def main():
# Title
st.title("MLAI: An Integrated Software platform for AI Automation")
# Sidebar
activities = [
"Home", "Dataset Explorer", "ML Classifiers", "ML Regression",
"Text Summarizer"
]
choice = st.sidebar.selectbox("Choose Activity", activities)
if choice == "Home":
st.header(
'Empowering companies to jumpstart AI and generate real-world value'
)
st.subheader(
'Use exponential technologies to your advantage and lead your industry with confidence through innovation.'
)
image = Image.open('images/img0.jpg')
st.image(image, use_column_width=True, caption='Data Mining')
if choice == "Dataset Explorer":
st.subheader("Dataset Explorer")
dataset_analysis.main()
if choice == "ML Classifiers":
classification.main()
if choice == "ML Regression":
regresion.main()
if choice == "Text Summarizer":
text_summ.main()
def main():
import classification
from util import reLu, reLu_derivative
prefix = 'projekt1-oddanie/clasification/data'
modes = ['circles']
quantities = [500, 1000]
create_nn = [[1000]]
# (sigmoid, sigmoid_derivative, 'sigmoid'),
activation = [(reLu, reLu_derivative, 'reLu')]
for mode in modes:
for quantity in quantities:
def get_filename(t):
return prefix + '.' + mode + '.' + t + '.' + str(
quantity) + '.csv'
train_filename = get_filename('train')
test_filename = get_filename('test')
n_epochs = 10000
for nn in create_nn:
for ff in activation:
activation_f, activation_f_derivative, name_f = ff
save_nn = name_f + '1-oddanie/classification.' + mode + '.' + str(
quantity) + str(nn)
print(save_nn)
# activation_f, activation_f_derivative = reLu, reLu_derivative
classification.main(train_filename, test_filename, nn,
save_nn, None, n_epochs, n_epochs / 10,
0.001, True, activation_f,
activation_f_derivative)
def test_classification(model_name: str, batch_size: int):
testargs = f"""
classification.py
{model_name}
--batch_size {batch_size}
""".split()
with patch.object(sys, "argv", testargs):
classification.main()
def predict_classify():
collection = request.get_json()['collection']
features = request.get_json()['features']
target = request.get_json()['target']
inputType = request.get_json()['inputType']
ml_result = classification.main(collection, features, target, inputType)
return jsonify({
"Linear_SVM": ml_result['Linear_SVM'].tolist(),
"RandomForest": ml_result['RandomForest'].tolist(),
"DecisionTree": ml_result['DecisionTree'].tolist(),
"Adaptive_GB": ml_result['Adaptive_GB'].tolist(),
"files": ml_result.index.tolist()
})
def main():
# Title
st.title("AlphaAI")
# Sidebar
activities = [
"Home", "Dataset Explorer", "ML Classifiers", "ML Regression",
"News Classification", "Text Summarizer",
"Real World Data Distribution", "Vision API"
]
choice = st.sidebar.selectbox("Choose Activity", activities)
if choice == "Home":
st.header(
'Empowering companies to jumpstart AI and generate real-world value'
)
st.subheader(
'Use exponential technologies to your advantage and lead your industry with confidence through innovation.'
)
image = Image.open('images/img0.jpg')
st.image(image, use_column_width=True, caption='Data Mining')
if choice == "Dataset Explorer":
st.subheader("Dataset Explorer")
dataset_analysis.main()
if choice == "Real World Data Distribution":
geo_climate.main()
if choice == "ML Regression":
regression.main()
if choice == "ML Classifiers":
classification.main()
if choice == "Vision API":
vision_api.main()
if choice == "Text Summarizer":
text_summ.main()
if choice == "News Classification":
newsclass.main()
print('[-] Zero values detected!')
print('Number of missing values in original dataset: ' +
str(read_input.isnull().sum().sum()))
print('[+] Creating dataset with predicted missing values:')
predict_missing(read_input)
print('[+] Missing values predicted')
read_input.dropna(inplace=True)
#Anzahl der Datensätze und Merkmale zählen
print('Checking instances and dimensionality:')
instances = read_input.shape[0]
dimensions = read_input.shape[1]
print('[+] number of instances: ' + str(instances))
print('[+] number of dimensions: ' + str(dimensions))
#Kodieren kategorieller Daten
le = LabelEncoder()
for elem in read_input.columns:
read_input[elem] = le.fit_transform(read_input[elem])
#Schreiben der bearbeiteten Daten in CSV
read_input.to_csv('mushrooms_encoded.csv', ',', encoding='utf-8')
print('[+] Removed rows with missing values from original dataset')
#Verteilung der Klassen errechnen
print('Checking distribution of classes')
print(read_input['class'].value_counts(True))
print('[+] Data transformation completed succesfully\n')
if __name__ == '__main__':
main()
feature_selection.main()
classification.main()
#!/usr/bin/env python
if __name__ == '__main__':
# Add src to $PYTHONPATH:
from os.path import dirname, abspath
from sys import path
path.append(dirname(abspath(__file__)) + '/src/')
# run classification pipeline
from classification import main
main()
def main():
import argparse
parser = argparse.ArgumentParser(description='Neural Network framework.')
parser.add_argument(
'action',
choices=['regression', 'classification'],
help='Choose mode either \'regression\' or \'classification\'.')
parser.add_argument(
'activation',
choices=['sigmoid', 'relu', 'tanh'],
help='Choose mode either \'sigmoid\' or \'relu\' or \'tanh\'.')
parser.add_argument('--train_filename',
type=str,
help='Name of a file containing training data',
required=False)
parser.add_argument('--test_filename',
type=str,
help='Name of a file containing testing data')
parser.add_argument(
'--create_nn',
nargs='*',
type=int,
help=
'When creating a nn from scratch; number of neurons for each layer',
required=False)
parser.add_argument('--save_nn',
type=str,
help='Name of a file to save trained model to.')
parser.add_argument('--savefig_filename',
type=str,
help='Name of a file to save plot to.')
parser.add_argument('-e',
'--number_of_epochs',
type=int,
help='Number of epochs (iterations) for the NN to run',
required=False,
default=10000)
parser.add_argument('--read_nn',
type=str,
help='When reading existing nn from a file; filename')
parser.add_argument(
'-v',
'--visualize_every',
type=int,
help='How ofter (every n iterations) print neuron\'s weights.',
required=False)
parser.add_argument('--l_rate',
type=float,
help='Learning rate',
required=False,
default=0.001)
parser.add_argument('--seed',
type=int,
help='Random seed int',
required=False,
default=1)
parser.add_argument('--biases', dest='biases', action='store_true')
parser.add_argument('--no_biases', dest='biases', action='store_false')
parser.set_defaults(biases=True)
args = parser.parse_args()
# Seed the random number generator
random.seed(args.seed)
if args.create_nn is None and args.read_nn is None:
print('Either \'--create_nn\' or \'--read_nn\' has to be provided.')
exit(1)
if args.train_filename is None and args.save_nn is not None:
print(
'\'--save_nn\' cannot be provided when \'--train_filename\' is not provided.'
)
exit(1)
if args.train_filename is None and args.create_nn is not None:
print(
'\'--create_nn\' cannot be provided when \'--train_filename\' is not provided.'
)
exit(1)
if args.activation == 'sigmoid':
from util import sigmoid, sigmoid_derivative
activation_f, activation_f_derivative = sigmoid, sigmoid_derivative
elif args.activation == 'relu':
from util import reLu, reLu_derivative
activation_f, activation_f_derivative = reLu, reLu_derivative
elif args.activation == 'tanh':
from util import tanh, tanh_derivative
activation_f, activation_f_derivative = tanh, tanh_derivative
else:
print(
'Sorry, second positional argument has to be either \'sigmoid\' or \'relu\' or \'tanh\'.'
)
exit(1)
if args.action == 'regression':
import regression
regression.main(args.train_filename, args.test_filename,
args.create_nn, args.save_nn, args.read_nn,
args.number_of_epochs, args.visualize_every,
args.l_rate, args.savefig_filename, activation_f,
activation_f_derivative)
elif args.action == 'classification':
import classification
classification.main(args.train_filename, args.test_filename,
args.create_nn, args.save_nn, args.read_nn,
args.number_of_epochs, args.visualize_every,
args.l_rate, args.biases, activation_f,
activation_f_derivative)
else:
print(
'Sorry, first positional argument has to be either \'regression\' or \'classification\'.'
)
exit(1)
def main():
part1.main() # Problem 1 and Problem 3.1
classification.main() # Problem 2
part3.main() # Problem 3.2
