def submit():
api_key = "acbb79b92da3e408762784310464ec42"
challenge = crowdai.Challenge("crowdAIMappingChallenge", api_key)
result = challenge.submit("predictions.json")
print(result)
import crowdai
import mido
midi_file_path="C:\\Users\\Deeps\\Documents\\School\\MIT807\\Code\\output\\sixtEncoDeco.mid"
API_KEY="<2a428240c13c65e18cea6b8a2f73ef03>"
midifile = mido.MidiFile(midi_file_path)
assert midifile.length >20 - 10 and midifile.length < 3600 + 10
assert len(midifile.tracks) == 1
assert midifile.type == 0
challenge = crowdai.Challenge("AIGeneratedMusicChallenge", API_KEY)
challenge.submit(midi_file_path)
"""
Common pitfall: `challenge.submit` takes the `midi_file_path`
and not the `midifile` object
"""
# Remember to generate the random predictions by running inside the repository
# python generate_random_prediction.py > data/predictions.txt
from __future__ import print_function
import crowdai
import argparse
parser = argparse.ArgumentParser(description='Submit the result to crowdAI')
parser.add_argument('--api_key', dest='api_key', action='store', required=True)
parser.add_argument('--predictions', dest='predictions', action='store', required=True)
args = parser.parse_args()
challenge = crowdai.Challenge("CriteoAdPlacementNIPS2017", args.api_key)
scores = challenge.submit(args.predictions, small_test=True)
"""
NOTE: In case of predictions for the actual test set, please pass `small_Test=False`
scores = challenge.submit(args.predictions, small_test=False)
"""
print(scores)
"""
{
"impwt_std": 0.00064745289554913,
"ips_std": 2.6075584296658,
"snips": 6603.0581686235,
"max_instances": 4027,
"ips": 24.30130041425,
"impwt": 0.0036803099099937,
"message": "",
"snips_std": 17.529346134878,
import crowdai
import argparse
from sklearn.svm import SVR
import numpy as np
parser = argparse.ArgumentParser(description='Submit the result to crowdAI')
parser.add_argument('--api_key', dest='api_key', action='store', required=True)
args = parser.parse_args()
# Create the challenge object by authentication with crowdAI with your API_KEY
challenge = crowdai.Challenge("OpenSNPChallenge2017", args.api_key)
#Load training data
x_train = np.load("data/subset_cm_train.npy")
y_train = np.load("data/train_heights.npy")
x_test = np.load("data/subset_cm_test.npy")
#Replace nan values in the training and testing set with an arbitrary number
inds = np.where(np.isnan(x_train))
x_train[inds] = -100
inds = np.where(np.isnan(x_test))
x_test[inds] = -100
# Instantiate a linear model
clf = SVR(C=1.0, epsilon=0.2)
clf.fit(x_train, y_train)
# Predict the heights for the test set
heights = clf.predict(x_test)
def make_submission(submission_filepath, logger, params):
api_key = params.api_key
challenge = crowdai.Challenge("crowdAIMappingChallenge", api_key)
logger.info('submitting predictions to crowdai')
challenge.submit(submission_filepath)
raise Exception("Unable to find the test files at: "
"`data/crowdai_fma_test/*.mp3`.\n"
"Are you sure you downloaded the test set and "
"placed it at the right location ? ")
for _file in TEST_FILES:
# NOTE: This expects that you have already downloaded the test set
# and it is available inside the data folder.
_track_id = _file.split("/")[-1].replace(".mp3", "")
"""
Generate predictions
"""
predictions = np.random.rand((len(CLASSES)))
predictions = softmax(predictions)
if np.sum(predictions) > 1.1:
print(predictions)
row = {}
row['file_id'] = _track_id
for _idx, _class in enumerate(CLASSES):
row[_class] = predictions[_idx]
writer.writerow(row)
csvfile.close()
challenge = crowdai.Challenge("WWWMusicalGenreRecognitionChallenge", API_KEY)
response = challenge.submit(output_path)
print(response['message'])
from __future__ import print_function
import crowdai
import argparse
import mido
parser = argparse.ArgumentParser(description='Submit the result to crowdAI')
parser.add_argument('--api_key', dest='api_key', action='store', required=True)
parser.add_argument('--midi_file', dest='midi_file', action='store', required=True)
args = parser.parse_args()
midifile = mido.MidiFile(args.midi_file)
assert midifile.length > 3600 - 10 and midifile.length < 3600 + 10
assert len(midifile.tracks) == 1
challenge = crowdai.Challenge("AIGeneratedMusicChallenge", args.api_key)
challenge.submit(args.midi_file)
x = random.randint(0, IMAGE_WIDTH - padding)
y = random.randint(0, IMAGE_HEIGHT - padding)
width = random.randint(0, IMAGE_WIDTH - x)
height = random.randint(0, IMAGE_WIDTH - y)
_result["bbox"] = [x, y, width, height]
_segmentation = []
for k in range(SEGMENTATION_LENGTH * 2):
_segmentation.append(np.random.randint(0, IMAGE_WIDTH))
_segmentation.append(np.random.randint(0, IMAGE_HEIGHT))
_result["segmentation"] = [_segmentation]
results.append(_result)
print("Writing results to : data/result_annotations.json")
with open("data/result_annotations.json", "w") as fp:
fp.write(json.dumps(results))
print("Writing Complete !")
print("Submitting to crowdAI...")
parser = argparse.ArgumentParser(description='Submit the result to crowdAI')
parser.add_argument('--api_key', dest='api_key', action='store', required=True)
args = parser.parse_args()
import crowdai
api_key = args.api_key
challenge = crowdai.Challenge("crowdAIMappingChallenge", api_key)
result = challenge.submit("data/result_annotations.json")
print(result)
import json
import numpy as np
import crowdai
api_key = 'bc2f7cddee3eb0c65f0737ec22bf1c48' #'a96a541f34d8c0d688871eab01ba8057'
challenge = crowdai.Challenge('crowdAIMappingChallenge', api_key)
result = challenge.submit('newpredictions.json')
print(result)
#!/usr/bin/env python
try:
import crowdai
except:
raise Exception("Please install the `crowdai` python client by : pip install crowdai")
import argparse
parser = argparse.ArgumentParser(description='Upload saved docker environments to crowdai for grading')
parser.add_argument('--api_key', dest='api_key', action='store', required=True)
parser.add_argument('--docker_container', dest='docker_container', action='store', required=True)
args = parser.parse_args()
challenge = crowdai.Challenge("Learning2RunChallengeNIPS2017", args.api_key)
result = challenge.submit(args.docker_container)
print(result)
#!/usr/bin/env python
import argparse
import crowdai
desc = 'Submit a prediction to be graded by crowdAI.'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('--api_key',
dest='api_key',
type=str,
required=True,
help='your crowdAI API key')
parser.add_argument('file',
metavar='submission.csv',
type=str,
help='the CSV file to be submitted')
args = parser.parse_args()
ID = 'WWWMusicalGenreRecognitionChallenge'
challenge = crowdai.Challenge(ID, args.api_key)
response = challenge.submit(args.file)
print(response['message'])
def submit_results(self, api_key="8229d44990cdd9496c4ae53cc9308aed"):
challenge = crowdai.Challenge("IEEEInvestmentRankingChallenge", api_key)
result = challenge.submit(os.path.join(self.path_to_results, self.result_file_name), round=2)
print(result)
