Handling missing values in a Dataset before training

How to impute missing values in a dataset before feeding to a classifier is often a difficult decision. Imputing with a wrong value can significantly skew the data and result in wrong classifier. The ideal solution is to get a clean data set without any NULL values but then, we might have to throw out most data. There are no perfect workarounds as most classifiers are built based on the information from data and lack thereof results in the wrong classifier. Continue reading “Handling missing values in a Dataset before training”

Extracting top feature names for a trained classifier in order

Post describes how to extract top feature names from a supervised learning classifier in sklearn.

Note: The training dataset X_train and y_train are pandas dataframe with column names.

After fitting/training a classifier clf, the scoring for features can be accessed (method varies depending on the classifier used).

  • For example, for logistic regression it is the magnitude of the coefficients and can be accessed as clf.coef_
  • For DecisionTree, it is clf.feature_importances_

Sort the scores in descending order using np.argsort() and pass it as an index to the column names of X_train.columns.

# For Decision Tree classifier

from sklearn.tree import DecisionTreeClassifier
import numpy as np

clf = DecisionTreeClassifier(random_state=42)
clf.fit(X_train, y_train)

importances = clf.feature_importances_

# printing top 5 features of fitted classifier
print (X_train.columns[(np.argsort(importances)[::-1])][:5])

Horizontal bar chart with 3 encodings using matplotlib


The chart explains the gender difference in school performance based on different states of india. Full project report

View python source code inside packages


Often we want to know how a function is written in an imported package. This post explains how to examine the source code of a function/class.

To know where the package is installed:


For the package pandas:

import pandas

To examine the source code of a given function or class, import the package inspect.

import inspect as insp
print insp.getsourcefile(pandas.DataFrame) # prints the path to source file

print insp.getsourcelines(pandas.DataFrame) # prints the source code

A documentation of inspect package can be found here.

Viewing the source code from IPython Notebook

Append ? to the function name inside the ipython-notebook cell to view source code and ?? for the entire source code.

import pandas

pandas.DataFrame? # shows the docstring</code>​

pandas.DataFrame?? # shows the source code and docstring

Changing the title of github pages


If you are a frequent user of github, you might have come across github pages, a service  to publish websites. Github pages are often helpful to explain/showcase your small projects with a neat webpage for each repository.

  • All you need is to include a markdown in your github repository by name INDEX.md and github pages will generate a webpage from it. There are many options as mentioned in documentation.
  • 3 easy steps to setup a webpage for a project.
    • If you are using ipython notebook, download your notebook as a markdown file
    • place “markdown file+resources” in your github repository inside docs folder (create one if doesn’t exist).
    • Rename the markdown as docs/INDEX.md to make it the default loader. Your project website is ready at the link “[usename].github.io/[projectname]”
    • By default project webpage shows repository name as the title of webpage. This can be edited if you create/edit the _config.yml file and place this content inside as key:value pairs. Make sure the value is passed as a string in quotes.
      theme: jekyll-theme-cayman
      title: "Title of the project goes here"
      description: "Subtitle/description which comes after title goes here"
      show_downloads: true # displays download button on the .io page

You can even use this to publish a Web-resume (get a resume template from w3schools).