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Merge 8b8c613 into 4e78cb5
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lucianolorenti authored Aug 18, 2022
2 parents 4e78cb5 + 8b8c613 commit 4957334
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11 changes: 11 additions & 0 deletions .github/workflows/publish.yml
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,16 @@ jobs:
- name: Set up Python ${ matrix.python-version }}
uses: actions/setup-python@v3
with:
<<<<<<< HEAD
python-version: "3.10"
- name: FragileTech/bump-version
uses: FragileTech/bump-version@main
with:
commit_name: Version bumper
commit_email: lucianolorenti@gmail.com
login: lucianolorenti@gmail.com
token: "${{ secrets.TOKEN_GITHUB }}"
=======
python-version: "3.10"
- name: Bump version
run: |
Expand All @@ -34,6 +44,7 @@ jobs:
github_token: ${{ secrets.GITHUB_TOKEN }}
branch: main
tags: true
>>>>>>> 4e78cb51c368737a91101483a4120e737525b7d6
- name: Install pypa/build
run: >-
python -m
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8 changes: 5 additions & 3 deletions ceruleo/dataset/analysis/numerical_features.py
Original file line number Diff line number Diff line change
Expand Up @@ -133,7 +133,8 @@ def mutual_information(x:np.ndarray, y:np.ndarray):
"monotonicity": lambda x,y:monotonicity(x),
"number_of_unique_elements": lambda x,y:n_unique(x),
'mutual_information': mutual_information,
'null': lambda x, y: null(x)
'null': lambda x, y: null(x),
'entropy': lambda x, y: entropy(x)
}


Expand Down Expand Up @@ -164,7 +165,7 @@ def analysis_single_time_series(
data = defaultdict(lambda: defaultdict(list))
if len(what_to_compute) == 0:
what_to_compute = list(sorted(metrics.keys()))
for column_index in range(X.shape[1]):
for column_index in range(len(column_names)):
column_name = column_names[column_index]
for what in what_to_compute:
x_ts = np.squeeze(X.loc[:, column_name].values)
Expand Down Expand Up @@ -209,6 +210,7 @@ def analysis(
- number_of_unique_elements
- mutual_information
- null
- entropy
Returns:
Expand All @@ -225,7 +227,7 @@ def analysis(
if isinstance(dataset, TransformedDataset):
column_names = dataset.transformer.column_names
else:
column_names = dataset[0].columns
column_names = dataset.numeric_features()
for X, y in iterate_over_features_and_target(dataset):
y = np.squeeze(y)
data = analysis_single_time_series(
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