (x86)\\Graphviz2.38\\bin\\dot.exe", "-Tpng", "C:\\Users\\Owner\\Desktop\\A\\Python_2016_A\\dt.dot", "-o", "dt.png"]
    
       
    try:
        subprocess.check_call(command)
    except:
        exit("Could not run dot, ie graphviz, to "
             "produce visualization")
    
data = pd.read_csv('adwords_data.csv', sep= ',' , header = 1)



X = data.values[:, [3,17,18,19,20,21,22]]
Y = data.values[:,8]

                           
X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.3, random_state = 100)                           
                           
clf_gini = DecisionTreeClassifier(criterion = "gini", random_state = 100, 
                               max_depth=8, min_samples_leaf=4)
clf_gini.fit(X_train, y_train)   


visualize_tree(clf_gini, ["Words in Key Phrase", "AdSense",	"Mortgage",	"Money",	"loan", 	"lawyer", 	"attorney"])

References
1. Decision tree Wikipedia
2. MLlib – Decision Trees
3. Visual analysis of AdWords data: a primer
4. 2 main differences between classification and regression trees
5. strings as features in decision tree/random forest
6. Decision Trees with scikit-learn
7. Classification: Basic Concepts, Decision Trees, and Model Evaluation
8. Understanding decision tree output from export_graphviz
9. Building Decision Trees in Python
10. Converting Categorical Text Variable into Binary Variables

A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm.
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning. [1]

Decision trees are widely used since they are easy to interpret, handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions. [2] Decision trees are also one of the most widely used predictive analytics techniques.

Recently I decided to build python code for decision tree for AdWords data. This was motivated by the post [3] about visual analytics used for AdWords dataset. Below are the main components that I used for implementing decision tree.

Dataset
AdWords dataset – the dataset was obtained on Internet. Below is the table with few rows to show data. Only the columns that were used for decision tree, are shown in the table.

Keyword	Number of words	CPC	Clicks	CTR	Cost	Impressions
car insurance premium	3	176	7	0.012	1399	484
AdSense data	2	119	13	0.025	1061	466

The following independent variables were selected:
Number of words in keyword phrase – this column was added based on the keyword phrase column.
CTR – click through rate

For the dependent variable it was selected CPC – Average Cost per Click.

Python Module
As the dependent variable is numeric and continuos, the regression decision tree from python module sklearn.tree was used in the script:
from sklearn.tree import DecisionTreeRegressor

In sklearn.tree Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. [5]

Visualization
For visualization of decision tree graphviz was installed. “Graphviz is open source graph visualization software. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks. It has important applications in networking, bioinformatics, software engineering, database and web design, machine learning, and in visual interfaces for other technical domains.”

Python Script
The created code consisted of the following steps:
reading data from csv data file
selecting needed columns
splitting dataset for testing and training
initializing DecisionTreeRegressor
visualizing decision tree via function. Note that the path to Graphviz are specified inside of scripts.

Decision tree and Python code are shown below. Online resources used for this post are provided in the reference section.

Python computer code:

# -*- coding: utf-8 -*-

import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeRegressor


import subprocess

from sklearn.tree import  export_graphviz


def visualize_tree(tree, feature_names):
    
    with open("dt.dot", 'w') as f:
        
        export_graphviz(tree, out_file=f, feature_names=feature_names,  filled=True, rounded=True )

    command = ["C:\\Program Files (x86)\\Graphviz2.38\\bin\\dot.exe", "-Tpng", "C:\\Users\\Owner\\Desktop\\A\\Python_2016_A\\dt.dot", "-o", "dt.png"]
    
        
    try:
        subprocess.check_call(command)
    except:
        exit("Could not run dot, ie graphviz, to "
             "produce visualization")
    
data = pd.read_csv('adwords_data.csv', sep= ',' , header = 1)


X = data.values[:, [3,13]]
Y = data.values[:,11]

                       
X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.3, random_state = 100)                           
                           
clf = DecisionTreeRegressor( random_state = 100,
                               max_depth=3, min_samples_leaf=4)
clf.fit(X_train, y_train)   


visualize_tree(clf, ["Words in Key Phrase", "CTR"])

References
1. Decision tree Wikipedia
2. MLlib – Decision Trees
3. Visual analysis of AdWords data: a primer
4. Decision trees in python with scikit_learn and pandas
5. Decision Tree
6. Graphviz – Graph Visualization Software

Do we need to normalize input data for neural network? How differently will be results from running normalized and non normalized data? This will be explored in the post using Online Machine Learning Algorithms tool for classification of iris data set with feed-forward neural network.

Feed-forward Neural Network
Feed-forward neural networks are commonly used for classification. In this example we choose feed-forward neural network with back propagation training and gradient descent optimization method.

Our neural network has one hidden layer. The number of neuron units in this hidden layer and learning rate can be set by user. We do not need to code neural network because we use online tool that is taking training, testing data, number of neurons in hidden layer and learning rate parameters as input.

The output from this tool is classification results for testing data set, neural network weights and deltas at different iterations.

Data Set
As mentioned above we use iris data set for data input to neural network. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. [1] We remove first 3 rows for each class and save for testing data set. Thus the input data set has 141 rows and testing data set has 9 rows.

Normalization
We run neural network first time without normalization of input data. Then we run neural network with normalized data.
We use min max normalization which can be described by formula [2]:
xij = (xij – colmmin) /(colmmax – colmmin)

Where
colmmin = minimum value of the column
colmmax = maximum value of the column
xij = x(data item) present at ithrow and jthcolumn

Normalization is applied to both X and Y, so the possible values for Y become 0, 05, 1
and X become like :
0.083333333 0.458333333 0.084745763 0.041666667

You can view normalized data at this link : Iris Data Set – Normalized Data

Online Tool
Once data is ready we can to load data to neural network and run classification. Here are the steps how to use online Machine Learning Algorithms tool:
1.Access the link Online Machine Learning Algorithms with feed-forward neural network.
2.Select algorithm and click Load parameters for this model.
3.Input the data that you want to run.
4.Click Run now.
5.Click results link
6.Click Refresh button on this new page, you maybe will need click few times untill you see data output.
7.Scroll to the bottom page to see calculations.
For more detailed instruction use this link: How to Run Online Machine Learning Algorithms Tool

Experiments
We do 4 experiments as shown in the table below: 1 without normalization, and 3 with normalization and various learning rate and number of neurons hidden. Results are shown in the same table and the error graph shown below under the table. When iris data set was inputted without normalization, the text label is changed to numerical variable with values -1,0,1 The result is not satisfying with needed accuracy level. So decision is made to normalize data using min max method.

Here is the sample result from the last experiment, with the lowest error. The learning rate is 0.1 and the number of hidden neurons is 36. The delta error in the end of training is 1.09.

[[ 0.00416852]
[ 0.01650389]
[ 0.01021347]
[ 0.43996485]
[ 0.50235484]
[ 0.58338683]
[ 0.80222148]
[ 0.92640374]
[ 0.93291573]]

Results

Norma lization	Learn. Rate	Hidden units #	Correct	Total	Accuracy	Delta Error
No	0.5	4	3	9	33%	9.7
Yes (Min Max)	0.5	4	8	9	89%	1.25
Yes (Min Max)	0.1	4	9	9	100%	1.19
Yes (Min Max)	0.1	36	9	9	100%	1.09

Conclusion
Normalization can make huge difference on results. Further improvements are posible by adjusting learning rate or number hidden units in the hidden layer.

References

1. Iris Data Set
2. AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL NETWORK