add examples of small plots in the main readme file
add more examples in the example directory with readme file cointaining some short eamples

methods:
add_columns_one_to_many(self,df_master,df_details,function,master_columns_lst,details_columns_lst)
for row in df_master[master_columns_lst,:]:
df_details[details_columns_lst] where joined with row
add columns to master_columns_lst function(row,)

 returns df_master with_new_columns

from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score

def plot_confusion_matrix(self,
cm, classes,
normalize=True,
title='Confusion matrix',
cmap=plt.cm.Blues):

This method should prints and plots the confusion matrix.
Normalization can be applied by setting normalize=True.

class LinearModelUtils
def get_formula
def rmse
def rmsle
def mae
def model_info_ record
return ModelInfoRecord

class ModelInfo(list)

class ModelInfoRecord
def init

• self
• model_name
• col_list
• actual_lbl
• formula
  -prediction_column
• clf
  class LinearModelInfoRecord(ModelInfoRecord)
  def init
• self
  -rmse
  -rmsle
  -mae
  -formula
  class ModelInfoReport

Don't test visualization, test different datasets using None and np.NaN
Test for the returned object and their attributes.
Check that the Utils does not throw exceptions

• split_to_columns(column,split_func,splited_columns_name) : returns tuple of colomns

• drop_columns(df,columns_list) returns df without the columns_list

• apply_by_dict(df, **kward)
  for key, value in kwargs.iteritems():
  if key not in list(df):
  print warn not in columns list
  continue
  if value is function:
  value=[value]
  if value int iterable:
  print warn
  for f in value
  df[key]=df[key].apply(f)
  return df

def init(self,df,targert_name)
def plot_releshenships()
"""
smart plot the relashenships between target and ther colums
"""

aget fig and matrix of axess and return fig

linear regression:
better_dummies:
splitting values by ranges or function
for example:
(
column: hour,
{
rush_hours : [7,8,9, ],
back_hour : [15,16,17]
others: [1:6. 10:14, 18:24]
}
)

will split the "hour" column in our df into 3 columns : rush_hours ,back_hour,others

from sklearn import datasets
from MachineLearningUtils.UsefulPlots import VisPlotPlayGround
from matplotlib import cm
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np

def main():

iris = datasets.load_iris()
_df=data1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                 columns= iris['feature_names'] + ['target'])
print (_df.info())
# exit()
plotter=VisPlotPlayGround(df=_df, ggplot=True, cmap=cm.jet)
cmaps = [m for m in cm.datad if not m.endswith("_r")]
print(sorted(cmaps))
for c in sorted(cmaps):
    plotter.show_colormap(c)
plt.show()

if name == 'main':
main()

http://scikit-learn.org/stable/modules/learning_curve.html