\n')
f.write(f"Evaluation Results Table
\n') @@ -85,7 +115,7 @@ def write_to_html(lpo_lco_ldo: str, f: TextIO, prefix: str = "", *args, **kwargs return f -def _write_table(f: TextIO, table: str, prefix: str = ""): +def _write_table(f: TextIOWrapper, table: str, prefix: str = ""): with open(os.path.join(prefix, table)) as eval_f: eval_results = eval_f.readlines() eval_results[0] = eval_results[0].replace( diff --git a/drevalpy/visualization/outplot.py b/drevalpy/visualization/outplot.py index 953cd1c..cbe858a 100644 --- a/drevalpy/visualization/outplot.py +++ b/drevalpy/visualization/outplot.py @@ -1,39 +1,37 @@ +"""Abstract wrapper class for all visualizations.""" + from abc import ABC, abstractmethod -from typing import TextIO +from io import TextIOWrapper class OutPlot(ABC): - """ - Abstract wrapper class for all visualizations - """ + """Abstract wrapper class for all visualizations.""" @abstractmethod def draw_and_save(self, out_prefix: str, out_suffix: str) -> None: """ - Draw and save the plot + Draw and save the plot. + :param out_prefix: path to output directory for python package :param out_suffix: custom suffix for output file - :return: """ pass @abstractmethod - def __draw__(self) -> None: - """ - Draw the plot - :return: - """ + def _draw(self) -> None: + """Draw the plot.""" pass @staticmethod @abstractmethod - def write_to_html(lpo_lco_ldo: str, f: TextIO, *args, **kwargs) -> TextIO: + def write_to_html(lpo_lco_ldo: str, f: TextIOWrapper, *args, **kwargs) -> TextIOWrapper: """ - Write the plot to html - :param lpo_lco_ldo: - :param f: - :param args: - :param kwargs: - :return: + Write the plot to the final report file. + + :param lpo_lco_ldo: LPO, LCO, LDO + :param f: the file to write to + :param args: additional arguments + :param kwargs: additional keyword arguments + :return: the file to write to """ pass diff --git a/drevalpy/visualization/regression_slider_plot.py b/drevalpy/visualization/regression_slider_plot.py index d63d888..349f74f 100644 --- a/drevalpy/visualization/regression_slider_plot.py +++ b/drevalpy/visualization/regression_slider_plot.py @@ -1,14 +1,21 @@ -from typing import TextIO +"""Module for generating regression plots with a slider for Pearson correlation coefficient.""" + +from io import TextIOWrapper import numpy as np import pandas as pd import plotly.express as px +import plotly.graph_objects as go from scipy.stats import pearsonr -from drevalpy.visualization.outplot import OutPlot +from ..pipeline_function import pipeline_function +from .outplot import OutPlot class RegressionSliderPlot(OutPlot): + """Generates regression plots with a slider for the Pearson correlation coefficient.""" + + @pipeline_function def __init__( self, df: pd.DataFrame, @@ -17,11 +24,20 @@ def __init__( group_by: str = "drug", normalize=False, ): + """ + Initialize the RegressionSliderPlot class. + + :param df: true vs. predicted values + :param lpo_lco_ldo: setting, e.g., LPO + :param model: model name + :param group_by: either "drug" or "cell_line" + :param normalize: whether to normalize the true and predicted values by the mean of the group + """ self.df = df[(df["LPO_LCO_LDO"] == lpo_lco_ldo) & (df["rand_setting"] == "predictions")] self.df = self.df[(self.df["algorithm"] == model)] self.group_by = group_by self.normalize = normalize - self.fig = None + self.fig = go.Figure() self.model = model if self.normalize: @@ -32,22 +48,39 @@ def __init__( self.df.loc[:, "y_true"] = self.df["y_true"] - self.df["mean_y_true_per_cell_line"] self.df.loc[:, "y_pred"] = self.df["y_pred"] - self.df["mean_y_true_per_cell_line"] + @pipeline_function def draw_and_save(self, out_prefix: str, out_suffix: str) -> None: - self.__draw__() + """ + Draw the regression plot and save it to a file. + + :param out_prefix: e.g., results/my_run/regression_plots/ + :param out_suffix: e.g., LPO_drug_SimpleNeuralNetwork + """ + self._draw() self.fig.write_html(f"{out_prefix}regression_lines_{out_suffix}.html") - def __draw__(self): + def _draw(self): + """Draw the regression plot.""" print(f"Generating regression plots for {self.group_by}, normalize={self.normalize}...") self.df = self.df.groupby(self.group_by).filter(lambda x: len(x) > 1) pccs = self.df.groupby(self.group_by).apply(lambda x: pearsonr(x["y_true"], x["y_pred"])[0]) pccs = pccs.reset_index() pccs.columns = [self.group_by, "pcc"] self.df = self.df.merge(pccs, on=self.group_by) - self.__render_plot__() + self._render_plot() @staticmethod - def write_to_html(lpo_lco_ldo: str, f: TextIO, *args, **kwargs) -> TextIO: - files = kwargs.get("files") + def write_to_html(lpo_lco_ldo: str, f: TextIOWrapper, *args, **kwargs) -> TextIOWrapper: + """ + Write the plot to the final report file. + + :param lpo_lco_ldo: setting, e.g., LPO + :param f: final report file + :param args: additional arguments + :param kwargs: additional keyword arguments, in this case all files + :return: the final report file + """ + files: list[str] = kwargs.get("files", []) f.write('Regression plots
\n') f.write("- \n")
regr_files = [f for f in files if lpo_lco_ldo in f and f.startswith("regression_lines")]
@@ -57,7 +90,8 @@ def write_to_html(lpo_lco_ldo: str, f: TextIO, *args, **kwargs) -> TextIO:
f.write("
\n')
f.write('\n')
f.write(f"
\n')
f.write(f"