def create_image_classifier():
# Create a dataset on the MetaMind servers.
# You can view your existing datasets here: https://www.metamind.io/my_stuff#my-datasets
# Each dataset is assigned an id by the server.
#
# To create a local representation of any of your datasets, you can simply pass the id into the constructor:
#
# ClassificationData(id=123)
#
training_data = ClassificationData(private=True, data_type='image', name='image_demo')
# You can find more information about how to add data to a dataset here: http://docs.metamind.io/docs/datasets
# There are multiple ways of adding data to both text and image datasets
training_data.add_samples([
(apple_pie_url, 'food'),
(salad_url, 'food'),
(deer_url, 'animal')
], input_type='urls')
image_directory = os.path.dirname(os.path.realpath(__file__)) + '/images'
training_data.add_samples([
(image_directory + '/bonsai.jpg', 'animal'),
(image_directory + '/dog.jpg', 'animal')
], input_type='files')
# Each classifier is assigned an id by the server, much like a dataset.
# As with a dataset, you can create a representation of a classifier by passing its id into the constructor:
#
# ClassificationModel(id=123)
#
classifier = ClassificationModel(private=True, name='image_demo')
classifier.fit(training_data)
return classifier
def create_image_classifier():
# Create a dataset on the MetaMind servers.
# You can view your existing datasets here: https://www.metamind.io/my_stuff#my-datasets
# Each dataset is assigned an id by the server.
#
# To create a local representation of any of your datasets, you can simply pass the id into the constructor:
#
# ClassificationData(id=123)
#
training_data = ClassificationData(private=True,
data_type='image',
name='image_demo')
# You can find more information about how to add data to a dataset here: http://docs.metamind.io/docs/datasets
# There are multiple ways of adding data to both text and image datasets
training_data.add_samples([(apple_pie_url, 'food'), (salad_url, 'food'),
(deer_url, 'animal')],
input_type='urls')
image_directory = os.path.dirname(os.path.realpath(__file__)) + '/images'
training_data.add_samples([(image_directory + '/bonsai.jpg', 'animal'),
(image_directory + '/dog.jpg', 'animal')],
input_type='files')
# Each classifier is assigned an id by the server, much like a dataset.
# As with a dataset, you can create a representation of a classifier by passing its id into the constructor:
#
# ClassificationModel(id=123)
#
classifier = ClassificationModel(private=True, name='image_demo')
classifier.fit(training_data)
return classifier
def train(names):
training_data = ClassificationData(private=True, data_type='image', name='fish')
inputs = []
for name in names:
urls = fetch(name)
for url in urls:
inputs.append((url, name))
training_data.add_samples(inputs, input_type='urls')
classifier = ClassificationModel(private=True, name='fish')
classifier.fit(training_data)
def create_text_classifier():
training_data = ClassificationData(private=True, data_type='text', name='text_demo')
training_data.add_samples([
('The horse got out of the barn.', 'rural'),
('It is very quiet at night', 'rural'),
('There are 300 cattle in the field.', 'rural'),
('The roads are empty', 'rural'),
('There is so much traffic today.', 'urban'),
('Newer buildings are often made of glass.', 'urban'),
('The subways are quite loud.', 'urban'),
('How many skyscrapers do you see?', 'urban')
], input_type='text')
classifier = ClassificationModel(private=True, name='text_demo')
classifier.fit(training_data)
return classifier
def create_text_classifier():
training_data = ClassificationData(private=True,
data_type='text',
name='text_demo')
training_data.add_samples(
[('The horse got out of the barn.', 'rural'),
('It is very quiet at night', 'rural'),
('There are 300 cattle in the field.', 'rural'),
('The roads are empty', 'rural'),
('There is so much traffic today.', 'urban'),
('Newer buildings are often made of glass.', 'urban'),
('The subways are quite loud.', 'urban'),
('How many skyscrapers do you see?', 'urban')],
input_type='text')
classifier = ClassificationModel(private=True, name='text_demo')
classifier.fit(training_data)
return classifier
def __init__(self, api_key=None, classifier=None):
ImageExtractor.__init__(self)
api_key = get_api_key() if api_key is None else api_key
if api_key is None:
raise ValueError("A valid MetaMind API key must be passed the "
"first time a MetaMind extractor is initialized.")
set_api_key(api_key, verbose=False)
# TODO: Can add a lookup dictionary somewhere that has name --> ID
# translation for commonly used classifiers.
if classifier is None:
self.classifier = general_image_classifier
else:
self.classifier = ClassificationModel(id=classifier)
def main():
# MetaMind makes it simple to create custom classifiers for both text and images
# Create and use a custom image classifier
# This classifier classifies an image as 'food' or 'animal'
image_classifier = create_image_classifier()
print 'Custom image classifier predictions:'
pprint.pprint(
image_classifier.predict([blueberry_pie_url, deer_url],
input_type='urls'))
# Create and use a custom text classifier
# This classifier classifies text as 'rural' or 'urban'
text_classifier = create_text_classifier()
print 'Custom text classifier predictions:'
pprint.pprint(
text_classifier.predict(
['We sheared the sheep yesterday.', 'The traffic is loud.'],
input_type='text'))
# Use builtin general image classifier
print 'MetaMind builtin general image classifier predictions:'
pprint.pprint(
general_image_classifier.predict([apple_pie_url, zebra_url],
input_type='urls'))
# Use builtin food image classifier
print 'MetaMind builtin food image classifier predictions:'
pprint.pprint(
food_image_classifier.predict([apple_pie_url, salad_url],
input_type='urls'))
# Use builtin twitter sentiment classifier
# This classifier finds tweets by a given key word, and classifies each tweet as
# 'positive', 'negative' or 'neutral'
print 'MetaMind builtin twitter sentiment classifier:'
pprint.pprint(twitter_text_classifier.query_and_predict('trump')[:3])
# You can create a representation of a given classifier by passing its id into the constructor.
# You can explore additional public classifiers here: https://www.metamind.io/vision/explore
# You can explore your private classifiers and data here: https://www.metamind.io/my_stuff
# You can find more details about the classifier used below here: https://www.metamind.io/classifiers/155
print 'Public sentiment classifier with id=155:'
pprint.pprint(
ClassificationModel(id=155).predict(
"This is such a great, wonderful sentiment", input_type="text"))
class MetamindFeaturesExtractor(ImageExtractor):
''' Uses the MetaMind API to extract features with an existing classifier.
Args:
api_key (str): A valid key for the MetaMind API. Only needs to be
passed the first time a MetaMindExtractor is initialized.
classifier (str, int): The name or ID of the MetaMind classifier to
use. If None or 'general', defaults to the general image
classifier. Otherwise, must be an integer ID for the desired
classifier.
'''
def __init__(self, api_key=None, classifier=None):
ImageExtractor.__init__(self)
api_key = get_api_key() if api_key is None else api_key
if api_key is None:
raise ValueError("A valid MetaMind API key must be passed the "
"first time a MetaMind extractor is initialized.")
set_api_key(api_key, verbose=False)
# TODO: Can add a lookup dictionary somewhere that has name --> ID
# translation for commonly used classifiers.
if classifier is None:
self.classifier = general_image_classifier
else:
self.classifier = ClassificationModel(id=classifier)
def apply(self, img):
data = img.data
temp_file = tempfile.mktemp() + '.png'
cv2.imwrite(temp_file, data)
labels = self.classifier.predict(temp_file, input_type='files')
os.remove(temp_file)
time.sleep(1.0) # Prevents server error somewhat
return Value(img, self, {'labels': labels})
from metamind.api import set_api_key, twitter_text_classifier, ClassificationData, ClassificationModel, set_api_key
import os, re
#Set the metamind API key for my account
set_api_key("IpdP8N0nsPmYstaqwqL1CWpPWfxxETCj5BzQWa7ANN6ChZ9PYS")
#Load the classifier we trained on the 3 class Rotten Tomatoes movie reviews
classifier = ClassificationModel(id=25412)
#Define the file paths to our data
tsv_tweets = "datasets/tweets/"
tweets_out = "datasets/tweets_out/"
tweet_types = ["recent"]
#Loop through each folder of good, bad and recent tweets and run
#the classifier on it
for type in tweet_types:
rel_path = tsv_tweets + type + "/"
rel_out_path = tweets_out + type + "/"
#Loop through each movie tweet file
for movie_tweets_file in os.listdir(rel_path):
movie_name = movie_tweets_file[:-4]
print "Predicting... " + rel_path + movie_tweets_file
#Write the output to a new file
f = open(rel_out_path + movie_name + ".json", 'w')
print >>f, classifier.predict(rel_path + movie_tweets_file, input_type="tsv")
def pick_song(predict_list):
mood_counts = {
"sad": 0.0,
"excited": 0.0,
"happy": 0.0,
"motivated": 0.0,
"angry": 0.0,
"energetic": 0.0
}
for input in predict_list["content"]["statuses"]:
class_result = ClassificationModel(id=25073).predict(input["text"],
input_type="text")
jsonres = json.loads(json.dumps(class_result[0]))
mood = jsonres['label'].lower()
mood_counts[mood] += 1.0
moods = max(mood_counts.iteritems(), key=operator.itemgetter(1))
total = sum(mood_counts.values())
proportion = {
key: (mood_counts[key] / float(total))
for key in mood_counts.keys()
}
sad = {
"max_danceability": .3,
"max_tempo": 110.0,
"min_acousticness": .3,
"min_speechiness": .3
}
excited = {
"min_danceability": .3,
"min_tempo": 100.0,
"min_energy": .5,
"max_acousticness": .4
}
happy = {"max_danceability": .5, "max_energy": .6}
motivated = {
"min_danceability": .4,
"min_energy": .5,
"max_acousticness": .4,
"max_speechiness": .5,
"min_tempo": 100.0
}
angry = {"min_energy": .5, "max_acousticness": .3}
energetic = {
"min_energy": .65,
"min_tempo": 110.0,
"max_acousticness": .5,
"max_speechiness": .6
}
sad = {key: (sad[key] * proportion["sad"]) for key in sad.iterkeys()}
sad["proportion"] = proportion["sad"]
excited = {
key: (excited[key] * proportion["excited"])
for key in excited.iterkeys()
}
excited["proportion"] = proportion["excited"]
happy = {
key: (happy[key] * proportion["happy"])
for key in happy.iterkeys()
}
happy["proportion"] = proportion["happy"]
motivated = {
key: (motivated[key] * proportion["motivated"])
for key in motivated.iterkeys()
}
motivated["proportion"] = proportion["motivated"]
angry = {
key: (angry[key] * proportion["angry"])
for key in angry.iterkeys()
}
angry["proportion"] = proportion["angry"]
energetic = {
key: (energetic[key] * proportion["energetic"])
for key in energetic.iterkeys()
}
energetic["proportion"] = proportion["energetic"]
stuff = [sad, excited, happy, motivated, angry, energetic]
newlist = sorted(stuff, key=operator.itemgetter("proportion"))
result = newlist[5]
songs_results = song.search(
artist_min_familiarity=.6,
style="pop",
artist_start_year_after="1999",
max_tempo=result.get("max_tempo", 160),
min_tempo=result.get("min_tempo", 0),
max_danceability=result.get("max_danceability", 1),
min_danceability=result.get("min_danceability", 0),
max_speechiness=result.get("max_speechiness", 1),
min_speechiness=result.get("min_speechiness", 0),
max_energy=result.get("max_energy", 1),
min_energy=result.get("min_energy", 0),
max_acousticness=result.get("max_acousticness", 1),
min_acousticness=result.get("min_acousticness", 0))
oursong = songs_results[
0] # is a slammin screen door, stayin out late, sneakin out your window
print oursong.title + " - " + oursong.artist_name
return oursong
#!/usr/bin/python
from Slicer import Slicer
from metamind.api import ClassificationData, ClassificationModel, set_api_key
import cv2
import sys
if len(sys.argv) < 3:
print ("Usage: Tester.py <key file> <picture>")
exit(1)
with open(sys.argv[1], "r") as apikey:
key = apikey.read()
key = key.rstrip()
set_api_key(key)
classifier = ClassificationModel(id=25011)
print ("-----")
s = Slicer(sys.argv[2], "out.jpg", True)
s.create_slices(150)
i = 0
for slic in s.slics:
cv2.imwrite("CURRENT" + str(i) + ".jpg", slic)
print classifier.predict(["CURRENT" + str(i) + ".jpg"], input_type="files")
i += 1
from metamind.api import set_api_key, twitter_text_classifier, ClassificationData, ClassificationModel, set_api_key
set_api_key("5eqwiKI50ym253djlf84VEgQptIb5odohKFpgS1SSWOdeGDzQ3")
training_data = ClassificationData(private=True, data_type="text", name="RT snippets training data")
training_data.add_samples()
classifier = ClassificationModel(private=True, name="RT movie classifier")
classifier.fit(training_data)
print classifier.predict("This company is the worst and is losing money", input_type="text")
from metamind.api import ClassificationData, ClassificationModel, set_api_key
from story_teller import *
import os
set_api_key(os.environ.get('METAMIND_KEY'))
#print getPostBetweenScores((200,300), 1)
#print getContentWithLabel(1)
training_data = ClassificationData(private=True, data_type='text', name='hn_stories_2labels_800_samples')
#training_data = ClassificationData(id=184417)
labels = ('0To15', '150Plus')
samples = getContentWithLabel(400, labels)
training_data.add_samples(samples, input_type='text')
classifier = ClassificationModel(private=True, name='HN score predictor_2labels')
#classifier = ClassificationModel(id=27906)
classifier.fit(training_data)
randomPost = getRandomPost()
prediction = classifier.predict(randomPost['content'], input_type='text')
print randomPost['score']
print prediction[0]['label'], prediction[0]['confidence']
#print 'prediction of score %d is %s with confidence %f' %(randomPost['score'], prediction['label'], prediction['probability'])
import metamind.api
from metamind.api import set_api_key, ClassificationData, ClassificationModel, food_image_classifier
#import time
import json
from datetime import date
fruit = ClassificationModel(private=True, name='fruits')
def train_data():
set_api_key('bHIqZD7ZJgRDn4oDWwMiSkDdGiuX3YvHKeCdNV1VF2WkQJO5gR')
training_data = ClassificationData(private=True, data_type='image',name='training images')
training_data.add_samples([
('./imgs/banana.jpg','fruits'),('./imgs/blueberries.jpg','fruits'),('./imgs/fruit_collection2.jpg','fruits'),('./imgs/fruit_collection.jpg','fruits'),('./imgs/grapefruit.jpg','fruits'),
('./imgs/grapes.jpg','fruits'),('./imgs/oranges.jpg','fruits'),('./imgs/peaches.jpg','fruits'),('./imgs/pears.jpg','fruits'),('./imgs/strawberries.jpg','fruits'),('./imgs/watermelon.jpg','fruits'),('./imgs/carrots.jpg','vegetables'),('./imgs/lettuce.jpg','vegetables'),('./imgs/radish.jpg','vegetables')], input_type='files')
training_data.add_samples([
('http://punchbowlsocial.com/wp-content/uploads/2015/02/eyg.jpg','eggs'),('http://media.thefowlergroup.com.s3.amazonaws.com/wp-content/uploads/2012/05/copywriting-deli-ham.jpg','meat'),('http://www.tyson.com/~/media/Consumer/Call-Outs/fresh-package.ashx?la=en','meat'),('http://homeguides.sfgate.com/DM-Resize/photos.demandstudios.com/gett/article/83/5/86544602_XS.jpg?w=442&h=442&keep_ratio=1','dairy'),('http://i-store.walmart.ca/images/WMTCNPE/155/016/155016_Large_1.jpeg','dairy'),('http://www.10tv.com/content/graphics/2014/08/29/kraft-singles-american.jpg','dairy')], input_type='urls')
fruit.fit(training_data)
#train_data()
def classify(in_img, ingredients):
train_data()
specific_descript = food_image_classifier.predict(in_img, input_type='files')
print(specific_descript)
general_descript = fruit.predict(in_img, input_type='files')
entry = date(2015, 10, 11)
if(general_descript[0]['label'] == 'meat'):
expiary = date(entry.year, entry.month, entry.day + 5)
elif(general_descript[0]['label'] == 'vegetables'):
expiary = date(entry.year, entry.month, entry.day + 14)
elif(general_descript[0]['label'] == 'fruits'):
for root, directories, files in os.walk(DIR):
for filename in files:
filepath = os.path.join(root, filename)
dataset[filename] = filepath # Add it to the list.
return dataset
data = []
ds = get_dataset(VAL_DIR)
for key in ds.keys():
data.append(ds[key])
# setup metamind
api_key = 'bZQv0loHZItIA6f6Nkw1vZCbyzSgrBb3wGSaQoPnCX0lOo0dAE'
path = '/Users/patrickhop/Desktop/metamind/backups/'
set_api_key(api_key)
cf = ClassificationModel(id=40417)
# split up dataset
data = random.sample(data, 4000)
# inference
pred = cf.predict(data, input_type="files")
probs = map(lambda x: x['probability'], pred)
inferences = map(lambda x: x['label'], pred)
# fill confusion matrix
# get max-gain ims
ims_and_probs = zip(data, probs, inferences)
hards = filter(lambda x: x[1] <= .6 and x[1] >= .4, ims_and_probs)
print ''
print hards
from metamind.api import set_api_key, twitter_text_classifier, ClassificationData, ClassificationModel, set_api_key
#Set the metamind API key for my account
set_api_key("IpdP8N0nsPmYstaqwqL1CWpPWfxxETCj5BzQWa7ANN6ChZ9PYS")
#Using the MetaMind API we can look up things
#print twitter_text_classifier.query_and_predict("comcast")
#Create the classification training data to feed into the model
training_data = ClassificationData(private=True, data_type="text", name="RT snippets 3 feature training data")
training_data.add_samples("rt.train-3.tsv", input_type="tsv")
#Train the classifier
classifier = ClassificationModel(private=True, name="RT movie 3-value classifier")
classifier.fit(training_data)
#print classifier.predict("Furious7 was the worst movie I've ever seen. Period.", input_type="text")
from metamind.api import set_api_key, ClassificationData, ClassificationModel
import pymongo
from pymongo import MongoClient
import pickle
import time
### metamind setup
set_api_key("k3U0ZYw5U7BiQWnXYCAJGzKHmSk42VSNUoVebKxPC9jlchnXzk")
training_data = ClassificationData(private=True, data_type='text', name="text snippets")
clf = ClassificationModel(private=True, name='SCF_category_classifier')
### mongodb setup
client = MongoClient()
db = client.nh1
### extract samples
with open('/home/allan/Desktop/new_haven_seeclickfix_visualization/data/wrangling/training_samples.pkl','r') as f: #load the samples...
training_samples = pickle.load(f)
count=0
training_samples_2=[]
for sample in training_samples:
count+=1
training_samples_2.append((sample[0]+' '+sample[1],sample[2]))
training_data.add_samples(training_samples_2,input_type='text') #add them to the training data.
clf.fit(training_data) # train the classifier...
## put all cleaning operations under a single function
# install metamind api first
# run the command: pip install MetaMindApi --upgrade
try:
from metamind.api import ClassificationData, ClassificationModel, set_api_key
except ImportError:
print "Could not import metamind.api packages"
# api key in your profile, might need to change it to work
set_api_key(
'Authorization: Basic wC5gH0A9hi37QAQA3i5oH045ofG1jNV07FhLQ1iwe5rmIJBtET')
# need classifier id, classifier has to be public
classifier = ClassificationModel(id='YOUR_CLASSIFIER_ID')
# change urls to image urls for ingredients we trained for
print classifier.predict([
'http://www.grubdaily.com/wp-content/uploads/2011/01/IMG_4514-copy.jpg',
'http://static.chefkoch-cdn.de/ck.de/rezepte/1/1642/103048-960x720-spaghetti-carbonara.jpg'
],
input_type='urls')
import json
import urllib2
from application_only_auth import Client
from metamind.api import ClassificationData, ClassificationModel, set_api_key
#twitter
API_KEY = "H5lXt22xepXuRUq2Y9zIFWyTk"
API_SECRET = "aj4PVeRsmRJjvqYPDO2Jk57qeeFkofWA4n3JQisEiCQQtD78JP"
client = Client(API_KEY, API_SECRET)
#meta mind
set_api_key("dEt77byHr0OuQpqmBbn6HycesPndJ77wMpwUKXudDznYZbf70e")
classifier = ClassificationModel(id=88)
# to be filled with all matched/possible statuses
master = {}
sentiments = ["sad", "depressed", "upset", "heartbroken"]
quants = ["", "really ", "very ", "extremely "]
me = ["I am ", "I'm ", "I'm feeling "]
sad_boys = []
for s in sentiments:
for q in quants:
for m in me:
sad_boys.append(m + q + s)
i = 0
j = 0
for s in sad_boys:
print "query#: " + str(i) + " Using: " + s
query = urllib2.quote(s).encode("utf8")