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import pandas as pd
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sample = pd.read_csv('clearn/data/fixtures/tinyCrimeSample.csv')
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sample
Out[4]:
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from clearn import munge
In [7]:
munge.make_clean_timestamps(sample)
Out[7]:
In [10]:
every_community_area = munge.get_master_dict()
In [11]:
where_wills_sister_lives = every_community_area['Edgewater']
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where_wills_sister_lives[-5:]
Out[14]:
In [16]:
from clearn.predict import NonsequentialPredictor
with_history = NonsequentialPredictor.preprocess(every_community_area)
with_history['Edgewater'][-5:]
Out[16]:
In [17]:
from datetime import date
log_reg_predictor = NonsequentialPredictor(with_history['Edgewater'])
log_reg_predictor.predict(date(2015, 4, 3))
Out[17]:
In [19]:
from clearn.evaluate import evaluate
# Generate a sample of 2500 days to predict
evaluate(2500)
... and come back in 9 hours
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