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