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timeseries_lstm.py
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timeseries_lstm.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
crimes = pd.read_csv('./Chicago_Crimes_2012_to_2017_new.csv', error_bad_lines=False, index_col=0)
print('Dataset Shape before drop_duplicate : ', crimes.shape)
print (crimes.head(5))
# convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(data) is list else data.shape[1]
df = DataFrame(data)
cols, names = list(), list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i)) # print(df.shift(-i).head())
if i == 0:
names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
else:
names += [('var%d(t+%d)' % (j + 1, i)) for j in range(n_vars)]
# put it all together
agg = concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg