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- def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
- """
- Frame a time series as a supervised learning dataset.
- Arguments:
- data: Sequence of observations as a list or NumPy array.
- n_in: Number of lag observations as input (X).
- n_out: Number of observations as output (y).
- dropnan: Boolean whether or not to drop rows with NaN values.
- Returns:
- Pandas DataFrame of series framed for supervised learning.
- """
- 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))
- 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
- # load dataset
- dataset = pd.read_csv('newdf2.csv', header=0, index_col=0)
- dataset = dataset.drop('Monthday.Key', axis = 1)
- dataset.head()
- values = dataset.values
- # integer encode direction
- encoder = LabelEncoder()
- values[:,4] = encoder.fit_transform(values[:,4])
- # ensure all data is float
- values = values.astype('float32')
- # normalize features
- scaler = MinMaxScaler(feature_range=(0, 1))
- scaled = scaler.fit_transform(values)
- # frame as supervised learning
- reframed = series_to_supervised(scaled, 1, 1)
- # drop columns we don't want to predict
- reframed.drop(reframed.columns[[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,20,21,22,23,24]], axis=1, inplace=True)
- print(reframed.head())
- # split into train and test sets
- values = reframed.values
- n_train_hours = round(len(dataset) *.7)
- train = values[:n_train_hours, :]
- test = values[n_train_hours:, :]
- # split into input and outputs
- train_X, train_y = train[:, :-1], train[:, -1]
- test_X, test_y = test[:, :-1], test[:, -1]
- # reshape input to be 3D [samples, timesteps, features]
- train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
- test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
- print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
- #(799, 1, 22) (799,) (342, 1, 22) (342,)
- # make a prediction
- yhat = model.predict(test_X)
- test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
- inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
- inv_yhat = scaler.inverse_transform(inv_yhat)
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