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