visualize_ML
visualize_ML is a python package made to visualize some of the steps involved while dealing with a Machine Learning problem. It is build on libraries like matplotlib for visualization and sklean,scipy for statistical computations.
Table of content:
Requirement
 python 2.x or python 3.x
Install
Install dependencies needed for matplotlib
sudo aptget builddep pythonmatplotlib
Install it using pip
pip install visualize_ML
Let's Code
While dealing with a Machine Learning problem some of the initial steps involved are data exploration,analysis followed by feature selection.Below are the modules for these tasks.
1) Data Exploration
At this stage, we explore variables one by one using Univariate Analysis which depends on whether the variable type is categorical or continuous .To deal with this we have the explore module.
>>> explore module
visualize_ML.explore.plot(data_input,categorical_name=[],drop=[],PLOT_COLUMNS_SIZE=4,bin_size=20,
bar_width=0.2,wspace=0.5,hspace=0.8)
Continuous Variables : In case of continous variables it plots the Histogram for every variable and gives descriptive statistics for them.
Categorical Variables : In case on categorical variables with 2 or more classes it plots the Bar chart for every variable and gives descriptive statistics for them.
Parameters  Type  Description 

data_input  Dataframe  This is the input Dataframe with all data.(Right now the input can be only be a dataframe input.) 
categorical_name  list (default=[ ])  Names of all categorical variable columns with more than 2 classes, to distinguish them with the continuous variablesEmply list implies that there are no categorical features with more than 2 classes. 
drop  list default=[ ]  Names of columns to be dropped. 
PLOT_COLUMNS_SIZE  int (default=4)  Number of plots to display vertically in the display window.The row size is adjusted accordingly. 
bin_size  int (default="auto")  Number of bins for the histogram displayed in the categorical vs categorical category. 
wspace  float32 (default = 0.5)  Horizontal padding between subplot on the display window. 
hspace  float32 (default = 0.8)  Vertical padding between subplot on the display window. 
Code Snippet
/* The data set is taken from famous Titanic data(Kaggle)*/
import pandas as pd
from visualize_ML import explore
df = pd.read_csv("dataset/train.csv")
explore.plot(df,["Survived","Pclass","Sex","SibSp","Ticket","Embarked"],drop=["PassengerId","Name"])
see the dataset
Note: While plotting all the rows with NaN values and columns with Character values are removed(except if values are True and False ),only numeric data is plotted.
2) Feature Selection
This is one of the challenging task to deal with for a ML task.Here we have to do Bivariate Analysis to find out the relationship between two variables. Here, we look for association and disassociation between variables at a predefined significance level.
relation module helps in visualizing the analysis done on various combination of variables and see relation between them.
>>> relation module
visualize_ML.relation.plot(data_input,target_name="",categorical_name=[],drop=[],bin_size=10)
Continuous vs Continuous variables: To do the Bivariate analysis scatter plots are made as their pattern indicates the relationship between variables. To indicates the strength of relationship amongst them we use Correlation between them.
The graph displays the correlation coefficient along with other information.
Correlation = Covariance(X,Y) / SQRT( Var(X)*Var(Y))
 1: perfect negative linear correlation
 +1:perfect positive linear correlation and
 0: No correlation
Categorical vs Categorical variables: Stacked Column Charts are made to visualize the relation.Chi square test is used to derive the statistical significance of relationship between the variables. It returns probability for the computed chisquare distribution with the degree of freedom. For more information on Chi Test see this
Probability of 0: It indicates that both categorical variable are dependent
Probability of 1: It shows that both variables are independent.
The graph displays the p_value along with other information. If it is leass than 0.05 it states that the variables are dependent.
Categorical vs Continuous variables: To explore the relation between categorical and continuous variables,box plots re drawn at each level of categorical variables. If levels are small in number, it will not show the statistical significance. ANOVA test is used to derive the statistical significance of relationship between the variables.
The graph displays the p_value along with other information. If it is leass than 0.05 it states that the variables are dependent.
For more information on ANOVA test see this
Parameters  Type  Description 

data_input  Dataframe  This is the input Dataframe with all data.(Right now the input can be only be a dataframe input.) 
target_name  String  The name of the target column. 
categorical_name  list (default=[ ])  Names of all categorical variable columns with more than 2 classes, to distinguish them with the continuous variablesEmply list implies that there are no categorical features with more than 2 classes. 
drop  list default=[ ]  Names of columns to be dropped. 
PLOT_COLUMNS_SIZE  int (default=4)  Number of plots to display vertically in the display window.The row size is adjusted accordingly. 
bin_size  int (default="auto")  Number of bins for the histogram displayed in the categorical vs categorical category. 
wspace  float32 (default = 0.5)  Horizontal padding between subplot on the display window. 
hspace  float32 (default = 0.8)  Vertical padding between subplot on the display window. 
Code Snippet
/* The data set is taken from famous Titanic data(Kaggle)*/
import pandas as pd
from visualize_ML import relation
df = pd.read_csv("dataset/train.csv")
relation.plot(df,"Survived",["Survived","Pclass","Sex","SibSp","Ticket","Embarked"],drop=["PassengerId","Name"],bin_size=10)
see the dataset
Note: While plotting all the rows with NaN values and columns with Non numeric values are removed only numeric data is plotted.Only categorical taget variable with string values are allowed.
Contribute
If you want to contribute and add new feature feel free to send Pull request here
This project is still under development so to report any bugs or request new features, head over to the Issues page
Tasks To Do

Make input compatible with other formats like Numpy.

Visualize best fit lines and decision boundaries for various models to make Parameter Tuning task easy.
and many others!
Licence
Licensed under The MIT License (MIT).
Copyright
ayush1997(c) 2016