forked from bigmlcom/python
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathmodelhandler.py
More file actions
167 lines (141 loc) · 6.34 KB
/
modelhandler.py
File metadata and controls
167 lines (141 loc) · 6.34 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
# -*- coding: utf-8 -*-
#!/usr/bin/env python
#
# Copyright 2014-2019 BigML
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""Base class for models' REST calls
https://bigml.com/api/models
"""
try:
import simplejson as json
except ImportError:
import json
from bigml.resourcehandler import ResourceHandler
from bigml.resourcehandler import (check_resource_type, resource_is_ready,
get_resource_type, check_resource,
get_model_id, get_cluster_id)
from bigml.constants import (MODEL_PATH, CLUSTER_PATH, DATASET_PATH,
TINY_RESOURCE)
class ModelHandler(ResourceHandler):
"""This class is used by the BigML class as
a mixin that provides the REST calls models. It should not
be instantiated independently.
"""
def __init__(self):
"""Initializes the ModelHandler. This class is intended to be
used as a mixin on ResourceHandler, that inherits its
attributes and basic method from BigMLConnection, and must not be
instantiated independently.
"""
self.model_url = self.url + MODEL_PATH
def create_model(self, origin_resource, args=None, wait_time=3, retries=10):
"""Creates a model from an origin_resource.
Uses a remote resource to create a new model using the
arguments in `args`.
The allowed remote resources can be:
- dataset
- list of datasets
- cluster
In the case of using cluster id as origin_resource, a centroid must
also be provided in the args argument. The first centroid is used
otherwise.
"""
create_args = {}
if args is not None:
create_args.update(args)
if isinstance(origin_resource, list):
# mutidatasets
create_args = self._set_create_from_datasets_args(
origin_resource, args=create_args, wait_time=wait_time,
retries=retries)
else:
resource_type = get_resource_type(origin_resource)
# model from cluster and centroid
if resource_type == CLUSTER_PATH:
cluster_id = get_cluster_id(origin_resource)
cluster = check_resource(cluster_id,
query_string=TINY_RESOURCE,
wait_time=wait_time,
retries=retries,
raise_on_error=True, api=self)
if 'centroid' not in create_args:
try:
centroid = cluster['object'][
'cluster_models'].keys()[0]
create_args.update({'centroid': centroid})
except KeyError:
raise KeyError("Failed to generate the model. A "
"centroid id is needed in the args "
"argument to generate a model from "
"a cluster.")
create_args.update({'cluster': cluster_id})
elif resource_type == DATASET_PATH:
create_args = self._set_create_from_datasets_args(
origin_resource, args=create_args, wait_time=wait_time,
retries=retries)
else:
raise Exception("A dataset, list of dataset ids"
" or cluster id plus centroid id are needed"
" to create a"
" dataset. %s found." % resource_type)
body = json.dumps(create_args)
return self._create(self.model_url, body)
def get_model(self, model, query_string='',
shared_username=None, shared_api_key=None):
"""Retrieves a model.
The model parameter should be a string containing the
model id or the dict returned by create_model.
As model is an evolving object that is processed
until it reaches the FINISHED or FAULTY state, the function will
return a dict that encloses the model values and state info
available at the time it is called.
If this is a shared model, the username and sharing api key must
also be provided.
"""
check_resource_type(model, MODEL_PATH,
message="A model id is needed.")
model_id = get_model_id(model)
if model_id:
return self._get("%s%s" % (self.url, model_id),
query_string=query_string,
shared_username=shared_username,
shared_api_key=shared_api_key)
def model_is_ready(self, model, **kwargs):
"""Checks whether a model's status is FINISHED.
"""
check_resource_type(model, MODEL_PATH,
message="A model id is needed.")
resource = self.get_model(model, **kwargs)
return resource_is_ready(resource)
def list_models(self, query_string=''):
"""Lists all your models.
"""
return self._list(self.model_url, query_string)
def update_model(self, model, changes):
"""Updates a model.
"""
check_resource_type(model, MODEL_PATH,
message="A model id is needed.")
model_id = get_model_id(model)
if model_id:
body = json.dumps(changes)
return self._update("%s%s" % (self.url, model_id), body)
def delete_model(self, model):
"""Deletes a model.
"""
check_resource_type(model, MODEL_PATH,
message="A model id is needed.")
model_id = get_model_id(model)
if model_id:
return self._delete("%s%s" % (self.url, model_id))