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
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
|
#pragma once
#include <AggregateFunctions/IAggregateFunction.h>
#include <AggregateFunctions/StatCommon.h>
#include <Columns/ColumnVector.h>
#include <Columns/ColumnTuple.h>
#include <Common/assert_cast.h>
#include <Core/Types.h>
#include <DataTypes/DataTypesNumber.h>
#include <DataTypes/DataTypeTuple.h>
#include <cmath>
#include <cfloat>
/// This function is used in implementations of different T-Tests.
/// On Darwin it's unavailable in math.h but actually exists in the library (can be linked successfully).
#if defined(OS_DARWIN)
extern "C"
{
double lgamma_r(double x, int * signgamp);
}
#endif
namespace DB
{
struct Settings;
class ReadBuffer;
class WriteBuffer;
namespace ErrorCodes
{
extern const int BAD_ARGUMENTS;
}
/// Returns tuple of (t-statistic, p-value)
/// https://cpb-us-w2.wpmucdn.com/voices.uchicago.edu/dist/9/1193/files/2016/01/05b-TandP.pdf
template <typename Data>
class AggregateFunctionTTest :
public IAggregateFunctionDataHelper<Data, AggregateFunctionTTest<Data>>
{
private:
bool need_confidence_interval = false;
Float64 confidence_level;
public:
AggregateFunctionTTest(const DataTypes & arguments, const Array & params)
: IAggregateFunctionDataHelper<Data, AggregateFunctionTTest<Data>>({arguments}, params, createResultType(!params.empty()))
{
if (!params.empty())
{
need_confidence_interval = true;
confidence_level = params.at(0).safeGet<Float64>();
if (!std::isfinite(confidence_level))
{
throw Exception(ErrorCodes::BAD_ARGUMENTS, "Aggregate function {} requires finite parameter values.", Data::name);
}
if (confidence_level <= 0.0 || confidence_level >= 1.0 || fabs(confidence_level - 0.0) < DBL_EPSILON || fabs(confidence_level - 1.0) < DBL_EPSILON)
{
throw Exception(ErrorCodes::BAD_ARGUMENTS, "Confidence level parameter must be between 0 and 1 in aggregate function {}.", Data::name);
}
}
}
String getName() const override
{
return Data::name;
}
static DataTypePtr createResultType(bool need_confidence_interval_)
{
if (need_confidence_interval_)
{
DataTypes types
{
std::make_shared<DataTypeNumber<Float64>>(),
std::make_shared<DataTypeNumber<Float64>>(),
std::make_shared<DataTypeNumber<Float64>>(),
std::make_shared<DataTypeNumber<Float64>>(),
};
Strings names
{
"t_statistic",
"p_value",
"confidence_interval_low",
"confidence_interval_high",
};
return std::make_shared<DataTypeTuple>(
std::move(types),
std::move(names)
);
}
else
{
DataTypes types
{
std::make_shared<DataTypeNumber<Float64>>(),
std::make_shared<DataTypeNumber<Float64>>(),
};
Strings names
{
"t_statistic",
"p_value",
};
return std::make_shared<DataTypeTuple>(
std::move(types),
std::move(names)
);
}
}
bool allocatesMemoryInArena() const override { return false; }
void add(AggregateDataPtr __restrict place, const IColumn ** columns, size_t row_num, Arena *) const override
{
Float64 value = columns[0]->getFloat64(row_num);
UInt8 is_second = columns[1]->getUInt(row_num);
if (is_second)
this->data(place).addY(value);
else
this->data(place).addX(value);
}
void merge(AggregateDataPtr __restrict place, ConstAggregateDataPtr rhs, Arena *) const override
{
this->data(place).merge(this->data(rhs));
}
void serialize(ConstAggregateDataPtr __restrict place, WriteBuffer & buf, std::optional<size_t> /* version */) const override
{
this->data(place).write(buf);
}
void deserialize(AggregateDataPtr __restrict place, ReadBuffer & buf, std::optional<size_t> /* version */, Arena *) const override
{
this->data(place).read(buf);
}
void insertResultInto(AggregateDataPtr __restrict place, IColumn & to, Arena *) const override
{
auto & data = this->data(place);
auto & column_tuple = assert_cast<ColumnTuple &>(to);
if (!data.hasEnoughObservations() || data.isEssentiallyConstant())
{
auto & column_stat = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(0));
auto & column_value = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(1));
column_stat.getData().push_back(std::numeric_limits<Float64>::quiet_NaN());
column_value.getData().push_back(std::numeric_limits<Float64>::quiet_NaN());
if (need_confidence_interval)
{
auto & column_ci_low = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(2));
auto & column_ci_high = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(3));
column_ci_low.getData().push_back(std::numeric_limits<Float64>::quiet_NaN());
column_ci_high.getData().push_back(std::numeric_limits<Float64>::quiet_NaN());
}
return;
}
auto [t_statistic, p_value] = data.getResult();
/// Because p-value is a probability.
p_value = std::min(1.0, std::max(0.0, p_value));
auto & column_stat = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(0));
auto & column_value = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(1));
column_stat.getData().push_back(t_statistic);
column_value.getData().push_back(p_value);
if (need_confidence_interval)
{
auto [ci_low, ci_high] = data.getConfidenceIntervals(confidence_level, data.getDegreesOfFreedom());
auto & column_ci_low = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(2));
auto & column_ci_high = assert_cast<ColumnVector<Float64> &>(column_tuple.getColumn(3));
column_ci_low.getData().push_back(ci_low);
column_ci_high.getData().push_back(ci_high);
}
}
};
}
|
