def __init__(self): ''' Constructor ''' ml_alg_base.__init__(self) self.dsr = DatasetReader() self.learning_model = naive_bayes.GaussianNB()
def __init__(self): ''' Constructor ''' self.dsr = DatasetReader() self.fenc = FreemanEncoder() self.training_data = []
def build_binary_classifiers(path_g1_sg2m, path_g1s_g2m): """ Build the stacked neural network with single output neuron for binary classification to G1 vs. S+G2M and G1+S vs. G2M phases, and evaluate its performance :param path_g1_sg2m: Path to the labeled dataset in two labels : G1 and SG2M :param path_g1s_g2m: Path to the labeled dataset in two labels : G1S and G2M :return: Accuracy of classification of each model. """ ############### Ordinal Classifier ################# dr1 = DatasetReader(path_g1_sg2m) dr2 = DatasetReader(path_g1s_g2m) binary_train1 = dr1.load_data() binary_train2 = dr2.load_data() oc1 = OrdinalClassifier(binary_train1[0], binary_train1[1]) oc2 = OrdinalClassifier(binary_train2[0], binary_train2[1]) r1 = oc1.classify() r2 = oc2.classify() return r1, r2
def __init__(self, n_neighbors=1): ''' Constructor ''' self.dsr = DatasetReader() self.fenc = FreemanEncoder() self.data = [] self.knn = KNeighborsClassifier(n_neighbors=n_neighbors, algorithm='auto', metric=self.lev_metric)
def __init__(self): ''' Constructor ''' self.dsr = DatasetReader() self.fenc = FreemanEncoder() states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] symbols = ['0', '1', '2', '3', '4', '5', '6', '7'] self.learning_model = HiddenMarkovModelTrainer(states=states, symbols=symbols) self.model = None
def build_stacked_ae(path): """ Build the stacked auto-encoder neural network, and evaluate its performance :param path: Path to the genetic dataset :return: Accuracy of classification of cell cycle phase. """ ############### Stacked Auto-Encoders ############## dr = DatasetReader(path) train = dr.load_data() ae = StackedAutoencoder(train[0], train[1], train[2], 3) ae.create_autoencoder() result = ae.evaluate_autoencoder() return result[1] * 100 print("Accuracy: %.2f%%" % (result[1] * 100))
def __init__(self, dataset_path, args): self._dataset_path = dataset_path self._documents = DatasetReader(dataset_path, args).read_dataset()
m20Connector.initDB() # Init to read moviesDS = DatasetReader.initWithFraction('datasets/data/movies.csv', 1.0, ',', init=True) gtagsDS = DatasetReader.initWithFraction( 'datasets/data/genome-tags.csv', 1.0, ',', init=True) linksDS = DatasetReader.initWithFraction('datasets/data/links.csv', 1.0, ',', init=True) #Just init ratingsDS = DatasetReader("datasets/data/ratings.csv", init=True) tagsDS = DatasetReader("datasets/data/tags.csv", init=True) gscoresDS = DatasetReader("datasets/data/genome-scores.csv", init=True) if (init_clear == False): for movie in moviesDS.readPercentage(): # print str(movie) m20Connector.insert( M20Movie(movie['movieId'], movie['title'], movie['genres'])) for tag in gtagsDS.readPercentage(): # print str(tag) m20Connector.insert(M20GenomeTag(tag['tagId'], tag['tag'])) for link in linksDS.readPercentage():
from datasets.caltechpedestrian import CaltechPedestrian from datasets.bdd100k import BDD100K from datasets.citypersons import CityPersons from DatasetReader import DatasetReader import logging import os logging.basicConfig(filename='example.log',level=logging.DEBUG) base_dir_bdd100k = '/data/stars/share/STARSDATASETS/bdd100k' for subset in ['train', 'val']: db = BDD100K(name='bdd100k-{}'.format(subset), base_dir=base_dir_bdd100k, save_dir='./bdd100k-{}'.format(subset), subset=subset) db.writedataframe() reader = DatasetReader('./bdd100k-{}'.format(subset)) df = reader.get_annotations(query='category == "person"') reader.plot_annotations(df=df, plot_cols=['xmin', 'ymin', 'xmax', 'ymax', 'category']) base_dir_caltech = '/data/stars/user/uujjwal/datasets/pedestrian/caltech/caltechall-train' db = CaltechPedestrian(name='caltechall-train', base_dir=base_dir_caltech, save_dir='./caltechall-train') db.writedataframe() reader = DatasetReader('./caltechall-train') df = reader.get_annotations(query='object == "person"') reader.plot_annotations(df=df, plot_cols=['xmin_full', 'ymin_full', 'xmax_full', 'ymax_full', 'object']) base_dir_caltech = '/data/stars/user/uujjwal/datasets/pedestrian/caltech/caltechall-test' db = CaltechPedestrian(name='caltechall-test', base_dir=base_dir_caltech, save_dir='./caltechall-test') db.writedataframe() reader = DatasetReader('./caltechall-test')
def __init__(self): self.reader = DatasetReader()
import numpy as np from matplotlib import pyplot as plt from Args import DIM, ROOT, EPOCHS, BATCH_SIZE, NUM_WORKERS, LEARNING_RATE from DatasetReader import DatasetReader from model import UNet import torch.optim as optim from copy import deepcopy from Evaluation import MeanDiceCoefficient if (__name__ == "__main__"): model = UNet().cuda() loss_fn = nn.BCELoss() optimiser = optim.Adam(model.parameters(), lr=LEARNING_RATE) trainset = DatasetReader(ROOT + "train/") testset = deepcopy(trainset) testset.setTrainMode(False) trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS) testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS) for epoch in range(EPOCHS): # Training phase
moviesDS = DatasetReader.initWithFraction(dataset_path + '/movies.csv', 1.0, ',', init=True) gtagsDS = DatasetReader.initWithFraction(dataset_path + '/genome-tags.csv', 1.0, ',', init=True) linksDS = DatasetReader.initWithFraction(dataset_path + '/links.csv', 1.0, ',', init=True) #Just init ratingsDS = DatasetReader(dataset_path + "/ratings.csv", init=True) tagsDS = DatasetReader(dataset_path + "/tags.csv", init=True) gscoresDS = DatasetReader(dataset_path + "/genome-scores.csv", init=True) if (init_clear == False): for movie in moviesDS.readPercentage(): # print str(movie) m20Connector.insert( M20Movie(movie['movieId'], movie['title'], movie['genres'])) for tag in gtagsDS.readPercentage(): # print str(tag) m20Connector.insert(M20GenomeTag(tag['tagId'], tag['tag']))