help="Multiple directories from other predictors",
    required=True)

parser.add_argument(
    "--test-data-input-sep",
    default="\s+",
    help="Separator to use for loading test data CSV/TSV files",
    type=str)


parser.add_argument(
    "--test-data-output-dir",
    help="Save combined test datasets to this directory",
    required=True)

if __name__ == "__main__":
    args = parser.parse_args()
    dataframes, predictor_names = load_test_data(
        args.test_data_input_dirs,
        sep=args.test_data_input_sep)
    if not exists(args.test_data_output_dir):
        makedirs(args.test_data_output_dir)

    print("Loaded test data:")
    for (allele, df) in dataframes.items():
        df.index.name = "sequence"
        print("%s: %d results" % (allele, len(df)))
        filename = "%s.csv" % allele
        filepath = join(args.test_data_output_dir, filename)
        df.to_csv(filepath)
model1.load_weights('./model_snapshots/X0001/weights-improvement-44-0.8930.h5')
# model1.load_weights('./model_snapshots/X0002/weights-improvement-49-0.8977.h5')
# model1.load_weights('./model_snapshots/X0003/weights-improvement-49-0.8874.h5')
# model1.load_weights('./model_snapshots/X0004/weights-improvement-48-0.9002.h5')
# model1.load_weights('./model_snapshots/X0005/weights-improvement-42-0.8846.h5')

# 1,3,4,5
intermediate_model1 = Model(
    inputs=model1.input,
    outputs=[model1.get_layer('global_average_pooling2d_1').output])
# # 2
# intermediate_model1 = Model(inputs=model1.input, outputs=[model1.get_layer('global_average_pooling2d_17').output])

from test_data import load_test_data

img_paths, test_data = load_test_data(input_size, preprocess_input)

from svm_data import load_test_data_train

train_data, train_label = load_test_data_train(input_size, preprocess_input)

print(
    '###################  intermediate_model train_going  ##############################'
)
from keras.preprocessing.image import ImageDataGenerator

test_datagen1 = ImageDataGenerator(horizontal_flip=True, )
predictions_1 = []
for i in range(tta_steps):
    preds = intermediate_model1.predict_generator(test_datagen1.flow(
        train_data, batch_size=bs, shuffle=False),
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import load_data as ld
import text_tokenizing as tt
import embedding_layer as emb_layer
import test_data as td

#Some constant values
maxlen = 100  # We will cut reviews after 50 words
max_words = 50000  # We will only consider the top 50,000 words in the dataset


#labeling the data set
texts,labels = ld.data_label()

#sampling the text data
x_train,y_train,x_val,y_val,word_index = tt.tokenize(texts,labels,maxlen,max_words)


clf = RandomForestClassifier(n_estimators=25)
clf.fit(x_train, y_train)
x_test,y_test = td.load_test_data()
clf_probs = clf.predict(x_test)
print(accuracy_score(clf_probs,y_test))
Beispiel #4
0
 def setUp(self):
     self.elements = segmentation.get_elements(test_data.load_test_data(), detailed=False)
Beispiel #5
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def post_test_data():
    """Loads test data"""
    load_test_data()