import gspread import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score from sklearn.impute import SimpleImputer from sklearn.ensemble import RandomForestClassifier from skopt import BayesSearchCV from skopt.space import Real, Categorical, Integer from transformers import pipeline

def generate_code(description: str, language: str, model: str) -> str: """ Generates boilerplate code for a specified description and language. """ if model.lower() == "codegen": return "Under Construction: Codegen model support not implemented yet." function_name = description.replace(" ", "_") code = f"""def {function_name}(): """Auto-generated function in {language}.""" pass """ return code

def zero_shot_classification(text: str, possible_labels: list, model_name: str = "facebook/bart-large-mnli") -> dict: """ Performs zero-shot classification using Hugging Face transformers. """ if not isinstance(possible_labels, list) or not possible_labels: raise ValueError("possible_labels must be a non-empty list of strings.") classifier = pipeline( "zero-shot-classification", model=model_name, device=0 # Use GPU if available ) results = classifier(text, possible_labels, multi_label=True) return results

def chain_of_thought(prompt: str, hint: str, model: str): """ Placeholder for chain-of-thought reasoning functionality. """ print("Chain-of-thought reasoning is under construction.")

def load_spreadsheet(gc_client, sheet_name: str): """ Loads all values from a Google Sheet into a pandas DataFrame. """ try: worksheet = gc_client.open(sheet_name).sheet1 rows = worksheet.get_all_values() # Assuming the first row is the header if not rows: print(f"Spreadsheet '{sheet_name}' is empty.") return pd.DataFrame() df = pd.DataFrame(rows[1:], columns=rows[0]) # Attempt to convert columns to numeric where possible for col in df.columns: df[col] = pd.to_numeric(df[col], errors='coerce') return df except Exception as e: print(f"Error loading spreadsheet '{sheet_name}': {e}") return pd.DataFrame()

def clean_data(df: pd.DataFrame) -> pd.DataFrame: """ Handles missing values using median imputation. """ if df.empty: print("DataFrame is empty, skipping cleaning.") return df imputer = SimpleImputer(strategy='median') # Only impute numerical columns numerical_cols = df.select_dtypes(include=np.number).columns if not numerical_cols.empty: df[numerical_cols] = imputer.fit_transform(df[numerical_cols]) return df

def feature_engineering(df: pd.DataFrame) -> pd.DataFrame: """ Placeholder for feature engineering steps. """ print("Feature engineering is under construction.") return df

def train_model(X_train: pd.DataFrame, y_train: pd.Series, model_type: str = 'logistic_regression'): """ Trains a specified machine learning model. """ if model_type.lower() == 'logistic_regression': model = LogisticRegression(max_iter=1000) elif model_type.lower() == 'random_forest': model = RandomForestClassifier(random_state=42) else: raise ValueError(f"Unsupported model type: {model_type}") model.fit(X_train, y_train) return model

def evaluate_model(model, X_test: pd.DataFrame, y_test: pd.Series) -> float: """ Evaluates the model's accuracy on the test set. """ predictions = model.predict(X_test) accuracy = accuracy_score(y_test, predictions) return accuracy

def optimize_model(model_instance, X_train: pd.DataFrame, y_train: pd.Series, param_space: dict): """ Optimizes model hyperparameters using BayesSearchCV. """ if not param_space: print("No parameter space provided for optimization.") return None, None bayes_search = BayesSearchCV( model_instance, param_space, n_iter=50, # Number of optimization iterations random_state=42, cv=3, # Cross-validation folds n_jobs=-1 # Use all available cores ) bayes_search.fit(X_train, y_train) print("Best parameters found: ", bayes_search.best_params_) print("Best accuracy found: ", bayes_search.best_score_) return bayes_search.best_estimator_, bayes_search.best_params_

def plot_distribution(df: pd.DataFrame, column: str): """ Plots the distribution of a specified column. """ if column not in df.columns: print(f"Column '{column}' not found in DataFrame.") return plt.figure(figsize=(10, 6)) sns.histplot(df[column].dropna(), kde=True) plt.title(f'Distribution of {column}') plt.xlabel(column) plt.ylabel('Frequency') plt.show() def plot_correlation_matrix(df: pd.DataFrame): """ Plots the correlation matrix for numerical columns. """ numerical_df = df.select_dtypes(include=np.number) if numerical_df.empty: print("No numerical columns to plot correlation matrix.") return plt.figure(figsize=(12, 10)) sns.heatmap(numerical_df.corr(), annot=True, cmap='coolwarm', fmt=".2f") plt.title('Correlation Matrix') plt.show()

def authenticate_google(): """ Authenticates the user for Google services (Colab/Jupyter). Returns the gspread client. """ from google.colab import auth import gspread from google.auth import default try: auth.authenticate_user() creds, _ = default() gc = gspread.authorize(creds) print("Google authentication successful.") return gc except Exception as e: print(f"Google authentication failed: {e}") return None

if name == 'main': # Example Usage: # 1. Authenticate gc_client = authenticate_google() if gc_client: # 2. Load data from a Google Sheet sheet_name = 'Your Google Sheet Name' # Replace with your sheet name df = load_spreadsheet(gc_client, sheet_name) if not df.empty: print("Original DataFrame head:") print(df.head()) print("\nDataFrame Info:") df.info() # 3. Clean data df_cleaned = clean_data(df) print("\nCleaned DataFrame head:") print(df_cleaned.head()) # 4. Feature Engineering (placeholder) df_engineered = feature_engineering(df_cleaned) # 5. Prepare data for modeling # Assuming 'target_column' is your target variable and other numerical columns are features target_column = 'Your Target Column Name' # Replace with your target column name if target_column in df_engineered.columns and df_engineered[target_column].dtype in [np.number, 'object']: # Convert target to numeric if it's not already, coercing errors df_engineered[target_column] = pd.to_numeric(df_engineered[target_column], errors='coerce') # Drop rows where the target is NaN after conversion df_engineered.dropna(subset=[target_column], inplace=True) if not df_engineered.empty: features = df_engineered.select_dtypes(include=np.number).drop(columns=[target_column], errors='ignore') target = df_engineered[target_column] if not features.empty: # Split data X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42) print(f"\nTraining data shape: {X_train.shape}") print(f"Testing data shape: {X_test.shape}") # 6. Train a model # Choose 'logistic_regression' or 'random_forest' model_type = 'random_forest' model = train_model(X_train, y_train, model_type=model_type) print(f"\n{model_type} model trained.") # 7. Evaluate the model accuracy = evaluate_model(model, X_test, y_test) print(f"\nModel accuracy on test set: {accuracy:.4f}") # 8. Optimize the model (Example for Random Forest) if model_type == 'random_forest': rf_param_space = { 'n_estimators': Integer(50, 200), 'max_depth': Integer(3, 10), 'min_samples_split': Integer(2, 10), 'min_samples_leaf': Integer(1, 5), 'criterion': Categorical(['gini', 'entropy']) } optimized_model, best_params = optimize_model(RandomForestClassifier(random_state=42), X_train, y_train, rf_param_space) if optimized_model: optimized_accuracy = evaluate_model(optimized_model, X_test, y_test) print(f"\nOptimized {model_type} model accuracy on test set: {optimized_accuracy:.4f}") # 9. Visualize data # Example: Plot distribution of a feature column (replace 'Your Feature Column Name' with an actual column) feature_to_plot = features.columns[0] if not features.empty else None if feature_to_plot: plot_distribution(df_engineered, feature_to_plot) # Plot correlation matrix plot_correlation_matrix(df_engineered) else: print("No numerical features found for modeling.") else: print(f"No data remaining after dropping rows with missing target values in '{target_column}'.") else: print(f"Target column '{target_column}' not found or is not suitable for classification/regression.") else: print("Failed to load spreadsheet data.") else: print("Google authentication failed. Cannot proceed with spreadsheet operations.") # Example Zero-Shot Classification text_to_classify = "This movie was fantastic and I loved the acting." possible_labels = ["positive", "negative", "neutral"] try: classification_results = zero_shot_classification(text_to_classify, possible_labels) print("\nZero-Shot Classification Results:") print(classification_results) except Exception as e: print(f"\nZero-Shot Classification failed: {e}") # Example Code Generation (Placeholder) generated_code = generate_code("create a data visualization", "python", "placeholder") print("\nGenerated Code Example:") print(generated_code) # Example Chain-of-Thought (Placeholder) chain_of_thought("Explain the concept of overfitting.", "Hint: Mention bias-variance trade-off.", "placeholder")