mirror of
https://github.com/ClickHouse/ClickHouse.git
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54786902c3
added test [#CLICKHOUSE-3305] added test [#CLICKHOUSE-3305] added test [#CLICKHOUSE-3305] added test [#CLICKHOUSE-3305] added test [#CLICKHOUSE-3305] added test [#CLICKHOUSE-3305] added test [#CLICKHOUSE-3305]
70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
from server import ClickHouseServer
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from client import ClickHouseClient
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from pandas import DataFrame
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import os
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import threading
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import tempfile
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class ClickHouseTable:
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def __init__(self, server, port, table_name, df):
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self.server = server
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self.port = port
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self.table_name = table_name
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self.df = df
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if not isinstance(self.server, ClickHouseServer):
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raise Exception('Expected ClickHouseServer, got ' + repr(self.server))
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if not isinstance(self.df, DataFrame):
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raise Exception('Expected DataFrame, got ' + repr(self.df))
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self.server.wait_for_request(port)
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self.client = ClickHouseClient(server.binary_path, port)
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def _convert(self, name):
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types_map = {
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'float64': 'Float64',
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'int64': 'Int64',
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'float32': 'Float32',
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'int32': 'Int32'
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}
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if name in types_map:
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return types_map[name]
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return 'String'
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def _create_table_from_df(self):
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self.client.query('create database if not exists test')
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self.client.query('drop table if exists test.{}'.format(self.table_name))
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column_types = list(self.df.dtypes)
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column_names = list(self.df)
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schema = ', '.join((name + ' ' + self._convert(str(t)) for name, t in zip(column_names, column_types)))
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print 'schema:', schema
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create_query = 'create table test.{} (date Date DEFAULT today(), {}) engine = MergeTree(date, (date), 8192)'
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self.client.query(create_query.format(self.table_name, schema))
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insert_query = 'insert into test.{} ({}) format CSV'
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with tempfile.TemporaryFile() as tmp_file:
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self.df.to_csv(tmp_file, header=False, index=False)
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tmp_file.seek(0)
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self.client.query(insert_query.format(self.table_name, ', '.join(column_names)), pipe=tmp_file)
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def apply_model(self, model_name, float_columns, cat_columns):
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columns = ', '.join(list(float_columns) + list(cat_columns))
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query = "select modelEvaluate('{}', {}) from test.{} format TSV"
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result = self.client.query(query.format(model_name, columns, self.table_name))
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return tuple(map(float, filter(len, map(str.strip, result.split()))))
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def _drop_table(self):
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self.client.query('drop table test.{}'.format(self.table_name))
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def __enter__(self):
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self._create_table_from_df()
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return self
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def __exit__(self, type, value, traceback):
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self._drop_table()
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