def main():
params = {
'num_leaves': 256,
'min_child_samples': 79,
'objective': 'binary',
'max_depth': 13,
'learning_rate': 0.03,
"boosting_type": "gbdt",
"subsample_freq": 3,
"subsample": 0.9,
# "bagging_seed": 11,
# "eval_metric": 'auc',
# "verbosity": -1,
'reg_alpha': 0.3,
'reg_lambda': 0.3,
'colsample_bytree': 0.9,
#'categorical_feature': cat_cols
}
# df_train_glo, df_test_glo = read_all_data('small')
df_train_glo, df_test_glo = read_all_data('all')
X, y, X_test = data_preprocess(df_train_glo, df_test_glo)
# ypred = train_lgb_cla(X, y, X_test, params)
# write(ypred, '1006_1')
ypred = train_xgb(X, y, X_test, params)
write(ypred, '1006_2')
def main():
time_now = time.time()
# df_train_glo, df_test_glo = read_all_data('small')
df_train_glo, df_test_glo = read_all_data('all')
global get_dummies_fea
must_delete = delete_null_feature(0.4)
best_loss = 10000000
best_ratio = 0.8
best_mask = 'delete_row'
delete_cols = delete_both_feature(best_ratio) + must_delete
df_train, df_test = data_preprocess(df_train_glo, df_test_glo, delete_cols,
best_mask)
pred = train(df_train, df_test)
write(pred, '1004_2')
for ratio in [0.8, 0.5, 0.3, 0.2]:
# for ratio in []:
delete_cols = delete_both_feature(ratio) + must_delete
for drop_mask in ['fillna_mode', 'delete_row']:
print '\n\ndelete_null_ratio = ', ratio, 'drop_mask = ', drop_mask, '-' * 100
df_train, df_test = data_preprocess(df_train_glo, df_test_glo,
delete_cols, drop_mask)
for model in [xgboost.XGBRegressor()]:
loss, mse = kFold_cross(df_train, model) # 0.97
if best_loss > loss:
best_loss = loss
best_mask = drop_mask
best_ratio = ratio
print time.time() - time_now, '\n\n'
delete_cols = delete_both_feature(best_ratio) + must_delete
df_train, df_test = data_preprocess(df_train_glo, df_test_glo, delete_cols,
best_mask)
pred = train(df_train, df_test)
write(pred, '1004_3')
def plot1():
data_size = sys.argv[1] # all or small
df_train, df_test = read_all_data(data_size)
plt.figure(figsize=(15, 5))
plt.scatter(df_train.TransactionDT, df_train.D15)
plt.title('Original D15')
plt.xlabel('Time')
plt.ylabel('D15')
plt.show()
def main():
data_size = sys.argv[1] # all or small
df_train, df_test = read_all_data(data_size)
print df_train.shape, df_test.shape
row = 3
col = 5
for i in range(1, 16):
plt.subplot(row, col, i)
plt.scatter(df_train['TransactionDT'], df_train['D' + str(i)])
plt.title('D' + str(i))
plt.savefig('./picture/Transaction_D_index.jpg')
plt.show()
def main():
time_now = time.time()
# df_train_glo, df_test_glo = read_all_data('small')
df_train_glo, df_test_glo = read_all_data('all')
global get_dummies_fea
must_delete = delete_null_feature(0.4)
best_ratio = 0.8
best_mask = 'delete_row'
delete_cols = delete_both_feature(best_ratio) + must_delete
df_train, df_test = data_preprocess(df_train_glo, df_test_glo, delete_cols, best_mask)
print df_train.columns.values
print df_test.columns.values
y = df_train['isFraud']
X = df_train.drop(['isFraud', 'TransactionDT'], axis = 1)
X_test = df_test.drop(['TransactionDT'], axis = 1)
y_pred = train_lgb(X, y, X_test)
write(y_pred, '1008_1')
def main():
begin_time = time.time()
df_train, df_test = read_all_data(int(sys.argv[1]))
y_train = df_train['isFraud'].copy()
print df_train.shape, df_test.shape
for i in range(1, 10):
print 'M' + str(i) + '.most_common = ', Counter(
df_train['M1'].tolist()).most_common(10)
df_train['M' +
str(i)] = df_train['M' + str(i)].apply(lambda x: 1 if str(
x) == 'T' else -1 if str(x) == 'F' else 0).astype(np.int)
df_test['M' + str(i)] = df_test['M' + str(i)].apply(lambda x: 1 if str(
x) == 'T' else -1 if str(x) == 'F' else 0).astype(np.int)
print 'M' + str(i) + '.most_common = ', Counter(
df_train['M' + str(i)].tolist()).most_common(10)
df_train, df_test = data_normalize(df_train, df_test)
df_train, df_test = add_datetime_feature(df_train, df_test)
df_train, df_test = encode(df_train, df_test)
cols = remove_cols(df_train)
df_train, df_test = get_uid(df_train, df_test)
df_train, df_test = encode2(df_train, df_test, cols)
for col in ['ProductCD', 'card6', 'P_emaildomain', 'R_emaildomain', 'id_12', 'id_15', 'id_16', 'id_28', 'id_29', 'id_31', 'id_35', \
'id_36', 'id_37', 'id_38', 'DeviceType', 'DeviceInfo']:
le = LabelEncoder()
le.fit(df_train[col].tolist() + df_test[col].tolist())
df_train[col] = le.transform(df_train[col].tolist())
df_test[col] = le.transform(df_test[col].tolist())
print df_train[cols].info()
print df_train[['TransactionAmt']].info()
for col in cols:
if col == 'isFraud': continue
if col not in df_train.columns.values or col not in df_test.columns.values:
print '\nerror', col, '\n'
mean = np.mean(df_train[~df_train[col].isna()][col].tolist() +
df_test[~df_test[col].isna()][col].tolist())
df_train[col] = df_train[col].fillna(mean)
df_test[col] = df_test[col].fillna(mean)
oof, preds = BUILD96(df_train, df_test, y_train, cols)
BUILD96_output(preds)
print 'spend time = ', time.time() - begin_time
# encoding:utf-8 # FileName: main # Author: xiaoyi | 小一 # email: [email protected] # Date: 2020/2/22 21:05 # Description: 分析疫情数据| 拐点来了吗？ import os from plot_data import plot_map, plot_line_chart from preprocess import summary_data from read_data import read_latest_data, read_all_data if __name__ == '__main__': # 读取数据 df_data = read_all_data('province') # 汇总每天的全国成绩 df_result_all = summary_data(df_data, 'all') df_result_excep_HB = summary_data(df_data, 'excep_HB') df_result_HB = summary_data(df_data, 'HB') line_chart_title = [ '累计确诊人数 (by:『知秋小梦』)', '新增确诊人数 (by:『知秋小梦』)', '累计治愈人数 (by:『知秋小梦』)', '累计死亡人数 (by:『知秋小梦』)', '治愈率 (by:『知秋小梦』)', '死亡率 (by:『知秋小梦』)' ] # 绘制折线图 plot_line_chart('全国数据', line_chart_title, df_result_all) plot_line_chart('全国数据（除湖北省）', line_chart_title, df_result_excep_HB) plot_line_chart('湖北省数据', line_chart_title, df_result_HB) # 获取最新日期的疫情数据
def json_data(selectedDate):
df_date = merged[merged["Date"]==str(selectedDate)]
json_data = json.dumps(json.loads(df_date.to_json()))
return df_date.to_json()
def update_date(attr, old, new):
yr = date_slider.value
new_data = json_data(yr)
geosource.geojson = new_data
p.title.text = 'covid 19 deaths, %s' %yr
def update_color(attr, old, new):
color_mapper.high = color_slider.value
merged = read_all_data()
geosource = GeoJSONDataSource(geojson = json_data(str(date(2020, 4,25))))
palette = brewer['YlGnBu'][8]
palette = palette[::-1]
color_cap_deaths = round(np.nanmax(merged["Deaths"]) + 500, -3)
color_mapper = LinearColorMapper(palette = palette, low = 0, high = color_cap_deaths, nan_color = '#d9d9d9')
hover = HoverTool(tooltips = [ ('Country/region','@country'),('deaths', '@Deaths')])
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 900, height = 20, border_line_color=None,location = (0,0), orientation = 'horizontal')
p = figure(title = 'Covid 19 deaths', plot_height = 900 , plot_width = 1600, tools = [BoxZoomTool(), ResetTool(), hover])
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.patches('xs','ys', source = geosource,fill_color = {'field' :'Deaths', 'transform' : color_mapper}, line_color = 'black', line_width = 0.25, fill_alpha = 1)
p.add_layout(color_bar, 'below')
date_slider = DateSlider(title="Date Range: ", start=date(2020, 1, 31), end=date(2020, 4, 25), value=date(2020, 4, 25), step=1)
date_slider.on_change('value', update_date)
def main():
df_train_glo, df_test_glo = read_all_data('small')
# df_train_glo, df_test_glo = read_all_data('all')
X, y, X_test = data_preprocess(df_train_glo, df_test_glo)
ypred = train_xgb(X, y, X_test)
write(ypred, '1006_3')
def main():
df_train_glo, df_test_glo = read_all_data('small')
# df_train_glo, df_test_glo = read_all_data('all')
data_preprocess(df_train_glo, df_test_glo)
import threading
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
import numpy as np
from collections import defaultdict
from read_data import read_all_data
from extract_word import extract_feature
from exture_feature_vec import extract_feature_of_a_seq
all_family_data = read_all_data()
sum_num = 0
for data in all_family_data.values():
sum_num += len(data)
print(time.ctime(), ":", "数据总量：", sum_num)
features = extract_feature(all_family_data)
dict_feature = {}
for feature in features:
if len(feature) in dict_feature.keys():
dict_feature[len(feature)] += 1
else:
dict_feature[len(feature)] = 1
print(sorted(dict_feature.items(), key=lambda d: d[0], reverse=False))
print(features, len(features))
family_names = {}
'TEST_VALIDATION', 'TOP_K'
]
if all_para[2] == 'LCFN': para_name += ['FREQUENCY_USER', 'FREQUENCY_ITEM']
if all_para[2] == 'LightLCFN':
para_name += [
'FREQUENCY_USER', 'FREQUENCY_ITEM', 'FREQUENCY', 'KEEP_PORB',
'SAMPLE_RATE', 'GRAPH_CONV', 'PREDICTION', 'LOSS_FUNCTION',
'GENERALIZATION', 'OPTIMIZATION', 'IF_TRASFORMATION', 'ACTIVATION',
'POOLING'
]
if all_para[2] == 'SGNN': para_name += ['PROP_DIM', 'PROP_EMB', 'IF_NORM']
# if testing the model, we need to read in test set
if tuning_method == 'test': all_para[11] = para[11] = 'Test'
## read data
data = read_all_data(all_para)
para[10] = data[-1]
## tuning the model
os.environ["CUDA_VISIBLE_DEVICES"] = all_para[0]
if tuning_method == 'tuning':
tuning(path_excel_dir, para_name, para, data, lr_coarse, lamda_coarse,
min_num_coarse, max_num_coarse, min_num_fine, max_num_fine)
if tuning_method == 'fine_tuning':
fine_tuning(path_excel_dir, para_name, para, data, lr_fine, lamda_fine,
min_num_fine, max_num_fine)
if tuning_method == 'cross_tuning':
cross_tuning(path_excel_dir, para_name, para, data, lr_fine,
lamda_fine, min_num_fine, max_num_fine)
if tuning_method == 'coarse_tuning':
coarse_tuning(path_excel_dir, para_name, para, data, lr_coarse,