Merge pull request #476 from SameerMahajan-GSLab/Sameer_Mahajan_473

Sameer Mahajan 473
HealthCatalyst · Jul 10, 2018 · 25cb3ae · 25cb3ae
2 parents 2ab52b5 + 6bdbaa8
commit 25cb3ae
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Showing 2 changed files with 9 additions and 4 deletions.
diff --git a/healthcareai/common/top_factors.py b/healthcareai/common/top_factors.py
@@ -1,10 +1,12 @@
 import numpy as np
 import pandas as pd
+from distutils.version import StrictVersion
 
 from sklearn.linear_model import LogisticRegression, LinearRegression
 
 from healthcareai.common.healthcareai_error import HealthcareAIError
 
+CHECK_PANDAS_VERSION = StrictVersion("0.23.1")
 
 def descending_sort(row):
     # TODO Low priority, consider testing
@@ -44,7 +46,11 @@ def top_k_features(dataframe, linear_model, k=3):
 
     # Multiply the values with the coefficients from the trained model and take the magnitude
     step1 = pd.DataFrame(np.abs(dataframe.values * linear_model.coef_), columns=dataframe.columns)
-    step2 = step1.apply(descending_sort, axis=1)
+
+    if StrictVersion(pd.__version__) >= CHECK_PANDAS_VERSION:
+        step2 = step1.apply(descending_sort, axis=1, result_type='expand')
+    else:
+        step2 = step1.apply(descending_sort, axis=1)
 
     results = list(step2.values[:, :k])
     return results

diff --git a/healthcareai/common/transformers.py b/healthcareai/common/transformers.py
@@ -10,7 +10,6 @@
 from imblearn.under_sampling import RandomUnderSampler
 from sklearn.preprocessing import StandardScaler
 
-
 class DataFrameImputer(TransformerMixin):
     """
     Impute missing values in a dataframe.
@@ -36,7 +35,7 @@ def fit(self, X, y=None):
 
         self.fill = pd.Series([X[c].value_counts().index[0]
                                if X[c].dtype == np.dtype('O')
-                                  or pd.core.common.is_categorical_dtype(X[c])
+                                  or pd.api.types.is_categorical_dtype(X[c])
                                else X[c].mean() for c in X], index=X.columns)
 
         if self.verbose:
@@ -105,7 +104,7 @@ def fit(self, X, y=None):
         return self
 
     def transform(self, X, y=None):
-        columns_to_dummify = X.select_dtypes(include=[object, 'category'])
+        columns_to_dummify = list(X.select_dtypes(include=[object, 'category']))
 
         # remove excluded columns (if they are still in the list)
         for column in columns_to_dummify: