► Keras 3 API documentation / Metrics / Classification metrics based on True/False positives & negatives

Classification metrics based on True/False positives & negatives

`AUC` class

keras.metrics.AUC(
    num_thresholds=200,
    curve="ROC",
    summation_method="interpolation",
    name=None,
    dtype=None,
    thresholds=None,
    multi_label=False,
    num_labels=None,
    label_weights=None,
    from_logits=False,
)

Approximates the AUC (Area under the curve) of the ROC or PR curves.

The AUC (Area under the curve) of the ROC (Receiver operating characteristic; default) or PR (Precision Recall) curves are quality measures of binary classifiers. Unlike the accuracy, and like cross-entropy losses, ROC-AUC and PR-AUC evaluate all the operational points of a model.

This class approximates AUCs using a Riemann sum. During the metric accumulation phrase, predictions are accumulated within predefined buckets by value. The AUC is then computed by interpolating per-bucket averages. These buckets define the evaluated operational points.

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the AUC. To discretize the AUC curve, a linearly spaced set of thresholds is used to compute pairs of recall and precision values. The area under the ROC-curve is therefore computed using the height of the recall values by the false positive rate, while the area under the PR-curve is the computed using the height of the precision values by the recall.

This value is ultimately returned as auc, an idempotent operation that computes the area under a discretized curve of precision versus recall values (computed using the aforementioned variables). The num_thresholds variable controls the degree of discretization with larger numbers of thresholds more closely approximating the true AUC. The quality of the approximation may vary dramatically depending on num_thresholds. The thresholds parameter can be used to manually specify thresholds which split the predictions more evenly.

For a best approximation of the real AUC, predictions should be distributed approximately uniformly in the range [0, 1] (if from_logits=False). The quality of the AUC approximation may be poor if this is not the case. Setting summation_method to 'minoring' or 'majoring' can help quantify the error in the approximation by providing lower or upper bound estimate of the AUC.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Arguments

num_thresholds: (Optional) The number of thresholds to use when discretizing the roc curve. Values must be > 1. Defaults to 200.
curve: (Optional) Specifies the name of the curve to be computed, 'ROC' (default) or 'PR' for the Precision-Recall-curve.
summation_method: (Optional) Specifies the Riemann summation method used. 'interpolation' (default) applies mid-point summation scheme for ROC. For PR-AUC, interpolates (true/false) positives but not the ratio that is precision (see Davis & Goadrich 2006 for details); 'minoring' applies left summation for increasing intervals and right summation for decreasing intervals; 'majoring' does the opposite.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.
thresholds: (Optional) A list of floating point values to use as the thresholds for discretizing the curve. If set, the num_thresholds parameter is ignored. Values should be in [0, 1]. Endpoint thresholds equal to {-epsilon, 1+epsilon} for a small positive epsilon value will be automatically included with these to correctly handle predictions equal to exactly 0 or 1.
multi_label: boolean indicating whether multilabel data should be treated as such, wherein AUC is computed separately for each label and then averaged across labels, or (when False) if the data should be flattened into a single label before AUC computation. In the latter case, when multilabel data is passed to AUC, each label-prediction pair is treated as an individual data point. Should be set to False for multi-class data.
num_labels: (Optional) The number of labels, used when multi_label is True. If num_labels is not specified, then state variables get created on the first call to update_state.
label_weights: (Optional) list, array, or tensor of non-negative weights used to compute AUCs for multilabel data. When multi_label is True, the weights are applied to the individual label AUCs when they are averaged to produce the multi-label AUC. When it's False, they are used to weight the individual label predictions in computing the confusion matrix on the flattened data. Note that this is unlike class_weights in that class_weights weights the example depending on the value of its label, whereas label_weights depends only on the index of that label before flattening; therefore label_weights should not be used for multi-class data.
from_logits: boolean indicating whether the predictions (y_pred in update_state) are probabilities or sigmoid logits. As a rule of thumb, when using a keras loss, the from_logits constructor argument of the loss should match the AUC from_logits constructor argument.

Example

>>> m = keras.metrics.AUC(num_thresholds=3)
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
>>> # threshold values are [0 - 1e-7, 0.5, 1 + 1e-7]
>>> # tp = [2, 1, 0], fp = [2, 0, 0], fn = [0, 1, 2], tn = [0, 2, 2]
>>> # tp_rate = recall = [1, 0.5, 0], fp_rate = [1, 0, 0]
>>> # auc = ((((1 + 0.5) / 2) * (1 - 0)) + (((0.5 + 0) / 2) * (0 - 0)))
>>> #     = 0.75
>>> m.result()
0.75

>>> m.reset_state()
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
...                sample_weight=[1, 0, 0, 1])
>>> m.result()
1.0

Usage with compile() API:

# Reports the AUC of a model outputting a probability.
model.compile(optimizer='sgd',
              loss=keras.losses.BinaryCrossentropy(),
              metrics=[keras.metrics.AUC()])

# Reports the AUC of a model outputting a logit.
model.compile(optimizer='sgd',
              loss=keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=[keras.metrics.AUC(from_logits=True)])

[source]

`Precision` class

keras.metrics.Precision(
    thresholds=None, top_k=None, class_id=None, name=None, dtype=None
)

Computes the precision of the predictions with respect to the labels.

The metric creates two local variables, true_positives and false_positives that are used to compute the precision. This value is ultimately returned as precision, an idempotent operation that simply divides true_positives by the sum of true_positives and false_positives.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

If top_k is set, we'll calculate precision as how often on average a class among the top-k classes with the highest predicted values of a batch entry is correct and can be found in the label for that entry.

If class_id is specified, we calculate precision by considering only the entries in the batch for which class_id is above the threshold and/or in the top-k highest predictions, and computing the fraction of them for which class_id is indeed a correct label.

Arguments

thresholds: (Optional) A float value, or a Python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is True, below is False). If used with a loss function that sets from_logits=True (i.e. no sigmoid applied to predictions), thresholds should be set to 0. One metric value is generated for each threshold value. If neither thresholds nor top_k are set, the default is to calculate precision with thresholds=0.5.
top_k: (Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating precision.
class_id: (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example

>>> m = keras.metrics.Precision()
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
>>> m.result()
0.6666667

>>> m.reset_state()
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.result()
1.0

>>> # With top_k=2, it will calculate precision over y_true[:2]
>>> # and y_pred[:2]
>>> m = keras.metrics.Precision(top_k=2)
>>> m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
>>> m.result()
0.0

>>> # With top_k=4, it will calculate precision over y_true[:4]
>>> # and y_pred[:4]
>>> m = keras.metrics.Precision(top_k=4)
>>> m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
>>> m.result()
0.5

Usage with compile() API:

model.compile(optimizer='sgd',
              loss='binary_crossentropy',
              metrics=[keras.metrics.Precision()])

Usage with a loss with from_logits=True:

model.compile(optimizer='adam',
              loss=keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=[keras.metrics.Precision(thresholds=0)])

[source]

`Recall` class

keras.metrics.Recall(
    thresholds=None, top_k=None, class_id=None, name=None, dtype=None
)

Computes the recall of the predictions with respect to the labels.

This metric creates two local variables, true_positives and false_negatives, that are used to compute the recall. This value is ultimately returned as recall, an idempotent operation that simply divides true_positives by the sum of true_positives and false_negatives.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

If top_k is set, recall will be computed as how often on average a class among the labels of a batch entry is in the top-k predictions.

If class_id is specified, we calculate recall by considering only the entries in the batch for which class_id is in the label, and computing the fraction of them for which class_id is above the threshold and/or in the top-k predictions.

Arguments

thresholds: (Optional) A float value, or a Python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is True, below is False). If used with a loss function that sets from_logits=True (i.e. no sigmoid applied to predictions), thresholds should be set to 0. One metric value is generated for each threshold value. If neither thresholds nor top_k are set, the default is to calculate recall with thresholds=0.5.
top_k: (Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating recall.
class_id: (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example

>>> m = keras.metrics.Recall()
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
>>> m.result()
0.6666667

>>> m.reset_state()
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.result()
1.0

Usage with compile() API:

model.compile(optimizer='sgd',
              loss='binary_crossentropy',
              metrics=[keras.metrics.Recall()])

Usage with a loss with from_logits=True:

model.compile(optimizer='adam',
              loss=keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=[keras.metrics.Recall(thresholds=0)])

[source]

`TruePositives` class

keras.metrics.TruePositives(thresholds=None, name=None, dtype=None)

Calculates the number of true positives.

If sample_weight is given, calculates the sum of the weights of true positives. This metric creates one local variable, true_positives that is used to keep track of the number of true positives.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Arguments

thresholds: (Optional) Defaults to 0.5. A float value, or a Python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is True, below is False). If used with a loss function that sets from_logits=True (i.e. no sigmoid applied to predictions), thresholds should be set to 0. One metric value is generated for each threshold value.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example

>>> m = keras.metrics.TruePositives()
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
>>> m.result()
2.0

>>> m.reset_state()
>>> m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.result()
1.0

[source]

`TrueNegatives` class

keras.metrics.TrueNegatives(thresholds=None, name=None, dtype=None)

Calculates the number of true negatives.

If sample_weight is given, calculates the sum of the weights of true negatives. This metric creates one local variable, accumulator that is used to keep track of the number of true negatives.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Arguments

thresholds: (Optional) Defaults to 0.5. A float value, or a Python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is True, below is False). If used with a loss function that sets from_logits=True (i.e. no sigmoid applied to predictions), thresholds should be set to 0. One metric value is generated for each threshold value.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example

>>> m = keras.metrics.TrueNegatives()
>>> m.update_state([0, 1, 0, 0], [1, 1, 0, 0])
>>> m.result()
2.0

>>> m.reset_state()
>>> m.update_state([0, 1, 0, 0], [1, 1, 0, 0], sample_weight=[0, 0, 1, 0])
>>> m.result()
1.0

[source]

`FalsePositives` class

keras.metrics.FalsePositives(thresholds=None, name=None, dtype=None)

Calculates the number of false positives.

If sample_weight is given, calculates the sum of the weights of false positives. This metric creates one local variable, accumulator that is used to keep track of the number of false positives.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Arguments

thresholds: (Optional) Defaults to 0.5. A float value, or a Python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is True, below is False). If used with a loss function that sets from_logits=True (i.e. no sigmoid applied to predictions), thresholds should be set to 0. One metric value is generated for each threshold value.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Examples

>>> m = keras.metrics.FalsePositives()
>>> m.update_state([0, 1, 0, 0], [0, 0, 1, 1])
>>> m.result()
2.0

>>> m.reset_state()
>>> m.update_state([0, 1, 0, 0], [0, 0, 1, 1], sample_weight=[0, 0, 1, 0])
>>> m.result()
1.0

[source]

`FalseNegatives` class

keras.metrics.FalseNegatives(thresholds=None, name=None, dtype=None)

Calculates the number of false negatives.

If sample_weight is given, calculates the sum of the weights of false negatives. This metric creates one local variable, accumulator that is used to keep track of the number of false negatives.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Arguments

thresholds: (Optional) Defaults to 0.5. A float value, or a Python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is True, below is False). If used with a loss function that sets from_logits=True (i.e. no sigmoid applied to predictions), thresholds should be set to 0. One metric value is generated for each threshold value.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example

>>> m = keras.metrics.FalseNegatives()
>>> m.update_state([0, 1, 1, 1], [0, 1, 0, 0])
>>> m.result()
2.0

>>> m.reset_state()
>>> m.update_state([0, 1, 1, 1], [0, 1, 0, 0], sample_weight=[0, 0, 1, 0])
>>> m.result()
1.0

[source]

`PrecisionAtRecall` class

keras.metrics.PrecisionAtRecall(
    recall, num_thresholds=200, class_id=None, name=None, dtype=None
)

Computes best precision where recall is >= specified value.

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the precision at the given recall. The threshold for the given recall value is computed and used to evaluate the corresponding precision.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

If class_id is specified, we calculate precision by considering only the entries in the batch for which class_id is above the threshold predictions, and computing the fraction of them for which class_id is indeed a correct label.

Arguments

recall: A scalar value in range [0, 1].
num_thresholds: (Optional) Defaults to 200. The number of thresholds to use for matching the given recall.
class_id: (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example

>>> m = keras.metrics.PrecisionAtRecall(0.5)
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
>>> m.result()
0.5

>>> m.reset_state()
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
...                sample_weight=[2, 2, 2, 1, 1])
>>> m.result()
0.33333333

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='binary_crossentropy',
    metrics=[keras.metrics.PrecisionAtRecall(recall=0.8)])

[source]

`RecallAtPrecision` class

keras.metrics.RecallAtPrecision(
    precision, num_thresholds=200, class_id=None, name=None, dtype=None
)

Computes best recall where precision is >= specified value.

For a given score-label-distribution the required precision might not be achievable, in this case 0.0 is returned as recall.

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the recall at the given precision. The threshold for the given precision value is computed and used to evaluate the corresponding recall.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Arguments

precision: A scalar value in range [0, 1].
num_thresholds: (Optional) Defaults to 200. The number of thresholds to use for matching the given precision.
class_id: (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example

>>> m = keras.metrics.RecallAtPrecision(0.8)
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
>>> m.result()
0.5

>>> m.reset_state()
>>> m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
...                sample_weight=[1, 0, 0, 1])
>>> m.result()
1.0

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='binary_crossentropy',
    metrics=[keras.metrics.RecallAtPrecision(precision=0.8)])

[source]

`SensitivityAtSpecificity` class

keras.metrics.SensitivityAtSpecificity(
    specificity, num_thresholds=200, class_id=None, name=None, dtype=None
)

Computes best sensitivity where specificity is >= specified value.

Sensitivity measures the proportion of actual positives that are correctly identified as such (tp / (tp + fn)). Specificity measures the proportion of actual negatives that are correctly identified as such (tn / (tn + fp)).

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the sensitivity at the given specificity. The threshold for the given specificity value is computed and used to evaluate the corresponding sensitivity.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

For additional information about specificity and sensitivity, see the following.

Arguments

specificity: A scalar value in range [0, 1].
num_thresholds: (Optional) Defaults to 200. The number of thresholds to use for matching the given specificity.
class_id: (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example

>>> m = keras.metrics.SensitivityAtSpecificity(0.5)
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
>>> m.result()
0.5

>>> m.reset_state()
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
...                sample_weight=[1, 1, 2, 2, 1])
>>> m.result()
0.333333

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='binary_crossentropy',
    metrics=[keras.metrics.SensitivityAtSpecificity(specificity=0.5)])

[source]

`SpecificityAtSensitivity` class

keras.metrics.SpecificityAtSensitivity(
    sensitivity, num_thresholds=200, class_id=None, name=None, dtype=None
)

Computes best specificity where sensitivity is >= specified value.

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the specificity at the given sensitivity. The threshold for the given sensitivity value is computed and used to evaluate the corresponding specificity.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

For additional information about specificity and sensitivity, see the following.

Arguments

sensitivity: A scalar value in range [0, 1].
num_thresholds: (Optional) Defaults to 200. The number of thresholds to use for matching the given sensitivity.
class_id: (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example

>>> m = keras.metrics.SpecificityAtSensitivity(0.5)
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
>>> m.result()
0.66666667

>>> m.reset_state()
>>> m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
...                sample_weight=[1, 1, 2, 2, 2])
>>> m.result()
0.5

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='binary_crossentropy',
    metrics=[keras.metrics.SpecificityAtSensitivity(sensitivity=0.3)])

[source]

`F1Score` class

keras.metrics.F1Score(average=None, threshold=None, name="f1_score", dtype=None)

Computes F-1 Score.

Formula:

f1_score = 2 * (precision * recall) / (precision + recall)

This is the harmonic mean of precision and recall. Its output range is [0, 1]. It works for both multi-class and multi-label classification.

Arguments

average: Type of averaging to be performed on data. Acceptable values are None, "micro", "macro" and "weighted". Defaults to None. If None, no averaging is performed and result() will return the score for each class. If "micro", compute metrics globally by counting the total true positives, false negatives and false positives. If "macro", compute metrics for each label, and return their unweighted mean. This does not take label imbalance into account. If "weighted", compute metrics for each label, and return their average weighted by support (the number of true instances for each label). This alters "macro" to account for label imbalance. It can result in an score that is not between precision and recall.
threshold: Elements of y_pred greater than threshold are converted to be 1, and the rest 0. If threshold is None, the argmax of y_pred is converted to 1, and the rest to 0.
name: Optional. String name of the metric instance.
dtype: Optional. Data type of the metric result.

Returns

F-1 Score: float.

Example

>>> metric = keras.metrics.F1Score(threshold=0.5)
>>> y_true = np.array([[1, 1, 1],
...                    [1, 0, 0],
...                    [1, 1, 0]], np.int32)
>>> y_pred = np.array([[0.2, 0.6, 0.7],
...                    [0.2, 0.6, 0.6],
...                    [0.6, 0.8, 0.0]], np.float32)
>>> metric.update_state(y_true, y_pred)
>>> result = metric.result()
array([0.5      , 0.8      , 0.6666667], dtype=float32)

[source]

`FBetaScore` class

keras.metrics.FBetaScore(
    average=None, beta=1.0, threshold=None, name="fbeta_score", dtype=None
)

Computes F-Beta score.

Formula:

b2 = beta ** 2
f_beta_score = (1 + b2) * (precision * recall) / (precision * b2 + recall)

This is the weighted harmonic mean of precision and recall. Its output range is [0, 1]. It works for both multi-class and multi-label classification.

Arguments

average: Type of averaging to be performed across per-class results in the multi-class case. Acceptable values are None, "micro", "macro" and "weighted". Defaults to None. If None, no averaging is performed and result() will return the score for each class. If "micro", compute metrics globally by counting the total true positives, false negatives and false positives. If "macro", compute metrics for each label, and return their unweighted mean. This does not take label imbalance into account. If "weighted", compute metrics for each label, and return their average weighted by support (the number of true instances for each label). This alters "macro" to account for label imbalance. It can result in an score that is not between precision and recall.
beta: Determines the weight of given to recall in the harmonic mean between precision and recall (see pseudocode equation above). Defaults to 1.
threshold: Elements of y_pred greater than threshold are converted to be 1, and the rest 0. If threshold is None, the argmax of y_pred is converted to 1, and the rest to 0.
name: Optional. String name of the metric instance.
dtype: Optional. Data type of the metric result.

Returns

F-Beta Score: float.

Example

>>> metric = keras.metrics.FBetaScore(beta=2.0, threshold=0.5)
>>> y_true = np.array([[1, 1, 1],
...                    [1, 0, 0],
...                    [1, 1, 0]], np.int32)
>>> y_pred = np.array([[0.2, 0.6, 0.7],
...                    [0.2, 0.6, 0.6],
...                    [0.6, 0.8, 0.0]], np.float32)
>>> metric.update_state(y_true, y_pred)
>>> result = metric.result()
>>> result
[0.3846154 , 0.90909094, 0.8333334 ]