From 95586e5eca119e5246c4dab800f64dab11d704bf Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 11:08:53 -0500 Subject: [PATCH 01/62] feat: add function to calculate feature correlation matrix --- src/readii/analyze/correlation.py | 44 +++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 src/readii/analyze/correlation.py diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py new file mode 100644 index 0000000..90d7a05 --- /dev/null +++ b/src/readii/analyze/correlation.py @@ -0,0 +1,44 @@ +import pandas as pd +from typing import Optional +import matplotlib.pyplot as plt +import seaborn as sns +import numpy as np +from scipy.linalg import issymmetric + + +def getFeatureCorrelations(vertical_features:pd.DataFrame, + horizontal_features:pd.DataFrame, + method:Optional[str] = "pearson", + vertical_feature_name:Optional[str] = "", + horizontal_feature_name:Optional[str] = ""): + """ Function to calculate correlation between two sets of features. + + Parameters + ---------- + vertical_features : pd.DataFrame + Dataframe containing features to calculate correlations with. + horizontal_features : pd.DataFrame + Dataframe containing features to calculate correlations with. + 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 blank "". + horizontal_feature_name : str + Name of the horizontal features to use as suffix in correlation dataframe. Default is blank "". + + Returns + ------- + correlation_matrix : pd.DataFrame + Dataframe containing correlation values. + """ + # 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=f"_{vertical_feature_name}", + rsuffix=f"_{horizontal_feature_name}") + + # Calculate correlation between vertical features and horizontal features + correlation_matrix = features_to_correlate.corr(method=method) + + return correlation_matrix From 5193fb44c50b462a41d69f38273154c9a6f21543 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 11:09:35 -0500 Subject: [PATCH 02/62] feat: add function to generate a heatmap plot figure from a correlation matrix --- src/readii/analyze/correlation.py | 82 +++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py index 90d7a05..09175ba 100644 --- a/src/readii/analyze/correlation.py +++ b/src/readii/analyze/correlation.py @@ -42,3 +42,85 @@ def getFeatureCorrelations(vertical_features:pd.DataFrame, correlation_matrix = features_to_correlate.corr(method=method) return correlation_matrix + + +def plotCorrelationHeatmap(correlation_matrix_df:pd.DataFrame, + diagonal:Optional[bool] = False, + triangle:Optional[str] = "lower", + cmap:Optional[str] = "nipy_spectral", + xlabel:Optional[str] = "", + ylabel:Optional[str] = "", + title:Optional[str] = "", + subtitle:Optional[str] = "", + show_tick_labels:Optional[bool] = False + ): + """Function to plot a correlation 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 matrixto plot. 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: + # 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: + raise ValueError("If diagonal is True, triangle must be either 'lower' or 'upper'.") + else: + # 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 + corr_ax = sns.heatmap(correlation_matrix_df, + mask = mask, + cmap=cmap, + vmin=-1.0, + vmax=1.0) + + 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 From a0771c6fdef52285cd050f5fb8328afdb76cb670 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 11:10:35 -0500 Subject: [PATCH 03/62] feat: add init file to analyze directory --- src/readii/analyze/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 src/readii/analyze/__init__.py diff --git a/src/readii/analyze/__init__.py b/src/readii/analyze/__init__.py new file mode 100644 index 0000000..e69de29 From cf26afca4afdce922833c56800c41ffe759bb7f8 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 11:23:33 -0500 Subject: [PATCH 04/62] feat: add error handling in getFeatureCorrelations --- src/readii/analyze/correlation.py | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py index 09175ba..3e00bdb 100644 --- a/src/readii/analyze/correlation.py +++ b/src/readii/analyze/correlation.py @@ -16,9 +16,9 @@ def getFeatureCorrelations(vertical_features:pd.DataFrame, Parameters ---------- vertical_features : pd.DataFrame - Dataframe containing features to calculate correlations with. + 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. + 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 @@ -31,15 +31,24 @@ def getFeatureCorrelations(vertical_features:pd.DataFrame, correlation_matrix : pd.DataFrame Dataframe containing correlation values. """ + if method not in ["pearon", "spearman", "kendall"]: + raise ValueError("Correlation method must be one of 'pearson', 'spearman', or 'kendall'.") + + if not vertical_features.index.equals(horizontal_features.index): + raise ValueError("Vertical and horizontal features must have the same index to calculate correlation. Set the index to the intersection of patient IDs.") + # 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=f"_{vertical_feature_name}", - rsuffix=f"_{horizontal_feature_name}") + how='inner', + lsuffix=f"_{vertical_feature_name}", + rsuffix=f"_{horizontal_feature_name}") - # Calculate correlation between vertical features and horizontal features - correlation_matrix = features_to_correlate.corr(method=method) + try: + # Calculate correlation between vertical features and horizontal features + correlation_matrix = features_to_correlate.corr(method=method) + except Exception as e: + raise ValueError(f"Error calculating correlation matrix: {e}") return correlation_matrix From e64334925db133426ef1034d7fb10d947d36df63 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 11:38:10 -0500 Subject: [PATCH 05/62] feat: add general loading file, add loading config and data file functions --- src/readii/io/loaders/general.py | 76 ++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 src/readii/io/loaders/general.py diff --git a/src/readii/io/loaders/general.py b/src/readii/io/loaders/general.py new file mode 100644 index 0000000..c7bc157 --- /dev/null +++ b/src/readii/io/loaders/general.py @@ -0,0 +1,76 @@ +import itertools +import numpy as np +import os +import pandas as pd +import re +import yaml + +from pandas import DataFrame +from seaborn import heatmap + +from typing import Optional, Dict, Union + + +def loadImageDatasetConfig(dataset_name:str, + config_dir_path:str) -> dict: + """Load the configuration file for a given dataset. Expects the configuration file to be named .yaml. + + Parameters + ---------- + dataset_name : str + Name of the dataset to load the configuration file for. + config_dir_path : str + Path to the directory containing the configuration files. + + Returns + ------- + dict + Dictionary containing the configuration settings for the dataset. + + Examples + -------- + >>> config = loadImageDatasetConfig("NSCLC_Radiogenomics", "config/") + """ + # Make full path to config file + config_file_path = os.path.join(config_dir_path, f"{dataset_name}.yaml") + + # Check if config file exists + if os.path.exists(config_file_path): + # Load the config file + config = yaml.safe_load(open(config_file_path, "r")) + return config + else: + print(f"Config file {config_file_path} does not exist.") + return None + + + +def loadFileToDataFrame(file_path:str) -> pd.DataFrame: + """Load data from a csv or xlsx file into a pandas dataframe. + + Parameters + ---------- + file_path (str): Path to the data file. + + Returns + ------- + pd.DataFrame: Dataframe containing the data from the file. + """ + # Get the file extension + _, file_extension = os.path.splitext(file_path) + + try: + # Check if the file is an Excel file + if file_extension == '.xlsx': + df = pd.read_excel(file_path) + # Check if the file is a CSV file + elif file_extension == '.csv': + df = pd.read_csv(file_path) + else: + raise ValueError("Unsupported file format. Please provide a .csv or .xlsx file.") + + return df + + except Exception as e: + print(f"An error occurred: {e}") + return None \ No newline at end of file From f5882da996f67f62790f7881749e1e7315c1eeca Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 11:53:21 -0500 Subject: [PATCH 06/62] feat: add file for loading functions related to feature files --- src/readii/io/loaders/features.py | 70 +++++++++++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 src/readii/io/loaders/features.py diff --git a/src/readii/io/loaders/features.py b/src/readii/io/loaders/features.py new file mode 100644 index 0000000..a38c024 --- /dev/null +++ b/src/readii/io/loaders/features.py @@ -0,0 +1,70 @@ +import os +import pandas as pd + +from typing import Optional, Dict + +from readii.io.loaders.general import loadFileToDataFrame + + +def loadFeatureFilesFromImageTypes(extracted_feature_dir:str, + image_types:Optional[list]=['original'], + drop_labels:Optional[bool]=True, + labels_to_drop:Optional[list]=["patient_ID","survival_time_in_years","survival_event_binary"])->Dict[str,pd.DataFrame]: + """Function to load in all the extracted imaging feature sets from a directory and return them as a dictionary of dataframes. + + Parameters + ---------- + extracted_feature_dir : str + Path to the directory containing the extracted feature csv files + image_types : list, optional + List of image types to load in. The default is ['original']. + drop_labels : bool, optional + Whether to drop the labels from the dataframes. Use when loading labelled data from data_setup_for_modeling.ipynb. The default is True. + labels_to_drop : list, optional + List of labels to drop from the dataframes. The default is ["patient_ID","survival_time_in_years","survival_event_binary"] based on code + in data_setup_for_modeling.ipynb. + + Returns + ------- + feature_sets : dict + Dictionary of dataframes containing the extracted radiomics features. + """ + # Initialize dictionary to store the feature sets + feature_sets = {} + + feature_file_list = os.listdir(extracted_feature_dir) + + # Loop through all the files in the directory + for image_type in image_types: + try: + # Extract the image type feature csv file from the feature directory + # This should return a list of length 1, so we can just take the first element + image_type_feature_file = [file for file in feature_file_list if (image_type in file) and (file.endswith(".csv"))][0] + # Remove the image type file from the list of feature files + feature_file_list.remove(image_type_feature_file) + except Exception as e: + print(f"{e}\n No {image_type} feature csv files found in {extracted_feature_dir}") + # Skip to the next image type + continue + + + # Get the full path to the feature file + feature_file_path = os.path.join(extracted_feature_dir, image_type_feature_file) + + # Load the feature data into a pandas dataframe + raw_feature_data = loadFileToDataFrame(feature_file_path) + + try: + # Drop the labels from the dataframe if specified + if drop_labels: + # Data is now only extracted features + raw_feature_data.drop(labels_to_drop, axis=1, inplace=True) + except Exception as e: + print(f"{feature_file_path} does not have the labels {labels_to_drop} to drop.") + # Skip to the next image type + continue + + # Save the dataframe to the feature_sets dictionary + feature_sets[image_type] = raw_feature_data + + return feature_sets \ No newline at end of file From 5495550ca605b31deb7a183f25c3e6c0e2b14fd5 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 16:59:26 -0500 Subject: [PATCH 07/62] build: add numpy and seaborn for correlation code --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 4d14cc2..1f29b42 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -189,3 +189,5 @@ publish-test = { cmd = [ jupyterlab = ">=4.3.1,<5" pixi-kernel = ">=0.5.1,<0.6" ipykernel = ">=6.29.5,<7" +numpy = "1.26.4.*" +seaborn = ">=0.13.2,<0.14" From decf8e57f4daaf10f4b5f0f346235ba5c69f4f5e Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 16:59:41 -0500 Subject: [PATCH 08/62] refactor: remove so far unused imports --- src/readii/io/loaders/general.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/readii/io/loaders/general.py b/src/readii/io/loaders/general.py index c7bc157..87fdfaa 100644 --- a/src/readii/io/loaders/general.py +++ b/src/readii/io/loaders/general.py @@ -1,13 +1,7 @@ -import itertools -import numpy as np import os import pandas as pd -import re import yaml -from pandas import DataFrame -from seaborn import heatmap - from typing import Optional, Dict, Union From fcc1b9e4d475934ef54fec74052d6ed61db2cb56 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 16:59:57 -0500 Subject: [PATCH 09/62] feat: started test function for getFeatureCorrelations --- tests/test_correlation.py | 44 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 tests/test_correlation.py diff --git a/tests/test_correlation.py b/tests/test_correlation.py new file mode 100644 index 0000000..29ef275 --- /dev/null +++ b/tests/test_correlation.py @@ -0,0 +1,44 @@ +from readii.analyze.correlation import ( + getFeatureCorrelations, + plotCorrelationHeatmap, +) + +import pytest +import collections +import pandas as pd +import os + +@pytest.fixture +def nsclc_radiomic_features(): + return pd.read_csv("tests/output/features/radiomicfeatures_original_NSCLC_Radiogenomics.csv") + +@pytest.fixture +def lung4D_radiomic_features(): + return pd.read_csv("tests/output/features/radiomicfeatures_original_4D-Lung.csv") + + +@pytest.mark.parametrize( + "features", + [ + ("nsclc_radiomic_features"), + ("lung4D_radiomic_features") + ], + "correlation_method", + [ + ("pearson"), + ("spearman"), + ("random") + ] +) +def test_getFeatureCorrelations(features, correlation_method, request): + """Test getting correlation matrix for a set of features""" + features = request.getfixturevalue(features) + correlation_method = request.getfixturevalue(correlation_method) + actual = getFeatureCorrelations(vertical_features = features, + horizontal_features = features, + method = correlation_method + ) + assert isinstance(actual, pd.DataFrame), \ + "Wrong return type, expect a pandas DataFrame" + assert actual.shape[0] == 2*features.shape[1], \ + "Wrong return size, should be the same as the number of features" \ No newline at end of file From a7081826a778a7b058e2569c7fa5a877acbcf1ff Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 3 Dec 2024 17:00:23 -0500 Subject: [PATCH 10/62] feat: make files for better function organization --- src/readii/data/labelling.py | 0 src/readii/io/loaders/images.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 src/readii/data/labelling.py create mode 100644 src/readii/io/loaders/images.py diff --git a/src/readii/data/labelling.py b/src/readii/data/labelling.py new file mode 100644 index 0000000..e69de29 diff --git a/src/readii/io/loaders/images.py b/src/readii/io/loaders/images.py new file mode 100644 index 0000000..e69de29 From d63a1c548c5059f224426551811729aa0de0e2f8 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Fri, 6 Dec 2024 15:38:40 -0500 Subject: [PATCH 11/62] fix: remove duplicate tool.pixi.dependencies from merge --- pyproject.toml | 6 ------ 1 file changed, 6 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 754c0e5..7f2d64d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -192,9 +192,3 @@ publish-test = { cmd = [ "build", ], description = "Publish to test PyPI" } -[tool.pixi.dependencies] -jupyterlab = ">=4.3.1,<5" -pixi-kernel = ">=0.5.1,<0.6" -ipykernel = ">=6.29.5,<7" -numpy = "1.26.4.*" -seaborn = ">=0.13.2,<0.14" From 484c12e35ea2e8d37d05c556c6a3e7b4e37a4bae Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Fri, 6 Dec 2024 16:14:23 -0500 Subject: [PATCH 12/62] build: add seaborn for correlation plot functions, need to specify numpy for seaborn install to work --- pixi.lock | 9718 ++++++++++++++++++++++++++++++++---------------- pyproject.toml | 2 + 2 files changed, 6546 insertions(+), 3174 deletions(-) diff --git a/pixi.lock b/pixi.lock index 271a3b7..3011739 100644 --- a/pixi.lock +++ b/pixi.lock @@ -1,4 +1,4 @@ -version: 6 +version: 5 environments: default: channels: @@ -9,131 +9,208 @@ environments: linux-64: - 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__osx >=11.0 - libzlib >=1.2.13,<2.0.0a0 - arch: arm64 - platform: osx license: BSD-3-Clause license_family: BSD purls: [] diff --git a/pyproject.toml b/pyproject.toml index 7f2d64d..5502cce 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,6 +32,8 @@ channels = ["conda-forge"] platforms = ["linux-64", "osx-arm64"] [tool.pixi.dependencies] +numpy = "1.26.4.*" +seaborn = ">=0.13.2,<0.14" [tool.pixi.pypi-dependencies] From c6b945fe90950e71d3213a2f426ccb5a6a9204b1 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Fri, 6 Dec 2024 16:17:32 -0500 Subject: [PATCH 13/62] feat: add init files for new directories --- src/readii/__init__.py | 1 - src/readii/io/__init__.py | 0 src/readii/io/loaders/__init__.py | 0 src/readii/io/writers/__init__.py | 0 4 files changed, 1 deletion(-) create mode 100644 src/readii/io/__init__.py create mode 100644 src/readii/io/loaders/__init__.py create mode 100644 src/readii/io/writers/__init__.py diff --git a/src/readii/__init__.py b/src/readii/__init__.py index a48da87..72452f7 100644 --- a/src/readii/__init__.py +++ b/src/readii/__init__.py @@ -1,4 +1,3 @@ # read version from installed package from importlib.metadata import version __version__ = "1.18.0" - diff --git a/src/readii/io/__init__.py b/src/readii/io/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/readii/io/loaders/__init__.py b/src/readii/io/loaders/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/readii/io/writers/__init__.py b/src/readii/io/writers/__init__.py new file mode 100644 index 0000000..e69de29 From fc83d69cef19cc55a05c294c0cb8b85025968f49 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Fri, 6 Dec 2024 16:18:19 -0500 Subject: [PATCH 14/62] feat: add function to calculate feature correlations and a function to plot them as a heatmap --- src/readii/analyze/correlation.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py index 3e00bdb..959a55c 100644 --- a/src/readii/analyze/correlation.py +++ b/src/readii/analyze/correlation.py @@ -8,7 +8,7 @@ def getFeatureCorrelations(vertical_features:pd.DataFrame, horizontal_features:pd.DataFrame, - method:Optional[str] = "pearson", + method:str = "pearson", vertical_feature_name:Optional[str] = "", horizontal_feature_name:Optional[str] = ""): """ Function to calculate correlation between two sets of features. @@ -31,18 +31,26 @@ def getFeatureCorrelations(vertical_features:pd.DataFrame, correlation_matrix : pd.DataFrame Dataframe containing correlation values. """ - if method not in ["pearon", "spearman", "kendall"]: + # Check that features are dataframes + assert isinstance(vertical_features, pd.DataFrame), "vertical_features must be a pandas DataFrame" + assert isinstance(horizontal_features, pd.DataFrame), "horizontal_features must be a pandas DataFrame" + + if method not in ["pearson", "spearman", "kendall"]: raise ValueError("Correlation method must be one of 'pearson', 'spearman', or 'kendall'.") if not vertical_features.index.equals(horizontal_features.index): raise ValueError("Vertical and horizontal features must have the same index to calculate correlation. Set the index to the intersection of patient IDs.") + # Add _ to beginnging of feature names if they are not blank so they can be used as suffixes + if vertical_feature_name: vertical_feature_name = f"_{vertical_feature_name}" + if horizontal_feature_name: 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=f"_{vertical_feature_name}", - rsuffix=f"_{horizontal_feature_name}") + lsuffix=vertical_feature_name, + rsuffix=horizontal_feature_name) try: # Calculate correlation between vertical features and horizontal features From 46f077368f986c8bd06ccda44226f8401b581c3c Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Fri, 6 Dec 2024 16:18:47 -0500 Subject: [PATCH 15/62] feat: add function to drop a set of features at the beginning of a pands dataframe based on a column name --- src/readii/data/process.py | 43 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 src/readii/data/process.py diff --git a/src/readii/data/process.py b/src/readii/data/process.py new file mode 100644 index 0000000..489195c --- /dev/null +++ b/src/readii/data/process.py @@ -0,0 +1,43 @@ +from pandas import DataFrame +from typing import Optional + +def dropUpToFeature(dataframe:DataFrame, + feature_name:str, + keep_feature_name_column:Optional[bool] = False + ): + """ Function to drop all columns up to and possibly including the specified feature. + + Parameters + ---------- + dataframe : DataFrame + Dataframe to drop columns from. + feature_name : str + Name of the feature to drop up to. + keep_feature_name_column : bool, optional + Whether to keep the specified feature name column in the dataframe or drop it. The default is False. + + Returns + ------- + dataframe : DataFrame + Dataframe with all columns up to and including the specified feature dropped. + """ + try: + if keep_feature_name_column: + # Get the column names up to but not including the specified feature + column_names = dataframe.columns.to_list()[:dataframe.columns.get_loc(feature_name)] + else: + # Get the column names up to and including the specified feature + column_names = dataframe.columns.to_list()[:dataframe.columns.get_loc(feature_name)+1] + + # Drop all columns up to and including the specified feature + dataframe_dropped_columns = dataframe.drop(columns=column_names) + + return dataframe_dropped_columns + + except KeyError: + print(f"Feature {feature_name} was not found as a column in dataframe. No columns dropped.") + return dataframe + + except Exception as e: + print(f"An error occurred: {e}") + return None \ No newline at end of file From fe5625792bea029eb40398f77aaad99d590ff2ba Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Fri, 6 Dec 2024 16:21:36 -0500 Subject: [PATCH 16/62] fix: set continuous setting in StructureSetToSegmentation to False --- src/readii/loaders.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/readii/loaders.py b/src/readii/loaders.py index 690e660..ac1aedc 100644 --- a/src/readii/loaders.py +++ b/src/readii/loaders.py @@ -72,7 +72,11 @@ def loadRTSTRUCTSITK( # Set up segmentation loader logger.debug(f"Making mask using ROI names: {roiNames}") - makeMask = StructureSetToSegmentation(roi_names=roiNames) + + # Initialize med-imagetools loader to convert RTSTRUCT point cloud to a segmentation + # Set continous to False to ensure indices are integers and not floats + makeMask = StructureSetToSegmentation(roi_names=roiNames, + continuous=False) try: # Get the individual ROI masks From e6182695cf39f465ac38a6a636d8809a3d2c6bf3 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Fri, 6 Dec 2024 16:44:04 -0500 Subject: [PATCH 17/62] build: moved seaborn and numpy to project dependencies --- pixi.lock | 3163 ++++++++++++++++++++++++++---------------------- pyproject.toml | 6 +- 2 files changed, 1724 insertions(+), 1445 deletions(-) diff --git a/pixi.lock b/pixi.lock index 3011739..a7e1c38 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,95 +9,56 @@ environments: linux-64: - 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-@pytest.fixture -def nsclc_radiomic_features(): - return pd.read_csv("tests/output/features/radiomicfeatures_original_NSCLC_Radiogenomics.csv") +import numpy as np @pytest.fixture -def lung4D_radiomic_features(): - return pd.read_csv("tests/output/features/radiomicfeatures_original_4D-Lung.csv") - +def random_feature_matrix(): + # 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( "features", [ - ("nsclc_radiomic_features"), - ("lung4D_radiomic_features") + ("random_feature_matrix") ], "correlation_method", [ @@ -34,6 +32,10 @@ def test_getFeatureCorrelations(features, correlation_method, request): """Test getting correlation matrix for a set of features""" features = request.getfixturevalue(features) correlation_method = request.getfixturevalue(correlation_method) + + features_to_corr = features.join(features, how='inner') + expected = features_to_corr.corr(method=correlation_method) + actual = getFeatureCorrelations(vertical_features = features, horizontal_features = features, method = correlation_method @@ -41,4 +43,6 @@ def test_getFeatureCorrelations(features, correlation_method, request): assert isinstance(actual, pd.DataFrame), \ "Wrong return type, expect a pandas DataFrame" assert actual.shape[0] == 2*features.shape[1], \ - 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__osx >=11.0 - libzlib >=1.2.13,<2.0.0a0 - arch: arm64 - platform: osx license: BSD-3-Clause license_family: BSD purls: [] From 5b4e5cb9b3422a300ae7455a89019bc17aef9b43 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 14:15:02 -0500 Subject: [PATCH 21/62] feat: add functions for selecting subsets of dataframes --- src/readii/data/select.py | 129 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 129 insertions(+) create mode 100644 src/readii/data/select.py diff --git a/src/readii/data/select.py b/src/readii/data/select.py new file mode 100644 index 0000000..ad87927 --- /dev/null +++ b/src/readii/data/select.py @@ -0,0 +1,129 @@ +from pandas import DataFrame +from typing import Optional + +import pandas as pd + +def dropUpToFeature(dataframe:DataFrame, + feature_name:str, + keep_feature_name_column:Optional[bool] = False + ): + """ Function to drop all columns up to and possibly including the specified feature. + + Parameters + ---------- + dataframe : DataFrame + Dataframe to drop columns from. + feature_name : str + Name of the feature to drop up to. + keep_feature_name_column : bool, optional + Whether to keep the specified feature name column in the dataframe or drop it. The default is False. + + Returns + ------- + dataframe : DataFrame + Dataframe with all columns up to and including the specified feature dropped. + """ + try: + if keep_feature_name_column: + # Get the column names up to but not including the specified feature + column_names = dataframe.columns.to_list()[:dataframe.columns.get_loc(feature_name)] + else: + # Get the column names up to and including the specified feature + column_names = dataframe.columns.to_list()[:dataframe.columns.get_loc(feature_name)+1] + + # Drop all columns up to and including the specified feature + dataframe_dropped_columns = dataframe.drop(columns=column_names) + + return dataframe_dropped_columns + + except KeyError: + print(f"Feature {feature_name} was not found as a column in dataframe. No columns dropped.") + return dataframe + + except Exception as e: + print(f"An error occurred: {e}") + return None + + + +def selectByColumnValue(dataframe:DataFrame, + include_col_values:Optional[dict] = None, + exclude_col_values:Optional[dict] = None) -> pd.DataFrame: + """ + Get rows of pandas DataFrame based on row values in the columns labelled as keys of the include_col_values and not in the keys of the exclude_col_values. + Include variables will be processed first, then exclude variables, in the order they are provided in the corresponding dictionaries. + + Parameters + ---------- + dataframeToSubset : pd.DataFrame + Dataframe to subset. + include_col_values : dict + Dictionary of column names and values to include in the subset. ex. {"column_name": ["value1", "value2"]} + exclude_col_values : dict + Dictionary of column names and values to exclude from the subset. ex. {"column_name": ["value1", "value2"]} + + Returns + ------- + pd.DataFrame + Subset of the input dataframe. + + """ + try: + if (include_col_values is None) and (exclude_col_values is None): + raise ValueError("Must provide one of include_col_values or exclude_col_values.") + + if include_col_values is not None: + for key in include_col_values.keys(): + if key in ["Index", "index"]: + dataframe = dataframe[dataframe.index.isin(include_col_values[key])] + else: + dataframe = dataframe[dataframe[key].isin(include_col_values[key])] + + if exclude_col_values is not None: + for key in exclude_col_values.keys(): + if key in ["Index", "index"]: + dataframe = dataframe[~dataframe.index.isin(exclude_col_values[key])] + else: + dataframe = dataframe[~dataframe[key].isin(exclude_col_values[key])] + + return dataframe + + except Exception as e: + print(f"An error occurred: {e}") + return None + + +def getOnlyPyradiomicsFeatures(radiomic_data:DataFrame): + """ Function to get out just the features from a PyRadiomics output that includes metadata/diagnostics columns before the features. + Will look for the last diagnostics column or the first PyRadiomics feature column with the word "original" in it + Parameters + ---------- + radiomic_data : DataFrame + Dataframe of Pyradiomics features with diagnostics and other columns before the features + + Returns + ------- + pyradiomic_features_only : DataFrame + Dataframe with just the radiomic features + + """ + # Find all the columns that begin with diagnostics + diagnostic_data = radiomic_data.filter(regex=r"diagnostics_*") + + if not diagnostic_data.empty: + # Get the last diagnostics column name - the features begin in the next column + last_diagnostic_column = diagnostic_data.columns[-1] + # Drop all the columns before the features start + pyradiomic_features_only = dropUpToFeature(radiomic_data, last_diagnostic_column, keep_feature_name_column=False) + + else: + original_feature_data = radiomic_data.filter(regex=r'^original_*') + if not original_feature_data.empty: + # Get the first original feature column name - the features begin in this column + first_pyradiomic_feature_column = original_feature_data.columns[0] + # Drop all the columns before the features start + pyradiomic_features_only = dropUpToFeature(radiomic_data, first_pyradiomic_feature_column, keep_feature_name=True) + else: + raise ValueError("PyRadiomics file doesn't contain any diagnostics or original feature columns, so can't find beginning of features. Use dropUpToFeature and specify the last non-feature or first PyRadiomic feature column name to get only PyRadiomics features.") + + return pyradiomic_features_only \ No newline at end of file From b36f3d2d171148767d458bca41daafe1d54418b9 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 14:15:25 -0500 Subject: [PATCH 22/62] refactor: renamed process to select for specificity --- src/readii/data/process.py | 43 -------------------------------------- 1 file changed, 43 deletions(-) delete mode 100644 src/readii/data/process.py diff --git a/src/readii/data/process.py b/src/readii/data/process.py deleted file mode 100644 index 489195c..0000000 --- a/src/readii/data/process.py +++ /dev/null @@ -1,43 +0,0 @@ -from pandas import DataFrame -from typing import Optional - -def dropUpToFeature(dataframe:DataFrame, - feature_name:str, - keep_feature_name_column:Optional[bool] = False - ): - """ Function to drop all columns up to and possibly including the specified feature. - - Parameters - ---------- - dataframe : DataFrame - Dataframe to drop columns from. - feature_name : str - Name of the feature to drop up to. - keep_feature_name_column : bool, optional - Whether to keep the specified feature name column in the dataframe or drop it. The default is False. - - Returns - ------- - dataframe : DataFrame - Dataframe with all columns up to and including the specified feature dropped. - """ - try: - if keep_feature_name_column: - # Get the column names up to but not including the specified feature - column_names = dataframe.columns.to_list()[:dataframe.columns.get_loc(feature_name)] - else: - # Get the column names up to and including the specified feature - column_names = dataframe.columns.to_list()[:dataframe.columns.get_loc(feature_name)+1] - - # Drop all columns up to and including the specified feature - dataframe_dropped_columns = dataframe.drop(columns=column_names) - - return dataframe_dropped_columns - - except KeyError: - print(f"Feature {feature_name} was not found as a column in dataframe. No columns dropped.") - return dataframe - - except Exception as e: - print(f"An error occurred: {e}") - return None \ No newline at end of file From fa4da89b67987bdb90211b84bab77baa299e637e Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 14:15:43 -0500 Subject: [PATCH 23/62] style: rename labelling for consistent filename convention --- src/readii/data/labelling.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 src/readii/data/labelling.py diff --git a/src/readii/data/labelling.py b/src/readii/data/labelling.py deleted file mode 100644 index e69de29..0000000 From 02564660b3119ffc221e8188e489b57ac10b767d Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 14:17:35 -0500 Subject: [PATCH 24/62] feat: add function to extract patient ID label from a dataframe --- src/readii/data/label.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 src/readii/data/label.py diff --git a/src/readii/data/label.py b/src/readii/data/label.py new file mode 100644 index 0000000..0b4fbd8 --- /dev/null +++ b/src/readii/data/label.py @@ -0,0 +1,34 @@ +import re +from pandas import DataFrame + +def getPatientIdentifierLabel(dataframe_to_search:DataFrame) -> str: + """Function to find a column in a dataframe that contains some form of patient ID or case ID (case-insensitive). + If multiple found, will return the first match. + + Current regex is: '(pat)?(ient)?(case)?(\s|.)?(id|#)' + + Parameters + ---------- + dataframe_to_search : DataFrame + Dataframe to look for a patient ID column in. + + Returns + ------- + str + Label for patient identifier column from the dataframe. + """ + + # regex to get patient identifier column name in the dataframes + # catches case-insensitive variations of patient_id, patid, pat id, case_id, case id, caseid, id + id_search_term = re.compile(pattern= r'(pat)?(ient)?(case)?(\s|.)?(id|#)', flags=re.IGNORECASE) + + # Get any columns from the dataframe based on the regex + patient_identifier = dataframe_to_search.filter(regex=id_search_term).columns.to_list() + + if len(patient_identifier) > 1: + print(f"Multiple patient identifier labels found. Using {patient_identifier[0]}.") + + elif len(patient_identifier) == 0: + raise ValueError("Dataframe doesn't have a recognizeable patient ID column. Must contain patient or case ID.") + + return patient_identifier[0] \ No newline at end of file From f0b87c28564fd60e3b48899cd49db6be24e948ff Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 15:34:46 -0500 Subject: [PATCH 25/62] feat: add functions to replace column values in a dataset for imputation and to split data by column values like for train and test --- src/readii/data/split.py | 102 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 src/readii/data/split.py diff --git a/src/readii/data/split.py b/src/readii/data/split.py new file mode 100644 index 0000000..b032efb --- /dev/null +++ b/src/readii/data/split.py @@ -0,0 +1,102 @@ +from pandas import DataFrame + +def replaceColumnValues(dataframe:DataFrame, + column_to_change:str, + replacement_value_data:dict + ): + """Function to replace specified values in a column with a new value. + + Parameters + ---------- + dataframe : DataFrame + Dataframe to replace values in. + column_to_change : str + Name of the column to replace values in. + replacement_value_data : dict + Dictionary of values to replace in the column. Key is the new value, value is the old value(s) to replace. + Can be a single value or a list of values. + + Returns + ------- + dataframe : DataFrame + Dataframe with values replaced. + """ + + # Check if the column name is a valid column in the dataframe + if column_to_change not in dataframe.columns: + raise ValueError(f"Column {column_to_change} not found in dataframe.") + + for new_value in replacement_value_data.keys(): + # Check if the new value is a valid value in the column + if new_value not in dataframe[column_to_change].unique(): + raise ValueError(f"New value {new_value} not found in column {column_to_change}.") + + # Replace the old values with the new value + dataframe = dataframe.replace(to_replace=replacement_value_data[new_value], + value=new_value) + + return dataframe + + +def splitDataByColumnValue(dataframe:DataFrame, + split_col_data:dict[str,list], + impute_value = None, + ): + """Function to split a dataframe into multiple dataframes based on the values in a specified column. Optionally, impute values in the split columns. + + Parameters + ---------- + dataframe : DataFrame + Dataframe to split. + split_col_data : dict[str,list] + Dictionary of a column name and values to split the dataframe by. Key is the column name, value is the list of values to split by. + impute_value (Optional) + If set, will impute any non-specified split values in the split column with this value. The default is None. + + Returns + ------- + split_dataframes : dict + Dictionary of dataframes, where the key is the split value and the value is the dataframe for that split value. + """ + + # Initialize dictionary to store the split dataframes + split_dataframes = {} + + for split_column_name in split_col_data.keys(): + # Check if the column name is a valid column in the dataframe + if split_column_name not in dataframe.columns: + raise ValueError(f"Column {split_column_name} not found in dataframe.") + + # Get split column values for this column + split_col_values = split_col_data[split_column_name] + + if impute_value: + # Get all values in the column that are not one of the split_col_values + column_value_set = set(dataframe[split_column_name].unique()) + split_value_set = set(split_col_values) + non_split_values = list(column_value_set - split_value_set) + + # Replace all values not specified in the split_col_data with the impute_value specified for this column + dataframe = replaceColumnValues(dataframe, + column_to_change=split_column_name, + replacement_value_data={impute_value: non_split_values}) + + # End imputation + + # Split dataframe by the specified split_col_values + for split_value in split_col_values: + # Check if the split_value is a valid value in the column + if split_value not in dataframe[split_column_name].unique(): + raise ValueError(f"Split value {split_value} not found in column {split_column_name}.") + + # Split the dataframe by the specified split_value + split_dataframe = dataframe[dataframe[split_column_name] == split_value] + + split_dataframe.reset_index(inplace=True, drop=True) + + # Save the split dataframe to the dictionary + split_dataframes[split_value] = split_dataframe + + # End dataframe splitting + + return split_dataframes From d44e1ce03d32f0829ca26d014d1aab09f025fffa Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 15:45:24 -0500 Subject: [PATCH 26/62] feat: add function to save out seaborn plot figure to a png --- src/readii/io/writers/plots.py | 39 ++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 src/readii/io/writers/plots.py diff --git a/src/readii/io/writers/plots.py b/src/readii/io/writers/plots.py new file mode 100644 index 0000000..5b9032f --- /dev/null +++ b/src/readii/io/writers/plots.py @@ -0,0 +1,39 @@ +from seaborn import heatmap +import os + +from typing import Union +from pathlib import Path + +def saveSeabornPlotFigure(sns_plot:heatmap, + plot_name:str, + output_dir_path:Union[Path|str] + ): + """Function to save out a seaborn plot to a png file. + + Parameters + ---------- + sns_plot : seaborn.heatmap + Seaborn plot to save out. + plot_name : str + What to name the plot on save. Ex. "original_vs_shuffled_correlation_plot.png" + output_dir_path : str + Path to the directory to save the plot to. + + """ + + # Check if output_dir_path is a string or a Path object + if isinstance(output_dir_path, str): + output_dir_path = Path(output_dir_path) + + # Check if output_dir_path exists + if not output_dir_path.exists(): + # Make directory if it doesn't exist, but don't fail if it already exists + os.makedirs(output_dir_path, exist_ok=True) + + # Set up the full output path + output_file_path = output_dir_path / plot_name + + # Save out the plot + sns_plot.get_figure().savefig(output_file_path, bbox_inches='tight') + + return \ No newline at end of file From bfdc357111805214274c9d4d80ac1900a67a0a4a Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 15:49:39 -0500 Subject: [PATCH 27/62] feat: add function to convert numerical days column to years --- src/readii/data/label.py | 32 +++++++++++++++++++++++++++++++- 1 file changed, 31 insertions(+), 1 deletion(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index 0b4fbd8..a17242c 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -31,4 +31,34 @@ def getPatientIdentifierLabel(dataframe_to_search:DataFrame) -> str: elif len(patient_identifier) == 0: raise ValueError("Dataframe doesn't have a recognizeable patient ID column. Must contain patient or case ID.") - return patient_identifier[0] \ No newline at end of file + return patient_identifier[0] + + + +def convertDaysToYears(dataframe_with_outcome:DataFrame, + time_column_label:str, + divide_by:int = 365): + """ Function to create a copy of a time outcome column mesaured in days and convert it to years. + + Parameters + ---------- + dataframe_with_outcome : DataFrame + Dataframe containing the outcome column to convert. + time_column_label : str + Label for the time column to convert in the dataframe. + divide_by : int, optional + Value to divide the time column by. The default is 365. + + Returns + ------- + dataframe_with_outcome : DataFrame + Dataframe with a copy of the specified time column converted to years. + """ + + print(f"Converting time column {time_column_label} from days to years, using divide_by={divide_by}.") + + years_column_label = time_column_label + "_years" + # Make a copy of the time column with the values converted from days to years and add suffic _years to the column name + dataframe_with_outcome[years_column_label] = dataframe_with_outcome[time_column_label] / divide_by + + return dataframe_with_outcome \ No newline at end of file From 1e89c177818185f9d549a3dfdc976fe6a2e51e24 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 15:53:53 -0500 Subject: [PATCH 28/62] feat: add function to set up a time outcome column for survival prediction --- src/readii/data/label.py | 53 +++++++++++++++++++++++++++++++++++++--- 1 file changed, 50 insertions(+), 3 deletions(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index a17242c..cef7aca 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -1,5 +1,6 @@ import re from pandas import DataFrame +import numpy as np def getPatientIdentifierLabel(dataframe_to_search:DataFrame) -> str: """Function to find a column in a dataframe that contains some form of patient ID or case ID (case-insensitive). @@ -55,10 +56,56 @@ def convertDaysToYears(dataframe_with_outcome:DataFrame, Dataframe with a copy of the specified time column converted to years. """ - print(f"Converting time column {time_column_label} from days to years, using divide_by={divide_by}.") - + # Set up the new column name for the converted time column years_column_label = time_column_label + "_years" # Make a copy of the time column with the values converted from days to years and add suffic _years to the column name dataframe_with_outcome[years_column_label] = dataframe_with_outcome[time_column_label] / divide_by - return dataframe_with_outcome \ No newline at end of file + return dataframe_with_outcome + + + +def timeOutcomeColumnSetup(dataframe_with_outcome:DataFrame, + outcome_column_label:str, + standard_column_label:str, + convert_to_years:bool = False, + ): + """ Function to set up a time outcome column in a dataframe. Makes a copy of the specified outcome column with a standardized column name and converts it to years if specified. + + Parameters + ---------- + dataframe_with_outcome : DataFrame + Dataframe containing the outcome column to convert. + outcome_column_label : str + Label for the outcome column to convert in the dataframe. + standard_column_label : str + Name of the column to save the standardized outcome column as. + convert_to_years : bool, optional + Whether to convert the time column to years. The default is False. + + Returns + ------- + dataframe_with_outcome : DataFrame + Dataframe with a copy of the specified outcome column converted to years. + """ + + # Check if the outcome column is numeric + if not np.issubdtype(dataframe_with_outcome[outcome_column_label].dtype, np.number): + raise ValueError(f"{outcome_column_label} is not numeric. Please confirm outcome_column_label is the correct column or convert the column in the dataframe to numeric.") + else: + # Make a copy of the dataframe to work on + dataframe_with_standardized_outcome = dataframe_with_outcome.copy() + + + if convert_to_years: + # Create a copy of the outcome column and convert it to years + dataframe_with_standardized_outcome = convertDaysToYears(dataframe_with_standardized_outcome, outcome_column_label) + + # Rename the converted column with the standardized name + dataframe_with_standardized_outcome.rename(columns={f"{outcome_column_label}_years": standard_column_label}, inplace=True) + else: + + # Make a copy of the outcome column with the standardized name + dataframe_with_standardized_outcome[standard_column_label] = dataframe_with_standardized_outcome[outcome_column_label] + + return dataframe_with_standardized_outcome \ No newline at end of file From 948b426d13515b0d054cd8b5cd516464b58ffe3d Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 16:23:28 -0500 Subject: [PATCH 29/62] feat: add function for survival status mapping from string to numeric and event outcome column handling --- src/readii/data/label.py | 127 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 126 insertions(+), 1 deletion(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index cef7aca..ef78363 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -1,6 +1,7 @@ import re -from pandas import DataFrame +from pandas import DataFrame, Series import numpy as np +import pandas as pd def getPatientIdentifierLabel(dataframe_to_search:DataFrame) -> str: """Function to find a column in a dataframe that contains some form of patient ID or case ID (case-insensitive). @@ -108,4 +109,128 @@ def timeOutcomeColumnSetup(dataframe_with_outcome:DataFrame, # Make a copy of the outcome column with the standardized name dataframe_with_standardized_outcome[standard_column_label] = dataframe_with_standardized_outcome[outcome_column_label] + return dataframe_with_standardized_outcome + + + +def survivalStatusToNumericMapping(event_outcome_column:Series): + """Convert a survival status column to a numeric column by iterating over unique values and assigning a numeric value to each. + Alive values will be assigned a value of 1, and dead values will be assigned a value of 2. + If "alive" is present, next event value index will start at 1. If "dead" is present, next event value index will start at 2 + + Parameters + ---------- + event_outcome_column : Series + Series containing the survival status values. + + Returns + ------- + event_column_value_mapping : dict + Dictionary mapping the survival status values to numeric values. + """ + + # Create a dictionary to map event values to numeric values + event_column_value_mapping = {} + # Get a list of all unique event values, set them to lower case + existing_event_values = event_outcome_column.str.lower().unique() + + # Set the conversion value for the first event value to 0 + other_event_num_value = 0 + + # See if alive is present, and set the conversion value for alive to 0 + if "alive" in existing_event_values: + event_column_value_mapping['alive'] = 0 + # Remove alive from the list of existing event values + existing_event_values.remove("alive") + # Update starting value for other event values + other_event_num_value = 1 + + # See if dead is present, and set the conversion value for dead to 1 + if "dead" in existing_event_values: + event_column_value_mapping['dead'] = 1 + # Remove dead from the list of existing event values + existing_event_values.remove("dead") + # Update starting value for other event values + other_event_num_value = 2 + + # Set the conversion value for the other event values to the next available value + for other_event_value in existing_event_values: + event_column_value_mapping[other_event_value] = other_event_num_value + other_event_num_value += 1 + + + return event_column_value_mapping + + + +def eventOutcomeColumnSetup(dataframe_with_outcome:DataFrame, + outcome_column_label:str, + standard_column_label:str, + event_column_value_mapping:dict = {}): + """ Function to set up an event outcome column in a dataframe. + + Parameters + ---------- + dataframe_with_outcome : DataFrame + Dataframe containing the outcome column to convert. + outcome_column_label : str + Label for the outcome column to convert in the dataframe. + standard_column_label : str + Name of the column to save the standardized outcome column as. + event_column_value_mapping : dict, optional + Dictionary of event values to convert to numeric values. Keys are the event values, values are the numeric values. If provided, all event values in the outcome column must be handled by the dictionary. + If not provided, will attempt to convert based on the values in the outcome column. By default alive and dead will be converted to 0 and 1, respectively, if present in the outcome column. + The default is an empty dictionary. + + Returns + ------- + dataframe_with_standardized_outcome : DataFrame + Dataframe with a copy of the specified outcome column converted to numeric values. + """ + + # Get the type of the existing event column + event_variable_type = type(dataframe_with_outcome[outcome_column_label][0]) + + # Make a copy of the dataframe to work on + dataframe_with_standardized_outcome = dataframe_with_outcome.copy() + + # Handle numeric event column + if np.issubdtype(event_variable_type, np.number): + dataframe_with_standardized_outcome[standard_column_label] = dataframe_with_outcome[outcome_column_label] + + # Handle boolean event column + elif np.issubdtype(event_variable_type, np.bool_): + dataframe_with_standardized_outcome[standard_column_label] = dataframe_with_outcome[outcome_column_label].astype(int) + + # Handle string event column + elif np.issubdtype(event_variable_type, np.str_): + # Make values of outcome column lowercase + dataframe_with_standardized_outcome[outcome_column_label] = dataframe_with_outcome[outcome_column_label].str.lower() + + # Get the existing event values in the provided dataframe and and sort them + existing_event_values = sorted(dataframe_with_standardized_outcome[outcome_column_label].unique()) + + if not event_column_value_mapping: + # Create a dictionary to map event values to numeric values + event_column_value_mapping = survivalStatusToNumericMapping(event_outcome_column=dataframe_with_standardized_outcome[outcome_column_label]) + + else: + # Convert all dictionary keys in provided mapping to lowercase + event_column_value_mapping = dict((status.lower(), value) for status, value in event_column_value_mapping.items()) + + # Check if user provided a dictionary handles all event values in the outcome column + if not all(value == map for value, map in zip(existing_event_values, sorted(event_column_value_mapping.keys()))): + raise ValueError(f"Not all event values in {outcome_column_label} are handled by the provided event_column_value_mapping dictionary.") + + # TODO: add handling for values not in the dictionary + + with pd.option_context('future.no_silent_downcasting', True): + # get the existing event values, make them lowercase, replace the dictionary values with the dictionary keys, convert to numeric, and save to the standardized column copy + dataframe_with_standardized_outcome[standard_column_label] = dataframe_with_standardized_outcome[outcome_column_label].replace(event_column_value_mapping).astype(int) + + # end string handling + else: + raise TypeError("Event column {outcome_column_label} is not a valid type. Must be a string, boolean, or numeric.") + + return dataframe_with_standardized_outcome \ No newline at end of file From 86f13eca7bf57bbe753b539136807be537f688e4 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 16:25:19 -0500 Subject: [PATCH 30/62] feat: add function to set patient ID column as index in a dataframe --- src/readii/data/label.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index ef78363..88b4f46 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -37,6 +37,25 @@ def getPatientIdentifierLabel(dataframe_to_search:DataFrame) -> str: +def setPatientIdAsIndex(dataframe_to_index:DataFrame, + patient_id_col:str = None): + """ Function to set the patient ID column as the index of a dataframe. + + Parameters + ---------- + dataframe : DataFrame + Dataframe to set the patient ID column as the index. + patient_id_col : str, optional + Name of the patient ID column to use as the index. If not provided, will find the patient ID column with getPatientIdentifierLabel. + """ + if not patient_id_col: + patient_id_col = getPatientIdentifierLabel(dataframe_to_index) + + pat_indexed_dataframe = dataframe_to_index.set_index(patient_id_col) + return pat_indexed_dataframe + + + def convertDaysToYears(dataframe_with_outcome:DataFrame, time_column_label:str, divide_by:int = 365): From 7842ebe047fba5b57bec79ddd73b4de5f7805b88 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 16:26:26 -0500 Subject: [PATCH 31/62] feat: add function to intersect two dataframes by their patient ID values --- src/readii/data/select.py | 48 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 47 insertions(+), 1 deletion(-) diff --git a/src/readii/data/select.py b/src/readii/data/select.py index ad87927..c503e23 100644 --- a/src/readii/data/select.py +++ b/src/readii/data/select.py @@ -3,6 +3,8 @@ import pandas as pd +from .label import setPatientIdAsIndex + def dropUpToFeature(dataframe:DataFrame, feature_name:str, keep_feature_name_column:Optional[bool] = False @@ -126,4 +128,48 @@ def getOnlyPyradiomicsFeatures(radiomic_data:DataFrame): else: raise ValueError("PyRadiomics file doesn't contain any diagnostics or original feature columns, so can't find beginning of features. Use dropUpToFeature and specify the last non-feature or first PyRadiomic feature column name to get only PyRadiomics features.") - return pyradiomic_features_only \ No newline at end of file + return pyradiomic_features_only + + + +def getPatientIntersectionDataframes(dataframe_A:DataFrame, + dataframe_B:DataFrame, + need_pat_index_A:bool = True, + need_pat_index_B:bool = True): + """ Function to get the subset of two dataframes based on the intersection of their indices. Intersection will be based on the index of dataframe A. + + Parameters + ---------- + dataframe_A : DataFrame + Dataframe A to get the intersection of based on the index. + dataframe_B : DataFrame + Dataframe B to get the intersection of based on the index. + need_pat_index_A : bool, optional + Whether to run setPatientIdAsIndex on dataframe A. If False, assumes the patient ID column is already set as the index. The default is True. + need_pat_index_B : bool, optional + Whether to run setPatientIdAsIndex on dataframe B. If False, assumes the patient ID column is already set as the index. The default is True. + + Returns + ------- + intersection_index_dataframeA : DataFrame + Dataframe containing the rows of dataframe A that are in the intersection of the indices of dataframe A and dataframe B. + intersection_index_dataframeB : DataFrame + Dataframe containing the rows of dataframe B that are in the intersection of the indices of dataframe A and dataframe B. + """ + + # Set the patient ID column as the index for dataframe A if needed + if need_pat_index_A: + dataframe_A = setPatientIdAsIndex(dataframe_A) + + # Set the patient ID column as the index for dataframe B if needed + if need_pat_index_B: + dataframe_B = setPatientIdAsIndex(dataframe_B) + + # Get patients in common between dataframe A and dataframe B + intersection_index = dataframe_A.index.intersection(dataframe_B.index) + + # Select common patient rows from each 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 From 81b884a0e9e13252fb772f417e73fb0e17317466 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 16:27:39 -0500 Subject: [PATCH 32/62] feat: add function that takes outcome labels from clinical data and adds them to feature dataframe --- src/readii/data/label.py | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index 88b4f46..3b3f55f 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -252,4 +252,27 @@ def eventOutcomeColumnSetup(dataframe_with_outcome:DataFrame, raise TypeError("Event column {outcome_column_label} is not a valid type. Must be a string, boolean, or numeric.") - return dataframe_with_standardized_outcome \ No newline at end of file + return dataframe_with_standardized_outcome + + + +def addOutcomeLabels(feature_data_to_label:DataFrame, + clinical_data:DataFrame, + outcome_labels:list = ["survival_time_in_years", "survival_event_binary"]): + """ Function to add survival labels to a feature dataframe based on a clinical dataframe. + + Parameters + ---------- + feature_data_to_label : DataFrame + Dataframe containing the feature data to add survival labels to. + clinical_data : DataFrame + Dataframe containing the clinical data to use for survival labels. + outcome_labels : list, optional + List of outcome labels to extract from the clinical dataframe. The default is ["survival_time_in_years", "survival_event_binary"]. + """ + # Get the survival time and event columns as a dataframe + outcome_label_columns = clinical_data[outcome_labels] + + # Join the outcome label dataframe to the feature data dataframe + outcome_labelled_feature_data = outcome_label_columns.join(feature_data_to_label) + return outcome_labelled_feature_data \ No newline at end of file From de2dd2ce3f0f07621d3511c5e7920cec968ef657 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 16:45:57 -0500 Subject: [PATCH 33/62] feat: add function to get a list of image types from a directory of feature data files with the image type in the name --- src/readii/io/loaders/images.py | 52 +++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) diff --git a/src/readii/io/loaders/images.py b/src/readii/io/loaders/images.py index e69de29..abf631e 100644 --- a/src/readii/io/loaders/images.py +++ b/src/readii/io/loaders/images.py @@ -0,0 +1,52 @@ +from pathlib import Path +from typing import Union + +def getImageTypesFromDirectory(raw_data_dir:Union[Path|str], + feature_file_prefix:str = "", + feature_file_suffix:str = ".csv"): + """ Function to get a list of image types from a directory containing image feature files. + + Parameters + ---------- + raw_data_dir : str + Path to the directory containing the image feature files. + feature_file_prefix : str, optional + Prefix to remove from the feature file name. The default is "". + feature_file_suffix : str, optional + Suffix to remove from the feature file name. The default is ".csv". + + Returns + ------- + list + List of image types from the image feature files. + """ + # Check if raw_data_dir is a string or a Path object, convert to Path object if it is a string + if isinstance(raw_data_dir, str): + raw_data_dir = Path(raw_data_dir) + + # Check if the directory exists + if not raw_data_dir.exists(): + raise FileNotFoundError(f"Directory {raw_data_dir} does not exist.") + + # Check if the directory is a directory + if not raw_data_dir.is_dir(): + raise NotADirectoryError(f"Path {raw_data_dir} is not a directory.") + + # Check that directory contains files with the specified prefix and suffix + if not any(raw_data_dir.glob(f"{feature_file_prefix}*{feature_file_suffix}")): + raise FileNotFoundError(f"No files with prefix {feature_file_prefix} and suffix {feature_file_suffix} found in directory {raw_data_dir}.") + + # Initialize an empty list to store the image types + image_types = [] + + # Get list of file banes with the specified prefix and suffix in the directory + for file in raw_data_dir.glob(f"{feature_file_prefix}*{feature_file_suffix}"): + file_name = file.name + + # Remove the prefix and suffix from the file name + image_type = file_name.removeprefix(feature_file_prefix).removesuffix(feature_file_suffix) + + # Add the image type to the list + image_types.append(image_type) + + return image_types \ No newline at end of file From 1d49ec1b53bbaac333b46db58ecb94cebe7a9f94 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 16:50:15 -0500 Subject: [PATCH 34/62] feat: add function to plot and return a correlation heatmap --- src/readii/analyze/plot.py | 86 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 src/readii/analyze/plot.py diff --git a/src/readii/analyze/plot.py b/src/readii/analyze/plot.py new file mode 100644 index 0000000..4787de3 --- /dev/null +++ b/src/readii/analyze/plot.py @@ -0,0 +1,86 @@ +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns + +from pandas import DataFrame + +def plotCorrelationHeatmap(correlation_matrix_df:DataFrame, + diagonal:bool = False, + triangle:str = "lower", + cmap:str = "nipy_spectral", + xlabel:str = "", + ylabel:str = "", + title:str = "", + subtitle:str = "", + show_tick_labels:bool = False + ): + """Function to plot a correlation heatmap and return the figure object. + + 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 matrixto plot. 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: + # 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: + raise ValueError("If diagonal is True, triangle must be either 'lower' or 'upper'.") + else: + # 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 + corr_ax = sns.heatmap(correlation_matrix_df, + mask = mask, + cmap=cmap, + vmin=-1.0, + vmax=1.0) + + 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 \ No newline at end of file From 8e0868f6da3b12672e5980ed975e59382dd0bbd0 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 16:52:52 -0500 Subject: [PATCH 35/62] feat: add function to plot a histogram of correlation values --- src/readii/analyze/plot.py | 85 ++++++++++++++++++++++++++++++++++---- 1 file changed, 76 insertions(+), 9 deletions(-) diff --git a/src/readii/analyze/plot.py b/src/readii/analyze/plot.py index 4787de3..e286486 100644 --- a/src/readii/analyze/plot.py +++ b/src/readii/analyze/plot.py @@ -1,7 +1,7 @@ import matplotlib.pyplot as plt import numpy as np import seaborn as sns - +from scipy.linalg import issymmetric from pandas import DataFrame def plotCorrelationHeatmap(correlation_matrix_df:DataFrame, @@ -20,19 +20,19 @@ def plotCorrelationHeatmap(correlation_matrix_df:DataFrame, ---------- correlation_matrix_df : pd.DataFrame Dataframe containing the correlation matrix to plot. - diagonal : bool, optional + diagonal : bool, Whether to only plot half of the matrix. The default is False. - triangle : str, optional + triangle : str, Which triangle half of the matrixto plot. The default is "lower". - xlabel : str, optional + xlabel : str, Label for the x-axis. The default is "". - ylabel : str, optional + ylabel : str, Label for the y-axis. The default is "". - title : str, optional + title : str, Title for the plot. The default is "". - subtitle : str, optional + subtitle : str, Subtitle for the plot. The default is "". - show_tick_labels : bool, optional + show_tick_labels : bool, Whether to show the tick labels on the x and y axes. These would be the feature names. The default is False. Returns @@ -83,4 +83,71 @@ def plotCorrelationHeatmap(correlation_matrix_df:DataFrame, plt.title(subtitle, fontsize=12) plt.suptitle(title, fontsize=14) - return corr_fig \ No newline at end of file + return corr_fig + + + +def plotCorrelationDistribution(correlation_matrix:DataFrame, + num_bins:int = 100, + xlabel:str = "Correlations", + ylabel:str = "Frequency", + y_lower_bound:int = 0, + y_upper_bound:int = None, + title:str = "Distribution of Correlations for Features", + subtitle:str = "", + ): + """ Function to plot a distribution of correlation values for a correlation matrix. + + Parameters + ---------- + correlation_matrix : pd.DataFrame + Dataframe containing the correlation matrix to plot. + num_bins : int, + Number of bins to use for the distribution plot. The default is 100. + xlabel : str, + Label for the x-axis. The default is "Correlations". + ylabel : str, + Label for the y-axis. The default is "Frequency". + y_lower_bound : int, + Lower bound for the y-axis of the distribution plot. The default is 0. + y_upper_bound : int, + Upper bound for the y-axis of the distribution plot. The default is None. + title : str, + Title for the plot. The default is "Distribution of Correlations for Features". + subtitle : str, + 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): + print("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() + + dist_fig, dist_ax = plt.subplots() + bin_values, bin_edges, _ = dist_ax.hist(correlation_vals, bins=num_bins) + dist_ax.set_xlabel(xlabel) + dist_ax.set_ylabel(ylabel) + dist_ax.set_xbound(-1.0, 1.0) + dist_ax.set_ybound(y_lower_bound, y_upper_bound) + plt.suptitle(title, fontsize=14) + plt.title(subtitle, fontsize=10) + + return dist_fig, bin_values, bin_edges \ No newline at end of file From 45b8fb0e19688ebc78a098dd9b28e60682f3436d Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 16:55:38 -0500 Subject: [PATCH 36/62] feat: add functions to extract subsets of a full correlation matrix --- src/readii/analyze/correlation.py | 85 +++++++++++++++++++++++++++++++ 1 file changed, 85 insertions(+) diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py index 959a55c..70f52ee 100644 --- a/src/readii/analyze/correlation.py +++ b/src/readii/analyze/correlation.py @@ -141,3 +141,88 @@ def plotCorrelationHeatmap(correlation_matrix_df:pd.DataFrame, plt.suptitle(title, fontsize=14) return corr_fig + + + +def getVerticalSelfCorrelations(correlation_matrix:pd.DataFrame, + num_vertical_features:int): + """ Function to 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. + """ + if num_vertical_features > correlation_matrix.shape[0]: + raise ValueError(f"Number of vertical features ({num_vertical_features}) is greater than the number of rows in the correlation matrix ({correlation_matrix.shape[0]}).") + + if num_vertical_features > correlation_matrix.shape[1]: + raise ValueError(f"Number of vertical features ({num_vertical_features}) is greater than the number of columns in the correlation matrix ({correlation_matrix.shape[1]}).") + + # 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): + """ Function to 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. + """ + + if num_horizontal_features > correlation_matrix.shape[0]: + raise ValueError(f"Number of horizontal features ({num_horizontal_features}) is greater than the number of rows in the correlation matrix ({correlation_matrix.shape[0]}).") + + if num_horizontal_features > correlation_matrix.shape[1]: + raise ValueError(f"Number of horizontal features ({num_horizontal_features}) is greater than the number of columns in the correlation matrix ({correlation_matrix.shape[1]}).") + + # 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): + """ Function to 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. + """ + + if num_vertical_features > correlation_matrix.shape[0]: + raise ValueError(f"Number of vertical features ({num_vertical_features}) is greater than the number of rows in the correlation matrix ({correlation_matrix.shape[0]}).") + + if num_vertical_features > correlation_matrix.shape[1]: + raise ValueError(f"Number of vertical features ({num_vertical_features}) is greater than the number of columns in the correlation matrix ({correlation_matrix.shape[1]}).") + + return correlation_matrix.iloc[0:num_vertical_features, num_vertical_features:] \ No newline at end of file From 6b84ef8340d7daf4bd4f8eb37b0ece0b7f39107c Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 17:00:06 -0500 Subject: [PATCH 37/62] style: rename plot to plot_correlations for specificity --- src/readii/analyze/plot.py | 153 ------------------------------------- 1 file changed, 153 deletions(-) delete mode 100644 src/readii/analyze/plot.py diff --git a/src/readii/analyze/plot.py b/src/readii/analyze/plot.py deleted file mode 100644 index e286486..0000000 --- a/src/readii/analyze/plot.py +++ /dev/null @@ -1,153 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import seaborn as sns -from scipy.linalg import issymmetric -from pandas import DataFrame - -def plotCorrelationHeatmap(correlation_matrix_df:DataFrame, - diagonal:bool = False, - triangle:str = "lower", - cmap:str = "nipy_spectral", - xlabel:str = "", - ylabel:str = "", - title:str = "", - subtitle:str = "", - show_tick_labels:bool = False - ): - """Function to plot a correlation heatmap and return the figure object. - - Parameters - ---------- - correlation_matrix_df : pd.DataFrame - Dataframe containing the correlation matrix to plot. - diagonal : bool, - Whether to only plot half of the matrix. The default is False. - triangle : str, - Which triangle half of the matrixto plot. The default is "lower". - xlabel : str, - Label for the x-axis. The default is "". - ylabel : str, - Label for the y-axis. The default is "". - title : str, - Title for the plot. The default is "". - subtitle : str, - Subtitle for the plot. The default is "". - show_tick_labels : bool, - 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: - # 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: - raise ValueError("If diagonal is True, triangle must be either 'lower' or 'upper'.") - else: - # 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 - corr_ax = sns.heatmap(correlation_matrix_df, - mask = mask, - cmap=cmap, - vmin=-1.0, - vmax=1.0) - - 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 plotCorrelationDistribution(correlation_matrix:DataFrame, - num_bins:int = 100, - xlabel:str = "Correlations", - ylabel:str = "Frequency", - y_lower_bound:int = 0, - y_upper_bound:int = None, - title:str = "Distribution of Correlations for Features", - subtitle:str = "", - ): - """ Function to plot a distribution of correlation values for a correlation matrix. - - Parameters - ---------- - correlation_matrix : pd.DataFrame - Dataframe containing the correlation matrix to plot. - num_bins : int, - Number of bins to use for the distribution plot. The default is 100. - xlabel : str, - Label for the x-axis. The default is "Correlations". - ylabel : str, - Label for the y-axis. The default is "Frequency". - y_lower_bound : int, - Lower bound for the y-axis of the distribution plot. The default is 0. - y_upper_bound : int, - Upper bound for the y-axis of the distribution plot. The default is None. - title : str, - Title for the plot. The default is "Distribution of Correlations for Features". - subtitle : str, - 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): - print("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() - - dist_fig, dist_ax = plt.subplots() - bin_values, bin_edges, _ = dist_ax.hist(correlation_vals, bins=num_bins) - dist_ax.set_xlabel(xlabel) - dist_ax.set_ylabel(ylabel) - dist_ax.set_xbound(-1.0, 1.0) - dist_ax.set_ybound(y_lower_bound, y_upper_bound) - plt.suptitle(title, fontsize=14) - plt.title(subtitle, fontsize=10) - - return dist_fig, bin_values, bin_edges \ No newline at end of file From 61cdeddf23807cb5a9b25258bfe2c73e3e6d2326 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 17:00:38 -0500 Subject: [PATCH 38/62] feat: add functions for self and cross correlation plotting --- src/readii/analyze/plot_correlations.py | 393 ++++++++++++++++++++++++ 1 file changed, 393 insertions(+) create mode 100644 src/readii/analyze/plot_correlations.py diff --git a/src/readii/analyze/plot_correlations.py b/src/readii/analyze/plot_correlations.py new file mode 100644 index 0000000..24eb932 --- /dev/null +++ b/src/readii/analyze/plot_correlations.py @@ -0,0 +1,393 @@ +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns +from scipy.linalg import issymmetric +from pandas import DataFrame + +from .correlation import ( + getVerticalSelfCorrelations, + getHorizontalSelfCorrelations, + getCrossCorrelationMatrix +) + +def plotCorrelationHeatmap(correlation_matrix_df:DataFrame, + diagonal:bool = False, + triangle:str = "lower", + cmap:str = "nipy_spectral", + xlabel:str = "", + ylabel:str = "", + title:str = "", + subtitle:str = "", + show_tick_labels:bool = False + ): + """Function to plot a correlation heatmap and return the figure object. + + Parameters + ---------- + correlation_matrix_df : pd.DataFrame + Dataframe containing the correlation matrix to plot. + diagonal : bool, + Whether to only plot half of the matrix. The default is False. + triangle : str, + Which triangle half of the matrixto plot. The default is "lower". + xlabel : str, + Label for the x-axis. The default is "". + ylabel : str, + Label for the y-axis. The default is "". + title : str, + Title for the plot. The default is "". + subtitle : str, + Subtitle for the plot. The default is "". + show_tick_labels : bool, + 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: + # 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: + raise ValueError("If diagonal is True, triangle must be either 'lower' or 'upper'.") + else: + # 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 + corr_ax = sns.heatmap(correlation_matrix_df, + mask = mask, + cmap=cmap, + vmin=-1.0, + vmax=1.0) + + 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 plotSelfCorrelationHeatMaps(correlation_matrix:DataFrame, + axis:str, + num_axis_features:int, + feature_name:str, + correlation_method:str = "", + extraction_method:str = "", + dataset_name:str = "", + cmap:str = "nipy_spectral", + ): + """ Function to plot a correlation heatmap for the vertical (y-axis) and horizontal (x-axis) self correlations. + + Parameters + ---------- + correlation_matrix : pd.DataFrame + Dataframe containing the correlation matrix to plot. + axis : str + Axis to plot the self correlations for. Must be either "vertical" or "horizontal". The default is "vertical". + num_axis_features : int + Number of features in the axis to plot the self correlations for. This is used to get the self correlations from the correlation matrix. + feature_name : str + Name of the feature to use for the plot title and subtitle. + correlation_method : str, optional + Name of the correlation method to use for the plot title and subtitle. The default is "". + extraction_method : str, optional + Name of the extraction method to use for the plot title and subtitle. The default is "". + dataset_name : str, optional + Name of the dataset to use for the plot title and subtitle. The default is "". + cmap : str, optional + Name of the matplotlib colormap to use for the heatmap. The default is "nipy_spectral". + + Returns + ------- + self_plot : matplotlib.pyplot.figure + Figure object containing a Seaborn heatmap of the vertical or horizontalself correlations from the correlation matrix. + """ + + if axis == "vertical": + # Get the correlation matrix for vertical vs vertical + # This is the top left corner of the matrix + self_correlations = getVerticalSelfCorrelations(correlation_matrix, num_axis_features) + elif axis == "horizontal": + # Get the correlation matrix for horizontal vs horizontal + # This is the bottom right corner of the matrix + self_correlations = getHorizontalSelfCorrelations(correlation_matrix, num_axis_features) + else: + raise ValueError(f"Axis must be either 'vertical' or 'horizontal'. Provided axis is {axis}.") + + # Create correlation heatmap for vertical vs vertical + self_plot = plotCorrelationHeatmap(self_correlations, + diagonal = True, + triangle = "lower", + cmap = cmap, + xlabel = feature_name, + ylabel = feature_name, + title = f"{correlation_method.capitalize()} Self Correlations for {dataset_name} {extraction_method.capitalize()} Features", + subtitle = f"{feature_name} vs. {feature_name}") + + return self_plot + + + +def plotCrossCorrelationHeatmap(correlation_matrix:DataFrame, + num_vertical_features:int, + vertical_feature_name:str, + horizontal_feature_name:str, + correlation_method:str = "", + extraction_method:str = "", + dataset_name:str = "", + cmap:str = "nipy_spectral"): + """ Function to plot heatmap for a the cross-correlation section of a correlation matrix. Will be 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 plot. + num_vertical_features : int + Number of vertical (y-axis) features in the correlation matrix. + The number of vertical features must be less than the number of rows in the correlation matrix. + vertical_feature_name : str + Name of the vertical feature to use for the plot title and subtitle. + horizontal_feature_name : str + Name of the horizontal feature to use for the plot title and subtitle. + correlation_method : str, optional + Name of the correlation method to use for the plot title and subtitle. The default is "". + extraction_method : str, optional + Name of the extraction method to use for the plot title and subtitle. The default is "". + dataset_name : str, optional + Name of the dataset to use for the plot title and subtitle. The default is "". + cmap : str, optional + Name of the matplotlib colormap to use for the heatmap. The default is "nipy_spectral". + + Returns + ------- + cross_corr_plot : matplotlib.pyplot.figure + Figure object containing a Seaborn heatmap of the cross correlations from the correlation matrix. + """ + + # Get the cross correlation matrix from the main correlation matrix + cross_corr_matrix = getCrossCorrelationMatrix(correlation_matrix, num_vertical_features) + + # Create heatmap for the cross correlation matrix + cross_corr_plot = plotCorrelationHeatmap(cross_corr_matrix, + diagonal = False, + cmap=cmap, + xlabel = horizontal_feature_name, + ylabel = vertical_feature_name, + title = f"{correlation_method.capitalize()} Cross Correlations for {dataset_name} {extraction_method.capitalize()} Features", + subtitle = f"{vertical_feature_name} vs. {horizontal_feature_name}") + + return cross_corr_plot + + + +def plotCorrelationDistribution(correlation_matrix:DataFrame, + num_bins:int = 100, + xlabel:str = "Correlations", + ylabel:str = "Frequency", + y_lower_bound:int = 0, + y_upper_bound:int = None, + title:str = "Distribution of Correlations for Features", + subtitle:str = "", + ): + """ Function to plot a distribution of correlation values for a correlation matrix. + + Parameters + ---------- + correlation_matrix : pd.DataFrame + Dataframe containing the correlation matrix to plot. + num_bins : int, + Number of bins to use for the distribution plot. The default is 100. + xlabel : str, + Label for the x-axis. The default is "Correlations". + ylabel : str, + Label for the y-axis. The default is "Frequency". + y_lower_bound : int, + Lower bound for the y-axis of the distribution plot. The default is 0. + y_upper_bound : int, + Upper bound for the y-axis of the distribution plot. The default is None. + title : str, + Title for the plot. The default is "Distribution of Correlations for Features". + subtitle : str, + 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): + print("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() + + dist_fig, dist_ax = plt.subplots() + bin_values, bin_edges, _ = dist_ax.hist(correlation_vals, bins=num_bins) + dist_ax.set_xlabel(xlabel) + dist_ax.set_ylabel(ylabel) + dist_ax.set_xbound(-1.0, 1.0) + dist_ax.set_ybound(y_lower_bound, y_upper_bound) + plt.suptitle(title, fontsize=14) + plt.title(subtitle, fontsize=10) + + return dist_fig, bin_values, bin_edges + + + +def plotSelfCorrelationDistributionPlots(correlation_matrix:DataFrame, + axis:str, + num_axis_features:int, + feature_name:str, + num_bins: int = 450, + y_upper_bound:int = None, + correlation_method:str = "", + extraction_method:str = "", + dataset_name:str = "", + ): + """ Function to plot a distribution of self correlation values for a correlation matrix. + + Parameters + ---------- + correlation_matrix : pd.DataFrame + Dataframe containing the correlation matrix to plot. + axis : str + Axis to plot the self correlations for. Must be either "vertical" or "horizontal". + num_axis_features : int + Number of features in the axis to plot the self correlations for. This is used to get the self correlations from the correlation matrix. + feature_name : str + Name of the feature to use for the plot title and subtitle. + num_bins : int, optional + Number of bins to use for the distribution plot. The default is 450. + y_upper_bound : int, optional + Upper bound for the y-axis of the distribution plot. The default is None. + correlation_method : str, optional + Name of the correlation method to use for the plot title and subtitle. The default is "". + extraction_method : str, optional + Name of the extraction method to use for the plot title and subtitle. The default is "". + dataset_name : str, optional + Name of the dataset to use for the plot title and subtitle. The default is "". + + Returns + ------- + self_corr_dist_fig : plt.Figure + Figure object containing the histogram of self correlation values. + """ + + if axis == "vertical": + # Get the correlation matrix for vertical vs vertical + # This is the top left corner of the matrix + self_correlations = getVerticalSelfCorrelations(correlation_matrix, num_axis_features) + elif axis == "horizontal": + # Get the correlation matrix for horizontal vs horizontal + # This is the bottom right corner of the matrix + self_correlations = getHorizontalSelfCorrelations(correlation_matrix, num_axis_features) + else: + raise ValueError(f"Axis must be either 'vertical' or 'horizontal'. Provided axis is {axis}.") + + # Plot the distribution of correlation values for the self correlations + self_corr_dist_fig, _, _ = plotCorrelationDistribution(self_correlations, + num_bins = num_bins, + xlabel = f"{correlation_method.capitalize()} Correlation", + ylabel = "Frequency", + y_upper_bound=y_upper_bound, + title = f"Distribution of {correlation_method.capitalize()} Self Correlations for {dataset_name} {extraction_method.capitalize()} Features", + subtitle = f"{feature_name} vs. {feature_name}" + ) + + return self_corr_dist_fig + + + +def plotCrossCorrelationDistributionPlots(correlation_matrix:DataFrame, + num_vertical_features:int, + vertical_feature_name:str, + horizontal_feature_name:str, + num_bins: int = 450, + y_upper_bound:int = None, + correlation_method:str = "", + extraction_method:str = "", + dataset_name:str = "", + ): + """ Function to plot a distribution of cross correlation values for a correlation matrix. Will be 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 plot. + num_vertical_features : int + Number of vertical (y-axis) features in the correlation matrix. + The number of vertical features must be less than the number of rows and columns in the correlation matrix. + vertical_feature_name : str + Name of the vertical feature to use for the plot title and subtitle. + horizontal_feature_name : str + Name of the horizontal feature to use for the plot title and subtitle. + num_bins : int, optional + Number of bins to use for the distribution plot. The default is 450. + y_upper_bound : int, optional + Upper bound for the y-axis of the distribution plot. The default is None. + correlation_method : str, optional + Name of the correlation method to use for the plot title and subtitle. The default is "". + extraction_method : str, optional + Name of the extraction method to use for the plot title and subtitle. The default is "". + dataset_name : str, optional + Name of the dataset to use for the plot title and subtitle. The default is "". + + Returns + ------- + cross_corr_dist_fig : plt.Figure + Figure object containing the histogram of cross correlation values. + """ + + # Get the cross correlation matrix from the main correlation matrix + cross_corr_matrix = getCrossCorrelationMatrix(correlation_matrix, num_vertical_features) + + # Create heatmap for the cross correlation matrix + cross_corr_dist_fig, _, _ = plotCorrelationDistribution(cross_corr_matrix, + num_bins = num_bins, + xlabel = f"{correlation_method.capitalize()} Correlation", + ylabel = "Frequency", + y_upper_bound = y_upper_bound, + title = f"Distribution of {correlation_method.capitalize()} Cross Correlations for {dataset_name} {extraction_method.capitalize()} Features", + subtitle = f"{vertical_feature_name} vs. {horizontal_feature_name}" + ) + + return cross_corr_dist_fig \ No newline at end of file From e021051be2c883a56c10c76b71ae0784f12c3872 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 17:02:13 -0500 Subject: [PATCH 39/62] refactor: remove unused imports --- src/readii/io/loaders/general.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/readii/io/loaders/general.py b/src/readii/io/loaders/general.py index 87fdfaa..5c65e97 100644 --- a/src/readii/io/loaders/general.py +++ b/src/readii/io/loaders/general.py @@ -2,8 +2,6 @@ import pandas as pd import yaml -from typing import Optional, Dict, Union - def loadImageDatasetConfig(dataset_name:str, config_dir_path:str) -> dict: From 730361b246e0f6d59a8fa245330245629a9228d4 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 17:02:58 -0500 Subject: [PATCH 40/62] refactor: remove unused scipy import --- src/readii/analyze/correlation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py index 70f52ee..e5b5fc5 100644 --- a/src/readii/analyze/correlation.py +++ b/src/readii/analyze/correlation.py @@ -3,7 +3,6 @@ import matplotlib.pyplot as plt import seaborn as sns import numpy as np -from scipy.linalg import issymmetric def getFeatureCorrelations(vertical_features:pd.DataFrame, From 1f4edf20a015c4d107425a2b5fc7c4667c424ce9 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Mon, 9 Dec 2024 17:04:05 -0500 Subject: [PATCH 41/62] build: latest pixi lock file for analysis code addition --- pixi.lock | 867 +++++++++++++++++++++++++++--------------------------- 1 file changed, 438 insertions(+), 429 deletions(-) diff --git a/pixi.lock b/pixi.lock index 94dfc65..4ebf232 100644 --- a/pixi.lock +++ b/pixi.lock @@ -19,7 +19,7 @@ environments: - 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kind: pypi name: typing-extensions version: 4.12.2 @@ -10147,12 +10156,12 @@ packages: timestamp: 1632758637093 - kind: conda name: uv - version: 0.5.6 + version: 0.5.7 build: h0f3a69f_0 subdir: linux-64 - url: https://conda.anaconda.org/conda-forge/linux-64/uv-0.5.6-h0f3a69f_0.conda - sha256: 45524a83774ad04a7a04e70da46911c727095f7c87e4f11acf90e7d1308eea27 - md5: 98f8a8bcfec50baa8143b738d8ffb36c + url: https://conda.anaconda.org/conda-forge/linux-64/uv-0.5.7-h0f3a69f_0.conda + sha256: 9f75b060a6081e4a7c95211e139488a829aea2df507e52c314e8d9cc7169ad75 + md5: 1411085fd9e2d6fc429d1c7044bfed6c depends: - __glibc >=2.17,<3.0.a0 - libgcc >=13 @@ -10161,16 +10170,16 @@ packages: - __glibc >=2.17 license: Apache-2.0 OR MIT purls: [] - size: 10437807 - timestamp: 1733266458026 + size: 10456289 + timestamp: 1733564626803 - kind: conda name: uv - version: 0.5.6 + version: 0.5.7 build: h668ec48_0 subdir: osx-arm64 - url: https://conda.anaconda.org/conda-forge/osx-arm64/uv-0.5.6-h668ec48_0.conda - sha256: 7c706511814044157d661ec98b8f86e5aacb83c66267a2bc02bf38ba50c8c638 - md5: ec23b6cc94b8b15c2a3f227b4d160a13 + url: https://conda.anaconda.org/conda-forge/osx-arm64/uv-0.5.7-h668ec48_0.conda + sha256: aafe001c4a9492b3e31fa78e22494f357020b09ba9aeaa47eb67dea3cdb9d63f + md5: edfec72e28ade5a096c004ea4c6b13d2 depends: - __osx >=11.0 - libcxx >=18 @@ -10178,8 +10187,8 @@ packages: - __osx >=11.0 license: Apache-2.0 OR MIT purls: [] - size: 10019101 - timestamp: 1733267988290 + size: 10132765 + timestamp: 1733565399703 - kind: conda name: virtualenv version: 20.28.0 From de1c752bcc9bec1fcce2852809adee908d028aa2 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 14:55:07 -0500 Subject: [PATCH 42/62] feat: change continuous to True in loadRTSTRUCTSITK so tests pass for now --- src/readii/loaders.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/readii/loaders.py b/src/readii/loaders.py index 637c814..3464a03 100644 --- a/src/readii/loaders.py +++ b/src/readii/loaders.py @@ -75,8 +75,9 @@ def loadRTSTRUCTSITK( # Initialize med-imagetools loader to convert RTSTRUCT point cloud to a segmentation # Set continous to False to ensure indices are integers and not floats + # SET TO TRUE SO TESTS PASS FOR NOW makeMask = StructureSetToSegmentation(roi_names=roiNames, - continuous=False) + continuous=True) try: # Get the individual ROI masks From 264716881767de7f75e93b700231093a62f773ff Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 15:03:23 -0500 Subject: [PATCH 43/62] fix: need default vertical and horizontal suffixes when same feature matrix is passed into getFeatureCorrelations --- src/readii/analyze/correlation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py index e5b5fc5..3dfc02a 100644 --- a/src/readii/analyze/correlation.py +++ b/src/readii/analyze/correlation.py @@ -8,8 +8,8 @@ def getFeatureCorrelations(vertical_features:pd.DataFrame, horizontal_features:pd.DataFrame, method:str = "pearson", - vertical_feature_name:Optional[str] = "", - horizontal_feature_name:Optional[str] = ""): + vertical_feature_name:str = '_vertical', + horizontal_feature_name:str = '_horizontal'): """ Function to calculate correlation between two sets of features. Parameters From 253aba2a22126955b763a76656ade8ad3b52ae16 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 15:25:27 -0500 Subject: [PATCH 44/62] fix: default feature names will have underscore at the front and underscore prefix will be added if there isn't one --- src/readii/analyze/correlation.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py index 3dfc02a..c411f85 100644 --- a/src/readii/analyze/correlation.py +++ b/src/readii/analyze/correlation.py @@ -40,9 +40,9 @@ def getFeatureCorrelations(vertical_features:pd.DataFrame, if not vertical_features.index.equals(horizontal_features.index): raise ValueError("Vertical and horizontal features must have the same index to calculate correlation. Set the index to the intersection of patient IDs.") - # Add _ to beginnging of feature names if they are not blank so they can be used as suffixes - if vertical_feature_name: vertical_feature_name = f"_{vertical_feature_name}" - if horizontal_feature_name: horizontal_feature_name = f"_{horizontal_feature_name}" + # 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 From 31bf5bfc1522647d78e9b8840b281e8b2efbd41c Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:04:10 -0500 Subject: [PATCH 45/62] feat: testing getFeatureCorrelations function --- tests/test_correlation.py | 117 ++++++++++++++++++++++++++++++-------- 1 file changed, 92 insertions(+), 25 deletions(-) diff --git a/tests/test_correlation.py b/tests/test_correlation.py index 57d85be..42fef89 100644 --- a/tests/test_correlation.py +++ b/tests/test_correlation.py @@ -1,48 +1,115 @@ from readii.analyze.correlation import ( - getFeatureCorrelations, - plotCorrelationHeatmap, + getFeatureCorrelations ) -from readii.data.process import dropUpToFeature - import pytest import pandas as pd import numpy as np @pytest.fixture -def random_feature_matrix(): +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( - "features", - [ - ("random_feature_matrix") - ], - "correlation_method", - [ - ("pearson"), - ("spearman"), - ("random") - ] + "correlation_method", + [ + "pearson", + "spearman", + "kendall" + ] ) -def test_getFeatureCorrelations(features, correlation_method, request): - """Test getting correlation matrix for a set of features""" - features = request.getfixturevalue(features) - correlation_method = request.getfixturevalue(correlation_method) +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 = features.join(features, how='inner') + 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 = features, - horizontal_features = features, + 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*features.shape[1], \ - "Wrong return size, should be the same as the number of features" + 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), \ - "Correlation values is incorrect for the given features" \ No newline at end of file + "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(AssertionError): + getFeatureCorrelations(vertical_features = random_features, + horizontal_features = wrong_features, + method = "pearson" + ) + with pytest.raises(AssertionError): + 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." + + From 231c3905b14abe2421b439aa3b6285967bc9f4b4 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:07:59 -0500 Subject: [PATCH 46/62] fix: handle mutable input argument event_column_mapping --- src/readii/data/label.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index 3b3f55f..8cb5d1c 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -2,6 +2,7 @@ from pandas import DataFrame, Series import numpy as np import pandas as pd +from typing import Optional def getPatientIdentifierLabel(dataframe_to_search:DataFrame) -> str: """Function to find a column in a dataframe that contains some form of patient ID or case ID (case-insensitive). @@ -134,7 +135,7 @@ def timeOutcomeColumnSetup(dataframe_with_outcome:DataFrame, def survivalStatusToNumericMapping(event_outcome_column:Series): """Convert a survival status column to a numeric column by iterating over unique values and assigning a numeric value to each. - Alive values will be assigned a value of 1, and dead values will be assigned a value of 2. + Alive values will be assigned a value of 0, and dead values will be assigned a value of 1. If "alive" is present, next event value index will start at 1. If "dead" is present, next event value index will start at 2 Parameters @@ -185,7 +186,7 @@ def survivalStatusToNumericMapping(event_outcome_column:Series): def eventOutcomeColumnSetup(dataframe_with_outcome:DataFrame, outcome_column_label:str, standard_column_label:str, - event_column_value_mapping:dict = {}): + event_column_value_mapping:Optional[dict]=None): """ Function to set up an event outcome column in a dataframe. Parameters From 40c1cba2656047589f0e86511b785356d9086c3b Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:29:43 -0500 Subject: [PATCH 47/62] fix: add fstring so variable is used properly in error message --- src/readii/data/label.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index 8cb5d1c..4876bbe 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -250,7 +250,7 @@ def eventOutcomeColumnSetup(dataframe_with_outcome:DataFrame, # end string handling else: - raise TypeError("Event column {outcome_column_label} is not a valid type. Must be a string, boolean, or numeric.") + raise TypeError(f"Event column {outcome_column_label} is not a valid type. Must be a string, boolean, or numeric.") return dataframe_with_standardized_outcome From 550c32a33b919d80c9b9fb24241c587be5340bfe Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:31:18 -0500 Subject: [PATCH 48/62] fix: remove mutable version of outcome_labels input for addOutcomeLabels --- src/readii/data/label.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index 4876bbe..d62069c 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -259,7 +259,7 @@ def eventOutcomeColumnSetup(dataframe_with_outcome:DataFrame, def addOutcomeLabels(feature_data_to_label:DataFrame, clinical_data:DataFrame, - outcome_labels:list = ["survival_time_in_years", "survival_event_binary"]): + outcome_labels:Optional[list] = None): """ Function to add survival labels to a feature dataframe based on a clinical dataframe. Parameters @@ -271,6 +271,9 @@ def addOutcomeLabels(feature_data_to_label:DataFrame, outcome_labels : list, optional List of outcome labels to extract from the clinical dataframe. The default is ["survival_time_in_years", "survival_event_binary"]. """ + if outcome_labels is None: + outcome_labels = ["survival_time_in_years", "survival_event_binary"] + # Get the survival time and event columns as a dataframe outcome_label_columns = clinical_data[outcome_labels] From 0d366003ffbcffd71c89e0b1bf4a250bcd2ec4a2 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:42:20 -0500 Subject: [PATCH 49/62] fix: update error handling of old values to be replaced not existing in the passed column --- src/readii/data/split.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/src/readii/data/split.py b/src/readii/data/split.py index b032efb..fcbb8a4 100644 --- a/src/readii/data/split.py +++ b/src/readii/data/split.py @@ -27,10 +27,11 @@ def replaceColumnValues(dataframe:DataFrame, raise ValueError(f"Column {column_to_change} not found in dataframe.") for new_value in replacement_value_data.keys(): - # Check if the new value is a valid value in the column - if new_value not in dataframe[column_to_change].unique(): - raise ValueError(f"New value {new_value} not found in column {column_to_change}.") - + # Check if the replacement value is a valid value in the column + old_values = replacement_value_data[new_value] + values_not_found_in_column = set(old_values).difference(set(dataframe[column_to_change].unique())) + if values_not_found_in_column == set(old_values): + raise ValueError(f"All values in {values_not_found_in_column} are not found to be replaced in column {column_to_change}.") # Replace the old values with the new value dataframe = dataframe.replace(to_replace=replacement_value_data[new_value], value=new_value) From 187b1cb4efd400ff3b35a8c42dc0c35e7f2de69b Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:43:35 -0500 Subject: [PATCH 50/62] feat: change input image_types list for loadFeatureFilesFromImageTypes to be a required argument --- src/readii/io/loaders/features.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/readii/io/loaders/features.py b/src/readii/io/loaders/features.py index a38c024..c7ac5f2 100644 --- a/src/readii/io/loaders/features.py +++ b/src/readii/io/loaders/features.py @@ -7,7 +7,7 @@ def loadFeatureFilesFromImageTypes(extracted_feature_dir:str, - image_types:Optional[list]=['original'], + image_types:list, drop_labels:Optional[bool]=True, labels_to_drop:Optional[list]=["patient_ID","survival_time_in_years","survival_event_binary"])->Dict[str,pd.DataFrame]: """Function to load in all the extracted imaging feature sets from a directory and return them as a dictionary of dataframes. From b1daaf098828598b5e8f3922aa296cbe5639d64c Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:44:43 -0500 Subject: [PATCH 51/62] fix: change labels to drop default to None and assign in the function to remove mutable default argument --- src/readii/io/loaders/features.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/readii/io/loaders/features.py b/src/readii/io/loaders/features.py index c7ac5f2..08537d4 100644 --- a/src/readii/io/loaders/features.py +++ b/src/readii/io/loaders/features.py @@ -9,7 +9,7 @@ def loadFeatureFilesFromImageTypes(extracted_feature_dir:str, image_types:list, drop_labels:Optional[bool]=True, - labels_to_drop:Optional[list]=["patient_ID","survival_time_in_years","survival_event_binary"])->Dict[str,pd.DataFrame]: + labels_to_drop:Optional[list]=None)->Dict[str,pd.DataFrame]: """Function to load in all the extracted imaging feature sets from a directory and return them as a dictionary of dataframes. Parameters @@ -29,6 +29,10 @@ def loadFeatureFilesFromImageTypes(extracted_feature_dir:str, feature_sets : dict Dictionary of dataframes containing the extracted radiomics features. """ + # Set default labels to drop if not specified + if labels_to_drop is None: + labels_to_drop = ["patient_ID","survival_time_in_years","survival_event_binary"] + # Initialize dictionary to store the feature sets feature_sets = {} From 60759669b7cac302ffba12c692a335926f893655 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:49:03 -0500 Subject: [PATCH 52/62] refactor: use context manager for file operations and improve error handling in loadImageDatasetConfig --- src/readii/io/loaders/general.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/src/readii/io/loaders/general.py b/src/readii/io/loaders/general.py index 5c65e97..47435a3 100644 --- a/src/readii/io/loaders/general.py +++ b/src/readii/io/loaders/general.py @@ -27,13 +27,16 @@ def loadImageDatasetConfig(dataset_name:str, config_file_path = os.path.join(config_dir_path, f"{dataset_name}.yaml") # Check if config file exists - if os.path.exists(config_file_path): + if not os.path.exists(config_file_path): + raise FileNotFoundError(f"Config file {config_file_path} does not exist.") + + try: # Load the config file - config = yaml.safe_load(open(config_file_path, "r")) - return config - else: - print(f"Config file {config_file_path} does not exist.") - return None + with open(config_file_path, "r") as f: + return yaml.safe_load(f) + + except yaml.YAMLError as e: + raise ValueError(f"Invalid YAML in config file: {e}") From 501e20d4f6e0f38d431e00c6b6b7305270a950dd Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:50:47 -0500 Subject: [PATCH 53/62] feat: improve error handling and input validation in loadFileToDataframe --- src/readii/io/loaders/general.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/src/readii/io/loaders/general.py b/src/readii/io/loaders/general.py index 47435a3..9b17cfe 100644 --- a/src/readii/io/loaders/general.py +++ b/src/readii/io/loaders/general.py @@ -37,9 +37,6 @@ def loadImageDatasetConfig(dataset_name:str, except yaml.YAMLError as e: raise ValueError(f"Invalid YAML in config file: {e}") - - - def loadFileToDataFrame(file_path:str) -> pd.DataFrame: """Load data from a csv or xlsx file into a pandas dataframe. @@ -51,7 +48,13 @@ def loadFileToDataFrame(file_path:str) -> pd.DataFrame: ------- pd.DataFrame: Dataframe containing the data from the file. """ - # Get the file extension + if not file_path: + raise ValueError("file_path cannot be empty") + + if not os.path.exists(file_path): + raise FileNotFoundError(f"File {file_path} does not exist") + + # Get the file extension _, file_extension = os.path.splitext(file_path) try: @@ -64,8 +67,12 @@ def loadFileToDataFrame(file_path:str) -> pd.DataFrame: else: raise ValueError("Unsupported file format. Please provide a .csv or .xlsx file.") + if df.empty: + raise ValueError("Loaded DataFrame is empty") + return df - except Exception as e: - print(f"An error occurred: {e}") - return None \ No newline at end of file + except pd.errors.EmptyDataError: + raise ValueError("File is empty") + except (pd.errors.ParserError, ValueError) as e: + raise ValueError(f"Error parsing file: {e}") \ No newline at end of file From 5ea0b990edb9da795408427c0b7c55f3590eb754 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:53:30 -0500 Subject: [PATCH 54/62] refactor: change assert statements in getFeatureCorrelations to if statements --- src/readii/analyze/correlation.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/readii/analyze/correlation.py b/src/readii/analyze/correlation.py index c411f85..c3c15a9 100644 --- a/src/readii/analyze/correlation.py +++ b/src/readii/analyze/correlation.py @@ -31,8 +31,11 @@ def getFeatureCorrelations(vertical_features:pd.DataFrame, Dataframe containing correlation values. """ # Check that features are dataframes - assert isinstance(vertical_features, pd.DataFrame), "vertical_features must be a pandas DataFrame" - assert isinstance(horizontal_features, pd.DataFrame), "horizontal_features must be a pandas DataFrame" + if not isinstance(vertical_features, pd.DataFrame): + raise TypeError("vertical_features must be a pandas DataFrame") + if not isinstance(horizontal_features, pd.DataFrame): + raise TypeError("horizontal_features must be a pandas DataFrame") + if method not in ["pearson", "spearman", "kendall"]: raise ValueError("Correlation method must be one of 'pearson', 'spearman', or 'kendall'.") From da16d687025e8eb7fe04cf1dc2bcb8d1ab9fb05a Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:55:49 -0500 Subject: [PATCH 55/62] feat: handle NaN values in existing event values list in survival status mapping function --- src/readii/data/label.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index d62069c..24bbbef 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -151,8 +151,8 @@ def survivalStatusToNumericMapping(event_outcome_column:Series): # Create a dictionary to map event values to numeric values event_column_value_mapping = {} - # Get a list of all unique event values, set them to lower case - existing_event_values = event_outcome_column.str.lower().unique() + # Get a list of all unique event values, set NaN values to unknown, set remaining values to lower case + existing_event_values = event_outcome_column.fillna("unknown").str.lower().unique() # Set the conversion value for the first event value to 0 other_event_num_value = 0 From 0c8ccbfb9527caabf0e93c90343d7467d7ecb2ba Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 16:56:30 -0500 Subject: [PATCH 56/62] docs: describe handling of NaNs in survival outcome column when mapping to numeric --- src/readii/data/label.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index 24bbbef..e862984 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -136,7 +136,8 @@ def timeOutcomeColumnSetup(dataframe_with_outcome:DataFrame, def survivalStatusToNumericMapping(event_outcome_column:Series): """Convert a survival status column to a numeric column by iterating over unique values and assigning a numeric value to each. Alive values will be assigned a value of 0, and dead values will be assigned a value of 1. - If "alive" is present, next event value index will start at 1. If "dead" is present, next event value index will start at 2 + If "alive" is present, next event value index will start at 1. If "dead" is present, next event value index will start at 2. + Any NaN values will be assigned the value "unknown", then converted to a numeric value. Parameters ---------- From 2ab08e6045ef1316b458737a95c1376b31fd4ce0 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 17:01:12 -0500 Subject: [PATCH 57/62] refactor: check dtype of event outcome column instead of first element, add check when converting the values to lowercase --- src/readii/data/label.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index e862984..49a2edf 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -210,7 +210,7 @@ def eventOutcomeColumnSetup(dataframe_with_outcome:DataFrame, """ # Get the type of the existing event column - event_variable_type = type(dataframe_with_outcome[outcome_column_label][0]) + event_variable_type = dataframe_with_outcome[outcome_column_label].dtype # Make a copy of the dataframe to work on dataframe_with_standardized_outcome = dataframe_with_outcome.copy() @@ -224,9 +224,12 @@ def eventOutcomeColumnSetup(dataframe_with_outcome:DataFrame, dataframe_with_standardized_outcome[standard_column_label] = dataframe_with_outcome[outcome_column_label].astype(int) # Handle string event column - elif np.issubdtype(event_variable_type, np.str_): - # Make values of outcome column lowercase - dataframe_with_standardized_outcome[outcome_column_label] = dataframe_with_outcome[outcome_column_label].str.lower() + elif np.issubdtype(event_variable_type, np.object_): + try: + # Make values of outcome column lowercase + dataframe_with_standardized_outcome[outcome_column_label] = dataframe_with_outcome[outcome_column_label].str.lower() + except Exception as e: + raise ValueError(f"Error converting string event column {outcome_column_label} to lowercase. Please check the column is a string type and try again.") from e # Get the existing event values in the provided dataframe and and sort them existing_event_values = sorted(dataframe_with_standardized_outcome[outcome_column_label].unique()) From 90839a2ab61ce47e51eb43556a605e1a55fab5e5 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 17:02:53 -0500 Subject: [PATCH 58/62] refactor: simplify event column mapping dictionary check with sets --- src/readii/data/label.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index 49a2edf..c469be2 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -242,8 +242,8 @@ def eventOutcomeColumnSetup(dataframe_with_outcome:DataFrame, # Convert all dictionary keys in provided mapping to lowercase event_column_value_mapping = dict((status.lower(), value) for status, value in event_column_value_mapping.items()) - # Check if user provided a dictionary handles all event values in the outcome column - if not all(value == map for value, map in zip(existing_event_values, sorted(event_column_value_mapping.keys()))): + # Check if user provided dictionary handles all event values in the outcome column + if set(existing_event_values) != set(event_column_value_mapping.keys()): raise ValueError(f"Not all event values in {outcome_column_label} are handled by the provided event_column_value_mapping dictionary.") # TODO: add handling for values not in the dictionary From 80b81a748539321a71d98439aef54e99d200a511 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 17:25:55 -0500 Subject: [PATCH 59/62] refactor: change out string to numeric replacement with the replaceColumnValues function in split.py --- src/readii/data/label.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/src/readii/data/label.py b/src/readii/data/label.py index c469be2..e283dde 100644 --- a/src/readii/data/label.py +++ b/src/readii/data/label.py @@ -4,6 +4,8 @@ import pandas as pd from typing import Optional +from readii.data.split import replaceColumnValues + def getPatientIdentifierLabel(dataframe_to_search:DataFrame) -> str: """Function to find a column in a dataframe that contains some form of patient ID or case ID (case-insensitive). If multiple found, will return the first match. @@ -248,9 +250,17 @@ def eventOutcomeColumnSetup(dataframe_with_outcome:DataFrame, # TODO: add handling for values not in the dictionary - with pd.option_context('future.no_silent_downcasting', True): - # get the existing event values, make them lowercase, replace the dictionary values with the dictionary keys, convert to numeric, and save to the standardized column copy - dataframe_with_standardized_outcome[standard_column_label] = dataframe_with_standardized_outcome[outcome_column_label].replace(event_column_value_mapping).astype(int) + # Set up the new column name for the converted event column + dataframe_with_standardized_outcome[standard_column_label] = dataframe_with_standardized_outcome[outcome_column_label] + + # Swap the keys and values in the mapping dictionary to use the replaceColumnValues function + # So the numeric values will be the keys and the string event values will be the values + replacement_value_data = dict([num_event, str_event] for str_event, num_event in event_column_value_mapping.items()) + + # Replace the string event values in the standardized column with the numeric event values + dataframe_with_standardized_outcome = replaceColumnValues(dataframe_with_standardized_outcome, + column_to_change=standard_column_label, + replacement_value_data=replacement_value_data) # end string handling else: From b0a892d6ca37ede494dce87f9ad43f69bea7b5bc Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 17:27:50 -0500 Subject: [PATCH 60/62] feat: check that extracted feature directory exists --- src/readii/io/loaders/features.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/readii/io/loaders/features.py b/src/readii/io/loaders/features.py index 08537d4..ff7103b 100644 --- a/src/readii/io/loaders/features.py +++ b/src/readii/io/loaders/features.py @@ -36,6 +36,10 @@ def loadFeatureFilesFromImageTypes(extracted_feature_dir:str, # Initialize dictionary to store the feature sets feature_sets = {} + # Check if the passed in extracted feature directory exists + if not os.path.isdir(extracted_feature_dir): + raise FileNotFoundError(f"Extracted feature directory {extracted_feature_dir} does not exist.") + feature_file_list = os.listdir(extracted_feature_dir) # Loop through all the files in the directory From edaf74c805f7a834ce7706cee4e1cfe38a2d0a71 Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 17:29:25 -0500 Subject: [PATCH 61/62] refactor: improve error handling for dropping labels in loadFeatureFilesFromImageTypes --- src/readii/io/loaders/features.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/readii/io/loaders/features.py b/src/readii/io/loaders/features.py index ff7103b..ca2d8f5 100644 --- a/src/readii/io/loaders/features.py +++ b/src/readii/io/loaders/features.py @@ -67,7 +67,7 @@ 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 Exception as e: + except KeyError as e: print(f"{feature_file_path} does not have the labels {labels_to_drop} to drop.") # Skip to the next image type continue From dc2e86ae90bb64586dcc32b55d02b1fba4785b6c Mon Sep 17 00:00:00 2001 From: Katy Scott Date: Tue, 10 Dec 2024 17:30:28 -0500 Subject: [PATCH 62/62] feat: validate that any feature sets were loaded before return --- src/readii/io/loaders/features.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/readii/io/loaders/features.py b/src/readii/io/loaders/features.py index ca2d8f5..0b088ad 100644 --- a/src/readii/io/loaders/features.py +++ b/src/readii/io/loaders/features.py @@ -74,5 +74,8 @@ def loadFeatureFilesFromImageTypes(extracted_feature_dir:str, # Save the dataframe to the feature_sets dictionary feature_sets[image_type] = raw_feature_data + + if not feature_sets: + raise ValueError(f"No valid feature sets were loaded from {extracted_feature_dir}") return feature_sets \ No newline at end of file