Deployment¶
Deployment of machine learning models requires repeating feature engineering steps on new data. In some cases, these steps need to be performed in near real-time. Featuretools has capabilities to ease the deployment of feature engineering.
Saving Features¶
First, let’s build some generate some training and test data in the same format. We use a random seed to generate different data for the test.
Note
Features saved in one version of Featuretools are not guaranteed to load in another. This means the features might need to be re-created after upgrading Featuretools.
In [1]: import featuretools as ft
In [2]: es_train = ft.demo.load_mock_customer(return_entityset=True)
In [3]: es_test = ft.demo.load_mock_customer(return_entityset=True, random_seed=33)
Now let’s build some features definitions using DFS. Because we have categorical features, we also encode them with one hot encoding based on the values in the training data.
In [4]: feature_matrix, feature_defs = ft.dfs(entityset=es_train,
...: target_entity="customers")
...:
In [5]: feature_matrix_enc, features_enc = ft.encode_features(feature_matrix, feature_defs)
In [6]: feature_matrix_enc
Out[6]:
zip_code = 60091 zip_code = 13244 zip_code is unknown COUNT(sessions) NUM_UNIQUE(sessions.device) MODE(sessions.device) = mobile MODE(sessions.device) = desktop MODE(sessions.device) is unknown SUM(transactions.amount) STD(transactions.amount) MAX(transactions.amount) SKEW(transactions.amount) MIN(transactions.amount) MEAN(transactions.amount) COUNT(transactions) NUM_UNIQUE(transactions.product_id) MODE(transactions.product_id) = 4 MODE(transactions.product_id) = 5 MODE(transactions.product_id) = 2 MODE(transactions.product_id) = 1 MODE(transactions.product_id) is unknown DAY(date_of_birth) = 18 DAY(date_of_birth) = 28 DAY(date_of_birth) = 21 DAY(date_of_birth) = 15 DAY(date_of_birth) is unknown DAY(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) is unknown YEAR(date_of_birth) = 2006 YEAR(date_of_birth) = 2003 YEAR(date_of_birth) = 1994 YEAR(date_of_birth) = 1986 YEAR(date_of_birth) = 1984 YEAR(date_of_birth) is unknown YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(join_date) is unknown MONTH(date_of_birth) = 8 MONTH(date_of_birth) = 7 MONTH(date_of_birth) = 11 MONTH(date_of_birth) is unknown MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(join_date) is unknown WEEKDAY(date_of_birth) = 0 WEEKDAY(date_of_birth) = 5 WEEKDAY(date_of_birth) = 4 WEEKDAY(date_of_birth) = 1 WEEKDAY(date_of_birth) is unknown WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(join_date) is unknown SUM(sessions.MIN(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MIN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MAX(transactions.amount)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MEAN(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) MODE(sessions.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.MODE(transactions.product_id)) = 3 MODE(sessions.MODE(transactions.product_id)) = 1 MODE(sessions.MODE(transactions.product_id)) = 4 MODE(sessions.MODE(transactions.product_id)) is unknown MODE(sessions.DAY(session_start)) = 1 MODE(sessions.DAY(session_start)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(session_start)) is unknown NUM_UNIQUE(transactions.sessions.customer_id) NUM_UNIQUE(transactions.sessions.device) MODE(transactions.sessions.customer_id) = 5 MODE(transactions.sessions.customer_id) = 4 MODE(transactions.sessions.customer_id) = 3 MODE(transactions.sessions.customer_id) = 2 MODE(transactions.sessions.customer_id) = 1 MODE(transactions.sessions.customer_id) is unknown MODE(transactions.sessions.device) = mobile MODE(transactions.sessions.device) = desktop MODE(transactions.sessions.device) is unknown
customer_id
5 1 0 0 6 3 1 0 0 6349.66 44.095630 149.02 -0.025941 7.55 80.375443 79 5 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 86.49 0.014384 259.873954 30 839.76 472.231119 3.600926 4.961414 0.415426 402.775486 0.000000 7.928001 11.007471 18 20.65 0.602209 1700.67 51.149250 5 94.481667 -0.317685 -0.470410 0.472342 0.204548 0.000000 -0.333796 0.335175 8 -0.539060 543.18 36.734681 5 128.51 66.666667 13.166667 14.415000 0.002397 1058.276667 43.312326 5.000000 139.960000 78.705187 1 5 1 1 1 1 0 1 0 0 0 1 0 1 0 1 0 1 3 1 0 0 0 0 0 1 0 0
4 1 0 0 8 3 1 0 0 8727.68 45.068765 149.95 -0.036348 5.73 80.070459 109 5 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 131.51 0.002764 356.125829 37 1157.99 649.657515 3.335416 16.960575 0.387884 235.992478 0.517549 3.514421 13.027258 18 54.83 0.382868 1351.46 54.293903 5 110.450000 0.282488 2.103510 -0.391805 -1.065663 -0.644061 0.027256 1.980948 10 -0.711744 771.68 29.026424 4 139.20 70.638182 13.625000 16.438750 0.000346 1090.960000 44.515729 4.625000 144.748750 81.207189 1 5 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 3 0 1 0 0 0 0 1 0 0
1 1 0 0 8 3 1 0 0 9025.62 40.442059 139.43 0.019698 5.81 71.631905 126 5 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 78.59 -0.476122 312.745952 40 1057.97 582.193117 4.062019 6.954507 0.589386 279.510713 0.000000 7.322191 13.759314 25 26.36 0.640252 1613.93 46.905665 5 88.755625 1.946018 2.440005 0.778170 -0.312355 0.000000 -0.780493 -0.424949 12 -1.038434 809.97 30.450261 5 118.90 50.623125 15.750000 9.823750 -0.059515 1128.202500 39.093244 5.000000 132.246250 72.774140 1 4 1 1 1 1 0 0 0 1 0 1 0 1 0 1 0 1 3 0 0 0 0 1 0 1 0 0
3 0 1 0 6 3 0 1 0 6236.62 43.683296 149.15 0.418230 5.89 67.060430 93 5 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 66.21 2.286086 257.299895 29 847.63 405.237462 2.428992 5.424407 0.429374 219.021420 0.408248 10.724241 11.174282 18 20.06 0.854976 1477.97 50.110120 5 82.109444 -1.507217 1.000771 2.246479 -0.245703 -2.449490 -0.941078 0.678544 11 -0.289466 889.21 35.704680 4 126.74 55.579412 15.500000 11.035000 0.381014 1039.436667 42.883316 4.833333 141.271667 67.539577 1 4 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 3 0 0 1 0 0 0 0 1 0
2 0 1 0 7 3 0 1 0 7200.28 37.705178 146.81 0.098259 8.73 77.422366 93 5 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 154.60 -0.277640 258.700528 35 931.63 548.905851 3.450328 15.874374 0.509798 251.609234 0.000000 17.221593 11.477071 18 56.46 0.755711 1320.64 47.935920 5 96.581000 -0.303276 2.154929 -0.440929 0.013087 0.000000 -1.539467 0.235296 8 -0.763603 634.84 27.839228 5 100.04 61.910000 13.285714 22.085714 -0.039663 1028.611429 36.957218 5.000000 133.090000 78.415122 1 4 1 1 1 1 0 1 0 0 0 1 0 1 0 1 0 1 3 0 0 0 1 0 0 0 1 0
Now, we can use featuretools.save_features() to save a list features to a json file
In [7]: ft.save_features(features_enc, "feature_definitions.json")
Calculating Feature Matrix for New Data¶
We can use featuretools.load_features() to read in a list of saved features to calculate for our new entity set.
In [8]: saved_features = ft.load_features('feature_definitions.json')
After we load the features back in, we can calculate the feature matrix.
In [9]: feature_matrix = ft.calculate_feature_matrix(saved_features, es_test)
In [10]: feature_matrix
Out[10]:
zip_code = 60091 zip_code = 13244 zip_code is unknown COUNT(sessions) NUM_UNIQUE(sessions.device) MODE(sessions.device) = mobile MODE(sessions.device) = desktop MODE(sessions.device) is unknown SUM(transactions.amount) STD(transactions.amount) MAX(transactions.amount) SKEW(transactions.amount) MIN(transactions.amount) MEAN(transactions.amount) COUNT(transactions) NUM_UNIQUE(transactions.product_id) MODE(transactions.product_id) = 4 MODE(transactions.product_id) = 5 MODE(transactions.product_id) = 2 MODE(transactions.product_id) = 1 MODE(transactions.product_id) is unknown DAY(date_of_birth) = 18 DAY(date_of_birth) = 28 DAY(date_of_birth) = 21 DAY(date_of_birth) = 15 DAY(date_of_birth) is unknown DAY(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) is unknown YEAR(date_of_birth) = 2006 YEAR(date_of_birth) = 2003 YEAR(date_of_birth) = 1994 YEAR(date_of_birth) = 1986 YEAR(date_of_birth) = 1984 YEAR(date_of_birth) is unknown YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(join_date) is unknown MONTH(date_of_birth) = 8 MONTH(date_of_birth) = 7 MONTH(date_of_birth) = 11 MONTH(date_of_birth) is unknown MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(join_date) is unknown WEEKDAY(date_of_birth) = 0 WEEKDAY(date_of_birth) = 5 WEEKDAY(date_of_birth) = 4 WEEKDAY(date_of_birth) = 1 WEEKDAY(date_of_birth) is unknown WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(join_date) is unknown SUM(sessions.MIN(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MIN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MAX(transactions.amount)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MEAN(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) MODE(sessions.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.MODE(transactions.product_id)) = 3 MODE(sessions.MODE(transactions.product_id)) = 1 MODE(sessions.MODE(transactions.product_id)) = 4 MODE(sessions.MODE(transactions.product_id)) is unknown MODE(sessions.DAY(session_start)) = 1 MODE(sessions.DAY(session_start)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(session_start)) is unknown NUM_UNIQUE(transactions.sessions.customer_id) NUM_UNIQUE(transactions.sessions.device) MODE(transactions.sessions.customer_id) = 5 MODE(transactions.sessions.customer_id) = 4 MODE(transactions.sessions.customer_id) = 3 MODE(transactions.sessions.customer_id) = 2 MODE(transactions.sessions.customer_id) = 1 MODE(transactions.sessions.customer_id) is unknown MODE(transactions.sessions.device) = mobile MODE(transactions.sessions.device) = desktop MODE(transactions.sessions.device) is unknown
customer_id
1 True False False 6 3 False True False 5776.41 41.998795 147.64 -0.173042 6.47 79.128904 73 5 True False False False False False False False False True False False False False True False False False False False True False False False True False False False True False False False True False False False False True True False False False 198.92 -1.546156 217.064024 25 834.08 524.919674 5.741661 45.471977 0.326628 327.331994 1.329160 11.742343 25.801702 19 124.58 0.164928 1418.99 51.192518 5 136.672500 -0.243198 2.290693 0.130163 -1.336611 -1.206607 -1.165955 1.796000 4 -0.614442 546.69 9.176046 2 119.86 64.238824 12.166667 33.153333 -0.257693 962.735000 36.177337 4.166667 139.013333 87.486612 1 4 1 1 1 True False False False True False True False True False True False 1 3 False False False False True False True False False
4 False True False 9 3 False True False 10178.43 36.523849 147.55 -0.179621 6.19 80.781190 126 5 False False False False True False False False False True False False False False True False False False False False True False False False True False False True False False False True False False False True False False False False True False 193.08 -1.797214 319.497611 43 1180.90 733.862898 4.272002 17.851716 0.324894 333.923377 0.440959 11.457114 11.875823 21 60.29 0.417250 1650.65 41.627134 5 104.565000 -0.086578 1.490781 -0.385392 -1.743267 -1.619848 0.230847 1.078619 8 -0.624344 557.32 22.026552 4 118.59 69.665000 14.000000 21.453333 -0.199690 1130.936667 35.499735 4.777778 131.211111 81.540322 1 5 1 1 1 True False False True False False True False True False True False 1 3 False True False False False False False True False
3 True False False 5 2 True False False 5258.95 42.416322 148.09 -0.081427 10.66 82.171094 64 5 False True False False False False False False False True False False False False True True False False False False False False False True False False False False True False False False True False False False False True False True False False 108.69 0.353061 215.417211 23 715.80 407.390549 4.868265 10.829338 0.530094 450.318768 0.547723 5.596950 10.012657 19 38.69 0.785376 1691.27 46.402508 5 92.004000 0.570300 1.010658 0.727969 -0.627238 -0.608581 -1.598919 -0.730554 8 -0.608419 653.11 37.945636 4 133.75 66.563000 12.800000 21.738000 0.070612 1051.790000 43.083442 4.600000 143.160000 81.478110 1 2 1 1 1 True False False True False False True False True False True False 1 2 False False True False False False True False False
2 False True False 8 3 False True False 9877.67 39.913352 148.34 0.040395 8.00 76.571085 129 5 False False False True False False False False False True False False False False True False False False False False True False False True False False False False True False False False True False False False False True True False False False 136.01 -0.082021 315.817331 39 1100.82 615.714934 4.086126 10.517928 0.305345 346.152626 0.353553 9.491736 10.757169 21 40.88 0.454842 1690.97 43.950396 5 87.669412 -1.158217 1.981423 -0.576079 -0.403715 -2.828427 -0.964539 -2.048650 8 -0.522578 619.93 33.618728 4 120.06 52.288421 16.125000 17.001250 -0.010253 1234.708750 39.477166 4.875000 137.602500 76.964367 1 4 1 1 1 True False False True False False True False True False True False 1 3 False False False True False False False True False
5 True False False 7 3 False True False 9018.74 37.514054 149.53 -0.107234 6.35 83.506852 108 5 False False False False True False True False False False False False False False True False False False False False True False False False True False False False True False False False True False False False True False False False True False 137.50 -0.595128 261.535265 33 997.48 584.302915 2.699206 10.431664 0.440504 263.424967 0.487950 5.679896 7.689676 19 34.93 0.538086 1705.25 44.978308 5 95.488000 -0.566615 0.013052 0.215180 -0.405840 -1.229634 -0.308063 0.246613 11 -0.618136 921.73 26.724118 4 133.41 73.576471 15.428571 19.642857 -0.085018 1288.391429 37.362181 4.714286 142.497143 83.471845 1 4 1 1 1 True False False True False False True False True False True False 1 3 True False False False False False False True False
As you can see above, we have the exact same features as before, but calculated using the test data.
Exporting Feature Matrix¶
Save as csv¶
The feature matrix is a pandas dataframe that we can save to disk
In [11]: feature_matrix.to_csv("feature_matrix.csv")
We can also read it back in as follows:
In [12]: saved_fm = pd.read_csv("feature_matrix.csv", index_col="customer_id")
In [13]: saved_fm
Out[13]:
zip_code = 60091 zip_code = 13244 zip_code is unknown COUNT(sessions) NUM_UNIQUE(sessions.device) MODE(sessions.device) = mobile MODE(sessions.device) = desktop MODE(sessions.device) is unknown SUM(transactions.amount) STD(transactions.amount) MAX(transactions.amount) SKEW(transactions.amount) MIN(transactions.amount) MEAN(transactions.amount) COUNT(transactions) NUM_UNIQUE(transactions.product_id) MODE(transactions.product_id) = 4 MODE(transactions.product_id) = 5 MODE(transactions.product_id) = 2 MODE(transactions.product_id) = 1 MODE(transactions.product_id) is unknown DAY(date_of_birth) = 18 DAY(date_of_birth) = 28 DAY(date_of_birth) = 21 DAY(date_of_birth) = 15 DAY(date_of_birth) is unknown DAY(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) is unknown YEAR(date_of_birth) = 2006 YEAR(date_of_birth) = 2003 YEAR(date_of_birth) = 1994 YEAR(date_of_birth) = 1986 YEAR(date_of_birth) = 1984 YEAR(date_of_birth) is unknown YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(join_date) is unknown MONTH(date_of_birth) = 8 MONTH(date_of_birth) = 7 MONTH(date_of_birth) = 11 MONTH(date_of_birth) is unknown MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(join_date) is unknown WEEKDAY(date_of_birth) = 0 WEEKDAY(date_of_birth) = 5 WEEKDAY(date_of_birth) = 4 WEEKDAY(date_of_birth) = 1 WEEKDAY(date_of_birth) is unknown WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(join_date) is unknown SUM(sessions.MIN(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MIN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MAX(transactions.amount)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MEAN(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) MODE(sessions.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.MODE(transactions.product_id)) = 3 MODE(sessions.MODE(transactions.product_id)) = 1 MODE(sessions.MODE(transactions.product_id)) = 4 MODE(sessions.MODE(transactions.product_id)) is unknown MODE(sessions.DAY(session_start)) = 1 MODE(sessions.DAY(session_start)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(session_start)) is unknown NUM_UNIQUE(transactions.sessions.customer_id) NUM_UNIQUE(transactions.sessions.device) MODE(transactions.sessions.customer_id) = 5 MODE(transactions.sessions.customer_id) = 4 MODE(transactions.sessions.customer_id) = 3 MODE(transactions.sessions.customer_id) = 2 MODE(transactions.sessions.customer_id) = 1 MODE(transactions.sessions.customer_id) is unknown MODE(transactions.sessions.device) = mobile MODE(transactions.sessions.device) = desktop MODE(transactions.sessions.device) is unknown
customer_id
1 True False False 6 3 False True False 5776.41 41.998795 147.64 -0.173042 6.47 79.128904 73 5 True False False False False False False False False True False False False False True False False False False False True False False False True False False False True False False False True False False False False True True False False False 198.92 -1.546156 217.064024 25 834.08 524.919674 5.741661 45.471977 0.326628 327.331994 1.329160 11.742343 25.801702 19 124.58 0.164928 1418.99 51.192518 5 136.672500 -0.243198 2.290693 0.130163 -1.336611 -1.206607 -1.165955 1.796000 4 -0.614442 546.69 9.176046 2 119.86 64.238824 12.166667 33.153333 -0.257693 962.735000 36.177337 4.166667 139.013333 87.486612 1 4 1 1 1 True False False False True False True False True False True False 1 3 False False False False True False True False False
4 False True False 9 3 False True False 10178.43 36.523849 147.55 -0.179621 6.19 80.781190 126 5 False False False False True False False False False True False False False False True False False False False False True False False False True False False True False False False True False False False True False False False False True False 193.08 -1.797214 319.497611 43 1180.90 733.862898 4.272002 17.851716 0.324894 333.923377 0.440959 11.457114 11.875823 21 60.29 0.417250 1650.65 41.627134 5 104.565000 -0.086578 1.490781 -0.385392 -1.743267 -1.619848 0.230847 1.078619 8 -0.624344 557.32 22.026552 4 118.59 69.665000 14.000000 21.453333 -0.199690 1130.936667 35.499735 4.777778 131.211111 81.540322 1 5 1 1 1 True False False True False False True False True False True False 1 3 False True False False False False False True False
3 True False False 5 2 True False False 5258.95 42.416322 148.09 -0.081427 10.66 82.171094 64 5 False True False False False False False False False True False False False False True True False False False False False False False True False False False False True False False False True False False False False True False True False False 108.69 0.353061 215.417211 23 715.80 407.390549 4.868265 10.829338 0.530094 450.318768 0.547723 5.596950 10.012657 19 38.69 0.785376 1691.27 46.402508 5 92.004000 0.570300 1.010658 0.727969 -0.627238 -0.608581 -1.598919 -0.730554 8 -0.608419 653.11 37.945636 4 133.75 66.563000 12.800000 21.738000 0.070612 1051.790000 43.083442 4.600000 143.160000 81.478110 1 2 1 1 1 True False False True False False True False True False True False 1 2 False False True False False False True False False
2 False True False 8 3 False True False 9877.67 39.913352 148.34 0.040395 8.00 76.571085 129 5 False False False True False False False False False True False False False False True False False False False False True False False True False False False False True False False False True False False False False True True False False False 136.01 -0.082021 315.817331 39 1100.82 615.714934 4.086126 10.517928 0.305345 346.152626 0.353553 9.491736 10.757169 21 40.88 0.454842 1690.97 43.950396 5 87.669412 -1.158217 1.981423 -0.576079 -0.403715 -2.828427 -0.964539 -2.048650 8 -0.522578 619.93 33.618728 4 120.06 52.288421 16.125000 17.001250 -0.010253 1234.708750 39.477166 4.875000 137.602500 76.964367 1 4 1 1 1 True False False True False False True False True False True False 1 3 False False False True False False False True False
5 True False False 7 3 False True False 9018.74 37.514054 149.53 -0.107234 6.35 83.506852 108 5 False False False False True False True False False False False False False False True False False False False False True False False False True False False False True False False False True False False False True False False False True False 137.50 -0.595128 261.535265 33 997.48 584.302915 2.699206 10.431664 0.440504 263.424967 0.487950 5.679896 7.689676 19 34.93 0.538086 1705.25 44.978308 5 95.488000 -0.566615 0.013052 0.215180 -0.405840 -1.229634 -0.308063 0.246613 11 -0.618136 921.73 26.724118 4 133.41 73.576471 15.428571 19.642857 -0.085018 1288.391429 37.362181 4.714286 142.497143 83.471845 1 4 1 1 1 True False False True False False True False True False True False 1 3 True False False False False False False True False