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fix examples 2
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@tvdboom
tvdboom committed Nov 29, 2023
1 parent b756dbc commit 5fca7a1
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Showing 12 changed files with 78 additions and 89 deletions.
10 changes: 9 additions & 1 deletion .github/workflows/config.yml
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Expand Up @@ -124,4 +124,12 @@ jobs:
pip install -U pytest pytest-xdist nbmake scikeras tensorflow
pip install -e .[full]
- name: Run example notebooks
run: pytest --nbmake -n=auto --nbmake-timeout=600 --ignore=./examples/webapp/ --ignore=./examples/accelerating_cuml.ipynb ./examples/
run: |
pytest \
-n=auto \
--nbmake \
--nbmake-timeout=600 \
--ignore=./examples/webapp/ \
--ignore=./examples/accelerating_cuml.ipynb \
--ignore=./examples/ray_backend.ipynb \
./examples/
6 changes: 3 additions & 3 deletions atom/atom.py
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Expand Up @@ -548,12 +548,12 @@ def eda(
rows_c = [(self.branch._get_rows(v), k) for k, v in rows.items()]

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Accessing a protected member of a class or a module 

Access to a protected member _get_rows of a class

if len(rows_c) == 1:

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Unbound local variables 

Local variable 'rows_c' might be referenced before assignment
self.report = self.memory.cache(sv.analyze)(
self.report = sv.analyze(
source=rows_c[0],
target_feat=self.branch._get_target(target, only_columns=True),

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Accessing a protected member of a class or a module 

Access to a protected member _get_target of a class
)
elif len(rows_c) == 2:
self.report = self.memory.cache(sv.compare)(
self.report = sv.compare(

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An instance attribute is defined outside `__init__` 

Instance attribute report defined outside __init__
source=rows_c[0],
compare=rows_c[1],
target_feat=self.branch._get_target(target, only_columns=True),

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Accessing a protected member of a class or a module 

Access to a protected member _get_target of a class
Expand Down Expand Up @@ -1222,7 +1222,7 @@ def _add_transformer(
name = transformer_c.__class__.__name__
while name in self.pipeline:
counter += 1
name = f"{transformer_c.__class__.__name__}{counter}"
name = f"{transformer_c.__class__.__name__.lower()}-{counter}"

self.branch.pipeline.steps.append((name, transformer_c))

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99 changes: 51 additions & 48 deletions atom/basemodel.py
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Expand Up @@ -64,7 +64,7 @@
from atom.utils.constants import DF_ATTRS
from atom.utils.types import (
HT, Backend, Bool, DataFrame, Engine, FHSelector, Float, FloatZeroToOneExc,
Int, IntLargerEqualZero, MetricConstructor, NJobs, Pandas,
Int, IntLargerEqualZero, MetricConstructor, MetricFunction, NJobs, Pandas,
PredictionMethods, PredictionMethodsTS, Predictor, RowSelector, Scalar,
Scorer, Sequence, Stages, TargetSelector, Verbose, Warnings, XSelector,
YSelector, dataframe_t, float_t, int_t,
Expand Down Expand Up @@ -2431,7 +2431,7 @@ def _prediction(
self,
X: RowSelector | XSelector,

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PEP 8 naming convention violation 

Argument name should be lowercase
y: YSelector | None = ...,
metric: MetricConstructor = ...,
metric: str | MetricFunction | Scorer | None = ...,
sample_weight: Sequence[Scalar] | None = ...,
verbose: Int | None = ...,
method: Literal["score"] = ...,

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'Literal' may be parameterized with literal ints, byte and unicode strings, bools, Enum values, None, other literal types, or type aliases to other literal types
Expand All @@ -2442,7 +2442,7 @@ def _prediction(
self,
X: RowSelector | XSelector,

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PEP 8 naming convention violation 

Argument name should be lowercase
y: YSelector | None = ...,
metric: MetricConstructor = ...,
metric: str | MetricFunction | Scorer | None = ...,
sample_weight: Sequence[Scalar] | None = ...,
verbose: Int | None = ...,
method: PredictionMethods = ...,
Expand All @@ -2452,7 +2452,7 @@ def _prediction(
self,
X: RowSelector | XSelector,

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PEP 8 naming convention violation 

Argument name should be lowercase
y: YSelector | None = None,
metric: MetricConstructor = None,
metric: str | MetricFunction | Scorer | None = None,
sample_weight: Sequence[Scalar] | None = None,
verbose: Int | None = None,
method: PredictionMethods = "predict",
Expand All @@ -2465,7 +2465,7 @@ def _prediction(
Parameters
----------
X: hashable, range, slice, sequence or dataframe-like
X: hashable, segment, sequence or dataframe-like
[Selection of rows][row-and-column-selection] or feature
set with shape=(n_samples, n_features) to make predictions
on.
Expand Down Expand Up @@ -2619,7 +2619,7 @@ def decision_function(
Parameters
----------
X: hashable, range, slice, sequence or dataframe-like
X: hashable, segment, sequence or dataframe-like
[Selection of rows][row-and-column-selection] or feature
set with shape=(n_samples, n_features) to make predictions
on.
Expand Down Expand Up @@ -2656,7 +2656,7 @@ def predict(
Parameters
----------
X: hashable, range, slice, sequence or dataframe-like
X: hashable, segment, sequence or dataframe-like
[Selection of rows][row-and-column-selection] or feature
set with shape=(n_samples, n_features) to make predictions
on.
Expand Down Expand Up @@ -2692,7 +2692,7 @@ def predict_log_proba(
Parameters
----------
X: hashable, range, slice, sequence or dataframe-like
X: hashable, segment, sequence or dataframe-like
[Selection of rows][row-and-column-selection] or feature
set with shape=(n_samples, n_features) to make predictions
on.
Expand Down Expand Up @@ -2728,7 +2728,7 @@ def predict_proba(
Parameters
----------
X: hashable, range, slice, sequence or dataframe-like
X: hashable, segment, sequence or dataframe-like
[Selection of rows][row-and-column-selection] or feature
set with shape=(n_samples, n_features) to make predictions
on.
Expand All @@ -2754,7 +2754,7 @@ def score(
X: RowSelector | XSelector,

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PEP 8 naming convention violation 

Argument name should be lowercase
y: YSelector | None = None,
*,
metric: MetricConstructor = None,
metric: str | MetricFunction | Scorer | None = None,
sample_weight: Sequence[Scalar] | None = None,
verbose: Int | None = None,
) -> Float:
Expand All @@ -2773,7 +2773,7 @@ def score(
Parameters
----------
X: hashable, range, slice, sequence or dataframe-like
X: hashable, segment, sequence or dataframe-like
[Selection of rows][row-and-column-selection] or feature
set with shape=(n_samples, n_features) to make predictions
on.
Expand Down Expand Up @@ -2825,9 +2825,9 @@ class ForecastModel(BaseModel):
@overload
def _prediction(
self,
y: YSelector | None = None,
X: RowSelector | XSelector | None = None,
metric: MetricConstructor = None,
y: RowSelector | YSelector | None = None,
X: XSelector | None = None,

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PEP 8 naming convention violation 

Argument name should be lowercase
metric: str | MetricFunction | Scorer | None = None,
verbose: Int | None = None,
method: Literal["score"] = ...,

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'Literal' may be parameterized with literal ints, byte and unicode strings, bools, Enum values, None, other literal types, or type aliases to other literal types
**kwargs,
Expand All @@ -2836,19 +2836,19 @@ def _prediction(
@overload
def _prediction(
self,
y: YSelector | None = None,
X: RowSelector | XSelector | None = None,
metric: MetricConstructor = None,
y: RowSelector | YSelector | None = None,
X: XSelector | None = None,

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PEP 8 naming convention violation 

Argument name should be lowercase
metric: str | MetricFunction | Scorer | None = None,
verbose: Int | None = None,
method: PredictionMethodsTS = ...,
**kwargs,
) -> Pandas: ...

def _prediction(

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Redeclared names without usages 

Redeclared '_prediction' defined above without usage
self,
y: YSelector | None = None,
X: RowSelector | XSelector | None = None,
metric: MetricConstructor = None,
y: RowSelector | YSelector | None = None,
X: XSelector | None = None,

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Argument name should be lowercase
metric: str | MetricFunction | Scorer | None = None,
verbose: Int | None = None,

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Unused local symbols 

Parameter 'verbose' value is not used
method: PredictionMethodsTS = "predict",
**kwargs,
Expand All @@ -2861,11 +2861,11 @@ def _prediction(
Parameters
----------
y: sequence or dataframe-like
y: int, str, dict, sequence, dataframe or None, default=None
Ground truth observations.
X: dataframe-like or None, default=None
Exogenous time series corresponding to fh.
X: hashable, segment, sequence, dataframe-like or None, default=None
Exogenous time series corresponding to `fh`.
metric: str, func, scorer or None, default=None
Metric to calculate. Choose from any of sklearn's scorers,
Expand All @@ -2890,10 +2890,13 @@ def _prediction(
called.
"""
Xt, yt = self.transform(X, y, verbose=verbose)
Xt, yt = X, y # self.transform(X, y, verbose=verbose) TODO: Fix pipeline ts

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PEP 8 naming convention violation 

Variable in function should be lowercase

if method != "score":
return self.memory.cache(getattr(self.estimator, method))(**kwargs)
if "y" in sign(func := getattr(self.estimator, method)):
return self.memory.cache(func)(y=yt, X=Xt, **kwargs)
else:
return self.memory.cache(func)(X=Xt, **kwargs)
else:
if metric is None:
scorer = self._metric[0]
Expand Down Expand Up @@ -2925,8 +2928,8 @@ def predict(
The forecasting horizon encoding the time stamps to
forecast at.
X: dataframe-like or None, default=None
Exogenous time series corresponding to fh.
X: hashable, segment, sequence, dataframe-like or None, default=None
Exogenous time series corresponding to `fh`.
verbose: int or None, default=None
Verbosity level for the transformers in the pipeline. If None,
Expand Down Expand Up @@ -2965,8 +2968,8 @@ def predict_interval(
The forecasting horizon encoding the time stamps to
forecast at.
X: dataframe-like or None, default=None
Exogenous time series corresponding to fh.
X: hashable, segment, sequence, dataframe-like or None, default=None
Exogenous time series corresponding to `fh`.
coverage: float or sequence, default=0.9
Nominal coverage(s) of predictive interval(s).
Expand Down Expand Up @@ -3014,8 +3017,8 @@ def predict_proba(
The forecasting horizon encoding the time stamps to
forecast at.
X: dataframe-like or None, default=None
Exogenous time series corresponding to fh.
X: hashable, segment, sequence, dataframe-like or None, default=None
Exogenous time series corresponding to `fh`.
marginal: bool, default=True
Whether returned distribution is marginal by time index.
Expand Down Expand Up @@ -3062,8 +3065,8 @@ def predict_quantiles(
The forecasting horizon encoding the time stamps to
forecast at.
X: dataframe-like or None, default=None
Exogenous time series corresponding to fh.
X: hashable, segment, sequence, dataframe-like or None, default=None
Exogenous time series corresponding to `fh`.
alpha: float or list of float, default=[0.05, 0.95]
A probability or list of, at which quantile forecasts are
Expand Down Expand Up @@ -3093,7 +3096,7 @@ def predict_quantiles(
@composed(crash, method_to_log, beartype)
def predict_residuals(
self,
y: Sequence[Any] | DataFrame,
y: RowSelector | YSelector,
X: XSelector | None = None,

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Argument name should be lowercase
*,
verbose: Int | None = None,
Expand All @@ -3108,11 +3111,11 @@ def predict_residuals(
Parameters
----------
y: sequence or dataframe-like
Ground truth observations to compute residuals to.
y: int, str, dict, sequence or dataframe
Ground truth observations.
X: dataframe-like or None, default=None
Exogenous time series corresponding to fh.
X: hashable, segment, sequence, dataframe-like or None, default=None
Exogenous time series corresponding to `y`.
verbose: int or None, default=None
Verbosity level for the transformers in the pipeline. If None,
Expand All @@ -3131,7 +3134,7 @@ def predict_residuals(
@composed(crash, method_to_log, beartype)
def predict_var(
self,
fh: FHSelector,
fh: RowSelector | FHSelector,
X: XSelector | None = None,

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Argument name should be lowercase
*,
cov: Bool = False,
Expand All @@ -3151,11 +3154,11 @@ def predict_var(
The forecasting horizon encoding the time stamps to
forecast at.
X: dataframe-like or None, default=None
Exogenous time series corresponding to fh.
X: hashable, segment, sequence, dataframe-like or None, default=None
Exogenous time series corresponding to `fh`.
cov: bool, default=False
Whether to computes covariance matrix forecast or marginal
Whether to compute covariance matrix forecast or marginal
variance forecasts.
verbose: int or None, default=None
Expand All @@ -3181,11 +3184,11 @@ def predict_var(
@composed(crash, method_to_log, beartype)
def score(
self,
y: Sequence[Any] | DataFrame,
X: DataFrame | None = None,
y: RowSelector | YSelector,
X: XSelector | None = None,

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PEP 8 naming convention violation 

Argument name should be lowercase
fh: FHSelector | None = None,
*,
metric: MetricConstructor = None,
metric: str | MetricFunction | Scorer | None = None,
verbose: Int | None = None,
) -> Float:
"""Get a metric score on new data.
Expand All @@ -3203,11 +3206,11 @@ def score(
Parameters
----------
y: sequence or dataframe-like
y: int, str, dict, sequence or dataframe
Ground truth observations.
X: dataframe-like or None, default=None
Exogenous time series corresponding to fh.
X: hashable, segment, sequence, dataframe-like or None, default=None
Exogenous time series corresponding to `fh`.
fh: int, sequence or [ForecastingHorizon][] or None, default=None
The forecasting horizon encoding the time stamps to
Expand Down
4 changes: 2 additions & 2 deletions atom/plots/predictionplot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -273,7 +273,7 @@ def plot_confusion_matrix(
models: int, str, Model, segment, sequence or None, default=None
Models to plot. If None, all models are selected.
rows: hashable, range, slice or sequence, default="test"
rows: hashable, segment or sequence, default="test"
[Selection of rows][row-and-column-selection] on which to
calculate the confusion matrix.
Expand Down Expand Up @@ -2678,7 +2678,7 @@ def plot_probabilities(
models: int, str, Model, segment, sequence or None, default=None
Models to plot. If None, all models are selected.
rows: hashable, range, slice or sequence, default="test"
rows: hashable, segment or sequence, default="test"
[Selection of rows][row-and-column-selection] on which to
calculate the metric.
Expand Down
12 changes: 2 additions & 10 deletions atom/utils/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@
from atom.utils.constants import __version__
from atom.utils.types import (
Bool, DataFrame, Estimator, Float, Index, IndexSelector, Int,
IntLargerEqualZero, MetricConstructor, Model, Pandas, Predictor, Scalar,
IntLargerEqualZero, MetricFunction, Model, Pandas, Predictor, Scalar,
Scorer, Segment, Sequence, Series, Transformer, TReturn, TReturns, Verbose,
XSelector, YSelector, YTypes, dataframe_t, int_t, pandas_t, segment_t,
sequence_t, series_t,
Expand Down Expand Up @@ -2054,7 +2054,7 @@ def check_attr(attr: str) -> bool:
return True


def get_custom_scorer(metric: MetricConstructor) -> Scorer:
def get_custom_scorer(metric: str | MetricFunction | Scorer) -> Scorer:
"""Get a scorer from a str, func or scorer.
Scorers used by ATOM have a name attribute.
Expand Down Expand Up @@ -2780,14 +2780,6 @@ def wrap_methods(f: Callable) -> Callable:
- Check if the instance is fitted before transforming.
- Convert output to pyarrow dtypes if specified in config.
Parameters
----------
f: callable
Function to decorate.
check_fitted: bool
Whether to check if the instance is fitted.
"""

@wraps(f)
Expand Down
3 changes: 2 additions & 1 deletion docs_sources/dependencies.md
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Expand Up @@ -31,6 +31,7 @@ packages are necessary for its correct functioning.
* **[gplearn](https://gplearn.readthedocs.io/en/stable/index.html)** (>=0.4.2)
* **[imbalanced-learn](https://imbalanced-learn.readthedocs.io/en/stable/api.html)** (>=0.11.0)
* **[ipython](https://ipython.readthedocs.io/en/stable/)** (>=8.11.0)
* **[ipywidgets](https://pypi.org/project/ipywidgets/)** (>=8.1.1)
* **[featuretools](https://www.featuretools.com/)** (>=1.28.0)
* **[joblib](https://joblib.readthedocs.io/en/latest/)** (>=1.3.1)
* **[matplotlib](https://matplotlib.org/)** (>=3.7.2)
Expand Down Expand Up @@ -63,7 +64,7 @@ additional libraries. You can install all the optional dependencies using
* **[lightgbm](https://lightgbm.readthedocs.io/en/latest/)** (>=4.1.0)
* **[pmdarima](http://alkaline-ml.com/pmdarima/)** (>=2.0.3)
* **[schemdraw](https://schemdraw.readthedocs.io/en/latest/index.html)** (>=0.16)
* **[sweetviz](https://github.com/fbdesignpro/sweetviz)** (>=2.2.1)
* **[sweetviz](https://github.com/fbdesignpro/sweetviz)** (>=2.3.1)
* **[wordcloud](http://amueller.github.io/word_cloud/)** (>=1.9.2)
* **[xgboost](https://xgboost.readthedocs.io/en/latest/)** (>=2.0.0)

Expand Down
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