6 examples of 'python extract data from json file' in Python

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37def return_train_data_from_json(input_json):
38 """Returns train data set from input JSON
39
40 Args:
41 input_json (dict): "Extraction" dictionary
42
43 Returns:
44 X (numpy.ndarray): Features
45
46 y (numpy.ndarray): Labels
47 """
48 X, y = return_main_data_from_json(input_json)
49
50 if input_json['test_dataset']['method'] == 'split_from_main':
51 X, X_test, y, y_test = train_test_split(
52 X,
53 y,
54 test_size=input_json['test_dataset']['split_ratio'],
55 random_state=input_json['test_dataset']['split_seed'],
56 stratify=y
57 )
58
59 if input_json['meta_feature_generation']['method'] == 'holdout_split':
60 X, X_test, y, y_test = train_test_split(
61 X,
62 y,
63 test_size=input_json['meta_feature_generation']['split_ratio'],
64 random_state=input_json['meta_feature_generation']['seed'],
65 stratify=y
66 )
67
68 return X, y
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33def _get_parsed_data(file_obj, for_page=False):
34 raw = file_obj.read().decode('utf-8')
35 return json.loads(raw, object_hook=_object_version_data_hook)
43def read_data(log_file):
44 """
45 Read, parse and return the JSON data for the given log file name.
46 (As a side effect sets the global _last_log_file to the log file name.)
47 """
48 global _last_log_file
49 try:
50 with open(log_file, "r") as f:
51 f.readline()
52 data = json.load(f)
53 # Keep track of the current log file in global variable in case we need to
54 # identify it later if there's a problem. (This works because the files are
55 # processed lazily.)
56 _last_log_file = log_file
57 except IOError:
58 sys.exit("Could not read log file %s" % log_file)
59 return data
107def return_holdout_data_from_json(input_json):
108 """Returns holdout data set from input JSON
109
110 Args:
111 input_json (dict): "Extraction" dictionary
112
113 Returns:
114 X (numpy.ndarray): Features
115
116 y (numpy.ndarray): Labels
117 """
118 if input_json['meta_feature_generation']['method'] == 'holdout_split':
119 X, y = return_main_data_from_json(input_json)
120
121 if input_json['test_dataset']['method'] == 'split_from_main':
122 X, X_test, y, y_test = train_test_split(
123 X,
124 y,
125 test_size=input_json['test_dataset']['split_ratio'],
126 random_state=input_json['test_dataset']['split_seed'],
127 stratify=y
128 )
129
130 X, X_holdout, y, y_holdout = train_test_split(
131 X,
132 y,
133 test_size=input_json['meta_feature_generation']['split_ratio'],
134 random_state=input_json['meta_feature_generation']['seed'],
135 stratify=y
136 )
137
138 return X_holdout, y_holdout
139
140 if input_json['meta_feature_generation']['method'] == 'holdout_source':
141 if 'source' not in input_json['meta_feature_generation'] or \
142 not input_json['meta_feature_generation']['source']:
143 raise exceptions.UserError('Source is empty')
144
145 extraction_code = "".join(input_json['meta_feature_generation']["source"])
146 extraction_function = import_object_from_string_code(extraction_code,
147 "extract_holdout_dataset")
148 X_holdout, y_holdout = extraction_function()
149
150 return np.array(X_holdout), np.array(y_holdout)
36def load_and_parse_json(filename):
37 try:
38 return json.loads(read_file(filename))
39 except:
40 print 'ERROR: Failed to parse %s' % filename
41 raise
27def load_file(self):
28 with open('datafile.json', 'r') as f:
29 return json.loads(f.read())

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