def generator():
ll_data_2g = utils.gongcan_to_ll()
train_data = utils.ll_to_grid(ll_data_2g)
# 将空余的信号强度，用0补填补
train_data = train_data.fillna(0)
train_data.to_csv("X.csv")
# 接下来是针对 cnn 进行的预处理
train_scaled = DataFrame()
# 归一化
labels = [
'RNCID_', 'CellID_', 'AsuLevel_', 'SignalLevel_', 'RSSI_', 'Latitude_',
'Longitude_'
]
for label in labels:
tmp = DataFrame()
for i in range(1, 8):
tmp = pd.concat([tmp, train_data[label + str(i)]], axis=1)
tmp_index = tmp.columns.tolist()
tmp = tmp.as_matrix()
# tmp_scaled = scale(tmp)
min_max_scaler = MinMaxScaler(copy=True, feature_range=(0, 1))
tmp_scaled = min_max_scaler.fit_transform(tmp)
tmp_scaled = DataFrame(tmp_scaled, columns=tmp_index)
train_scaled = pd.concat([train_scaled, tmp_scaled], axis=1)
# train_scaled.to_csv('X_scaled.csv')
train_scaled = pd.concat([
train_scaled,
train_data[['IMSI', 'MRTime', 'Longitude', 'Latitude', 'grid_num']]
],
axis=1)
X_ = []
y_ = []
for index, row in train_scaled.iterrows():
y_.append(row['grid_num'])
x_ = []
for i in range(1, 8):
tmp = []
for label in labels:
tmp.append(row[label + str(i)])
x_.append(tmp)
X_.append(x_)
# X 是生成好的 7*7 数组，作为 feature
# y_ 是生成好的 label
X = np.array(X_)
y_ = np.array(y_)
# 对生成好的 label 做 onehot 编码
y = np.zeros(shape=(y_.shape[0], 13 * 17))
# print(y.shape)
for i in range(y_.shape[0]):
y[i][int(y_[i])] = 1
return X, y
# test_index_label_sampled_np = test_index_label_sampled.as_matrix()
test_index_sampled = test_index_label_sampled['id']
test_index_sampled_np = test_index_sampled.as_matrix()
# test_label_sampled = test_index_label_sampled['type']
# test_label_sampled_np = test_label_sampled.as_matrix()
count1 = 0
predict_data_pd = pd.DataFrame(np.zeros((test_index_sampled_np.shape[0], 2)),
columns=['id', 'type'])
for test_index, test_filename_str in enumerate(test_index_sampled_np):
test_filename = test_data_path + str(test_filename_str) + '.txt'
test_file = open(test_filename)
data_test = test_file.read()
data_test = data_test.split(',')
data_test = DataFrame(data_test).T
data_test_np = data_test.as_matrix()
print(data_test_np)
# y_predict = bgc.predict(data_test)
y_predict = bst.predict(data_test_np)
y_predict = y_predict.tolist()
y_predict = y_predict[0].index(max(y_predict[0]))
print(y_predict)
predict_data_pd.iloc[count1, 0] = test_filename_str
predict_data_pd.iloc[count1, 1] = y_predict
print(y_predict)
# test_data_pd.iloc[count1, 0:2600] = data_test.iloc[0,:]
# test_data_pd.iloc[count1, 2600] = test_filename_str
count1 = count1 + 1
print(count1)
print(predict_data_pd.head())
map_result = {0: 'star', 1: 'unknown', 2: 'galaxy', 3: 'qso'}
# test_index_label_sampled = test_index_label.sample(frac = 0.01, replace = False, axis = 0)
test_index_label_sampled = test_index_label
# test_index_label_sampled_np = test_index_label_sampled.as_matrix()
test_index_sampled = test_index_label_sampled['id']
test_index_sampled_np = test_index_sampled.as_matrix()
# test_label_sampled = test_index_label_sampled['type']
# test_label_sampled_np = test_label_sampled.as_matrix()
count1 = 0
predict_data_pd = pd.DataFrame(np.zeros((test_index_sampled_np.shape[0], 2)), columns = ['id','type'])
for test_index, test_filename_str in enumerate(test_index_sampled_np):
test_filename = test_data_path + str(test_filename_str) + '.txt'
test_file = open(test_filename)
data_test = test_file.read()
data_test = data_test.split(',')
data_test = DataFrame(data_test).T
data_test_np = data_test.as_matrix()
# y_predict = bgc.predict(data_test)
y_predict = xgb1.predict(data_test_np)
predict_data_pd.iloc[count1,0] = test_filename_str
predict_data_pd.iloc[count1,1] = y_predict
print(y_predict)
# test_data_pd.iloc[count1, 0:2600] = data_test.iloc[0,:]
# test_data_pd.iloc[count1, 2600] = test_filename_str
count1 = count1 + 1
print(count1)
print(predict_data_pd.head())
map_result = {0:'star', 1:'unknown', 2:'galaxy',3:'qso'}
predict_data_pd['type'] = predict_data_pd['type'].map(map_result)
predict_data_pd.to_csv('G:/Python/Tianwen/predict_data_0.16_XGBoost.csv')
