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Fix and update run_all_model

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Jactus
2020-12-01 11:54:26 +08:00
parent bb5f3cb33d
commit 77884db3a5
12 changed files with 42 additions and 26 deletions
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1
examples/benchmarks/ALSTM/workflow_config_alstm.yaml
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@@ -54,7 +54,6 @@ task:
batch_size: 800
metric: loss
loss: mse
seed: 0
GPU: 0
rnn_type: GRU
dataset:

1
examples/benchmarks/GATs/workflow_config_gats.yaml
View File

@@ -56,7 +56,6 @@ task:
base_model: LSTM
with_pretrain: True
model_path: "benchmarks/LSTM/model_lstm_csi300.pkl"
seed: 0
GPU: 0
dataset:
class: DatasetH

1
examples/benchmarks/GRU/workflow_config_gru.yaml
View File

@@ -54,7 +54,6 @@ task:
batch_size: 800
metric: loss
loss: mse
seed: 0
GPU: 0
dataset:
class: DatasetH

1
examples/benchmarks/LSTM/workflow_config_lstm.yaml
View File

@@ -54,7 +54,6 @@ task:
batch_size: 800
metric: loss
loss: mse
seed: 0
GPU: 0
dataset:
class: DatasetH

1
examples/benchmarks/SFM/workflow_config_sfm.yaml
View File

@@ -58,7 +58,6 @@ task:
loss: mse
optimizer: adam
GPU: 1
seed: 710
dataset:
class: DatasetH
module_path: qlib.data.dataset

19
examples/run_all_model.py
View File

@@ -152,6 +152,10 @@ def get_all_results(folders) -> dict:
result["annualized_return_with_cost"] = list()
result["information_ratio_with_cost"] = list()
result["max_drawdown_with_cost"] = list()
result["ic"] = list()
result["icir"] = list()
result["rank_ic"] = list()
result["rank_icir"] = list()
for recorder_id in recorders:
if recorders[recorder_id].status == "FINISHED":
recorder = R.get_recorder(recorder_id=recorder_id, experiment_name=fn)
@@ -159,19 +163,27 @@ def get_all_results(folders) -> dict:
result["annualized_return_with_cost"].append(metrics["excess_return_with_cost.annualized_return"])
result["information_ratio_with_cost"].append(metrics["excess_return_with_cost.information_ratio"])
result["max_drawdown_with_cost"].append(metrics["excess_return_with_cost.max_drawdown"])
result["ic"].append(metrics["IC"])
result["icir"].append(metrics["ICIR"])
result["rank_ic"].append(metrics["Rank IC"])
result["rank_icir"].append(metrics["Rank ICIR"])
results[fn] = result
return results
# function to generate and save markdown table
def gen_and_save_md_table(metrics):
table = "| Model Name | Annualized Return | Information Ratio | Max Drawdown |\n"
table += "|---|---|---|---|\n"
table = "| Model Name | Annualized Return | Information Ratio | Max Drawdown | IC | ICIR | Rank IC | Rank ICIR |\n"
table += "|---|---|---|---|---|---|---|---|\n"
for fn in metrics:
ar = metrics[fn]["annualized_return_with_cost"]
ir = metrics[fn]["information_ratio_with_cost"]
md = metrics[fn]["max_drawdown_with_cost"]
table += f"| {fn} | {ar[0]:9.4f}±{ar[1]:9.2f} | {ir[0]:9.4f}±{ir[1]:9.2f}| {md[0]:9.4f}±{md[1]:9.2f} |\n"
ic = metrics[fn]["ic"]
icir = metrics[fn]["icir"]
ric = metrics[fn]["rank_ic"]
ricir = metrics[fn]["rank_icir"]
table += f"| {fn} | {ar[0]:5.4f}±{ar[1]:2.2f} | {ir[0]:5.4f}±{ir[1]:2.2f}| {md[0]:5.4f}±{md[1]:2.2f} | {ic[0]:5.4f}±{ic[1]:2.2f} | {icir[0]:5.4f}±{icir[1]:2.2f}| {ric[0]:5.4f}±{ric[1]:2.2f} | {ricir[0]:5.4f}±{ricir[1]:2.2f} |\n"
pprint(table)
with open("table.md", "w") as f:
f.write(table)
@@ -240,6 +252,7 @@ def run(times=1, models=None, exclude=False):
sys.stderr.write("\n")
# install qlib
sys.stderr.write("Installing qlib...\n")
execute(f"{python_path} -m pip install --upgrade pip") # TODO: FIX ME!
execute(f"{python_path} -m pip install --upgrade cython") # TODO: FIX ME!
if fn == "TFT":
execute(

7
qlib/contrib/model/pytorch_alstm.py
View File

@@ -57,7 +57,7 @@ class ALSTM(Model):
loss="mse",
optimizer="adam",
GPU="0",
seed=0,
seed=None,
**kwargs
):
# Set logger.
@@ -113,8 +113,9 @@ class ALSTM(Model):
)
)
np.random.seed(self.seed)
torch.manual_seed(self.seed)
if self.seed is not None:
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.ALSTM_model = ALSTMModel(
d_feat=self.d_feat,

9
qlib/contrib/model/pytorch_gats.py
View File

@@ -62,7 +62,7 @@ class GATs(Model):
model_path=None,
optimizer="adam",
GPU="0",
seed=0,
seed=None,
**kwargs
):
# Set logger.
@@ -123,8 +123,11 @@ class GATs(Model):
seed,
)
)
np.random.seed(self.seed)
torch.manual_seed(self.seed)
if self.seed is not None:
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.GAT_model = GATModel(
d_feat=self.d_feat,
hidden_size=self.hidden_size,

7
qlib/contrib/model/pytorch_gru.py
View File

@@ -57,7 +57,7 @@ class GRU(Model):
loss="mse",
optimizer="adam",
GPU="0",
seed=0,
seed=None,
**kwargs
):
# Set logger.
@@ -113,8 +113,9 @@ class GRU(Model):
)
)
np.random.seed(self.seed)
torch.manual_seed(self.seed)
if self.seed is not None:
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.gru_model = GRUModel(
d_feat=self.d_feat,

7
qlib/contrib/model/pytorch_lstm.py
View File

@@ -57,7 +57,7 @@ class LSTM(Model):
loss="mse",
optimizer="adam",
GPU="0",
seed=0,
seed=None,
**kwargs
):
# Set logger.
@@ -113,8 +113,9 @@ class LSTM(Model):
)
)
np.random.seed(self.seed)
torch.manual_seed(self.seed)
if self.seed is not None:
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.lstm_model = LSTMModel(
d_feat=self.d_feat,

7
qlib/contrib/model/pytorch_nn.py
View File

@@ -61,7 +61,7 @@ class DNNModelPytorch(Model):
optimizer="gd",
loss="mse",
GPU="0",
seed=0,
seed=None,
**kwargs
):
# Set logger.
@@ -116,8 +116,9 @@ class DNNModelPytorch(Model):
)
)
np.random.seed(self.seed)
torch.manual_seed(self.seed)
if self.seed is not None:
np.random.seed(self.seed)
torch.manual_seed(self.seed)
if loss not in {"mse", "binary"}:
raise NotImplementedError("loss {} is not supported!".format(loss))

7
qlib/contrib/model/pytorch_sfm.py
View File

@@ -217,7 +217,7 @@ class SFM(Model):
loss="mse",
optimizer="gd",
GPU="0",
seed=0,
seed=None,
**kwargs
):
# Set logger.
@@ -282,8 +282,9 @@ class SFM(Model):
)
)
np.random.seed(self.seed)
torch.manual_seed(self.seed)
if self.seed is not None:
np.random.seed(self.seed)
torch.manual_seed(self.seed)
self.sfm_model = SFM_Model(
d_feat=self.d_feat,