def transform(self, X: dt.Frame):
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
try:
X = dt.Frame(X)
original_zip_column_name = X.names[0]
X.names = ['zip_key']
X = X[:, str('zip_key')]
zip_list = dt.unique(X[~dt.isna(dt.f.zip_key), 0]).to_list()[0]
zip_features = [self.get_zipcode_features(x) for x in zip_list]
X_g = dt.Frame({"zip_key": zip_list})
X_g.cbind(dt.Frame(zip_features))
X_g.key = 'zip_key'
X_result = X[:, :, dt.join(X_g)]
self._output_feature_names = [
"{}.{}".format(original_zip_column_name, f)
for f in list(X_result[:, 1:].names)
]
self._feature_desc = [
"Property '{}' of US zipcode found in '{}'".format(
f, original_zip_column_name)
for f in list(X_result[:, 1:].names)
]
return X_result[:, 1:]
except Exception as ex:
loggerwarning(
logger, "USZipcodeDatabaseTransformer got exception {}".format(
type(ex).__name__))
return np.zeros(X.shape[0])
def get_experiment_logger(self):
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
return logger
def logger(self):
from h2oaicore import application_context
from h2oaicore.systemutils import exp_dir
# Don't assign to self, not picklable
return make_experiment_logger(
experiment_id=application_context.context.experiment_id,
tmp_dir=None,
experiment_tmp_dir=exp_dir())
def _get_experiment_logger(self):
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
return logger
def transform(self, X: dt.Frame):
"""
Uses fitted models (1 per time group) to predict the target
If self.is_train exists, it means we are doing in-sample predictions
if it does not then we Arima is used to predict the future
:param X: Datatable Frame containing the features
:return: ARIMA predictions
"""
X = X.to_pandas()
XX = X[self.tgc].copy()
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
nb_groups = len(XX_grp)
preds = []
for _i_g, (key, X) in enumerate(XX_grp):
# Just say where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(
logger, "Auto ARIMA : %d%% of groups transformed" %
(100 * (_i_g + 1) // nb_groups))
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
# print("auto arima - transforming data of shape: %s for group: %s" % (str(X.shape), grp_hash))
order = np.argsort(X[self.time_column])
if grp_hash in self.models:
model = self.models[grp_hash]
if model is not None:
yhat = model.predict_in_sample() \
if hasattr(self, 'is_train') else model.predict(n_periods=X.shape[0])
yhat = yhat[order]
XX = pd.DataFrame(yhat, columns=['yhat'])
else:
XX = pd.DataFrame(np.full((X.shape[0], 1), self.nan_value),
columns=['yhat']) # invalid model
else:
XX = pd.DataFrame(np.full((X.shape[0], 1), self.nan_value),
columns=['yhat']) # unseen groups
XX.index = X.index
preds.append(XX)
XX = pd.concat(tuple(preds), axis=0).sort_index()
return XX
def fit(self, X, y, sample_weight=None, eval_set=None, sample_weight_eval_set=None, **kwargs):
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(experiment_id=self.context.experiment_id, tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
maybe_download_language_model(logger,
save_directory=self.__class__._model_path,
model_link=self.__class__._model_link,
config_link=self.__class__._config_link,
vocab_link=self.__class__._vocab_link)
super().fit(X, y, sample_weight, eval_set, sample_weight_eval_set, **kwargs)
def fit(self, X: dt.Frame, y: np.array = None):
"""
Fits ARIMA models (1 per time group) using historical target values contained in y
:param X: Datatable frame containing the features
:param y: numpy array containing the historical values of the target
:return: self
"""
# Import the ARIMA python module
pm = importlib.import_module('pmdarima')
# Init models
self.models = {}
# Convert to pandas
X = X.to_pandas()
XX = X[self.tgc].copy()
XX['y'] = np.array(y)
self.nan_value = np.mean(y)
self.ntrain = X.shape[0]
# Group the input by TGC (Time group column) excluding the time column itself
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
# Build 1 ARIMA model per time group columns
nb_groups = len(XX_grp)
for _i_g, (key, X) in enumerate(XX_grp):
# Just say where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(
logger, "Auto ARIMA : %d%% of groups fitted" %
(100 * (_i_g + 1) // nb_groups))
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
# print("auto arima - fitting on data of shape: %s for group: %s" % (str(X.shape), grp_hash))
order = np.argsort(X[self.time_column])
try:
model = pm.auto_arima(X['y'].values[order],
error_action='ignore')
except:
model = None
self.models[grp_hash] = model
return self
def transform(self, X: dt.Frame):
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
X = dt.Frame(X)
original_zip_column_name = X.names[0]
X = X[:, dt.str64(dt.f[0])]
X.names = ['zip_key']
try:
zip_list = dt.unique(X[~dt.isna(dt.f.zip_key),
0]).to_list()[0] + ['79936']
zip_features = [self.get_zipcode_features(x) for x in zip_list]
X_g = dt.Frame({"zip_key": zip_list})
X_g.cbind(dt.Frame(zip_features))
X_g.key = 'zip_key'
X_result = X[:, :, dt.join(X_g)]
self._output_feature_names = [
"{}:{}.{}".format(self.transformer_name,
original_zip_column_name,
self.replaceBannedCharacters(f))
for f in list(X_result[:, 1:].names)
]
self._feature_desc = [
"Property '{}' of zipcode column ['{}'] from US zipcode database (recipe '{}')"
.format(f, original_zip_column_name, self.transformer_name)
for f in list(X_result[:, 1:].names)
]
return X_result[:, 1:]
except ValueError as ve:
loggerinfo(
logger, "Column '{}' is not a zipcode: {}".format(
original_zip_column_name, str(ve)))
return self.get_zipcode_null_result(X, original_zip_column_name)
except TypeError as te:
loggerwarning(
logger, "Column '{}' triggered TypeError: {}".format(
original_zip_column_name, str(te)))
raise te
def fit_transform(self, X: dt.Frame, y: np.array = None, **kwargs):
X_original = X
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir,
username=self.context.username,
)
self._output_feature_names = [
"pre:" + x for x in list(X_original.names)
]
self._feature_desc = [
"Pre-transformed feature " + x for x in list(X_original.names)
]
return X_original
def mutate_params(self, accuracy, time_tolerance, interpretability,
**kwargs):
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
# Default version is do no mutation
# Otherwise, change self.params for this model
holiday_choice = [None, "US", "UK", "DE", "FRA"]
if accuracy >= 8:
weekly_choice = [False, 'auto', 5, 7, 10, 15]
yearly_choice = [False, 'auto', 5, 10, 15, 20, 30]
monthly_choice = [False, 3, 5, 7, 10]
quarterly_choice = [False, 3, 5, 7, 10]
elif accuracy >= 5:
weekly_choice = [False, 'auto', 10, 20]
yearly_choice = [False, 'auto', 10, 20]
monthly_choice = [False, 5]
quarterly_choice = [False, 5]
else:
# No alternative seasonality, and no seasonality override for weekly and yearly
weekly_choice = [False, 'auto']
yearly_choice = [False, 'auto']
monthly_choice = [False]
quarterly_choice = [False]
self.params["country_holidays"] = np.random.choice(holiday_choice)
self.params["seasonality_mode"] = np.random.choice(
["additive", "multiplicative"])
self.params["weekly_seasonality"] = np.random.choice(weekly_choice)
self.params["monthly_seasonality"] = np.random.choice(monthly_choice)
self.params["quarterly_seasonality"] = np.random.choice(
quarterly_choice)
self.params["yearly_seasonality"] = np.random.choice(yearly_choice)
self.params["growth"] = np.random.choice(["linear", "logistic"])
def fit(self,
X,
y,
sample_weight=None,
eval_set=None,
sample_weight_eval_set=None,
**kwargs):
# Get column names
orig_cols = list(X.names)
from h2oaicore.tensorflow_dynamic import got_cpu_tf, got_gpu_tf
import tensorflow as tf
import shap
import scipy
import pandas as pd
self.setup_keras_session()
import h2oaicore.keras as keras
import matplotlib.pyplot as plt
if not hasattr(self, 'save_model_path'):
model_id = str(uuid.uuid4())[:8]
self.save_model_path = os.path.join(user_dir(),
"custom_xnn_model.hdf5")
np.random.seed(self.random_state)
my_init = keras.initializers.RandomUniform(seed=self.random_state)
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
# Set up temp folter
tmp_folder = self._create_tmp_folder(logger)
# define base model
def xnn_initialize(features,
ridge_functions=3,
arch=[20, 12],
learning_rate=0.01,
bg_samples=100,
beta1=0.9,
beta2=0.999,
dec=0.0,
ams=True,
bseed=None,
is_categorical=False):
#
# Prepare model architecture
#
# Input to the network, our observation containing all the features
input = keras.layers.Input(shape=(features, ), name='main_input')
# Record current column names
loggerinfo(logger, "XNN LOG")
loggerdata(logger, "Feature list:")
loggerdata(logger, str(orig_cols))
# Input to ridge function number i is the dot product of our original input vector times coefficients
ridge_input = keras.layers.Dense(ridge_functions,
name="projection_layer",
activation='linear')(input)
ridge_networks = []
# Each subnetwork uses only 1 neuron from the projection layer as input so we need to split it
ridge_inputs = SplitLayer(ridge_functions)(ridge_input)
for i, ridge_input in enumerate(ridge_inputs):
# Generate subnetwork i
mlp = _mlp(ridge_input, i, arch)
ridge_networks.append(mlp)
added = keras.layers.Concatenate(
name='concatenate_1')(ridge_networks)
# Add the correct output layer for the problem
if is_categorical:
out = keras.layers.Dense(1,
activation='sigmoid',
input_shape=(ridge_functions, ),
name='main_output')(added)
else:
out = keras.layers.Dense(1,
activation='linear',
input_shape=(ridge_functions, ),
name='main_output')(added)
model = keras.models.Model(inputs=input, outputs=out)
optimizer = keras.optimizers.Adam(lr=learning_rate,
beta_1=beta1,
beta_2=beta2,
decay=dec,
amsgrad=ams)
# Use the correct loss for the problem
if is_categorical:
model.compile(loss={'main_output': 'binary_crossentropy'},
optimizer=optimizer)
else:
model.compile(loss={'main_output': 'mean_squared_error'},
optimizer=optimizer)
return model
def _mlp(input, idx, arch=[20, 12], activation='relu'):
# Set up a submetwork
# Hidden layers
mlp = keras.layers.Dense(arch[0],
activation=activation,
name='mlp_{}_dense_0'.format(idx),
kernel_initializer=my_init)(input)
for i, layer in enumerate(arch[1:]):
mlp = keras.layers.Dense(layer,
activation=activation,
name='mlp_{}_dense_{}'.format(
idx, i + 1),
kernel_initializer=my_init)(mlp)
# Output of the MLP
mlp = keras.layers.Dense(
1,
activation='linear',
name='mlp_{}_dense_last'.format(idx),
kernel_regularizer=keras.regularizers.l1(1e-3),
kernel_initializer=my_init)(mlp)
return mlp
def get_shap(X, model):
# Calculate the Shap values
np.random.seed(24)
bg_samples = min(X.shape[0], 1000)
if isinstance(X, pd.DataFrame):
background = X.iloc[np.random.choice(X.shape[0],
bg_samples,
replace=False)]
else:
background = X[np.random.choice(X.shape[0],
bg_samples,
replace=False)]
# Explain predictions of the model on the subset
explainer = shap.DeepExplainer(model, background)
shap_values = explainer.shap_values(X)
# Return the mean absolute value of each shap value for each dataset
xnn_shap = np.abs(shap_values[0]).mean(axis=0)
return xnn_shap
# Initialize the xnn's
features = X.shape[1]
orig_cols = list(X.names)
if self.num_classes >= 2:
lb = LabelEncoder()
lb.fit(self.labels)
y = lb.transform(y)
self.is_cat = True
xnn1 = xnn_initialize(features=features,
ridge_functions=features,
arch=self.params["arch"],
learning_rate=self.params["lr"],
beta1=self.params["beta_1"],
beta2=self.params["beta_1"],
dec=self.params["decay"],
ams=self.params["amsgrad"],
is_categorical=self.is_cat)
xnn = xnn_initialize(features=features,
ridge_functions=features,
arch=self.params["arch"],
learning_rate=self.params["lr"],
beta1=self.params["beta_1"],
beta2=self.params["beta_1"],
dec=self.params["decay"],
ams=self.params["amsgrad"],
is_categorical=self.is_cat)
else:
self.is_cat = False
xnn1 = xnn_initialize(features=features,
ridge_functions=features,
arch=self.params["arch"],
learning_rate=self.params["lr"],
beta1=self.params["beta_1"],
beta2=self.params["beta_1"],
dec=self.params["decay"],
ams=self.params["amsgrad"],
is_categorical=self.is_cat)
xnn = xnn_initialize(features=features,
ridge_functions=features,
arch=self.params["arch"],
learning_rate=self.params["lr"],
beta1=self.params["beta_1"],
beta2=self.params["beta_1"],
dec=self.params["decay"],
ams=self.params["amsgrad"],
is_categorical=self.is_cat)
# Replace missing values with a value smaller than all observed values
self.min = dict()
for col in X.names:
XX = X[:, col]
self.min[col] = XX.min1()
if self.min[col] is None or np.isnan(self.min[col]):
self.min[col] = -1e10
else:
self.min[col] -= 1
XX.replace(None, self.min[col])
X[:, col] = XX
assert X[dt.isna(dt.f[col]), col].nrows == 0
X = X.to_numpy()
inputs = {'main_input': X}
validation_set = 0
verbose = 0
# Train the neural network once with early stopping and a validation set
history = keras.callbacks.History()
es = keras.callbacks.EarlyStopping(monitor='val_loss', mode='min')
history = xnn1.fit(inputs,
y,
epochs=self.params["n_estimators"],
batch_size=self.params["batch_size"],
validation_split=0.3,
verbose=verbose,
callbacks=[history, es])
# Train again on the full data
number_of_epochs_it_ran = len(history.history['loss'])
xnn.fit(inputs,
y,
epochs=number_of_epochs_it_ran,
batch_size=self.params["batch_size"],
validation_split=0.0,
verbose=verbose)
# Get the mean absolute Shapley values
importances = np.array(get_shap(X, xnn))
int_output = {}
int_weights = {}
int_bias = {}
int_input = {}
original_activations = {}
x_labels = list(map(lambda x: 'x' + str(x), range(features)))
intermediate_output = []
# Record and plot the projection weights
#
weight_list = []
for layer in xnn.layers:
layer_name = layer.get_config()['name']
if layer_name != "main_input":
print(layer_name)
weights = layer.get_weights()
# Record the biases
try:
bias = layer.get_weights()[1]
int_bias[layer_name] = bias
except:
print("No Bias")
# Record outputs for the test set
intermediate_layer_model = keras.models.Model(
inputs=xnn.input, outputs=xnn.get_layer(layer_name).output)
# Record the outputs from the training set
if self.is_cat and (layer_name == 'main_output'):
original_activations[layer_name] = scipy.special.logit(
intermediate_layer_model.predict(X))
original_activations[
layer_name +
"_p"] = intermediate_layer_model.predict(X)
else:
original_activations[
layer_name] = intermediate_layer_model.predict(X)
# Record other weights, inputs, and outputs
int_weights[layer_name] = weights
int_input[layer_name] = layer.input
int_output[layer_name] = layer.output
# Plot the projection layers
if "projection_layer" in layer.get_config()['name']:
# print(layer.get_config()['name'])
# Record the weights for each projection layer
weights = [np.transpose(layer.get_weights()[0])]
weight_list2 = []
for i, weight in enumerate(weights[0]):
weight_list.append(weight)
weight_list2.append(
list(np.reshape(weight, (1, features))[0]))
# Plot weights
plt.bar(orig_cols,
abs(np.reshape(weight, (1, features))[0]),
1,
color="blue")
plt.ylabel("Coefficient value")
plt.title("Projection Layer Weights {}".format(i),
fontdict={'fontsize': 10})
plt.xticks(rotation=90)
plt.show()
plt.savefig(os.path.join(
tmp_folder, 'projection_layer_' + str(i) + '.png'),
bbox_inches="tight")
plt.clf()
if "main_output" in layer.get_config()['name']:
weights_main = layer.get_weights()
print(weights_main)
pd.DataFrame(weight_list2).to_csv(os.path.join(tmp_folder,
"projection_data.csv"),
index=False)
intermediate_output = []
for feature_num in range(features):
intermediate_layer_model = keras.models.Model(
inputs=xnn.input,
outputs=xnn.get_layer('mlp_' + str(feature_num) +
'_dense_last').output)
intermediate_output.append(intermediate_layer_model.predict(X))
# Record and plot the ridge functions
ridge_x = []
ridge_y = []
for weight_number in range(len(weight_list)):
ridge_x.append(
list(
sum(X[:, ii] * weight_list[weight_number][ii]
for ii in range(features))))
ridge_y.append(list(intermediate_output[weight_number]))
plt.plot(
sum(X[:, ii] * weight_list[weight_number][ii]
for ii in range(features)),
intermediate_output[weight_number], 'o')
plt.xlabel("Input")
plt.ylabel("Subnetwork " + str(weight_number))
plt.title("Ridge Function {}".format(i), fontdict={'fontsize': 10})
plt.show()
plt.savefig(
os.path.join(tmp_folder,
'ridge_' + str(weight_number) + '.png'))
plt.clf()
# Output the ridge function importance
weights2 = np.array([item[0] for item in list(weights)[0]])
output_activations = np.abs(
np.array([
item * weights2
for item in list(original_activations["concatenate_1"])
])).mean(axis=0)
loggerinfo(logger, str(output_activations))
pd.DataFrame(output_activations).to_csv(os.path.join(
tmp_folder, "ridge_weights.csv"),
index=False)
plt.bar(x_labels, output_activations, 1, color="blue")
plt.xlabel("Ridge function number")
plt.ylabel("Feature importance")
plt.title("Ridge function importance", fontdict={'fontsize': 10})
plt.show()
plt.savefig(os.path.join(tmp_folder, 'Ridge_function_importance.png'))
pd.DataFrame(ridge_y).applymap(lambda x: x[0]).to_csv(os.path.join(
tmp_folder, "ridge_y.csv"),
index=False)
pd.DataFrame(ridge_x).to_csv(os.path.join(tmp_folder, "ridge_x.csv"),
index=False)
pd.DataFrame(orig_cols).to_csv(os.path.join(tmp_folder,
"input_columns.csv"),
index=False)
self.set_model_properties(model=xnn,
features=orig_cols,
importances=importances.tolist(),
iterations=self.params['n_estimators'])
def transform(self, X: dt.Frame):
"""
Uses fitted models (1 per time group) to predict the target
If self.is_train exists, it means we are doing in-sample predictions
if it does not then we Arima is used to predict the future
:param X: Datatable Frame containing the features
:return: ARIMA predictions
"""
# Get the logger if it exists
logger = None
tmp_folder = str(uuid.uuid4()) + "_arima_folder/"
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir
)
tmp_folder = self.context.experiment_tmp_dir + "/" + str(uuid.uuid4()) + "_arima_folder/"
# Create a temp folder to store files used during multi processing experiment
# This temp folder will be removed at the end of the process
loggerinfo(logger, "Arima temp folder {}".format(tmp_folder))
try:
os.mkdir(tmp_folder)
except PermissionError:
# This not occur so log a warning
loggerwarning(logger, "Arima was denied temp folder creation rights")
tmp_folder = temporary_files_path + "/" + str(uuid.uuid4()) + "_arima_folder/"
os.mkdir(tmp_folder)
except FileExistsError:
# We should never be here since temp dir name is expected to be unique
loggerwarning(logger, "Arima temp folder already exists")
tmp_folder = self.context.experiment_tmp_dir + "/" + str(uuid.uuid4()) + "_arima_folder/"
os.mkdir(tmp_folder)
except:
# Revert to temporary file path
loggerwarning(logger, "Arima defaulted to create folder inside tmp directory.")
tmp_folder = temporary_files_path + "/" + str(uuid.uuid4()) + "_arima_folder/"
os.mkdir(tmp_folder)
X = X.to_pandas()
XX = X[self.tgc].copy()
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
assert len(XX_grp) > 0
num_tasks = len(XX_grp)
def processor(out, res):
out.append(res)
pool_to_use = small_job_pool
pool = pool_to_use(logger=None, processor=processor, num_tasks=num_tasks)
XX_paths = []
model_paths = []
nb_groups = len(XX_grp)
for _i_g, (key, X) in enumerate(XX_grp):
# Just print where we are in the process of fitting models
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(logger, "Auto ARIMA : %d%% of groups transformed" % (100 * (_i_g + 1) // nb_groups))
# Create time group key to store and retrieve fitted models
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
# Create file path to store data and pass it to the fitting pool
X_path = os.path.join(tmp_folder, "autoarima_Xt" + str(uuid.uuid4()))
# Commented for performance, uncomment for debug
# print("ARIMA - transforming data of shape: %s for group: %s" % (str(X.shape), grp_hash))
if grp_hash in self.models:
model = self.models[grp_hash]
model_path = os.path.join(tmp_folder, "autoarima_modelt" + str(uuid.uuid4()))
save_obj(model, model_path)
save_obj(X, X_path)
model_paths.append(model_path)
args = (model_path, X_path, self.nan_value, hasattr(self, 'is_train'), self.time_column, tmp_folder)
kwargs = {}
pool.submit_tryget(None, MyParallelAutoArimaTransformer_transform_async, args=args, kwargs=kwargs,
out=XX_paths)
else:
# Don't go through pools
XX = pd.DataFrame(np.full((X.shape[0], 1), self.nan_value), columns=['yhat']) # unseen groups
# Sync indices
XX.index = X.index
save_obj(XX, X_path)
XX_paths.append(X_path)
pool.finish()
XX = pd.concat((load_obj(XX_path) for XX_path in XX_paths), axis=0).sort_index()
for p in XX_paths + model_paths:
remove(p)
try:
shutil.rmtree(tmp_folder)
loggerinfo(logger, "Arima cleaned up temporary file folder.")
except:
loggerwarning(logger, "Arima could not delete the temporary file folder.")
return XX
def transform(self, X: dt.Frame, **kwargs):
"""
Uses fitted models (1 per time group) to predict the target
:param X: Datatable Frame containing the features
:return: FB Prophet predictions
"""
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
tmp_folder = self._create_tmp_folder(logger)
n_jobs = self._get_n_jobs(logger, **kwargs)
XX = X[:, self.tgc].to_pandas()
XX = XX.replace([None, np.nan], 0)
XX.rename(columns={self.time_column: "ds"}, inplace=True)
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
assert len(XX_grp) > 0
num_tasks = len(XX_grp)
def processor(out, res):
out.append(res)
pool_to_use = small_job_pool
loggerinfo(logger,
"Prophet will use {} workers for transform".format(n_jobs))
pool = pool_to_use(logger=None,
processor=processor,
num_tasks=num_tasks,
max_workers=n_jobs)
XX_paths = []
model_paths = []
nb_groups = len(XX_grp)
print("Nb Groups = ", nb_groups)
for _i_g, (key, X) in enumerate(XX_grp):
# Log where we are in the transformation of the dataset
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(
logger, "FB Prophet : %d%% of groups transformed" %
(100 * (_i_g + 1) // nb_groups))
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
X_path = os.path.join(tmp_folder,
"fbprophet_Xt" + str(uuid.uuid4()))
# Commented for performance, uncomment for debug
# print("prophet - transforming data of shape: %s for group: %s" % (str(X.shape), grp_hash))
if grp_hash in self.models:
model = self.models[grp_hash]
model_path = os.path.join(
tmp_folder, "fbprophet_modelt" + str(uuid.uuid4()))
save_obj(model, model_path)
save_obj(X, X_path)
model_paths.append(model_path)
args = (model_path, X_path, self.priors[grp_hash], tmp_folder)
kwargs = {}
pool.submit_tryget(
None,
MyParallelProphetTransformer_transform_async,
args=args,
kwargs=kwargs,
out=XX_paths)
else:
XX = pd.DataFrame(np.full((X.shape[0], 1), self.nan_value),
columns=['yhat']) # unseen groups
XX.index = X.index
save_obj(XX, X_path)
XX_paths.append(X_path)
pool.finish()
XX = pd.concat((load_obj(XX_path) for XX_path in XX_paths),
axis=0).sort_index()
for p in XX_paths + model_paths:
remove(p)
self._clean_tmp_folder(logger, tmp_folder)
return XX
def fit(self, X: dt.Frame, y: np.array = None, **kwargs):
"""
Fits FB Prophet models (1 per time group) using historical target values contained in y
Model fitting is distributed over a pool of processes and uses file storage to share the data with workers
:param X: Datatable frame containing the features
:param y: numpy array containing the historical values of the target
:return: self
"""
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
tmp_folder = self._create_tmp_folder(logger)
n_jobs = self._get_n_jobs(logger, **kwargs)
# Convert to pandas
XX = X[:, self.tgc].to_pandas()
XX = XX.replace([None, np.nan], 0)
XX.rename(columns={self.time_column: "ds"}, inplace=True)
# Make sure labales are numeric
if self.labels is not None:
y = LabelEncoder().fit(self.labels).transform(y)
XX['y'] = np.array(y)
# Set target prior
self.nan_value = np.mean(y)
# Group the input by TGC (Time group column) excluding the time column itself
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
self.models = {}
self.priors = {}
# Prepare for multi processing
num_tasks = len(XX_grp)
def processor(out, res):
out[res[0]] = res[1]
pool_to_use = small_job_pool
loggerinfo(logger,
"Prophet will use {} workers for fitting".format(n_jobs))
loggerinfo(
logger, "Prophet parameters holidays {} / monthly {}".format(
self.country_holidays, self.monthly_seasonality))
pool = pool_to_use(logger=None,
processor=processor,
num_tasks=num_tasks,
max_workers=n_jobs)
# Fit 1 FB Prophet model per time group columns
nb_groups = len(XX_grp)
for _i_g, (key, X) in enumerate(XX_grp):
# Just log where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(
logger, "FB Prophet : %d%% of groups fitted" %
(100 * (_i_g + 1) // nb_groups))
X_path = os.path.join(tmp_folder,
"fbprophet_X" + str(uuid.uuid4()))
X = X.reset_index(drop=True)
save_obj(X, X_path)
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
self.priors[grp_hash] = X['y'].mean()
params = {
"country_holidays": self.country_holidays,
"monthly_seasonality": self.monthly_seasonality
}
args = (X_path, grp_hash, tmp_folder, params)
kwargs = {}
pool.submit_tryget(None,
MyParallelProphetTransformer_fit_async,
args=args,
kwargs=kwargs,
out=self.models)
pool.finish()
for k, v in self.models.items():
self.models[k] = load_obj(v) if v is not None else None
remove(v)
self._clean_tmp_folder(logger, tmp_folder)
return self
def fit(self,
X,
y,
sample_weight=None,
eval_set=None,
sample_weight_eval_set=None,
**kwargs):
# Specify these parameters for the dataset.
#
# Also set feature engineering effort to 0
# under the features section of expert settings.
########################
# Specify the protected column.
# The protected column must be numeric.
self.protected_name = "black"
# Specify the level of the protected group in the protected column
self.protected_label = 1
# Specify the target level considered to be a positive outcome
# Must be encoded as 0/1
self.positive_target = 0
# Set minimum mean protected ratio needed to avoid a penalty
# (mean protected ratio = mean predictions for the protected group/mean predictions for all other groups)
#
# Try tuning this to values at or a little above
# the mean of the positive target for the protected group
# divided by the mean of the positive target for the unprotected group.
# If it's set too large, the accuracy will be poor, so there
# is a limit to the debiasing that can be obtained.
self.mean_protected_prediction_ratio_minimum = 0.92
########################
orig_cols = list(X.names)
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from collections import Counter
import xgboost as xgb
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
# Current mu value
mu = self.params["mu"]
def fair_metric(predt: np.ndarray, dtrain: xgb.DMatrix):
''' FairXGB Error Metric'''
# predt is the prediction array
# Find the right protected group vector
if len(predt) == len(protected_train):
protected_feature = np.array(protected_train.copy())
elif len(predt) == len(protected_full):
protected_feature = np.array(protected_full.copy())
elif len(predt) == len(protected_valid):
protected_feature = np.array(protected_valid.copy())
else:
protected_feature = 0
y = dtrain.get_label()
answer = -y * np.log(
sigmoid(predt)) - (1 - y) * np.log(1 - sigmoid(predt))
answer += mu * (
protected_feature * np.log(sigmoid(predt)) +
(1 - protected_feature) * np.log(1 - sigmoid(predt)))
return 'Fair_Metric', float(np.sum(answer) / len(answer))
def sigmoid(x):
z = 1.0 / (1.0 + np.exp(-x))
return z
def gradient(predt: np.ndarray, dtrain: xgb.DMatrix):
'''Fair Xgboost Gradient'''
# predt is the prediction array
# Find the right protected group vector
if len(predt) == len(protected_train):
protected_feature = np.array(protected_train.copy())
elif len(predt) == len(protected_full):
protected_feature = np.array(protected_full.copy())
elif len(predt) == len(protected_valid):
protected_feature = np.array(protected_valid.copy())
else:
protected_feature = 0
y = dtrain.get_label()
answer = sigmoid(predt) - y
answer += mu * (protected_feature - sigmoid(predt))
return answer
def hessian(predt: np.ndarray, dtrain: xgb.DMatrix):
'''Fair Xgboost Hessian'''
# predt is the prediction array
answer = (1 - mu) * sigmoid(predt) * (1 - sigmoid(predt))
return answer
def fair(predt: np.ndarray, dtrain: xgb.DMatrix):
''' Fair xgb objective function
'''
grad = gradient(predt, dtrain)
hess = hessian(predt, dtrain)
return grad, hess
# Set up model
if self.num_classes >= 2:
lb = LabelEncoder()
lb.fit(self.labels)
y = lb.transform(y)
params = {}
params['eta'] = self.params["eta"]
params['max_depth'] = self.params['max_depth']
params['min_child_weight'] = self.params['min_child_weight']
params['reg_lambda'] = self.params['reg_lambda']
params['reg_alpha'] = self.params['reg_alpha']
params['colsample_bytree'] = self.params['colsample_bytree']
params['subsample'] = self.params['subsample']
params['silent'] = 1
params['seed'] = self.params['random_state']
else:
# fairxgb doesn't work for regression
loggerinfo(logger, "PASS, no fairxgboost model")
pass
# Switch to pandas
X = X.to_pandas()
X.columns = orig_cols
# Find the protected group column if it is present
self.protected = "none"
for col in X.columns:
if col.find(self.protected_name) > -1:
self.protected = col
X_datatypes = [str(item) for item in list(X.dtypes)]
# List the categorical and numerical features
self.X_categorical = [
orig_cols[col_count] for col_count in range(len(orig_cols))
if (X_datatypes[col_count] == 'category') or (
X_datatypes[col_count] == 'object')
]
self.X_numeric = [
item for item in orig_cols if item not in self.X_categorical
]
self.encoded_categories = []
# Find the levels and mode for each categorical feature
# for use in the test set
self.train_levels = {}
for item in self.X_categorical:
self.train_levels[item] = list(set(X[item]))
self.train_mode[item] = Counter(X[item]).most_common(1)[0][0]
# One hot encode the categorical features
# And replace missing values with a Missing category
if len(self.X_categorical) > 0:
loggerinfo(logger, "Categorical encode")
for colname in self.X_categorical:
X[colname] = list(X[colname].fillna("Missing"))
self.enc = OneHotEncoder(handle_unknown='ignore')
if self.protected in self.X_categorical:
self.X_categorical.remove(self.protected)
if len(self.X_categorical) > 0:
self.enc.fit(X[self.X_categorical])
self.encoded_categories = list(
self.enc.get_feature_names(
input_features=self.X_categorical))
X_enc = self.enc.transform(X[self.X_categorical]).toarray()
X = pd.concat([
X[self.X_numeric],
pd.DataFrame(X_enc, columns=self.encoded_categories)
],
axis=1)
# Replace missing values with a missing value code
if len(self.X_numeric) > 0:
for colname in self.X_numeric:
X[colname] = list(X[colname].fillna(-999))
# Make sure the target that represents a positive outcome is 1
if self.positive_target == 0:
y = 1 - y
X_full = X.copy()
y_full = y.copy()
# Set up a validation step to find the optimal number of trees
X_valid = X.iloc[int(0.7 * len(X_full)):, :]
y_valid = y[int(0.7 * len(X_full)):]
X = X.iloc[0:int(0.7 * len(X_full)), :]
y = y[0:int(0.7 * len(X_full))]
if self.protected != "none":
# Set the protected group to 0 and all others 1
protected_full = [
int(item) for item in
~(np.array(X_full[self.protected]) == self.protected_label)
]
protected_train = [
int(item) for item in
~(np.array(X[self.protected]) == self.protected_label)
]
protected_valid = [
int(item) for item in
~(np.array(X_valid[self.protected]) == self.protected_label)
]
else:
mu = 0
protected_full = []
protected_train = []
protected_valid = []
# Remove the protected value from the model
if self.protected != "none":
X = X.drop(self.protected, axis=1)
X_full = X_full.drop(self.protected, axis=1)
X_valid = X_valid.drop(self.protected, axis=1)
d_train = xgb.DMatrix(X, label=y, missing=np.nan)
d_valid = xgb.DMatrix(X_valid, label=y_valid, missing=np.nan)
# Initial run to find the optimal number of trees
num_iterations = 10000
watchlist = [(d_train, 'train'), (d_valid, 'valid')]
clf = xgb.train(params,
d_train,
num_iterations,
watchlist,
feval=fair_metric,
verbose_eval=10,
obj=fair,
early_stopping_rounds=10)
# Second xgboost run with the full dataset and optimal number of trees
attribute_dict = clf.attributes()
new_iterations = int(attribute_dict['best_iteration'])
d_train = xgb.DMatrix(X_full, label=y_full, missing=np.nan)
watchlist = [(d_train, 'train')]
clf = xgb.train(params,
d_train,
new_iterations,
watchlist,
feval=fair_metric,
verbose_eval=10,
obj=fair)
# Calculate feature importances
importances_dict = clf.get_score(importance_type='gain')
# Make sure the protected group has high feature importance
# so that it doesn't get dropped by driverless
if self.protected != "none":
if len(importances_dict) > 0:
importances_dict[self.protected] = max(
importances_dict.values())
else:
importances_dict[self.protected] = 1
for col in list(X.columns):
importances_dict[col] = 1
# Make sure any dropped columns are listed with 0 importance
for col in list(X.columns):
if col not in importances_dict:
importances_dict[col] = 0
self.mean_target = np.array(sum(y) / len(y))
loggerinfo(logger, "End fair check")
loggerinfo(logger, str(mu))
loggerdata(logger, str(importances_dict))
self.is_train = True
# Set model properties
self.set_model_properties(model=clf,
features=list(importances_dict.keys()),
importances=list(importances_dict.values()),
iterations=num_iterations)
def transform(self, X: dt.Frame, **kwargs):
"""
Uses fitted models (1 per time group) to predict the target
If self.is_train exists, it means we are doing in-sample predictions
if it does not then we Arima is used to predict the future
:param X: Datatable Frame containing the features
:return: ARIMA predictions
"""
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
tmp_folder = self._create_tmp_folder(logger)
n_jobs = self._get_n_jobs(logger, **kwargs)
X = X.to_pandas()
XX = X[self.tgc].copy()
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
assert len(XX_grp) > 0
num_tasks = len(XX_grp)
def processor(out, res):
out.append(res)
pool_to_use = small_job_pool
loggerinfo(logger,
"Arima will use {} workers for transform".format(n_jobs))
pool = pool_to_use(logger=None,
processor=processor,
num_tasks=num_tasks,
max_workers=n_jobs)
XX_paths = []
model_paths = []
nb_groups = len(XX_grp)
for _i_g, (key, X) in enumerate(XX_grp):
# Just print where we are in the process of fitting models
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(
logger, "Auto ARIMA : %d%% of groups transformed" %
(100 * (_i_g + 1) // nb_groups))
# Create time group key to store and retrieve fitted models
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
# Create file path to store data and pass it to the fitting pool
X_path = os.path.join(tmp_folder,
"autoarima_Xt" + str(uuid.uuid4()))
# Commented for performance, uncomment for debug
# print("ARIMA - transforming data of shape: %s for group: %s" % (str(X.shape), grp_hash))
if grp_hash in self.models:
model = self.models[grp_hash]
model_path = os.path.join(
tmp_folder, "autoarima_modelt" + str(uuid.uuid4()))
save_obj(model, model_path)
save_obj(X, X_path)
model_paths.append(model_path)
args = (model_path, X_path, self.nan_value,
hasattr(self, 'is_train'), self.time_column,
self.pred_gap, tmp_folder)
kwargs = {}
pool.submit_tryget(
None,
MyParallelAutoArimaTransformer_transform_async,
args=args,
kwargs=kwargs,
out=XX_paths)
else:
# Don't go through pools
XX = pd.DataFrame(np.full((X.shape[0], 1), self.nan_value),
columns=['yhat']) # unseen groups
# Sync indices
XX.index = X.index
save_obj(XX, X_path)
XX_paths.append(X_path)
pool.finish()
XX = pd.concat((load_obj(XX_path) for XX_path in XX_paths),
axis=0).sort_index()
for p in XX_paths + model_paths:
remove(p)
self._clean_tmp_folder(logger, tmp_folder)
return XX
def transform(self, X: dt.Frame, **kwargs):
"""
Uses fitted models (1 per time group) to predict the target
:param X: Datatable Frame containing the features
:return: FB Prophet predictions
"""
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir
)
tmp_folder = self._create_tmp_folder(logger)
n_jobs = self._get_n_jobs(logger, **kwargs)
# Reduce X to TGC
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
X = X[:, self.tgc].to_pandas()
# Fill NaNs or None
X = X.replace([None, np.nan], 0)
# Change date feature name to match Prophet requirements
X.rename(columns={self.time_column: "ds"}, inplace=True)
# Predict y using unique dates
X_time = X[['ds']].groupby('ds').first().reset_index()
with suppress_stdout_stderr():
y_avg = self.model.predict(X_time)[['ds', 'yhat']]
# Prophet transforms the date column to datetime so we need to transfrom that to merge back
X_time.sort_values('ds', inplace=True)
X_time['yhat'] = y_avg['yhat']
X_time.sort_index(inplace=True)
# Merge back into original frame on 'ds'
# pd.merge wipes the index ... so keep it to provide it again
indices = X.index
X = pd.merge(
left=X,
right=X_time[['ds', 'yhat']],
on='ds',
how='left'
)
X.index = indices
# Go through groups and recover the scaled target for knowed groups
if len(tgc_wo_time) > 0:
X_groups = X.groupby(tgc_wo_time)
else:
X_groups = [([None], X)]
inverted_ys = []
for key, X_grp in X_groups:
grp_hash = self.get_hash(key)
# Scale target for current group
if grp_hash in self.scalers.keys():
inverted_y = self.scalers[grp_hash].inverse_transform(X_grp[['yhat']])
else:
inverted_y = self.general_scaler.inverse_transform(X_grp[['yhat']])
# Put back in a DataFrame to keep track of original index
inverted_df = pd.DataFrame(inverted_y, columns=['yhat'])
inverted_df.index = X_grp.index
inverted_ys.append(inverted_df)
XX_general = pd.concat(tuple(inverted_ys), axis=0).sort_index()
if self.top_groups:
# Go though the groups and predict only top
XX_paths = []
model_paths = []
def processor(out, res):
out.append(res)
num_tasks = len(self.top_groups)
pool_to_use = small_job_pool
pool = pool_to_use(logger=None, processor=processor, num_tasks=num_tasks, max_workers=n_jobs)
nb_groups = len(X_groups)
for _i_g, (key, X_grp) in enumerate(X_groups):
# Just log where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(logger, "FB Prophet : %d%% of groups predicted" % (100 * (_i_g + 1) // nb_groups))
# Create dict key to store the min max scaler
grp_hash = self.get_hash(key)
X_path = os.path.join(tmp_folder, "fbprophet_Xt" + str(uuid.uuid4()))
if grp_hash not in self.top_groups:
XX = pd.DataFrame(np.full((X_grp.shape[0], 1), np.nan), columns=['yhat']) # unseen groups
XX.index = X_grp.index
save_obj(XX, X_path)
XX_paths.append(X_path)
continue
if self.grp_models[grp_hash] is None:
XX = pd.DataFrame(np.full((X_grp.shape[0], 1), np.nan), columns=['yhat']) # unseen groups
XX.index = X_grp.index
save_obj(XX, X_path)
XX_paths.append(X_path)
continue
model = self.grp_models[grp_hash]
model_path = os.path.join(tmp_folder, "fbprophet_modelt" + str(uuid.uuid4()))
save_obj(model, model_path)
save_obj(X_grp, X_path)
model_paths.append(model_path)
args = (model_path, X_path, self.priors[grp_hash], tmp_folder)
kwargs = {}
pool.submit_tryget(None, MyParallelProphetTransformer_transform_async, args=args, kwargs=kwargs,
out=XX_paths)
pool.finish()
XX_top_groups = pd.concat((load_obj(XX_path) for XX_path in XX_paths), axis=0).sort_index()
for p in XX_paths + model_paths:
remove(p)
self._clean_tmp_folder(logger, tmp_folder)
features_df = pd.DataFrame()
features_df[self.display_name + '_GrpAvg'] = XX_general['yhat']
if self.top_groups:
features_df[self.display_name + f'_Top{self.top_n}Grp'] = XX_top_groups['yhat']
self._output_feature_names = list(features_df.columns)
self._feature_desc = list(features_df.columns)
return features_df
def fit(self, X: dt.Frame, y: np.array = None):
"""
Fits ARIMA models (1 per time group) using historical target values contained in y
Model fitting is distributed over a pool of processes and uses file storage to share the data with workers
:param X: Datatable frame containing the features
:param y: numpy array containing the historical values of the target
:return: self
"""
# Get the logger if it exists
logger = None
tmp_folder = str(uuid.uuid4()) + "_arima_folder/"
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir
)
tmp_folder = self.context.experiment_tmp_dir + "/" + str(uuid.uuid4()) + "_arima_folder/"
# Create a temp folder to store files used during multi processing experiment
# This temp folder will be removed at the end of the process
loggerinfo(logger, "Arima temp folder {}".format(tmp_folder))
try:
os.mkdir(tmp_folder)
except PermissionError:
# This not occur so log a warning
loggerwarning(logger, "Arima was denied temp folder creation rights")
tmp_folder = temporary_files_path + "/" + str(uuid.uuid4()) + "_arima_folder/"
os.mkdir(tmp_folder)
except FileExistsError:
# We should never be here since temp dir name is expected to be unique
loggerwarning(logger, "Arima temp folder already exists")
tmp_folder = self.context.experiment_tmp_dir + "/" + str(uuid.uuid4()) + "_arima_folder/"
os.mkdir(tmp_folder)
except:
# Revert to temporary file path
tmp_folder = temporary_files_path + "/" + str(uuid.uuid4()) + "_arima_folder/"
os.mkdir(tmp_folder)
# Import the ARIMA python module
pm = importlib.import_module('pmdarima')
# Init models
self.models = {}
# Convert to pandas
X = X.to_pandas()
XX = X[self.tgc].copy()
XX['y'] = np.array(y)
self.nan_value = np.mean(y)
self.ntrain = X.shape[0]
# Group the input by TGC (Time group column) excluding the time column itself
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
# Prepare for multi processing
num_tasks = len(XX_grp)
def processor(out, res):
out[res[0]] = res[1]
pool_to_use = small_job_pool
if hasattr(self, "params_base"):
max_workers = self.params_base['n_jobs']
else:
loggerinfo(logger, "Custom Recipe does not have a params_base attribute")
# Beware not to use the disable_gpus keyword here. looks like cython does not like it
# max_workers = get_max_workers(True)
# Just set default to 2
max_workers = 2
loggerinfo(logger, "Arima will use {} workers for parallel processing".format(max_workers))
pool = pool_to_use(
logger=None, processor=processor,
num_tasks=num_tasks, max_workers=max_workers
)
# Build 1 ARIMA model per time group columns
nb_groups = len(XX_grp)
for _i_g, (key, X) in enumerate(XX_grp):
# Just say where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(logger, "Auto ARIMA : %d%% of groups fitted" % (100 * (_i_g + 1) // nb_groups))
X_path = os.path.join(tmp_folder, "autoarima_X" + str(uuid.uuid4()))
X = X.reset_index(drop=True)
save_obj(X, X_path)
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
args = (X_path, grp_hash, self.time_column, tmp_folder)
kwargs = {}
pool.submit_tryget(None, MyParallelAutoArimaTransformer_fit_async, args=args, kwargs=kwargs,
out=self.models)
pool.finish()
for k, v in self.models.items():
self.models[k] = load_obj(v) if v is not None else None
remove(v)
try:
shutil.rmtree(tmp_folder)
loggerinfo(logger, "Arima cleaned up temporary file folder.")
except:
loggerwarning(logger, "Arima could not delete the temporary file folder.")
return self
def predict(self, X, **kwargs):
model_config, _, _, _ = self.get_model_properties()
models = model_config['models']
cap = model_config['cap']
priors = model_config['priors']
prior = model_config['prior']
if self.tgc is None or not all([x in X.names for x in self.tgc]):
return np.ones(X.shape[0]) * self.nan_value
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
loggerinfo(logger, "Start Predicting with Prophet")
# Reduce to TimeGroupColumns
if isinstance(X, dt.Frame):
# Convert to pandas
XX = X[:, self.tgc].to_pandas()
else:
XX = X[:, self.tgc].copy()
XX = XX.replace([None, np.nan], 0)
XX.rename(columns={self.time_column: "ds"}, inplace=True)
if self.params["growth"] == "logistic":
XX["cap"] = cap
# Compute groups
# Group the input by TGC (Time group column) excluding the time column itself
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
# Go Through groups and predict
#
nb_groups = len(XX_grp)
preds = []
for _i_g, (key, X) in enumerate(XX_grp):
# Just log where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(
logger, "FB Prophet Model : %d%% of groups transformed" %
(100 * (_i_g + 1) // nb_groups))
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
# Facebook Prophet returns the predictions ordered by time
# So we should keep track of the time order for each group so that
# predictions are ordered the same as the imput frame
# Keep track of the order
order = np.argsort(pd.to_datetime(X["ds"]))
if grp_hash in models.keys():
model = models[grp_hash]
if model is not None:
# Run prophet
yhat = model.predict(X)
XX = yhat
else:
if grp_hash in priors.keys():
XX = pd.DataFrame(np.full((X.shape[0], 1),
priors[grp_hash]),
columns=['yhat'])
else:
# This should not happen
loggerinfo(logger, "Group in models but not in priors")
XX = pd.DataFrame(np.full((X.shape[0], 1), prior),
columns=['yhat'])
else:
# print("No Group")
XX = pd.DataFrame(np.full((X.shape[0], 1), prior),
columns=['yhat']) # unseen groups
# Reorder the index like prophet re-ordered the predictions
XX.index = X.index[order]
# print("Transformed Output for Group")
# print(XX.sort_index().head(20), flush=True)
preds.append(XX[['yhat']])
XX = pd.concat(tuple(preds), axis=0).sort_index()
return XX['yhat'].values
def fit(self,
X,
y,
sample_weight=None,
eval_set=None,
sample_weight_eval_set=None,
**kwargs):
orig_cols = list(X.names)
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from collections import Counter
import pygam
from pygam import LinearGAM, LogisticGAM
import matplotlib.pyplot as plt
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
# Set up temp folder
tmp_folder = self._create_tmp_folder(logger)
# Set up model
if self.num_classes >= 2:
lb = LabelEncoder()
lb.fit(self.labels)
y = lb.transform(y)
clf = LogisticGAM(terms="auto",
lam=self.params["lam"],
max_iter=self.params["max_iter"])
self.is_classifier = True
else:
clf = LinearGAM(terms="auto",
lam=self.params["lam"],
max_iter=self.params["max_iter"])
self.is_classifier = False
X = self.basic_impute(X)
# Find the datatypes
X = X.to_pandas()
X.columns = orig_cols
# Change continuous features to categorical
X_datatypes = [str(item) for item in list(X.dtypes)]
# Change all float32 values to float64
for ii in range(len(X_datatypes)):
if X_datatypes[ii] == 'float32':
X = X.astype({orig_cols[ii]: np.float64})
X_datatypes = [str(item) for item in list(X.dtypes)]
# List the categorical and numerical features
self.X_categorical = [
orig_cols[col_count] for col_count in range(len(orig_cols))
if (X_datatypes[col_count] == 'category') or (
X_datatypes[col_count] == 'object')
]
self.X_numeric = [
item for item in orig_cols if item not in self.X_categorical
]
# Find the levels and mode for each categorical feature
# for use in the test set
self.train_levels = {}
for item in self.X_categorical:
self.train_levels[item] = list(set(X[item]))
self.train_mode[item] = Counter(X[item]).most_common(1)[0][0]
# One hot encode the categorical features
# And replace missing values with a Missing category
if len(self.X_categorical) > 0:
X.loc[:, self.X_categorical] = X[self.X_categorical].fillna(
"Missing").copy()
self.enc = OneHotEncoder(handle_unknown='ignore')
self.enc.fit(X[self.X_categorical])
self.encoded_categories = list(
self.enc.get_feature_names(input_features=self.X_categorical))
X_enc = self.enc.transform(X[self.X_categorical]).toarray()
X = pd.concat([
X[self.X_numeric],
pd.DataFrame(X_enc, columns=self.encoded_categories)
],
axis=1)
# Replace missing values with a missing value code
self.median_train = {}
if len(self.X_numeric) > 0:
for colname in self.X_numeric:
self.median_train[colname] = X[colname].quantile(0.5)
X.loc[:, colname] = X[colname].fillna(
self.median_train[colname]).copy()
try:
clf.fit(X, y)
except np.linalg.LinAlgError as e:
raise IgnoreError("np.linalg.LinAlgError") from e
except pygam.utils.OptimizationError as e:
raise IgnoreError("pygam.utils.OptimizationError") from e
except ValueError as e:
if 'On entry to DLASCL parameter number' in str(e):
raise IgnoreError('On entry to DLASCL parameter number') from e
raise
p_values = np.array(clf.statistics_['p_values'])
# Plot the partial dependence plots for each feature
for ii in range(X.shape[1]):
XX = clf.generate_X_grid(term=ii)
plt.figure()
plt.plot(XX[:, ii], clf.partial_dependence(term=ii, X=XX))
plt.plot(XX[:, ii],
clf.partial_dependence(term=ii, X=XX, width=.95)[1],
c='r',
ls='--')
plt.title("Partial Dependence " + str(ii),
fontdict={'fontsize': 10})
plt.show()
plt.savefig(os.path.join(
tmp_folder, 'Feature_partial_dependence_' + str(ii) + '.png'),
bbox_inches="tight")
if max(p_values[0:(len(p_values) - 1)]) > 0:
importances = -np.log(p_values[0:(len(p_values) - 1)] + 10**(-16))
importances = list(importances / max(importances))
else:
importances = [1] * (len(p_values) - 1)
self.mean_target = np.array(sum(y) / len(y))
self.set_model_properties(model=clf,
features=list(X.columns),
importances=importances,
iterations=self.params['n_estimators'])
def fit(self,
X,
y,
sample_weight=None,
eval_set=None,
sample_weight_eval_set=None,
**kwargs):
orig_cols = list(X.names)
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from collections import Counter
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn import tree
import matplotlib.pyplot as plt
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
# Set up temp folter
tmp_folder = self._create_tmp_folder(logger)
# Set up model
if self.num_classes >= 2:
lb = LabelEncoder()
lb.fit(self.labels)
y = lb.transform(y)
clf = DecisionTreeClassifier(random_state=42,
max_depth=self.params["tree_depth"])
self.is_classifier = True
else:
clf = DecisionTreeRegressor(random_state=42,
max_depth=self.params["tree_depth"])
self.is_classifier = False
# Find the datatypes
X = X.to_pandas()
X.columns = orig_cols
# Change continuous features to categorical
X_datatypes = [str(item) for item in list(X.dtypes)]
# Change all float32 values to float64
for ii in range(len(X_datatypes)):
if X_datatypes[ii] == 'float32':
X = X.astype({orig_cols[ii]: np.float64})
X_datatypes = [str(item) for item in list(X.dtypes)]
# List the categorical and numerical features
self.X_categorical = [
orig_cols[col_count] for col_count in range(len(orig_cols))
if (X_datatypes[col_count] == 'category') or (
X_datatypes[col_count] == 'object')
]
self.X_numeric = [
item for item in orig_cols if item not in self.X_categorical
]
# Find the levels and mode for each categorical feature
# for use in the test set
self.train_levels = {}
for item in self.X_categorical:
self.train_levels[item] = list(set(X[item]))
self.train_mode[item] = Counter(X[item]).most_common(1)[0][0]
# One hot encode the categorical features
# And replace missing values with a Missing category
if len(self.X_categorical) > 0:
X.loc[:, self.X_categorical] = X[self.X_categorical].fillna(
"Missing").copy()
self.enc = OneHotEncoder(handle_unknown='ignore')
self.enc.fit(X[self.X_categorical])
self.encoded_categories = list(
self.enc.get_feature_names(input_features=self.X_categorical))
X_enc = self.enc.transform(X[self.X_categorical]).toarray()
X = pd.concat([
X[self.X_numeric],
pd.DataFrame(X_enc, columns=self.encoded_categories)
],
axis=1)
# Replace missing values with a missing value code
if len(self.X_numeric) > 0:
X.loc[:, self.X_numeric] = X[self.X_numeric].fillna(-999).copy()
# Fit the decision tree
clf.fit(X, y)
if self.is_classifier:
yy = clf.predict_proba(X)
p = np.round_(yy[:, 1], 5)
else:
yy = clf.predict(X)
p = np.round_(yy, 5)
self.leaf_categories = list(set(p))
# Fit linear or logistic models to each leaf node
model_array = {}
equation_log = []
for cat in self.leaf_categories:
if self.is_classifier:
if (np.mean(y[p == cat]) < 1) and (np.mean(y[p == cat]) > 0):
lm = LogisticRegression(random_state=42)
lm.fit(X[p == cat], y[p == cat])
model_array[cat] = lm
equation_log.append([[
int(round((1 - cat) * sum(p == cat))),
int(round(cat * sum(p == cat)))
],
sum(p == cat), lm.intercept_[0]] +
list(lm.coef_[0]))
else:
loggerinfo(logger, "No leaf fit")
model_array[cat] = "dt"
else:
try:
lm = LinearRegression()
lm.fit(X[p == cat], y[p == cat])
model_array[cat] = lm
equation_log.append(
[cat, sum(p == cat), lm.intercept_] + list(lm.coef_))
except:
loggerinfo(logger, "No leaf fit")
model_array[cat] = "dt"
# Save the leaf models
pd.DataFrame(equation_log,
columns=['leaf value', 'number of samples', 'intercept'] +
list(X.columns)).to_csv(
os.path.join(tmp_folder, 'Leaf_model_coef.csv'))
# Plot the decision tree
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 8), dpi=1600)
tree.plot_tree(clf, feature_names=list(X.columns))
fig.savefig(os.path.join(tmp_folder, 'Decision_tree_plot.png'))
importances = clf.feature_importances_
loggerinfo(logger, str(importances))
self.mean_target = np.array(sum(y) / len(y))
model = [clf, model_array]
# Set model properties
self.set_model_properties(model=model,
features=list(X.columns),
importances=importances,
iterations=self.params['n_estimators'])
def fit(self,
X,
y,
sample_weight=None,
eval_set=None,
sample_weight_eval_set=None,
**kwargs):
logger = None
if self._make_logger:
# Example use of logger, with required import of:
# from h2oaicore.systemutils import make_experiment_logger, loggerinfo
# Can use loggerwarning, loggererror, etc. for different levels
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
if self._show_logger_test:
loggerinfo(logger, "TestLOGGER: Fit CatBoost")
if self._show_task_test:
# Example task sync operations
if hasattr(self, 'testcount'):
self.test_count += 1
else:
self.test_count = 0
# The below generates a message in the GUI notifications panel
if self.test_count == 0 and self.context and self.context.experiment_id:
warning = "TestWarning: First CatBoost fit for this model instance"
loggerwarning(logger, warning)
task = kwargs.get('task')
if task:
task.sync(key=self.context.experiment_id,
progress=dict(type='warning', data=warning))
task.flush()
# The below generates a message in the GUI top-middle panel above the progress wheel
if self.test_count == 0 and self.context and self.context.experiment_id:
message = "Tuning CatBoost"
loggerinfo(logger, message)
task = kwargs.get('task')
if task:
task.sync(key=self.context.experiment_id,
progress=dict(type='update', message=message))
task.flush()
from catboost import CatBoostClassifier, CatBoostRegressor, EFstrType
# label encode target and setup type of problem
lb = LabelEncoder()
if self.num_classes >= 2:
lb.fit(self.labels)
y = lb.transform(y)
if eval_set is not None:
valid_X = eval_set[0][0]
valid_y = eval_set[0][1]
valid_y = lb.transform(valid_y)
eval_set = [(valid_X, valid_y)]
self.params.update({'objective': 'Logloss'})
if self.num_classes > 2:
self.params.update({'objective': 'MultiClass'})
if isinstance(X, dt.Frame):
orig_cols = list(X.names)
numeric_cols = list(X[:, [bool, int, float]].names)
else:
orig_cols = list(X.columns)
numeric_cols = list(X.select_dtypes([np.number]).columns)
# unlike lightgbm that needs label encoded categoricals, catboots can take raw strings etc.
self.params['cat_features'] = [
i for i, x in enumerate(orig_cols)
if 'CatOrig:' in x or 'Cat:' in x or x not in numeric_cols
]
if not self.get_uses_gpus(self.params):
# monotonicity constraints not available for GPU for catboost
# get names of columns in same order
X_names = list(dt.Frame(X).names)
X_numeric = self.get_X_ordered_numerics(X)
X_numeric_names = list(X_numeric.names)
_, _, constraints, self.set_monotone_constraints(X=X_numeric, y=y)
# if non-numerics, then fix those to have 0 constraint
self.params['monotone_constraints'] = [0] * len(X_names)
colnumi = 0
for coli in X_names:
if X_names[coli] in X_numeric_names:
self.params['monotone_constraints'][coli] = constraints[
colnumi]
colnumi += 1
if isinstance(X, dt.Frame) and len(self.params['cat_features']) == 0:
# dt -> catboost internally using buffer leaks, so convert here
# assume predict is after pipeline collection or in subprocess so needs no protection
X = X.to_numpy(
) # don't assign back to X so don't damage during predict
X = np.ascontiguousarray(X,
dtype=np.float32 if config.data_precision
== "float32" else np.float64)
if eval_set is not None:
valid_X = eval_set[0][0].to_numpy(
) # don't assign back to X so don't damage during predict
valid_X = np.ascontiguousarray(
valid_X,
dtype=np.float32
if config.data_precision == "float32" else np.float64)
valid_y = eval_set[0][1]
eval_set = [(valid_X, valid_y)]
if eval_set is not None:
valid_X_shape = eval_set[0][0].shape
else:
valid_X_shape = None
X, eval_set = self.process_cats(X, eval_set, orig_cols)
# modify self.params_base['gpu_id'] based upon actually-available GPU based upon training and valid shapes
self.acquire_gpus_function(train_shape=X.shape,
valid_shape=valid_X_shape)
params = copy.deepcopy(
self.params
) # keep separate, since then can be pulled form lightgbm params
params = self.transcribe_params(params=params, **kwargs)
if logger is not None:
loggerdata(
logger,
"CatBoost parameters: params_base : %s params: %s catboost_params: %s"
% (str(self.params_base), str(self.params), str(params)))
if self.num_classes == 1:
self.model = CatBoostRegressor(**params)
else:
self.model = CatBoostClassifier(**params)
# Hit sometimes: Exception: catboost/libs/data_new/quantization.cpp:779: All features are either constant or ignored.
if self.num_classes == 1:
# assume not mae, which would use median
# baseline = [np.mean(y)] * len(y)
baseline = None
else:
baseline = None
kwargs_fit = dict(baseline=baseline, eval_set=eval_set)
pickle_path = None
if config.debug_daimodel_level >= 2:
self.uuid = str(uuid.uuid4())[:6]
pickle_path = os.path.join(exp_dir(),
"catboost%s.tmp.pickle" % self.uuid)
save_obj((self.model, X, y, sample_weight, kwargs_fit),
pickle_path)
# FIT (with migration safety before hyperopt/Optuna function added)
try:
if hasattr(self, 'dask_or_hyper_or_normal_fit'):
self.dask_or_hyper_or_normal_fit(X,
y,
sample_weight=sample_weight,
kwargs=kwargs,
**kwargs_fit)
else:
self.model.fit(X, y, sample_weight=sample_weight, **kwargs_fit)
except Exception as e:
if "All features are either constant or ignored" in str(e):
raise IgnoreEntirelyError(str(e))
raise
if config.debug_daimodel_level <= 2:
remove(pickle_path)
# https://catboost.ai/docs/concepts/python-reference_catboostclassifier.html
# need to move to wrapper
if self.model.get_best_iteration() is not None:
iterations = self.model.get_best_iteration() + 1
else:
iterations = self.params['n_estimators']
# must always set best_iterations
self.model_path = None
importances = copy.deepcopy(self.model.feature_importances_)
if not self._save_by_pickle:
self.uuid = str(uuid.uuid4())[:6]
model_file = "catboost_%s.bin" % str(self.uuid)
self.model_path = os.path.join(self.context.experiment_tmp_dir,
model_file)
self.model.save_model(self.model_path)
with open(self.model_path, mode='rb') as f:
model = f.read()
else:
model = self.model
self.set_model_properties(
model=
model, # overwrites self.model object with bytes if not using pickle
features=orig_cols,
importances=importances,
iterations=iterations)
def predict(self, X, **kwargs):
orig_cols = list(X.names)
import pandas as pd
import xgboost as xgb
import numpy as np
def sigmoid(x):
z = 1.0 / (1.0 + np.exp(-x))
return z
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
X = dt.Frame(X)
X = X.to_pandas()
if self.protected in list(X.columns):
# Set the protected group to 0 and all others 1
loggerdebug(logger, "Protected test found")
protected_test = np.array([
int(item) for item in
~(np.array(X[self.protected]) == self.protected_label)
])
else:
loggerdebug(logger, "Protected test not found")
protected_test = np.array([])
if self.protected in list(X.columns):
X = X.drop(self.protected, axis=1)
# Replace missing values with a missing category
# Replace categories that weren't in the training set with the mode
if len(self.X_categorical) > 0:
for colname in self.X_categorical:
if colname in list(X.columns):
X[colname] = list(X[colname].fillna("Missing"))
for label in self.X_categorical:
if label in list(X.columns):
# Replace anything not in the test set
train_categories = self.train_levels[label]
X_label = np.array(X[label])
mmode = self.train_mode[label]
X_label[~np.isin(X_label, train_categories)] = mmode
X[label] = X_label
# Replace missing values with a missing value code
if len(self.X_numeric) > 0:
for colname in self.X_numeric:
if colname in list(X.columns):
X[colname] = list(X[colname].fillna(-999))
# Get model
model, _, _, _ = self.get_model_properties()
# Remove the protected group
if self.protected in self.X_categorical:
self.X_categorical.remove(self.protected)
# One hot encode categorical features
if len(self.X_categorical) > 0:
X_enc = self.enc.transform(X[self.X_categorical]).toarray()
X = pd.concat([
X[self.X_numeric],
pd.DataFrame(X_enc, columns=self.encoded_categories)
],
axis=1)
d_test = xgb.DMatrix(X, missing=np.nan)
# If the positive target was 0, change the final result to 1-p
if self.positive_target == 0:
preds = 1.0 - sigmoid(model.predict(d_test))
else:
preds = sigmoid(model.predict(d_test))
mean_preds = np.mean(preds)
# Set a penalty value to which some probabilities will be changed
# if the fairness threshold isn't reached
epsilon = 0.0001
if mean_preds > 0.5:
penalty = epsilon
else:
penalty = 1.0 - epsilon
# Only apply penalties in the training stage
if self.is_train:
# If the protected value was removed, use the maximum penalty
# by changing all probabilities to the penalty value
# (the recipe needs to be able to use the protected values)
if self.protected == "none":
preds[0:len(preds)] = penalty
loggerdata(logger, str(preds))
loggerdata(logger, "Removal_penalty")
else:
# The mean ratio calculation for target=0 and target=1
if self.positive_target == 0:
if np.mean(preds[protected_test == 1]) < 1.0:
DI = (1.0 - np.mean(preds[protected_test == 0])) / (
1.0 - np.mean(preds[protected_test == 1]))
else:
DI = 1
else:
if np.mean(preds[protected_test == 1]) > 0.0:
DI = np.mean(preds[protected_test == 0]) / np.mean(
preds[protected_test == 1])
else:
DI = 1
loggerdata(logger, "Mean ratio Check")
loggerdata(logger, str(DI))
if DI < self.mean_protected_prediction_ratio_minimum:
# Create a penalty proportional to the distance below the specified threshold
len_preds = len(preds)
num_penalty = min(
len_preds,
int((self.mean_protected_prediction_ratio_minimum - DI)
/ self.mean_protected_prediction_ratio_minimum *
len_preds))
preds[0:num_penalty] = penalty
loggerdata(logger, "num_penalty1")
loggerdata(logger, str(num_penalty),
str(num_penalty / len(preds)))
self.is_train = False
return preds
def fit(self, X, y, sample_weight=None, eval_set=None, sample_weight_eval_set=None, **kwargs):
# Example use of logger, with required import of:
# from h2oaicore.systemutils import make_experiment_logger, loggerinfo
# Can use loggerwarning, loggererror, etc. for different levels
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(experiment_id=self.context.experiment_id, tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
loggerinfo(logger, "TestLOGGER: Fit CatBoost")
# Example task sync operations
if hasattr(self, 'testcount'):
self.test_count += 1
else:
self.test_count = 0
# The below generates a message in the GUI notifications panel
if self.test_count == 0 and self.context and self.context.experiment_id:
warning = "TestWarning: First CatBoost fit for this model instance"
loggerwarning(logger, warning)
task = kwargs.get('task')
if task:
task.sync(key=self.context.experiment_id, progress=dict(type='warning', data=warning))
task.flush()
# The below generates a message in the GUI top-middle panel above the progress wheel
if self.test_count == 0 and self.context and self.context.experiment_id:
message = "TestMessage: CatBoost"
loggerinfo(logger, message)
task = kwargs.get('task')
if task:
task.sync(key=self.context.experiment_id, progress=dict(type='update', message=message))
task.flush()
from catboost import CatBoostClassifier, CatBoostRegressor, EFstrType
lb = LabelEncoder()
if self.num_classes >= 2:
lb.fit(self.labels)
y = lb.transform(y)
if isinstance(X, dt.Frame):
orig_cols = list(X.names)
# dt -> lightgbm internally using buffer leaks, so convert here
# assume predict is after pipeline collection or in subprocess so needs no protection
X = X.to_numpy() # don't assign back to X so don't damage during predict
X = np.ascontiguousarray(X, dtype=np.float32 if config.data_precision == "float32" else np.float64)
if eval_set is not None:
valid_X = eval_set[0][0].to_numpy() # don't assign back to X so don't damage during predict
valid_X = np.ascontiguousarray(valid_X,
dtype=np.float32 if config.data_precision == "float32" else np.float64)
valid_y = eval_set[0][1]
if self.num_classes >= 2:
valid_y = lb.transform(valid_y)
eval_set[0] = (valid_X, valid_y)
else:
orig_cols = list(X.columns)
if self.num_classes == 1:
model = CatBoostRegressor(**self.params)
else:
model = CatBoostClassifier(**self.params)
# Hit sometimes: Exception: catboost/libs/data_new/quantization.cpp:779: All features are either constant or ignored.
if self.num_classes == 1:
# assume not mae, which would use median
# baseline = [np.mean(y)] * len(y)
baseline = None
else:
baseline = None
model.fit(X, y=y,
sample_weight=sample_weight,
baseline=baseline,
eval_set=eval_set,
early_stopping_rounds=kwargs.get('early_stopping_rounds', None),
verbose=self.params.get('verbose', False)
)
# need to move to wrapper
if model.get_best_iteration() is not None:
iterations = model.get_best_iteration() + 1
else:
iterations = self.params['iterations'] + 1
# must always set best_iterations
self.set_model_properties(model=model,
features=orig_cols,
importances=model.feature_importances_,
iterations=iterations)
def fit(self, X: dt.Frame, y: np.array = None, **kwargs):
"""
Fits FB Prophet models (1 per time group) using historical target values contained in y
Model fitting is distributed over a pool of processes and uses file storage to share the data with workers
:param X: Datatable frame containing the features
:param y: numpy array containing the historical values of the target
:return: self
"""
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir
)
try:
# Add value of prophet_top_n in recipe_dict variable inside of config.toml file
# eg1: recipe_dict="{'prophet_top_n': 200}"
# eg2: recipe_dict="{'prophet_top_n':10}"
self.top_n = config.recipe_dict['prophet_top_n']
except KeyError:
self.top_n = 50
loggerinfo(logger, f"Prophet will use {self.top_n} groups as well as average target data.")
tmp_folder = self._create_tmp_folder(logger)
n_jobs = self._get_n_jobs(logger, **kwargs)
# Reduce X to TGC
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
X = X[:, self.tgc].to_pandas()
# Fill NaNs or None
X = X.replace([None, np.nan], 0)
# Add target, Label encoder is only used for Classif. which we don't support...
if self.labels is not None:
y = LabelEncoder().fit(self.labels).transform(y)
X['y'] = np.array(y)
self.nan_value = X['y'].mean()
# Change date feature name to match Prophet requirements
X.rename(columns={self.time_column: "ds"}, inplace=True)
# Create a general scale now that will be used for unknown groups at prediction time
# Can we do smarter than that ?
self.general_scaler = MinMaxScaler().fit(X[['y', 'ds']].groupby('ds').median().values)
# Go through groups and standard scale them
if len(tgc_wo_time) > 0:
X_groups = X.groupby(tgc_wo_time)
else:
X_groups = [([None], X)]
self.scalers = {}
scaled_ys = []
print(f'{datetime.now()} Start of group scaling')
for key, X_grp in X_groups:
# Create dict key to store the min max scaler
grp_hash = self.get_hash(key)
# Scale target for current group
self.scalers[grp_hash] = MinMaxScaler()
y_skl = self.scalers[grp_hash].fit_transform(X_grp[['y']].values)
# Put back in a DataFrame to keep track of original index
y_skl_df = pd.DataFrame(y_skl, columns=['y'])
# (0, 'A') (1, 4) (100, 1) (100, 1)
# print(grp_hash, X_grp.shape, y_skl.shape, y_skl_df.shape)
y_skl_df.index = X_grp.index
scaled_ys.append(y_skl_df)
print(f'{datetime.now()} End of group scaling')
# Set target back in original frame but keep original
X['y_orig'] = X['y']
X['y'] = pd.concat(tuple(scaled_ys), axis=0)
# Now Average groups
X_avg = X[['ds', 'y']].groupby('ds').mean().reset_index()
# Send that to Prophet
params = {
"country_holidays": self.country_holidays,
"monthly_seasonality": self.monthly_seasonality
}
mod = importlib.import_module('fbprophet')
Prophet = getattr(mod, "Prophet")
self.model = Prophet(yearly_seasonality=True, weekly_seasonality=True, daily_seasonality=True)
if params["country_holidays"] is not None:
self.model.add_country_holidays(country_name=params["country_holidays"])
if params["monthly_seasonality"]:
self.model.add_seasonality(name='monthly', period=30.5, fourier_order=5)
with suppress_stdout_stderr():
self.model.fit(X[['ds', 'y']])
print(f'{datetime.now()} General Model Fitted')
self.top_groups = None
if len(tgc_wo_time) > 0:
if self.top_n > 0:
top_n_grp = X.groupby(tgc_wo_time).size().sort_values().reset_index()[tgc_wo_time].iloc[-self.top_n:].values
self.top_groups = [
'_'.join(map(str, key))
for key in top_n_grp
]
if self.top_groups:
self.grp_models = {}
self.priors = {}
# Prepare for multi processing
num_tasks = len(self.top_groups)
def processor(out, res):
out[res[0]] = res[1]
pool_to_use = small_job_pool
loggerinfo(logger, f"Prophet will use {n_jobs} workers for fitting.")
loggerinfo(logger, "Prophet parameters holidays {} / monthly {}".format(self.country_holidays, self.monthly_seasonality))
pool = pool_to_use(
logger=None, processor=processor,
num_tasks=num_tasks, max_workers=n_jobs
)
#
# Fit 1 FB Prophet model per time group columns
nb_groups = len(X_groups)
# Put y back to its unscaled value for top groups
X['y'] = X['y_orig']
for _i_g, (key, X) in enumerate(X_groups):
# Just log where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(logger, "FB Prophet : %d%% of groups fitted" % (100 * (_i_g + 1) // nb_groups))
X_path = os.path.join(tmp_folder, "fbprophet_X" + str(uuid.uuid4()))
X = X.reset_index(drop=True)
save_obj(X, X_path)
grp_hash = self.get_hash(key)
if grp_hash not in self.top_groups:
continue
self.priors[grp_hash] = X['y'].mean()
params = {
"country_holidays": self.country_holidays,
"monthly_seasonality": self.monthly_seasonality
}
args = (X_path, grp_hash, tmp_folder, params)
kwargs = {}
pool.submit_tryget(None, MyParallelProphetTransformer_fit_async,
args=args, kwargs=kwargs, out=self.grp_models)
pool.finish()
for k, v in self.grp_models.items():
self.grp_models[k] = load_obj(v) if v is not None else None
remove(v)
self._clean_tmp_folder(logger, tmp_folder)
return self
def fit(self,
X,
y,
sample_weight=None,
eval_set=None,
sample_weight_eval_set=None,
**kwargs):
# Get TGC and time column
self.tgc = self.params_base.get('tgc', None)
self.time_column = self.params_base.get('time_column', None)
self.nan_value = np.mean(y)
self.cap = np.max(
y
) * 1.5 # TODO Don't like this we should compute a cap from average yearly growth
self.prior = np.mean(y)
if self.time_column is None:
self.time_column = self.tgc[0]
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
loggerinfo(
logger,
"Start Fitting Prophet Model with params : {}".format(self.params))
# Get temporary folders for multi process communication
tmp_folder = self._create_tmp_folder(logger)
n_jobs = self._get_n_jobs(logger, **kwargs)
# Convert to pandas
XX = X[:, self.tgc].to_pandas()
XX = XX.replace([None, np.nan], 0)
XX.rename(columns={self.time_column: "ds"}, inplace=True)
# Make target available in the Frame
XX['y'] = np.array(y)
# Set target prior
self.nan_value = np.mean(y)
# Group the input by TGC (Time group column) excluding the time column itself
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
self.models = {}
self.priors = {}
# Prepare for multi processing
num_tasks = len(XX_grp)
def processor(out, res):
out[res[0]] = res[1]
pool_to_use = small_job_pool
loggerdebug(logger,
"Prophet will use {} workers for fitting".format(n_jobs))
pool = pool_to_use(logger=None,
processor=processor,
num_tasks=num_tasks,
max_workers=n_jobs)
# Fit 1 FB Prophet model per time group columns
nb_groups = len(XX_grp)
for _i_g, (key, X) in enumerate(XX_grp):
# Just log where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(
logger, "FB Prophet : %d%% of groups fitted" %
(100 * (_i_g + 1) // nb_groups))
X_path = os.path.join(tmp_folder,
"fbprophet_X" + str(uuid.uuid4()))
X = X.reset_index(drop=True)
save_obj(X, X_path)
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
self.priors[grp_hash] = X['y'].mean()
args = (X_path, grp_hash, tmp_folder, self.params, self.cap)
kwargs = {}
pool.submit_tryget(None,
MyParallelProphetTransformer_fit_async,
args=args,
kwargs=kwargs,
out=self.models)
pool.finish()
for k, v in self.models.items():
self.models[k] = load_obj(v) if v is not None else None
remove(v)
self._clean_tmp_folder(logger, tmp_folder)
return None
def fit(self, X: dt.Frame, y: np.array = None, **kwargs):
"""
Fits ARIMA models (1 per time group) using historical target values contained in y
Model fitting is distributed over a pool of processes and uses file storage to share the data with workers
:param X: Datatable frame containing the features
:param y: numpy array containing the historical values of the target
:return: self
"""
# Get the logger if it exists
logger = None
tmp_folder = str(uuid.uuid4()) + "_arima_folder/"
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
tmp_folder = self._create_tmp_folder(logger)
n_jobs = self._get_n_jobs(logger, **kwargs)
# Import the ARIMA python module
pm = importlib.import_module('pmdarima')
# Init models
self.models = {}
# Convert to pandas
X = X.to_pandas()
XX = X[self.tgc].copy()
XX['y'] = np.array(y)
self.nan_value = np.mean(y)
self.ntrain = X.shape[0]
# Group the input by TGC (Time group column) excluding the time column itself
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
if len(tgc_wo_time) > 0:
XX_grp = XX.groupby(tgc_wo_time)
else:
XX_grp = [([None], XX)]
# Prepare for multi processing
num_tasks = len(XX_grp)
def processor(out, res):
out[res[0]] = res[1]
pool_to_use = small_job_pool
loggerinfo(
logger,
"Arima will use {} workers for parallel processing".format(n_jobs))
pool = pool_to_use(logger=None,
processor=processor,
num_tasks=num_tasks,
max_workers=n_jobs)
# Build 1 ARIMA model per time group columns
nb_groups = len(XX_grp)
for _i_g, (key, X) in enumerate(XX_grp):
# Just say where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(
logger, "Auto ARIMA : %d%% of groups fitted" %
(100 * (_i_g + 1) // nb_groups))
X_path = os.path.join(tmp_folder,
"autoarima_X" + str(uuid.uuid4()))
X = X.reset_index(drop=True)
save_obj(X, X_path)
key = key if isinstance(key, list) else [key]
grp_hash = '_'.join(map(str, key))
args = (X_path, grp_hash, self.time_column, tmp_folder)
kwargs = {}
pool.submit_tryget(None,
MyParallelAutoArimaTransformer_fit_async,
args=args,
kwargs=kwargs,
out=self.models)
pool.finish()
for k, v in self.models.items():
self.models[k] = load_obj(v) if v is not None else None
remove(v)
self._clean_tmp_folder(logger, tmp_folder)
return self
def fit(self, X, y, sample_weight=None, eval_set=None, sample_weight_eval_set=None, **kwargs):
orig_cols = list(X.names)
import pandas as pd
import numpy as np
from skrules import SkopeRules
from sklearn.preprocessing import OneHotEncoder
from collections import Counter
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
# Set up temp folder
tmp_folder = self._create_tmp_folder(logger)
# Set up model
if self.num_classes >= 2:
lb = LabelEncoder()
lb.fit(self.labels)
y = lb.transform(y)
model = SkopeRules(max_depth_duplication=self.params["max_depth_duplication"],
n_estimators=self.params["n_estimators"],
precision_min=self.params["precision_min"],
recall_min=self.params["recall_min"],
max_samples=self.params["max_samples"],
max_samples_features=self.params["max_samples_features"],
max_depth=self.params["max_depth"],
max_features=self.params["max_features"],
min_samples_split=self.params["min_samples_split"],
bootstrap=self.params["bootstrap"],
bootstrap_features=self.params["bootstrap_features"],
random_state=self.params["random_state"],
feature_names=orig_cols)
else:
# Skopes doesn't work for regression
loggerinfo(logger, "PASS, no skopes model")
pass
# Find the datatypes
X = X.to_pandas()
X.columns = orig_cols
# Change continuous features to categorical
X_datatypes = [str(item) for item in list(X.dtypes)]
# Change all float32 values to float64
for ii in range(len(X_datatypes)):
if X_datatypes[ii] == 'float32':
X = X.astype({orig_cols[ii]: np.float64})
X_datatypes = [str(item) for item in list(X.dtypes)]
# List the categorical and numerical features
self.X_categorical = [orig_cols[col_count] for col_count in range(len(orig_cols)) if
(X_datatypes[col_count] == 'category') or (X_datatypes[col_count] == 'object')]
self.X_numeric = [item for item in orig_cols if item not in self.X_categorical]
# Find the levels and mode for each categorical feature
# for use in the test set
self.train_levels = {}
for item in self.X_categorical:
self.train_levels[item] = list(set(X[item]))
self.train_mode[item] = Counter(X[item]).most_common(1)[0][0]
# One hot encode the categorical features
# And replace missing values with a Missing category
if len(self.X_categorical) > 0:
loggerinfo(logger, "PCategorical encode")
for colname in self.X_categorical:
X[colname] = list(X[colname].fillna("Missing"))
self.enc = OneHotEncoder(handle_unknown='ignore')
self.enc.fit(X[self.X_categorical])
self.encoded_categories = list(self.enc.get_feature_names(input_features=self.X_categorical))
X_enc = self.enc.transform(X[self.X_categorical]).toarray()
X = pd.concat([X[self.X_numeric], pd.DataFrame(X_enc, columns=self.encoded_categories)], axis=1)
# Replace missing values with a missing value code
if len(self.X_numeric) > 0:
for colname in self.X_numeric:
X[colname] = list(X[colname].fillna(-999))
model.fit(np.array(X), np.array(y))
# Find the rule list
self.rule_list = model.rules_
# Calculate feature importances
var_imp = []
for var in orig_cols:
var_imp.append(sum(int(var in item[0]) for item in self.rule_list))
if max(var_imp) != 0:
importances = list(np.array(var_imp) / max(var_imp))
else:
importances = [1] * len(var_imp)
pd.DataFrame(model.rules_, columns=['Rule', '(Precision, Recall, nb)']).to_csv(
os.path.join(tmp_folder, 'Skope_rules.csv'), index=False)
self.mean_target = np.array(sum(y) / len(y))
# Set model properties
self.set_model_properties(model=model,
features=list(X.columns),
importances=importances,
iterations=self.params['n_estimators'])
def fit(self,
X,
y,
sample_weight=None,
eval_set=None,
sample_weight_eval_set=None,
**kwargs):
# Get TGC and time column
self.tgc = self.params_base.get('tgc', None)
self.time_column = self.params_base.get('time_column', None)
self.nan_value = np.mean(y)
self.cap = np.max(
y
) * 1.5 # TODO Don't like this we should compute a cap from average yearly growth
self.prior = np.mean(y)
if self.time_column is None:
self.time_column = self.tgc[0]
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
loggerinfo(
logger,
"Start Fitting Prophet Model with params : {}".format(self.params))
try:
# Add value of prophet_top_n in recipe_dict variable inside of config.toml file
# eg1: recipe_dict="{'prophet_top_n': 200}"
# eg2: recipe_dict="{'prophet_top_n':10}"
self.top_n = config.recipe_dict['prophet_top_n']
except KeyError:
self.top_n = 50
loggerinfo(
logger,
f"Prophet will use {self.top_n} groups as well as average target data."
)
# Get temporary folders for multi process communication
tmp_folder = self._create_tmp_folder(logger)
n_jobs = self._get_n_jobs(logger, **kwargs)
# Reduce X to TGC
tgc_wo_time = list(np.setdiff1d(self.tgc, self.time_column))
X = X[:, self.tgc].to_pandas()
# Fill NaNs or None
X = X.replace([None, np.nan], 0)
# Add target, Label encoder is only used for Classif. which we don't support...
if self.labels is not None:
y = LabelEncoder().fit(self.labels).transform(y)
X['y'] = np.array(y)
self.nan_value = X['y'].mean()
# Change date feature name to match Prophet requirements
X.rename(columns={self.time_column: "ds"}, inplace=True)
# Create a general scale now that will be used for unknown groups at prediction time
# Can we do smarter than that ?
general_scaler = MinMaxScaler().fit(
X[['y', 'ds']].groupby('ds').median().values)
# Go through groups and standard scale them
if len(tgc_wo_time) > 0:
X_groups = X.groupby(tgc_wo_time)
else:
X_groups = [([None], X)]
scalers = {}
scaled_ys = []
print('Number of groups : ', len(X_groups))
for g in tgc_wo_time:
print(f'Number of groups in {g} groups : {X[g].unique().shape}')
for key, X_grp in X_groups:
# Create dict key to store the min max scaler
grp_hash = self.get_hash(key)
# Scale target for current group
scalers[grp_hash] = MinMaxScaler()
y_skl = scalers[grp_hash].fit_transform(X_grp[['y']].values)
# Put back in a DataFrame to keep track of original index
y_skl_df = pd.DataFrame(y_skl, columns=['y'])
y_skl_df.index = X_grp.index
scaled_ys.append(y_skl_df)
# Set target back in original frame but keep original
X['y_orig'] = X['y']
X['y'] = pd.concat(tuple(scaled_ys), axis=0)
# Now Average groups
X_avg = X[['ds', 'y']].groupby('ds').mean().reset_index()
# Send that to Prophet
mod = importlib.import_module('fbprophet')
Prophet = getattr(mod, "Prophet")
nrows = X[['ds', 'y']].shape[0]
n_changepoints = max(1, int(nrows * (2 / 3)))
if n_changepoints < 25:
model = Prophet(yearly_seasonality=True,
weekly_seasonality=True,
daily_seasonality=True,
n_changepoints=n_changepoints)
else:
model = Prophet(yearly_seasonality=True,
weekly_seasonality=True,
daily_seasonality=True)
if self.params["country_holidays"] is not None:
model.add_country_holidays(
country_name=self.params["country_holidays"])
if self.params["monthly_seasonality"]:
model.add_seasonality(
name='monthly',
period=30.5,
fourier_order=self.params["monthly_seasonality"])
if self.params["quarterly_seasonality"]:
model.add_seasonality(
name='quarterly',
period=92,
fourier_order=self.params["quarterly_seasonality"])
with suppress_stdout_stderr():
model.fit(X[['ds', 'y']])
top_groups = None
if len(tgc_wo_time) > 0:
if self.top_n > 0:
top_n_grp = X.groupby(tgc_wo_time).size().sort_values(
).reset_index()[tgc_wo_time].iloc[-self.top_n:].values
top_groups = ['_'.join(map(str, key)) for key in top_n_grp]
grp_models = {}
priors = {}
if top_groups:
# Prepare for multi processing
num_tasks = len(top_groups)
def processor(out, res):
out[res[0]] = res[1]
pool_to_use = small_job_pool
loggerinfo(logger,
f"Prophet will use {n_jobs} workers for fitting.")
pool = pool_to_use(logger=None,
processor=processor,
num_tasks=num_tasks,
max_workers=n_jobs)
#
# Fit 1 FB Prophet model per time group columns
nb_groups = len(X_groups)
# Put y back to its unscaled value for top groups
X['y'] = X['y_orig']
for _i_g, (key, X) in enumerate(X_groups):
# Just log where we are in the fitting process
if (_i_g + 1) % max(1, nb_groups // 20) == 0:
loggerinfo(
logger, "FB Prophet : %d%% of groups fitted" %
(100 * (_i_g + 1) // nb_groups))
X_path = os.path.join(tmp_folder,
"fbprophet_X" + str(uuid.uuid4()))
X = X.reset_index(drop=True)
save_obj(X, X_path)
grp_hash = self.get_hash(key)
if grp_hash not in top_groups:
continue
priors[grp_hash] = X['y'].mean()
args = (X_path, grp_hash, tmp_folder, self.params, self.cap)
kwargs = {}
pool.submit_tryget(None,
MyParallelProphetTransformer_fit_async,
args=args,
kwargs=kwargs,
out=grp_models)
pool.finish()
for k, v in grp_models.items():
grp_models[k] = load_obj(v) if v is not None else None
remove(v)
self._clean_tmp_folder(logger, tmp_folder)
self.set_model_properties(
model={
'avg': model,
'group': grp_models,
'priors': priors,
'topgroups': top_groups,
'skl': scalers,
'gen_scaler': general_scaler
},
features=self.tgc, # Prophet uses time and timegroups
importances=np.ones(len(self.tgc)),
iterations=-1 # Does not have iterations
)
return None
def fit_transform(self, X: dt.Frame, y: np.array = None, **kwargs):
X_original = X
X = X[:, dt.f[int].extend(dt.f[float]).extend(dt.f[bool]).
extend(dt.f[str])]
if hasattr(self, 'runcount'):
self.run_count += 1
else:
self.run_count = 0
# Get the logger if it exists
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(
experiment_id=self.context.experiment_id,
tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir,
username=self.context.username,
)
survival_event = self.__class__._survival_event
if survival_event in X.names:
raise ValueError(
"Consider renaming feature '{}'.".format(survival_event))
# bind y to X to use as event in CoxPH
X[:, survival_event] = np.array(LabelEncoder().fit_transform(y))
# sanity check that target is binary
if X[survival_event].nunique()[0, 0] != 2:
raise ValueError(
"Too many values {} in event column - must be exactly 2.".
format(X[survival_event].nunique()[0, 0]))
# redress target values into 0, 1
event_max = X[survival_event].max()[0, 0]
X[dt.f[survival_event] != event_max, survival_event] = 0
X[dt.f[survival_event] == event_max, survival_event] = 1
stop_column_name = self.__class__._stop_column_name
ignored_columns = self.__class__._ignored_columns
if stop_column_name is None:
raise ValueError("Stop column name can't be null.")
main_message = "Survival Analysis CoxPH pre-transformer will use event '{}' and time '{}' columns.". \
format(survival_event, stop_column_name)
# in accpetance test simply return input X
if stop_column_name not in X.names:
loggerwarning(
logger,
"Survival Analysis CoxPH pre-transformer found no time column '{}'."
.format(stop_column_name))
return X_original
if not X[:, stop_column_name].stype in [
dt.bool8, dt.int8, dt.int16, dt.int32, dt.int64, dt.float32,
dt.float64
]:
raise ValueError(
"Stop column `{}' type must be numeric, but found '{}'".format(
stop_column_name, X[:, stop_column_name].stype))
# remove stop column from X
del X_original[:, stop_column_name]
self._output_feature_names = list(X_original.names)
self._feature_desc = list(X_original.names)
if self.run_count == 0 and self.context and self.context.experiment_id:
loggerinfo(logger, main_message)
task = kwargs.get('task')
if task and main_message is not None:
task.sync(key=self.context.experiment_id,
progress=dict(type='update', message=main_message))
task.flush()
# Validate CoxPH requirements on stop column
if X[stop_column_name].min()[0, 0] < 0:
X[dt.f[stop_column_name] < 0, stop_column_name] = 0
loggerwarning(
logger,
"Stop column can't be negative: replaced negative values with 0."
)
if X[stop_column_name].countna()[0, 0] > 0:
X[dt.isna(dt.f[stop_column_name]), stop_column_name] = 0
loggerwarning(
logger,
"Stop column can't contain NULLs: replaced NULL with 0.")
h2o.init(port=config.h2o_recipes_port, log_dir=self.my_log_dir)
model = H2OCoxProportionalHazardsEstimator(
stop_column=stop_column_name,
ties=self.ties,
max_iterations=self.max_iterations)
frame = h2o.H2OFrame(X.to_pandas())
model_path = None
risk_frame = None
try:
model.train(y=survival_event,
training_frame=frame,
ignored_columns=ignored_columns)
self.id = model.model_id
model_path = os.path.join(temporary_files_path,
"h2o_model." + str(uuid.uuid4()))
model_path = h2o.save_model(model=model, path=model_path)
with open(model_path, "rb") as f:
self.raw_model_bytes = f.read()
risk_frame = model.predict(frame)
X_original[:, "risk_score_coxph_{}_{}".format(
self.ties, self.max_iterations)] = risk_frame.as_data_frame(
header=False)
self._output_feature_names.append(
f"{self.display_name}{orig_feat_prefix}riskscore_coxph{extra_prefix}{self.ties}_{self.max_iterations}"
)
self._feature_desc.append(
f"CoxPH model risk score [ties={self.ties}, max.iter={self.max_iterations}"
)
return X_original
finally:
if model_path is not None:
remove(model_path)
h2o.remove(model)
h2o.remove(frame)
if risk_frame is not None:
h2o.remove(risk_frame)