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Fix imports in examples
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unboxapi/__init__.py

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -161,7 +161,7 @@ def add_model(
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The first set of variables needed by Unbox are:
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>>> from unboxapi.tasks import TaskType
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>>> from unboxapi import TaskType
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>>>
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>>> task_type = TaskType.TabularClassification
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>>> class_names = ['Retained', 'Churned']
@@ -204,7 +204,7 @@ def add_model(
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The other model-specific variables needed by Unbox are:
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>>> from unboxapi.models import ModelType
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>>> from unboxapi import ModelType
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>>>
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>>> model_type = ModelType.sklearn
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>>> train_sample_df = df.sample(5000)
@@ -243,7 +243,7 @@ def add_model(
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The first set of variables needed by Unbox are:
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>>> from unboxapi.tasks import TaskType
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>>> from unboxapi import TaskType
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>>>
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>>> task_type = TaskType.TextClassification
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>>> class_names = ['Negative', 'Positive']
@@ -287,7 +287,7 @@ def add_model(
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The other model-specific variables needed by Unbox are:
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>>> from unboxapi.models import ModelType
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>>> from unboxapi import ModelType
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>>>
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>>> model_type = ModelType.sklearn
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@@ -506,7 +506,7 @@ def add_dataset(
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The variables are needed by Unbox are:
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>>> from unboxapi.tasks import TaskType
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>>> from unboxapi import TaskType
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>>>
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>>> task_type = TaskType.TabularClassification
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>>> class_names = ['Retained', 'Churned']
@@ -540,7 +540,7 @@ def add_dataset(
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The variables are needed by Unbox are:
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>>> from unboxapi.tasks import TaskType
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>>> from unboxapi import TaskType
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>>>
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>>> task_type = TaskType.TextClassification
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>>> class_names = ['Negative', 'Positive']
@@ -696,7 +696,7 @@ def add_dataframe(
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The variables are needed by Unbox are:
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>>> from unboxapi.tasks import TaskType
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>>> from unboxapi import TaskType
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>>>
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>>> task_type = TaskType.TabularClassification
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>>> class_names = ['Retained', 'Churned']
@@ -729,7 +729,7 @@ def add_dataframe(
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The variables are needed by Unbox are:
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>>> from unboxapi.tasks import TaskType
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>>> from unboxapi import TaskType
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>>>
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>>> task_type = TaskType.TextClassification
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>>> class_names = ['Negative', 'Positive']

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