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.SKEW(transactions.amount)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.STD(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MAX(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.MIN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.STD(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.STD(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.STD(transactions.amount)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.DAY(session_start)) MODE(sessions.MODE(transactions.product_id)) = 3 MODE(sessions.MODE(transactions.product_id)) = 1 MODE(sessions.MODE(transactions.product_id)) = 4 MODE(sessions.MODE(transactions.product_id)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(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 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 0 839.76 0.014384 86.49 30 472.231119 259.873954 3.600926 7.928001 0.415426 4.961414 402.775486 0.000000 11.007471 18 0.602209 20.65 1700.67 5 94.481667 51.149250 -0.317685 -0.333796 -0.470410 0.472342 0.000000 0.335175 0.204548 8 128.51 -0.539060 543.18 5 66.666667 36.734681 13.166667 139.960000 0.002397 14.415000 1058.276667 5.000000 78.705187 43.312326 5 1 1 1 1 1 0 0 0 1 0 1 0 1 0 1 0 1 3 1 0 0 0 0 0 1 0 0
4 1 0 0 8 3 1 0 0 8727.68 45.068765 149.95 -0.036348 5.73 80.070459 109 5 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1157.99 0.002764 131.51 37 649.657515 356.125829 3.335416 3.514421 0.387884 16.960575 235.992478 0.517549 13.027258 18 0.382868 54.83 1351.46 5 110.450000 54.293903 0.282488 0.027256 2.103510 -0.391805 -0.644061 1.980948 -1.065663 10 139.20 -0.711744 771.68 4 70.638182 29.026424 13.625000 144.748750 0.000346 16.438750 1090.960000 4.625000 81.207189 44.515729 5 1 1 1 1 0 1 0 0 1 0 1 0 1 0 1 0 1 3 0 1 0 0 0 0 1 0 0
1 1 0 0 8 3 1 0 0 9025.62 40.442059 139.43 0.019698 5.81 71.631905 126 5 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1057.97 -0.476122 78.59 40 582.193117 312.745952 4.062019 7.322191 0.589386 6.954507 279.510713 0.000000 13.759314 25 0.640252 26.36 1613.93 5 88.755625 46.905665 1.946018 -0.780493 2.440005 0.778170 0.000000 -0.424949 -0.312355 12 118.90 -1.038434 809.97 5 50.623125 30.450261 15.750000 132.246250 -0.059515 9.823750 1128.202500 5.000000 72.774140 39.093244 4 1 1 1 1 0 0 1 0 1 0 1 0 1 0 1 0 1 3 0 0 0 0 1 0 1 0 0
3 0 1 0 6 3 0 1 0 6236.62 43.683296 149.15 0.418230 5.89 67.060430 93 5 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 847.63 2.286086 66.21 29 405.237462 257.299895 2.428992 10.724241 0.429374 5.424407 219.021420 0.408248 11.174282 18 0.854976 20.06 1477.97 5 82.109444 50.110120 -1.507217 -0.941078 1.000771 2.246479 -2.449490 0.678544 -0.245703 11 126.74 -0.289466 889.21 4 55.579412 35.704680 15.500000 141.271667 0.381014 11.035000 1039.436667 4.833333 67.539577 42.883316 4 1 1 1 1 0 1 0 0 1 0 1 0 1 0 1 0 1 3 0 0 1 0 0 0 0 1 0
2 0 1 0 7 3 0 1 0 7200.28 37.705178 146.81 0.098259 8.73 77.422366 93 5 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 931.63 -0.277640 154.60 35 548.905851 258.700528 3.450328 17.221593 0.509798 15.874374 251.609234 0.000000 11.477071 18 0.755711 56.46 1320.64 5 96.581000 47.935920 -0.303276 -1.539467 2.154929 -0.440929 0.000000 0.235296 0.013087 8 100.04 -0.763603 634.84 5 61.910000 27.839228 13.285714 133.090000 -0.039663 22.085714 1028.611429 5.000000 78.415122 36.957218 4 1 1 1 1 1 0 0 0 1 0 1 0 1 0 1 0 1 3 0 0 0 1 0 0 0 1 0
Now, we can use featuretools.save_features() to save a list features to a json file
In [7]: ft.save_features(features_enc, "feature_definitions.json")
Calculating Feature Matrix for New Data¶
We can use featuretools.load_features() to read in a list of saved features to calculate for our new entity set.
In [8]: saved_features = ft.load_features('feature_definitions.json')
After we load the features back in, we can calculate the feature matrix.
In [9]: feature_matrix = ft.calculate_feature_matrix(saved_features, es_test)
In [10]: feature_matrix
Out[10]:
zip_code = 60091 zip_code = 13244 zip_code is unknown COUNT(sessions) NUM_UNIQUE(sessions.device) MODE(sessions.device) = mobile MODE(sessions.device) = desktop MODE(sessions.device) is unknown SUM(transactions.amount) STD(transactions.amount) MAX(transactions.amount) SKEW(transactions.amount) MIN(transactions.amount) MEAN(transactions.amount) COUNT(transactions) NUM_UNIQUE(transactions.product_id) MODE(transactions.product_id) = 4 MODE(transactions.product_id) = 5 MODE(transactions.product_id) = 2 MODE(transactions.product_id) = 1 MODE(transactions.product_id) is unknown DAY(date_of_birth) = 18 DAY(date_of_birth) = 28 DAY(date_of_birth) = 21 DAY(date_of_birth) = 15 DAY(date_of_birth) is unknown DAY(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) is unknown YEAR(date_of_birth) = 2006 YEAR(date_of_birth) = 2003 YEAR(date_of_birth) = 1994 YEAR(date_of_birth) = 1986 YEAR(date_of_birth) = 1984 YEAR(date_of_birth) is unknown YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(join_date) is unknown MONTH(date_of_birth) = 8 MONTH(date_of_birth) = 7 MONTH(date_of_birth) = 11 MONTH(date_of_birth) is unknown MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(join_date) is unknown WEEKDAY(date_of_birth) = 0 WEEKDAY(date_of_birth) = 5 WEEKDAY(date_of_birth) = 4 WEEKDAY(date_of_birth) = 1 WEEKDAY(date_of_birth) is unknown WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(join_date) is unknown SUM(sessions.MAX(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.STD(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MAX(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.MIN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.STD(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.STD(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.STD(transactions.amount)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.DAY(session_start)) MODE(sessions.MODE(transactions.product_id)) = 3 MODE(sessions.MODE(transactions.product_id)) = 1 MODE(sessions.MODE(transactions.product_id)) = 4 MODE(sessions.MODE(transactions.product_id)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(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 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 -1.546156 198.92 25 524.919674 217.064024 5.741661 11.742343 0.326628 45.471977 327.331994 1.329160 25.801702 19 0.164928 124.58 1418.99 5 136.672500 51.192518 -0.243198 -1.165955 2.290693 0.130163 -1.206607 1.796000 -1.336611 4 119.86 -0.614442 546.69 2 64.238824 9.176046 12.166667 139.013333 -0.257693 33.153333 962.735000 4.166667 87.486612 36.177337 4 1 1 1 1 False False True False True False True False True False True False 1 3 False False False False True False True False False
4 False True False 9 3 False True False 10178.43 36.523849 147.55 -0.179621 6.19 80.781190 126 5 False False False False True False False False False True False False False False True False False False False False True False False False True False False True False False False True False False False True False False False False True False 1180.90 -1.797214 193.08 43 733.862898 319.497611 4.272002 11.457114 0.324894 17.851716 333.923377 0.440959 11.875823 21 0.417250 60.29 1650.65 5 104.565000 41.627134 -0.086578 0.230847 1.490781 -0.385392 -1.619848 1.078619 -1.743267 8 118.59 -0.624344 557.32 4 69.665000 22.026552 14.000000 131.211111 -0.199690 21.453333 1130.936667 4.777778 81.540322 35.499735 5 1 1 1 1 False True False False True False True False True False True False 1 3 False True False False False False False True False
3 True False False 5 2 True False False 5258.95 42.416322 148.09 -0.081427 10.66 82.171094 64 5 False True False False False False False False False True False False False False True True False False False False False False False True False False False False True False False False True False False False False True False True False False 715.80 0.353061 108.69 23 407.390549 215.417211 4.868265 5.596950 0.530094 10.829338 450.318768 0.547723 10.012657 19 0.785376 38.69 1691.27 5 92.004000 46.402508 0.570300 -1.598919 1.010658 0.727969 -0.608581 -0.730554 -0.627238 8 133.75 -0.608419 653.11 4 66.563000 37.945636 12.800000 143.160000 0.070612 21.738000 1051.790000 4.600000 81.478110 43.083442 2 1 1 1 1 False True False False True False True False True False True False 1 2 False False True False False False True False False
2 False True False 8 3 False True False 9877.67 39.913352 148.34 0.040395 8.00 76.571085 129 5 False False False True False False False False False True False False False False True False False False False False True False False True False False False False True False False False True False False False False True True False False False 1100.82 -0.082021 136.01 39 615.714934 315.817331 4.086126 9.491736 0.305345 10.517928 346.152626 0.353553 10.757169 21 0.454842 40.88 1690.97 5 87.669412 43.950396 -1.158217 -0.964539 1.981423 -0.576079 -2.828427 -2.048650 -0.403715 8 120.06 -0.522578 619.93 4 52.288421 33.618728 16.125000 137.602500 -0.010253 17.001250 1234.708750 4.875000 76.964367 39.477166 4 1 1 1 1 False True False False True False True False True False True False 1 3 False False False True False False False True False
5 True False False 7 3 False True False 9018.74 37.514054 149.53 -0.107234 6.35 83.506852 108 5 False False False False True False True False False False False False False False True False False False False False True False False False True False False False True False False False True False False False True False False False True False 997.48 -0.595128 137.50 33 584.302915 261.535265 2.699206 5.679896 0.440504 10.431664 263.424967 0.487950 7.689676 19 0.538086 34.93 1705.25 5 95.488000 44.978308 -0.566615 -0.308063 0.013052 0.215180 -1.229634 0.246613 -0.405840 11 133.41 -0.618136 921.73 4 73.576471 26.724118 15.428571 142.497143 -0.085018 19.642857 1288.391429 4.714286 83.471845 37.362181 4 1 1 1 1 False True False False True False True False True False True False 1 3 True False False False False False False True False
As you can see above, we have the exact same features as before, but calculated using the test data.
Exporting Feature Matrix¶
Save as csv¶
The feature matrix is a pandas dataframe that we can save to disk
In [11]: feature_matrix.to_csv("feature_matrix.csv")
We can also read it back in as follows:
In [12]: saved_fm = pd.read_csv("feature_matrix.csv", index_col="customer_id")
In [13]: saved_fm
Out[13]:
zip_code = 60091 zip_code = 13244 zip_code is unknown COUNT(sessions) NUM_UNIQUE(sessions.device) MODE(sessions.device) = mobile MODE(sessions.device) = desktop MODE(sessions.device) is unknown SUM(transactions.amount) STD(transactions.amount) MAX(transactions.amount) SKEW(transactions.amount) MIN(transactions.amount) MEAN(transactions.amount) COUNT(transactions) NUM_UNIQUE(transactions.product_id) MODE(transactions.product_id) = 4 MODE(transactions.product_id) = 5 MODE(transactions.product_id) = 2 MODE(transactions.product_id) = 1 MODE(transactions.product_id) is unknown DAY(date_of_birth) = 18 DAY(date_of_birth) = 28 DAY(date_of_birth) = 21 DAY(date_of_birth) = 15 DAY(date_of_birth) is unknown DAY(join_date) = 17 DAY(join_date) = 15 DAY(join_date) = 13 DAY(join_date) = 8 DAY(join_date) is unknown YEAR(date_of_birth) = 2006 YEAR(date_of_birth) = 2003 YEAR(date_of_birth) = 1994 YEAR(date_of_birth) = 1986 YEAR(date_of_birth) = 1984 YEAR(date_of_birth) is unknown YEAR(join_date) = 2011 YEAR(join_date) = 2012 YEAR(join_date) = 2010 YEAR(join_date) is unknown MONTH(date_of_birth) = 8 MONTH(date_of_birth) = 7 MONTH(date_of_birth) = 11 MONTH(date_of_birth) is unknown MONTH(join_date) = 4 MONTH(join_date) = 8 MONTH(join_date) = 7 MONTH(join_date) is unknown WEEKDAY(date_of_birth) = 0 WEEKDAY(date_of_birth) = 5 WEEKDAY(date_of_birth) = 4 WEEKDAY(date_of_birth) = 1 WEEKDAY(date_of_birth) is unknown WEEKDAY(join_date) = 6 WEEKDAY(join_date) = 5 WEEKDAY(join_date) = 4 WEEKDAY(join_date) is unknown SUM(sessions.MAX(transactions.amount)) SUM(sessions.SKEW(transactions.amount)) SUM(sessions.MIN(transactions.amount)) SUM(sessions.NUM_UNIQUE(transactions.product_id)) SUM(sessions.MEAN(transactions.amount)) SUM(sessions.STD(transactions.amount)) STD(sessions.COUNT(transactions)) STD(sessions.MAX(transactions.amount)) STD(sessions.SKEW(transactions.amount)) STD(sessions.MIN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.NUM_UNIQUE(transactions.product_id)) STD(sessions.MEAN(transactions.amount)) MAX(sessions.COUNT(transactions)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) MAX(sessions.SUM(transactions.amount)) MAX(sessions.NUM_UNIQUE(transactions.product_id)) MAX(sessions.MEAN(transactions.amount)) MAX(sessions.STD(transactions.amount)) SKEW(sessions.COUNT(transactions)) SKEW(sessions.MAX(transactions.amount)) SKEW(sessions.MIN(transactions.amount)) SKEW(sessions.SUM(transactions.amount)) SKEW(sessions.NUM_UNIQUE(transactions.product_id)) SKEW(sessions.MEAN(transactions.amount)) SKEW(sessions.STD(transactions.amount)) MIN(sessions.COUNT(transactions)) MIN(sessions.MAX(transactions.amount)) MIN(sessions.SKEW(transactions.amount)) MIN(sessions.SUM(transactions.amount)) MIN(sessions.NUM_UNIQUE(transactions.product_id)) MIN(sessions.MEAN(transactions.amount)) MIN(sessions.STD(transactions.amount)) MEAN(sessions.COUNT(transactions)) MEAN(sessions.MAX(transactions.amount)) MEAN(sessions.SKEW(transactions.amount)) MEAN(sessions.MIN(transactions.amount)) MEAN(sessions.SUM(transactions.amount)) MEAN(sessions.NUM_UNIQUE(transactions.product_id)) MEAN(sessions.MEAN(transactions.amount)) MEAN(sessions.STD(transactions.amount)) NUM_UNIQUE(sessions.MODE(transactions.product_id)) NUM_UNIQUE(sessions.MONTH(session_start)) NUM_UNIQUE(sessions.YEAR(session_start)) NUM_UNIQUE(sessions.WEEKDAY(session_start)) NUM_UNIQUE(sessions.DAY(session_start)) MODE(sessions.MODE(transactions.product_id)) = 3 MODE(sessions.MODE(transactions.product_id)) = 1 MODE(sessions.MODE(transactions.product_id)) = 4 MODE(sessions.MODE(transactions.product_id)) is unknown MODE(sessions.MONTH(session_start)) = 1 MODE(sessions.MONTH(session_start)) is unknown MODE(sessions.YEAR(session_start)) = 2014 MODE(sessions.YEAR(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 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 -1.546156 198.92 25 524.919674 217.064024 5.741661 11.742343 0.326628 45.471977 327.331994 1.329160 25.801702 19 0.164928 124.58 1418.99 5 136.672500 51.192518 -0.243198 -1.165955 2.290693 0.130163 -1.206607 1.796000 -1.336611 4 119.86 -0.614442 546.69 2 64.238824 9.176046 12.166667 139.013333 -0.257693 33.153333 962.735000 4.166667 87.486612 36.177337 4 1 1 1 1 False False True False True False True False True False True False 1 3 False False False False True False True False False
4 False True False 9 3 False True False 10178.43 36.523849 147.55 -0.179621 6.19 80.781190 126 5 False False False False True False False False False True False False False False True False False False False False True False False False True False False True False False False True False False False True False False False False True False 1180.90 -1.797214 193.08 43 733.862898 319.497611 4.272002 11.457114 0.324894 17.851716 333.923377 0.440959 11.875823 21 0.417250 60.29 1650.65 5 104.565000 41.627134 -0.086578 0.230847 1.490781 -0.385392 -1.619848 1.078619 -1.743267 8 118.59 -0.624344 557.32 4 69.665000 22.026552 14.000000 131.211111 -0.199690 21.453333 1130.936667 4.777778 81.540322 35.499735 5 1 1 1 1 False True False False True False True False True False True False 1 3 False True False False False False False True False
3 True False False 5 2 True False False 5258.95 42.416322 148.09 -0.081427 10.66 82.171094 64 5 False True False False False False False False False True False False False False True True False False False False False False False True False False False False True False False False True False False False False True False True False False 715.80 0.353061 108.69 23 407.390549 215.417211 4.868265 5.596950 0.530094 10.829338 450.318768 0.547723 10.012657 19 0.785376 38.69 1691.27 5 92.004000 46.402508 0.570300 -1.598919 1.010658 0.727969 -0.608581 -0.730554 -0.627238 8 133.75 -0.608419 653.11 4 66.563000 37.945636 12.800000 143.160000 0.070612 21.738000 1051.790000 4.600000 81.478110 43.083442 2 1 1 1 1 False True False False True False True False True False True False 1 2 False False True False False False True False False
2 False True False 8 3 False True False 9877.67 39.913352 148.34 0.040395 8.00 76.571085 129 5 False False False True False False False False False True False False False False True False False False False False True False False True False False False False True False False False True False False False False True True False False False 1100.82 -0.082021 136.01 39 615.714934 315.817331 4.086126 9.491736 0.305345 10.517928 346.152626 0.353553 10.757169 21 0.454842 40.88 1690.97 5 87.669412 43.950396 -1.158217 -0.964539 1.981423 -0.576079 -2.828427 -2.048650 -0.403715 8 120.06 -0.522578 619.93 4 52.288421 33.618728 16.125000 137.602500 -0.010253 17.001250 1234.708750 4.875000 76.964367 39.477166 4 1 1 1 1 False True False False True False True False True False True False 1 3 False False False True False False False True False
5 True False False 7 3 False True False 9018.74 37.514054 149.53 -0.107234 6.35 83.506852 108 5 False False False False True False True False False False False False False False True False False False False False True False False False True False False False True False False False True False False False True False False False True False 997.48 -0.595128 137.50 33 584.302915 261.535265 2.699206 5.679896 0.440504 10.431664 263.424967 0.487950 7.689676 19 0.538086 34.93 1705.25 5 95.488000 44.978308 -0.566615 -0.308063 0.013052 0.215180 -1.229634 0.246613 -0.405840 11 133.41 -0.618136 921.73 4 73.576471 26.724118 15.428571 142.497143 -0.085018 19.642857 1288.391429 4.714286 83.471845 37.362181 4 1 1 1 1 False True False False True False True False True False True False 1 3 True False False False False False False True False