API reference: scikit-learn integration#

You can use the Neptune integration with scikit-learn to track your classifiers, regressors, and k-means clustering results.

`create_regressor_summary()`#

Returns a scikit-learn regressor summary that includes:

All regressor parameters
Pickled estimator (model)
Test predictions
Test scores
Model performance visualizations

The regressor should be fitted before calling this function.

Parameters

Name	Type	Default	Description
`regressor`	`regressor`	-	Fitted scikit-learn regressor object.
`X_train`	`ndarray`	-	Training data matrix.
`X_test`	`ndarray`	-	Testing data matrix.
`y_train`	`ndarray`	-	The regression target for training.
`y_test`	`ndarray`	-	The regression target for testing.
`nrows`	`int`, optional	`1000`	Log first `nrows` rows of test predictions.
`log_charts`	`bool`, optional	`True`	If `True`, calculate and log chart visualizations. This is equivalent to calling the `create_learning_curve_chart()` `create_feature_importance_chart()`, `create_residuals_chart()`, `create_prediction_error_chart()`, and `create_cooks_distance_chart()` functions from this module. Note: Calculating visualizations is potentially expensive depending on input data and regressor, and may take some time to finish.

Returns

dict with all metadata, which can be assigned to a run namespace:

run["summary"] = create_regressor_summary(...)

Example

# Create a run
import neptune
run = neptune.init_run()

# Log random forest regressor summary
rfr = RandomForestRegressor()
rfr.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils
run["random_forest/summary"] = npt_utils.create_regressor_summary(
    rfr, X_train, X_test, y_train, y_test
)

`create_classifier_summary()`#

Returns a scikit-learn classifier summary that includes:

All classifier parameters
Pickled estimator (model)
Test predictions
Test predictions probabilities
Test scores
Model performance visualizations

The classifier should be fitted before calling this function.

Parameters

Name	Type	Default	Description
`classifier`	`classifier`	-	Fitted scikit-learn classifier object.
`X_train`	`ndarray`	-	Training data matrix.
`X_test`	`ndarray`	-	Testing data matrix.
`y_train`	`ndarray`	-	The classification target for training.
`y_test`	`ndarray`	-	The classification target for testing.
`nrows`	`int`, optional	`1000`	Log first `nrows` rows of test predictions and prediction probabilities.
`log_charts`	`bool`, optional	`True`	If `True`, calculate and log chart visualizations. This is equivalent to calling the `create_classification_report_chart()` `create_confusion_matrix_chart()`, `create_roc_auc_chart()`, `create_prediction_error_chart()`, `create_precision_recall_chart()` and `create_class_prediction_error_chart()` functions from this module. Note: Calculating visualizations is potentially expensive depending on input data and regressor, and may take some time to finish.

Returns

dict with all metadata, which can be assigned to the run namespace:

run["summary"] = create_classifier_summary(...)

Example

# Create a run
import neptune

run = neptune.init_run()

# Log random forest classifier summary
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils

run["random_forest/summary"] = npt_utils.create_classifier_summary(
    rfc, X_train, X_test, y_train, y_test
)

`create_kmeans_summary()`#

Returns a scikit-learn k-means summary.

This method fits the k-means model to data and logs:

All KMeans parameters
Cluster labels
Clustering visualizations: k-means elbow chart and silhouette coefficients chart

Parameters

Name	Type	Default	Description
`model`	`KMeans`	-	KMeans object
`X`	`ndarray`	-	Training instances to cluster
`nrows`	`int`, optional	`1000`	Number of rows to log in the cluster labels
`kwargs`	-	-	KMeans parameters

Returns

dict with all metadata, which can be assigned to a run namespace: run["summary"] = create_kmeans_summary(...)

Example

# Create a run
import neptune
run = neptune.init_run()

# Log random forest classifier summary
km = KMeans(n_init=11, max_iter=270)
X, y = make_blobs(n_samples=579, n_features=17, centers=7, random_state=28743)

import neptune.integrations.sklearn as npt_utils
run["kmeans/summary"] = npt_utils.create_kmeans_summary(km, X)

`get_estimator_params()`#

Get estimator parameters.

Parameters

Name	Type	Description
`estimator`	`estimator`	Scikit-learn estimator to log parameters for.

Returns

dict with all parameters mapped to their values.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log estimator parameters
rfr = RandomForestRegressor()

import neptune.integrations.sklearn as npt_utils
from neptune.utils import stringify_unsupported

run["estimator/params"] = stringify_unsupported(npt_utils.get_estimator_params(rfr))

`get_pickled_model()`#

Get pickled estimator.

Parameters

Name	Type	Description
`estimator`	`estimator`	Scikit-learn estimator to pickle.

Returns

File value object with a pickled model that you can log to the run.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log pickled model
rfr = RandomForestRegressor()

import neptune.integrations.sklearn as npt_utils
run["estimator/pickled_model"] = npt_utils.get_pickled_model(rfr)

`get_test_preds()`#

Get test predictions as a table.

If you pass y_pred, predictions are not computed from X_test data.

The estimator should be fitted before calling this function.

Parameters

Name	Type	Default	Description
`estimator`	`estimator`	-	scikit-learn estimator to compute predictions.
`X_test`	`ndarray`	-	Testing data matrix.
`y_test`	`ndarray`	-	The regression target for testing.
`y_pred`	`ndarray`, optional	`None`	Estimator predictions on test data.
`nrows`	`int`, optional	`1000`	Number of rows to log.

Returns

File value object with test predictions as a table that you can log to the run.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log test predictions as a table
rfr = RandomForestRegressor()

import neptune.integrations.sklearn as npt_utils
run["estimator/test_preds"] = npt_utils.get_test_preds(rfr, X_test, y_test)

`get_test_preds_proba()`#

Get test prediction probabilities.

If you pass X_test, prediction probabilities are computed from data.
If you pass y_pred_proba, prediction probabilities are not computed from X_test data.

The estimator should be fitted before calling this function.

Parameters

Name	Type	Default	Description
`classifier`	`classifier`	-	scikit-learn classifier to compute prediction probabilities.
`X_test`	`ndarray`	-	Testing data matrix.
`y_pred_proba`	`ndarray`, optional	`None`	Classifier prediction probabilities on test data.
`nrows`	`int`, optional	`1000`	Number of rows to log.

Returns

File value object with test prediction probabilities as a table that you can log to the run.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log classifier test predictions probabilities
rfr = RandomForestRegressor()

import neptune.integrations.sklearn as npt_utils
run["estimator/test_preds_proba"] = npt_utils.get_test_preds_proba(rfr, X_test)

`get_scores()`#

Get estimator scores on X.

If you pass y_pred, predictions are not computed from X and y data.

The estimator should be fitted before calling this function.

Estimator	Logged scores
Single output regressors	Explained variance, max error, mean absolute error, \(r^2\)
Multi output regressors	\(r^2\)
Classifiers	Precision, recall, f beta score, support

Parameters

Name	Type	Default	Description
`estimator`	`estimator`	-	scikit-learn estimator to compute scores.
`X`	`ndarray`	-	Data matrix.
`y`	`ndarray`	-	Target for testing.
`y_pred`	`ndarray`, optional	`None`	Estimator predictions on data.

Returns

dict with scores.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log estimator scores
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils
run["estimator/scores"] = npt_utils.get_scores(rfc, X, y)

`create_learning_curve_chart()`#

Returns a learning curve chart.

Parameters

Name	Type	Default	Description
`regressor`	`regressor`	-	Fitted scikit-learn regressor object
`X_train`	`ndarray`	-	Training data matrix
`y_train`	`ndarray`	-	The regression target for training

Returns

File value object with a learning curve chart that you can log to the run.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log a learning curve chart
rfr = RandomForestRegressor()
rfr.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils
run["visuals/learning_curve"] = npt_utils.create_learning_curve_chart(
    rfr, X_train, y_train
)

`create_feature_importance_chart()`#

Returns a feature importance chart.

Parameters

Name	Type	Default	Description
`regressor`	`regressor`	-	Fitted scikit-learn regressor object
`X_train`	`ndarray`	-	Training data matrix
`y_train`	`ndarray`	-	The regression target for training

Returns

File value object with a feature importance chart that you can log to the run.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log a feature importance chart
rfr = RandomForestRegressor()
rfr.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils
run["visuals/feature_importance"] = npt_utils.create_feature_importance_chart(
    rfr, X_train, y_train
)

`create_residuals_chart()`#

Returns a residuals chart.

Parameters

Name	Type	Default	Description
`regressor`	`regressor`	-	Fitted scikit-learn regressor object
`X_train`	`ndarray`	-	Training data matrix
`X_test`	`ndarray`	-	Testing data matrix
`y_train`	`ndarray`	-	The regression target for training
`y_test`	`ndarray`	-	The regression target for testing

Returns

File value object with a residuals chart that you can log to the run.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log a residuals chart
rfr = RandomForestRegressor()
rfr.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils
run["visuals/residuals"] = npt_utils.create_residuals_chart(
    rfr, X_train, X_test, y_train, y_test
)

`create_prediction_error_chart()`#

Returns a prediction error chart.

Parameters

Name	Type	Default	Description
`regressor`	`regressor`	-	Fitted scikit-learn regressor object
`X_train`	`ndarray`	-	Training data matrix
`X_test`	`ndarray`	-	Testing data matrix
`y_train`	`ndarray`	-	The regression target for training
`y_test`	`ndarray`	-	The regression target for testing

Returns

File value object with a prediction error chart that you can log to the run.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log a prediction error chart
rfr = RandomForestRegressor()
rfr.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils
run["visuals/prediction_error"] = npt_utils.create_prediction_error_chart(
    rfr, X_train, X_test, y_train, y_test
)

`create_cooks_distance_chart()`#

Returns a Cook's distance chart.

Parameters

Name	Type	Default	Description
`regressor`	`regressor`	-	Fitted scikit-learn regressor object
`X_train`	`ndarray`	-	Training data matrix
`y_train`	`ndarray`	-	The regression target for training

Returns

File value object with a Cook's distance chart that you can log to the run.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log a prediction error chart
rfr = RandomForestRegressor()
rfr.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils
run["visuals/cooks_distance"] = npt_utils.create_cooks_distance_chart(
    rfr, X_train, y_train
)

`create_classification_report_chart()`#

Returns a classification report chart.

Parameters

Name	Type	Default	Description
`classifier`	`classifier`	-	Fitted scikit-learn classifier object
`X_train`	`ndarray`	-	Training data matrix
`X_test`	`ndarray`	-	Testing data matrix
`y_train`	`ndarray`	-	The classification target for training
`y_test`	`ndarray`	-	The classification target for testing

Returns

File value object with a classification report chart that you can log to the run.

Example

# Create a run
import neptune
run = neptune.init_run()

# Log a classification report chart
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils
run["visuals/cls_report"] = npt_utils.create_classification_report_chart(
    rfc, X_train, X_test, y_train, y_test
)

`create_confusion_matrix_chart()`#

Returns a confusion matrix.

Parameters

Name	Type	Default	Description
`classifier`	`classifier`	-	Fitted scikit-learn classifier object.
`X_train`	`ndarray`	-	Training data matrix.
`X_test`	`ndarray`	-	Testing data matrix.
`y_train`	`ndarray`	-	The classification target for training.
`y_test`	`ndarray`	-	The classification target for testing.

Returns

File value object that you can log to the run.

Example

Create a run:

import neptune

run = neptune.init_run()

Log the chart:

rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils

run["visuals/confusion_matrix"] = npt_utils.create_confusion_matrix_chart(
    rfc, X_train, X_test, y_train, y_test
)

`create_roc_auc_chart()`#

Returns a ROC-AUC chart.

Parameters

Name	Type	Default	Description
`classifier`	`classifier`	-	Fitted scikit-learn classifier object.
`X_train`	`ndarray`	-	Training data matrix.
`X_test`	`ndarray`	-	Testing data matrix.
`y_train`	`ndarray`	-	The classification target for training.
`y_test`	`ndarray`	-	The classification target for testing.

Returns

File value object that you can log to the run.

Example

Create a run:

import neptune

run = neptune.init_run()

Log the chart:

rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils

run["visuals/roc_auc"] = npt_utils.create_roc_auc_chart(
    rfc, X_train, X_test, y_train, y_test
)

`create_precision_recall_chart()`#

Returns a precision-recall chart.

Parameters

Name	Type	Default	Description
`classifier`	`classifier`	-	Fitted scikit-learn classifier object.
`X_test`	`ndarray`	-	Testing data matrix.
`y_test`	`ndarray`	-	The classification target for testing.
`y_pred_proba`	`ndarray`	-	Classifier predictions probabilities on test data.

Returns

File value object that you can log to the run.

Example

Create a run:

import neptune

run = neptune.init_run()

Log the chart:

rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils

run["visuals/precision_recall"] = npt_utils.create_precision_recall_chart(
    rfc, X_test, y_test
)

`create_class_prediction_error_chart()`#

Returns a class prediction error chart.

Parameters

Name	Type	Default	Description
`classifier`	`classifier`	-	Fitted scikit-learn classifier object.
`X_train`	`ndarray`	-	Training data matrix.
`X_test`	`ndarray`	-	Testing data matrix.
`y_train`	`ndarray`	-	The classification target for training.
`y_test`	`ndarray`	-	The classification target for testing.

Returns

File value object that you can log to the run.

Example

Create a run:

import neptune

run = neptune.init_run()

Log the chart:

rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)

import neptune.integrations.sklearn as npt_utils

run["visuals/class_pred_error"] = npt_utils.create_class_prediction_error_chart(
    rfc, X_train, X_test, y_train, y_test
)

`get_cluster_labels()`#

Logs the index of the cluster label each sample belongs to.

Parameters

Name	Type	Default	Description
`model`	`KMeans`	-	KMeans object.
`X`	`ndarray`	-	Training instances to cluster.
`nrows`	`int`, optional	`1000`	Number of rows to log.
`kwargs`	-	-	KMeans parameters.

Returns

File value object that you can log to the run.

Example

Create a run:

import neptune

run = neptune.init_run()

Log the labels:

km = KMeans(n_init=11, max_iter=270)
X, y = make_blobs(n_samples=579, n_features=17, centers=7, random_state=28743)

import neptune.integrations.sklearn as npt_utils

run["kmeans/cluster_labels"] = npt_utils.get_cluster_labels(km, X)

`create_kelbow_chart()`#

Returns the K-elbow chart for the KMeans clusterer.

Parameters

Name	Type	Default	Description
`model`	`KMeans`	-	KMeans object.
`X`	`ndarray`	-	Training instances to cluster.
`kwargs`	-	-	KMeans parameters.

Returns

File value object that you can log to the run.

Example

Create a run:

import neptune

run = neptune.init_run()

Log the chart:

km = KMeans(n_init=11, max_iter=270)
X, y = make_blobs(n_samples=579, n_features=17, centers=7, random_state=28743)

import neptune.integrations.sklearn as npt_utils

run["kmeans/kelbow"] = npt_utils.create_kelbow_chart(km, X)

`create_silhouette_chart()`#

Returns the silhouette coefficient charts for the KMeans clusterer.

Charts are computed for j = 2, 3, ..., n_clusters.

Parameters

Name	Type	Default	Description
`model`	`KMeans`	-	KMeans object.
`X`	`ndarray`	-	Training instances to cluster.
`kwargs`	-	-	KMeans parameters.

Returns

File value object that you can log to the run.

Example

Create a run:

import neptune

run = neptune.init_run()

Log the charts:

km = KMeans(n_init=11, max_iter=270)
X, y = make_blobs(n_samples=579, n_features=17, centers=7, random_state=28743)

import neptune.integrations.sklearn as npt_utils

run["kmeans/silhouette"] = npt_utils.create_silhouette_chart(km, X, n_clusters=12)

API reference: scikit-learn integration#

create_regressor_summary()#

create_classifier_summary()#

create_kmeans_summary()#

get_estimator_params()#

get_pickled_model()#

get_test_preds()#

get_test_preds_proba()#

get_scores()#

create_learning_curve_chart()#

create_feature_importance_chart()#

create_residuals_chart()#

create_prediction_error_chart()#

create_cooks_distance_chart()#

create_classification_report_chart()#

create_confusion_matrix_chart()#

create_roc_auc_chart()#

create_precision_recall_chart()#

create_class_prediction_error_chart()#

get_cluster_labels()#

create_kelbow_chart()#

create_silhouette_chart()#

`create_regressor_summary()`#

`create_classifier_summary()`#

`create_kmeans_summary()`#

`get_estimator_params()`#

`get_pickled_model()`#

`get_test_preds()`#

`get_test_preds_proba()`#

`get_scores()`#

`create_learning_curve_chart()`#

`create_feature_importance_chart()`#

`create_residuals_chart()`#

`create_prediction_error_chart()`#

`create_cooks_distance_chart()`#

`create_classification_report_chart()`#

`create_confusion_matrix_chart()`#

`create_roc_auc_chart()`#

`create_precision_recall_chart()`#

`create_class_prediction_error_chart()`#

`get_cluster_labels()`#

`create_kelbow_chart()`#

`create_silhouette_chart()`#