snapshot...

modules/machine_learning/classification.py

@@ -50,13 +50,13 @@ def plot_confusion_matrix(cm, classes,
     plt.xlabel('Predicted label')
 
 
-def get_csv_as_dataframe(csv_file, reduce_frac=None):
+def get_csv_as_dataframe(csv_file, reduce_frac=None, random_state=42):
     icing_df = pd.read_csv(csv_file)
+
     # Random selection of reduce_frac of the rows
     if reduce_frac is not None:
-        icing_df = icing_df.sample(frac=reduce_frac)
-    print(icing_df.describe())
-    print(icing_df.shape)
+        icing_df = icing_df.sample(axis=0, frac=reduce_frac, random_state=random_state)
+
     return icing_df
 
 
@@ -72,9 +72,8 @@ def get_feature_target_data(data_frame, standardize=True):
     # Remove rows with NaN values
     # icing_df = icing_df.dropna()
 
-    print('num obs, features: ', icing_df.shape)
-
     x = np.asarray(icing_df[params])
+    print('num obs, features: ', x.shape)
     if standardize:
         x = preprocessing.StandardScaler().fit(x).transform(x)