Added tuner file

Author: PyDataBlog

Tutorials/divorce_tuner.py

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+# Import Libraries
+import tensorflow
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from rarfile import RarFile
+from urllib.request import urlretrieve
+from kerastuner import HyperModel
+from kerastuner.tuners import RandomSearch, Hyperband, BayesianOptimization
+from tensorflow import keras
+from sklearn.metrics import confusion_matrix
+
+
+sns.set(rc={'figure.figsize': (20, 10)})
+
+# download the compressed divorce file
+urlretrieve('https://archive.ics.uci.edu/ml/machine-learning-databases/00497/divorce.rar', 'divorce_file.rar')
+
+# extract rar file
+with RarFile('divorce_file.rar', mode='r') as rf:
+  rf.extractall()
+
+# read divorce data
+df = pd.read_excel('divorce.xlsx')
+
+# clean columns
+clean_cols = [x.lower() for x in df.columns.to_list()]
+df.columns = clean_cols
+
+# Separate the target and features as separate dataframes
+X = df.drop('class', axis=1)
+y = df[['class']].astype('int')
+
+# Stratified split based on the distribution of the target vector, y
+X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                    stratify=y,
+                                                    test_size=0.20,
+                                                    random_state=30)
+
+
+class MyHyperModel(HyperModel):
+
+    def __init__(self, num_classes):
+        self.num_classes = num_classes
+
+    def build(self, hp):
+        
+        # specify model
+        model = keras.Sequential()
+
+        # range of models to build
+        for i in range(hp.Int('num_layers', 2, 20)):
+
+            model.add(keras.layers.Dense(units=hp.Int('units_' + str(i),
+                                                min_value=32,
+                                                max_value=512, 
+                                                step=32),
+                                   activation='relu'))
+
+        model.add(keras.layers.Dense(self.num_classes, activation='sigmoid'))
+        
+        model.compile(
+            optimizer=keras.optimizers.Adam(
+                hp.Choice('learning_rate',
+                          values=[1e-2, 1e-3, 1e-4])),
+            loss='binary_crossentropy',
+            metrics=['accuracy'])
+
+        return model
+    
+
+
+hypermodel = MyHyperModel(num_classes=1)    
+
+tuner = Hyperband(
+    hypermodel,
+    objective='accuracy',
+    max_epochs=10,
+    seed=10,
+    project_name='divorce test')
+
+
+tuner.search(X_train.values, y_train.values.flatten(),
+             epochs=10,
+             validation_data=(X_test.values, y_test.values.flatten()))
+
+params = tuner.get_best_hyperparameters()[0]
+
+model = tuner.hypermodel.build(params)
+
+model.fit(X.values, y.values.flatten(), epochs=20)
+
+hyperband_accuracy_df = pd.DataFrame(model.history.history)
+
+hyperband_accuracy_df[['loss', 'accuracy']].plot()
+plt.title('Loss & Accuracy Per EPOCH')
+plt.xlabel('EPOCH')
+plt.ylabel('Accruacy')
+plt.show()
+
+
+random_tuner = RandomSearch(
+    hypermodel,
+    objective='accuracy',
+    max_trials=10,
+    seed=10, 
+    project_name='divorce test')
+
+
+random_tuner.search(X_train.values, y_train.values.flatten(),
+             epochs=10,
+             validation_data=(X_test.values, y_test.values.flatten()))
+
+random_params = random_tuner.get_best_hyperparameters()[0]
+
+random_model = random_tuner.hypermodel.build(params)
+
+random_model.fit(X.values, y.values.flatten(), epochs=15)
+
+random_accuracy_df = pd.DataFrame(random_model.history.history)
+
+random_accuracy_df[['loss', 'accuracy']].plot()
+plt.title('Loss & Accuracy Per EPOCH For Random Model')
+plt.xlabel('EPOCH')
+plt.ylabel('Accruacy')
+plt.show()
+
+
+
+bayesian_tuner = BayesianOptimization(
+    hypermodel,
+    objective='accuracy',
+    max_trials=10,
+    seed=10,
+    project_name='divorce test')
+
+bayesian_tuner.search(X_train.values, y_train.values.flatten(),
+             epochs=10,
+             validation_data=(X_test.values, y_test.values.flatten()))
+             
+bayesian_params = bayesian_tuner.get_best_hyperparameters()[0]
+
+bayesian_model = bayesian_tuner.hypermodel.build(bayesian_params)
+
+bayesian_model.fit(X.values, y.values.flatten(), epochs=15)
+
+bayesian_accuracy_df = pd.DataFrame(bayesian_model.history.history)
+
+bayesian_accuracy_df[['loss', 'accuracy']].plot()
+plt.title('Loss & Accuracy Per EPOCH For Bayesian Optimisation Model')
+plt.xlabel('EPOCH')
+plt.ylabel('Accruacy')
+plt.show()
+
+
+
+