def main():
# TODO List out which components are supplied by Automater
# In this example, we're utilizing X and y generated by the Automater, auto.input_nub, auto.input_layers,
# auto.output_nub, and auto.suggest_loss
save_results = True
# TODO Load data
observations = None
print('Observation columns: {}'.format(list(observations.columns)))
# TODO Train /test split
train_observations, test_observations = train_test_split(observations)
train_observations = train_observations.copy()
test_observations = test_observations.copy()
# TODO List out variable types
data_type_dict = {
'numerical': [],
'categorical': [],
'text': [],
'timeseries': []
}
output_var = None
# Create and fit Automater
auto = Automater(data_type_dict=data_type_dict, output_var=output_var)
auto.fit(train_observations)
# Transform data
train_X, train_y = auto.fit_transform(train_observations)
test_X, test_y = auto.transform(test_observations)
# TODO Create and fit keras (deep learning) model.
x = auto.input_nub
x = Dense(32)(x)
x = Dense(32)(x)
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='adam', loss=auto.suggest_loss())
model.fit(train_X, train_y)
# Make model predictions and inverse transform model predictions, to get usable results
pred_test_y = model.predict(test_X)
auto.inverse_transform_output(pred_test_y)
# Save all results
if save_results:
temp_dir = lib.get_temp_dir()
model.save(os.path.join(temp_dir, 'model.h5py'))
pickle.dump(train_X, open(os.path.join(temp_dir, 'train_X.pkl'), 'wb'))
pickle.dump(train_y, open(os.path.join(temp_dir, 'train_y.pkl'), 'wb'))
pickle.dump(test_X, open(os.path.join(temp_dir, 'test_X.pkl'), 'wb'))
pickle.dump(test_y, open(os.path.join(temp_dir, 'test_y.pkl'), 'wb'))
pickle.dump(pred_test_y,
open(os.path.join(temp_dir, 'pred_test_y.pkl'), 'wb'))
def main():
observations = load_titanic()
# Transform the data set, using keras_pandas
categorical_vars = ['pclass', 'sex', 'survived']
numerical_vars = [
'age', 'siblings_spouses_aboard', 'parents_children_aboard', 'fare'
]
text_vars = ['name']
auto = Automater(categorical_vars=categorical_vars,
numerical_vars=numerical_vars,
text_vars=text_vars,
response_var='survived')
X, y = auto.fit_transform(observations)
# Start model with provided input nub
x = auto.input_nub
# Fill in your own hidden layers
x = Dense(256)(x)
x = Dense(256, activation='relu')(x)
x = Dense(256)(x)
# End model with provided output nub
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='Adam', loss=auto.loss, metrics=['accuracy'])
# Train model
model.fit(X, y, epochs=15, validation_split=.2)
def main():
# List out which components are supplied by Automater
# In this example, we're utilizing X and y generated by the Automater, auto.input_nub, auto.input_layers,
# auto.output_nub, and auto.suggest_loss
save_results = True
# Load data
observations = lib.load_lending_club()
print('Observation columns: {}'.format(list(observations.columns)))
print('Class balance:\n {}'.format(observations['loan_status'].value_counts()))
# Train /test split
train_observations, test_observations = train_test_split(observations)
train_observations = train_observations.copy()
test_observations = test_observations.copy()
# List out variable types
data_type_dict = {'numerical': ['loan_amnt', 'annual_inc', 'open_acc', 'dti', 'delinq_2yrs',
'inq_last_6mths', 'mths_since_last_delinq', 'pub_rec', 'revol_bal',
'revol_util',
'total_acc', 'pub_rec_bankruptcies'],
'categorical': ['term', 'grade', 'emp_length', 'home_ownership', 'loan_status', 'addr_state',
'application_type', 'disbursement_method'],
'text': ['desc', 'purpose', 'title']}
output_var = 'loan_status'
# Create and fit Automater
auto = Automater(data_type_dict=data_type_dict, output_var=output_var)
auto.fit(train_observations)
# Transform data
train_X, train_y = auto.fit_transform(train_observations)
test_X, test_y = auto.transform(test_observations)
# Create and fit keras (deep learning) model.
x = auto.input_nub
x = Dense(32)(x)
x = Dense(32)(x)
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='adam', loss=auto.suggest_loss())
model.fit(train_X, train_y)
# Make model predictions and inverse transform model predictions, to get usable results
pred_test_y = model.predict(test_X)
auto.inverse_transform_output(pred_test_y)
# Save all results
if save_results:
temp_dir = lib.get_temp_dir()
model.save(os.path.join(temp_dir, 'model.h5py'))
pickle.dump(train_X, open(os.path.join(temp_dir, 'train_X.pkl'), 'wb'))
pickle.dump(train_y, open(os.path.join(temp_dir, 'train_y.pkl'), 'wb'))
pickle.dump(test_X, open(os.path.join(temp_dir, 'test_X.pkl'), 'wb'))
pickle.dump(test_y, open(os.path.join(temp_dir, 'test_y.pkl'), 'wb'))
pickle.dump(pred_test_y, open(os.path.join(temp_dir, 'pred_test_y.pkl'), 'wb'))
def main():
logging.getLogger().setLevel(logging.INFO)
# Reference variables
test_run = True
observations = load_lending_club()
if test_run:
observations = observations.sample(n=100)
# Transform the data set, using keras_pandas
categorical_vars = [
'term', 'grade', 'sub_grade', 'emp_length', 'home_ownership',
'verification_status', 'issue_d', 'pymnt_plan', 'purpose',
'addr_state', 'initial_list_status', 'application_type',
'disbursement_method', 'loan_status'
]
numerical_vars = [
'loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'annual_inc',
'installment', 'dti', 'inq_last_6mths', 'open_acc', 'pub_rec',
'revol_bal', 'total_acc', 'pub_rec_bankruptcies', 'int_rate',
'revol_util'
]
text_vars = ['desc', 'title']
for categorical_var in categorical_vars:
observations[categorical_var] = observations[categorical_var].fillna(
'None')
observations[categorical_var] = observations[categorical_var].apply(
str)
auto = Automater(categorical_vars=categorical_vars,
numerical_vars=numerical_vars,
text_vars=text_vars,
response_var='loan_status')
X, y = auto.fit_transform(observations)
# Start model with provided input nub
x = auto.input_nub
# Fill in your own hidden layers
x = Dense(8)(x)
x = Dense(16, activation='relu')(x)
x = Dense(8)(x)
# End model with provided output nub
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='Adam', loss=auto.loss, metrics=['accuracy'])
# Train model
logging.warning(
'Settle in! This training normally takes about 5-20 minutes on CPU')
model.fit(X, y, epochs=1, validation_split=.2)
pass
def test_inverse_transform_numerical_response(self):
# :oad data
observations = lib.load_lending_club()
# Set to test run
observations = observations.sample(n=100)
# Declare variable types
categorical_vars = ['term', 'grade', 'sub_grade', 'emp_length', 'home_ownership', 'verification_status',
'issue_d',
'pymnt_plan', 'purpose', 'addr_state', 'initial_list_status', 'application_type',
'disbursement_method', 'loan_status']
numerical_vars = ['loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'annual_inc', 'installment', 'dti',
'inq_last_6mths', 'open_acc', 'pub_rec', 'revol_bal', 'total_acc', 'pub_rec_bankruptcies',
'int_rate', 'revol_util']
text_vars = ['desc', 'title']
# Manual null filling
for categorical_var in categorical_vars:
observations[categorical_var] = observations[categorical_var].fillna('None')
observations[categorical_var] = observations[categorical_var].apply(str)
auto = Automater(categorical_vars=categorical_vars, numerical_vars=numerical_vars, text_vars=text_vars,
response_var='funded_amnt')
X, y = auto.fit_transform(observations)
# Start model with provided input nub
x = auto.input_nub
# Fill in your own hidden layers
x = Dense(8)(x)
x = Dense(16, activation='relu')(x)
x = Dense(8)(x)
# End model with provided output nub
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='Adam', loss=auto.loss, metrics=['accuracy'])
# Train model
logging.warning('Settle in! This training normally takes about 5-20 minutes on CPU')
model.fit(X, y, epochs=1, validation_split=.2)
unscaled_preds = model.predict(X)
logging.debug('unscaled_preds: {}'.format(list(unscaled_preds)))
scaled_preds = auto.inverse_transform_output(unscaled_preds)
logging.debug('scaled_preds: {}'.format(list(scaled_preds)))
self.assertNotAlmostEquals(0, numpy.mean(scaled_preds))
self.assertNotAlmostEquals(1, numpy.std(scaled_preds))
def main():
logging.getLogger().setLevel(logging.DEBUG)
observations = load_mushrooms()
# Transform the data set, using keras_pandas
auto = Automater(categorical_vars=observations.columns,
response_var='class')
X, y = auto.fit_transform(observations)
# Create model
x = auto.input_nub
x = Dense(30)(x)
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='Adam', loss=auto.loss, metrics=['accuracy'])
# Train model
model.fit(X, y, epochs=10, validation_split=.5)
pass
def main():
# List out which components are supplied by Automater
# In this example, we're utilizing X and y generated by the Automater, auto.input_nub, auto.input_layers,
# auto.output_nub, and auto.suggest_loss
save_results = True
# Load data
observations = lib.load_instanbul_stocks(as_ts=True)
print('Observation columns: {}'.format(list(observations.columns)))
# Notice that the lagged variables are an array of values
print('One of the lagged variables: \n{}'.format(
observations['ise_lagged']))
# Train /test split
train_observations, test_observations = train_test_split(observations)
train_observations = train_observations.copy()
test_observations = test_observations.copy()
# List out variable types
data_type_dict = {
'numerical':
['ise', 'ise.1', 'sp', 'dax', 'ftse', 'nikkei', 'bovespa', 'eu', 'em'],
'categorical': [],
'text': [],
'timeseries':
['ise_lagged', 'ise.1_lagged', 'sp_lagged', 'dax_lagged']
}
output_var = 'ise'
# Create and fit Automater
auto = Automater(data_type_dict=data_type_dict, output_var=output_var)
auto.fit(train_observations)
# Transform data
train_X, train_y = auto.fit_transform(train_observations)
test_X, test_y = auto.transform(test_observations)
# Create and fit keras (deep learning) model.
x = auto.input_nub
x = Dense(32)(x)
x = Dense(32)(x)
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='adam', loss=auto.suggest_loss())
model.fit(train_X, train_y)
# Make model predictions and inverse transform model predictions, to get usable results
pred_test_y = model.predict(test_X)
auto.inverse_transform_output(pred_test_y)
# Save all results
if save_results:
temp_dir = lib.get_temp_dir()
model.save(os.path.join(temp_dir, 'model.h5py'))
pickle.dump(train_X, open(os.path.join(temp_dir, 'train_X.pkl'), 'wb'))
pickle.dump(train_y, open(os.path.join(temp_dir, 'train_y.pkl'), 'wb'))
pickle.dump(test_X, open(os.path.join(temp_dir, 'test_X.pkl'), 'wb'))
pickle.dump(test_y, open(os.path.join(temp_dir, 'test_y.pkl'), 'wb'))
pickle.dump(pred_test_y,
open(os.path.join(temp_dir, 'pred_test_y.pkl'), 'wb'))
def main():
# TODO List out which components are supplied by Automater
# In this example, we're utilizing X and y generated by the Automater, auto.input_nub, auto.input_layers,
# auto.output_nub, and auto.suggest_loss
save_results = False
# TODO Load data
observations = lib.load_titanic()
print('Observation columns: {}'.format(list(observations.columns)))
# TODO Train /test split
train_observations, test_observations = train_test_split(observations)
train_observations = train_observations.copy()
test_observations = test_observations.copy()
# TODO List out variable types
data_type_dict = {'numerical': ['age', 'siblings_spouses_aboard', 'parents_children_aboard', 'fare'],
'categorical': ['survived', 'pclass', 'sex'],
'text': ['name'],
'timeseries': []
}
output_var = 'survived'
# Create and fit Automater
auto = Automater(data_type_dict=data_type_dict, output_var=output_var)
auto.fit(train_observations)
# Transform data
train_X, train_y = auto.fit_transform(train_observations)
test_X, test_y = auto.transform(test_observations)
# TODO Create and fit keras (deep learning) model.
x = auto.input_nub
x = Dense(32)(x)
x = Dense(32)(x)
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
print(f'Suggested loss: {auto.suggest_loss()}\n\n')
model.compile(optimizer='adam', loss=auto.suggest_loss(), metrics=['acc'])
# model.fit(train_X, train_y)
model.summary()
print('\n\n' + '^' * 21)
print(train_X)
print('\n\n' + '^' * 21)
print(train_y)
model.fit(train_X, train_y, batch_size=32, epochs=1, validation_split=0.1)
# Make model predictions and inverse transform model predictions, to get usable results
pred_test_y = model.predict(test_X)
auto.inverse_transform_output(pred_test_y)
# Save all results
if save_results:
temp_dir = lib.get_temp_dir()
model.save(os.path.join(temp_dir, 'model.h5py'))
pickle.dump(train_X, open(os.path.join(temp_dir, 'train_X.pkl'), 'wb'))
pickle.dump(train_y, open(os.path.join(temp_dir, 'train_y.pkl'), 'wb'))
pickle.dump(test_X, open(os.path.join(temp_dir, 'test_X.pkl'), 'wb'))
pickle.dump(test_y, open(os.path.join(temp_dir, 'test_y.pkl'), 'wb'))
pickle.dump(pred_test_y, open(os.path.join(temp_dir, 'pred_test_y.pkl'), 'wb'))
from keras_pandas.lib import load_titanic
observations = load_titanic()
# Transform the data set, using keras_pandas
categorical_vars = ['pclass', 'sex', 'survived']
numerical_vars = [
'age', 'siblings_spouses_aboard', 'parents_children_aboard', 'fare'
]
text_vars = ['name']
auto = Automater(categorical_vars=categorical_vars,
numerical_vars=numerical_vars,
text_vars=text_vars,
response_var='survived')
X, y = auto.fit_transform(observations)
# Start model with provided input nub
x = auto.input_nub
# Fill in your own hidden layers
x = Dense(32)(x)
x = Dense(32, activation='relu')(x)
x = Dense(32)(x)
# End model with provided output nub
x = auto.output_nub(x)
model = Model(inputs=auto.input_layers, outputs=x)
model.compile(optimizer='Adam',
loss='sparse_categorical_crossentropy',
