The art of using pipelines

Pipelines are a natural way to think about a machine learning system. Indeed with some practice a data scientist can visualise data "flowing" through a series of steps. The input is typically some raw data which has to be processed in some manner. The goal is to represent the data in such a way that is can be ingested by a machine learning algorithm. Along the way some steps will extract features, while others will normalize the data and remove undesirable elements. Pipelines are simple, and yet they are a powerful way of designing sophisticated machine learning systems.

Both scikit-learn and pandas make it possible to use pipelines. However it's quite rare to see pipelines being used in practice (at least on Kaggle). Sometimes you get to see people using scikit-learn's pipeline module, however the pipe method from pandas is sadly underappreciated. A big reason why pipelines are not given much love is that it's easier to think of batch learning in terms of a script or a notebook. Indeed many people doing data science seem to prefer a procedural style to a declarative style. Moreover in practice pipelines can be a bit rigid if one wishes to do non-orthodox operations.

Although pipelines may be a bit of an odd fit for batch learning, they make complete sense when they are used for online learning. Indeed the UNIX philosophy has advocated the use of pipelines for data processing for many decades. If you can visualise data as a stream of observations then using pipelines should make a lot of sense to you. We'll attempt to convince you by writing a machine learning algorithm in a procedural way and then converting it to a declarative pipeline in small steps. Hopefully by the end you'll be convinced, or not!

In this notebook we'll manipulate data from the Kaggle Recruit Restaurants Visitor Forecasting competition. The data is directly available through river's datasets module.

from pprint import pprint
from river import datasets

for x, y in datasets.Restaurants():
    pprint(x)
    pprint(y)
    break

Downloading https://maxhalford.github.io/files/datasets/kaggle_recruit_restaurants.zip (4.28 MB)
Uncompressing into /home/runner/river_data/Restaurants
{'area_name': 'Tōkyō-to Nerima-ku Toyotamakita',
 'date': datetime.datetime(2016, 1, 1, 0, 0),
 'genre_name': 'Izakaya',
 'is_holiday': True,
 'latitude': 35.7356234,
 'longitude': 139.6516577,
 'store_id': 'air_04341b588bde96cd'}
10

We'll start by building and running a model using a procedural coding style. The performance of the model doesn't matter, we're simply interested in the design of the model.

from river import feature_extraction
from river import linear_model
from river import metrics
from river import preprocessing
from river import stats

means = (
    feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7)),
    feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14)),
    feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(21))
)

scaler = preprocessing.StandardScaler()
lin_reg = linear_model.LinearRegression()
metric = metrics.MAE()

for x, y in datasets.Restaurants():

    # Derive date features
    x['weekday'] = x['date'].weekday()
    x['is_weekend'] = x['date'].weekday() in (5, 6)

    # Process the rolling means of the target  
    for mean in means:
        x = {**x, **mean.transform_one(x)}
        mean.learn_one(x, y)

    # Remove the key/value pairs that aren't features
    for key in ['store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude']:
        x.pop(key)

    # Rescale the data
    x = scaler.learn_one(x).transform_one(x)

    # Fit the linear regression
    y_pred = lin_reg.predict_one(x)
    lin_reg.learn_one(x, y)

    # Update the metric using the out-of-fold prediction
    metric.update(y, y_pred)

print(metric)

MAE: 8.465114

We're not using many features. We can print the last x to get an idea of the features (don't forget they've been scaled!)

pprint(x)

{'is_holiday': -0.23103573677646685,
 'is_weekend': 1.6249280076334165,
 'target_rollingmean_14_by_store_id': -1.4125913815779154,
 'target_rollingmean_21_by_store_id': -1.3980979075298519,
 'target_rollingmean_7_by_store_id': -1.3502314499809096,
 'weekday': 1.0292832579142892}

The above chunk of code is quite explicit but it's a bit verbose. The whole point of libraries such as river is to make life easier for users. Moreover there's too much space for users to mess up the order in which things are done, which increases the chance of there being target leakage. We'll now rewrite our model in a declarative fashion using a pipeline à la sklearn.

from river import compose


def get_date_features(x):
    weekday =  x['date'].weekday()
    return {'weekday': weekday, 'is_weekend': weekday in (5, 6)}


model = compose.Pipeline(
    ('features', compose.TransformerUnion(
        ('date_features', compose.FuncTransformer(get_date_features)),
        ('last_7_mean', feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7))),
        ('last_14_mean', feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14))),
        ('last_21_mean', feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(21)))
    )),
    ('drop_non_features', compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude')),
    ('scale', preprocessing.StandardScaler()),
    ('lin_reg', linear_model.LinearRegression())
)

metric = metrics.MAE()

for x, y in datasets.Restaurants():

    # Make a prediction without using the target
    y_pred = model.predict_one(x)

    # Update the model using the target
    model.learn_one(x, y)

    # Update the metric using the out-of-fold prediction
    metric.update(y, y_pred)

print(metric)

MAE: 8.38533

We use a Pipeline to arrange each step in a sequential order. A TransformerUnion is used to merge multiple feature extractors into a single transformer. The for loop is now much shorter and is thus easier to grok: we get the out-of-fold prediction, we fit the model, and finally we update the metric. This way of evaluating a model is typical of online learning, and so we put it wrapped it inside a function called progressive_val_score part of the evaluate module. We can use it to replace the for loop.

from river import evaluate

model = compose.Pipeline(
    ('features', compose.TransformerUnion(
        ('date_features', compose.FuncTransformer(get_date_features)),
        ('last_7_mean', feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7))),
        ('last_14_mean', feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14))),
        ('last_21_mean', feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(21)))
    )),
    ('drop_non_features', compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude')),
    ('scale', preprocessing.StandardScaler()),
    ('lin_reg', linear_model.LinearRegression())
)

evaluate.progressive_val_score(dataset=datasets.Restaurants(), model=model, metric=metrics.MAE())

MAE: 8.38533

Notice that you couldn't have used the progressive_val_score method if you wrote the model in a procedural manner.

Our code is getting shorter, but it's still a bit difficult on the eyes. Indeed there is a lot of boilerplate code associated with pipelines that can get tedious to write. However river has some special tricks up it's sleeve to save you from a lot of pain.

The first trick is that the name of each step in the pipeline can be omitted. If no name is given for a step then river automatically infers one.

model = compose.Pipeline(
    compose.TransformerUnion(
        compose.FuncTransformer(get_date_features),
        feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7)),
        feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14)),
        feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(21))
    ),
    compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude'),
    preprocessing.StandardScaler(),
    linear_model.LinearRegression()
)

evaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())

MAE: 8.38533

Under the hood a Pipeline inherits from collections.OrderedDict. Indeed this makes sense because if you think about it a Pipeline is simply a sequence of steps where each step has a name. The reason we mention this is because it means you can manipulate a Pipeline the same way you would manipulate an ordinary dict. For instance we can print the name of each step by using the keys method.

for name in model.steps:
    print(name)

TransformerUnion
Discard
StandardScaler
LinearRegression

The first step is a FeatureUnion and it's string representation contains the string representation of each of it's elements. Not having to write names saves up some time and space and is certainly less tedious.

The next trick is that we can use mathematical operators to compose our pipeline. For example we can use the + operator to merge Transformers into a TransformerUnion.

model = compose.Pipeline(
    compose.FuncTransformer(get_date_features) + \
    feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7)) + \
    feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14)) + \
    feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(21)),

    compose.Discard('store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude'),
    preprocessing.StandardScaler(),
    linear_model.LinearRegression()
)

evaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())

MAE: 8.38533

Likewhise we can use the | operator to assemble steps into a Pipeline.

model = (
    compose.FuncTransformer(get_date_features) +
    feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(7)) +
    feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(14)) +
    feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(21))
)

to_discard = ['store_id', 'date', 'genre_name', 'area_name', 'latitude', 'longitude']

model = model | compose.Discard(*to_discard) | preprocessing.StandardScaler()

model |= linear_model.LinearRegression()

evaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())

MAE: 8.38533

Hopefully you'll agree that this is a powerful way to express machine learning pipelines. For some people this should be quite remeniscent of the UNIX pipe operator. One final trick we want to mention is that functions are automatically wrapped with a FuncTransformer, which can be quite handy.

model = get_date_features

for n in [7, 14, 21]:
    model += feature_extraction.TargetAgg(by='store_id', how=stats.RollingMean(n))

model |= compose.Discard(*to_discard)
model |= preprocessing.StandardScaler()
model |= linear_model.LinearRegression()

evaluate.progressive_val_score(datasets.Restaurants(), model, metrics.MAE())

MAE: 8.38533

Naturally some may prefer the procedural style we first used because they find it easier to work with. It all depends on your style and you should use what you feel comfortable with. However we encourage you to use operators because we believe that this will increase the readability of your code, which is very important. To each their own!

Before finishing we can take an interactive look at our pipeline.

model

get_date_features

def get_date_features(x): weekday = x['date'].weekday() return {'weekday': weekday, 'is_weekend': weekday in (5, 6)}

target_rollingmean_7_by_store_id

{'by': ['store_id'], 'feature_name': 'target_rollingmean_7_by_store_id', 'groups': defaultdict(functools.partial(<function deepcopy at 0x7fe1bf000310>, RollingMean: 0.), {'air_00a91d42b08b08d9': RollingMean: 31.571429, 'air_0164b9927d20bcc3': RollingMean: 6.428571, 'air_0241aa3964b7f861': RollingMean: 11.428571, 'air_0328696196e46f18': RollingMean: 10., 'air_034a3d5b40d5b1b1': RollingMean: 28.428571, 'air_036d4f1ee7285390': RollingMean: 27.142857, 'air_0382c794b73b51ad': RollingMean: 29.142857, 'air_03963426c9312048': RollingMean: 45.714286, 'air_04341b588bde96cd': RollingMean: 32.857143, 'air_049f6d5b402a31b2': RollingMean: 16.571429, 'air_04cae7c1bc9b2a0b': RollingMean: 18.571429, 'air_0585011fa179bcce': RollingMean: 5.571429, 'air_05c325d315cc17f5': RollingMean: 29.714286, 'air_0647f17b4dc041c8': RollingMean: 29.714286, 'air_064e203265ee5753': RollingMean: 19.142857, 'air_066f0221b8a4d533': RollingMean: 11.428571, 'air_06f95ac5c33aca10': RollingMean: 27.142857, 'air_0728814bd98f7367': RollingMean: 8.285714, 'air_0768ab3910f7967f': RollingMean: 32.571429, 'air_07b314d83059c4d2': RollingMean: 41.285714, 'air_07bb665f9cdfbdfb': RollingMean: 24.142857, 'air_082908692355165e': RollingMean: 48., 'air_083ddc520ea47e1e': RollingMean: 14.857143, 'air_0845d8395f30c6bb': RollingMean: 25.285714, 'air_084d98859256acf0': RollingMean: 12.714286, 'air_0867f7bebad6a649': RollingMean: 19.142857, 'air_08ba8cd01b3ba010': RollingMean: 10., 'air_08cb3c4ee6cd6a22': RollingMean: 10.857143, 'air_08ef81d5b7a0d13f': RollingMean: 12.857143, 'air_08f994758a1e76d4': RollingMean: 29.285714, 'air_09040f6df960ddb8': RollingMean: 17.571429, 'air_0919d54f0c9a24b8': RollingMean: 37., 'air_09661c0f3259cc04': RollingMean: 26.428571, 'air_09a845d5b5944b01': RollingMean: 7.571429, 'air_09fd1f5c58583141': RollingMean: 8.571429, 'air_0a74a5408a0b8642': RollingMean: 28.857143, 'air_0b184ec04c741a6a': RollingMean: 11.857143, 'air_0b1e72d2d4422b20': RollingMean: 20.857143, 'air_0b9038300f8b2b50': RollingMean: 13.428571, 'air_0e1eae99b8723bc1': RollingMean: 12.571429, 'air_0e7c11b9abc50163': RollingMean: 37., 'air_0ead98dd07e7a82a': RollingMean: 10.571429, 'air_0f0cdeee6c9bf3d7': RollingMean: 25.571429, 'air_0f2f96335f274801': RollingMean: 11.428571, 'air_0f60e1576a7d397d': RollingMean: 4.857143, 'air_1033310359ceeac1': RollingMean: 20.857143, 'air_10393f12e9069760': RollingMean: 13.285714, 'air_105a7954e32dba9b': RollingMean: 50.714286, 'air_10713fbf3071c361': RollingMean: 12.857143, 'air_10bbe8acd943d8f6': RollingMean: 28., 'air_12c4fb7a423df20d': RollingMean: 21.142857, 'air_138ee734ac79ff90': RollingMean: 6.285714, 'air_138ff410757b845f': RollingMean: 49.714286, 'air_1408dd53f31a8a65': RollingMean: 24.285714, 'air_142e78ba7001da9c': RollingMean: 14.142857, 'air_1509881b22965b34': RollingMean: 17., 'air_152c1f08d7d20e07': RollingMean: 12., 'air_15ae33469e9ea2dd': RollingMean: 8.285714, 'air_15e6e15c7ea2c162': RollingMean: 19.571429, 'air_16179d43b6ee5fd8': RollingMean: 8.428571, 'air_1653a6c513865af3': RollingMean: 35., 'air_168441ada3e878e1': RollingMean: 54.571429, 'air_16c4cfddeb2cf69b': RollingMean: 9.428571, 'air_16cf0a73233896de': RollingMean: 13., 'air_1707a3f18bb0da07': RollingMean: 23.142857, 'air_17a6ab40f97fd4d8': RollingMean: 6.571429, 'air_17bed6dbf7c8b0fc': RollingMean: 20.428571, 'air_1979eaff8189d086': RollingMean: 9.857143, 'air_1ab60ce33bfed8a8': RollingMean: 10.142857, 'air_1ae94f514a0bce13': RollingMean: 6.571429, 'air_1ba4e87ef7422183': RollingMean: 35.571429, 'air_1c0b150f9e696a5f': RollingMean: 100.714286, 'air_1c95a84924d72500': RollingMean: 9.285714, 'air_1d1e8860ae04f8e9': RollingMean: 15.142857, 'air_1d25ca6c76df48b4': RollingMean: 42.857143, 'air_1d3f797dd1f7cf1c': RollingMean: 39., 'air_1dd8f6f47480d1a2': RollingMean: 39.142857, 'air_1dea9815ccd36620': RollingMean: 10.285714, 'air_1e23210b584540e7': RollingMean: 3.857143, 'air_1e665503b8474c55': RollingMean: 6.142857, 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9.857143, 'air_9fc607777ad76b26': RollingMean: 16.285714, 'air_a083834e7ffe187e': RollingMean: 20.857143, 'air_a11473cc1eb9a27f': RollingMean: 31.714286, 'air_a17f0778617c76e2': RollingMean: 30.285714, 'air_a1fe8c588c8d2f30': RollingMean: 17.285714, 'air_a218912784bf767d': RollingMean: 13., 'air_a21ffca0bea1661a': RollingMean: 1.142857, 'air_a239a44805932bab': RollingMean: 35.142857, 'air_a24bf50c3e90d583': RollingMean: 18.285714, 'air_a2567267116a3b75': RollingMean: 16.285714, 'air_a257c9749d8d0ff6': RollingMean: 18.142857, 'air_a271c9ba19e81d17': RollingMean: 29.142857, 'air_a2b29aa7feb4e36f': RollingMean: 21.571429, 'air_a304330715435390': RollingMean: 8.142857, 'air_a33461f4392ec62c': RollingMean: 30.142857, 'air_a373500730e2a9e0': RollingMean: 10.142857, 'air_a38f25e3399d1b25': RollingMean: 41.142857, 'air_a41b032371a63427': RollingMean: 9.428571, 'air_a49f1cf0634f13e5': RollingMean: 16.857143, 'air_a510dcfe979f09eb': RollingMean: 12.714286, 'air_a546cbf478a8b6e4': RollingMean: 28.714286, 'air_a55d17bd3f3033cb': RollingMean: 12.142857, 'air_a563896da3777078': RollingMean: 23.142857, 'air_a678e5b144ca24ce': RollingMean: 18.571429, 'air_a7404a854919e990': RollingMean: 8.571429, 'air_a8533b7a992bb0ca': RollingMean: 19.285714, 'air_a85f0c0c889f6b7e': RollingMean: 50.571429, 'air_a85f8c0bfd61889f': RollingMean: 17.142857, 'air_a88ac559064dec08': RollingMean: 28., 'air_a9133955abccf071': RollingMean: 27., 'air_a9178f19da58fe99': RollingMean: 7.714286, 'air_a9a380530c1e121f': RollingMean: 45.285714, 'air_aa0049fe3cc6f4d6': RollingMean: 11.142857, 'air_ab3ae0e410b20069': RollingMean: 16.714286, 'air_ab9746a0f83084b7': RollingMean: 1.142857, 'air_abcdc8115988a010': RollingMean: 9.857143, 'air_abf06fcca748dca5': RollingMean: 9., 'air_ac7a7427c9ae12a5': RollingMean: 63.857143, 'air_ad13e71e21235131': RollingMean: 10.285714, 'air_ad60f6b76c9df7ed': RollingMean: 30.571429, 'air_ad7777590c884721': RollingMean: 9.428571, 'air_add9a575623726c8': RollingMean: 47.571429, 'air_ade6e836ffd1da64': RollingMean: 11.571429, 'air_aed3a8b49abe4a48': RollingMean: 5., 'air_af03c277a167b2bd': RollingMean: 36.285714, 'air_af24e3e817dea1e5': RollingMean: 15.285714, 'air_af63df35857b16e6': RollingMean: 25.714286, 'air_b0a6a4c5e95c74cf': RollingMean: 19.571429, 'air_b162fb07fbbdea33': RollingMean: 15.285714, 'air_b192fb5310436005': RollingMean: 8., 'air_b1a72bf1ebf4b8ef': RollingMean: 46.285714, 'air_b1bb1fae86617d7a': RollingMean: 36.428571, 'air_b1d822f75c9fc615': RollingMean: 10.714286, 'air_b2395df0e874078d': RollingMean: 6.571429, 'air_b23d0f519291247d': RollingMean: 34., 'air_b259b4e4a51a690d': RollingMean: 18.142857, 'air_b28bed4b2e7167b7': RollingMean: 19.142857, 'air_b2a639cc7e02edf1': RollingMean: 20.142857, 'air_b2d8bc9c88b85f96': RollingMean: 8.142857, 'air_b2d97bd2337c5ba7': RollingMean: 36.142857, 'air_b2dcec37b83e2494': RollingMean: 7.857143, 'air_b30fffd7ab1e75a5': RollingMean: 9.428571, 'air_b3180b74332ba886': RollingMean: 14.285714, 'air_b3a824511477a4ed': RollingMean: 6.285714, 'air_b439391e72899756': RollingMean: 20.142857, 'air_b45b8e456f53942a': RollingMean: 9.285714, 'air_b4f32bcc399da2b9': RollingMean: 20.428571, 'air_b5598d12d1b84890': RollingMean: 5.428571, 'air_b5bdd318005d9aa4': RollingMean: 39.428571, 'air_b60cc7d6aee68194': RollingMean: 13.285714, 'air_b711b43ae472cb6b': RollingMean: 22.571429, 'air_b7fa3d2fca744dd2': RollingMean: 41.428571, 'air_b80fed1a07c817d2': RollingMean: 4.142857, 'air_b88192b35ac03c24': RollingMean: 21., 'air_b8925441167c3152': RollingMean: 1.714286, 'air_b8a5ee69e5fdcc5b': RollingMean: 31.571429, 'air_b8d9e1624baaadc2': RollingMean: 6.857143, 'air_b9e27558fb8bd5c4': RollingMean: 13.714286, 'air_ba495cccc8f0f237': RollingMean: 18.714286, 'air_ba937bf13d40fb24': RollingMean: 15.714286, 'air_bac5f4441db21db9': RollingMean: 45.857143, 'air_baf28ac9f13a307d': RollingMean: 14.571429, 'air_bb09595bab7d5cfb': RollingMean: 33.857143, 'air_bb26d6d079594414': RollingMean: 11.428571, 'air_bb4ff06cd661ee9b': RollingMean: 33.285714, 'air_bbe1c1a47e09f161': RollingMean: 1.428571, 'air_bc991c51d6613745': RollingMean: 19.714286, 'air_bc9a129e11a2efe0': RollingMean: 30.857143, 'air_bcce1ea4350b7b72': RollingMean: 26., 'air_bd74a9222edfdfe1': RollingMean: 13.428571, 'air_bdd32aa407c16335': RollingMean: 16.714286, 'air_bebd55ed63ab2422': RollingMean: 10.857143, 'air_bed603c423b7d9d4': RollingMean: 5.857143, 'air_bedd35489e666605': RollingMean: 35.857143, 'air_bf13014b6e3e60ca': RollingMean: 31.857143, 'air_bf21b8350771879b': RollingMean: 22.142857, 'air_bf617aa68d5f1cfa': RollingMean: 7.571429, 'air_bf7591560077332d': RollingMean: 9.142857, 'air_bfafaed35e213fd7': RollingMean: 14.714286, 'air_bfda7731a6c6fc61': RollingMean: 20.285714, 'air_c027e2b560442808': RollingMean: 17.285714, 'air_c0385db498b391e5': RollingMean: 34.285714, 'air_c1d5d165c055b866': RollingMean: 30.142857, 'air_c1ff20617c54fee7': RollingMean: 8.142857, 'air_c225148c0fcc5c72': RollingMean: 39.428571, 'air_c2626f5f86d57342': RollingMean: 16.142857, 'air_c26f027b5bc1f081': RollingMean: 5.857143, 'air_c28983412a7eefcf': RollingMean: 40.571429, 'air_c2c8435bdb3516d4': RollingMean: 35., 'air_c31472d14e29cee8': RollingMean: 16.714286, 'air_c3585b0fba3998d0': RollingMean: 9.285714, 'air_c3bc011cca3bec65': RollingMean: 4.285714, 'air_c3dcaf3aeb18e20e': RollingMean: 15.714286, 'air_c47aa7493b15f297': RollingMean: 23., 'air_c4fa5c562d5409ca': RollingMean: 15.857143, 'air_c52c63c781fe48f6': RollingMean: 29.428571, 'air_c5459218282bedd5': RollingMean: 21.714286, 'air_c66dbd2c37832d00': RollingMean: 16.714286, 'air_c6a164dd4060e960': RollingMean: 15.857143, 'air_c6aa2efba0ffc8eb': RollingMean: 26.428571, 'air_c6ffd6a93e6b68d6': RollingMean: 15.714286, 'air_c73d319ffabf287a': RollingMean: 18.428571, 'air_c759b6abeb552160': RollingMean: 7.142857, 'air_c77ee2b7d36da265': RollingMean: 42., 'air_c7d30ab0e07f31d5': RollingMean: 20.285714, 'air_c7f78b4f3cba33ff': RollingMean: 25.142857, 'air_c8265ecc116f2284': RollingMean: 9.142857, 'air_c88467d88b2c8ecd': RollingMean: 19.428571, 'air_c8a657c8c5c93d69': RollingMean: 9.142857, 'air_c8c0ef02ed72053f': RollingMean: 27.428571, 'air_c8fe396d6c46275d': RollingMean: 18., 'air_c92745dfdd2ec68a': RollingMean: 21.857143, 'air_c9ed65554b6edffb': RollingMean: 12.714286, 'air_c9f6de13be8b8f25': RollingMean: 3.571429, 'air_ca1315af9e073bd1': RollingMean: 43.714286, 'air_ca6ae8d49a2f1eaf': RollingMean: 24.142857, 'air_ca957d3a1529fbd3': RollingMean: 31.285714, 'air_cadf9cfb510a1d78': RollingMean: 29.714286, 'air_caf996ac27206301': RollingMean: 4.714286, 'air_cb083b4789a8d3a2': RollingMean: 18.714286, 'air_cb25551c4cd8d9f3': RollingMean: 5.714286, 'air_cb7467aed805e7fe': RollingMean: 40.428571, 'air_cb935ff8610ba3d3': RollingMean: 4.142857, 'air_cbe139af83feb388': RollingMean: 10.428571, 'air_cbe867adcf44e14f': RollingMean: 15.714286, 'air_cc1a0e985ce63711': RollingMean: 27.285714, 'air_cc35590cd1da8554': RollingMean: 24.571429, 'air_ccd19a5bc5573ae5': RollingMean: 34.857143, 'air_cd4b301d5d3918d8': RollingMean: 5.857143, 'air_cd5f54969be9ed08': RollingMean: 6.285714, 'air_ced6297e5bdf5130': RollingMean: 23.428571, 'air_cf2229e64408d9fe': RollingMean: 20.428571, 'air_cf22e368c1a71d53': RollingMean: 34.857143, 'air_cf5ab75a0afb8af9': RollingMean: 48., 'air_cfcc94797d2b5d3d': RollingMean: 18.285714, 'air_cfdeb326418194ff': RollingMean: 17.285714, 'air_d00161e19f08290b': RollingMean: 28.714286, 'air_d00a15343325e5f7': RollingMean: 21.142857, 'air_d07e57b21109304a': RollingMean: 11.428571, 'air_d0a1e69685259c92': RollingMean: 32., 'air_d0a7bd3339c3d12a': RollingMean: 46.714286, 'air_d0e8a085d8dc83aa': RollingMean: 6.571429, 'air_d138b593ebda55cc': RollingMean: 6., 'air_d1418d6fd6d634f2': RollingMean: 17.714286, 'air_d186b2cb0b9ce022': RollingMean: 14.428571, 'air_d1f20424f76cc78e': RollingMean: 19.857143, 'air_d34c0861a2be94cb': RollingMean: 41.285714, 'air_d3e7b5952cd09ccb': RollingMean: 17.857143, 'air_d44d210d2994f01b': RollingMean: 7., 'air_d473620754bf9fc2': RollingMean: 11., 'air_d477b6339b8ce69f': RollingMean: 11., 'air_d4981cdde163b172': RollingMean: 27.142857, 'air_d4b5a4b04c5f2d04': RollingMean: 15.142857, 'air_d4d218b451f82c3d': RollingMean: 9.428571, 'air_d500b48a8735fbd3': RollingMean: 20.142857, 'air_d54d6fcb116fbed3': RollingMean: 4.714286, 'air_d5e0a20370c325c7': RollingMean: 33.285714, 'air_d63cfa6d6ab78446': RollingMean: 20.857143, 'air_d69b08a175bc0387': RollingMean: 13.571429, 'air_d6b3e67261f07646': RollingMean: 11.714286, 'air_d8abb9e490abf94f': RollingMean: 12.714286, 'air_d97dabf7aae60da5': RollingMean: 34.714286, 'air_d98380a4aeb0290b': RollingMean: 44.285714, 'air_daa7947e1c47f5ed': RollingMean: 31.142857, 'air_dabfbd0ec951925a': RollingMean: 8.142857, 'air_dad0b6a36138f309': RollingMean: 5.142857, 'air_db1233ad855b34d5': RollingMean: 25.857143, 'air_db4b38ebe7a7ceff': RollingMean: 19.714286, 'air_db80363d35f10926': RollingMean: 36.142857, 'air_dbf64f1ce38c7442': RollingMean: 14., 'air_dc0e080ba0a5e5af': RollingMean: 9.428571, 'air_dc71c6cc06cd1aa2': RollingMean: 6.857143, 'air_de692863bb2dd758': RollingMean: 22., 'air_de803f7e324936b8': RollingMean: 24.571429, 'air_de88770300008624': RollingMean: 21.857143, 'air_dea0655f96947922': RollingMean: 37., 'air_df507aec929ce5f6': RollingMean: 24.857143, 'air_df554c4527a1cfe6': RollingMean: 57.285714, 'air_df5cf5cd03eb68d0': RollingMean: 7.285714, 'air_df843e6b22e8d540': RollingMean: 12.285714, 'air_df9355c47c5df9d3': RollingMean: 32.428571, 'air_dfad598ff642dab7': RollingMean: 28.285714, 'air_dfe068a1bf85f395': RollingMean: 41.142857, 'air_e00fe7853c0100d6': RollingMean: 18.142857, 'air_e0118664da63a2d0': RollingMean: 15.428571, 'air_e01d99390355408d': RollingMean: 10.571429, 'air_e053c561f32acc28': RollingMean: 16.428571, 'air_e08b9cf82057a170': RollingMean: 34., 'air_e0aee25b56a069f2': RollingMean: 14.857143, 'air_e0e69668214ff972': RollingMean: 11.142857, 'air_e0f241bd406810c0': RollingMean: 34.714286, 'air_e1b76fcb5208fb6b': RollingMean: 18.714286, 'air_e2208a79e2678432': RollingMean: 47.428571, 'air_e270aff84ac7e4c8': RollingMean: 22.714286, 'air_e3020992d5fe5dfd': RollingMean: 14.285714, 'air_e34c631c766f2766': RollingMean: 24.857143, 'air_e42bdc3377d1eee7': RollingMean: 23.428571, 'air_e483f5b3c4f310e0': RollingMean: 6., 'air_e524c6a9e06cc3a1': RollingMean: 9., 'air_e55abd740f93ecc4': RollingMean: 45.285714, 'air_e57dd6884595f60d': RollingMean: 37.714286, 'air_e58f669b6f1a08ce': RollingMean: 12.142857, 'air_e5cf003abcc5febb': RollingMean: 18.285714, 'air_e64de0a6bf0739af': RollingMean: 38.285714, 'air_e657ca554b0c008c': RollingMean: 28.285714, 'air_e700e390226d9985': RollingMean: 18.714286, 'air_e76a668009c5dabc': RollingMean: 9.285714, 'air_e7d2ac6d53d1b744': RollingMean: 11.714286, 'air_e7fbee4e3cfe65c5': RollingMean: 39., 'air_e88bbe2ede3467aa': RollingMean: 21.285714, 'air_e89735e80d614a7e': RollingMean: 31.428571, 'air_e8ed9335d0c38333': RollingMean: 30.142857, 'air_e9ebf7fc520ac76a': RollingMean: 29.142857, 'air_ea6d0c3acf00b22a': RollingMean: 25.571429, 'air_ea7c16131980c837': RollingMean: 7., 'air_eb120e6d384a17a8': RollingMean: 48.428571, 'air_eb20a89bba7dd3d0': RollingMean: 3.142857, 'air_eb2d2653586315dd': RollingMean: 35.285714, 'air_eb5788dba285e725': RollingMean: 31.142857, 'air_ebd31e812960f517': RollingMean: 30.571429, 'air_ebe02c3090271fa9': RollingMean: 11.285714, 'air_ec0fad2def4dcff0': RollingMean: 16., 'air_eca4a5a191e8d993': RollingMean: 32.571429, 'air_eca5e0064dc9314a': RollingMean: 33.714286, 'air_ecab54b57a71b10d': RollingMean: 13., 'air_eceb97ad6a7d4c07': RollingMean: 30.714286, 'air_ecf7f141339f1d57': RollingMean: 20.142857, 'air_eda179770dfa9f91': RollingMean: 10.428571, 'air_edd5e3d696a5811b': RollingMean: 38.142857, 'air_ee3a01f0c71a769f': RollingMean: 28.142857, 'air_ee3ba9af184c6c82': RollingMean: 21.285714, 'air_eec5e572b9eb9c23': RollingMean: 15.571429, 'air_eeeadee005c006a2': RollingMean: 13.714286, 'air_ef47430bcd6f6a89': RollingMean: 13.857143, 'air_ef789667e2e6fe96': RollingMean: 36.428571, 'air_ef920fa6f4b085f6': RollingMean: 32.142857, 'air_efc80d3f96b3aff7': RollingMean: 8.428571, 'air_efd70b04de878f25': RollingMean: 30., 'air_efef1e3daecce07e': RollingMean: 43.428571, 'air_f068442ebb6c246c': RollingMean: 11.571429, 'air_f0c7272956e62f12': RollingMean: 5.428571, 'air_f0fb0975bdc2cdf9': RollingMean: 10.142857, 'air_f1094dbf2aef85d9': RollingMean: 7.142857, 'air_f180301886c21375': RollingMean: 18.142857, 'air_f183a514cb8ff4fa': RollingMean: 38., 'air_f1f9027d4fa8f653': RollingMean: 22., 'air_f267dd70a6a6b5d3': RollingMean: 31.857143, 'air_f26f36ec4dc5adb0': RollingMean: 39.857143, 'air_f2985de32bb792e0': RollingMean: 24.714286, 'air_f2c5a1f24279c531': RollingMean: 18.571429, 'air_f3602e4fa2f12993': RollingMean: 11., 'air_f3f9824b7d70c3cf': RollingMean: 15.714286, 'air_f4936b91c9addbf0': RollingMean: 16.142857, 'air_f593fa60ac3541e2': RollingMean: 8., 'air_f690c42545146e0a': RollingMean: 11.714286, 'air_f6b2489ccf873c3b': RollingMean: 16.285714, 'air_f6bfd27e2e174d16': RollingMean: 14.142857, 'air_f6cdaf7b7fdc6d78': RollingMean: 12.285714, 'air_f8233ad00755c35c': RollingMean: 28.714286, 'air_f85e21e543cf44f2': RollingMean: 5.714286, 'air_f88898cd09f40496': RollingMean: 7.714286, 'air_f911308e19d64236': RollingMean: 47., 'air_f9168b23fdfc1e52': RollingMean: 14.142857, 'air_f927b2da69a82341': RollingMean: 9.428571, 'air_f957c6d6467d4d90': RollingMean: 9.285714, 'air_f96765e800907c77': RollingMean: 38.428571, 'air_fa12b40b02fecfd8': RollingMean: 14.571429, 'air_fa4ffc9057812fa2': RollingMean: 6., 'air_fab092c35776a9b1': RollingMean: 10.571429, 'air_fb44f566d4f64a4e': RollingMean: 14.857143, 'air_fbadf737162a5ce3': RollingMean: 15., 'air_fc477473134e9ae5': RollingMean: 15.714286, 'air_fcd4492c83f1c6b9': RollingMean: 24.142857, 'air_fcfbdcf7b1f82c6e': RollingMean: 39.857143, 'air_fd154088b1de6fa7': RollingMean: 4.428571, 'air_fd6aac1043520e83': RollingMean: 39.428571, 'air_fdc02ec4a3d21ea4': RollingMean: 8.142857, 'air_fdcfef8bd859f650': RollingMean: 3.714286, 'air_fe22ef5a9cbef123': RollingMean: 24.428571, 'air_fe58c074ec1445ea': RollingMean: 35., 'air_fea5dc9594450608': RollingMean: 17.428571, 'air_fee8dcf4d619598e': RollingMean: 29.571429, 'air_fef9ccb3ba0da2f7': RollingMean: 8.714286, 'air_ffcc2d5087e1b476': RollingMean: 24.714286, 'air_fff68b929994bfbd': RollingMean: 4.142857}), 'how': RollingMean: 0., 'target_name': 'target'}

target_rollingmean_14_by_store_id

{'by': ['store_id'], 'feature_name': 'target_rollingmean_14_by_store_id', 'groups': defaultdict(functools.partial(<function deepcopy at 0x7fe1bf000310>, RollingMean: 0.), {'air_00a91d42b08b08d9': RollingMean: 28.428571, 'air_0164b9927d20bcc3': RollingMean: 6.571429, 'air_0241aa3964b7f861': RollingMean: 9.928571, 'air_0328696196e46f18': RollingMean: 8.142857, 'air_034a3d5b40d5b1b1': RollingMean: 24.285714, 'air_036d4f1ee7285390': RollingMean: 22.714286, 'air_0382c794b73b51ad': RollingMean: 26.571429, 'air_03963426c9312048': RollingMean: 47.142857, 'air_04341b588bde96cd': RollingMean: 32.5, 'air_049f6d5b402a31b2': RollingMean: 13.571429, 'air_04cae7c1bc9b2a0b': RollingMean: 20., 'air_0585011fa179bcce': RollingMean: 4.785714, 'air_05c325d315cc17f5': RollingMean: 28.142857, 'air_0647f17b4dc041c8': RollingMean: 30.928571, 'air_064e203265ee5753': RollingMean: 18.714286, 'air_066f0221b8a4d533': RollingMean: 11.142857, 'air_06f95ac5c33aca10': RollingMean: 29.714286, 'air_0728814bd98f7367': RollingMean: 7.785714, 'air_0768ab3910f7967f': RollingMean: 28.214286, 'air_07b314d83059c4d2': RollingMean: 38.571429, 'air_07bb665f9cdfbdfb': RollingMean: 25.785714, 'air_082908692355165e': RollingMean: 46.071429, 'air_083ddc520ea47e1e': RollingMean: 12.642857, 'air_0845d8395f30c6bb': RollingMean: 22.714286, 'air_084d98859256acf0': RollingMean: 16.142857, 'air_0867f7bebad6a649': RollingMean: 21., 'air_08ba8cd01b3ba010': RollingMean: 10.285714, 'air_08cb3c4ee6cd6a22': RollingMean: 12.642857, 'air_08ef81d5b7a0d13f': RollingMean: 14.5, 'air_08f994758a1e76d4': RollingMean: 28., 'air_09040f6df960ddb8': RollingMean: 15.571429, 'air_0919d54f0c9a24b8': RollingMean: 35.857143, 'air_09661c0f3259cc04': RollingMean: 28.857143, 'air_09a845d5b5944b01': RollingMean: 6.928571, 'air_09fd1f5c58583141': RollingMean: 8.642857, 'air_0a74a5408a0b8642': RollingMean: 28.428571, 'air_0b184ec04c741a6a': RollingMean: 12.857143, 'air_0b1e72d2d4422b20': RollingMean: 20.928571, 'air_0b9038300f8b2b50': RollingMean: 11.428571, 'air_0e1eae99b8723bc1': RollingMean: 10.642857, 'air_0e7c11b9abc50163': RollingMean: 35.071429, 'air_0ead98dd07e7a82a': RollingMean: 13.571429, 'air_0f0cdeee6c9bf3d7': RollingMean: 22.071429, 'air_0f2f96335f274801': RollingMean: 9.928571, 'air_0f60e1576a7d397d': RollingMean: 6.214286, 'air_1033310359ceeac1': RollingMean: 18.642857, 'air_10393f12e9069760': RollingMean: 13.571429, 'air_105a7954e32dba9b': RollingMean: 51.5, 'air_10713fbf3071c361': RollingMean: 12.285714, 'air_10bbe8acd943d8f6': RollingMean: 30.857143, 'air_12c4fb7a423df20d': RollingMean: 20.071429, 'air_138ee734ac79ff90': RollingMean: 7.214286, 'air_138ff410757b845f': RollingMean: 54.428571, 'air_1408dd53f31a8a65': RollingMean: 27.857143, 'air_142e78ba7001da9c': RollingMean: 13.642857, 'air_1509881b22965b34': RollingMean: 19., 'air_152c1f08d7d20e07': RollingMean: 11.857143, 'air_15ae33469e9ea2dd': RollingMean: 10.285714, 'air_15e6e15c7ea2c162': RollingMean: 18.642857, 'air_16179d43b6ee5fd8': RollingMean: 7.714286, 'air_1653a6c513865af3': RollingMean: 32.5, 'air_168441ada3e878e1': RollingMean: 45.714286, 'air_16c4cfddeb2cf69b': RollingMean: 12.071429, 'air_16cf0a73233896de': RollingMean: 11.428571, 'air_1707a3f18bb0da07': RollingMean: 26.214286, 'air_17a6ab40f97fd4d8': RollingMean: 8.285714, 'air_17bed6dbf7c8b0fc': RollingMean: 19.785714, 'air_1979eaff8189d086': RollingMean: 9.071429, 'air_1ab60ce33bfed8a8': RollingMean: 9.642857, 'air_1ae94f514a0bce13': RollingMean: 7., 'air_1ba4e87ef7422183': RollingMean: 38.714286, 'air_1c0b150f9e696a5f': RollingMean: 100.785714, 'air_1c95a84924d72500': RollingMean: 8.714286, 'air_1d1e8860ae04f8e9': RollingMean: 15.071429, 'air_1d25ca6c76df48b4': RollingMean: 42.214286, 'air_1d3f797dd1f7cf1c': RollingMean: 33.571429, 'air_1dd8f6f47480d1a2': RollingMean: 37.428571, 'air_1dea9815ccd36620': RollingMean: 10.5, 'air_1e23210b584540e7': RollingMean: 3.857143, 'air_1e665503b8474c55': RollingMean: 5.785714, 'air_1eeff462acb24fb7': RollingMean: 16.428571, 'air_1f1390a8be2272b3': RollingMean: 18.214286, 'air_1f34e9beded2231a': RollingMean: 8.214286, 'air_1f7f8fa557bc0d55': RollingMean: 3.928571, 'air_2009041dbf9264de': RollingMean: 51.285714, 'air_20619d21192aa571': RollingMean: 16.785714, 'air_20add8092c9bb51d': RollingMean: 31.642857, 'air_2195cd5025a98033': RollingMean: 29.428571, 'air_21f5052d5330528d': RollingMean: 30.857143, 'air_220cba70c890b119': RollingMean: 7.928571, 'air_22682e965418936f': RollingMean: 10., 'air_228f10bec0bda9c8': RollingMean: 16.785714, 'air_229d7e508d9f1b5e': RollingMean: 9.571429, 'air_232dcee6f7c51d37': RollingMean: 5.571429, 'air_234d3dbf7f3d5a50': RollingMean: 7.642857, 'air_23e1b11aee2a1407': RollingMean: 46., 'air_23ee674e91469086': RollingMean: 20.714286, 'air_24b9b2a020826ede': RollingMean: 31.5, 'air_24e8414b9b07decb': RollingMean: 6.071429, 'air_2545dd3a00f265e2': RollingMean: 46.785714, 'air_256be208a979e023': RollingMean: 7.428571, 'air_2570ccb93badde68': RollingMean: 39.357143, 'air_258ad2619d7bff9a': RollingMean: 36.714286, 'air_258dc112912fc458': RollingMean: 61.428571, 'air_25c583983246b7b0': RollingMean: 24.428571, 'air_25d8e5cc57dd87d9': RollingMean: 24.928571, 'air_25e9888d30b386df': RollingMean: 4.928571, 'air_2634e41551e9807d': RollingMean: 14.857143, 'air_26c5bbeb7bb82bf1': RollingMean: 27.071429, 'air_26f10355d9b4d82a': RollingMean: 33.785714, 'air_2703dcb33192b181': RollingMean: 52.428571, 'air_275732a5db46f4d3': RollingMean: 15.928571, 'air_27e991812b0d9c92': RollingMean: 41.428571, 'air_28064154614b2e6c': RollingMean: 22.857143, 'air_287d2de7d3c93406': RollingMean: 13., 'air_28a9fa1ec0839375': RollingMean: 29.071429, 'air_28dbe91c4c9656be': RollingMean: 33.571429, 'air_290e7a57b390f78e': RollingMean: 13.857143, 'air_298513175efdf261': RollingMean: 26.428571, 'air_2a184c1745274b2b': RollingMean: 4.214286, 'air_2a24aec099333f39': RollingMean: 7.285714, 'air_2a3743e37aab04b4': RollingMean: 20.071429, 'air_2a485b92210c98b5': RollingMean: 18.142857, 'air_2a7f14da7fe0f699': RollingMean: 27.214286, 'air_2aab19554f91ff82': RollingMean: 49.714286, 'air_2ac361b97630e2df': RollingMean: 12.071429, 'air_2b8b29ddfd35018e': RollingMean: 10.928571, 'air_2b9bc9f5f5168ea1': RollingMean: 21.428571, 'air_2bffb19a24d11729': RollingMean: 12.428571, 'air_2c505f9ad67d4635': RollingMean: 16.5, 'air_2c6c79d597e48096': RollingMean: 17.928571, 'air_2c6fef1ce0e13a5a': RollingMean: 30.785714, 'air_2c989829acbd1c6b': RollingMean: 28.357143, 'air_2cee51fa6fdf6c0d': RollingMean: 16.571429, 'air_2d3afcb91762fe01': RollingMean: 45.142857, 'air_2d78d9a1f4dd02ca': RollingMean: 11.357143, 'air_2e7cb1f1a2a9cd6a': RollingMean: 27.571429, 'air_2f8ced25216df926': RollingMean: 14.142857, 'air_2fc149abe33adcb4': RollingMean: 35.214286, 'air_2fc478dc9f0a6b31': RollingMean: 11.428571, 'air_2fed81034f8834e5': RollingMean: 20.714286, 'air_303bac187b53083a': RollingMean: 10.428571, 'air_310e467e6e625004': RollingMean: 15.214286, 'air_3155ee23d92202da': RollingMean: 16.5, 'air_31c753b48a657b6c': RollingMean: 24.285714, 'air_32460819c7600037': RollingMean: 51.071429, 'air_324f7c39a8410e7c': RollingMean: 10.928571, 'air_326ca454ef3558bc': RollingMean: 22.214286, 'air_32b02ba5dc2027f4': RollingMean: 27.5, 'air_32c61b620a766138': RollingMean: 26.785714, 'air_32f5d7cd696e3c4a': RollingMean: 23.142857, 'air_33b01025210d6007': RollingMean: 11.785714, 'air_3440e0ea1b70a99b': RollingMean: 28., 'air_346ade7d29230634': RollingMean: 7.857143, 'air_347be2c4feeb408b': RollingMean: 21.071429, 'air_349278fa964bb12f': RollingMean: 19.214286, 'air_3525f11ef0bf0c35': RollingMean: 41.142857, 'air_35512c42db0868da': RollingMean: 6.071429, 'air_3561fd1c0bce6a95': RollingMean: 10.714286, 'air_35c4732dcbfe31be': RollingMean: 6.785714, 'air_36429b5ca4407b3e': RollingMean: 20., 'air_36bcf77d3382d36e': RollingMean: 32.5, 'air_37189c92b6c761ec': RollingMean: 14.857143, 'air_375a5241615b5e22': RollingMean: 7., 'air_382f5ace4e2247b8': RollingMean: 7.785714, 'air_383f5b2f8d345a49': RollingMean: 12.357143, 'air_38746ffe9aa20c7e': RollingMean: 3.785714, 'air_396166d47733d5c9': RollingMean: 24.714286, 'air_396942e6423a2145': RollingMean: 21.857143, 'air_397d3f32a7196aa2': RollingMean: 35.5, 'air_3980af67be35afdb': RollingMean: 15.785714, 'air_3982a2c4ea2ed431': RollingMean: 30.5, 'air_399904bdb7685ca0': RollingMean: 27.785714, 'air_39dccf7df20b1c6a': RollingMean: 26.714286, 'air_3a8a3f8fb5cd7f88': RollingMean: 21.214286, 'air_3aa839e8e0cb6c87': RollingMean: 27.5, 'air_3ac24136722e2291': RollingMean: 13.142857, 'air_3b20733899b5287f': RollingMean: 39.857143, 'air_3b6438b125086430': RollingMean: 13.285714, 'air_3bb99a1fe0583897': RollingMean: 40.285714, 'air_3bd49f98ab7f36ab': RollingMean: 13., 'air_3c05c8f26c611eb9': RollingMean: 25.285714, 'air_3c938075889fc059': RollingMean: 30.5, 'air_3cad29d1a23209d2': RollingMean: 8.357143, 'air_3caef3f76b8f26c5': RollingMean: 25.642857, 'air_3d3a2b509180e798': RollingMean: 17.714286, 'air_3e6cea17a9d2c0f1': RollingMean: 17.071429, 'air_3e93f3c81008696d': RollingMean: 34.214286, 'air_3f91d592acd6cc0b': RollingMean: 24.928571, 'air_401b39f97e56b939': RollingMean: 11.785714, 'air_4043b7ccfbffa732': RollingMean: 44.571429, 'air_4092cfbd95a3ac1b': RollingMean: 23., 'air_40953e2d8b4f2857': RollingMean: 15.428571, 'air_40f6193ea3ed1b91': RollingMean: 16.714286, 'air_414ff459ed18fa48': RollingMean: 16., 'air_41bbf6e1d9814c4b': RollingMean: 8.714286, 'air_421670f21da5ba31': RollingMean: 17.928571, 'air_4254c3fc3ad078bd': RollingMean: 11.357143, 'air_42c9aa6d617c5057': RollingMean: 42.714286, 'air_42d41eb58cad170e': RollingMean: 32.285714, 'air_43b65e4b05bff2d3': RollingMean: 20.642857, 'air_43d577e0c9460e64': RollingMean: 35.142857, 'air_4433ab8e9999915f': RollingMean: 21.142857, 'air_4481a87c1d7c9896': RollingMean: 20.928571, 'air_452100f5305dde64': RollingMean: 7.428571, 'air_45326ebb8dc72cfb': RollingMean: 29.142857, 'air_4570f52104fe0982': RollingMean: 7.357143, 'air_4579cb0669fd411b': RollingMean: 18.071429, 'air_457efe8c3a30ea17': RollingMean: 6.285714, 'air_464a62de0d57be1e': RollingMean: 26.857143, 'air_465bddfed3353b23': RollingMean: 27.071429, 'air_47070be6093f123e': RollingMean: 46.285714, 'air_472b19e3b5bffa41': RollingMean: 13.5, 'air_473cf23b9e7c0a37': RollingMean: 9.357143, 'air_473f98b212d37b4a': RollingMean: 27.357143, 'air_47beaffd3806c979': RollingMean: 19.214286, 'air_483eba479dc9910d': RollingMean: 21.642857, 'air_48e9fc98b62495a7': RollingMean: 17.071429, 'air_48f4da6223571da4': RollingMean: 22.571429, 'air_48ffd31594bc3263': RollingMean: 4.928571, 'air_49211568cab5fdee': RollingMean: 25., 'air_4974785f48853db9': RollingMean: 6.928571, 'air_4b251b9f8373f1ae': RollingMean: 26.928571, 'air_4b380b4db9d37883': RollingMean: 25.571429, 'air_4b55d8aea1d2b395': RollingMean: 34.285714, 'air_4b9085d0d46a6211': RollingMean: 28.142857, 'air_4beac252540f865e': RollingMean: 41.571429, 'air_4c2ed28f3f19ca52': RollingMean: 14.642857, 'air_4c665a2bfff0da3b': RollingMean: 8.857143, 'air_4c727b55acdee495': RollingMean: 15.357143, 'air_4cab15ad29c0ffbc': RollingMean: 21.214286, 'air_4cab91146e3d1897': RollingMean: 15., 'air_4cca5666eaf5c709': RollingMean: 34.428571, 'air_4ce7b17062a1bf73': RollingMean: 7.857143, 'air_4d21676ed11f0bac': RollingMean: 32.857143, 'air_4d71826793c09b22': RollingMean: 21.642857, 'air_4d90a22572fa1ec9': RollingMean: 25.785714, 'air_4de6d887a7b1c1fc': RollingMean: 15.357143, 'air_4dea8d17f6f59c56': RollingMean: 25.214286, 'air_4e1c38f68f435596': RollingMean: 34.071429, 'air_4f762e840b3996e1': RollingMean: 11.285714, 'air_4feeb8600f131e43': RollingMean: 57.714286, 'air_500641aca4cf673c': RollingMean: 15.142857, 'air_506fe758114df773': RollingMean: 28.428571, 'air_51281cd059d7b89b': RollingMean: 13.928571, 'air_51319e7acf0438cf': RollingMean: 13.571429, 'air_52a08ef3efdb4bb0': RollingMean: 47.928571, 'air_52e2a1fd42bc917a': RollingMean: 11.642857, 'air_536043fcf1a4f8a4': RollingMean: 30.071429, 'air_539d693f7317c62d': RollingMean: 18., 'air_546b353cbea4a45b': RollingMean: 17.357143, 'air_5485912b44f976de': RollingMean: 8.428571, 'air_54d6c25d33f5260e': RollingMean: 40.5, 'air_54ed43163b7596c4': RollingMean: 16.5, 'air_55390f784018349a': RollingMean: 46., 'air_55c3627912b9c849': RollingMean: 10., 'air_55e11c33d4758131': RollingMean: 22.071429, 'air_56cd12f31a0afc04': RollingMean: 29.785714, 'air_56cebcbd6906e04c': RollingMean: 17.357143, 'air_56ea46c14b2dd967': RollingMean: 42.214286, 'air_57013002b912772b': RollingMean: 5.571429, 'air_573ecdf81b157d22': RollingMean: 28.285714, 'air_57c9eea1a2b66e65': RollingMean: 14.357143, 'air_57ed725a1930a5b9': RollingMean: 15.642857, 'air_5878b6f2a9da12c1': RollingMean: 13.357143, 'air_59cc9b2b209c6331': RollingMean: 9.071429, 'air_5a9a6cbeeb434c08': RollingMean: 23.071429, 'air_5acc13d655a6e8b2': RollingMean: 24.071429, 'air_5afb1cca48ceaa19': RollingMean: 42.142857, 'air_5b6d18c470bbfaf9': RollingMean: 37.285714, 'air_5b704df317ed1962': RollingMean: 2.214286, 'air_5bd22f9cc1426a90': RollingMean: 32.357143, 'air_5c65468938c07fa5': RollingMean: 16.214286, 'air_5c7489c9ec755e2d': RollingMean: 33.428571, 'air_5c817ef28f236bdf': RollingMean: 42.428571, 'air_5cb030b9f0b91537': RollingMean: 11.071429, 'air_5cfc537125d97f16': RollingMean: 9.571429, 'air_5d7c744c3a2ef624': RollingMean: 32.428571, 'air_5d945ade487cdf4d': RollingMean: 13.5, 'air_5dea8a7a5bf5eb71': RollingMean: 28.285714, 'air_5e339a1f364cdb00': RollingMean: 11.285714, 'air_5e34c6fe6fabd10e': RollingMean: 20.357143, 'air_5e70fe82f9e4fab6': RollingMean: 15.5, 'air_5e939e005bd34633': RollingMean: 2.571429, 'air_5ed3198e4a5eed0f': RollingMean: 38.071429, 'air_5f3a3ef4cba110a4': RollingMean: 32.214286, 'air_5f6fa1b897fe80d5': RollingMean: 29.785714, 'air_5fbda8e9302f7c13': RollingMean: 27.928571, 'air_602ca92c0db34f8f': RollingMean: 19., 'air_609050e4e4f79ae1': RollingMean: 11.857143, 'air_60a7057184ec7ec7': RollingMean: 27.857143, 'air_60aa54ecbc602348': RollingMean: 5.428571, 'air_6108821ffafa9b72': RollingMean: 24.785714, 'air_614e2f7e76dff854': RollingMean: 11.857143, 'air_61668cc2b0778898': RollingMean: 8.428571, 'air_61b8d37c33617f21': RollingMean: 27.428571, 'air_61de73b097513f58': RollingMean: 9.714286, 'air_622375b4815cf5cb': RollingMean: 37.5, 'air_627cabe2fe53f33f': RollingMean: 14.5, 'air_629d9935273c82ae': RollingMean: 23.142857, 'air_629edf21ea38ac2d': RollingMean: 35.214286, 'air_632ba66e1f75aa28': RollingMean: 19.142857, 'air_638c35eb25e53eea': RollingMean: 19.5, 'air_63a750d8b4b6a976': RollingMean: 23.428571, 'air_63a88d81295195ed': RollingMean: 29.5, 'air_63b13c56b7201bd9': RollingMean: 22.285714, 'air_63e28ee0b0c955a7': RollingMean: 22., 'air_640cf4835f0d9ba3': RollingMean: 26.714286, 'air_6411203a47b5ec77': RollingMean: 8.928571, 'air_645cb18b33f938cf': RollingMean: 17.071429, 'air_646b93e336f0dded': RollingMean: 7.428571, 'air_64a5d5c1381837af': RollingMean: 40., 'air_64d4491ad8cdb1c6': RollingMean: 13.714286, 'air_650f9b9de0c5542c': 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'air_6a15e4eae523189d': RollingMean: 17.785714, 'air_6b15edd1b4fbb96a': RollingMean: 31.785714, 'air_6b2268863b14a2af': RollingMean: 18.857143, 'air_6b65745d432fd77f': RollingMean: 23.571429, 'air_6b7678aae65d2d59': RollingMean: 6.928571, 'air_6b942d5ebbc759c2': RollingMean: 14.714286, 'air_6b9fa44a9cf504a1': RollingMean: 5.785714, 'air_6c1128955c58b690': RollingMean: 13., 'air_6c91a28278a16f64': RollingMean: 8.642857, 'air_6c952e3c6e590945': RollingMean: 15.642857, 'air_6ca1d941c8199a67': RollingMean: 26.071429, 'air_6cbe54f0aa30b615': RollingMean: 15.571429, 'air_6ced51c24fb54262': RollingMean: 7.785714, 'air_6d64dba2edd4fc0c': RollingMean: 9.428571, 'air_6d65542aa43b598b': RollingMean: 27.785714, 'air_6d65dd11d96e00fb': RollingMean: 4.428571, 'air_6e06824d0934dd81': RollingMean: 21.928571, 'air_6e3fd96320d24324': RollingMean: 8.285714, 'air_6e64fb5821402cd2': RollingMean: 8.357143, 'air_6ff5fca957798daa': RollingMean: 7.857143, 'air_707d4b6328f2c2df': RollingMean: 28.5, 'air_709262d948dd0b6e': RollingMean: 11.071429, 'air_70e9e8cd55879414': RollingMean: 10.928571, 'air_70f834596eb99fee': RollingMean: 21.428571, 'air_710d6537cb7623df': RollingMean: 32.071429, 'air_712dd258f7f91b4b': RollingMean: 15.714286, 'air_71903025d39a4571': RollingMean: 14.642857, 'air_722297e7f26db91d': RollingMean: 11.071429, 'air_728ff578acc6ac6e': RollingMean: 10.571429, 'air_72f5146cf0c49beb': RollingMean: 12.214286, 'air_735bcbe1763d6e98': RollingMean: 8.642857, 'air_73f316e6a18d8aa9': RollingMean: 22.785714, 'air_7420042ff75f9aca': RollingMean: 35., 'air_746211c0b532e8aa': RollingMean: 53.5, 'air_747f375eb3900e1e': RollingMean: 5.5, 'air_74cf22153214064c': RollingMean: 10.142857, 'air_7514d90009613cd6': RollingMean: 66.928571, 'air_754ae581ad80cc9f': RollingMean: 13.928571, 'air_75864c80d2fb334a': RollingMean: 10.357143, 'air_75bd5d1b6dc6670d': RollingMean: 14.142857, 'air_764f71040a413d4d': RollingMean: 49.428571, 'air_77488fa378cf98c3': RollingMean: 9., 'air_77dfc83450cbc89c': RollingMean: 41.571429, 'air_7831b00996701c0f': RollingMean: 23.5, 'air_789103bf53b8096b': RollingMean: 55.928571, 'air_789466e488705c93': RollingMean: 24.928571, 'air_78df4dc6a7e83e41': RollingMean: 17.5, 'air_79afb3f52b4d062c': RollingMean: 10., 'air_79f528087f49df06': RollingMean: 34.071429, 'air_7a81bd7fadcbf3d8': RollingMean: 5., 'air_7a946aada80376a4': RollingMean: 16., 'air_7bacc4d36fb094c9': RollingMean: 6.142857, 'air_7bc6ca04d7b0f3b8': RollingMean: 12.071429, 'air_7bda6048a4a78837': RollingMean: 23.857143, 'air_7c7774c66fb237f7': RollingMean: 8.714286, 'air_7cc17a324ae5c7dc': RollingMean: 14.714286, 'air_7cf5a02c0e01b647': RollingMean: 33.214286, 'air_7d65049f9d275c0d': RollingMean: 10.857143, 'air_7dacea2f22afccfb': RollingMean: 32.357143, 'air_7db266904cb0d72a': RollingMean: 13., 'air_7e12c5d27f44a8de': RollingMean: 23.785714, 'air_7ef9a5ea5c8fe39f': RollingMean: 11.142857, 'air_7f3dc18494bce98b': RollingMean: 14.071429, 'air_7f9e15afafcf4c75': 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36.142857, 'air_915558a55c2bc56c': RollingMean: 17.785714, 'air_91beafbba9382b0a': RollingMean: 36.5, 'air_91d72e16c4bcba55': RollingMean: 14.785714, 'air_9241121af22ff1d6': RollingMean: 29.928571, 'air_929d8513e3cdb423': RollingMean: 7., 'air_931a8a4321b6e7d1': RollingMean: 5.142857, 'air_9352c401d5adb01b': RollingMean: 25.571429, 'air_9387ff95e886ebc7': RollingMean: 13.214286, 'air_938ef91ecdde6878': RollingMean: 23.214286, 'air_939964477035ef0b': RollingMean: 17.928571, 'air_93b9bb641f8fc982': RollingMean: 28.285714, 'air_93dd7070c9bf5453': RollingMean: 29.428571, 'air_93ebe490d4abb8e9': RollingMean: 17.714286, 'air_9438d67241c81314': RollingMean: 37., 'air_947eb2cae4f3e8f2': RollingMean: 32.071429, 'air_9483d65e9cc9a6b7': RollingMean: 15.285714, 'air_950381108f839348': RollingMean: 28.142857, 'air_95d28905941fd4cb': RollingMean: 30.785714, 'air_95e917913cd988f3': RollingMean: 23.642857, 'air_96005f79124e12bf': RollingMean: 40.428571, 'air_965b2e0cf4119003': RollingMean: 47.357143, 'air_96743eee94114261': RollingMean: 13.428571, 'air_96773a6236d279b1': RollingMean: 24.785714, 'air_968d72c12eed09f0': RollingMean: 18., 'air_96929a799b12a43e': RollingMean: 27.214286, 'air_96ec3cfe78cb0652': RollingMean: 18.5, 'air_97159fc4e90053fe': RollingMean: 24., 'air_97958e7fce98b6a3': RollingMean: 16.928571, 'air_97b2a9f975fc702c': RollingMean: 40.142857, 'air_97cf68dc1a9beac0': RollingMean: 13.428571, 'air_97e0f2feec4d577a': RollingMean: 15.071429, 'air_9828505fefc77d75': RollingMean: 12.5, 'air_98b54e32ccddd896': RollingMean: 18.214286, 'air_990a642a3deb2903': RollingMean: 33.5, 'air_99157b6163835eec': RollingMean: 33.785714, 'air_99a5183695b849f9': RollingMean: 30.928571, 'air_99b01136f451fc0e': RollingMean: 41.428571, 'air_99c3eae84130c1cb': RollingMean: 39.928571, 'air_9a30407764f4ff84': RollingMean: 17., 'air_9a6f6e7f623003d2': RollingMean: 3.142857, 'air_9aa32b3db0fab3a5': RollingMean: 17.142857, 'air_9aa92007e3628dbc': RollingMean: 31.642857, 'air_9ae7081cb77dc51e': 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'air_c28983412a7eefcf': RollingMean: 35.285714, 'air_c2c8435bdb3516d4': RollingMean: 31.214286, 'air_c31472d14e29cee8': RollingMean: 14.857143, 'air_c3585b0fba3998d0': RollingMean: 9.357143, 'air_c3bc011cca3bec65': RollingMean: 5.642857, 'air_c3dcaf3aeb18e20e': RollingMean: 17.428571, 'air_c47aa7493b15f297': RollingMean: 21.142857, 'air_c4fa5c562d5409ca': RollingMean: 13.785714, 'air_c52c63c781fe48f6': RollingMean: 27.714286, 'air_c5459218282bedd5': RollingMean: 22.571429, 'air_c66dbd2c37832d00': RollingMean: 16.714286, 'air_c6a164dd4060e960': RollingMean: 12.428571, 'air_c6aa2efba0ffc8eb': RollingMean: 28.857143, 'air_c6ffd6a93e6b68d6': RollingMean: 17.357143, 'air_c73d319ffabf287a': RollingMean: 15.285714, 'air_c759b6abeb552160': RollingMean: 5.071429, 'air_c77ee2b7d36da265': RollingMean: 42.071429, 'air_c7d30ab0e07f31d5': RollingMean: 17.142857, 'air_c7f78b4f3cba33ff': RollingMean: 22.785714, 'air_c8265ecc116f2284': RollingMean: 9.142857, 'air_c88467d88b2c8ecd': RollingMean: 18.428571, 'air_c8a657c8c5c93d69': RollingMean: 7.928571, 'air_c8c0ef02ed72053f': RollingMean: 26.5, 'air_c8fe396d6c46275d': RollingMean: 14.357143, 'air_c92745dfdd2ec68a': RollingMean: 18.428571, 'air_c9ed65554b6edffb': RollingMean: 12.928571, 'air_c9f6de13be8b8f25': RollingMean: 4.357143, 'air_ca1315af9e073bd1': RollingMean: 45.285714, 'air_ca6ae8d49a2f1eaf': RollingMean: 20.928571, 'air_ca957d3a1529fbd3': RollingMean: 33.714286, 'air_cadf9cfb510a1d78': RollingMean: 31.071429, 'air_caf996ac27206301': RollingMean: 5.642857, 'air_cb083b4789a8d3a2': RollingMean: 17.214286, 'air_cb25551c4cd8d9f3': RollingMean: 9., 'air_cb7467aed805e7fe': RollingMean: 36.571429, 'air_cb935ff8610ba3d3': RollingMean: 4.857143, 'air_cbe139af83feb388': RollingMean: 9.928571, 'air_cbe867adcf44e14f': RollingMean: 15.5, 'air_cc1a0e985ce63711': RollingMean: 27.785714, 'air_cc35590cd1da8554': RollingMean: 20.857143, 'air_ccd19a5bc5573ae5': RollingMean: 37.714286, 'air_cd4b301d5d3918d8': RollingMean: 6.928571, 'air_cd5f54969be9ed08': RollingMean: 7.214286, 'air_ced6297e5bdf5130': RollingMean: 24.428571, 'air_cf2229e64408d9fe': RollingMean: 17.857143, 'air_cf22e368c1a71d53': RollingMean: 37.285714, 'air_cf5ab75a0afb8af9': RollingMean: 45.214286, 'air_cfcc94797d2b5d3d': RollingMean: 17.285714, 'air_cfdeb326418194ff': RollingMean: 15.142857, 'air_d00161e19f08290b': RollingMean: 26., 'air_d00a15343325e5f7': RollingMean: 18.714286, 'air_d07e57b21109304a': RollingMean: 10.142857, 'air_d0a1e69685259c92': RollingMean: 29.285714, 'air_d0a7bd3339c3d12a': RollingMean: 39.785714, 'air_d0e8a085d8dc83aa': RollingMean: 7.571429, 'air_d138b593ebda55cc': RollingMean: 5.714286, 'air_d1418d6fd6d634f2': RollingMean: 16.285714, 'air_d186b2cb0b9ce022': RollingMean: 13.357143, 'air_d1f20424f76cc78e': RollingMean: 20.928571, 'air_d34c0861a2be94cb': RollingMean: 42.571429, 'air_d3e7b5952cd09ccb': RollingMean: 18.642857, 'air_d44d210d2994f01b': RollingMean: 6.071429, 'air_d473620754bf9fc2': RollingMean: 12.571429, 'air_d477b6339b8ce69f': RollingMean: 8.857143, 'air_d4981cdde163b172': RollingMean: 23.428571, 'air_d4b5a4b04c5f2d04': RollingMean: 12.642857, 'air_d4d218b451f82c3d': RollingMean: 9.785714, 'air_d500b48a8735fbd3': RollingMean: 18.071429, 'air_d54d6fcb116fbed3': RollingMean: 4.214286, 'air_d5e0a20370c325c7': RollingMean: 29.857143, 'air_d63cfa6d6ab78446': RollingMean: 18.428571, 'air_d69b08a175bc0387': RollingMean: 10.857143, 'air_d6b3e67261f07646': RollingMean: 13.714286, 'air_d8abb9e490abf94f': RollingMean: 12., 'air_d97dabf7aae60da5': RollingMean: 33.285714, 'air_d98380a4aeb0290b': RollingMean: 43.357143, 'air_daa7947e1c47f5ed': RollingMean: 34.357143, 'air_dabfbd0ec951925a': RollingMean: 7.642857, 'air_dad0b6a36138f309': RollingMean: 5.5, 'air_db1233ad855b34d5': RollingMean: 24.142857, 'air_db4b38ebe7a7ceff': RollingMean: 24.5, 'air_db80363d35f10926': RollingMean: 28.785714, 'air_dbf64f1ce38c7442': RollingMean: 14.928571, 'air_dc0e080ba0a5e5af': RollingMean: 8.928571, 'air_dc71c6cc06cd1aa2': RollingMean: 6.285714, 'air_de692863bb2dd758': RollingMean: 20.857143, 'air_de803f7e324936b8': RollingMean: 24.285714, 'air_de88770300008624': RollingMean: 20., 'air_dea0655f96947922': RollingMean: 38.571429, 'air_df507aec929ce5f6': RollingMean: 19.071429, 'air_df554c4527a1cfe6': RollingMean: 53.5, 'air_df5cf5cd03eb68d0': RollingMean: 7.857143, 'air_df843e6b22e8d540': RollingMean: 10.285714, 'air_df9355c47c5df9d3': RollingMean: 30.285714, 'air_dfad598ff642dab7': RollingMean: 26.071429, 'air_dfe068a1bf85f395': RollingMean: 36.428571, 'air_e00fe7853c0100d6': RollingMean: 20.214286, 'air_e0118664da63a2d0': RollingMean: 16.714286, 'air_e01d99390355408d': RollingMean: 11.571429, 'air_e053c561f32acc28': RollingMean: 17.928571, 'air_e08b9cf82057a170': RollingMean: 34.5, 'air_e0aee25b56a069f2': RollingMean: 14.214286, 'air_e0e69668214ff972': RollingMean: 9.785714, 'air_e0f241bd406810c0': RollingMean: 33.142857, 'air_e1b76fcb5208fb6b': RollingMean: 14.571429, 'air_e2208a79e2678432': RollingMean: 45.142857, 'air_e270aff84ac7e4c8': RollingMean: 25.928571, 'air_e3020992d5fe5dfd': RollingMean: 13.5, 'air_e34c631c766f2766': RollingMean: 24.642857, 'air_e42bdc3377d1eee7': RollingMean: 20.785714, 'air_e483f5b3c4f310e0': RollingMean: 5.285714, 'air_e524c6a9e06cc3a1': RollingMean: 8.785714, 'air_e55abd740f93ecc4': RollingMean: 46., 'air_e57dd6884595f60d': RollingMean: 39.428571, 'air_e58f669b6f1a08ce': RollingMean: 11.142857, 'air_e5cf003abcc5febb': RollingMean: 12.571429, 'air_e64de0a6bf0739af': RollingMean: 43.214286, 'air_e657ca554b0c008c': RollingMean: 25., 'air_e700e390226d9985': RollingMean: 15.928571, 'air_e76a668009c5dabc': RollingMean: 7.785714, 'air_e7d2ac6d53d1b744': RollingMean: 9.357143, 'air_e7fbee4e3cfe65c5': RollingMean: 36., 'air_e88bbe2ede3467aa': RollingMean: 22.857143, 'air_e89735e80d614a7e': RollingMean: 33.785714, 'air_e8ed9335d0c38333': RollingMean: 24.857143, 'air_e9ebf7fc520ac76a': RollingMean: 28.428571, 'air_ea6d0c3acf00b22a': RollingMean: 22.285714, 'air_ea7c16131980c837': RollingMean: 7.071429, 'air_eb120e6d384a17a8': RollingMean: 42.785714, 'air_eb20a89bba7dd3d0': RollingMean: 3., 'air_eb2d2653586315dd': RollingMean: 31.428571, 'air_eb5788dba285e725': RollingMean: 27.071429, 'air_ebd31e812960f517': RollingMean: 26.857143, 'air_ebe02c3090271fa9': RollingMean: 11.142857, 'air_ec0fad2def4dcff0': RollingMean: 15.142857, 'air_eca4a5a191e8d993': RollingMean: 28.357143, 'air_eca5e0064dc9314a': RollingMean: 34.571429, 'air_ecab54b57a71b10d': RollingMean: 13.071429, 'air_eceb97ad6a7d4c07': RollingMean: 27.857143, 'air_ecf7f141339f1d57': RollingMean: 19.857143, 'air_eda179770dfa9f91': RollingMean: 10.642857, 'air_edd5e3d696a5811b': RollingMean: 39.357143, 'air_ee3a01f0c71a769f': RollingMean: 25.285714, 'air_ee3ba9af184c6c82': RollingMean: 18.642857, 'air_eec5e572b9eb9c23': RollingMean: 11.142857, 'air_eeeadee005c006a2': RollingMean: 12.142857, 'air_ef47430bcd6f6a89': RollingMean: 14.857143, 'air_ef789667e2e6fe96': RollingMean: 36.357143, 'air_ef920fa6f4b085f6': RollingMean: 33.928571, 'air_efc80d3f96b3aff7': RollingMean: 9.428571, 'air_efd70b04de878f25': RollingMean: 31., 'air_efef1e3daecce07e': RollingMean: 40.428571, 'air_f068442ebb6c246c': RollingMean: 10.071429, 'air_f0c7272956e62f12': RollingMean: 4.285714, 'air_f0fb0975bdc2cdf9': RollingMean: 10.285714, 'air_f1094dbf2aef85d9': RollingMean: 6.714286, 'air_f180301886c21375': RollingMean: 14.428571, 'air_f183a514cb8ff4fa': RollingMean: 42.071429, 'air_f1f9027d4fa8f653': RollingMean: 26.5, 'air_f267dd70a6a6b5d3': RollingMean: 44.5, 'air_f26f36ec4dc5adb0': RollingMean: 38.785714, 'air_f2985de32bb792e0': RollingMean: 26.285714, 'air_f2c5a1f24279c531': RollingMean: 14.357143, 'air_f3602e4fa2f12993': RollingMean: 11.571429, 'air_f3f9824b7d70c3cf': RollingMean: 16.642857, 'air_f4936b91c9addbf0': RollingMean: 16., 'air_f593fa60ac3541e2': RollingMean: 13.642857, 'air_f690c42545146e0a': RollingMean: 12.928571, 'air_f6b2489ccf873c3b': RollingMean: 15.928571, 'air_f6bfd27e2e174d16': RollingMean: 12.928571, 'air_f6cdaf7b7fdc6d78': RollingMean: 10., 'air_f8233ad00755c35c': RollingMean: 29.571429, 'air_f85e21e543cf44f2': RollingMean: 4.785714, 'air_f88898cd09f40496': RollingMean: 8.857143, 'air_f911308e19d64236': RollingMean: 40.571429, 'air_f9168b23fdfc1e52': RollingMean: 19.5, 'air_f927b2da69a82341': RollingMean: 8.357143, 'air_f957c6d6467d4d90': RollingMean: 10.642857, 'air_f96765e800907c77': RollingMean: 43.857143, 'air_fa12b40b02fecfd8': RollingMean: 15., 'air_fa4ffc9057812fa2': RollingMean: 5.142857, 'air_fab092c35776a9b1': RollingMean: 10.714286, 'air_fb44f566d4f64a4e': RollingMean: 15.857143, 'air_fbadf737162a5ce3': RollingMean: 14.571429, 'air_fc477473134e9ae5': RollingMean: 16.857143, 'air_fcd4492c83f1c6b9': RollingMean: 22.357143, 'air_fcfbdcf7b1f82c6e': RollingMean: 35.642857, 'air_fd154088b1de6fa7': RollingMean: 6.428571, 'air_fd6aac1043520e83': RollingMean: 36.071429, 'air_fdc02ec4a3d21ea4': RollingMean: 8.071429, 'air_fdcfef8bd859f650': RollingMean: 3.642857, 'air_fe22ef5a9cbef123': RollingMean: 21.714286, 'air_fe58c074ec1445ea': RollingMean: 32.785714, 'air_fea5dc9594450608': RollingMean: 15.571429, 'air_fee8dcf4d619598e': RollingMean: 26.785714, 'air_fef9ccb3ba0da2f7': RollingMean: 9.714286, 'air_ffcc2d5087e1b476': RollingMean: 20., 'air_fff68b929994bfbd': RollingMean: 4.285714}), 'how': RollingMean: 0., 'target_name': 'target'}

target_rollingmean_21_by_store_id

{'by': ['store_id'], 'feature_name': 'target_rollingmean_21_by_store_id', 'groups': defaultdict(functools.partial(<function deepcopy at 0x7fe1bf000310>, RollingMean: 0.), {'air_00a91d42b08b08d9': RollingMean: 29.190476, 'air_0164b9927d20bcc3': RollingMean: 8.380952, 'air_0241aa3964b7f861': RollingMean: 8.904762, 'air_0328696196e46f18': RollingMean: 8.904762, 'air_034a3d5b40d5b1b1': RollingMean: 22.52381, 'air_036d4f1ee7285390': RollingMean: 21.238095, 'air_0382c794b73b51ad': RollingMean: 27.380952, 'air_03963426c9312048': RollingMean: 40.428571, 'air_04341b588bde96cd': RollingMean: 35.333333, 'air_049f6d5b402a31b2': RollingMean: 12.47619, 'air_04cae7c1bc9b2a0b': RollingMean: 19.238095, 'air_0585011fa179bcce': RollingMean: 5.333333, 'air_05c325d315cc17f5': RollingMean: 27., 'air_0647f17b4dc041c8': RollingMean: 30.52381, 'air_064e203265ee5753': RollingMean: 19.47619, 'air_066f0221b8a4d533': RollingMean: 12.428571, 'air_06f95ac5c33aca10': RollingMean: 30.047619, 'air_0728814bd98f7367': 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RollingMean: 12.380952, 'air_0e1eae99b8723bc1': RollingMean: 11.952381, 'air_0e7c11b9abc50163': RollingMean: 36.904762, 'air_0ead98dd07e7a82a': RollingMean: 13.857143, 'air_0f0cdeee6c9bf3d7': RollingMean: 25.238095, 'air_0f2f96335f274801': RollingMean: 10., 'air_0f60e1576a7d397d': RollingMean: 6.619048, 'air_1033310359ceeac1': RollingMean: 18.809524, 'air_10393f12e9069760': RollingMean: 15.714286, 'air_105a7954e32dba9b': RollingMean: 50.857143, 'air_10713fbf3071c361': RollingMean: 12.047619, 'air_10bbe8acd943d8f6': RollingMean: 26.809524, 'air_12c4fb7a423df20d': RollingMean: 20.857143, 'air_138ee734ac79ff90': RollingMean: 8.714286, 'air_138ff410757b845f': RollingMean: 54.285714, 'air_1408dd53f31a8a65': RollingMean: 25.190476, 'air_142e78ba7001da9c': RollingMean: 13.619048, 'air_1509881b22965b34': RollingMean: 16.666667, 'air_152c1f08d7d20e07': RollingMean: 13.238095, 'air_15ae33469e9ea2dd': RollingMean: 11.428571, 'air_15e6e15c7ea2c162': RollingMean: 18.952381, 'air_16179d43b6ee5fd8': RollingMean: 8.285714, 'air_1653a6c513865af3': RollingMean: 32.52381, 'air_168441ada3e878e1': RollingMean: 41.47619, 'air_16c4cfddeb2cf69b': RollingMean: 11.52381, 'air_16cf0a73233896de': RollingMean: 10.52381, 'air_1707a3f18bb0da07': RollingMean: 27.285714, 'air_17a6ab40f97fd4d8': RollingMean: 6.904762, 'air_17bed6dbf7c8b0fc': RollingMean: 19.095238, 'air_1979eaff8189d086': RollingMean: 9.666667, 'air_1ab60ce33bfed8a8': RollingMean: 9.714286, 'air_1ae94f514a0bce13': RollingMean: 6.904762, 'air_1ba4e87ef7422183': RollingMean: 37.619048, 'air_1c0b150f9e696a5f': RollingMean: 107.238095, 'air_1c95a84924d72500': RollingMean: 9.619048, 'air_1d1e8860ae04f8e9': RollingMean: 12.904762, 'air_1d25ca6c76df48b4': RollingMean: 39.666667, 'air_1d3f797dd1f7cf1c': RollingMean: 33.428571, 'air_1dd8f6f47480d1a2': RollingMean: 37.857143, 'air_1dea9815ccd36620': RollingMean: 10.428571, 'air_1e23210b584540e7': RollingMean: 4.047619, 'air_1e665503b8474c55': RollingMean: 5.571429, 'air_1eeff462acb24fb7': 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'air_f2c5a1f24279c531': RollingMean: 15.428571, 'air_f3602e4fa2f12993': RollingMean: 12.52381, 'air_f3f9824b7d70c3cf': RollingMean: 15.809524, 'air_f4936b91c9addbf0': RollingMean: 15.666667, 'air_f593fa60ac3541e2': RollingMean: 12.285714, 'air_f690c42545146e0a': RollingMean: 12.142857, 'air_f6b2489ccf873c3b': RollingMean: 15.857143, 'air_f6bfd27e2e174d16': RollingMean: 12.714286, 'air_f6cdaf7b7fdc6d78': RollingMean: 8.714286, 'air_f8233ad00755c35c': RollingMean: 30.333333, 'air_f85e21e543cf44f2': RollingMean: 5.666667, 'air_f88898cd09f40496': RollingMean: 7.952381, 'air_f911308e19d64236': RollingMean: 43.666667, 'air_f9168b23fdfc1e52': RollingMean: 17.47619, 'air_f927b2da69a82341': RollingMean: 10.47619, 'air_f957c6d6467d4d90': RollingMean: 10.47619, 'air_f96765e800907c77': RollingMean: 45.190476, 'air_fa12b40b02fecfd8': RollingMean: 15.52381, 'air_fa4ffc9057812fa2': RollingMean: 4.904762, 'air_fab092c35776a9b1': RollingMean: 10.809524, 'air_fb44f566d4f64a4e': RollingMean: 15.809524, 'air_fbadf737162a5ce3': RollingMean: 15.047619, 'air_fc477473134e9ae5': RollingMean: 13.333333, 'air_fcd4492c83f1c6b9': RollingMean: 21.714286, 'air_fcfbdcf7b1f82c6e': RollingMean: 37.47619, 'air_fd154088b1de6fa7': RollingMean: 6.52381, 'air_fd6aac1043520e83': RollingMean: 33.380952, 'air_fdc02ec4a3d21ea4': RollingMean: 7.619048, 'air_fdcfef8bd859f650': RollingMean: 3.52381, 'air_fe22ef5a9cbef123': RollingMean: 23.142857, 'air_fe58c074ec1445ea': RollingMean: 31.285714, 'air_fea5dc9594450608': RollingMean: 15.428571, 'air_fee8dcf4d619598e': RollingMean: 26.190476, 'air_fef9ccb3ba0da2f7': RollingMean: 8.857143, 'air_ffcc2d5087e1b476': RollingMean: 19.952381, 'air_fff68b929994bfbd': RollingMean: 4.428571}), 'how': RollingMean: 0., 'target_name': 'target'}

~['area_name', 'date', 'genre_name', 'latitude', 'longitude', 'store_id']

{'blacklist': {'area_name', 'date', 'genre_name', 'latitude', 'longitude', 'store_id'}}

StandardScaler

{'counts': Counter({'target_rollingmean_21_by_store_id': 252108, 'target_rollingmean_14_by_store_id': 252108, 'target_rollingmean_7_by_store_id': 252108, 'weekday': 252108, 'is_weekend': 252108}), 'means': defaultdict(<class 'float'>, {'is_weekend': 0.27469576530693085, 'target_rollingmean_14_by_store_id': 20.897453818387582, 'target_rollingmean_21_by_store_id': 20.901790561001423, 'target_rollingmean_7_by_store_id': 20.895968753375726, 'weekday': 3.0196780744759444}), 'vars': defaultdict(<class 'float'>, {'is_weekend': 0.1992380018293697, 'target_rollingmean_14_by_store_id': 139.4838567930601, 'target_rollingmean_21_by_store_id': 135.6375578980582, 'target_rollingmean_7_by_store_id': 148.74195644058028, 'weekday': 3.7017047339732057})}

LinearRegression

{'_weights': {'target_rollingmean_21_by_store_id': 8.267976730511931, 'target_rollingmean_14_by_store_id': 3.686020877446722, 'target_rollingmean_7_by_store_id': -0.7363408297986078, 'weekday': 6.318523886308755, 'is_weekend': -2.3783326136424945}, '_y_name': None, 'clip_gradient': 1000000000000.0, 'initializer': Zeros (), 'intercept': 19.9310628051135, 'intercept_init': 0.0, 'intercept_lr': Constant({'learning_rate': 0.01}), 'l2': 0.0, 'loss': Squared({}), 'optimizer': SGD({'lr': Constant({'learning_rate': 0.01}), 'n_iterations': 252108})}