Python PenguinAggregationの例

コード例 #1

ファイル: penguinScript1.py プロジェクト: JiaminXuan/aggregation

#!/usr/bin/env python
__author__ = 'ggdhines'
from penguinAggregation import PenguinAggregation
import random
import os
import sys

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from divisiveKmeans import DivisiveKmeans

clusterAlg = DivisiveKmeans().__fit__

penguin = PenguinAggregation()
subject_ids = penguin.__get_subjects_per_site__("APZ00035mv",complete=True,remove_blanks=True)

for i,subject in enumerate(random.sample(subject_ids,50)):
    print i
    penguin.__readin_subject__(subject)
    blankImage = penguin.__cluster_subject__(subject, clusterAlg)

    if not blankImage:
        penguin.__save_raw_markings__(subject)
        break

コード例 #2

ファイル: checkIBCC.py プロジェクト: JiaminXuan/aggregation

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
elif os.path.exists("/Users/greg"):
    sys.path.append("/Users/greg/Code/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from divisiveKmeans import DivisiveKmeans
from multiClickCorrect import MultiClickCorrect
correctionAlg = MultiClickCorrect(overlap_threshold=1,min_cluster_size=2).__fix__

clusterAlg = DivisiveKmeans().__fit__

penguin = PenguinAggregation()

gold_subjects = penguin.__get_gold_subjects__()
gold_sample = gold_subjects[:50]

penguin.__readin_users__()

for count,zooniverse_id in enumerate(gold_sample):
    if count == 50:
        break
    print count, zooniverse_id
    penguin.__readin_subject__(zooniverse_id,read_in_gold=True)

    blankImage = penguin.__cluster_subject__(zooniverse_id, clusterAlg,fix_distinct_clusters=True,correction_alg=correctionAlg)
    penguin.__soy_it__(zooniverse_id)

コード例 #3

ファイル: clusterCompare.py プロジェクト: lelou6666/aggregation

import numpy as np

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append(
        "/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from agglomerativeClustering import Ward
from divisiveKmeans import DivisiveKmeans

clusterAlg2 = DivisiveKmeans().__fit__
clusterAlg = Ward().__fit__

penguin = PenguinAggregation()
subject_ids = pickle.load(
    open(aggregation.base_directory + "/Databases/penguin_gold.pickle", "rb"))

X1 = []
Y1 = []
X2 = []
Y2 = []
Z1 = []
Z2 = []
nonEmpty = 0
index = -1
random.shuffle(subject_ids)
while True:
    index += 1
    #for i,subject in enumerate(random.sample(subject_ids,50)):

コード例 #4

ファイル: clusterCompare2.py プロジェクト: JiaminXuan/aggregation

import aggregation
import matplotlib.pyplot as plt
import numpy as np

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from agglomerativeClustering import Ward
from divisiveKmeans import DivisiveKmeans

clusterAlg2 = DivisiveKmeans().__fit__

dkmeans = PenguinAggregation(clustering_alg= DivisiveKmeans().__fit__)
agglomerative = PenguinAggregation(clustering_alg = Ward().__fit__)
subject_ids = pickle.load(open(aggregation.base_directory+"/Databases/penguin_gold.pickle","rb"))


X1 = []
Y1 = []
X2 = []
Y2 = []
Z1 = []
Z2 = []
nonEmpty = 0
index = -1
random.shuffle(subject_ids)
while True:
    index += 1

コード例 #5

ファイル: clusterCompare2.py プロジェクト: lelou6666/aggregation

import matplotlib.pyplot as plt
import numpy as np

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append(
        "/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from agglomerativeClustering import Ward
from divisiveKmeans import DivisiveKmeans

clusterAlg2 = DivisiveKmeans().__fit__

dkmeans = PenguinAggregation(clustering_alg=DivisiveKmeans().__fit__)
agglomerative = PenguinAggregation(clustering_alg=Ward().__fit__)
subject_ids = pickle.load(
    open(aggregation.base_directory + "/Databases/penguin_gold.pickle", "rb"))

X1 = []
Y1 = []
X2 = []
Y2 = []
Z1 = []
Z2 = []
nonEmpty = 0
index = -1
random.shuffle(subject_ids)
while True:
    index += 1

コード例 #6

ファイル: errorCheck.py プロジェクト: JiaminXuan/aggregation

import matplotlib.cbook as cbook

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
elif os.path.exists("/Users/greg"):
    sys.path.append("/Users/greg/Code/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from divisiveKmeans import DivisiveKmeans
from zeroFix import ZeroFix

clusterAlg = DivisiveKmeans().__fit__
fixAlg = ZeroFix().__fix__

penguin = PenguinAggregation()



client = pymongo.MongoClient()
db = client['penguin_2015-01-18']
collection = db["penguin_classifications"]
subject_collection = db["penguin_subjects"]

accuracy = []
numGold = []

penguin.__readin_subject__("APZ00035nr")

penguin.__display_raw_markings__("APZ00035nr")

コード例 #7

ファイル: penguinScript2.py プロジェクト: JiaminXuan/aggregation

from penguinAggregation import PenguinAggregation
import random
import os
import sys

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from divisiveKmeans import DivisiveKmeans

clusterAlg = DivisiveKmeans().__fit__

penguin = PenguinAggregation()
zooniverse_id_list = random.sample(penguin.__get_subjects_per_site__("APZ0001x3p"),40)

for i,zooniverse_id in enumerate(zooniverse_id_list):
    print i
    penguin.__readin_subject__(zooniverse_id)
    blankImage = penguin.__cluster_subject__(zooniverse_id, clusterAlg)

    if not blankImage:
        print "+--"
        penguin.__find_closest_neighbour__(zooniverse_id)

#penguin.__plot_cluster_size__(zooniverse_id_list)
penguin.__find_one__(zooniverse_id_list)

コード例 #8

ファイル: errorCheck.py プロジェクト: lelou6666/aggregation

import urllib
import matplotlib.cbook as cbook

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append(
        "/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
elif os.path.exists("/Users/greg"):
    sys.path.append("/Users/greg/Code/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from divisiveKmeans import DivisiveKmeans
from zeroFix import ZeroFix

clusterAlg = DivisiveKmeans().__fit__
fixAlg = ZeroFix().__fix__

penguin = PenguinAggregation()

client = pymongo.MongoClient()
db = client['penguin_2015-01-18']
collection = db["penguin_classifications"]
subject_collection = db["penguin_subjects"]

accuracy = []
numGold = []

penguin.__readin_subject__("APZ00035nr")

penguin.__display_raw_markings__("APZ00035nr")

コード例 #9

import random
import os
import sys

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append(
        "/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from divisiveKmeans import DivisiveKmeans

clusterAlg = DivisiveKmeans().__fit__

penguin = PenguinAggregation()
zooniverse_id_list = random.sample(
    penguin.__get_subjects_per_site__("APZ0001x3p"), 40)

for i, zooniverse_id in enumerate(zooniverse_id_list):
    print i
    penguin.__readin_subject__(zooniverse_id)
    blankImage = penguin.__cluster_subject__(zooniverse_id, clusterAlg)

    if not blankImage:
        print "+--"
        penguin.__find_closest_neighbour__(zooniverse_id)

#penguin.__plot_cluster_size__(zooniverse_id_list)
penguin.__find_one__(zooniverse_id_list)

コード例 #10

ファイル: clusterCompare.py プロジェクト: alexbfree/aggregation

import matplotlib.pyplot as plt
import numpy as np

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from agglomerativeClustering import Ward
from divisiveKmeans import DivisiveKmeans

clusterAlg2 = DivisiveKmeans().__fit__
clusterAlg = Ward().__fit__

penguin = PenguinAggregation()
subject_ids = pickle.load(open(aggregation.base_directory + "/Databases/penguin_gold.pickle", "rb"))


X1 = []
Y1 = []
X2 = []
Y2 = []
Z1 = []
Z2 = []
nonEmpty = 0
index = -1
random.shuffle(subject_ids)
while True:
    index += 1
    # for i,subject in enumerate(random.sample(subject_ids,50)):

コード例 #11

#!/usr/bin/env python
__author__ = 'ggdhines'
from penguinAggregation import PenguinAggregation
import random
import os
import sys

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append(
        "/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from divisiveKmeans import DivisiveKmeans

clusterAlg = DivisiveKmeans().__fit__

penguin = PenguinAggregation()
subject_ids = penguin.__get_subjects_per_site__("APZ00035mv",
                                                complete=True,
                                                remove_blanks=True)

for i, subject in enumerate(random.sample(subject_ids, 50)):
    print i
    penguin.__readin_subject__(subject)
    blankImage = penguin.__cluster_subject__(subject, clusterAlg)

    if not blankImage:
        penguin.__save_raw_markings__(subject)
        break

コード例 #12

ファイル: penguinAgglomerative.py プロジェクト: lelou6666/aggregation

import sys
import cPickle as pickle
import aggregation

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append(
        "/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from agglomerativeClustering import Ward, TooBig

clusterAlg = Ward().__fit__

penguin = PenguinAggregation()
subject_ids = pickle.load(
    open(aggregation.base_directory + "/Databases/penguin_gold.pickle", "rb"))

for i, subject in enumerate(random.sample(subject_ids, 50)):
    #subject = "APZ000173v"
    print i, subject

    penguin.__readin_subject__(subject, users_to_skip=["caitlin.black"])
    try:
        numClusters, time = penguin.__cluster_subject__(subject, clusterAlg)
    except TooBig:
        print "too big"
        continue

    if not blankImage:

コード例 #13

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append(
        "/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
elif os.path.exists("/Users/greg"):
    sys.path.append("/Users/greg/Code/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from divisiveKmeans import DivisiveKmeans
from multiClickCorrect import __ibcc__2

clusterAlg = DivisiveKmeans().__fit__

penguin = PenguinAggregation()

client = pymongo.MongoClient()
db = client['penguin_2015-01-18']
collection = db["penguin_classifications"]
subject_collection = db["penguin_subjects"]

subjects = subject_collection.find({"metadata.path": {"$regex": "MAIVb2012a"}})
accuracy = []
num_gold = 0
could_have = 0
missed = 0
false_pos = 0
overlaps = {}
#overlaps2 = []
Xt = []

コード例 #14

ファイル: soyman.py プロジェクト: JiaminXuan/aggregation

import matplotlib.cbook as cbook

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
elif os.path.exists("/Users/greg"):
    sys.path.append("/Users/greg/Code/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from divisiveKmeans import DivisiveKmeans
from multiClickCorrect import __ibcc__2

clusterAlg = DivisiveKmeans().__fit__

penguin = PenguinAggregation()



client = pymongo.MongoClient()
db = client['penguin_2015-01-18']
collection = db["penguin_classifications"]
subject_collection = db["penguin_subjects"]

subjects = subject_collection.find({"metadata.path":{"$regex":"MAIVb2012a"}})
accuracy = []
num_gold =0
could_have = 0
missed = 0
false_pos = 0
overlaps = {}

コード例 #15

ファイル: penguinAgglomerative.py プロジェクト: alexbfree/aggregation

import os
import sys
import cPickle as pickle
import aggregation

# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
    sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
    sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")

from agglomerativeClustering import Ward, TooBig

clusterAlg = Ward().__fit__

penguin = PenguinAggregation()
subject_ids = pickle.load(open(aggregation.base_directory + "/Databases/penguin_gold.pickle", "rb"))

for i, subject in enumerate(random.sample(subject_ids, 50)):
    # subject = "APZ000173v"
    print i, subject

    penguin.__readin_subject__(subject, users_to_skip=["caitlin.black"])
    try:
        numClusters, time = penguin.__cluster_subject__(subject, clusterAlg)
    except TooBig:
        print "too big"
        continue

    if not blankImage:
        penguin.__display_raw_markings__(subject)