From ed86d63d5760e506c2f2f2bbe0cc3b3707b82b5e Mon Sep 17 00:00:00 2001 From: Michal Tyrolski Date: Tue, 22 Aug 2023 13:17:39 +0200 Subject: [PATCH] Neptune, disable caching, remove unused files (#30) * versionizing * neptune --- docs/__init__.html | 30 -- docs/aggregators.html | 294 ---------------- docs/clustering.html | 474 -------------------------- docs/downloader.html | 89 ----- docs/experiment.html | 465 ------------------------- docs/experiment_test.html | 333 ------------------ docs/metric.html | 80 ----- docs/neptune_downloader.html | 351 ------------------- docs/noise.html | 59 ---- docs/paths.html | 95 ------ docs/paths_test.html | 80 ----- docs/pycco.css | 190 ----------- docs/wizzards.html | 319 ----------------- exphub/download/neptune_downloader.py | 22 +- exphub/relaunchers/__init__.py | 0 exphub/relaunchers/validator.py | 141 -------- poetry.lock | 8 +- pyproject.toml | 2 +- 18 files changed, 16 insertions(+), 3016 deletions(-) delete mode 100644 docs/__init__.html delete mode 100644 docs/aggregators.html delete mode 100644 docs/clustering.html delete mode 100644 docs/downloader.html delete mode 100644 docs/experiment.html delete mode 100644 docs/experiment_test.html delete mode 100644 docs/metric.html delete mode 100644 docs/neptune_downloader.html delete mode 100644 docs/noise.html delete mode 100644 docs/paths.html delete mode 100644 docs/paths_test.html delete mode 100644 docs/pycco.css delete mode 100644 docs/wizzards.html delete mode 100644 exphub/relaunchers/__init__.py delete mode 100644 exphub/relaunchers/validator.py diff --git a/docs/__init__.html b/docs/__init__.html deleted file mode 100644 index c24a87f..0000000 --- a/docs/__init__.html +++ /dev/null @@ -1,30 +0,0 @@ - - - - - __init__.py - - - -
-
-
-

__init__.py

-
-
-
-
-
- # -
- -
-
- 

-
-
-
-
-
-
- diff --git a/docs/aggregators.html b/docs/aggregators.html deleted file mode 100644 index 3208f92..0000000 --- a/docs/aggregators.html +++ /dev/null @@ -1,294 +0,0 @@ - - - - - aggregators.py - - - -
-
-
-

aggregators.py

-
-
-
-
-
- # -
- -
-
- 
import pandas as pd
-from typing import List, Any
-import functools
-
-import functools
-import pandas as pd
-from typing import Any, List
-
-
-
-
-
-
- # -
-

A class that represents an aggregator function to be applied to a DataFrame.

-
-
- 
class Aggregator:
-
-
-
-
-
-
- # -
-

An Aggregator object is a callable that takes a DataFrame and an optional inplace -parameter and returns the modified DataFrame with a new column appended to it. The -new column’s values are computed using the aggregator function specified in the -Aggregator object’s constructor.

-
-
- 

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Constructs an Aggregator object.

-    

-    

-      
    def __init__(self, fn, label: Any) -> None:

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Args:
-    fn (function): The aggregator function that will be applied to the
-        DataFrame. The function should take a DataFrame as input and return
-        a Series or a DataFrame.
-    label (Any): The label to be used for the new column in the DataFrame.

-    

-    

-      
        self.fn = fn
-        self.label = label

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Modifies a DataFrame by adding a new column computed using the aggregator function.

-    

-    

-      
    def __call__(self, incoming_df: pd.DataFrame, inplace: bool = False) -> Any:

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Args:
-    incoming_df (pd.DataFrame): The DataFrame to be modified.
-    inplace (bool, optional): If True, the incoming DataFrame will be modified in place.
-        If False, a copy of the DataFrame will be created and modified. Defaults to False.

-
Returns:
-    Any: The modified DataFrame.

-    

-    

-      
        df = incoming_df.copy() if not inplace else incoming_df
-        df[self.label] = self.fn(df)
-        return df

-    

-  

-  

-  

-    

-      

-        #
-      

-      
A class that represents a chain of Aggregator objects.

-    

-    

-      
class AggregatorChain:

-    

-  

-  

-  

-    

-      

-        #
-      

-      
An AggregatorChain object is a callable that takes a DataFrame and an optional
-inplace parameter and returns the modified DataFrame after applying all the
-Aggregator objects in the chain, in the order they were added.

-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Constructs an AggregatorChain object.

-    

-    

-      
    def __init__(self, aggs: List[Aggregator]) -> None:

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Args:
-    aggs (List[Aggregator]): A list of Aggregator objects to be applied in the chain.

-    

-    

-      
        self.aggs = aggs

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Modifies a DataFrame by applying a chain of Aggregator objects.

-    

-    

-      
    def __call__(self, df: pd.DataFrame, inplace=False) -> Any:

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Args:
-    df (pd.DataFrame): The DataFrame to be modified.
-    inplace (bool, optional): If True, the incoming DataFrame will be modified in place.
-        If False, a copy of the DataFrame will be created and modified. Defaults to False.

-
Returns:
-    Any: The modified DataFrame.

-    

-    

-      
        df_n = df.copy() if not inplace else df
-        return functools.reduce(lambda x, y: y(x, inplace=False), self.aggs, df_n)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Returns a list of labels for the new columns added

-    

-    

-      
    def labels(self) -> List[str]:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        return list(map(lambda x: x.label, self.aggs))

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Adds another AggregatorChain object to the current one.

-    

-    

-      
    def __add__(self, other: 'AggregatorChain') -> 'AggregatorChain':

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Args:
-    other (AggregatorChain): The AggregatorChain object to be added.

-
Returns:
-    AggregatorChain: A new AggregatorChain object that is the result of adding the two
-        AggregatorChain objects.

-    

-    

-      
        return AggregatorChain(self.aggs + other.aggs)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
A class that stores a collection of predefined Aggregator chains.

-    

-    

-      
class Vault:

-    

-  

-  

-  

-    

-      

-        #
-      

-      
The Vault class provides a convenient way to retrieve predefined
-Aggregator chains by name. The available Aggregator chains are stored as
-class attributes.

-    

-    

-      
    MEAN = AggregatorChain([Aggregator(lambda x: x.mean(axis=1, numeric_only=True), 'mean')])
-    _NO_AGGR = AggregatorChain([Aggregator(lambda x: x.mean(axis=1, numeric_only=True), 'no_aggr')])
-    MEAN_STD = AggregatorChain([
-        Aggregator(lambda df: df.mean(axis=1, numeric_only=True), 'mean'),
-        Aggregator(lambda df: df.mean(axis=1, numeric_only=True) - df.std(axis=1), 'mean_std_minus'),
-        Aggregator(lambda df: df.mean(axis=1, numeric_only=True) + df.std(axis=1), 'mean_std_plus'),
-    ])
-    MIN = AggregatorChain([Aggregator(lambda x: x.min(axis=1, numeric_only=True), 'min')])
-    MAX = AggregatorChain([Aggregator(lambda x: x.max(axis=1, numeric_only=True), 'max')])
-    SUM = AggregatorChain([Aggregator(lambda x: x.sum(axis=1, numeric_only=True), 'sum')])
-    MEDIAN = AggregatorChain([Aggregator(lambda x: x.median(axis=1, numeric_only=True), 'median')])
-    VAR = AggregatorChain([Aggregator(lambda x: x.var(axis=1, numeric_only=True), 'var')])
-    STD = AggregatorChain([Aggregator(lambda x: x.std(axis=1, numeric_only=True), 'std')])
-
-

-    

-  

-  

-

-
diff --git a/docs/clustering.html b/docs/clustering.html
deleted file mode 100644
index 1a329db..0000000
--- a/docs/clustering.html
+++ /dev/null
@@ -1,474 +0,0 @@
-
-
-
-  
-  clustering.py
-  
-
-
-

-  

-  

-    
clustering.py

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
from exphub.metrics.metric import Metric
-from abc import abstractmethod
-from typing import Any
-from sklearn.metrics import (rand_score, adjusted_rand_score, mutual_info_score, normalized_mutual_info_score,
-                             adjusted_mutual_info_score, fowlkes_mallows_score, silhouette_score,
-                             calinski_harabasz_score, davies_bouldin_score)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
An abstract base class for clustering extrinsic metrics. Extrinsic metrics evaluate the
-quality of a clustering algorithm by comparing its output to a ground truth clustering.

-    

-    

-      
class ClusterExtrinsicMetric(Metric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __init__(self) -> None:
-        super().__init__()

-    

-  

-  

-  

-    

-      

-        #
-      

-      
An abstract base class for clustering intrinsic metrics. Intrinsic metrics evaluate the
-quality of a clustering algorithm without reference to a ground truth clustering.

-    

-    

-      
    @abstractmethod
-    def __call__(self, y_pred, y_gt, **kwargs) -> Any:
-        pass
-
-
-class ClusterIntrinsicMetric(Metric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __init__(self) -> None:
-        super().__init__()

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Calculates the Rand Index, a measure of the similarity between two clusterings.

-    

-    

-      
    @abstractmethod
-    def __call__(self, embeddings, labels, **kwargs) -> Any:
-        pass
-
-
-class RandIndex(ClusterExtrinsicMetric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __call__(self, y_pred, y_gt, **kwargs) -> Any:
-        return rand_score(y_gt, y_pred)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Calculates the Adjusted Rand Index, a measure of the similarity between two clusterings
-that is adjusted for chance.

-    

-    

-      
class AdjustedRandIndex(ClusterExtrinsicMetric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __call__(self, y_pred, y_gt, **kwargs) -> Any:
-        return adjusted_rand_score(y_gt, y_pred)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Calculates the Mutual Information Score, a measure of the similarity between two clusterings
-based on the amount of shared information.

-    

-    

-      
class MutualInfoScore(ClusterExtrinsicMetric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __call__(self, y_pred, y_gt, **kwargs) -> Any:
-        return mutual_info_score(y_gt, y_pred)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Calculates the Normalized Mutual Information Score, a measure of the similarity between
-two clusterings based on the normalized amount of shared information.

-    

-    

-      
class NormalizedMutualInfoScore(ClusterExtrinsicMetric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __call__(self, y_pred, y_gt, **kwargs) -> Any:
-        return normalized_mutual_info_score(y_gt, y_pred)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Calculates the Adjusted Mutual Information Score, a measure of the similarity between
-two clusterings based on the amount of shared information, adjusted for chance.

-    

-    

-      
class AdjustedMutualInfoScore(ClusterExtrinsicMetric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __call__(self, y_pred, y_gt, **kwargs) -> Any:
-        return adjusted_mutual_info_score(y_gt, y_pred)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Calculates the Fowlkes-Mallows Score, a measure of the similarity between two clusterings
-based on the geometric mean of precision and recall.

-    

-    

-      
class FowlkesMallowsScore(ClusterExtrinsicMetric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __call__(self, y_pred, y_gt, **kwargs) -> Any:
-        return fowlkes_mallows_score(y_gt, y_pred)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Calculates the Silhouette Score, a measure of how similar an object is to its own cluster
-compared to other clusters. A higher score indicates better clustering quality.

-    

-    

-      
class SilhouetteScore(ClusterIntrinsicMetric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __call__(self, embeddings, labels, **kwargs) -> Any:
-        if len(embeddings) != len(labels):
-            raise ValueError(
-                f"Embeddings and cluster y_gt must have the same length. Got {len(embeddings)} and {len(labels)}")
-        return silhouette_score(embeddings, labels)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Calculates the Calinski-Harabasz Score, also known as the Variance Ratio Criterion. This score
-is a measure of cluster dispersion, where a higher value indicates better clustering quality.
-The score is defined as the ratio of the between-cluster dispersion to the within-cluster dispersion.

-    

-    

-      
class CalinskiHarabaszScore(ClusterIntrinsicMetric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __call__(self, embeddings, labels, **kwargs) -> Any:
-        if len(embeddings) != len(labels):
-            raise ValueError(
-                f"Embeddings and cluster y_gt must have the same length. Got {len(embeddings)} and {len(labels)}")
-        return calinski_harabasz_score(embeddings, labels)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Calculates the Davies-Bouldin Score, a measure of cluster quality based on the average
-similarity between clusters. A lower score indicates better clustering quality.
-The score is defined as the average of the maximum similarity between each cluster and
-all other clusters, where similarity is the ratio of within-cluster distances to between-cluster distances.

-    

-    

-      
class DaviesBouldinScore(ClusterIntrinsicMetric):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __call__(self, embeddings, labels, **kwargs) -> Any:
-        if len(embeddings) != len(labels):
-            raise ValueError(
-                f"Embeddings and cluster y_gt must have the same length. Got {len(embeddings)} and {len(labels)}")
-        return davies_bouldin_score(embeddings, labels)
-
-

-    

-  

-  

-

-
diff --git a/docs/downloader.html b/docs/downloader.html
deleted file mode 100644
index 1d3b372..0000000
--- a/docs/downloader.html
+++ /dev/null
@@ -1,89 +0,0 @@
-
-
-
-  
-  downloader.py
-  
-
-
-

-  

-  

-    
downloader.py

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
from abc import ABC, abstractmethod
-import pandas as pd
-
-from exphub.download.experiment import Experiment

-    

-  

-  

-  

-    

-      

-        #
-      

-      
An abstract base class for a data downloader.

-
This class should be subclassed to create custom downloaders for specific data sources.

-    

-    

-      
class Downloader(ABC):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Downloads data and returns it as an Experiment instance.

-
This method should be implemented by subclasses to define the data downloading logic.

-
Args:
-    args: Variable-length arguments specific to the downloader implementation.
-    *kwargs: Arbitrary keyword arguments specific to the downloader implementation.

-
Returns:
-    Experiment: An Experiment instance containing the downloaded data.

-    

-    

-      
    @abstractmethod
-    def download(self, *args, **kwargs) -> Experiment:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        pass
-
-

-    

-  

-  

-

-
diff --git a/docs/experiment.html b/docs/experiment.html
deleted file mode 100644
index 1576f9d..0000000
--- a/docs/experiment.html
+++ /dev/null
@@ -1,465 +0,0 @@
-
-
-
-  
-  experiment.py
-  
-
-
-

-  

-  

-    
experiment.py

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
from dataclasses import dataclass, field
-from itertools import product
-from typing import Dict, List
-import pandas as pd

-    

-  

-  

-  

-    

-      

-        #
-      

-      
A class representing an experiment with its parameters and series.

-
Attributes:
-    params (pd.DataFrame): A DataFrame containing the parameters of the experiment.
-    series (Dict[str, pd.DataFrame]): A dictionary mapping metric names to DataFrames.

-    

-    

-      
@dataclass
-class Experiment:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    params: pd.DataFrame
-    series: field(default_factory=dict)  # metric_name -> df

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __str__(self) -> str:
-        return f'Experiment Instance\n{len(self.params_names)} parameters: {self.params_names}\n including...\n\t * Attributes: {self.attributes_names}\n\t * Series: {self.series_names}'

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Returns a new Experiment instance with a subset of the parameters.

-
Args:
-    params_names_to_keep (List[str]): A list of the names of the parameters to keep.

-
Returns:
-    Experiment: A new Experiment instance with a subset of the parameters.

-    

-    

-      
    def subset_params(self, params_names_to_keep: List[str]) -> 'Experiment':

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        attributes_params = [p for p in params_names_to_keep if p in self.attributes_names]
-        series_params = [p for p in params_names_to_keep if p in self.series_names]
-
-        if any((p not in self.params_names and p not in self.attributes_names) for p in params_names_to_keep):
-            raise ValueError(f'Invalid parameter name. Valid names are {self.params_names}')
-
-        new_series = {s: self.series[s] for s in series_params}
-        new_params = self.params[attributes_params + series_params]
-
-        return Experiment(new_params, new_series)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Returns a new Experiment instance with a subset of the runs.

-
Args:
-    runs_ids_to_keep (List[str]): A list of the IDs of the runs to keep.

-
Returns:
-    Experiment: A new Experiment instance with a subset of the runs.

-    

-    

-      
    def subset_runs(self, runs_ids_to_keep: List[str]) -> 'Experiment':

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        new_params = self.params[self.params[self.id_column_name].isin(runs_ids_to_keep)]
-        new_series = {s: self.series[s][runs_ids_to_keep] for s in self.series_names}
-
-        return Experiment(new_params, new_series)

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def without(self, params_names_to_drop: List[str]) -> 'Experiment':
-        all_params = self.params_names
-        return self.subset_params([p for p in all_params if p not in params_names_to_drop])

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Returns the names of the series.

-
Returns:
-    List[str]: A list of the names of the series.

-    

-    

-      
    @property
-    def series_names(self) -> List[str]:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        return list(self.series.keys())

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Returns the names of the attributes.

-
Returns:
-    List[str]: A list of the names of the attributes.

-    

-    

-      
    @property
-    def attributes_names(self) -> List[str]:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        return [c for c in self.params.columns if c not in self.series_names]

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Returns the names of the parameters.

-
Returns:
-    List[str]: A list of the names of the parameters.

-    

-    

-      
    @property
-    def params_names(self) -> List[str]:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        return self.params.columns.tolist()

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Returns the name of the ID column.

-
Returns:
-    str: The name of the ID column.

-    

-    

-      
    @property
-    def id_column_name(self) -> str:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        return 'sys/id' if 'sys/id' in self.params.columns else 'id'

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Filters the experiment based on the given conditions.

-
Args:
-    conditions (list): A list of functions to filter the experiment’s parameters.

-
Returns:
-    Experiment: A new Experiment instance with filtered parameters and series.

-    

-    

-      
    def filter_via_hyperparams(self, conditions: list) -> 'Experiment':

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        df_meta = self.params.copy()
-        for fn in conditions:
-            df_meta = df_meta[fn(df_meta)]

-    

-  

-  

-  

-    

-      

-        #
-      

-      
If no rows left, return empty experiment

-    

-    

-      
        if len(df_meta) == 0:
-            return Experiment(pd.DataFrame(), {})

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Filter series columns only if they are present in the meta df

-    

-    

-      
        series = {}

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Shorten compatibility

-    

-    

-      
        id_col = 'sys/id' if 'sys/id' in df_meta.columns else 'id'
-
-        for metric_name, df in self.series.items():

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Initialize new series from index

-    

-    

-      
            new_series = pd.DataFrame(index=df.index)
-            new_series.index.name = df.index.name
-
-            for col in df.columns:
-                if col.split('_')[-1] in df_meta[id_col].values:
-                    new_series[col] = df[col].copy()
-
-            series[metric_name] = new_series
-
-        return Experiment(df_meta, series)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Splits the experiment into sub-experiments based on unique combinations of values in the specified columns.

-
Args:
-    columns (List[str]): A list of column names to split the experiment by.

-
Returns:
-    Dict[str, Experiment]: A dictionary mapping split descriptions to sub-experiments.

-    

-    

-      
    def split_by_columns(self, columns: List[str]) -> Dict[str, 'Experiment']:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        unique_values_by_columns = {col: self.params[col].unique() for col in columns}
-        splits = {}
-        import copy
-        for values in product(*unique_values_by_columns.values()):
-            split_describtion = '\n'.join([f'{col} = {val}' for col, val in zip(columns, values)])
-            experiment = copy.deepcopy(self)
-            for col, val in zip(columns, values):
-                experiment = experiment.filter_via_hyperparams([lambda df: df[col] == val])
-            splits[split_describtion] = experiment

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Final filtering to remove empty experiments

-    

-    

-      
        splits = {k: v for k, v in splits.items() if len(v.params) > 0}
-
-        return splits

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Merges the experiment with another experiment.

-
Args:
-    other (Experiment): The experiment to merge with.

-
Returns:
-    Experiment: A new Experiment instance with merged parameters and series.

-    

-    

-      
    def merge(self, other: 'Experiment') -> 'Experiment':

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        if self.params_names != other.params_names:
-            raise ValueError(
-                f'Cannot merge experiments with different parameters. Parameters are {self.params_names} and {other.params_names}'
-            )
-
-        new_params = pd.concat([self.params, other.params])
-        new_series = {s: pd.concat([self.series[s], other.series[s]]) for s in self.series_names}
-
-        return Experiment(new_params, new_series)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Drops all runs that have NaN values in any of the series params.

-    

-    

-      
    def drop_runs_with_nan(self) -> 'Experiment':

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        return self.filter_via_hyperparams([lambda df: ~df.isnull().any(axis=1)])
-
-

-    

-  

-  

-

-
diff --git a/docs/experiment_test.html b/docs/experiment_test.html
deleted file mode 100644
index 42d8b18..0000000
--- a/docs/experiment_test.html
+++ /dev/null
@@ -1,333 +0,0 @@
-
-
-
-  
-  experiment_test.py
-  
-
-
-

-  

-  

-    
experiment_test.py

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
import pytest
-import pandas as pd
-import numpy as np
-from exphub.download.experiment import Experiment

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Create a sample Experiment instance for testing

-    

-    

-      
params_data = {
-    'id': ['run1', 'run2', 'run3'],
-    'attr1': [1, 2, 3],
-    'attr2': [2, 3, 1],
-    'metric1': [0.1, 0.2, 0.3],
-    'metric2': [0.3, 0.2, 0.1],
-}
-params_df = pd.DataFrame(params_data)
-sample_series = {
-    'metric1': pd.DataFrame(data=[[0.1, 0.2, 0.3]], columns=['run1', 'run2', 'run3']),
-    'metric2': pd.DataFrame(data=[[0.3, 0.2, 0.1]], columns=['run1', 'run2', 'run3']),
-}
-sample_experiment = Experiment(params=params_df, series=sample_series)

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
@pytest.mark.parametrize(
-    "params_names_to_keep,expected",
-    [
-        (['attr1'], 1),
-        (['attr2'], 1),
-        (['metric1'], 1),
-        (['attr1', 'attr2'], 2),
-        (['attr1', 'metric1'], 2),
-    ],
-)
-def test_subset_params(params_names_to_keep, expected):
-    result = sample_experiment.subset_params(params_names_to_keep)
-    assert len(result.params.columns) == expected
-
-
-@pytest.mark.parametrize(
-    "runs_ids_to_keep,expected",
-    [
-        (['run1'], 1),
-        (['run2'], 1),
-        (['run1', 'run2'], 2),
-    ],
-)
-def test_subset_runs(runs_ids_to_keep, expected):
-    result = sample_experiment.subset_runs(runs_ids_to_keep)
-    assert len(result.series[sample_experiment.series_names[0]].columns) == expected
-
-
-@pytest.mark.parametrize(
-    "params_names_to_drop,expected",
-    [
-        (['attr1'], 4),
-        (['attr2'], 4),
-        (['metric1'], 4),
-        (['attr1', 'attr2'], 3),
-        (['attr1', 'metric1'], 3),
-    ],
-)
-def test_without(params_names_to_drop, expected):
-    result = sample_experiment.without(params_names_to_drop)
-    assert len(result.params.columns) == expected
-
-
-@pytest.mark.parametrize(
-    "conditions,expected",
-    [
-        ([lambda df: df['attr1'] > 1], 2),
-        ([lambda df: df['attr2'] > 1], 2),
-        ([lambda df: df['attr1'] > 1, lambda df: df['attr2'] > 1], 1),
-    ],
-)
-def test_filter_via_hyperparams(conditions, expected):
-    result = sample_experiment.filter_via_hyperparams(conditions)
-    assert len(result.params) == expected
-
-
-@pytest.mark.parametrize(
-    "columns,expected",
-    [
-        (['attr1'], 3),
-        (['attr2'], 3),
-    ],
-)
-def test_split_by_columns(columns, expected):
-    result = sample_experiment.split_by_columns(columns)
-    assert len(result) == expected
-
-
-@pytest.mark.parametrize(
-    "params_names_to_keep,expected_columns",
-    [
-        (['attr1'], ['attr1']),
-        (['attr2'], ['attr2']),
-        (['metric1'], ['metric1']),
-        (['attr1', 'attr2'], ['attr1', 'attr2']),
-        (['attr1', 'metric1'], ['attr1', 'metric1']),
-    ],
-)
-def test_subset_params2(params_names_to_keep, expected_columns):
-    result = sample_experiment.subset_params(params_names_to_keep)
-    assert len(result.params.columns) == len(expected_columns)
-    assert set(result.params.columns) == set(expected_columns)
-
-
-@pytest.mark.parametrize(
-    "runs_ids_to_keep,expected_rows",
-    [
-        (['run1'], ['run1']),
-        (['run2'], ['run2']),
-        (['run1', 'run2'], ['run1', 'run2']),
-    ],
-)
-def test_subset_runs2(runs_ids_to_keep, expected_rows):
-    result = sample_experiment.subset_runs(runs_ids_to_keep)
-    assert len(result.params) == len(expected_rows)
-    assert set(result.params['id'].values) == set(expected_rows)
-
-
-@pytest.mark.parametrize(
-    "params_names_to_drop,expected_columns",
-    [
-        (['attr1'], ['id', 'attr2', 'metric1', 'metric2']),
-        (['attr2'], ['id', 'attr1', 'metric1', 'metric2']),
-        (['metric1'], ['id', 'attr1', 'attr2', 'metric2']),
-        (['attr1', 'attr2'], ['id', 'metric1', 'metric2']),
-        (['attr1', 'metric1'], ['id', 'attr2', 'metric2']),
-    ],
-)
-def test_without2(params_names_to_drop, expected_columns):
-    result = sample_experiment.without(params_names_to_drop)
-    assert len(result.params.columns) == len(expected_columns)
-    assert set(result.params.columns) == set(expected_columns)
-
-
-def create_test_experiment():
-    params = pd.DataFrame({'param1': [1, 2], 'param2': [3, 4]})
-    series = {'metric1': pd.DataFrame({'value': [5, 6]}), 'metric2': pd.DataFrame({'value': [7, 8]})}
-    return Experiment(params, series)
-
-
-def test_merge_success():
-    exp1 = create_test_experiment()
-    exp2 = create_test_experiment()
-
-    merged_exp = exp1.merge(exp2)
-
-    assert len(merged_exp.params) == 4
-    assert len(merged_exp.series['metric1']) == 4
-    assert len(merged_exp.series['metric2']) == 4
-
-
-def test_merge_failure_different_params():
-    exp1 = create_test_experiment()
-
-    params = pd.DataFrame({'param3': [1, 2], 'param4': [3, 4]})
-    series = {'metric1': pd.DataFrame({'value': [5, 6]}), 'metric2': pd.DataFrame({'value': [7, 8]})}
-    exp2 = Experiment(params, series)
-
-    with pytest.raises(ValueError) as excinfo:
-        exp1.merge(exp2)
-
-    assert "Cannot merge experiments with different parameters" in str(excinfo.value)
-
-
-def test_merge_correct_dfs():
-    exp1 = create_test_experiment()
-    exp2 = create_test_experiment()
-
-    merged_exp = exp1.merge(exp2)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Check if params DataFrame is correct

-    

-    

-      
    expected_params = pd.DataFrame({'param1': [1, 2, 1, 2], 'param2': [3, 4, 3, 4]})
-
-    pd.testing.assert_frame_equal(merged_exp.params.reset_index(drop=True), expected_params.reset_index(drop=True))

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Check if metric1 DataFrame is correct

-    

-    

-      
    expected_metric1 = pd.DataFrame({'value': [5, 6, 5, 6]})
-    pd.testing.assert_frame_equal(merged_exp.series['metric1'].reset_index(drop=True),
-                                  expected_metric1.reset_index(drop=True))

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Check if metric2 DataFrame is correct

-    

-    

-      
    expected_metric2 = pd.DataFrame({'value': [7, 8, 7, 8]})
-    pd.testing.assert_frame_equal(merged_exp.series['metric2'].reset_index(drop=True),
-                                  expected_metric2.reset_index(drop=True))

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
def create_test_experiment_with_nan():
-    params = pd.DataFrame({
-        'id': [0, 1],
-        'param1': [1, 2],
-        'param2': [3, 4],
-        'metric1': [5, float('nan')],
-        'metric2': [7, 8]
-    })
-    series = {}
-    return Experiment(params, series)

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
def test_drop_runs_with_nan():
-    exp = create_test_experiment_with_nan()
-    exp_no_nan = exp.drop_runs_with_nan()

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Check if the resulting Experiment has the correct number of runs

-    

-    

-      
    assert len(exp_no_nan.params) == 1

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Check if the remaining run has the correct ID

-    

-    

-      
    assert exp_no_nan.params['id'].iloc[0] == 0

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Check if the remaining run has the correct values

-    

-    

-      
    assert exp_no_nan.params['param1'].iloc[0] == 1
-    assert exp_no_nan.params['param2'].iloc[0] == 3
-    assert exp_no_nan.params['metric1'].iloc[0] == 5
-    assert exp_no_nan.params['metric2'].iloc[0] == 7
-
-

-    

-  

-  

-

-
diff --git a/docs/metric.html b/docs/metric.html
deleted file mode 100644
index 3d97458..0000000
--- a/docs/metric.html
+++ /dev/null
@@ -1,80 +0,0 @@
-
-
-
-  
-  metric.py
-  
-
-
-

-  

-  

-    
metric.py

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
from abc import ABC, abstractmethod
-from typing import Any

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
class Metric(ABC):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __init__(self) -> None:
-        super().__init__()

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    @abstractmethod
-    def __call__(self, **kwargs: Any) -> Any:
-        return super().__call__(**kwargs)
-
-
-class MetricsCollection:
-
-    def __init__(self, metrics: list[Metric]) -> None:
-        super().__init__()
-        self.metrics = metrics
-
-    def __call__(self, **kwargs) -> Any:
-        return {metric.__class__.__name__: metric(**kwargs) for metric in self.metrics}
-
-

-    

-  

-  

-

-
diff --git a/docs/neptune_downloader.html b/docs/neptune_downloader.html
deleted file mode 100644
index fadb365..0000000
--- a/docs/neptune_downloader.html
+++ /dev/null
@@ -1,351 +0,0 @@
-
-
-
-  
-  neptune_downloader.py
-  
-
-
-

-  

-  

-    
neptune_downloader.py

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
from exphub.download.downloader import Downloader
-import pandas as pd
-from typing import Optional, Union, List
-import os
-import neptune.new as neptune
-from exphub.download.experiment import Experiment
-from exphub.utils.noise import Suppressor
-from exphub.utils.paths import shorten_paths

-    

-  

-  

-  

-    

-      

-        #
-      

-      
A NeptuneDownloader class for downloading experiment data from Neptune.ai.

-
Attributes:
-    project_name (str): The name of the Neptune project.
-    api_token (Optional[str]): The Neptune API token. If not provided, it should be set as an environment variable.

-    

-    

-      
class NeptuneDownloader(Downloader):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    NEPTUNE_API_TOKEN = 'NEPTUNE_API_TOKEN'

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Download experiment data from Neptune.ai with the specified filters and settings.

-
Args:
-    id (Optional[Union[str, List[str]]]): A list of experiment IDs to download.
-    state (Optional[Union[str, List[str]]]): A list of experiment states to download.
-    owner (Optional[Union[str, List[str]]]): A list of experiment owners to download.
-    tag (Optional[Union[str, List[str]]]): A list of experiment tags to download.
-    attributes (Optional[List[str]]): A list of experiment attributes to download.
-    short_names (bool): Whether to shorten the column names in the resulting data. Defaults to True.
-    series (List[str]): A list of experiment series to download.

-
Returns:
-    Experiment: An Experiment object containing the downloaded data.

-    

-    

-      
    def __init__(self, project_name: str, api_token: Optional[str] = None):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        self.api_token = api_token
-        self.project_name = project_name
-        if self.api_token is None:
-            if NeptuneDownloader.NEPTUNE_API_TOKEN not in os.environ:
-                raise ValueError(f'Environment variable {NeptuneDownloader.NEPTUNE_API_TOKEN} not found.')
-            self.api_token = os.environ[NeptuneDownloader.NEPTUNE_API_TOKEN]
-        else:
-            self.api_token = api_token
-            os.environ[NeptuneDownloader.NEPTUNE_API_TOKEN] = api_token
-        self.project = neptune.init_project(project=self.project_name, mode="read-only", api_token=self.api_token)

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def download(self,
-                 id: Optional[Union[str, List[str]]] = None,
-                 state: Optional[Union[str, List[str]]] = None,
-                 owner: Optional[Union[str, List[str]]] = None,
-                 tag: Optional[Union[str, List[str]]] = None,
-                 attributes: Optional[List[str]] = None,
-                 short_names: bool = True,
-                 series: List[str] = []) -> Experiment:
-        if all([id is None, state is None, owner is None, tag is None]):
-            raise ValueError('At least one of id, state, owner, or tag must be provided.')
-        columns = [*attributes, *series]
-        params = self.project.fetch_runs_table(owner=owner, id=id, state=state, tag=tag, columns=columns).to_pandas()
-        series = {}
-        for series_col in series:
-            series[series_col] = self._download_series(series_col, id=id, state=state, owner=owner, tag=tag)
-
-        self.short_names = short_names
-
-        if short_names:

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Modify params in place

-    

-    

-      
            meta_long2short = shorten_paths(params)
-            params.rename(columns=meta_long2short, inplace=True)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Modify series in place

-    

-    

-      
            short_df_series = {}
-            for series_col, df in series.items():
-                long2short = shorten_paths(df)
-                short_df_series[meta_long2short[series_col]] = df.rename(columns=long2short)
-            series = short_df_series
-
-        return Experiment(params, series)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Downloads a specified series of data from Neptune.ai based on filtering criteria.

-
Args:
-    series_column (Union[List[str], str]): The name of the series to download.
-    id (Optional[Union[str, List[str]]]): The run ID(s) to filter by.
-    state (Optional[Union[str, List[str]]]): The run state(s) to filter by.
-    owner (Optional[Union[str, List[str]]]): The run owner(s) to filter by.
-    tag (Optional[Union[str, List[str]]]): The run tag(s) to filter by.

-
Returns:
-    pd.DataFrame: A pandas DataFrame containing the downloaded series data.

-    

-    

-      
    def _download_series(self,
-                         series_column: Union[List[str], str],
-                         id: Optional[Union[str, List[str]]] = None,
-                         state: Optional[Union[str, List[str]]] = None,
-                         owner: Optional[Union[str, List[str]]] = None,
-                         tag: Optional[Union[str, List[str]]] = None) -> pd.DataFrame:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        if all([id is None, state is None, owner is None, tag is None]):
-            raise ValueError('At least one of id, state, owner, or tag must be provided.')
-
-        ids = self.project.fetch_runs_table(
-            owner=owner, id=id, state=state, tag=tag, columns='sys/id').to_pandas()['sys/id'].values

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Run initialization

-    

-    

-      
        runs = [
-            Suppressor.exec_no_stdout(
-                neptune.init_run, project=self.project_name, with_id=run_id, mode="read-only", api_token=self.api_token)
-            for run_id in ids
-        ]

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        def _fetch_values(col_label):
-            if isinstance(col_label, list):
-                assert len(col_label) == 1
-                col_label = col_label[0]

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Fetching values and counting the number of values

-    

-    

-      
            id2value = {}
-            missing = 0
-            for id, run in zip(ids, runs):
-                try:
-                    id2value[id] = Suppressor.exec_no_stdout(run[col_label].fetch_values, include_timestamp=False)
-                except neptune.exceptions.NeptuneException:
-                    print(f'[WARNING] Run {id} does not have a column named {col_label}')
-                    missing += 1
-            if missing == len(ids):
-                raise ValueError(f'No runs have a column named {col_label}')
-
-            df = pd.DataFrame({})
-            for id, value in id2value.items():
-                df[id] = value['value']
-
-            return df
-
-        return _fetch_values(series_column)

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
class NeptuneDownloaderParentExpLinked(NeptuneDownloader):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def __init__(self, project_name: str, api_token: Optional[str] = None, param_name_of_parent_exp_id: str = None):
-        super().__init__(project_name, api_token)
-        self.param_name_of_parent_exp_id = param_name_of_parent_exp_id

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def download(self,
-                 id: Optional[Union[str, List[str]]] = None,
-                 state: Optional[Union[str, List[str]]] = None,
-                 owner: Optional[Union[str, List[str]]] = None,
-                 tag: Optional[Union[str, List[str]]] = None,
-                 attributes: Optional[List[str]] = None,
-                 short_names: bool = True,
-                 series: List[str] = [],
-                 attributes_parent: List[str] = [],
-                 series_parent: List[str] = []) -> Experiment:
-        current_experiment = super().download(id, state, owner, tag, attributes, short_names, series)
-        parent_experiment = self._download_parent_experiment(current_experiment, attributes_parent, series_parent)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Collapse params and metrics of parent expeirment into current_experiment, ‘parent’ namespace

-    

-    

-      
        parent_experiment.params = parent_experiment.params.add_prefix('parent/')
-        parent_experiment.series = {k: v.add_prefix('parent/') for k, v in parent_experiment.series.items()}
-
-        return Experiment(
-            pd.concat([current_experiment.params, parent_experiment.params], axis=1), {
-                **current_experiment.series,
-                **parent_experiment.series
-            })

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def _download_parent_experiment(self, current_experiment, attributes_parent: List[str], series_parent: List[str]):
-        parent_experiment_id = current_experiment.params[self.param_name_of_parent_exp_id].values[0]
-        return super().download(id=parent_experiment_id, attributes=attributes_parent, series=series_parent)
-
-

-    

-  

-  

-

-
diff --git a/docs/noise.html b/docs/noise.html
deleted file mode 100644
index d166619..0000000
--- a/docs/noise.html
+++ /dev/null
@@ -1,59 +0,0 @@
-
-
-
-  
-  noise.py
-  
-
-
-

-  

-  

-    
noise.py

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
import os
-import contextlib

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
class Suppressor:

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    @classmethod
-    def exec_no_stdout(cls, fn, **kwargs):
-        with open(os.devnull, 'w') as devnull:
-            with contextlib.redirect_stdout(devnull):
-                return fn(**kwargs)
-
-

-    

-  

-  

-

-
diff --git a/docs/paths.html b/docs/paths.html
deleted file mode 100644
index f9145cc..0000000
--- a/docs/paths.html
+++ /dev/null
@@ -1,95 +0,0 @@
-
-
-
-  
-  paths.py
-  
-
-
-

-  

-  

-    
paths.py

-  

-  

-  

-    

-      

-        #
-      

-      
Find the longest common suffix between the current_path and all other paths in all_paths.

-
Args:
-    all_paths (List[str]): A list of all path strings.
-    current_path (str): The path string for which the longest common suffix is calculated.

-
Returns:
-    int: The length of the longest common suffix.

-    

-    

-      
def find_longest_common_suffix(all_paths, current_path):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    max_common_suffix_length = 1
-    paths = [path for path in all_paths if path != current_path]
-    for i in range(1, len(current_path.split('/'))):
-        if any('/'.join((path.split('/')[-i:])) == '/'.join(current_path.split('/')[-i:])
-               for path in paths
-               if len(path.split('/')) >= i):
-            max_common_suffix_length += 1
-        else:
-            break
-
-    return max_common_suffix_length

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Shorten the input paths by finding the longest common suffix for each path and keeping only the required parts to distinguish between them. Also, return a dictionary mapping the original paths to their shortened versions.

-
Args:
-    paths (List[str]): A list of input path strings.

-
Returns:
-     Dict[str, str]: A dictionary mapping the original paths to their shortened versions.

-    

-    

-      
def shorten_paths(paths):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    shortened_paths = []
-    path_mapping = {}
-
-    for path in paths:
-        split_path = path.split('/')
-        max_common_suffix_length = find_longest_common_suffix(paths, path)
-        shortened_path = '/'.join(split_path[-max_common_suffix_length:])
-        shortened_paths.append(shortened_path)
-        path_mapping[path] = shortened_path
-
-    return path_mapping
-
-

-    

-  

-  

-

-
diff --git a/docs/paths_test.html b/docs/paths_test.html
deleted file mode 100644
index 2d21f78..0000000
--- a/docs/paths_test.html
+++ /dev/null
@@ -1,80 +0,0 @@
-
-
-
-  
-  paths_test.py
-  
-
-
-

-  

-  

-    
paths_test.py

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
import pytest
-from exphub.utils.paths import shorten_paths

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
@pytest.mark.parametrize(
-    "paths,expected",
-    [
-        (
-            ["/a/b/c/d", "/a/b/c/e"],
-            {
-                "/a/b/c/d": "d",
-                "/a/b/c/e": "e"
-            },
-        ),
-        (
-            ["/a/b/c/d", "/a/b/c/e", "/a/b/c/f"],
-            {
-                "/a/b/c/d": "d",
-                "/a/b/c/e": "e",
-                "/a/b/c/f": "f"
-            },
-        ),
-        (
-            ["/a/b/c/d/e", "/a/b/c/e"],
-            {
-                "/a/b/c/d/e": "d/e",
-                "/a/b/c/e": "c/e"
-            },
-        ),
-        (
-            ["/a/b/c/d/e", "/a/b/c/d/f", "/a/b/c/d/g"],
-            {
-                "/a/b/c/d/e": "e",
-                "/a/b/c/d/f": "f",
-                "/a/b/c/d/g": "g"
-            },
-        ),
-    ],
-)
-def test_shorten_paths(paths, expected):
-    result = shorten_paths(paths)
-    assert result == expected
-
-

-    

-  

-  

-

-
diff --git a/docs/pycco.css b/docs/pycco.css
deleted file mode 100644
index aef571a..0000000
--- a/docs/pycco.css
+++ /dev/null
@@ -1,190 +0,0 @@
-/*--------------------- Layout and Typography ----------------------------*/
-body {
-  font-family: 'Palatino Linotype', 'Book Antiqua', Palatino, FreeSerif, serif;
-  font-size: 16px;
-  line-height: 24px;
-  color: #252519;
-  margin: 0; padding: 0;
-  background: #f5f5ff;
-}
-a {
-  color: #261a3b;
-}
-  a:visited {
-    color: #261a3b;
-  }
-p {
-  margin: 0 0 15px 0;
-}
-h1, h2, h3, h4, h5, h6 {
-  margin: 40px 0 15px 0;
-}
-h2, h3, h4, h5, h6 {
-    margin-top: 0;
-  }
-#container {
-  background: white;
- }
-#container, div.section {
-  position: relative;
-}
-#background {
-  position: absolute;
-  top: 0; left: 580px; right: 0; bottom: 0;
-  background: #f5f5ff;
-  border-left: 1px solid #e5e5ee;
-  z-index: 0;
-}
-#jump_to, #jump_page {
-  background: white;
-  -webkit-box-shadow: 0 0 25px #777; -moz-box-shadow: 0 0 25px #777;
-  -webkit-border-bottom-left-radius: 5px; -moz-border-radius-bottomleft: 5px;
-  font: 10px Arial;
-  text-transform: uppercase;
-  cursor: pointer;
-  text-align: right;
-}
-#jump_to, #jump_wrapper {
-  position: fixed;
-  right: 0; top: 0;
-  padding: 5px 10px;
-}
-  #jump_wrapper {
-    padding: 0;
-    display: none;
-  }
-    #jump_to:hover #jump_wrapper {
-      display: block;
-    }
-    #jump_page {
-      padding: 5px 0 3px;
-      margin: 0 0 25px 25px;
-    }
-      #jump_page .source {
-        display: block;
-        padding: 5px 10px;
-        text-decoration: none;
-        border-top: 1px solid #eee;
-      }
-        #jump_page .source:hover {
-          background: #f5f5ff;
-        }
-        #jump_page .source:first-child {
-        }
-div.docs {
-  float: left;
-  max-width: 500px;
-  min-width: 500px;
-  min-height: 5px;
-  padding: 10px 25px 1px 50px;
-  vertical-align: top;
-  text-align: left;
-}
-  .docs pre {
-    margin: 15px 0 15px;
-    padding-left: 15px;
-  }
-  .docs p tt, .docs p code {
-    background: #f8f8ff;
-    border: 1px solid #dedede;
-    font-size: 12px;
-    padding: 0 0.2em;
-  }
-  .octowrap {
-    position: relative;
-  }
-    .octothorpe {
-      font: 12px Arial;
-      text-decoration: none;
-      color: #454545;
-      position: absolute;
-      top: 3px; left: -20px;
-      padding: 1px 2px;
-      opacity: 0;
-      -webkit-transition: opacity 0.2s linear;
-    }
-      div.docs:hover .octothorpe {
-        opacity: 1;
-      }
-div.code {
-  margin-left: 580px;
-  padding: 14px 15px 16px 50px;
-  vertical-align: top;
-}
-  .code pre, .docs p code {
-    font-size: 12px;
-  }
-    pre, tt, code {
-      line-height: 18px;
-      font-family: Monaco, Consolas, "Lucida Console", monospace;
-      margin: 0; padding: 0;
-    }
-div.clearall {
-    clear: both;
-}
-
-
-/*---------------------- Syntax Highlighting -----------------------------*/
-td.linenos { background-color: #f0f0f0; padding-right: 10px; }
-span.lineno { background-color: #f0f0f0; padding: 0 5px 0 5px; }
-body .hll { background-color: #ffffcc }
-body .c { color: #408080; font-style: italic }  /* Comment */
-body .err { border: 1px solid #FF0000 }         /* Error */
-body .k { color: #954121 }                      /* Keyword */
-body .o { color: #666666 }                      /* Operator */
-body .cm { color: #408080; font-style: italic } /* Comment.Multiline */
-body .cp { color: #BC7A00 }                     /* Comment.Preproc */
-body .c1 { color: #408080; font-style: italic } /* Comment.Single */
-body .cs { color: #408080; font-style: italic } /* Comment.Special */
-body .gd { color: #A00000 }                     /* Generic.Deleted */
-body .ge { font-style: italic }                 /* Generic.Emph */
-body .gr { color: #FF0000 }                     /* Generic.Error */
-body .gh { color: #000080; font-weight: bold }  /* Generic.Heading */
-body .gi { color: #00A000 }                     /* Generic.Inserted */
-body .go { color: #808080 }                     /* Generic.Output */
-body .gp { color: #000080; font-weight: bold }  /* Generic.Prompt */
-body .gs { font-weight: bold }                  /* Generic.Strong */
-body .gu { color: #800080; font-weight: bold }  /* Generic.Subheading */
-body .gt { color: #0040D0 }                     /* Generic.Traceback */
-body .kc { color: #954121 }                     /* Keyword.Constant */
-body .kd { color: #954121; font-weight: bold }  /* Keyword.Declaration */
-body .kn { color: #954121; font-weight: bold }  /* Keyword.Namespace */
-body .kp { color: #954121 }                     /* Keyword.Pseudo */
-body .kr { color: #954121; font-weight: bold }  /* Keyword.Reserved */
-body .kt { color: #B00040 }                     /* Keyword.Type */
-body .m { color: #666666 }                      /* Literal.Number */
-body .s { color: #219161 }                      /* Literal.String */
-body .na { color: #7D9029 }                     /* Name.Attribute */
-body .nb { color: #954121 }                     /* Name.Builtin */
-body .nc { color: #0000FF; font-weight: bold }  /* Name.Class */
-body .no { color: #880000 }                     /* Name.Constant */
-body .nd { color: #AA22FF }                     /* Name.Decorator */
-body .ni { color: #999999; font-weight: bold }  /* Name.Entity */
-body .ne { color: #D2413A; font-weight: bold }  /* Name.Exception */
-body .nf { color: #0000FF }                     /* Name.Function */
-body .nl { color: #A0A000 }                     /* Name.Label */
-body .nn { color: #0000FF; font-weight: bold }  /* Name.Namespace */
-body .nt { color: #954121; font-weight: bold }  /* Name.Tag */
-body .nv { color: #19469D }                     /* Name.Variable */
-body .ow { color: #AA22FF; font-weight: bold }  /* Operator.Word */
-body .w { color: #bbbbbb }                      /* Text.Whitespace */
-body .mf { color: #666666 }                     /* Literal.Number.Float */
-body .mh { color: #666666 }                     /* Literal.Number.Hex */
-body .mi { color: #666666 }                     /* Literal.Number.Integer */
-body .mo { color: #666666 }                     /* Literal.Number.Oct */
-body .sb { color: #219161 }                     /* Literal.String.Backtick */
-body .sc { color: #219161 }                     /* Literal.String.Char */
-body .sd { color: #219161; font-style: italic } /* Literal.String.Doc */
-body .s2 { color: #219161 }                     /* Literal.String.Double */
-body .se { color: #BB6622; font-weight: bold }  /* Literal.String.Escape */
-body .sh { color: #219161 }                     /* Literal.String.Heredoc */
-body .si { color: #BB6688; font-weight: bold }  /* Literal.String.Interpol */
-body .sx { color: #954121 }                     /* Literal.String.Other */
-body .sr { color: #BB6688 }                     /* Literal.String.Regex */
-body .s1 { color: #219161 }                     /* Literal.String.Single */
-body .ss { color: #19469D }                     /* Literal.String.Symbol */
-body .bp { color: #954121 }                     /* Name.Builtin.Pseudo */
-body .vc { color: #19469D }                     /* Name.Variable.Class */
-body .vg { color: #19469D }                     /* Name.Variable.Global */
-body .vi { color: #19469D }                     /* Name.Variable.Instance */
-body .il { color: #666666 }                     /* Literal.Number.Integer.Long */
diff --git a/docs/wizzards.html b/docs/wizzards.html
deleted file mode 100644
index e3e2650..0000000
--- a/docs/wizzards.html
+++ /dev/null
@@ -1,319 +0,0 @@
-
-
-
-  
-  wizzards.py
-  
-
-
-

-  

-  

-    
wizzards.py

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
from abc import ABC, abstractmethod
-
-import pandas as pd
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-
-from exphub.download.experiment import Experiment

-    

-  

-  

-  

-    

-      

-        #
-      

-      
An abstract base class for creating wizards to render various visualizations.

-    

-    

-      
class Wizard(ABC):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Renders the visualization. This method should be implemented by subclasses.

-
:param kwargs: Optional keyword arguments specific to each subclass.

-    

-    

-      
    @abstractmethod
-    def render(self, **kwargs):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        pass

-    

-  

-  

-  

-    

-      

-        #
-      

-      
A wizard class for rendering tables with custom styling. This class is used for visualizing
-experiment data by applying different background colors to attributes and series in the table.

-    

-    

-      
class TableWizard(Wizard):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Initializes the TableWizard with the given experiment.

-
:param experiment: The experiment object containing data to be visualized.

-    

-    

-      
    def __init__(self, experiment: Experiment):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        self.experiment = experiment

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Renders the table with the specified background colors for attributes and series.

-
:param attributes_color: The background color to be applied to attribute names. Default is ‘#211b1b’.
-:param series_color: The background color to be applied to series names. Default is ‘#022b11’.
-:return: The styled table with the specified background colors applied.

-    

-    

-      
    def render(self, attributes_color: str = '#211b1b', series_color: str = '#022b11'):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        return self.experiment.params.style.set_properties(
-            **{
-                'background-color': attributes_color
-            }, subset=self.experiment.attributes_names).set_properties(
-                **{'background-color': series_color}, subset=self.experiment.series_names)

-    

-  

-  

-  

-    

-      

-        #
-      

-      
A wizard class for rendering line plots of time series data from experiments. This class is used
-for visualizing experiment data by plotting single or multiple series on a line chart or subplots.

-    

-    

-      
class SeriesWizard(Wizard):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Initializes the SeriesWizard with the given experiment.

-
:param experiment: The experiment object containing data to be visualized.

-    

-    

-      
    def __init__(self, experiment: Experiment):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        self.experiment = experiment

-    

-  

-  

-  

-    

-      

-        #
-      

-      
Renders the line plot visualization for single or multiple series.

-
:return: A plotly figure containing the line plot(s) of the time series data.

-    

-    

-      
    def render(self):

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
        if len(self.experiment.series_names) == 0:
-            raise ValueError('No series to plot.')
-
-        if len(self.experiment.series_names) > 1:
-            return self._render_multiple_series()
-
-        return self._render_single_series()

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def _render_single_series(self):
-        fig = go.Figure()
-        metric_name = self.experiment.series_names[0]
-
-        xaxis_title = 'step'
-        yaxis_title = metric_name
-        title = f'{yaxis_title} per {xaxis_title}'
-
-        traces = self._generate_traces(self.experiment.series[self.experiment.series_names[0]])
-
-        fig.add_traces(traces)
-        fig.update_layout(title=title, xaxis_title=xaxis_title, yaxis_title=metric_name, legend_title='runs')
-
-        return fig

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def _generate_traces(self, series: pd.DataFrame):
-        traces = []
-        for run_id in series.columns:
-            traces.append(go.Scatter(x=series.index, y=series[run_id], name=run_id))
-        return traces

-    

-  

-  

-  

-    

-      

-        #
-      

-
-    

-    

-      
    def _render_multiple_series(self):
-        n_plots = len(self.experiment.series_names)
-        xaxis_title = 'step'
-
-        fig = make_subplots(
-            rows=1,
-            cols=n_plots,
-            shared_xaxes=False,
-            shared_yaxes=True,
-            subplot_titles=[f'{metric_name} per step' for metric_name in self.experiment.series_names])
-        grid = [(x, 1) for x in range(1, n_plots + 1)]
-
-        for (x, y), series_name in zip(grid, self.experiment.series_names):
-            traces = self._generate_traces(self.experiment.series[series_name])
-            fig.add_traces(traces, rows=y, cols=x)
-            fig.update_yaxes(title_text=series_name, row=y, col=x)
-            fig.update_xaxes(title_text=xaxis_title, row=y, col=x)
-
-        return fig
-
-

-    

-  

-  

-

-
diff --git a/exphub/download/neptune_downloader.py b/exphub/download/neptune_downloader.py
index 9c9a813..9c446e4 100644
--- a/exphub/download/neptune_downloader.py
+++ b/exphub/download/neptune_downloader.py
@@ -66,15 +66,15 @@ def download(self,
             raise ValueError('At least one of id, state, owner, or tag must be provided.')
         columns = [*attributes, *series]
         
-        hsh = tag if tag is not None else 'x'
-        logger.info(f'Hash of columns: {hsh}')
-        logger.info(f'Cache path: {os.path.join(NeptuneDownloader.EXPHUB_CACHE, f"{hsh}.joblib")}')
-        if (not force) and os.path.exists(os.path.join(NeptuneDownloader.EXPHUB_CACHE, f'{hsh}.joblib')):
-            logger.info(f'Loading experiment from cache {hsh}.joblib')
-            logger.warning(f'Loading experiment from cache {hsh}.joblib is based on the columns.\nIf you have new runs with the same columns, please override the cache by using the override_cache argument=True')
-            return jl.load(os.path.join(NeptuneDownloader.EXPHUB_CACHE, f'{hsh}.joblib'))
-        else:
-            logger.info(f'No cache found. Downloading experiment from Neptune.ai')
+        # hsh = tag if tag is not None else 'x'
+        # logger.info(f'Hash of columns: {hsh}')
+        # logger.info(f'Cache path: {os.path.join(NeptuneDownloader.EXPHUB_CACHE, f"{hsh}.joblib")}')
+        # if (not force) and os.path.exists(os.path.join(NeptuneDownloader.EXPHUB_CACHE, f'{hsh}.joblib')):
+        #     logger.info(f'Loading experiment from cache {hsh}.joblib')
+        #     logger.warning(f'Loading experiment from cache {hsh}.joblib is based on the columns.\nIf you have new runs with the same columns, please override the cache by using the override_cache argument=True')
+        #     return jl.load(os.path.join(NeptuneDownloader.EXPHUB_CACHE, f'{hsh}.joblib'))
+        # else:
+        #     logger.info(f'No cache found. Downloading experiment from Neptune.ai')
         params = self.project.fetch_runs_table(owner=owner, id=id, state=state, tag=tag, columns=columns).to_pandas()
         series_dict = {}
         for series_col in series:
@@ -100,8 +100,8 @@ def download(self,
         logger.info(f'dtypes of params: {exp.params.dtypes}')
         
         # Cache experiment
-        jl.dump(exp, os.path.join(NeptuneDownloader.EXPHUB_CACHE, f'{hsh}.joblib'))
-        logger.info(f'Experiment cached at {os.path.join(NeptuneDownloader.EXPHUB_CACHE, f"{hsh}.joblib")}')
+        # jl.dump(exp, os.path.join(NeptuneDownloader.EXPHUB_CACHE, f'{hsh}.joblib'))
+        # logger.info(f'Experiment cached at {os.path.join(NeptuneDownloader.EXPHUB_CACHE, f"{hsh}.joblib")}')
         
         return exp
 
diff --git a/exphub/relaunchers/__init__.py b/exphub/relaunchers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/exphub/relaunchers/validator.py b/exphub/relaunchers/validator.py
deleted file mode 100644
index 42ef084..0000000
--- a/exphub/relaunchers/validator.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from typing import List
-from loguru import logger
-
-from exphub.download.experiment import Experiment
-
-class RelaunchTrigger(ABC):
-    """Abstract base class for relaunch triggers.
-
-    A relaunch trigger is used to determine whether an experiment should be relaunched based on certain conditions.
-    """
-    def filter(self, experiment: Experiment) -> bool:
-        """Filter experiments based on the conditions defined in the derived class.
-
-        Args:
-            experiment (Experiment): The experiment to be filtered.
-
-        Returns:
-            bool: True if the experiment meets the conditions, False otherwise.
-        """
-        return experiment.filter_via_hyperparams(self.conditions)
-    
-    @property
-    @abstractmethod
-    def conditions(self) -> List[str]:
-        """Abstract property that should be implemented in derived classes.
-
-        Returns:
-            List[str]: A list of conditions that the experiment must meet to be relaunched.
-        """
-        raise NotImplementedError
-    
-class FailedStatus(RelaunchTrigger):
-    """Relaunch trigger for experiments that have failed."""
-    @property
-    def conditions(self) -> List[str]:
-        """Conditions for relaunching experiments that have failed.
-
-        Returns:
-            List[str]: A list of conditions that the experiment must meet to be relaunched.
-        """
-        return [
-            lambda df: df['failed'] == 'True'
-        ]
-    
-class NotEnoughEpochs(RelaunchTrigger):
-    """Relaunch trigger for experiments that have not reached a minimum number of epochs."""
-    def __init__(self, min_epochs: int) -> None:
-        """Initialize the trigger with a minimum number of epochs.
-
-        Args:
-            min_epochs (int): The minimum number of epochs required for an experiment.
-        """
-        self.min_epochs = min_epochs
-    @property
-    def conditions(self) -> List[str]:
-        """Conditions for relaunching experiments that have not reached a minimum number of epochs.
-
-        Returns:
-            List[str]: A list of conditions that the experiment must meet to be relaunched.
-        """
-        return [
-            lambda df: df['epoch'] < self.min_epochs
-        ]
-
-def _load_default_triggers() -> List[RelaunchTrigger]:
-    """Load the default relaunch triggers.
-
-    Returns:
-        List[RelaunchTrigger]: A list of default relaunch triggers.
-    """
-    return [
-        FailedStatus(),
-        NotEnoughEpochs(5)
-    ]
-    
-@dataclass
-class ValidationResult:
-    """Data class for storing the results of experiment validation."""
-    ids_success: List[str] # all triggers passed
-    ids_failed: List[str] # at least one trigger failed    
-    
-    def summary(self):
-        """Print a summary of the validation results."""
-        logger.info(f'Validation summary:')
-        logger.info(f'* {len(self.ids_success)} runs passed all triggers')
-        logger.info(f'* {len(self.ids_failed)} runs failed at least one trigger')
-        logger.info(f'* {len(self.ids_success) + len(self.ids_failed)} runs total')
-        
-        if len(self.ids_failed) == 0:
-            logger.success(f'* {len(self.ids_success) / (len(self.ids_success) + len(self.ids_failed)) * 100:.2f}% success rate')
-            logger.success(f'* {len(self.ids_failed) / (len(self.ids_success) + len(self.ids_failed)) * 100:.2f}% failure rate')
-        else:
-            logger.critical(f'* {len(self.ids_success) / (len(self.ids_success) + len(self.ids_failed)) * 100:.2f}% success rate')
-            logger.critical(f'* {len(self.ids_failed) / (len(self.ids_success) + len(self.ids_failed)) * 100:.2f}% failure rate')
-
-class Validator:
-    """Validator class.
-
-    This class is used to relaunch experiments that have crashed.
-    """
-    def __init__(self, relaunch_triggers: List[RelaunchTrigger] = _load_default_triggers()) -> None:
-        """Initialize the validator with a list of relaunch triggers.
-
-        Args:
-            relaunch_triggers (List[RelaunchTrigger], optional): A list of relaunch triggers. Defaults to _load_default_triggers().
-        """
-        self.relaunch_triggers = relaunch_triggers
-        
-    def validate(self, experiment):
-        """Validate an experiment based on the relaunch triggers.
-
-        Args:
-            experiment (Experiment): The experiment to be validated.
-
-        Returns:
-            ValidationResult: The results of the validation.
-        """
-        logger.info(f'Validating experiment')
-        experiment_ids = experiment.params[experiment.id_column_name].unique()
-        
-        trigger2experiment = {
-            type(trigger).__name__: trigger.filter(experiment) for trigger in self.relaunch_triggers
-        }
-        all_failed_ids = []
-        
-        for trigger_name, trigger_result in trigger2experiment.items():
-            if len(trigger_result) == 0:
-                logger.success(f'Experiment is empty after applying following trigger: {trigger_name}')
-                continue
-            ids_failed = trigger_result.params[experiment.id_column_name].unique()
-            for id in ids_failed:
-                logger.error(f'Run {id} failed trigger {trigger_name}')
-            
-            all_failed_ids.extend(ids_failed)
-            
-        all_failed_ids = set(all_failed_ids)        
-        ids_success = [id for id in experiment_ids if id not in all_failed_ids]
-        
-        return ValidationResult(ids_success, ids_failed)
diff --git a/poetry.lock b/poetry.lock
index eb58513..4d1bad7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -747,13 +747,13 @@ files = [
 
 [[package]]
 name = "neptune"
-version = "1.5.0"
+version = "1.6.0"
 description = "Neptune Client"
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
-    {file = "neptune-1.5.0-py3-none-any.whl", hash = "sha256:e0d150febcaa2fa516c17cdb37066bcbfd1528c1898b0cf638e6d6742b8f2ff5"},
-    {file = "neptune-1.5.0.tar.gz", hash = "sha256:677c14c242609e76de9577d2c9e9f6aa41a8bdc473a01db55ef34e3c154a9d2a"},
+    {file = "neptune-1.6.0-py3-none-any.whl", hash = "sha256:355bbb0a3e353e4c66f39c2c5bf9b0bc547ceba583d38c6c76618e4acde1450f"},
+    {file = "neptune-1.6.0.tar.gz", hash = "sha256:b3ca24e81bd0ebdbc27a93a7274263570f834a97f808fe06ba9e863c098a24a9"},
 ]
 
 [package.dependencies]
@@ -1608,4 +1608,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "3.10.10"
-content-hash = "666a031051c953e64e998c4c54acf7b12720a3484af99013d0633452bdd4e423"
+content-hash = "013e2ae377cd3f3aa98619f0bc1ee3cd9f711873c465b2c908396d3040d86499"
diff --git a/pyproject.toml b/pyproject.toml
index 7aa4e9f..caf8dbd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,7 @@ loguru = "^0.7.0"
 moto = "*"
 boto3 = "*"
 urllib3 = "<2"
-neptune = "1.5.0"
+neptune = "^1.6.0"
 
 [build-system]
 requires = ["poetry-core"]