Display Kedro node metadata and outputs

​​
​​
​See code examples on GitHub​
​​
​​
​See results in Neptune​
You can log and display learning curves, diagnostic charts, images, video, and other metadata from Kedro pipelines in Neptune.
This guide shows how to:
Log diagnostic charts from Kedro pipeline nodes
Save node output as a file
Display charts, outputs, and other metadata from Kedro pipelines
By the end of this guide, you will log a ROC curve as image and node output as JSON from a Kedro pipeline to Neptune and display them in the Neptune UI.
​See this example in Neptune​
Display images and node outputs in Neptune UI
Keywords: Kedro Neptune, Display Kedro pipeline outputs, Log images from Kedro pipelines
Before you start
Make sure you meet the following prerequisites before starting:
​Have Kedro installed​
​Have neptune-client and kedro-neptune plugin installed​
​Have Kedro Iris starter created​
​Have Kedro-Neptune integration initialized​
Step 1: Add training and prediction nodes
Define model training parameters in conf/base/parameters.yml.
snippet
parameters.yml
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# Random forest parameters
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rf_max_depth: 3
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rf_max_features: 3
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rf_n_estimators: 25
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# Parameters for the example pipeline. Feel free to delete these once you
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# remove the example pipeline from hooks.py and the example nodes in
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# `src/pipelines/`
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​
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# Data split parameters
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example_test_data_ratio: 0.2
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​
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# Random forest parameters
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rf_max_depth: 3
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rf_max_features: 3
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rf_n_estimators: 25
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​
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# MLP parameters
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mlp_alpha: 0.02
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mlp_max_iter: 50
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Create a model training node in the src/KEDRO_PROJECT/pipelines/data_science/nodes.py.
Use parameters you defined in conf/base/parameters.yml. 
This node should output a trained model.
snippet
nodes.py
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def train_rf_model(train_x: pd.DataFrame, 
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                   train_y: pd.DataFrame, 
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                   parameters: Dict[str, Any]):
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    max_depth = parameters["rf_max_depth"]
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    n_estimators = parameters["rf_n_estimators"]
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    max_features = parameters["rf_max_features"]
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​
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    clf = RandomForestClassifier(max_depth=max_depth,
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                                 n_estimators=n_estimators,
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                                 max_features=max_features)
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    clf.fit(train_x, train_y.idxmax(axis=1))
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​
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    return clf
Copied!
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# Copyright 2021 QuantumBlack Visual Analytics Limited
2
#
3
# Licensed under the Apache License, Version 2.0 (the "License");
4
# you may not use this file except in compliance with the License.
5
# You may obtain a copy of the License at
6
#
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#     http://www.apache.org/licenses/LICENSE-2.0
8
#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
10
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
11
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
12
# NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS
13
# BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN
14
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN
15
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
16
#
17
# The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo
18
# (either separately or in combination, "QuantumBlack Trademarks") are
19
# trademarks of QuantumBlack. The License does not grant you any right or
20
# license to the QuantumBlack Trademarks. You may not use the QuantumBlack
21
# Trademarks or any confusingly similar mark as a trademark for your product,
22
# or use the QuantumBlack Trademarks in any other manner that might cause
23
# confusion in the marketplace, including but not limited to in advertising,
24
# on websites, or on software.
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#
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# See the License for the specific language governing permissions and
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# limitations under the License.
28
​
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"""Example code for the nodes in the example pipeline. This code is meant
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just for illustrating basic Kedro features.
31
​
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Delete this when you start working on your own Kedro project.
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"""
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# pylint: disable=invalid-name
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​
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import logging
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import matplotlib.pyplot as plt
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import neptune.new as neptune
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import numpy as np
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import pandas as pd
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from scikitplot.metrics import plot_roc_curve, plot_precision_recall_curve
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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from sklearn.neural_network import MLPClassifier
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from typing import Any, Dict
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​
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​
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def train_rf_model(
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        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
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):
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    """Node for training Random Forest model"""
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    max_depth = parameters["rf_max_depth"]
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    n_estimators = parameters["rf_n_estimators"]
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    max_features = parameters["rf_max_features"]
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​
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    clf = RandomForestClassifier(max_depth=max_depth,
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                                 n_estimators=n_estimators,
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                                 max_features=max_features)
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    clf.fit(train_x, train_y.idxmax(axis=1))
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​
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    return clf
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​
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​
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def train_mlp_model(
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        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
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):
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    """Node for training MLP model"""
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    alpha = parameters["mlp_alpha"]
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    max_iter = parameters["mlp_max_iter"]
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​
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    clf = MLPClassifier(alpha=alpha,
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                        max_iter=max_iter)
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    clf.fit(train_x, train_y)
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​
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    return clf
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​
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​
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def get_predictions(rf_model: RandomForestClassifier, mlp_model: MLPClassifier,
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                    test_x: pd.DataFrame) -> Dict[str, Any]:
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    """Node for making predictions given a pre-trained model and a test set."""
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    predictions = {}
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    for name, model in zip(['rf', 'mlp'], [rf_model, mlp_model]):
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        y_pred = model.predict_proba(test_x).tolist()
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        predictions[name] = y_pred
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​
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    return predictions
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​
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​
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def evaluate_models(predictions: dict, test_y: pd.DataFrame,
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                    neptune_run: neptune.run.Handler):
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    """Node for evaluating Random Forest and MLP models and creating ROC and Precision-Recall Curves"""
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​
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    for name, y_pred in predictions.items():
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        y_true = test_y.to_numpy().argmax(axis=1)
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        y_pred = np.array(y_pred)
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​
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        accuracy = accuracy_score(y_true, y_pred.argmax(axis=1).ravel())
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        neptune_run[f'nodes/evaluate_models/metrics/accuracy_{name}'] = accuracy
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​
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        fig, ax = plt.subplots()
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        plot_roc_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'ROC curve {name}')
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        neptune_run['nodes/evaluate_models/plots/plot_roc_curve'].log(fig)
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​
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        fig, ax = plt.subplots()
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        plot_precision_recall_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'PR curve {name}')
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        neptune_run['nodes/evaluate_models/plots/plot_precision_recall_curve'].log(fig)
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​
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​
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def ensemble_models(predictions: dict, test_y: pd.DataFrame,
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                    neptune_run: neptune.run.Handler) -> np.ndarray:
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    """Node for averaging predictions of Random Forest and MLP models"""
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    y_true = test_y.to_numpy().argmax(axis=1)
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    y_pred_averaged = np.stack(predictions.values()).mean(axis=0)
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​
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    accuracy = accuracy_score(y_true, y_pred_averaged.argmax(axis=1).ravel())
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    neptune_run[f'nodes/ensemble_models/metrics/accuracy_ensemble'] = accuracy
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​
Copied!
In this example, you will create a Kedro pipeline that trains and ensembles predictions from two models Random Forest and MLPClassifier. 
For simplicity, we showed just the Random Forest code snippets below. See the full nodes.py for the MLPClassifier. 
Create a model prediction node in the src/KEDRO_PROJECT/pipelines/data_science/nodes.py.
This node should output a dictionary with predictions for two models Random Forest and MLPClassifier. 
snippet
nodes.py
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def get_predictions(rf_model: RandomForestClassifier, 
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                    mlp_model: MLPClassifier,
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                    test_x: pd.DataFrame):
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    """Node for making predictions given a pre-trained model and a test set."""
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    predictions = {}
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    for name, model in zip(['rf', 'mlp'], [rf_model, mlp_model]):
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        y_pred = model.predict_proba(test_x).tolist()
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        predictions[name] = y_pred
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​
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    return predictions
Copied!
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# Copyright 2021 QuantumBlack Visual Analytics Limited
2
#
3
# Licensed under the Apache License, Version 2.0 (the "License");
4
# you may not use this file except in compliance with the License.
5
# You may obtain a copy of the License at
6
#
7
#     http://www.apache.org/licenses/LICENSE-2.0
8
#
9
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
10
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
11
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
12
# NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS
13
# BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN
14
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN
15
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
16
#
17
# The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo
18
# (either separately or in combination, "QuantumBlack Trademarks") are
19
# trademarks of QuantumBlack. The License does not grant you any right or
20
# license to the QuantumBlack Trademarks. You may not use the QuantumBlack
21
# Trademarks or any confusingly similar mark as a trademark for your product,
22
# or use the QuantumBlack Trademarks in any other manner that might cause
23
# confusion in the marketplace, including but not limited to in advertising,
24
# on websites, or on software.
25
#
26
# See the License for the specific language governing permissions and
27
# limitations under the License.
28
​
29
"""Example code for the nodes in the example pipeline. This code is meant
30
just for illustrating basic Kedro features.
31
​
32
Delete this when you start working on your own Kedro project.
33
"""
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# pylint: disable=invalid-name
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​
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import logging
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import matplotlib.pyplot as plt
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import neptune.new as neptune
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import numpy as np
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import pandas as pd
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from scikitplot.metrics import plot_roc_curve, plot_precision_recall_curve
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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from sklearn.neural_network import MLPClassifier
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from typing import Any, Dict
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​
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​
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def train_rf_model(
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        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
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):
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    """Node for training Random Forest model"""
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    max_depth = parameters["rf_max_depth"]
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    n_estimators = parameters["rf_n_estimators"]
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    max_features = parameters["rf_max_features"]
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​
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    clf = RandomForestClassifier(max_depth=max_depth,
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                                 n_estimators=n_estimators,
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                                 max_features=max_features)
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    clf.fit(train_x, train_y.idxmax(axis=1))
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​
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    return clf
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​
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​
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def train_mlp_model(
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        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
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):
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    """Node for training MLP model"""
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    alpha = parameters["mlp_alpha"]
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    max_iter = parameters["mlp_max_iter"]
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​
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    clf = MLPClassifier(alpha=alpha,
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                        max_iter=max_iter)
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    clf.fit(train_x, train_y)
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​
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    return clf
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​
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​
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def get_predictions(rf_model: RandomForestClassifier, mlp_model: MLPClassifier,
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                    test_x: pd.DataFrame) -> Dict[str, Any]:
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    """Node for making predictions given a pre-trained model and a test set."""
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    predictions = {}
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    for name, model in zip(['rf', 'mlp'], [rf_model, mlp_model]):
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        y_pred = model.predict_proba(test_x).tolist()
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        predictions[name] = y_pred
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​
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    return predictions
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​
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​
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def evaluate_models(predictions: dict, test_y: pd.DataFrame,
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                    neptune_run: neptune.run.Handler):
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    """Node for evaluating Random Forest and MLP models and creating ROC and Precision-Recall Curves"""
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​
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    for name, y_pred in predictions.items():
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        y_true = test_y.to_numpy().argmax(axis=1)
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        y_pred = np.array(y_pred)
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​
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        accuracy = accuracy_score(y_true, y_pred.argmax(axis=1).ravel())
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        neptune_run[f'nodes/evaluate_models/metrics/accuracy_{name}'] = accuracy
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​
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        fig, ax = plt.subplots()
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        plot_roc_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'ROC curve {name}')
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        neptune_run['nodes/evaluate_models/plots/plot_roc_curve'].log(fig)
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​
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        fig, ax = plt.subplots()
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        plot_precision_recall_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'PR curve {name}')
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        neptune_run['nodes/evaluate_models/plots/plot_precision_recall_curve'].log(fig)
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​
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​
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def ensemble_models(predictions: dict, test_y: pd.DataFrame,
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                    neptune_run: neptune.run.Handler) -> np.ndarray:
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    """Node for averaging predictions of Random Forest and MLP models"""
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    y_true = test_y.to_numpy().argmax(axis=1)
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    y_pred_averaged = np.stack(predictions.values()).mean(axis=0)
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​
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    accuracy = accuracy_score(y_true, y_pred_averaged.argmax(axis=1).ravel())
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    neptune_run[f'nodes/ensemble_models/metrics/accuracy_ensemble'] = accuracy
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​
Copied!
Step 2: Add evaluation node and log ROC curve to Neptune
Import Neptune client toward the top of the nodes.py
snippet
nodes.py
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import neptune.new as neptune
Copied!
1
# Copyright 2021 QuantumBlack Visual Analytics Limited
2
#
3
# Licensed under the Apache License, Version 2.0 (the "License");
4
# you may not use this file except in compliance with the License.
5
# You may obtain a copy of the License at
6
#
7
#     http://www.apache.org/licenses/LICENSE-2.0
8
#
9
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
10
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
11
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
12
# NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS
13
# BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN
14
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN
15
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
16
#
17
# The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo
18
# (either separately or in combination, "QuantumBlack Trademarks") are
19
# trademarks of QuantumBlack. The License does not grant you any right or
20
# license to the QuantumBlack Trademarks. You may not use the QuantumBlack
21
# Trademarks or any confusingly similar mark as a trademark for your product,
22
# or use the QuantumBlack Trademarks in any other manner that might cause
23
# confusion in the marketplace, including but not limited to in advertising,
24
# on websites, or on software.
25
#
26
# See the License for the specific language governing permissions and
27
# limitations under the License.
28
​
29
"""Example code for the nodes in the example pipeline. This code is meant
30
just for illustrating basic Kedro features.
31
​
32
Delete this when you start working on your own Kedro project.
33
"""
34
# pylint: disable=invalid-name
35
​
36
import logging
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import matplotlib.pyplot as plt
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import neptune.new as neptune
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import numpy as np
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import pandas as pd
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from scikitplot.metrics import plot_roc_curve, plot_precision_recall_curve
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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from sklearn.neural_network import MLPClassifier
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from typing import Any, Dict
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​
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​
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def train_rf_model(
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        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
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):
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    """Node for training Random Forest model"""
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    max_depth = parameters["rf_max_depth"]
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    n_estimators = parameters["rf_n_estimators"]
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    max_features = parameters["rf_max_features"]
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​
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    clf = RandomForestClassifier(max_depth=max_depth,
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                                 n_estimators=n_estimators,
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                                 max_features=max_features)
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    clf.fit(train_x, train_y.idxmax(axis=1))
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​
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    return clf
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​
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​
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def train_mlp_model(
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        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
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):
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    """Node for training MLP model"""
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    alpha = parameters["mlp_alpha"]
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    max_iter = parameters["mlp_max_iter"]
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​
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    clf = MLPClassifier(alpha=alpha,
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                        max_iter=max_iter)
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    clf.fit(train_x, train_y)
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​
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    return clf
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​
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​
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def get_predictions(rf_model: RandomForestClassifier, mlp_model: MLPClassifier,
79
                    test_x: pd.DataFrame) -> Dict[str, Any]:
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    """Node for making predictions given a pre-trained model and a test set."""
81
    predictions = {}
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    for name, model in zip(['rf', 'mlp'], [rf_model, mlp_model]):
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        y_pred = model.predict_proba(test_x).tolist()
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        predictions[name] = y_pred
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​
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    return predictions
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​
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​
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def evaluate_models(predictions: dict, test_y: pd.DataFrame,
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                    neptune_run: neptune.run.Handler):
91
    """Node for evaluating Random Forest and MLP models and creating ROC and Precision-Recall Curves"""
92
​
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    for name, y_pred in predictions.items():
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        y_true = test_y.to_numpy().argmax(axis=1)
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        y_pred = np.array(y_pred)
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​
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        accuracy = accuracy_score(y_true, y_pred.argmax(axis=1).ravel())
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        neptune_run[f'nodes/evaluate_models/metrics/accuracy_{name}'] = accuracy
99
​
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        fig, ax = plt.subplots()
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        plot_roc_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'ROC curve {name}')
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        neptune_run['nodes/evaluate_models/plots/plot_roc_curve'].log(fig)
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​
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        fig, ax = plt.subplots()
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        plot_precision_recall_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'PR curve {name}')
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        neptune_run['nodes/evaluate_models/plots/plot_precision_recall_curve'].log(fig)
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​
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​
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def ensemble_models(predictions: dict, test_y: pd.DataFrame,
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                    neptune_run: neptune.run.Handler) -> np.ndarray:
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    """Node for averaging predictions of Random Forest and MLP models"""
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    y_true = test_y.to_numpy().argmax(axis=1)
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    y_pred_averaged = np.stack(predictions.values()).mean(axis=0)
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​
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    accuracy = accuracy_score(y_true, y_pred_averaged.argmax(axis=1).ravel())
116
    neptune_run[f'nodes/ensemble_models/metrics/accuracy_ensemble'] = accuracy
117
​
Copied!
Add neptune_run argument of type neptune.run.Handler to the report_accuracy function 
snippet
nodes.py
1
def evaluate_models(predictions: dict, test_y: pd.DataFrame,
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                    neptune_run: neptune.run.Handler):
3
...
Copied!
1
# Copyright 2021 QuantumBlack Visual Analytics Limited
2
#
3
# Licensed under the Apache License, Version 2.0 (the "License");
4
# you may not use this file except in compliance with the License.
5
# You may obtain a copy of the License at
6
#
7
#     http://www.apache.org/licenses/LICENSE-2.0
8
#
9
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
10
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
11
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
12
# NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS
13
# BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN
14
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN
15
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
16
#
17
# The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo
18
# (either separately or in combination, "QuantumBlack Trademarks") are
19
# trademarks of QuantumBlack. The License does not grant you any right or
20
# license to the QuantumBlack Trademarks. You may not use the QuantumBlack
21
# Trademarks or any confusingly similar mark as a trademark for your product,
22
# or use the QuantumBlack Trademarks in any other manner that might cause
23
# confusion in the marketplace, including but not limited to in advertising,
24
# on websites, or on software.
25
#
26
# See the License for the specific language governing permissions and
27
# limitations under the License.
28
​
29
"""Example code for the nodes in the example pipeline. This code is meant
30
just for illustrating basic Kedro features.
31
​
32
Delete this when you start working on your own Kedro project.
33
"""
34
# pylint: disable=invalid-name
35
​
36
import logging
37
import matplotlib.pyplot as plt
38
import neptune.new as neptune
39
import numpy as np
40
import pandas as pd
41
from scikitplot.metrics import plot_roc_curve, plot_precision_recall_curve
42
from sklearn.ensemble import RandomForestClassifier
43
from sklearn.metrics import accuracy_score
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from sklearn.neural_network import MLPClassifier
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from typing import Any, Dict
46
​
47
​
48
def train_rf_model(
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        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
50
):
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    """Node for training Random Forest model"""
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    max_depth = parameters["rf_max_depth"]
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    n_estimators = parameters["rf_n_estimators"]
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    max_features = parameters["rf_max_features"]
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​
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    clf = RandomForestClassifier(max_depth=max_depth,
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                                 n_estimators=n_estimators,
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                                 max_features=max_features)
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    clf.fit(train_x, train_y.idxmax(axis=1))
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​
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    return clf
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​
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​
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def train_mlp_model(
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        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
66
):
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    """Node for training MLP model"""
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    alpha = parameters["mlp_alpha"]
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    max_iter = parameters["mlp_max_iter"]
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​
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    clf = MLPClassifier(alpha=alpha,
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                        max_iter=max_iter)
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    clf.fit(train_x, train_y)
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​
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    return clf
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​
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​
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def get_predictions(rf_model: RandomForestClassifier, mlp_model: MLPClassifier,
79
                    test_x: pd.DataFrame) -> Dict[str, Any]:
80
    """Node for making predictions given a pre-trained model and a test set."""
81
    predictions = {}
82
    for name, model in zip(['rf', 'mlp'], [rf_model, mlp_model]):
83
        y_pred = model.predict_proba(test_x).tolist()
84
        predictions[name] = y_pred
85
​
86
    return predictions
87
​
88
​
89
def evaluate_models(predictions: dict, test_y: pd.DataFrame,
90
                    neptune_run: neptune.run.Handler):
91
    """Node for evaluating Random Forest and MLP models and creating ROC and Precision-Recall Curves"""
92
​
93
    for name, y_pred in predictions.items():
94
        y_true = test_y.to_numpy().argmax(axis=1)
95
        y_pred = np.array(y_pred)
96
​
97
        accuracy = accuracy_score(y_true, y_pred.argmax(axis=1).ravel())
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        neptune_run[f'nodes/evaluate_models/metrics/accuracy_{name}'] = accuracy
99
​
100
        fig, ax = plt.subplots()
101
        plot_roc_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'ROC curve {name}')
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        neptune_run['nodes/evaluate_models/plots/plot_roc_curve'].log(fig)
103
​
104
        fig, ax = plt.subplots()
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        plot_precision_recall_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'PR curve {name}')
106
        neptune_run['nodes/evaluate_models/plots/plot_precision_recall_curve'].log(fig)
107
​
108
​
109
def ensemble_models(predictions: dict, test_y: pd.DataFrame,
110
                    neptune_run: neptune.run.Handler) -> np.ndarray:
111
    """Node for averaging predictions of Random Forest and MLP models"""
112
    y_true = test_y.to_numpy().argmax(axis=1)
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    y_pred_averaged = np.stack(predictions.values()).mean(axis=0)
114
​
115
    accuracy = accuracy_score(y_true, y_pred_averaged.argmax(axis=1).ravel())
116
    neptune_run[f'nodes/ensemble_models/metrics/accuracy_ensemble'] = accuracy
117
​
Copied!
You can treat neptune_run like a normal Neptune Run and log any ML metadata to it. 
You have to use a special string "neptune_run" to use the Neptune Run handler in Kedro pipelines.
Create the ROC curve as matplotlib figure and log it to 'nodes/evaluate_models/plots/plot_roc_curve' namespace with the .log() method.
snippet
nodes.py
1
def evaluate_models(predictions: dict, test_y: pd.DataFrame,
2
                    neptune_run: neptune.run.Handler):
3
    ...
4
​
5
    for name, y_pred in predictions.items():
6
        y_true = test_y.to_numpy().argmax(axis=1)
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        y_pred = np.array(y_pred)
8
    
9
        fig, ax = plt.subplots()
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        plot_roc_curve(test_y.idxmax(axis=1), y_pred, 
11
                       ax=ax, title=f'ROC curve {name}')
12
        neptune_run['nodes/evaluate_models/plots/plot_roc_curve'].log(fig)
Copied!
1
# Copyright 2021 QuantumBlack Visual Analytics Limited
2
#
3
# Licensed under the Apache License, Version 2.0 (the "License");
4
# you may not use this file except in compliance with the License.
5
# You may obtain a copy of the License at
6
#
7
#     http://www.apache.org/licenses/LICENSE-2.0
8
#
9
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
10
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
11
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
12
# NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS
13
# BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN
14
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN
15
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
16
#
17
# The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo
18
# (either separately or in combination, "QuantumBlack Trademarks") are
19
# trademarks of QuantumBlack. The License does not grant you any right or
20
# license to the QuantumBlack Trademarks. You may not use the QuantumBlack
21
# Trademarks or any confusingly similar mark as a trademark for your product,
22
# or use the QuantumBlack Trademarks in any other manner that might cause
23
# confusion in the marketplace, including but not limited to in advertising,
24
# on websites, or on software.
25
#
26
# See the License for the specific language governing permissions and
27
# limitations under the License.
28
​
29
"""Example code for the nodes in the example pipeline. This code is meant
30
just for illustrating basic Kedro features.
31
​
32
Delete this when you start working on your own Kedro project.
33
"""
34
# pylint: disable=invalid-name
35
​
36
import logging
37
import matplotlib.pyplot as plt
38
import neptune.new as neptune
39
import numpy as np
40
import pandas as pd
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from scikitplot.metrics import plot_roc_curve, plot_precision_recall_curve
42
from sklearn.ensemble import RandomForestClassifier
43
from sklearn.metrics import accuracy_score
44
from sklearn.neural_network import MLPClassifier
45
from typing import Any, Dict
46
​
47
​
48
def train_rf_model(
49
        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
50
):
51
    """Node for training Random Forest model"""
52
    max_depth = parameters["rf_max_depth"]
53
    n_estimators = parameters["rf_n_estimators"]
54
    max_features = parameters["rf_max_features"]
55
​
56
    clf = RandomForestClassifier(max_depth=max_depth,
57
                                 n_estimators=n_estimators,
58
                                 max_features=max_features)
59
    clf.fit(train_x, train_y.idxmax(axis=1))
60
​
61
    return clf
62
​
63
​
64
def train_mlp_model(
65
        train_x: pd.DataFrame, train_y: pd.DataFrame, parameters: Dict[str, Any]
66
):
67
    """Node for training MLP model"""
68
    alpha = parameters["mlp_alpha"]
69
    max_iter = parameters["mlp_max_iter"]
70
​
71
    clf = MLPClassifier(alpha=alpha,
72
                        max_iter=max_iter)
73
    clf.fit(train_x, train_y)
74
​
75
    return clf
76
​
77
​
78
def get_predictions(rf_model: RandomForestClassifier, mlp_model: MLPClassifier,
79
                    test_x: pd.DataFrame) -> Dict[str, Any]:
80
    """Node for making predictions given a pre-trained model and a test set."""
81
    predictions = {}
82
    for name, model in zip(['rf', 'mlp'], [rf_model, mlp_model]):
83
        y_pred = model.predict_proba(test_x).tolist()
84
        predictions[name] = y_pred
85
​
86
    return predictions
87
​
88
​
89
def evaluate_models(predictions: dict, test_y: pd.DataFrame,
90
                    neptune_run: neptune.run.Handler):
91
    """Node for evaluating Random Forest and MLP models and creating ROC and Precision-Recall Curves"""
92
​
93
    for name, y_pred in predictions.items():
94
        y_true = test_y.to_numpy().argmax(axis=1)
95
        y_pred = np.array(y_pred)
96
​
97
        accuracy = accuracy_score(y_true, y_pred.argmax(axis=1).ravel())
98
        neptune_run[f'nodes/evaluate_models/metrics/accuracy_{name}'] = accuracy
99
​
100
        fig, ax = plt.subplots()
101
        plot_roc_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'ROC curve {name}')
102
        neptune_run['nodes/evaluate_models/plots/plot_roc_curve'].log(fig)
103
​
104
        fig, ax = plt.subplots()
105
        plot_precision_recall_curve(test_y.idxmax(axis=1), y_pred, ax=ax, title=f'PR curve {name}')
106
        neptune_run['nodes/evaluate_models/plots/plot_precision_recall_curve'].log(fig)
107
​
108
​
109
def ensemble_models(predictions: dict, test_y: pd.DataFrame,
110
                    neptune_run: neptune.run.Handler) -> np.ndarray:
111
    """Node for averaging predictions of Random Forest and MLP models"""
112
    y_true = test_y.to_numpy().argmax(axis=1)
113
    y_pred_averaged = np.stack(predictions.values()).mean(axis=0)
114
​
115
    accuracy = accuracy_score(y_true, y_pred_averaged.argmax(axis=1).ravel())
116
    neptune_run[f'nodes/ensemble_models/metrics/accuracy_ensemble'] = accuracy
117
​
Copied!
Step 3: Save node output as JSON file in Neptune
Add predictions dataset to the Kedro catalog in 'conf/base/catalog.yml':
snippet
catalog.yml
1
predictions:
2
  type: kedro.extras.datasets.json.JSONDataSet
3
  filepath: data/07_model_output/predictions.json
Copied!
1
# Here you can define all your data sets by using simple YAML syntax.
2
#
3
# Documentation for this file format can be found in "The Data Catalog"
4
# Link: https://kedro.readthedocs.io/en/stable/05_data/01_data_catalog.html
5
#
6
# We support interacting with a variety of data stores including local file systems, cloud, network and HDFS
7
#
8
# An example data set definition can look as follows:
9
#
10
#bikes:
11
#  type: pandas.CSVDataSet
12
#  filepath: "data/01_raw/bikes.csv"
13
#
14
#weather:
15
#  type: spark.SparkDataSet
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#  filepath: s3a://your_bucket/data/01_raw/weather*
17
#  file_format: csv
18
#  credentials: dev_s3
19
#  load_args:
20
#    header: True
21
#    inferSchema: True
22
#  save_args:
23
#    sep: '|'
24
#    header: True
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#
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#scooters:
27
#  type: pandas.SQLTableDataSet
28
#  credentials: scooters_credentials
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#  table_name: scooters
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#  load_args:
31
#    index_col: ['name']
32
#    columns: ['name', 'gear']
33
#  save_args:
34
#    if_exists: 'replace'
35
#    # if_exists: 'fail'
36
#    # if_exists: 'append'
37
#
38
# The Data Catalog supports being able to reference the same file using two different DataSet implementations
39
# (transcoding), templating and a way to reuse arguments that are frequently repeated. See more here:
40
# https://kedro.readthedocs.io/en/stable/05_data/01_data_catalog.html
41
#
42
# This is a data set used by the "Hello World" example pipeline provided with the project
43
# template. Please feel free to remove it once you remove the example pipeline.
44
​
45
example_iris_data:
46
  type: pandas.CSVDataSet
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  filepath: data/01_raw/iris_v2.csv
48
​
49
rf_model:
50
  type: kedro.extras.datasets.pickle.PickleDataSet
51
  filepath: data/06_models/rf_model.pkl
52
​
53
mlp_model:
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  type: kedro.extras.datasets.pickle.PickleDataSet
55
  filepath: data/06_models/mlp_model.pkl
56
​
57
predictions:
58
  type: kedro.extras.datasets.json.JSONDataSet
59
  filepath: data/07_model_output/predictions.json
60
​
61
[email protected]:
62
  type: kedro_neptune.NeptuneFileDataSet
63
  filepath: data/07_model_output/predictions.json
64
​
Copied!
Add [email protected] dataset to the catalog in 'conf/base/catalog.yml' to log Kedro DataSet as file to Neptune:
snippet
catalog.yml
1
[email protected]:
2
  type: kedro_neptune.NeptuneFileDataSet
3
  filepath: data/07_model_output/predictions.json
Copied!
1
# Here you can define all your data sets by using simple YAML syntax.
2
#
3
# Documentation for this file format can be found in "The Data Catalog"
4
# Link: https://kedro.readthedocs.io/en/stable/05_data/01_data_catalog.html
5
#
6
# We support interacting with a variety of data stores including local file systems, cloud, network and HDFS
7
#
8
# An example data set definition can look as follows:
9
#
10
#bikes:
11
#  type: pandas.CSVDataSet
12
#  filepath: "data/01_raw/bikes.csv"
13
#
14
#weather:
15
#  type: spark.SparkDataSet
16
#  filepath: s3a://your_bucket/data/01_raw/weather*
17
#  file_format: csv
18
#  credentials: dev_s3
19
#  load_args:
20
#    header: True
21
#    inferSchema: True
22
#  save_args:
23
#    sep: '|'
24
#    header: True
25
#
26
#scooters:
27
#  type: pandas.SQLTableDataSet
28
#  credentials: scooters_credentials
29
#  table_name: scooters
30
#  load_args:
31
#    index_col: ['name']
32
#    columns: ['name', 'gear']
33
#  save_args:
34
#    if_exists: 'replace'
35
#    # if_exists: 'fail'
36
#    # if_exists: 'append'
37
#
38
# The Data Catalog supports being able to reference the same file using two different DataSet implementations
39
# (transcoding), templating and a way to reuse arguments that are frequently repeated. See more here:
40
# https://kedro.readthedocs.io/en/stable/05_data/01_data_catalog.html
41
#
42
# This is a data set used by the "Hello World" example pipeline provided with the project
43
# template. Please feel free to remove it once you remove the example pipeline.
44
​
45
example_iris_data:
46
  type: pandas.CSVDataSet
47
  filepath: data/01_raw/iris_v2.csv
48
​
49
rf_model:
50
  type: kedro.extras.datasets.pickle.PickleDataSet
51
  filepath: data/06_models/rf_model.pkl
52
​
53
mlp_model:
54
  type: kedro.extras.datasets.pickle.PickleDataSet
55
  filepath: data/06_models/mlp_model.pkl
56
​
57
predictions:
58
  type: kedro.extras.datasets.json.JSONDataSet
59
  filepath: data/07_model_output/predictions.json
60
​
61
[email protected]:
62
  type: kedro_neptune.NeptuneFileDataSet
63
  filepath: data/07_model_output/predictions.json
64
​
Copied!
You can log any file format to Neptune not just JSON with kedro_neptune.NeptuneFileDataSet.
Step 4: Add Neptune Run handler to the Kedro pipeline
Go to a pipeline definition, src/KEDRO_PROJECT/pipelines/data_science/pipelines.py
Add neptune_run Run handler as an input to the evaluate_models node
snippet
pipelines.py
1
node(
2
    evaluate_models,
3
    dict(predictions="predictions", 
4
         test_y="example_test_y", 
5
         neptune_run="neptune_run"),
6
    None,
7
    name="evaluate_models",
8
    ),
Copied!
pipelines/data_science/pipelines.py
1
# Copyright 2021 QuantumBlack Visual Analytics Limited
2
#
3
# Licensed under the Apache License, Version 2.0 (the "License");
4
# you may not use this file except in compliance with the License.
5
# You may obtain a copy of the License at
6
#
7
#     http://www.apache.org/licenses/LICENSE-2.0
8
#
9
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
10
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
11
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND
12
# NONINFRINGEMENT. IN NO EVENT WILL THE LICENSOR OR OTHER CONTRIBUTORS
13
# BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER LIABILITY, WHETHER IN AN
14
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF, OR IN
15
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
16
#
17
# The QuantumBlack Visual Analytics Limited ("QuantumBlack") name and logo
18
# (either separately or in combination, "QuantumBlack Trademarks") are
19
# trademarks of QuantumBlack. The License does not grant you any right or
20
# license to the QuantumBlack Trademarks. You may not use the QuantumBlack
21
# Trademarks or any confusingly similar mark as a trademark for your product,
22
# or use the QuantumBlack Trademarks in any other manner that might cause
23
# confusion in the marketplace, including but not limited to in advertising,
24
# on websites, or on software.
25
#
26
# See the License for the specific language governing permissions and
27
# limitations under the License.
28
​
29
"""Example code for the nodes in the example pipeline. This code is meant
30
just for illustrating basic Kedro features.
31
​
32
Delete this when you start working on your own Kedro project.
33
"""
34
​
35
from kedro.pipeline import Pipeline, node
36
​
​See this example in Neptune​

Before you start

Step 1: Add training and prediction nodes

Step 2: Add evaluation node and log ROC curve to Neptune

Step 3: Save node output as JSON file in Neptune

Step 4: Add Neptune Run handler to the Kedro pipeline

See this example in Neptune
