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analytical_code/forest.py

+#!/usr/bin/env python
+
+import pandas as pd
+import numpy as np
+import pickle
+
+# Open file with pd.read_csv
+df = pd.read_csv("matrix_model_complete.tsv", sep='\t')
+#print(df.head())
+
+X = df.drop('class', axis=1)
+X = X.drop('#', axis=1)
+y = df['class']
+
+#print(X)
+#print(y)
+
+from sklearn.model_selection import train_test_split
+
+# implementing train-test-split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=66)
+
+from sklearn import model_selection
+from sklearn.ensemble import RandomForestClassifier
+
+#random forest model_selection
+rfc = RandomForestClassifier(n_estimators=1600, max_depth=340, max_features='auto')
+rfc.fit(X_train, y_train)
+
+# predictions
+rfc_predict = rfc.predict(X_test)
+
+from sklearn.model_selection import cross_val_score
+from sklearn.metrics import classification_report, confusion_matrix
+
+rfc_cv_score= cross_val_score(rfc, X, y, cv=10, scoring='roc_auc')
+
+print("=== Confusion Matrix ===")
+print(confusion_matrix(y_test, rfc_predict))
+print('\n')
+print("=== Classification Report ===")
+print(classification_report(y_test, rfc_predict))
+print('\n')
+print("=== All AUC Scores ===")
+print(rfc_cv_score)
+print('\n')
+print("=== Mean AUC Score ===")
+print("Mean AUC Score - Random Forest: ", rfc_cv_score.mean())
+
+#filename = 'finalized_model_3f.sav'
+#pickle.dump(rfc, open(filename, 'wb'))
+# Tuning Hyperparameters
+#from sklearn.model_selection import RandomizedSearchCV
+
+# number of trees in random forest
+#n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
+
+# number of features as every split
+#max_features = ['auto', 'sqrt']
+
+# max depth
+#max_depth = [int(x) for x in np.linspace(100, 500, num=11)]
+#max_depth.append(None)
+
+# create random grid
+#random_grid = {
+#	'n_estimators': n_estimators,
+#	'max_features': max_features,
+#	'max_depth': max_depth
+#	}
+
+# Random search of parameters
+#rfc_random = RandomizedSearchCV(estimator = rfc, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)
+
+# Fit the model
+#rfc_random.fit(X_train, y_train)
+
+#print(results)
+#print(rfc_random.best_params_)
+