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flake8 fix

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This commit is contained in:
Stefano Ariestasia 2022-12-08 20:06:02 +09:00
parent 404df7ae20
commit 3b9052247f
1 changed files with 9 additions and 7 deletions
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16
freqtrade/data/converter.py
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@ -121,16 +121,17 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
logger.debug(message) logger.debug(message)
return df return df
def reduce_mem_usage(pair: str, df: DataFrame) -> DataFrame: def reduce_mem_usage(pair: str, df: DataFrame) -> DataFrame:
""" iterate through all the columns of a dataframe and modify the data type """ iterate through all the columns of a dataframe and modify the data type
to reduce memory usage. to reduce memory usage.
""" """
# start_mem = df.memory_usage().sum() / 1024**2 # start_mem = df.memory_usage().sum() / 1024**2
# logger.info(f"Memory usage of dataframe for {pair} is {start_mem:.2f} MB") # logger.info(f"Memory usage of dataframe for {pair} is {start_mem:.2f} MB")
for col in df.columns[1:]: for col in df.columns[1:]:
col_type = df[col].dtype col_type = df[col].dtype
if col_type != object: if col_type != object:
c_min = df[col].min() c_min = df[col].min()
c_max = df[col].max() c_max = df[col].max()
@ -142,7 +143,7 @@ def reduce_mem_usage(pair: str, df: DataFrame) -> DataFrame:
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32) df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64) df[col] = df[col].astype(np.int64)
elif str(col_type)[:5] == "float": elif str(col_type)[:5] == "float":
if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max: if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
df[col] = df[col].astype(np.float16) df[col] = df[col].astype(np.float16)
@ -158,9 +159,10 @@ def reduce_mem_usage(pair: str, df: DataFrame) -> DataFrame:
# end_mem = df.memory_usage().sum() / 1024**2 # end_mem = df.memory_usage().sum() / 1024**2
# logger.info("Memory usage after optimization is: {:.2f} MB".format(end_mem)) # logger.info("Memory usage after optimization is: {:.2f} MB".format(end_mem))
# logger.info("Decreased by {:.1f}%".format(100 * (start_mem - end_mem) / start_mem)) # logger.info("Decreased by {:.1f}%".format(100 * (start_mem - end_mem) / start_mem))
return df return df
def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date', def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
startup_candles: int = 0) -> DataFrame: startup_candles: int = 0) -> DataFrame:
""" """
@ -196,10 +198,10 @@ def trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles) trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles)
if not trimed_df.empty: if not trimed_df.empty:
# start_mem = trimed_df.memory_usage().sum() / 1024**2 # start_mem = trimed_df.memory_usage().sum() / 1024**2
# logger.info(f"Memory usage of dataframe for {pair} before reduced is {start_mem:.2f} MB") # logger.info(f"Memory usage of df for {pair} before reduced is {start_mem:.2f} MB")
trimed_df = reduce_mem_usage(pair, trimed_df) trimed_df = reduce_mem_usage(pair, trimed_df)
# end_mem = trimed_df.memory_usage().sum() / 1024**2 # end_mem = trimed_df.memory_usage().sum() / 1024**2
# logger.info(f"Memory usage of dataframe for {pair} after reduced is {end_mem:.2f} MB") # logger.info(f"Memory usage of df for {pair} after reduced is {end_mem:.2f} MB")
processed[pair] = trimed_df processed[pair] = trimed_df
else: else:
logger.warning(f'{pair} has no data left after adjusting for startup candles, ' logger.warning(f'{pair} has no data left after adjusting for startup candles, '