notebook.community

Edit and run



In [258]:

    
# rattle package in R has weather dataset
#(see help at http://artax.karlin.mff.cuni.cz/r-help/library/rattle/html/weather.html)



In [259]:

    
import os as os



In [260]:

    
import pandas as pd



In [261]:

    
os.getcwd()









    Out[261]:





'/home/ajayohri'



In [262]:

    
os.listdir()









    Out[262]:





['.hplip',
 '.xsession-errors.old',
 'VirtualBox VMs',
 'filename.pkl_04.npy',
 '.thunderbird',
 'SVM.R',
 'R',
 'Desktop',
 'filename.pkl_07.npy',
 '.cache',
 '.webex',
 'file.R',
 '.ipython',
 'unique_ids_for_list.html',
 'filename.pkl_11.npy',
 '.Xauthority',
 'Dropbox',
 'examples.desktop',
 'machine learning-plot and bagged pima indians.ipynb',
 'date time.ipynb',
 'Untitled.ipynb',
 '.rstudio-desktop',
 'filename.pkl_01.npy',
 'anaconda3',
 '.dropbox',
 'Music',
 '.pki',
 'rsconnect',
 'GoodReads.ipynb',
 '.config',
 'diamsum.html',
 'filename.pkl_06.npy',
 'data inspection .ipynb',
 '.sudo_as_admin_successful',
 '.continuum',
 '.java',
 'unique ids for list.R',
 '.bashrc-anaconda3.bak',
 '.texmf-var',
 'numpy scipy pandas.ipynb',
 'mozilla.pdf',
 '.dropbox-dist',
 '.bash_logout',
 '.jupyter',
 '.ecryptfs',
 '.dbus',
 '.local',
 '.lyx',
 '.xsession-errors',
 'hebrew',
 'RCommanderMarkdown.Rmd',
 '.bash_history',
 'SAS',
 'nbr2mp4.sh',
 '.adobe',
 '.Skype',
 'filename.pkl_05.npy',
 '.wajig',
 'ajay ohri.odt',
 '.macromedia',
 '.gphoto',
 '.oracle_jre_usage',
 'machine learning-rattle dataset from R.ipynb',
 '.profile',
 'file operations.ipynb',
 'Documents',
 'filename.pkl_09.npy',
 'Videos',
 'RCommander.R',
 'filename.pkl_08.npy',
 '.gstreamer-0.10',
 'SVM.html',
 '.Private',
 'RCommander.txt',
 're for searching strings.ipynb',
 '.Rhistory',
 'filename.pkl_02.npy',
 'RcmdrMarkdown.Rmd',
 'Scikit Tutorial',
 'machine learning.ipynb',
 '.ivy2',
 'assignment2.R',
 'assignment2.html',
 'filename.pkl_03.npy',
 'Public',
 'nbr2mp4.tar',
 'RcmdrMarkdown.md',
 '.bashrc',
 '.mozilla',
 'Pictures',
 'Data Viz Tutorial.ipynb',
 'filename.pkl_10.npy',
 '.RData',
 '.gconf',
 'data transformations.ipynb',
 'RcmdrMarkdown.html',
 'file.html',
 'Scikit Tutorial.ipynb',
 'Strings, Lists and Maps.ipynb',
 'filename.pkl',
 'weather.csv',
 'Downloads',
 '.gnupg',
 '.nano',
 'variables in strings.ipynb',
 'Templates',
 '.ICEauthority',
 '.ipynb_checkpoints']



In [263]:

    
#Finding only csv files in a directory using os and glob packages
import glob

path = os.getcwd()
extension = 'csv'
os.chdir(path)
result = [i for i in glob.glob('*.{}'.format(extension))]
print(result)









    



['weather.csv']



In [264]:

    
dataframe=pd.read_csv("weather.csv")



In [265]:

    
dataframe.head()









    Out[265]:






  
    
      
      Unnamed: 0
      Date
      Location
      MinTemp
      MaxTemp
      Rainfall
      Evaporation
      Sunshine
      WindGustDir
      WindGustSpeed
      ...
      Humidity3pm
      Pressure9am
      Pressure3pm
      Cloud9am
      Cloud3pm
      Temp9am
      Temp3pm
      RainToday
      RISK_MM
      RainTomorrow
    
  
  
    
      0
      1
      2007-11-01
      Canberra
      8.0
      24.3
      0.0
      3.4
      6.3
      NW
      30.0
      ...
      29
      1019.7
      1015.0
      7
      7
      14.4
      23.6
      No
      3.6
      Yes
    
    
      1
      2
      2007-11-02
      Canberra
      14.0
      26.9
      3.6
      4.4
      9.7
      ENE
      39.0
      ...
      36
      1012.4
      1008.4
      5
      3
      17.5
      25.7
      Yes
      3.6
      Yes
    
    
      2
      3
      2007-11-03
      Canberra
      13.7
      23.4
      3.6
      5.8
      3.3
      NW
      85.0
      ...
      69
      1009.5
      1007.2
      8
      7
      15.4
      20.2
      Yes
      39.8
      Yes
    
    
      3
      4
      2007-11-04
      Canberra
      13.3
      15.5
      39.8
      7.2
      9.1
      NW
      54.0
      ...
      56
      1005.5
      1007.0
      2
      7
      13.5
      14.1
      Yes
      2.8
      Yes
    
    
      4
      5
      2007-11-05
      Canberra
      7.6
      16.1
      2.8
      5.6
      10.6
      SSE
      50.0
      ...
      49
      1018.3
      1018.5
      7
      7
      11.1
      15.4
      Yes
      0.0
      No
    
  

5 rows � 25 columns



In [266]:

    
dataframe.info()









    



<class 'pandas.core.frame.DataFrame'>
RangeIndex: 366 entries, 0 to 365
Data columns (total 25 columns):
Unnamed: 0       366 non-null int64
Date             366 non-null object
Location         366 non-null object
MinTemp          366 non-null float64
MaxTemp          366 non-null float64
Rainfall         366 non-null float64
Evaporation      366 non-null float64
Sunshine         363 non-null float64
WindGustDir      363 non-null object
WindGustSpeed    364 non-null float64
WindDir9am       335 non-null object
WindDir3pm       365 non-null object
WindSpeed9am     359 non-null float64
WindSpeed3pm     366 non-null int64
Humidity9am      366 non-null int64
Humidity3pm      366 non-null int64
Pressure9am      366 non-null float64
Pressure3pm      366 non-null float64
Cloud9am         366 non-null int64
Cloud3pm         366 non-null int64
Temp9am          366 non-null float64
Temp3pm          366 non-null float64
RainToday        366 non-null object
RISK_MM          366 non-null float64
RainTomorrow     366 non-null object
dtypes: float64(12), int64(6), object(7)
memory usage: 71.6+ KB



In [267]:

    
dataframe=dataframe.drop('Unnamed: 0', 1)



In [268]:

    
dataframe.describe()









    



/home/ajayohri/anaconda3/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile
  RuntimeWarning)






    Out[268]:






  
    
      
      MinTemp
      MaxTemp
      Rainfall
      Evaporation
      Sunshine
      WindGustSpeed
      WindSpeed9am
      WindSpeed3pm
      Humidity9am
      Humidity3pm
      Pressure9am
      Pressure3pm
      Cloud9am
      Cloud3pm
      Temp9am
      Temp3pm
      RISK_MM
    
  
  
    
      count
      366.000000
      366.000000
      366.000000
      366.000000
      363.000000
      364.000000
      359.000000
      366.000000
      366.000000
      366.000000
      366.000000
      366.000000
      366.000000
      366.000000
      366.000000
      366.000000
      366.000000
    
    
      mean
      7.265574
      20.550273
      1.428415
      4.521858
      7.909366
      39.840659
      9.651811
      17.986339
      72.035519
      44.519126
      1019.709016
      1016.810383
      3.890710
      4.024590
      12.358470
      19.230874
      1.428415
    
    
      std
      6.025800
      6.690516
      4.225800
      2.669383
      3.481517
      13.059807
      7.951929
      8.856997
      13.137058
      16.850947
      6.686212
      6.469422
      2.956131
      2.666268
      5.630832
      6.640346
      4.225800
    
    
      min
      -5.300000
      7.600000
      0.000000
      0.200000
      0.000000
      13.000000
      0.000000
      0.000000
      36.000000
      13.000000
      996.500000
      996.800000
      0.000000
      0.000000
      0.100000
      5.100000
      0.000000
    
    
      25%
      2.300000
      15.025000
      0.000000
      2.200000
      NaN
      NaN
      NaN
      11.000000
      64.000000
      32.250000
      1015.350000
      1012.800000
      1.000000
      1.000000
      7.625000
      14.150000
      0.000000
    
    
      50%
      7.450000
      19.650000
      0.000000
      4.200000
      NaN
      NaN
      NaN
      17.000000
      72.000000
      43.000000
      1020.150000
      1017.400000
      3.500000
      4.000000
      12.550000
      18.550000
      0.000000
    
    
      75%
      12.500000
      25.500000
      0.200000
      6.400000
      NaN
      NaN
      NaN
      24.000000
      81.000000
      55.000000
      1024.475000
      1021.475000
      7.000000
      7.000000
      17.000000
      24.000000
      0.200000
    
    
      max
      20.900000
      35.800000
      39.800000
      13.800000
      13.600000
      98.000000
      41.000000
      52.000000
      99.000000
      96.000000
      1035.700000
      1033.200000
      8.000000
      8.000000
      24.700000
      34.500000
      39.800000



In [269]:

    
dataframe['RainTomorrow'].unique()









    Out[269]:





array(['Yes', 'No'], dtype=object)



In [270]:

    
dataframe['RainToday'].unique()









    Out[270]:





array(['No', 'Yes'], dtype=object)



In [271]:

    
dataframe['Location'].unique()









    Out[271]:





array(['Canberra'], dtype=object)



In [272]:

    
dataframe['Date'].unique()









    Out[272]:





array(['2007-11-01', '2007-11-02', '2007-11-03', '2007-11-04',
       '2007-11-05', '2007-11-06', '2007-11-07', '2007-11-08',
       '2007-11-09', '2007-11-10', '2007-11-11', '2007-11-12',
       '2007-11-13', '2007-11-14', '2007-11-15', '2007-11-16',
       '2007-11-17', '2007-11-18', '2007-11-19', '2007-11-20',
       '2007-11-21', '2007-11-22', '2007-11-23', '2007-11-24',
       '2007-11-25', '2007-11-26', '2007-11-27', '2007-11-28',
       '2007-11-29', '2007-11-30', '2007-12-01', '2007-12-02',
       '2007-12-03', '2007-12-04', '2007-12-05', '2007-12-06',
       '2007-12-07', '2007-12-08', '2007-12-09', '2007-12-10',
       '2007-12-11', '2007-12-12', '2007-12-13', '2007-12-14',
       '2007-12-15', '2007-12-16', '2007-12-17', '2007-12-18',
       '2007-12-19', '2007-12-20', '2007-12-21', '2007-12-22',
       '2007-12-23', '2007-12-24', '2007-12-25', '2007-12-26',
       '2007-12-27', '2007-12-28', '2007-12-29', '2007-12-30',
       '2007-12-31', '2008-01-01', '2008-01-02', '2008-01-03',
       '2008-01-04', '2008-01-05', '2008-01-06', '2008-01-07',
       '2008-01-08', '2008-01-09', '2008-01-10', '2008-01-11',
       '2008-01-12', '2008-01-13', '2008-01-14', '2008-01-15',
       '2008-01-16', '2008-01-17', '2008-01-18', '2008-01-19',
       '2008-01-20', '2008-01-21', '2008-01-22', '2008-01-23',
       '2008-01-24', '2008-01-25', '2008-01-26', '2008-01-27',
       '2008-01-28', '2008-01-29', '2008-01-30', '2008-01-31',
       '2008-02-01', '2008-02-02', '2008-02-03', '2008-02-04',
       '2008-02-05', '2008-02-06', '2008-02-07', '2008-02-08',
       '2008-02-09', '2008-02-10', '2008-02-11', '2008-02-12',
       '2008-02-13', '2008-02-14', '2008-02-15', '2008-02-16',
       '2008-02-17', '2008-02-18', '2008-02-19', '2008-02-20',
       '2008-02-21', '2008-02-22', '2008-02-23', '2008-02-24',
       '2008-02-25', '2008-02-26', '2008-02-27', '2008-02-28',
       '2008-02-29', '2008-03-01', '2008-03-02', '2008-03-03',
       '2008-03-04', '2008-03-05', '2008-03-06', '2008-03-07',
       '2008-03-08', '2008-03-09', '2008-03-10', '2008-03-11',
       '2008-03-12', '2008-03-13', '2008-03-14', '2008-03-15',
       '2008-03-16', '2008-03-17', '2008-03-18', '2008-03-19',
       '2008-03-20', '2008-03-21', '2008-03-22', '2008-03-23',
       '2008-03-24', '2008-03-25', '2008-03-26', '2008-03-27',
       '2008-03-28', '2008-03-29', '2008-03-30', '2008-03-31',
       '2008-04-01', '2008-04-02', '2008-04-03', '2008-04-04',
       '2008-04-05', '2008-04-06', '2008-04-07', '2008-04-08',
       '2008-04-09', '2008-04-10', '2008-04-11', '2008-04-12',
       '2008-04-13', '2008-04-14', '2008-04-15', '2008-04-16',
       '2008-04-17', '2008-04-18', '2008-04-19', '2008-04-20',
       '2008-04-21', '2008-04-22', '2008-04-23', '2008-04-24',
       '2008-04-25', '2008-04-26', '2008-04-27', '2008-04-28',
       '2008-04-29', '2008-04-30', '2008-05-01', '2008-05-02',
       '2008-05-03', '2008-05-04', '2008-05-05', '2008-05-06',
       '2008-05-07', '2008-05-08', '2008-05-09', '2008-05-10',
       '2008-05-11', '2008-05-12', '2008-05-13', '2008-05-14',
       '2008-05-15', '2008-05-16', '2008-05-17', '2008-05-18',
       '2008-05-19', '2008-05-20', '2008-05-21', '2008-05-22',
       '2008-05-23', '2008-05-24', '2008-05-25', '2008-05-26',
       '2008-05-27', '2008-05-28', '2008-05-29', '2008-05-30',
       '2008-05-31', '2008-06-01', '2008-06-02', '2008-06-03',
       '2008-06-04', '2008-06-05', '2008-06-06', '2008-06-07',
       '2008-06-08', '2008-06-09', '2008-06-10', '2008-06-11',
       '2008-06-12', '2008-06-13', '2008-06-14', '2008-06-15',
       '2008-06-16', '2008-06-17', '2008-06-18', '2008-06-19',
       '2008-06-20', '2008-06-21', '2008-06-22', '2008-06-23',
       '2008-06-24', '2008-06-25', '2008-06-26', '2008-06-27',
       '2008-06-28', '2008-06-29', '2008-06-30', '2008-07-01',
       '2008-07-02', '2008-07-03', '2008-07-04', '2008-07-05',
       '2008-07-06', '2008-07-07', '2008-07-08', '2008-07-09',
       '2008-07-10', '2008-07-11', '2008-07-12', '2008-07-13',
       '2008-07-14', '2008-07-15', '2008-07-16', '2008-07-17',
       '2008-07-18', '2008-07-19', '2008-07-20', '2008-07-21',
       '2008-07-22', '2008-07-23', '2008-07-24', '2008-07-25',
       '2008-07-26', '2008-07-27', '2008-07-28', '2008-07-29',
       '2008-07-30', '2008-07-31', '2008-08-01', '2008-08-02',
       '2008-08-03', '2008-08-04', '2008-08-05', '2008-08-06',
       '2008-08-07', '2008-08-08', '2008-08-09', '2008-08-10',
       '2008-08-11', '2008-08-12', '2008-08-13', '2008-08-14',
       '2008-08-15', '2008-08-16', '2008-08-17', '2008-08-18',
       '2008-08-19', '2008-08-20', '2008-08-21', '2008-08-22',
       '2008-08-23', '2008-08-24', '2008-08-25', '2008-08-26',
       '2008-08-27', '2008-08-28', '2008-08-29', '2008-08-30',
       '2008-08-31', '2008-09-01', '2008-09-02', '2008-09-03',
       '2008-09-04', '2008-09-05', '2008-09-06', '2008-09-07',
       '2008-09-08', '2008-09-09', '2008-09-10', '2008-09-11',
       '2008-09-12', '2008-09-13', '2008-09-14', '2008-09-15',
       '2008-09-16', '2008-09-17', '2008-09-18', '2008-09-19',
       '2008-09-20', '2008-09-21', '2008-09-22', '2008-09-23',
       '2008-09-24', '2008-09-25', '2008-09-26', '2008-09-27',
       '2008-09-28', '2008-09-29', '2008-09-30', '2008-10-01',
       '2008-10-02', '2008-10-03', '2008-10-04', '2008-10-05',
       '2008-10-06', '2008-10-07', '2008-10-08', '2008-10-09',
       '2008-10-10', '2008-10-11', '2008-10-12', '2008-10-13',
       '2008-10-14', '2008-10-15', '2008-10-16', '2008-10-17',
       '2008-10-18', '2008-10-19', '2008-10-20', '2008-10-21',
       '2008-10-22', '2008-10-23', '2008-10-24', '2008-10-25',
       '2008-10-26', '2008-10-27', '2008-10-28', '2008-10-29',
       '2008-10-30', '2008-10-31'], dtype=object)



In [273]:

    
# Bagged Decision Trees for Classification
from sklearn import cross_validation
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree



In [274]:

    
dataframe.columns









    Out[274]:





Index(['Date', 'Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation',
       'Sunshine', 'WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm',
       'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm',
       'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',
       'Temp3pm', 'RainToday', 'RISK_MM', 'RainTomorrow'],
      dtype='object')



In [275]:

    
del dataframe['Date']



In [276]:

    
del dataframe['Location']



In [277]:

    
del dataframe['WindDir9am']



In [278]:

    
del dataframe['WindSpeed3pm']



In [279]:

    
del dataframe['WindGustDir']
del dataframe['WindDir3pm']
del dataframe['RISK_MM']



In [280]:

    
dataframe=dataframe.replace(['Yes', 'No'], [1, 0]) #using replace to change string to numeric values



In [281]:

    
dataframe=dataframe.dropna()



In [282]:

    
dataframe.head()









    Out[282]:






  
    
      
      MinTemp
      MaxTemp
      Rainfall
      Evaporation
      Sunshine
      WindGustSpeed
      WindSpeed9am
      Humidity9am
      Humidity3pm
      Pressure9am
      Pressure3pm
      Cloud9am
      Cloud3pm
      Temp9am
      Temp3pm
      RainToday
      RainTomorrow
    
  
  
    
      0
      8.0
      24.3
      0.0
      3.4
      6.3
      30.0
      6.0
      68
      29
      1019.7
      1015.0
      7
      7
      14.4
      23.6
      0
      1
    
    
      1
      14.0
      26.9
      3.6
      4.4
      9.7
      39.0
      4.0
      80
      36
      1012.4
      1008.4
      5
      3
      17.5
      25.7
      1
      1
    
    
      2
      13.7
      23.4
      3.6
      5.8
      3.3
      85.0
      6.0
      82
      69
      1009.5
      1007.2
      8
      7
      15.4
      20.2
      1
      1
    
    
      3
      13.3
      15.5
      39.8
      7.2
      9.1
      54.0
      30.0
      62
      56
      1005.5
      1007.0
      2
      7
      13.5
      14.1
      1
      1
    
    
      4
      7.6
      16.1
      2.8
      5.6
      10.6
      50.0
      20.0
      68
      49
      1018.3
      1018.5
      7
      7
      11.1
      15.4
      1
      0



In [283]:

    
len(dataframe)









    Out[283]:





354



In [284]:

    
len(dataframe.columns)









    Out[284]:





17



In [285]:

    
names=dataframe.columns
names









    Out[285]:





Index(['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine',
       'WindGustSpeed', 'WindSpeed9am', 'Humidity9am', 'Humidity3pm',
       'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am',
       'Temp3pm', 'RainToday', 'RainTomorrow'],
      dtype='object')



In [286]:

    
dataframe.describe()









    Out[286]:






  
    
      
      MinTemp
      MaxTemp
      Rainfall
      Evaporation
      Sunshine
      WindGustSpeed
      WindSpeed9am
      Humidity9am
      Humidity3pm
      Pressure9am
      Pressure3pm
      Cloud9am
      Cloud3pm
      Temp9am
      Temp3pm
      RainToday
      RainTomorrow
    
  
  
    
      count
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
      354.000000
    
    
      mean
      7.362429
      20.601412
      1.420904
      4.558192
      7.925424
      40.011299
      9.666667
      71.875706
      44.454802
      1019.562147
      1016.692090
      3.920904
      4.019774
      12.438701
      19.271469
      0.180791
      0.180791
    
    
      std
      6.010927
      6.708966
      4.235358
      2.667877
      3.510039
      13.034488
      7.978489
      13.161939
      16.944316
      6.602685
      6.373679
      2.962363
      2.672312
      5.630160
      6.663681
      0.385390
      0.385390
    
    
      min
      -5.300000
      7.600000
      0.000000
      0.200000
      0.000000
      13.000000
      0.000000
      36.000000
      13.000000
      996.500000
      996.800000
      0.000000
      0.000000
      0.100000
      5.100000
      0.000000
      0.000000
    
    
      25%
      2.400000
      15.100000
      0.000000
      2.400000
      5.925000
      31.000000
      6.000000
      64.000000
      32.000000
      1015.225000
      1012.725000
      1.000000
      1.000000
      7.725000
      14.300000
      0.000000
      0.000000
    
    
      50%
      7.500000
      19.750000
      0.000000
      4.200000
      8.650000
      39.000000
      7.000000
      72.000000
      43.000000
      1020.000000
      1017.200000
      4.000000
      4.000000
      12.600000
      18.600000
      0.000000
      0.000000
    
    
      75%
      12.500000
      25.500000
      0.200000
      6.400000
      10.600000
      46.000000
      13.000000
      80.000000
      54.750000
      1024.400000
      1021.350000
      7.000000
      7.000000
      17.000000
      24.000000
      0.000000
      0.000000
    
    
      max
      20.900000
      35.800000
      39.800000
      13.800000
      13.600000
      98.000000
      41.000000
      99.000000
      96.000000
      1035.700000
      1033.200000
      8.000000
      8.000000
      24.700000
      34.500000
      1.000000
      1.000000



In [287]:

    
type(dataframe)









    Out[287]:





pandas.core.frame.DataFrame



In [288]:

    
array = dataframe.values



In [289]:

    
pd.value_counts(dataframe["RainTomorrow"])









    Out[289]:





0    290
1     64
Name: RainTomorrow, dtype: int64



In [290]:

    
array









    Out[290]:





array([[  8. ,  24.3,   0. , ...,  23.6,   0. ,   1. ],
       [ 14. ,  26.9,   3.6, ...,  25.7,   1. ,   1. ],
       [ 13.7,  23.4,   3.6, ...,  20.2,   1. ,   1. ],
       ..., 
       [ 12.5,  19.9,   0. , ...,  18.3,   0. ,   0. ],
       [ 12.5,  26.9,   0. , ...,  25.9,   0. ,   0. ],
       [ 12.3,  30.2,   0. , ...,  28.6,   0. ,   0. ]])



In [291]:

    
X = array[:,0:16]
Y = array[:,16]
num_folds = 10
num_instances = len(X)
seed = 7



In [292]:

    
type(X)









    Out[292]:





numpy.ndarray



In [293]:

    
X









    Out[293]:





array([[  8. ,  24.3,   0. , ...,  14.4,  23.6,   0. ],
       [ 14. ,  26.9,   3.6, ...,  17.5,  25.7,   1. ],
       [ 13.7,  23.4,   3.6, ...,  15.4,  20.2,   1. ],
       ..., 
       [ 12.5,  19.9,   0. , ...,  14.5,  18.3,   0. ],
       [ 12.5,  26.9,   0. , ...,  15.8,  25.9,   0. ],
       [ 12.3,  30.2,   0. , ...,  23.8,  28.6,   0. ]])



In [294]:

    
#Y[Y == "Yes"] = 1 An alternative way to make a NumPy arraye change values
#Y[Y == "No"] = 0
Y









    Out[294]:





array([ 1.,  1.,  1.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  1.,  1.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  1.,  1.,  1.,  1.,
        0.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,
        1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,
        1.,  0.,  0.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  1.,  1.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,
        1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,
        0.,  1.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  1.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.,
        0.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,
        1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  1.,  0.,  0.,  0.,
        0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  1.,  1.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.])



In [295]:

    
dtr = tree.DecisionTreeRegressor(max_depth=3)
dtr.fit(X, Y)









    Out[295]:





DecisionTreeRegressor(criterion='mse', max_depth=3, max_features=None,
           max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, presort=False, random_state=None,
           splitter='best')



In [296]:

    
# from sklearn.metrics import roc_curve, auc



In [297]:

    
#!sudo pip install pydotplus
# http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
# http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/
# http://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/



In [298]:

    
#!pip freeze
#checking if we have the right packages



In [299]:

    
#!pip install --upgrade pip



In [300]:

    
#!pip install pydotplus



In [301]:

    
import pydotplus as pydot

from IPython.display import Image

from sklearn.externals.six import StringIO



In [302]:

    
# Graphviz
#sudo add-apt-repository ppa:gviz-adm/graphviz-dev
# sudo apt-get update
# http://www.graphviz.org/Download_linux_ubuntu.php



In [303]:

    
dot_data = StringIO()



In [304]:

    
tree.export_graphviz(dtr, out_file=dot_data,feature_names=names[:-1])



In [305]:

    
graph = pydot.graph_from_dot_data(dot_data.getvalue())



In [306]:

    
Image(graph.create_png())









    Out[306]:



In [307]:

    
kfold = cross_validation.KFold(n=num_instances, n_folds=num_folds, random_state=seed)
cart = DecisionTreeClassifier()
num_trees = 100
model = BaggingClassifier(base_estimator=cart, n_estimators=num_trees, random_state=seed)



In [308]:

    
model









    Out[308]:





BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best'),
         bootstrap=True, bootstrap_features=False, max_features=1.0,
         max_samples=1.0, n_estimators=100, n_jobs=1, oob_score=False,
         random_state=7, verbose=0, warm_start=False)



In [309]:

    
kfold









    Out[309]:





sklearn.cross_validation.KFold(n=354, n_folds=10, shuffle=False, random_state=7)



In [310]:

    
results = cross_validation.cross_val_score(model, X, Y, cv=kfold)
print(results.mean())









    



0.850873015873



In [311]:

    
results









    Out[311]:





array([ 0.75      ,  0.86111111,  0.69444444,  0.88888889,  0.88571429,
        0.82857143,  0.91428571,  0.85714286,  0.94285714,  0.88571429])

	Unnamed: 0	Date	Location	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustDir	WindGustSpeed	...	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm	RainToday	RISK_MM	RainTomorrow
0	1	2007-11-01	Canberra	8.0	24.3	0.0	3.4	6.3	NW	30.0	...	29	1019.7	1015.0	7	7	14.4	23.6	No	3.6	Yes
1	2	2007-11-02	Canberra	14.0	26.9	3.6	4.4	9.7	ENE	39.0	...	36	1012.4	1008.4	5	3	17.5	25.7	Yes	3.6	Yes
2	3	2007-11-03	Canberra	13.7	23.4	3.6	5.8	3.3	NW	85.0	...	69	1009.5	1007.2	8	7	15.4	20.2	Yes	39.8	Yes
3	4	2007-11-04	Canberra	13.3	15.5	39.8	7.2	9.1	NW	54.0	...	56	1005.5	1007.0	2	7	13.5	14.1	Yes	2.8	Yes
4	5	2007-11-05	Canberra	7.6	16.1	2.8	5.6	10.6	SSE	50.0	...	49	1018.3	1018.5	7	7	11.1	15.4	Yes	0.0	No

	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustSpeed	WindSpeed9am	WindSpeed3pm	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm	RISK_MM
count	366.000000	366.000000	366.000000	366.000000	363.000000	364.000000	359.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000	366.000000
mean	7.265574	20.550273	1.428415	4.521858	7.909366	39.840659	9.651811	17.986339	72.035519	44.519126	1019.709016	1016.810383	3.890710	4.024590	12.358470	19.230874	1.428415
std	6.025800	6.690516	4.225800	2.669383	3.481517	13.059807	7.951929	8.856997	13.137058	16.850947	6.686212	6.469422	2.956131	2.666268	5.630832	6.640346	4.225800
min	-5.300000	7.600000	0.000000	0.200000	0.000000	13.000000	0.000000	0.000000	36.000000	13.000000	996.500000	996.800000	0.000000	0.000000	0.100000	5.100000	0.000000
25%	2.300000	15.025000	0.000000	2.200000	NaN	NaN	NaN	11.000000	64.000000	32.250000	1015.350000	1012.800000	1.000000	1.000000	7.625000	14.150000	0.000000
50%	7.450000	19.650000	0.000000	4.200000	NaN	NaN	NaN	17.000000	72.000000	43.000000	1020.150000	1017.400000	3.500000	4.000000	12.550000	18.550000	0.000000
75%	12.500000	25.500000	0.200000	6.400000	NaN	NaN	NaN	24.000000	81.000000	55.000000	1024.475000	1021.475000	7.000000	7.000000	17.000000	24.000000	0.200000
max	20.900000	35.800000	39.800000	13.800000	13.600000	98.000000	41.000000	52.000000	99.000000	96.000000	1035.700000	1033.200000	8.000000	8.000000	24.700000	34.500000	39.800000

	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustSpeed	WindSpeed9am	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm	RainToday	RainTomorrow
count	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000	354.000000
mean	7.362429	20.601412	1.420904	4.558192	7.925424	40.011299	9.666667	71.875706	44.454802	1019.562147	1016.692090	3.920904	4.019774	12.438701	19.271469	0.180791	0.180791
std	6.010927	6.708966	4.235358	2.667877	3.510039	13.034488	7.978489	13.161939	16.944316	6.602685	6.373679	2.962363	2.672312	5.630160	6.663681	0.385390	0.385390
min	-5.300000	7.600000	0.000000	0.200000	0.000000	13.000000	0.000000	36.000000	13.000000	996.500000	996.800000	0.000000	0.000000	0.100000	5.100000	0.000000	0.000000
25%	2.400000	15.100000	0.000000	2.400000	5.925000	31.000000	6.000000	64.000000	32.000000	1015.225000	1012.725000	1.000000	1.000000	7.725000	14.300000	0.000000	0.000000
50%	7.500000	19.750000	0.000000	4.200000	8.650000	39.000000	7.000000	72.000000	43.000000	1020.000000	1017.200000	4.000000	4.000000	12.600000	18.600000	0.000000	0.000000
75%	12.500000	25.500000	0.200000	6.400000	10.600000	46.000000	13.000000	80.000000	54.750000	1024.400000	1021.350000	7.000000	7.000000	17.000000	24.000000	0.000000	0.000000
max	20.900000	35.800000	39.800000	13.800000	13.600000	98.000000	41.000000	99.000000	96.000000	1035.700000	1033.200000	8.000000	8.000000	24.700000	34.500000	1.000000	1.000000