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