def _run(self, runobj: RunObject, execution: MLClientCtx):
if runobj.metadata.iteration:
self.store_run(runobj)
meta = self._get_meta(runobj, True)
job = self._generate_mpi_job(runobj, execution, meta)
resp = self._submit_mpijob(job, meta.namespace)
state = None
timeout = int(config.submit_timeout) or 120
for _ in range(timeout):
resp = self.get_job(meta.name, meta.namespace)
state = self._get_job_launcher_status(resp)
if resp and state:
break
time.sleep(1)
if resp:
logger.info("MpiJob {} state={}".format(meta.name, state or "unknown"))
if state:
state = state.lower()
launcher, _ = self._get_launcher(meta.name, meta.namespace)
execution.set_hostname(launcher)
execution.set_state("running" if state == "active" else state)
if self.kfp:
writer = AsyncLogWriter(self._db_conn, runobj)
status = self._get_k8s().watch(
launcher, meta.namespace, writer=writer
)
logger.info(
"MpiJob {} finished with state {}".format(meta.name, status)
)
if status == "succeeded":
execution.set_state("completed")
else:
execution.set_state(
"error",
"MpiJob {} finished with state {}".format(
meta.name, status
),
)
else:
txt = "MpiJob {} launcher pod {} state {}".format(
meta.name, launcher, state
)
logger.info(txt)
runobj.status.status_text = txt
else:
txt = "MpiJob status unknown or failed, check pods: {}".format(
self.get_pods(meta.name, meta.namespace)
)
logger.warning(txt)
runobj.status.status_text = txt
if self.kfp:
execution.set_state("error", txt)
return None
def table_summary(context: MLClientCtx,
dask_client: Union[DataItem, str],
dask_key: str = 'my_dask_dataframe',
target_path: str = '',
name: str = 'table_summary.csv',
key: str = 'table_summary') -> None:
"""Summarize a table
:param context: the function context
:param dask_client: path to the dask client scheduler json file, as
string or artifact
:param dask_key: key of dataframe in dask client 'datasets' attribute
:param target_path: destimation folder for table summary file
:param name: name of table summary file (with extension like .csv)
:param key: key of table summary in artifact store
"""
print(context.__dict__)
dask_client = Client(scheduler_file=str(dask_client))
df = dask_client.get_dataset('dask_key')
print(df.head())
dscr = df.describe()
filepath = os.path.join(target_path, name)
dd.to_csv(dscr, filepath, single_file=True, index=False)
context.log_artifact(key, target_path=filepath)
def create_classification_data(context: MLClientCtx,
n_samples: int,
m_features: int,
k_classes: int,
header: Optional[List[str]],
label_column: Optional[str] = 'labels',
weight: float = 0.5,
random_state: int = 1,
filename: Optional[str] = None,
key: str = 'classifier-data',
file_ext: str = 'pqt',
sk_params={}):
"""Create a binary classification sample dataset and save.
If no filename is given it will default to:
'simdata-{n_samples}X{m_features}.parquet'.
Additional scikit-learn parameters can be set using **sk_params, please see https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html for more details.
:param context: function context
:param n_samples: number of rows/samples
:param m_features: number of cols/features
:param k_classes: number of classes
:param header: header for features array
:param label_column: column name of ground-truth series
:param weight: fraction of sample negative value (ground-truth=0)
:param random_state: rng seed (see https://scikit-learn.org/stable/glossary.html#term-random-state)
:param filename: optional name for saving simulated data file
:param key: key of data in artifact store
:param file_ext: (pqt) extension for parquet file
:param sk_params: additional `sklearn.datasets.make_classification`
outputs filename of created data (includes path) in the artifact store.
"""
if not filename:
name = f"simdata-{n_samples:0.0e}X{m_features}.{file_ext}".replace(
"+", "")
filename = os.path.join(context.artifact_path, name)
else:
filename = os.path.join(context.artifact_path, filename)
features, labels = make_classification(n_samples=n_samples,
n_features=m_features,
weights=weight,
n_classes=k_classes,
random_state=random_state,
**sk_params)
# make dataframes, add column names, concatenate (X, y)
X = pd.DataFrame(features)
if not header:
X.columns = ["feat_" + str(x) for x in range(m_features)]
else:
X.columns = header
y = pd.DataFrame(labels, columns=[label_column])
data = pd.concat([X, y], axis=1)
pq.write_table(pa.Table.from_pandas(data), filename)
context.log_artifact(key, local_path=name)
def get_toy_data(context: MLClientCtx,
dataset: str,
params: dict = {}) -> None:
"""Loads a scikit-learn toy dataset for classification or regression
The following datasets are available ('name' : desription):
'boston' : boston house-prices dataset (regression)
'iris' : iris dataset (classification)
'diabetes' : diabetes dataset (regression)
'digits' : digits dataset (classification)
'linnerud' : linnerud dataset (multivariate regression)
'wine' : wine dataset (classification)
'cancer' : breast cancer wisconsin dataset (classification)
The scikit-learn functions return a data bunch including the following items:
- data the features matrix
- target the ground truth labels
- DESCR a description of the dataset
- feature_names header for data
The features (and their names) are stored with the target labels in a DataFrame.
For further details see https://scikit-learn.org/stable/datasets/index.html#toy-datasets
:param context: function execution context
:param dataset: name of the dataset to load
:param params: params of the sklearn load_data method
"""
filepath = os.path.join(context.artifact_path, dataset) + '.pqt'
# check to see if we haven't already downloaded the file
if not os.path.isfile(filepath):
artifact_path = context.artifact_path
# reach into module and import the appropriate load_xxx function
pkg_module = 'sklearn.datasets'
fname = f'load_{dataset}'
pkg_module = __import__(pkg_module, fromlist=[fname])
load_data_fn = getattr(pkg_module, fname)
data = load_data_fn(**params)
feature_names = data['feature_names']
# save
xy = np.concatenate([data['data'], data['target'].reshape(-1, 1)],
axis=1)
feature_names.append('labels')
df = pd.DataFrame(data=xy, columns=feature_names)
df.to_parquet(filepath, engine='pyarrow', index=False)
# either we just downloaded file, or it exists, log it:
context.log_artifact(dataset, local_path=filepath.split('/')[-1])
def load_dataset(
context: MLClientCtx,
dataset: str,
name: str = "",
file_ext: str = "parquet",
params: dict = {},
) -> None:
"""Loads a scikit-learn toy dataset for classification or regression
The following datasets are available ('name' : desription):
'boston' : boston house-prices dataset (regression)
'iris' : iris dataset (classification)
'diabetes' : diabetes dataset (regression)
'digits' : digits dataset (classification)
'linnerud' : linnerud dataset (multivariate regression)
'wine' : wine dataset (classification)
'breast_cancer' : breast cancer wisconsin dataset (classification)
The scikit-learn functions return a data bunch including the following items:
- data the features matrix
- target the ground truth labels
- DESCR a description of the dataset
- feature_names header for data
The features (and their names) are stored with the target labels in a DataFrame.
For further details see https://scikit-learn.org/stable/datasets/index.html#toy-datasets
:param context: function execution context
:param dataset: name of the dataset to load
:param name: artifact name (defaults to dataset)
:param file_ext: output file_ext: parquet or csv
:param params: params of the sklearn load_data method
"""
dataset = str(dataset)
pkg_module = "sklearn.datasets"
fname = f"load_{dataset}"
pkg_module = __import__(pkg_module, fromlist=[fname])
load_data_fn = getattr(pkg_module, fname)
data = load_data_fn(**params)
feature_names = data["feature_names"]
xy = np.concatenate([data["data"], data["target"].reshape(-1, 1)], axis=1)
if hasattr(feature_names, "append"):
feature_names.append("labels")
else:
feature_names = np.append(feature_names, "labels")
df = pd.DataFrame(data=xy, columns=feature_names)
context.log_dataset(name or dataset, df=df, format=file_ext, index=False)
def parquet_to_dask(context: MLClientCtx,
parquet_url: Union[DataItem, str, Path, IO[AnyStr]],
inc_cols: Optional[List[str]] = None,
index_cols: Optional[List[str]] = None,
shards: int = 4,
threads_per: int = 4,
processes: bool = False,
memory_limit: str = '2GB',
persist: bool = True,
dask_key: str = 'my_dask_dataframe',
target_path: str = '') -> None:
"""Load parquet dataset into dask cluster
If no cluster is found loads a new one and persist the data to it. It
shouold not be necessary to create a new cluster when the function
is run as a 'dask' job.
:param context: the function context
:param parquet_url: url of the parquet file or partitioned dataset as either
artifact DataItem, string, or path object (see pandas read_csv)
:param inc_cols: include only these columns (very fast)
:param index_cols: list of index column names (can be a long-running process)
:param shards: number of workers to launch
:param threads_per: number of threads per worker
:param processes:
"""
if hasattr(context, 'dask_client'):
context.logger.info('found cluster...')
dask_client = context.dask_client
else:
context.logger.info('starting new cluster...')
cluster = LocalCluster(n_workers=shards,
threads_per_worker=threads_per,
processes=processes,
memory_limit=memory_limit)
dask_client = Client(cluster)
context.logger.info(dask_client)
df = dd.read_parquet(parquet_url)
if persist and context:
df = dask_client.persist(df)
dask_client.publish_dataset(dask_key=df)
context.dask_client = dask_client
# share the scheduler
filepath = os.path.join(target_path, 'scheduler.json')
dask_client.write_scheduler_file(filepath)
context.log_artifact('scheduler', target_path=filepath)
print(df.head())
def _generate_mpi_job(self, runobj: RunObject, execution: MLClientCtx, meta: client.V1ObjectMeta) -> dict:
pod_labels = deepcopy(meta.labels)
pod_labels['mlrun/job'] = meta.name
# Populate mpijob object
# start by populating pod templates
launcher_pod_template = deepcopy(self._mpijob_pod_template)
worker_pod_template = deepcopy(self._mpijob_pod_template)
# configuration for both launcher and workers
for pod_template in [launcher_pod_template, worker_pod_template]:
if self.spec.image:
self._update_container(pod_template, 'image', self.full_image_path())
self._update_container(pod_template, 'volumeMounts', self.spec.volume_mounts)
extra_env = {'MLRUN_EXEC_CONFIG': runobj.to_json()}
# if self.spec.rundb:
# extra_env['MLRUN_DBPATH'] = self.spec.rundb
extra_env = [{'name': k, 'value': v} for k, v in extra_env.items()]
self._update_container(pod_template, 'env', extra_env + self.spec.env)
if self.spec.image_pull_policy:
self._update_container(
pod_template, 'imagePullPolicy', self.spec.image_pull_policy)
if self.spec.workdir:
self._update_container(pod_template, 'workingDir', self.spec.workdir)
if self.spec.image_pull_secret:
update_in(pod_template, 'spec.imagePullSecrets',
[{'name': self.spec.image_pull_secret}])
update_in(pod_template, 'metadata.labels', pod_labels)
update_in(pod_template, 'spec.volumes', self.spec.volumes)
# configuration for workers only
# update resources only for workers because the launcher doesn't require
# special resources (like GPUs, Memory, etc..)
self._enrich_worker_configurations(worker_pod_template)
# configuration for launcher only
self._enrich_launcher_configurations(launcher_pod_template)
# generate mpi job using both pod templates
job = self._generate_mpi_job_template(launcher_pod_template, worker_pod_template)
# update the replicas only for workers
update_in(job, 'spec.mpiReplicaSpecs.Worker.replicas', self.spec.replicas or 1)
if execution.get_param('slots_per_worker'):
update_in(job, 'spec.slotsPerWorker', execution.get_param('slots_per_worker'))
update_in(job, 'metadata', meta.to_dict())
return job
def training(context: MLClientCtx, p1: int = 1, p2: int = 2) -> None:
"""Train a model.
:param context: The runtime context object.
:param p1: A model parameter.
:param p2: Another model parameter.
"""
# access input metadata, values, and inputs
print(f'Run: {context.name} (uid={context.uid})')
print(f'Params: p1={p1}, p2={p2}')
context.logger.info('started training')
# <insert training code here>
# log the run results (scalar values)
context.log_result('accuracy', p1 * 2)
context.log_result('loss', p1 * 3)
# add a lable/tag to this run
context.set_label('category', 'tests')
# log a simple artifact + label the artifact
# If you want to upload a local file to the artifact repo add src_path=<local-path>
context.log_artifact('model',
body=b'abc is 123',
local_path='model.txt',
labels={'framework': 'tfkeras'})
def validation(context: MLClientCtx, model: DataItem) -> None:
"""Model validation.
Dummy validation function.
:param context: The runtime context object.
:param model: The extimated model object.
"""
# access input metadata, values, files, and secrets (passwords)
print(f'Run: {context.name} (uid={context.uid})')
print(f'file - {model.url}:\n{model.get()}\n')
context.logger.info('started validation')
context.log_artifact('validation',
body=b'<b> validated </b>',
format='html')
def load_dask(
context: MLClientCtx,
src_data: DataItem,
dask_key: str = "dask_key",
inc_cols: Optional[List[str]] = None,
index_cols: Optional[List[str]] = None,
dask_persist: bool = True,
refresh_data: bool = True,
scheduler_key: str = "scheduler"
) -> None:
"""Load dataset into an existing dask cluster
dask jobs define the dask client parameters at the job level, this method will raise an error if no client is detected.
:param context: the function context
:param src_data: url of the data file or partitioned dataset as either
artifact DataItem, string, or path object (similar to
pandas read_csv)
:param dask_key: destination key of data on dask cluster and artifact store
:param inc_cols: include only these columns (very fast)
:param index_cols: list of index column names (can be a long-running process)
:param dask_persist: (True) should the data be persisted (through the `client.persist` op)
:param refresh_data: (False) if the dask_key already exists in the dask cluster, this will
raise an Exception. Set to True to replace the existing cluster data.
:param scheduler_key: (scheduler) the dask scheduler configuration, json also logged as an artifact
"""
if hasattr(context, "dask_client"):
dask_client = context.dask_client
else:
raise Exception("a dask client was not found in the execution context")
df = src_data.as_df(df_module=dd)
if dask_persist:
df = dask_client.persist(df)
if dask_client.datasets and dask_key in dask_client.datasets:
dask_client.unpublish_dataset(dask_key)
dask_client.publish_dataset(df, name=dask_key)
if context:
context.dask_client = dask_client
# share the scheduler, whether data is persisted or not
dask_client.write_scheduler_file(scheduler_key + ".json")
# we don't use log_dataset here until it can take into account
# dask origin and apply dask describe.
context.log_artifact(scheduler_key, local_path=scheduler_key + ".json")
def gen_class_data(context: MLClientCtx,
n_samples: int,
m_features: int,
k_classes: int,
header: Optional[List[str]],
label_column: Optional[str] = "labels",
weight: float = 0.5,
random_state: int = 1,
key: str = "classifier-data",
file_ext: str = "parquet",
sk_params={}):
"""Create a binary classification sample dataset and save.
If no filename is given it will default to:
"simdata-{n_samples}X{m_features}.parquet".
Additional scikit-learn parameters can be set using **sk_params, please see https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html for more details.
:param context: function context
:param n_samples: number of rows/samples
:param m_features: number of cols/features
:param k_classes: number of classes
:param header: header for features array
:param label_column: column name of ground-truth series
:param weight: fraction of sample negative value (ground-truth=0)
:param random_state: rng seed (see https://scikit-learn.org/stable/glossary.html#term-random-state)
:param key: key of data in artifact store
:param file_ext: (pqt) extension for parquet file
:param sk_params: additional parameters for `sklearn.datasets.make_classification`
"""
features, labels = make_classification(n_samples=n_samples,
n_features=m_features,
weights=weight,
n_classes=k_classes,
random_state=random_state,
**sk_params)
# make dataframes, add column names, concatenate (X, y)
X = pd.DataFrame(features)
if not header:
X.columns = ["feat_" + str(x) for x in range(m_features)]
else:
X.columns = header
y = pd.DataFrame(labels, columns=[label_column])
data = pd.concat([X, y], axis=1)
context.log_dataset(key, df=data, format=file_ext, index=False)
def learning_curves(context: MLClientCtx,
results: dict,
figsz: Tuple[int, int] = (10, 10),
plots_dest: str = "plots") -> None:
"""plot xgb learning curves
this will also log a model's learning curves
"""
plt.clf()
plt.figure(figsize=figsz)
plt.plot(results["train"]["my_rmsle"], label="train-my-rmsle")
plt.plot(results["valid"]["my_rmsle"], label="valid-my-rmsle")
plt.title(f"learning curves")
plt.legend()
context.log_artifact(PlotArtifact(f"learning-curves", body=plt.gcf()),
local_path=f"{plots_dest}/learning-curves.html")
def pandas_profiling_report(
context: MLClientCtx,
data: DataItem,
) -> None:
"""Create a Pandas Profiling Report for a dataset.
:param context: the function context
:param data: Dataset to create report for
"""
df = data.as_df()
profile = df.profile_report(title="Pandas Profiling Report")
context.log_artifact(
"Pandas Profiling Report",
body=profile.to_html(),
local_path="pandas_profiling_report.html",
)
def sql_to_file(
context: MLClientCtx,
sql_query: str,
database_url: str,
file_ext: str = "parquet",
) -> None:
"""SQL Ingest - Ingest data using SQL query
:param context: the function context
:param sql_query: the sql query used to retrieve the data
:param database_url: database connection URL
:param file_ext: ("parquet") format for result file
"""
engine = create_engine(database_url)
df = pd.read_sql(sql_query, engine)
context.log_dataset(
"query result",
df=df,
format=file_ext,
artifact_path=context.artifact_subpath("data"),
)
def validation(context: MLClientCtx, model: DataItem) -> None:
"""Model validation.
Dummy validation function.
:param context: The runtime context object.
:param model: The extimated model object.
"""
# access input metadata, values, files, and secrets (passwords)
print(f"Run: {context.name} (uid={context.uid})")
context.logger.info("started validation")
# get the model file, class (metadata), and extra_data (dict of key: DataItem)
model_file, model_obj, _ = get_model(model)
# update model object elements and data
update_model(model_obj, parameters={"one_more": 5})
print(f"path to local copy of model file - {model_file}")
print("parameters:", model_obj.parameters)
print("metrics:", model_obj.metrics)
context.log_artifact("validation",
body=b"<b> validated </b>",
format="html")
def plot_confusion_matrix(context: MLClientCtx,
labels,
predictions,
key: str = "confusion_matrix",
plots_dir: str = "plots",
colormap: str = "Blues",
fmt: str = "png",
sample_weight=None):
"""Create a confusion matrix.
Plot and save a confusion matrix using test data from a
modelline step.
See https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
TODO: fix label alignment
TODO: consider using another packaged version
TODO: refactor to take params dict for plot options
:param context: function context
:param labels: validation data ground-truth labels
:param predictions: validation data predictions
:param key: str
:param plots_dir: relative path of plots in artifact store
:param colormap: colourmap for confusion matrix
:param fmt: plot format
:param sample_weight: sample weights
"""
_gcf_clear(plt)
cm = metrics.confusion_matrix(labels, predictions, sample_weight=None)
sns.heatmap(cm, annot=True, cmap=colormap, square=True)
fig = plt.gcf()
fname = f"{plots_dir}/{key}.{fmt}"
fig.savefig(os.path.join(context.artifact_path, fname))
context.log_artifact(PlotArtifact(key, body=fig), local_path=fname)
def open_archive(
context: MLClientCtx,
archive_url: DataItem,
subdir: str = "content",
key: str = "content",
target_path: str = None,
):
"""Open a file/object archive into a target directory
Currently supports zip and tar.gz
:param context: function execution context
:param archive_url: url of archive file
:param subdir: path within artifact store where extracted files
are stored
:param key: key of archive contents in artifact store
:param target_path: file system path to store extracted files (use either this or subdir)
"""
os.makedirs(target_path or subdir, exist_ok=True)
archive_url = archive_url.local()
if archive_url.endswith("gz"):
with tarfile.open(archive_url, mode="r|gz") as ref:
ref.extractall(target_path or subdir)
elif archive_url.endswith("zip"):
with zipfile.ZipFile(archive_url, "r") as ref:
ref.extractall(target_path or subdir)
else:
raise ValueError(f"unsupported archive type in {archive_url}")
kwargs = {}
if target_path:
kwargs = {"target_path": target_path}
else:
kwargs = {"local_path": subdir}
context.log_artifact(key, **kwargs)
def send_email(
context: MLClientCtx,
sender: str,
to: str,
subject: str,
content: str = "",
server_addr: str = None,
attachments: List[str] = [],
) -> None:
"""Send an email.
:param sender: Sender email address
:param context: The function context
:param to: Email address of mail recipient
:param subject: Email subject
:param content: Optional mail text
:param server_addr: Address of SMTP server to use. Use format <addr>:<port>
:param attachments: List of attachments to add.
"""
email_user = context.get_secret("SMTP_USER")
email_pass = context.get_secret("SMTP_PASSWORD")
if email_user is None or email_pass is None:
context.logger.error(
"Missing sender email or password - cannot send email.")
return
if server_addr is None:
context.logger.error("Server not specified - cannot send email.")
return
msg = EmailMessage()
msg["From"] = sender
msg["Subject"] = subject
msg["To"] = to
msg.set_content(content)
for filename in attachments:
context.logger.info(f"Looking at attachment: {filename}")
if not os.path.isfile(filename):
context.logger.warning(f"Filename does not exist {filename}")
continue
ctype, encoding = mimetypes.guess_type(filename)
if ctype is None or encoding is not None:
ctype = "application/octet-stream"
maintype, subtype = ctype.split("/", 1)
with open(filename, "rb") as fp:
msg.add_attachment(
fp.read(),
maintype=maintype,
subtype=subtype,
filename=os.path.basename(filename),
)
context.logger.info(
f"Added attachment: Filename: {filename}, of mimetype: {maintype}, {subtype}"
)
try:
s = smtplib.SMTP(host=server_addr)
s.starttls()
s.login(email_user, email_pass)
s.send_message(msg)
context.logger.info("Email sent successfully.")
except smtplib.SMTPException as exp:
context.logger.error(f"SMTP exception caught in SMTP code: {exp}")
except ConnectionError as ce:
context.logger.error(f"Connection error caught in SMTP code: {ce}")
def train_model(
context: MLClientCtx,
model_pkg_class: str,
dataset: DataItem,
label_column: str = "labels",
encode_cols: List[str] = [],
sample: int = -1,
test_size: float = 0.30,
train_val_split: float = 0.75,
test_set_key: str = "test_set",
model_evaluator=None,
models_dest: str = "",
plots_dest: str = "plots",
file_ext: str = "parquet",
model_pkg_file: str = "",
random_state: int = 1,
) -> None:
"""train a classifier
An optional cutom model evaluator can be supplied that should have the signature:
`my_custom_evaluator(context, xvalid, yvalid, model)` and return a dictionary of
scalar "results", a "plots" keys with a list of PlotArtifacts, and
and "tables" key containing a returned list of TableArtifacts.
:param context: the function context
:param model_pkg_class: the model to train, e.g, "sklearn.neural_networks.MLPClassifier",
or json model config
:param dataset: ("data") name of raw data file
:param label_column: ground-truth (y) labels
:param encode_cols: dictionary of names and prefixes for columns that are
to hot be encoded.
:param sample: Selects the first n rows, or select a sample
starting from the first. If negative <-1, select
a random sample
:param test_size: (0.05) test set size
:param train_val_split: (0.75) Once the test set has been removed the
training set gets this proportion.
:param test_set_key: key of held out data in artifact store
:param model_evaluator: (None) a custom model evaluator can be specified
:param models_dest: ("") models subfolder on artifact path
:param plots_dest: plot subfolder on artifact path
:param file_ext: ("parquet") format for test_set_key hold out data
:param random_state: (1) sklearn rng seed
"""
models_dest = models_dest or "model"
raw, labels, header = get_sample(dataset, sample, label_column)
if encode_cols:
raw = pd.get_dummies(raw,
columns=list(encode_cols.keys()),
prefix=list(encode_cols.values()),
drop_first=True)
(xtrain, ytrain), (xvalid, yvalid), (xtest, ytest) = get_splits(
raw, labels, 3, test_size, 1 - train_val_split, random_state)
context.log_dataset(test_set_key,
df=pd.concat([xtest, ytest.to_frame()], axis=1),
format=file_ext,
index=False,
labels={"data-type": "held-out"},
artifact_path=context.artifact_subpath('data'))
model_config = gen_sklearn_model(model_pkg_class,
context.parameters.items())
model_config["FIT"].update({"X": xtrain, "y": ytrain.values})
ClassifierClass = create_class(model_config["META"]["class"])
model = ClassifierClass(**model_config["CLASS"])
model.fit(**model_config["FIT"])
artifact_path = context.artifact_subpath(models_dest)
plots_path = context.artifact_subpath(models_dest, plots_dest)
if model_evaluator:
eval_metrics = model_evaluator(context,
xvalid,
yvalid,
model,
plots_artifact_path=plots_path)
else:
eval_metrics = eval_model_v2(context,
xvalid,
yvalid,
model,
plots_artifact_path=plots_path)
context.set_label('class', model_pkg_class)
context.log_model("model",
body=dumps(model),
artifact_path=artifact_path,
extra_data=eval_metrics,
model_file="model.pkl",
metrics=context.results,
labels={"class": model_pkg_class})
def arc_to_parquet(context: MLClientCtx,
archive_url: DataItem,
header: List[str] = [None],
chunksize: int = 0,
dtype=None,
encoding: str = "latin-1",
key: str = "data",
dataset: str = "None",
part_cols=[],
file_ext: str = "parquet",
index: bool = False,
refresh_data: bool = False,
stats: bool = False) -> None:
"""Open a file/object archive and save as a parquet file or dataset
Notes
-----
* this function is typically for large files, please be sure to check all settings
* partitioning requires precise specification of column types.
* the archive_url can be any file readable by pandas read_csv, which includes tar files
* if the `dataset` parameter is not empty, then a partitioned dataset will be created
instead of a single file in the folder `dataset`
* if a key exists already then it will not be re-acquired unless the `refresh_data` param
is set to `True`. This is in case the original file is corrupt, or a refresh is
required.
:param context: the function context
:param archive_url: MLRun data input (DataItem object)
:param chunksize: (0) when > 0, row size (chunk) to retrieve
per iteration
:param dtype destination data type of specified columns
:param encoding ("latin-8") file encoding
:param key: key in artifact store (when log_data=True)
:param dataset: (None) if not None then "target_path/dataset"
is folder for partitioned files
:param part_cols: ([]) list of partitioning columns
:param file_ext: (parquet) csv/parquet file extension
:param index: (False) pandas save index option
:param refresh_data: (False) overwrite existing data at that location
:param stats: (None) calculate table stats when logging artifact
"""
base_path = context.artifact_path
os.makedirs(base_path, exist_ok=True)
archive_url = archive_url.local()
if dataset is not None:
dest_path = os.path.join(base_path, dataset)
exists = os.path.isdir(dest_path)
else:
dest_path = os.path.join(base_path, key + f".{file_ext}")
exists = os.path.isfile(dest_path)
if not exists:
context.logger.info("destination file does not exist, downloading")
if chunksize > 0:
header = _chunk_readwrite(archive_url, dest_path, chunksize,
encoding, dtype, dataset)
context.log_dataset(key=key,
stats=stats,
format='parquet',
target_path=dest_path)
else:
df = pd.read_csv(archive_url)
context.log_dataset(key, df=df, format=file_ext, index=index)
else:
context.logger.info("destination file already exists, nothing done")
def summarize(
context: MLClientCtx,
table: DataItem,
label_column: str = None,
class_labels: List[str] = [],
plot_hist: bool = True,
plots_dest: str = "plots",
update_dataset=False,
) -> None:
"""Summarize a table
:param context: the function context
:param table: MLRun input pointing to pandas dataframe (csv/parquet file path)
:param label_column: ground truth column label
:param class_labels: label for each class in tables and plots
:param plot_hist: (True) set this to False for large tables
:param plots_dest: destination folder of summary plots (relative to artifact_path)
:param update_dataset: when the table is a registered dataset update the charts in-place
"""
df = table.as_df()
header = df.columns.values
extra_data = {}
try:
gcf_clear(plt)
snsplt = sns.pairplot(df, hue=label_column) # , diag_kws={"bw": 1.5})
extra_data["histograms"] = context.log_artifact(
PlotArtifact("histograms", body=plt.gcf()),
local_path=f"{plots_dest}/hist.html",
db_key=False,
)
except Exception as e:
context.logger.error(
f"Failed to create pairplot histograms due to: {e}")
try:
gcf_clear(plt)
plot_cols = 3
plot_rows = int((len(header) - 1) / plot_cols) + 1
fig, ax = plt.subplots(plot_rows, plot_cols, figsize=(15, 4))
fig.tight_layout(pad=2.0)
for i in range(plot_rows * plot_cols):
if i < len(header):
sns.violinplot(
x=df[header[i]],
ax=ax[int(i / plot_cols)][i % plot_cols],
orient="h",
width=0.7,
inner="quartile",
)
else:
fig.delaxes(ax[int(i / plot_cols)][i % plot_cols])
i += 1
extra_data["violin"] = context.log_artifact(
PlotArtifact("violin", body=plt.gcf(), title="Violin Plot"),
local_path=f"{plots_dest}/violin.html",
db_key=False,
)
except Exception as e:
context.logger.warn(
f"Failed to create violin distribution plots due to: {e}")
if label_column:
labels = df.pop(label_column)
imbtable = labels.value_counts(normalize=True).sort_index()
try:
gcf_clear(plt)
balancebar = imbtable.plot(kind="bar",
title="class imbalance - labels")
balancebar.set_xlabel("class")
balancebar.set_ylabel("proportion of total")
extra_data["imbalance"] = context.log_artifact(
PlotArtifact("imbalance", body=plt.gcf()),
local_path=f"{plots_dest}/imbalance.html",
)
except Exception as e:
context.logger.warn(
f"Failed to create class imbalance plot due to: {e}")
context.log_artifact(
TableArtifact("imbalance-weights-vec",
df=pd.DataFrame({"weights": imbtable})),
local_path=f"{plots_dest}/imbalance-weights-vec.csv",
db_key=False,
)
tblcorr = df.corr()
mask = np.zeros_like(tblcorr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
dfcorr = pd.DataFrame(data=tblcorr, columns=header, index=header)
dfcorr = dfcorr[
np.arange(dfcorr.shape[0])[:, None] > np.arange(dfcorr.shape[1])]
context.log_artifact(
TableArtifact("correlation-matrix", df=tblcorr, visible=True),
local_path=f"{plots_dest}/correlation-matrix.csv",
db_key=False,
)
try:
gcf_clear(plt)
ax = plt.axes()
sns.heatmap(tblcorr, ax=ax, mask=mask, annot=False, cmap=plt.cm.Reds)
ax.set_title("features correlation")
extra_data["correlation"] = context.log_artifact(
PlotArtifact("correlation",
body=plt.gcf(),
title="Correlation Matrix"),
local_path=f"{plots_dest}/corr.html",
db_key=False,
)
except Exception as e:
context.logger.warn(
f"Failed to create features correlation plot due to: {e}")
gcf_clear(plt)
if update_dataset and table.meta and table.meta.kind == "dataset":
from mlrun.artifacts import update_dataset_meta
update_dataset_meta(table.meta, extra_data=extra_data)
def describe_spark(context: MLClientCtx,
dataset: DataItem,
artifact_path,
bins: int = 30,
describe_extended: bool = True):
location = dataset.local()
spark = SparkSession.builder.appName("Spark job").getOrCreate()
df = spark.read.csv(location, header=True, inferSchema=True)
kwargs = []
float_cols = [
item[0] for item in df.dtypes
if item[1].startswith('float') or item[1].startswith('double')
]
if describe_extended == True:
table, variables, freq = describe(df, bins, float_cols, kwargs)
tbl_1 = variables.reset_index()
if len(freq) != 0:
tbl_2 = pd.DataFrame.from_dict(
freq, orient="index").sort_index().stack().reset_index()
tbl_2.columns = ['col', 'key', 'val']
tbl_2['Merged'] = [{
key: val
} for key, val in zip(tbl_2.key, tbl_2.val)]
tbl_2 = tbl_2.groupby(
'col',
as_index=False).agg(lambda x: tuple(x))[['col', 'Merged']]
summary = pd.merge(tbl_1,
tbl_2,
how='left',
left_on='index',
right_on='col')
else:
summary = tbl_1
context.log_dataset("summary_stats",
df=summary,
format="csv",
index=False,
artifact_path=context.artifact_subpath('data'))
context.log_results(table)
else:
tbl_1 = df.describe().toPandas()
summary = tbl_1.T
context.log_dataset("summary_stats",
df=summary,
format="csv",
index=False,
artifact_path=context.artifact_subpath('data'))
spark.stop()
def data_clean(
context: MLClientCtx,
src: DataItem,
file_ext: str = "csv",
models_dest: str = "models/encoders",
cleaned_key: str = "cleaned-data",
encoded_key: str = "encoded-data",
):
"""process a raw churn data file
Data has 3 states here: `raw`, `cleaned` and `encoded`
* `raw` kept by default, the pipeline begins with a raw data artifact
* `cleaned` kept for charts, presentations
* `encoded` is input for a cross validation and training function
steps (not necessarily in correct order, some parallel)
* column name maps
* deal with nans and other types of missings/junk
* label encode binary and ordinal category columns
* create category ranges from numerical columns
And finally,
* test
Why we don't one-hot-encode here? One hot encoding isn't a necessary
step for all algorithms. It can also generate a very large feature
matrix that doesn't need to be serialized (even if sparse).
So we leave one-hot-encoding for the training step.
What about scaling numerical columns? Same as why we don't one hot
encode here. Do we scale before train-test split? IMHO, no. Scaling
before splitting introduces a type of data leakage. In addition,
many estimators are completely immune to the monotonic transformations
implied by scaling, so why waste the cycles?
TODO:
* parallelize where possible
* more abstraction (more parameters, chain sklearn transformers)
* convert to marketplace function
:param context: the function execution context
:param src: an artifact or file path
:param file_ext: file type for artifacts
:param models_dest: label encoders and other preprocessing steps
should be saved together with other pipeline
models
:param cleaned_key: key of cleaned data table in artifact store
:param encoded_key: key of encoded data table in artifact store
"""
df = src.as_df()
# drop columns
drop_cols_list = ["customerID", "TotalCharges"]
df.drop(drop_cols_list, axis=1, inplace=True)
# header transformations
rename_cols_map = {
"SeniorCitizen": "senior",
"Partner": "partner",
"Dependents": "deps",
"Churn": "labels",
}
df.rename(rename_cols_map, axis=1, inplace=True)
# add drop column to logs:
for col in drop_cols_list:
rename_cols_map.update({col: "_DROPPED_"})
# log the op
tp = os.path.join(models_dest, "preproc-column_map.json")
context.log_artifact("preproc-column_map.json",
body=json.dumps(rename_cols_map),
local_path=tp)
# VALUE transformations
# clean
# truncate reply to "No"
df = df.applymap(lambda x: "No" if str(x).startswith("No ") else x)
# encode numerical type as category bins (ordinal)
bins = [0, 12, 24, 36, 48, 60, np.inf]
labels = [0, 1, 2, 3, 4, 5]
df["tenure_map"] = pd.cut(df.tenure, bins, labels=False)
tenure_map = dict(zip(bins, labels))
# save this transformation
tp = os.path.join(models_dest, "preproc-numcat_map.json")
context.log_artifact(
"preproc-numcat_map.json",
body=bytes(json.dumps(tenure_map).encode("utf-8")),
local_path=tp,
)
context.log_dataset(cleaned_key, df=df, format=file_ext, index=False)
# label encoding - generate model for each column saved in dict
# some of these columns may be hot encoded in the training step
fix_cols = [
"gender",
"partner",
"deps",
"OnlineSecurity",
"OnlineBackup",
"DeviceProtection",
"TechSupport",
"StreamingTV",
"StreamingMovies",
"PhoneService",
"MultipleLines",
"PaperlessBilling",
"InternetService",
"Contract",
"PaymentMethod",
"labels",
]
d = defaultdict(LabelEncoder)
df[fix_cols] = df[fix_cols].apply(
lambda x: d[x.name].fit_transform(x.astype(str)))
context.log_dataset(encoded_key, df=df, format=file_ext, index=False)
model_bin = dumps(d)
context.log_model(
"model",
body=model_bin,
artifact_path=os.path.join(context.artifact_path, models_dest),
model_file="model.pkl",
)
def _generate_mpi_job(
self,
runobj: RunObject,
execution: MLClientCtx,
meta: client.V1ObjectMeta,
) -> dict:
pod_labels = deepcopy(meta.labels)
pod_labels["mlrun/job"] = meta.name
# Populate mpijob object
# start by populating pod templates
launcher_pod_template = deepcopy(self._mpijob_pod_template)
worker_pod_template = deepcopy(self._mpijob_pod_template)
# configuration for both launcher and workers
for pod_template in [launcher_pod_template, worker_pod_template]:
if self.spec.image:
self._update_container(pod_template, "image",
self.full_image_path())
self._update_container(pod_template, "volumeMounts",
self.spec.volume_mounts)
extra_env = self._generate_runtime_env(runobj)
extra_env = [{"name": k, "value": v} for k, v in extra_env.items()]
self._update_container(pod_template, "env",
extra_env + self.spec.env)
if self.spec.image_pull_policy:
self._update_container(
pod_template,
"imagePullPolicy",
self.spec.image_pull_policy,
)
if self.spec.workdir:
self._update_container(pod_template, "workingDir",
self.spec.workdir)
if self.spec.image_pull_secret:
update_in(
pod_template,
"spec.imagePullSecrets",
[{
"name": self.spec.image_pull_secret
}],
)
update_in(pod_template, "metadata.labels", pod_labels)
update_in(pod_template, "spec.volumes", self.spec.volumes)
# configuration for workers only
# update resources only for workers because the launcher
# doesn't require special resources (like GPUs, Memory, etc..)
self._enrich_worker_configurations(worker_pod_template)
# configuration for launcher only
self._enrich_launcher_configurations(launcher_pod_template)
# generate mpi job using both pod templates
job = self._generate_mpi_job_template(launcher_pod_template,
worker_pod_template)
# update the replicas only for workers
update_in(
job,
"spec.mpiReplicaSpecs.Worker.replicas",
self.spec.replicas or 1,
)
update_in(
job,
"spec.cleanPodPolicy",
self.spec.clean_pod_policy,
)
if execution.get_param("slots_per_worker"):
update_in(
job,
"spec.slotsPerWorker",
execution.get_param("slots_per_worker"),
)
update_in(job, "metadata", meta.to_dict())
return job
def train_model(context: MLClientCtx,
dataset: DataItem,
model_pkg_class: str,
label_column: str = "label",
train_validation_size: float = 0.75,
sample: float = 1.0,
models_dest: str = "models",
test_set_key: str = "test_set",
plots_dest: str = "plots",
dask_key: str = "dask_key",
dask_persist: bool = False,
scheduler_key: str = '',
file_ext: str = "parquet",
random_state: int = 42) -> None:
"""
Train a sklearn classifier with Dask
:param context: Function context.
:param dataset: Raw data file.
:param model_pkg_class: Model to train, e.g, "sklearn.ensemble.RandomForestClassifier",
or json model config.
:param label_column: (label) Ground-truth y labels.
:param train_validation_size: (0.75) Train validation set proportion out of the full dataset.
:param sample: (1.0) Select sample from dataset (n-rows/% of total), randomzie rows as default.
:param models_dest: (models) Models subfolder on artifact path.
:param test_set_key: (test_set) Mlrun db key of held out data in artifact store.
:param plots_dest: (plots) Plot subfolder on artifact path.
:param dask_key: (dask key) Key of dataframe in dask client "datasets" attribute.
:param dask_persist: (False) Should the data be persisted (through the `client.persist`)
:param scheduler_key: (scheduler) Dask scheduler configuration, json also logged as an artifact.
:param file_ext: (parquet) format for test_set_key hold out data
:param random_state: (42) sklearn seed
"""
if scheduler_key:
client = Client(scheduler_key)
else:
client = Client()
context.logger.info("Read Data")
df = dataset.as_df(df_module=dd)
context.logger.info("Prep Data")
numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
df = df.select_dtypes(include=numerics)
if df.isna().any().any().compute() == True:
raise Exception('NAs valus found')
df_header = df.columns
df = df.sample(frac=sample).reset_index(drop=True)
encoder = LabelEncoder()
encoder = encoder.fit(df[label_column])
X = df.drop(label_column, axis=1).to_dask_array(lengths=True)
y = encoder.transform(df[label_column])
classes = df[label_column].drop_duplicates() # no unique values in dask
classes = [str(i) for i in classes]
context.logger.info("Split and Train")
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, train_size=train_validation_size, random_state=random_state)
scaler = StandardScaler()
scaler = scaler.fit(X_train)
X_train_transformed = scaler.transform(X_train)
X_test_transformed = scaler.transform(X_test)
model_config = gen_sklearn_model(model_pkg_class,
context.parameters.items())
model_config["FIT"].update({"X": X_train_transformed, "y": y_train})
ClassifierClass = create_class(model_config["META"]["class"])
model = ClassifierClass(**model_config["CLASS"])
with joblib.parallel_backend("dask"):
model = model.fit(**model_config["FIT"])
artifact_path = context.artifact_subpath(models_dest)
plots_path = context.artifact_subpath(models_dest, plots_dest)
context.logger.info("Evaluate")
extra_data_dict = {}
for report in (ROCAUC, ClassificationReport, ConfusionMatrix):
report_name = str(report.__name__)
plt.cla()
plt.clf()
plt.close()
viz = report(model, classes=classes, per_class=True, is_fitted=True)
viz.fit(X_train_transformed,
y_train) # Fit the training data to the visualizer
viz.score(X_test_transformed,
y_test.compute()) # Evaluate the model on the test data
plot = context.log_artifact(PlotArtifact(report_name,
body=viz.fig,
title=report_name),
db_key=False)
extra_data_dict[str(report)] = plot
if report_name == 'ROCAUC':
context.log_results({
"micro": viz.roc_auc.get("micro"),
"macro": viz.roc_auc.get("macro")
})
elif report_name == 'ClassificationReport':
for score_name in viz.scores_:
for score_class in viz.scores_[score_name]:
context.log_results({
score_name + "-" + score_class:
viz.scores_[score_name].get(score_class)
})
viz = FeatureImportances(model,
classes=classes,
per_class=True,
is_fitted=True,
labels=df_header.delete(
df_header.get_loc(label_column)))
viz.fit(X_train_transformed, y_train)
viz.score(X_test_transformed, y_test)
plot = context.log_artifact(PlotArtifact("FeatureImportances",
body=viz.fig,
title="FeatureImportances"),
db_key=False)
extra_data_dict[str("FeatureImportances")] = plot
plt.cla()
plt.clf()
plt.close()
context.logger.info("Log artifacts")
artifact_path = context.artifact_subpath(models_dest)
plots_path = context.artifact_subpath(models_dest, plots_dest)
context.set_label('class', model_pkg_class)
context.log_model("model",
body=dumps(model),
artifact_path=artifact_path,
model_file="model.pkl",
extra_data=extra_data_dict,
metrics=context.results,
labels={"class": model_pkg_class})
context.log_artifact("standard_scaler",
body=dumps(scaler),
artifact_path=artifact_path,
model_file="scaler.gz",
label="standard_scaler")
context.log_artifact("label_encoder",
body=dumps(encoder),
artifact_path=artifact_path,
model_file="encoder.gz",
label="label_encoder")
df_to_save = delayed(np.column_stack)((X_test, y_test)).compute()
context.log_dataset(
test_set_key,
df=pd.DataFrame(df_to_save,
columns=df_header), # improve log dataset ability
format=file_ext,
index=False,
labels={"data-type": "held-out"},
artifact_path=context.artifact_subpath('data'))
context.logger.info("Done!")
def permutation_importance(
context: MLClientCtx,
model: DataItem,
dataset: DataItem,
labels: str,
figsz=(10, 5),
plots_dest: str = "plots",
fitype: str = "permute",
) -> pd.DataFrame:
"""calculate change in metric
type 'permute' uses a pre-estimated model
type 'dropcol' uses a re-estimates model
:param context: the function's execution context
:param model: a trained model
:param dataset: features and ground truths, regression targets
:param labels name of the ground truths column
:param figsz: matplotlib figure size
:param plots_dest: path within artifact store
:
"""
model_file, model_data, _ = get_model(model.url, suffix=".pkl")
model = load(open(str(model_file), "rb"))
X = dataset.as_df()
y = X.pop(labels)
header = X.columns
metric = _oob_classifier_accuracy
baseline = metric(model, X, y)
imp = []
for col in X.columns:
if fitype is "permute":
save = X[col].copy()
X[col] = np.random.permutation(X[col])
m = metric(model, X, y)
X[col] = save
imp.append(baseline - m)
elif fitype is "dropcol":
X_ = X.drop(col, axis=1)
model_ = clone(model)
#model_.random_state = random_state
model_.fit(X_, y)
o = model_.oob_score_
imp.append(baseline - o)
else:
raise ValueError(
"unknown fitype, only 'permute' or 'dropcol' permitted")
zipped = zip(imp, header)
feature_imp = pd.DataFrame(sorted(zipped),
columns=["importance", "feature"])
feature_imp.sort_values(by="importance", ascending=False, inplace=True)
plt.clf()
plt.figure(figsize=figsz)
sns.barplot(x="importance", y="feature", data=feature_imp)
plt.title(f"feature importances-{fitype}")
plt.tight_layout()
context.log_artifact(
PlotArtifact(f"feature importances-{fitype}", body=plt.gcf()),
local_path=f"{plots_dest}/feature-permutations.html",
)
context.log_dataset(f"feature-importances-{fitype}-tbl",
df=feature_imp,
index=False)
def training(context: MLClientCtx, p1: int = 1, p2: int = 2) -> None:
"""Train a model.
:param context: The runtime context object.
:param p1: A model parameter.
:param p2: Another model parameter.
"""
# access input metadata, values, and inputs
print(f"Run: {context.name} (uid={context.uid})")
print(f"Params: p1={p1}, p2={p2}")
context.logger.info("started training")
# <insert training code here>
# log the run results (scalar values)
context.log_result("accuracy", p1 * 2)
context.log_result("loss", p1 * 3)
# add a lable/tag to this run
context.set_label("category", "tests")
# log a simple artifact + label the artifact
# If you want to upload a local file to the artifact repo add src_path=<local-path>
context.log_artifact("somefile",
body=b"abc is 123",
local_path="myfile.txt")
# create a dataframe artifact
df = pd.DataFrame([{
"A": 10,
"B": 100
}, {
"A": 11,
"B": 110
}, {
"A": 12,
"B": 120
}])
context.log_dataset("mydf", df=df)
# Log an ML Model artifact, add metrics, params, and labels to it
# and place it in a subdir ('models') under artifacts path
context.log_model(
"mymodel",
body=b"abc is 123",
model_file="model.txt",
metrics={"accuracy": 0.85},
parameters={"xx": "abc"},
labels={"framework": "xgboost"},
artifact_path=context.artifact_subpath("models"),
)
def fit(context: MLClientCtx,
dataset: DataItem,
num_boost_round: int = 10,
evals: List[Tuple[DMatrix, str]] = [],
obj: Union[Callable, str] = "",
feval: Union[Callable, str] = None,
maximize: bool = False,
early_stopping_rounds: int = None,
evals_result: dict = {},
verbose_eval: bool = True,
xgb_model: DataItem = None,
callbacks: List[Callable] = [],
label_column: str = "labels",
encode_cols: dict = {},
sample: int = -1,
test_size: float = 0.25,
valid_size: float = 0.75,
random_state: int = 1994,
models_dest: str = "models",
plots_dest: str = "plots",
file_ext: str = "csv",
test_set_key: str = "test-set",
gpus: bool = False) -> None:
"""low level xgboost train api
for the xgboost `train` params see:
https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.train
Note: the first parameter of xgboost's `train` method is a dict of parameters
supplied to the booster (engine). To modify one of those simply
add a task parameter (when running you supply an mlrun NewTask) with the
prefix "XGB_". So for example, to set the 'tree_method' parameter to 'approx',
add {"XGB_tree_method":"approx"} to the task params key.
:param context: the function context
:param dataset: the full data set, train, valid and test will be extracted and
each converted to a DMatrix for input to xgboost's `train`
:param label_column: ground-truth (y) labels
:param encode_cols: dictionary of names and prefixes for columns that are
to hot be encoded.
:param sample: Selects the first n rows, or select a sample
starting from the first. If negative <-1, select
a random sample
:param test_size: (0.05) test set size
:param valid_size: (0.75) Once the test set has been removed the
training set gets this proportion.
:param random_state: (1) sklearn rng seed
:param models_dest: destination subfolder for model artifacts
:param plots_dest: destination subfolder for plot artifacts
:param file_ext: format for test_set_key hold out data
:param test_set_key: (test-set), key of held out data in artifact store
:param gpus: (False), run on gpus
"""
raw, labels, header = get_sample(dataset, sample, label_column)
# hot-encode
if encode_cols:
raw = pd.get_dummies(raw,
columns=list(encode_cols.keys()),
prefix=list(encode_cols.values()),
drop_first=True)
# split the sample into train validate, test and calibration sets:
(xtrain, ytrain), (xvalid, yvalid), (xtest, ytest) = \
get_splits(raw, labels, 3, test_size, valid_size, random_state)
# save test data as regular dataframe as it may be used by other process
context.log_dataset(test_set_key,
df=pd.concat([xtest, ytest], axis=1),
format=file_ext,
index=False)
# convert to xgboost DMatrix (todo - dask, gpu)
dtrain = DMatrix(xtrain, label=ytrain)
dvalid = DMatrix(xvalid, label=yvalid)
boost_params = {
"tree_method": "gpu_hist" if gpus else "hist",
"seed": random_state,
"disable_default_eval_metric": 1,
"objective": "reg:squaredlogerror",
"eval_metric": "rmsle"
}
# enable user to customize `booster param` parameters
for k, v in context.parameters.items():
if k.startswith('XGB_'):
boost_params[k[4:]] = v
# collect learning curves / training history
results = dict()
booster = train(
boost_params,
dtrain=dtrain,
num_boost_round=num_boost_round,
evals=[(dtrain, "train"), (dvalid, "valid")],
evals_result=results,
obj=squared_log,
feval=rmsle,
maximize=maximize,
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
# xgb_model=xgb_model,
# callbacks: List[Callable] = []
)
context.log_model("model",
body=dumps(booster),
model_file="model.pkl",
artifact_path='/User/artifacts/tttt')
learning_curves(context, results)
def _generate_mpi_job(
self,
runobj: RunObject,
execution: MLClientCtx,
meta: client.V1ObjectMeta,
) -> dict:
pod_labels = deepcopy(meta.labels)
pod_labels["mlrun/job"] = meta.name
# Populate mpijob object
# start by populating pod templates
launcher_pod_template = deepcopy(self._mpijob_pod_template)
worker_pod_template = deepcopy(self._mpijob_pod_template)
command, args, extra_env = self._get_cmd_args(runobj)
# configuration for both launcher and workers
for pod_template in [launcher_pod_template, worker_pod_template]:
if self.spec.image:
self._update_container(pod_template, "image",
self.full_image_path())
self._update_container(pod_template, "volumeMounts",
self.spec.volume_mounts)
self._update_container(pod_template, "env",
extra_env + self.spec.env)
if self.spec.image_pull_policy:
self._update_container(
pod_template,
"imagePullPolicy",
self.spec.image_pull_policy,
)
if self.spec.workdir:
self._update_container(pod_template, "workingDir",
self.spec.workdir)
if self.spec.image_pull_secret:
update_in(
pod_template,
"spec.imagePullSecrets",
[{
"name": self.spec.image_pull_secret
}],
)
update_in(pod_template, "metadata.labels", pod_labels)
update_in(pod_template, "spec.volumes", self.spec.volumes)
update_in(pod_template, "spec.nodeName", self.spec.node_name)
update_in(pod_template, "spec.nodeSelector",
self.spec.node_selector)
update_in(pod_template, "spec.affinity",
self.spec._get_sanitized_affinity())
if self.spec.priority_class_name and len(
mlconf.get_valid_function_priority_class_names()):
update_in(
pod_template,
"spec.priorityClassName",
self.spec.priority_class_name,
)
# configuration for workers only
# update resources only for workers because the launcher
# doesn't require special resources (like GPUs, Memory, etc..)
self._enrich_worker_configurations(worker_pod_template)
# configuration for launcher only
self._enrich_launcher_configurations(launcher_pod_template,
[command] + args)
# generate mpi job using both pod templates
job = self._generate_mpi_job_template(launcher_pod_template,
worker_pod_template)
# update the replicas only for workers
update_in(
job,
"spec.mpiReplicaSpecs.Worker.replicas",
self.spec.replicas or 1,
)
update_in(
job,
"spec.cleanPodPolicy",
self.spec.clean_pod_policy,
)
if execution.get_param("slots_per_worker"):
update_in(
job,
"spec.slotsPerWorker",
execution.get_param("slots_per_worker"),
)
update_in(job, "metadata", meta.to_dict())
return job
def data_clean(context: MLClientCtx,
src: DataItem,
file_ext: str = "csv",
models_dest: str = "models/encoders",
cleaned_key: str = "cleaned-data",
encoded_key: str = "encoded-data"):
df = src.as_df()
# drop columns
drop_cols_list = ["customerID", "TotalCharges"]
df.drop(drop_cols_list, axis=1, inplace=True)
# header transformations
old_cols = df.columns
rename_cols_map = {
"SeniorCitizen": "senior",
"Partner": "partner",
"Dependents": "deps",
"Churn": "labels"
}
df.rename(rename_cols_map, axis=1, inplace=True)
# add drop column to logs:
for col in drop_cols_list:
rename_cols_map.update({col: "_DROPPED_"})
# log the op
tp = os.path.join(models_dest, "preproc-column_map.json")
context.log_artifact("preproc-column_map.json",
body=json.dumps(rename_cols_map),
local_path=tp)
df = df.applymap(lambda x: "No" if str(x).startswith("No ") else x)
# encode numerical type as category bins (ordinal)
bins = [0, 12, 24, 36, 48, 60, np.inf]
labels = [0, 1, 2, 3, 4, 5]
tenure = df.tenure.copy(deep=True)
df["tenure_map"] = pd.cut(df.tenure, bins, labels=False)
tenure_map = dict(zip(bins, labels))
# save this transformation
tp = os.path.join(models_dest, "preproc-numcat_map.json")
context.log_artifact("preproc-numcat_map.json",
body=bytes(json.dumps(tenure_map).encode("utf-8")),
local_path=tp)
context.log_dataset(cleaned_key, df=df, format=file_ext, index=False)
fix_cols = [
"gender", "partner", "deps", "OnlineSecurity", "OnlineBackup",
"DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies",
"PhoneService", "MultipleLines", "PaperlessBilling", "InternetService",
"Contract", "PaymentMethod", "labels"
]
d = defaultdict(LabelEncoder)
df[fix_cols] = df[fix_cols].apply(
lambda x: d[x.name].fit_transform(x.astype(str)))
context.log_dataset(encoded_key, df=df, format=file_ext, index=False)
model_bin = dumps(d)
context.log_model("model",
body=model_bin,
artifact_path=os.path.join(context.artifact_path,
models_dest),
model_file="model.pkl")
