Added: Linear regression time series forecast and XGBoost customer churn notebooks

Author: djarpin

linear_time_series_forecast/AmazonAIAlgorithmsIO_pb2.py

@@ -0,0 +1,90 @@
+import struct
+import io
+import boto3
+import sys
+
+import AmazonAIAlgorithmsIO_pb2
+from record_pb2 import Record
+
+
+def write_recordio(f, data):
+    kmagic = 0xced7230a
+    length = len(data)
+    f.write(struct.pack('I', kmagic))
+    f.write(struct.pack('I', length))
+    upper_align = ((length + 3) >> 2) << 2
+    padding = bytes([0x00 for _ in range(upper_align - length)])
+    f.write(data)
+    f.write(padding)
+
+
+def list_to_record_bytes(values, keys=None, label=None, feature_size=None):
+    record = Record()
+
+    record.features['values'].float32_tensor.values.extend(values)
+
+    if keys is not None:
+        if feature_size is None:
+            raise ValueError("For sparse tensors the feature size must be specified.")
+
+        record.features['values'].float32_tensor.keys.extend(keys)
+
+    if feature_size is not None:
+        record.features['values'].float32_tensor.shape.extend([feature_size])
+
+    if label is not None:
+        record.label['values'].float32_tensor.values.extend([label])
+
+    return record.SerializeToString()
+
+
+def read_next(f):
+     kmagic = 0xced7230a
+     raw_bytes = f.read(4)
+     if not raw_bytes:
+         return
+     m = struct.unpack('I', raw_bytes)[0]
+     if m != kmagic:
+         raise ValueError("Incorrect encoding")
+     length = struct.unpack('I', f.read(4))[0]
+     upper_align = ((length + 3) >> 2) << 2
+     data = f.read(upper_align)
+     return data[:length]
+
+ 
+def to_proto(f, labels, vectors):
+     for label, vec in zip(labels, vectors):
+         record = AmazonAIAlgorithmsIO_pb2.Record()
+         record.values.extend(vec)
+         record.label = label
+         write_recordio(f, record.SerializeToString())
+ 
+
+def to_libsvm(f, labels, values):
+     f.write('\n'.join(
+         ['{} {}'.format(label, ' '.join(['{}:{}'.format(i + 1, el) for i, el in enumerate(vec)])) for label, vec in
+          zip(labels, values)]))
+     return f
+
+
+def write_to_s3(fobj, bucket, key):
+    return boto3.Session().resource('s3').Bucket(bucket).Object(key).upload_fileobj(fobj)
+
+
+def upload_to_s3(partition_name, partition, bucket):
+    labels = [t.tolist() for t in partition[1]]
+    vectors = [t.tolist() for t in partition[0]]
+    f = io.BytesIO()
+    to_proto(f, labels, vectors)
+    f.seek(0)
+    key = "{}/examples".format(partition_name)
+    url = 's3n://{}/{}'.format(bucket, key)
+    print('Writing to {}'.format(url))
+    write_to_s3(f, bucket, key)
+    print('Done writing to {}'.format(url))
+
+
+def convert_data(partitions, bucket):
+    for partition_name, partition in partitions:
+        print('{}: {} {}'.format(partition_name, partition[0].shape, partition[1].shape))
+        upload_to_s3(partition_name, partition, bucket)