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.MAX(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MAX(transactions.amount)) STD(sessions.MEAN(transactions.amount)) STD(sessions.MIN(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.SKEW(transactions.amount)) STD(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.SUM(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) MODE(sessions.DAY(session_start)) = 1 MODE(sessions.DAY(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.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(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 True False False 6 3 True False False 6349.66 44.095630 149.02 -0.025941 7.55 80.375443 79 5 False True False False False False True False False False True False False False False False False False False True False False False True False False True False False False False True False False True False False False False True False False 839.76 472.231119 86.49 30 0.014384 259.873954 3.600926 7.928001 11.007471 4.961414 0.000000 0.415426 402.775486 18 94.481667 20.65 5 0.602209 51.149250 1700.67 -0.317685 -0.333796 0.335175 -0.470410 0.000000 0.204548 0.472342 8 128.51 66.666667 5 -0.539060 36.734681 543.18 13.166667 139.960000 78.705187 14.415000 5.000000 0.002397 43.312326 1058.276667 1 5 1 1 1 True False True False False False True False True False True False 1 3 True False False False False False True False False
4 True False False 8 3 True False False 8727.68 45.068765 149.95 -0.036348 5.73 80.070459 109 5 False False True False False False False False True False False False False True False True False False False False False True False False False True False False False True False False False False False False True False False False True False 1157.99 649.657515 131.51 37 0.002764 356.125829 3.335416 3.514421 13.027258 16.960575 0.517549 0.387884 235.992478 18 110.450000 54.83 5 0.382868 54.293903 1351.46 0.282488 0.027256 1.980948 2.103510 -0.644061 -1.065663 -0.391805 10 139.20 70.638182 4 -0.711744 29.026424 771.68 13.625000 144.748750 81.207189 16.438750 4.625000 0.000346 44.515729 1090.960000 1 5 1 1 1 True False False True False False True False True False True False 1 3 False True False False False False True False False
1 True False False 8 3 True False False 9025.62 40.442059 139.43 0.019698 5.81 71.631905 126 5 True False False False False True False False False False True False False False False False False True False False False True False False False False True False False True False False False True False False False False True False False False 1057.97 582.193117 78.59 40 -0.476122 312.745952 4.062019 7.322191 13.759314 6.954507 0.000000 0.589386 279.510713 25 88.755625 26.36 5 0.640252 46.905665 1613.93 1.946018 -0.780493 -0.424949 2.440005 0.000000 -0.312355 0.778170 12 118.90 50.623125 5 -1.038434 30.450261 809.97 15.750000 132.246250 72.774140 9.823750 5.000000 -0.059515 39.093244 1128.202500 1 4 1 1 1 True False False False True False True False True False True False 1 3 False False False False True False True False False
3 False True False 6 3 False True False 6236.62 43.683296 149.15 0.418230 5.89 67.060430 93 5 False False False True False False False True False False False False True 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 847.63 405.237462 66.21 29 2.286086 257.299895 2.428992 10.724241 11.174282 5.424407 0.408248 0.429374 219.021420 18 82.109444 20.06 5 0.854976 50.110120 1477.97 -1.507217 -0.941078 0.678544 1.000771 -2.449490 -0.245703 2.246479 11 126.74 55.579412 4 -0.289466 35.704680 889.21 15.500000 141.271667 67.539577 11.035000 4.833333 0.381014 42.883316 1039.436667 1 4 1 1 1 True False False True False False True False True False True False 1 3 False False True False False False False True False
2 False True False 7 3 False True False 7200.28 37.705178 146.81 0.098259 8.73 77.422366 93 5 True False False False False True False False False False False True False 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 False False 931.63 548.905851 154.60 35 -0.277640 258.700528 3.450328 17.221593 11.477071 15.874374 0.000000 0.509798 251.609234 18 96.581000 56.46 5 0.755711 47.935920 1320.64 -0.303276 -1.539467 0.235296 2.154929 0.000000 0.013087 -0.440929 8 100.04 61.910000 5 -0.763603 27.839228 634.84 13.285714 133.090000 78.415122 22.085714 5.000000 -0.039663 36.957218 1028.611429 1 4 1 1 1 True False True False False False True False True False True False 1 3 False False False True False False False True False
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.MAX(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MAX(transactions.amount)) STD(sessions.MEAN(transactions.amount)) STD(sessions.MIN(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.SKEW(transactions.amount)) STD(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.SUM(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) MODE(sessions.DAY(session_start)) = 1 MODE(sessions.DAY(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.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(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 834.08 524.919674 198.92 25 -1.546156 217.064024 5.741661 11.742343 25.801702 45.471977 1.329160 0.326628 327.331994 19 136.672500 124.58 5 0.164928 51.192518 1418.99 -0.243198 -1.165955 1.796000 2.290693 -1.206607 -1.336611 0.130163 4 119.86 64.238824 2 -0.614442 9.176046 546.69 12.166667 139.013333 87.486612 33.153333 4.166667 -0.257693 36.177337 962.735000 1 4 1 1 1 True False False 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 1180.90 733.862898 193.08 43 -1.797214 319.497611 4.272002 11.457114 11.875823 17.851716 0.440959 0.324894 333.923377 21 104.565000 60.29 5 0.417250 41.627134 1650.65 -0.086578 0.230847 1.078619 1.490781 -1.619848 -1.743267 -0.385392 8 118.59 69.665000 4 -0.624344 22.026552 557.32 14.000000 131.211111 81.540322 21.453333 4.777778 -0.199690 35.499735 1130.936667 1 5 1 1 1 True False False True False 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 715.80 407.390549 108.69 23 0.353061 215.417211 4.868265 5.596950 10.012657 10.829338 0.547723 0.530094 450.318768 19 92.004000 38.69 5 0.785376 46.402508 1691.27 0.570300 -1.598919 -0.730554 1.010658 -0.608581 -0.627238 0.727969 8 133.75 66.563000 4 -0.608419 37.945636 653.11 12.800000 143.160000 81.478110 21.738000 4.600000 0.070612 43.083442 1051.790000 1 2 1 1 1 True False False True False 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 1100.82 615.714934 136.01 39 -0.082021 315.817331 4.086126 9.491736 10.757169 10.517928 0.353553 0.305345 346.152626 21 87.669412 40.88 5 0.454842 43.950396 1690.97 -1.158217 -0.964539 -2.048650 1.981423 -2.828427 -0.403715 -0.576079 8 120.06 52.288421 4 -0.522578 33.618728 619.93 16.125000 137.602500 76.964367 17.001250 4.875000 -0.010253 39.477166 1234.708750 1 4 1 1 1 True False False True False 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 997.48 584.302915 137.50 33 -0.595128 261.535265 2.699206 5.679896 7.689676 10.431664 0.487950 0.440504 263.424967 19 95.488000 34.93 5 0.538086 44.978308 1705.25 -0.566615 -0.308063 0.246613 0.013052 -1.229634 -0.405840 0.215180 11 133.41 73.576471 4 -0.618136 26.724118 921.73 15.428571 142.497143 83.471845 19.642857 4.714286 -0.085018 37.362181 1288.391429 1 4 1 1 1 True False False True False 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.MAX(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MAX(transactions.amount)) STD(sessions.MEAN(transactions.amount)) STD(sessions.MIN(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.SKEW(transactions.amount)) STD(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.SUM(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) MODE(sessions.DAY(session_start)) = 1 MODE(sessions.DAY(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.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(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 834.08 524.919674 198.92 25 -1.546156 217.064024 5.741661 11.742343 25.801702 45.471977 1.329160 0.326628 327.331994 19 136.672500 124.58 5 0.164928 51.192518 1418.99 -0.243198 -1.165955 1.796000 2.290693 -1.206607 -1.336611 0.130163 4 119.86 64.238824 2 -0.614442 9.176046 546.69 12.166667 139.013333 87.486612 33.153333 4.166667 -0.257693 36.177337 962.735000 1 4 1 1 1 True False False 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 1180.90 733.862898 193.08 43 -1.797214 319.497611 4.272002 11.457114 11.875823 17.851716 0.440959 0.324894 333.923377 21 104.565000 60.29 5 0.417250 41.627134 1650.65 -0.086578 0.230847 1.078619 1.490781 -1.619848 -1.743267 -0.385392 8 118.59 69.665000 4 -0.624344 22.026552 557.32 14.000000 131.211111 81.540322 21.453333 4.777778 -0.199690 35.499735 1130.936667 1 5 1 1 1 True False False True False 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 715.80 407.390549 108.69 23 0.353061 215.417211 4.868265 5.596950 10.012657 10.829338 0.547723 0.530094 450.318768 19 92.004000 38.69 5 0.785376 46.402508 1691.27 0.570300 -1.598919 -0.730554 1.010658 -0.608581 -0.627238 0.727969 8 133.75 66.563000 4 -0.608419 37.945636 653.11 12.800000 143.160000 81.478110 21.738000 4.600000 0.070612 43.083442 1051.790000 1 2 1 1 1 True False False True False 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 1100.82 615.714934 136.01 39 -0.082021 315.817331 4.086126 9.491736 10.757169 10.517928 0.353553 0.305345 346.152626 21 87.669412 40.88 5 0.454842 43.950396 1690.97 -1.158217 -0.964539 -2.048650 1.981423 -2.828427 -0.403715 -0.576079 8 120.06 52.288421 4 -0.522578 33.618728 619.93 16.125000 137.602500 76.964367 17.001250 4.875000 -0.010253 39.477166 1234.708750 1 4 1 1 1 True False False True False 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 997.48 584.302915 137.50 33 -0.595128 261.535265 2.699206 5.679896 7.689676 10.431664 0.487950 0.440504 263.424967 19 95.488000 34.93 5 0.538086 44.978308 1705.25 -0.566615 -0.308063 0.246613 0.013052 -1.229634 -0.405840 0.215180 11 133.41 73.576471 4 -0.618136 26.724118 921.73 15.428571 142.497143 83.471845 19.642857 4.714286 -0.085018 37.362181 1288.391429 1 4 1 1 1 True False False True False False True False True False True False 1 3 True False False False False False False True False