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