bureau2_path = args[1]
print "make_crowd_plots: illustrating behaviour captured in bureau files: "
print "make_crowd_plots: ", bureau1_path
print "make_crowd_plots: ", bureau2_path
else:
print make_crowd_plots.__doc__
return
cornerplotter_path = cornerplotter_path + 'CornerPlotter.py'
output_directory = './'
# ------------------------------------------------------------------
# Read in bureau objects:
bureau1 = swap.read_pickle(bureau1_path, 'bureau')
bureau2 = swap.read_pickle(bureau2_path, 'bureau')
print "make_crowd_plots: stage 1, 2 agent numbers: ", len(
bureau1.list()), len(bureau2.list())
# make lists by going through agents
N_early = 10
stage2_veteran_members = []
list1 = bureau1.list()
for ID in bureau2.list():
if ID in list1:
stage2_veteran_members.append(ID)
print "make_crowd_plots: ", len(
stage2_veteran_members
), " volunteers stayed on for Stage 2 from Stage 1"
# How will we decide if a sim has been seen?
try: use_marker_positions = tonights.parameters['use_marker_positions']
except: use_marker_positions = False
print "SWAP: should we use the marker positions on sims? ",use_marker_positions
# How will we make decisions based on probability?
thresholds = {}
thresholds['detection'] = tonights.parameters['detection_threshold']
thresholds['rejection'] = tonights.parameters['rejection_threshold']
# ------------------------------------------------------------------
# Read in, or create, a bureau of agents who will represent the
# volunteers:
bureau = swap.read_pickle(tonights.parameters['bureaufile'],'bureau')
# ------------------------------------------------------------------
# Read in, or create, an object representing the candidate list:
sample = swap.read_pickle(tonights.parameters['samplefile'],'collection')
# ------------------------------------------------------------------
# Open up database:
if practise:
db = swap.read_pickle(tonights.parameters['dbfile'],'database')
if db is None:
print "SWAP: making a new Toy database..."
from skimage import io from subprocess import call #from colors import blues_r import numpy as np from matplotlib import pyplot as plt import swap #cornerplotter_path = '/Users/cpd/SWAP/pappy/CornerPlotter.py' #bureau_path = '/Users/cpd/SWAP/SpaceWarps/analysis/CFHTLS_bureau.pickle' #output_directory = '/Users/cpd/SWAP/swap_analysis/' cornerplotter_path = '/Users/mbaumer/SWAP/pappy/CornerPlotter.py' bureau_path = '/Users/mbaumer/SpaceWarps/analysis/CFHTLS_bureau.pickle' output_directory = '/Users/mbaumer/SWAP/swap_analysis' bureau = swap.read_pickle(bureau_path, 'bureau') len(bureau.list()) # make lists buy going through agents N_early = 10 skill = [] contribution = [] experience = [] education = [] early_contribution = [] early_skill = [] skill_all = [] contribution_all = [] experience_all = [] education_all = []
def make_roc_curves(args):
"""
NAME
make_roc_curves
PURPOSE
Given some collection pickles, this script
produces the one roc plot that will be put someplace in the SW system
paper.
COMMENTS
FLAGS
-h Print this message
INPUTS
collection pickles
colors for the lines
line styles
labels
OUTPUTS
roc png plot
EXAMPLE
BUGS
- Code is not tested yet...
AUTHORS
This file is part of the Space Warps project, and is distributed
under the MIT license by the Space Warps Science Team.
http://spacewarps.org/
HISTORY
2013-07-01 started Davis (KIPAC)
2014-09-06 updated to only use collections.
"""
# ------------------------------------------------------------------
flags = {
'output_directory': './',
'collections': [],
'labels': [],
'line_styles': [],
'colors': []
}
for arg in args:
if arg in flags:
flags[arg] = args[arg]
else:
print "make_roc_curves: unrecognized flag ", arg
print flags
# check that collections etc are equal length
if len(flags['collections']) != len(flags['labels']):
raise Exception('Collections and labels must be same length!')
if len(flags['collections']) != len(flags['line_styles']):
raise Exception('Collections and line_styles must be same length!')
if len(flags['collections']) != len(flags['colors']):
raise Exception('Collections and colors must be same length!')
n_min = 0
output_directory = flags['output_directory']
collections = flags['collections']
fig, ax = plt.subplots(figsize=(10, 8))
for i, collection_path in enumerate(collections):
# ------------------------------------------------------------------
# Read in collection object:
collection = swap.read_pickle(collection_path, 'collection')
print "make_roc_curves: collection {0} subject numbers: {1}".format(
i, len(collection.list()))
# ------------------------------------------------------------------
# set up data for roc plots
y_true = np.array([])
y_score = np.array([])
for ID in collection.list():
subject = collection.member[ID]
if (subject.category == 'training'):
n_assessment = len(subject.annotationhistory['ItWas'])
if (n_assessment > n_min):
truth = {'LENS': 1, 'NOT': 0}[subject.truth]
y_true = np.append(y_true, truth)
y_score = np.append(y_score, subject.mean_probability)
fpr, tpr, threshold = roc_curve(y_true, y_score)
color = flags['colors'][i]
label = flags['labels'][i]
line_style = flags['line_styles'][i]
ax.plot(fpr,
tpr,
color,
label=label,
linestyle=line_style,
linewidth=3)
ax.set_xlim(0, 0.1)
ax.set_ylim(0.8, 1)
ax.set_xlabel('False Positive Rate')
ax.set_ylabel('True Positive Rate')
plt.legend(loc='lower right')
pngfile = output_directory + 'roc_curve.png'
plt.savefig(pngfile)
print "make_roc_curves: roc curve saved to " + pngfile
# ------------------------------------------------------------------
print "make_roc_curves: all done!"
return
def make_roc_curves(args):
"""
NAME
make_roc_curves
PURPOSE
Given some collection pickles, this script
produces the one roc plot that will be put someplace in the SW system
paper.
COMMENTS
FLAGS
-h Print this message
INPUTS
collection pickles
colors for the lines
line styles
labels
OUTPUTS
roc png plot
EXAMPLE
BUGS
- Code is not tested yet...
AUTHORS
This file is part of the Space Warps project, and is distributed
under the GPL v2 by the Space Warps Science Team.
http://spacewarps.org/
HISTORY
2013-07-01 started Davis (KIPAC)
2014-09-06 updated to only use collections.
"""
# ------------------------------------------------------------------
flags = {'output_directory': './',
'collections': [],
'labels': [],
'line_styles': [],
'colors': []}
for arg in args:
if arg in flags:
flags[arg] = args[arg]
else:
print "make_roc_curves: unrecognized flag ",arg
print flags
# check that collections etc are equal length
if len(flags['collections']) != len(flags['labels']):
raise Exception('Collections and labels must be same length!')
if len(flags['collections']) != len(flags['line_styles']):
raise Exception('Collections and line_styles must be same length!')
if len(flags['collections']) != len(flags['colors']):
raise Exception('Collections and colors must be same length!')
n_min = 0
output_directory = flags['output_directory']
collections = flags['collections']
fig, ax = plt.subplots(figsize=(10,8))
for i, collection_path in enumerate(collections):
# ------------------------------------------------------------------
# Read in collection object:
collection = swap.read_pickle(collection_path, 'collection')
print "make_roc_curves: collection {0} subject numbers: {1}".format(i, len(collection.list()))
# ------------------------------------------------------------------
# set up data for roc plots
y_true = np.array([])
y_score = np.array([])
for ID in collection.list():
subject = collection.member[ID]
if (subject.category == 'training'):
n_assessment = len(subject.annotationhistory['ItWas'])
if (n_assessment > n_min):
truth = {'LENS': 1, 'NOT': 0}[subject.truth]
y_true = np.append(y_true, truth)
y_score = np.append(y_score, subject.mean_probability)
fpr, tpr, threshold = roc_curve(y_true, y_score)
color = flags['colors'][i]
label = flags['labels'][i]
line_style = flags['line_styles'][i]
ax.plot(fpr, tpr, color, label=label, linestyle=line_style, linewidth=3)
ax.set_xlim(0, 0.1)
ax.set_ylim(0.8, 1)
ax.set_xlabel('False Positive Rate')
ax.set_ylabel('True Positive Rate')
plt.legend(loc='lower right')
pngfile = output_directory + 'roc_curve.png'
plt.savefig(pngfile)
print "make_roc_curves: roc curve saved to "+pngfile
# ------------------------------------------------------------------
print "make_roc_curves: all done!"
return
def make_lens_atlas(args):
"""
NAME
make_lens_atlas
PURPOSE
Given location of bureau and collection pickles as well as a list of
subjects, this script produces a set of annotated images of lenses
(heatmaps for lens locations, markers for where clicks were, etc).
COMMENTS
You have to download the file so it chooses whever your output
directory is to also download the raw images.
This should be pretty customizable.
FLAGS
-h Print this message
--heatmap Do heatmaps
--contour Do contours
--field Do full image
--stamp Do cutouts
--alpha Do alpha
--points N Take N agents and plot them. Any number < 0 = do all
--skill Weight agent markers by skill
INPUTS
collection collection.pickle
catalog catalog.dat
Assumed format:
ID kind x y Prob N0 Skill Dist
Here:
ID = Space Warps subject ID
kind = Space Warps subject type (sim, dud, test)
x,y = object (cluster) centroid, in pixels
P = Space Warps subject probability
N0 = number of markers in the cluster
S = total skill per cluster, summed over markers
D = biggest distance within cluster
OUTPUTS
EXAMPLE
BUGS
TODO: incorporate some of these defaults into the flags dictionary
AUTHORS
This file is part of the Space Warps project, and is distributed
under the GPL v2 by the Space Warps Science Team.
http://spacewarps.org/
HISTORY
2013-07-16 started Davis (KIPAC)
"""
# ------------------------------------------------------------------
# Some defaults:
flags = {'points': 30,
'heatmap': False,
'contour': False,
'field': False,
'stamp': False,
'alpha': False,
'skill': False,
'output_directory': './',
'output_format': 'png',
'stamp_size': 50,
'dist_max': 30,
'stamp_min': 1,
'smooth_click': 3,
'figsize_stamp': 5,
'figsize_field': 10,
'image_y_size': 440,
'diagnostics': False,
}
# ------------------------------------------------------------------
# Read in options:
# this has to be easier to do...
for arg in args:
if arg in flags:
flags[arg] = args[arg]
elif arg == 'collection':
collection_path = args[arg]
elif arg == 'catalog':
catalog_path = args[arg]
else:
print "make_lens_atlas: unrecognized flag ",arg
xbins = np.arange(flags['stamp_size'] * 2)
ybins = np.arange(flags['stamp_size'] * 2)
figsize_stamp = (flags['figsize_stamp'], flags['figsize_stamp'])
figsize_field = (flags['figsize_field'], flags['figsize_field'])
image_y_size = flags['image_y_size']
print "make_lens_atlas: illustrating behaviour captured in collection, and lens files: "
print "make_lens_atlas: ", collection_path
print "make_lens_atlas: ", catalog_path
# ------------------------------------------------------------------
# Read in files:
#bureau = swap.read_pickle(bureau_path, 'bureau') # TODO: needed?
collection = swap.read_pickle(collection_path, 'collection')
catalog = csv2rec(catalog_path)
#print "make_lens_atlas: bureau numbers ", len(bureau.list())
print "make_lens_atlas: collection numbers ", len(collection.list())
print "make_lens_atlas: catalog numbers ", len(catalog)
# ------------------------------------------------------------------
# Run through data:
# ------------------------------------------------------------------
# Stamps:
if flags['stamp']:
print "make_lens_atlas: running stamps"
for lens_i in range(len(catalog)):
ID = catalog[lens_i]['id']
kind = catalog[lens_i]['kind']
x = catalog[lens_i]['x']
# flip y axis
y = image_y_size - catalog[lens_i]['y']
N0 = catalog[lens_i]['n0']
if 'dist' in catalog.dtype.names:
if catalog[lens_i]['dist'] == 0:
continue
if ((x < 0)):
# this is one of the 'non points'; skip
continue
if (N0 < flags['stamp_min']):
# not enough points!
continue
subject = collection.member[ID]
annotationhistory = subject.annotationhistory
# ------------------------------------------------------------------
# download png
url = subject.location
outname = flags['output_directory'] + '{0}_field.png'.format(ID)
im = get_online_png(url, outname)
min_x = np.int(np.max((x - flags['stamp_size'], 0)))
max_x = np.int(np.min((x + flags['stamp_size'], im.shape[0])))
min_y = np.int(np.max((y - flags['stamp_size'], 0)))
max_y = np.int(np.min((y + flags['stamp_size'], im.shape[1])))
min_member_x = np.int(np.max((x - flags['dist_max'], 0)))
max_member_x = np.int(np.min((x + flags['dist_max'], im.shape[0])))
min_member_y = np.int(np.max((y - flags['dist_max'], 0)))
max_member_y = np.int(np.min((y + flags['dist_max'], im.shape[1])))
if (min_x >= max_x) + (min_y >= max_y):
print "make_lens_atlas: misshapen lens for ID ", ID
continue
# if it is a training image, claim the alpha parameter
if im.shape[2] == 4:
alpha = im[:, :, 3][min_y: max_y, min_x: max_x]
im = im[:, :, :3][min_y: max_y, min_x: max_x]
else:
alpha = None
im = im[min_y: max_y, min_x: max_x]
fig = plt.figure(figsize=figsize_stamp)
ax = fig.add_subplot(111)
ax.imshow(im, origin=origin)
ax.scatter(x - min_x,
y - min_y,
marker='d',
c=(0, 1.0, 0), s=100,
alpha=0.75)
if ((flags['contour'])
+ (flags['heatmap'])
+ (flags['points'] != 0)):
itwas = annotationhistory['ItWas']
x_all = annotationhistory['At_X']
y_all = annotationhistory['At_Y']
x_markers_all = np.array([xi for xj in x_all for xi in xj])
y_markers_all = np.array([yi for yj in y_all for yi in yj])
agents_numbers = np.arange(
x_markers_all.size)
if 'labels' in annotationhistory:
# find which label is closest to your folks
labels_all = annotationhistory['labels']
labels = np.array([xi for xj in labels_all for xi in xj])
cluster_labels = list(set(labels))
data = np.vstack((x_markers_all, y_markers_all)).T
cluster_centers = np.array([np.mean(data[labels == i], axis=0)
for i in cluster_labels])
# find which label is closest to the (x,y)
label_center = cluster_labels[np.argmin(np.sum(np.square(cluster_centers - np.vstack((x, y)).T), axis=1))]
conds = (labels == label_center)
else:
# now filter markers by those that are within
# dist_max of the center (since I don't record cluster
# members...)
conds = ((x_markers_all >= min_member_x) *
(x_markers_all <= max_member_x) *
(y_markers_all >= min_member_y) *
(y_markers_all <= max_member_y))
agents = agents_numbers[conds]
x_markers = x_markers_all[agents]
y_markers = y_markers_all[agents]
# filter markers
n_catalog = len(agents)
if (flags['points'] > 0) * \
(flags['points'] < n_catalog):
agents_points = np.random.choice(
agents,
size=flags['points'], replace=False)
else:
agents_points = agents
x_markers_filtered = x_markers_all[agents_points]
y_markers_filtered = y_markers_all[agents_points]
if (flags['skill']) * (len(agents) > 0):
PL_all = annotationhistory['PL']
PD_all = annotationhistory['PD']
# filter out the empty clicks
PL_list = []
for i, xj in enumerate(x_all):
PL_list.append([PL_all[i]] * len(xj))
PL = np.array([PLi for PLj in PL_list for PLi in PLj])
# filter out the empty clicks
PD_list = []
for i, xj in enumerate(x_all):
PD_list.append([PD_all[i]] * len(xj))
PD = np.array([PDi for PDj in PD_list for PDi in PDj])
skill_all = swap.expectedInformationGain(0.5, PL, PD)
skill = skill_all[agents]
smax = 100
smin = 5
if np.max(skill) != np.min(skill):
sizes_all = (skill_all - np.min(skill)) * (smax - smin) / \
(np.max(skill) - np.min(skill))
sizes_filtered = sizes_all[agents_points]
else:
sizes_filtered = 50
else:
skill = None
sizes_filtered = 50
colors = (0, 1.0, 0)
# ----------------------------------------------------------
# heatmaps
if (flags['heatmap']) * (len(agents) > 0):
fig_heatmap = plt.figure(figsize=figsize_stamp)
ax_heatmap = fig_heatmap.add_subplot(111)
# now do the lens locations
# don't need to filter the x's since that is filtered by
# xbins and ybins anyways
pdf2d(x_markers - min_x, y_markers - min_y,
xbins=xbins, ybins=ybins,
weights=skill, smooth=flags['smooth_click'],
color=(0, 1.0, 0),
style='hist',
axis=ax_heatmap)
if flags['alpha'] * (alpha != None):
contour_hist(alpha.T,
extent=(xbins[0], xbins[-1], ybins[0], ybins[-1]),
color='w', style='contour', axis=ax_heatmap)
ax_heatmap.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off',
right='off',
labelleft='off',
labelbottom='off') # labels along the bottom edge are off
# CPD 04.08.14: Flip axis to old conventions
ax_heatmap.invert_yaxis()
try:
outfile = flags['output_directory'] + \
'{0}_cluster_{1}_heatmap.{2}'.format(
ID, lens_i, flags['output_format'])
# fig_heatmap.savefig(outfile)
#fig_heatmap.canvas.print_png(outfile)
fig_heatmap.savefig(outfile, bbox_inches='tight', pad_inches=0)
except:
print 'make_lens_catalog: heatmap problem with ', ID, lens_i
# import ipdb; ipdb.set_trace()
# ---------------------------------------------------------
# back to our other plots
# contours
if (flags['contour']) * (len(agents) > 0):
# now do the lens locations
# don't need to filter the x's since that is filtered by
# xbins and ybins anyways
pdf2d(x_markers - min_x, y_markers - min_y,
xbins=xbins, ybins=ybins,
weights=skill, smooth=flags['smooth_click'],
color=(0, 1.0, 0),
style='contour',
axis=ax)
# plot points
if (flags['points'] != 0) * (len(agents) > 0):
ax.scatter(x_markers_filtered - min_x,
y_markers_filtered - min_y,
c=colors, s=sizes_filtered,
alpha=0.25)
# plot alpha
if flags['alpha'] * (alpha != None):
contour_hist(alpha.T,
extent=(xbins[0], xbins[-1], ybins[0], ybins[-1]),
color='w', style='contour', axis=ax)
# ----------------------------------------------------------
ax.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off',
right='off',
labelleft='off',
labelbottom='off') # labels along the bottom edge are off
ax.invert_yaxis()
try:
outfile = flags['output_directory'] + \
'{0}_cluster_{1}_contour.{2}'.format(
ID, lens_i, flags['output_format']
)
# fig.savefig(outfile)
fig.savefig(outfile, bbox_inches='tight', pad_inches=0)
# fig.canvas.print_png(outfile)
except:
print 'make_lens_catalog: contour problem with ', ID, lens_i
# import ipdb; ipdb.set_trace()
plt.close('all')
# ------------------------------------------------------------------
# Fields
if flags['field']:
print "make_lens_atlas: running fields"
# find the unique IDs. mark centers and also centrals if clustering is
# done
#import ipdb; ipdb.set_trace()
unique_IDs = np.unique(catalog['id'])
for ID in unique_IDs:
mini_catalog = catalog[catalog['id'] == ID]
subject = collection.member[ID]
annotationhistory = subject.annotationhistory
# plot cluster centers
kind = mini_catalog['kind']
x_centers = mini_catalog['x']
# flip y from catalog
y_centers = image_y_size - mini_catalog['y']
skill_centers = mini_catalog['skill']
# filter out the -1 entry
center_cond = (x_centers > 0) * (y_centers > 0)
# filter outliers if possible
if 'dist' in mini_catalog.dtype.names:
center_cond *= mini_catalog['dist'] > 0
skill_centers = skill_centers[center_cond]
x_centers = x_centers[center_cond]
y_centers = y_centers[center_cond]
colors_centers = [(0, 1.0, 0) for i in x_centers]
if len(colors_centers) == 0:
#welp, nothing here
continue
# ------------------------------------------------------------------
# download png
url = subject.location
outname = flags['output_directory'] + '{0}_field.png'.format(ID)
im = get_online_png(url, outname)
# if it is a training image, claim the alpha parameter
if im.shape[2] == 4:
alpha = im[:, :, 3]
im = im[:, :, :3]
else:
alpha = None
fig = plt.figure(figsize=figsize_field)
ax = fig.add_subplot(111)
ax.imshow(im, origin=origin)
xbins = np.arange(im.shape[0])
ybins = np.arange(im.shape[1])
min_x = 0
min_y = 0
max_x = im.shape[0]
max_y = im.shape[1]
if (flags['skill']) * (np.max(skill_centers) != np.min(skill_centers)):
sizes_centers = (
(skill_centers - np.min(skill_centers)) *
(200 - 10) /
(np.max(skill_centers) - np.min(skill_centers)))
else:
sizes_centers = [100 for i in x_centers]
sizes_centers = [100 for i in x_centers]
ax.scatter(x_centers, y_centers,
marker='d', c=colors_centers,
s=sizes_centers, alpha=0.75)
if flags['diagnostics']:
r = flags['dist_max']
b = flags['stamp_size']
b_ones = np.ones(100) * b
b_arr = np.linspace(-b, b, 100)
def xy(x0, y0, r, phi):
return x0 + r * np.cos(phi), y0 + r * np.sin(phi)
phis = np.arange(0, 6.28, 0.01)
for i in xrange(len(x_centers)):
x_center = x_centers[i]
y_center = y_centers[i]
ax.plot( *xy(x_center, y_center, r, phis), c='w', ls='-', linewidth=4)
# plot box
ax.plot(x_center + b_ones, y_center + b_arr, c='r', ls='--', linewidth=4)
ax.plot(x_center - b_ones, y_center + b_arr, c='r', ls='--', linewidth=4)
ax.plot(x_center + b_arr, y_center + b_ones, c='r', ls='--', linewidth=4)
ax.plot(x_center + b_arr, y_center - b_ones, c='r', ls='--', linewidth=4)
itwas = annotationhistory['ItWas']
x_all = annotationhistory['At_X']
y_all = annotationhistory['At_Y']
x_markers_all = np.array([xi for xj in x_all for xi in xj])
y_markers_all = np.array([yi for yj in y_all for yi in yj])
# now filter markers by those that are within
# stamp_size of the stamp
# I'm pretty sure this step is redundant when going over the full
# image?
agents_numbers = np.arange(
x_markers_all.size)
conds = ((x_markers_all >= min_x) * (x_markers_all <= max_x) *
(y_markers_all >= min_y) * (y_markers_all <= max_y))
agents = agents_numbers[conds]
x_markers = x_markers_all[agents]
y_markers = y_markers_all[agents]
# filter markers
n_catalog = len(agents)
if (flags['points'] > 0) * \
(flags['points'] < n_catalog):
agents_points = np.random.choice(
agents,
size=flags['points'], replace=False)
else:
agents_points = agents
x_markers_filtered = x_markers_all[agents_points]
y_markers_filtered = y_markers_all[agents_points]
if flags['skill']:
PL_all = annotationhistory['PL']
PD_all = annotationhistory['PD']
# filter out the empty clicks
PL_list = []
for i, xj in enumerate(x_all):
PL_list.append([PL_all[i]] * len(xj))
PL = np.array([PLi for PLj in PL_list for PLi in PLj])
# filter out the empty clicks
PD_list = []
for i, xj in enumerate(x_all):
PD_list.append([PD_all[i]] * len(xj))
PD = np.array([PDi for PDj in PD_list for PDi in PDj])
skill_all = swap.expectedInformationGain(0.5, PL, PD)
skill = skill_all[agents]
smax = 100
smin = 5
if np.max(skill) != np.min(skill):
sizes_all = (skill_all - np.min(skill)) * (smax - smin) / \
(np.max(skill) - np.min(skill))
sizes_filtered = sizes_all[agents_points]
else:
sizes_filtered = 50
else:
skill = None
sizes_filtered = 50
if 'labels' in annotationhistory:
# find which label is closest to your folks
labels_all = annotationhistory['labels']
labels = np.array([xi for xj in labels_all for xi in xj])
labels_filtered = labels[agents_points]
colors = []
alpha = 0.75
for label in labels_filtered:
if label == -1:
colors.append((1.0, 0.0, 0))
else:
colors.append((0, 1.0, 0))
else:
colors = (0, 1.0, 0)
alpha = 0.25
# ----------------------------------------------------------
# contours
if flags['contour'] * (len(x_markers) >= flags['stamp_min']):
# now do the lens locations
# don't need to filter the x's since that is filtered by
# xbins and ybins anyways
pdf2d(x_markers - min_x, y_markers - min_y,
xbins=xbins, ybins=ybins,
weights=skill, smooth=flags['smooth_click'],
color=(0, 1.0, 0),
style='contour',
axis=ax)
# ----------------------------------------------------------
# plot points
if flags['points'] != 0:
ax.scatter(x_markers_filtered - min_x,
y_markers_filtered - min_y,
c=colors, s=sizes_filtered,
alpha=alpha)
# ----------------------------------------------------------
# do alpha
if flags['alpha'] * (alpha != None):
contour_hist(alpha.T,
extent=(xbins[0], xbins[-1], ybins[0], ybins[-1]),
color='w', style='contour', axis=ax)
ax.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off',
right='off',
labelleft='off',
labelbottom='off') # labels along the bottom edge are off
ax.invert_yaxis()
try:
outfile = flags['output_directory'] + '{0}_field_output.{1}'.format(ID, flags['output_format'])
# fig.savefig(outfile)
fig.savefig(outfile, bbox_inches='tight', pad_inches=0)
#fig.canvas.print_png(outfile)
except:
print 'make_lens_catalog: field problem with field ', ID
plt.close('all')
print 'make_lens_catalog: All done!'
tonights = swap.Configuration(configfile)
thresholds = {}
thresholds['detection'] = tonights.parameters['detection_threshold']
thresholds['rejection'] = tonights.parameters['rejection_threshold']
# Use the following directory for output lists and plots:
tonights.parameters['trunk'] = tonights.parameters['survey']
tonights.parameters['dir'] = '.'
# ------------------------------------------------------------------
# Read in, or create, an object representing the candidate list:
print "SWAG: reading in subjects..."
sample = swap.read_pickle(tonights.parameters['samplefile'],'collection')
# ------------------------------------------------------------------
# Write out a catalog of subjects, including the ZooID, subject ID,
# how many classifications, and probability:
catalog = swap.get_new_filename(tonights.parameters,'candidate_catalog')
print "SWAG: saving catalog of high probability subjects..."
Nlenses,Nsubjects = swap.write_catalog(sample,catalog,thresholds,kind='test')
print "SWAG: "+str(Nsubjects)+" subjects classified,"
print "SWAG: "+str(Nlenses)+" candidates (with P > rejection) written to "+catalog
# Also write out the sims, and the duds:
catalog = swap.get_new_filename(tonights.parameters,'sim_catalog')
def MachineShop(args):
# Buh. I never built in the ability to change directories on the fly
#machine_sim_directory = 'sims_Machine/redo_with_circular_morphs'
"""
Sometimes you just need to the run the Machine on a bunch of already
made SWAP-runs / simulations.
If so, this script is for you!
"""
# Get parameters from the SWAP run of interest
the = swap.Configuration(args.config)
params = the.parameters
# This pulls up the FIDUCIAL SWAP simulation
sim = Simulation(config=args.config,
directory='sims_SWAP/S_PLPD5_p5_ff_norand',
variety='feat_or_not')
# this was originally set to 2/17/09 which is WRONG
# 11/2/17: WHY?? F**k you, Past Melanie. What am I supposed to do here??
first_day = dt.datetime(2009, 2, 12)
today = dt.datetime.strptime(params['start'], '%Y-%m-%d_%H:%M:%S')
start_day = dt.datetime(2009, 2, 17)
last_day = dt.datetime.strptime(params['end'], '%Y-%m-%d_%H:%M:%S')
yesterday = None
run_machine = False
SWAP_retired = 0
notfound = 0
last_night = None
for idx, filename in enumerate(sim.retiredFileList[(today -
first_day).days:]):
print ""
print "----------------------- The Machine Shop ----------------------------"
print "Today is {}".format(today)
if today >= last_day:
print "Get outta the machine shop!"
exit()
# ---------------------------------------------------------------------
# OPEN METADATA PICKLE (updated each time MachineClassifier is run)
# ---------------------------------------------------------------------
backup_meta_file = params['metadatafile'].replace(
'.pickle', '_orig.pickle')
if today == first_day:
try:
storage = swap.read_pickle(backup_meta_file, 'metadata')
except:
print "MachineShop: Backup metadata pickle not yet created."
print "MachineShop: Opening original metadata pickle file instead"
storage = swap.read_pickle(params['metadatafile'], 'metadata')
if 'retired_date' not in storage.subjects.colnames:
storage.subjects['retired_date'] = '2016-09-10'
if 'valid' not in np.unique(storage.subjects['MLsample']):
expert = (storage.subjects['Expert_label'] != -1)
storage.subjects['MLsample'][expert] = 'valid'
# save an untouched copy for reference later
print "MachineShop: Creating a backup metadata pickle"
swap.write_pickle(storage, backup_meta_file)
else:
storage = swap.read_pickle(params['metadatafile'], 'metadata')
# Regardless of which metadata you open, make sure it has these columns
# (old metadata files WON'T have them!)
if 'retired_date' not in storage.subjects.colnames:
storage.subjects['retired_date'] = '2016-09-10'
if 'valid' not in np.unique(storage.subjects['MLsample']):
expert = (storage.subjects['Expert_label'] != -1)
storage.subjects['MLsample'][expert] = 'valid'
subjects = storage.subjects
# I just need to know what was retired TONIGHT --
# compare what's retired UP TILL tonight with what was
# retired up till LAST NIGHT
SWAP_retired_by_tonight = sim.fetchCatalog(filename)
# If we're picking up where we left off, grab previous training sample
#if today>start_day and last_night is None:
# print 'MachineShop: getting previous training sample'
# last_night = subjects[subjects['MLsample']=='train']
# last_night['zooid'] = last_night['SDSS_id']
try:
ids_retired_tonight = set(SWAP_retired_by_tonight['zooid']) - \
set(last_night['zooid'])
except:
ids_retired_tonight = set(SWAP_retired_by_tonight['zooid'])
print "Newly retired subjects: {}".format(len(ids_retired_tonight))
# Now that I have the ids from the previous night, adjust the
# metadata file to reflect what was retired / add SWAP info
for ID in list(ids_retired_tonight):
# Locate this subject in the metadata file
mask = subjects['SDSS_id'] == int(ID)
# Update them in metadata file as training sample for MC
# DOUBLE CHECK THAT IT HAS NOT BEEN RETIRED BY MACHINE!!!
#if subjects['MLsample'][mask] == 'test ':
if subjects['MLsample'][mask] == 'test':
SWAP_retired += 1
subjects['MLsample'][mask] = 'train'
subjects['retired_date'][mask] = dt.datetime.strftime(
today, '%Y-%m-%d')
subjects['SWAP_prob'][mask] = SWAP_retired_by_tonight['P'][
SWAP_retired_by_tonight['zooid'] == ID]
run_machine = True
else:
notfound += 1
if len(subjects[subjects['MLsample'] == 'train']) >= 10000:
run_machine = True
last_night = SWAP_retired_by_tonight
print "Retired by this day:", len(last_night)
print ""
print "MachineShop: Found {0} subjects retired by SWAP on {1}"\
.format(SWAP_retired, today)
print "MachineShop: {0} total subjects retired so far"\
.format(np.sum(subjects['MLsample']=='train'))
print "MachineShop: Found {0} subjects retired by Machine."\
.format(np.sum(subjects['MLsample']=='mclas'))
print "MachineShop: Saving updated StorageLocker."
params['dir'] = os.getcwd()
# Save our new metadata file -- MC needs this -- save to NOT the original
params['metadatafile'] = params['dir'] + '/' + params[
'survey'] + '_metadata.pickle'
swap.write_pickle(storage, params['metadatafile'])
if run_machine:
# Need to doctor the config to refect the "correct date"
params['start'] = today.strftime('%Y-%m-%d_%H:%M:%S')
swap.write_config(args.config, params)
# Run MachineClassifier.py using this subject file
os.system("python MachineClassifier.py -c %s" % args.config)
"""os.system("python test_Machine.py -c {0}".format(args.config))"""
# MachineClassifier updates the configfile so now we need to open the NEW one
the = swap.Configuration(args.config)
params = the.parameters
# Update date (since we're not running SWAP)
today += dt.timedelta(days=1)
def main():
"""
This script makes ALL THE MUTHAFUCKIN FIGURES FOR MAH PAYPAH.
1. VOLUNTEER PROBABILITIES
NAME
plot_user_probabilities()
REQUIRES
swap bureau file and # of users to plot
2. VOTE DISTRIBUTIONS COMPARED TO GZ2
NAME
plot_vote_distributions()
REQUIRES
gz2_metadata and simulation to compare to
3. BASELINE SWAP SIMULATION COMPARED TO GZ2
NAME
plot_GZX_baseline()
REQUIRES
baseline simulation, evaluation ascii file for baseline run,
gz2_retired (cumulative retired subjects in GZ2)
NOTES
this plots the retired subject rate AND the corresponding
quality metrics ON THE SAME AXES
The eval file and the GZ2 retired subjects file must be
created in separate script: (generate_SWAP_eval_report)
4. VARIATIONS IN SWAP
NAME
plot_GZX_evaluation_spread()
plot_GZX_cumulative_retirement_spread()
REQUIRES
three simulations to compare (for spread in retirement)
three evaluation files to compare (for spread in eval)
NOTES
the eval files have to be created with generate_SWAP_eval_report
5. SWAP AND GZ2 DISAGREE
swap_gets_it_wrong()
6. MONEYPLOT
MONEYPLOT()
7. 1D MORPHOLOGY DISTRIBUTIONS
NAME
plot_morph_params_1D()
REQUIRES
"""
make_volunteer_probabilties_plot = False
make_subject_trajectory_plot = False
make_vote_distributions_plot = False
make_baseline_simulation_plot = False
make_swap_variations_plot = False
make_swap_gets_it_wrong_plot = False
make_moneyplot = True
make_morph_distributions_plot = False
make_roc_curves = False
calculate_GX_human_effort = False
survey = 'GZ2_sup_PLPD5_p5_flipfeature2b'
dir_tertiary = 'tertiary_simulation_output'
dir_sim_machine = 'sims_Machine/redo_with_correct_ell_morphs'
dir_sim_swap = 'sims_SWAP/S_PLPD5_p5_ff_norand/'
# Load up some GZ2 data
# -----------------------------------------------
gz2_metadata = Table.read('metadata_ground_truth_labels.fits')
if 'GZ2_deb_combo' not in gz2_metadata.colnames:
gz2_metadata['GZ2_raw_combo'] = GZ2_label_SMOOTH_NOT(bigfuckingtable,
type='raw')
gz2_metadata.write('metadata_ground_truth_labels.fits', overwrite=True)
gz2_metadata['zooid'] = gz2_metadata['SDSS_id']
gz2_metadata['id'] = gz2_metadata['asset_id']
F = open('GZ2_cumulative_retired_subjects_expert.pickle', 'r')
gz2_cum_sub_retired = cPickle.load(F)
morph = Table.read("metadata_ground_truth_labels.fits")
pdb.set_trace()
# Load up BASELINE simulation
# ------------------------------------------------------
mid_name = 'sup_PLPD5_p5_flipfeature2b_norandom2'
#stuff = generate_SWAP_eval_report(mid_sim, gz2_metadata, outname=mid_name+'_raw_combo',
# write_file=True, gz_kind='raw_combo')
mid_eval2 = Table.read('{0}/GZX_evaluation_{1}.txt'.format(
dir_tertiary, mid_name + '_raw_combo'),
format='ascii')
mid_sim = Simulation(
config='configfiles/update_sup_PLPD5_p5_flipfeature2b_norandom2.config',
directory=dir_sim_swap,
variety='feat_or_not')
""" MAKE VOLUNTEER PROBABILTIES PLOT """
if make_volunteer_probabilties_plot:
# Load up the SWAP Simulation AGENT BUREAU
picklename = '{0}/{1}_bureau.pickle'.format(dir_sim_swap, survey)
bureau = swap.read_pickle(picklename, 'bureau')
plot_user_probabilities(bureau, 200)
if make_subject_trajectory_plot:
# Load up the SWAP Simulation AGENT BUREAU
picklename = '{0}/{1}_collection.pickle'.format(dir_sim_swap, survey)
collection = swap.read_pickle(picklename, 'collection')
plot_subject_trajectories(collection, 200)
""" MAKE BASELINE SIMULATION PLOT """
if make_baseline_simulation_plot:
# BASELINE fig requires BASELINE Simulation,
# evaluation output for that sim,
# cumulative retirement for GZ2
plot_GZX_baseline(mid_sim, mid_eval2, gz2_cum_sub_retired)
""" MAKE MONEY PLOT """
if make_moneyplot:
outfile = '{}/{}'.format(dir_sim_machine, survey)
# this file made by analaze_GZX_simulation.py
filename = glob.glob('{}*_combo_analysis*.pickle'.format(outfile))
F = open(filename[0], 'rb')
combo_run = cPickle.load(F)
F.close()
# Load up the Machine bureau
F = open('{0}/{1}_MLbureau.pickle'.format(dir_sim_machine, survey),
'rb')
MLbureau = cPickle.load(F)
F.close()
MONEYPLOT(92,
mid_sim,
mid_eval2,
gz2_cum_sub_retired,
combo_run,
MLbureau,
outfile=outfile)
""" MORPH DISTRIBUTIONS """
if make_morph_distributions_plot:
# Plotting FEAT vs NOT, FALSE POS & FALSE NEGs, RETIRED vs NOT RETIRED
# to do all that, need files that were created.... GZX_SWAP_eval?
filename = glob.glob(
'{}/*_machine_retired_subjects.fits'.format(dir_sim_machine))
machine_retired = Table.read(filename[0])
#machine_not_retired = Table.read('tertiary_simulation_output/{}_machine_not_retired_subjects.fits'.format(outfile))
plot_morph_params_1D(machine_retired,
gz2_metadata,
outfile=dir_sim_machine)
""" MAKE SWAP GETS IT WRONG PLOT """
if make_swap_gets_it_wrong_plot:
# Compare SWAP-retired subjects to various parameters in the GZ2 Main Catalog
bigfuckingtable = Table.read(
'../SpaceWarps/analysis/GZ2ASSETS_NAIR_MORPH_MAIN.fits')
gz2_bigfuckingtable = join(gz2_metadata, bigfuckingtable, keys='id')
all_retired = mid_sim.fetchCatalog(mid_sim.retiredFileList[-1])
gz2_baseline = join(gz2_bigfuckingtable, all_retired, keys='zooid')
tps2, fps2, tns2, fns2 = calculate_confusion_matrix(
gz2_baseline[gz2_baseline['P'] > 0.3],
gz2_baseline[gz2_baseline['P'] < 0.3],
smooth_or_not=False,
gz_kind='raw_combo')
correct = vstack([tps2, tns2])
#print len(correct)
swap_gets_it_wrong(fps2, fns2, correct)
""" MAKE VOTE DISTRIBUTION PLOT """
if make_vote_distributions_plot:
# Requires the Vote Distributions for GZ2 and those from the Simulation
plot_vote_distributions(gz2_metadata, mid_sim)
if calculate_GX_human_effort:
mlbureaufile = 'sims_Machine/redo_with_correct_ell_morphs/GZ2_sup_PLPD5_p5_flipfeature2b_MLbureau.pickle'
MLbureau = swap.read_pickle(mlbureaufile, 'bureau')
machine_meta = 'sims_Machine/redo_with_correct_ell_morphs/GZ2_sup_PLPD5_p5_flipfeature2b_metadata.pickle'
all_subjects = swap.read_pickle(machine_meta, 'metadata').subjects
#subjects = all_subjects[all_subjects['retired_date']!='2016-09-10']
mclass = all_subjects[all_subjects['MLsample'] == 'mclas']
swaps = all_subjects[(all_subjects['MLsample'] == 'train') |
(all_subjects['MLsample'] == 'valid')]
catalog = mid_sim.fetchCatalog(mid_sim.retiredFileList[-1])
catalog['SDSS_id'] = catalog['zooid']
# How many machine-retired subjects would have been retired by SWAP anyway?
#swap_mach_retired = join(catalog, mclass, keys='SDSS_id')
swap_retired = join(catalog, swaps, keys='SDSS_id')
# Assume that only Human Effort came from training sample
effort = np.sum(swap_retired['Nclass'])
print "Human effort for GZX:", effort
# LOOK AT MOST IMPORTANT FEATURES FOR MACHINE
machine = MLbureau.member['RF_accuracy']
trainhist = machine.traininghistory
models = trainhist['Model']
for i, model in enumerate(models):
if i == 0:
feature_importances = model.feature_importances_
else:
feature_importances = np.vstack(
[feature_importances, model.feature_importances_])
labels = ['M$_{20}$', '$C$', '$1-b/a$', '$A$', '$G$']
fi = feature_importances
avg, std = [], []
for i in range(5):
avg.append(np.mean(fi[:, i]))
std.append(np.std(fi[:, i]))
avg = np.array(avg)
std = np.array(std)
labels = np.array(labels)
sort_indices = np.argsort(avg)
ind = np.arange(len(labels))
#pdb.set_trace()
fig = plt.figure(figsize=(11, 8))
ax = fig.add_subplot(111)
rects1 = ax.bar(ind,
avg[sort_indices],
yerr=std[sort_indices],
color='red',
edgecolor='black',
capsize=5,
align='center')
ax.set_ylabel('Feature Importance')
ax.set_xticks(ind)
ax.set_xticklabels(labels[sort_indices])
ax.set_ylim(0, 0.45)
ax.set_yticks([0., .1, .2, .3, .4])
plt.savefig('RF_feature_importance_4paper.pdf', bbox_inches='tight')
plt.show()
#pdb.set_trace()
if make_roc_curves:
candidateFileList = mid_sim.fetchFileList(kind='candidate')
"""
# SWAP situation at ~30 days into simulation
candidates1 = mid_sim.fetchCatalog(candidateFileList[30])
rejected1 = mid_sim.fetchCatalog(mid_sim.rejectedFileList[30])
swap_subjects1 = np.concatenate([candidates1, rejected1])
subjects1 = join(gz2_metadata, swap_subjects1, keys='zooid')
# SWAP situation at ~60 days into simualtion
candidates2 = mid_sim.fetchCatalog(candidateFileList[60])
rejected2 = mid_sim.fetchCatalog(mid_sim.rejectedFileList[60])
swap_subjects2 = np.concatenate([candidates2, rejected2])
subjects2 = join(gz2_metadata, swap_subjects2, keys='zooid')
"""
# SWAP situation at the end of the siulation
candidates3 = mid_sim.fetchCatalog(candidateFileList[-1])
rejected3 = mid_sim.fetchCatalog(mid_sim.rejectedFileList[-1])
swap_subjects3 = np.concatenate([candidates3, rejected3])
subjects3 = join(gz2_metadata, swap_subjects3, keys='zooid')
subject_sets = [subjects3] #subjects1, subjects2,
plot_roc_curve(subject_sets,
smooth_or_not=False,
gz_kind='raw_combo',
swap=True,
outname=None)
""" MAKE SWAP VARIATIONS PLOT(S) """
if make_swap_variations_plot:
#"""
# Load up simulations varying subject PRIOR
# -------------------------------------------------------
low_p = 'sup_PLPD5_p2_flipfeature2_norand'
high_p = 'sup_PLPD5_p8_flipfeature2_norand'
p35 = 'sup_PLPD5_p35_flipfeature2_norand'
low_p_eval2 = Table.read(
'tertiary_simulation_output/GZX_evaluation_{0}.txt'.format(
low_p + '_raw_combo'),
format='ascii')
high_p_eval2 = Table.read(
'tertiary_simulation_output/GZX_evaluation_{0}.txt'.format(
high_p + '_raw_combo'),
format='ascii')
#p35_eval2 = Table.read('tertiary_simulation_output/GZX_evaluation_{0}.txt'.format(p35+'_raw_combo'), format='ascii')
low_p_sim = Simulation(
config='configfiles/update_sup_PLPD5_p2_flipfeature2_norand.config',
directory='sims_SWAP/S_PLPD5_p2_ff_norand/',
variety='feat_or_not')
high_p_sim = Simulation(
config='configfiles/update_sup_PLPD5_p8_flipfeature2_norand.config',
directory='sims_SWAP/S_PLPD5_p8_ff_norand/',
variety='feat_or_not')
#p35_sim = Simulation(config='configfiles/update_sup_PLPD5_p35_flipfeature2_norand.config',
# directory='sims_SWAP/S_PLPD5_p35_ff_norand/',
# variety='feat_or_not')
#"""
# Load up simulations for varying user PL/PD
# -------------------------------------------------------
low_plpd = 'sup_PLPD4_p5_flipfeature2_norand'
high_plpd = 'sup_PLPD6_p5_flipfeature2_norand'
low_plpd_eval2 = Table.read(
'tertiary_simulation_output/GZX_evaluation_{0}.txt'.format(
low_plpd + '_raw_combo'),
format='ascii')
high_plpd_eval2 = Table.read(
'tertiary_simulation_output/GZX_evaluation_{0}.txt'.format(
high_plpd + '_raw_combo'),
format='ascii')
low_plpd_sim = Simulation(
config='configfiles/update_sup_PLPD4_p5_flipfeature2_norand.config',
directory='sims_SWAP/S_PLPD4_p5_ff_norand/',
variety='feat_or_not')
high_plpd_sim = Simulation(
config='configfiles/update_sup_PLPD6_p5_flipfeature2_norand.config',
directory='sims_SWAP/S_PLPD6_p5_ff_norand/',
variety='feat_or_not')
#"""
# VARY PRIOR
fig = plt.figure(figsize=(11, 16))
plt.rc('text', usetex=True)
gs = gridspec.GridSpec(2, 1)
gs.update(wspace=0.05, hspace=0.01)
ax = fig.add_subplot(gs[0])
plot_GZX_evaluation_spread(92,
low_p_eval2,
mid_eval2,
high_p_eval2,
outfile='compare_PLPD_4paper',
ax=ax)
ax2 = fig.add_subplot(gs[1])
plot_GZX_cumulative_retirement_spread(92,
low_p_sim,
mid_sim,
high_p_sim,
gz2_cum_sub_retired,
outfile='compare_prior_4paper',
ax=ax2)
fig.suptitle(r'$0.1 \le \mathrm{Subject~Prior} \le 0.8$', fontsize=30)
gs.tight_layout(fig, rect=[0, 0, 1, 0.97])
plt.savefig('GZX_eval_and_retirement_prior_spread_4paper_v2.pdf')
plt.show()
plt.close()
#"""
# -----------------------------------------------------------
# VARY PLPD
fig = plt.figure(figsize=(11, 16))
plt.rc('text', usetex=True)
gs = gridspec.GridSpec(2, 1)
gs.update(wspace=0.01, hspace=0.01)
ax = fig.add_subplot(gs[0])
plot_GZX_evaluation_spread(92,
low_plpd_eval2,
mid_eval2,
high_plpd_eval2,
outfile='compare_PLPD_4paper',
ax=ax)
ax2 = fig.add_subplot(gs[1])
plot_GZX_cumulative_retirement_spread(92,
low_plpd_sim,
mid_sim,
high_plpd_sim,
gz2_cum_sub_retired,
outfile='compare_prior_4paper',
ax=ax2)
fig.suptitle(
r'$(0.4, 0.4) \le \mathrm{Confusion~Matrix} \le (0.6, 0.6)$',
fontsize=30)
gs.tight_layout(fig, rect=[0, 0, 1, 0.97])
plt.savefig('GZX_eval_and_retirement_PLPD_spread_4paper_v2.pdf')
plt.show()
plt.close()
# Check for pickles in array args:
if len(args) == 1:
collectionfile = args[0]
print "make_trajectory_plots: illustrating subject trajectories in: "
print "make_trajectory_plots: ",collectionfile
else:
print make_trajectory_plots.__doc__
return
output_directory = './'
# ------------------------------------------------------------------
# Read in collection:
sample = swap.read_pickle(collectionfile, 'collection')
print "make_trajectory_plots: total no. of available subjects: ",len(sample.list())
if highlights:
# Read in subjects to be highlighted:
highlightIDs = swap.read_list(listfile)
print highlightIDs
print "make_trajectory_plots: total no. of special subjects: ",len(highlightIDs)
print "make_trajectory_plots: special subjects: ",highlightIDs
# ------------------------------------------------------------------
# Start plot:
figure = sample.start_trajectory_plot(title=title,histogram=histogram,logscale=False)
pngfile = 'trajectories.png'
else:
t1 = datetime.datetime.strptime(tonights.parameters['start'],
'%Y-%m-%d')
print "SWAP: updating all subjects with classifications made since " + tonights.parameters[
'start']
# How will we make decisions based on probability?
thresholds = {}
thresholds['detection'] = tonights.parameters['detection_threshold']
thresholds['rejection'] = tonights.parameters['rejection_threshold']
# ------------------------------------------------------------------
# Read in, or create, a bureau of agents who will represent the
# volunteers:
bureau = swap.read_pickle(tonights.parameters['crowdfile'], 'crowd')
# ------------------------------------------------------------------
# Read in, or create, an object representing the candidate list:
sample = swap.read_pickle(tonights.parameters['samplefile'], 'collection')
# ------------------------------------------------------------------
# Open up database:
if practise:
db = swap.read_pickle(tonights.parameters['dbfile'], 'database')
if db is None:
print "SWAP: making a new Toy database..."
# Check for pickles in array args:
if len(args) == 1:
collectionfile = args[0]
print "make_trajectory_plots: illustrating subject trajectories in: "
print "make_trajectory_plots: ", collectionfile
else:
print make_trajectory_plots.__doc__
return
output_directory = "./"
# ------------------------------------------------------------------
# Read in collection:
sample = swap.read_pickle(collectionfile, "collection")
print "make_trajectory_plots: total no. of available subjects: ", len(sample.list())
if highlights:
# Read in subjects to be highlighted:
highlightIDs = swap.read_list(listfile)
print highlightIDs
print "make_trajectory_plots: total no. of special subjects: ", len(highlightIDs)
print "make_trajectory_plots: special subjects: ", highlightIDs
# ------------------------------------------------------------------
# Start plot:
figure = sample.start_trajectory_plot(title=title, histogram=histogram, logscale=False)
pngfile = "trajectories.png"
def make_lens_catalog(args):
"""
NAME
make_lens_catalog
PURPOSE
Given location of collection pickle, this script produces a set of
annotated images of lenses (heatmaps for lens locations, markers for
where clicks were, etc).
COMMENTS
You have to download the file so it chooses whever your output
directory is to also download the raw images.
This should be pretty customizable.
FLAGS
-h Print this message
--skill Weight by skill
INPUTS
collection.pickle
OUTPUTS
lens.dat
Assumed format:
ID kind x y Prob N0 Skill Dist
Here:
ID = Space Warps subject ID
kind = Space Warps subject type (sim, dud, test)
x,y = object (cluster) centroid, in pixels
P = Space Warps subject probability
N0 = number of markers in the cluster
S = total skill per cluster, summed over markers
D = biggest distance within cluster
EXAMPLE
BUGS
AUTHORS
This file is part of the Space Warps project, and is distributed
under the GPL v2 by the Space Warps Science Team.
http://spacewarps.org/
HISTORY
2013-07-16 started Davis (KIPAC)
"""
# ------------------------------------------------------------------
# Some defaults:
flags = {
'skill': False,
'output_directory': './',
'output_name': 'catalog.dat',
'image_y_size': 440,
'catalog_path': '',
'update_collection': '',
}
# ------------------------------------------------------------------
# Read in options:
# this has to be easier to do...
for arg in args:
if arg in flags:
flags[arg] = args[arg]
elif arg == 'collection_path':
collection_path = args[arg]
else:
print "make_lens_atlas: unrecognized flag ", arg
print "make_lens_catalog: illustrating behaviour captured in collection file: "
print "make_lens_catalog: ", collection_path
memory = joblib.Memory(cachedir=flags['output_directory'])
memory.clear()
catalog_path = flags['output_directory'] + flags['output_name']
if len(flags['output_name']) > 0:
F = open(catalog_path, 'w')
F.write('id,kind,x,y,prob,n0,skill,dist\n')
# ------------------------------------------------------------------
# Read in files:
collection = swap.read_pickle(collection_path, 'collection')
ID_list = collection.list()
print "make_lens_catalog: collection numbers ", len(ID_list)
if flags['catalog_path'] != '':
print "make_lens_catalog: filtering from catalog ", flags[
'catalog_path']
catalog_in = csv2rec(flags['catalog_path'])
ID_list = np.unique(catalog_in['id'])
# ------------------------------------------------------------------
# Run through data:
catalog = {}
for ID in ID_list:
subject = collection.member[ID]
kind = subject.kind
P = subject.mean_probability
itwas = subject.annotationhistory['ItWas']
x_all = subject.annotationhistory['At_X']
y_all = subject.annotationhistory['At_Y']
x_markers = np.array([xi for xj in x_all for xi in xj])
y_markers = np.array([yi for yj in y_all for yi in yj])
catalog.update(
{ID: {
'agents_reject': [],
'x': x_markers,
'y': y_markers,
}})
PL_all = subject.annotationhistory['PL']
PD_all = subject.annotationhistory['PD']
# filter out the empty clicks
PL_list = []
PL_nots = []
for i, xj in enumerate(x_all):
# len(xj) of empty = 0
PL_list.append([PL_all[i]] * len(xj))
if len(xj) == 0:
PL_nots.append(PL_all[i])
PL = np.array([PLi for PLj in PL_list for PLi in PLj])
PL_nots = np.array(PL_nots)
# filter out the empty clicks
PD_list = []
PD_nots = []
for i, xj in enumerate(x_all):
PD_list.append([PD_all[i]] * len(xj))
if len(xj) == 0:
PD_nots.append(PD_all[i])
catalog[ID]['agents_reject'].append(i)
PD = np.array([PDi for PDj in PD_list for PDi in PDj])
PD_nots = np.array(PD_nots)
skill = swap.expectedInformationGain(0.5, PL, PD) # skill
# it is only fair to write out the NOTs, too
# do the empty guys
skill_nots = swap.expectedInformationGain(0.5, PL_nots,
PD_nots) # skill
x, y = -1, -1
N0 = len(skill_nots)
S = np.sum(skill_nots)
D = 0
## catalog.append((ID, kind, x, y, P, N0, S, D))
if len(catalog) % 500 == 0:
print len(catalog)
if len(flags['output_name']) > 0:
F.write('{0},{1},{2},{3},{4},{5},{6},{7}\n'.format(
ID, kind, x, y, P, N0, S, D))
if len(x_markers) == 0:
# apparently everyone was a not...
catalog[ID]['agents_labels'] = np.array([])
continue
# ------------------------------------------------------------------
# cluster
print 'make_lens_catalog: subject ID = ', ID
if flags['skill']:
cluster_centers, cluster_center_labels, cluster_labels, \
n_clusters, dist_within = outlier_clusters(x_markers, y_markers, skill, memory=memory)
else:
cluster_centers, cluster_center_labels, cluster_labels, \
n_clusters, dist_within = outlier_clusters(x_markers, y_markers, None, memory=memory)
# need to get: x, y, N0, S
catalog[ID]['agents_labels'] = cluster_labels
for cluster_center_label in cluster_center_labels:
cluster_center = cluster_centers[cluster_center_label]
members = (cluster_labels == cluster_center_label)
x, y = cluster_center
# convert y to catalog convention
y = flags['image_y_size'] - y
N0 = np.sum(members)
S = np.sum(skill[members])
D = dist_within[cluster_center_label]
if cluster_center_label == -1:
# outlier cluster
# so really every point is its own cluster...
D = 0
## catalog.append((ID, kind, x, y, P, N0, S, D))
## if len(catalog)%500 == 0:
## print len(catalog)
# TODO: make some requirement to be included (exclude outliers)
if len(flags['output_name']) > 0:
F.write('{0},{1},{2},{3},{4},{5},{6},{7}\n'.format(
ID, kind, x, y, P, N0, S, D))
print 'make_lens_catalog: Clearing memory'
# clear memory
memory.clear()
if len(flags['output_name']) > 0:
print 'make_lens_catalog: closing file!'
F.close()
if len(flags['update_collection']) > 0:
print 'make_lens_catalog: writing updated collection to', flags[
'update_collection']
# TODO: get the other params correct!!!!
collection_fat = swap.collection.Collection()
for ID in catalog:
subject = collection.member[ID]
atx = subject.annotationhistory['At_X']
labels_in = list(catalog[ID]['agents_labels'])
labels_fat = []
for atx_i in atx:
labels_fat.append([])
for atx_ij in atx_i:
labels_fat[-1].append(labels_in.pop(0))
subject.annotationhistory.update({'labels': labels_fat})
collection_fat.member.update({ID: subject})
swap.write_pickle(collection_fat, flags['update_collection'])
print 'make_lens_catalog: All done!'
return catalog
def make_lens_atlas(args):
"""
NAME
make_lens_atlas
PURPOSE
Given location of bureau and collection pickles as well as a list of
subjects, this script produces a set of annotated images of lenses
(heatmaps for lens locations, markers for where clicks were, etc).
COMMENTS
You have to download the file so it chooses whever your output
directory is to also download the raw images.
This should be pretty customizable.
FLAGS
-h Print this message
--heatmap Do heatmaps
--contour Do contours
--field Do full image
--stamp Do cutouts
--alpha Do alpha
--points N Take N agents and plot them. Any number < 0 = do all
--skill Weight agent markers by skill
INPUTS
collection collection.pickle
catalog catalog.dat
Assumed format:
ID kind x y Prob N0 Skill Dist
Here:
ID = Space Warps subject ID
kind = Space Warps subject type (sim, dud, test)
x,y = object (cluster) centroid, in pixels
P = Space Warps subject probability
N0 = number of markers in the cluster
S = total skill per cluster, summed over markers
D = biggest distance within cluster
OUTPUTS
EXAMPLE
BUGS
TODO: incorporate some of these defaults into the flags dictionary
AUTHORS
This file is part of the Space Warps project, and is distributed
under the GPL v2 by the Space Warps Science Team.
http://spacewarps.org/
HISTORY
2013-07-16 started Davis (KIPAC)
"""
# ------------------------------------------------------------------
# Some defaults:
flags = {
'points': 30,
'heatmap': False,
'contour': False,
'field': False,
'stamp': False,
'alpha': False,
'skill': False,
'output_directory': './',
'output_format': 'png',
'stamp_size': 50,
'dist_max': 30,
'stamp_min': 1,
'smooth_click': 3,
'figsize_stamp': 5,
'figsize_field': 10,
'image_y_size': 440,
'diagnostics': False,
}
# ------------------------------------------------------------------
# Read in options:
# this has to be easier to do...
for arg in args:
if arg in flags:
flags[arg] = args[arg]
elif arg == 'collection':
collection_path = args[arg]
elif arg == 'catalog':
catalog_path = args[arg]
else:
print "make_lens_atlas: unrecognized flag ", arg
print(flags)
xbins = np.arange(flags['stamp_size'] * 2)
ybins = np.arange(flags['stamp_size'] * 2)
figsize_stamp = (flags['figsize_stamp'], flags['figsize_stamp'])
figsize_field = (flags['figsize_field'], flags['figsize_field'])
image_y_size = flags['image_y_size']
print "make_lens_atlas: illustrating behaviour captured in collection, and lens files: "
print "make_lens_atlas: ", collection_path
print "make_lens_atlas: ", catalog_path
# ------------------------------------------------------------------
# Read in files:
#bureau = swap.read_pickle(bureau_path, 'bureau') # TODO: needed?
collection = swap.read_pickle(collection_path, 'collection')
catalog = csv2rec(catalog_path)
#print "make_lens_atlas: bureau numbers ", len(bureau.list())
print "make_lens_atlas: collection numbers ", len(collection.list())
print "make_lens_atlas: catalog numbers ", len(catalog)
# ------------------------------------------------------------------
# Run through data:
# ------------------------------------------------------------------
# Stamps:
if flags['stamp']:
print "make_lens_atlas: running stamps"
for lens_i in range(len(catalog)):
ID = catalog[lens_i]['id']
kind = catalog[lens_i]['kind']
x = catalog[lens_i]['x']
# flip y axis
y = image_y_size - catalog[lens_i]['y']
N0 = catalog[lens_i]['n0']
if 'dist' in catalog.dtype.names:
if catalog[lens_i]['dist'] == 0:
continue
if ((x < 0)):
# this is one of the 'non points'; skip
print(lens_i, 'x < 0!')
continue
if (N0 < flags['stamp_min']):
# not enough points!
print(lens_i, '{0} < {1}'.format(N0, flags['stamp_min']))
continue
subject = collection.member[ID]
annotationhistory = subject.annotationhistory
# ------------------------------------------------------------------
# download png
url = subject.location
outname = flags['output_directory'] + '{0}_field.png'.format(ID)
im = get_online_png(url, outname)
min_x = np.int(np.max((x - flags['stamp_size'], 0)))
max_x = np.int(np.min((x + flags['stamp_size'], im.shape[0])))
min_y = np.int(np.max((y - flags['stamp_size'], 0)))
max_y = np.int(np.min((y + flags['stamp_size'], im.shape[1])))
min_member_x = np.int(np.max((x - flags['dist_max'], 0)))
max_member_x = np.int(np.min((x + flags['dist_max'], im.shape[0])))
min_member_y = np.int(np.max((y - flags['dist_max'], 0)))
max_member_y = np.int(np.min((y + flags['dist_max'], im.shape[1])))
if (min_x >= max_x) + (min_y >= max_y):
print "make_lens_atlas: misshapen lens for ID ", ID
continue
# if it is a training image, claim the alpha parameter
if im.shape[2] == 4:
alpha = im[:, :, 3][min_y:max_y, min_x:max_x]
im = im[:, :, :3][min_y:max_y, min_x:max_x]
else:
alpha = None
im = im[min_y:max_y, min_x:max_x]
fig = plt.figure(figsize=figsize_stamp)
ax = fig.add_subplot(111)
ax.imshow(im, origin=origin)
ax.scatter(x - min_x,
y - min_y,
marker='d',
c=(0, 1.0, 0),
s=100,
alpha=0.75)
if ((flags['contour']) + (flags['heatmap']) +
(flags['points'] != 0)):
itwas = annotationhistory['ItWas']
x_all = annotationhistory['At_X']
y_all = annotationhistory['At_Y']
x_markers_all = np.array([xi for xj in x_all for xi in xj])
y_markers_all = np.array([yi for yj in y_all for yi in yj])
agents_numbers = np.arange(x_markers_all.size)
if 'labels' in annotationhistory:
# find which label is closest to your folks
labels_all = annotationhistory['labels']
labels = np.array([xi for xj in labels_all for xi in xj])
cluster_labels = list(set(labels))
data = np.vstack((x_markers_all, y_markers_all)).T
cluster_centers = np.array([
np.mean(data[labels == i], axis=0)
for i in cluster_labels
])
# find which label is closest to the (x,y)
label_center = cluster_labels[np.argmin(
np.sum(np.square(cluster_centers -
np.vstack((x, y)).T),
axis=1))]
conds = (labels == label_center)
else:
# now filter markers by those that are within
# dist_max of the center (since I don't record cluster
# members...)
conds = ((x_markers_all >= min_member_x) *
(x_markers_all <= max_member_x) *
(y_markers_all >= min_member_y) *
(y_markers_all <= max_member_y))
agents = agents_numbers[conds]
x_markers = x_markers_all[agents]
y_markers = y_markers_all[agents]
# filter markers
n_catalog = len(agents)
if n_catalog < 1:
print(lens_i, n_catalog)
if (flags['points'] > 0) * \
(flags['points'] < n_catalog):
agents_points = np.random.choice(agents,
size=flags['points'],
replace=False)
else:
agents_points = agents
x_markers_filtered = x_markers_all[agents_points]
y_markers_filtered = y_markers_all[agents_points]
if (flags['skill']) * (len(agents) > 0):
PL_all = annotationhistory['PL']
PD_all = annotationhistory['PD']
# filter out the empty clicks
PL_list = []
for i, xj in enumerate(x_all):
PL_list.append([PL_all[i]] * len(xj))
PL = np.array([PLi for PLj in PL_list for PLi in PLj])
# filter out the empty clicks
PD_list = []
for i, xj in enumerate(x_all):
PD_list.append([PD_all[i]] * len(xj))
PD = np.array([PDi for PDj in PD_list for PDi in PDj])
skill_all = swap.expectedInformationGain(0.5, PL, PD)
skill = skill_all[agents]
smax = 100
smin = 5
if np.max(skill) != np.min(skill):
sizes_all = (skill_all - np.min(skill)) * (smax - smin) / \
(np.max(skill) - np.min(skill))
sizes_filtered = sizes_all[agents_points]
else:
sizes_filtered = 50
else:
skill = None
sizes_filtered = 50
colors = (0, 1.0, 0)
# ----------------------------------------------------------
# heatmaps
if (flags['heatmap']) * (len(agents) > 0):
fig_heatmap = plt.figure(figsize=figsize_stamp)
ax_heatmap = fig_heatmap.add_subplot(111)
# now do the lens locations
# don't need to filter the x's since that is filtered by
# xbins and ybins anyways
pdf2d(x_markers - min_x,
y_markers - min_y,
xbins=xbins,
ybins=ybins,
weights=skill,
smooth=flags['smooth_click'],
color=(0, 1.0, 0),
style='hist',
axis=ax_heatmap)
if flags['alpha'] * (alpha != None):
contour_hist(alpha.T,
extent=(xbins[0], xbins[-1], ybins[0],
ybins[-1]),
color='w',
style='contour',
axis=ax_heatmap)
ax_heatmap.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off',
right='off',
labelleft='off',
labelbottom='off') # labels along the bottom edge are off
# CPD 04.08.14: Flip axis to old conventions
ax_heatmap.invert_yaxis()
try:
outfile = flags['output_directory'] + \
'{0}_cluster_{1}_heatmap.{2}'.format(
ID, lens_i, flags['output_format'])
# fig_heatmap.savefig(outfile)
#fig_heatmap.canvas.print_png(outfile)
fig_heatmap.savefig(outfile,
bbox_inches='tight',
pad_inches=0)
except:
print 'make_lens_catalog: heatmap problem with ', ID, lens_i
# import ipdb; ipdb.set_trace()
# ---------------------------------------------------------
# back to our other plots
# contours
if (flags['contour']) * (len(agents) > 0):
# now do the lens locations
# don't need to filter the x's since that is filtered by
# xbins and ybins anyways
pdf2d(x_markers - min_x,
y_markers - min_y,
xbins=xbins,
ybins=ybins,
weights=skill,
smooth=flags['smooth_click'],
color=(0, 1.0, 0),
style='contour',
axis=ax)
# plot points
if (flags['points'] != 0) * (len(agents) > 0):
ax.scatter(x_markers_filtered - min_x,
y_markers_filtered - min_y,
c=colors,
s=sizes_filtered,
alpha=0.25)
# plot alpha
if flags['alpha'] * (alpha != None):
contour_hist(alpha.T,
extent=(xbins[0], xbins[-1], ybins[0], ybins[-1]),
color='w',
style='contour',
axis=ax)
# ----------------------------------------------------------
ax.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off',
right='off',
labelleft='off',
labelbottom='off') # labels along the bottom edge are off
ax.invert_yaxis()
try:
outfile = flags['output_directory'] + \
'{0}_cluster_{1}_contour.{2}'.format(
ID, lens_i, flags['output_format']
)
# fig.savefig(outfile)
fig.savefig(outfile, bbox_inches='tight', pad_inches=0)
# fig.canvas.print_png(outfile)
except:
print 'make_lens_catalog: contour problem with ', ID, lens_i
# import ipdb; ipdb.set_trace()
plt.close('all')
# ------------------------------------------------------------------
# Fields
if flags['field']:
print "make_lens_atlas: running fields"
# find the unique IDs. mark centers and also centrals if clustering is
# done
#import ipdb; ipdb.set_trace()
unique_IDs = np.unique(catalog['id'])
for ID in unique_IDs:
mini_catalog = catalog[catalog['id'] == ID]
subject = collection.member[ID]
annotationhistory = subject.annotationhistory
# plot cluster centers
kind = mini_catalog['kind']
x_centers = mini_catalog['x']
# flip y from catalog
y_centers = image_y_size - mini_catalog['y']
skill_centers = mini_catalog['skill']
# filter out the -1 entry
center_cond = (x_centers > 0) * (y_centers > 0)
# filter outliers if possible
if 'dist' in mini_catalog.dtype.names:
center_cond *= mini_catalog['dist'] > 0
skill_centers = skill_centers[center_cond]
x_centers = x_centers[center_cond]
y_centers = y_centers[center_cond]
colors_centers = [(0, 1.0, 0) for i in x_centers]
if len(colors_centers) == 0:
#welp, nothing here
continue
# ------------------------------------------------------------------
# download png
url = subject.location
outname = flags['output_directory'] + '{0}_field.png'.format(ID)
im = get_online_png(url, outname)
# if it is a training image, claim the alpha parameter
if im.shape[2] == 4:
alpha = im[:, :, 3]
im = im[:, :, :3]
else:
alpha = None
fig = plt.figure(figsize=figsize_field)
ax = fig.add_subplot(111)
ax.imshow(im, origin=origin)
xbins = np.arange(im.shape[0])
ybins = np.arange(im.shape[1])
min_x = 0
min_y = 0
max_x = im.shape[0]
max_y = im.shape[1]
if (flags['skill']) * (np.max(skill_centers) !=
np.min(skill_centers)):
sizes_centers = (
(skill_centers - np.min(skill_centers)) * (200 - 10) /
(np.max(skill_centers) - np.min(skill_centers)))
else:
sizes_centers = [100 for i in x_centers]
sizes_centers = [100 for i in x_centers]
ax.scatter(x_centers,
y_centers,
marker='d',
c=colors_centers,
s=sizes_centers,
alpha=0.75)
if flags['diagnostics']:
r = flags['dist_max']
b = flags['stamp_size']
b_ones = np.ones(100) * b
b_arr = np.linspace(-b, b, 100)
def xy(x0, y0, r, phi):
return x0 + r * np.cos(phi), y0 + r * np.sin(phi)
phis = np.arange(0, 6.28, 0.01)
for i in xrange(len(x_centers)):
x_center = x_centers[i]
y_center = y_centers[i]
ax.plot(*xy(x_center, y_center, r, phis),
c='w',
ls='-',
linewidth=4)
# plot box
ax.plot(x_center + b_ones,
y_center + b_arr,
c='r',
ls='--',
linewidth=4)
ax.plot(x_center - b_ones,
y_center + b_arr,
c='r',
ls='--',
linewidth=4)
ax.plot(x_center + b_arr,
y_center + b_ones,
c='r',
ls='--',
linewidth=4)
ax.plot(x_center + b_arr,
y_center - b_ones,
c='r',
ls='--',
linewidth=4)
itwas = annotationhistory['ItWas']
x_all = annotationhistory['At_X']
y_all = annotationhistory['At_Y']
x_markers_all = np.array([xi for xj in x_all for xi in xj])
y_markers_all = np.array([yi for yj in y_all for yi in yj])
# now filter markers by those that are within
# stamp_size of the stamp
# I'm pretty sure this step is redundant when going over the full
# image?
agents_numbers = np.arange(x_markers_all.size)
conds = ((x_markers_all >= min_x) * (x_markers_all <= max_x) *
(y_markers_all >= min_y) * (y_markers_all <= max_y))
agents = agents_numbers[conds]
x_markers = x_markers_all[agents]
y_markers = y_markers_all[agents]
# filter markers
n_catalog = len(agents)
if (flags['points'] > 0) * \
(flags['points'] < n_catalog):
agents_points = np.random.choice(agents,
size=flags['points'],
replace=False)
else:
agents_points = agents
x_markers_filtered = x_markers_all[agents_points]
y_markers_filtered = y_markers_all[agents_points]
if flags['skill']:
PL_all = annotationhistory['PL']
PD_all = annotationhistory['PD']
# filter out the empty clicks
PL_list = []
for i, xj in enumerate(x_all):
PL_list.append([PL_all[i]] * len(xj))
PL = np.array([PLi for PLj in PL_list for PLi in PLj])
# filter out the empty clicks
PD_list = []
for i, xj in enumerate(x_all):
PD_list.append([PD_all[i]] * len(xj))
PD = np.array([PDi for PDj in PD_list for PDi in PDj])
skill_all = swap.expectedInformationGain(0.5, PL, PD)
skill = skill_all[agents]
smax = 100
smin = 5
if np.max(skill) != np.min(skill):
sizes_all = (skill_all - np.min(skill)) * (smax - smin) / \
(np.max(skill) - np.min(skill))
sizes_filtered = sizes_all[agents_points]
else:
sizes_filtered = 50
else:
skill = None
sizes_filtered = 50
if 'labels' in annotationhistory:
# find which label is closest to your folks
labels_all = annotationhistory['labels']
labels = np.array([xi for xj in labels_all for xi in xj])
labels_filtered = labels[agents_points]
colors = []
alpha = 0.75
for label in labels_filtered:
if label == -1:
colors.append((1.0, 0.0, 0))
else:
colors.append((0, 1.0, 0))
else:
colors = (0, 1.0, 0)
alpha = 0.25
# ----------------------------------------------------------
# contours
if flags['contour'] * (len(x_markers) >= flags['stamp_min']):
# now do the lens locations
# don't need to filter the x's since that is filtered by
# xbins and ybins anyways
pdf2d(x_markers - min_x,
y_markers - min_y,
xbins=xbins,
ybins=ybins,
weights=skill,
smooth=flags['smooth_click'],
color=(0, 1.0, 0),
style='contour',
axis=ax)
# ----------------------------------------------------------
# plot points
if flags['points'] != 0:
ax.scatter(x_markers_filtered - min_x,
y_markers_filtered - min_y,
c=colors,
s=sizes_filtered,
alpha=alpha)
# ----------------------------------------------------------
# do alpha
if flags['alpha'] * (alpha != None):
contour_hist(alpha.T,
extent=(xbins[0], xbins[-1], ybins[0], ybins[-1]),
color='w',
style='contour',
axis=ax)
ax.tick_params(\
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
left='off',
right='off',
labelleft='off',
labelbottom='off') # labels along the bottom edge are off
ax.invert_yaxis()
try:
outfile = flags[
'output_directory'] + '{0}_field_output.{1}'.format(
ID, flags['output_format'])
# fig.savefig(outfile)
fig.savefig(outfile, bbox_inches='tight', pad_inches=0)
#fig.canvas.print_png(outfile)
except:
print 'make_lens_catalog: field problem with field ', ID
plt.close('all')
print 'make_lens_catalog: All done!'
def MachineClassifier(options, args):
"""
NAME
MachineClassifier.py
PURPOSE
Machine learning component of Galaxy Zoo Express
Read in a training sample generated by human users (which have
previously been analyzed by SWAP).
Learn on the training sample and moniter progress.
Once "fully trained", apply learned model to test sample.
COMMENTS
Lots I'm sure.
FLAGS
-h Print this message
-c config file name
"""
#-----------------------------------------------------------------------
# LOAD CONFIG FILE PARAMETERS
#-----------------------------------------------------------------------
# Check for config file in array args:
if (len(args) >= 1) or (options.configfile):
if args: config = args[0]
elif options.configfile: config = options.configfile
print swap.doubledashedline
print swap.ML_hello
print swap.doubledashedline
print "ML: taking instructions from",config
else:
print MachineClassifier.__doc__
return
machine_sim_directory = 'sims_Machine/redo_with_circular_morphs/'
tonights = swap.Configuration(config)
# Read the pickled random state file
random_file = open(tonights.parameters['random_file'],"r");
random_state = cPickle.load(random_file);
random_file.close();
np.random.set_state(random_state)
time = tonights.parameters['start']
# Get the machine threshold (to make retirement decisions)
swap_thresholds = {}
swap_thresholds['detection'] = tonights.parameters['detection_threshold']
swap_thresholds['rejection'] = tonights.parameters['rejection_threshold']
threshold = tonights.parameters['machine_threshold']
prior = tonights.parameters['prior']
# Get list of evaluation metrics and criteria
eval_metrics = tonights.parameters['evaluation_metrics']
# How much cross-validation should we do?
cv = tonights.parameters['cross_validation']
survey = tonights.parameters['survey']
# To generate training labels based on the subject probability,
# we need to know what should be considered the positive label:
# i.e., GZ2 has labels (in metadatafile) Smooth = 1, Feat = 0
# Doing a Smooth or Not run, the positive label is 1
# Doing a Featured or Not run, the positive label is 0
pos_label = tonights.parameters['positive_label']
#----------------------------------------------------------------------
# read in the metadata for all subjects
storage = swap.read_pickle(tonights.parameters['metadatafile'], 'metadata')
# 11TH HOUR QUICK FIX CUZ I F****D UP. MB 10/27/16
if 'GZ2_raw_combo' not in storage.subjects.colnames:
gz2_metadata = Table.read('metadata_ground_truth_labels.fits')
storage.subjects['GZ2_raw_combo'] = gz2_metadata['GZ2_raw_combo']
swap.write_pickle(storage, tonights.parameters['metadatafile'])
subjects = storage.subjects
#----------------------------------------------------------------------
# read in the PROJECT COLLECTION -- (shared between SWAP/Machine)
#sample = swap.read_pickle(tonights.parameters['samplefile'],'collection')
# read in or create the ML bureau for machine agents (history for Machines)
MLbureau = swap.read_pickle(tonights.parameters['MLbureaufile'],'bureau')
#-----------------------------------------------------------------------
# FETCH TRAINING & VALIDATION SAMPLES
#-----------------------------------------------------------------------
train_sample = storage.fetch_subsample(sample_type='train',
class_label='GZ2_raw_combo')
""" Notes about the training sample:
# this will select only those which have my morphology measured for them
# AND which have "ground truth" according to GZ2
# Eventually we could open this up to include the ~10k that aren't in the
# GZ Main Sample but I think, for now, we should reduce ourselves to this
# stricter sample so that we always have back-up "truth" for each galaxy.
"""
try:
train_meta, train_features = ml.extract_features(train_sample,
keys=['M20_corr', 'C_corr', 'E', 'A_corr', 'G_corr'])
original_length = len(train_meta)
except TypeError:
print "ML: can't extract features from subsample."
print "ML: Exiting MachineClassifier.py"
sys.exit()
else:
# TODO: consider making this part of SWAP's duties?
# 5/18/16: Only use those subjects which are no longer on the prior
off_the_fence = np.where(train_meta['SWAP_prob']!=prior)
train_meta = train_meta[off_the_fence]
train_features = train_features[off_the_fence]
train_labels = np.array([pos_label if p > prior else 1-pos_label
for p in train_meta['SWAP_prob']])
shortened_length = len(train_meta)
print "ML: found a training sample of %i subjects"%shortened_length
removed = original_length - shortened_length
print "ML: %i subjects removed to create balanced training sample"%removed
valid_sample = storage.fetch_subsample(sample_type='valid',
class_label='Expert_label')
try:
valid_meta, valid_features = ml.extract_features(valid_sample,
keys=['M20_corr', 'C_corr', 'E', 'A_corr', 'G_corr'])
except:
print "ML: there are no subjects with the label 'valid'!"
else:
valid_labels = valid_meta['Expert_label'].filled()
print "ML: found a validation sample of %i subjects"%len(valid_meta)
# ---------------------------------------------------------------------
# Require a minimum size training sample [Be reasonable, my good man!]
# ---------------------------------------------------------------------
if len(train_sample) < 10000:
print "ML: training sample is too small to be worth anything."
print "ML: Exiting MachineClassifier.py"
sys.exit()
else:
print "ML: training sample is large enough to give it a shot."
# TODO: LOOP THROUGH DIFFERENT MACHINES?
# 5/12/16 -- no... need to make THIS a class and create multiple
# instances? Each one can be passed an instance of a machine?
# Machine can be trained to optimize different metrics
# (ACC, completeness, purity, etc. Have a list of acceptable ones.)
# Minimize a Loss function.
for metric in eval_metrics:
# REGISTER Machine Classifier
# Construct machine name --> Machine+Metric
machine = 'RF'
Name = machine+'_'+metric
# register an Agent for this Machine
try:
test = MLbureau.member[Name]
except:
MLbureau.member[Name] = swap.Agent_ML(Name, metric)
MLagent = MLbureau.member[Name]
#---------------------------------------------------------------
# TRAIN THE MACHINE; EVALUATE ON VALIDATION SAMPLE
#---------------------------------------------------------------
# Now we run the machine -- need cross validation on whatever size
# training sample we have ..
# Fixed until we build in other machine options
# Need to dynamically determine appropriate parameters...
#max_neighbors = get_max_neighbors(train_features, cv)
#n_neighbors = np.arange(1, (cv-1)*max_neighbors/cv, 5, dtype=int)
#params = {'n_neighbors':n_neighbors,
# 'weights':('uniform','distance')}
num_features = train_features.shape[1]
min_features = int(round(np.sqrt(num_features)))
params = {'max_features':np.arange(min_features, num_features+1),
'max_depth':np.arange(2,16)}
# Create the model
# for "estimator=XXX" all you need is an instance of a machine --
# any scikit-learn machine will do. However, non-sklearn machines..
# That will be a bit trickier! (i.e. Phil's conv-nets)
general_model = GridSearchCV(estimator=RF(n_estimators=30),
param_grid=params, n_jobs=31,
error_score=0, scoring=metric, cv=cv)
# Train the model -- k-fold cross validation is embedded
print "ML: Searching the hyperparameter space for values that "\
"optimize the %s."%metric
trained_model = general_model.fit(train_features, train_labels)
MLagent.model = trained_model
# Test accuracy (metric of choice) on validation sample
score = trained_model.score(valid_features, valid_labels)
ratio = np.sum(train_labels==pos_label) / len(train_labels)
MLagent.record_training(model_described_by=
trained_model.best_estimator_,
with_params=trained_model.best_params_,
trained_on=len(train_features),
with_ratio=ratio,
at_time=time,
with_train_score=trained_model.best_score_,
and_valid_score=trained_model.score(
valid_features, valid_labels))
valid_prob_thresh = trained_model.predict_proba(valid_features)[:,pos_label]
fps, tps, thresh = mtrx.roc_curve(valid_labels,valid_prob_thresh, pos_label=pos_label)
metric_list = compute_binary_metrics(fps, tps)
ACC, TPR, FPR, FNR, TNR, PPV, FDR, FOR, NPV = metric_list
MLagent.record_validation(accuracy=ACC, recall=TPR, precision=PPV,
false_pos=FPR, completeness_f=TNR,
contamination_f=NPV)
#MLagent.plot_ROC()
# ---------------------------------------------------------------
# IF TRAINED MACHINE PREDICTS WELL ON VALIDATION ....
# ---------------------------------------------------------------
if MLagent.is_trained(metric) or MLagent.trained:
print "ML: %s has successfully trained and will be applied "\
"to the test sample."%Name
# Retrieve the test sample
test_sample = storage.fetch_subsample(sample_type='test',
class_label='GZ2_raw_combo')
""" Notes on test sample:
The test sample will, in real life, be those subjects for which
we don't have an answer a priori. However, for now, this sample
is how we will judge, in part, the performance of the overall
method. As such, we only include those subjects which have
GZ2 labels in the Main Sample.
"""
try:
test_meta, test_features = ml.extract_features(test_sample,
keys=['M20_corr', 'C_corr', 'E', 'A_corr', 'G_corr'])
except:
print "ML: there are no subjects with the label 'test'!"
print "ML: Either there is nothing more to do or there is a BIG mistake..."
else:
print "ML: found test sample of %i subjects"%len(test_meta)
#-----------------------------------------------------------
# APPLY MACHINE TO TEST SAMPLE
#-----------------------------------------------------------
predictions = MLagent.model.predict(test_features)
probabilities = MLagent.model.predict_proba(test_features)[:,pos_label]
print "ML: %s has finished predicting labels for the test "\
"sample."%Name
print "ML: Generating performance report on the test sample:"
test_labels = test_meta['GZ2_raw_combo'].filled()
print mtrx.classification_report(test_labels, predictions)
test_accuracy = mtrx.accuracy_score(test_labels,predictions)
test_precision = mtrx.precision_score(test_labels,predictions,pos_label=pos_label)
test_recall = mtrx.recall_score(test_labels,predictions,pos_label=pos_label)
MLagent.record_evaluation(accuracy_score=test_accuracy,
precision_score=test_precision,
recall_score=test_recall,
at_time=time)
# ----------------------------------------------------------
# Save the predictions and probabilities to a new pickle
test_meta['predictions'] = predictions
test_meta['machine_probability'] = probabilities
# If is hasn't been done already, save the current directory
# ---------------------------------------------------------------------
tonights.parameters['trunk'] = survey+'_'+tonights.parameters['start']
# This is the standard directory...
#tonights.parameters['dir'] = os.getcwd()+'/'+tonights.parameters['trunk']
# This is to put files into the sims_Machine/... directory.
tonights.parameters['dir'] = os.getcwd()
filename=tonights.parameters['dir']+'/'+tonights.parameters['trunk']+'_'+Name+'.fits'
test_meta.write(filename)
count=0
noSWAP=0
for sub, pred, prob in zip(test_meta, predictions, probabilities):
# IF MACHINE P >= THRESHOLD, INSERT INTO SWAP COLLECTION
# --------------------------------------------------------
if (prob >= threshold) or (1-prob >= threshold):
# Flip the set label in the metadata file --
# don't want to use this as a training sample!
idx = np.where(subjects['asset_id'] == sub['asset_id'])
storage.subjects['MLsample'][idx] = 'mclass'
storage.subjects['retired_date'][idx] = time
count+=1
print "MC: Machine classifed {0} subjects with >= 90% confidence".format(count)
print "ML: Of those, {0} had never been seen by SWAP".format(noSWAP)
tonights.parameters['trunk'] = survey+'_'+tonights.parameters['start']
tonights.parameters['dir'] = os.getcwd()
if not os.path.exists(tonights.parameters['dir']):
os.makedirs(tonights.parameters['dir'])
# Repickle all the shits
# -----------------------------------------------------------------------
if tonights.parameters['repickle']:
#new_samplefile = swap.get_new_filename(tonights.parameters,'collection')
#print "ML: saving SWAP subjects to "+new_samplefile
#swap.write_pickle(sample, new_samplefile)
#tonights.parameters['samplefile'] = new_samplefile
new_bureaufile=swap.get_new_filename(tonights.parameters,'bureau','ML')
print "ML: saving MLbureau to "+new_bureaufile
swap.write_pickle(MLbureau, new_bureaufile)
tonights.parameters['MLbureaufile'] = new_bureaufile
metadatafile = swap.get_new_filename(tonights.parameters,'metadata')
print "ML: saving metadata to "+metadatafile
swap.write_pickle(storage, metadatafile)
tonights.parameters['metadatafile'] = metadatafile
# UPDATE CONFIG FILE with pickle filenames, dir/trunk, and (maybe) new day
# ----------------------------------------------------------------------
configfile = config.replace('startup','update')
# Random_file needs updating, else we always start from the same random
# state when update.config is reread!
random_file = open(tonights.parameters['random_file'],"w");
random_state = np.random.get_state();
cPickle.dump(random_state,random_file);
random_file.close();
swap.write_config(configfile, tonights.parameters)
return
collection2_path = args[3]
print "make_roc_curves: illustrating behaviour captured in bureau and collection files: "
print "make_roc_curves: ",bureau1_path
print "make_roc_curves: ",bureau2_path
print "make_roc_curves: ",collection1_path
print "make_roc_curves: ",collection2_path
else:
print make_roc_curves.__doc__
return
output_directory = './'
# ------------------------------------------------------------------
# Read in bureau and collection objects:
bureau1 = swap.read_pickle(bureau1_path, 'bureau')
collection1 = swap.read_pickle(collection1_path, 'collection')
bureau2 = swap.read_pickle(bureau2_path, 'bureau')
collection2 = swap.read_pickle(collection2_path, 'collection')
print "make_roc_curves: stage 1, 2 agent numbers: ",len(bureau1.list()), len(bureau2.list())
print "make_roc_curves: stage 1, 2 subject numbers: ",len(collection1.list()), len(collection2.list())
# ------------------------------------------------------------------
# set up data for roc plots
n_min = 1
fprs = []
def MachineClassifier(options, args):
"""
NAME
MachineClassifier.py
PURPOSE
Machine learning component of Galaxy Zoo Express
Read in a training sample generated by human users (which have
preferentially been analyzed by SWAP).
Learn on the training sample and moniter progress.
Once "fully trained", apply learned model to test sample.
COMMENTS
Lots I'm sure.
FLAGS
-h Print this message
-c config file name
"""
# Check for setup file in array args:
if (len(args) >= 1) or (options.configfile):
if args: config = args[0]
elif options.configfile: config = options.configfile
print swap.doubledashedline
print swap.ML_hello
print swap.doubledashedline
print "ML: taking instructions from",config
else:
print MachineClassifier.__doc__
return
tonights = swap.Configuration(config)
# Read the pickled random state file
random_file = open(tonights.parameters['random_file'],"r");
random_state = cPickle.load(random_file);
random_file.close();
np.random.set_state(random_state);
time = tonights.parameters['start']
print time
# Get the machine threshold (make retirement decisions)
threshold = tonights.parameters['machine_threshold']
prior = tonights.parameters['prior']
# Get list of evaluation metrics and criteria
eval_metrics = tonights.parameters['evaluation_metrics']
# How much cross-validation should we do?
cv = tonights.parameters['cross_validation']
survey = tonights.parameters['survey']
#----------------------------------------------------------------------
# read in the metadata for all subjects (Test or Training sample?)
storage = swap.read_pickle(tonights.parameters['metadatafile'], 'metadata')
subjects = storage.subjects
#----------------------------------------------------------------------
# read in the SWAP collection
sample = swap.read_pickle(tonights.parameters['samplefile'],'collection')
#----------------------------------------------------------------------
# read in or create the ML collection
MLsample = swap.read_pickle(tonights.parameters['MLsamplefile'],
'MLcollection')
# read in or create the ML bureau for machine agents (history)
MLbureau = swap.read_pickle(tonights.parameters['MLbureaufile'],'bureau')
#if not tonights.parameters['MLbureaufile']:
# MLbureaufile = swap.get_new_filename(tonights.parameters,'bureau','ML')
# tonights.parameters['MLbureaufile'] = MLbureaufile
#MLbureau = swap.read_pickle(tonights.parameters['MLbureaufile'],'bureau')
#-----------------------------------------------------------------------
# SELECT TRAINING & VALIDATION SAMPLES
#-----------------------------------------------------------------------
# TO DO: training sample should only select those which are NOT part of
# validation sample (Nair catalog objects) 2/22/16
train_sample = storage.fetch_subsample(sample_type='train',
class_label='GZ2_label')
""" Notes about the training sample:
# this will select only those which have my morphology measured for them
# AND which have a true "answer" according to GZ2
# Eventually we could open this up to include the ~10k that aren't in the
# GZ Main Sample but I think, for now, we should reduce ourselves to this
# stricter sample so that we always have back-up "truth" for each galaxy.
"""
try:
train_meta, train_features = ml.extract_features(train_sample)
original_length = len(train_meta)
except TypeError:
print "ML: can't extract features from subsample."
print "ML: Exiting MachineClassifier.py"
sys.exit()
else:
# TODO: consider making this part of SWAP's duties?
# 5/18/16: Only use those subjects which are no longer on the prior
off_the_fence = np.where(train_meta['SWAP_prob']!=prior)
train_meta = train_meta[off_the_fence]
train_features = train_features[off_the_fence]
train_labels = np.array([1 if p > prior else 0 for p in
train_meta['SWAP_prob']])
#train_labels = train_meta['Nair_label'].filled()
shortened_length = len(train_meta)
print "ML: found a training sample of %i subjects"%shortened_length
removed = original_length - shortened_length
print "ML: %i subjects had prior probability and were removed"%removed
valid_sample = storage.fetch_subsample(sample_type='valid',
class_label='Expert_label')
try:
valid_meta, valid_features = ml.extract_features(valid_sample)
except:
print "ML: there are no subjects with the label 'valid'!"
else:
valid_labels = valid_meta['Expert_label'].filled()
print "ML: found a validation sample of %i subjects"%len(valid_meta)
# ---------------------------------------------------------------------
# Require a minimum size training sample [Be reasonable, my good man!]
# ---------------------------------------------------------------------
if len(train_sample) < 10000:
print "ML: training sample is too small to be worth anything."
print "ML: Exiting MachineClassifier.py"
sys.exit()
else:
print "ML: training sample is large enough to give it a shot."
# TODO: LOOP THROUGH DIFFERENT MACHINES?
# 5/12/16 -- no... need to make THIS a class and create multiple
# instances? Each one can be passed an instance of a machine?
# Machine can be trained to maximize/minimize different metrics
# (ACC, completeness, purity, etc. Have a list of acceptable ones.)
# Minimize a Loss function (KNC doesn't have a loss fcn).
for metric in eval_metrics:
# REGISTER Machine Classifier
# Construct machine name --> Machine+Metric? For now: KNC
machine = 'KNC'
machine = 'RF'
Name = machine+'_'+metric
# register an Agent for this Machine
# This "Agent" doesn't behave like a SW agent... at least not yet
try:
test = MLbureau.member[Name]
except:
MLbureau.member[Name] = swap.Agent_ML(Name, metric)
MLagent = MLbureau.member[Name]
#---------------------------------------------------------------
# TRAIN THE MACHINE; EVALUATE ON VALIDATION SAMPLE
#---------------------------------------------------------------
# Now we run the machine -- need cross validation on whatever size
# training sample we have ..
# Fixed until we build in other machine options
# Need to dynamically determine appropriate parameters...
#max_neighbors = get_max_neighbors(train_features, cv)
#n_neighbors = np.arange(1, (cv-1)*max_neighbors/cv, 5, dtype=int)
#params = {'n_neighbors':n_neighbors,
# 'weights':('uniform','distance')}
num_features = train_features.shape[1]
min_features = int(round(np.sqrt(num_features)))
params = {'max_features':np.arange(min_features, num_features+1),
'max_depth':np.arange(2,16)}
# Create the model
# for "estimator=XXX" all you need is an instance of a machine --
# any scikit-learn machine will do. However, non-sklearn machines..
# That will be a bit trickier! (i.e. Phil's conv-nets)
general_model = GridSearchCV(estimator=RF(n_estimators=30),
param_grid=params, n_jobs=-1,
error_score=0, scoring=metric, cv=cv)
# Train the model -- k-fold cross validation is embedded
print "ML: Searching the hyperparameter space for values that "\
"optimize the %s."%metric
trained_model = general_model.fit(train_features, train_labels)
MLagent.model = trained_model
# Test "accuracy" (metric of choice) on validation sample
score = trained_model.score(valid_features, valid_labels)
ratio = np.sum(train_labels==1) / len(train_labels)
MLagent.record_training(model_described_by=
trained_model.best_estimator_,
with_params=trained_model.best_params_,
trained_on=len(train_features),
with_ratio=ratio,
at_time=time,
with_train_score=trained_model.best_score_,
and_valid_score=trained_model.score(
valid_features, valid_labels))
fps, tps, thresh = mtrx.roc_curve(valid_labels,
trained_model.predict_proba(valid_features)[:,1])
metric_list = compute_binary_metrics(fps, tps)
ACC, TPR, FPR, FNR, TNR, PPV, FDR, FOR, NPV = metric_list
MLagent.record_validation(accuracy=ACC, recall=TPR, precision=PPV,
false_pos=FPR, completeness_f=TNR,
contamination_f=NPV)
#MLagent.plot_ROC()
# ---------------------------------------------------------------
# IF TRAINED MACHINE PREDICTS WELL ON VALIDATION ....
# ---------------------------------------------------------------
if MLagent.is_trained(metric):
print "ML: %s has successfully trained and will be applied "\
"to the test sample."
# Retrieve the test sample
test_sample = storage.fetch_subsample(sample_type='test',
class_label='GZ2_label')
""" Notes on test sample:
The test sample will, in real life, be those subjects for which
we don't have an answer a priori. However, for now, this sample
is how we will judge, in part, the performance of the overall
method. As such, we only include those subjects which have
GZ2 labels in the Main Sample.
"""
try:
test_meta, test_features = ml.extract_features(test_sample)
except:
print "ML: there are no subjects with the label 'test'!"
print "ML: which means there's nothing more to do!"
else:
print "ML: found test sample of %i subjects"%len(test_meta)
#-----------------------------------------------------------
# APPLY MACHINE TO TEST SAMPLE
#-----------------------------------------------------------
predictions = MLagent.model.predict(test_features)
probabilities = MLagent.model.predict_proba(test_features)
print "ML: %s has finished predicting labels for the test "\
"sample."%Name
print "ML: Generating performance report on the test sample:"
test_labels = test_meta['GZ2_label'].filled()
print mtrx.classification_report(test_labels, predictions)
test_accuracy=mtrx.accuracy_score(test_labels,predictions)
test_precision=mtrx.precision_score(test_labels,predictions)
test_recall=mtrx.recall_score(test_labels,predictions)
MLagent.record_evaluation(accuracy_score=test_accuracy,
precision_score=test_precision,
recall_score=test_recall,
at_time=time)
#pdb.set_trace()
# ----------------------------------------------------------
# Save the predictions and probabilities to a new pickle
test_meta['predictions'] = predictions
test_meta['probability_of_smooth'] = probabilities[:,1]
filename=tonights.parameters['trunk']+'_'+Name+'.pickle'
swap.write_pickle(test_meta, filename)
"""
for thing, pred, p in zip(test_meta, predictions,
probabitilies):
# IF MACHINE P >= THRESHOLD, INSERT INTO SWAP COLLECTION
# --------------------------------------------------------
if (p >= threshold) or (1-p >= threshold):
print "BOOM! WE'VE GOT A MACHINE-CLASSIFIED SUBJECT:"
print "Probability:", p
# Initialize the subject in SWAP Collection
ID = thing['asset_id']
sample.member[ID] = swap.Subject(ID, str(s['SDSS_id']),
location=s['external_ref'])
sample.member[ID].retiredby = 'machine'
# Flag subject as 'INACTIVE' / 'DETECTED' / 'REJECTED'
# ----------------------------------------------------------
if p >= threshold:
sample.member[str(s['id'])].state = 'inactive'
elif 1-p >= threshold:
sample.member[str(s['id'])].status = 'rejected'
#"""
# If is hasn't been done already, save the current directory
# ---------------------------------------------------------------------
tonights.parameters['dir'] = os.getcwd()+'/'+tonights.parameters['trunk']
if not os.path.exists(tonights.parameters['dir']):
os.makedirs(tonights.parameters['dir'])
# Repickle all the shits
# -----------------------------------------------------------------------
if tonights.parameters['repickle']:
new_samplefile = swap.get_new_filename(tonights.parameters,'collection')
print "ML: saving SWAP subjects to "+new_samplefile
swap.write_pickle(sample, new_samplefile)
tonights.parameters['samplefile'] = new_samplefile
new_samplefile=swap.get_new_filename(tonights.parameters,'MLcollection')
print "ML: saving test sample subjects to "+new_samplefile
swap.write_pickle(MLsample,new_samplefile)
tonights.parameters['MLsamplefile'] = new_samplefile
new_bureaufile=swap.get_new_filename(tonights.parameters,'bureau','ML')
print "ML: saving MLbureau to "+new_bureaufile
swap.write_pickle(MLbureau, new_bureaufile)
tonights.parameters['MLbureaufile'] = new_bureaufile
metadatafile = swap.get_new_filename(tonights.parameters,'metadata')
print "ML: saving metadata to "+metadatafile
swap.write_pickle(storage, metadatafile)
tonights.parameters['metadatafile'] = metadatafile
# UPDATE CONFIG FILE with pickle filenames, dir/trunk, and (maybe) new day
# ----------------------------------------------------------------------
configfile = config.replace('startup','update')
# Random_file needs updating, else we always start from the same random
# state when update.config is reread!
random_file = open(tonights.parameters['random_file'],"w");
random_state = np.random.get_state();
cPickle.dump(random_state,random_file);
random_file.close();
swap.write_config(configfile, tonights.parameters)
return
def MachineClassifier(options, args):
try: config = options.configfile
except: pdb.set_trace()
tonights = swap.Configuration(config)
#"""
# Read the pickled random state file
random_file = open(tonights.parameters['random_file'],"r");
random_state = cPickle.load(random_file);
random_file.close();
np.random.set_state(random_state);
#"""
# Get the machine threshold (make retirement decisions)
threshold = tonights.parameters['machine_threshold']
# Get list of evaluation metrics and criteria
eval_metrics = tonights.parameters['evaluation_metrics']
survey = tonights.parameters['survey']
subdir = 'sup_run4'
#----------------------------------------------------------------------
# read in the metadata for all subjects (Test or Training sample?)
subjects = swap.read_pickle(tonights.parameters['metadatafile'], 'metadata')
#----------------------------------------------------------------------
# read in the SWAP collection
sample = swap.read_pickle(tonights.parameters['samplefile'],'collection')
#----------------------------------------------------------------------
# read in or create the ML collection
MLsample = swap.read_pickle(tonights.parameters['MLsamplefile'],
'MLcollection')
# read in or create the ML bureau for machine agents (history)
MLbureau = swap.read_pickle(tonights.parameters['MLbureaufile'], 'MLbureau')
#-----------------------------------------------------------------------
# DETERMINE IF THERE IS A TRAINING SAMPLE TO WORK WITH
#-----------------------------------------------------------------------
# TO DO: training sample should only select those which are NOT part of
# validation sample (Nair catalog objects) 2/22/16
# IDENTIFY TRAINING SAMPLE
train_sample = subjects[subjects['MLsample']=='train']
train_meta, train_features = ml.extract_training(train_sample)
train_labels = np.array([1 if p > 0.3 else 0 \
for p in train_meta['SWAP_prob']])
# IDENTIFY VALIDATION SAMPLE (FINAL)
valid_sample = subjects[subjects['MLsample']=='valid']
valid_meta, valid_features = ml.extract_training(valid_sample)
valid_labels = valid_meta['Expert_label'].filled()
#if len(train_sample) >= 100:
# TO DO: LOOP THROUGH DIFFERENT MACHINES? HOW MANY MACHINES?
for metric in eval_metrics:
# REGISTER Machine Classifier
# Construct machine name --> Machine+Metric? For now: KNC
machine = 'KNC'
Name = machine+'_'+metric
# register an Agent for this Machine
try:
test = MLbureau.member[Name]
except:
MLbureau.member[Name] = swap.Agent_ML(Name, metric)
#---------------------------------------------------------------
# TRAIN THE MACHINE; EVALUATE ON VALIDATION SAMPLE
#---------------------------------------------------------------
# Now we run the machine -- need cross validation on whatever size
# training sample we have ..
# For now this will be fixed until we build in other machine options
params = {'n_neighbors':np.arange(1, 2*(len(train_sample)-1) / 3, 2),
'weights':('uniform','distance')}
# Create the model
general_model = GridSearchCV(estimator=KNC(), param_grid=params,
error_score=0, scoring=metric)
# Train the model -- k-fold cross validation is embedded
trained_model = general_model.fit(train_features, train_labels)
# Test "accuracy" (metric of choice) on validation sample
score = trained_model.score(valid_features, valid_labels)
MLbureau.member[Name].record_training(\
model_described_by=trained_model.best_estimator_,
with_params=trained_model.best_params_,
trained_on=len(train_features),
at_time=TIME,
with_train_acc=traineed_model.best_score_,
and_valid_acc=trained_model.score(valid_features,
valid_labels))
# Store the trained machine
MLbureau.member[Name].model = trained_model
# Compute / store confusion matrix as a function of threshold
# produced by this machine on the Expert Validation sample
fps, tps, thresh = mtrx._binary_clf_curve(valid_labels,
trained_model.predict_proba(valid_features)[:,1])
metric_list = mtrx.compute_binary_metrics(fps, tps)
ACC, TPR, FPR, FNR, TNR, PPV, FDR, FOR, NPV = metric_list
MLbureau.member[Name].record_evaluation(accuracy=ACC,
completeness_s=TPR,
contamination_s=FDR,
completeness_f=TNR,
contamination_f=NPV)
pdb.set_trace()
# 3. compare the metric of choice with the evaluation criterion to
# see if this machine has sufficiently learned?
# ... what if my criterion is simply "Maximize Accuracy"?
# ... or minimize feature contamination? these require that we
# compare tonight's machine with the previous night's machine
# But if my criterion is simply "have feature contam less than 20%"
# then it's easy....
# IF TRAINED MACHINE PREDICTS WELL ON VALIDATION ....
if MLbureau.member[Name].evaluate():
#---------------------------------------------------------------
# APPLY MACHINE TO TEST SAMPLE
#---------------------------------------------------------------
# This requires that my runKNC function returns the Machine Object
shitski=5
#---------------------------------------------------------------
# PROCESS PREDICTIONS/PROBS
#---------------------------------------------------------------
for s,p,l in zip(test_meta, probas, predictions):
ID = str(s['id'])
descriptions = Nair_or_Not(s)
category, kind, flavor, truth = descriptions
# LOAD EACH TEST SUBJECT INTO MACHINE COLLECTION
# -------------------------------------------------------------
try:
test = MLsample.member[ID]
except: MLsample.member[ID] = swap.Subject_ML(ID,
str(s['name']), category, kind,
truth,threshold,s['external_ref'])
tstring = datetime.now().strftime('%Y-%m-%d_%H:%M:%S')
MLsample.member[ID].was_described(by='knn', as_being=1,
withp=p, at_time=tstring)
# NOTE: if subject is Nair (training) it doesn't get flagged as
# inactive but it can be flagged as detected/rejected
# IF MACHINE P >= THRESHOLD, INSERT INTO SWAP COLLECTION
# -------------------------------------------------------------
thresholds = {'detection':0.,'rejection':0.}
if (p >= threshold) or (1-p >= threshold):
print "BOOM! WE'VE GOT A MACHINE-CLASSIFIED SUBJECT:"
print "Probability:",p
# Initialize the subject in SWAP Collection
sample.member[ID] = swap.Subject(ID, str(s['name']),
category, kind,flavor,truth,
thresholds, s['external_ref'],0.)
sample.member[ID].retiredby = 'machine'
# Flag subject as 'INACTIVE' / 'DETECTED' / 'REJECTED'
# ----------------------------------------------------------
if p >= threshold:
sample.member[str(s['id'])].state = 'inactive'
elif 1-p >= threshold:
sample.member[str(s['id'])].status = 'rejected'
#---------------------------------------------------------------
# SAVE MACHINE METADATA?
#---------------------------------------------------------------
print "Size of SWAP sample:", sample.size()
print "Size of ML sample:", MLsample.size()
if tonights.parameters['report']:
# Output list of subjects to retire, based on this batch of
# classifications. Note that what is needed here is the ZooID,
# not the subject ID:
new_retirementfile = swap.get_new_filename(tonights.parameters,\
'retire_these', source='ML')
print "SWAP: saving Machine-retired subject Zooniverse IDs..."
N = swap.write_list(MLsample,new_retirementfile,
item='retired_subject', source='ML')
print "SWAP: "+str(N)+" lines written to "+new_retirementfile
# write catalogs of smooth/not over MLthreshold
# -------------------------------------------------------------
catalog = swap.get_new_filename(tonights.parameters,
'retired_catalog', source='ML')
print "SWAP: saving catalog of Machine-retired subjects..."
Nretired, Nsubjects = swap.write_catalog(MLsample,bureau,
catalog, threshold,
kind='rejected', source='ML')
print "SWAP: From "+str(Nsubjects)+" subjects classified,"
print "SWAP: "+str(Nretired)+" retired (with P < rejection) "\
"written to "+catalog
catalog = swap.get_new_filename(tonights.parameters,
'detected_catalog', source='ML')
print "SWAP: saving catalog of Machine detected subjects..."
Ndetected, Nsubjects = swap.write_catalog(MLsample, bureau,
catalog, threshold,
kind='detected', source='ML')
print "SWAP: From "+str(Nsubjects)+" subjects classified,"
print "SWAP: %i detected (with P > MLthreshold) "\
"written to %s"%(Ndetected, catalog)
# If is hasn't been done already, save the current directory
# ---------------------------------------------------------------------
tonights.parameters['dir'] = os.getcwd()+'/'+tonights.parameters['trunk']
if not os.path.exists(tonights.parameters['dir']):
os.makedirs(tonights.parameters['dir'])
# Repickle all the shits
# -----------------------------------------------------------------------
if tonights.parameters['repickle']:
new_samplefile = swap.get_new_filename(tonights.parameters,'collection')
print "SWAP: saving SWAP subjects to "+new_samplefile
swap.write_pickle(sample,new_samplefile)
tonights.parameters['samplefile'] = new_samplefile
new_samplefile=swap.get_new_filename(tonights.parameters,'MLcollection')
print "SWAP: saving test sample subjects to "+new_samplefile
swap.write_pickle(MLsample,new_samplefile)
tonights.parameters['MLsamplefile'] = new_samplefile
metadatafile = swap.get_new_filename(tonights.parameters,'metadata')
print "SWAP: saving metadata to "+metadatafile
swap.write_pickle(subjects,metadatafile)
tonights.parameters['metadatafile'] = metadatafile
# Update the time increment for SWAP's next run
# -----------------------------------------------------------------------
t2 = datetime.datetime.strptime(tonights.parameters['start'],
'%Y-%m-%d_%H:%M:%S') + \
datetime.timedelta(days=tonights.parameters['increment'])
tstop = datetime.datetime.strptime(tonights.parameters['end'],
'%Y-%m-%d_%H:%M:%S')
if t2 == tstop:
plots = True
else:
tonights.parameters['start'] = t2.strftime('%Y-%m-%d_%H:%M:%S')
# Update configfile to reflect Machine additions
# -----------------------------------------------------------------------
configfile = 'update.config'
random_file = open(tonights.parameters['random_file'],"w");
random_state = np.random.get_state();
cPickle.dump(random_state,random_file);
random_file.close();
swap.write_config(configfile, tonights.parameters)
pdb.set_trace()
def bureau(self):
if self._bureau is None:
self.bureau = swap.read_pickle(self.parameters['bureaufile'],
'bureau')
return self._bureau
# Check for pickles in array args:
if len(args) == 1:
collectionfile = args[0]
print "make_trajectory_plots: illustrating subject trajectories in: "
print "make_trajectory_plots: ", collectionfile
else:
print make_trajectory_plots.__doc__
return
output_directory = './'
# ------------------------------------------------------------------
# Read in collection:
sample = swap.read_pickle(collectionfile, 'collection')
print "make_trajectory_plots: total no. of available subjects: ", len(
sample.list())
if highlights:
# Read in subjects to be highlighted:
highlightIDs = swap.read_list(listfile)
print highlightIDs
print "make_trajectory_plots: total no. of special subjects: ", len(
highlightIDs)
print "make_trajectory_plots: special subjects: ", highlightIDs
# ------------------------------------------------------------------
# Start plot:
figure = sample.start_trajectory_plot(title=title,
def collection(self):
if self._collection is None:
self._collection = swap.read_pickle(self.parameters['samplefile'],
'collection')
return self._collection
def main(args):
params = fetch_parameters(args.config)
num_days = fetch_num_days(params)
# ---------------------------------------------------------------------
# Fetch lists of relevant filenames over the course of the run
detectedfilelist = fetch_filelist(params, kind='detected')
if args.old_run:
rejectedfilelist = fetch_filelist(params, kind='retired')
else:
rejectedfilelist = fetch_filelist(params, kind='rejected')
# ---------------------------------------------------------------------
# Fetch the cumulative number of classified subjects from SWAP
detected = fetch_number_of_subjects(detectedfilelist, kind='detected')
rejected = fetch_number_of_subjects(rejectedfilelist, kind='rejected')
GZX_retired_subjects = np.vstack([detected, rejected])
# ---------------------------------------------------------------------
# Fetch the cumulative number of classified subjects from GZ2
GZ2_retired_subjects = fetch_num_retired_GZ2(num_days,expert=args.expert)
# Generate appropriate output filename
if args.combined_subjects:
outname = args.config[len('update_'):-len('.config')]+'_combo'
GZX_retired_subjects = np.sum(GZX_retired_subjects,axis=0)
else:
outname = args.config[len('update_'):-len('.config')]
# ---------------------------------------------------------------------
# Plot that shit
plot_retired_GZ_vs_SWAP(GZX_retired_subjects, GZ2_retired_subjects,
num_days, outfilename=outname)
# ---------------------------------------------------------------------
### Generate evaluation report as a function of time
if args.eval_report:
try:
eval_report = Table.read('GZXevaluation_%s.txt'%outname,
format='ascii')
recall = eval_report['recall']
accuracy = eval_report['accuracy']
precision = eval_report['precision']
except:
meta = swap.read_pickle(params['metadatafile'], 'storage')
subjects = meta.subjects
accuracy, recall, precision = generate_SWAP_eval_report(
detectedfilelist, rejectedfilelist, subjects)
plot_GZX_evaluation(num_days, accuracy, precision, recall, outname)
def subjectMetadata(self):
if self._subjectMetadata is None:
self._subjectMetadata = swap.read_pickle(
self.parameters['metadatafile'], 'metadata').subjects
return self._subjectMetadata
def make_lens_catalog(args):
"""
NAME
make_lens_catalog
PURPOSE
Given location of collection pickle, this script produces a set of
annotated images of lenses (heatmaps for lens locations, markers for
where clicks were, etc).
COMMENTS
You have to download the file so it chooses whever your output
directory is to also download the raw images.
This should be pretty customizable.
FLAGS
-h Print this message
--skill Weight by skill
INPUTS
collection.pickle
OUTPUTS
lens.dat
Assumed format:
ID kind x y Prob N0 Skill Dist
Here:
ID = Space Warps subject ID
kind = Space Warps subject type (sim, dud, test)
x,y = object (cluster) centroid, in pixels
P = Space Warps subject probability
N0 = number of markers in the cluster
S = total skill per cluster, summed over markers
D = biggest distance within cluster
EXAMPLE
BUGS
AUTHORS
This file is part of the Space Warps project, and is distributed
under the GPL v2 by the Space Warps Science Team.
http://spacewarps.org/
HISTORY
2013-07-16 started Davis (KIPAC)
"""
# ------------------------------------------------------------------
# Some defaults:
flags = {'skill': False,
'output_directory': './',
'output_name': 'catalog.dat',
'image_y_size': 440,
'catalog_path': '',
'update_collection': '',}
# ------------------------------------------------------------------
# Read in options:
# this has to be easier to do...
for arg in args:
if arg in flags:
flags[arg] = args[arg]
elif arg == 'collection_path':
collection_path = args[arg]
else:
print "make_lens_atlas: unrecognized flag ",arg
print "make_lens_catalog: illustrating behaviour captured in collection file: "
print "make_lens_catalog: ",collection_path
memory = joblib.Memory(cachedir=flags['output_directory'])
memory.clear()
catalog_path = flags['output_directory'] + flags['output_name']
if len(flags['output_name']) > 0:
F = open(catalog_path, 'w')
F.write('id,kind,x,y,prob,n0,skill,dist\n')
# ------------------------------------------------------------------
# Read in files:
collection = swap.read_pickle(collection_path, 'collection')
ID_list = collection.list()
print "make_lens_catalog: collection numbers ", len(ID_list)
if flags['catalog_path'] != '':
print "make_lens_catalog: filtering from catalog ",flags['catalog_path']
catalog_in = csv2rec(flags['catalog_path'])
ID_list = np.unique(catalog_in['id'])
# ------------------------------------------------------------------
# Run through data:
catalog = {}
for ID in ID_list:
subject = collection.member[ID]
kind = subject.kind
P = subject.mean_probability
itwas = subject.annotationhistory['ItWas']
x_all = subject.annotationhistory['At_X']
y_all = subject.annotationhistory['At_Y']
x_markers = np.array([xi for xj in x_all for xi in xj])
y_markers = np.array([yi for yj in y_all for yi in yj])
catalog.update({ID: {'agents_reject': [],
'x': x_markers,
'y': y_markers,}})
PL_all = subject.annotationhistory['PL']
PD_all = subject.annotationhistory['PD']
# filter out the empty clicks
PL_list = []
PL_nots = []
for i, xj in enumerate(x_all):
# len(xj) of empty = 0
PL_list.append([PL_all[i]] * len(xj))
if len(xj) == 0:
PL_nots.append(PL_all[i])
PL = np.array([PLi for PLj in PL_list for PLi in PLj])
PL_nots = np.array(PL_nots)
# filter out the empty clicks
PD_list = []
PD_nots = []
for i, xj in enumerate(x_all):
PD_list.append([PD_all[i]] * len(xj))
if len(xj) == 0:
PD_nots.append(PD_all[i])
catalog[ID]['agents_reject'].append(i)
PD = np.array([PDi for PDj in PD_list for PDi in PDj])
PD_nots = np.array(PD_nots)
skill = swap.expectedInformationGain(0.5, PL, PD) # skill
# it is only fair to write out the NOTs, too
# do the empty guys
skill_nots = swap.expectedInformationGain(0.5, PL_nots, PD_nots) # skill
x, y = -1, -1
N0 = len(skill_nots)
S = np.sum(skill_nots)
D = 0
## catalog.append((ID, kind, x, y, P, N0, S, D))
if len(catalog)%500 == 0:
print len(catalog)
if len(flags['output_name']) > 0:
F.write('{0},{1},{2},{3},{4},{5},{6},{7}\n'.format(
ID, kind, x, y, P, N0, S, D))
if len(x_markers) == 0:
# apparently everyone was a not...
catalog[ID]['agents_labels'] = np.array([])
continue
# ------------------------------------------------------------------
# cluster
print 'make_lens_catalog: subject ID = ', ID
if flags['skill']:
cluster_centers, cluster_center_labels, cluster_labels, \
n_clusters, dist_within = outlier_clusters(x_markers, y_markers, skill, memory=memory)
else:
cluster_centers, cluster_center_labels, cluster_labels, \
n_clusters, dist_within = outlier_clusters(x_markers, y_markers, None, memory=memory)
# need to get: x, y, N0, S
catalog[ID]['agents_labels'] = cluster_labels
for cluster_center_label in cluster_center_labels:
cluster_center = cluster_centers[cluster_center_label]
members = (cluster_labels == cluster_center_label)
x, y = cluster_center
# convert y to catalog convention
y = flags['image_y_size'] - y
N0 = np.sum(members)
S = np.sum(skill[members])
D = dist_within[cluster_center_label]
if cluster_center_label == -1:
# outlier cluster
# so really every point is its own cluster...
D = 0
## catalog.append((ID, kind, x, y, P, N0, S, D))
## if len(catalog)%500 == 0:
## print len(catalog)
# TODO: make some requirement to be included (exclude outliers)
if len(flags['output_name']) > 0:
F.write('{0},{1},{2},{3},{4},{5},{6},{7}\n'.format(
ID, kind, x, y, P, N0, S, D))
print 'make_lens_catalog: Clearing memory'
# clear memory
memory.clear()
if len(flags['output_name']) > 0:
print 'make_lens_catalog: closing file!'
F.close()
if len(flags['update_collection']) > 0:
print 'make_lens_catalog: writing updated collection to', flags['update_collection']
# TODO: get the other params correct!!!!
collection_fat = swap.collection.Collection()
for ID in catalog:
subject = collection.member[ID]
atx = subject.annotationhistory['At_X']
labels_in = list(catalog[ID]['agents_labels'])
labels_fat = []
for atx_i in atx:
labels_fat.append([])
for atx_ij in atx_i:
labels_fat[-1].append(labels_in.pop(0))
subject.annotationhistory.update({'labels': labels_fat})
collection_fat.member.update({ID: subject})
swap.write_pickle(collection_fat, flags['update_collection'])
print 'make_lens_catalog: All done!'
return catalog
# How will we make decisions based on probability?
thresholds = {}
thresholds['detection'] = tonights.parameters['detection_threshold']
thresholds['rejection'] = tonights.parameters['rejection_threshold']
# will we perform machine learning after SWAP?
try: machine = tonights.parameters['machine']
except: machine = False
print "SWAP: running MachineClassifier.py after this run?",machine
#pdb.set_trace()
# ------------------------------------------------------------------
# Read in, or create, a bureau of agents who will represent the
# volunteers:
bureau = swap.read_pickle(tonights.parameters['bureaufile'],'bureau')
# ------------------------------------------------------------------
# Read in, or create, an object representing the candidate list:
sample = swap.read_pickle(tonights.parameters['samplefile'],'collection')
# ------------------------------------------------------------------
# Read in metadata (all subjects for which we have morph params)
subjects = swap.read_pickle(tonights.parameters['metadatafile'], 'metadata')
# ------------------------------------------------------------------
# Open up database:
db = swap.MySQLdb()
# Read in a batch of classifications, made since the aforementioned
# start time:
# Check for pickles in array args:
if len(args) == 2:
bureau1_path = args[0]
bureau2_path = args[1]
print "make_info_plots: illustrating behaviour captured in bureau files: "
print "make_info_plots: ",bureau1_path
print "make_info_plots: ",bureau2_path
else:
print make_info_plots.__doc__
return
# Read in bureau objects:
bureau1 = swap.read_pickle(bureau1_path, 'bureau')
bureau2 = swap.read_pickle(bureau2_path, 'bureau')
print "make_info_plots: stage 1, 2 agent numbers: ",len(bureau1.list()), len(bureau2.list())
experience1 = []
effort1 = []
final_skill1 = []
final_PL1 =[]
final_PD1 =[]
information1 = []
contribution1 = []
experience2 = []
effort2 = []
final_skill2 = []
final_PL2 =[]
if not path.exists(cluster_directory):
makedirs(cluster_directory)
if not path.exists(field_directory):
makedirs(field_directory)
# load up requisite files
collection_path = args['collection']
if '.pickle' in collection_path:
# will need PJM's swap to interpret the spacewarps database
try:
import swap
except:
raise Exception('create_catalogs: Unable to import SpaceWarps analysis code!')
#collection_path = base_collection_path + 'stage{0}'.format(stage) + '/CFHTLS_collection.pickle'
collection_pickle = swap.read_pickle(collection_path, 'collection')
stage = -1
collection = convert_swap_collection_to_dataframe(collection_pickle, stage)
elif '.csv' in collection_path:
collection = pd.read_csv(collection_path)
else:
raise Exception('create_catalogs: No idea what kind of collection you are giving me here for {0}'.format(collection_path))
if args['knownlens']:
knownlens = pd.read_csv(args['knownlens'])
else:
knownlens = None
# create the cluster args
cluster_args = {'collection': collection,
'knownlens': knownlens,
def make_offline_reports(args):
"""
NAME
make_offline_reports
PURPOSE
Given an offline tuple as well as other bureau tuples etc,
this script produces the reports made at the end of SWAP
COMMENTS
FLAGS
-h Print this message
--out Output directory, otherwise is '.'
--do_offline Do offline analysis?
INPUTS
configfile Plain text file containing SW experiment configuration
bureaufile
collectionfile
OUTPUTS
EXAMPLE
BUGS
AUTHORS
This file is part of the Space Warps project, and is distributed
under the MIT license by the Space Warps Science Team.
http://spacewarps.org/
HISTORY
2014-09-16 started Davis (KIPAC)
"""
# ------------------------------------------------------------------
# Some defaults:
# default settings are for offline using only exact training info
flags = {'do_offline': False,
'output_directory': '.',
'PL0': 0.5, # initial PL guess
'PD0': 0.5, # initial PD guess
'pi': 4e-2, # initial lens probability
'n_min_assessment': 0, # minimum number of assessments before included in analysis
'use_training_info': True,
'exclude_test_info': True,
'exclude_training_info': False,
'N_min': 10, # min number of EM steps required
'N_max': 100, # max number of EM steps
'epsilon_min': 1e-6, # escape condition
}
# this has to be easier to do...
for arg in args:
if arg in flags:
flags[arg] = args[arg]
elif arg == 'config':
configfile = args[arg]
elif arg == 'collection':
collectionfile = args[arg]
elif arg == 'bureau':
bureaufile = args[arg]
else:
print "make_offline_reports: unrecognized flag ",arg
out_dir = flags['output_directory']
# ------------------------------------------------------------------
# Read in run configuration:
tonights = swap.Configuration(configfile)
# TODO: do this correctly
tonights.parameters['finish'] = 'now'
tonights.parameters['start'] = 'now'
tonights.parameters['trunk'] = \
tonights.parameters['survey']+'_'+tonights.parameters['finish']
tonights.parameters['dir'] = out_dir
# How will we make decisions based on probability?
thresholds = {}
thresholds['detection'] = tonights.parameters['detection_threshold']
thresholds['rejection'] = tonights.parameters['rejection_threshold']
t = -1 # for now?!
# ------------------------------------------------------------------
# Read in, or create, a bureau of agents who will represent the
# volunteers:
bureau = swap.read_pickle(bureaufile, 'bureau')
# ------------------------------------------------------------------
# Read in, or create, an object representing the candidate list:
sample = swap.read_pickle(collectionfile, 'collection')
# ------------------------------------------------------------------
# if do_offline, run offline analysis here:
if flags['do_offline']:
PL0 = flags['PL0']
PD0 = flags['PD0']
pi = flags['pi']
n_min_assessment = flags['n_min_assessment']
use_training_info = flags['use_training_info']
exclude_test_info = flags['exclude_test_info']
exclude_training_info = flags['exclude_training_info']
N_min = flags['N_min']
N_max = flags['N_max']
epsilon_min = flags['epsilon_min']
# initialize offline params
bureau_offline = {}
probabilities = {}
online_probabilities = {}
training_IDs = {} # which entries in collection are training
set_aside_subject = {} # which subjects do we set aside? Here we set aside none
set_aside_agent = {} # which agents do we set aside? Here we set aside none
collection = {}
for ID in sample.list():
if ID in set_aside_subject:
continue
else:
collection.update({ID: sample.member[ID]})
for ID in collection.keys():
subject = collection[ID]
n_assessment = len(subject.annotationhistory['ItWas'])
if (n_assessment > n_min_assessment):
if (subject.category == 'training'):
if use_training_info:
truth = {'LENS': 1, 'NOT': 0}[subject.truth]
training_IDs.update({ID: truth})
if exclude_training_info:
# when doing M step, don't use these to update parameters
training_IDs.update({ID: -1})
elif (subject.category == 'test'):
if exclude_test_info:
# when doing M step, don't use these to update parameters
training_IDs.update({ID: -1})
probabilities.update({ID: pi})
online_probabilities.update({ID: subject.mean_probability})
for agent_i in xrange(len(subject.annotationhistory['Name'])):
name = subject.annotationhistory['Name'][agent_i]
if name in set_aside_agent:
continue
xij = subject.annotationhistory['ItWas'][agent_i]
if name not in bureau_offline:
bureau_offline.update({name: {
'PD': PD0, 'PL': PL0,
'PL_in': bureau.member[name].PL,
'PD_in': bureau.member[name].PD,
'Pi': pi,
'Subjects': {ID: xij}}})
else:
bureau_offline[name]['Subjects'].update({ID: xij})
# Run EM Algorithm
bureau_offline, pi, probabilities, information_dict = EM_algorithm(
bureau_offline, pi, probabilities, training_IDs,
N_min=N_min, N_max=N_max, epsilon_min=epsilon_min,
return_information=True)
tup = (bureau_offline, pi, probabilities, information_dict)
offlinefile = out_dir + '/offline.pickle'
swap.write_pickle(tup, offlinefile)
# ------------------------------------------------------------------
# Now replace sample member probabilities with offline probabilities
# Also update bureau with offline results
for ID in sample.list():
# just in case any IDs didn't get into offline somehow?!
if ID not in probabilities.keys():
sample.member.pop(ID)
continue
# This is a bit hackish: update mean_probability,
# median_probability, and do the rejection threshold stuff
subject = sample.member[ID]
subject.mean_probability = probabilities[ID]
subject.median_probability = probabilities[ID]
# ripped from subject.py
if subject.mean_probability < subject.rejection_threshold:
subject.status = 'rejected'
if subject.kind == 'test':
subject.state = 'inactive'
subject.retirement_time = -1#at_time
subject.retirement_age = subject.exposure
elif subject.mean_probability > subject.detection_threshold:
subject.status = 'detected'
if subject.kind == 'test':
# Let's keep the detections live!
# subject.state = 'inactive'
# subject.retirement_time = at_time
# subject.retirement_age = subject.exposure
pass
else:
# Keep the subject alive! This code is only reached if
# we are not being hasty.
subject.status = 'undecided'
if subject.kind == 'test':
subject.state = 'active'
subject.retirement_time = 'not yet'
subject.retirement_age = 0.0
# I don't think this is necessary, but just in case
sample.member[ID] = subject
for kind in ['sim', 'dud', 'test']:
sample.collect_probabilities(kind)
# now save
collectionfile = out_dir + '/collection_offline.pickle'
swap.write_pickle(collection, collectionfile)
# now update bureau
for ID in bureau.list():
# just in case any IDs didn't make it to offline?
if ID not in bureau_offline.keys():
bureau.member.pop(ID)
continue
# update PL, PD, then update_skill
agent = bureau.member[ID]
agent.PL = bureau_offline[ID]['PL']
agent.PD = bureau_offline[ID]['PD']
agent.update_skill()
# I don't think this is necessary, but just in case
bureau.member[ID] = agent
bureau.collect_probabilities()
# now save
bureaufile = out_dir + '/bureau_offline.pickle'
swap.write_pickle(bureau, bureaufile)
# ------------------------------------------------------------------
# now we can pretend we're in SWAP.py
new_retirementfile = swap.get_new_filename(tonights.parameters,'retire_these')
print "make_offline_reports: saving retiree subject Zooniverse IDs..."
N = swap.write_list(sample,new_retirementfile,item='retired_subject')
print "make_offline_reports: "+str(N)+" lines written to "+new_retirementfile
# Also print out lists of detections etc! These are urls of images.
new_samplefile = swap.get_new_filename(tonights.parameters,'candidates')
print "make_offline_reports: saving lens candidates..."
N = swap.write_list(sample,new_samplefile,item='candidate')
print "make_offline_reports: "+str(N)+" lines written to "+new_samplefile
# Now save the training images, for inspection:
new_samplefile = swap.get_new_filename(tonights.parameters,'training_true_positives')
print "make_offline_reports: saving true positives..."
N = swap.write_list(sample,new_samplefile,item='true_positive')
print "make_offline_reports: "+str(N)+" lines written to "+new_samplefile
new_samplefile = swap.get_new_filename(tonights.parameters,'training_false_positives')
print "make_offline_reports: saving false positives..."
N = swap.write_list(sample,new_samplefile,item='false_positive')
print "make_offline_reports: "+str(N)+" lines written to "+new_samplefile
new_samplefile = swap.get_new_filename(tonights.parameters,'training_false_negatives')
print "make_offline_reports: saving false negatives..."
N = swap.write_list(sample,new_samplefile,item='false_negative')
print "make_offline_reports: "+str(N)+" lines written to "+new_samplefile
# Also write out catalogs of subjects, including the ZooID, subject ID,
# how many classifications, and probability:
catalog = swap.get_new_filename(tonights.parameters,'candidate_catalog')
print "make_offline_reports: saving catalog of high probability subjects..."
Nlenses,Nsubjects = swap.write_catalog(sample,catalog,thresholds,kind='test')
print "make_offline_reports: From "+str(Nsubjects)+" subjects classified,"
print "make_offline_reports: "+str(Nlenses)+" candidates (with P > rejection) written to "+catalog
catalog = swap.get_new_filename(tonights.parameters,'sim_catalog')
print "make_offline_reports: saving catalog of high probability subjects..."
Nsims,Nsubjects = swap.write_catalog(sample,catalog,thresholds,kind='sim')
print "make_offline_reports: From "+str(Nsubjects)+" subjects classified,"
print "make_offline_reports: "+str(Nsims)+" sim 'candidates' (with P > rejection) written to "+catalog
catalog = swap.get_new_filename(tonights.parameters,'dud_catalog')
print "make_offline_reports: saving catalog of high probability subjects..."
Nduds,Nsubjects = swap.write_catalog(sample,catalog,thresholds,kind='dud')
print "make_offline_reports: From "+str(Nsubjects)+" subjects classified,"
print "make_offline_reports: "+str(Nduds)+" dud 'candidates' (with P > rejection) written to "+catalog
# ------------------------------------------------------------------
# Make plots! Can't plot everything - uniformly sample 200 of each
# thing (agent or subject).
# Agent histories:
fig1 = bureau.start_history_plot()
pngfile = swap.get_new_filename(tonights.parameters,'histories')
Nc = np.min([200,bureau.size()])
print "make_offline_reports: plotting "+str(Nc)+" agent histories in "+pngfile
for Name in bureau.shortlist(Nc):
bureau.member[Name].plot_history(fig1)
bureau.finish_history_plot(fig1,t,pngfile)
tonights.parameters['historiesplot'] = pngfile
# Agent probabilities:
pngfile = swap.get_new_filename(tonights.parameters,'probabilities')
print "make_offline_reports: plotting "+str(Nc)+" agent probabilities in "+pngfile
bureau.plot_probabilities(Nc,t,pngfile)
tonights.parameters['probabilitiesplot'] = pngfile
# Subject trajectories:
fig3 = sample.start_trajectory_plot()
pngfile = swap.get_new_filename(tonights.parameters,'trajectories')
# Random 500 for display purposes:
Ns = np.min([500,sample.size()])
print "make_offline_reports: plotting "+str(Ns)+" subject trajectories in "+pngfile
for ID in sample.shortlist(Ns):
sample.member[ID].plot_trajectory(fig3)
# To plot only false negatives, or only true positives:
# for ID in sample.shortlist(Ns,kind='sim',status='rejected'):
# sample.member[ID].plot_trajectory(fig3)
# for ID in sample.shortlist(Ns,kind='sim',status='detected'):
# sample.member[ID].plot_trajectory(fig3)
sample.finish_trajectory_plot(fig3,pngfile,t=t)
tonights.parameters['trajectoriesplot'] = pngfile
# Candidates! Plot all undecideds or detections:
fig4 = sample.start_trajectory_plot(final=True)
pngfile = swap.get_new_filename(tonights.parameters,'sample')
# BigN = 100000 # Would get them all...
BigN = 500 # Can't see them all!
candidates = []
candidates += sample.shortlist(BigN,kind='test',status='detected')
candidates += sample.shortlist(BigN,kind='test',status='undecided')
sims = []
sims += sample.shortlist(BigN,kind='sim',status='detected')
sims += sample.shortlist(BigN,kind='sim',status='undecided')
duds = []
duds += sample.shortlist(BigN,kind='dud',status='detected')
duds += sample.shortlist(BigN,kind='dud',status='undecided')
print "make_offline_reports: plotting "+str(len(sims))+" sims in "+pngfile
for ID in sims:
sample.member[ID].plot_trajectory(fig4)
print "make_offline_reports: plotting "+str(len(duds))+" duds in "+pngfile
for ID in duds:
sample.member[ID].plot_trajectory(fig4)
print "make_offline_reports: plotting "+str(len(candidates))+" candidates in "+pngfile
for ID in candidates:
sample.member[ID].plot_trajectory(fig4)
# They will all show up in the histogram though:
sample.finish_trajectory_plot(fig4,pngfile,final=True)
tonights.parameters['candidatesplot'] = pngfile
# ------------------------------------------------------------------
# Finally, write a PDF report:
swap.write_report(tonights.parameters,bureau,sample)
def main():
parser = OptionParser()
parser.add_option("-w", dest="weight", default='uniform',
help="Run KNC with preferred weighting.")
parser.add_option("-t", dest="thresh", default=None, help="Set threshold")
parser.add_option("-p", dest="plotonly", action='store_true', default=False,
help="Skip machine learning and go straight to plotting")
parser.add_option("-n", dest="name_modifier", default=None,
help="Additional naming identification for output files")
(options, args) = parser.parse_args()
if options.thresh: thresh = float(options.thresh)
else: thresh = None
if options.plotonly:
#metrics_uni = read_pickle('KNC_uniform_eval.pickle')
#metrics_dist = read_pickle('KNC_distance_eval.pickle')
###################### JUST PLOT THE SHITS ###########################
kwargs = {'thresh':thresh,
'keys':['accuracy','contamination', 'completeness',
'falseomis','trueneg'],
'labels':['Accuracy', 'Contamination (S)', 'Completeness (S)',
'Contamination (F)', 'Completeness (F)'],
'name_modifier':options.name_modifier}
print options.weight
plot_the_shits(metric='all', method='KNC_%s'%options.weight, **kwargs)
#explore_accuracy(method='KNC_uniform')
exit()
################### READ IN TRAINING / VALIDATION DATA #############
filename = 'GZ2_testML2_metadata.pickle'
data = swap.read_pickle(filename, 'metadata')
# This is the "New" validation sample -- Expertly classified
valid_idx = np.where((data['MLsample']=='valid') & (data['GZ2_label']!=-1)
& (data['Nair_label']!=-1)
& (data['Expert_label']!=-1))
valid = data[valid_idx]
valid_meta, valid_features = ml.extract_training(valid)
valid_labels_ex = valid_meta['Expert_label'].filled()
valid_labels_gz = valid_meta['GZ2_label'].filled()
valid_labels_nr = valid_meta['Nair_label'].filled()
# Let's try to recreate what I had before.
valid2_idx = np.where((data['Nair_label']!=-1))
valid2 = data[valid2_idx]
valid2_meta, valid2_features = ml.extract_training(valid2)
valid2_labels = valid2_meta['Nair_label'].filled()
# Which validation sample do I want to use? BLAH.
# --> Used this to try to replicate what I had a month ago (Nair "truth")
#valid_features = valid2_features
#valid_labels = valid2_labels
# Now test on the new, smaller validation sample
# --> first, still with Nair "truth"
valid_labels = valid_labels_nr
# --> second, using GZ2 user "truth"
#valid_labels = valid_labels_gz
# --> finally, using Expert "truth"
#valid_labels = valid_labels_ex
# Load up the training set (ALL GZ labels)
train_idx = np.where((data['MLsample']!='valid') & (data['GZ2_label']!=-1))
train = data[train_idx]
train_meta, train_features = ml.extract_training(train)
train_labels = train_meta['GZ2_label'].filled()
# select various and increasing size training samples
# -------------------------------------------------------------------
N = [100,500,1000,5000,10000,50000]#
K = [5,10,15,20,25,30,35,40,45,50]
evaluation_metrics = {'precision':[], 'recall':[], 'pr_thresh':[],
'falsepos':[], 'truepos':[], 'roc_thresh':[],
'accuracy':[], 'thresh':[], 'falseomis':[],
'falseneg':[], 'trueneg':[],
'contamination':[], 'completeness':[],
'precision_score':[], 'recall_score':[],
'accuracy_score':[], 'roc_auc_score1':[],
'roc_auc_score2':[], 'f1_score':[], 'k':[],'n':[]}
################### RUN CLASSIFIERS WITIH VARIOUS PARAMS ############
############## this is running through various K manually ##########
for j,n in enumerate(N):
train_features_sub = train_features[:n]
train_labels_sub = train_labels[:n]
#ratio = float(np.sum(train_labels_sub==1))/len(train_labels_sub)
#print "Ratio of Smooth / Total for training sample (%i): %f"\
# %(n, ratio)
for i,k in enumerate(K):
# Adjust k because it can't be => sample size
if n <= k: k = n-1
preds, probs, machine = ml.runKNC(train_features_sub,
train_labels_sub,
valid_features,
N=k, weights=options.weight)
#preds = ml.runRNC(train_sample, labels, valid_sample, R=k,
# weights='distance', outlier=0)
fps, tps, thresh = mtrx._binary_clf_curve(valid_labels,probs[:,1])
metrics_list = mtrx.compute_binary_metrics(fps, tps)
[acc, tpr, fpr, fnr, tnr, prec, fdr, fomis, npv] = metrics_list
evaluation_metrics['completeness'].append(tpr)
evaluation_metrics['contamination'].append(fdr)
evaluation_metrics['falseneg'].append(fnr)
evaluation_metrics['trueneg'].append(tnr)
evaluation_metrics['falseomis'].append(fomis)
evaluation_metrics['accuracy'].append(acc)
evaluation_metrics['thresh'].append(thresh)
# Curves -- for plotting ROC and PR
pp, rr, thresh2 = mx.precision_recall_curve(valid_labels,probs[:,1])
evaluation_metrics['precision'].append(pp)
evaluation_metrics['recall'].append(rr)
evaluation_metrics['pr_thresh'].append(thresh2)
fpr, tpr, thresh3=mx.roc_curve(valid_labels, probs[:,1],pos_label=1)
evaluation_metrics['falsepos'].append(fpr)
evaluation_metrics['truepos'].append(tpr)
evaluation_metrics['roc_thresh'].append(thresh3)
# Single value metrics -- for plotting against N? K? whatever...
evaluation_metrics['roc_auc_score1'].append(mx.auc(fpr, tpr))
evaluation_metrics['roc_auc_score2'].append(mx.roc_auc_score(
valid_labels,preds))
evaluation_metrics['precision_score'].append(mx.precision_score(
valid_labels,preds))
evaluation_metrics['recall_score'].append(mx.recall_score(
valid_labels,preds))
evaluation_metrics['accuracy_score'].append(mx.accuracy_score(
valid_labels,preds))
evaluation_metrics['f1_score'].append(mx.f1_score(
valid_labels,preds))
# current k and n so I don't have to backstrapolate
evaluation_metrics['k'].append(k)
evaluation_metrics['n'].append(n)
for key, val in evaluation_metrics.iteritems():
evaluation_metrics[key] = np.array(evaluation_metrics[key])
# If everything works... Let's save this huge structure as a pickle
filename = 'KNC_%s_eval_%s.pickle'%(options.weight, options.name_modifier)
F = open(filename,'wb')
cPickle.dump(evaluation_metrics, F, protocol=2)
print "Saved evaluation metrics %s"%filename
######################### PLOT THE SHITS #############################
#kwargs = {'thresh':.5, 'keys':['accuracy','precision','recall']}
#plot_the_shits(method='KNC_uniform', metric='all', **kwargs)
#explore_accuracy(method='KNC_uniform')
exit()
# Simple naming scheme for output lists and plots:
tonights.parameters['trunk'] = \
tonights.parameters['survey']
tonights.parameters['dir'] = os.getcwd()
# What was the last time a subject was touched?
t = datetime.datetime.strptime(tonights.parameters['start'],
'%Y-%m-%d_%H:%M:%S')
# ------------------------------------------------------------------
# Read in, or create, a bureau of agents who will represent the
# volunteers:
bureau = swap.read_pickle(tonights.parameters['bureaufile'], 'bureau')
# ------------------------------------------------------------------
# Read in, or create, an object representing the candidate list:
sample = swap.read_pickle(tonights.parameters['samplefile'], 'collection')
# ------------------------------------------------------------------
# Output list of subjects to retire, based on this batch of
# classifications. Note that what is needed here is the ZooID,
# not the subject ID. Also print out lists of detections etc!
# These are urls of images.
new_samplefile = swap.get_new_filename(tonights.parameters, 'candidates')
print "SWEAR: saving lens candidates..."
makedirs(cluster_directory)
if not path.exists(field_directory):
makedirs(field_directory)
# load up requisite files
collection_path = args['collection']
if '.pickle' in collection_path:
# will need PJM's swap to interpret the spacewarps database
try:
import swap
except:
raise Exception(
'create_catalogs: Unable to import SpaceWarps analysis code!')
#collection_path = base_collection_path + 'stage{0}'.format(stage) + '/CFHTLS_collection.pickle'
collection_pickle = swap.read_pickle(collection_path, 'collection')
stage = -1
collection = convert_swap_collection_to_dataframe(
collection_pickle, stage)
elif '.csv' in collection_path:
collection = pd.read_csv(collection_path)
else:
raise Exception(
'create_catalogs: No idea what kind of collection you are giving me here for {0}'
.format(collection_path))
if args['knownlens']:
knownlens = pd.read_csv(args['knownlens'])
else:
knownlens = None
