pandas 映射

tensnsorflow/feature_engineering.py

@@ -59,8 +59,8 @@ def get_data():
     value_map = {v: k for k, v in enumerate(unique_values)}
 
     df = df.drop("device_id", axis=1)
-    train = df[df["stat_date"] != validate_date]
-    test = df[df["stat_date"] == validate_date]
+    train = df[df["stat_date"] != validate_date+"stat_date"]
+    test = df[df["stat_date"] == validate_date+"stat_date"]
     for i in features:
         train[i] = train[i].map(value_map)
         test[i] = test[i].map(value_map)

tensnsorflow/test.py

@@ -26,8 +26,7 @@ def gen_tfrecords(in_file):
     out_file = os.path.join(FLAGS.output_dir, basename)
     tfrecord_out = tf.python_io.TFRecordWriter(out_file)
     df = pd.read_csv(in_file)
-    ["", "", "", "device_type", "manufacturer",
-     , "level2_ids", "time", "stat_date"]
+
     for i in range(df.shape[0]):
         features = tf.train.Features(feature={
             "y": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["y"][i]])),
@@ -37,11 +36,13 @@ def gen_tfrecords(in_file):
             "ucity_id": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["ucity_id"][i]])),
             "clevel1_id": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["clevel1_id"][i]])),
             "ccity_name": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["ccity_name"][i]])),
-            "channel": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["channel"][i]])),
-
+            "device_type": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["device_type"][i]])),
+            "manufacturer": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["manufacturer"][i]])),
+            "level2_ids": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["level2_ids"][i]])),
+            "time": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["time"][i]])),
+            "stat_date": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["stat_date"][i]]))
         })
-
-        example = tf.train.Example(features = tf.train.Features(feature = feature))
+        example = tf.train.Example(features = tf.train.Features(feature = features))
         serialized = example.SerializeToString()
         tfrecord_out.write(serialized)
     tfrecord_out.close()