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Merged
rhiever merged 4 commits into from
May 31, 2017
Merged

Rename "classes" to "target" throughout TPOT #477

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2cbe6d6
rename classes to target in codes
May 31, 2017
6290b31
clean up
May 31, 2017
da3f369
update doc
May 31, 2017
f6f9a22
clean up
May 31, 2017
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14 changes: 7 additions & 7 deletions docs_sources/api.md
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Original file line number Diff line number Diff line change
Expand Up @@ -647,7 +647,7 @@ tpot.export('tpot_boston_pipeline.py')

<table width="100%">
<tr>
<td width="25%"><a href="#tpotregressor-fit">fit</a>(features, classes[, sample_weight, groups])</td>
<td width="25%"><a href="#tpotregressor-fit">fit</a>(features, target[, sample_weight, groups])</td>
<td>Run the TPOT optimization process on the given training data.</td>
</tr>

Expand All @@ -657,7 +657,7 @@ tpot.export('tpot_boston_pipeline.py')
</tr>

<tr>
<td><a href="#tpotregressor-score">score</a>(testing_features, testing_classes)</td>
<td><a href="#tpotregressor-score">score</a>(testing_features, testing_target)</td>
<td>Returns the optimized pipeline's score on the given testing data using the user-specified scoring function.</td>
</tr>

Expand All @@ -670,13 +670,13 @@ tpot.export('tpot_boston_pipeline.py')

<a name="tpotregressor-fit"></a>
```Python
fit(features, classes, sample_weight=None, groups=None)
fit(features, target, sample_weight=None, groups=None)
```

<div style="padding-left:5%" width="100%">
Run the TPOT optimization process on the given training data.
<br /><br />
Uses genetic programming to optimize a machine learning pipeline that maximizes the score on the provided features and classes. Performs internal k-fold cross-validaton to avoid overfitting on the provided data.
Uses genetic programming to optimize a machine learning pipeline that maximizes the score on the provided features and target. Performs internal k-fold cross-validaton to avoid overfitting on the provided data.
<br /><br />
<table width="100%">
<tr>
Expand All @@ -693,7 +693,7 @@ using <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preproce
If you wish to use a different imputation strategy than median imputation, please make sure to apply imputation to your feature set prior to passing it to TPOT.
</blockquote>

<strong>classes</strong>: array-like {n_samples}
<strong>target</strong>: array-like {n_samples}
<blockquote>
List of target labels for prediction
</blockquote>
Expand Down Expand Up @@ -757,7 +757,7 @@ Predicted target values for the samples in the feature matrix

<a name="tpotregressor-score"></a>
```Python
score(testing_features, testing_classes)
score(testing_features, testing_target)
```

<div style="padding-left:5%" width="100%">
Expand All @@ -774,7 +774,7 @@ The default scoring function for TPOTClassifier is 'mean_squared_error'.
Feature matrix of the testing set
</blockquote>

<strong>testing_classes</strong>: array-like {n_samples}
<strong>testing_target</strong>: array-like {n_samples}
<blockquote>
List of target labels for prediction in the testing set
</blockquote>
Expand Down
12 changes: 6 additions & 6 deletions docs_sources/examples.md
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Expand Up @@ -36,15 +36,15 @@ from sklearn.preprocessing import Normalizer
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1),
tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = \
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['class'], random_state=42)

exported_pipeline = make_pipeline(
Normalizer(),
GaussianNB()
)

exported_pipeline.fit(training_features, training_classes)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
```

Expand Down Expand Up @@ -81,12 +81,12 @@ from sklearn.neighbors import KNeighborsClassifier
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1),
tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = \
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['class'], random_state=42)

exported_pipeline = KNeighborsClassifier(n_neighbors=6, weights="distance")

exported_pipeline.fit(training_features, training_classes)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
```

Expand Down Expand Up @@ -125,14 +125,14 @@ from sklearn.model_selection import train_test_split
tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)
features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1),
tpot_data.dtype.names.index('class'), axis=1)
training_features, testing_features, training_classes, testing_classes = \
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['class'], random_state=42)

exported_pipeline = GradientBoostingRegressor(alpha=0.85, learning_rate=0.1, loss="ls",
max_features=0.9, min_samples_leaf=5,
min_samples_split=6)

exported_pipeline.fit(training_features, training_classes)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
```

Expand Down
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