def loadData(design):
param_bounds, param_names, params_no, problem = setupProblem(design)
samples, rows_to_keep = getSamples(design, params_no, param_bounds)
df = pd.DataFrame({'mu0':samples[:,0],'sigma0':samples[:,1],\
'mu1':samples[:,2],'sigma1':samples[:,3],\
'p00':samples[:,4],'p11':samples[:,5]})
return df
def main():
if len(argv) != 8:
print(
'Usage: FsPlusRpPlusGfs NormalFilePath TumorFilePath TargetDimensionalityFS TargetDimensionalityRP TargetDimensionalityGFS NumberOfNormalSamples NumberOfTumorSamples'
)
exit()
"""Parameters for executing the script"""
# File Path of Normal People's Data
normalFilePath = argv[1]
# File Path of Tumor People's Data
tumorFilePath = argv[2]
# The Dimensionality of Subspace after Feature Selection Stage
targetDimensionalityFS = int(argv[3])
# The Dimensionality of Subspace after Random Projection Stage
targetDimensionalityRP = int(argv[4])
# The Dimensionality of Subspace after Greedy Feature Selection Stage
targetDimensionalityGFS = int(argv[5])
# Number of Samples for Normal People
numberOfNormalTestingSamples = int(argv[6])
# Number of Samples for Tumor People
numberOfTumorTestingSamples = int(argv[7])
"""Read data from files"""
# Sample of Normal People
normalSamples = getSamples(normalFilePath)
numberOfNormalSamples = len(normalSamples)
# Samples of Tumor People
tumorSamples = getSamples(tumorFilePath)
numberOfTumorSamples = len(tumorSamples)
# All Samples
samples = normalSamples + tumorSamples
numberOfSamples = len(samples)
# Gene Indexes in List: samples
normalSampleIndexes = range(0, numberOfNormalSamples)
tumorSampleIndexes = range(numberOfNormalSamples, numberOfSamples)
numberOfGenes = len(samples[0]) if len(samples) != 0 else 0
print('Original Data Matrix: {} Samples with {} Genes'.format(
numberOfSamples, numberOfGenes))
"""Data preprocessing"""
zScoreNormalization(samples)
"""Runtime Result"""
trainingErrorSamples, tp, fp, fn, tn = \
featureSelectionPlusRandomProjectionPlusGreedyFeatureSelectionProceduce(
samples, normalSampleIndexes, tumorSampleIndexes, numberOfGenes, targetDimensionalityFS,
targetDimensionalityRP, targetDimensionalityGFS, numberOfNormalTestingSamples, numberOfTumorTestingSamples)
def makeFigure8_ResponseSurfaces():
sns.set_style("white")
# constants, vectors
design = 'LHsamples_wider_1000_AnnQonly'
structure = '53_ADC022'
short_idx = np.arange(2, 22, 2)
demand_idx = np.arange(1, 21, 2)
percentiles = [50, 90]
nrealizations = 10
nyears = 105
nmonths = 12
# plotting characteristics
shortage_cmap = mpl.cm.get_cmap('RdBu_r')
colors = [
'#de2d26', '#fb6a4a', '#3182bd', '#6baed6', '#a50f15', '#08519c',
'#9e9ac8'
]
# find which samples are still in param_bounds after flipping misidentified wet and dry states
param_bounds, param_names, params_no, problem = setupProblem(design)
samples, rows_to_keep = getSamples(design, params_no, param_bounds)
nsamples = len(rows_to_keep)
# load historical shortage data and convert acre-ft to m^3
hist_short = np.loadtxt('../Simulation_outputs/' + structure +
'_info_hist.txt')[:, 2] * 1233.48
hist_demand = np.loadtxt('../Simulation_outputs/' + structure +
'_info_hist.txt')[:, 1] * 1233.48
# replace failed runs with np.nan (currently -999.9)
hist_short[hist_short < 0] = np.nan
# load shortage data for this experimental design
SYN = np.load('../Simulation_outputs/' + design + '/' + structure +
'_info.npy')
# extract columns for year shortage and demand and convert acre-ft to ^3
SYN_short = SYN[:, short_idx, :] * 1233.48
SYN_demand = SYN[:, demand_idx, :] * 1233.48
# use just the samples within the experimental design
SYN_short = SYN_short[:, :, rows_to_keep]
SYN_demand = SYN_demand[:, :, rows_to_keep]
# replace failed runs with np.nan (currently -999.9)
SYN_short[SYN_short < 0] = np.nan
# Identify droughts at percentiles
syn_magnitude = calc_syn_magnitude(nyears, nmonths, nrealizations,
nsamples, percentiles, SYN_short)
# reshape synthetic shortage data into 12*nyears x nsamples*nrealizations
SYN_short = SYN_short.reshape([
np.shape(SYN_short)[0],
np.shape(SYN_short)[1] * np.shape(SYN_short)[2]
])
SYN_demand = SYN_demand.reshape([
np.shape(SYN_demand)[0],
np.shape(SYN_demand)[1] * np.shape(SYN_demand)[2]
])
# create data frames of shortage and SOWs
CMIPsamples = np.loadtxt('../Qgen/CMIPunscaled_SOWs.txt')[:, 7:13]
PaleoSamples = np.loadtxt('../Qgen/Paleo_SOWs.txt')[:, 7:13]
CMIP = pd.DataFrame(data=np.repeat(CMIPsamples, nrealizations, axis=0),
columns=param_names)
Paleo = pd.DataFrame(data=np.repeat(PaleoSamples, nrealizations, axis=0),
columns=param_names)
dta = pd.DataFrame(data=np.repeat(samples, nrealizations, axis=0),
columns=param_names)
R2_scores = pd.read_csv('../Simulation_outputs/' + design + '/' +
structure + '_R2.csv')
fig, axes = plt.subplots(2, 3, figsize=(19.2, 9.5))
fig.subplots_adjust(hspace=0.3, wspace=0.3)
# plot shortage distribution for this structure under all-encompassing experiment
ax1 = axes[0, 0]
handles, labels = plotSDC(ax1, SYN_short, SYN_demand, hist_short,
hist_demand, nsamples, nrealizations)
ax1.set_ylim([0, 6200000])
ax1.ticklabel_format(style='sci', axis='y', scilimits=(6, 6))
ax1.tick_params(axis='both', labelsize=14)
ax1.set_ylabel('Shortage (m' + r'$^3$' + ')', fontsize=14)
# add lines at percentiles
for percentile in percentiles:
ax1.plot([percentile, percentile], [0, 6200000], c='k')
# plot variance decomposition for this structure under all-encompassing experiment
ax2 = axes[1, 0]
S1_values = pd.read_csv('../Simulation_outputs/' + design + '/' +
structure + '_S1.csv')
plotSums(S1_values, ax2, colors)
ax2.set_ylim([0, 1])
ax2.tick_params(axis='both', labelsize=14)
ax2.set_ylabel('Portion of Variance', fontsize=14)
ax2.set_xlabel('Shortage Percentile', fontsize=14)
# add lines at percentiles
for percentile in percentiles:
ax2.plot([percentile, percentile], [0, 1], c='k')
for i in range(len(percentiles)):
# get shortage magnitudes at this percentile
dta['Shortage'] = syn_magnitude[i, :]
# find average shortage across realizations in each SOW
avg_dta = dta.groupby(['mu0', 'mu1', 'sigma0', 'sigma1', 'p00', 'p11'],
as_index=False)[['Shortage']].mean()
percentile_scores = R2_scores[str(int(percentiles[i] - 1))]
if percentile_scores[0] > 0:
# get top two predictors of shortage
top_two = list(np.argsort(percentile_scores)[::-1][:2])
predictors = list(
[param_names[top_two[0]], param_names[top_two[1]]])
avg_dta['Interaction'] = avg_dta[predictors[0]] * avg_dta[
predictors[1]]
# fit OLS model with top two predictors and their interaction
result = fitOLS_interact(avg_dta, predictors)
xgrid = np.arange(param_bounds[top_two[0]][0], param_bounds[top_two[0]][1], \
np.around((param_bounds[top_two[0]][1]-param_bounds[top_two[0]][0])/100,decimals=4))
ygrid = np.arange(param_bounds[top_two[1]][0], param_bounds[top_two[1]][1], \
np.around((param_bounds[top_two[1]][1]-param_bounds[top_two[1]][0])/100,decimals=4))
# plot average shortage in each SOW and prediction from regression
plotResponseSurface(axes[0,i+1], result, avg_dta, CMIP, Paleo, shortage_cmap, shortage_cmap, \
xgrid, ygrid, predictors[0], predictors[1], otherSOWs = False)
axes[0, i + 1].set_title(str(percentiles[i]) + 'th Percentile',
fontsize=16)
fig.savefig('Figure8_ResponseSurfaces.pdf')
# plot prediction from regression with CMIP and Paleo samples on top
plotResponseSurface(axes[1,i+1], result, avg_dta, CMIP, Paleo, shortage_cmap, shortage_cmap, \
xgrid, ygrid, predictors[0], predictors[1], otherSOWs = True)
fig.savefig('Figure8_ResponseSurfaces.pdf')
fig.savefig('Figure8_ResponseSurfaces.pdf')
fig.clf()
return None
def makeFigureS18_FactorMaps_User3():
sns.set_style("white")
# constants, vectors
design = 'LHsamples_wider_1000_AnnQonly'
structure = '3704614'
short_idx = np.arange(2,22,2)
demand_idx = np.arange(1,21,2)
percentiles = [40, 90]
nrealizations = 10
# plotting characteristics
probability_cmap = mpl.cm.get_cmap('RdBu')
success_cmap = mpl.colors.ListedColormap(np.array([[227,26,28],[166,206,227]])/255.0)
contour_levels = np.arange(0.0, 1.05,0.1)
# find which samples are still in param_bounds after flipping misidentified wet and dry states
param_bounds, param_names, params_no, problem = setupProblem(design)
samples, rows_to_keep = getSamples(design, params_no, param_bounds)
nsamples = len(rows_to_keep)
# load historical shortage data and convert acre-ft to m^3
hist_short = np.loadtxt('../Simulation_outputs/' + structure + '_info_hist.txt')[:,2]*1233.48
hist_demand = np.loadtxt('../Simulation_outputs/' + structure + '_info_hist.txt')[:,1]*1233.48
# replace failed runs with np.nan (currently -999.9)
hist_short[hist_short < 0] = np.nan
# load shortage data for this experimental design
SYN = np.load('../Simulation_outputs/' + design + '/' + structure + '_info.npy')
# extract columns for year shortage and demand and convert acre-ft to ^3
SYN_short = SYN[:,short_idx,:]*1233.48
SYN_demand = SYN[:,demand_idx,:]*1233.48
# use just the samples within the experimental design
SYN_short = SYN_short[:,:,rows_to_keep]
SYN_demand = SYN_demand[:,:,rows_to_keep]
# replace failed runs with np.nan (currently -999.9)
SYN_short[SYN_short < 0] = np.nan
# reshape synthetic shortage data into 12*nyears x nsamples*nrealizations
SYN_short = SYN_short.reshape([np.shape(SYN_short)[0],np.shape(SYN_short)[1]*np.shape(SYN_short)[2]])
SYN_demand = SYN_demand.reshape([np.shape(SYN_demand)[0],np.shape(SYN_demand)[1]*np.shape(SYN_demand)[2]])
# create data frames of shortage and SOWs
dta = pd.DataFrame(data = np.repeat(samples, nrealizations, axis = 0), columns=param_names)
fig, axes = plt.subplots(2,4,figsize=(24.3,9.1))
fig.subplots_adjust(hspace=0.5,right=0.8,wspace=0.5)
# plot shortage distribution for this structure under all-encompassing experiment
ax1 = axes[0,0]
handles, labels = plotSDC(ax1, SYN_short, SYN_demand, hist_short, hist_demand, nsamples, nrealizations, True)
ax1.set_ylim([0,1])
ax1.tick_params(axis='both',labelsize=14)
ax1.set_ylabel('Shortage/Demand',fontsize=14)
ax1.set_xlabel('Shortage Percentile',fontsize=14)
# add lines at percentiles
for percentile in percentiles:
ax1.plot([percentile, percentile],[0,1],c='k')
# plotfailure heatmap for this structure under all-encompassing experiment
ax2 = axes[1,0]
allSOWs, historic_percents, frequencies, magnitudes, gridcells, im = plotFailureHeatmap(ax2, design, structure)
addPercentileBlocks(historic_percents, gridcells, percentiles, ax2)
allSOWsperformance = allSOWs/100
historic_percents = [roundup(x) for x in historic_percents]
#all_pseudo_r_scores = calcPseudoR2(frequencies, magnitudes, params_no, allSOWsperformance, dta, structure, design)
all_pseudo_r_scores = pd.read_csv("../Simulation_outputs/" + design + "/" + structure + "_pseudo_r_scores.csv")
for i in range(len(percentiles)):
for j in range(3):
# magnitude of shortage at this percentile to plot
h = np.where(np.array(historic_percents) == 100 - percentiles[i])[0][0]
if j == 0:
h -= 2
elif j == 2:
h += 2
# find out if each realization was a success or failure at this magnitude/frequency combination
dta['Success'] = allSOWsperformance[list(frequencies).index(100-percentiles[i]),h,:]
# consider each SOW a success if 50% or more realizations were a success
avg_dta = dta.groupby(['mu0','mu1','sigma0','sigma1','p00','p11'],as_index=False)[['Success']].mean()
avg_dta.loc[np.where(avg_dta['Success']>=0.5)[0],'Success'] = 1
avg_dta.loc[np.where(avg_dta['Success']<0.5)[0],'Success'] = 0
# load pseudo R2 of predictors for this magnitude/frequency combination
pseudo_r_scores = all_pseudo_r_scores[str((100-percentiles[i]))+'yrs_'+str(magnitudes[h])+'prc'].values
if pseudo_r_scores.any():
top_predictors = np.argsort(pseudo_r_scores)[::-1][:2]
ranges = param_bounds[top_predictors]
# define grid of x (1st predictor), and y (2nd predictor) dimensions
# to plot contour map over
xgrid = np.arange(param_bounds[top_predictors[0]][0],
param_bounds[top_predictors[0]][1], np.around((ranges[0][1]-ranges[0][0])/100,decimals=4))
ygrid = np.arange(param_bounds[top_predictors[1]][0],
param_bounds[top_predictors[1]][1], np.around((ranges[1][1]-ranges[1][0])/100,decimals=4))
all_predictors = [ dta.columns.tolist()[k] for k in top_predictors]
# fit logistic regression model with top two predictors of success and their interaction
avg_dta['Interaction'] = avg_dta[all_predictors[0]]*dta[all_predictors[1]]
result = fitLogit_interact(avg_dta, [all_predictors[k] for k in [0,1]])
# plot success/failure for each SOW on top of logistic regression estimate of probability of success
contourset = plotFactorMap(axes[i,j+1], result, avg_dta, probability_cmap, success_cmap, contour_levels, xgrid, ygrid, \
all_predictors[0], all_predictors[1])
axes[i,j+1].set_title("Success if " + str(magnitudes[h]) + "% shortage\n<" + str((100-percentiles[i])) + "% of the time", fontsize=16)
fig.savefig('FigureS18_FactorMaps_User3.pdf')
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
cbar = fig.colorbar(contourset, cax=cbar_ax)
cbar.ax.set_ylabel("Predicted Probability of Success", rotation=-90, va="bottom",fontsize=16)
cbar.ax.tick_params(labelsize=16)
fig.savefig("FigureS18_FactorMaps_User3.pdf")
fig.clf()
return None
def makeFigureS11_VarianceDecomposition_User3():
sns.set_style("white")
designs = [
'LHsamples_original_1000_AnnQonly', 'CMIPunscaled_SOWs', 'Paleo_SOWs',
'LHsamples_wider_1000_AnnQonly'
]
nsamples = [1000, 97, 366, 1000] # before removing those out of bounds
titles = ['Box Around Historical', 'CMIP', 'Paleo', 'All-Encompassing']
structure = '3704614'
nrealizations = 10
short_idx = np.arange(2, 22, 2)
demand_idx = np.arange(1, 21, 2)
colors = [
"#de2d26", "#fb6a4a", "#3182bd", "#6baed6", "#a50f15", "#08519c",
"#9e9ac8"
]
mu0 = plt.Rectangle((0, 0), 1, 1, fc=colors[0], edgecolor='none')
sigma0 = plt.Rectangle((0, 0), 1, 1, fc=colors[1], edgecolor='none')
mu1 = plt.Rectangle((0, 0), 1, 1, fc=colors[2], edgecolor='none')
sigma1 = plt.Rectangle((0, 0), 1, 1, fc=colors[3], edgecolor='none')
p00 = plt.Rectangle((0, 0), 1, 1, fc=colors[4], edgecolor='none')
p11 = plt.Rectangle((0, 0), 1, 1, fc=colors[5], edgecolor='none')
Interact = plt.Rectangle((0, 0), 1, 1, fc=colors[6], edgecolor='none')
# perform variance decomposition
#for i, design in enumerate(designs):
# Sobol_per_structure(design, structure)
# plot shotrage distributions
fig = plt.figure()
count = 1 # subplot counter
# load historical shortage and demand data and convert acre-ft to m^3
hist_short = np.loadtxt('../Simulation_outputs/' + structure +
'_info_hist.txt')[:, 2] * 1233.48 / 1E6
hist_demand = np.loadtxt('../Simulation_outputs/' + structure +
'_info_hist.txt')[:, 1] * 1233.48 / 1E6
# replace failed runs with np.nan (currently -999.9)
hist_short[hist_short < 0] = np.nan
for i, design in enumerate(designs):
# find which samples are still in param_bounds after flipping misidentified wet and dry states
param_bounds, param_names, params_no, problem = setupProblem(design)
_, rows_to_keep = getSamples(design, params_no, param_bounds)
nsamples[i] = len(
rows_to_keep
) # after removing those out of bounds after reclassification
# load shortage data for this experimental design
SYN = np.load('../Simulation_outputs/' + design + '/' + structure +
'_info.npy')
# extract columns for year shortage and demand and convert acre-ft to m^3
SYN_short = SYN[:, short_idx, :] * 1233.48 / 1E6
SYN_demand = SYN[:, demand_idx, :] * 1233.48 / 1E6
# use just the samples within the experimental design
SYN_short = SYN_short[:, :, rows_to_keep]
SYN_demand = SYN_demand[:, :, rows_to_keep]
# reshape into 12*nyears x nsamples*nrealizations
SYN_short = SYN_short.reshape([
np.shape(SYN_short)[0],
np.shape(SYN_short)[1] * np.shape(SYN_short)[2]
])
SYN_demand = SYN_demand.reshape([
np.shape(SYN_demand)[0],
np.shape(SYN_demand)[1] * np.shape(SYN_demand)[2]
])
# replace failed runs with np.nan (currently -999.9)
SYN_short[SYN_short < 0] = np.nan
# plot shortage distribution
ax = fig.add_subplot(2, 4, count)
handles, labels = plotSDC(ax, SYN_short, SYN_demand, hist_short,
hist_demand, nsamples[i], nrealizations)
# only put labels left column, make y ranges consistent, title experiment
if count == 1:
ax.tick_params(axis='y', labelsize=14)
ax.set_ylabel('Annual Shortage\n(millions of m' + r'$^3$' + ')',
fontsize=16)
else:
ax.tick_params(axis='y', labelleft='off')
ax.set_title(titles[count - 1], fontsize=16)
ax.tick_params(axis='x', labelbottom='off')
# iterature subplot counter
count += 1
# plot variance decomposition
for design in designs:
# load sensitivity indices
S1_values = pd.read_csv('../Simulation_outputs/' + design + '/' +
structure + '_S1.csv')
# plot shortage distribution
ax = fig.add_subplot(2, 4, count)
plotSums(S1_values, ax, colors)
ax.tick_params(axis='x', labelsize=14)
if count == 5:
ax.tick_params(axis='y', labelsize=14)
ax.set_ylabel('Portion of\nVariance Explained', fontsize=16)
else:
ax.tick_params(axis='y', labelleft='off')
# iterate subplot counter
count += 1
fig.set_size_inches([16, 8])
fig.subplots_adjust(bottom=0.22)
fig.text(0.5, 0.15, 'Percentile of Shortage', ha='center', fontsize=16)
fig.savefig('FigureS11_VarianceDecomposition_User3.pdf')
fig.clf()
return None
def makeFigure6_ShortageDistns():
sns.set_style("white")
designs = [
'LHsamples_original_1000_AnnQonly', 'CMIPunscaled_SOWs', 'Paleo_SOWs',
'LHsamples_wider_1000_AnnQonly'
]
nsamples = [1000, 97, 366, 1000] # before removing those out of bounds
titles = ['Box Around Historical', 'CMIP', 'Paleo', 'All-Encompassing']
structures = ['53_ADC022', '7200645']
nrealizations = 10
short_idx = np.arange(2, 22, 2)
demand_idx = np.arange(1, 21, 2)
fig = plt.figure()
count = 1 # subplot counter
for structure in structures:
# load historical shortage and demand data and convert acre-ft to m^3
hist_short = np.loadtxt('../Simulation_outputs/' + structure +
'_info_hist.txt')[:, 2] * 1233.48 / 1E6
hist_demand = np.loadtxt('../Simulation_outputs/' + structure +
'_info_hist.txt')[:, 1] * 1233.48 / 1E6
# replace failed runs with np.nan (currently -999.9)
hist_short[hist_short < 0] = np.nan
for i, design in enumerate(designs):
# find which samples are still in param_bounds after flipping misidentified wet and dry states
param_bounds, param_names, params_no, problem = setupProblem(
design)
_, rows_to_keep = getSamples(design, params_no, param_bounds)
nsamples[i] = len(
rows_to_keep
) # after removing those out of bounds after reclassification
# load shortage data for this experimental design
SYN = np.load('../Simulation_outputs/' + design + '/' + structure +
'_info.npy')
# extract columns for year shortage and demand and convert acre-ft to m^3
SYN_short = SYN[:, short_idx, :] * 1233.48 / 1E6
SYN_demand = SYN[:, demand_idx, :] * 1233.48 / 1E6
# use just the samples within the experimental design
SYN_short = SYN_short[:, :, rows_to_keep]
SYN_demand = SYN_demand[:, :, rows_to_keep]
# reshape into 12*nyears x nsamples*nrealizations
SYN_short = SYN_short.reshape([
np.shape(SYN_short)[0],
np.shape(SYN_short)[1] * np.shape(SYN_short)[2]
])
SYN_demand = SYN_demand.reshape([
np.shape(SYN_demand)[0],
np.shape(SYN_demand)[1] * np.shape(SYN_demand)[2]
])
# replace failed runs with np.nan (currently -999.9)
SYN_short[SYN_short < 0] = np.nan
# plot shortage distribution
ax = fig.add_subplot(2, 4, count)
handles, labels = plotSDC(ax, SYN_short, SYN_demand, hist_short,
hist_demand, nsamples[i], nrealizations)
# only put labels on bottom row/left column, make y ranges consistent, title experiment
if count == 1 or count == 5:
ax.tick_params(axis='y', labelsize=14)
else:
ax.tick_params(axis='y', labelleft='off')
if count <= 4:
ax.tick_params(axis='x', labelbottom='off')
ax.set_title(titles[count - 1], fontsize=16)
ax.set_ylim(0, 6.2)
#ax.ticklabel_format(style='sci', axis='y', scilimits=(6,6))
else:
ax.tick_params(axis='x', labelsize=14)
ax.set_ylim(0, 370)
#ax.ticklabel_format(style='sci', axis='y', scilimits=(8,8))
# iterature subplot counter
count += 1
fig.set_size_inches([16, 8])
fig.text(0.5, 0.15, 'Percentile', ha='center', fontsize=16)
fig.text(0.05,
0.5,
'Annual Shortage (millions of m' + r'$^3$' + ')',
va='center',
rotation=90,
fontsize=16)
fig.subplots_adjust(bottom=0.22)
labels_transposed = [
labels[9], labels[4], labels[8], labels[3], labels[7], labels[2],
labels[6], labels[1], labels[5], labels[0]
]
handles_transposed = [
handles[9], handles[4], handles[8], handles[3], handles[7], handles[2],
handles[6], handles[1], handles[5], handles[0]
]
legend = fig.legend(handles=handles_transposed,
labels=labels_transposed,
fontsize=16,
loc='lower center',
title='Cumulative frequency in experiment',
ncol=5)
plt.setp(legend.get_title(), fontsize=16)
fig.savefig('Figure6_ShortageDistns.pdf')
fig.clf()
return None
