From 8743576f7238003530ae55e78fa50554d8d6ba33 Mon Sep 17 00:00:00 2001 From: bxdd Date: Mon, 29 Mar 2021 20:16:00 +0800 Subject: [PATCH] black format --- examples/highfreq/highfreq_processor.py | 2 +- examples/highfreq/workflow.py | 4 ++-- examples/rolling_process_data/workflow.py | 6 ++++-- qlib/data/dataset/__init__.py | 14 +++++--------- qlib/data/dataset/handler.py | 4 ++-- qlib/data/dataset/loader.py | 1 + qlib/data/dataset/processor.py | 4 +++- 7 files changed, 18 insertions(+), 17 deletions(-) diff --git a/examples/highfreq/highfreq_processor.py b/examples/highfreq/highfreq_processor.py index 6ed68ff38..d843c6ac0 100644 --- a/examples/highfreq/highfreq_processor.py +++ b/examples/highfreq/highfreq_processor.py @@ -70,7 +70,7 @@ class HighFreqNorm(Processor): columns=["FEATURE_%d" % i for i in range(12 * 240)], ).sort_index() return df_new_features - + def config(self, fit_start_time=None, fit_end_time=None, **kwargs): if fit_start_time: self.fit_start_time = fit_start_time diff --git a/examples/highfreq/workflow.py b/examples/highfreq/workflow.py index 97762f182..94c9b689f 100644 --- a/examples/highfreq/workflow.py +++ b/examples/highfreq/workflow.py @@ -177,8 +177,8 @@ class HighfreqWorkflow(object): dataset_backtest.setup_data(handler_kwargs={}) ##=============get data============= - xtest, = dataset.prepare(["test"]) - backtest_test, = dataset_backtest.prepare(["test"]) + (xtest,) = dataset.prepare(["test"]) + (backtest_test,) = dataset_backtest.prepare(["test"]) print(xtest, backtest_test) return diff --git a/examples/rolling_process_data/workflow.py b/examples/rolling_process_data/workflow.py index ffdd8329a..02f43889d 100644 --- a/examples/rolling_process_data/workflow.py +++ b/examples/rolling_process_data/workflow.py @@ -105,7 +105,7 @@ class RollingDataWorkflow(object): handler_kwargs={ "start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]), "end_time": datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]), - "processor_kwargs":{ + "processor_kwargs": { "fit_start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]), "fit_end_time": datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]), }, @@ -126,7 +126,9 @@ class RollingDataWorkflow(object): }, ) dataset.setup_data( - handler_kwargs={"init_type": DataHandlerLP.IT_FIT_SEQ,} + handler_kwargs={ + "init_type": DataHandlerLP.IT_FIT_SEQ, + } ) dtrain, dvalid, dtest = dataset.prepare(["train", "valid", "test"]) diff --git a/qlib/data/dataset/__init__.py b/qlib/data/dataset/__init__.py index aa90cee2f..d8a9e0209 100644 --- a/qlib/data/dataset/__init__.py +++ b/qlib/data/dataset/__init__.py @@ -35,7 +35,7 @@ class Dataset(Serializable): def config(self, *arg, **kwargs): """ - config is designed to configure and parameters that cannot be learned from the data + config is designed to configure and parameters that cannot be learned from the data """ super().config(*arg, **kwargs) @@ -117,7 +117,7 @@ class DatasetH(Dataset): self.segments = segments.copy() super().__init__(**kwargs) - def config(self, handler_kwargs:dict = None, segments:dict = None, **kwargs): + def config(self, handler_kwargs: dict = None, segments: dict = None, **kwargs): """ Initialize the DatasetH @@ -130,7 +130,7 @@ class DatasetH(Dataset): kwargs : dict Config of DatasetH, such as - + - segments : dict Config of segments which is same as 'segments' in self.__init__ @@ -141,8 +141,6 @@ class DatasetH(Dataset): if segments is not None: self.segments = segments.copy() - - def setup_data(self, handler_kwargs: dict = None, **kwargs): """ Setup the Data @@ -151,16 +149,15 @@ class DatasetH(Dataset): ---------- handler_kwargs : dict init arguments of DataHanlder, which could include the following arguments: - + - init_type : Init Type of Handler - + - enable_cache : wheter to enable cache """ super().setup_data(**kwargs) if handler_kwargs is not None: self.handler.setup_data(**handler_kwargs) - def __repr__(self): return "{name}(handler={handler}, segments={segments})".format( @@ -464,7 +461,6 @@ class TSDatasetH(DatasetH): cal = self.handler.fetch(col_set=self.handler.CS_RAW).index.get_level_values("datetime").unique() cal = sorted(cal) self.cal = cal - def _prepare_seg(self, slc: slice, **kwargs) -> TSDataSampler: # Dataset decide how to slice data(Get more data for timeseries). diff --git a/qlib/data/dataset/handler.py b/qlib/data/dataset/handler.py index 4adef23a0..2190deeb1 100644 --- a/qlib/data/dataset/handler.py +++ b/qlib/data/dataset/handler.py @@ -119,7 +119,7 @@ class DataHandler(Serializable): self.start_time = start_time if end_time: self.end_time = end_time - + def setup_data(self, enable_cache: bool = False): """ Set Up the data. @@ -407,7 +407,7 @@ class DataHandlerLP(DataHandler): if self.drop_raw: del self._data - def config(self, processor_kwargs:dict = None, **kwargs): + def config(self, processor_kwargs: dict = None, **kwargs): """ configuration of data. # what data to be loaded from data source diff --git a/qlib/data/dataset/loader.py b/qlib/data/dataset/loader.py index a58bca5e8..1cda5c025 100644 --- a/qlib/data/dataset/loader.py +++ b/qlib/data/dataset/loader.py @@ -53,6 +53,7 @@ class DataLoader(abc.ABC): """ pass + class DLWParser(DataLoader): """ (D)ata(L)oader (W)ith (P)arser for features and names diff --git a/qlib/data/dataset/processor.py b/qlib/data/dataset/processor.py index 5be178c5c..d25d36c88 100755 --- a/qlib/data/dataset/processor.py +++ b/qlib/data/dataset/processor.py @@ -202,6 +202,7 @@ class MinMaxNorm(Processor): self.fit_end_time = fit_end_time super().config(**kwargs) + class ZScoreNorm(Processor): """ZScore Normalization""" @@ -229,7 +230,7 @@ class ZScoreNorm(Processor): df.loc(axis=1)[self.cols] = normalize(df[self.cols].values) return df - + def config(self, fit_start_time=None, fit_end_time=None, **kwargs): if fit_start_time: self.fit_start_time = fit_start_time @@ -280,6 +281,7 @@ class RobustZScoreNorm(Processor): self.fit_end_time = fit_end_time super().config(**kwargs) + class CSZScoreNorm(Processor): """Cross Sectional ZScore Normalization"""