211 lines
4.7 KiB
Python
211 lines
4.7 KiB
Python
import pprint
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import xarray
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import numpy
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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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def kernel_3(should_exist=None):
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if should_exist is None:
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should_exist = False
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t3 = [
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('playerTwitterFollowers', None),
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('teamTwitterFollowers', None),
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('games', None),
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('events', 'events-v2.nc'),
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]
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o_1 = None
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o_2 = None
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t4 = '/kaggle/input/garbage'
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t5 = {}
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for k, v in t3:
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if v is None:
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t1 = os.path.join(
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t4,
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'%s.nc' % k,
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)
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else:
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t1 = os.path.join(
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t4,
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v,
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)
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if os.path.exists(t1):
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t2 = xarray.load_dataset(t1)
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else:
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if should_exist:
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pprint.pprint([k, v, t1])
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raise NotImplementedError
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if o_1 is None:
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o_1 = kernel_1()
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if o_2 is None:
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o_2 = kernel_2(
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o_1=o_1
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)
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t2 = o_2['events']
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t5[k] = t2
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return dict(
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t5=t5,
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)
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def kernel_4(
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o_3=None,
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):
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[
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print(
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o_3['t5']['events'].to_dataframe().iloc[k].to_json(indent=4)
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)
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for k in range(-10, -1)
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]
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[
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print(
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o_3['t5']['games'].to_dataframe().iloc[k].to_json(indent=4)
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)
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for k in range(-10, -1)
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]
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t4 = 'https://www.youtube.com/watch?v=reaC7BHgL3M'
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r"""
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{
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"gamePk":634280,
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"gameType":"R",
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"season":2021,
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"gameDate":"2021-04-30",
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"gameTimeUTC":"2021-04-30T23:37:00Z",
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"resumeDate":"",
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"resumedFrom":"",
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"codedGameState":"F",
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"detailedGameState":"Final",
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"isTie":0.0,
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"gameNumber":1,
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"doubleHeader":"N",
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"dayNight":"night",
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"scheduledInnings":9,
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"gamesInSeries":3.0,
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"seriesDescription":"Regular Season",
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"homeId":141,
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"homeName":"Toronto Blue Jays",
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"homeAbbrev":"TOR",
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"homeWins":12,
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"homeLosses":12,
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"homeWinPct":0.5,
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"homeWinner":true,
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"homeScore":13.0,
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"awayId":144,
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"awayName":"Atlanta Braves",
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"awayAbbrev":"ATL",
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"awayWins":12.0,
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"awayLosses":14.0,
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"awayWinPct":0.462,
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"awayWinner":false,
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"awayScore":5.0
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}
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"""
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t1 = numpy.where(o_3['t5']['events']['gamePk'] == 634280)[0]
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t5 = o_3['t5']['events'].index.data
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t6 = t5[t1]
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t2 = o_3['t5']['events'].sel(index=t6)
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t3 = o_3['t5']['games'].to_dataframe().iloc[-19].to_dict()
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pprint.pprint(t3)
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assert t3['gamePk'] == 634280
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dict(
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t2=t2,
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t3=t3,
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t4=t4,
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)
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