def main(): X = data_io.load_train_features() if (type(X) == type(None)): print("No feature file found!") exit(1) y = data_io.read_train_target() #min_max_scaler = preprocessing.MinMaxScaler() #X = min_max_scaler.fit_transform(X) get_top_features(X, y.Target, 20)
def main(): X = data_io.load_train_features() if(type(X) == type(None)): print("No feature file found!") exit(1) y = data_io.read_train_target() #min_max_scaler = preprocessing.MinMaxScaler() #X = min_max_scaler.fit_transform(X) get_top_features(X,y.Target,20)
def grid_search(): X = data_io.load_train_features() if(type(X) == type(None)): print("No feature file found!") exit(1) y = data_io.read_train_target() tree_depth = [5, 7, 9 , 10, 12, 14] learning_rate = [0.01, 0.05, 0.1, 0.2] scorer = Scorer(X,y) for d in tree_depth: for l in learning_rate: r = scorer.score([d,l]) print "Score", d,l,r
def grid_search(): X = data_io.load_train_features() if (type(X) == type(None)): print("No feature file found!") exit(1) y = data_io.read_train_target() tree_depth = [5, 7, 9, 10, 12, 14] learning_rate = [0.01, 0.05, 0.1, 0.2] scorer = Scorer(X, y) for d in tree_depth: for l in learning_rate: r = scorer.score([d, l]) print "Score", d, l, r
def main(): fp.n_threads = int(data_io.get_json()["feature_extraction_threads"]) print("extracting train data set features") X = data_io.load_train_features() if(X is None): extract_train_features() else: print("Feature already extracted!") print("extracting valid data set features") X = data_io.load_valid_features() if(X is None): extract_valid_features() else: print("Feature already extracted!")
def main(): fp.n_threads = int(data_io.get_json()["feature_extraction_threads"]) print("extracting train data set features") X = data_io.load_train_features() if (X is None): extract_train_features() else: print("Feature already extracted!") print("extracting valid data set features") X = data_io.load_valid_features() if (X is None): extract_valid_features() else: print("Feature already extracted!")
def main(): y = data_io.read_train_target() X = data_io.load_train_features() if(type(X) == type(None)): print("No feature file found!") exit(1) X_old = data_io.load_features("./Models/old_csv/features_train_en_python.csv") print X.shape X = X_old.join(X) print X.shape #print X data_io.save_train_features(X,y) X = data_io.load_valid_features() X_old = data_io.load_features("./Models/old_csv/features_valid_en_python.csv") print X.shape X = X_old.join(X) print X.shape data_io.save_valid_features(X)
def main(): y = data_io.read_train_target() X = data_io.load_train_features() if (type(X) == type(None)): print("No feature file found!") exit(1) X_old = data_io.load_features( "./Models/old_csv/features_train_en_python.csv") print X.shape X = X_old.join(X) print X.shape #print X data_io.save_train_features(X, y) X = data_io.load_valid_features() X_old = data_io.load_features( "./Models/old_csv/features_valid_en_python.csv") print X.shape X = X_old.join(X) print X.shape data_io.save_valid_features(X)
def main(valid): X_train = data_io.load_train_features() X_valid = data_io.load_valid_features() t_file = merge("train", X_train) v_file = merge(valid, X_valid)