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.SKEW(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MAX(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MIN(transactions.amount)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.MEAN(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.STD(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) 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.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(session_start)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown NUM_UNIQUE(transactions.sessions.device) NUM_UNIQUE(transactions.sessions.customer_id) MODE(transactions.sessions.device) = mobile MODE(transactions.sessions.device) = desktop MODE(transactions.sessions.device) is unknown 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
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 0.014384 30 839.76 86.49 259.873954 472.231119 0.415426 0.000000 7.928001 402.775486 3.600926 4.961414 11.007471 0.602209 5 1700.67 18 20.65 51.149250 94.481667 0.000000 -0.333796 0.472342 -0.317685 -0.470410 0.204548 0.335175 -0.539060 5 128.51 543.18 8 36.734681 66.666667 0.002397 5.000000 139.960000 1058.276667 13.166667 14.415000 43.312326 78.705187 5 1 1 1 1 1 0 0 0 1 0 1 0 1 0 1 0 3 1 1 0 0 1 0 0 0 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 0.002764 37 1157.99 131.51 356.125829 649.657515 0.387884 0.517549 3.514421 235.992478 3.335416 16.960575 13.027258 0.382868 5 1351.46 18 54.83 54.293903 110.450000 -0.644061 0.027256 -0.391805 0.282488 2.103510 -1.065663 1.980948 -0.711744 4 139.20 771.68 10 29.026424 70.638182 0.000346 4.625000 144.748750 1090.960000 13.625000 16.438750 44.515729 81.207189 5 1 1 1 1 0 1 0 0 1 0 1 0 1 0 1 0 3 1 1 0 0 0 1 0 0 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 -0.476122 40 1057.97 78.59 312.745952 582.193117 0.589386 0.000000 7.322191 279.510713 4.062019 6.954507 13.759314 0.640252 5 1613.93 25 26.36 46.905665 88.755625 0.000000 -0.780493 0.778170 1.946018 2.440005 -0.312355 -0.424949 -1.038434 5 118.90 809.97 12 30.450261 50.623125 -0.059515 5.000000 132.246250 1128.202500 15.750000 9.823750 39.093244 72.774140 4 1 1 1 1 0 0 1 0 1 0 1 0 1 0 1 0 3 1 1 0 0 0 0 0 0 1 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 2.286086 29 847.63 66.21 257.299895 405.237462 0.429374 0.408248 10.724241 219.021420 2.428992 5.424407 11.174282 0.854976 5 1477.97 18 20.06 50.110120 82.109444 -2.449490 -0.941078 2.246479 -1.507217 1.000771 -0.245703 0.678544 -0.289466 4 126.74 889.21 11 35.704680 55.579412 0.381014 4.833333 141.271667 1039.436667 15.500000 11.035000 42.883316 67.539577 4 1 1 1 1 0 1 0 0 1 0 1 0 1 0 1 0 3 1 0 1 0 0 0 1 0 0 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 -0.277640 35 931.63 154.60 258.700528 548.905851 0.509798 0.000000 17.221593 251.609234 3.450328 15.874374 11.477071 0.755711 5 1320.64 18 56.46 47.935920 96.581000 0.000000 -1.539467 -0.440929 -0.303276 2.154929 0.013087 0.235296 -0.763603 5 100.04 634.84 8 27.839228 61.910000 -0.039663 5.000000 133.090000 1028.611429 13.285714 22.085714 36.957218 78.415122 4 1 1 1 1 1 0 0 0 1 0 1 0 1 0 1 0 3 1 0 1 0 0 0 0 1 0 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.SKEW(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MAX(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MIN(transactions.amount)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.MEAN(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.STD(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) 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.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(session_start)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown NUM_UNIQUE(transactions.sessions.device) NUM_UNIQUE(transactions.sessions.customer_id) MODE(transactions.sessions.device) = mobile MODE(transactions.sessions.device) = desktop MODE(transactions.sessions.device) is unknown 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
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 -1.546156 25 834.08 198.92 217.064024 524.919674 0.326628 1.329160 11.742343 327.331994 5.741661 45.471977 25.801702 0.164928 5 1418.99 19 124.58 51.192518 136.672500 -1.206607 -1.165955 0.130163 -0.243198 2.290693 -1.336611 1.796000 -0.614442 2 119.86 546.69 4 9.176046 64.238824 -0.257693 4.166667 139.013333 962.735000 12.166667 33.153333 36.177337 87.486612 4 1 1 1 1 False False True False True False True False True False True False 3 1 True False False False False False False True 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 -1.797214 43 1180.90 193.08 319.497611 733.862898 0.324894 0.440959 11.457114 333.923377 4.272002 17.851716 11.875823 0.417250 5 1650.65 21 60.29 41.627134 104.565000 -1.619848 0.230847 -0.385392 -0.086578 1.490781 -1.743267 1.078619 -0.624344 4 118.59 557.32 8 22.026552 69.665000 -0.199690 4.777778 131.211111 1130.936667 14.000000 21.453333 35.499735 81.540322 5 1 1 1 1 False True False False True False True False True False True False 3 1 False True False False True False False False 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 0.353061 23 715.80 108.69 215.417211 407.390549 0.530094 0.547723 5.596950 450.318768 4.868265 10.829338 10.012657 0.785376 5 1691.27 19 38.69 46.402508 92.004000 -0.608581 -1.598919 0.727969 0.570300 1.010658 -0.627238 -0.730554 -0.608419 4 133.75 653.11 8 37.945636 66.563000 0.070612 4.600000 143.160000 1051.790000 12.800000 21.738000 43.083442 81.478110 2 1 1 1 1 False True False False True False True False True False True False 2 1 True False False False False True False 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 -0.082021 39 1100.82 136.01 315.817331 615.714934 0.305345 0.353553 9.491736 346.152626 4.086126 10.517928 10.757169 0.454842 5 1690.97 21 40.88 43.950396 87.669412 -2.828427 -0.964539 -0.576079 -1.158217 1.981423 -0.403715 -2.048650 -0.522578 4 120.06 619.93 8 33.618728 52.288421 -0.010253 4.875000 137.602500 1234.708750 16.125000 17.001250 39.477166 76.964367 4 1 1 1 1 False True False False True False True False True False True False 3 1 False True False False False False True False 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 -0.595128 33 997.48 137.50 261.535265 584.302915 0.440504 0.487950 5.679896 263.424967 2.699206 10.431664 7.689676 0.538086 5 1705.25 19 34.93 44.978308 95.488000 -1.229634 -0.308063 0.215180 -0.566615 0.013052 -0.405840 0.246613 -0.618136 4 133.41 921.73 11 26.724118 73.576471 -0.085018 4.714286 142.497143 1288.391429 15.428571 19.642857 37.362181 83.471845 4 1 1 1 1 False True False False True False True False True False True False 3 1 False True False True False False False False 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.SKEW(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MAX(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MIN(transactions.amount)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.MEAN(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.STD(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) 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.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(session_start)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown NUM_UNIQUE(transactions.sessions.device) NUM_UNIQUE(transactions.sessions.customer_id) MODE(transactions.sessions.device) = mobile MODE(transactions.sessions.device) = desktop MODE(transactions.sessions.device) is unknown 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
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 -1.546156 25 834.08 198.92 217.064024 524.919674 0.326628 1.329160 11.742343 327.331994 5.741661 45.471977 25.801702 0.164928 5 1418.99 19 124.58 51.192518 136.672500 -1.206607 -1.165955 0.130163 -0.243198 2.290693 -1.336611 1.796000 -0.614442 2 119.86 546.69 4 9.176046 64.238824 -0.257693 4.166667 139.013333 962.735000 12.166667 33.153333 36.177337 87.486612 4 1 1 1 1 False False True False True False True False True False True False 3 1 True False False False False False False True 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 -1.797214 43 1180.90 193.08 319.497611 733.862898 0.324894 0.440959 11.457114 333.923377 4.272002 17.851716 11.875823 0.417250 5 1650.65 21 60.29 41.627134 104.565000 -1.619848 0.230847 -0.385392 -0.086578 1.490781 -1.743267 1.078619 -0.624344 4 118.59 557.32 8 22.026552 69.665000 -0.199690 4.777778 131.211111 1130.936667 14.000000 21.453333 35.499735 81.540322 5 1 1 1 1 False True False False True False True False True False True False 3 1 False True False False True False False False 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 0.353061 23 715.80 108.69 215.417211 407.390549 0.530094 0.547723 5.596950 450.318768 4.868265 10.829338 10.012657 0.785376 5 1691.27 19 38.69 46.402508 92.004000 -0.608581 -1.598919 0.727969 0.570300 1.010658 -0.627238 -0.730554 -0.608419 4 133.75 653.11 8 37.945636 66.563000 0.070612 4.600000 143.160000 1051.790000 12.800000 21.738000 43.083442 81.478110 2 1 1 1 1 False True False False True False True False True False True False 2 1 True False False False False True False 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 -0.082021 39 1100.82 136.01 315.817331 615.714934 0.305345 0.353553 9.491736 346.152626 4.086126 10.517928 10.757169 0.454842 5 1690.97 21 40.88 43.950396 87.669412 -2.828427 -0.964539 -0.576079 -1.158217 1.981423 -0.403715 -2.048650 -0.522578 4 120.06 619.93 8 33.618728 52.288421 -0.010253 4.875000 137.602500 1234.708750 16.125000 17.001250 39.477166 76.964367 4 1 1 1 1 False True False False True False True False True False True False 3 1 False True False False False False True False 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 -0.595128 33 997.48 137.50 261.535265 584.302915 0.440504 0.487950 5.679896 263.424967 2.699206 10.431664 7.689676 0.538086 5 1705.25 19 34.93 44.978308 95.488000 -1.229634 -0.308063 0.215180 -0.566615 0.013052 -0.405840 0.246613 -0.618136 4 133.41 921.73 11 26.724118 73.576471 -0.085018 4.714286 142.497143 1288.391429 15.428571 19.642857 37.362181 83.471845 4 1 1 1 1 False True False False True False True False True False True False 3 1 False True False True False False False False False