def get_best_quest_classifier(train, label_col, skf_tune):
quest = QuestConstructor()
alphas = [10e-5, 10e-4, 10e-3, 10e-2, 0.25, 0.5, 0.9,
0.99] #np.arange(0.001, 1, 0.01)
# max_nr_nodes = np.arange(1,20,2)
errors = {}
for alpha in alphas:
# for max_nr_node in max_nr_nodes:
errors[alpha] = []
for train_tune_idx, val_tune_idx in skf_tune:
train_tune = train.iloc[train_tune_idx, :]
X_train_tune = train_tune.drop(label_col, axis=1)
y_train_tune = train_tune[label_col]
val_tune = train.iloc[val_tune_idx, :]
X_val_tune = val_tune.drop(label_col, axis=1)
y_val_tune = val_tune[label_col]
for alpha in alphas:
quest.alpha = alpha
tree = quest.construct_tree(X_train_tune, y_train_tune)
predictions = tree.evaluate_multiple(X_val_tune).astype(int)
errors[alpha].append(
1 - accuracy_score(predictions, y_val_tune, normalize=True))
for alpha in alphas:
# for max_nr_node in max_nr_nodes:
errors[alpha] = np.mean(errors[alpha])
best_params = min(errors.items(), key=operator.itemgetter(1))[0]
quest.alpha = best_params
return quest
for feature in feature_column_names:
feature_mins[feature] = np.min(df[feature])
feature_maxs[feature] = np.max(df[feature])
df = df.reset_index(drop=True)
labels_df = DataFrame()
labels_df['cat'] = df['disease'].copy()
features_df = df.copy()
features_df = features_df.drop('disease', axis=1)
features_df = features_df / features_df.max()
train_labels_df = labels_df
train_features_df = features_df
c45 = C45Constructor(cf=0.01)
cart = CARTConstructor(min_samples_leaf=10, max_depth=6)
quest = QuestConstructor(default=1,
max_nr_nodes=1,
discrete_thresh=25,
alpha=0.05)
tree_constructors = [c45, cart, quest]
tree_confusion_matrices = {}
titles = ["C4.5", "Boosted C4.5", "Genetic"]
for title in titles:
tree_confusion_matrices[title] = []
skf = sklearn.cross_validation.StratifiedKFold(labels_df['cat'],
n_folds=N_FOLDS,
shuffle=True,
random_state=SEED)
for train_index, test_index in skf:
trees = []
feature_column_names = list(set(df.columns) - set(['Name']))
for feature in feature_column_names:
feature_mins[feature] = np.min(df[feature])
feature_maxs[feature] = np.max(df[feature])
df=df.reset_index(drop=True)
labels_df = DataFrame()
labels_df['cat'] = df['Name'].copy()
features_df = df.copy()
features_df = features_df.drop('Name', axis=1)
train_labels_df = labels_df
train_features_df = features_df
c45 = C45Constructor(cf=1.0)
cart = CARTConstructor(max_depth=5, min_samples_leaf=2)
quest = QuestConstructor(default=1, max_nr_nodes=2, discrete_thresh=1, alpha=0.0000001)
tree_constructors = [c45, cart, quest]
tree_confusion_matrices = {}
for tree_constructor in tree_constructors:
tree_confusion_matrices[tree_constructor.get_name()] = []
tree_confusion_matrices["Genetic"] = []
skf = sklearn.cross_validation.StratifiedKFold(labels_df['cat'], n_folds=N_FOLDS, shuffle=True, random_state=SEED)
for train_index, test_index in skf:
train_features_df, test_features_df = features_df.iloc[train_index,:].copy(), features_df.iloc[test_index,:].copy()
train_labels_df, test_labels_df = labels_df.iloc[train_index,:].copy(), labels_df.iloc[test_index,:].copy()
train_features_df = train_features_df.reset_index(drop=True)
test_features_df = test_features_df.reset_index(drop=True)
train_labels_df = train_labels_df.reset_index(drop=True)
new_features = DataFrame()
for k in range(num_features):
new_features[feature_column_names[best_features[k]]] = features_df[feature_column_names[best_features[k]]]
features_df = new_features
feature_column_names = list(set(features_df.columns) - set(['Survived']))
feature_mins = {}
feature_maxs = {}
for feature in feature_column_names:
feature_mins[feature] = np.min(df[feature])
feature_maxs[feature] = np.max(df[feature])
c45 = C45Constructor(cf=0.15)
cart = CARTConstructor(max_depth=3, min_samples_split=3)
quest = QuestConstructor(default=1, max_nr_nodes=3, discrete_thresh=10, alpha=0.1)
tree_constructors = [c45, cart, quest]
merger = DecisionTreeMerger()
train_df = features_df.copy()
train_df['cat'] = labels_df['cat'].copy()
best_tree = merger.genetic_algorithm(train_df, 'cat', tree_constructors, seed=SEED, num_iterations=10,
num_mutations=5, population_size=10, max_samples=1, val_fraction=0.25,
num_boosts=7)
# best_tree = merger.genetic_algorithm(train_df, 'cat', tree_constructors, seed=SEED, num_iterations=1,
# num_mutations=1, population_size=1, max_samples=1, val_fraction=0.05)
c45_tree = c45.construct_tree(features_df, labels_df)
quest_tree = quest.construct_tree(features_df, labels_df)
c45_tree.populate_samples(features_df, labels_df['cat'])
quest_tree.populate_samples(features_df, labels_df['cat'])
cart_tree = cart.construct_tree(features_df, labels_df)
# test_labels_df = test_labels_df.reset_index(drop=True)
# train_features_df = features_df.head(int(0.8*len(features_df.index)))
# test_features_df = features_df.tail(int(0.2*len(features_df.index)))
# train_labels_df = labels_df.head(int(0.8*len(labels_df.index)))
# test_labels_df = labels_df.tail(int(0.2*len(labels_df.index)))
train_df = train_features_df.copy()
train_df['cat'] = train_labels_df['cat'].copy()
c45 = C45Constructor(cf=0.15)
cart = CARTConstructor(min_samples_leaf=10)
#c45_2 = C45Constructor(cf=0.15)
#c45_3 = C45Constructor(cf=0.75)
quest = QuestConstructor(default=1,
max_nr_nodes=5,
discrete_thresh=10,
alpha=0.1)
tree_constructors = [c45, cart, quest]
trees = []
for tree_constructor in tree_constructors:
tree = tree_constructor.construct_tree(train_features_df, train_labels_df)
tree.visualise(
os.path.join(os.path.join('..', 'data'), tree_constructor.get_name()))
trees.append(tree)
merger = DecisionTreeMerger()
best_tree, constructed_trees = merger.genetic_algorithm(train_df,
'cat',
tree_constructors,
seed=SEED,
num_iterations=2)
measurements_temp[dataset][algorithm] = measurements[dataset][algorithm]
measurements_list.append(measurements_temp)
target.write(write_preamble())
for measurements_ in measurements_list:
target.write(write_measurements(measurements_))
target.write(write_figures(figure))
target.write(write_footing())
target.close()
datasets = load_all_datasets()
quest_bench = QUESTBenchConstructor()
guide = GUIDEConstructor()
quest = QuestConstructor()
inTrees = inTreesClassifier()
merger = DecisionTreeMergerClean()
NR_FOLDS = 3
for dataset in datasets:
print dataset['name'], len(dataset['dataframe'])
conf_matrices = {'QUESTGilles': [], 'GUIDE': [], 'C4.5': [], 'CART': [], 'ISM': [], 'ISM_pruned': [],
'Genetic': [], 'CN2': [], 'QUESTLoh': [], 'inTrees': [], 'XGBoost': [], 'RF': []} #
avg_nodes = {'QUESTGilles': [], 'GUIDE': [], 'C4.5': [], 'CART': [], 'ISM': [], 'ISM_pruned': [],
'Genetic': [], 'CN2': [], 'QUESTLoh': [], 'inTrees': [], 'XGBoost': [], 'RF': []} #
times = {'QUESTGilles': [], 'GUIDE': [], 'C4.5': [], 'CART': [], 'ISM': [], 'ISM_pruned': [],
'Genetic': [], 'CN2': [], 'QUESTLoh': [], 'inTrees': [], 'XGBoost': [], 'RF': []} #
df = dataset['dataframe']
label_col = dataset['label_col']
feature_cols = dataset['feature_cols']
skf = StratifiedKFold(df[label_col], n_folds=NR_FOLDS, shuffle=True, random_state=None)
