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Preprocess Julia floats before pandas reading
Browse files- pysr/sr.py +10 -6
pysr/sr.py
CHANGED
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@@ -1,5 +1,6 @@
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"""Define the PySRRegressor scikit-learn interface."""
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import copy
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import os
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import sys
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import numpy as np
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@@ -2023,7 +2024,11 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
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cur_filename = str(self.equation_file_) + f".out{i}" + ".bkup"
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if not os.path.exists(cur_filename):
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cur_filename = str(self.equation_file_) + f".out{i}"
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-
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# Rename Complexity column to complexity:
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df.rename(
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columns={
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@@ -2033,14 +2038,16 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
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},
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inplace=True,
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)
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df["equation"] = df["equation"].apply(_preprocess_julia_floats)
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all_outputs.append(df)
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else:
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filename = str(self.equation_file_) + ".bkup"
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if not os.path.exists(filename):
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filename = str(self.equation_file_)
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-
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all_outputs[-1].rename(
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columns={
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"Complexity": "complexity",
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@@ -2049,9 +2056,6 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
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},
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inplace=True,
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)
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all_outputs[-1]["equation"] = all_outputs[-1]["equation"].apply(
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_preprocess_julia_floats
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)
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except FileNotFoundError:
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raise RuntimeError(
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"""Define the PySRRegressor scikit-learn interface."""
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import copy
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from io import StringIO
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import os
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import sys
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import numpy as np
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cur_filename = str(self.equation_file_) + f".out{i}" + ".bkup"
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if not os.path.exists(cur_filename):
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cur_filename = str(self.equation_file_) + f".out{i}"
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with open(cur_filename, "r") as f:
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buf = f.read()
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buf = _preprocess_julia_floats(buf)
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df = pd.read_csv(StringIO(buf))
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# Rename Complexity column to complexity:
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df.rename(
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columns={
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},
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inplace=True,
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)
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all_outputs.append(df)
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else:
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filename = str(self.equation_file_) + ".bkup"
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if not os.path.exists(filename):
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filename = str(self.equation_file_)
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with open(filename, "r") as f:
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buf = f.read()
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buf = _preprocess_julia_floats(buf)
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all_outputs = [pd.read_csv(StringIO(buf))]
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all_outputs[-1].rename(
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columns={
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"Complexity": "complexity",
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},
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inplace=True,
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)
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except FileNotFoundError:
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raise RuntimeError(
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