89 lines
1.9 KiB
Python
89 lines
1.9 KiB
Python
import xarray
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import json
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import glob
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import io
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import os
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import pandas
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import pickle
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def kernel_1():
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t4 = 'kernel_1-t3.dat'
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def preprocess(t4):
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t1 = '/kaggle/input/mlb-player-digital-engagement-forecasting'
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t2 = glob.glob(
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os.path.join(
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t1,
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'*.csv'
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)
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)
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t3 = {
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o : pandas.read_csv(o).to_xarray()
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for o in t2
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}
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with io.open(t4, 'wb') as f:
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pickle.dump(t3, f)
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if not os.path.exists(t4):
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preprocess(t4=t4)
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with io.open(t4, 'rb') as f:
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t3 = pickle.load(f)
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return dict(
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t3=t3,
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)
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def kernel_2(
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o_1=None,
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):
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t1 = {}
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for k in [
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'playerTwitterFollowers',
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'teamTwitterFollowers',
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'games',
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'events'
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]:
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t4 = '%s.nc' % k
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if not os.path.exists(t4):
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print('started %s' % t4)
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t2 = '/kaggle/input/mlb-player-digital-engagement-forecasting/train.csv'
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t3 = pandas.DataFrame(
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sum(
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[
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json.loads(o)
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for o in o_1['t3'][t2][k].values
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if isinstance(o, str)
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],
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[]
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)
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).to_xarray()
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t3.to_netcdf(t4)
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print('cached %s' % t4)
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if k == 'events':
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t5 = '%s-v2.nc' % k
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if not os.path.exists(t5):
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t2 = xarray.load_dataset(t4)
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t3 = t2.sel(
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index=numpy.arange(
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2017653 - 10 * 1000,
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2017653 + 1
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)
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)
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t3.to_netcdf(t5)
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t1[k] = xarray.load_dataset(t5)
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print('loaded %s' % t5)
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else:
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t1[k] = xarray.load_dataset(t4)
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print('loaded %s' % t4)
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return dict(
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t1=t1,
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)
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