def mlflow_run(self, df, r_name="Lab-1:RF Petrol Regression Experiment"):
"""
This method trains, computes metrics, and logs all metrics, parameters,
and artifacts for the current run
:param df: pandas dataFrame
:param r_name: Name of the experiment as logged by MLflow
:return: MLflow Tuple (ExperimentID, runID)
"""
with mlflow.start_run(run_name=r_name) as run:
# get all rows and columns but the last column
X = dataset.iloc[:, 0:4].values
# get all the last columns, which is what we want to predict
y = dataset.iloc[:, 4].values
# create train and test data
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
# Feature Scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# train and predict
self.rf.fit(X_train, y_train)
y_pred = self.rf.predict(X_test)
# Log model and params using the MLflow sklearn APIs
mlflow.sklearn.log_model(self.rf, "random-forest-reg-model")
mlflow.log_params(self.params)
# compute metrics
mae = metrics.mean_absolute_error(y_test, y_pred)
mse = metrics.mean_squared_error(y_test, y_pred)
rsme = np.sqrt(mse)
r2 = metrics.r2_score(y_test, y_pred)
# Log metrics
mlflow.log_metric("mae", mae)
mlflow.log_metric("mse", mse)
mlflow.log_metric("rsme", rsme)
mlflow.log_metric("r2", r2)
# update global class instance variable with values
self.rsme.append(rsme)
self.r2.append(r2)
self.estimators.append(params["n_estimators"])
# plot RSME graph and save as artifacts
(fig, ax) = Utils.plot_graphs(rfr.estimators, rfr.rsme,
"Random Forest Estimators",
"Root Mean Square",
"Root Mean Square vs Estimators")
# get current run and experiment id
runID = run.info.run_uuid
experimentID = run.info.experiment_id
# create temporary artifact file name and log artifact
temp_file_name = Utils.get_temporary_directory_path(
"rsme_estimators-", ".png")
temp_name = temp_file_name.name
try:
fig.savefig(temp_name)
mlflow.log_artifact(temp_name, "rsme_estimators_plots")
finally:
temp_file_name.close() # Delete the temp file
# plot R2 graph and save as artifacts
(fig_2, ax) = Utils.plot_graphs(rfr.estimators, rfr.r2,
"Random Forest Estimators", "R2",
"R2 vs Estimators")
# create temporary artifact file name and log artifact
temp_file_name = Utils.get_temporary_directory_path(
"r2-estimators-", ".png")
temp_name = temp_file_name.name
try:
fig_2.savefig(temp_name)
mlflow.log_artifact(temp_name, "r2_estimators_plots")
finally:
temp_file_name.close() # Delete the temp file
# print some data
print("-" * 100)
print(
"Inside MLflow Run with run_id {} and experiment_id {}".format(
runID, experimentID))
print("Estimator trees :", self.params["n_estimators"])
print("Estimator trees depth :", self.params["max_depth"])
print('Mean Absolute Error :', mae)
print('Mean Squared Error :', mse)
print('Root Mean Squared Error:', rsme)
print('R2 :', r2)
return (experimentID, runID)
def mlflow_run(self, df, r_name="Lab-1:RF Petrol Regression Experiment"):
"""
This method trains, computes metrics, and logs all metrics, parameters,
and artifacts for the current run
:param df: pandas dataFrame
:param r_name: Name of the run as logged by MLflow
:return: MLflow Tuple (ExperimentID, runID)
"""
with mlflow.start_run(run_name=r_name) as run:
# get all feature independent attributes
X = df.iloc[:, 0:4].values
# get all the values of last columns, dependent variables,
# which is what we want to predict as our values, the petrol consumption
y = df.iloc[:, 4].values
# create train and test data
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
# Feature Scaling, though for RF is not necessary.
# z = (X - u)/ s, where u is the man, s the standard deviation
# get the handle to the transformer
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# train and predict
self.rf.fit(X_train, y_train)
y_pred = self.rf.predict(X_test)
# Log model and params using the MLflow sklearn APIs
mlflow.sklearn.log_model(self.model, "random-forest-reg-model")
mlflow.log_params(self.params)
# compute metrics; r2 is a statistical measure of how well the
# data fits the model: higher the value indicates better fit.
mae = metrics.mean_absolute_error(y_test, y_pred)
mse = metrics.mean_squared_error(y_test, y_pred)
rsme = np.sqrt(mse)
r2 = metrics.r2_score(y_test, y_pred)
# Log metrics
mlflow.log_metric("mae", mae)
mlflow.log_metric("mse", mse)
mlflow.log_metric("rsme", rsme)
mlflow.log_metric("r2", r2)
# update global class instance variable with values
self.rsme.append(rsme)
self.estimators.append(self._params["n_estimators"])
# plot graphs and save as artifacts
(fig, ax) = Utils.plot_graphs(self.estimators, self.rsme,
"Random Forest Estimators",
"Root Mean Square",
"Root Mean Square vs Estimators")
# get current run and experiment id
runID = run.info.run_uuid
experimentID = run.info.experiment_id
# create temporary artifact file name and log artifact
temp_file_name = Utils.get_temporary_directory_path(
"rsme_estimators-", ".png")
temp_name = temp_file_name.name
try:
fig.savefig(temp_name)
mlflow.log_artifact(temp_name, "rsme_estimators_plots")
finally:
temp_file_name.close() # Delete the temp file
# print some data
print("-" * 100)
print(
"Inside MLflow Run with run_id {} and experiment_id {}".format(
runID, experimentID))
print("Estimator trees :", self.params["n_estimators"])
print('Mean Absolute Error :', mae)
print('Mean Squared Error :', mse)
print('Root Mean Squared Error:', rsme)
print('R2 :', r2)
return (experimentID, runID)