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 = 0.70,
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 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 feature_selection(
context,
df_artifact,
k=2,
min_votes=0.5,
label_column: str = 'Y',
stat_filters=[
'f_classif', 'mutual_info_classif', 'chi2', 'f_regression'
],
model_filters={
'LinearSVC': 'LinearSVC',
'LogisticRegression': 'LogisticRegression',
'ExtraTreesClassifier': 'ExtraTreesClassifier'
},
max_scaled_scores=True):
"""Applies selected feature selection statistical functions
or models on our 'df_artifact'.
Each statistical function or model will vote for it's best K selected features.
If a feature has >= 'min_votes' votes, it will be selected.
:param context: the function context
:param k: number of top features to select from each statistical
function or model
:param min_votes: minimal number of votes (from a model or by statistical
function) needed for a feature to be selected.
Can be specified by percentage of votes or absolute
number of votes
:param label_column: ground-truth (y) labels
:param stat_filters: statistical functions to apply to the features
(from sklearn.feature_selection)
:param model_filters: models to use for feature evaluation, can be specified by
model name (ex. LinearSVC), formalized json (contains 'CLASS',
'FIT', 'META') or a path to such json file.
:param max_scaled_scores: produce feature scores table scaled with max_scaler
"""
# Read input DF
df_path = str(df_artifact)
context.logger.info(f'input dataset {df_path}')
if df_path.endswith('csv'):
df = pd.read_csv(df_path)
elif df_path.endswith('parquet') or df_path.endswith('pq'):
df = pd.read_parquet(df_path)
# Set feature vector and labels
y = df.pop(label_column)
X = df
# Create selected statistical estimators
stat_functions_list = {
stat_name:
SelectKBest(create_class(f'sklearn.feature_selection.{stat_name}'), k)
for stat_name in stat_filters
}
requires_abs = ['chi2']
# Run statistic filters
selected_features_agg = {}
stats_df = pd.DataFrame(index=X.columns)
for stat_name, stat_func in stat_functions_list.items():
try:
# Compute statistics
params = (X, y) if stat_name in requires_abs else (abs(X), y)
stat = stat_func.fit(*params)
# Collect stat function results
stat_df = pd.DataFrame(index=X.columns,
columns=[stat_name],
data=stat.scores_)
plot_stat(context, stat_name, stat_df)
stats_df = stats_df.join(stat_df)
# Select K Best features
selected_features = X.columns[stat_func.get_support()]
selected_features_agg[stat_name] = selected_features
except Exception as e:
context.logger.info(
f"Couldn't calculate {stat_name} because of: {e}")
# Create models from class name / json file / json params
all_sklearn_estimators = dict(
all_estimators()) if len(model_filters) > 0 else {}
selected_models = {}
for model_name, model in model_filters.items():
if '.json' in model:
current_model = json.load(open(model, 'r'))
ClassifierClass = create_class(current_model["META"]["class"])
selected_models[model_name] = ClassifierClass(
**current_model["CLASS"])
elif model in all_sklearn_estimators:
selected_models[model_name] = all_sklearn_estimators[model_name]()
else:
try:
current_model = json.loads(model) if isinstance(
model, str) else current_model
ClassifierClass = create_class(current_model["META"]["class"])
selected_models[model_name] = ClassifierClass(
**current_model["CLASS"])
except:
context.logger.info(f'unable to load {model}')
# Run model filters
models_df = pd.DataFrame(index=X.columns)
for model_name, model in selected_models.items():
# Train model and get feature importance
select_from_model = SelectFromModel(model).fit(X, y)
feature_idx = select_from_model.get_support()
feature_names = X.columns[feature_idx]
selected_features_agg[model_name] = feature_names.tolist()
# Collect model feature importance
if hasattr(select_from_model.estimator_, 'coef_'):
stat_df = select_from_model.estimator_.coef_
elif hasattr(select_from_model.estimator_, 'feature_importances_'):
stat_df = select_from_model.estimator_.feature_importances_
stat_df = pd.DataFrame(index=X.columns,
columns=[model_name],
data=stat_df[0])
models_df = models_df.join(stat_df)
plot_stat(context, model_name, stat_df)
# Create feature_scores DF with stat & model filters scores
result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
context.log_dataset(key='feature_scores',
df=result_matrix_df,
local_path='feature_scores.parquet',
format='parquet')
if max_scaled_scores:
normalized_df = result_matrix_df.replace([np.inf, -np.inf],
np.nan).values
min_max_scaler = MinMaxScaler()
normalized_df = min_max_scaler.fit_transform(normalized_df)
normalized_df = pd.DataFrame(data=normalized_df,
columns=result_matrix_df.columns,
index=result_matrix_df.index)
context.log_dataset(
key='max_scaled_scores_feature_scores',
df=normalized_df,
local_path='max_scaled_scores_feature_scores.parquet',
format='parquet')
# Create feature count DataFrame
for test_name in selected_features_agg:
result_matrix_df[test_name] = [
1 if x in selected_features_agg[test_name] else 0
for x in X.columns
]
result_matrix_df.loc[:, 'num_votes'] = result_matrix_df.sum(axis=1)
context.log_dataset(key='selected_features_count',
df=result_matrix_df,
local_path='selected_features_count.parquet',
format='parquet')
# How many votes are needed for a feature to be selected?
if isinstance(min_votes, int):
votes_needed = min_votes
else:
num_filters = len(stat_filters) + len(model_filters)
votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
context.logger.info(f'votes needed to be selected: {votes_needed}')
# Create final feature dataframe
selected_features = result_matrix_df[
result_matrix_df.num_votes >= votes_needed].index.tolist()
good_feature_df = df.loc[:, selected_features]
final_df = pd.concat([good_feature_df, y], axis=1)
context.log_dataset(key='selected_features',
df=final_df,
local_path='selected_features.parquet',
format='parquet')
def feature_selection(context,
df_artifact,
k: int=5,
min_votes: float=0.5,
label_column: str=None,
stat_filters: list=['f_classif', 'mutual_info_classif', 'chi2', 'f_regression'],
model_filters: dict={'LinearSVC': 'LinearSVC',
'LogisticRegression': 'LogisticRegression',
'ExtraTreesClassifier': 'ExtraTreesClassifier'},
max_scaled_scores: bool=True,
sample_ratio: float=None,
output_vector_name: float=None,
ignore_type_errors: bool=False,
is_feature_vector: bool=False):
"""Applies selected feature selection statistical functions
or models on our 'df_artifact'.
Each statistical function or model will vote for it's best K selected features.
If a feature has >= 'min_votes' votes, it will be selected.
:param context: the function context.
:param k: number of top features to select from each statistical
function or model.
:param min_votes: minimal number of votes (from a model or by statistical
function) needed for a feature to be selected.
Can be specified by percentage of votes or absolute
number of votes.
:param label_column: ground-truth (y) labels.
:param stat_filters: statistical functions to apply to the features
(from sklearn.feature_selection).
:param model_filters: models to use for feature evaluation, can be specified by
model name (ex. LinearSVC), formalized json (contains 'CLASS',
'FIT', 'META') or a path to such json file.
:param max_scaled_scores: produce feature scores table scaled with max_scaler.
:param sample_ratio: percentage of the dataset the user whishes to compute the feature selection process on.
:param output_vector_name: creates a new feature vector containing only the identifies features.
:param ignore_type_errors: skips datatypes that are neither float or int within the feature vector.
:param is_feature_vector: bool stating if the data is passed as a feature vector.
"""
# Check if df.meta is valid, if it is, look for a feature vector
if df_artifact.meta:
if df_artifact.meta.kind == mlrun.api.schemas.ObjectKind.feature_vector:
is_feature_vector = True
# Look inside meta.spec.label_feature to identify the label_column if the user did not specify it
if label_column is None:
if is_feature_vector:
label_column = df_artifact.meta.spec.label_feature.split('.')[1]
else:
raise ValueError('No label_column was given, please add a label_column.')
# Use the feature vector as dataframe
df = df_artifact.as_df()
# Ensure k is not bigger than the the total number of features
if k > df.shape[1]:
raise ValueError(f'K cannot be bigger than the total number of features ({df.shape[1]}). Please choose a smaller K.')
elif k < 1:
raise ValueError(f'K cannot be smaller than 1. Please choose a bigger K.')
# Create a sample dataframe of the original feature vector
if sample_ratio:
df = df.groupby(label_column).apply(lambda x: x.sample(frac=sample_ratio)).reset_index(drop=True)
df = df.dropna()
# Set feature vector and labels
y = df.pop(label_column)
X = df
if np.object in list(X.dtypes) and ignore_type_errors is False:
raise ValueError(f"{df.select_dtypes(include=['object']).columns.tolist()} are neither float or int.")
# Create selected statistical estimators
stat_functions_list = {stat_name: SelectKBest(create_class(f'sklearn.feature_selection.{stat_name}'), k)
for stat_name in stat_filters}
requires_abs = ['chi2']
# Run statistic filters
selected_features_agg = {}
stats_df = pd.DataFrame(index=X.columns).dropna()
for stat_name, stat_func in stat_functions_list.items():
try:
params = (X, y) if stat_name in requires_abs else (abs(X), y)
stat = stat_func.fit(*params)
# Collect stat function results
stat_df = pd.DataFrame(index=X.columns,
columns=[stat_name],
data=stat.scores_)
plot_stat(context, stat_name, stat_df)
stats_df = stats_df.join(stat_df)
# Select K Best features
selected_features = X.columns[stat_func.get_support()]
selected_features_agg[stat_name] = selected_features
except Exception as e:
context.logger.info(f"Couldn't calculate {stat_name} because of: {e}")
# Create models from class name / json file / json params
all_sklearn_estimators = dict(all_estimators()) if len(model_filters) > 0 else {}
selected_models = {}
for model_name, model in model_filters.items():
if '.json' in model:
current_model = json.load(open(model, 'r'))
ClassifierClass = create_class(current_model["META"]["class"])
selected_models[model_name] = ClassifierClass(**current_model["CLASS"])
elif model in all_sklearn_estimators:
selected_models[model_name] = all_sklearn_estimators[model_name]()
else:
try:
current_model = json.loads(model) if isinstance(model, str) else current_model
ClassifierClass = create_class(current_model["META"]["class"])
selected_models[model_name] = ClassifierClass(**current_model["CLASS"])
except:
context.logger.info(f'unable to load {model}')
# Run model filters
models_df = pd.DataFrame(index=X.columns)
for model_name, model in selected_models.items():
if model_name == 'LogisticRegression':
model.set_params(solver='liblinear')
# Train model and get feature importance
select_from_model = SelectFromModel(model).fit(X, y)
feature_idx = select_from_model.get_support()
feature_names = X.columns[feature_idx]
selected_features_agg[model_name] = feature_names.tolist()
# Collect model feature importance
if hasattr(select_from_model.estimator_, 'coef_'):
stat_df = select_from_model.estimator_.coef_
elif hasattr(select_from_model.estimator_, 'feature_importances_'):
stat_df = select_from_model.estimator_.feature_importances_
stat_df = pd.DataFrame(index=X.columns,
columns=[model_name],
data=stat_df[0])
models_df = models_df.join(stat_df)
plot_stat(context, model_name, stat_df)
# Create feature_scores DF with stat & model filters scores
result_matrix_df = pd.concat([stats_df, models_df], axis=1, sort=False)
context.log_dataset(key='feature_scores',
df=result_matrix_df,
local_path='feature_scores.parquet',
format='parquet')
if max_scaled_scores:
normalized_df = result_matrix_df.replace([np.inf, -np.inf], np.nan).values
min_max_scaler = MinMaxScaler()
normalized_df = min_max_scaler.fit_transform(normalized_df)
normalized_df = pd.DataFrame(data=normalized_df,
columns=result_matrix_df.columns,
index=result_matrix_df.index)
context.log_dataset(key='max_scaled_scores_feature_scores',
df=normalized_df,
local_path='max_scaled_scores_feature_scores.parquet',
format='parquet')
# Create feature count DataFrame
for test_name in selected_features_agg:
result_matrix_df[test_name] = [1 if x in selected_features_agg[test_name] else 0 for x in X.columns]
result_matrix_df.loc[:, 'num_votes'] = result_matrix_df.sum(axis=1)
context.log_dataset(key='selected_features_count',
df=result_matrix_df,
local_path='selected_features_count.parquet',
format='parquet')
# How many votes are needed for a feature to be selected?
if isinstance(min_votes, int):
votes_needed = min_votes
else:
num_filters = len(stat_filters) + len(model_filters)
votes_needed = int(np.floor(num_filters * max(min(min_votes, 1), 0)))
context.logger.info(f'votes needed to be selected: {votes_needed}')
# Create final feature dataframe
selected_features = result_matrix_df[result_matrix_df.num_votes >= votes_needed].index.tolist()
good_feature_df = df.loc[:, selected_features]
final_df = pd.concat([good_feature_df, y], axis=1)
context.log_dataset(key='selected_features',
df=final_df,
local_path='selected_features.parquet',
format='parquet')
# Creating a new feature vector containing only the identified top features
if is_feature_vector and df_artifact.meta.spec.features and output_vector_name:
# Selecting the top K features from our top feature dataframe
selected_features = result_matrix_df.head(k).index
# Match the selected feature names to the FS Feature annotations
matched_selections = [feature for feature in list(df_artifact.meta.spec.features) for selected in list(selected_features) if feature.endswith(selected)]
# Defining our new feature vector
top_features_fv = fs.FeatureVector(output_vector_name,
matched_selections,
label_feature="labels.label",
description='feature vector composed strictly of our top features')
# Saving
top_features_fv.save()
fs.get_offline_features(top_features_fv, target=ParquetTarget())
# Logging our new feature vector URI
context.log_result('top_features_vector', top_features_fv.uri)
def train_model(
context: MLClientCtx,
model_type: str,
dataset: Union[DataItem, pd.core.frame.DataFrame],
label_column: str = "labels",
encode_cols: dict = {},
sample: int = -1,
imbal_vec=[],
test_size: float = 0.25,
valid_size: float = 0.75,
random_state: int = 1,
models_dest: str = "models",
plots_dest: str = "plots",
eval_metrics: list = ["error", "auc"],
file_ext: str = "parquet",
test_set: str = "test_set",
) -> None:
"""train an xgboost model.
Note on imabalanced data: the `imbal_vec` parameter represents the measured
class representations in the sample and can be used as a first step in tuning
an XGBoost model. This isn't a hyperparamter, merely an estimate that should
be set as 'constant' throughout tuning process.
:param context: the function context
:param model_type: the model type to train, "classifier", "regressor"...
: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 imbal_vec: ([]) vector of class weights seen in 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 eval_metrics: (["error", "auc"]) learning curve metrics
:param file_ext: format for test_set_key hold out data
:param test-set: (test_set) key of held out data in artifact store
"""
models_dest = models_dest or "models"
plots_dest = plots_dest or f"plots/{context.name}"
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, valid_size, random_state)
context.log_dataset(test_set,
df=pd.concat([xtest, ytest], axis=1),
format=file_ext,
index=False)
model_config = _gen_xgb_model(model_type, context.parameters.items())
XGBBoostClass = create_class(model_config["META"]["class"])
model = XGBBoostClass(**model_config["CLASS"])
model_config["FIT"].update({
"X": xtrain,
"y": ytrain.values,
"eval_set": [(xtrain, ytrain), (xvalid, yvalid)],
"eval_metric": eval_metrics,
})
model.fit(**model_config["FIT"])
eval_metrics = eval_model_v2(context, xvalid, yvalid, model)
model_bin = dumps(model)
context.log_model(
"model",
body=model_bin,
artifact_path=os.path.join(context.artifact_path, models_dest),
model_file="model.pkl",
)