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Peter Hamfelt
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add lib parameter checkers
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‎pylint_ml/checkers/numpy/numpy_parameter.py‎

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# Licensed under the MIT: https://mit-license.org/
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# For details: https://github.com/pylint-dev/pylint-ml/LICENSE
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# Copyright (c) https://github.com/pylint-dev/pylint-ml/CONTRIBUTORS.txt
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"""Check for proper usage of numpy functions with required parameters."""
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from astroid import nodes
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from pylint.checkers import BaseChecker
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from pylint.checkers.utils import only_required_for_messages
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from pylint.interfaces import HIGH
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class NumPyParameterChecker(BaseChecker):
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name = "numpy-parameter"
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msgs = {
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"W8111": (
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"Ensure that required parameters %s are explicitly specified in numpy method %s.",
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"numpy-parameter",
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"Explicitly specifying required parameters improves model performance and prevents unintended behavior.",
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),
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}
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REQUIRED_PARAMS = {
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# Array Creation
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'array': ['object'],
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'zeros': ['shape'],
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'ones': ['shape'],
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'full': ['shape', 'fill_value'],
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'empty': ['shape'],
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'arange': ['start'],
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'linspace': ['start', 'stop'],
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'logspace': ['start', 'stop'],
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'eye': ['N'],
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'identity': ['n'],
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# Random Sampling
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'random.rand': ['d0'],
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'random.randn': ['d0'],
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'random.randint': ['low', 'high'],
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'random.choice': ['a'],
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'random.uniform': ['low', 'high'],
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'random.normal': ['loc', 'scale'],
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# Mathematical Functions
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'sum': ['a'],
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'mean': ['a'],
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'median': ['a'],
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'std': ['a'],
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'var': ['a'],
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'prod': ['a'],
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'min': ['a'],
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'max': ['a'],
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'ptp': ['a'],
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# Array Manipulation
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'reshape': ['newshape'],
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'transpose': [],
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'concatenate': ['arrays'],
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'stack': ['arrays'],
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'vstack': ['arrays'],
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'hstack': ['arrays'],
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# Linear Algebra
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'dot': ['a', 'b'],
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'matmul': ['a', 'b'],
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'linalg.inv': ['a'],
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'linalg.eig': ['a'],
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'linalg.solve': ['a', 'b'],
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# Statistical Functions
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'percentile': ['a', 'q'],
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'quantile': ['a', 'q'],
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'corrcoef': ['x'],
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'cov': ['m'],
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}
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@only_required_for_messages("numpy-parameter")
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def visit_call(self, node: nodes.Call) -> None:
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method_name = self._get_full_method_name(node)
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if method_name in self.REQUIRED_PARAMS:
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provided_keywords = {kw.arg for kw in node.keywords if kw.arg is not None}
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# Collect all missing parameters
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missing_params = [param for param in self.REQUIRED_PARAMS[method_name] if param not in provided_keywords]
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if missing_params:
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self.add_message(
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"numpy-parameter",
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node=node,
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confidence=HIGH,
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args=(", ".join(missing_params), method_name),
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)
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@staticmethod
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def _get_full_method_name(node: nodes.Call) -> str:
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"""
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Extracts the full method name, including chained attributes (e.g., np.random.rand).
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"""
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func = node.func
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method_chain = []
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# Traverse the attribute chain
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while isinstance(func, nodes.Attribute):
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method_chain.insert(0, func.attrname)
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func = func.expr
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# Check if the root of the chain is "np" (as NumPy functions are expected to use np. prefix)
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if isinstance(func, nodes.Name) and func.name == "np":
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return '.'.join(method_chain)
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return ""
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‎pylint_ml/checkers/pandas/pandas_dataframe_merge.py‎

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‎pylint_ml/checkers/pandas/pandas_dtype_param.py‎

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‎pylint_ml/checkers/pandas/pandas_parameter.py‎

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# Licensed under the MIT: https://mit-license.org/
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# For details: https://github.com/pylint-dev/pylint-ml/LICENSE
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# Copyright (c) https://github.com/pylint-dev/pylint-ml/CONTRIBUTORS.txt
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"""Check for proper usage of Pandas functions with required parameters."""
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from astroid import nodes
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from pylint.checkers import BaseChecker
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from pylint.checkers.utils import only_required_for_messages
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from pylint.interfaces import HIGH
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class PandasParameterChecker(BaseChecker):
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name = "pandas-parameter"
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msgs = {
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"W8111": (
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"Ensure that required parameters %s are explicitly specified in Pandas method %s.",
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"pandas-parameter",
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"Explicitly specifying required parameters improves model performance and prevents unintended behavior.",
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),
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}
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# Define required parameters for specific Pandas classes and methods
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REQUIRED_PARAMS = {
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# DataFrame creation
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'DataFrame': ['data'], # The primary input data for DataFrame creation
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# Concatenation
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'concat': ['objs'], # The list or dictionary of DataFrames/Series to concatenate
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# DataFrame I/O (Input/Output)
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'read_csv': ['filepath_or_buffer', 'dtype'], # Path to CSV file or file-like object; column data types
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'read_excel': ['io', 'dtype'], # Path to Excel file or file-like object; column data types
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'read_table': ['filepath_or_buffer', 'dtype'], # Path to delimited text-file or file object; column data types
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'to_csv': ['path_or_buf'], # File path or buffer to write the DataFrame to
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'to_excel': ['excel_writer'], # File path or ExcelWriter object to write the data to
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# Merging and Joining
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'merge': ['right', 'how', 'on', 'validate'], # The DataFrame or Serie to merge with
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'join': ['other'], # The DataFrame or Series to join
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# DataFrame Operations
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'pivot_table': ['index'], # The column to pivot on (values and columns have defaults)
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'groupby': ['by'], # The key or list of keys to group by
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'resample': ['rule'], # The frequency rule to resample by
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# Data Cleaning and Transformation
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'fillna': ['value'], # Value to use to fill NA/NaN values
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'drop': ['labels'], # Labels to drop
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'drop_duplicates': ['subset'], # Subset of columns to consider when dropping duplicates
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'replace': ['to_replace'], # Values to replace
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# Plotting
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'plot': ['x'], # x-values or index for plotting
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'hist': ['column'], # Column to plot the histogram for
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'boxplot': ['column'], # Column(s) to plot boxplot for
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# DataFrame Sorting
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'sort_values': ['by'], # Column(s) to sort by
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'sort_index': ['axis'], # Axis to sort along (index=0, columns=1)
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# Statistical Functions
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'corr': ['method'], # Method to use for correlation ('pearson', 'kendall', 'spearman')
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'describe': [], # No required parameters, but additional ones could be specified
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# Windowing/Resampling Functions
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'rolling': ['window'], # Size of the moving window
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'ewm': ['span'], # Span for exponentially weighted calculations
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# Miscellaneous Functions
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'apply': ['func'], # Function to apply to the data
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'agg': ['func'], # Function or list of functions for aggregation
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}
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@only_required_for_messages("pandas-parameter")
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def visit_call(self, node: nodes.Call) -> None:
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method_name = self._get_method_name(node)
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if method_name in self.REQUIRED_PARAMS:
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provided_keywords = {kw.arg for kw in node.keywords if kw.arg is not None}
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# Collect all missing parameters
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missing_params = [param for param in self.REQUIRED_PARAMS[method_name] if param not in provided_keywords]
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if missing_params:
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self.add_message(
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"pandas-parameter",
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node=node,
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confidence=HIGH,
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args=(", ".join(missing_params), method_name),
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)
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@staticmethod
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def _get_method_name(node: nodes.Call) -> str:
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"""Extracts the method name from a Call node, including handling chained calls."""
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func = node.func
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while isinstance(func, nodes.Attribute):
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func = func.expr
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return (
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node.func.attrname
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if isinstance(node.func, nodes.Attribute)
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else func.name if isinstance(func, nodes.Name) else ""
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)

‎pylint_ml/checkers/scipy/scipy_import.py‎

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@@ -25,8 +25,9 @@ class ScipyImportChecker(BaseChecker):
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),
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}
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@only_required_for_messages("scipy-import")
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@only_required_for_messages("scipy-import", "scipy-wildcard-import")
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def visit_import(self, node: nodes.Import) -> None:
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for name, _ in node.names:
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for name, _alias in node.names:
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if name == "scipy":
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# Flag direct or aliased imports of scipy
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self.add_message("scipy-import", node=node, confidence=HIGH)

‎pylint_ml/checkers/scipy/scipy_parameter.py‎

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# Licensed under the MIT: https://mit-license.org/
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# For details: https://github.com/pylint-dev/pylint-ml/LICENSE
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# Copyright (c) https://github.com/pylint-dev/pylint-ml/CONTRIBUTORS.txt
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"""Check for proper usage of Scipy functions with required parameters."""
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from astroid import nodes
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from pylint.checkers import BaseChecker
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from pylint.checkers.utils import only_required_for_messages
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from pylint.interfaces import HIGH
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class ScipyParameterChecker(BaseChecker):
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name = "scipy-parameter"
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msgs = {
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"W8111": (
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"Ensure that required parameters %s are explicitly specified in scipy method %s.",
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"scipy-parameter",
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"Explicitly specifying required parameters improves model performance and prevents unintended behavior.",
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),
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}
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# Define required parameters for specific Scipy classes and methods
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REQUIRED_PARAMS = {
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# scipy.optimize
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'minimize': ['fun', 'x0'],
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'curve_fit': ['f', 'xdata', 'ydata'],
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'root': ['fun', 'x0'],
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# scipy.integrate
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'quad': ['func', 'a', 'b'],
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'dblquad': ['func', 'a', 'b', 'gfun', 'hfun'],
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'solve_ivp': ['fun', 't_span', 'y0'],
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# scipy.stats
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'ttest_ind': ['a', 'b'],
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'ttest_rel': ['a', 'b'],
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'norm.pdf': ['x'],
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# scipy.spatial
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'distance.euclidean': ['u', 'v'], # Full chain
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'euclidean': ['u', 'v'], # Direct import of euclidean
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'KDTree.query': ['x'],
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}
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@only_required_for_messages("scipy-parameter")
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def visit_call(self, node: nodes.Call) -> None:
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method_name = self._get_full_method_name(node)
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if method_name in self.REQUIRED_PARAMS:
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provided_keywords = {kw.arg for kw in node.keywords if kw.arg is not None}
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# Collect all missing parameters
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missing_params = [param for param in self.REQUIRED_PARAMS[method_name] if param not in provided_keywords]
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if missing_params:
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self.add_message(
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"scipy-parameter",
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node=node,
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confidence=HIGH,
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args=(", ".join(missing_params), method_name),
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)
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def _get_full_method_name(self, node: nodes.Call) -> str:
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"""
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Extracts the full method name, including handling chained attributes (e.g., scipy.spatial.distance.euclidean)
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and also handles direct imports like euclidean.
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"""
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func = node.func
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method_chain = []
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# Traverse the attribute chain to get the full method name
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while isinstance(func, nodes.Attribute):
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method_chain.insert(0, func.attrname)
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func = func.expr
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# If it's a direct function name, like `euclidean`, return it
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if isinstance(func, nodes.Name):
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method_chain.insert(0, func.name)
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return '.'.join(method_chain)

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