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(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) 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 YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(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 MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(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 WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(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 SUM(sessions.MAX(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.MIN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MAX(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MEAN(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.SKEW(transactions.amount)) STD(sessions.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.STD(transactions.amount)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MIN(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.STD(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.SKEW(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.MIN(transactions.amount)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(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.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) 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 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 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 839.76 259.873954 472.231119 30 0.014384 86.49 402.775486 7.928001 3.600926 11.007471 0.000000 0.415426 4.961414 1700.67 18 51.149250 94.481667 5 0.602209 20.65 0.472342 -0.333796 -0.317685 0.204548 0.335175 0.000000 -0.470410 543.18 128.51 8 36.734681 66.666667 5 -0.539060 1058.276667 139.960000 13.166667 43.312326 78.705187 5.000000 0.002397 14.415000 1 5 1 1 1 1 0 1 0 0 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 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1157.99 356.125829 649.657515 37 0.002764 131.51 235.992478 3.514421 3.335416 13.027258 0.517549 0.387884 16.960575 1351.46 18 54.293903 110.450000 5 0.382868 54.83 -0.391805 0.027256 0.282488 -1.065663 1.980948 -0.644061 2.103510 771.68 139.20 10 29.026424 70.638182 4 -0.711744 1090.960000 144.748750 13.625000 44.515729 81.207189 4.625000 0.000346 16.438750 1 5 1 1 1 1 0 0 1 0 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 1 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 1057.97 312.745952 582.193117 40 -0.476122 78.59 279.510713 7.322191 4.062019 13.759314 0.000000 0.589386 6.954507 1613.93 25 46.905665 88.755625 5 0.640252 26.36 0.778170 -0.780493 1.946018 -0.312355 -0.424949 0.000000 2.440005 809.97 118.90 12 30.450261 50.623125 5 -1.038434 1128.202500 132.246250 15.750000 39.093244 72.774140 5.000000 -0.059515 9.823750 1 4 1 1 1 1 0 0 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 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 847.63 257.299895 405.237462 29 2.286086 66.21 219.021420 10.724241 2.428992 11.174282 0.408248 0.429374 5.424407 1477.97 18 50.110120 82.109444 5 0.854976 20.06 2.246479 -0.941078 -1.507217 -0.245703 0.678544 -2.449490 1.000771 889.21 126.74 11 35.704680 55.579412 4 -0.289466 1039.436667 141.271667 15.500000 42.883316 67.539577 4.833333 0.381014 11.035000 1 4 1 1 1 1 0 0 1 0 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 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 931.63 258.700528 548.905851 35 -0.277640 154.60 251.609234 17.221593 3.450328 11.477071 0.000000 0.509798 15.874374 1320.64 18 47.935920 96.581000 5 0.755711 56.46 -0.440929 -1.539467 -0.303276 0.013087 0.235296 0.000000 2.154929 634.84 100.04 8 27.839228 61.910000 5 -0.763603 1028.611429 133.090000 13.285714 36.957218 78.415122 5.000000 -0.039663 22.085714 1 4 1 1 1 1 0 1 0 0 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(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) 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 YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(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 MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(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 WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(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 SUM(sessions.MAX(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.MIN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MAX(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MEAN(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.SKEW(transactions.amount)) STD(sessions.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.STD(transactions.amount)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MIN(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.STD(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.SKEW(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.MIN(transactions.amount)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(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.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) 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 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 True False False False False False True False False False True False False False True True False False False False False False False True 834.08 217.064024 524.919674 25 -1.546156 198.92 327.331994 11.742343 5.741661 25.801702 1.329160 0.326628 45.471977 1418.99 19 51.192518 136.672500 5 0.164928 124.58 0.130163 -1.165955 -0.243198 -1.336611 1.796000 -1.206607 2.290693 546.69 119.86 4 9.176046 64.238824 2 -0.614442 962.735000 139.013333 12.166667 36.177337 87.486612 4.166667 -0.257693 33.153333 1 4 1 1 1 True False False 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 True False False False False False True False False True False False False True False False False True False False False True False False 1180.90 319.497611 733.862898 43 -1.797214 193.08 333.923377 11.457114 4.272002 11.875823 0.440959 0.324894 17.851716 1650.65 21 41.627134 104.565000 5 0.417250 60.29 -0.385392 0.230847 -0.086578 -1.743267 1.078619 -1.619848 1.490781 557.32 118.59 8 22.026552 69.665000 4 -0.624344 1130.936667 131.211111 14.000000 35.499735 81.540322 4.777778 -0.199690 21.453333 1 5 1 1 1 True False False True False 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 False False True False True False False False False False False False False True False False False True False True False False False False False False True 715.80 215.417211 407.390549 23 0.353061 108.69 450.318768 5.596950 4.868265 10.012657 0.547723 0.530094 10.829338 1691.27 19 46.402508 92.004000 5 0.785376 38.69 0.727969 -1.598919 0.570300 -0.627238 -0.730554 -0.608581 1.010658 653.11 133.75 8 37.945636 66.563000 4 -0.608419 1051.790000 143.160000 12.800000 43.083442 81.478110 4.600000 0.070612 21.738000 1 2 1 1 1 True False False True False 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 True False False False False False False True False False False True False False False True True False False False False False False False True 1100.82 315.817331 615.714934 39 -0.082021 136.01 346.152626 9.491736 4.086126 10.757169 0.353553 0.305345 10.517928 1690.97 21 43.950396 87.669412 5 0.454842 40.88 -0.576079 -0.964539 -1.158217 -0.403715 -2.048650 -2.828427 1.981423 619.93 120.06 8 33.618728 52.288421 4 -0.522578 1234.708750 137.602500 16.125000 39.477166 76.964367 4.875000 -0.010253 17.001250 1 4 1 1 1 True False False True False 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 False False False True False True False False False False False False True False False False False False True False False False True False False False True False False True False False False False True False 997.48 261.535265 584.302915 33 -0.595128 137.50 263.424967 5.679896 2.699206 7.689676 0.487950 0.440504 10.431664 1705.25 19 44.978308 95.488000 5 0.538086 34.93 0.215180 -0.308063 -0.566615 -0.405840 0.246613 -1.229634 0.013052 921.73 133.41 11 26.724118 73.576471 4 -0.618136 1288.391429 142.497143 15.428571 37.362181 83.471845 4.714286 -0.085018 19.642857 1 4 1 1 1 True False False True False 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(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) 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 YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(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 MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(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 WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(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 SUM(sessions.MAX(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.MIN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MAX(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MEAN(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.SKEW(transactions.amount)) STD(sessions.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.STD(transactions.amount)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MIN(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.STD(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.SKEW(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.MIN(transactions.amount)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(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.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) 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 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 True False False False False False True False False False True False False False True True False False False False False False False True 834.08 217.064024 524.919674 25 -1.546156 198.92 327.331994 11.742343 5.741661 25.801702 1.329160 0.326628 45.471977 1418.99 19 51.192518 136.672500 5 0.164928 124.58 0.130163 -1.165955 -0.243198 -1.336611 1.796000 -1.206607 2.290693 546.69 119.86 4 9.176046 64.238824 2 -0.614442 962.735000 139.013333 12.166667 36.177337 87.486612 4.166667 -0.257693 33.153333 1 4 1 1 1 True False False 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 True False False False False False True False False True False False False True False False False True False False False True False False 1180.90 319.497611 733.862898 43 -1.797214 193.08 333.923377 11.457114 4.272002 11.875823 0.440959 0.324894 17.851716 1650.65 21 41.627134 104.565000 5 0.417250 60.29 -0.385392 0.230847 -0.086578 -1.743267 1.078619 -1.619848 1.490781 557.32 118.59 8 22.026552 69.665000 4 -0.624344 1130.936667 131.211111 14.000000 35.499735 81.540322 4.777778 -0.199690 21.453333 1 5 1 1 1 True False False True False 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 False False True False True False False False False False False False False True False False False True False True False False False False False False True 715.80 215.417211 407.390549 23 0.353061 108.69 450.318768 5.596950 4.868265 10.012657 0.547723 0.530094 10.829338 1691.27 19 46.402508 92.004000 5 0.785376 38.69 0.727969 -1.598919 0.570300 -0.627238 -0.730554 -0.608581 1.010658 653.11 133.75 8 37.945636 66.563000 4 -0.608419 1051.790000 143.160000 12.800000 43.083442 81.478110 4.600000 0.070612 21.738000 1 2 1 1 1 True False False True False 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 True False False False False False False True False False False True False False False True True False False False False False False False True 1100.82 315.817331 615.714934 39 -0.082021 136.01 346.152626 9.491736 4.086126 10.757169 0.353553 0.305345 10.517928 1690.97 21 43.950396 87.669412 5 0.454842 40.88 -0.576079 -0.964539 -1.158217 -0.403715 -2.048650 -2.828427 1.981423 619.93 120.06 8 33.618728 52.288421 4 -0.522578 1234.708750 137.602500 16.125000 39.477166 76.964367 4.875000 -0.010253 17.001250 1 4 1 1 1 True False False True False 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 False False False True False True False False False False False False True False False False False False True False False False True False False False True False False True False False False False True False 997.48 261.535265 584.302915 33 -0.595128 137.50 263.424967 5.679896 2.699206 7.689676 0.487950 0.440504 10.431664 1705.25 19 44.978308 95.488000 5 0.538086 34.93 0.215180 -0.308063 -0.566615 -0.405840 0.246613 -1.229634 0.013052 921.73 133.41 11 26.724118 73.576471 4 -0.618136 1288.391429 142.497143 15.428571 37.362181 83.471845 4.714286 -0.085018 19.642857 1 4 1 1 1 True False False True False False True False True False True False 3 1 False True False True False False False False False