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make release-tag: Merge branch 'main' into stable
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,4 @@ | ||
| Faker==24.11.0 | ||
| Faker==25.0.1 | ||
| copulas==0.11.0 | ||
| numpy==1.26.4 | ||
| pandas==2.2.2 | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -2,10 +2,9 @@ | |
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| """Top-level package for RDT.""" | ||
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| __author__ = 'DataCebo, Inc.' | ||
| __email__ = '[email protected]' | ||
| __version__ = '1.12.0' | ||
| __version__ = '1.12.1.dev2' | ||
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| import sys | ||
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@@ -20,10 +19,7 @@ | |
| from rdt import transformers | ||
| from rdt.hyper_transformer import HyperTransformer | ||
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| __all__ = [ | ||
| 'HyperTransformer', | ||
| 'transformers' | ||
| ] | ||
| __all__ = ['HyperTransformer', 'transformers'] | ||
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| RANDOM_SEED = 42 | ||
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@@ -42,9 +38,10 @@ def get_demo(num_rows=5): | |
| pd.DataFrame | ||
| """ | ||
| # Hard code first five rows | ||
| login_dates = pd.Series([ | ||
| '2021-06-26', '2021-02-10', 'NAT', '2020-09-26', '2020-12-22' | ||
| ], dtype='datetime64[ns]') | ||
| login_dates = pd.Series( | ||
| ['2021-06-26', '2021-02-10', 'NAT', '2020-09-26', '2020-12-22'], | ||
| dtype='datetime64[ns]', | ||
| ) | ||
| email_optin = pd.Series([False, False, False, True, np.nan], dtype='object') | ||
| credit_card = ['VISA', 'VISA', 'AMEX', np.nan, 'DISCOVER'] | ||
| age = [29, 18, 21, 45, 32] | ||
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@@ -55,7 +52,7 @@ def get_demo(num_rows=5): | |
| 'email_optin': email_optin, | ||
| 'credit_card': credit_card, | ||
| 'age': age, | ||
| 'dollars_spent': dollars_spent | ||
| 'dollars_spent': dollars_spent, | ||
| }) | ||
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| if num_rows <= 5: | ||
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@@ -67,14 +64,18 @@ def get_demo(num_rows=5): | |
| try: | ||
| num_rows -= 5 | ||
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| login_dates = np.array([ | ||
| np.datetime64('2000-01-01') + np.timedelta64(np.random.randint(0, 10000), 'D') | ||
| for _ in range(num_rows) | ||
| ], dtype='datetime64[ns]') | ||
| login_dates = np.array( | ||
| [ | ||
| np.datetime64('2000-01-01') + np.timedelta64(np.random.randint(0, 10000), 'D') | ||
| for _ in range(num_rows) | ||
| ], | ||
| dtype='datetime64[ns]', | ||
| ) | ||
| login_dates[np.random.random(size=num_rows) > 0.8] = np.datetime64('NaT') | ||
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| email_optin = pd.Series([True, False, np.nan], dtype='object').sample( | ||
| num_rows, replace=True) | ||
| num_rows, replace=True | ||
| ) | ||
| credit_card = np.random.choice(['VISA', 'AMEX', np.nan, 'DISCOVER'], size=num_rows) | ||
| age = np.random.randint(18, 100, size=num_rows) | ||
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@@ -84,16 +85,19 @@ def get_demo(num_rows=5): | |
| finally: | ||
| np.random.set_state(random_state) | ||
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| return pd.concat([ | ||
| data, | ||
| pd.DataFrame({ | ||
| 'last_login': login_dates, | ||
| 'email_optin': email_optin, | ||
| 'credit_card': credit_card, | ||
| 'age': age, | ||
| 'dollars_spent': dollars_spent | ||
| }) | ||
| ], ignore_index=True) | ||
| return pd.concat( | ||
| [ | ||
| data, | ||
| pd.DataFrame({ | ||
| 'last_login': login_dates, | ||
| 'email_optin': email_optin, | ||
| 'credit_card': credit_card, | ||
| 'age': age, | ||
| 'dollars_spent': dollars_spent, | ||
| }), | ||
| ], | ||
| ignore_index=True, | ||
| ) | ||
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| def _get_addon_target(addon_path_name): | ||
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@@ -159,7 +163,7 @@ def _find_addons(): | |
| try: | ||
| addon = entry_point.load() | ||
| except Exception: # pylint: disable=broad-exception-caught | ||
| msg = f'Failed to load "{entry_point.name}" from "{entry_point.version}".' | ||
| msg = f'Failed to load "{entry_point.name}" from "{entry_point.value}".' | ||
| warnings.warn(msg) | ||
| continue | ||
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