import sys
from data_loader import load_data
from sklearn import preprocessing
from models import LogisticRegressionModel, SVMClassifier, BoostingTree, MixModel
from evaluation import plot_confusion_matrix
import argparse
import pandas as pd
import numpy as np
import pdb
import matplotlib.pyplot as plt

parser = argparse.ArgumentParser()

parser.add_argument(
    '--model',
    type=str,
    choices=['mix', 'linear', 'lr', 'svm', 'bt', 'cnn', 'ft'],
    help='data file surfix')

parser.add_argument(
    '--train_file', type=str, help='training data')
parser.add_argument(
    '--valid_file', type=str, help='valid data')
parser.add_argument(
    '--test_file', type=str, help='testing data')

args = parser.parse_args()

models = {
    "lr": LogisticRegressionModel(),
    "svm": SVMClassifier(),
    "bt": BoostingTree(),
    "mix": MixModel(),
}


def multiclass_roc_auc_score(y_test, y_pred, average="macro"):
    lb = LabelBinarizer()
    lb.fit(y_test)
    y_test = lb.transform(y_test)
    y_pred = lb.transform(y_pred)
    return roc_auc_score(y_test, y_pred, average=average)

if __name__ == '__main__':

    # try feature selection
    #col_sel = ['Board', 'Developer', 'In-app Purchases', 'Size', 'Subtitle', 'Update_Gap', 'User Rating Count']
    #X_train, y_train = load_data(pd.read_csv(args.train_file), col_sel)
    #X_test, y_test = load_data(pd.read_csv(args.test_file), col_sel)

    X_train, y_train = load_data(pd.read_csv(args.train_file))
    X_test, y_test = load_data(pd.read_csv(args.test_file))
    #X_test, y_test = load_data(pd.read_csv(args.train_file))

    le = preprocessing.LabelEncoder()
    le.fit(y_train)
    y_train = le.transform(y_train)
    y_test = le.transform(y_test)

    model = models[args.model]
    model.fit(X_train, y_train)
    y_pred, y_pred_prob, y_pred_prob_vec = model.predict(X_test)

    from sklearn.metrics import roc_auc_score
    print('auroc:{0:.3f}'.format(roc_auc_score(y_test, y_pred_prob_vec[:,1])))
    #plot_confusion_matrix(y_test, y_pred_prob_vec, len(le.classes_), args.model + '.auc.png');




7044466807e6080e6fc1484bc514987a8eecb551
<mask token>


def multiclass_roc_auc_score(y_test, y_pred, average='macro'):
    lb = LabelBinarizer()
    lb.fit(y_test)
    y_test = lb.transform(y_test)
    y_pred = lb.transform(y_pred)
    return roc_auc_score(y_test, y_pred, average=average)


<mask token>

<mask token>
parser.add_argument('--model', type=str, choices=['mix', 'linear', 'lr',
    'svm', 'bt', 'cnn', 'ft'], help='data file surfix')
parser.add_argument('--train_file', type=str, help='training data')
parser.add_argument('--valid_file', type=str, help='valid data')
parser.add_argument('--test_file', type=str, help='testing data')
<mask token>


def multiclass_roc_auc_score(y_test, y_pred, average='macro'):
    lb = LabelBinarizer()
    lb.fit(y_test)
    y_test = lb.transform(y_test)
    y_pred = lb.transform(y_pred)
    return roc_auc_score(y_test, y_pred, average=average)


if __name__ == '__main__':
    X_train, y_train = load_data(pd.read_csv(args.train_file))
    X_test, y_test = load_data(pd.read_csv(args.test_file))
    le = preprocessing.LabelEncoder()
    le.fit(y_train)
    y_train = le.transform(y_train)
    y_test = le.transform(y_test)
    model = models[args.model]
    model.fit(X_train, y_train)
    y_pred, y_pred_prob, y_pred_prob_vec = model.predict(X_test)
    from sklearn.metrics import roc_auc_score
    print('auroc:{0:.3f}'.format(roc_auc_score(y_test, y_pred_prob_vec[:, 1])))

<mask token>
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, choices=['mix', 'linear', 'lr',
    'svm', 'bt', 'cnn', 'ft'], help='data file surfix')
parser.add_argument('--train_file', type=str, help='training data')
parser.add_argument('--valid_file', type=str, help='valid data')
parser.add_argument('--test_file', type=str, help='testing data')
args = parser.parse_args()
models = {'lr': LogisticRegressionModel(), 'svm': SVMClassifier(), 'bt':
    BoostingTree(), 'mix': MixModel()}


def multiclass_roc_auc_score(y_test, y_pred, average='macro'):
    lb = LabelBinarizer()
    lb.fit(y_test)
    y_test = lb.transform(y_test)
    y_pred = lb.transform(y_pred)
    return roc_auc_score(y_test, y_pred, average=average)


if __name__ == '__main__':
    X_train, y_train = load_data(pd.read_csv(args.train_file))
    X_test, y_test = load_data(pd.read_csv(args.test_file))
    le = preprocessing.LabelEncoder()
    le.fit(y_train)
    y_train = le.transform(y_train)
    y_test = le.transform(y_test)
    model = models[args.model]
    model.fit(X_train, y_train)
    y_pred, y_pred_prob, y_pred_prob_vec = model.predict(X_test)
    from sklearn.metrics import roc_auc_score
    print('auroc:{0:.3f}'.format(roc_auc_score(y_test, y_pred_prob_vec[:, 1])))

import sys
from data_loader import load_data
from sklearn import preprocessing
from models import LogisticRegressionModel, SVMClassifier, BoostingTree, MixModel
from evaluation import plot_confusion_matrix
import argparse
import pandas as pd
import numpy as np
import pdb
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, choices=['mix', 'linear', 'lr',
    'svm', 'bt', 'cnn', 'ft'], help='data file surfix')
parser.add_argument('--train_file', type=str, help='training data')
parser.add_argument('--valid_file', type=str, help='valid data')
parser.add_argument('--test_file', type=str, help='testing data')
args = parser.parse_args()
models = {'lr': LogisticRegressionModel(), 'svm': SVMClassifier(), 'bt':
    BoostingTree(), 'mix': MixModel()}


def multiclass_roc_auc_score(y_test, y_pred, average='macro'):
    lb = LabelBinarizer()
    lb.fit(y_test)
    y_test = lb.transform(y_test)
    y_pred = lb.transform(y_pred)
    return roc_auc_score(y_test, y_pred, average=average)


if __name__ == '__main__':
    X_train, y_train = load_data(pd.read_csv(args.train_file))
    X_test, y_test = load_data(pd.read_csv(args.test_file))
    le = preprocessing.LabelEncoder()
    le.fit(y_train)
    y_train = le.transform(y_train)
    y_test = le.transform(y_test)
    model = models[args.model]
    model.fit(X_train, y_train)
    y_pred, y_pred_prob, y_pred_prob_vec = model.predict(X_test)
    from sklearn.metrics import roc_auc_score
    print('auroc:{0:.3f}'.format(roc_auc_score(y_test, y_pred_prob_vec[:, 1])))

import sys
from data_loader import load_data
from sklearn import preprocessing
from models import LogisticRegressionModel, SVMClassifier, BoostingTree, MixModel
from evaluation import plot_confusion_matrix
import argparse
import pandas as pd
import numpy as np
import pdb
import matplotlib.pyplot as plt

parser = argparse.ArgumentParser()

parser.add_argument(
    '--model',
    type=str,
    choices=['mix', 'linear', 'lr', 'svm', 'bt', 'cnn', 'ft'],
    help='data file surfix')

parser.add_argument(
    '--train_file', type=str, help='training data')
parser.add_argument(
    '--valid_file', type=str, help='valid data')
parser.add_argument(
    '--test_file', type=str, help='testing data')

args = parser.parse_args()

models = {
    "lr": LogisticRegressionModel(),
    "svm": SVMClassifier(),
    "bt": BoostingTree(),
    "mix": MixModel(),
}


def multiclass_roc_auc_score(y_test, y_pred, average="macro"):
    lb = LabelBinarizer()
    lb.fit(y_test)
    y_test = lb.transform(y_test)
    y_pred = lb.transform(y_pred)
    return roc_auc_score(y_test, y_pred, average=average)

if __name__ == '__main__':

    # try feature selection
    #col_sel = ['Board', 'Developer', 'In-app Purchases', 'Size', 'Subtitle', 'Update_Gap', 'User Rating Count']
    #X_train, y_train = load_data(pd.read_csv(args.train_file), col_sel)
    #X_test, y_test = load_data(pd.read_csv(args.test_file), col_sel)

    X_train, y_train = load_data(pd.read_csv(args.train_file))
    X_test, y_test = load_data(pd.read_csv(args.test_file))
    #X_test, y_test = load_data(pd.read_csv(args.train_file))

    le = preprocessing.LabelEncoder()
    le.fit(y_train)
    y_train = le.transform(y_train)
    y_test = le.transform(y_test)

    model = models[args.model]
    model.fit(X_train, y_train)
    y_pred, y_pred_prob, y_pred_prob_vec = model.predict(X_test)

    from sklearn.metrics import roc_auc_score
    print('auroc:{0:.3f}'.format(roc_auc_score(y_test, y_pred_prob_vec[:,1])))
    #plot_confusion_matrix(y_test, y_pred_prob_vec, len(le.classes_), args.model + '.auc.png');



