search_rf.py

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from skopt import BayesSearchCV

# %%
#Open csv file
data = pd.read_feather('../../data_ugi/data10.feather')



# %%
#Split into training and test data
y = data['READ30']
X = data.drop(['READ30'], axis=1)

# Number of trees in random forest
n_estimators = [500, 750, 1000, 1250, 1500]
# Number of features to consider at every split
max_features = ['auto','sqrt']
# Maximum number of levels in tree
max_depth = [20, 40, 60, 80, 100, 120]
max_depth.append(None)
# Minimum number of samples required to split a node
min_samples_split = [2, 3, 4, 6]
# Minimum number of samples required at each leaf node
min_samples_leaf = [2, 4, 6, 8]
# Method of selecting samples for training each tree
bootstrap = [True]

random_grid = {'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'min_samples_split': min_samples_split,
               'min_samples_leaf': min_samples_leaf,
               'bootstrap': bootstrap}

# Use the random grid to search for best hyperparameters
# First create the base model to tune
rf = RandomForestClassifier()
rf_random = BayesSearchCV(rf, random_grid, n_iter = 50, cv = 5, verbose=2, scoring='roc_auc', random_state=42, n_jobs = -1)
# Fit the random search model
rf_random.fit(X, y)
results = pd.DataFrame(rf_random.cv_results_)
results.sort_values(by='rank_test_score').to_csv('../../results_ugi/results_rf3_data10.csv')