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