Fixed #181

EpistasisLab · Jun 22, 2016 · 3fdf711 · 3fdf711
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commit 3fdf711
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Showing 5 changed files with 7 additions and 7 deletions.
diff --git a/docs/sources/documentation/pipeline_operators/kernel_approximation/Nystroem.md b/docs/sources/documentation/pipeline_operators/kernel_approximation/Nystroem.md
@@ -34,6 +34,7 @@ import pandas as pd
 
 from sklearn.cross_validation import train_test_split
 from sklearn.kernel_approximation import Nystroem
+from sklearn.tree import DecisionTreeClassifier
 
 # NOTE: Make sure that the class is labeled 'class' in the data file
 tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR')

diff --git a/docs/sources/documentation/pipeline_operators/kernel_approximation/RBFSampler.md b/docs/sources/documentation/pipeline_operators/kernel_approximation/RBFSampler.md
@@ -29,6 +29,7 @@ import pandas as pd
 
 from sklearn.cross_validation import train_test_split
 from sklearn.kernel_approximation import RBFSampler
+from sklearn.tree import DecisionTreeClassifier
 
 # NOTE: Make sure that the class is labeled 'class' in the data file
 tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR')

diff --git a/...ources/documentation/pipeline_operators/models/classifiers/ensemble/AdaBoost.md b/...ources/documentation/pipeline_operators/models/classifiers/ensemble/AdaBoost.md
@@ -27,7 +27,7 @@ Example Exported Code
 import numpy as np
 import pandas as pd
 from sklearn.cross_validation import train_test_split
-from sklearn.ensumble import AdaBoostClassifier
+from sklearn.ensemble import AdaBoostClassifier
 
 # NOTE: Make sure that the class is labeled 'class' in the data file
 tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR')

diff --git a/.../sources/documentation/pipeline_operators/preprocessing/scaling/RobustScaler.md b/.../sources/documentation/pipeline_operators/preprocessing/scaling/RobustScaler.md
@@ -39,9 +39,8 @@ if len(training_features.columns.values) > 0:
     scaler = RobustScaler()
     scaler.fit(training_features.values.astype(np.float64))
     scaled_features = scaler.transform(result1.drop('class', axis=1).values.astype(np.float64))
-
-    for col_num, column in enumerate(result1.drop('class', axis=1).columns.values):
-        result1.loc[:, column] = scaled_features[:, col_num]
+    result1 = pd.DataFrame(data=scaled_features)
+    result1['class'] = tpot_data['class'].values
 
 # Perform classification with a decision tree classifier
 result2 = result1.copy()

diff --git a/...ources/documentation/pipeline_operators/preprocessing/scaling/StandardScaler.md b/...ources/documentation/pipeline_operators/preprocessing/scaling/StandardScaler.md
@@ -39,9 +39,8 @@ if len(training_features.columns.values) > 0:
     scaler = StandardScaler()
     scaler.fit(training_features.values.astype(np.float64))
     scaled_features = scaler.transform(result1.drop('class', axis=1).values.astype(np.float64))
-
-    for col_num, column in enumerate(result1.drop('class', axis=1).columns.values):
-        result1.loc[:, column] = scaled_features[:, col_num]
+    result1 = pd.DataFrame(data=scaled_features)
+    result1['class'] = tpot_data['class'].values
 
 
 # Perform classification with a decision tree classifier