comparison base_model_trainer.py @ 3:ccd798db5abb draft

planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit cf47efb521b91a9cb44ae5c5ade860627f9b9030

2:0314dad38aaa

import base64
import logging
import os
import tempfile

from feature_importance import FeatureImportanceAnalyzer

import h5py

import joblib

import numpy as np

import pandas as pd

from sklearn.metrics import average_precision_score

from utils import get_html_closing, get_html_template

logging.basicConfig(level=logging.DEBUG)
LOG = logging.getLogger(__name__)

            target_col,
            output_dir,
            task_type,
            random_seed,
            test_file=None,
            **kwargs
            ):
        self.exp = None  # This will be set in the subclass
        self.input_file = input_file
        self.target_col = target_col
        self.output_dir = output_dir
        self.task_type = task_type

        if len(non_numeric_cols) > 0:
            LOG.info(f"Non-numeric columns found: {non_numeric_cols.tolist()}")

        names = self.data.columns.to_list()
        target_index = int(self.target_col)-1
        self.target = names[target_index]
        self.features_name = [name
                              for i, name in enumerate(names)
                              if i != target_index]
        if hasattr(self, 'missing_value_strategy'):

                self.test_file, sep=None, engine='python')
            self.test_data = self.test_data[numeric_cols].apply(
                pd.to_numeric, errors='coerce')
            self.test_data.columns = self.test_data.columns.str.replace(
                '.', '_'
                )

    def setup_pycaret(self):
        LOG.info("Initializing PyCaret")
        self.setup_params = {
            'target': self.target,

        filtered_setup_params = {
            k: v
            for k, v in self.setup_params.items() if k not in excluded_params
        }
        setup_params_table = pd.DataFrame(
            list(filtered_setup_params.items()),
            columns=['Parameter', 'Value'])

        best_model_params = pd.DataFrame(
            self.best_model.get_params().items(),
            columns=['Parameter', 'Value'])
        best_model_params.to_csv(
            os.path.join(self.output_dir, 'best_model.csv'),
            index=False)
        self.results.to_csv(os.path.join(
            self.output_dir, "comparison_results.csv"))
        self.test_result_df.to_csv(os.path.join(
            self.output_dir, "test_results.csv"))

        plots_html = ""
        length = len(self.plots)
        for i, (plot_name, plot_path) in enumerate(self.plots.items()):
            encoded_image = self.encode_image_to_base64(plot_path)

        analyzer = FeatureImportanceAnalyzer(
            data=self.data,
            target_col=self.target_col,
            task_type=self.task_type,
            output_dir=self.output_dir)
        feature_importance_html = analyzer.run()

        html_content = f"""
        {get_html_template()}
            <h1>PyCaret Model Training Report</h1>

                Setup & Best Model</div>
                <div class="tab" onclick="openTab(event, 'plots')">
                Best Model Plots</div>
                <div class="tab" onclick="openTab(event, 'feature')">
                Feature Importance</div>
            """
        if self.plots_explainer_html:
            html_content += """
                "<div class="tab" onclick="openTab(event, 'explainer')">"
                Explainer Plots</div>
            """
        html_content += f"""
            </div>
            <div id="summary" class="tab-content">
                <h2>Setup Parameters</h2>
                <table>
                    <tr><th>Parameter</th><th>Value</th></tr>
                    {setup_params_table.to_html(
                        index=False, header=False, classes='table')}
                </table>
                <h5>If you want to know all the experiment setup parameters,
                  please check the PyCaret documentation for
                  the classification/regression <code>exp</code> function.</h5>
                <h2>Best Model: {model_name}</h2>
                <table>
                    <tr><th>Parameter</th><th>Value</th></tr>
                    {best_model_params.to_html(
                        index=False, header=False, classes='table')}
                </table>
                <h2>Comparison Results on the Cross-Validation Set</h2>
                <table>
                    {self.results.to_html(index=False, classes='table')}
                </table>
                <h2>Results on the Test Set for the best model</h2>
                <table>
                    {self.test_result_df.to_html(index=False, classes='table')}
                </table>
            </div>
            <div id="plots" class="tab-content">
                <h2>Best Model Plots on the testing set</h2>
                {plots_html}
            </div>

            html_content += f"""
            <div id="explainer" class="tab-content">
                {self.plots_explainer_html}
                {tree_plots}
            </div>
            {get_html_closing()}
            """
        else:
            html_content += f"""
            {get_html_closing()}
            """
        with open(os.path.join(
                self.output_dir, "comparison_result.html"), "w") as file:
            file.write(html_content)

    def save_dashboard(self):
        raise NotImplementedError("Subclasses should implement this method")

3:ccd798db5abb

import base64
import logging
import os
import tempfile

import h5py
import joblib
import numpy as np
import pandas as pd
from feature_importance import FeatureImportanceAnalyzer
from sklearn.metrics import average_precision_score
from utils import get_html_closing, get_html_template

logging.basicConfig(level=logging.DEBUG)
LOG = logging.getLogger(__name__)

            target_col,
            output_dir,
            task_type,
            random_seed,
            test_file=None,
            **kwargs):
        self.exp = None  # This will be set in the subclass
        self.input_file = input_file
        self.target_col = target_col
        self.output_dir = output_dir
        self.task_type = task_type

        if len(non_numeric_cols) > 0:
            LOG.info(f"Non-numeric columns found: {non_numeric_cols.tolist()}")

        names = self.data.columns.to_list()
        target_index = int(self.target_col) - 1
        self.target = names[target_index]
        self.features_name = [name
                              for i, name in enumerate(names)
                              if i != target_index]
        if hasattr(self, 'missing_value_strategy'):

                self.test_file, sep=None, engine='python')
            self.test_data = self.test_data[numeric_cols].apply(
                pd.to_numeric, errors='coerce')
            self.test_data.columns = self.test_data.columns.str.replace(
                '.', '_'
            )

    def setup_pycaret(self):
        LOG.info("Initializing PyCaret")
        self.setup_params = {
            'target': self.target,

        filtered_setup_params = {
            k: v
            for k, v in self.setup_params.items() if k not in excluded_params
        }
        setup_params_table = pd.DataFrame(
            list(filtered_setup_params.items()), columns=['Parameter', 'Value']
        )

        best_model_params = pd.DataFrame(
            self.best_model.get_params().items(),
            columns=['Parameter', 'Value']
        )
        best_model_params.to_csv(
            os.path.join(self.output_dir, "best_model.csv"), index=False
        )
        self.results.to_csv(
            os.path.join(self.output_dir, "comparison_results.csv")
        )
        self.test_result_df.to_csv(
            os.path.join(self.output_dir, "test_results.csv")
        )

        plots_html = ""
        length = len(self.plots)
        for i, (plot_name, plot_path) in enumerate(self.plots.items()):
            encoded_image = self.encode_image_to_base64(plot_path)

        analyzer = FeatureImportanceAnalyzer(
            data=self.data,
            target_col=self.target_col,
            task_type=self.task_type,
            output_dir=self.output_dir,
        )
        feature_importance_html = analyzer.run()

        html_content = f"""
        {get_html_template()}
            <h1>PyCaret Model Training Report</h1>

                Setup & Best Model</div>
                <div class="tab" onclick="openTab(event, 'plots')">
                Best Model Plots</div>
                <div class="tab" onclick="openTab(event, 'feature')">
                Feature Importance</div>
        """
        if self.plots_explainer_html:
            html_content += """
                <div class="tab" onclick="openTab(event, 'explainer')">
                Explainer Plots</div>
            """
        html_content += f"""
            </div>
            <div id="summary" class="tab-content">
                <h2>Setup Parameters</h2>
                {setup_params_table.to_html(
                    index=False,
                    header=True,
                    classes='table sortable'
                )}
                <h5>If you want to know all the experiment setup parameters,
                  please check the PyCaret documentation for
                  the classification/regression <code>exp</code> function.</h5>
                <h2>Best Model: {model_name}</h2>
                {best_model_params.to_html(
                    index=False,
                    header=True,
                    classes='table sortable'
                )}
                <h2>Comparison Results on the Cross-Validation Set</h2>
                {self.results.to_html(index=False, classes='table sortable')}
                <h2>Results on the Test Set for the best model</h2>
                {self.test_result_df.to_html(
                    index=False,
                    classes='table sortable'
                )}
            </div>
            <div id="plots" class="tab-content">
                <h2>Best Model Plots on the testing set</h2>
                {plots_html}
            </div>

            html_content += f"""
            <div id="explainer" class="tab-content">
                {self.plots_explainer_html}
                {tree_plots}
            </div>
            """
        html_content += """
        <script>
        document.addEventListener("DOMContentLoaded", function() {
            var tables = document.querySelectorAll("table.sortable");
            tables.forEach(function(table) {
                var headers = table.querySelectorAll("th");
                headers.forEach(function(header, index) {
                    header.style.cursor = "pointer";
                    // Add initial arrow (up) to indicate sortability
                    header.innerHTML += '<span class="sort-arrow"> ↑</span>';
                    header.addEventListener("click", function() {
                        var direction = this.getAttribute(
                            "data-sort-direction"
                        ) || "asc";
                        // Reset arrows in all headers of this table
                        headers.forEach(function(h) {
                            var arrow = h.querySelector(".sort-arrow");
                            if (arrow) arrow.textContent = " ↑";
                        });
                        // Set arrow for clicked header
                        var arrow = this.querySelector(".sort-arrow");
                        arrow.textContent = direction === "asc" ? " ↓" : " ↑";
                        sortTable(table, index, direction);
                        this.setAttribute("data-sort-direction",
                        direction === "asc" ? "desc" : "asc");
                    });
                });
            });
        });

        function sortTable(table, colNum, direction) {
            var tb = table.tBodies[0];
            var tr = Array.prototype.slice.call(tb.rows, 0);
            var multiplier = direction === "asc" ? 1 : -1;
            tr = tr.sort(function(a, b) {
                var aText = a.cells[colNum].textContent.trim();
                var bText = b.cells[colNum].textContent.trim();
                // Remove arrow from text comparison
                aText = aText.replace(/[↑↓]/g, '').trim();
                bText = bText.replace(/[↑↓]/g, '').trim();
                if (!isNaN(aText) && !isNaN(bText)) {
                    return multiplier * (
                        parseFloat(aText) - parseFloat(bText)
                    );
                } else {
                    return multiplier * aText.localeCompare(bText);
                }
            });
            for (var i = 0; i < tr.length; ++i) tb.appendChild(tr[i]);
        }
        </script>
        """
        html_content += f"""
        {get_html_closing()}
        """
        with open(
            os.path.join(self.output_dir, "comparison_result.html"),
            "w"
        ) as file:
            file.write(html_content)

    def save_dashboard(self):
        raise NotImplementedError("Subclasses should implement this method")