BaseEvaluator
ADLStream.evaluation.BaseEvaluator
Abstract base evaluator
This is the base class for implementing a custom evaluator.
Every Evaluator
must have the properties below and implement evaluate
with the
signature (new_results, instances) = evaluate()
. The evaluate
function should
contain the logic to:
- Get validation metrics from validation data (self.y_eval
, self.o_eval
and self.x_eval
).
- Save metrics in self.metric_history
.
- Remove already evaluated data (y_eval
, o_eval
and x_eval
) to keep memory
free.
- Return new computed accuracy and count of number of instances evaluated.
Examples:
class MinimalEvaluator(BaseEvaluator):
def __init__(self, metric='kappa', **kwargs):
self.metric = metric
super().__init__(**kwargs)
def evaluate(self):
new_results = []
instances = []
current_instance = len(self.metric_history)
while self.y_eval and self.o_eval:
# Get metric
new_metric = metrics.evaluate(
self.metric,
self.y_eval[0]
self.o_eval[0]
)
# Save metric
self.metric_history.append(new_metric)
# Remove evaluated data
self.y_eval = self.y_eval[1:]
self.o_eval = self.o_eval[1:]
self.x_eval = self.x_eval[1:]
# Add number of instances evaluated
current_instance += 1
instances.append(current_instance)
retun new_results, instances
Parameters:
Name | Type | Description | Default |
---|---|---|---|
results_file |
str |
Name of the csv file where to write results. If None, no csv file is created. Defaults to "ADLStream.csv". |
None |
dataset_name |
str |
Name of the data to validate. Defaults to None. |
None |
show_plot |
bool |
Whether to plot the evolution of the metric. Defaults to True. |
True |
plot_file |
str |
Name of the plot image file. If None, no image is saved. Defaults to None. |
None |
ylabel |
str |
y-axis label of the evolution plot. Defaults to "". |
'' |
Source code in ADLStream/evaluation/base_evaluator.py
class BaseEvaluator(ABC):
"""Abstract base evaluator
This is the base class for implementing a custom evaluator.
Every `Evaluator` must have the properties below and implement `evaluate` with the
signature `(new_results, instances) = evaluate()`. The `evaluate` function should
contain the logic to:
- Get validation metrics from validation data (`self.y_eval`, `self.o_eval`
and `self.x_eval`).
- Save metrics in `self.metric_history`.
- Remove already evaluated data (`y_eval`, `o_eval` and `x_eval`) to keep memory
free.
- Return new computed accuracy and count of number of instances evaluated.
Examples:
```python
class MinimalEvaluator(BaseEvaluator):
def __init__(self, metric='kappa', **kwargs):
self.metric = metric
super().__init__(**kwargs)
def evaluate(self):
new_results = []
instances = []
current_instance = len(self.metric_history)
while self.y_eval and self.o_eval:
# Get metric
new_metric = metrics.evaluate(
self.metric,
self.y_eval[0]
self.o_eval[0]
)
# Save metric
self.metric_history.append(new_metric)
# Remove evaluated data
self.y_eval = self.y_eval[1:]
self.o_eval = self.o_eval[1:]
self.x_eval = self.x_eval[1:]
# Add number of instances evaluated
current_instance += 1
instances.append(current_instance)
retun new_results, instances
```
Arguments:
results_file (str, optional): Name of the csv file where to write results.
If None, no csv file is created.
Defaults to "ADLStream.csv".
dataset_name (str, optional): Name of the data to validate.
Defaults to None.
show_plot (bool, optional): Whether to plot the evolution of the metric.
Defaults to True.
plot_file (str, optional): Name of the plot image file.
If None, no image is saved.
Defaults to None.
ylabel (str, optional): y-axis label of the evolution plot.
Defaults to "".
"""
def __init__(
self,
results_file=None,
predictions_file=None,
dataset_name=None,
show_plot=True,
plot_file=None,
ylabel="",
):
self.results_file = results_file
self.dataset_name = dataset_name
self.predictions_file = predictions_file
self.show_plot = show_plot
self.plot_file = plot_file
self.ylabel = ylabel
self.x_eval = []
self.y_eval = []
self.o_eval = []
self.metric_history = []
self._create_results_file()
self.visualizer = None
if self.show_plot or self.plot_file is not None:
self.visualizer = EvaluationVisualizer(self.dataset_name, self.ylabel)
def _create_results_file(self):
if self.results_file is not None:
with open(self.results_file, "w") as f:
f.write("timestamp,instances,metric\n")
def start(self):
if self.visualizer is not None:
self.visualizer.start()
if self.predictions_file:
self.predictions_file = open(self.predictions_file, "a")
if self.results_file:
self.results_file = open(self.results_file, "a")
def end(self):
self.predictions_file.close()
self.results_file.close()
@abstractmethod
def evaluate(self):
"""Function that contains the main logic of the evaluator.
In a generic scheme, this function should:
- Get validation metrics from validation data (`self.y_eval`, `self.o_eval`
and `self.x_eval`).
- Save metrics in `self.metric_history`.
- Remove already evaluated data (`y_eval`, `o_eval` and `x_eval`) to keep
memory free.
- Return new computed metrics and count of number of instances evaluated.
Raises:
NotImplementedError: This is an abstract method which should be implemented.
Returns:
tuple[list, list]: new_metrics (list), instances(list)
"""
raise NotImplementedError("Abstract method")
def write_results(self, new_results, instances):
if self.results_file is not None:
for i, value in enumerate(new_results):
self.results_file.write(
"{},{},{}\n".format(
str(datetime.now()),
instances[i],
value,
)
)
def write_predictions(self, preds):
if self.predictions_file is not None:
for _, prediction in enumerate(preds):
self.predictions_file.write(f"{','.join(map(str, prediction))}\n")
def update_plot(self, new_results, instances):
if self.show_plot or self.plot_file is not None:
self.visualizer.append_data(instances, new_results)
def update_predictions(self, context):
"""Gets new predictions from ADLStream context
Args:
context (ADLStreamContext): ADLStream context
"""
x, y, o = context.get_predictions()
self.x_eval += x
self.y_eval += y
self.o_eval += o
self.write_predictions(o)
def run(self, context):
"""Run evaluator
This function update predictions from context, evaluate them and update result
file and result plot.
Args:
context (ADLStreamContext): ADLStream context
"""
self.start()
while not context.is_finished():
self.update_predictions(context)
new_results, instances = self.evaluate()
if new_results:
self.write_results(new_results, instances)
self.update_plot(new_results, instances)
if self.plot_file:
self.visualizer.savefig(self.plot_file)
if self.show_plot:
self.visualizer.show()
self.end()
evaluate(self)
¶
Function that contains the main logic of the evaluator.
In a generic scheme, this function should:
- Get validation metrics from validation data (self.y_eval
, self.o_eval
and self.x_eval
).
- Save metrics in self.metric_history
.
- Remove already evaluated data (y_eval
, o_eval
and x_eval
) to keep
memory free.
- Return new computed metrics and count of number of instances evaluated.
Exceptions:
Type | Description |
---|---|
NotImplementedError |
This is an abstract method which should be implemented. |
Returns:
Type | Description |
---|---|
tuple[list, list] |
new_metrics (list), instances(list) |
Source code in ADLStream/evaluation/base_evaluator.py
@abstractmethod
def evaluate(self):
"""Function that contains the main logic of the evaluator.
In a generic scheme, this function should:
- Get validation metrics from validation data (`self.y_eval`, `self.o_eval`
and `self.x_eval`).
- Save metrics in `self.metric_history`.
- Remove already evaluated data (`y_eval`, `o_eval` and `x_eval`) to keep
memory free.
- Return new computed metrics and count of number of instances evaluated.
Raises:
NotImplementedError: This is an abstract method which should be implemented.
Returns:
tuple[list, list]: new_metrics (list), instances(list)
"""
raise NotImplementedError("Abstract method")
run(self, context)
¶
Run evaluator
This function update predictions from context, evaluate them and update result file and result plot.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
context |
ADLStreamContext |
ADLStream context |
required |
Source code in ADLStream/evaluation/base_evaluator.py
def run(self, context):
"""Run evaluator
This function update predictions from context, evaluate them and update result
file and result plot.
Args:
context (ADLStreamContext): ADLStream context
"""
self.start()
while not context.is_finished():
self.update_predictions(context)
new_results, instances = self.evaluate()
if new_results:
self.write_results(new_results, instances)
self.update_plot(new_results, instances)
if self.plot_file:
self.visualizer.savefig(self.plot_file)
if self.show_plot:
self.visualizer.show()
self.end()
update_predictions(self, context)
¶
Gets new predictions from ADLStream context
Parameters:
Name | Type | Description | Default |
---|---|---|---|
context |
ADLStreamContext |
ADLStream context |
required |
Source code in ADLStream/evaluation/base_evaluator.py
def update_predictions(self, context):
"""Gets new predictions from ADLStream context
Args:
context (ADLStreamContext): ADLStream context
"""
x, y, o = context.get_predictions()
self.x_eval += x
self.y_eval += y
self.o_eval += o
self.write_predictions(o)