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