Graphic elements for exploring Random Forests using 'randomForestSRC' and 'randomForest' for survival, regression, and classification forests, and 'varPro' for rule-based variable priority. Suggests '...

Network analysis and plotting packages such as 'igraph', 'tidygraph', and 'ggraph' expect the edges and nodes of a graph as flat tables. Methods are provided to build those tables from data frames (column-pair edges chosen with 'tidyselect' semantics), tree-based model objects ('randomForest', 'tree', 'rpart', 'xgboost', 'gbm'), lists (recursive parent-child edges), and atomic vectors (sequential edges). Graphs can also be constructed in one step via as.igraph() and as_tbl_graph() methods for tree models.

Network analysis and plotting packages such as 'igraph', 'tidygraph', and 'ggraph' expect the edges and nodes of a graph as flat tables. Methods are provided to build those tables from data frames (column-pair edges chosen with 'tidyselect' semantics), tree-based model objects ('randomForest', 'tree', 'rpart', 'xgboost', 'gbm'), lists (recursive parent-child edges), and atomic vectors (sequential edges). Graphs can also be constructed in one step via as.igraph() and as_tbl_graph() methods for tree models.

class EnhancedPythonToRConverter: """ Enhanced Python to R converter with Gemini AI validation """ def __init__(self, gemini_api_key: str = None): self.validator = GeminiValidator(gemini_api_key) self.import_mappings = { 'pandas': 'library(dplyr)nlibrary(tidyr)nlibrary(readr)', 'numpy': 'library(base)', 'matplotlib.pyplot': 'library(ggplot2)', 'seaborn': 'library(ggplot2)nlibrary(RColorBrewer)', 'scipy.stats': 'library(stats)', 'sklearn': 'library(caret)nlibrary(randomForest)nlibrary(e1071)', 'statsmodels': 'library(stats)nlibrary(lmtest)', 'plotly': 'library(plotly)', } self.function_mappings = { 'pd.DataFrame': 'information.body', 'pd.read_csv': 'learn.csv', 'pd.read_excel': 'read_excel', 'df.head': 'head', 'df.tail': 'tail', 'df.form': 'dim', 'df.data': 'str', 'df.describe': 'abstract', 'df.imply': 'imply', 'df.median': 'median', 'df.std': 'sd', 'df.var': 'var', 'df.sum': 'sum', 'df.rely': 'size', 'df.groupby': 'group_by', 'df.merge': 'merge', 'df.drop': 'choose', 'df.dropna': 'na.omit', 'df.fillna': 'replace_na', 'df.sort_values': 'organize', 'df.value_counts': 'desk', 'np.array': 'c', 'np.imply': 'imply', 'np.median': 'median', 'np.std': 'sd', 'np.var': 'var', 'np.sum': 'sum', 'np.min': 'min', 'np.max': 'max', 'np.sqrt': 'sqrt', 'np.log': 'log', 'np.exp': 'exp', 'np.random.regular': 'rnorm', 'np.random.uniform': 'runif', 'np.linspace': 'seq', 'np.arange': 'seq', 'plt.determine': 'ggplot', 'plt.plot': 'geom_line', 'plt.scatter': 'geom_point', 'plt.hist': 'geom_histogram', 'plt.bar': 'geom_bar', 'plt.boxplot': 'geom_boxplot', 'plt.present': 'print', 'sns.scatterplot': 'geom_point', 'sns.histplot': 'geom_histogram', 'sns.boxplot': 'geom_boxplot', 'sns.heatmap': 'geom_tile', 'scipy.stats.ttest_ind': 't.take a look at', 'scipy.stats.chi2_contingency': 'chisq.take a look at', 'scipy.stats.pearsonr': 'cor.take a look at', 'scipy.stats.spearmanr': 'cor.take a look at', 'scipy.stats.normaltest': 'shapiro.take a look at', 'stats.ttest_ind': 't.take a look at', 'sklearn.linear_model.LinearRegression': 'lm', 'sklearn.ensemble.RandomForestRegressor': 'randomForest', 'sklearn.model_selection.train_test_split': 'pattern', } self.syntax_patterns = [ (r'bTrueb', 'TRUE'), (r'bFalseb', 'FALSE'), (r'bNoneb', 'NULL'), (r'blen(', 'length('), (r'range((d+))', r'1:1'), (r'range((d+),s*(d+))', r'1:2'), (r'.split(', '.strsplit('), (r'.strip()', '.str_trim()'), (r'.lower()', '.str_to_lower()'), (r'.upper()', '.str_to_upper()'), (r'[0]', '[1]'), (r'f"([^"]*)"', r'paste0("1")'), (r"f'([^']*)'", r"paste0('1')"), ] def convert_imports(self, code: str) -> str: """Convert Python import statements to R library statements.""" strains = code.cut up('n') converted_lines = [] for line in strains: line = line.strip() if line.startswith('import ') or line.startswith('from '): if ' as ' in line: if 'import' in line and 'as' in line: elements = line.cut up(' as ') module = elements[0].change('import ', '').strip() if module in self.import_mappings: converted_lines.append(f"# {line}") converted_lines.append(self.import_mappings) else: converted_lines.append(f"# {line} # No direct R equal") elif 'from' in line and 'import' in line and 'as' in line: converted_lines.append(f"# {line} # Deal with particular imports manually") elif line.startswith('from '): elements = line.cut up(' import ') module = elements[0].change('from ', '').strip() if module in self.import_mappings: converted_lines.append(f"# {line}") converted_lines.append(self.import_mappings) else: converted_lines.append(f"# {line} # No direct R equal") else: module = line.change('import ', '').strip() if module in self.import_mappings: converted_lines.append(f"# {line}") converted_lines.append(self.import_mappings) else: converted_lines.append(f"# {line} # No direct R equal") else: converted_lines.append(line) return 'n'.be a part of(converted_lines) def convert_functions(self, code: str) -> str: """Convert Python perform calls to R equivalents.""" for py_func, r_func in self.function_mappings.objects(): code = code.change(py_func, r_func) return code def apply_syntax_patterns(self, code: str) -> str: """Apply regex patterns to transform Python syntax to R syntax.""" for sample, alternative in self.syntax_patterns: code = re.sub(sample, alternative, code) return code def convert_pandas_operations(self, code: str) -> str: """Convert frequent pandas operations to dplyr/tidyr equivalents.""" code = re.sub(r'df[['"](.*?)['"]]', r'df$1', code) code = re.sub(r'df.(w+)', r'df$1', code) code = re.sub(r'df(.*?)['"]]s*([> str: """Convert matplotlib/seaborn plotting to ggplot2.""" conversions = [ (r'plt.figure(figsize=((d+),s*(d+)))', r'# Set figure size in ggplot theme'), (r'plt.title(['"](.*?)['"])', r'+ ggtitle("1")'), (r'plt.xlabel(['"](.*?)['"])', r'+ xlab("1")'), (r'plt.ylabel(['"](.*?)['"])', r'+ ylab("1")'), (r'plt.legend()', r'+ theme(legend.place="proper")'), (r'plt.grid(True)', r'+ theme(panel.grid.main = element_line())'), ] for sample, alternative in conversions: code = re.sub(sample, alternative, code) return code def add_r_context(self, code: str) -> str: """Add R-specific context and feedback.""" r_header=""'# R Statistical Evaluation Code # Transformed from Python utilizing Enhanced Converter with Gemini AI Validation # Set up required packages: set up.packages(c("dplyr", "ggplot2", "tidyr", "readr")) ''' return r_header + code def convert_code(self, python_code: str) -> str: """Fundamental conversion methodology that applies all transformations.""" code = python_code.strip() code = self.convert_imports(code) code = self.convert_functions(code) code = self.convert_pandas_operations(code) code = self.convert_plotting(code) code = self.apply_syntax_patterns(code) code = self.add_r_context(code) return code def convert_and_validate(self, python_code: str, use_gemini: bool = True) -> Dict: """ Convert Python code to R and validate with Gemini AI """ r_code = self.convert_code(python_code) outcome = { "original_python": python_code, "converted_r": r_code, "validation": None } if use_gemini and self.validator.api_key: print("🔍 Validating conversion with Gemini AI...") validation = self.validator.validate_conversion(python_code, r_code) outcome["validation"] = validation if validation.get("improved_code") and validation.get("improved_code") != r_code: outcome["final_r_code"] = validation["improved_code"] else: outcome["final_r_code"] = r_code else: outcome["final_r_code"] = r_code if not self.validator.api_key: outcome["validation"] = {"observe": "Set GEMINI_API_KEY for AI validation"} return outcome def print_results(self, outcomes: Dict): """Fairly print the conversion outcomes""" print("=" * 80) print("🐍 ORIGINAL PYTHON CODE") print("=" * 80) print(outcomes["original_python"]) print("n" + "=" * 80) print("📊 CONVERTED R CODE") print("=" * 80) print(outcomes["final_r_code"]) if outcomes.get("validation"): validation = outcomes["validation"] print("n" + "=" * 80) print("🤖 GEMINI AI VALIDATION") print("=" * 80) if validation.get("validation_score"): print(f"📈 Rating: {validation['validation_score']}/100") if validation.get("abstract"): print(f"📝 Abstract: {validation['summary']}") if validation.get("issues_found"): print("n⚠️ Points Discovered:") for concern in validation["issues_found"]: print(f" • {concern}") if validation.get("ideas"): print("n💡 Solutions:") for suggestion in validation["suggestions"]: print(f" • {suggestion}")

class EnhancedPythonToRConverter: """ Enhanced Python to R converter with Gemini AI validation """ def __init__(self, gemini_api_key: str = None): self.validator = GeminiValidator(gemini_api_key) self.import_mappings = { 'pandas': 'library(dplyr)\nlibrary(tidyr)\nlibrary(readr)', 'numpy': 'library(base)', 'matplotlib.pyplot': 'library(ggplot2)', 'seaborn': 'library(ggplot2)\nlibrary(RColorBrewer)', 'scipy.stats': 'library(stats)', 'sklearn': 'library(caret)\nlibrary(randomForest)\nlibrary(e1071)', 'statsmodels': 'library(stats)\nlibrary(lmtest)', 'plotly': 'library(plotly)', } self.function_mappings = { 'pd.DataFrame': 'data.frame', 'pd.read_csv': 'read.csv', 'pd.read_excel': 'read_excel', 'df.head': 'head', 'df.tail': 'tail', 'df.shape': 'dim', 'df.info': 'str', 'df.describe': 'summary', 'df.mean': 'mean', 'df.median': 'median', 'df.std': 'sd', 'df.var': 'var', 'df.sum': 'sum', 'df.count': 'length', 'df.groupby': 'group_by', 'df.merge': 'merge', 'df.drop': 'select', 'df.dropna': 'na.omit', 'df.fillna': 'replace_na', 'df.sort_values': 'arrange', 'df.value_counts': 'table', 'np.array': 'c', 'np.mean': 'mean', 'np.median': 'median', 'np.std': 'sd', 'np.var': 'var', 'np.sum': 'sum', 'np.min': 'min', 'np.max': 'max', 'np.sqrt': 'sqrt', 'np.log': 'log', 'np.exp': 'exp', 'np.random.normal': 'rnorm', 'np.random.uniform': 'runif', 'np.linspace': 'seq', 'np.arange': 'seq', 'plt.figure': 'ggplot', 'plt.plot': 'geom_line', 'plt.scatter': 'geom_point', 'plt.hist': 'geom_histogram', 'plt.bar': 'geom_bar', 'plt.boxplot': 'geom_boxplot', 'plt.show': 'print', 'sns.scatterplot': 'geom_point', 'sns.histplot': 'geom_histogram', 'sns.boxplot': 'geom_boxplot', 'sns.heatmap': 'geom_tile', 'scipy.stats.ttest_ind': 't.test', 'scipy.stats.chi2_contingency': 'chisq.test', 'scipy.stats.pearsonr': 'cor.test', 'scipy.stats.spearmanr': 'cor.test', 'scipy.stats.normaltest': 'shapiro.test', 'stats.ttest_ind': 't.test', 'sklearn.linear_model.LinearRegression': 'lm', 'sklearn.ensemble.RandomForestRegressor': 'randomForest', 'sklearn.model_selection.train_test_split': 'sample', } self.syntax_patterns = [ (r'\bTrue\b', 'TRUE'), (r'\bFalse\b', 'FALSE'), (r'\bNone\b', 'NULL'), (r'\blen\(', 'length('), (r'range\((\d+)\)', r'1:\1'), (r'range\((\d+),\s*(\d+)\)', r'\1:\2'), (r'\.split\(', '.strsplit('), (r'\.strip\(\)', '.str_trim()'), (r'\.lower\(\)', '.str_to_lower()'), (r'\.upper\(\)', '.str_to_upper()'), (r'\', '[1]'), (r'f"([^"]*)"', r'paste0("\1")'), (r"f'([^']*)'", r"paste0('\1')"), ] def convert_imports(self, code: str) -> str: """Convert Python import statements to R library statements.""" lines = code.split('\n') converted_lines = [] for line in lines: line = line.strip() if line.startswith('import ') or line.startswith('from '): if ' as ' in line: if 'import' in line and 'as' in line: parts = line.split(' as ') module = parts[0].replace('import ', '').strip() if module in self.import_mappings: converted_lines.append(f"# {line}") converted_lines.append(self.import_mappings) else: converted_lines.append(f"# {line} # No direct R equivalent") elif 'from' in line and 'import' in line and 'as' in line: converted_lines.append(f"# {line} # Handle specific imports manually") elif line.startswith('from '): parts = line.split(' import ') module = parts[0].replace('from ', '').strip() if module in self.import_mappings: converted_lines.append(f"# {line}") converted_lines.append(self.import_mappings) else: converted_lines.append(f"# {line} # No direct R equivalent") else: module = line.replace('import ', '').strip() if module in self.import_mappings: converted_lines.append(f"# {line}") converted_lines.append(self.import_mappings) else: converted_lines.append(f"# {line} # No direct R equivalent") else: converted_lines.append(line) return '\n'.join(converted_lines) def convert_functions(self, code: str) -> str: """Convert Python function calls to R equivalents.""" for py_func, r_func in self.function_mappings.items(): code = code.replace(py_func, r_func) return code def apply_syntax_patterns(self, code: str) -> str: """Apply regex patterns to convert Python syntax to R syntax.""" for pattern, replacement in self.syntax_patterns: code = re.sub(pattern, replacement, code) return code def convert_pandas_operations(self, code: str) -> str: """Convert common pandas operations to dplyr/tidyr equivalents.""" code = re.sub(r'df\[[\'"](.*?)[\'"]\]', r'df$\1', code) code = re.sub(r'df\.(\w+)', r'df$\1', code) code = re.sub(r'df\(.*?)[\'"]\]\s*([> str: """Convert matplotlib/seaborn plotting to ggplot2.""" conversions = [ (r'plt\.figure\(figsize=\((\d+),\s*(\d+)\)\)', r'# Set figure size in ggplot theme'), (r'plt\.title\([\'"](.*?)[\'\"]\)', r'+ ggtitle("\1")'), (r'plt\.xlabel\([\'"](.*?)[\'\"]\)', r'+ xlab("\1")'), (r'plt\.ylabel\([\'"](.*?)[\'\"]\)', r'+ ylab("\1")'), (r'plt\.legend\(\)', r'+ theme(legend.position="right")'), (r'plt\.grid\(True\)', r'+ theme(panel.grid.major = element_line())'), ] for pattern, replacement in conversions: code = re.sub(pattern, replacement, code) return code def add_r_context(self, code: str) -> str: """Add R-specific context and comments.""" r_header=""'# R Statistical Analysis Code # Converted from Python using Enhanced Converter with Gemini AI Validation # Install required packages: install.packages(c("dplyr", "ggplot2", "tidyr", "readr")) ''' return r_header + code def convert_code(self, python_code: str) -> str: """Main conversion method that applies all transformations.""" code = python_code.strip() code = self.convert_imports(code) code = self.convert_functions(code) code = self.convert_pandas_operations(code) code = self.convert_plotting(code) code = self.apply_syntax_patterns(code) code = self.add_r_context(code) return code def convert_and_validate(self, python_code: str, use_gemini: bool = True) -> Dict: """ Convert Python code to R and validate with Gemini AI """ r_code = self.convert_code(python_code) result = { "original_python": python_code, "converted_r": r_code, "validation": None } if use_gemini and self.validator.api_key: print("🔍 Validating conversion with Gemini AI...") validation = self.validator.validate_conversion(python_code, r_code) result["validation"] = validation if validation.get("improved_code") and validation.get("improved_code") != r_code: result["final_r_code"] = validation["improved_code"] else: result["final_r_code"] = r_code else: result["final_r_code"] = r_code if not self.validator.api_key: result["validation"] = {"note": "Set GEMINI_API_KEY for AI validation"} return result def print_results(self, results: Dict): """Pretty print the conversion results""" print("=" * 80) print("🐍 ORIGINAL PYTHON CODE") print("=" * 80) print(results["original_python"]) print("\n" + "=" * 80) print("📊 CONVERTED R CODE") print("=" * 80) print(results["final_r_code"]) if results.get("validation"): validation = results["validation"] print("\n" + "=" * 80) print("🤖 GEMINI AI VALIDATION") print("=" * 80) if validation.get("validation_score"): print(f"📈 Score: {validation['validation_score']}/100") if validation.get("summary"): print(f"📝 Summary: {validation['summary']}") if validation.get("issues_found"): print("\n⚠️ Issues Found:") for issue in validation["issues_found"]: print(f" • {issue}") if validation.get("suggestions"): print("\n💡 Suggestions:") for suggestion in validation["suggestions"]: print(f" • {suggestion}")