def score(data_conf, model_conf, evaluation=False, **kwargs):
try:
print()
print("-----------------------------------")
print(" Model Serving ")
print("-----------------------------------")
print()
# ==============================
# 1.0 Data Loading
# ==============================
# # USING IRIS DATASET:
# iris = load_iris() #The Iris dataset is available through the scikit-learn API
# idx = list(range(len(iris.target)))
# np.random.shuffle(idx) #We shuffle it (important if we want to split in train and test sets)
# X = iris.data[idx]
# y = iris.target[idx]
# # Load data in Pandas dataFrame and then in a Pyspark dataframe
# data_pd = pd.DataFrame(data=np.column_stack((X,y)), columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'label'])
# data_df = spark.createDataFrame(data_pd)
#if not evaluation: table_in = data_conf[env]['input_to_score'] # for scoring new data
#if evaluation: table_in = data_conf[env]['input_test'] # for performance evaluation on historical data
#data_df = spark.table(table_in)
data_df = spark.read.format("delta").load(
"/mnt/delta/{0}".format('test_data_spark_rf'))
data_df.show(5)
print("Step 1.0 completed: Loaded dataset in Spark")
except Exception as e:
print("Errored on 1.0: data loading")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ===================
# 1.1 Model serving
# ===================
# Load model from MLflow model registry #https://www.mlflow.org/docs/latest/model-registry.html
if env == 'PROD':
mlflow_model_stage = 'Production'
else:
mlflow_model_stage = 'Staging'
# Detecting the model dictionary among available models in MLflow model registry.
client = MlflowClient()
for mv in client.search_model_versions(
"name='{0}'".format(mlflow_model_name)):
if dict(mv)['current_stage'] == mlflow_model_stage:
model_dict = dict(mv)
break
print('Model extracted run_id: ', model_dict['run_id'])
print('Model extracted version number: ', model_dict['version'])
print('Model extracted stage: ', model_dict['current_stage'])
def get_local_path_from_dbfs(dbfs_path):
'''
This get the local version of the dbfs path, i.e. replaces "dbfs:" by "/dbfs", for local APIs use.
'''
return "/dbfs" + dbfs_path.lstrip("dbfs:")
mlflow_path = model_dict['source']
print("mlflow_path: ", mlflow_path)
# De-serialize the model
#model = PipelineModel.load("/tmp/rf_model_test")
model = mlflow.spark.load_model(mlflow_path)
# Make predictions
predictions = model.transform(data_df)
# Select example rows to display.
predictions.select("prediction", "indexedLabel", "features").show(5)
# Saving the result of the scoring
if not evaluation: table_out = data_conf[env]['output_to_score']
if evaluation: table_out = data_conf[env]['output_test']
#predictions.write.format("ORC").saveAsTable(table_out, mode='overwrite')
print(
"Step 1.1 completed: model loading, data scoring and writing to hive"
)
print()
except Exception as e:
print("Errored on step 1.1: model serving")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
def evaluate(data_conf, model_conf, scoring=True, **kwargs):
try:
# ===========================
# E.1 Scoring of test data
# ===========================
if scoring: # switch, in case we want to skip score (if score already computed earlier)
score(
data_conf, model_conf, evaluation=True
) # the score function is applied on test dataset for performance evaluation
except Exception as e:
print("Errored on step E.1: scoring of test data")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ===========================
# E.2 Metrics & Visualization
# ===========================
# Load model from MLflow model registry #https://www.mlflow.org/docs/latest/model-registry.html
if env == 'PROD':
mlflow_model_stage = 'Production'
else:
mlflow_model_stage = 'Staging'
# Detecting the model dictionary among available models in MLflow model registry.
client = MlflowClient()
for mv in client.search_model_versions(
"name='{0}'".format(mlflow_model_name)):
if dict(mv)['current_stage'] == mlflow_model_stage:
model_dict = dict(mv)
break
print('Model extracted run_id: ', model_dict['run_id'])
print('Model extracted version number: ', model_dict['version'])
print('Model extracted stage: ', model_dict['current_stage'])
#MLflow logging of metrics for trained model
mlflow.end_run() # in case mlfow run_id defined before here
mlflow.start_run(run_id=model_dict['run_id'])
# Loading dataset
table_in = data_conf[env]['output_test']
predictions = spark.table(table_in)
# Select (prediction, true label) and compute test error
evaluator = MulticlassClassificationEvaluator(
labelCol="indexedLabel",
predictionCol="prediction",
metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Accuracy = %g" % (accuracy))
# Extracting the test set to Pandas
pred_pd = predictions.toPandas()
y_test = pred_pd['indexedLabel'].values
y_pred = pred_pd['prediction'].values
# Accuracy and Confusion Matrix
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy = ', accuracy)
print('Confusion matrix:')
Classes = ['setosa', 'versicolor', 'virginica']
C = confusion_matrix(y_test, y_pred)
C_normalized = C / C.astype(np.float).sum()
C_normalized_pd = pd.DataFrame(C_normalized,
columns=Classes,
index=Classes)
print(C_normalized_pd)
#labels = ['business', 'health']
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(C, cmap='Blues')
plt.title('Confusion matrix of the classifier')
fig.colorbar(cax)
ax.set_xticklabels([''] + Classes)
ax.set_yticklabels([''] + Classes)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()
fig.savefig('/dbfs/mnt/delta/confusion_matrix_spark_rf.png')
# Tracking performance metrics
mlflow.log_metric("Accuracy", accuracy)
mlflow.log_artifact("/dbfs/mnt/delta/confusion_matrix_spark_rf.png")
print("Step E.2 completed metrics & visualisation")
print()
except Exception as e:
print("Errored on step E.2: metrics & visualisation")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
)
# COMMAND ----------
# MAGIC %md ### Fetch the new model version ID using MLflow Model Registry Search
# MAGIC
# MAGIC The `MlflowClient.search_model_versions()` function searches for model versions by model name, MLflow run ID, or artifact source location. All model versions satisfying a particular filter query are returned.
# MAGIC
# MAGIC The following cell uses this search function to fetch the version ID of the new model, which is assumed to be the largest (e.g., most recent) version ID.
# COMMAND ----------
from mlflow.tracking.client import MlflowClient
client = MlflowClient()
model_version_infos = client.search_model_versions("name = '%s'" % model_name)
new_model_version = max([model_version_info.version for model_version_info in model_version_infos])
# COMMAND ----------
# MAGIC %md Wait for the new model version to become ready.
# COMMAND ----------
wait_until_ready(model_name, new_model_version)
# COMMAND ----------
# MAGIC %md ## Add a description to the new model version
# COMMAND ----------
input_data,
headers=headers)
print("Predicted : ", http_res.text)
# COMMAND ----------
# MAGIC %md ### Cleanup
# COMMAND ----------
# delete AML webservice
svc.delete()
# loop over registered models in MLflow
models = client.search_model_versions("name='{}'".format(model_name))
for model in models:
try:
# set model stage to Archive
client.transition_model_version_stage(name=model_name,
version=model.version,
stage='Archived')
except:
pass
# delete version of model
client.delete_model_version(model_name, model.version)
# delete model
client.delete_registered_model(model_name)
# COMMAND ----------
def train(data_conf, model_conf, **kwargs):
try:
print("-----------------------------------")
print("Starting Cashflow DL Model Training")
print("-----------------------------------")
print()
# ==============================
# 0. Main parameters definitions
# ==============================
# Size of X and y arrays definition
N_days_X, N_days_y = int(data_conf['number_of_historical_days']), int(
data_conf['number_of_predicted_days']) #365, 92
print('Number of days used for prediction (X): ', N_days_X)
print('Number of days predicted (y): ', N_days_y)
print()
# Date range definition
start_date, end_date = data_conf['start_date'], data_conf['end_date']
start_date_dt, end_date_dt, start_date_prediction, end_date_prediction, end_date_plusOneDay, end_date_minus_6month = dates_definitions(
start_date, end_date, N_days_X, N_days_y)
print('Date range: ', start_date, end_date)
print()
model_name = model_conf['model_name']
except Exception as e:
print("Errored on initialization")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ========================================
# T.1 Pre-processing before model training
# ========================================
# Loading dataset
table_in = data_conf[environment]['table_to_train_on']
#ts_balance = spark.read.parquet("/mnt/test/{0}.parquet".format(table_in)).cache()
ts_balance = spark.read.format("delta").load(
"/mnt/delta/{0}".format(table_in))
# Cleaning of the time series
ts_balance = ts_balance.withColumn(
'balance', ts_balance.balance.cast("array<float>"))
ts_balance = ts_balance.withColumn(
'keep_ts',
F.udf(lambda x, y: time_series_cleaning(x, y), "int")('balance',
F.lit(20))
) #at least 10 transactions in the ts, to be used in the training
ts_balance = ts_balance.where('keep_ts == 1')
# Creating the dataset on which we train (and test and validate) the model
ts_balance_model = ts_balance.sample(
False, 0.7,
seed=0) #now 0.7, but in real case would be 0.1 at best... or 0.05
print('ts_balance_model.count()', ts_balance_model.count())
# Pre-processing before model training
ts_balance_model = pre_processing(ts_balance_model,
end_date,
spark,
serving=False)
ts_balance_model.show(3)
print('ts_balance_model.rdd.getNumPartitions()',
ts_balance_model.rdd.getNumPartitions())
ts_balance_model.show(3)
# Saving prepared dataset
table_out = 'cashflow_training_step1'
#ts_balance_model.write.format("parquet").mode("overwrite").save("/mnt/test/{0}.parquet".format(table_out))
ts_balance_model.write.format("delta").mode("overwrite").save(
"/mnt/delta/{0}".format(table_out))
except Exception as e:
print("Errored on step T.1: pre-processing before model training")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ========================================
# T.2 Generating TRAIN, VAL, TEST datasets
# ========================================
# Loading datasets
table_model = 'cashflow_training_step1'
#ts_balance_model = spark.read.parquet("/mnt/test/{0}.parquet".format(table_model)).cache()
ts_balance_model = spark.read.format("delta").load(
"/mnt/delta/{0}".format(table_model)).cache()
ts_balance_model.show(3)
print('ts_balance_model.count()', ts_balance_model.count())
print('ts_balance_model.rdd.getNumPartitions()',
ts_balance_model.rdd.getNumPartitions())
train_set, val_set, test_set = ts_balance_model.randomSplit(
[0.6, 0.2, 0.2], seed=12345)
train_set.show(3)
print(
'train_set.rdd.getNumPartitions(), val_set.rdd.getNumPartitions(), test_set.rdd.getNumPartitions()',
train_set.rdd.getNumPartitions(), val_set.rdd.getNumPartitions(),
test_set.rdd.getNumPartitions())
# Saving prepared datasets (train, val, test sets to parquet)
table_train = 'cashflow_train'
table_val = 'cashflow_val'
table_test = data_conf[environment][
'table_test_for_performance'] #'cashflow_test'
train_set.select('X',
'y').write.format("delta").mode("overwrite").save(
"/mnt/delta/{0}".format(table_train))
val_set.select('X', 'y').write.format("delta").mode("overwrite").save(
"/mnt/delta/{0}".format(table_val))
test_set.select('primaryaccountholder','transactiondate','balance')\
.write.format("delta").mode("overwrite").save("/mnt/delta/{0}".format(table_test))
except Exception as e:
print("Errored on step T.2: pre-processings")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ==============================
# T.3 MODEL DEFINITION AND TRAIN
# ==============================
table_train = 'cashflow_train'
table_val = 'cashflow_val'
#table_train = spark.read.parquet("/mnt/test/{0}.parquet".format(table_train))
table_train = spark.read.format("delta").load(
"/mnt/delta/{0}".format(table_train))
#table_val = spark.read.parquet("/mnt/test/{0}.parquet".format(table_val))
table_val = spark.read.format("delta").load(
"/mnt/delta/{0}".format(table_val))
table_train_count = table_train.count()
table_val_count = table_val.count()
#table_train_count, table_val_count
from pyspark.sql.functions import col
from petastorm.spark import SparkDatasetConverter, make_spark_converter
# Set a cache directory on DBFS FUSE for intermediate data.
spark.conf.set(SparkDatasetConverter.PARENT_CACHE_DIR_URL_CONF,
"file:///dbfs/tmp/petastorm/cache")
converter_train = make_spark_converter(table_train)
converter_val = make_spark_converter(table_val)
print(f"train: {len(converter_train)}, val: {len(converter_val)}")
def get_compiled_model(N_days_X, N_days_y, model_conf): #lr=0.001
#model = get_model(lr=lr)
model = define_1dcnn_model(N_days_X, N_days_y, model_conf)
hyperparameters = model_conf['hyperParameters']
opt = tf.keras.optimizers.Adam()
# Model compilation
model.compile(optimizer=opt, loss=hyperparameters['loss'])
return model
# Enable auto-logging to MLflow to capture TensorBoard metrics.
mlflow.tensorflow.autolog(every_n_iter=1)
model_name = model_conf['model_name']
mlflow_model_name = model_name
model_dir = "/tmp/" + model_name
try:
dbutils.fs.rm(model_dir, recurse=True)
except OSError:
pass
with mlflow.start_run():
NUM_EPOCHS = model_conf['hyperParameters']['epochs'] #5
BATCH_SIZE = model_conf['hyperParameters']['batch_size'] #500
def train_and_evaluate(N_days_X, N_days_y, model_conf): #lr=0.001
model = get_compiled_model(N_days_X, N_days_y, model_conf) #lr
with converter_train.make_tf_dataset(batch_size=BATCH_SIZE) as train_dataset, \
converter_val.make_tf_dataset(batch_size=BATCH_SIZE) as val_dataset:
#train_dataset = train_dataset.map(lambda x: (x.features, x.label_index))
train_dataset = train_dataset.map(
lambda x: (tf.reshape(x.X, [-1, N_days_X, 1]),
tf.reshape(x.y, [-1, N_days_y])))
steps_per_epoch = len(converter_train) // BATCH_SIZE
#val_dataset = val_dataset.map(lambda x: (x.features, x.label_index))
val_dataset = val_dataset.map(
lambda x: (tf.reshape(x.X, [-1, N_days_X, 1]),
tf.reshape(x.y, [-1, N_days_y])))
validation_steps = max(1, len(converter_val) // BATCH_SIZE)
print(
f"steps_per_epoch: {steps_per_epoch}, validation_steps: {validation_steps}"
)
hist = model.fit(train_dataset,
steps_per_epoch=steps_per_epoch,
epochs=NUM_EPOCHS,
validation_data=val_dataset,
validation_steps=validation_steps,
verbose=2)
return model, hist
model, hist = train_and_evaluate(N_days_X, N_days_y, model_conf)
print(hist.history['val_loss'][-1])
#MLflow logging
#mlflow.log_artifact(cwd + "data.json")
#mlflow.log_artifact(cwd + "config.json")
mlflow.log_param("model_name", str(model_name))
mlflow.log_param("N_days_X", N_days_X)
mlflow.log_param("N_days_y", N_days_y)
mlflow.log_param("start_date", start_date)
mlflow.log_param("end_date", end_date)
mlflow.log_param("num_epochs", str(NUM_EPOCHS))
mlflow.log_param("batch_size", str(BATCH_SIZE))
#mlflow.log_param("steps_per_epoch", str(steps_per_epoch)) #validation_steps
# saving using tf.keras.models.save_model
tf.keras.models.save_model(model, filepath=model_dir +
'/model') #SavedModel format
#model.save(filepath=model_dir+'model', save_format="h5") #H5 format (todo, and look how to register that)
# saving using mlflow.tensorflow.save_model (this does NOT log nor register the model) does not overwrites...
#mlflow.tensorflow.save_model(tf_saved_model_dir=model_dir+'/model',
# tf_meta_graph_tags=[tf.compat.v1.saved_model.tag_constants.SERVING],
# tf_signature_def_key='serving_default',
# path = 'model')
# logging already saved model
mlflow.tensorflow.log_model(
tf_saved_model_dir=model_dir + '/model',
tf_meta_graph_tags=[
tf.compat.v1.saved_model.tag_constants.SERVING
],
tf_signature_def_key='serving_default',
registered_model_name=model_name,
artifact_path='model')
# Getting the version number of the newly registered MLflow model (useful for next steps)
mlflow_model_version = 0
client_current_model = MlflowClient()
for mv in client_current_model.search_model_versions(
"name='{0}'".format(mlflow_model_name)):
#if int(dict(mv)['version']) == mlflow_model_version:
if int(
dict(mv)['version']
) >= mlflow_model_version: # finding the last version registered
mlflow_model_version = int(dict(mv)['version'])
model_dict = dict(mv)
#update 2020-07017: to grab the latest model version, we can also do like this: (TO BE TESTED!!!)
#model_version_infos = client_current_model.search_model_versions(f"name = '{model_name}'")
#mlflow_model_version = max([model_version_info.version for model_version_info in model_version_infos])
# Wait until the model is ready
def wait_until_model_ready(model_name, model_version):
client = MlflowClient()
for _ in range(20):
model_version_details = client.get_model_version(
name=model_name,
version=model_version,
)
status = ModelVersionStatus.from_string(
model_version_details.status)
print("Model status: %s" %
ModelVersionStatus.to_string(status))
if status == ModelVersionStatus.READY:
break
tm.sleep(5)
wait_until_model_ready(mlflow_model_name, mlflow_model_version)
# Transition the registered model stage from "None" to "Staging"
client_current_model.transition_model_version_stage(
name=mlflow_model_name,
version=mlflow_model_version,
stage="Staging",
)
# Copy the file from the driver node and save it to DBFS (so that they can be accessed e.g. after the current cluster terminates.):
dbutils.fs.cp("file:/tmp/{0}/model".format(model_name),
"dbfs:/mnt/test/{0}/model".format(model_name),
recurse=True)
print('Model copied here: ',
"dbfs:/mnt/test/{0}/model/".format(model_name))
#mlflow.end_run()
except Exception as e:
print("Errored on step T.3: model definition and train")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
def evaluate(data_conf, model_conf, scoring=True, **kwargs):
try:
print("-------------------------------------")
print("Starting Cashflow DL Model Evaluation")
print("-------------------------------------")
print()
# ==============================
# 0. Main parameters definitions
# ==============================
# Size of X and y arrays definition
N_days_X, N_days_y = int(data_conf['number_of_historical_days']), int(
data_conf['number_of_predicted_days']) #365, 92
print('Number of days used for prediction (X): ', N_days_X)
print('Number of days predicted (y): ', N_days_y)
print()
# Date range definition
start_date, end_date = data_conf['start_date'], data_conf['end_date']
start_date_dt, end_date_dt, start_date_prediction, end_date_prediction, end_date_plusOneDay, end_date_minus_6month = dates_definitions(
start_date, end_date, N_days_X, N_days_y)
print('Date range: ', start_date, end_date)
print()
model_name = model_conf['model_name']
except Exception as e:
print("Errored on initialization")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ===========================
# E.1 Scoring of test data
# ===========================
#if kwargs['do_we_score'] is True: # switch, in case we want to skip score (if score already computed earlier)
if scoring: # switch, in case we want to skip score (if score already computed earlier)
score(
data_conf, model_conf, evaluation=True
) # the score function is applied on test dataset for performance evaluation
except Exception as e:
print("Errored on step E.1: scoring of test data")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ===========================
# E.2 Metrics & Visualization
# ===========================
# Load model from MLflow model registry #https://www.mlflow.org/docs/latest/model-registry.html
#mlflow_model_name = 'cashflow-poc'
mlflow_model_name = model_conf['model_name']
if environment == 'prod':
mlflow_model_stage = 'Production'
else:
mlflow_model_stage = 'Staging'
# Detecting the model dictionary among available models in MLflow model registry.
client = MlflowClient()
for mv in client.search_model_versions(
"name='{0}'".format(mlflow_model_name)):
if dict(mv)['current_stage'] == mlflow_model_stage:
model_dict = dict(mv)
break
print('Model extracted run_id: ', model_dict['run_id'])
print('Model extracted version number: ', model_dict['version'])
print('Model extracted stage: ', model_dict['current_stage'])
#MLflow logging of metrics for trained model
mlflow.end_run() # in case mlfow run_id defined before here
mlflow.start_run(run_id=model_dict['run_id'])
#mlflow.start_run() # specify the runid!!!
# Loading dataset
table_in = data_conf[environment]['table_scored']
#ts_balance = spark.read.parquet("/mnt/test/{0}.parquet".format(table_in)).cache()
ts_balance = spark.read.format("delta").load(
"/mnt/delta/{0}".format(table_in))
# Extracting the test set to Pandas
ts_balance_pd = ts_balance.select(
'balance', 'X', 'y', 'y_pred',
'y_pred_rescaled_retrended').toPandas()
# Extraction of metrics
R2_all_3month, R2_array_3month, R2_all_1month, R2_array_1month = metric_extraction(
ts_balance_pd, N_days_y)
# Visualization of prediction
#fig1, fig2 = visualization_prediction(ts_balance_pd, start_date, end_date, N_days_X, N_days_y, R2_array_1month, R2_array_3month, serving=False)
fig1, fig2 = visualization_time_series_pred_only(ts_balance_pd,
start_date,
end_date,
N_days_X,
N_days_y,
R2_array_1month,
R2_array_3month,
serving=False)
fig1.savefig('/dbfs/mnt/delta/performance.png')
fig2.savefig('/dbfs/mnt/delta/performance_R2.png')
mlflow.log_artifact('/dbfs/mnt/delta/performance.png')
mlflow.log_artifact('/dbfs/mnt/delta/performance_R2.png')
# Saving the metric
print('Test R2 metric (3-months window): {}'.format(R2_all_3month))
print('Test R2 metric (1-months window): {}'.format(R2_all_1month))
mlflow.log_metric("R2_all_3month", R2_all_3month)
mlflow.log_metric("R2_all_1month", R2_all_1month)
with open("/dbfs/mnt/delta/evaluation.json", "w+") as f:
json.dump({
'R2_3month': R2_all_3month,
'R2_1month': R2_all_1month
}, f)
mlflow.log_artifact("/dbfs/mnt/delta/evaluation.json")
mlflow.end_run()
ts_balance.unpersist()
print("Step E.2 completed visualisation")
except Exception as e:
print("Errored on step E.2: visualisation")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
def score(data_conf, model_conf, evaluation=False, **kwargs):
try:
print("----------------------------------")
print("Starting Cashflow DL Model Scoring")
print("----------------------------------")
print("")
# ==============================
# 0. Main parameters definitions
# ==============================
# Size of X and y arrays definition
N_days_X, N_days_y = int(data_conf['number_of_historical_days']), int(
data_conf['number_of_predicted_days']) #365, 92
print('Number of days used for prediction (X): {0}'.format(N_days_X))
print('Number of days predicted (y): {0}'.format(N_days_y))
print('')
# Date range definition
start_date, end_date = data_conf['start_date'], data_conf['end_date']
start_date_dt, end_date_dt, start_date_prediction, end_date_prediction, end_date_plusOneDay, end_date_minus_6month = dates_definitions(
start_date, end_date, N_days_X, N_days_y)
print('Date range: [{0}, {1}]'.format(start_date, end_date))
print('')
model_name = model_conf['model_name']
#print("Step 0 completed (main parameters definition)")
except Exception as e:
print("Errored on initialization")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ==================================
# S.1 Pre-processings before serving
# ==================================
start_time_S1 = time.time()
# Loading dataset
table_in = data_conf[environment]['table_to_score']
#ts_balance = spark.read.parquet("/mnt/test/{0}.parquet".format(table_in)).cache()
ts_balance = spark.read.format("delta").load(
"/mnt/delta/{0}".format(table_in))
print('Reading table {0}'.format(table_in))
#print('Size of table: ',ts_balance.count())
#print('ts_balance.rdd.getNumPartitions()',ts_balance.rdd.getNumPartitions())
if not evaluation:
ts_balance = pre_processing(ts_balance,
end_date,
spark,
serving=True)
if evaluation:
ts_balance = pre_processing(ts_balance,
end_date,
spark,
serving=False)
ts_balance.show(3)
# Saving prepared dataset
table_out = data_conf[environment]['cashflow_s1_out_scoring']
#ts_balance.write.format("parquet").mode("overwrite").save("/mnt/test/{0}.parquet".format(table_out))
ts_balance.write.format("delta").mode("overwrite").save(
"/mnt/delta/{0}".format(table_out))
ts_balance.unpersist()
spark.catalog.clearCache()
end_time_S1 = time.time()
print("Step S.1 completed: pre-processings before serving")
print("Time spent: ", end_time_S1 - start_time_S1)
except Exception as e:
print("Errored on step S.1: pre-processings before serving")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ===================
# S.2 Model serving
# ===================
start_time_S2 = time.time()
# Loading dataset
table_in = data_conf[environment]['cashflow_s1_out_scoring']
#ts_balance = spark.read.parquet("/mnt/test/{0}.parquet".format(table_in))
ts_balance = spark.read.format("delta").load(
"/mnt/delta/{0}".format(table_in))
ts_balance.cache()
print('Number of partitions: ', ts_balance.rdd.getNumPartitions())
# Load model from MLflow model registry #https://www.mlflow.org/docs/latest/model-registry.html
mlflow_model_name = model_conf['model_name']
if environment == 'prod':
mlflow_model_stage = 'Production'
else:
mlflow_model_stage = 'Staging'
# Detecting the model dictionary among available models in MLflow model registry.
client = MlflowClient()
for mv in client.search_model_versions(
"name='{0}'".format(mlflow_model_name)):
if dict(mv)['current_stage'] == mlflow_model_stage:
model_dict = dict(mv)
break
print('Model extracted run_id: ', model_dict['run_id'])
print('Model extracted version number: ', model_dict['version'])
print('Model extracted stage: ', model_dict['current_stage'])
def get_local_path_from_dbfs(dbfs_path):
'''
This get the local version of the dbfs path, i.e. replaces "dbfs:" by "/dbfs", for local APIs use.
'''
#os.path.join("/dbfs", dbfs_path.lstrip("dbfs:")) #why does not work???
return "/dbfs" + dbfs_path.lstrip("dbfs:")
mlflow_path = get_local_path_from_dbfs(
model_dict['source']) + '/tfmodel'
print("mlflow_path: ", mlflow_path)
# It detects the name id of the pb model file
file = [
f
for f in os.listdir('/dbfs/mnt/test/{0}/model/'.format(model_name))
]
print(file)
export_dir_saved = "/dbfs/mnt/test/{0}/model/".format(
model_name) #+file[0] # TODO!!! GET THE MODEL FROM MLFLOW !!!!
print(export_dir_saved)
#def rdd_scoring(numpy_array):
# predictor_fn = tf.contrib.predictor.from_saved_model(export_dir = export_dir_saved)
# return predictor_fn({'input': numpy_array.reshape(-1, N_days_X, 1) })
#@F.udf("array<float>")
#def udf_scoring(x):
# predictor_fn = tf.contrib.predictor.from_saved_model(export_dir = mlflow_path) #export_dir_saved)
# return np.around(predictor_fn({'input': np.array(x).reshape(-1, N_days_X, 1) })['output'][0].tolist(), decimals=3).tolist()
@F.pandas_udf("array<float>")
def pandas_udf_scoring(x):
#predictor_fn = tf.contrib.predictor.from_saved_model(export_dir = export_dir_saved) #mlflow_path)
#return Series([np.around(predictor_fn({'input': np.array(v).reshape(-1, N_days_X, 1)})['output'][0], decimals=3) for v in x])
new_model = tf.keras.models.load_model(export_dir_saved)
#new_model = mlflow.tensorflow.load_model(mlflow_path)
return Series([
np.around(new_model.predict(
np.array(v).reshape(-1, N_days_X, 1)).reshape(N_days_y),
decimals=3) for v in x
])
ts_balance = ts_balance.withColumn('y_pred', pandas_udf_scoring('X'))
#ts_balance = ts_balance.withColumn('y_pred', udf_scoring('X'))
print('ts_balance.rdd.getNumPartitions()',
ts_balance.rdd.getNumPartitions())
ts_balance.show(3)
#print('Size of table: ',ts_balance.count())
# Saving prepared dataset
table_out = data_conf[environment]['cashflow_s2_out_scoring']
#ts_balance.write.format("parquet").mode("overwrite").save("/mnt/test/{0}.parquet".format(table_out))
ts_balance.write.format("delta").mode("overwrite").save(
"/mnt/delta/{0}".format(table_out))
ts_balance.unpersist()
spark.catalog.clearCache()
end_time_S2 = time.time()
print("Step S.2 completed: model serving")
print("Time spent: ", end_time_S2 - start_time_S2)
except Exception as e:
print("Errored on step S.2: model serving")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ===================
# S.3 Post-processing
# ===================
start_time_S3 = time.time()
# Loading dataset
table_in = data_conf[environment]['cashflow_s2_out_scoring']
#ts_balance = spark.read.parquet("/mnt/test/{0}.parquet".format(table_in)).cache()
ts_balance = spark.read.format("delta").load(
"/mnt/delta/{0}".format(table_in))
ts_balance = post_processing(ts_balance)
ts_balance.show(3)
# Saving prepared dataset
table_out = data_conf[environment]['table_scored']
#ts_balance.write.format("parquet").mode("overwrite").save("/mnt/test/{0}.parquet".format(table_out))
ts_balance.write.format("delta").mode("overwrite").save(
"/mnt/delta/{0}".format(table_out))
ts_balance.unpersist()
end_time_S3 = time.time()
print("Step S.3 completed: post-processing")
print("Time spent: ", end_time_S3 - start_time_S3)
except Exception as e:
print("Errored on step S.3: post-processing")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
# If run in production, in MLflow model registry, the last model currently in "Staging" is transitioned to "Production"
# ---------------------------------------------------------------------------------------------------------------------
from mlflow.tracking.client import MlflowClient
# Define the environment (dev, test or prod)
environment = dbutils.widgets.getArgument("environment")
if environment == 'prod':
# Detect the last model currently in "Staging" in MLflow model registry.
mlflow_model_name = 'super_test'
mlflow_model_stage = 'Staging'
client = MlflowClient()
for mv in client.search_model_versions(
"name='{0}'".format(mlflow_model_name)):
if dict(mv)['current_stage'] == mlflow_model_stage:
model_dict = dict(mv)
print('Model extracted run_id: ', model_dict['run_id'])
print('Model extracted version number: ', model_dict['version'])
print('Model extracted stage: ', model_dict['current_stage'])
# Transition the registered model stage from "None" to "Staging"
client.transition_model_version_stage(
name=mlflow_model_name,
version=model_dict['version'],
stage="Production",
)
print()
def score(data_conf, model_conf, evaluation=False, **kwargs):
try:
print()
print("-----------------------------------")
print(" Model Serving ")
print("-----------------------------------")
print()
# ==============================
# 1.0 Data Loading
# ==============================
#if not evaluation: table_in = data_conf[env]['input_to_score'] # for scoring new data
#if evaluation: table_in = data_conf[env]['input_test'] # for performance evaluation on historical data
#data_df = spark.table(table_in)
data_df = spark.read.format("delta").load("/mnt/delta/{0}".format('test_data_sklearn_rf'))
data_pd = data_df.toPandas()
# Feature selection
feature_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
target = 'label'
x_test = data_pd[feature_cols].values
y_test = data_pd[target].values
# Creation of train and test datasets
#x_train, x_test, y_train, y_test = train_test_split(X,y,train_size=0.7, stratify=y) #stratify=y ensures that the same proportion of labels are in both train and test sets!
print("Step 1.0 completed: Loaded dataset in Spark")
except Exception as e:
print("Errored on 1.0: data loading")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
try:
# ===================
# 1.1 Model serving
# ===================
# Load model from MLflow model registry #https://www.mlflow.org/docs/latest/model-registry.html
if env == 'PROD' :
mlflow_model_stage = 'Production'
else:
mlflow_model_stage = 'Staging'
print(mlflow_model_stage)
# Detecting the model dictionary among available models in MLflow model registry.
client = MlflowClient()
for mv in client.search_model_versions("name='{0}'".format(mlflow_model_name)):
if dict(mv)['current_stage'] == mlflow_model_stage:
model_dict = dict(mv)
break
print('Model extracted run_id: ', model_dict['run_id'])
print('Model extracted version number: ', model_dict['version'])
print('Model extracted stage: ', model_dict['current_stage'])
# def get_local_path_from_dbfs(dbfs_path):
# '''
# This get the local version of the dbfs path, i.e. replaces "dbfs:" by "/dbfs", for local APIs use.
# '''
# return "/dbfs"+dbfs_path.lstrip("dbfs:")
mlflow_path = model_dict['source']
print("mlflow_path: ", mlflow_path)
# De-serialize the model
# model = mlflow.sklearn.load_model(mlflow_path) # works but using the mlflow.sklearn API (not general)
model = mlflow.pyfunc.load_model(mlflow_path) # Load model as a PyFuncModel.
# Make predictions
#y_pred = model.predict(x_test)
y_pred = model.predict(pd.DataFrame(x_test)) # when using the PyFuncModel, the input expected is a Pandas df
# Saving the result of the scoring
if not evaluation: table_out = data_conf[env]['output_to_score']
if evaluation: table_out = data_conf[env]['output_test']
#predictions.write.format("ORC").saveAsTable(table_out, mode='overwrite')
pred_pd = pd.DataFrame(data=np.column_stack((y_test,y_pred)), columns=['y_test', 'y_pred'])
pred_df = spark.createDataFrame(pred_pd)
pred_df.write.format("delta").mode("overwrite").save("/mnt/delta/{0}".format('prediction_sklearn_rf'))
# Select example rows to display.
pred_df.show(5)
print("Step 1.1 completed: model loading, data scoring and writing to hive")
print()
except Exception as e:
print("Errored on step 1.1: model serving")
print("Exception Trace: {0}".format(e))
print(traceback.format_exc())
raise e
