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krb5 >=1.21.3,<1.22.0a0 - libcxx >=18 - libsodium >=1.0.20,<1.0.21.0a0 + arch: x86_64 + platform: osx license: MPL-2.0 license_family: MOZILLA purls: [] @@ -15619,6 +18089,8 @@ packages: - ucrt >=10.0.20348.0 - vc >=14.2,<15 - vc14_runtime >=14.29.30139 + arch: x86_64 + platform: win license: MPL-2.0 license_family: MOZILLA purls: [] @@ -15662,6 +18134,8 @@ packages: - python_abi 3.12.* *_cp312 - zstd >=1.5.6,<1.5.7.0a0 - zstd >=1.5.6,<1.6.0a0 + arch: x86_64 + platform: osx license: BSD-3-Clause license_family: BSD purls: @@ -15697,6 +18171,8 @@ packages: - vc14_runtime >=14.29.30139 - zstd >=1.5.6,<1.5.7.0a0 - zstd >=1.5.6,<1.6.0a0 + arch: x86_64 + platform: win license: BSD-3-Clause license_family: BSD purls: @@ -15721,6 +18197,8 @@ packages: depends: - __osx >=10.9 - libzlib >=1.2.13,<2.0.0a0 + arch: x86_64 + platform: osx license: BSD-3-Clause license_family: BSD purls: [] @@ -15745,6 +18223,8 @@ packages: - ucrt >=10.0.20348.0 - vc >=14.2,<15 - vc14_runtime >=14.29.30139 + arch: x86_64 + platform: win license: BSD-3-Clause license_family: BSD purls: [] diff --git a/pyproject.toml b/pyproject.toml index b61f051..21e0154 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,6 +13,9 @@ dependencies = [ "pydicom>=2.3.1", "pyradiomics-bhklab>=3.1.4,<4", "orcestra-downloader>=0.9.0,<1", + "numpy==1.26.4.*", + "seaborn>=0.13.2,<0.14", + "pandas>=2.2.3,<3" ] requires-python = ">=3.10, <3.13" diff --git a/src/readii/analyze/__init__.py b/src/readii/analyze/__init__.py new file mode 100644 index 0000000..73b5a90 --- /dev/null +++ b/src/readii/analyze/__init__.py @@ -0,0 +1,17 @@ +"""Module to perform analysis on READII outputs.""" +from .correlation import ( + getCrossCorrelationMatrix, + getFeatureCorrelations, + getHorizontalSelfCorrelations, + getVerticalSelfCorrelations, +) +from .plot_correlation import plotCorrelationHeatmap, plotCorrelationHistogram + +__all__ = [ + 'getFeatureCorrelations', + 'getVerticalSelfCorrelations', + 'getHorizontalSelfCorrelations', + 'getCrossCorrelationMatrix', + 'plotCorrelationHeatmap', + 'plotCorrelationHistogram' +] \ No newline at end of file diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py new file mode 100644 index 0000000..6de9a93 --- /dev/null +++ b/src/readii/analyze/correlation.py @@ -0,0 +1,165 @@ +import pandas as pd + +from readii.data.select import validateDataframeSubsetSelection +from readii.utils import logger + + +def getFeatureCorrelations(vertical_features:pd.DataFrame, + horizontal_features:pd.DataFrame, + method:str = "pearson", + vertical_feature_name:str = '_vertical', + horizontal_feature_name:str = '_horizontal') -> pd.DataFrame: + """Calculate correlation between two sets of features. + + Parameters + ---------- + vertical_features : pd.DataFrame + Dataframe containing features to calculate correlations with. Index must be the same as the index of the horizontal_features dataframe. + horizontal_features : pd.DataFrame + Dataframe containing features to calculate correlations with. Index must be the same as the index of the vertical_features dataframe. + method : str + Method to use for calculating correlations. Default is "pearson". + vertical_feature_name : str + Name of the vertical features to use as suffix in correlation dataframe. Default is "_vertical". + horizontal_feature_name : str + Name of the horizontal features to use as suffix in correlation dataframe. Default is "_horizontal". + + Returns + ------- + correlation_matrix : pd.DataFrame + Dataframe containing correlation values. + """ + # Check that features are dataframes + if not isinstance(vertical_features, pd.DataFrame): + msg = "vertical_features must be a pandas DataFrame" + logger.exception(msg) + raise TypeError() + if not isinstance(horizontal_features, pd.DataFrame): + msg = "horizontal_features must be a pandas DataFrame" + logger.exception(msg) + raise TypeError() + + # Check for empty DataFrames + if vertical_features.empty or horizontal_features.empty: + msg = "Cannot calculate correlations with empty DataFrames" + logger.exception(msg) + raise ValueError(msg) + + if method not in ["pearson", "spearman", "kendall"]: + msg = "Correlation method must be one of 'pearson', 'spearman', or 'kendall'." + logger.exception(msg) + raise ValueError() + + if not vertical_features.index.equals(horizontal_features.index): + msg = "Vertical and horizontal features must have the same index to calculate correlation. Set the index to the intersection of patient IDs." + logger.exception(msg) + raise ValueError() + + # Add _ to beginnging of feature names if they don't start with _ so they can be used as suffixes + if not vertical_feature_name.startswith("_"): + vertical_feature_name = f"_{vertical_feature_name}" + if not horizontal_feature_name.startswith("_"): + horizontal_feature_name = f"_{horizontal_feature_name}" + + # Join the features into one dataframe + # Use inner join to keep only the rows that have a value in both vertical and horizontal features + features_to_correlate = vertical_features.join(horizontal_features, + how='inner', + lsuffix=vertical_feature_name, + rsuffix=horizontal_feature_name) + + try: + # Calculate correlation between vertical features and horizontal features + correlation_matrix = features_to_correlate.corr(method=method) + except Exception as e: + msg = f"Error calculating correlation matrix: {e}" + logger.exception(msg) + raise e + + return correlation_matrix + + + +def getVerticalSelfCorrelations(correlation_matrix:pd.DataFrame, + num_vertical_features:int) -> pd.DataFrame: + """Get the vertical (y-axis) self correlations from a correlation matrix. Gets the top left quadrant of the correlation matrix. + + Parameters + ---------- + correlation_matrix : pd.DataFrame + Dataframe containing the correlation matrix to get the vertical self correlations from. + num_vertical_features : int + Number of vertical features in the correlation matrix. + + Returns + ------- + pd.DataFrame + Dataframe containing the vertical self correlations from the correlation matrix. + """ + try: + validateDataframeSubsetSelection(correlation_matrix, num_vertical_features, num_vertical_features) + except ValueError as e: + msg = "Number of vertical features provided is greater than the number of rows or columns in the correlation matrix." + logger.exception(msg) + raise e + + # Get the correlation matrix for vertical vs vertical - this is the top left corner of the matrix + return correlation_matrix.iloc[0:num_vertical_features, 0:num_vertical_features] + + + +def getHorizontalSelfCorrelations(correlation_matrix:pd.DataFrame, + num_horizontal_features:int) -> pd.DataFrame: + """Get the horizontal (x-axis) self correlations from a correlation matrix. Gets the bottom right quadrant of the correlation matrix. + + Parameters + ---------- + correlation_matrix : pd.DataFrame + Dataframe containing the correlation matrix to get the horizontal self correlations from. + num_horizontal_features : int + Number of horizontal features in the correlation matrix. + + Returns + ------- + pd.DataFrame + Dataframe containing the horizontal self correlations from the correlation matrix. + """ + try: + validateDataframeSubsetSelection(correlation_matrix, num_horizontal_features, num_horizontal_features) + except ValueError as e: + msg = "Number of horizontalfeatures provided is greater than the number of rows or columns in the correlation matrix." + logger.exception(msg) + raise e + + # Get the index of the start of the horizontal correlations + start_of_horizontal_correlations = len(correlation_matrix.columns) - num_horizontal_features + + # Get the correlation matrix for horizontal vs horizontal - this is the bottom right corner of the matrix + return correlation_matrix.iloc[start_of_horizontal_correlations:, start_of_horizontal_correlations:] + + + +def getCrossCorrelationMatrix(correlation_matrix:pd.DataFrame, + num_vertical_features:int) -> pd.DataFrame: + """Get the cross correlation matrix subsection for a correlation matrix. Gets the top right quadrant of the correlation matrix so vertical and horizontal features are correctly labeled. + + Parameters + ---------- + correlation_matrix : pd.DataFrame + Dataframe containing the correlation matrix to get the cross correlation matrix subsection from. + num_vertical_features : int + Number of vertical features in the correlation matrix. + + Returns + ------- + pd.DataFrame + Dataframe containing the cross correlations from the correlation matrix. + """ + try: + validateDataframeSubsetSelection(correlation_matrix, num_vertical_features, num_vertical_features) + except ValueError as e: + msg = "Number of vertical features provided is greater than the number of rows or columns in the correlation matrix." + logger.exception(msg) + raise e + + return correlation_matrix.iloc[0:num_vertical_features, num_vertical_features:] \ No newline at end of file diff --git a/src/readii/analyze/plot_correlation.py b/src/readii/analyze/plot_correlation.py new file mode 100644 index 0000000..8ebf068 --- /dev/null +++ b/src/readii/analyze/plot_correlation.py @@ -0,0 +1,188 @@ +from typing import Optional + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from matplotlib.figure import Figure +from scipy.linalg import issymmetric + +from readii.utils import logger + + +def plotCorrelationHeatmap(correlation_matrix_df:pd.DataFrame, + diagonal:bool = False, + triangle:Optional[str] = "lower", + cmap:str = "nipy_spectral", + xlabel:str = "", + ylabel:Optional[str] = "", + title:Optional[str] = "", + subtitle:Optional[str] = "", + show_tick_labels:bool = False + ) -> Figure: + """Plot a correlation dataframe as a heatmap. + + Parameters + ---------- + correlation_matrix_df : pd.DataFrame + Dataframe containing the correlation matrix to plot. + diagonal : bool, optional + Whether to only plot half of the matrix. The default is False. + triangle : str, optional + Which triangle half of the matrix to plot. Either "lower" or "upper". + The default is "lower". + xlabel : str, optional + Label for the x-axis. The default is "". + ylabel : str, optional + Label for the y-axis. The default is "". + title : str, optional + Title for the plot. The default is "". + subtitle : str, optional + Subtitle for the plot. The default is "". + show_tick_labels : bool, optional + Whether to show the tick labels on the x and y axes. These would be the feature names. The default is False. + + Returns + ------- + corr_fig : matplotlib.pyplot.figure + Figure object containing a Seaborn heatmap. + """ + if diagonal: + logger.debug(f"Creating {triangle} traingle mask for diagonal correlation plot.") + # Set up mask for hiding half the matrix in the plot + if triangle == "lower": + # Mask out the upper right triangle half of the matrix + mask = np.triu(correlation_matrix_df) + elif triangle == "upper": + # Mask out the lower left triangle half of the matrix + mask = np.tril(correlation_matrix_df) + else: + msg = f"If diagonal is True, triangle must be either 'lower' or 'upper'. Got {triangle}." + logger.exception(msg) + raise ValueError() + else: + logger.debug("Creating full square correlation matrix plot.") + # The entire correlation matrix will be visisble in the plot + mask = None + + # Set a default title if one is not provided + if not title: + title = "Correlation Heatmap" + + # Set up figure and axes for the plot + corr_fig, corr_ax = plt.subplots() + + # Plot the correlation matrix + try: + corr_ax = sns.heatmap(correlation_matrix_df, + mask = mask, + cmap=cmap, + vmin=-1.0, + vmax=1.0) + except Exception as e: + msg = f"Error generating heatmap: {e}" + logger.exception(msg) + raise e + + if not show_tick_labels: + # Remove the individual feature names from the axes + corr_ax.set_xticklabels(labels=[]) + corr_ax.set_yticklabels(labels=[]) + + # Set axis labels + corr_ax.set_xlabel(xlabel) + corr_ax.set_ylabel(ylabel) + + # Set title and subtitle + # Suptitle is the super title, which will be above the title + plt.title(subtitle, fontsize=12) + plt.suptitle(title, fontsize=14) + + return corr_fig + + + +def plotCorrelationHistogram(correlation_matrix:pd.DataFrame, + num_bins:int = 100, + xlabel:Optional[str] = "Correlations", + ylabel:Optional[str] = "Frequency", + y_lower_bound:int = 0, + y_upper_bound:Optional[int] = None, + title:Optional[str] = "Distribution of Correlations for Features", + subtitle:Optional[str] = "", + ) -> Figure: + """Plot a histogram to show thedistribution of correlation values for a correlation matrix. + + Parameters + ---------- + correlation_matrix : pd.DataFrame + Dataframe containing the correlation matrix to plot. + num_bins : int, optional + Number of bins to use for the distribution plot. The default is 100. + xlabel : str, optional + Label for the x-axis. The default is "Correlations". + ylabel : str, optional + Label for the y-axis. The default is "Frequency". + y_lower_bound : int, optional + Lower bound for the y-axis of the distribution plot. The default is 0. + y_upper_bound : int, optional + Upper bound for the y-axis of the distribution plot. The default is None. + title : str, optional + Title for the plot. The default is "Distribution of Correlations for Features". + subtitle : str, optional + Subtitle for the plot. The default is "". + + Returns + ------- + dist_fig : plt.Figure + Figure object containing the histogram of correlation values. + bin_values : np.ndarray or list of arrays + Numpy array containing the values in each bin for the histogram. + bin_edges : np.ndarray + Numpy array containing the bin edges for the histogram. + """ + # Convert to numpy to use histogram function + feature_correlation_arr = correlation_matrix.to_numpy() + + # Check if matrix is symmetric + if issymmetric(feature_correlation_arr): + logger.debug("Correlation matrix is symmetric.") + # Get only the bottom left triangle of the correlation matrix since the matrix is symmetric + lower_half_idx = np.mask_indices(feature_correlation_arr.shape[0], np.tril) + # This is a 1D array for binning and plotting + correlation_vals = feature_correlation_arr[lower_half_idx] + else: + # Flatten the matrix to a 1D array for binning and plotting + correlation_vals = feature_correlation_arr.flatten() + + # Set up figure and axes for the plot + dist_fig, dist_ax = plt.subplots() + + # Plot the histogram of correlation values + # Validate num_bins + if num_bins <= 0: + msg = f"Number of bins must be positive, got {num_bins}" + logger.exception(msg) + raise ValueError() + + try: + bin_values, bin_edges, _ = dist_ax.hist(correlation_vals, bins=num_bins) + except Exception as e: + msg = f"Error generating histogram: {e}" + logger.exception(msg) + raise e + + # Set up axis labels + dist_ax.set_xlabel(xlabel) + dist_ax.set_ylabel(ylabel) + + # Set axis bounds + dist_ax.set_xbound(-1.0, 1.0) + dist_ax.set_ybound(y_lower_bound, y_upper_bound) + + # Set title and subtitle + # Suptitle is the super title, which will be above the title + plt.suptitle(title, fontsize=14) + plt.title(subtitle, fontsize=10) + + return dist_fig, bin_values, bin_edges \ No newline at end of file diff --git a/src/readii/data/select.py b/src/readii/data/select.py index f2d4a51..d854d40 100644 --- a/src/readii/data/select.py +++ b/src/readii/data/select.py @@ -174,4 +174,49 @@ def getPatientIntersectionDataframes(dataframe_A:DataFrame, intersection_index_dataframeA = dataframe_A.loc[intersection_index] intersection_index_dataframeB = dataframe_B.loc[intersection_index] - return intersection_index_dataframeA, intersection_index_dataframeB \ No newline at end of file + return intersection_index_dataframeA, intersection_index_dataframeB + + +def validateDataframeSubsetSelection(dataframe:DataFrame, + num_rows:Optional[int] = None, + num_cols:Optional[int] = None): + + # Check if dataframe is a DataFrame + if not isinstance(dataframe, DataFrame): + msg = f"dataframe must be a pandas DataFrame, got {type(dataframe)}" + logger.error(msg) + raise TypeError(msg) + + if num_rows is not None: + # Check if num_rows is an integer + if not isinstance(num_rows, int): + msg = f"num_rows must be an integer, got {type(num_rows)}" + logger.error(msg) + raise TypeError(msg) + + if num_rows > dataframe.shape[0]: + msg = f"num_rows ({num_rows}) is greater than the number of rows in the dataframe ({dataframe.shape[0]})" + logger.error(msg) + raise ValueError() + else: + logger.debug("Number of rows is within the size of the dataframe.") + + + if num_cols is not None: + # Check if num_cols is an integer + if not isinstance(num_cols, int): + msg = f"num_cols must be an integer, got {type(num_cols)}" + logger.error(msg) + raise TypeError(msg) + + if num_cols > dataframe.shape[1]: + msg = f"num_cols ({num_cols}) is greater than the number of columns in the dataframe ({dataframe.shape[1]})" + logger.error(msg) + raise ValueError() + else: + logger.debug("Number of columns is within the size of the dataframe.") + + return None + + + diff --git a/src/readii/image_processing.py b/src/readii/image_processing.py index 94379fa..a64dc4e 100644 --- a/src/readii/image_processing.py +++ b/src/readii/image_processing.py @@ -8,7 +8,6 @@ from readii.loaders import ( loadDicomSITK, - loadRTSTRUCTSITK, loadSegmentation, ) diff --git a/src/readii/io/loaders/features.py b/src/readii/io/loaders/features.py index a499798..b7dbbde 100644 --- a/src/readii/io/loaders/features.py +++ b/src/readii/io/loaders/features.py @@ -53,7 +53,7 @@ def loadFeatureFilesFromImageTypes(extracted_feature_dir:str, image_type_feature_file = matching_files[0] # Remove the image type file from the list of feature files feature_file_list.remove(image_type_feature_file) - except IndexError as e: + except IndexError: logger.warning(f"No {image_type} feature csv files found in {extracted_feature_dir}") # Skip to the next image type continue @@ -70,7 +70,7 @@ def loadFeatureFilesFromImageTypes(extracted_feature_dir:str, if drop_labels: # Data is now only extracted features raw_feature_data.drop(labels_to_drop, axis=1, inplace=True) - except KeyError as e: + except KeyError: logger.warning(f"{feature_file_path} does not have the labels {labels_to_drop} to drop.") # Skip to the next image type continue diff --git a/src/readii/io/writers/plot_writer.py b/src/readii/io/writers/plot_writer.py new file mode 100644 index 0000000..3effc80 --- /dev/null +++ b/src/readii/io/writers/plot_writer.py @@ -0,0 +1,113 @@ +from dataclasses import dataclass, field +from pathlib import Path +from typing import ClassVar + +from matplotlib.figure import Figure + +from readii.io.writers.base_writer import BaseWriter +from readii.utils import logger + + +class PlotWriterError(Exception): + """Base exception for PlotWriter errors.""" + + pass + + +class PlotWriterIOError(PlotWriterError): + """Raised when I/O operations fail.""" + + pass + + +class PlotWriterValidationError(PlotWriterError): + """Raised when validation of writer configuration fails.""" + + pass + +@dataclass +class PlotWriter(BaseWriter): + """Class for managing file writing with customizable paths and filenames for plot figure files.""" + + overwrite: bool = field( + default=False, + metadata={ + "help": "If True, allows overwriting existing files. If False, raises PlotWriterIOError." + }, + ) + # Make extensions immutable + VALID_EXTENSIONS: ClassVar[list[str]] = ( + ".png", + ".pdf", + ".ps", + ".eps", + ".svg" + ) + + def __post_init__(self) -> None: + """Validate writer configuration.""" + super().__post_init__() + + if not any(self.filename_format.endswith(ext) for ext in self.VALID_EXTENSIONS): + msg = f"Invalid filename format {self.filename_format}. Must end with one of {self.VALID_EXTENSIONS}." + raise PlotWriterValidationError(msg) + + def save(self, plot:Figure, **kwargs: str) -> Path: + """Save the plot to a .png file with extra whitespace removed. + + Parameters + ---------- + plot : matplotlib.figure.Figure + The plot to save. + kwargs : dict + Additional keyword arguments to pass to the save method. + + Returns + ------- + Path + The path to the saved file. + + Raises + ------ + PlotWriterIOError + If there is an error saving the plot. + PlotWriterValidationError + If the filename format is invalid. + """ + logger.debug("Saving.", kwargs=kwargs) + + # Generate the output path + out_path = self.resolve_path(**kwargs) + + # Check if the output path already exists + if out_path.exists(): + if not self.overwrite: + msg = f"File {out_path} already exists. \nSet {self.__class__.__name__}.overwrite to True to overwrite." + raise PlotWriterIOError(msg) + else: + logger.warning(f"File {out_path} already exists. Overwriting.") + + logger.debug("Saving plot to file", out_path=out_path) + try: + # Save the plot to the output path, remove extra whitespace around the figure + plot.savefig(out_path, bbox_inches='tight') + + except Exception as e: + msg = f"Error saving plot to file {out_path}: {e}" + raise PlotWriterIOError(msg) from e + else: + logger.info("Plot saved successfully.", out_path=out_path) + return out_path + + +if __name__ == "__main__": # pragma: no cover + from rich import print # noqa + + plot_writer = PlotWriter( + root_directory=Path("TRASH", "plot_writer_examples"), + filename_format="{DatasetName}_{PlotType}_{FeatureType}_{ImageType}_plot.png", + overwrite=True, + create_dirs=True + ) + + print(plot_writer) \ No newline at end of file diff --git a/tests/analyze/test_correlation.py b/tests/analyze/test_correlation.py new file mode 100644 index 0000000..e614401 --- /dev/null +++ b/tests/analyze/test_correlation.py @@ -0,0 +1,114 @@ +from readii.analyze.correlation import ( + getFeatureCorrelations +) + +import pytest +import pandas as pd +import numpy as np + +@pytest.fixture +def random_features(): + # Create a 10x10 matrix with random float values between 0 and 1 + random_matrix = np.random.default_rng(seed=10).random((10,10)) + # Convert to dataframe and name the columns feature1, feature2, etc. + return pd.DataFrame(random_matrix, columns=[f"feature_{i+1}" for i in range(10)]) + +@pytest.mark.parametrize( + "correlation_method", + [ + "pearson", + "spearman", + "kendall" + ] +) +def test_methods_getFeatureCorrelation(correlation_method, random_features): + """Test getting correlation matrix for a set of random_features with pearson and spearman correlation methods""" + + features_to_corr = random_features.join(random_features, how='inner', lsuffix='_vertical', rsuffix='_horizontal') + expected = features_to_corr.corr(method=correlation_method) + + actual = getFeatureCorrelations(vertical_features = random_features, + horizontal_features = random_features, + method = correlation_method + ) + assert isinstance(actual, pd.DataFrame), \ + "Wrong return type, expect a pandas DataFrame" + assert actual.shape[0] == 2*random_features.shape[1], \ + "Wrong return size, should be double the number of input features" + assert actual.equals(expected), \ + f"{correlation_method} correlation values are incorrect for the input features" + + +def test_defaults_getFeatureCorrelations(random_features): + """Test the default argument behaviours of getFeatureCorrelations. Should be a Pearson correlation matrix with _vertical and _horizontal suffixes on the feature names""" + features_to_corr = random_features.join(random_features, how='inner', lsuffix='_vertical', rsuffix='_horizontal') + expected = features_to_corr.corr(method="pearson") + + actual = getFeatureCorrelations(vertical_features = random_features, + horizontal_features = random_features) + assert isinstance(actual, pd.DataFrame), \ + "Wrong return type, expect a pandas DataFrame" + assert actual.shape[0] == 2*random_features.shape[1], \ + "Wrong return size, should be double the number of the input features" + assert actual.equals(expected), \ + "Pearson correlation values are incorrect for the input features" + assert actual.columns.equals(expected.columns), \ + "Column names are incorrect. Should be the input features with _vertical and _horizontal suffixes" + + +@pytest.mark.parametrize( + "correlation_method", + [ + "random", + "" + ] +) +def test_wrongMethod_getFeatureCorrelations(random_features, correlation_method): + """Check ValueError is raised when incorrect correlation method is passed""" + with pytest.raises(ValueError): + getFeatureCorrelations(vertical_features = random_features, + horizontal_features = random_features, + method = correlation_method + ) + +@pytest.mark.parametrize( + "wrong_features", + [ + np.random.default_rng(seed=10).random((10,10)), + "Just a string", + {"feat1": 34, "feat2": 10000, "feat3": 3.141592} + ] +) +def test_wrongFeatures_getFeatureCorrelations(random_features, wrong_features): + """Check ValueError is raised when incorrect features are passed""" + with pytest.raises(TypeError): + getFeatureCorrelations(vertical_features = random_features, + horizontal_features = wrong_features, + method = "pearson" + ) + with pytest.raises(TypeError): + getFeatureCorrelations(vertical_features = wrong_features, + horizontal_features = random_features, + method = "pearson" + ) + +@pytest.mark.parametrize( + "vertical_feature_name, horizontal_feature_name, expected_vertical, expected_horizontal", + [ + ("type_A", "type_B", [f"feature_{i+1}_type_A" for i in range(10)], [f"feature_{i+1}_type_B" for i in range(10)]), + ("_type_C", "_type_D", [f"feature_{i+1}_type_C" for i in range(10)], [f"feature_{i+1}_type_D" for i in range(10)]), + ] +) +def test_featureNames_getFeatureCorrelations(random_features, vertical_feature_name, horizontal_feature_name, expected_vertical, expected_horizontal): + """ Check that feature names with and without _ prefix are handled correctly""" + + actual = getFeatureCorrelations(vertical_features=random_features, + horizontal_features=random_features, + vertical_feature_name=vertical_feature_name, + horizontal_feature_name=horizontal_feature_name) + + assert list(actual.columns) == expected_vertical + expected_horizontal, \ + "Column names are incorrect, check underscore prefix handling when adding the vertical and horizontal feature names. Should be [feature]_vertical_feature_name and [feature]_horizontal_feature_name." + assert list(actual.index) == expected_vertical + expected_horizontal, \ + "Index values are incorrect, check underscore prefix handling when adding the vertical and horizontal feature names. Should be [feature]_vertical_feature_name and [feature]_horizontal_feature_name." + \ No newline at end of file