fix: fixed link functions (#69)

Author: Darinochka

Makefile

@@ -38,5 +38,11 @@ test-docs: docs
 serve-docs: docs
 	$(poetry) python -m http.server -d docs/build/html 8333
 
+.PHONY: clean-docs
+clean-docs:
+	rm -rf docs/build
+	rm -rf docs/source/autoapi
+	rm -rf docs/source/tutorials
+
 .PHONY: all
 all: lint

autointent/context/data_handler/_dataset.py

@@ -7,7 +7,6 @@
 
 from datasets import ClassLabel, Sequence, concatenate_datasets, get_dataset_config_names, load_dataset
 from datasets import Dataset as HFDataset
-from typing_extensions import Self
 
 from autointent.custom_types import LabelType
 
@@ -141,7 +140,7 @@ def dump(self) -> dict[str, list[dict[str, Any]]]:
         """
         return {split_name: split.to_list() for split_name, split in self.items()}
 
-    def encode_labels(self) -> Self:
+    def encode_labels(self) -> "Dataset":
         """
         Encode dataset labels into one-hot or multilabel format.
 
@@ -152,7 +151,7 @@ def encode_labels(self) -> Self:
         self._encoded_labels = True
         return self
 
-    def to_multilabel(self) -> Self:
+    def to_multilabel(self) -> "Dataset":
         """
         Convert dataset labels to multilabel format.
 

autointent/context/data_handler/_schemas.py

@@ -4,7 +4,6 @@
 """
 
 from pydantic import BaseModel, model_validator
-from typing_extensions import Self
 
 from autointent.custom_types import LabelType
 
@@ -34,7 +33,7 @@ class Sample(BaseModel):
     label: LabelType | None = None
 
     @model_validator(mode="after")
-    def validate_sample(self) -> Self:
+    def validate_sample(self) -> "Sample":
         """
         Validate the sample after model instantiation.
 
@@ -48,7 +47,7 @@ def validate_sample(self) -> Self:
         """
         return self._validate_label()
 
-    def _validate_label(self) -> Self:
+    def _validate_label(self) -> "Sample":
         """
         Validate the `label` field of the sample.
 

autointent/context/data_handler/_validation.py

@@ -1,7 +1,6 @@
 """File with definitions of DatasetReader and DatasetValidator."""
 
 from pydantic import BaseModel, model_validator
-from typing_extensions import Self
 
 from ._schemas import Intent, Sample
 
@@ -21,7 +20,7 @@ class DatasetReader(BaseModel):
     intents: list[Intent] = []
 
     @model_validator(mode="after")
-    def validate_dataset(self) -> Self:
+    def validate_dataset(self) -> "DatasetReader":
         """
         Validate the dataset by ensuring intents and data splits are consistent.
 
@@ -33,7 +32,7 @@ def validate_dataset(self) -> Self:
             self._validate_split(split)
         return self
 
-    def _validate_intents(self) -> Self:
+    def _validate_intents(self) -> "DatasetReader":
         """
         Validate the intents by checking their IDs for sequential order.
 
@@ -52,7 +51,7 @@ def _validate_intents(self) -> Self:
             raise ValueError(message)
         return self
 
-    def _validate_split(self, split: list[Sample]) -> Self:
+    def _validate_split(self, split: list[Sample]) -> "DatasetReader":
         """
         Validate a dataset split to ensure all sample labels reference valid intent IDs.
 

autointent/metrics/retrieval.py

@@ -115,8 +115,7 @@ def retrieval_map(query_labels: LABELS_VALUE_TYPE, candidates_labels: CANDIDATE_
     Calculate the mean average precision at position k.
 
     The Mean Average Precision (MAP) is computed as the average of the average precision
-    (AP) scores for all queries. The average precision for a single query is calculated using
-    the :func:`average_precision` function, which computes the precision at each rank
+    (AP) scores for all queries. The average precision for a single query computes the precision at each rank
     position considering the top-k retrieved items.
 
     MAP is given by:
@@ -192,8 +191,7 @@ def retrieval_map_intersecting(
 
     The Mean Average Precision (MAP) for intersecting labels is computed as
     the average of the average precision (AP) scores for all queries. The average
-    precision for a single query is calculated using the :func:`average_precision_intersecting`
-    function, which considers the intersecting true and predicted labels for the
+    precision for a single query considers the intersecting true and predicted labels for the
     top-k retrieved items.
 
     MAP is given by:
@@ -226,8 +224,8 @@ def retrieval_map_macro(
     r"""
     Calculate the mean average precision at position k for the intersecting labels.
 
-    This function internally uses :func:`retrieval_map` to calculate the MAP for each query and then
-    applies :func:`macrofy` to perform macro-averaging across multiple queries.
+    This function internally uses :func:`retrieval_map` to calculate the MAP for each query and
+    performs macro-averaging across multiple queries.
 
     :param query_labels: For each query, this list contains its class labels
     :param candidates_labels: For each query, these lists contain class labels of items ranked by a retrieval model
@@ -372,7 +370,7 @@ def retrieval_hit_rate_macro(
     Calculate the hit rate at position k for the intersecting labels.
 
     This function internally uses :func:`retrieval_hit_rate` to calculate the hit rate at position :math:`k`
-    for each query and applies :func:`macrofy` to perform macro-averaging across multiple queries.
+    for each query and performs macro-averaging across multiple queries.
 
     :param query_labels: For each query, this list contains its class labels
     :param candidates_labels: For each query, these lists contain class labels of items ranked by a retrieval model
@@ -508,7 +506,7 @@ def retrieval_precision_macro(
     Calculate the precision at position k for the intersecting labels.
 
     This function internally uses :func:`retrieval_precision` to calculate the precision at position :math:`k`
-    for each query and applies :func:`macrofy` to perform macro-averaging across multiple queries.
+    for each query and performs macro-averaging across multiple queries.
 
     :param query_labels: For each query, this list contains its class labels
     :param candidates_labels: For each query, these lists contain class labels of items ranked by a retrieval model
@@ -685,8 +683,7 @@ def retrieval_ndcg_macro(
     r"""
     Calculate the Normalized Discounted Cumulative Gain (NDCG) at position k for the intersecting labels.
 
-    This function calculates NDCG using :func:`retrieval_ndcg` and applies it to each
-    query using :func:`macrofy` to compute the macro-averaged score.
+    This function calculates NDCG using :func:`retrieval_ndcg` and computes the macro-averaged score.
 
     :param query_labels: For each query, this list contains its class labels
     :param candidates_labels: For each query, these lists contain class labels of items ranked by a retrieval model (from most to least relevant)
@@ -778,8 +775,7 @@ def retrieval_mrr_macro(
     r"""
     Calculate the Mean Reciprocal Rank (MRR) at position k for the intersecting labels.
 
-    This function calculates MRR using :func:`retrieval_mrr` and applies it to each
-    query using :func:`macrofy` to compute the macro-averaged score.
+    This function calculates MRR using :func:`retrieval_mrr` and computes the macro-averaged score.
 
     :param query_labels: For each query, this list contains its class labels
     :param candidates_labels: For each query, these lists contain class labels of items ranked by a retrieval model (from most to least relevant)

autointent/metrics/scoring.py

@@ -105,9 +105,9 @@ def _calculate_prediction_metric(
     r"""
     Calculate prediction metric.
 
-    This function applies the given prediction metric function :func:`func` to evaluate the predictions.
+    This function applies the given prediction metric function `func` to evaluate the predictions.
     It transforms the inputs and computes predictions based on the input scores:
-    - For multiclass classification, predictions are generated using :func:`np.argmax`.
+    - For multiclass classification, predictions are generated using `np.argmax`.
     - For multilabel classification, predictions are generated using a threshold of 0.5.
 
     :param func: prediction metric function
@@ -131,8 +131,8 @@ def scoring_accuracy(labels: LABELS_VALUE_TYPE, scores: SCORES_VALUE_TYPE) -> fl
     r"""
     Calculate accuracy for multiclass and multilabel classification.
 
-    This function computes accuracy by using :func:`prediction_accuracy` to evaluate predictions and
-    :func:`calculate_prediction_metric` to handle the computation.
+    This function computes accuracy by using :func:`autointent.metrics.prediction.prediction_accuracy`
+    to evaluate predictions.
 
     :param labels: ground truth labels for each utterance
     :param scores: for each utterance, this list contains scores for each of `n_classes` classes
@@ -145,8 +145,8 @@ def scoring_f1(labels: LABELS_VALUE_TYPE, scores: SCORES_VALUE_TYPE) -> float:
     r"""
     Calculate the F1 score for multiclass and multilabel classification.
 
-    This function computes the F1 score by using :func:`prediction_f1` to evaluate predictions and
-    :func:`calculate_prediction_metric` to handle the computation.
+    This function computes the F1 score by using :func:`autointent.metrics.prediction.prediction_f1`
+    to evaluate predictions.
 
     :param labels: Ground truth labels for each sample
     :param scores: For each sample, this list contains scores for each of `n_classes` classes
@@ -159,8 +159,8 @@ def scoring_precision(labels: LABELS_VALUE_TYPE, scores: SCORES_VALUE_TYPE) -> f
     r"""
     Calculate precision for multiclass and multilabel classification.
 
-    This function computes precision by using :func:`prediction_precision` to evaluate predictions and
-    :func:`calculate_prediction_metric` to handle the computation.
+    This function computes precision by using :func:`autointent.metrics.prediction.prediction_precision`
+    to evaluate predictions.
 
     :param labels: Ground truth labels for each sample
     :param scores: For each sample, this list contains scores for each of `n_classes` classes
@@ -173,8 +173,8 @@ def scoring_recall(labels: LABELS_VALUE_TYPE, scores: SCORES_VALUE_TYPE) -> floa
     r"""
     Calculate recall for multiclass and multilabel classification.
 
-    This function computes recall by using :func:`prediction_recall` to evaluate predictions and
-    :func:`calculate_prediction_metric` to handle the computation.
+    This function computes recall by using :func:`autointent.metrics.prediction.prediction_recall`
+    to evaluate predictions.
 
     :param labels: Ground truth labels for each sample
     :param scores: For each sample, this list contains scores for each of `n_classes` classes

autointent/modules/_base.py

@@ -4,7 +4,6 @@
 from typing import Any, Literal
 
 import numpy.typing as npt
-from typing_extensions import Self
 
 from autointent.context import Context
 from autointent.context.optimization_info import Artifact
@@ -91,7 +90,7 @@ def predict_with_metadata(
 
     @classmethod
     @abstractmethod
-    def from_context(cls, context: Context, **kwargs: dict[str, Any]) -> Self:
+    def from_context(cls, context: Context, **kwargs: dict[str, Any]) -> "Module":
         """
         Initialize self from context.
 

autointent/modules/_regexp.py

@@ -5,8 +5,6 @@
 from pathlib import Path
 from typing import Any, Literal, TypedDict
 
-from typing_extensions import Self
-
 from autointent import Context
 from autointent.context.data_handler._data_handler import RegexPatterns
 from autointent.context.data_handler._schemas import Intent
@@ -32,7 +30,7 @@ class RegExp(Module):
     """Regular expressions based intent detection module."""
 
     @classmethod
-    def from_context(cls, context: Context) -> Self:
+    def from_context(cls, context: Context) -> "RegExp":
         """Initialize from context."""
         return cls()
 

autointent/modules/prediction/_adaptive.py

@@ -7,7 +7,6 @@
 import numpy as np
 import numpy.typing as npt
 from sklearn.metrics import f1_score
-from typing_extensions import Self
 
 from autointent import Context
 from autointent.context.data_handler import Tag
@@ -86,7 +85,7 @@ def __init__(self, search_space: list[float] | None = None) -> None:
         self.search_space = search_space if search_space is not None else default_search_space
 
     @classmethod
-    def from_context(cls, context: Context, search_space: list[float] | None = None) -> Self:
+    def from_context(cls, context: Context, search_space: list[float] | None = None) -> "AdaptivePredictor":
         """
         Create an AdaptivePredictor instance using a Context object.
 

autointent/modules/prediction/_argmax.py

@@ -6,7 +6,6 @@
 
 import numpy as np
 import numpy.typing as npt
-from typing_extensions import Self
 
 from autointent import Context
 from autointent.context.data_handler import Tag
@@ -60,7 +59,7 @@ def __init__(self) -> None:
         """Init."""
 
     @classmethod
-    def from_context(cls, context: Context) -> Self:
+    def from_context(cls, context: Context) -> "ArgmaxPredictor":
         """
         Initialize form context.