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* add regression restructure classes * better html report
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -7,4 +7,5 @@ env | |
| *.txt | ||
| *.pkl | ||
| *.html | ||
| *.ipynb | ||
| *.ipynb | ||
| __pycache__ | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,164 @@ | ||
| import sys | ||
| import pandas as pd | ||
| import os | ||
| import logging | ||
| import base64 | ||
|
|
||
| logging.basicConfig(level=logging.DEBUG) | ||
| LOG = logging.getLogger(__name__) | ||
|
|
||
| class BaseModelTrainer: | ||
| def __init__(self, input_file, target_col, output_dir): | ||
| 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.data = None | ||
| self.target = None | ||
| self.best_model = None | ||
| self.results = None | ||
| self.plots = {} | ||
|
|
||
| def load_data(self): | ||
| LOG.info(f"Loading data from {self.input_file}") | ||
| self.data = pd.read_csv(self.input_file, sep=None, engine='python') | ||
| names = self.data.columns.to_list() | ||
| self.target = names[int(self.target_col)-1] | ||
| self.data = self.data.fillna(self.data.median(numeric_only=True)) | ||
| self.data.columns = self.data.columns.str.replace('.', '_') | ||
|
|
||
| def setup_pycaret(self): | ||
| LOG.info("Initializing PyCaret") | ||
| self.exp.setup(self.data, target=self.target, | ||
| session_id=123, html=True, | ||
| log_experiment=False, system_log=False) | ||
|
|
||
| def train_model(self): | ||
| LOG.info("Training and selecting the best model") | ||
| self.best_model = self.exp.compare_models() | ||
| self.results = self.exp.pull() | ||
|
|
||
| def save_model(self): | ||
| LOG.info("Saving the model") | ||
| self.exp.save_model(self.best_model, "model.pkl") | ||
|
|
||
| def generate_plots(self): | ||
| raise NotImplementedError("Subclasses should implement this method") | ||
|
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| def encode_image_to_base64(self, img_path): | ||
| with open(img_path, 'rb') as img_file: | ||
| return base64.b64encode(img_file.read()).decode('utf-8') | ||
|
|
||
| def save_html_report(self): | ||
| LOG.info("Saving HTML report") | ||
|
|
||
| model_name = type(self.best_model).__name__ | ||
|
|
||
| # Save model summary | ||
| 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) | ||
|
|
||
| # Save comparison results | ||
| self.results.to_csv(os.path.join(self.output_dir, "comparison_results.csv")) | ||
|
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| # Read and encode plot images | ||
| plots_html = "" | ||
| for plot_name, plot_path in self.plots.items(): | ||
| encoded_image = self.encode_image_to_base64(plot_path) | ||
| plots_html += f""" | ||
| <div class="plot"> | ||
| <h3>{plot_name.capitalize()}</h3> | ||
| <img src="data:image/png;base64,{encoded_image}" alt="{plot_name}"> | ||
| </div> | ||
| """ | ||
|
|
||
| # Generate HTML content | ||
| html_content = f""" | ||
| <!DOCTYPE html> | ||
| <html lang="en"> | ||
| <head> | ||
| <meta charset="UTF-8"> | ||
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | ||
| <title>PyCaret Model Training Report</title> | ||
| <style> | ||
| body {{ | ||
| font-family: Arial, sans-serif; | ||
| margin: 0; | ||
| padding: 20px; | ||
| background-color: #f4f4f4; | ||
| }} | ||
| .container {{ | ||
| max-width: 800px; | ||
| margin: auto; | ||
| background: white; | ||
| padding: 20px; | ||
| box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); | ||
| }} | ||
| h1 {{ | ||
| text-align: center; | ||
| color: #333; | ||
| }} | ||
| h2 {{ | ||
| border-bottom: 2px solid #4CAF50; | ||
| color: #4CAF50; | ||
| padding-bottom: 5px; | ||
| }} | ||
| table {{ | ||
| width: 100%; | ||
| border-collapse: collapse; | ||
| margin: 20px 0; | ||
| }} | ||
| table, th, td {{ | ||
| border: 1px solid #ddd; | ||
| }} | ||
| th, td {{ | ||
| padding: 8px; | ||
| text-align: left; | ||
| }} | ||
| th {{ | ||
| background-color: #4CAF50; | ||
| color: white; | ||
| }} | ||
| .plot {{ | ||
| text-align: center; | ||
| margin: 20px 0; | ||
| }} | ||
| .plot img {{ | ||
| max-width: 100%; | ||
| height: auto; | ||
| }} | ||
| </style> | ||
| </head> | ||
| <body> | ||
| <div class="container"> | ||
| <h1>PyCaret Model Training Report</h1> | ||
| <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</h2> | ||
| <table> | ||
| {self.results.to_html(index=False, classes='table')} | ||
| </table> | ||
| <h2>Plots</h2> | ||
| {plots_html} | ||
| </div> | ||
| </body> | ||
| </html> | ||
| """ | ||
|
|
||
| with open(os.path.join(self.output_dir, "comparison_result.html"), "w") as file: | ||
| file.write(html_content) | ||
|
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||
| def save_dashboard(self): | ||
| raise NotImplementedError("Subclasses should implement this method") | ||
|
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||
| def run(self): | ||
| self.load_data() | ||
| self.setup_pycaret() | ||
| self.train_model() | ||
| self.save_model() | ||
| self.generate_plots() | ||
| self.save_html_report() | ||
| self.save_dashboard() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,130 +1,23 @@ | ||
| import sys | ||
| import pandas as pd | ||
| from base_model_trainer import BaseModelTrainer | ||
| from pycaret.classification import ClassificationExperiment | ||
| import os | ||
| import logging | ||
| from dashboard import generate_dashboard | ||
| from jinja_report.generate_report import main as generate_report | ||
| import base64 | ||
| from dashboard import generate_classifier_explainer_dashboard | ||
|
|
||
| logging.basicConfig(level=logging.DEBUG) | ||
| LOG = logging.getLogger(__name__) | ||
|
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||
| class ModelTrainer: | ||
| class ClassificationModelTrainer(BaseModelTrainer): | ||
| def __init__(self, input_file, target_col, output_dir): | ||
| super().__init__(input_file, target_col, output_dir) | ||
| self.exp = ClassificationExperiment() | ||
| self.input_file = input_file | ||
| self.target_col = target_col | ||
| self.output_dir = output_dir | ||
| self.data = None | ||
| self.target = None | ||
| self.best_model = None | ||
| self.results = None | ||
| self.plots = {} | ||
|
|
||
| def load_data(self): | ||
| LOG.info(f"Loading data from {self.input_file}") | ||
| self.data = pd.read_csv(self.input_file, sep=None, engine='python') | ||
| names = self.data.columns.to_list() | ||
| self.target = names[int(self.target_col)-1] | ||
| self.data = self.data.fillna(self.data.median(numeric_only=True)) | ||
| self.data.columns = self.data.columns.str.replace('.', '_') | ||
|
|
||
| def setup_pycaret(self): | ||
| LOG.info("Initializing PyCaret") | ||
| self.exp.setup(self.data, target=self.target, | ||
| session_id=123, html=True, | ||
| log_experiment=False, system_log=False) | ||
|
|
||
| def train_model(self): | ||
| LOG.info("Training and selecting the best model") | ||
| self.best_model = self.exp.compare_models() | ||
| self.results = self.exp.pull() | ||
|
|
||
| def save_model(self): | ||
| LOG.info("Saving the model") | ||
| self.exp.save_model(self.best_model, "model") | ||
|
|
||
| def save_dashboard(self): | ||
| LOG.info("Saving explainer dashboard") | ||
| dashboard = generate_classifier_explainer_dashboard(self.exp, self.best_model) | ||
| dashboard.save_html("dashboard.html") | ||
|
|
||
| def generate_plots(self): | ||
| LOG.info("Generating and saving plots") | ||
| # Generate PyCaret plots | ||
| plots = ['auc', 'confusion_matrix', | ||
| 'threshold', | ||
| 'pr', 'error', | ||
| 'class_report', 'learning', | ||
| 'calibration', 'vc', | ||
| 'dimension', | ||
| 'manifold', 'rfe', | ||
| 'feature', 'feature_all'] | ||
| plots = ['auc', 'confusion_matrix', 'threshold', 'pr', 'error', 'class_report', 'learning', 'calibration', 'vc', 'dimension', 'manifold', 'rfe', 'feature', 'feature_all'] | ||
| for plot_name in plots: | ||
| plot_path = self.exp.plot_model(self.best_model, plot=plot_name, | ||
| save=True) | ||
| plot_path = self.exp.plot_model(self.best_model, plot=plot_name, save=True) | ||
| self.plots[plot_name] = plot_path | ||
|
|
||
| def encode_image_to_base64(self, img_path): | ||
| with open(img_path, 'rb') as img_file: | ||
| return base64.b64encode(img_file.read()).decode('utf-8') | ||
|
|
||
| def save_html_report(self): | ||
| LOG.info("Saving HTML report") | ||
|
|
||
| model_name = type(self.best_model).__name__ | ||
|
|
||
| report_data = { | ||
| "title": "PyCaret Model Training Report", | ||
| 'Best Model': [ | ||
| { | ||
| 'type': 'table', | ||
| 'src': os.path.join(self.output_dir, 'best_model.csv'), | ||
| 'label': f'Best Model: {model_name}' | ||
| } | ||
| ], | ||
| 'Comparison Results': [ | ||
| { | ||
| 'type': 'table', | ||
| 'src': os.path.join(self.output_dir, 'comparison_results.csv'), | ||
| 'label': 'Comparison Result <br> The scoring grid with average cross-validation scores' | ||
| } | ||
| ], | ||
| "Plots": [] | ||
| } | ||
|
|
||
| # Save model summary | ||
| 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) | ||
|
|
||
| # Save comparison results | ||
| self.results.to_csv(os.path.join(self.output_dir, "comparison_results.csv")) | ||
|
|
||
| # Add plots to the report data | ||
| for plot_name, plot_path in self.plots.items(): | ||
| encoded_image = self.encode_image_to_base64(plot_path) | ||
| report_data['Plots'].append({ | ||
| 'type': 'html', | ||
| 'src': f'data:image/png;base64,{encoded_image}', | ||
| 'label': plot_name.capitalize() | ||
| }) | ||
|
|
||
| generate_report(inputs=report_data, outfile=os.path.join(self.output_dir, "comparison_result.html")) | ||
|
|
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| def save_dashboard(self): | ||
| LOG.info("Saving explainer dashboard") | ||
| dashboard = generate_dashboard(self.exp, self.best_model) | ||
| dashboard.save_html("dashboard.html") | ||
|
|
||
| def run(self): | ||
| self.load_data() | ||
| self.setup_pycaret() | ||
| self.train_model() | ||
| self.save_model() | ||
| self.generate_plots() | ||
| self.save_html_report() | ||
| self.save_dashboard() | ||
|
|
||
| if __name__ == "__main__": | ||
| input_file = sys.argv[1] | ||
| target_col = sys.argv[2] | ||
| output_dir = sys.argv[3] | ||
|
|
||
| trainer = ModelTrainer(input_file, target_col, output_dir) | ||
| trainer.run() |
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