add option to force single precision
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@ -1246,6 +1246,9 @@ class FreqaiDataKitchen:
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self.get_unique_classes_from_labels(dataframe)
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if self.freqai_config.get('convert_df_to_float32', False):
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dataframe = self.reduce_dataframe_footprint(dataframe)
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return dataframe
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def fit_labels(self) -> None:
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@ -1344,3 +1347,35 @@ class FreqaiDataKitchen:
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f"Could not find backtesting prediction file at {path_to_predictionfile}"
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)
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return False
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def reduce_dataframe_footprint(self, df: DataFrame) -> DataFrame:
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"""
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Ensure all values are float32
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"""
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start_mem = df.memory_usage().sum() / 1024**2
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print("Memory usage of dataframe is {:.2f} MB".format(start_mem))
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for col in df.columns[1:]:
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col_type = df[col].dtype
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if col_type != object:
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c_min = df[col].min()
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c_max = df[col].max()
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if str(col_type)[:3] == "int":
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if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
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df[col] = df[col].astype(np.int8)
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elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
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df[col] = df[col].astype(np.int16)
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elif c_min > np.iinfo(np.int32).min:
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df[col] = df[col].astype(np.int32)
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else:
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if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
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df[col] = df[col].astype(np.float16)
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elif c_min > np.finfo(np.float32).min:
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df[col] = df[col].astype(np.float32)
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end_mem = df.memory_usage().sum() / 1024**2
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print("Memory usage after optimization is: {:.2f} MB".format(end_mem))
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print("Decreased by {:.1f}%".format(100 * (start_mem - end_mem) / start_mem))
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return df
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