Removed dangling, non-NSGA experiments
This commit is contained in:
Varyngoth
@@ -1,92 +1,52 @@
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import pandas as pd
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from loading_climate_data import load_and_process_climate_data
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from loading_crop_data import engineer_crop_features
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from loading_ndvi_data import process_ndvi_data
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from loading_soil_data import get_soil_data
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def combine_datasets_single_csv(
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csv_file: str,
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target_column: str = None,
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exclude_features: list = None
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):
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"""
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Load and process a single CSV file into a DataFrame suitable for modeling.
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Mimics the original combine_datasets() structure, returning a single DataFrame.
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def combine_datasets(crop_data_file,
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soil_data_file,
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climate_data_file,
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ndvi_data_file,
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ndvi=False):
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dataset_loaders = {
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'soil': lambda: get_soil_data(soil_data_file),
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'climate': lambda: load_and_process_climate_data(climate_data_file),
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'ndvi': lambda: process_ndvi_data(ndvi_data_file),
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'crop': lambda: engineer_crop_features(crop_data_file)
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}
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Parameters
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----------
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csv_file : str
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Path to the CSV file to load.
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target_column : str, optional
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Column to use as target. If None, assumes last column is the target.
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exclude_features : list of str, optional
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List of features to exclude from the DataFrame.
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merge_keys = {
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'soil': ['PostalCode'],
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'climate': ['PostalCode', 'Year'],
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'ndvi': ['PostalCode', 'Year'],
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'crop': ['PostalCode', 'Year']
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}
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Returns
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-------
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combined_data : pd.DataFrame
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Processed DataFrame, target column removed if specified.
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The format is compatible with previous combine_datasets outputs.
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"""
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# Load CSV
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combined_data = pd.read_csv(csv_file)
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combined_data = dataset_loaders['crop']()
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# Determine target
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if target_column is None:
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target_column = combined_data.columns[-1] # default last column
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# Merge climate data
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climate_data = dataset_loaders['climate']()
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combined_data = pd.merge(combined_data, climate_data, on=merge_keys['climate'], how='left')
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if target_column not in combined_data.columns:
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raise ValueError(f"Target column '{target_column}' not found in CSV.")
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# Merge NDVI data if required
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if ndvi:
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ndvi_data = dataset_loaders['ndvi']()
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combined_data = combined_data[combined_data['Year'] >= 2000]
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combined_data = pd.merge(combined_data, ndvi_data, on=merge_keys['ndvi'], how='left')
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# Separate target column internally (optional)
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target_series = combined_data[target_column]
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# Merge soil data
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soil_data = dataset_loaders['soil']()
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combined_data = pd.merge(combined_data, soil_data, on=merge_keys['soil'], how='left')
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postal_codes = combined_data['PostalCode']
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years = combined_data['Year']
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# Drop the target from features
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combined_data = combined_data.drop(columns=[target_column])
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# Drop irrelevant or redundant columns
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features_to_exclude = ['PostalCode', 'Year', 'SoilID']
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combined_data = combined_data.drop(columns=features_to_exclude, errors='ignore')
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return combined_data
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def combine_dataset_pc(crop_data_file,
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soil_data_file,
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climate_data_file,
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ndvi_data_file,
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ndvi=False):
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dataset_loaders = {
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'soil': lambda: get_soil_data(soil_data_file),
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'climate': lambda: load_and_process_climate_data(climate_data_file),
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'ndvi': lambda: process_ndvi_data(ndvi_data_file),
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'crop': lambda: engineer_crop_features(crop_data_file)
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}
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merge_keys = {
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'soil': ['PostalCode'],
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'climate': ['PostalCode', 'Year'],
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'ndvi': ['PostalCode', 'Year'],
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'crop': ['PostalCode', 'Year']
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}
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combined_data = dataset_loaders['crop']()
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# Merge climate data
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climate_data = dataset_loaders['climate']()
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combined_data = pd.merge(combined_data, climate_data, on=merge_keys['climate'], how='left')
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# Merge NDVI data if required
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if ndvi:
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ndvi_data = dataset_loaders['ndvi']()
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combined_data = combined_data[combined_data['Year'] >= 2000]
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combined_data = pd.merge(combined_data, ndvi_data, on=merge_keys['ndvi'], how='left')
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# Merge soil data
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soil_data = dataset_loaders['soil']()
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combined_data = pd.merge(combined_data, soil_data, on=merge_keys['soil'], how='left')
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# Drop irrelevant or redundant columns
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features_to_exclude = ['Year', 'SoilID']
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combined_data = combined_data.drop(columns=features_to_exclude, errors='ignore')
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# Drop additional user-specified features
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if exclude_features:
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for col in exclude_features:
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if col in combined_data.columns:
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combined_data = combined_data.drop(columns=[col])
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else:
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print(f"Warning: '{col}' not found in CSV and cannot be excluded.")
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# Mimic original structure: return combined_data as a DataFrame
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return combined_data
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@@ -1,23 +0,0 @@
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#!/bin/bash
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#SBATCH --account=def-xander # Replace with your account
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#SBATCH --mem=10G # Memory allocation
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#SBATCH --time=21:00:00 # Total run time limit (11 hours)
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#SBATCH --cpus-per-task=4 # Number of CPU cores per task
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#SBATCH --job-name=grid_search_%A # Job name with job ID appended
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#SBATCH --output=%x-%j.out # Standard output and error log
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#SBATCH --error=%x-%j.err # Separate error log
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# Load necessary modules
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#module load python/3.8
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# Activate your virtual environment
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source /env0/bin/activate
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# Parameters
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TIME=$1
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# Run the Python script with the specified time parameter
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srun python $WORK_DIR/src/grid_search_exp.py --time $TIME
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# Deactivate the virtual environment
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deactivate
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@@ -1,186 +0,0 @@
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import os
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import time
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import numpy as np
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import pandas as pd
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import warnings
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import shap
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from sklearn.model_selection import KFold, ParameterGrid
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import StandardScaler, PolynomialFeatures, RobustScaler, PowerTransformer
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from sklearn.impute import SimpleImputer
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from sklearn.feature_selection import SelectKBest, f_regression
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from sklearn.metrics import mean_squared_error
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from sklearn.cluster import KMeans
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from sklearn.exceptions import ConvergenceWarning
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from joblib import dump
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from combine_datasets import combine_datasets
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from algorithms import lasso, random_forest, gradient_boosting, decision_tree_regressor, ridge_regressor, stacking_lasso
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warnings.filterwarnings("ignore", category=ConvergenceWarning)
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# Preprocess the data and separate features and target
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def preprocess_data(input_data, pipeline):
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X = input_data.drop(columns=['yield_t/ha'], errors='ignore')
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y = input_data['yield_t/ha']
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# Fit-transform the pipeline steps without `y` first
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for name, step in pipeline.steps:
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if name != 'feature_selection':
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X = step.fit_transform(X)
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# Apply `SelectKBest` separately, which requires `y`
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if 'feature_selection' in [name for name, _ in pipeline.steps]:
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X = pipeline.named_steps['feature_selection'].fit_transform(X, y)
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return X, y
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# Function to compute SHAP values using KMeans clustering
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def compute_shap_values_with_kmeans(model, X_train, X_test, n_clusters=10):
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# Fit KMeans to the training data with explicit n_init parameter
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kmeans = KMeans(n_clusters=n_clusters, n_init=10, random_state=42).fit(X_train)
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cluster_centers = kmeans.cluster_centers_
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# Compute SHAP values on cluster centers
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explainer = shap.KernelExplainer(model.predict, cluster_centers)
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shap_values = explainer.shap_values(X_test)
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return shap_values
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# Iterative grid search with time monitoring
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def iterative_grid_search(model, param_grid, X, y, cv, time_limit):
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best_model = None
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best_score = -np.inf
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start_time = time.time()
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# Manually iterate over parameter combinations
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for params in ParameterGrid(param_grid):
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elapsed_time = time.time() - start_time
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if elapsed_time > time_limit:
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print("Time limit exceeded. Stopping search.")
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break
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model.set_params(**params)
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scores = []
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for train_idx, test_idx in cv.split(X, y):
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X_train, X_test = X[train_idx], X[test_idx]
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y_train, y_test = y[train_idx], y[test_idx]
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model.fit(X_train, y_train)
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predictions = model.predict(X_test)
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score = -mean_squared_error(y_test, predictions)
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scores.append(score)
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mean_score = np.mean(scores)
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if mean_score > best_score:
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best_score = mean_score
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best_model = model
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return best_model
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# Main experiment function
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def run_experiment_with_dynamic_grid(total_hours=10.0, output_base_directory="./results"):
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time_per_algorithm = total_hours * 3600 / 5
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# Load and combine datasets
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data = combine_datasets(
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"./data/potatoes_dataset.csv",
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"./data/updated_soil_data_with_awc.csv",
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"./data/final_augmented_climate_data.csv",
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"./data/NDVI.csv",
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ndvi=True
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)
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pipelines = [
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Pipeline([
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('normalize', StandardScaler()),
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('imputer', SimpleImputer(strategy='mean')),
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('poly_features', PolynomialFeatures(degree=2)),
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('feature_selection', SelectKBest(score_func=f_regression, k=25))
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]),
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Pipeline([
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('robust', RobustScaler()),
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('feature_selection', SelectKBest(score_func=f_regression, k=25)),
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('power_transformation', PowerTransformer(method='yeo-johnson'))
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])
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]
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models = {
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'lasso': lasso,
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'ridge': ridge_regressor,
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'random_forest': random_forest,
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'gradient_boosting': gradient_boosting,
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'decision_tree': decision_tree_regressor,
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'stacking_ensemble': stacking_lasso
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}
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output_directory = os.path.join(output_base_directory, f"grid_search_results_{total_hours}h")
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os.makedirs(output_directory, exist_ok=True)
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kf = KFold(n_splits=5, shuffle=True, random_state=42)
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for model_name, model_func in models.items():
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best_pipeline_result = None
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for pipeline_idx, pipeline in enumerate(pipelines):
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X, y = preprocess_data(data, pipeline)
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model, params = model_func()
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best_model = iterative_grid_search(
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model, params, X, y, cv=kf, time_limit=time_per_algorithm
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)
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shap_stabilities = []
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for fold, (train_idx, test_idx) in enumerate(kf.split(X, y)):
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X_train_fold, X_test_fold = X[train_idx], X[test_idx]
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y_train_fold = y[train_idx]
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best_model.fit(X_train_fold, y_train_fold)
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shap_values = compute_shap_values_with_kmeans(best_model, X_train_fold, X_test_fold)
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fold_shap_stability = np.std(shap_values, axis=0).mean()
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shap_stabilities.append(fold_shap_stability)
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shap_stability = np.mean(shap_stabilities)
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predictions = best_model.predict(X)
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mse = mean_squared_error(y, predictions)
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if best_pipeline_result is None or (
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mse < best_pipeline_result['mse'] and shap_stability < best_pipeline_result['shap_stability']):
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best_pipeline_result = {
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'model': best_model,
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'pipeline_idx': pipeline_idx,
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'mse': mse,
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'shap_stability': shap_stability
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}
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if best_pipeline_result:
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model_output_dir = os.path.join(output_directory, model_name)
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os.makedirs(model_output_dir, exist_ok=True)
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model_file_path = os.path.join(model_output_dir, f"{model_name}_best_model.joblib")
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dump(best_pipeline_result['model'], model_file_path)
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metrics_df = pd.DataFrame({
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'Model': [model_name],
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'Pipeline': [f"Pipeline_{best_pipeline_result['pipeline_idx'] + 1}"],
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'MSE': [best_pipeline_result['mse']],
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'SHAP_Stability': [best_pipeline_result['shap_stability']]
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})
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metrics_csv_path = os.path.join(output_directory, "metrics_summary_GS.csv")
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metrics_df.to_csv(metrics_csv_path, index=False, mode='a', header=not os.path.exists(metrics_csv_path))
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print(f"Saved best results for {model_name} from Pipeline {best_pipeline_result['pipeline_idx'] + 1}")
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print(f"All results saved to {output_directory}")
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description='Run Grid Search for different models.')
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parser.add_argument('--time', type=float, required=True, help='Time for the model to run in hours')
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args = parser.parse_args()
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run_experiment_with_dynamic_grid(total_hours=args.time, output_base_directory="./results_grid_search")
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@@ -1,166 +0,0 @@
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import os
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import shap
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import numpy as np
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import pandas as pd
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import h2o
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from h2o.automl import H2OAutoML
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from sklearn.preprocessing import StandardScaler, RobustScaler, PolynomialFeatures, PowerTransformer
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from sklearn.impute import SimpleImputer
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from sklearn.pipeline import Pipeline
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from sklearn.model_selection import KFold
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from sklearn.feature_selection import SelectKBest, f_regression
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from sklearn.metrics import mean_squared_error
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from sklearn.cluster import KMeans
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from combine_datasets import combine_datasets
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# Initialize H2O
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h2o.init()
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# Preprocess the data and separate features and target
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def preprocess_data(input_data, pipeline):
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X = input_data.drop(columns=['yield_t/ha'], errors='ignore')
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y = input_data['yield_t/ha']
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# Fit-transform the pipeline steps without `y` first
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for name, step in pipeline.steps:
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if name != 'feature_selection':
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X = step.fit_transform(X)
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# Apply `SelectKBest` separately, which requires `y`
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if 'feature_selection' in [name for name, _ in pipeline.steps]:
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X = pipeline.named_steps['feature_selection'].fit_transform(X, y)
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return X, y
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# Function to compute SHAP values using KMeans clustering
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def compute_shap_values_with_kmeans(model, X_train, X_test, n_clusters=10):
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# Fit KMeans to the training data with explicit n_init parameter
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kmeans = KMeans(n_clusters=n_clusters, n_init=10, random_state=42).fit(X_train)
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cluster_centers = kmeans.cluster_centers_
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# Convert cluster centers to H2OFrame
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cluster_centers_h2o = h2o.H2OFrame(cluster_centers)
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# Convert X_test to H2OFrame
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X_test_h2o = h2o.H2OFrame(X_test)
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# Compute SHAP values on cluster centers
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explainer = shap.KernelExplainer(lambda x: model.predict(h2o.H2OFrame(x)).as_data_frame().values.flatten(),
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cluster_centers_h2o.as_data_frame().values)
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shap_values = explainer.shap_values(X_test_h2o.as_data_frame().values)
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return shap_values
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# Function to load and combine datasets
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def load_and_combine_datasets(use_ndvi=True):
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data = combine_datasets(
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"./data/potatoes_dataset.csv",
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"./data/updated_soil_data_with_awc.csv",
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"./data/final_augmented_climate_data.csv",
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"./data/NDVI.csv",
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ndvi=use_ndvi
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)
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return data
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# Main experiment function with H2O AutoML
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def run_h2o_experiment(use_ndvi=True, automl_time=10, output_base_directory="./results_h2o"):
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data = load_and_combine_datasets(use_ndvi=use_ndvi)
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pipelines = [
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Pipeline([
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('normalize', StandardScaler()),
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('imputer', SimpleImputer(strategy='mean')),
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('poly_features', PolynomialFeatures(degree=2)),
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('feature_selection', SelectKBest(score_func=f_regression, k=25))
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]),
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Pipeline([
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('robust', RobustScaler()),
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('feature_selection', SelectKBest(score_func=f_regression, k=25)),
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('power_transformation', PowerTransformer(method='yeo-johnson'))
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])
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]
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output_directory = os.path.join(output_base_directory, f"h2o_automl_results_{automl_time}h")
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os.makedirs(output_directory, exist_ok=True)
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metrics_df = pd.DataFrame(columns=['Model', 'Pipeline', 'MSE', 'SHAP_Stability'])
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kf = KFold(n_splits=5, shuffle=True, random_state=42)
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for pipeline_idx, pipeline in enumerate(pipelines):
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X_processed, y = preprocess_data(data, pipeline)
|
||||
|
||||
X_h2o = h2o.H2OFrame(X_processed)
|
||||
y_h2o = h2o.H2OFrame(y.to_frame())
|
||||
X_h2o['target'] = y_h2o
|
||||
|
||||
shap_stabilities = []
|
||||
all_shap_values = []
|
||||
|
||||
for fold, (train_idx, test_idx) in enumerate(kf.split(X_processed)):
|
||||
train = X_h2o[train_idx.tolist(), :]
|
||||
test = X_h2o[test_idx.tolist(), :]
|
||||
|
||||
aml = H2OAutoML(max_runtime_secs=int(automl_time * 3600 / kf.get_n_splits()), max_models=5, seed=42,
|
||||
sort_metric='MSE')
|
||||
aml.train(x=list(X_h2o.columns[:-1]), y='target', training_frame=train)
|
||||
|
||||
best_model = aml.leader
|
||||
|
||||
leaderboard = aml.leaderboard.as_data_frame()
|
||||
leaderboard_csv_path = os.path.join(output_directory, f"leaderboard_pipeline_{pipeline_idx + 1}_fold_{fold + 1}.csv")
|
||||
leaderboard.to_csv(leaderboard_csv_path, index=False)
|
||||
|
||||
model_output_dir = os.path.join(output_directory, f"pipeline_{pipeline_idx + 1}")
|
||||
os.makedirs(model_output_dir, exist_ok=True)
|
||||
model_file_path = os.path.join(model_output_dir, f"h2o_best_model_{pipeline_idx + 1}_fold_{fold + 1}.zip")
|
||||
h2o.save_model(best_model, path=model_file_path)
|
||||
|
||||
predictions = best_model.predict(test).as_data_frame()['predict']
|
||||
|
||||
mse = mean_squared_error(test['target'].as_data_frame().values, predictions.values)
|
||||
|
||||
shap_values = compute_shap_values_with_kmeans(best_model, train.as_data_frame().values,
|
||||
test.as_data_frame().values)
|
||||
shap_stability = np.std(shap_values, axis=0).mean()
|
||||
shap_stabilities.append(shap_stability)
|
||||
all_shap_values.append(shap_values)
|
||||
|
||||
|
||||
print(f"Completed H2O AutoML and SHAP computation for Pipeline {pipeline_idx + 1}, Fold {fold + 1}")
|
||||
|
||||
mean_shap_stability = np.mean(shap_stabilities)
|
||||
for fold_idx, shap_values in enumerate(all_shap_values):
|
||||
shap_file_name = f"shap_values_pipeline_{pipeline_idx + 1}_fold_{fold_idx + 1}.npy"
|
||||
shap_file_path = os.path.join(model_output_dir, shap_file_name)
|
||||
np.save(shap_file_path, shap_values)
|
||||
|
||||
metrics_df = pd.concat([metrics_df, pd.DataFrame([{
|
||||
'Model': best_model.model_id,
|
||||
'Pipeline': f"Pipeline_{pipeline_idx + 1}",
|
||||
'MSE': mse,
|
||||
'SHAP_Stability': mean_shap_stability
|
||||
}])], ignore_index=True)
|
||||
|
||||
metrics_csv_path = os.path.join(output_directory, "metrics_summary.csv")
|
||||
metrics_df.to_csv(metrics_csv_path, index=False)
|
||||
|
||||
print(f"All results saved to {output_directory}")
|
||||
|
||||
h2o.shutdown(prompt=False)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description='Run H2O AutoML for different models.')
|
||||
parser.add_argument('--time', type=float, required=True, help='Time in hours for the AutoML process to run.')
|
||||
args = parser.parse_args()
|
||||
|
||||
# Run the H2O AutoML experiment
|
||||
run_h2o_experiment(use_ndvi=True, automl_time=args.time, output_base_directory="./results_h2o")
|
||||
|
||||
@@ -1,23 +0,0 @@
|
||||
#!/bin/bash
|
||||
#SBATCH --account=def-xander # Replace with your account
|
||||
#SBATCH --mem=10G # Memory allocation
|
||||
#SBATCH --time=11:00:00 # Total run time limit (11 hours)
|
||||
#SBATCH --cpus-per-task=4 # Number of CPU cores per task
|
||||
#SBATCH --job-name=h20_%A # Job name with job ID appended
|
||||
#SBATCH --output=%x-%j.out # Standard output and error log
|
||||
#SBATCH --error=%x-%j.err # Separate error log
|
||||
|
||||
# Load necessary modules
|
||||
module load python/3.8
|
||||
|
||||
# Activate your virtual environment
|
||||
source ~/envs/workdir/bin/activate
|
||||
|
||||
# Parameters
|
||||
TIME=$1
|
||||
|
||||
# Run the Python script with the specified time parameter
|
||||
srun python /home/dvera/scratch/Framework_EXP/h20_autoML.py --time $TIME
|
||||
|
||||
# Deactivate the virtual environment
|
||||
deactivate
|
||||
Reference in New Issue
Block a user