我有以下数据框：
#create some data with Names column
data = pd.DataFrame({'Names': ['Joe', 'John', 'Jasper', 'Jez'] *4, 'Add': ['Lo', 'Po', 'Fa',
'It']*4,'Ob1' : np.random.rand(16), 'Ob2' : np.random.rand(16)})
我想仅基于“名称”和“添加”的几个组合来运行 hyperopt。话虽如此，我已使用以下代码将此数据帧拆分为一个子集：
# create a dataframe of all combinations
combinations=data[['Names','Add']].drop_duplicates()
comboList=list(zip(combinations['Names'],combinations['Add']))
comboList
[('Joe', 'Lo'), ('John', 'Po'), ('Jasper', 'Fa'), ('Jez', 'It')]
In [14]:
for i,combo in enumerate(comboList):
print(combo)
subset=data[(data['Names']==combo[0]) & (data['Add']==combo[1])]
# run multiple functions on subset here
print(subset.head())
使用上述逻辑，我构建了函数：
from tqdm._tqdm_notebook import tqdm_notebook as tqdm
from sklearn.model_selection import train_test_split
from keras.layers import Dense, Activation, Flatten
from keras import optimizers
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
from sklearn.metrics import roc_auc_score
import sys
def trim_dataset(mat, batch_size):
"""
trims dataset to a size that's divisible by BATCH_SIZE
"""
no_of_rows_drop = mat.shape[0] % batch_size
if no_of_rows_drop > 0:
return mat[:-no_of_rows_drop]
else:
return mat
def build_timeseries(mat, y_col_index, time_steps):
# total number of time-series samples would be len(mat) - TIME_STEPS
dim_0 = mat.shape[0] - time_steps
dim_1 = mat.shape[1]
x = np.zeros((dim_0, time_steps, dim_1))
y = np.zeros((x.shape[0],))
for i in tqdm(range(dim_0)):
x[i] = mat[i:time_steps + i]
y[i] = mat[time_steps + i, y_col_index]
# print("length of time-series i/o {} {}".format(x.shape, y.shape))
return x, y
def data(df,batch_size, time_steps):
global mat
BATCH_SIZE = 10
TIME_STEPS = 1
x_train, x_test = train_test_split(df, train_size=883, shuffle=False)
# scale the train and test dataset
min_max_scaler = MinMaxScaler()
x_train = min_max_scaler.fit_transform(x_train)
x_test = min_max_scaler.transform(x_test)
x_train_ts, y_train_ts = build_timeseries(x_train, 0, TIME_STEPS)
x_test_ts, y_test_ts = build_timeseries(x_test, 0, TIME_STEPS)
x_train_ts = trim_dataset(x_train_ts, BATCH_SIZE)
y_train_ts = trim_dataset(y_train_ts, BATCH_SIZE)
x_test_ts = trim_dataset(x_test_ts, BATCH_SIZE)
y_test_ts = trim_dataset(y_test_ts, BATCH_SIZE)
str(np.isnan(x_test).any())))
return x_train_ts, y_train_ts, x_test_ts, y_test_ts
stime = time.time()
def print_time(text, stime):
seconds = (time.time() - stime)
print(text + " " + str(seconds / 60) + " minutes : " + str(np.round(seconds % 60)) + " seconds")
def get_readable_ctime():
return time.strftime("%d-%m-%Y %H_%M_%S")
从 keras.callbacks 导入回调
class LogMetrics(Callback):
def __init__(self, search_params, param, comb_no):
self.param = param
self.self_params = search_params
self.comb_no = comb_no
def on_epoch_end(self, epoch, logs):
for i, key in enumerate(self.self_params.keys()):
logs[key] = self.param[key]
logs["combination_number"] = self.comb_no
search_space = {
'batch_size': hp.choice('bs', [10]),
'time_steps': hp.choice('ts', [1]),
'lstm1_nodes':hp.choice('units_lsmt1', [100,150,200,250,300]),
'lstm1_dropouts':hp.uniform('dos_lstm1',0,1),
'lstm_layers': hp.choice('num_layers_lstm',[
{
'layers':'one',
},
{
'layers':'two',
'lstm2_nodes':hp.choice('units_lstm2', [50,100,150]),
'lstm2_dropouts':hp.uniform('dos_lstm2',0,1)
}
]),
'dense_layers': hp.choice('num_layers_dense',[
{
'layers':'one'
},
{
'layers':'two',
'dense2_nodes':hp.choice('units_dense', [10,20,30,40])
}
]),
"lr": hp.uniform('lr',0,1),
"epochs": hp.choice('epochs', [50,100,150,200,300,500,1000,2000]),
"optimizer": hp.choice('optmz',["rms"])
}
def create_model_hypopt(params):
print("Trying params:",params)
batch_size = params["batch_size"]
time_steps = params["time_steps"]
start_time = time.clock()
comboList=list(zip(Roaming_Map['roaming_partner_country'],Roaming_Map['opco']))
for i,combo in enumerate(comboList):
print("As above for the country & opco - ",i,combo)
subset=data[(data['Names']==combo[0]) & (data['add']==combo[1])]
subset=subset[["Ob1","Ob2"]]
x_train_ts, y_train_ts, x_test_ts, y_test_ts = data(subset,10, 1)
lstm_model = Sequential()
lstm_model.add(LSTM(params["lstm1_nodes"], batch_input_shape=(batch_size, time_steps, x_train_ts.shape[2]), dropout=params["lstm1_dropouts"],
recurrent_dropout=params["lstm1_dropouts"], stateful=True, return_sequences=True,
kernel_initializer='random_uniform'))
# ,return_sequences=True #LSTM params => dropout=0.2, recurrent_dropout=0.2
if params["lstm_layers"]["layers"] == "two":
lstm_model.add(LSTM(params["lstm_layers"]["lstm2_nodes"], dropout=params["lstm_layers"]["lstm2_dropouts"]))
else:
lstm_model.add(Flatten())
if params["dense_layers"]["layers"] == 'two':
lstm_model.add(Dense(params["dense_layers"]["dense2_nodes"], activation='relu'))
lstm_model.add(Dense(1, activation='sigmoid'))
lr = params["lr"]
epochs = params["epochs"]
if params["optimizer"] == 'rms':
optimizer = optimizers.RMSprop(lr=lr)
else:
optimizer = optimizers.SGD(lr=lr, decay=1e-6, momentum=0.9, nesterov=True)
lstm_model.compile(loss='mean_squared_error', optimizer=optimizer) # binary_crossentropy
history = lstm_model.fit(x_train_ts, y_train_ts, epochs=epochs, verbose=2, batch_size=batch_size,
validation_data=[x_test_ts, y_test_ts],
callbacks=[LogMetrics(search_space, params, -1)])
# for key in history.history.keys():
# print(key, "--",history.history[key])
# get the highest validation accuracy of the training epochs
val_error = np.amin(history.history['val_loss'])
print('Best validation error of epoch number',epochs,'for the combination:',combo, val_error)
return {'loss': val_error, 'status': STATUS_OK, 'model': lstm_model} # if accuracy use '-' sign
trials = Trials()
best = fmin(create_model_hypopt,
space=search_space,
algo=tpe.suggest,
max_evals=10,
trials=trials)
print_time("program completed in", stime)
best_trials = sorted(trials.results, key=lambda x: x['loss'], reverse=False)
best_trials.append(best_trials)
但这适用于 Name & Add 的所有组合，同时具有相同的超参数，然后转到下一组超参数。相反，我希望它为 name 和 add 的一个组合运行所有可能的超参数，然后去下一个组合。