#! /usr/bin/python

import math
import statistics
import visualizer
import numpy as np
from datagen import constructData
from sklearn import svm

# Applies Support Vector Regression to the electricity dataset,
# prints out the accuracy rate to the terminal and plots
# predictions against actual values
def suppVectorRegress():

    kernelList = ["linear","rbf",polyKernel]
    names = ["linear","radial basis","poly"]
    preds = []

    # Retrieve time series data & apply preprocessing
    data = constructData()

    # 2014 had 365 days, but we take the last 364 days since
    # the last day has no numerical value
    cutoff = len(data)-364
    xTrain = data[0][0:cutoff]
    yTrain = data[1][0:cutoff]
    xTest = data[0][cutoff:]
    yTest = data[1][cutoff:]

    # Fill in missing values denoted by zeroes as an average of
    # both neighbors
    statistics.estimateMissing(xTrain,0.0)
    statistics.estimateMissing(xTest,0.0)

    # Logarithmically scale the data
    xTrain = [[math.log(y) for y in x] for x in xTrain]
    xTest = [[math.log(y) for y in x] for x in xTest]
    yTrain = [math.log(x) for x in yTrain]

    # Detrend the time series
    indices = np.arange(len(data[1]))
    trainIndices = indices[0:cutoff]
    testIndices = indices[cutoff:]
    detrended,slope,intercept = statistics.detrend(trainIndices,yTrain)
    yTrain = detrended

    for gen in range(len(kernelList)):

        # Use SVR to predict test observations based upon training observations
        pred = svrPredictions(xTrain,yTrain,xTest,kernelList[gen])
        # Add the trend back into the predictions
        trendedPred = statistics.reapplyTrend(testIndices,pred,slope,intercept)
        # Reverse the normalization
        trendedPred = [math.exp(x) for x in trendedPred]
        # Compute the NRMSE
        err = statistics.normRmse(yTest,trendedPred)

        print "The Normalized Root-Mean Square Error is " + str(err) + " using kernel " + names[gen] + "..."

        preds.append(trendedPred)

    names.append("actual")
    preds.append(yTest)

    visualizer.comparisonPlot(2014,1,1,preds,names,plotName="Support Vector Regression Load Predictions vs. Actual", 
        yAxisName="Predicted Kilowatts")

# Construct a support vector machine and get predictions
# for the test set
# Returns a 1-d vector of predictions
def svrPredictions(xTrain,yTrain,xTest,k):
    clf = svm.SVR(C=2.0,kernel=k)
    clf.fit(xTrain,yTrain)
    return clf.predict(xTest)

# A scale invariant kernel (note only conditionally semi-definite)
def polyKernel(x,y):
    return (np.dot(x,y.T)+1.0)**0.95

if __name__=="__main__":
    suppVectorRegress()
#! /usr/bin/python

import math
import statistics
import visualizer
import numpy as np
from datagen import constructData
from sklearn import svm

# Applies Support Vector Regression to the electricity dataset,
# prints out the accuracy rate to the terminal and plots
# predictions against actual values
def suppVectorRegress():

    kernelList = ["linear","rbf",polyKernel]
    names = ["linear","radial basis","poly"]
    preds = []

    # Retrieve time series data & apply preprocessing
    data = constructData()

    # 2014 had 365 days, but we take the last 364 days since
    # the last day has no numerical value
    cutoff = len(data)-364
    xTrain = data[0][0:cutoff]
    yTrain = data[1][0:cutoff]
    xTest = data[0][cutoff:]
    yTest = data[1][cutoff:]

    # Fill in missing values denoted by zeroes as an average of
    # both neighbors
    statistics.estimateMissing(xTrain,0.0)
    statistics.estimateMissing(xTest,0.0)

    # Logarithmically scale the data
    xTrain = [[math.log(y) for y in x] for x in xTrain]
    xTest = [[math.log(y) for y in x] for x in xTest]
    yTrain = [math.log(x) for x in yTrain]

    # Detrend the time series
    indices = np.arange(len(data[1]))
    trainIndices = indices[0:cutoff]
    testIndices = indices[cutoff:]
    detrended,slope,intercept = statistics.detrend(trainIndices,yTrain)
    yTrain = detrended

    for gen in range(len(kernelList)):

        # Use SVR to predict test observations based upon training observations
        pred = svrPredictions(xTrain,yTrain,xTest,kernelList[gen])
        # Add the trend back into the predictions
        trendedPred = statistics.reapplyTrend(testIndices,pred,slope,intercept)
        # Reverse the normalization
        trendedPred = [math.exp(x) for x in trendedPred]
        # Compute the NRMSE
        err = statistics.normRmse(yTest,trendedPred)

        print "The Normalized Root-Mean Square Error is " + str(err) + " using kernel " + names[gen] + "..."

        preds.append(trendedPred)

    names.append("actual")
    preds.append(yTest)

    visualizer.comparisonPlot(2014,1,1,preds,names,plotName="Support Vector Regression Load Predictions vs. Actual", 
        yAxisName="Predicted Kilowatts")

# Construct a support vector machine and get predictions
# for the test set
# Returns a 1-d vector of predictions
def svrPredictions(xTrain,yTrain,xTest,k):
    clf = svm.SVR(C=2.0,kernel=k)
    clf.fit(xTrain,yTrain)
    return clf.predict(xTest)

# A scale invariant kernel (note only conditionally semi-definite)
def polyKernel(x,y):
    return (np.dot(x,y.T)+1.0)**0.95

if __name__=="__main__":
    suppVectorRegress()
#! /usr/bin/python # Applies Support Vector Regression to the electricity dataset, # prints out the accuracy rate to the terminal and plots # predictions against actual values # Retrieve time series data & apply preprocessing # 2014 had 365 days, but we take the last 364 days since # the last day has no numerical value # Fill in missing values denoted by zeroes as an average of # both neighbors # Logarithmically scale the data # Detrend the time series # Use SVR to predict test observations based upon training observations # Add the trend back into the predictions # Reverse the normalization # Compute the NRMSE # Construct a support vector machine and get predictions # for the test set # Returns a 1-d vector of predictions # A scale invariant kernel (note only conditionally semi-definite)