Detecting exoplanets in outer space

For the project explained in this chapter, we use the Kepler labeled time series data from Kaggle: https://www.kaggle.com/keplersmachines/kepler-labelled-time-series-data/home. This dataset is derived mainly from the Campaign 3 observations of the mission by NASA's Kepler space telescope.

In the dataset, column 1 values are the labels and columns 2 to 3198 values are the flux values over time. The training set has 5087 data points, 37 confirmed exoplanets, and 5050 non-exoplanet stars. The test set has 570 data points, 5 confirmed exoplanets, and 565 non-exoplanet stars.

We will carry out the following steps to download, and then preprocess our data to create the train and test datasets: 

1. Download the dataset using the Kaggle API. The following code will be used for the same:

armando@librenix:~/datasets/kaggle-kepler$ kaggle datasets download -d keplersmachines/kepler-labelled-time-series-data

Downloading kepler-labelled-time-series-data.zip to /mnt/disk1tb/datasets/kaggle-kepler
100%|██████████████████████████████████████| 57.4M/57.4M [00:03<00:00, 18.3MB/s]

The folder contains the following two files:

exoTest.csv
exoTrain.csv

1. Link the folder datasets to our home folder so we can access it from the ~/datasets/kaggle-kepler path and then we define the folder path and list the contents of the folder through the Notebook to confirm if we have access to the data files through the Notebook:

dsroot = os.path.join(os.path.expanduser('~'),'datasets','kaggle-kepler')
os.listdir(dsroot)

We get the following output:

['exoTest.csv', 'kepler-labelled-time-series-data.zip', 'exoTrain.csv']

1. We will then read the two .csv data files in the pandas DataFrames named train and test respectively:

import pandas as pd
train = pd.read_csv(os.path.join(dsroot,'exoTrain.csv'))
test = pd.read_csv(os.path.join(dsroot,'exoTest.csv'))
print('Training data\n',train.head())
print('Test data\n',test.head())

The first five lines of the training and test data look similar to the following:

Training data
    LABEL   FLUX.1   FLUX.2   FLUX.3  \
0      2    93.85    83.81    20.10     
1      2   -38.88   -33.83   -58.54   
2      2   532.64   535.92   513.73    
3      2   326.52   347.39   302.35    
4      2 -1107.21 -1112.59 -1118.95 
     FLUX.4   FLUX.5   FLUX.6  FLUX.7  \
0    -26.98   -39.56  -124.71 -135.18   
1    -40.09   -79.31   -72.81  -86.55   
2    496.92   456.45   466.00  464.50   
3    298.13   317.74   312.70  322.33   
4  -1095.10 -1057.55 -1034.48 -998.34   

    FLUX.8  FLUX.9    ...      FLUX.3188  \
0   -96.27  -79.89    ...         -78.07      
1   -85.33  -83.97    ...          -3.28   
2   486.39  436.56    ...         -71.69  
3   311.31  312.42    ...           5.71      
4 -1022.71 -989.57    ...        -594.37    

    FLUX.3189  FLUX.3190  FLUX.3191  \
0     -102.15    -102.15      25.13   
1      -32.21     -32.21     -24.89   
2       13.31      13.31     -29.89   
3       -3.73      -3.73      30.05   
4     -401.66    -401.66    -357.24  

   FLUX.3192  FLUX.3193  FLUX.3194  
0      48.57      92.54      39.32  
1      -4.86       0.76     -11.70     
2     -20.88       5.06     -11.80    
3      20.03     -12.67      -8.77      
4    -443.76    -438.54    -399.71   

   FLUX.3195  FLUX.3196  FLUX.3197  
0      61.42       5.08     -39.54  
1       6.46      16.00      19.93  
2     -28.91     -70.02     -96.67  
3     -17.31     -17.35      13.98  
4    -384.65    -411.79    -510.54  

[5 rows x 3198 columns]

Test data

    LABEL   FLUX.1   FLUX.2   FLUX.3   \
0      2   119.88   100.21    86.46      
1      2  5736.59  5699.98  5717.16    
2      2   844.48   817.49   770.07    
3      2  -826.00  -827.31  -846.12    
4      2   -39.57   -15.88    -9.16      

       FLUX.4   FLUX.5   FLUX.6   FLUX.7  \
0       48.68    46.12    39.39    18.57   
1     5692.73  5663.83  5631.16  5626.39   
2      675.01   605.52   499.45   440.77   
3     -836.03  -745.50  -784.69  -791.22   
4       -6.37   -16.13   -24.05    -0.90   

    FLUX.8   FLUX.9    ...      FLUX.3188  \
0     6.98     6.63    ...          14.52       
1  5569.47  5550.44    ...        -581.91    
2   362.95   207.27    ...          17.82     
3  -746.50  -709.53    ...         122.34       
4   -45.20    -5.04    ...         -37.87     
    FLUX.3189  FLUX.3190  FLUX.3191  \
0       19.29      14.44      -1.62   
1     -984.09   -1230.89   -1600.45   
2      -51.66     -48.29     -59.99   
3       93.03      93.03      68.81   
4      -61.85     -27.15     -21.18     

   FLUX.3192  FLUX.3193  FLUX.3194  \  
0      13.33      45.50      31.93    
1   -1824.53   -2061.17   -2265.98     
2     -82.10    -174.54     -95.23      
3       9.81      20.75      20.25    
4     -33.76     -85.34     -81.46    

   FLUX.3195  FLUX.3196  FLUX.3197  
0      35.78     269.43      57.72  
1   -2366.19   -2294.86   -2034.72  
2    -162.68     -36.79      30.63  
3    -120.81    -257.56    -215.41  
4     -61.98     -69.34     -17.84  

[5 rows x 3198 columns]

The training and test datasets have labels in the first column and 3197 features in the next columns. Now let us split the training and test data into labels and features with the following code:

x_train = train.drop('LABEL', axis=1)
y_train = train.LABEL-1 #subtract one because of TGBT
x_test = test.drop('LABEL', axis=1)
y_test = test.LABEL-1

In the preceding code, we subtract 1 from the labels, since the TFBT estimator assumes labels starting with numerical zero while the features in the datasets are numbers 1 and 2.

Now that we have the label and feature vectors for training and test data, let us build the boosted tree models.