from math import sqrt
from numpy import concatenate
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from datetime import datetime
from matplotlib import pyplot
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM

show_visualization=False

# ** Load and remove missing data **
# function to load data
def parse(x):
    return datetime.strptime(x, '%Y %m %d %H')

dataset = read_csv('raw.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
dataset.drop('No', axis=1, inplace=True)

# manually specify column names
dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
dataset.index.name = 'date'

# dealing with missing data...

# mark all NA values with 0
dataset['pollution'].fillna(0, inplace=True)

# drop the first 24 hours
dataset = dataset[24:]

# save to file
dataset.to_csv('pollution.csv')

# ** visualization ** 

if show_visualization:
# load dataset
    dataset = read_csv('pollution.csv', header=0, index_col=0)
    values = dataset.values

    # specify columns to plot
    groups = [0, 1, 2, 3, 5, 6, 7]
    i = 1

    # plot each column
    pyplot.figure()

    for group in groups:
        pyplot.subplot(len(groups), 1, i)
        pyplot.plot(values[:, group])
        pyplot.title(dataset.columns[group], y=0.5, loc='right')
        i += 1
    
    # pyplot.show()

# ** Data processing **

# 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))
            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
# we ignore the first column with index_col and .values
dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values

# integer encode wind 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[[9,10,11,12,13,14,15]], axis=1, inplace=True)

# ** Fit the model **

# split into train and test sets
values = reframed.values
n_train_hours = 365 * 24
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)

# design network
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')

# fit network
history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)

# plot history
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()

# ** Model evaluation **

# make a prediction
yhat = model.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))

# invert scaling for forecast
inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]

# invert scaling for actual
inv_y = scaler.inverse_transform(test_X)
inv_y = inv_y[:,0]

# calculate RMSE
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
print('Test RMSE: %.3f' % rmse)
