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def compare_on_dataset(data, target_variable=None, lr=0.001, patience=150):
from IPython.display import display
df = (
pd.read_csv(data)
# Rename columns to lowercase and underscores
.pipe(lambda d: d.rename(columns={
k: v for k, v in zip(
d.columns,
[c.lower().replace(' ', '_') for c in d.columns]
)
}))
# Switch categorical classes to integers
.assign(**{target_variable: lambda r: r[target_variable].astype('category').cat.codes})
.pipe(lambda d: pd.get_dummies(d))
)
y = df[target_variable].values
X = (
# Drop target variable
df.drop(target_variable, axis=1)
# Min-max-scaling (only needed for the DL model)
.pipe(lambda d: (d-d.min())/d.max()).fillna(0)
.as_matrix()
)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=seed
)
m = Sequential()
m.add(Dense(128, activation='relu', input_shape=(X.shape[1],)))
m.add(Dropout(0.5))
m.add(Dense(128, activation='relu'))
m.add(Dropout(0.5))
m.add(Dense(128, activation='relu'))
m.add(Dropout(0.5))
m.add(Dense(len(np.unique(y)), activation='softmax'))
m.compile(
optimizer=optimizers.Adam(lr=lr),
loss='categorical_crossentropy',
metrics=['accuracy']
)
m.fit(
# Feature matrix
X_train,
# Target class one-hot-encoded
pd.get_dummies(pd.DataFrame(y_train), columns=[0]).as_matrix(),
# Iterations to be run if not stopped by EarlyStopping
epochs=200,
callbacks=[
EarlyStopping(monitor='val_loss', patience=patience),
ModelCheckpoint(
'best.model',
monitor='val_loss',
save_best_only=True,
verbose=1
)
],
verbose=2,
validation_split=0.1,
batch_size=256,
)
# Keep track of what class corresponds to what index
mapping = (
pd.get_dummies(pd.DataFrame(y_train), columns=[0], prefix='', prefix_sep='')
.columns.astype(int).values
)
# Load the best model
m.load_weights("best.model")
y_test_preds = [mapping[pred] for pred in m.predict(X_test).argmax(axis=1)]
print 'Three layer deep neural net'
display(pd.crosstab(
pd.Series(y_test, name='Actual'),
pd.Series(y_test_preds, name='Predicted'),
margins=True
))
print 'Accuracy: {0:.3f}'.format(accuracy_score(y_test, y_test_preds))
boostrap_stats_samples = [
np.random.choice((y_test == y_test_preds), size=int(len(y_test)*.5)).mean()
for _ in range(10000)
]
print 'Boostrapped accuracy 95 % interval', np.percentile(boostrap_stats_samples, 5), np.percentile(boostrap_stats_samples, 95)
params_fixed = {
'objective': 'binary:logistic',
'silent': 1,
'seed': seed,
}
space = {
'max_depth': (1, 5),
'learning_rate': (10**-4, 10**-1),
'n_estimators': (10, 200),
'min_child_weight': (1, 20),
'subsample': (0, 1),
'colsample_bytree': (0.3, 1)
}
reg = XGBClassifier(**params_fixed)
def objective(params):
""" Wrap a cross validated inverted `accuracy` as objective func """
reg.set_params(**{k: p for k, p in zip(space.keys(), params)})
return 1-np.mean(cross_val_score(
reg, X_train, y_train, cv=5, n_jobs=-1,
scoring='accuracy')
)
res_gp = gp_minimize(objective, space.values(), n_calls=50, random_state=seed)
best_hyper_params = {k: v for k, v in zip(space.keys(), res_gp.x)}
params = best_hyper_params.copy()
params.update(params_fixed)
clf = XGBClassifier(**params)
clf.fit(X_train, y_train)
y_test_preds = clf.predict(X_test)
print ''
print 'Xgboost'
display(pd.crosstab(
pd.Series(y_test, name='Actual'),
pd.Series(y_test_preds, name='Predicted'),
margins=True
))
print 'Accuracy: {0:.3f}'.format(accuracy_score(y_test, y_test_preds))
boostrap_stats_samples = [
np.random.choice((y_test == y_test_preds), size=int(len(y_test)*.5)).mean()
for _ in range(10000)
]
print 'Boostrapped accuracy 95 % interval', np.percentile(boostrap_stats_samples, 5), '-', np.percentile(boostrap_stats_samples, 95)
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