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(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) 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 YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(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 MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(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 WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(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 SUM(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MIN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MEAN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.MAX(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.DAY(session_start)) MODE(sessions.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown MODE(sessions.MODE(transactions.product_id)) = 3 MODE(sessions.MODE(transactions.product_id)) = 1 MODE(sessions.MODE(transactions.product_id)) = 4 MODE(sessions.MODE(transactions.product_id)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(session_start)) is unknown MODE(sessions.DAY(session_start)) = 1 MODE(sessions.DAY(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 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 86.49 472.231119 0.014384 259.873954 839.76 30 4.961414 402.775486 3.600926 11.007471 0.415426 7.928001 0.000000 20.65 1700.67 18 94.481667 0.602209 51.149250 5 -0.470410 0.472342 -0.317685 0.335175 0.204548 -0.333796 0.000000 543.18 8 66.666667 -0.539060 36.734681 128.51 5 14.415000 1058.276667 13.166667 78.705187 0.002397 43.312326 139.960000 5.000000 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 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 131.51 649.657515 0.002764 356.125829 1157.99 37 16.960575 235.992478 3.335416 13.027258 0.387884 3.514421 0.517549 54.83 1351.46 18 110.450000 0.382868 54.293903 5 2.103510 -0.391805 0.282488 1.980948 -1.065663 0.027256 -0.644061 771.68 10 70.638182 -0.711744 29.026424 139.20 4 16.438750 1090.960000 13.625000 81.207189 0.000346 44.515729 144.748750 4.625000 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 1 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 78.59 582.193117 -0.476122 312.745952 1057.97 40 6.954507 279.510713 4.062019 13.759314 0.589386 7.322191 0.000000 26.36 1613.93 25 88.755625 0.640252 46.905665 5 2.440005 0.778170 1.946018 -0.424949 -0.312355 -0.780493 0.000000 809.97 12 50.623125 -1.038434 30.450261 118.90 5 9.823750 1128.202500 15.750000 72.774140 -0.059515 39.093244 132.246250 5.000000 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 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 66.21 405.237462 2.286086 257.299895 847.63 29 5.424407 219.021420 2.428992 11.174282 0.429374 10.724241 0.408248 20.06 1477.97 18 82.109444 0.854976 50.110120 5 1.000771 2.246479 -1.507217 0.678544 -0.245703 -0.941078 -2.449490 889.21 11 55.579412 -0.289466 35.704680 126.74 4 11.035000 1039.436667 15.500000 67.539577 0.381014 42.883316 141.271667 4.833333 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 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 154.60 548.905851 -0.277640 258.700528 931.63 35 15.874374 251.609234 3.450328 11.477071 0.509798 17.221593 0.000000 56.46 1320.64 18 96.581000 0.755711 47.935920 5 2.154929 -0.440929 -0.303276 0.235296 0.013087 -1.539467 0.000000 634.84 8 61.910000 -0.763603 27.839228 100.04 5 22.085714 1028.611429 13.285714 78.415122 -0.039663 36.957218 133.090000 5.000000 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(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) 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 YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(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 MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(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 WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(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 SUM(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MIN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MEAN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.MAX(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.DAY(session_start)) MODE(sessions.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown MODE(sessions.MODE(transactions.product_id)) = 3 MODE(sessions.MODE(transactions.product_id)) = 1 MODE(sessions.MODE(transactions.product_id)) = 4 MODE(sessions.MODE(transactions.product_id)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(session_start)) is unknown MODE(sessions.DAY(session_start)) = 1 MODE(sessions.DAY(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 True False False False False False True False False False True False False False True True False False False False False False False True 198.92 524.919674 -1.546156 217.064024 834.08 25 45.471977 327.331994 5.741661 25.801702 0.326628 11.742343 1.329160 124.58 1418.99 19 136.672500 0.164928 51.192518 5 2.290693 0.130163 -0.243198 1.796000 -1.336611 -1.165955 -1.206607 546.69 4 64.238824 -0.614442 9.176046 119.86 2 33.153333 962.735000 12.166667 87.486612 -0.257693 36.177337 139.013333 4.166667 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 True False False False False False True False False True False False False True False False False True False False False True False False 193.08 733.862898 -1.797214 319.497611 1180.90 43 17.851716 333.923377 4.272002 11.875823 0.324894 11.457114 0.440959 60.29 1650.65 21 104.565000 0.417250 41.627134 5 1.490781 -0.385392 -0.086578 1.078619 -1.743267 0.230847 -1.619848 557.32 8 69.665000 -0.624344 22.026552 118.59 4 21.453333 1130.936667 14.000000 81.540322 -0.199690 35.499735 131.211111 4.777778 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 False False True False True False False False False False False False False True False False False True False True False False False False False False True 108.69 407.390549 0.353061 215.417211 715.80 23 10.829338 450.318768 4.868265 10.012657 0.530094 5.596950 0.547723 38.69 1691.27 19 92.004000 0.785376 46.402508 5 1.010658 0.727969 0.570300 -0.730554 -0.627238 -1.598919 -0.608581 653.11 8 66.563000 -0.608419 37.945636 133.75 4 21.738000 1051.790000 12.800000 81.478110 0.070612 43.083442 143.160000 4.600000 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 True False False False False False False True False False False True False False False True True False False False False False False False True 136.01 615.714934 -0.082021 315.817331 1100.82 39 10.517928 346.152626 4.086126 10.757169 0.305345 9.491736 0.353553 40.88 1690.97 21 87.669412 0.454842 43.950396 5 1.981423 -0.576079 -1.158217 -2.048650 -0.403715 -0.964539 -2.828427 619.93 8 52.288421 -0.522578 33.618728 120.06 4 17.001250 1234.708750 16.125000 76.964367 -0.010253 39.477166 137.602500 4.875000 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 False False False True False True False False False False False False True False False False False False True False False False True False False False True False False True False False False False True False 137.50 584.302915 -0.595128 261.535265 997.48 33 10.431664 263.424967 2.699206 7.689676 0.440504 5.679896 0.487950 34.93 1705.25 19 95.488000 0.538086 44.978308 5 0.013052 0.215180 -0.566615 0.246613 -0.405840 -0.308063 -1.229634 921.73 11 73.576471 -0.618136 26.724118 133.41 4 19.642857 1288.391429 15.428571 83.471845 -0.085018 37.362181 142.497143 4.714286 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(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) 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 YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(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 MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(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 WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(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 SUM(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.STD(transactions.amount)) SUM(sessions.MAX(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MIN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MEAN(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.MAX(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.STD(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.STD(transactions.amount)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.STD(transactions.amount)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.DAY(session_start)) MODE(sessions.WEEKDAY(session_start)) = 2 MODE(sessions.WEEKDAY(session_start)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown MODE(sessions.MODE(transactions.product_id)) = 3 MODE(sessions.MODE(transactions.product_id)) = 1 MODE(sessions.MODE(transactions.product_id)) = 4 MODE(sessions.MODE(transactions.product_id)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(session_start)) is unknown MODE(sessions.DAY(session_start)) = 1 MODE(sessions.DAY(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 True False False False False False True False False False True False False False True True False False False False False False False True 198.92 524.919674 -1.546156 217.064024 834.08 25 45.471977 327.331994 5.741661 25.801702 0.326628 11.742343 1.329160 124.58 1418.99 19 136.672500 0.164928 51.192518 5 2.290693 0.130163 -0.243198 1.796000 -1.336611 -1.165955 -1.206607 546.69 4 64.238824 -0.614442 9.176046 119.86 2 33.153333 962.735000 12.166667 87.486612 -0.257693 36.177337 139.013333 4.166667 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 True False False False False False True False False True False False False True False False False True False False False True False False 193.08 733.862898 -1.797214 319.497611 1180.90 43 17.851716 333.923377 4.272002 11.875823 0.324894 11.457114 0.440959 60.29 1650.65 21 104.565000 0.417250 41.627134 5 1.490781 -0.385392 -0.086578 1.078619 -1.743267 0.230847 -1.619848 557.32 8 69.665000 -0.624344 22.026552 118.59 4 21.453333 1130.936667 14.000000 81.540322 -0.199690 35.499735 131.211111 4.777778 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 False False True False True False False False False False False False False True False False False True False True False False False False False False True 108.69 407.390549 0.353061 215.417211 715.80 23 10.829338 450.318768 4.868265 10.012657 0.530094 5.596950 0.547723 38.69 1691.27 19 92.004000 0.785376 46.402508 5 1.010658 0.727969 0.570300 -0.730554 -0.627238 -1.598919 -0.608581 653.11 8 66.563000 -0.608419 37.945636 133.75 4 21.738000 1051.790000 12.800000 81.478110 0.070612 43.083442 143.160000 4.600000 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 True False False False False False False True False False False True False False False True True False False False False False False False True 136.01 615.714934 -0.082021 315.817331 1100.82 39 10.517928 346.152626 4.086126 10.757169 0.305345 9.491736 0.353553 40.88 1690.97 21 87.669412 0.454842 43.950396 5 1.981423 -0.576079 -1.158217 -2.048650 -0.403715 -0.964539 -2.828427 619.93 8 52.288421 -0.522578 33.618728 120.06 4 17.001250 1234.708750 16.125000 76.964367 -0.010253 39.477166 137.602500 4.875000 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 False False False True False True False False False False False False True False False False False False True False False False True False False False True False False True False False False False True False 137.50 584.302915 -0.595128 261.535265 997.48 33 10.431664 263.424967 2.699206 7.689676 0.440504 5.679896 0.487950 34.93 1705.25 19 95.488000 0.538086 44.978308 5 0.013052 0.215180 -0.566615 0.246613 -0.405840 -0.308063 -1.229634 921.73 11 73.576471 -0.618136 26.724118 133.41 4 19.642857 1288.391429 15.428571 83.471845 -0.085018 37.362181 142.497143 4.714286 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