Merge pull request #70 from ananyas168/remove-stale-replica
removed the TRL-neurips folder
This commit is contained in:
josepablocam
GitHub
Коммит
0c5c19d51b
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@ -1,119 +0,0 @@
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ArbitraryColumnNames
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| | a | G8DkqkuE | b8T | H |
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|---:|:--------|-----------:|:------------|:----|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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ShuffleRows
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|
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| | Name | Age | City | Sex |
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|---:|:--------|------:|:------------|:------|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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TransposeTable
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| | 0 | 1 | 2 |
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|:-----|:---------|:------------|:--------|
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| Name | Alice | Bob | Charlie |
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| Age | 25 | 30 | 22 |
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| City | New York | Los Angeles | Chicago |
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| Sex | F | M | M |
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ColumnCluster
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||||
|
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| | Name-----Age | City-----Sex |
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|---:|:---------------|:------------------|
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| 0 | Alice-----25 | New York-----F |
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| 1 | Bob-----30 | Los Angeles-----M |
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| 2 | Charlie-----22 | Chicago-----M |
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SerializeTable
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Name:Alice,Age:25,City:New York,Sex:F
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Name:Bob,Age:30,City:Los Angeles,Sex:M
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Name:Charlie,Age:22,City:Chicago,Sex:M
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ShuffleColumns
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| | Age | Sex | Name | City |
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|---:|------:|:------|:--------|:------------|
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| 0 | 25 | F | Alice | New York |
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| 1 | 30 | M | Bob | Los Angeles |
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| 2 | 22 | M | Charlie | Chicago |
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ShuffleColumnNames
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| | Age | Sex | Name | City |
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|---:|:--------|------:|:------------|:-------|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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OriginalData
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| | Name | Age | City | Sex |
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|---:|:--------|------:|:------------|:------|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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SequentialColumnNames
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| | col_0 | col_1 | col_2 | col_3 |
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|---:|:--------|--------:|:------------|:--------|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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| | |
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|---:|:---------------------------------------|
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| 0 | Name:Alice,Age:25,City:New York,Sex:F |
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| 1 | Name:Bob,Age:30,City:Los Angeles,Sex:M |
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| 2 | Name:Charlie,Age:22,City:Chicago,Sex:M |
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>Name</th>
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<th>Age</th>
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<th>City</th>
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<th>Sex</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>0</th>
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<td>Alice</td>
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<td>25</td>
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<td>New York</td>
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<td>F</td>
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</tr>
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<tr>
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<th>1</th>
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<td>Bob</td>
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<td>30</td>
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<td>Los Angeles</td>
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<td>M</td>
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</tr>
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<tr>
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<th>2</th>
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<td>Charlie</td>
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<td>22</td>
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<td>Chicago</td>
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<td>M</td>
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</tr>
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</tbody>
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</table><table border="1" class="dataframe"> <thead> <tr style="text-align: right;"><th></th><th>Name</th><th>Age</th><th>City</th><th>Sex</th> </tr> </thead> <tbody> <tr><th>0</th><td>Alice</td><td>25</td><td>New York</td><td>F</td> </tr> <tr><th>1</th><td>Bob</td><td>30</td><td>Los Angeles</td><td>M</td> </tr> <tr><th>2</th><td>Charlie</td><td>22</td><td>Chicago</td><td>M</td> </tr> </tbody></table>
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@ -1,119 +0,0 @@
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ArbitraryColumnNames
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| | a | G8DkqkuE | b8T | H |
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|---:|:--------|-----------:|:------------|:----|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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ShuffleRows
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| | Name | Age | City | Sex |
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|---:|:--------|------:|:------------|:------|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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TransposeTable
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|
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| | 0 | 1 | 2 |
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|:-----|:---------|:------------|:--------|
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| Name | Alice | Bob | Charlie |
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| Age | 25 | 30 | 22 |
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| City | New York | Los Angeles | Chicago |
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| Sex | F | M | M |
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ColumnCluster
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|
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| | Name-----Age | City-----Sex |
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|---:|:---------------|:------------------|
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| 0 | Alice-----25 | New York-----F |
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| 1 | Bob-----30 | Los Angeles-----M |
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| 2 | Charlie-----22 | Chicago-----M |
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SerializeTable
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Name:Alice,Age:25,City:New York,Sex:F
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Name:Bob,Age:30,City:Los Angeles,Sex:M
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Name:Charlie,Age:22,City:Chicago,Sex:M
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|
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ShuffleColumns
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|
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| | Age | Sex | Name | City |
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|---:|------:|:------|:--------|:------------|
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| 0 | 25 | F | Alice | New York |
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| 1 | 30 | M | Bob | Los Angeles |
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| 2 | 22 | M | Charlie | Chicago |
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ShuffleColumnNames
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|
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| | Age | Sex | Name | City |
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|---:|:--------|------:|:------------|:-------|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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OriginalData
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|
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| | Name | Age | City | Sex |
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|---:|:--------|------:|:------------|:------|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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SequentialColumnNames
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| | col_0 | col_1 | col_2 | col_3 |
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|---:|:--------|--------:|:------------|:--------|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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| | |
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|---:|:---------------------------------------|
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| 0 | Name:Alice,Age:25,City:New York,Sex:F |
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| 1 | Name:Bob,Age:30,City:Los Angeles,Sex:M |
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| 2 | Name:Charlie,Age:22,City:Chicago,Sex:M |
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<table border="1" class="dataframe">
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<thead>
|
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<tr style="text-align: right;">
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<th></th>
|
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<th>Name</th>
|
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<th>Age</th>
|
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<th>City</th>
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<th>Sex</th>
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</tr>
|
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</thead>
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<tbody>
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<tr>
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<th>0</th>
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<td>Alice</td>
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<td>25</td>
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<td>New York</td>
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<td>F</td>
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</tr>
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<tr>
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<th>1</th>
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<td>Bob</td>
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<td>30</td>
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<td>Los Angeles</td>
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<td>M</td>
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</tr>
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<tr>
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<th>2</th>
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<td>Charlie</td>
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<td>22</td>
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<td>Chicago</td>
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<td>M</td>
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</tr>
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</tbody>
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</table><table border="1" class="dataframe"> <thead> <tr style="text-align: right;"><th></th><th>Name</th><th>Age</th><th>City</th><th>Sex</th> </tr> </thead> <tbody> <tr><th>0</th><td>Alice</td><td>25</td><td>New York</td><td>F</td> </tr> <tr><th>1</th><td>Bob</td><td>30</td><td>Los Angeles</td><td>M</td> </tr> <tr><th>2</th><td>Charlie</td><td>22</td><td>Chicago</td><td>M</td> </tr> </tbody></table>
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@ -1,63 +0,0 @@
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| | Name | Age | City | Sex |
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|---:|:----------|------:|:--------------|:------|
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| 0 | Alice | 25 | New York | F |
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| 1 | Bob | 30 | Los Angeles | M |
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| 2 | Charlie | 22 | Chicago | M |
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| 3 | David | 28 | Boston | M |
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| 4 | Emily | 35 | San Francisco | F |
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| 5 | Frank | 29 | Dallas | M |
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| 6 | Grace | 27 | Miami | F |
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| 7 | Henry | 32 | Seattle | M |
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| 8 | Ivy | 24 | Denver | F |
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| 9 | Jack | 33 | Houston | M |
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| 10 | Katherine | 26 | Atlanta | F |
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| 11 | Liam | 31 | Phoenix | M |
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| 12 | Mia | 36 | Philadelphia | F |
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| 13 | Noah | 23 | San Diego | M |
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| 14 | Olivia | 29 | Austin | F |
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NavigationTests
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What value is at row 14 and column Name?
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What value is at row 7 and column City?
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What value is at row 4 and column Name?
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ColumnLookupTests
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What column is the Olivia in?
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What column is the Seattle in?
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What column is the Emily in?
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RowLookupTests
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What row is the Olivia in?
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What row is the Seattle in?
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What row is the Emily in?
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|
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DataTypeLookupTests
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What type (using Pandas datatype notation) is column Age?
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What type (using Pandas datatype notation) is column City?
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What type (using Pandas datatype notation) is column Sex?
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TableColumnReorderTests
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Can you reorder the table such that the column are in this new order ['Sex', 'City', 'Age', 'Name']? Make sure to return the complete reordered table.
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Can you reorder the table such that the column are in this new order ['Name', 'Sex', 'City', 'Age']? Make sure to return the complete reordered table.
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Can you reorder the table such that the column are in this new order ['Sex', 'Name', 'Age', 'City']? Make sure to return the complete reordered table.
|
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|
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|
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TableReconstructionTests
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|
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Can you reconstruct the table by deserializing the table above?
|
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|
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|
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TableTransposeTests
|
||||
|
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Can you transpose the table?
|
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|
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|
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|
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@ -1,60 +0,0 @@
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# General Guideline
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This folder contains evaluation code and results associated with the paper[**Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs**](https://openreview.net/forum?id=Ld5UCpiT07) to appear at
|
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[TRL@NeurIPS 2023](https://table-representation-learning.github.io/)
|
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|
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## Setup
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Assuming you want to setup inside a Python virtual environment.
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```powershell
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python -m virtualenv venv/
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venv/scripts/activate
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pip install -r requirements.txt
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```
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You should have your OPENAI key as an environment variable, because we use the key to access the GPT3.5 `text-davinci-003 model`
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```
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$env:OPENAI_API_KEY=...
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```
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## Resources
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Inside the `resources` folder you will find `\BenchmarkDataset` subfolder which consists of seven datasets from kaggle. These seven datasets are used as benchmarks to evaluate LLM's understanding through our self-supervised evalution setup.
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In order to access the Benchmark results that we obtained, we request you to download the `BenchmarkResults.zip` from [TodO](). Make sure to unzip the file at root of `TRL-neurips\resources\` folder for sucessful code runs.
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## Code Usage Guideline
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|
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In the paper we talk introduce Self-Supervised Structural Tasks to evaluate the performance of the LLM.
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|
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For the fact-finding task you can refer the `.\code_\tableTestingMicroScript.py`. A notebook `.\notebooks\MicroTableTesting.ipynb` is also implemented to how our fact-finding evaluation suit is used.
|
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|
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For the table-transformation task you can refer the `code_\tableTestingMacroScript.py`. A notebook `.\notebooks\MacroTableTesting.ipynb` is also implemented to how our table-transformation evaluation suit is used.
|
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|
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## Metrics
|
||||
|
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### Fact-finding tasks
|
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We report the average pass@1 and p-value for the fact-finding tasks.
|
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|
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`.\notebooks\MicroTestMetricsCalculation.ipynb` takes the log file generated by the `.\notebooks\MicroTableTesting.ipynb`file and does some pre-requite metric calculation and finally generate the final csv files.
|
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|
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The final csv files are then used by `.\notebooks\MicroTestResults.ipynb` to provide the pass@1 scores on the Original data across all the fact-finding tasks, along with p-value and the average pass@1 delta from original to noisy operation.
|
||||
|
||||
### Table-transformation tasks
|
||||
We report the average F1 score and p-value for the table-transformation tasks.
|
||||
|
||||
`.\notebooks\MacroTestMetricsCalculation.ipynb` takes the log file generated by the `.\notebooks\MacroTableTesting.ipynb` file and does some pre-requite metric calculation and finally generate the final csv files.
|
||||
|
||||
The final csv files are then used by `.\notebooks\MacroTestResults_RQ1.ipynb` to provide the F1 scores on the Original data across all the fact-finding tasks, along with p-value. Whereas `.\notebooks\MacroTestResults_RQ2.ipynb` reports average F1 score delta from original to noisy for transformation tasks along with the p-values.
|
||||
|
||||
# Citation
|
||||
|
||||
If you find our work useful in your research, please consider citing the paper:
|
||||
|
||||
@inproceedings{singha2023tabular,
|
||||
title={Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs},
|
||||
author={Singha, Ananya and Cambronero, Jos{\'e} and Gulwani, Sumit and Le, Vu and Parnin, Chris},
|
||||
booktitle={NeurIPS 2023 Second Table Representation Learning Workshop},
|
||||
year={2023}
|
||||
}
|
||||
|
||||
# Contact
|
||||
For any questions or issues, please submit repository issues or reach us via email at `t-asingha@microsoft.com`
|
||||
|
|
@ -1,57 +0,0 @@
|
|||
import openai
|
||||
import json
|
||||
import time
|
||||
import re
|
||||
import os
|
||||
from typing import List, Dict
|
||||
from openai.embeddings_utils import get_embedding
|
||||
|
||||
DEFAULT_KEY = os.env("OPENAI_API_KEY")
|
||||
|
||||
|
||||
def set_openai_key_attribute(open_api_key: str):
|
||||
openai.api_type = "open_ai"
|
||||
openai.api_base = "https://api.openai.com/v1"
|
||||
openai.api_key = open_api_key
|
||||
openai.api_version = None
|
||||
|
||||
|
||||
def openapi_call_completions(prompt: str, modelName="text-davinci-003", temp=0.7, maxTok=500, num_n=1, open_api_key: str = DEFAULT_KEY):
|
||||
|
||||
set_openai_key_attribute(open_api_key)
|
||||
max_attempts = 100000
|
||||
attempt = 1
|
||||
|
||||
while attempt <= max_attempts:
|
||||
try:
|
||||
response = openai.Completion.create(
|
||||
model=modelName,
|
||||
prompt=prompt,
|
||||
temperature=temp,
|
||||
max_tokens=maxTok,
|
||||
top_p=1,
|
||||
frequency_penalty=0,
|
||||
presence_penalty=0,
|
||||
n=num_n,
|
||||
logprobs=1
|
||||
)
|
||||
|
||||
generation_list_all = []
|
||||
response = json.loads(str(response))
|
||||
meta_info = {"prompt": prompt, "response": response}
|
||||
for i in range(len(response["choices"])):
|
||||
output = response["choices"][i]["text"].strip()
|
||||
generation_list_all.append(output)
|
||||
meta_info["generations"] = generation_list_all
|
||||
return generation_list_all, meta_info
|
||||
|
||||
except openai.error.RateLimitError as e:
|
||||
# Rate limit error occurred, wait for a while before retrying
|
||||
wait_duration = 1 # Wait for 60 seconds
|
||||
print(
|
||||
f"Rate limit exceeded. Retrying in {wait_duration} seconds...")
|
||||
time.sleep(wait_duration)
|
||||
|
||||
attempt += 1
|
||||
|
||||
return
|
||||
|
|
@ -1,178 +0,0 @@
|
|||
from typing import Any, Union
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import datetime
|
||||
|
||||
|
||||
def parse_cell_value(reference_cell: Any, other_cell: Any) -> Any:
|
||||
"""
|
||||
Parses other_cell as reference_cell type if other_cell is string
|
||||
"""
|
||||
if not isinstance(other_cell, str):
|
||||
return other_cell
|
||||
try:
|
||||
if isinstance(reference_cell, bool):
|
||||
# check bool first since bool isinstance of int as well
|
||||
# tolerate capitalization differences for boolean
|
||||
return other_cell.lower() == "true"
|
||||
elif isinstance(reference_cell, int) or isinstance(reference_cell, float):
|
||||
# always parse as float to be safe with decimal points
|
||||
return float(other_cell)
|
||||
elif isinstance(reference_cell, datetime.date):
|
||||
# best guess parse
|
||||
return pd.to_datetime(other_cell).date()
|
||||
elif isinstance(reference_cell, datetime.datetime):
|
||||
# best guess parse
|
||||
return pd.to_datetime(other_cell).to_pydatetime()
|
||||
else:
|
||||
# fall back to str
|
||||
return other_cell
|
||||
except ValueError:
|
||||
# fall back to str if fails to parse
|
||||
return other_cell
|
||||
|
||||
|
||||
def cells_are_equal(cell1: Any, cell2: Any) -> int:
|
||||
try:
|
||||
if isinstance(cell1, (bool, str, datetime.date, datetime.datetime, pd.Timestamp)):
|
||||
return cell1 == cell2
|
||||
elif isinstance(cell1, (float, int, np.number)):
|
||||
return np.allclose(cell1, cell2)
|
||||
elif np.isnan(cell1):
|
||||
return np.isnan(cell2)
|
||||
elif np.isinf(cell1):
|
||||
return np.isinf(cell2) and (np.sign(cell1) == np.sign(cell2))
|
||||
else:
|
||||
# fall back
|
||||
return cell1 == cell2
|
||||
except:
|
||||
# try again in case it was numpy issues
|
||||
try:
|
||||
return cell1 == cell2
|
||||
except:
|
||||
return False
|
||||
|
||||
|
||||
def make_two_dim(obj):
|
||||
if obj.ndim == 2:
|
||||
return obj
|
||||
if obj.ndim == 1:
|
||||
return obj.reshape(-1, 1)
|
||||
else:
|
||||
raise Exception("Expect object with 1 or 2 dimensions")
|
||||
|
||||
|
||||
def compare_per_cell(
|
||||
reference_df: pd.DataFrame,
|
||||
other_df: pd.DataFrame,
|
||||
type_reference_df: pd.DataFrame = None,
|
||||
count_header_and_index: bool = True,
|
||||
return_fraction: bool = True,
|
||||
):
|
||||
"""
|
||||
Per-cell comparison with respect to reference_df (i.e.
|
||||
(other_df matches) / (reference_df cells)
|
||||
).
|
||||
|
||||
We cast other_df cells to match the type in type_reference_df (default to
|
||||
reference_df if not provided)
|
||||
|
||||
We treat header row and column indices as just other cells.
|
||||
"""
|
||||
if type_reference_df is None:
|
||||
type_reference_df = reference_df
|
||||
|
||||
as_dataframe = isinstance(reference_df, pd.DataFrame)
|
||||
if as_dataframe:
|
||||
ref_vals = reference_df.reset_index(drop=True)
|
||||
other_vals = other_df.reset_index(drop=True)
|
||||
type_ref_vals = type_reference_df.reset_index(drop=True)
|
||||
# iterate over reference dimensions
|
||||
rows_range = range(ref_vals.shape[0])
|
||||
cols_range = ref_vals.columns
|
||||
def lookup_ij(df, i, j): return df.loc[i, j]
|
||||
else:
|
||||
ref_vals = make_two_dim(reference_df.values)
|
||||
other_vals = make_two_dim(other_df.values)
|
||||
type_ref_vals = make_two_dim(type_reference_df.values)
|
||||
nrows, ncols = ref_vals.shape
|
||||
rows_range = range(nrows)
|
||||
cols_range = range(ncols)
|
||||
def lookup_ij(mat, i, j): return mat[i, j]
|
||||
|
||||
success_ct = 0
|
||||
total_ct = 0
|
||||
|
||||
for i in rows_range:
|
||||
for j in cols_range:
|
||||
# print(i,j)
|
||||
# print(ref_vals.index)
|
||||
ref_cell = lookup_ij(ref_vals, i, j)
|
||||
total_ct += 1
|
||||
try:
|
||||
other_cell = lookup_ij(other_vals, i, j)
|
||||
type_ref_cell = lookup_ij(type_ref_vals, i, j)
|
||||
except (IndexError, KeyError):
|
||||
# failed since out of bounds
|
||||
# print("failed")
|
||||
continue
|
||||
other_cell_parsed = parse_cell_value(type_ref_cell, other_cell)
|
||||
success_ct += int(cells_are_equal(ref_cell, other_cell_parsed))
|
||||
|
||||
if count_header_and_index:
|
||||
# compare header
|
||||
ref_header_row = reference_df.columns
|
||||
other_header_row = other_df.columns
|
||||
add_to_success, add_to_total = compare_per_cell(
|
||||
ref_header_row, other_header_row, count_header_and_index=False, return_fraction=False)
|
||||
total_ct += add_to_total
|
||||
success_ct += add_to_success
|
||||
|
||||
# compare column index
|
||||
ref_index_col = reference_df.index
|
||||
other_index_col = other_df.index
|
||||
add_to_success, add_to_total = compare_per_cell(
|
||||
ref_index_col, other_index_col, count_header_and_index=False, return_fraction=False)
|
||||
total_ct += add_to_total
|
||||
success_ct += add_to_success
|
||||
|
||||
if return_fraction:
|
||||
return float(success_ct) / float(total_ct)
|
||||
else:
|
||||
return success_ct, total_ct
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
data1 = {'A': [1, 2, 3],
|
||||
'B': [True, False, True],
|
||||
'C': ['apple', 'banana', 'cherry'],
|
||||
'D': [datetime.date(2023, 1, 1), datetime.date(2023, 2, 2), datetime.date(2023, 3, 3)]}
|
||||
df1 = pd.DataFrame(data1)
|
||||
|
||||
# Create the second DataFrame with overlapping values
|
||||
data2 = {'A': [1, 5, 3],
|
||||
'B': [True, True, False],
|
||||
'C': ['apple', 'elephant', 'cherry'],
|
||||
'D': [datetime.date(2023, 1, 1), datetime.date(2023, 2, 2), datetime.date(2023, 3, 3)]}
|
||||
df2 = pd.DataFrame(data2)
|
||||
|
||||
# equal to self
|
||||
assert compare_per_cell(df1, df1) == 1.0
|
||||
assert compare_per_cell(df2, df2) == 1.0
|
||||
# empty
|
||||
assert compare_per_cell(df1, pd.DataFrame()) == 0.0
|
||||
assert compare_per_cell(
|
||||
df1, df1.iloc[:0], count_header_and_index=False) == 0.0
|
||||
assert compare_per_cell(
|
||||
df1.iloc[:0], df1, count_header_and_index=False, return_fraction=False) == (0, 0)
|
||||
# both directions
|
||||
assert compare_per_cell(df1, df2, type_reference_df=df1,
|
||||
count_header_and_index=False, return_fraction=False) == (8, 12)
|
||||
assert compare_per_cell(df2, df1, type_reference_df=df2,
|
||||
count_header_and_index=False, return_fraction=False) == (8, 12)
|
||||
# subsets
|
||||
df1_subset = df1[["C", "D"]]
|
||||
assert compare_per_cell(df1_subset, df2, type_reference_df=df1_subset,
|
||||
count_header_and_index=False, return_fraction=False) == (5, 6)
|
||||
assert compare_per_cell(df2, df1_subset, type_reference_df=df2,
|
||||
count_header_and_index=False, return_fraction=False) == (5, 12)
|
||||
|
|
@ -1,57 +0,0 @@
|
|||
from typing import Options
|
||||
import os
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
|
||||
def compute_ecdf(data):
|
||||
"""Compute ECDF for a one-dimensional array of measurements."""
|
||||
n = len(data)
|
||||
x = np.sort(data)
|
||||
y = np.arange(1, n+1) / n
|
||||
return x, y
|
||||
|
||||
def ecdf_scatter_plot(attributes, save_path,pivot_table,name_save, type:str= Options['dots',"step"], figsize:tuple=(30,20), k:int=1,temperature:float=0.1):
|
||||
"""With table formats as columns and test cases as row. ecdf plot across table manipulation for a specific temperature and metric """
|
||||
# Define the number of subplots and their arrangement
|
||||
x_list = [[test, "", temperature ] for test in attributes["TestCase"] ]
|
||||
num_rows = len(x_list)
|
||||
num_cols = 6
|
||||
|
||||
# Create a figure and a grid of subplots
|
||||
fig, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
|
||||
|
||||
# Loop through each subplot and plot the scatter plot
|
||||
for i in range(num_rows):
|
||||
for j in range(num_cols):
|
||||
|
||||
TableFormat = attributes["tableFormat"][j]
|
||||
ax = axes[i][j]
|
||||
# Filter data for each subplot based on data_labels
|
||||
for manipulation in attributes["TableManipulation"]:
|
||||
x= x_list[i]
|
||||
x[1] = manipulation
|
||||
x = tuple(x)
|
||||
|
||||
y = ( f'pass_{k}', TableFormat)
|
||||
scores = pivot_table.loc[x][y]
|
||||
scores_x, scores_y = compute_ecdf(scores)
|
||||
if type == "dots":
|
||||
ax.plot(scores_x, scores_y, label=f'{manipulation}',marker = ".", linestyle = "none")
|
||||
else:
|
||||
ax.step(scores_x, scores_y, label=f'{manipulation}')
|
||||
|
||||
name = f'Test: {x[0]}, metric: pass@{k}\nTemperature: {x[2]}, Format: {y[1]}'
|
||||
|
||||
ax.legend()
|
||||
ax.set_title(name)
|
||||
ax.set_xlabel('score (%)')
|
||||
ax.set_ylabel('ecdf')
|
||||
|
||||
|
||||
# Adjust layout to prevent overlapping labels
|
||||
plt.tight_layout()
|
||||
plt.savefig(os.path.join(save_path, name_save.replace(".csv", "_") +f"ecdf_{type}_plot.pdf"))
|
||||
# Show the plot
|
||||
plt.show()
|
||||
plt.clf()
|
||||
|
|
@ -1,26 +0,0 @@
|
|||
import pandas
|
||||
import os
|
||||
import numpy as np
|
||||
from itertools import combinations
|
||||
from typing import Any
|
||||
|
||||
|
||||
def metric_pass_k(expected_answer, answer: Any, k: int) -> float:
|
||||
if not isinstance(answer, list):
|
||||
raise ValueError("Answer should be a list for pass@k metric.")
|
||||
|
||||
# Convert the test.expect to a list if it's not already
|
||||
expected_values = expected_answer if isinstance(
|
||||
expected_answer, list) else [expected_answer]
|
||||
expected_values = list(map(str, expected_values))
|
||||
boolean_answers = [
|
||||
answer[i] in expected_values for i in range(len(answer))]
|
||||
combinations_k = list(combinations(boolean_answers, k))
|
||||
passed_at_k = 0
|
||||
# Calculate the pass@k metric
|
||||
for comb in combinations_k:
|
||||
if any(comb):
|
||||
passed_at_k += 1
|
||||
pass_at_k_percentage = (passed_at_k / len(combinations_k))*100
|
||||
|
||||
return pass_at_k_percentage
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
from typing import Options
|
||||
import os
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from collections import Counter
|
||||
|
||||
|
||||
|
||||
def scatter_plot(attributes, save_path,pivot_table,name_save):
|
||||
x_list = [(test, manipulation, temp ) for test in attributes["TestCase"] for manipulation in attributes["TableManipulation"] for temp in attributes["temperature"]]
|
||||
# Define the number of subplots and their arrangement
|
||||
|
||||
num_rows = len(x_list)
|
||||
num_cols = 6
|
||||
|
||||
# Create a figure and a grid of subplots
|
||||
fig, axes = plt.subplots(num_rows, num_cols, figsize=(50,500))
|
||||
|
||||
|
||||
# Loop through each subplot and plot the scatter plot
|
||||
for i in range(num_rows):
|
||||
for j in range(num_cols):
|
||||
|
||||
TableFormat = attributes["tableFormat"][j]
|
||||
ax = axes[i][j]
|
||||
markerss= [".", "x", "p", "^","o","s"]
|
||||
# Filter data for each subplot based on data_labels
|
||||
for j, k in enumerate([1,3,5,10,15]):
|
||||
x= x_list[i]
|
||||
y = ( f'pass_{k}', TableFormat)
|
||||
scores = pivot_table.loc[x][y]
|
||||
ax.scatter(range(len(scores)), scores, label=f'pass@{k}', s=int(300/k), marker =markerss[j] )
|
||||
name = f'Test: {x[0]}, TableManipulation: {x[1]}\nTemperature: {x[2]}, Format: {y[1]}'
|
||||
ax.set_title(name)
|
||||
ax.set_xlabel('data-points')
|
||||
ax.set_ylabel('score(%)')
|
||||
|
||||
ax.legend()
|
||||
# Adjust layout to prevent overlapping labels
|
||||
plt.tight_layout()
|
||||
plt.savefig(os.path.join(save_path, name_save.replace(".csv", "_") +"_scatter_plot.pdf"))
|
||||
# Show the plot
|
||||
plt.show()
|
||||
plt.clf()
|
||||
|
||||
|
||||
def density_scatter_plot(attributes, save_path,pivot_table,name_save):
|
||||
x_list = [(test, manipulation, temp ) for test in attributes["TestCase"] for manipulation in attributes["TableManipulation"] for temp in attributes["temperature"]]
|
||||
# Define the number of subplots and their arrangement
|
||||
|
||||
num_rows = len(x_list)
|
||||
num_cols = 6
|
||||
|
||||
# Create a figure and a grid of subplots
|
||||
fig, axes = plt.subplots(num_rows, num_cols, figsize=(50,500))
|
||||
|
||||
|
||||
# Loop through each subplot and plot the scatter plot
|
||||
for i in range(num_rows):
|
||||
for j in range(num_cols):
|
||||
|
||||
TableFormat = attributes["tableFormat"][j]
|
||||
ax = axes[i][j]
|
||||
markerss= [".", "x", "p", "^","o","s"]
|
||||
# Filter data for each subplot based on data_labels
|
||||
for j, k in enumerate([1,3,5,10,15]):
|
||||
x= x_list[i]
|
||||
y = ( f'pass_{k}', TableFormat)
|
||||
scores = pivot_table.loc[x][y]
|
||||
counter_scores = Counter(scores)
|
||||
scores_x= counter_scores.keys()
|
||||
scores_y= counter_scores.values()
|
||||
ax.scatter(scores_x, scores_y, label=f'pass@{k}', s=int(300/k), marker =markerss[j] )
|
||||
name = f'Test: {x[0]}, TableManipulation: {x[1]}\nTemperature: {x[2]}, Format: {y[1]}'
|
||||
ax.set_xlim([-1,101])
|
||||
ax.set_title(name)
|
||||
ax.set_xlabel('score (%)')
|
||||
ax.set_ylabel('frequency')
|
||||
|
||||
ax.legend()
|
||||
# Adjust layout to prevent overlapping labels
|
||||
plt.tight_layout()
|
||||
plt.savefig(os.path.join(save_path, name_save.replace(".csv", "_") +"density_scatter_plot.pdf"))
|
||||
# Show the plot
|
||||
plt.show()
|
||||
plt.clf()
|
||||
|
||||
|
|
@ -1,195 +0,0 @@
|
|||
import random
|
||||
import pandas as pd
|
||||
from utils import stringify_serialzed_df
|
||||
data = {
|
||||
'Date': ['2023-01-01 00:00:00',
|
||||
'2023-01-02 00:00:00',
|
||||
'2023-01-03 00:00:00',
|
||||
'2023-01-04 00:00:00',
|
||||
'2023-01-05 00:00:00'],
|
||||
'Ticker': ['AAPL', 'GOOG', 'MSFT', 'AMZN', 'FB'],
|
||||
'Price': [150.20, 2700.50, 330.80, 3200.00, 350.75],
|
||||
'Shares': [100, 50, 200, 30, 80],
|
||||
'Transaction': ['Buy', 'Sell', 'Buy', 'Sell', 'Buy']
|
||||
}
|
||||
df_EX1 = pd.DataFrame(data)
|
||||
data2 = {'Name': ['Alice', 'Bob', 'Charlie'],
|
||||
'Age': [25, 30, 22],
|
||||
'City': ['New York', 'Los Angeles', 'Chicago'],
|
||||
"Sex": ['F', "M", "M"]}
|
||||
|
||||
df_EX2 = pd.DataFrame(data2)
|
||||
df_EX1 = pd.DataFrame(data)
|
||||
|
||||
|
||||
DATA_QUES_INSTRUCTION = """Given the tabular data your job is to provide answer to the question asked over the table.
|
||||
[Example]
|
||||
|
||||
Data:
|
||||
[Data_format]
|
||||
|
||||
Questions: [Ques]
|
||||
Answer:
|
||||
"""
|
||||
|
||||
EXAMPLES = """Data:
|
||||
[Data_format_example1]
|
||||
|
||||
[QA1]
|
||||
|
||||
Data:
|
||||
[Data_format_example2]
|
||||
|
||||
[QA2]
|
||||
"""
|
||||
|
||||
###### table formats: #########
|
||||
TF_EX1_MarkdownFormat = '| | Date | Ticker | Price | Shares | Transaction |\n|---:|:--------------------|:---------|--------:|---------:|:--------------|\n| 0 | 2023-01-01 00:00:00 | AAPL | 150.2 | 100 | Buy |\n| 1 | 2023-01-02 00:00:00 | GOOG | 2700.5 | 50 | Sell |\n| 2 | 2023-01-03 00:00:00 | MSFT | 330.8 | 200 | Buy |\n| 3 | 2023-01-04 00:00:00 | AMZN | 3200 | 30 | Sell |\n| 4 | 2023-01-05 00:00:00 | FB | 350.75 | 80 | Buy |'
|
||||
TF_EX1_DataMatrixFormat = """[['', 'Date', 'Ticker', 'Price', 'Shares', 'Transaction'],
|
||||
[0, '2023-01-01 00:00:00', 'AAPL', 150.2, 100, 'Buy'],
|
||||
[1, '2023-01-02 00:00:00', 'GOOG', 2700.5, 50, 'Sell'],
|
||||
[2, '2023-01-03 00:00:00', 'MSFT', 330.8, 200, 'Buy'],
|
||||
[3, '2023-01-04 00:00:00', 'AMZN', 3200.0, 30, 'Sell'],
|
||||
[4, '2023-01-05 00:00:00', 'FB', 350.75, 80, 'Buy']]
|
||||
"""
|
||||
TF_EX1_JsonFormat = '{"0":{"Date":2023-01-01 00:00:00,"Ticker":"AAPL","Price":150.2,"Shares":100,"Transaction":"Buy"},"1":{"Date":2023-01-02 00:00:00,"Ticker":"GOOG","Price":2700.5,"Shares":50,"Transaction":"Sell"},"2":{"Date":2023-01-03 00:00:00,"Ticker":"MSFT","Price":330.8,"Shares":200,"Transaction":"Buy"},"3":{"Date":2023-01-04 00:00:00,"Ticker":"AMZN","Price":3200.0,"Shares":30,"Transaction":"Sell"},"4":{"Date":2023-01-05 00:00:00,"Ticker":"FB","Price":350.75,"Shares":80,"Transaction":"Buy"}}'
|
||||
TF_EX1_DFloaderFormat = "pd.DataFrame({Date : ['2023-01-01 00:00:00', '2023-01-02 00:00:00', '2023-01-03 00:00:00', '2023-01-04 00:00:00', '2023-01-05 00:00:00'], Ticker : ['AAPL', 'GOOG', 'MSFT', 'AMZN', 'FB'], Price : [150.2, 2700.5, 330.8, 3200.0, 350.75], Shares : [100, 50, 200, 30, 80], Transaction : ['Buy', 'Sell', 'Buy', 'Sell', 'Buy']}, index=[0, 1, 2, 3, 4])"
|
||||
TF_EX1_HTMLFormat = '<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>Date</th>\n <th>Ticker</th>\n <th>Price</th>\n <th>Shares</th>\n <th>Transaction</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2023-01-01 00:00:00</td>\n <td>AAPL</td>\n <td>150.20</td>\n <td>100</td>\n <td>Buy</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2023-01-02 00:00:00</td>\n <td>GOOG</td>\n <td>2700.50</td>\n <td>50</td>\n <td>Sell</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2023-01-03 00:00:00</td>\n <td>MSFT</td>\n <td>330.80</td>\n <td>200</td>\n <td>Buy</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2023-01-04 00:00:00</td>\n <td>AMZN</td>\n <td>3200.00</td>\n <td>30</td>\n <td>Sell</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2023-01-05 00:00:00</td>\n <td>FB</td>\n <td>350.75</td>\n <td>80</td>\n <td>Buy</td>\n </tr>\n </tbody>\n</table>'
|
||||
TF_EX1_HTMLNoSpaceFormat = '<table border="1" class="dataframe"> <thead><tr style="text-align: right;"> <th></th> <th>Date</th> <th>Ticker</th> <th>Price</th> <th>Shares</th> <th>Transaction</th></tr> </thead> <tbody><tr> <th>0</th> <td>2023-01-01 00:00:00</td> <td>AAPL</td> <td>150.20</td> <td>100</td> <td>Buy</td></tr><tr> <th>1</th> <td>2023-01-02 00:00:00</td> <td>GOOG</td> <td>2700.50</td> <td>50</td> <td>Sell</td></tr><tr> <th>2</th> <td>2023-01-03 00:00:00</td> <td>MSFT</td> <td>330.80</td> <td>200</td> <td>Buy</td></tr><tr> <th>3</th> <td>2023-01-04 00:00:00</td> <td>AMZN</td> <td>3200.00</td> <td>30</td> <td>Sell</td></tr><tr> <th>4</th> <td>2023-01-05 00:00:00</td> <td>FB</td> <td>350.75</td> <td>80</td> <td>Buy</td></tr> </tbody></table>'
|
||||
|
||||
TF_EX1_TabSeparatedFormat = """ Date Ticker Price Shares Transaction
|
||||
0 2023-01-01 AAPL 150.2 100 Buy
|
||||
1 2023-01-02 GOOG 2700.5 50 Sell
|
||||
2 2023-01-03 MSFT 330.8 200 Buy
|
||||
3 2023-01-04 AMZN 3200.0 30 Sell
|
||||
4 2023-01-05 FB 350.75 80 Buy"""
|
||||
TF_EX1_CommaSeparatedFormat = """,Date,Ticker,Price,Shares,Transaction
|
||||
0,2023-01-01,AAPL,150.2,100,Buy
|
||||
1,2023-01-02,GOOG,2700.5,50,Sell
|
||||
2,2023-01-03,MSFT,330.8,200,Buy
|
||||
3,2023-01-04,AMZN,3200.0,30,Sell
|
||||
4,2023-01-05,FB,350.75,80,Buy"""
|
||||
|
||||
######################################
|
||||
###### Table ops Questionaire: #########
|
||||
|
||||
|
||||
def Example_TransposeTests(FormatName, TransposeTable, SerializeTable, ColumnCluster, df_EX1=df_EX1, df_EX2=df_EX2):
|
||||
Recontruction_Example_str = ""
|
||||
Recontruction_Example_str += f"Data:\n{FormatName.formatting(df_EX1)}\n\n"
|
||||
Recontruction_Example_str += f"""Question: Can you transpose the table?
|
||||
Answer:
|
||||
{FormatName.formatting(next(TransposeTable().modify(df_EX1)))}\n\n\n"""
|
||||
Recontruction_Example_str += f"Data:\n{FormatName.formatting(df_EX2)}\n\n"
|
||||
Recontruction_Example_str += f"""Question: Can you transpose the table?
|
||||
Answer:
|
||||
{FormatName.formatting(next(TransposeTable().modify(df_EX2)))}\n\n"""
|
||||
return Recontruction_Example_str
|
||||
|
||||
|
||||
def Example_ColumnReorder(FormatName, TransposeTable, SerializeTable, ColumnCluster, df_EX1=df_EX1, df_EX2=df_EX2):
|
||||
Reordering_Example_str = ""
|
||||
col_suffled1 = df_EX1.columns.to_list()
|
||||
random.shuffle(col_suffled1)
|
||||
Reordering_Example_str += f"Data:\n{FormatName.formatting(df_EX1)}\n\n"
|
||||
Reordering_Example_str += f"""Question: Can you reorder the table such that the column are in this new order {str(col_suffled1)}?
|
||||
Answer:
|
||||
{FormatName.formatting(df_EX1[col_suffled1])}\n\n\n"""
|
||||
col_suffled2 = df_EX2.columns.to_list()
|
||||
random.shuffle(col_suffled2)
|
||||
Reordering_Example_str += f"Data:\n{FormatName.formatting(df_EX2)}\n\n"
|
||||
Reordering_Example_str += f"""Question: Can you reorder the table such that the column are in this new order {str(col_suffled2)} ?
|
||||
Answer:
|
||||
{FormatName.formatting(df_EX2[col_suffled2])}\n"""
|
||||
return Reordering_Example_str
|
||||
|
||||
|
||||
def Example_Reconstruction(FormatName, TransposeTable, SerializeTable, ColumnCluster, df_EX1=df_EX1, df_EX2=df_EX2):
|
||||
Recontruction_Example_str = ""
|
||||
Recontruction_Example_str += f"Data:\n{FormatName.formatting(next(SerializeTable().modify(df_EX1)))}\n\n"
|
||||
Recontruction_Example_str += f"""Question: Can you reconstruct the table by deserializing the table above?
|
||||
Answer:
|
||||
{FormatName.formatting(df_EX1)}\n\n\n"""
|
||||
Recontruction_Example_str += f"Data:\n{FormatName.formatting(next(ColumnCluster().modify(df_EX2)))}\n\n"
|
||||
Recontruction_Example_str += f"""Question: Can you reconstruct the table by deserializing the table above?
|
||||
Answer:
|
||||
{FormatName.formatting(df_EX2)}\n\n"""
|
||||
return Recontruction_Example_str
|
||||
|
||||
|
||||
def Example_Reconstruction1(FormatName, TransposeTable, SerializeTable, ColumnCluster, df_EX1=df_EX1, df_EX2=df_EX2):
|
||||
""" Similar to reconstruction but the input table is just string"""
|
||||
Recontruction_Example_str = ""
|
||||
Recontruction_Example_str += f"Data:\n{stringify_serialzed_df(next(SerializeTable().modify(df_EX1)))}\n\n"
|
||||
Recontruction_Example_str += f"""Question: Can you reconstruct the table by deserializing the table above?
|
||||
Answer:
|
||||
{FormatName.formatting(df_EX1)}\n\n\n"""
|
||||
Recontruction_Example_str += f"Data:\n{stringify_serialzed_df(next(ColumnCluster().modify(df_EX2)))}\n\n"
|
||||
Recontruction_Example_str += f"""Question: Can you reconstruct the table by deserializing the table above?
|
||||
Answer:
|
||||
{FormatName.formatting(df_EX2)}\n\n"""
|
||||
return Recontruction_Example_str
|
||||
####################################################################
|
||||
|
||||
|
||||
###### Test case Questionaire: #########
|
||||
Ex1_QA1_NavigationTests = """Question: What value is at row 3 and column Ticker?
|
||||
Answer:
|
||||
AMZN"""
|
||||
|
||||
Ex1_QA2_NavigationTests = """Question: What value is at row 1 and column Transaction?
|
||||
Answer:
|
||||
Sell"""
|
||||
|
||||
Ex1_QA1_ColumnLookupTests = """Question: What column is the FB in?
|
||||
Answer:
|
||||
Ticker"""
|
||||
|
||||
Ex1_QA2_ColumnLookupTests = """Question: What column is the '2023-01-02 00:00:00' in?
|
||||
Answer:
|
||||
Date"""
|
||||
|
||||
Ex1_QA1_RowLookupTests = """Question: What row is the FB in?
|
||||
Answer:
|
||||
4"""
|
||||
|
||||
Ex1_QA2_RowLookupTests = """Question: What row is the '2023-01-02 00:00:00' in?
|
||||
Answer:
|
||||
1"""
|
||||
|
||||
Ex1_QA1_DataTypeLookupTests = """Question: What type (using Pandas datatype notation) is column Shares?
|
||||
Answer:
|
||||
int64"""
|
||||
|
||||
Ex1_QA2_DataTypeLookupTests = """Question: What type (using Pandas datatype notation) is column Price?
|
||||
Answer:
|
||||
float64"""
|
||||
|
||||
######################################
|
||||
|
||||
###### Example Dictionary defination: #########
|
||||
EXAMPLE_Dictionary = {"TF_EX1_MarkdownFormat": TF_EX1_MarkdownFormat,
|
||||
"TF_EX1_DataMatrixFormat": TF_EX1_DataMatrixFormat,
|
||||
"TF_EX1_JsonFormat": TF_EX1_JsonFormat,
|
||||
"TF_EX1_DFloaderFormat": TF_EX1_DFloaderFormat,
|
||||
"TF_EX1_HTMLFormat": TF_EX1_HTMLFormat,
|
||||
"TF_EX1_HTMLNoSpaceFormat": TF_EX1_HTMLNoSpaceFormat,
|
||||
"TF_EX1_TabSeparatedFormat": TF_EX1_TabSeparatedFormat,
|
||||
"TF_EX1_CommaSeparatedFormat": TF_EX1_CommaSeparatedFormat,
|
||||
"Ex1_QA1_NavigationTests": Ex1_QA1_NavigationTests,
|
||||
"Ex1_QA2_NavigationTests": Ex1_QA2_NavigationTests,
|
||||
"Ex1_QA1_ColumnLookupTests": Ex1_QA1_ColumnLookupTests,
|
||||
"Ex1_QA2_ColumnLookupTests": Ex1_QA2_ColumnLookupTests,
|
||||
"Ex1_QA1_RowLookupTests": Ex1_QA1_RowLookupTests,
|
||||
"Ex1_QA2_RowLookupTests": Ex1_QA2_RowLookupTests,
|
||||
"Ex1_QA1_DataTypeLookupTests": Ex1_QA1_DataTypeLookupTests,
|
||||
"Ex1_QA2_DataTypeLookupTests": Ex1_QA2_DataTypeLookupTests,
|
||||
"Ex_TableReconstructionTests": Example_Reconstruction,
|
||||
"Ex_TableTransposeTests": Example_TransposeTests,
|
||||
"Ex_TableColumnReorderTests": Example_ColumnReorder,
|
||||
"Ex_TableReconstructionTests1": Example_Reconstruction1
|
||||
|
||||
|
||||
}
|
||||
|
||||
######################################
|
||||
|
|
@ -1,703 +0,0 @@
|
|||
import pandas as pd
|
||||
from abc import ABC, abstractmethod
|
||||
import itertools
|
||||
from typing import Any, Callable, Iterator, List, Optional
|
||||
from dataclasses import dataclass
|
||||
import numpy as np
|
||||
import json
|
||||
import jsonlines
|
||||
import os
|
||||
import datetime
|
||||
from prompts import *
|
||||
from LLMCall import openapi_call_completions
|
||||
from tabulate import tabulate
|
||||
import random
|
||||
import string
|
||||
from collections import Counter
|
||||
from itertools import combinations
|
||||
from utils import Convert_back_to_df, num_tokens_from_string, stringify_serialized_df
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
|
||||
class CustomJSONEncoder(json.JSONEncoder):
|
||||
def default(self, obj):
|
||||
if isinstance(obj, np.integer):
|
||||
return int(obj)
|
||||
elif isinstance(obj, np.floating):
|
||||
return float(obj)
|
||||
elif isinstance(obj, np.ndarray):
|
||||
return obj.tolist()
|
||||
if isinstance(obj, pd.Series):
|
||||
return obj.tolist()
|
||||
elif isinstance(obj, datetime):
|
||||
# Handle datetime objects
|
||||
return obj.isoformat()
|
||||
elif isinstance(obj, set):
|
||||
# Handle sets
|
||||
return list(obj)
|
||||
return super().default(obj)
|
||||
|
||||
|
||||
@dataclass
|
||||
class TestCase:
|
||||
question: str
|
||||
expect: Any
|
||||
TestType: str
|
||||
|
||||
|
||||
RNG_SEED = 42
|
||||
|
||||
|
||||
def get_random_state(random_state=None):
|
||||
if random_state is None:
|
||||
random_state = np.random.RandomState(seed=RNG_SEED)
|
||||
return random_state
|
||||
|
||||
|
||||
def create_combinations(entries: List) -> List:
|
||||
result = []
|
||||
i = 0
|
||||
while i < len(entries):
|
||||
if (len(entries) - i)not in [1, 2]:
|
||||
if i < int(len(entries)/2):
|
||||
lower_bound = 2
|
||||
else:
|
||||
lower_bound = 1
|
||||
tuple_size = min(4, np.random.randint(
|
||||
lower_bound, (len(entries) - i)))
|
||||
else:
|
||||
tuple_size = len(entries) - i
|
||||
result.append(tuple(entries[i:i + tuple_size]))
|
||||
i += tuple_size
|
||||
return result
|
||||
|
||||
|
||||
def combine_columns(df, combinations_list):
|
||||
new_df = pd.DataFrame()
|
||||
column_names = df.columns.to_list()
|
||||
for en in combinations_list:
|
||||
column_subset_names = list(np.array(column_names)[list(en)])
|
||||
new_df[str("-----".join(column_subset_names))] = df[column_subset_names].apply(
|
||||
lambda row: '-----'.join(map(str, row)), axis=1)
|
||||
return new_df
|
||||
|
||||
|
||||
class TestCaseGenerator(ABC):
|
||||
|
||||
def generate(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
random_state: Optional[np.random.RandomState] = None
|
||||
) -> Iterator[TestCase]:
|
||||
raise NotImplementedError()
|
||||
|
||||
def check(self, test: TestCase, answer: pd.DataFrame) -> bool:
|
||||
|
||||
expected_df = Convert_back_to_df("JsonFormat", str(test.expect))
|
||||
expected_df = expected_df.astype(str)
|
||||
if expected_df.shape == answer.shape:
|
||||
return expected_df.equals(answer)
|
||||
else:
|
||||
return False
|
||||
|
||||
def metric_pass_k(self, test: TestCase, answer: List[pd.DataFrame], k: int) -> float:
|
||||
expected_df = Convert_back_to_df("JsonFormat", str(test.expect))
|
||||
expected_df = expected_df.astype(str)
|
||||
boolean_answers = [expected_df.equals(
|
||||
answer[i]) if expected_df.shape == answer[i].shape else False for i in range(len(answer))]
|
||||
combinations_k = list(combinations(boolean_answers, k))
|
||||
passed_at_k = 0
|
||||
# Calculate the pass@k metric
|
||||
for comb in combinations_k:
|
||||
if any(comb):
|
||||
passed_at_k += 1
|
||||
pass_at_k_percentage = (passed_at_k / len(combinations_k))*100
|
||||
|
||||
return pass_at_k_percentage
|
||||
|
||||
def per_cell_accuracies(self, test: TestCase, answer: List[pd.DataFrame]) -> List[float]:
|
||||
score_list = []
|
||||
for ans in answer:
|
||||
expected_df = Convert_back_to_df("JsonFormat", str(test.expect))
|
||||
expected_df = expected_df.astype(str)
|
||||
if expected_df.shape == ans.shape:
|
||||
matching_cells = (expected_df == ans).sum().sum()
|
||||
total_cells = expected_df.shape[0]*expected_df.shape[1]
|
||||
per_cell_score = matching_cells/total_cells
|
||||
else:
|
||||
per_cell_score = 0.0
|
||||
score_list.append(np.round(per_cell_score, 2))
|
||||
return score_list
|
||||
|
||||
|
||||
class NavigationTests(TestCaseGenerator):
|
||||
|
||||
def generate(self, df, random_state=None):
|
||||
random_state = get_random_state(random_state)
|
||||
cols = df.columns
|
||||
indexes = df.index.to_list()
|
||||
nrows = df.shape[0]
|
||||
while True:
|
||||
col_idx = random_state.choice(cols)
|
||||
row_idx = random_state.randint(0, nrows)
|
||||
question = f"What value is at row {indexes[row_idx]} and column {col_idx}?"
|
||||
yield TestCase(question, df.iloc[row_idx][col_idx], "NavigationTests")
|
||||
|
||||
|
||||
class ColumnLookupTests(TestCaseGenerator):
|
||||
|
||||
def generate(self, df, random_state=None):
|
||||
|
||||
random_state = get_random_state(random_state)
|
||||
cols = df.columns
|
||||
nrows = df.shape[0]
|
||||
while True:
|
||||
col_idx = random_state.choice(cols)
|
||||
row_idx = random_state.randint(0, nrows)
|
||||
|
||||
value = df.iloc[row_idx][col_idx]
|
||||
question = f"What column is the {value} in?"
|
||||
|
||||
_, col_indices = np.where(df.to_numpy() == value)
|
||||
col_indices = list(set([cols[i] for i in col_indices.tolist()]))
|
||||
|
||||
yield TestCase(question, col_indices, "ColumnLookupTests")
|
||||
|
||||
|
||||
class RowLookupTests(TestCaseGenerator):
|
||||
|
||||
def generate(self, df, random_state=None):
|
||||
|
||||
random_state = get_random_state(random_state)
|
||||
cols = df.columns
|
||||
indexes = df.index.to_list()
|
||||
nrows = df.shape[0]
|
||||
while True:
|
||||
col_idx = random_state.choice(cols)
|
||||
row_idx = random_state.randint(0, nrows)
|
||||
|
||||
value = df.iloc[row_idx][col_idx]
|
||||
question = f"What row is the {value} in?"
|
||||
|
||||
row_indices, _ = np.where(df.to_numpy() == value)
|
||||
row_indices = list(set(row_indices.tolist()))
|
||||
row_indices_ = [indexes[v] for v in row_indices]
|
||||
yield TestCase(question, row_indices_, "RowLookupTests")
|
||||
|
||||
|
||||
class DataTypeLookupTests(TestCaseGenerator):
|
||||
|
||||
def generate(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
|
||||
indices = list(df.index)
|
||||
transpose_bool = all(isinstance(item, str) for item in indices)
|
||||
header = "column"
|
||||
if transpose_bool:
|
||||
df = df.T
|
||||
header = "row"
|
||||
cols = list(df.columns)
|
||||
for col in cols*100:
|
||||
question = f"What type (using Pandas datatype notation) is {header} {col}?"
|
||||
answer = str(df.dtypes[col])
|
||||
yield TestCase(question, answer, "DataTypeLookupTests")
|
||||
|
||||
|
||||
class TableReconstructionTests(TestCaseGenerator):
|
||||
def generate(self, df, random_state=None):
|
||||
question = f"Can you reconstruct the table by deserializing the table above?"
|
||||
|
||||
answer = df.to_json(orient='index', index=True)
|
||||
|
||||
yield TestCase(question, answer, "TableReconstructionTests")
|
||||
|
||||
|
||||
class TableReconstructionTests1(TestCaseGenerator):
|
||||
def generate(self, df, random_state=None):
|
||||
question = f"Can you reconstruct the table by deserializing the table above?"
|
||||
|
||||
answer = df.to_json(orient='index', index=True)
|
||||
|
||||
yield TestCase(question, answer, "TableReconstructionTests1")
|
||||
|
||||
|
||||
class TableColumnReorderTests(TestCaseGenerator):
|
||||
def generate(self, df, random_state=None):
|
||||
random_state = get_random_state(random_state)
|
||||
cols = list(df.columns)
|
||||
while True:
|
||||
random_state.shuffle(cols)
|
||||
new_df = df[cols]
|
||||
new_column_order = new_df.columns.to_list()
|
||||
question = f"""Can you reorder the table such that the column are in this new order {str(new_column_order)}? Make sure to return the complete reordered table."""
|
||||
answer = new_df.to_json(orient='index', index=True)
|
||||
yield TestCase(question, answer, "ColumnShuffleTests")
|
||||
|
||||
|
||||
class TableTransposeTests(TestCaseGenerator):
|
||||
def generate(self, df, random_state=None):
|
||||
question = f"""Can you transpose the table?"""
|
||||
|
||||
answer = df.T.to_json(orient='index', index=True)
|
||||
yield TestCase(question, answer, "ColumnShuffleTests")
|
||||
|
||||
|
||||
class TableOperation(ABC):
|
||||
@abstractmethod
|
||||
def modify(
|
||||
self, df: pd.DataFrame, random_state: Optional[np.random.RandomState]
|
||||
) -> Iterator[pd.DataFrame]:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class ShuffleRows(TableOperation):
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
while True:
|
||||
yield df.sample(frac=1.0, random_state=random_state, replace=False)
|
||||
|
||||
|
||||
class ShuffleColumns(TableOperation):
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
cols = list(df.columns)
|
||||
while True:
|
||||
random_state.shuffle(cols)
|
||||
yield df[cols]
|
||||
|
||||
|
||||
class ArbitraryColumnNames(TableOperation):
|
||||
|
||||
def __init__(self, get_column_name: Optional[Callable[[int], str]] = None):
|
||||
if get_column_name is None:
|
||||
get_column_name = []
|
||||
while len(get_column_name) <= 200:
|
||||
arb_name = ''.join(random.choices(
|
||||
string.ascii_letters + string.digits, k=np.random.randint(1, 11)))
|
||||
if arb_name not in get_column_name:
|
||||
get_column_name.append(arb_name)
|
||||
self.get_column_name = get_column_name
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
while True:
|
||||
new_columns = random.sample(self.get_column_name, len(df.columns))
|
||||
df.columns = new_columns
|
||||
yield df
|
||||
|
||||
|
||||
class SequentialColumnNames(TableOperation):
|
||||
|
||||
def __init__(self, get_column_name: Optional[Callable[[int], str]] = None):
|
||||
if get_column_name is None:
|
||||
def get_column_name(col_idx): return f"col_{col_idx}"
|
||||
self.get_column_name = get_column_name
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
new_columns = [
|
||||
self.get_column_name(idx) for idx in range(len(df.columns))
|
||||
]
|
||||
df.columns = new_columns
|
||||
|
||||
while True:
|
||||
yield df.sample(frac=0.7,
|
||||
random_state=random_state,
|
||||
replace=False,
|
||||
)
|
||||
|
||||
|
||||
class OriginalData(TableOperation):
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
yield df
|
||||
|
||||
|
||||
class ShuffleColumnNames(TableOperation):
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
cols = list(df.columns)
|
||||
while True:
|
||||
random_state.shuffle(cols)
|
||||
df.columns = cols
|
||||
yield df
|
||||
|
||||
|
||||
class SampleRows(TableOperation):
|
||||
|
||||
def __init__(self, fraction: float = 0.5, replace: bool = True):
|
||||
self.fraction = fraction
|
||||
self.replace = replace
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
random_state = get_random_state(random_state)
|
||||
while True:
|
||||
yield df.sample(frac=self.fraction,
|
||||
random_state=random_state,
|
||||
replace=self.replace,
|
||||
ignore_index=True)
|
||||
|
||||
|
||||
class TransposeTable(TableOperation):
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
df = df.T
|
||||
random_state = get_random_state(random_state)
|
||||
while True:
|
||||
yield df.sample(frac=0.7,
|
||||
random_state=random_state,
|
||||
replace=False,
|
||||
)
|
||||
|
||||
|
||||
class ColumnCluster(TableOperation):
|
||||
def __init__(self, get_column_combination: Optional[Callable[[int], str]] = None):
|
||||
if get_column_combination is None:
|
||||
get_column_combination = create_combinations
|
||||
self.get_column_combination = get_column_combination
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
while True:
|
||||
combinations_list = self.get_column_combination(range(df.shape[1]))
|
||||
|
||||
new_df = pd.DataFrame()
|
||||
column_names = df.columns.to_list()
|
||||
for en in combinations_list:
|
||||
column_subset_names = list(np.array(column_names)[list(en)])
|
||||
new_df[str("-----".join(column_subset_names))] = df[column_subset_names].apply(
|
||||
lambda row: '-----'.join(map(str, row)), axis=1)
|
||||
|
||||
yield new_df
|
||||
|
||||
|
||||
class SerializeTable(TableOperation):
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
new_df = pd.DataFrame()
|
||||
new_df[""] = df.apply(lambda row: ','.join(
|
||||
[f'{col}:{value}' for col, value in row.items()]), axis=1)
|
||||
|
||||
random_state = get_random_state(random_state)
|
||||
while True:
|
||||
yield new_df.sample(frac=0.7,
|
||||
random_state=random_state,
|
||||
replace=False,
|
||||
)
|
||||
|
||||
|
||||
class CompositeTableOperation(object):
|
||||
|
||||
def __init__(self, table_ops: List[TableOperation]):
|
||||
self.table_ops = table_ops
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
random_state = get_random_state(random_state)
|
||||
|
||||
def recursive_app(val, table_ops, random_state):
|
||||
if len(table_ops) == 0:
|
||||
yield val
|
||||
else:
|
||||
table_op = table_ops[0]
|
||||
for new_val in table_op.modify(val, random_state=random_state):
|
||||
for recursed_val in recursive_app(new_val, table_ops[1:],
|
||||
random_state):
|
||||
yield recursed_val
|
||||
|
||||
for df_variant in recursive_app(df, self.table_ops, random_state):
|
||||
yield df_variant
|
||||
|
||||
|
||||
class TableFormats(ABC):
|
||||
@abstractmethod
|
||||
def formatting(
|
||||
self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class MarkdownFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
return tabulate(df, headers='keys', tablefmt='pipe', showindex=True)
|
||||
|
||||
|
||||
class DataMatrixFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
df_ = pd.DataFrame()
|
||||
df_[""] = df.index.to_list()
|
||||
df_[df.columns] = df.values.tolist()
|
||||
matrix = df_.columns.tolist() # Get column headers as a list
|
||||
matrix_data = df_.values.tolist() # Get data as a list of lists
|
||||
matrix_data.insert(0, matrix)
|
||||
return matrix_data
|
||||
|
||||
|
||||
class JsonFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
|
||||
return df.to_json(orient='index', index=True)
|
||||
|
||||
|
||||
class DFloaderFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
|
||||
str_df_loader = "pd.DataFrame({[data]}, index=[indices])"
|
||||
data = ""
|
||||
indi = str(df.index.to_list())
|
||||
for d in df.columns:
|
||||
data += f"'{str(d)}'" + " : " + str(df[d].to_list()) + ", "
|
||||
data = data[:-2]
|
||||
str_df_loader = str_df_loader.replace(
|
||||
"[data]", data).replace("[indices]", indi)
|
||||
return str_df_loader
|
||||
|
||||
|
||||
class HTMLFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
html = df.to_html(index=True)
|
||||
return html
|
||||
|
||||
|
||||
class HTMLNoSpaceFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
html = df.to_html(index=True)
|
||||
return str(html).replace("\t", "").replace("\n", "").replace(" ", "")
|
||||
|
||||
|
||||
class TabSeparatedFormat(TableFormats):
|
||||
def formatting(self, df: pd.DataFrame) -> str:
|
||||
return df.to_csv(sep='\t', index=True)
|
||||
|
||||
|
||||
class CommaSeparatedFormat(TableFormats):
|
||||
def formatting(self, df: pd.DataFrame) -> str:
|
||||
return df.to_csv(index=True)
|
||||
|
||||
|
||||
class SQLQueryFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
return
|
||||
|
||||
|
||||
def gather_Examples_Prompt(tab_format, test_case):
|
||||
|
||||
TF, TC = tab_format.__class__.__name__, test_case.__class__.__name__
|
||||
if TC in ["TableColumnReorderTests", "TableReconstructionTests", "TableTransposeTests", "TableReconstructionTests1"]:
|
||||
Examples = EXAMPLE_Dictionary[f"Ex_{TC}"](
|
||||
tab_format, TransposeTable, SerializeTable, ColumnCluster)
|
||||
|
||||
else:
|
||||
Examples = EXAMPLES.replace("[Data_format_example1]", EXAMPLE_Dictionary[f"TF_EX1_{TF}"])\
|
||||
.replace("[QA1]", EXAMPLE_Dictionary[f"Ex1_QA1_{TC}"])\
|
||||
.replace("[Data_format_example2]", EXAMPLE_Dictionary[f"TF_EX1_{TF}"])\
|
||||
.replace("[QA2]", EXAMPLE_Dictionary[f"Ex1_QA2_{TC}"])
|
||||
return Examples
|
||||
|
||||
|
||||
class LLMTableLearner():
|
||||
def get_prompt(self, examples, df_format, question: str, temperature: float) -> Any:
|
||||
prompt = DATA_QUES_INSTRUCTION.replace("[Ques]", str(question)).replace(
|
||||
"[Data_format]", str(df_format)).replace("[Example]", examples)
|
||||
total_tokens = num_tokens_from_string(prompt)
|
||||
no_of_token_left = 4051-total_tokens
|
||||
expected_token = num_tokens_from_string(str(df_format))+51
|
||||
maxTok = min(total_tokens, no_of_token_left)
|
||||
num_n = 3
|
||||
modelName = "text-davinci-003"
|
||||
prompt_cache = {"model": modelName,
|
||||
"prompt": prompt,
|
||||
"temperature": temperature,
|
||||
"max_tokens": maxTok,
|
||||
"top_p": 1,
|
||||
"frequency_penalty": 0,
|
||||
"presence_penalty": 0,
|
||||
"n": num_n,
|
||||
"logprobs": 1}
|
||||
return prompt_cache
|
||||
|
||||
def get_answer(self, examples, df_format, question: str, temperature: float, open_api_key: str) -> Any:
|
||||
prompt = DATA_QUES_INSTRUCTION.replace("[Ques]", str(question)).replace(
|
||||
"[Data_format]", str(df_format)).replace("[Example]", examples)
|
||||
total_tokens = num_tokens_from_string(prompt)
|
||||
no_of_token_left = 4051-total_tokens
|
||||
expected_token = num_tokens_from_string(str(df_format))+51
|
||||
maxTok = min(total_tokens, no_of_token_left)
|
||||
num_n = 3
|
||||
modelName = "text-davinci-003"
|
||||
answer, cache = openapi_call_completions(
|
||||
prompt, modelName=modelName, temp=temperature, maxTok=maxTok, num_n=num_n, open_api_key=open_api_key)
|
||||
return answer, cache
|
||||
|
||||
|
||||
class TableExperimentSuite(object):
|
||||
|
||||
def __init__(self, llm_learner: LLMTableLearner(),
|
||||
table_formats: List[TableFormats],
|
||||
table_ops: List[TableOperation],
|
||||
test_gens: List[TestCaseGenerator],
|
||||
cache_save_path: str,
|
||||
open_api_key: str):
|
||||
self.llm = llm_learner
|
||||
self.table_formats = table_formats
|
||||
self.table_ops = table_ops
|
||||
self.test_gens = test_gens
|
||||
self.cache_save_path = cache_save_path
|
||||
self.open_api_key = open_api_key
|
||||
|
||||
def run_experiment(self,
|
||||
df,
|
||||
per_table_op: int = 10,
|
||||
per_test_gen: int = 10,
|
||||
save_cache=True):
|
||||
results = []
|
||||
cacahe_all_dict = []
|
||||
print("OPEN AI KEY USED: ", self.open_api_key)
|
||||
if save_cache:
|
||||
current_datetime = datetime.datetime.now()
|
||||
formatted_datetime = current_datetime.strftime("%Y_%m_%d_%H_%M_%S")
|
||||
cache_path = os.path.join(self.cache_save_path, f"cache_logger")
|
||||
if not os.path.exists(cache_path):
|
||||
os.makedirs(cache_path)
|
||||
cache_path2 = os.path.join(
|
||||
self.cache_save_path, f"cache_logger_all")
|
||||
if not os.path.exists(cache_path2):
|
||||
os.makedirs(cache_path2)
|
||||
save_catch_file = os.path.join(
|
||||
cache_path, f"macro_test_log_{formatted_datetime}.json")
|
||||
save_catch_file2 = os.path.join(
|
||||
cache_path, f"macro_test_log_{formatted_datetime}.json")
|
||||
with open(save_catch_file, mode="w") as writer:
|
||||
pass
|
||||
table_ops = [OriginalData(), SampleRows(),
|
||||
ColumnCluster(), SerializeTable()]
|
||||
table_ops_test = {"ArbitraryColumnNames": {"TableColumnReorderTests": [5, 10],
|
||||
"TableTransposeTests": [50, 1],
|
||||
"TableReconstructionTests1": [50, 1]},
|
||||
"ShuffleRows": {"TableColumnReorderTests": [5, 10],
|
||||
"TableTransposeTests": [50, 1],
|
||||
"TableReconstructionTests1": [50, 1]},
|
||||
"ColumnCluster": {"TableColumnReorderTests": [5, 10],
|
||||
"TableTransposeTests": [50, 1],
|
||||
"TableReconstructionTests1": [50, 1]},
|
||||
"ShuffleColumns": {"TableColumnReorderTests": [5, 10],
|
||||
"TableTransposeTests": [50, 1],
|
||||
"TableReconstructionTests1": [50, 1]},
|
||||
"ShuffleColumnNames": {"TableColumnReorderTests": [5, 10],
|
||||
"TableTransposeTests": [50, 1],
|
||||
"TableReconstructionTests1": [50, 1]},
|
||||
"TransposeTable": {"TableColumnReorderTests": [5, 10],
|
||||
"TableTransposeTests": [50, 1],
|
||||
"TableReconstructionTests1": [50, 1]},
|
||||
"SequentialColumnNames": {"TableColumnReorderTests": [5, 10],
|
||||
"TableTransposeTests": [50, 1],
|
||||
"TableReconstructionTests1": [50, 1]}}
|
||||
|
||||
table_ops_test = {"OriginalData": ["TableColumnReorderTests"],
|
||||
"SampleRows": ["TableTransposeTests"],
|
||||
"ColumnCluster": ["TableReconstructionTests"],
|
||||
"SerializeTable": ["TableReconstructionTests", "TableReconstructionTests1"]}
|
||||
|
||||
for table_op in tqdm(self.table_ops):
|
||||
table_op_name = table_op.__class__.__name__
|
||||
count_df_variant = 0
|
||||
for df_variant in tqdm(itertools.islice(table_op.modify(df),
|
||||
per_table_op)):
|
||||
df_feed_test = df_variant
|
||||
count_df_variant += 1
|
||||
|
||||
for gen in tqdm(self.test_gens):
|
||||
|
||||
if table_op_name in ["ColumnCluster", "SerializeTable"] and gen.__class__.__name__ == "TableReconstructionTests1":
|
||||
df_feed_test = df
|
||||
for test in itertools.islice(gen.generate(df_feed_test),
|
||||
per_test_gen):
|
||||
|
||||
for tab_format in tqdm(self.table_formats):
|
||||
temperature_list = [0]
|
||||
for temp in temperature_list:
|
||||
|
||||
try:
|
||||
examples = gather_Examples_Prompt(
|
||||
tab_format, gen)
|
||||
|
||||
df_in_desired_format = tab_format.formatting(
|
||||
df_variant)
|
||||
prompt_cache_per_call = self.llm.get_prompt(
|
||||
examples, df_in_desired_format, test.question, temp)
|
||||
|
||||
if gen.__class__.__name__ == "TableReconstructionTests1":
|
||||
df_in_desired_format = stringify_serialized_df(
|
||||
df_variant)
|
||||
answer, cache_per_call = self.llm.get_answer(
|
||||
examples, df_in_desired_format, test.question, temp, self.open_api_key)
|
||||
error = None
|
||||
try:
|
||||
answer_changed_format = [Convert_back_to_df(
|
||||
tab_format.__class__.__name__, a) for a in answer]
|
||||
result = gen.check(
|
||||
test, answer_changed_format[0])
|
||||
pass_1 = gen.metric_pass_k(
|
||||
test, answer_changed_format, 1)
|
||||
pass_3 = gen.metric_pass_k(
|
||||
test, answer_changed_format, 3)
|
||||
pass_5 = gen.metric_pass_k(
|
||||
test, answer_changed_format, 5)
|
||||
pass_10 = gen.metric_pass_k(
|
||||
test, answer, 10)
|
||||
pass_15 = gen.metric_pass_k(
|
||||
test, answer, 15)
|
||||
per_cell_accracies = gen.per_cell_accuracies(
|
||||
test, answer_changed_format)
|
||||
per_cell_accracies_top1 = per_cell_accracies[0]
|
||||
error = None
|
||||
except Exception as Err:
|
||||
result = None
|
||||
pass_1 = pass_3 = pass_5 = pass_10 = pass_15 = None
|
||||
error = str(Err)
|
||||
per_cell_accracies = []
|
||||
per_cell_accracies_top1 = None
|
||||
print(f"error {Err} encountered")
|
||||
|
||||
except Exception as Err:
|
||||
answer, cache_per_call = None, None
|
||||
error = str(Err)
|
||||
print(f"error {Err} encountered")
|
||||
|
||||
cache_all = {"model-Temperature": str(temp),
|
||||
"tab_format": str(tab_format.__class__.__name__),
|
||||
"table_op": str(table_op_name),
|
||||
"gen": str(gen.__class__.__name__),
|
||||
"test-Question": test.question,
|
||||
"test-expected-answer": test.expect,
|
||||
"test-TestType": str(test.TestType),
|
||||
"test": str(test),
|
||||
"answer": answer,
|
||||
"result_match_top1": result,
|
||||
"error": str(error),
|
||||
"pass_1": str(pass_1),
|
||||
"pass_3": str(pass_3),
|
||||
"pass_5": str(pass_5),
|
||||
"pass_10": str(pass_10),
|
||||
"pass_15": str(pass_15),
|
||||
"per_cell_accracies": str(per_cell_accracies),
|
||||
"prompt_cache_per_call": prompt_cache_per_call,
|
||||
"LLMOutput": cache_per_call}
|
||||
results.append((temp, tab_format.__class__.__name__, table_op.__class__.__name__, gen.__class__.__name__, test.question, test.expect,
|
||||
test.TestType, pass_1, pass_3, pass_5, pass_10, pass_15, test, answer, answer, per_cell_accracies_top1, per_cell_accracies, result, error))
|
||||
# cacahe_all_dict.append(cache_all)
|
||||
if save_cache:
|
||||
|
||||
with open(save_catch_file, mode="a") as file:
|
||||
serializable_entry = json.dumps(
|
||||
cache_all, cls=CustomJSONEncoder)
|
||||
# Add a newline separator
|
||||
file.write(serializable_entry + '\n')
|
||||
|
||||
return results
|
||||
|
|
@ -1,612 +0,0 @@
|
|||
import pandas as pd
|
||||
from abc import ABC, abstractmethod
|
||||
import itertools
|
||||
from typing import Any, Callable, Iterator, List, Optional
|
||||
from dataclasses import dataclass
|
||||
import numpy as np
|
||||
import json
|
||||
import jsonlines
|
||||
import os
|
||||
import datetime
|
||||
from prompts import *
|
||||
from LLMCall import openapi_call_completions
|
||||
from tabulate import tabulate
|
||||
import random
|
||||
import string
|
||||
from collections import Counter
|
||||
from itertools import combinations
|
||||
from utils import num_tokens_from_string
|
||||
|
||||
|
||||
class CustomJSONEncoder(json.JSONEncoder):
|
||||
def default(self, obj):
|
||||
if isinstance(obj, np.integer):
|
||||
return int(obj)
|
||||
elif isinstance(obj, np.floating):
|
||||
return float(obj)
|
||||
elif isinstance(obj, np.ndarray):
|
||||
return obj.tolist()
|
||||
if isinstance(obj, pd.Series):
|
||||
return obj.tolist()
|
||||
elif isinstance(obj, datetime):
|
||||
# Handle datetime objects
|
||||
return obj.isoformat()
|
||||
elif isinstance(obj, set):
|
||||
# Handle sets
|
||||
return list(obj)
|
||||
return super().default(obj)
|
||||
|
||||
|
||||
@dataclass
|
||||
class TestCase:
|
||||
question: str
|
||||
expect: Any
|
||||
TestType: str
|
||||
|
||||
|
||||
RNG_SEED = 42
|
||||
|
||||
|
||||
def get_random_state(random_state=None):
|
||||
if random_state is None:
|
||||
random_state = np.random.RandomState(seed=RNG_SEED)
|
||||
return random_state
|
||||
|
||||
|
||||
def create_combinations(entries: List) -> List:
|
||||
result = []
|
||||
i = 0
|
||||
while i < len(entries):
|
||||
if (len(entries) - i)not in [1, 2]:
|
||||
if i < int(len(entries)/2):
|
||||
lower_bound = 2
|
||||
else:
|
||||
lower_bound = 1
|
||||
tuple_size = min(4, np.random.randint(
|
||||
lower_bound, (len(entries) - i)))
|
||||
else:
|
||||
tuple_size = len(entries) - i
|
||||
result.append(tuple(entries[i:i + tuple_size]))
|
||||
i += tuple_size
|
||||
return result
|
||||
|
||||
|
||||
def combine_columns(df, combinations_list):
|
||||
new_df = pd.DataFrame()
|
||||
column_names = df.columns.to_list()
|
||||
for en in combinations_list:
|
||||
column_subset_names = list(np.array(column_names)[list(en)])
|
||||
new_df[str("-----".join(column_subset_names))] = df[column_subset_names].apply(
|
||||
lambda row: '-----'.join(map(str, row)), axis=1)
|
||||
return new_df
|
||||
|
||||
|
||||
class TestCaseGenerator(ABC):
|
||||
|
||||
def generate(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
random_state: Optional[np.random.RandomState] = None
|
||||
) -> Iterator[TestCase]:
|
||||
raise NotImplementedError()
|
||||
|
||||
def check(self, test: TestCase, answer: Any) -> bool:
|
||||
if test.TestType in ["ColumnLookupTests", "RowLookupTests"]:
|
||||
if isinstance(test.expect, list):
|
||||
expected_values = test.expect if isinstance(
|
||||
test.expect, list) else [test.expect]
|
||||
expected_values = list(map(str, expected_values))
|
||||
matches = set(expected_values).intersection(set([answer]))
|
||||
else:
|
||||
print("Error: The expected Answer 'List' should be a list not string")
|
||||
return len(matches) > 0
|
||||
return str(test.expect) == answer
|
||||
|
||||
def metric_pass_k(self, test: TestCase, answer: Any, k: int) -> float:
|
||||
if not isinstance(answer, list):
|
||||
raise ValueError("Answer should be a list for pass@k metric.")
|
||||
|
||||
# Convert the test.expect to a list if it's not already
|
||||
expected_values = test.expect if isinstance(
|
||||
test.expect, list) else [test.expect]
|
||||
expected_values = list(map(str, expected_values))
|
||||
boolean_answers = [
|
||||
True if answer[i] in expected_values else False for i in range(len(answer))]
|
||||
combinations_k = list(combinations(boolean_answers, k))
|
||||
passed_at_k = 0
|
||||
# Calculate the pass@k metric
|
||||
for comb in combinations_k:
|
||||
if any(comb):
|
||||
passed_at_k += 1
|
||||
pass_at_k_percentage = (passed_at_k / len(combinations_k))*100
|
||||
|
||||
return pass_at_k_percentage
|
||||
|
||||
|
||||
class NavigationTests(TestCaseGenerator):
|
||||
|
||||
def generate(self, df, random_state=None):
|
||||
random_state = get_random_state(random_state)
|
||||
cols = df.columns
|
||||
indexes = df.index.to_list()
|
||||
nrows = df.shape[0]
|
||||
while True:
|
||||
col_idx = random_state.choice(cols)
|
||||
row_idx = random_state.randint(0, nrows)
|
||||
question = f"What value is at row {indexes[row_idx]} and column {col_idx}?"
|
||||
yield TestCase(question, df.iloc[row_idx][col_idx], "NavigationTests")
|
||||
|
||||
|
||||
class ColumnLookupTests(TestCaseGenerator):
|
||||
|
||||
def generate(self, df, random_state=None):
|
||||
|
||||
random_state = get_random_state(random_state)
|
||||
cols = df.columns
|
||||
nrows = df.shape[0]
|
||||
while True:
|
||||
col_idx = random_state.choice(cols)
|
||||
row_idx = random_state.randint(0, nrows)
|
||||
|
||||
value = df.iloc[row_idx][col_idx]
|
||||
question = f"What column is the {value} in?"
|
||||
|
||||
_, col_indices = np.where(df.to_numpy() == value)
|
||||
col_indices = list(set([cols[i] for i in col_indices.tolist()]))
|
||||
|
||||
yield TestCase(question, col_indices, "ColumnLookupTests")
|
||||
|
||||
|
||||
class RowLookupTests(TestCaseGenerator):
|
||||
|
||||
def generate(self, df, random_state=None):
|
||||
|
||||
random_state = get_random_state(random_state)
|
||||
cols = df.columns
|
||||
indexes = df.index.to_list()
|
||||
nrows = df.shape[0]
|
||||
while True:
|
||||
col_idx = random_state.choice(cols)
|
||||
row_idx = random_state.randint(0, nrows)
|
||||
|
||||
value = df.iloc[row_idx][col_idx]
|
||||
question = f"What row is the {value} in?"
|
||||
|
||||
row_indices, _ = np.where(df.to_numpy() == value)
|
||||
row_indices = list(set(row_indices.tolist()))
|
||||
row_indices_ = [indexes[v] for v in row_indices]
|
||||
yield TestCase(question, row_indices_, "RowLookupTests")
|
||||
|
||||
|
||||
class DataTypeLookupTests(TestCaseGenerator):
|
||||
|
||||
def generate(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
|
||||
indices = list(df.index)
|
||||
transpose_bool = all(isinstance(item, str) for item in indices)
|
||||
header = "column"
|
||||
if transpose_bool:
|
||||
df = df.T
|
||||
header = "row"
|
||||
cols = list(df.columns)
|
||||
for col in cols*100:
|
||||
question = f"What type (using Pandas datatype notation) is {header} {col}?"
|
||||
answer = str(df.dtypes[col])
|
||||
yield TestCase(question, answer, "DataTypeLookupTests")
|
||||
|
||||
|
||||
class TableReconstructionTests(TestCaseGenerator):
|
||||
def generate(self, df, random_state=None):
|
||||
|
||||
question = f"Can you reconstruct the table in a json 'index' format by deserializing the table above?"
|
||||
answer = df.to_json(orient='index', index=True)
|
||||
|
||||
yield TestCase(question, answer, "TableReconstructionTest")
|
||||
|
||||
|
||||
class TableColumnReorderTests(TestCaseGenerator):
|
||||
def generate(self, df, random_state=None):
|
||||
random_state = get_random_state(random_state)
|
||||
cols = list(df.columns)
|
||||
while True:
|
||||
random_state.shuffle(cols)
|
||||
new_df = df[cols]
|
||||
new_column_order = str(new_df.columns.to_list())
|
||||
question = f"""Can you reorder the table such that the column are in this new order {new_column_order}?
|
||||
Return the reordered table in json 'index' format. """
|
||||
answer = new_df.to_json(orient='index', index=True)
|
||||
yield TestCase(question, answer, "ColumnShuffleTests")
|
||||
|
||||
|
||||
class TableTransposeTests(TestCaseGenerator):
|
||||
def generate(self, df, random_state=None):
|
||||
|
||||
question = f"""Can you transpose the table? Return the transposed table in json 'index' format."""
|
||||
answer = df.to_json(orient='index', index=True)
|
||||
yield TestCase(question, answer, "ColumnShuffleTests")
|
||||
|
||||
|
||||
class TableOperation(ABC):
|
||||
@abstractmethod
|
||||
def modify(
|
||||
self, df: pd.DataFrame, random_state: Optional[np.random.RandomState]
|
||||
) -> Iterator[pd.DataFrame]:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class ShuffleRows(TableOperation):
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
while True:
|
||||
yield df.sample(frac=1.0, random_state=random_state, replace=False)
|
||||
|
||||
|
||||
class ShuffleColumns(TableOperation):
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
cols = list(df.columns)
|
||||
while True:
|
||||
random_state.shuffle(cols)
|
||||
yield df[cols]
|
||||
|
||||
|
||||
class ArbitraryColumnNames(TableOperation):
|
||||
|
||||
def __init__(self, get_column_name: Optional[Callable[[int], str]] = None):
|
||||
if get_column_name is None:
|
||||
get_column_name = [''.join(random.choices(
|
||||
string.ascii_letters + string.digits, k=np.random.randint(1, 11))) for _ in range(200)]
|
||||
self.get_column_name = get_column_name
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
while True:
|
||||
new_columns = random.sample(self.get_column_name, len(df.columns))
|
||||
df.columns = new_columns
|
||||
yield df
|
||||
|
||||
|
||||
class SequentialColumnNames(TableOperation):
|
||||
|
||||
def __init__(self, get_column_name: Optional[Callable[[int], str]] = None):
|
||||
if get_column_name is None:
|
||||
def get_column_name(col_idx): return f"col_{col_idx}"
|
||||
self.get_column_name = get_column_name
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
new_columns = [
|
||||
self.get_column_name(idx) for idx in range(len(df.columns))
|
||||
]
|
||||
df.columns = new_columns
|
||||
yield df
|
||||
|
||||
|
||||
class OriginalData(TableOperation):
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
yield df
|
||||
|
||||
|
||||
class ShuffleColumnNames(TableOperation):
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
random_state = get_random_state(random_state)
|
||||
cols = list(df.columns)
|
||||
while True:
|
||||
random_state.shuffle(cols)
|
||||
df.columns = cols
|
||||
yield df
|
||||
|
||||
|
||||
class SampleRows(TableOperation):
|
||||
|
||||
def __init__(self, fraction: float = 0.5, replace: bool = True):
|
||||
self.fraction = fraction
|
||||
self.replace = replace
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
random_state = get_random_state(random_state)
|
||||
while True:
|
||||
yield df.sample(frac=self.fraction,
|
||||
random_state=random_state,
|
||||
replace=self.replace)
|
||||
|
||||
|
||||
class TransposeTable(TableOperation):
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
yield df.T
|
||||
|
||||
|
||||
class ColumnCluster(TableOperation):
|
||||
def __init__(self, get_column_combination: Optional[Callable[[int], str]] = None):
|
||||
if get_column_combination is None:
|
||||
get_column_combination = create_combinations
|
||||
self.get_column_combination = get_column_combination
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
while True:
|
||||
combinations_list = self.get_column_combination(range(df.shape[1]))
|
||||
|
||||
new_df = pd.DataFrame()
|
||||
column_names = df.columns.to_list()
|
||||
for en in combinations_list:
|
||||
column_subset_names = list(np.array(column_names)[list(en)])
|
||||
new_df[str("-----".join(column_subset_names))] = df[column_subset_names].apply(
|
||||
lambda row: '-----'.join(map(str, row)), axis=1)
|
||||
|
||||
yield new_df
|
||||
|
||||
|
||||
class SerializeTable(TableOperation):
|
||||
def modify(self, df, random_state=None):
|
||||
df = df.copy(deep=True)
|
||||
new_df = pd.DataFrame()
|
||||
new_df[""] = df.apply(lambda row: ','.join(
|
||||
[f'{col}:{value}' for col, value in row.items()]), axis=1)
|
||||
|
||||
yield new_df
|
||||
|
||||
|
||||
class CompositeTableOperation(object):
|
||||
|
||||
def __init__(self, table_ops: List[TableOperation]):
|
||||
self.table_ops = table_ops
|
||||
|
||||
def modify(self, df, random_state=None):
|
||||
random_state = get_random_state(random_state)
|
||||
|
||||
def recursive_app(val, table_ops, random_state):
|
||||
if len(table_ops) == 0:
|
||||
yield val
|
||||
else:
|
||||
table_op = table_ops[0]
|
||||
for new_val in table_op.modify(val, random_state=random_state):
|
||||
for recursed_val in recursive_app(new_val, table_ops[1:],
|
||||
random_state):
|
||||
yield recursed_val
|
||||
|
||||
for df_variant in recursive_app(df, self.table_ops, random_state):
|
||||
yield df_variant
|
||||
|
||||
|
||||
class TableFormats(ABC):
|
||||
@abstractmethod
|
||||
def formatting(
|
||||
self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class MarkdownFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
return tabulate(df, headers='keys', tablefmt='pipe', showindex=True)
|
||||
|
||||
|
||||
class DataMatrixFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
df_ = pd.DataFrame()
|
||||
df_[""] = df.index.to_list()
|
||||
df_[df.columns] = df.values.tolist()
|
||||
matrix = df_.columns.tolist() # Get column headers as a list
|
||||
matrix_data = df_.values.tolist() # Get data as a list of lists
|
||||
matrix_data.insert(0, matrix)
|
||||
return matrix_data
|
||||
|
||||
|
||||
class JsonFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
return df.to_json(orient='index', index=True)
|
||||
|
||||
|
||||
class DFloaderFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
str_df_loader = "pd.DataFrame({[data]}, index=[indices])"
|
||||
data = ""
|
||||
indi = str(df.index.to_list())
|
||||
for d in df.columns:
|
||||
data += str(d) + " : " + str(df[d].to_list()) + ", "
|
||||
data = data[:-2]
|
||||
str_df_loader = str_df_loader.replace(
|
||||
"[data]", data).replace("[indices]", indi)
|
||||
return str_df_loader
|
||||
|
||||
|
||||
class HTMLFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
html = df.to_html(index=True)
|
||||
return html
|
||||
|
||||
|
||||
class HTMLNoSpaceFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
html = df.to_html(index=True)
|
||||
return str(html).replace("\t", "").replace("\n", "").replace(" ", "")
|
||||
|
||||
|
||||
class TabSeparatedFormat(TableFormats):
|
||||
def formatting(self, df: pd.DataFrame) -> str:
|
||||
return df.to_csv(sep='\t', index=True)
|
||||
|
||||
|
||||
class CommaSeparatedFormat(TableFormats):
|
||||
def formatting(self, df: pd.DataFrame) -> str:
|
||||
return df.to_csv(index=True)
|
||||
|
||||
|
||||
class SQLQueryFormat(TableFormats):
|
||||
def formatting(self, df):
|
||||
return
|
||||
|
||||
|
||||
def gather_Examples_Prompt(tab_format, test_case):
|
||||
|
||||
TF, TC = tab_format.__class__.__name__, test_case.__class__.__name__
|
||||
Examples = EXAMPLES.replace("[Data_format_example1]", EXAMPLE_Dictionary[f"TF_EX1_{TF}"])\
|
||||
.replace("[QA1]", EXAMPLE_Dictionary[f"Ex1_QA1_{TC}"])\
|
||||
.replace("[Data_format_example2]", EXAMPLE_Dictionary[f"TF_EX1_{TF}"])\
|
||||
.replace("[QA2]", EXAMPLE_Dictionary[f"Ex1_QA2_{TC}"])
|
||||
return Examples
|
||||
|
||||
|
||||
class LLMTableLearner():
|
||||
def get_prompt(self, examples, df_format, question: str, temperature: float) -> Any:
|
||||
prompt = DATA_QUES_INSTRUCTION.replace("[Ques]", str(question)).replace(
|
||||
"[Data_format]", str(df_format)).replace("[Example]", examples)
|
||||
total_tokens = num_tokens_from_string(prompt)
|
||||
maxTok = 100
|
||||
num_n = 15
|
||||
modelName = "text-davinci-003"
|
||||
prompt_cache = {"model": modelName,
|
||||
"prompt": prompt,
|
||||
"temperature": temperature,
|
||||
"max_tokens": maxTok,
|
||||
"top_p": 1,
|
||||
"frequency_penalty": 0,
|
||||
"presence_penalty": 0,
|
||||
"n": num_n,
|
||||
"logprobs": 1}
|
||||
return prompt_cache
|
||||
|
||||
def get_answer(self, examples, df_format, question: str, temperature: float, open_api_key: str) -> Any:
|
||||
prompt = DATA_QUES_INSTRUCTION.replace("[Ques]", str(question)).replace(
|
||||
"[Data_format]", str(df_format)).replace("[Example]", examples)
|
||||
|
||||
answer, cache = openapi_call_completions(
|
||||
prompt, modelName="text-davinci-003", temp=temperature, maxTok=100, num_n=15, open_api_key=open_api_key)
|
||||
return answer, cache
|
||||
|
||||
|
||||
class TableExperimentSuite(object):
|
||||
|
||||
def __init__(self, llm_learner: LLMTableLearner(),
|
||||
table_formats: List[TableFormats],
|
||||
table_ops: List[TableOperation],
|
||||
test_gens: List[TestCaseGenerator],
|
||||
cache_save_path: str,
|
||||
open_api_key: str):
|
||||
self.llm = llm_learner
|
||||
self.table_formats = table_formats
|
||||
self.table_ops = table_ops
|
||||
self.test_gens = test_gens
|
||||
self.cache_save_path = cache_save_path
|
||||
self.open_api_key = open_api_key
|
||||
|
||||
def run_experiment(self,
|
||||
df,
|
||||
per_table_op: int = 10,
|
||||
per_test_gen: int = 10,
|
||||
save_cache=True):
|
||||
results = []
|
||||
cacahe_all_dict = []
|
||||
if save_cache:
|
||||
current_datetime = datetime.datetime.now()
|
||||
formatted_datetime = current_datetime.strftime("%Y_%m_%d_%H_%M_%S")
|
||||
|
||||
cache_path = os.path.join(self.cache_save_path, f"cache_logger")
|
||||
if not os.path.exists(cache_path):
|
||||
os.makedirs(cache_path)
|
||||
cache_path2 = os.path.join(
|
||||
self.cache_save_path, f"cache_logger_all")
|
||||
if not os.path.exists(cache_path2):
|
||||
os.makedirs(cache_path2)
|
||||
save_catch_file = os.path.join(
|
||||
cache_path, f"micro_test_log_{formatted_datetime}.json")
|
||||
save_catch_file2 = os.path.join(
|
||||
cache_path2, f"micro_test_log_{formatted_datetime}.json")
|
||||
with open(save_catch_file, mode="w") as writer:
|
||||
pass
|
||||
|
||||
for table_op in self.table_ops:
|
||||
|
||||
for df_variant in itertools.islice(table_op.modify(df),
|
||||
per_table_op):
|
||||
df_variant_for_test = df_variant
|
||||
if table_op.__class__.__name__ in ["ColumnCluster", "SerializeTable"]:
|
||||
df_variant_for_test = df
|
||||
|
||||
for gen in self.test_gens:
|
||||
if table_op.__class__.__name__ in ["OriginalData", "SequentialColumnNames", "TransposeTable", "SerializeTable"]:
|
||||
no_of_test = per_test_gen*per_table_op
|
||||
else:
|
||||
no_of_test = per_test_gen
|
||||
|
||||
for test in itertools.islice(gen.generate(df_variant_for_test),
|
||||
no_of_test):
|
||||
|
||||
for tab_format in self.table_formats:
|
||||
temperature_list = [0]
|
||||
for temp in temperature_list:
|
||||
|
||||
try:
|
||||
examples = gather_Examples_Prompt(
|
||||
tab_format, gen)
|
||||
prompt_cache_per_call = self.llm.get_prompt(
|
||||
examples, tab_format.formatting(df_variant), test.question, temp)
|
||||
|
||||
answer, cache_per_call = self.llm.get_answer(examples, tab_format.formatting(
|
||||
df_variant), test.question, temp, self.open_api_key)
|
||||
result = gen.check(test, answer[0])
|
||||
pass_1 = gen.metric_pass_k(test, answer, 1)
|
||||
pass_3 = gen.metric_pass_k(test, answer, 3)
|
||||
pass_5 = gen.metric_pass_k(test, answer, 5)
|
||||
pass_10 = gen.metric_pass_k(
|
||||
test, answer, 10)
|
||||
pass_15 = gen.metric_pass_k(
|
||||
test, answer, 15)
|
||||
|
||||
error = None
|
||||
|
||||
except Exception as Err:
|
||||
answer, cache_per_call = None, None
|
||||
result = None
|
||||
pass_1 = pass_3 = pass_5 = pass_10 = pass_15 = None
|
||||
error = str(Err)
|
||||
print(f"error {Err} encountered")
|
||||
error = str(Err)
|
||||
print(f"error {Err} encountered")
|
||||
|
||||
cache_all = {"model-Temperature": str(temp),
|
||||
"tab_format": str(tab_format.__class__.__name__),
|
||||
"table_op": str(table_op.__class__.__name__),
|
||||
"gen": str(gen.__class__.__name__),
|
||||
"test-Question": test.question,
|
||||
"test-expected-answer": test.expect,
|
||||
"test-TestType": str(test.TestType),
|
||||
"test": str(test),
|
||||
"answer": answer,
|
||||
"result_match_top1": result,
|
||||
"error": str(error),
|
||||
"pass_1": str(pass_1),
|
||||
"pass_3": str(pass_3),
|
||||
"pass_5": str(pass_5),
|
||||
"pass_10": str(pass_10),
|
||||
"pass_15": str(pass_15),
|
||||
"prompt_cache_per_call": prompt_cache_per_call,
|
||||
"LLMOutput": cache_per_call}
|
||||
results.append((temp, tab_format.__class__.__name__, table_op.__class__.__name__, gen.__class__.__name__, test.question,
|
||||
test.expect, test.TestType, pass_1, pass_3, pass_5, pass_10, pass_15, test, answer, answer, result, error))
|
||||
cacahe_all_dict.append(cache_all)
|
||||
if save_cache:
|
||||
with open(save_catch_file, mode="a") as file:
|
||||
serializable_entry = json.dumps(
|
||||
cache_all, cls=CustomJSONEncoder)
|
||||
file.write(serializable_entry + '\n')
|
||||
|
||||
if save_cache:
|
||||
with open(save_catch_file2, mode="w") as file:
|
||||
for result_entry in cacahe_all_dict:
|
||||
serializable_entry = json.dumps(
|
||||
result_entry, cls=CustomJSONEncoder)
|
||||
file.write(serializable_entry + '\n')
|
||||
|
||||
return results
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
import tiktoken
|
||||
from io import StringIO
|
||||
import ast
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def Convert_back_to_df(Format: str, data_string: str):
|
||||
if Format == "JsonFormat":
|
||||
return pd.DataFrame(ast.literal_eval(data_string)).T
|
||||
if Format == "MarkdownFormat":
|
||||
extracted_df = pd.read_csv(
|
||||
StringIO(data_string.replace(' ', '')), # Get rid of whitespaces
|
||||
sep='|',
|
||||
index_col=1
|
||||
).dropna(
|
||||
axis=1,
|
||||
how='all'
|
||||
).iloc[1:]
|
||||
extracted_df_ = extracted_df.map(
|
||||
lambda x: x.strip() if isinstance(x, str) else x)
|
||||
extracted_df_.columns = [col.strip() for col in extracted_df.columns]
|
||||
return extracted_df_
|
||||
if Format == "DFloaderFormat":
|
||||
start_inx = data_string.find("pd.DataFrame(")+len("pd.DataFrame(")
|
||||
stop_idx = data_string.find(", index=[")
|
||||
string_dict = data_string[start_inx:stop_idx]
|
||||
string_dict
|
||||
dict_data_part = ast.literal_eval(data_string[start_inx:stop_idx])
|
||||
list_index = ast.literal_eval(data_string[stop_idx+len(", index="):-1])
|
||||
return pd.DataFrame(dict_data_part, index=list_index)
|
||||
|
||||
if Format == "DataMatrixFormat":
|
||||
Matrix = ast.literal_eval(data_string.replace("\n", ""))
|
||||
matrix_df = pd.DataFrame(Matrix)
|
||||
matrix_df.index = matrix_df[0].to_list()
|
||||
matrix_df = matrix_df.drop(columns=[0])
|
||||
matrix_df.columns = matrix_df.iloc[0, :].tolist()
|
||||
matrix_df = matrix_df.iloc[1:, :]
|
||||
return matrix_df
|
||||
|
||||
if Format == "CommaSeparatedFormat":
|
||||
dff = pd.read_csv(StringIO(data_string), sep=",")
|
||||
index_col = dff.columns[0]
|
||||
dff.index = dff[index_col].to_list()
|
||||
dff = dff.drop(columns=[index_col])
|
||||
return dff
|
||||
|
||||
if Format == "TabSeparatedFormat":
|
||||
dff = pd.read_csv(StringIO(data_string), sep="\t")
|
||||
index_col = dff.columns[0]
|
||||
dff.index = dff[index_col].to_list()
|
||||
dff = dff.drop(columns=[index_col])
|
||||
return dff
|
||||
|
||||
if Format in ["HTMLNoSpaceFormat", "HTMLFormat"]:
|
||||
dff = pd.read_html(StringIO(data_string))[0]
|
||||
index_col = dff.columns[0]
|
||||
dff.index = dff[index_col].to_list()
|
||||
dff = dff.drop(columns=[index_col])
|
||||
return dff
|
||||
|
||||
|
||||
def num_tokens_from_string(string: str, encoding_name: str = "p50k_base") -> int:
|
||||
"""Returns the number of tokens in a text string."""
|
||||
encoding = tiktoken.get_encoding(encoding_name)
|
||||
num_tokens = len(encoding.encode(string))
|
||||
return num_tokens
|
||||
|
||||
|
||||
num_tokens_from_string("tiktoken is great!", "cl100k_base")
|
||||
|
||||
|
||||
def stringify_serialized_df(serialized_df: pd.DataFrame):
|
||||
return "\n".join([v[0] for v in serialized_df.values])
|
||||
|
|
@ -1,173 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%cd ../code_"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"gather": {
|
||||
"logged": 1694699855951
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tableTestingMicroScript import *\n",
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"import ast\n",
|
||||
"from utils import num_tokens_from_string"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from prompts import EXAMPLES, EXAMPLE_Dictionary\n",
|
||||
"import pandas as pd\n",
|
||||
"from utils import num_tokens_from_string\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def gather_Examples_Prompt(tab_format, test_case):\n",
|
||||
"\n",
|
||||
" TF, TC = tab_format.__class__.__name__, test_case\n",
|
||||
" Examples = EXAMPLES.replace(\"[Data_format_example1]\", EXAMPLE_Dictionary[f\"TF_EX1_{TF}\"])\\\n",
|
||||
" .replace(\"[QA1]\", EXAMPLE_Dictionary[f\"Ex1_QA1_{TC}\"])\\\n",
|
||||
" .replace(\"[Data_format_example2]\", EXAMPLE_Dictionary[f\"TF_EX1_{TF}\"])\\\n",
|
||||
" .replace(\"[QA2]\", EXAMPLE_Dictionary[f\"Ex1_QA2_{TC}\"])\n",
|
||||
" return Examples\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_max_no_of_rows_feedable(df, FormatFunction, Micro=True):\n",
|
||||
"\n",
|
||||
" per_test = []\n",
|
||||
" test_case = [\"NavigationTests\", \"ColumnLookupTests\",\n",
|
||||
" \"RowLookupTests\", \"DataTypeLookupTests\"]\n",
|
||||
"\n",
|
||||
" for tt in test_case:\n",
|
||||
" exp = gather_Examples_Prompt(FormatFunction, tt)\n",
|
||||
"\n",
|
||||
" for i in range(min(df.shape[0], 100), 2, -1):\n",
|
||||
" if Micro:\n",
|
||||
" total_token = 30 + num_tokens_from_string(exp)+11+num_tokens_from_string(\n",
|
||||
" str(FormatFunction.formatting(df.head(i))))+100\n",
|
||||
" else:\n",
|
||||
" total_token = 100 + num_tokens_from_string(exp)+11+num_tokens_from_string(\n",
|
||||
" str(FormatFunction.formatting(df.head(i))))*2.5\n",
|
||||
"\n",
|
||||
" if total_token < 4081:\n",
|
||||
"\n",
|
||||
" per_test.append(i)\n",
|
||||
" break\n",
|
||||
" return (min(per_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"benchmark_dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"gather": {
|
||||
"logged": 1694736239538
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"benchmark_data_path = \"../resources/BenchmarkDataset\"\n",
|
||||
"benchmark_dataset = [f for f in os.listdir(\n",
|
||||
" benchmark_data_path) if f.endswith(\".csv\")]\n",
|
||||
"benchmark_save_path = \"../resources/BenchmarkResults/BenchmarkResults_Micro_tests/\"\n",
|
||||
"if not os.path.exists(benchmark_save_path):\n",
|
||||
" os.makedirs(benchmark_save_path)\n",
|
||||
"open_api_key = os.env(\"OPENAI_API_KEY\")\n",
|
||||
"for bd in benchmark_dataset:\n",
|
||||
" print(bd)\n",
|
||||
" df = pd.read_csv(os.path.join(benchmark_data_path, bd))\n",
|
||||
" save_path = os.path.join(benchmark_save_path, bd)\n",
|
||||
" if not os.path.exists(save_path):\n",
|
||||
" os.makedirs(save_path)\n",
|
||||
"\n",
|
||||
" table_formats = [HTMLFormat(), HTMLNoSpaceFormat(), MarkdownFormat(), JsonFormat(\n",
|
||||
" ), DataMatrixFormat(), DFloaderFormat(), TabSeparatedFormat(), CommaSeparatedFormat()],\n",
|
||||
" max_rows = min([get_max_no_of_rows_feedable(df, format)\n",
|
||||
" for format in table_formats])\n",
|
||||
"\n",
|
||||
" df = df.head(max_rows)\n",
|
||||
" print(df.shape)\n",
|
||||
" suite = TableExperimentSuite(LLMTableLearner(),\n",
|
||||
" [MarkdownFormat(), JsonFormat(), DataMatrixFormat(\n",
|
||||
" ), DFloaderFormat(), TabSeparatedFormat(), CommaSeparatedFormat()],\n",
|
||||
" [\n",
|
||||
" OriginalData(), SampleRows(), ColumnCluster(), SerializeTable()\n",
|
||||
" ],\n",
|
||||
" [TableColumnReorderTests(), TableTransposeTests(\n",
|
||||
" ), TableReconstructionTests(), TableReconstructionTests1()],\n",
|
||||
" save_path, open_api_key)\n",
|
||||
" output = suite.run_experiment(df, per_table_op=50, per_test_gen=1)\n",
|
||||
"\n",
|
||||
" col_names = [\"temperature\", \"tableFormat\", \"TableManipulation\", \"TestCase\", \"TestQuestion\", \"TestExpectedAnswer\", \"TestCaseType\", \"pass_1\", \"pass_3\",\n",
|
||||
" \"pass_5\", \"pass_10\", \"pass_15\", \"TestCaseTuple\", \"Answers_top_15\", \"Answer_top_1\", \"per_cell_accracies_top1\", \"per_cell_accracies\", \"Result\", \"Error\"]\n",
|
||||
" output_df = pd.DataFrame(output, columns=col_names)\n",
|
||||
"\n",
|
||||
" output_df.to_csv(os.path.join(\n",
|
||||
" save_path, f\"Macro_{bd}_output.csv\"), index=None)\n",
|
||||
" try:\n",
|
||||
" output_df.to_pickle(os.path.join(\n",
|
||||
" save_path, f\"{bd}_output.pkl\"), index=None)\n",
|
||||
" except:\n",
|
||||
" pass"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernel_info": {
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "env",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
},
|
||||
"microsoft": {
|
||||
"ms_spell_check": {
|
||||
"ms_spell_check_language": "en"
|
||||
}
|
||||
},
|
||||
"nteract": {
|
||||
"version": "nteract-front-end@1.0.0"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,228 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%cd ../code_\n",
|
||||
"from collections import Counter\n",
|
||||
"from tableTestingNewMacro import *\n",
|
||||
"from utils import Convert_back_to_df\n",
|
||||
"from compare import compare_per_cell\n",
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"import ast\n",
|
||||
"import json\n",
|
||||
"from typing import Any, List\n",
|
||||
"from itertools import combinations\n",
|
||||
"import tqdm\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def check(expected_df: pd.DataFrame, answer: pd.DataFrame) -> bool:\n",
|
||||
" expected_df = expected_df.astype(str)\n",
|
||||
" try:\n",
|
||||
" if expected_df.shape == answer.shape:\n",
|
||||
" return expected_df.equals(answer)\n",
|
||||
" else:\n",
|
||||
" return False\n",
|
||||
" except:\n",
|
||||
" return False\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def metric_pass_k(expected_df: pd.DataFrame, answer: List[pd.DataFrame], k: int) -> float:\n",
|
||||
" expected_df = expected_df.astype(str)\n",
|
||||
" boolean_answers = [check(expected_df, answer[i])\n",
|
||||
" for i in range(len(answer))]\n",
|
||||
" combinations_k = list(combinations(boolean_answers, k))\n",
|
||||
" passed_at_k = 0\n",
|
||||
" # Calculate the pass@k metric\n",
|
||||
" for comb in combinations_k:\n",
|
||||
" if any(comb):\n",
|
||||
" passed_at_k += 1\n",
|
||||
" pass_at_k_percentage = (passed_at_k / len(combinations_k))*100\n",
|
||||
"\n",
|
||||
" return pass_at_k_percentage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def serialize_it(expected_df, format_name):\n",
|
||||
" if format_name == \"JsonFormat\":\n",
|
||||
" return str(JsonFormat().formatting(next(SerializeTable().modify(expected_df))))\n",
|
||||
" if format_name == \"DFloaderFormat\":\n",
|
||||
" return str(DFloaderFormat().formatting(next(SerializeTable().modify(expected_df))))\n",
|
||||
" if format_name == \"DataMatrixFormat\":\n",
|
||||
" return str(DataMatrixFormat().formatting(next(SerializeTable().modify(expected_df))))\n",
|
||||
" if format_name == \"MarkdownFormat\":\n",
|
||||
" return str(MarkdownFormat().formatting(next(SerializeTable().modify(expected_df))))\n",
|
||||
" if format_name == \"CommaSeparatedFormat\":\n",
|
||||
" return str(CommaSeparatedFormat().formatting(next(SerializeTable().modify(expected_df))))\n",
|
||||
" if format_name == \"TabSeparatedFormat\":\n",
|
||||
" return str(TabSeparatedFormat().formatting(next(SerializeTable().modify(expected_df))))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"benchmark_data_path = \"../resources/BenchmarkDataset\"\n",
|
||||
"benchmark_dataset = os.listdir(benchmark_data_path)\n",
|
||||
"# 3----> macro test -noises 8 formats 3 test\n",
|
||||
"benchmark_save_path = \"../resources/BenchmarkResults_29_9_2023_Macro_Tests_all_noise_redo_redo/\"\n",
|
||||
"benchmark_dataset = [d for d in benchmark_dataset if d.endswith(\".csv\")]\n",
|
||||
"sp = spa = benchmark_save_path\n",
|
||||
"\n",
|
||||
"for bd in benchmark_dataset:\n",
|
||||
" try:\n",
|
||||
" pass_metrics = []\n",
|
||||
" t_F_bools = []\n",
|
||||
" scores_all_precision = []\n",
|
||||
" scores_all_recall = []\n",
|
||||
" print(bd)\n",
|
||||
" df = pd.read_csv(os.path.join(benchmark_save_path,\n",
|
||||
" bd, \"Macro_\"+bd+\"_output.csv\"))\n",
|
||||
" print(df.shape)\n",
|
||||
" dirs = os.listdir(os.path.join(\n",
|
||||
" benchmark_save_path, bd, \"cache_logger_new\"))\n",
|
||||
" print(dirs)\n",
|
||||
" error_lines = 0\n",
|
||||
" error_converts = 0\n",
|
||||
" found = 0\n",
|
||||
" table_ops = []\n",
|
||||
" error_converts_format = []\n",
|
||||
" ops_candidate_error = []\n",
|
||||
" test_candidate_error = []\n",
|
||||
" error_logs = []\n",
|
||||
" print(\"use:\", dirs[-1])\n",
|
||||
" with open(os.path.join(benchmark_save_path, bd, \"cache_logger_new\", dirs[-1]), \"r\") as file:\n",
|
||||
" for line in tqdm.tqdm(file):\n",
|
||||
" try:\n",
|
||||
" data_ = json.loads((line))\n",
|
||||
" expected_ans = data_[\"test-expected-answer\"]\n",
|
||||
" expected_df = Convert_back_to_df(\n",
|
||||
" \"JsonFormat\", str(expected_ans))\n",
|
||||
" gen = data_[\"gen\"]\n",
|
||||
" if gen == \"TableReconstructionTests\" and data_[\"table_op\"] == \"SampleRows\":\n",
|
||||
" if serialize_it(expected_df, data_[\"tab_format\"]) in data_[\"prompt_cache_per_call\"][\"prompt\"]:\n",
|
||||
" table_op = \"SerializeTable\"\n",
|
||||
" found += 1\n",
|
||||
" elif gen == \"TableReconstructionTests1\":\n",
|
||||
" table_op = \"SerializeTable\"\n",
|
||||
" else:\n",
|
||||
" table_op = data_[\"table_op\"]\n",
|
||||
" table_ops.append(table_op)\n",
|
||||
" answer = data_[\"answer\"]\n",
|
||||
" tab_format = data_[\"tab_format\"]\n",
|
||||
" answer_changed_format = []\n",
|
||||
" for a in answer:\n",
|
||||
" try:\n",
|
||||
" conv = Convert_back_to_df(tab_format, a)\n",
|
||||
" except Exception as err:\n",
|
||||
" conv = pd.DataFrame()\n",
|
||||
" error_converts += 1\n",
|
||||
" if a == answer[0]:\n",
|
||||
" error_converts_format.append(\n",
|
||||
" data_[\"tab_format\"])\n",
|
||||
"\n",
|
||||
" answer_changed_format.append(conv)\n",
|
||||
"\n",
|
||||
" a = answer_changed_format[0]\n",
|
||||
" try:\n",
|
||||
" scores_all_precision.append(\n",
|
||||
" compare_per_cell(a, expected_df)*100)\n",
|
||||
" except:\n",
|
||||
" scores_all_precision.append(0)\n",
|
||||
" try:\n",
|
||||
" # , type_reference_df=a, count_header_and_index=True, return_fraction=False))\n",
|
||||
" scores_all_recall.append(\n",
|
||||
" compare_per_cell(expected_df, a)*100)\n",
|
||||
" except:\n",
|
||||
" scores_all_recall.append(0)\n",
|
||||
"\n",
|
||||
" # break\n",
|
||||
" try:\n",
|
||||
" pass_metrics.append([metric_pass_k(\n",
|
||||
" expected_ans, answer_changed_format, k) for k in [1, 3, 5, 10, 15]])\n",
|
||||
" except Exception as Err:\n",
|
||||
" pass_metrics.append([None for k in [1, 3, 5, 10, 15]])\n",
|
||||
" try:\n",
|
||||
" t_F_bools.append(\n",
|
||||
" check(expected_ans, answer_changed_format[0]))\n",
|
||||
"\n",
|
||||
" except Exception as Err:\n",
|
||||
" t_F_bools.append(False)\n",
|
||||
" except Exception as err:\n",
|
||||
"\n",
|
||||
" ops_candidate_error.append(data_[\"table_op\"])\n",
|
||||
" test_candidate_error.append(data_[\"gen\"])\n",
|
||||
" error_logs.append(data_[\"error\"])\n",
|
||||
" error_lines += 1\n",
|
||||
" scores_all_precision.append(0)\n",
|
||||
" scores_all_recall.append(0)\n",
|
||||
" t_F_bools.append(False)\n",
|
||||
" pass_metrics.append([None for k in [1, 3, 5, 10, 15]])\n",
|
||||
"\n",
|
||||
" print(len(pass_metrics))\n",
|
||||
" print(Counter(ops_candidate_error).items())\n",
|
||||
" print(Counter(test_candidate_error).items())\n",
|
||||
" print(Counter(error_logs).items())\n",
|
||||
" print(\"NO of error lines:\", error_lines)\n",
|
||||
" print(\"error_converts:\", error_converts/15, error_converts)\n",
|
||||
" print(\"found serialized:\", found)\n",
|
||||
" print(Counter(table_ops).items())\n",
|
||||
" print(\"failed format counts:\", Counter(error_converts_format).items())\n",
|
||||
" df[\"table_ops\"] = table_ops\n",
|
||||
" df[['pass_1', 'pass_3', 'pass_5', 'pass_10', 'pass_15']] = pass_metrics\n",
|
||||
" df[\"Result\"] = df[\"Result\"].apply(lambda x: 1 if x else 0)\n",
|
||||
" df[\"T/F\"] = t_F_bools\n",
|
||||
" df[\"precision_per_cell_correctness_top1\"] = scores_all_precision\n",
|
||||
" df[\"recall_per_cell_correctness_top1\"] = scores_all_recall\n",
|
||||
" df.to_csv(os.path.join(benchmark_save_path, bd,\n",
|
||||
" \"Macro_\"+bd+\"_output_revamped.csv\"), index=None)\n",
|
||||
"\n",
|
||||
" print(f\"{'-'*90}\\n\")\n",
|
||||
" except Exception as Err:\n",
|
||||
" print(\"error encountered\")\n",
|
||||
" print(Err)\n",
|
||||
" pass\n",
|
||||
" print(f\"{'-'*90}\\n\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "env",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,231 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"import numpy as np\n",
|
||||
"import statsmodels.api as sm\n",
|
||||
"import statsmodels.stats.contingency_tables as ct"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"benchmark_data_path = \"../resources/BenchmarkDatasetClean/\"\n",
|
||||
"benchmark_dataset = os.listdir(benchmark_data_path)\n",
|
||||
"benchmark_save_path = \"../resources/BenchmarkResults_29_9_2023_Macro_Tests_all_noise_redo_redo\"\n",
|
||||
"benchmark_save_path2 = \"../resources/BenchmarkResults_23_9_2023_Macro_Tests_HTML_original\"\n",
|
||||
"benchmark_save_path1 = \"../resources/BenchmarkResults_23_9_2023_Macro_Tests_new_format_added_test_redo/\"\n",
|
||||
"\n",
|
||||
"benchmark_dataset = [data for data in benchmark_dataset if data.endswith(\n",
|
||||
" \".csv\") and \"breast\" not in data]\n",
|
||||
"dfs = []\n",
|
||||
"for bd in benchmark_dataset:\n",
|
||||
"\n",
|
||||
" pass_metrics = []\n",
|
||||
" print(bd)\n",
|
||||
" df1 = pd.read_csv(os.path.join(benchmark_save_path1, bd,\n",
|
||||
" \"Macro_\"+bd+\"_output_revamped.csv\"))\n",
|
||||
" df2 = pd.read_csv(os.path.join(benchmark_save_path2, bd,\n",
|
||||
" \"Macro_\"+bd+\"_output_revamped.csv\"))\n",
|
||||
" df3 = pd.read_csv(os.path.join(benchmark_save_path, bd,\n",
|
||||
" \"Macro_\"+bd+\"_output_revamped.csv\"))\n",
|
||||
" df2[\"precision_per_cell_correctness_top1\"] = df2[\"precision_per_cell_correctness_top1\"]*0.01\n",
|
||||
" df2[\"recall_per_cell_correctness_top1\"] = df2[\"recall_per_cell_correctness_top1\"]*0.01\n",
|
||||
" df3[\"precision_per_cell_correctness_top1\"] = df3[\"precision_per_cell_correctness_top1\"]*0.01\n",
|
||||
" df3[\"recall_per_cell_correctness_top1\"] = df3[\"recall_per_cell_correctness_top1\"]*0.01\n",
|
||||
" df2[\"TableManipulation\"] = \"OriginalData\"\n",
|
||||
" df1[\"TableManipulation\"] = \"OriginalData\"\n",
|
||||
" df = pd.concat([df1, df2, df3], ignore_index=True)\n",
|
||||
" print(df1.shape, df2.shape, df.shape)\n",
|
||||
" df[\"Dataset-name\"] = bd\n",
|
||||
" df[\"F1Score_per_cell_correctness_top1\"] = df.apply(lambda x: np.round(2*(x.recall_per_cell_correctness_top1*x.precision_per_cell_correctness_top1)/(\n",
|
||||
" x.recall_per_cell_correctness_top1+x.precision_per_cell_correctness_top1)) if (x.recall_per_cell_correctness_top1*x.precision_per_cell_correctness_top1) != 0 else 0, axis=1)\n",
|
||||
" dfs.append(df)\n",
|
||||
"all_data = pd.concat(dfs, ignore_index=True)\n",
|
||||
"\n",
|
||||
"attributes = {c: list(df[c].unique()) for c in df.columns if c in [\n",
|
||||
" \"temperature\", \"tableFormat\", \"table_ops\", \"TestCase\"]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### RQ1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_cases = [\"TableColumnReorderTests\",\n",
|
||||
" \"TableReconstructionTests1\", \"TableTransposeTests\"]\n",
|
||||
"formats = all_data[\"tableFormat\"].unique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_data_test_vs_format_on_all_noise_ops = all_data[(\n",
|
||||
" all_data[\"temperature\"] == 0.0)] \n",
|
||||
"print(all_data_test_vs_format_on_all_noise_ops.shape)\n",
|
||||
"test_vs_format_on_all_noise_ops = all_data_test_vs_format_on_all_noise_ops.pivot_table(\n",
|
||||
" index=[\"tableFormat\"],\n",
|
||||
" columns='TestCase',\n",
|
||||
" values=[\"F1Score_per_cell_correctness_top1\", 'recall_per_cell_correctness_top1',\n",
|
||||
" \"precision_per_cell_correctness_top1\"], \n",
|
||||
" aggfunc=[\"mean\"] # Custom aggregation function to append values to a list\n",
|
||||
")\n",
|
||||
"test_vs_format_on_all_noise_ops = test_vs_format_on_all_noise_ops*100\n",
|
||||
"test_vs_format_on_all_noise_ops = np.round(\n",
|
||||
" test_vs_format_on_all_noise_ops, decimals=2)\n",
|
||||
"cols = [(\"mean\", f\"{metric}_per_cell_correctness_top1\", T)\n",
|
||||
" for T in test_cases for metric in [\"precision\", \"recall\", \"F1Score\"]]\n",
|
||||
"test_vs_format_on_all_noise_ops = test_vs_format_on_all_noise_ops[cols]\n",
|
||||
"indi = ['CommaSeparatedFormat', 'DFloaderFormat', 'DataMatrixFormat',\n",
|
||||
" 'JsonFormat', 'MarkdownFormat',\n",
|
||||
" 'TabSeparatedFormat', 'HTMLFormat', 'HTMLNoSpaceFormat']\n",
|
||||
"test_vs_format_on_all_noise_ops = test_vs_format_on_all_noise_ops.reindex(indi)\n",
|
||||
"test_vs_format_on_all_noise_ops"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cols_f1 = [(\"mean\", f\"{metric}_per_cell_correctness_top1\", T)\n",
|
||||
" for T in test_cases for metric in [\"F1Score\"]]\n",
|
||||
"test_vs_format_on_all_noise_ops_f1 = test_vs_format_on_all_noise_ops[cols_f1]\n",
|
||||
"test_vs_format_on_all_noise_ops_f1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### P-values RQ1 Macro"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_data_test_vs_format_on_all_noise_ops = all_data[(\n",
|
||||
" all_data[\"temperature\"] == 0.0)] \n",
|
||||
"print(all_data_test_vs_format_on_all_noise_ops.shape)\n",
|
||||
"test_vs_format_on_all_noise_ops = all_data_test_vs_format_on_all_noise_ops.pivot_table(\n",
|
||||
" index=[\"TestCase\"],\n",
|
||||
" columns='tableFormat',\n",
|
||||
" values=[\"F1Score_per_cell_correctness_top1\"],\n",
|
||||
" # Custom aggregation function to append values to a list\n",
|
||||
" aggfunc=lambda x: list(x)\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import scipy\n",
|
||||
"\n",
|
||||
"p_val = {}\n",
|
||||
"for id in range(test_vs_format_on_all_noise_ops.shape[0]):\n",
|
||||
" print(id)\n",
|
||||
" if test_vs_format_on_all_noise_ops.index[id] != \"TableReconstructionTests\":\n",
|
||||
" print(\n",
|
||||
" f\"For Test: {test_vs_format_on_all_noise_ops.index[id]} pass@1 temp =0.1\")\n",
|
||||
" sorted_list = [i for i in test_vs_format_on_all_noise_ops_f1.sort_values(\n",
|
||||
" by=[(\"mean\", \"F1Score_per_cell_correctness_top1\", test_vs_format_on_all_noise_ops.index[id])], ascending=False).index]\n",
|
||||
" print(sorted_list)\n",
|
||||
" sub_vals = {}\n",
|
||||
" index_val = test_vs_format_on_all_noise_ops.index[id]\n",
|
||||
" for x in range(1, len(sorted_list)):\n",
|
||||
" vals1 = test_vs_format_on_all_noise_ops.loc[index_val, (\n",
|
||||
" \"F1Score_per_cell_correctness_top1\", sorted_list[0])]\n",
|
||||
" vals2 = test_vs_format_on_all_noise_ops.loc[index_val, (\n",
|
||||
" \"F1Score_per_cell_correctness_top1\", sorted_list[x])]\n",
|
||||
" min_val = min(len(vals1), len(vals2))\n",
|
||||
" print(min_val)\n",
|
||||
" p_value = scipy.stats.ttest_rel(vals1[:min_val], vals2[:min_val])\n",
|
||||
" sub_vals[f\"{sorted_list[0]}-{sorted_list[x]}\"] = {\"p-value\": p_value.pvalue,\n",
|
||||
" \"statistics\": p_value.statistic, \"df\": p_value.df, \"tests_count\": min_val}\n",
|
||||
" p_val[test_vs_format_on_all_noise_ops.index[id]] = sub_vals"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"import datetime\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class CustomJSONEncoder(json.JSONEncoder):\n",
|
||||
" def default(self, obj):\n",
|
||||
" if isinstance(obj, np.integer):\n",
|
||||
" return int(obj)\n",
|
||||
" elif isinstance(obj, np.floating):\n",
|
||||
" return float(obj)\n",
|
||||
" elif isinstance(obj, np.ndarray):\n",
|
||||
" return obj.tolist()\n",
|
||||
" if isinstance(obj, pd.Series):\n",
|
||||
" return obj.tolist()\n",
|
||||
" elif isinstance(obj, datetime):\n",
|
||||
" # Handle datetime objects\n",
|
||||
" return obj.isoformat()\n",
|
||||
" elif isinstance(obj, set):\n",
|
||||
" # Handle sets\n",
|
||||
" return list(obj)\n",
|
||||
" return super().default(obj)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"with open(\"../resources/all_p_vals/p_vals_macro_tests_RQ1.json\", \"w\") as f:\n",
|
||||
" json.dump(p_val, f, indent=3, cls=CustomJSONEncoder)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "env",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,297 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"import numpy as np\n",
|
||||
"import statsmodels.api as sm\n",
|
||||
"import statsmodels.stats.contingency_tables as ct"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"benchmark_data_path = \"../resources/BenchmarkDatasetClean/\"\n",
|
||||
"benchmark_dataset = os.listdir(benchmark_data_path)\n",
|
||||
"benchmark_save_path = \"../resources/BenchmarkResults_29_9_2023_Macro_Tests_all_noise_redo_redo\"\n",
|
||||
"benchmark_save_path2 = \"../resources/BenchmarkResults_23_9_2023_Macro_Tests_HTML_original\"\n",
|
||||
"benchmark_save_path1 = \"../resources/BenchmarkResults_23_9_2023_Macro_Tests_new_format_added_test_redo/\"\n",
|
||||
"\n",
|
||||
"benchmark_dataset = [data for data in benchmark_dataset if data.endswith(\n",
|
||||
" \".csv\") and \"breast\" not in data]\n",
|
||||
"dfs = []\n",
|
||||
"for bd in benchmark_dataset:\n",
|
||||
"\n",
|
||||
" pass_metrics = []\n",
|
||||
" print(bd)\n",
|
||||
" df1 = pd.read_csv(os.path.join(benchmark_save_path1, bd,\n",
|
||||
" \"Macro_\"+bd+\"_output_revamped.csv\"))\n",
|
||||
" df2 = pd.read_csv(os.path.join(benchmark_save_path2, bd,\n",
|
||||
" \"Macro_\"+bd+\"_output_revamped.csv\"))\n",
|
||||
" df3 = pd.read_csv(os.path.join(benchmark_save_path, bd,\n",
|
||||
" \"Macro_\"+bd+\"_output_revamped.csv\"))\n",
|
||||
" df2[\"TableManipulation\"] = \"OriginalData\"\n",
|
||||
" df1[\"TableManipulation\"] = \"OriginalData\"\n",
|
||||
" df2[\"precision_per_cell_correctness_top1\"] = df2[\"precision_per_cell_correctness_top1\"]*0.01\n",
|
||||
" df2[\"recall_per_cell_correctness_top1\"] = df2[\"recall_per_cell_correctness_top1\"]*0.01\n",
|
||||
" df3[\"precision_per_cell_correctness_top1\"] = df3[\"precision_per_cell_correctness_top1\"]*0.01\n",
|
||||
" df3[\"recall_per_cell_correctness_top1\"] = df3[\"recall_per_cell_correctness_top1\"]*0.01\n",
|
||||
" df = pd.concat([df1, df2, df3], ignore_index=True)\n",
|
||||
" df[\"Dataset-name\"] = bd\n",
|
||||
" df[\"F1Score_per_cell_correctness_top1\"] = df.apply(lambda x: np.round(2*(x.recall_per_cell_correctness_top1*x.precision_per_cell_correctness_top1)/(\n",
|
||||
" x.recall_per_cell_correctness_top1+x.precision_per_cell_correctness_top1)) if (x.recall_per_cell_correctness_top1*x.precision_per_cell_correctness_top1) != 0 else 0, axis=1)\n",
|
||||
" dfs.append(df)\n",
|
||||
"all_data = pd.concat(dfs, ignore_index=True)\n",
|
||||
"\n",
|
||||
"attributes = {c: list(df[c].unique()) for c in df.columns if c in [\n",
|
||||
" \"temperature\", \"tableFormat\", \"table_ops\", \"TestCase\"]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### RQ3: "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Average percell, @pass 1 formats- noise- vs test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_cases = ['TableColumnReorderTests',\n",
|
||||
" 'TableReconstructionTests1',\n",
|
||||
" 'TableTransposeTests']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_data_test_vs_format_on_all_noise_ops = all_data[(\n",
|
||||
" all_data[\"temperature\"] == 0.0)] \n",
|
||||
"print(all_data_test_vs_format_on_all_noise_ops.shape)\n",
|
||||
"test_vs_format_on_all_noise_ops = all_data_test_vs_format_on_all_noise_ops.pivot_table(\n",
|
||||
" index=[\"tableFormat\", \"TableManipulation\"],\n",
|
||||
" columns='TestCase',\n",
|
||||
" values=[\"F1Score_per_cell_correctness_top1\", 'recall_per_cell_correctness_top1',\n",
|
||||
" \"precision_per_cell_correctness_top1\"], \n",
|
||||
" aggfunc=[\"mean\"] # Custom aggregation function to append values to a list\n",
|
||||
")\n",
|
||||
"test_vs_format_on_all_noise_ops = test_vs_format_on_all_noise_ops*100\n",
|
||||
"test_vs_format_on_all_noise_ops = np.round(\n",
|
||||
" test_vs_format_on_all_noise_ops, decimals=2)\n",
|
||||
"print(test_vs_format_on_all_noise_ops.columns)\n",
|
||||
"noises = [\n",
|
||||
" 'OriginalData',\n",
|
||||
" 'ShuffleRows',\n",
|
||||
" 'ShuffleColumns',\n",
|
||||
" 'ShuffleColumnNames',\n",
|
||||
" 'SequentialColumnNames',\n",
|
||||
" 'ArbitraryColumnNames',\n",
|
||||
" 'TransposeTable',\n",
|
||||
" 'ColumnCluster',\n",
|
||||
" 'SerializeTable']\n",
|
||||
"table_formats = all_data[\"tableFormat\"].value_counts().index\n",
|
||||
"indi = [(formatType, noise)\n",
|
||||
" for formatType in table_formats for noise in noises]\n",
|
||||
"cols = [(\"mean\", f\"{metric}_per_cell_correctness_top1\", T)\n",
|
||||
" for T in test_cases for metric in [\"precision\", \"recall\", \"F1Score\"]]\n",
|
||||
"test_vs_format_on_all_noise_ops = test_vs_format_on_all_noise_ops[cols]\n",
|
||||
"test_vs_format_on_all_noise_ops = test_vs_format_on_all_noise_ops.reindex(indi)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops.tail(30)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### diff with p_val "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops_list = all_data_test_vs_format_on_all_noise_ops.pivot_table(\n",
|
||||
" index=[\"tableFormat\", \"TableManipulation\"],\n",
|
||||
" columns='TestCase',\n",
|
||||
" values=[\"F1Score_per_cell_correctness_top1\", 'recall_per_cell_correctness_top1',\n",
|
||||
" \"precision_per_cell_correctness_top1\"], \n",
|
||||
" # Custom aggregation function to append values to a list\n",
|
||||
" aggfunc=lambda x: list(x)\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"cols_agg = [(f\"{metric}_per_cell_correctness_top1\", T)\n",
|
||||
" for T in test_cases for metric in [\"precision\", \"recall\", \"F1Score\"]]\n",
|
||||
"\n",
|
||||
"test_vs_format_on_all_noise_ops_list = test_vs_format_on_all_noise_ops_list[cols_agg]\n",
|
||||
"test_vs_format_on_all_noise_ops_list = test_vs_format_on_all_noise_ops_list.reindex(\n",
|
||||
" indi)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops_list"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import scipy\n",
|
||||
"test_vs_format_on_all_noise_ops_diff_with_pval = test_vs_format_on_all_noise_ops.copy(\n",
|
||||
" deep=True)\n",
|
||||
"test_vs_format_on_all_noise_ops_pval = test_vs_format_on_all_noise_ops.copy(\n",
|
||||
" deep=True)\n",
|
||||
"for i in test_vs_format_on_all_noise_ops_diff_with_pval.index:\n",
|
||||
" for j in test_vs_format_on_all_noise_ops_diff_with_pval.columns:\n",
|
||||
" ix = (i[0], \"OriginalData\")\n",
|
||||
" jj = (j[1], j[2])\n",
|
||||
" original_values = test_vs_format_on_all_noise_ops_list.loc[ix, jj]\n",
|
||||
" noise_induced_values = test_vs_format_on_all_noise_ops_list.loc[i, jj]\n",
|
||||
"\n",
|
||||
" if i[1] != \"OriginalData\":\n",
|
||||
" min_val = min(len(original_values), len(noise_induced_values))\n",
|
||||
"\n",
|
||||
" p_value = scipy.stats.ttest_rel(\n",
|
||||
" original_values[:min_val], noise_induced_values[:min_val])\n",
|
||||
"\n",
|
||||
" benoffi_corrected_p_val_threshold = 0.01/8\n",
|
||||
" subtract_from = test_vs_format_on_all_noise_ops.loc[ix, j]\n",
|
||||
" difference = test_vs_format_on_all_noise_ops_diff_with_pval.loc[i,\n",
|
||||
" j]-subtract_from\n",
|
||||
" if \"-\" in str(difference):\n",
|
||||
" str_diff = \"{:.2f}\".format(difference)\n",
|
||||
" else:\n",
|
||||
" str_diff = \"+\"+\"{:.2f}\".format(difference)\n",
|
||||
" if p_value.pvalue < benoffi_corrected_p_val_threshold:\n",
|
||||
"\n",
|
||||
" str_diff += \"**\"\n",
|
||||
" test_vs_format_on_all_noise_ops_diff_with_pval.loc[i, j] = str_diff\n",
|
||||
" test_vs_format_on_all_noise_ops_pval.loc[i, j] = p_value.pvalue"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops_diff_with_pval"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### only diff Macro"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops_diff = test_vs_format_on_all_noise_ops.copy(\n",
|
||||
" deep=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i in test_vs_format_on_all_noise_ops_diff.index:\n",
|
||||
" for j in test_vs_format_on_all_noise_ops_diff.columns:\n",
|
||||
" ix = (i[0], \"OriginalData\")\n",
|
||||
" if i[1] != \"OriginalData\":\n",
|
||||
" subtract_from = test_vs_format_on_all_noise_ops.loc[ix, j]\n",
|
||||
" difference = subtract_from - \\\n",
|
||||
" test_vs_format_on_all_noise_ops_diff.loc[i, j]\n",
|
||||
" if \"-\" in str(difference):\n",
|
||||
" str_diff = \"{:.2f}\".format(difference)\n",
|
||||
" else:\n",
|
||||
" str_diff = \"+\"+\"{:.2f}\".format(difference)\n",
|
||||
"\n",
|
||||
" test_vs_format_on_all_noise_ops_diff.loc[i, j] = str_diff"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops_diff"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "env",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,169 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%cd ../code_"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"gather": {
|
||||
"logged": 1694699855951
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tableTestingMicroScript import *\n",
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"import ast\n",
|
||||
"from utils import num_tokens_from_string"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from prompts import EXAMPLES, EXAMPLE_Dictionary\n",
|
||||
"import pandas as pd\n",
|
||||
"from utils import num_tokens_from_string\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def gather_Examples_Prompt(tab_format, test_case):\n",
|
||||
"\n",
|
||||
" TF, TC = tab_format.__class__.__name__, test_case\n",
|
||||
" Examples = EXAMPLES.replace(\"[Data_format_example1]\", EXAMPLE_Dictionary[f\"TF_EX1_{TF}\"])\\\n",
|
||||
" .replace(\"[QA1]\", EXAMPLE_Dictionary[f\"Ex1_QA1_{TC}\"])\\\n",
|
||||
" .replace(\"[Data_format_example2]\", EXAMPLE_Dictionary[f\"TF_EX1_{TF}\"])\\\n",
|
||||
" .replace(\"[QA2]\", EXAMPLE_Dictionary[f\"Ex1_QA2_{TC}\"])\n",
|
||||
" return Examples\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_max_no_of_rows_feedable(df, FormatFunction, Micro=True):\n",
|
||||
"\n",
|
||||
" per_test = []\n",
|
||||
" test_case = [\"NavigationTests\", \"ColumnLookupTests\",\n",
|
||||
" \"RowLookupTests\", \"DataTypeLookupTests\"]\n",
|
||||
"\n",
|
||||
" for tt in test_case:\n",
|
||||
" exp = gather_Examples_Prompt(FormatFunction, tt)\n",
|
||||
"\n",
|
||||
" for i in range(min(df.shape[0], 100), 2, -1):\n",
|
||||
" if Micro:\n",
|
||||
" total_token = 30 + num_tokens_from_string(exp)+11+num_tokens_from_string(\n",
|
||||
" str(FormatFunction.formatting(df.head(i))))+100\n",
|
||||
" else:\n",
|
||||
" total_token = 100 + num_tokens_from_string(exp)+11+num_tokens_from_string(\n",
|
||||
" str(FormatFunction.formatting(df.head(i))))*2.5\n",
|
||||
"\n",
|
||||
" if total_token < 4081:\n",
|
||||
"\n",
|
||||
" per_test.append(i)\n",
|
||||
" break\n",
|
||||
" return (min(per_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"gather": {
|
||||
"logged": 1694736239538
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"benchmark_data_path = \"../BenchmarkDatasetClean\"\n",
|
||||
"benchmark_dataset = [f for f in os.listdir(\n",
|
||||
" benchmark_data_path) if f.endswith(\".csv\")]\n",
|
||||
"benchmark_save_path = \"../BenchmarkResults_Micro_tests/\"\n",
|
||||
"if not os.path.exists(benchmark_save_path):\n",
|
||||
" os.makedirs(benchmark_save_path)\n",
|
||||
"open_api_key = os.env(\"OPENAI_API_KEY\")\n",
|
||||
"for bd in benchmark_dataset:\n",
|
||||
" print(bd)\n",
|
||||
" df = pd.read_csv(os.path.join(benchmark_data_path, bd))\n",
|
||||
" save_path = os.path.join(benchmark_save_path, bd)\n",
|
||||
" if not os.path.exists(save_path):\n",
|
||||
" os.makedirs(save_path)\n",
|
||||
"\n",
|
||||
" table_formats = [HTMLFormat(), HTMLNoSpaceFormat(), MarkdownFormat(), JsonFormat(\n",
|
||||
" ), DataMatrixFormat(), DFloaderFormat(), TabSeparatedFormat(), CommaSeparatedFormat()],\n",
|
||||
" max_rows = min([get_max_no_of_rows_feedable(df, format)\n",
|
||||
" for format in table_formats])\n",
|
||||
"\n",
|
||||
" df = df.head(max_rows)\n",
|
||||
" print(df.shape)\n",
|
||||
" suite = TableExperimentSuite(LLMTableLearner(),\n",
|
||||
" [HTMLFormat(), HTMLNoSpaceFormat(), MarkdownFormat(), JsonFormat(), DataMatrixFormat(\n",
|
||||
" ), DFloaderFormat(), TabSeparatedFormat(), CommaSeparatedFormat()],\n",
|
||||
" [ArbitraryColumnNames(),\n",
|
||||
" ShuffleRows(),\n",
|
||||
" TransposeTable(),\n",
|
||||
" ColumnCluster(),\n",
|
||||
" SerializeTable(),\n",
|
||||
" ShuffleColumns(),\n",
|
||||
" ShuffleColumnNames(),\n",
|
||||
" OriginalData(),\n",
|
||||
" SequentialColumnNames()\n",
|
||||
" ],\n",
|
||||
" [NavigationTests(), ColumnLookupTests(),\n",
|
||||
" RowLookupTests(), DataTypeLookupTests()],\n",
|
||||
" save_path, open_api_key)\n",
|
||||
" output = suite.run_experiment(df, per_table_op=10, per_test_gen=10)\n",
|
||||
" col_names = [\"temperature\", \"tableFormat\", \"TableManipulation\", \"TestCase\", \"TestQuestion\", \"TestExpectedAnswer\", \"TestCaseType\",\n",
|
||||
" \"pass_1\", \"pass_3\", \"pass_5\", \"pass_10\", \"pass_15\", \"TestCaseTuple\", \"Answers_top_15\", \"Answer_top_1\", \"Result\", \"Error\"]\n",
|
||||
" output_df = pd.DataFrame(output, columns=col_names)\n",
|
||||
" output_df.to_csv(os.path.join(\n",
|
||||
" save_path, f\"Micro_{bd}_output.csv\"), index=None)\n",
|
||||
" try:\n",
|
||||
" output_df.to_pickle(os.path.join(\n",
|
||||
" save_path, f\"Micro_{bd}_output.pkl\"), index=None)\n",
|
||||
" except:\n",
|
||||
" pass"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernel_info": {
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "env",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
},
|
||||
"microsoft": {
|
||||
"ms_spell_check": {
|
||||
"ms_spell_check_language": "en"
|
||||
}
|
||||
},
|
||||
"nteract": {
|
||||
"version": "nteract-front-end@1.0.0"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,192 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%cd ../code_"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"gather": {
|
||||
"logged": 1693566160494
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from tableTesting import *\n",
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"import ast\n",
|
||||
"from itertools import combinations\n",
|
||||
"from typing import Any\n",
|
||||
"import json\n",
|
||||
"from collections import Counter"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def metric_pass_k(expected_answer, answer: Any, k: int) -> float:\n",
|
||||
" if not isinstance(answer, list):\n",
|
||||
" raise ValueError(\"Answer should be a list for pass@k metric.\")\n",
|
||||
"\n",
|
||||
" # Convert the test.expect to a list if it's not already\n",
|
||||
" expected_values = expected_answer if isinstance(\n",
|
||||
" expected_answer, list) else [expected_answer]\n",
|
||||
" expected_values = list(map(str, expected_values))\n",
|
||||
" boolean_answers = [True if answer[i]\n",
|
||||
" in expected_values else False for i in range(len(answer))]\n",
|
||||
" combinations_k = list(combinations(boolean_answers, k))\n",
|
||||
" passed_at_k = 0\n",
|
||||
" # Calculate the pass@k metric\n",
|
||||
" for comb in combinations_k:\n",
|
||||
" if any(comb):\n",
|
||||
" passed_at_k += 1\n",
|
||||
" pass_at_k_percentage = (passed_at_k / len(combinations_k))*100\n",
|
||||
"\n",
|
||||
" return pass_at_k_percentage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def bool_results(expected_answer, answer: Any) -> float:\n",
|
||||
" if not isinstance(answer, list):\n",
|
||||
" raise ValueError(\"Answer should be a list for pass@k metric.\")\n",
|
||||
"\n",
|
||||
" # Convert the test.expect to a list if it's not already\n",
|
||||
" expected_values = expected_answer if isinstance(\n",
|
||||
" expected_answer, list) else [expected_answer]\n",
|
||||
" expected_values = list(map(str, expected_values))\n",
|
||||
" if answer[0] in expected_values:\n",
|
||||
" return True\n",
|
||||
" return False"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def check(self, test, answer: Any) -> bool:\n",
|
||||
" if test.TestType in [\"ColumnLookupTests\", \"RowLookupTests\"]:\n",
|
||||
" if isinstance(test.expect, list):\n",
|
||||
" expected_values = test.expect if isinstance(\n",
|
||||
" test.expect, list) else [test.expect]\n",
|
||||
" expected_values = list(map(str, expected_values))\n",
|
||||
" matches = set(expected_values).intersection(set([answer]))\n",
|
||||
" else:\n",
|
||||
" print(\"Error: The expected Answer 'List' should be a list not string\")\n",
|
||||
" return len(matches) > 0\n",
|
||||
" return str(test.expect) == answer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"benchmark_data_path = \"../resources/BenchmarkDatasetClean/\"\n",
|
||||
"benchmark_dataset = os.listdir(benchmark_data_path)\n",
|
||||
"benchmark_save_path = \"../resources/BenchmarkResults31_8_2023/\"\n",
|
||||
"save_path_plot = \"../resources/BenchmarkResults31_8_2023_plots/\"\n",
|
||||
"if not os.path.exists(save_path_plot):\n",
|
||||
" os.makedirs(save_path_plot)\n",
|
||||
"sp = \"../BenchmarkFinalResults_31_8_2023/\"\n",
|
||||
"if not os.path.exists(sp):\n",
|
||||
" os.makedirs(sp)\n",
|
||||
"spa = \"../BenchmarkFinalArrangedResults_31_8_2023/\"\n",
|
||||
"if not os.path.exists(spa):\n",
|
||||
" os.makedirs(spa)\n",
|
||||
"benchmark_dataset = [\n",
|
||||
" data for data in benchmark_dataset if data.endswith(\".csv\")]\n",
|
||||
"for bd in benchmark_dataset:\n",
|
||||
" try:\n",
|
||||
" pass_metrics = []\n",
|
||||
" t_F_bools = []\n",
|
||||
" print(bd)\n",
|
||||
" df = pd.read_csv(os.path.join(\n",
|
||||
" benchmark_save_path, bd, bd+\"_output.csv\"))\n",
|
||||
" print(df.shape)\n",
|
||||
" dirs = os.listdir(os.path.join(\n",
|
||||
" benchmark_save_path, bd, \"cache_logger\"))\n",
|
||||
" dirs = [d for d in dirs if d.endswith(\".jsonl\")]\n",
|
||||
" print(dirs)\n",
|
||||
" with open(os.path.join(benchmark_save_path, bd, \"cache_logger\", dirs[-1]), \"r\") as file:\n",
|
||||
" for line in file:\n",
|
||||
" line = ast.literal_eval(line.strip())\n",
|
||||
" if line.startswith(\"\\\"\") and line.endswith(\"\\\"\"):\n",
|
||||
" line = line[1:-1]\n",
|
||||
" data_ = json.loads((line))\n",
|
||||
" expected_ans = data_[\"test-expected-answer\"]\n",
|
||||
" ans = data_[\"answer\"]\n",
|
||||
" try:\n",
|
||||
" pass_metrics.append(\n",
|
||||
" [metric_pass_k(expected_ans, ans, k) for k in [1, 3, 5, 10, 15]])\n",
|
||||
" t_F_bools.append(bool_results(expected_ans, ans))\n",
|
||||
" except Exception as err:\n",
|
||||
" print(f\"error {err} catched during metric calculation\")\n",
|
||||
" pass_metrics.append([None for k in [1, 3, 5, 10, 15]])\n",
|
||||
" t_F_bools.append(False)\n",
|
||||
" pass\n",
|
||||
" print(len(pass_metrics))\n",
|
||||
" df[['pass_1', 'pass_3', 'pass_5', 'pass_10', 'pass_15']] = pass_metrics\n",
|
||||
" df[\"T/F\"] = t_F_bools\n",
|
||||
" print(df.columns)\n",
|
||||
" df.to_csv(os.path.join(benchmark_save_path, bd, bd +\n",
|
||||
" \"_output_revamped_metrics_with_bools.csv\"), index=None)\n",
|
||||
"\n",
|
||||
" except Exception as Err:\n",
|
||||
" print(\"ERROR------------->\", Err)\n",
|
||||
" pass"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernel_info": {
|
||||
"name": "python38-azureml"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "env",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
},
|
||||
"microsoft": {
|
||||
"ms_spell_check": {
|
||||
"ms_spell_check_language": "en"
|
||||
}
|
||||
},
|
||||
"nteract": {
|
||||
"version": "nteract-front-end@1.0.0"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,413 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"import numpy as np\n",
|
||||
"import statsmodels.api as sm\n",
|
||||
"import statsmodels.stats.contingency_tables as ct\n",
|
||||
"import datetime"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"benchmark_data_path = \"../resources/BenchmarkDataset\"\n",
|
||||
"benchmark_dataset = os.listdir(benchmark_data_path)\n",
|
||||
"# ---> final micro results with HTML formats\n",
|
||||
"benchmark_save_path2 = \"../resources/BenchmarkResults/BenchmarkResults_21_9_2023_new_save_format_HTMLFormat_redo/\"\n",
|
||||
"# ---> final micro results with 6 format\n",
|
||||
"benchmark_save_path1 = \"../resources/BenchmarkResults/BenchmarkResults_21_9_2023_new_save_format_redo/\"\n",
|
||||
"benchmark_dataset = [data for data in benchmark_dataset if data.endswith(\n",
|
||||
" \".csv\") and \"breast\" not in data]\n",
|
||||
"dfs = []\n",
|
||||
"for bd in benchmark_dataset:\n",
|
||||
"\n",
|
||||
" pass_metrics = []\n",
|
||||
" print(bd)\n",
|
||||
" df1 = pd.read_csv(os.path.join(benchmark_save_path1, bd,\n",
|
||||
" \"Micro_\"+bd+\"_output_revamped.csv\"))\n",
|
||||
" try:\n",
|
||||
" df2 = pd.read_csv(os.path.join(benchmark_save_path2,\n",
|
||||
" bd, \"Micro_\"+bd+\"_output_revamped.csv\"))\n",
|
||||
" df = pd.concat([df1, df2], ignore_index=True)\n",
|
||||
" except:\n",
|
||||
" df = df1\n",
|
||||
" print(df1.shape, df2.shape, df.shape)\n",
|
||||
" df[\"Dataset-name\"] = bd\n",
|
||||
" dfs.append(df)\n",
|
||||
"all_data = pd.concat(dfs, ignore_index=True)\n",
|
||||
"pivot_table = all_data.pivot_table(\n",
|
||||
" index=[\"TestCase\", 'TableManipulation', \"temperature\"],\n",
|
||||
" columns='tableFormat',\n",
|
||||
" values=['pass_1', 'pass_3', 'pass_5', 'pass_10', 'pass_15', 'Result'],\n",
|
||||
" # Custom aggregation function to append values to a list\n",
|
||||
" aggfunc=lambda x: list(x)\n",
|
||||
")\n",
|
||||
"attributes = {c: list(df[c].unique()) for c in df.columns if c in [\n",
|
||||
" \"temperature\", \"tableFormat\", \"TableManipulation\", \"TestCase\"]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# RQ1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Average pass @ 1, temp =0 Tests Vs formats across all datasets"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_data_test_vs_format_on_original_data = all_data[(\n",
|
||||
" all_data[\"TableManipulation\"] == \"OriginalData\") & (all_data[\"temperature\"] == 0.0)]\n",
|
||||
"test_vs_format_on_original_data = all_data_test_vs_format_on_original_data.pivot_table(\n",
|
||||
" index=[\"TestCase\"],\n",
|
||||
" columns='tableFormat',\n",
|
||||
" values=['pass_1'],\n",
|
||||
" aggfunc=[\"mean\"] # Custom aggregation function to append values to a list\n",
|
||||
")\n",
|
||||
"test_vs_format_on_original_data = np.round(\n",
|
||||
" test_vs_format_on_original_data, decimals=2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_original_data.T.to_clipboard()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## p-value RQ1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import scipy\n",
|
||||
"data_id = all_data[all_data[\"TableManipulation\"].isin([\"OriginalData\"]) & (\n",
|
||||
" all_data[\"temperature\"] == 0.0)] \n",
|
||||
"pivot_table_across_formats = data_id.pivot_table(\n",
|
||||
" index=[\"TestCase\"],\n",
|
||||
" columns='tableFormat',\n",
|
||||
" values=['pass_1'],\n",
|
||||
" # Custom aggregation function to append values to a list\n",
|
||||
" aggfunc=lambda x: list(x)\n",
|
||||
")\n",
|
||||
"p_val = {}\n",
|
||||
"for id in range(pivot_table_across_formats.shape[0]):\n",
|
||||
" print(id)\n",
|
||||
" print(f\"For Test: {pivot_table_across_formats.index[id]} pass@1 temp =0.1\")\n",
|
||||
" sorted_list = [i[-1] for i in test_vs_format_on_original_data.T.sort_values(\n",
|
||||
" by=[pivot_table_across_formats.index[id]], ascending=False).index]\n",
|
||||
" sub_vals = {}\n",
|
||||
" index_val = pivot_table_across_formats.index[id]\n",
|
||||
" for x in range(1, len(sorted_list)):\n",
|
||||
" vals1 = pivot_table_across_formats.loc[index_val,\n",
|
||||
" (\"pass_1\", sorted_list[0])]\n",
|
||||
" vals2 = pivot_table_across_formats.loc[index_val,\n",
|
||||
" (\"pass_1\", sorted_list[x])]\n",
|
||||
" min_val = min(len(vals1), len(vals2))\n",
|
||||
" print(min_val)\n",
|
||||
" p_value = scipy.stats.ttest_rel(vals1[:min_val], vals2[:min_val])\n",
|
||||
" sub_vals[f\"{sorted_list[0]}-{sorted_list[x]}\"] = {\"p-value\": p_value.pvalue,\n",
|
||||
" \"statistics\": p_value.statistic, \"df\": p_value.df, \"tests_count\": min_val}\n",
|
||||
" p_val[pivot_table_across_formats.index[id]] = sub_vals"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class CustomJSONEncoder(json.JSONEncoder):\n",
|
||||
" def default(self, obj):\n",
|
||||
" if isinstance(obj, np.integer):\n",
|
||||
" return int(obj)\n",
|
||||
" elif isinstance(obj, np.floating):\n",
|
||||
" return float(obj)\n",
|
||||
" elif isinstance(obj, np.ndarray):\n",
|
||||
" return obj.tolist()\n",
|
||||
" if isinstance(obj, pd.Series):\n",
|
||||
" return obj.tolist()\n",
|
||||
" elif isinstance(obj, datetime):\n",
|
||||
" # Handle datetime objects\n",
|
||||
" return obj.isoformat()\n",
|
||||
" elif isinstance(obj, set):\n",
|
||||
" # Handle sets\n",
|
||||
" return list(obj)\n",
|
||||
" # Add more custom conversions for other data types if needed\n",
|
||||
" return super().default(obj)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"with open(\"../resources/all_p_vals/p_vals_micro_tests_RQ1.json\", \"w\") as f:\n",
|
||||
" json.dump(p_val, f, indent=3, cls=CustomJSONEncoder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Average pass@1, temp 0.0 over different data averaged across tests for different formats"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_data_dataset_vs_format_on_original_data = all_data[(\n",
|
||||
" all_data[\"TableManipulation\"] == \"OriginalData\") & (all_data[\"temperature\"] == 0.0)]\n",
|
||||
"dataset_vs_format_on_original_data = all_data_dataset_vs_format_on_original_data.pivot_table(\n",
|
||||
" index=[\"Dataset-name\"],\n",
|
||||
" columns='tableFormat',\n",
|
||||
" values=['pass_1'],\n",
|
||||
" aggfunc=[\"mean\"] # Custom aggregation function to append values to a list\n",
|
||||
")\n",
|
||||
"test_vs_format_on_original_data = np.round(\n",
|
||||
" dataset_vs_format_on_original_data, decimals=2)\n",
|
||||
"test_vs_format_on_original_data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# RQ2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## All noise vs test across formats"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_data[\"tableFormat\"].value_counts().index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_data_test_vs_format_on_all_noise_ops = all_data[(\n",
|
||||
" all_data[\"temperature\"] == 0.0)]\n",
|
||||
"test_vs_format_on_all_noise_ops = all_data_test_vs_format_on_all_noise_ops.pivot_table(\n",
|
||||
" index=[\"tableFormat\", \"TableManipulation\"],\n",
|
||||
" columns='TestCase',\n",
|
||||
" values=['pass_1'],\n",
|
||||
" aggfunc=[\"mean\"] # Custom aggregation function to append values to a list\n",
|
||||
")\n",
|
||||
"test_vs_format_on_all_noise_ops_list = all_data_test_vs_format_on_all_noise_ops.pivot_table(\n",
|
||||
" index=[\"tableFormat\", \"TableManipulation\"],\n",
|
||||
" columns='TestCase',\n",
|
||||
" values=['pass_1'],\n",
|
||||
" # Custom aggregation function to append values to a list\n",
|
||||
" aggfunc=lambda x: list(x)\n",
|
||||
")\n",
|
||||
"test_vs_format_on_all_noise_ops = np.round(\n",
|
||||
" test_vs_format_on_all_noise_ops, decimals=2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"noises = [\n",
|
||||
" 'OriginalData',\n",
|
||||
" 'ShuffleRows',\n",
|
||||
" 'ShuffleColumns',\n",
|
||||
" 'ShuffleColumnNames',\n",
|
||||
" 'SequentialColumnNames',\n",
|
||||
" 'ArbitraryColumnNames',\n",
|
||||
" 'TransposeTable',\n",
|
||||
" 'ColumnCluster',\n",
|
||||
" 'SerializeTable']\n",
|
||||
"table_formats = all_data[\"tableFormat\"].value_counts().index\n",
|
||||
"indi = [(formatType, noise)\n",
|
||||
" for formatType in table_formats for noise in noises]\n",
|
||||
"cols = [(metric, \"pass_1\", test)\n",
|
||||
" for test in attributes[\"TestCase\"] for metric in [\"mean\"]]\n",
|
||||
"cols_agg = [(\"pass_1\", test) for test in attributes[\"TestCase\"]]\n",
|
||||
"test_vs_format_on_all_noise_ops = test_vs_format_on_all_noise_ops[cols]\n",
|
||||
"test_vs_format_on_all_noise_ops = test_vs_format_on_all_noise_ops.reindex(indi)\n",
|
||||
"test_vs_format_on_all_noise_ops_list = test_vs_format_on_all_noise_ops_list[cols_agg]\n",
|
||||
"test_vs_format_on_all_noise_ops_list = test_vs_format_on_all_noise_ops_list.reindex(\n",
|
||||
" indi)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops.head(10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## RQ2: Delta values from original with p-values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import scipy\n",
|
||||
"test_vs_format_on_all_noise_ops_diff_with_pval = test_vs_format_on_all_noise_ops.copy(\n",
|
||||
" deep=True)\n",
|
||||
"test_vs_format_on_all_noise_ops_pval = test_vs_format_on_all_noise_ops.copy(\n",
|
||||
" deep=True)\n",
|
||||
"for i in test_vs_format_on_all_noise_ops_diff_with_pval.index:\n",
|
||||
" for j in test_vs_format_on_all_noise_ops_diff_with_pval.columns:\n",
|
||||
" ix = (i[0], \"OriginalData\")\n",
|
||||
" jj = (j[1], j[2])\n",
|
||||
" original_values = test_vs_format_on_all_noise_ops_list.loc[ix, jj]\n",
|
||||
" noise_induced_values = test_vs_format_on_all_noise_ops_list.loc[i, jj]\n",
|
||||
"\n",
|
||||
" if i[1] != \"OriginalData\":\n",
|
||||
" min_val = min(len(original_values), len(noise_induced_values))\n",
|
||||
" p_value = scipy.stats.ttest_rel(\n",
|
||||
" original_values[:min_val], noise_induced_values[:min_val])\n",
|
||||
" benoffi_corrected_p_val_threshold = 0.01/8\n",
|
||||
" subtract_from = test_vs_format_on_all_noise_ops.loc[ix, j]\n",
|
||||
" difference = test_vs_format_on_all_noise_ops_diff_with_pval.loc[i,\n",
|
||||
" j]-subtract_from\n",
|
||||
" if \"-\" in str(difference):\n",
|
||||
" str_diff = \"{:.2f}\".format(difference)\n",
|
||||
" else:\n",
|
||||
" str_diff = \"+\"+\"{:.2f}\".format(difference)\n",
|
||||
" if p_value.pvalue < benoffi_corrected_p_val_threshold:\n",
|
||||
" str_diff += \"**\"\n",
|
||||
" test_vs_format_on_all_noise_ops_diff_with_pval.loc[i, j] = str_diff\n",
|
||||
" test_vs_format_on_all_noise_ops_pval.loc[i, j] = p_value.pvalue"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops_diff_with_pval.head(30)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### only_diff"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops_diff = test_vs_format_on_all_noise_ops.copy(\n",
|
||||
" deep=True)\n",
|
||||
"for i in test_vs_format_on_all_noise_ops_diff.index:\n",
|
||||
" for j in test_vs_format_on_all_noise_ops_diff.columns:\n",
|
||||
" ix = (i[0], \"OriginalData\")\n",
|
||||
" if i[1] != \"OriginalData\":\n",
|
||||
" subtract_from = test_vs_format_on_all_noise_ops.loc[ix, j]\n",
|
||||
" difference = subtract_from - \\\n",
|
||||
" test_vs_format_on_all_noise_ops_diff.loc[i, j]\n",
|
||||
" if \"-\" in str(difference):\n",
|
||||
" str_diff = \"{:.2f}\".format(difference)\n",
|
||||
" else:\n",
|
||||
" str_diff = \"+\"+\"{:.2f}\".format(difference)\n",
|
||||
"\n",
|
||||
" test_vs_format_on_all_noise_ops_diff.loc[i, j] = str_diff"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_vs_format_on_all_noise_ops_diff"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "env",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,12 +0,0 @@
|
|||
pandas
|
||||
numpy
|
||||
scipy
|
||||
torch
|
||||
openai
|
||||
transformers
|
||||
jsonlines
|
||||
tabulate
|
||||
matplotlib
|
||||
scikit-learn
|
||||
plotly
|
||||
statsmodels
|
||||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -1,769 +0,0 @@
|
|||
X1,X2,X3,X4,X5,X6,X7,X8,Y1,Y2
|
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0.98,514.5,294.0,110.25,7.0,3,0.0,0,15.55,21.33
|
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0.98,514.5,294.0,110.25,7.0,4,0.0,0,15.55,21.33
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0.98,514.5,294.0,110.25,7.0,5,0.0,0,15.55,21.33
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0.86,588.0,294.0,147.0,7.0,5,0.0,0,18.31,27.87
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0.82,612.5,318.5,147.0,7.0,2,0.0,0,17.05,23.77
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0.82,612.5,318.5,147.0,7.0,3,0.0,0,17.41,21.46
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0.82,612.5,318.5,147.0,7.0,4,0.0,0,16.95,21.16
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0.76,661.5,416.5,122.5,7.0,5,0.0,0,23.93,29.4
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0.74,686.0,245.0,220.5,3.5,2,0.0,0,6.07,10.9
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0.74,686.0,245.0,220.5,3.5,3,0.0,0,6.05,11.19
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0.74,686.0,245.0,220.5,3.5,4,0.0,0,6.01,10.94
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0.74,686.0,245.0,220.5,3.5,5,0.0,0,6.04,11.17
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0.71,710.5,269.5,220.5,3.5,2,0.0,0,6.37,11.27
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0.71,710.5,269.5,220.5,3.5,3,0.0,0,6.4,11.72
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0.71,710.5,269.5,220.5,3.5,4,0.0,0,6.37,11.29
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0.71,710.5,269.5,220.5,3.5,5,0.0,0,6.4,11.67
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0.69,735.0,294.0,220.5,3.5,2,0.0,0,6.85,11.74
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0.69,735.0,294.0,220.5,3.5,4,0.0,0,6.77,11.73
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0.69,735.0,294.0,220.5,3.5,5,0.0,0,6.81,11.93
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0.66,759.5,318.5,220.5,3.5,2,0.0,0,7.18,12.4
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0.66,759.5,318.5,220.5,3.5,3,0.0,0,7.1,12.23
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0.66,759.5,318.5,220.5,3.5,4,0.0,0,7.1,12.4
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0.66,759.5,318.5,220.5,3.5,5,0.0,0,7.1,12.14
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0.64,784.0,343.0,220.5,3.5,2,0.0,0,10.85,16.78
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0.64,784.0,343.0,220.5,3.5,4,0.0,0,10.77,16.75
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0.62,808.5,367.5,220.5,3.5,2,0.0,0,8.6,12.07
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0.98,514.5,294.0,110.25,7.0,4,0.1,1,24.63,26.44
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0.9,563.5,318.5,122.5,7.0,2,0.1,1,29.03,32.92
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0.79,637.0,343.0,147.0,7.0,4,0.1,1,36.9,34.43
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|
||||
0.76,661.5,416.5,122.5,7.0,4,0.1,1,32.94,34.14
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0.82,612.5,318.5,147.0,7.0,5,0.1,5,23.86,27.69
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||||
0.98,514.5,294.0,110.25,7.0,4,0.4,3,32.23,33.86
|
||||
0.98,514.5,294.0,110.25,7.0,5,0.4,3,32.75,34.0
|
||||
0.9,563.5,318.5,122.5,7.0,2,0.4,3,34.24,37.26
|
||||
0.9,563.5,318.5,122.5,7.0,3,0.4,3,34.95,35.04
|
||||
0.9,563.5,318.5,122.5,7.0,4,0.4,3,35.05,33.82
|
||||
0.9,563.5,318.5,122.5,7.0,5,0.4,3,34.29,33.31
|
||||
0.86,588.0,294.0,147.0,7.0,2,0.4,3,31.28,35.22
|
||||
0.86,588.0,294.0,147.0,7.0,3,0.4,3,32.12,34.7
|
||||
0.86,588.0,294.0,147.0,7.0,4,0.4,3,32.05,30.11
|
||||
0.86,588.0,294.0,147.0,7.0,5,0.4,3,31.84,31.6
|
||||
0.82,612.5,318.5,147.0,7.0,2,0.4,3,28.67,32.43
|
||||
0.82,612.5,318.5,147.0,7.0,3,0.4,3,29.67,30.65
|
||||
0.82,612.5,318.5,147.0,7.0,4,0.4,3,29.47,29.77
|
||||
0.82,612.5,318.5,147.0,7.0,5,0.4,3,28.91,29.64
|
||||
0.79,637.0,343.0,147.0,7.0,2,0.4,3,41.26,46.44
|
||||
0.79,637.0,343.0,147.0,7.0,3,0.4,3,41.3,44.18
|
||||
0.79,637.0,343.0,147.0,7.0,4,0.4,3,42.49,38.81
|
||||
0.79,637.0,343.0,147.0,7.0,5,0.4,3,42.08,38.23
|
||||
0.76,661.5,416.5,122.5,7.0,2,0.4,3,39.32,38.17
|
||||
0.76,661.5,416.5,122.5,7.0,3,0.4,3,39.84,38.48
|
||||
0.76,661.5,416.5,122.5,7.0,4,0.4,3,38.89,39.66
|
||||
0.76,661.5,416.5,122.5,7.0,5,0.4,3,39.68,40.1
|
||||
0.74,686.0,245.0,220.5,3.5,2,0.4,3,13.97,16.08
|
||||
0.74,686.0,245.0,220.5,3.5,3,0.4,3,14.22,15.39
|
||||
0.74,686.0,245.0,220.5,3.5,4,0.4,3,14.1,16.57
|
||||
0.74,686.0,245.0,220.5,3.5,5,0.4,3,13.78,16.6
|
||||
0.71,710.5,269.5,220.5,3.5,2,0.4,3,14.07,16.11
|
||||
0.71,710.5,269.5,220.5,3.5,3,0.4,3,14.03,15.47
|
||||
0.71,710.5,269.5,220.5,3.5,4,0.4,3,13.94,16.7
|
||||
0.71,710.5,269.5,220.5,3.5,5,0.4,3,13.86,16.1
|
||||
0.69,735.0,294.0,220.5,3.5,2,0.4,3,14.32,16.35
|
||||
0.69,735.0,294.0,220.5,3.5,3,0.4,3,14.56,15.84
|
||||
0.69,735.0,294.0,220.5,3.5,4,0.4,3,14.33,16.99
|
||||
0.69,735.0,294.0,220.5,3.5,5,0.4,3,14.08,17.02
|
||||
0.66,759.5,318.5,220.5,3.5,2,0.4,3,15.16,17.04
|
||||
0.66,759.5,318.5,220.5,3.5,3,0.4,3,15.18,17.63
|
||||
0.66,759.5,318.5,220.5,3.5,4,0.4,3,14.72,18.1
|
||||
0.66,759.5,318.5,220.5,3.5,5,0.4,3,14.9,18.22
|
||||
0.64,784.0,343.0,220.5,3.5,2,0.4,3,18.48,20.78
|
||||
0.64,784.0,343.0,220.5,3.5,3,0.4,3,18.71,20.72
|
||||
0.64,784.0,343.0,220.5,3.5,4,0.4,3,18.48,21.54
|
||||
0.64,784.0,343.0,220.5,3.5,5,0.4,3,18.46,21.53
|
||||
0.62,808.5,367.5,220.5,3.5,2,0.4,3,16.47,16.9
|
||||
0.62,808.5,367.5,220.5,3.5,3,0.4,3,16.35,17.14
|
||||
0.62,808.5,367.5,220.5,3.5,4,0.4,3,16.55,16.56
|
||||
0.62,808.5,367.5,220.5,3.5,5,0.4,3,16.74,16.0
|
||||
0.98,514.5,294.0,110.25,7.0,2,0.4,4,32.85,32.95
|
||||
0.98,514.5,294.0,110.25,7.0,3,0.4,4,32.67,33.06
|
||||
0.98,514.5,294.0,110.25,7.0,4,0.4,4,32.21,33.95
|
||||
0.98,514.5,294.0,110.25,7.0,5,0.4,4,32.74,33.88
|
||||
0.9,563.5,318.5,122.5,7.0,2,0.4,4,36.45,33.98
|
||||
0.9,563.5,318.5,122.5,7.0,3,0.4,4,35.73,39.92
|
||||
0.9,563.5,318.5,122.5,7.0,4,0.4,4,35.4,39.22
|
||||
0.9,563.5,318.5,122.5,7.0,5,0.4,4,36.57,36.1
|
||||
0.86,588.0,294.0,147.0,7.0,2,0.4,4,32.38,31.53
|
||||
0.86,588.0,294.0,147.0,7.0,3,0.4,4,31.66,36.2
|
||||
0.86,588.0,294.0,147.0,7.0,4,0.4,4,32.15,36.21
|
||||
0.86,588.0,294.0,147.0,7.0,5,0.4,4,32.75,31.0
|
||||
0.82,612.5,318.5,147.0,7.0,2,0.4,4,28.93,28.2
|
||||
0.82,612.5,318.5,147.0,7.0,3,0.4,4,28.05,32.35
|
||||
0.82,612.5,318.5,147.0,7.0,4,0.4,4,28.64,31.14
|
||||
0.82,612.5,318.5,147.0,7.0,5,0.4,4,29.52,28.43
|
||||
0.79,637.0,343.0,147.0,7.0,2,0.4,4,42.77,38.33
|
||||
0.79,637.0,343.0,147.0,7.0,3,0.4,4,41.73,47.59
|
||||
0.79,637.0,343.0,147.0,7.0,4,0.4,4,41.32,46.23
|
||||
0.79,637.0,343.0,147.0,7.0,5,0.4,4,42.96,39.56
|
||||
0.76,661.5,416.5,122.5,7.0,2,0.4,4,40.68,40.36
|
||||
0.76,661.5,416.5,122.5,7.0,3,0.4,4,40.4,39.67
|
||||
0.76,661.5,416.5,122.5,7.0,4,0.4,4,40.6,39.85
|
||||
0.76,661.5,416.5,122.5,7.0,5,0.4,4,40.11,40.77
|
||||
0.74,686.0,245.0,220.5,3.5,2,0.4,4,14.37,16.61
|
||||
0.74,686.0,245.0,220.5,3.5,3,0.4,4,14.48,16.74
|
||||
0.74,686.0,245.0,220.5,3.5,4,0.4,4,14.32,16.9
|
||||
0.74,686.0,245.0,220.5,3.5,5,0.4,4,14.44,17.32
|
||||
0.71,710.5,269.5,220.5,3.5,2,0.4,4,14.6,16.85
|
||||
0.71,710.5,269.5,220.5,3.5,3,0.4,4,14.7,17.2
|
||||
0.71,710.5,269.5,220.5,3.5,4,0.4,4,14.47,17.23
|
||||
0.71,710.5,269.5,220.5,3.5,5,0.4,4,14.66,17.74
|
||||
0.69,735.0,294.0,220.5,3.5,2,0.4,4,14.54,16.81
|
||||
0.69,735.0,294.0,220.5,3.5,3,0.4,4,14.62,16.88
|
||||
0.69,735.0,294.0,220.5,3.5,4,0.4,4,14.53,16.9
|
||||
0.69,735.0,294.0,220.5,3.5,5,0.4,4,14.71,17.39
|
||||
0.66,759.5,318.5,220.5,3.5,2,0.4,4,15.34,17.86
|
||||
0.66,759.5,318.5,220.5,3.5,3,0.4,4,15.29,17.82
|
||||
0.66,759.5,318.5,220.5,3.5,4,0.4,4,15.09,18.36
|
||||
0.66,759.5,318.5,220.5,3.5,5,0.4,4,15.3,18.24
|
||||
0.64,784.0,343.0,220.5,3.5,2,0.4,4,19.06,21.68
|
||||
0.64,784.0,343.0,220.5,3.5,3,0.4,4,19.13,21.54
|
||||
0.64,784.0,343.0,220.5,3.5,4,0.4,4,19.0,22.25
|
||||
0.64,784.0,343.0,220.5,3.5,5,0.4,4,18.84,22.49
|
||||
0.62,808.5,367.5,220.5,3.5,2,0.4,4,16.44,17.1
|
||||
0.62,808.5,367.5,220.5,3.5,3,0.4,4,16.9,16.79
|
||||
0.62,808.5,367.5,220.5,3.5,4,0.4,4,16.94,16.58
|
||||
0.62,808.5,367.5,220.5,3.5,5,0.4,4,16.77,16.79
|
||||
0.98,514.5,294.0,110.25,7.0,2,0.4,5,32.84,32.88
|
||||
0.98,514.5,294.0,110.25,7.0,3,0.4,5,32.72,33.23
|
||||
0.98,514.5,294.0,110.25,7.0,4,0.4,5,32.21,33.76
|
||||
0.98,514.5,294.0,110.25,7.0,5,0.4,5,32.73,34.01
|
||||
0.9,563.5,318.5,122.5,7.0,2,0.4,5,35.67,33.94
|
||||
0.9,563.5,318.5,122.5,7.0,3,0.4,5,35.01,33.14
|
||||
0.9,563.5,318.5,122.5,7.0,4,0.4,5,34.72,38.79
|
||||
0.9,563.5,318.5,122.5,7.0,5,0.4,5,35.24,37.27
|
||||
0.86,588.0,294.0,147.0,7.0,2,0.4,5,32.31,29.69
|
||||
0.86,588.0,294.0,147.0,7.0,3,0.4,5,31.81,31.2
|
||||
0.86,588.0,294.0,147.0,7.0,4,0.4,5,31.12,36.26
|
||||
0.86,588.0,294.0,147.0,7.0,5,0.4,5,32.06,35.71
|
||||
0.82,612.5,318.5,147.0,7.0,2,0.4,5,30.0,29.93
|
||||
0.82,612.5,318.5,147.0,7.0,3,0.4,5,29.5,29.56
|
||||
0.82,612.5,318.5,147.0,7.0,4,0.4,5,29.06,33.84
|
||||
0.82,612.5,318.5,147.0,7.0,5,0.4,5,29.92,32.54
|
||||
0.79,637.0,343.0,147.0,7.0,2,0.4,5,42.11,38.56
|
||||
0.79,637.0,343.0,147.0,7.0,3,0.4,5,41.96,37.7
|
||||
0.79,637.0,343.0,147.0,7.0,4,0.4,5,41.09,47.01
|
||||
0.79,637.0,343.0,147.0,7.0,5,0.4,5,40.79,44.87
|
||||
0.76,661.5,416.5,122.5,7.0,2,0.4,5,38.82,39.37
|
||||
0.76,661.5,416.5,122.5,7.0,3,0.4,5,39.72,39.8
|
||||
0.76,661.5,416.5,122.5,7.0,4,0.4,5,39.31,37.79
|
||||
0.76,661.5,416.5,122.5,7.0,5,0.4,5,39.86,38.18
|
||||
0.74,686.0,245.0,220.5,3.5,2,0.4,5,14.41,16.69
|
||||
0.74,686.0,245.0,220.5,3.5,3,0.4,5,14.19,16.62
|
||||
0.74,686.0,245.0,220.5,3.5,4,0.4,5,14.17,16.94
|
||||
0.74,686.0,245.0,220.5,3.5,5,0.4,5,14.39,16.7
|
||||
0.71,710.5,269.5,220.5,3.5,2,0.4,5,12.43,15.59
|
||||
0.71,710.5,269.5,220.5,3.5,3,0.4,5,12.63,14.58
|
||||
0.71,710.5,269.5,220.5,3.5,4,0.4,5,12.76,15.33
|
||||
0.71,710.5,269.5,220.5,3.5,5,0.4,5,12.42,15.31
|
||||
0.69,735.0,294.0,220.5,3.5,2,0.4,5,14.12,16.63
|
||||
0.69,735.0,294.0,220.5,3.5,3,0.4,5,14.28,15.87
|
||||
0.69,735.0,294.0,220.5,3.5,4,0.4,5,14.37,16.54
|
||||
0.69,735.0,294.0,220.5,3.5,5,0.4,5,14.21,16.74
|
||||
0.66,759.5,318.5,220.5,3.5,2,0.4,5,14.96,17.64
|
||||
0.66,759.5,318.5,220.5,3.5,3,0.4,5,14.92,17.79
|
||||
0.66,759.5,318.5,220.5,3.5,4,0.4,5,14.92,17.55
|
||||
0.66,759.5,318.5,220.5,3.5,5,0.4,5,15.16,18.06
|
||||
0.64,784.0,343.0,220.5,3.5,2,0.4,5,17.69,20.82
|
||||
0.64,784.0,343.0,220.5,3.5,3,0.4,5,18.19,20.21
|
||||
0.64,784.0,343.0,220.5,3.5,4,0.4,5,18.16,20.71
|
||||
0.64,784.0,343.0,220.5,3.5,5,0.4,5,17.88,21.4
|
||||
0.62,808.5,367.5,220.5,3.5,2,0.4,5,16.54,16.88
|
||||
0.62,808.5,367.5,220.5,3.5,3,0.4,5,16.44,17.11
|
||||
0.62,808.5,367.5,220.5,3.5,4,0.4,5,16.48,16.61
|
||||
0.62,808.5,367.5,220.5,3.5,5,0.4,5,16.64,16.03
|
||||
|
|
|
@ -1,395 +0,0 @@
|
|||
CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV
|
||||
0.00632,18.0,2.31,0.0,0.5379999999999999,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24.0
|
||||
0.02731,0.0,7.07,0.0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
|
||||
0.02729,0.0,7.07,0.0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
|
||||
0.032369999999999996,0.0,2.18,0.0,0.45799999999999996,6.997999999999999,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
|
||||
0.02985,0.0,2.18,0.0,0.45799999999999996,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
|
||||
0.14455,12.5,7.87,0.0,0.524,6.172000000000001,96.1,5.9505,5,311,15.2,396.9,19.15,27.1
|
||||
0.21124,12.5,7.87,0.0,0.524,5.631,100.0,6.0821,5,311,15.2,386.63,29.93,16.5
|
||||
0.22489,12.5,7.87,0.0,0.524,6.377000000000001,94.3,6.3467,5,311,15.2,392.52,20.45,15.0
|
||||
0.11747,12.5,7.87,0.0,0.524,6.0089999999999995,82.9,6.2267,5,311,15.2,396.9,13.27,18.9
|
||||
0.09378,12.5,7.87,0.0,0.524,5.888999999999999,39.0,5.4509,5,311,15.2,390.5,15.71,21.7
|
||||
0.62976,0.0,8.14,0.0,0.5379999999999999,5.949,61.8,4.7075,4,307,21.0,396.9,8.26,20.4
|
||||
0.62739,0.0,8.14,0.0,0.5379999999999999,5.834,56.5,4.4986,4,307,21.0,395.62,8.47,19.9
|
||||
1.05393,0.0,8.14,0.0,0.5379999999999999,5.935,29.3,4.4986,4,307,21.0,386.85,6.58,23.1
|
||||
0.7842,0.0,8.14,0.0,0.5379999999999999,5.99,81.7,4.2579,4,307,21.0,386.75,14.67,17.5
|
||||
0.80271,0.0,8.14,0.0,0.5379999999999999,5.456,36.6,3.7965,4,307,21.0,288.99,11.69,20.2
|
||||
0.7258,0.0,8.14,0.0,0.5379999999999999,5.727,69.5,3.7965,4,307,21.0,390.95,11.28,18.2
|
||||
1.25179,0.0,8.14,0.0,0.5379999999999999,5.57,98.1,3.7979,4,307,21.0,376.57,21.02,13.6
|
||||
0.8520399999999999,0.0,8.14,0.0,0.5379999999999999,5.965,89.2,4.0123,4,307,21.0,392.53,13.83,19.6
|
||||
1.2324700000000002,0.0,8.14,0.0,0.5379999999999999,6.142,91.7,3.9769,4,307,21.0,396.9,18.72,15.2
|
||||
0.9884299999999999,0.0,8.14,0.0,0.5379999999999999,5.813,100.0,4.0952,4,307,21.0,394.54,19.88,14.5
|
||||
0.75026,0.0,8.14,0.0,0.5379999999999999,5.9239999999999995,94.1,4.3996,4,307,21.0,394.33,16.3,15.6
|
||||
0.84054,0.0,8.14,0.0,0.5379999999999999,5.599,85.7,4.4546,4,307,21.0,303.42,16.51,13.9
|
||||
0.67191,0.0,8.14,0.0,0.5379999999999999,5.813,90.3,4.6819999999999995,4,307,21.0,376.88,14.81,16.6
|
||||
0.9557700000000001,0.0,8.14,0.0,0.5379999999999999,6.047000000000001,88.8,4.4534,4,307,21.0,306.38,17.28,14.8
|
||||
0.77299,0.0,8.14,0.0,0.5379999999999999,6.495,94.4,4.4547,4,307,21.0,387.94,12.8,18.4
|
||||
1.00245,0.0,8.14,0.0,0.5379999999999999,6.6739999999999995,87.3,4.239,4,307,21.0,380.23,11.98,21.0
|
||||
1.13081,0.0,8.14,0.0,0.5379999999999999,5.712999999999999,94.1,4.2330000000000005,4,307,21.0,360.17,22.6,12.7
|
||||
1.3547200000000001,0.0,8.14,0.0,0.5379999999999999,6.072,100.0,4.175,4,307,21.0,376.73,13.04,14.5
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||||
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|
||||
3.6782199999999996,0.0,18.1,0.0,0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19,20.8
|
||||
4.22239,0.0,18.1,1.0,0.77,5.803,89.0,1.9047,24,666,20.2,353.04,14.64,16.8
|
||||
3.4742800000000003,0.0,18.1,1.0,0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29,21.9
|
||||
4.55587,0.0,18.1,0.0,0.718,3.5610000000000004,87.9,1.6132,24,666,20.2,354.7,7.12,27.5
|
||||
3.69695,0.0,18.1,0.0,0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14.0,21.9
|
||||
6.53876,0.0,18.1,1.0,0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96,50.0
|
||||
9.2323,0.0,18.1,0.0,0.631,6.216,100.0,1.1691,24,666,20.2,366.15,9.53,50.0
|
||||
8.26725,0.0,18.1,1.0,0.6679999999999999,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50.0
|
||||
11.1081,0.0,18.1,0.0,0.6679999999999999,4.906000000000001,100.0,1.1742,24,666,20.2,396.9,34.77,13.8
|
||||
18.4982,0.0,18.1,0.0,0.6679999999999999,4.138,100.0,1.137,24,666,20.2,396.9,37.97,13.8
|
||||
9.82349,0.0,18.1,0.0,0.6709999999999999,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3
|
||||
23.6482,0.0,18.1,0.0,0.6709999999999999,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1
|
||||
17.8667,0.0,18.1,0.0,0.6709999999999999,6.223,100.0,1.3861,24,666,20.2,393.74,21.78,10.2
|
||||
88.9762,0.0,18.1,0.0,0.6709999999999999,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4
|
||||
15.8744,0.0,18.1,0.0,0.6709999999999999,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9
|
||||
9.18702,0.0,18.1,0.0,0.7,5.5360000000000005,100.0,1.5804,24,666,20.2,396.9,23.6,11.3
|
||||
20.0849,0.0,18.1,0.0,0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8
|
||||
16.8118,0.0,18.1,0.0,0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2
|
||||
24.3938,0.0,18.1,0.0,0.7,4.652,100.0,1.4672,24,666,20.2,396.9,28.28,10.5
|
||||
22.5971,0.0,18.1,0.0,0.7,5.0,89.5,1.5184,24,666,20.2,396.9,31.99,7.4
|
||||
8.15174,0.0,18.1,0.0,0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5
|
||||
6.96215,0.0,18.1,0.0,0.7,5.712999999999999,97.0,1.9265,24,666,20.2,394.43,17.11,15.1
|
||||
5.29305,0.0,18.1,0.0,0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2
|
||||
11.5779,0.0,18.1,0.0,0.7,5.0360000000000005,97.0,1.77,24,666,20.2,396.9,25.68,9.7
|
||||
8.71675,0.0,18.1,0.0,0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1
|
||||
5.87205,0.0,18.1,0.0,0.693,6.405,96.0,1.6768,24,666,20.2,396.9,19.37,12.5
|
||||
7.67202,0.0,18.1,0.0,0.693,5.747000000000001,98.9,1.6334,24,666,20.2,393.1,19.92,8.5
|
||||
38.3518,0.0,18.1,0.0,0.693,5.452999999999999,100.0,1.4896,24,666,20.2,396.9,30.59,5.0
|
||||
9.91655,0.0,18.1,0.0,0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3
|
||||
25.0461,0.0,18.1,0.0,0.693,5.987,100.0,1.5888,24,666,20.2,396.9,26.77,5.6
|
||||
9.59571,0.0,18.1,0.0,0.693,6.404,100.0,1.639,24,666,20.2,376.11,20.31,12.1
|
||||
24.8017,0.0,18.1,0.0,0.693,5.349,96.0,1.7028,24,666,20.2,396.9,19.77,8.3
|
||||
41.5292,0.0,18.1,0.0,0.693,5.531000000000001,85.4,1.6074,24,666,20.2,329.46,27.38,8.5
|
||||
67.9208,0.0,18.1,0.0,0.693,5.683,100.0,1.4254,24,666,20.2,384.97,22.98,5.0
|
||||
7.40389,0.0,18.1,0.0,0.597,5.617000000000001,97.9,1.4547,24,666,20.2,314.64,26.4,17.2
|
||||
51.1358,0.0,18.1,0.0,0.597,5.757000000000001,100.0,1.413,24,666,20.2,2.6,10.11,15.0
|
||||
14.0507,0.0,18.1,0.0,0.597,6.657,100.0,1.5275,24,666,20.2,35.05,21.22,17.2
|
||||
18.811,0.0,18.1,0.0,0.597,4.628,100.0,1.5539,24,666,20.2,28.79,34.37,17.9
|
||||
28.6558,0.0,18.1,0.0,0.597,5.155,100.0,1.5894,24,666,20.2,210.97,20.08,16.3
|
||||
45.7461,0.0,18.1,0.0,0.693,4.519,100.0,1.6582,24,666,20.2,88.27,36.98,7.0
|
||||
18.0846,0.0,18.1,0.0,0.679,6.434,100.0,1.8347,24,666,20.2,27.25,29.05,7.2
|
||||
10.8342,0.0,18.1,0.0,0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79,7.5
|
||||
25.9406,0.0,18.1,0.0,0.679,5.303999999999999,89.1,1.6475,24,666,20.2,127.36,26.64,10.4
|
||||
73.5341,0.0,18.1,0.0,0.679,5.957000000000001,100.0,1.8026,24,666,20.2,16.45,20.62,8.8
|
||||
11.8123,0.0,18.1,0.0,0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4
|
||||
11.0874,0.0,18.1,0.0,0.718,6.4110000000000005,100.0,1.8589,24,666,20.2,318.75,15.02,16.7
|
||||
7.022589999999999,0.0,18.1,0.0,0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2
|
||||
12.0482,0.0,18.1,0.0,0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8
|
||||
8.792119999999999,0.0,18.1,0.0,0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7
|
||||
15.8603,0.0,18.1,0.0,0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3
|
||||
7.36711,0.0,18.1,0.0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11.0
|
||||
10.0623,0.0,18.1,0.0,0.584,6.832999999999999,94.3,2.0882,24,666,20.2,81.33,19.69,14.1
|
||||
6.44405,0.0,18.1,0.0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1
|
||||
5.5810699999999995,0.0,18.1,0.0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3
|
||||
13.9134,0.0,18.1,0.0,0.713,6.207999999999999,95.0,2.2222,24,666,20.2,100.63,15.17,11.7
|
||||
11.1604,0.0,18.1,0.0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4
|
||||
14.4208,0.0,18.1,0.0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6
|
||||
15.1772,0.0,18.1,0.0,0.74,6.152,100.0,1.9142,24,666,20.2,9.32,26.45,8.7
|
||||
13.6781,0.0,18.1,0.0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4
|
||||
9.39063,0.0,18.1,0.0,0.74,5.627000000000001,93.9,1.8172,24,666,20.2,396.9,22.88,12.8
|
||||
9.96654,0.0,18.1,0.0,0.74,6.485,100.0,1.9784,24,666,20.2,386.73,18.85,15.4
|
||||
12.8023,0.0,18.1,0.0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8
|
||||
10.6718,0.0,18.1,0.0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8
|
||||
6.288069999999999,0.0,18.1,0.0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9
|
||||
9.92485,0.0,18.1,0.0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6
|
||||
9.329089999999999,0.0,18.1,0.0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1
|
||||
7.52601,0.0,18.1,0.0,0.713,6.417000000000001,98.3,2.185,24,666,20.2,304.21,19.31,13.0
|
||||
5.09017,0.0,18.1,0.0,0.713,6.297000000000001,91.8,2.3682,24,666,20.2,385.09,17.27,16.1
|
||||
9.513630000000001,0.0,18.1,0.0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9
|
||||
4.75237,0.0,18.1,0.0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1
|
||||
4.668830000000001,0.0,18.1,0.0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7
|
||||
8.20058,0.0,18.1,0.0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5
|
||||
6.80117,0.0,18.1,0.0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20.0
|
||||
3.69311,0.0,18.1,0.0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7
|
||||
6.65492,0.0,18.1,0.0,0.713,6.317,83.0,2.7344,24,666,20.2,396.9,13.99,19.5
|
||||
5.82115,0.0,18.1,0.0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2
|
||||
7.83932,0.0,18.1,0.0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4
|
||||
4.422280000000001,0.0,18.1,0.0,0.584,6.002999999999999,94.5,2.5403,24,666,20.2,331.29,21.32,19.1
|
||||
15.5757,0.0,18.1,0.0,0.58,5.926,71.0,2.9084,24,666,20.2,368.74,18.13,19.1
|
||||
13.0751,0.0,18.1,0.0,0.58,5.712999999999999,56.7,2.8237,24,666,20.2,396.9,14.76,20.1
|
||||
4.34879,0.0,18.1,0.0,0.58,6.167000000000001,84.0,3.0334,24,666,20.2,396.9,16.29,19.9
|
||||
4.03841,0.0,18.1,0.0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6
|
||||
3.56868,0.0,18.1,0.0,0.58,6.437,75.0,2.8965,24,666,20.2,393.37,14.36,23.2
|
||||
8.05579,0.0,18.1,0.0,0.584,5.4270000000000005,95.4,2.4298,24,666,20.2,352.58,18.14,13.8
|
||||
6.39312,0.0,18.1,0.0,0.584,6.162000000000001,97.4,2.206,24,666,20.2,302.76,24.1,13.3
|
||||
4.87141,0.0,18.1,0.0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7
|
||||
15.0234,0.0,18.1,0.0,0.614,5.303999999999999,97.3,2.1007,24,666,20.2,349.48,24.91,12.0
|
||||
10.232999999999999,0.0,18.1,0.0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6
|
||||
5.8240099999999995,0.0,18.1,0.0,0.532,6.242000000000001,64.7,3.4242,24,666,20.2,396.9,10.74,23.0
|
||||
5.7081800000000005,0.0,18.1,0.0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
|
||||
2.81838,0.0,18.1,0.0,0.532,5.7620000000000005,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
|
||||
2.37857,0.0,18.1,0.0,0.583,5.871,41.9,3.7239999999999998,24,666,20.2,370.73,13.34,20.6
|
||||
3.6736699999999995,0.0,18.1,0.0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
|
||||
5.69175,0.0,18.1,0.0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
|
||||
4.8356699999999995,0.0,18.1,0.0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
|
||||
0.15086,0.0,27.74,0.0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
|
||||
0.18337,0.0,27.74,0.0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7.0
|
||||
0.20745999999999998,0.0,27.74,0.0,0.609,5.093,98.0,1.8226,4,711,20.1,318.43,29.68,8.1
|
||||
0.10574000000000001,0.0,27.74,0.0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
|
||||
0.11132,0.0,27.74,0.0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
|
||||
0.17331,0.0,9.69,0.0,0.585,5.707000000000001,54.0,2.3817,6,391,19.2,396.9,12.01,21.8
|
||||
0.27957,0.0,9.69,0.0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
|
||||
0.17899,0.0,9.69,0.0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
|
||||
0.2896,0.0,9.69,0.0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
|
||||
0.26838,0.0,9.69,0.0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
|
||||
0.23911999999999997,0.0,9.69,0.0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
|
||||
0.17783,0.0,9.69,0.0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
|
||||
0.22438000000000002,0.0,9.69,0.0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
|
||||
0.04527,0.0,11.93,0.0,0.573,6.12,76.7,2.2875,1,273,21.0,396.9,9.08,20.6
|
||||
0.06076,0.0,11.93,0.0,0.573,6.976,91.0,2.1675,1,273,21.0,396.9,5.64,23.9
|
||||
0.10959,0.0,11.93,0.0,0.573,6.794,89.3,2.3889,1,273,21.0,393.45,6.48,22.0
|
||||
|
|
|
@ -1,151 +0,0 @@
|
|||
sepal_length,sepal_width,petal_length,petal_width,species
|
||||
5.1,3.5,1.4,0.2,Iris-setosa
|
||||
4.9,3.0,1.4,0.2,Iris-setosa
|
||||
4.7,3.2,1.3,0.2,Iris-setosa
|
||||
4.6,3.1,1.5,0.2,Iris-setosa
|
||||
5.0,3.6,1.4,0.2,Iris-setosa
|
||||
5.4,3.9,1.7,0.4,Iris-setosa
|
||||
4.6,3.4,1.4,0.3,Iris-setosa
|
||||
5.0,3.4,1.5,0.2,Iris-setosa
|
||||
4.4,2.9,1.4,0.2,Iris-setosa
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
|
||||
5.4,3.7,1.5,0.2,Iris-setosa
|
||||
4.8,3.4,1.6,0.2,Iris-setosa
|
||||
4.8,3.0,1.4,0.1,Iris-setosa
|
||||
4.3,3.0,1.1,0.1,Iris-setosa
|
||||
5.8,4.0,1.2,0.2,Iris-setosa
|
||||
5.7,4.4,1.5,0.4,Iris-setosa
|
||||
5.4,3.9,1.3,0.4,Iris-setosa
|
||||
5.1,3.5,1.4,0.3,Iris-setosa
|
||||
5.7,3.8,1.7,0.3,Iris-setosa
|
||||
5.1,3.8,1.5,0.3,Iris-setosa
|
||||
5.4,3.4,1.7,0.2,Iris-setosa
|
||||
5.1,3.7,1.5,0.4,Iris-setosa
|
||||
4.6,3.6,1.0,0.2,Iris-setosa
|
||||
5.1,3.3,1.7,0.5,Iris-setosa
|
||||
4.8,3.4,1.9,0.2,Iris-setosa
|
||||
5.0,3.0,1.6,0.2,Iris-setosa
|
||||
5.0,3.4,1.6,0.4,Iris-setosa
|
||||
5.2,3.5,1.5,0.2,Iris-setosa
|
||||
5.2,3.4,1.4,0.2,Iris-setosa
|
||||
4.7,3.2,1.6,0.2,Iris-setosa
|
||||
4.8,3.1,1.6,0.2,Iris-setosa
|
||||
5.4,3.4,1.5,0.4,Iris-setosa
|
||||
5.2,4.1,1.5,0.1,Iris-setosa
|
||||
5.5,4.2,1.4,0.2,Iris-setosa
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
|
||||
5.0,3.2,1.2,0.2,Iris-setosa
|
||||
5.5,3.5,1.3,0.2,Iris-setosa
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
|
||||
4.4,3.0,1.3,0.2,Iris-setosa
|
||||
5.1,3.4,1.5,0.2,Iris-setosa
|
||||
5.0,3.5,1.3,0.3,Iris-setosa
|
||||
4.5,2.3,1.3,0.3,Iris-setosa
|
||||
4.4,3.2,1.3,0.2,Iris-setosa
|
||||
5.0,3.5,1.6,0.6,Iris-setosa
|
||||
5.1,3.8,1.9,0.4,Iris-setosa
|
||||
4.8,3.0,1.4,0.3,Iris-setosa
|
||||
5.1,3.8,1.6,0.2,Iris-setosa
|
||||
4.6,3.2,1.4,0.2,Iris-setosa
|
||||
5.3,3.7,1.5,0.2,Iris-setosa
|
||||
5.0,3.3,1.4,0.2,Iris-setosa
|
||||
7.0,3.2,4.7,1.4,Iris-versicolor
|
||||
6.4,3.2,4.5,1.5,Iris-versicolor
|
||||
6.9,3.1,4.9,1.5,Iris-versicolor
|
||||
5.5,2.3,4.0,1.3,Iris-versicolor
|
||||
6.5,2.8,4.6,1.5,Iris-versicolor
|
||||
5.7,2.8,4.5,1.3,Iris-versicolor
|
||||
6.3,3.3,4.7,1.6,Iris-versicolor
|
||||
4.9,2.4,3.3,1.0,Iris-versicolor
|
||||
6.6,2.9,4.6,1.3,Iris-versicolor
|
||||
5.2,2.7,3.9,1.4,Iris-versicolor
|
||||
5.0,2.0,3.5,1.0,Iris-versicolor
|
||||
5.9,3.0,4.2,1.5,Iris-versicolor
|
||||
6.0,2.2,4.0,1.0,Iris-versicolor
|
||||
6.1,2.9,4.7,1.4,Iris-versicolor
|
||||
5.6,2.9,3.6,1.3,Iris-versicolor
|
||||
6.7,3.1,4.4,1.4,Iris-versicolor
|
||||
5.6,3.0,4.5,1.5,Iris-versicolor
|
||||
5.8,2.7,4.1,1.0,Iris-versicolor
|
||||
6.2,2.2,4.5,1.5,Iris-versicolor
|
||||
5.6,2.5,3.9,1.1,Iris-versicolor
|
||||
5.9,3.2,4.8,1.8,Iris-versicolor
|
||||
6.1,2.8,4.0,1.3,Iris-versicolor
|
||||
6.3,2.5,4.9,1.5,Iris-versicolor
|
||||
6.1,2.8,4.7,1.2,Iris-versicolor
|
||||
6.4,2.9,4.3,1.3,Iris-versicolor
|
||||
6.6,3.0,4.4,1.4,Iris-versicolor
|
||||
6.8,2.8,4.8,1.4,Iris-versicolor
|
||||
6.7,3.0,5.0,1.7,Iris-versicolor
|
||||
6.0,2.9,4.5,1.5,Iris-versicolor
|
||||
5.7,2.6,3.5,1.0,Iris-versicolor
|
||||
5.5,2.4,3.8,1.1,Iris-versicolor
|
||||
5.5,2.4,3.7,1.0,Iris-versicolor
|
||||
5.8,2.7,3.9,1.2,Iris-versicolor
|
||||
6.0,2.7,5.1,1.6,Iris-versicolor
|
||||
5.4,3.0,4.5,1.5,Iris-versicolor
|
||||
6.0,3.4,4.5,1.6,Iris-versicolor
|
||||
6.7,3.1,4.7,1.5,Iris-versicolor
|
||||
6.3,2.3,4.4,1.3,Iris-versicolor
|
||||
5.6,3.0,4.1,1.3,Iris-versicolor
|
||||
5.5,2.5,4.0,1.3,Iris-versicolor
|
||||
5.5,2.6,4.4,1.2,Iris-versicolor
|
||||
6.1,3.0,4.6,1.4,Iris-versicolor
|
||||
5.8,2.6,4.0,1.2,Iris-versicolor
|
||||
5.0,2.3,3.3,1.0,Iris-versicolor
|
||||
5.6,2.7,4.2,1.3,Iris-versicolor
|
||||
5.7,3.0,4.2,1.2,Iris-versicolor
|
||||
5.7,2.9,4.2,1.3,Iris-versicolor
|
||||
6.2,2.9,4.3,1.3,Iris-versicolor
|
||||
5.1,2.5,3.0,1.1,Iris-versicolor
|
||||
5.7,2.8,4.1,1.3,Iris-versicolor
|
||||
6.3,3.3,6.0,2.5,Iris-virginica
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
7.1,3.0,5.9,2.1,Iris-virginica
|
||||
6.3,2.9,5.6,1.8,Iris-virginica
|
||||
6.5,3.0,5.8,2.2,Iris-virginica
|
||||
7.6,3.0,6.6,2.1,Iris-virginica
|
||||
4.9,2.5,4.5,1.7,Iris-virginica
|
||||
7.3,2.9,6.3,1.8,Iris-virginica
|
||||
6.7,2.5,5.8,1.8,Iris-virginica
|
||||
7.2,3.6,6.1,2.5,Iris-virginica
|
||||
6.5,3.2,5.1,2.0,Iris-virginica
|
||||
6.4,2.7,5.3,1.9,Iris-virginica
|
||||
6.8,3.0,5.5,2.1,Iris-virginica
|
||||
5.7,2.5,5.0,2.0,Iris-virginica
|
||||
5.8,2.8,5.1,2.4,Iris-virginica
|
||||
6.4,3.2,5.3,2.3,Iris-virginica
|
||||
6.5,3.0,5.5,1.8,Iris-virginica
|
||||
7.7,3.8,6.7,2.2,Iris-virginica
|
||||
7.7,2.6,6.9,2.3,Iris-virginica
|
||||
6.0,2.2,5.0,1.5,Iris-virginica
|
||||
6.9,3.2,5.7,2.3,Iris-virginica
|
||||
5.6,2.8,4.9,2.0,Iris-virginica
|
||||
7.7,2.8,6.7,2.0,Iris-virginica
|
||||
6.3,2.7,4.9,1.8,Iris-virginica
|
||||
6.7,3.3,5.7,2.1,Iris-virginica
|
||||
7.2,3.2,6.0,1.8,Iris-virginica
|
||||
6.2,2.8,4.8,1.8,Iris-virginica
|
||||
6.1,3.0,4.9,1.8,Iris-virginica
|
||||
6.4,2.8,5.6,2.1,Iris-virginica
|
||||
7.2,3.0,5.8,1.6,Iris-virginica
|
||||
7.4,2.8,6.1,1.9,Iris-virginica
|
||||
7.9,3.8,6.4,2.0,Iris-virginica
|
||||
6.4,2.8,5.6,2.2,Iris-virginica
|
||||
6.3,2.8,5.1,1.5,Iris-virginica
|
||||
6.1,2.6,5.6,1.4,Iris-virginica
|
||||
7.7,3.0,6.1,2.3,Iris-virginica
|
||||
6.3,3.4,5.6,2.4,Iris-virginica
|
||||
6.4,3.1,5.5,1.8,Iris-virginica
|
||||
6.0,3.0,4.8,1.8,Iris-virginica
|
||||
6.9,3.1,5.4,2.1,Iris-virginica
|
||||
6.7,3.1,5.6,2.4,Iris-virginica
|
||||
6.9,3.1,5.1,2.3,Iris-virginica
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
6.8,3.2,5.9,2.3,Iris-virginica
|
||||
6.7,3.3,5.7,2.5,Iris-virginica
|
||||
6.7,3.0,5.2,2.3,Iris-virginica
|
||||
6.3,2.5,5.0,1.9,Iris-virginica
|
||||
6.5,3.0,5.2,2.0,Iris-virginica
|
||||
6.2,3.4,5.4,2.3,Iris-virginica
|
||||
5.9,3.0,5.1,1.8,Iris-virginica
|
||||
|
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -1,769 +0,0 @@
|
|||
Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
|
||||
6,148,72,35,0,33.6,0.627,50,1
|
||||
1,85,66,29,0,26.6,0.35100000000000003,31,0
|
||||
8,183,64,0,0,23.3,0.672,32,1
|
||||
1,89,66,23,94,28.1,0.16699999999999998,21,0
|
||||
0,137,40,35,168,43.1,2.2880000000000003,33,1
|
||||
5,116,74,0,0,25.6,0.201,30,0
|
||||
3,78,50,32,88,31.0,0.248,26,1
|
||||
10,115,0,0,0,35.3,0.134,29,0
|
||||
2,197,70,45,543,30.5,0.158,53,1
|
||||
8,125,96,0,0,0.0,0.23199999999999998,54,1
|
||||
4,110,92,0,0,37.6,0.191,30,0
|
||||
10,168,74,0,0,38.0,0.537,34,1
|
||||
10,139,80,0,0,27.1,1.4409999999999998,57,0
|
||||
1,189,60,23,846,30.1,0.39799999999999996,59,1
|
||||
5,166,72,19,175,25.8,0.5870000000000001,51,1
|
||||
7,100,0,0,0,30.0,0.484,32,1
|
||||
0,118,84,47,230,45.8,0.551,31,1
|
||||
7,107,74,0,0,29.6,0.254,31,1
|
||||
1,103,30,38,83,43.3,0.183,33,0
|
||||
1,115,70,30,96,34.6,0.529,32,1
|
||||
3,126,88,41,235,39.3,0.7040000000000001,27,0
|
||||
8,99,84,0,0,35.4,0.38799999999999996,50,0
|
||||
7,196,90,0,0,39.8,0.451,41,1
|
||||
9,119,80,35,0,29.0,0.263,29,1
|
||||
11,143,94,33,146,36.6,0.254,51,1
|
||||
10,125,70,26,115,31.1,0.205,41,1
|
||||
7,147,76,0,0,39.4,0.257,43,1
|
||||
1,97,66,15,140,23.2,0.48700000000000004,22,0
|
||||
13,145,82,19,110,22.2,0.245,57,0
|
||||
5,117,92,0,0,34.1,0.337,38,0
|
||||
5,109,75,26,0,36.0,0.546,60,0
|
||||
3,158,76,36,245,31.6,0.851,28,1
|
||||
3,88,58,11,54,24.8,0.267,22,0
|
||||
6,92,92,0,0,19.9,0.188,28,0
|
||||
10,122,78,31,0,27.6,0.512,45,0
|
||||
4,103,60,33,192,24.0,0.966,33,0
|
||||
11,138,76,0,0,33.2,0.42,35,0
|
||||
9,102,76,37,0,32.9,0.665,46,1
|
||||
2,90,68,42,0,38.2,0.503,27,1
|
||||
4,111,72,47,207,37.1,1.39,56,1
|
||||
3,180,64,25,70,34.0,0.271,26,0
|
||||
7,133,84,0,0,40.2,0.696,37,0
|
||||
7,106,92,18,0,22.7,0.235,48,0
|
||||
9,171,110,24,240,45.4,0.721,54,1
|
||||
7,159,64,0,0,27.4,0.294,40,0
|
||||
0,180,66,39,0,42.0,1.893,25,1
|
||||
1,146,56,0,0,29.7,0.564,29,0
|
||||
2,71,70,27,0,28.0,0.586,22,0
|
||||
7,103,66,32,0,39.1,0.344,31,1
|
||||
7,105,0,0,0,0.0,0.305,24,0
|
||||
1,103,80,11,82,19.4,0.491,22,0
|
||||
1,101,50,15,36,24.2,0.526,26,0
|
||||
5,88,66,21,23,24.4,0.342,30,0
|
||||
8,176,90,34,300,33.7,0.467,58,1
|
||||
7,150,66,42,342,34.7,0.718,42,0
|
||||
1,73,50,10,0,23.0,0.248,21,0
|
||||
7,187,68,39,304,37.7,0.254,41,1
|
||||
0,100,88,60,110,46.8,0.9620000000000001,31,0
|
||||
0,146,82,0,0,40.5,1.781,44,0
|
||||
0,105,64,41,142,41.5,0.17300000000000001,22,0
|
||||
2,84,0,0,0,0.0,0.304,21,0
|
||||
8,133,72,0,0,32.9,0.27,39,1
|
||||
5,44,62,0,0,25.0,0.5870000000000001,36,0
|
||||
2,141,58,34,128,25.4,0.6990000000000001,24,0
|
||||
7,114,66,0,0,32.8,0.258,42,1
|
||||
5,99,74,27,0,29.0,0.203,32,0
|
||||
0,109,88,30,0,32.5,0.855,38,1
|
||||
2,109,92,0,0,42.7,0.845,54,0
|
||||
1,95,66,13,38,19.6,0.33399999999999996,25,0
|
||||
4,146,85,27,100,28.9,0.18899999999999997,27,0
|
||||
2,100,66,20,90,32.9,0.867,28,1
|
||||
5,139,64,35,140,28.6,0.41100000000000003,26,0
|
||||
13,126,90,0,0,43.4,0.583,42,1
|
||||
4,129,86,20,270,35.1,0.231,23,0
|
||||
1,79,75,30,0,32.0,0.396,22,0
|
||||
1,0,48,20,0,24.7,0.14,22,0
|
||||
7,62,78,0,0,32.6,0.391,41,0
|
||||
5,95,72,33,0,37.7,0.37,27,0
|
||||
0,131,0,0,0,43.2,0.27,26,1
|
||||
2,112,66,22,0,25.0,0.307,24,0
|
||||
3,113,44,13,0,22.4,0.14,22,0
|
||||
2,74,0,0,0,0.0,0.102,22,0
|
||||
7,83,78,26,71,29.3,0.767,36,0
|
||||
0,101,65,28,0,24.6,0.237,22,0
|
||||
5,137,108,0,0,48.8,0.22699999999999998,37,1
|
||||
2,110,74,29,125,32.4,0.698,27,0
|
||||
13,106,72,54,0,36.6,0.17800000000000002,45,0
|
||||
2,100,68,25,71,38.5,0.324,26,0
|
||||
15,136,70,32,110,37.1,0.153,43,1
|
||||
1,107,68,19,0,26.5,0.165,24,0
|
||||
1,80,55,0,0,19.1,0.258,21,0
|
||||
4,123,80,15,176,32.0,0.44299999999999995,34,0
|
||||
7,81,78,40,48,46.7,0.261,42,0
|
||||
4,134,72,0,0,23.8,0.27699999999999997,60,1
|
||||
2,142,82,18,64,24.7,0.7609999999999999,21,0
|
||||
6,144,72,27,228,33.9,0.255,40,0
|
||||
2,92,62,28,0,31.6,0.13,24,0
|
||||
1,71,48,18,76,20.4,0.32299999999999995,22,0
|
||||
6,93,50,30,64,28.7,0.35600000000000004,23,0
|
||||
1,122,90,51,220,49.7,0.325,31,1
|
||||
1,163,72,0,0,39.0,1.222,33,1
|
||||
1,151,60,0,0,26.1,0.179,22,0
|
||||
0,125,96,0,0,22.5,0.262,21,0
|
||||
1,81,72,18,40,26.6,0.28300000000000003,24,0
|
||||
2,85,65,0,0,39.6,0.93,27,0
|
||||
1,126,56,29,152,28.7,0.8009999999999999,21,0
|
||||
1,96,122,0,0,22.4,0.207,27,0
|
||||
4,144,58,28,140,29.5,0.287,37,0
|
||||
3,83,58,31,18,34.3,0.336,25,0
|
||||
0,95,85,25,36,37.4,0.247,24,1
|
||||
3,171,72,33,135,33.3,0.19899999999999998,24,1
|
||||
8,155,62,26,495,34.0,0.5429999999999999,46,1
|
||||
1,89,76,34,37,31.2,0.192,23,0
|
||||
4,76,62,0,0,34.0,0.391,25,0
|
||||
7,160,54,32,175,30.5,0.588,39,1
|
||||
4,146,92,0,0,31.2,0.539,61,1
|
||||
5,124,74,0,0,34.0,0.22,38,1
|
||||
5,78,48,0,0,33.7,0.654,25,0
|
||||
4,97,60,23,0,28.2,0.44299999999999995,22,0
|
||||
4,99,76,15,51,23.2,0.223,21,0
|
||||
0,162,76,56,100,53.2,0.759,25,1
|
||||
6,111,64,39,0,34.2,0.26,24,0
|
||||
2,107,74,30,100,33.6,0.40399999999999997,23,0
|
||||
5,132,80,0,0,26.8,0.18600000000000003,69,0
|
||||
0,113,76,0,0,33.3,0.278,23,1
|
||||
1,88,30,42,99,55.0,0.496,26,1
|
||||
3,120,70,30,135,42.9,0.452,30,0
|
||||
1,118,58,36,94,33.3,0.261,23,0
|
||||
1,117,88,24,145,34.5,0.40299999999999997,40,1
|
||||
0,105,84,0,0,27.9,0.741,62,1
|
||||
4,173,70,14,168,29.7,0.361,33,1
|
||||
9,122,56,0,0,33.3,1.114,33,1
|
||||
3,170,64,37,225,34.5,0.35600000000000004,30,1
|
||||
8,84,74,31,0,38.3,0.457,39,0
|
||||
2,96,68,13,49,21.1,0.647,26,0
|
||||
2,125,60,20,140,33.8,0.08800000000000001,31,0
|
||||
0,100,70,26,50,30.8,0.597,21,0
|
||||
0,93,60,25,92,28.7,0.532,22,0
|
||||
0,129,80,0,0,31.2,0.703,29,0
|
||||
5,105,72,29,325,36.9,0.159,28,0
|
||||
3,128,78,0,0,21.1,0.268,55,0
|
||||
5,106,82,30,0,39.5,0.28600000000000003,38,0
|
||||
2,108,52,26,63,32.5,0.318,22,0
|
||||
10,108,66,0,0,32.4,0.272,42,1
|
||||
4,154,62,31,284,32.8,0.237,23,0
|
||||
0,102,75,23,0,0.0,0.5720000000000001,21,0
|
||||
9,57,80,37,0,32.8,0.096,41,0
|
||||
2,106,64,35,119,30.5,1.4,34,0
|
||||
5,147,78,0,0,33.7,0.218,65,0
|
||||
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11,135,0,0,0,52.3,0.578,40,1
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1,96,64,27,87,33.2,0.289,21,0
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2,81,60,22,0,27.7,0.29,25,0
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12,151,70,40,271,41.8,0.742,38,1
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9,164,84,21,0,30.8,0.831,32,1
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1,91,64,24,0,29.2,0.192,21,0
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4,91,70,32,88,33.1,0.446,22,0
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6,119,50,22,176,27.1,1.318,33,1
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9,184,85,15,0,30.0,1.213,49,1
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10,101,86,37,0,45.6,1.136,38,1
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2,108,62,32,56,25.2,0.128,21,0
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10,129,76,28,122,35.9,0.28,39,0
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7,133,88,15,155,32.4,0.262,37,0
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7,161,86,0,0,30.4,0.165,47,1
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2,108,80,0,0,27.0,0.259,52,1
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7,136,74,26,135,26.0,0.647,51,0
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4,96,56,17,49,20.8,0.34,26,0
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1,128,48,45,194,40.5,0.613,24,1
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6,151,62,31,120,35.5,0.6920000000000001,28,0
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2,146,70,38,360,28.0,0.337,29,1
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0,126,84,29,215,30.7,0.52,24,0
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14,100,78,25,184,36.6,0.41200000000000003,46,1
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8,112,72,0,0,23.6,0.84,58,0
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2,144,58,33,135,31.6,0.42200000000000004,25,1
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5,77,82,41,42,35.8,0.156,35,0
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5,115,98,0,0,52.9,0.209,28,1
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3,150,76,0,0,21.0,0.207,37,0
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2,120,76,37,105,39.7,0.215,29,0
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10,161,68,23,132,25.5,0.326,47,1
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0,137,68,14,148,24.8,0.14300000000000002,21,0
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0,128,68,19,180,30.5,1.391,25,1
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2,124,68,28,205,32.9,0.875,30,1
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6,80,66,30,0,26.2,0.313,41,0
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0,106,70,37,148,39.4,0.605,22,0
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2,155,74,17,96,26.6,0.433,27,1
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3,113,50,10,85,29.5,0.626,25,0
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7,109,80,31,0,35.9,1.127,43,1
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2,112,68,22,94,34.1,0.315,26,0
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3,99,80,11,64,19.3,0.284,30,0
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3,182,74,0,0,30.5,0.345,29,1
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3,115,66,39,140,38.1,0.15,28,0
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6,194,78,0,0,23.5,0.129,59,1
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4,129,60,12,231,27.5,0.527,31,0
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3,112,74,30,0,31.6,0.19699999999999998,25,1
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0,124,70,20,0,27.4,0.254,36,1
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13,152,90,33,29,26.8,0.731,43,1
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2,112,75,32,0,35.7,0.14800000000000002,21,0
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1,157,72,21,168,25.6,0.12300000000000001,24,0
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1,122,64,32,156,35.1,0.6920000000000001,30,1
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10,179,70,0,0,35.1,0.2,37,0
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2,102,86,36,120,45.5,0.127,23,1
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6,105,70,32,68,30.8,0.122,37,0
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8,118,72,19,0,23.1,1.476,46,0
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2,87,58,16,52,32.7,0.166,25,0
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1,180,0,0,0,43.3,0.282,41,1
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12,106,80,0,0,23.6,0.13699999999999998,44,0
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1,95,60,18,58,23.9,0.26,22,0
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0,165,76,43,255,47.9,0.259,26,0
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5,115,76,0,0,31.2,0.34299999999999997,44,1
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1,95,74,21,73,25.9,0.6729999999999999,36,0
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1,0,68,35,0,32.0,0.389,22,0
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5,122,86,0,0,34.7,0.29,33,0
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8,95,72,0,0,36.8,0.485,57,0
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8,126,88,36,108,38.5,0.349,49,0
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1,139,46,19,83,28.7,0.654,22,0
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4,92,80,0,0,42.2,0.237,29,0
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4,137,84,0,0,31.2,0.252,30,0
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3,61,82,28,0,34.4,0.243,46,0
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1,90,62,12,43,27.2,0.58,24,0
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3,90,78,0,0,42.7,0.5589999999999999,21,0
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9,165,88,0,0,30.4,0.302,49,1
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1,125,50,40,167,33.3,0.9620000000000001,28,1
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13,129,0,30,0,39.9,0.569,44,1
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12,88,74,40,54,35.3,0.37799999999999995,48,0
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1,196,76,36,249,36.5,0.875,29,1
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5,189,64,33,325,31.2,0.583,29,1
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5,158,70,0,0,29.8,0.207,63,0
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5,103,108,37,0,39.2,0.305,65,0
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4,147,74,25,293,34.9,0.385,30,0
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5,99,54,28,83,34.0,0.499,30,0
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0,101,64,17,0,21.0,0.252,21,0
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3,81,86,16,66,27.5,0.306,22,0
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3,173,82,48,465,38.4,2.137,25,1
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0,84,64,22,66,35.8,0.545,21,0
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12,140,82,43,325,39.2,0.528,58,1
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0,98,82,15,84,25.2,0.299,22,0
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1,87,60,37,75,37.2,0.509,22,0
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4,156,75,0,0,48.3,0.23800000000000002,32,1
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0,93,100,39,72,43.4,1.021,35,0
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1,109,60,8,182,25.4,0.9470000000000001,21,0
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1,90,62,18,59,25.1,1.268,25,0
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1,125,70,24,110,24.3,0.221,25,0
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1,119,54,13,50,22.3,0.205,24,0
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5,116,74,29,0,32.3,0.66,35,1
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8,105,100,36,0,43.3,0.239,45,1
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5,144,82,26,285,32.0,0.452,58,1
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3,100,68,23,81,31.6,0.9490000000000001,28,0
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1,100,66,29,196,32.0,0.444,42,0
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5,166,76,0,0,45.7,0.34,27,1
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1,131,64,14,415,23.7,0.389,21,0
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4,116,72,12,87,22.1,0.46299999999999997,37,0
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4,158,78,0,0,32.9,0.8029999999999999,31,1
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2,127,58,24,275,27.7,1.6,25,0
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3,96,56,34,115,24.7,0.9440000000000001,39,0
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0,131,66,40,0,34.3,0.196,22,1
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3,82,70,0,0,21.1,0.389,25,0
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3,193,70,31,0,34.9,0.24100000000000002,25,1
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4,95,64,0,0,32.0,0.161,31,1
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6,137,61,0,0,24.2,0.151,55,0
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5,136,84,41,88,35.0,0.28600000000000003,35,1
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9,72,78,25,0,31.6,0.28,38,0
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5,168,64,0,0,32.9,0.135,41,1
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2,123,48,32,165,42.1,0.52,26,0
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4,115,72,0,0,28.9,0.376,46,1
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0,101,62,0,0,21.9,0.336,25,0
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8,197,74,0,0,25.9,1.1909999999999998,39,1
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1,172,68,49,579,42.4,0.7020000000000001,28,1
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6,102,90,39,0,35.7,0.674,28,0
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1,112,72,30,176,34.4,0.528,25,0
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1,143,84,23,310,42.4,1.0759999999999998,22,0
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1,143,74,22,61,26.2,0.256,21,0
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||||
2,56,56,28,45,24.2,0.332,22,0
|
||||
0,162,76,36,0,49.6,0.364,26,1
|
||||
0,95,64,39,105,44.6,0.366,22,0
|
||||
4,125,80,0,0,32.3,0.536,27,1
|
||||
5,136,82,0,0,0.0,0.64,69,0
|
||||
2,129,74,26,205,33.2,0.591,25,0
|
||||
3,130,64,0,0,23.1,0.314,22,0
|
||||
1,107,50,19,0,28.3,0.18100000000000002,29,0
|
||||
1,140,74,26,180,24.1,0.828,23,0
|
||||
1,144,82,46,180,46.1,0.335,46,1
|
||||
8,107,80,0,0,24.6,0.856,34,0
|
||||
13,158,114,0,0,42.3,0.257,44,1
|
||||
2,121,70,32,95,39.1,0.8859999999999999,23,0
|
||||
7,129,68,49,125,38.5,0.439,43,1
|
||||
2,90,60,0,0,23.5,0.191,25,0
|
||||
7,142,90,24,480,30.4,0.128,43,1
|
||||
3,169,74,19,125,29.9,0.268,31,1
|
||||
0,99,0,0,0,25.0,0.253,22,0
|
||||
4,127,88,11,155,34.5,0.598,28,0
|
||||
4,118,70,0,0,44.5,0.904,26,0
|
||||
2,122,76,27,200,35.9,0.483,26,0
|
||||
6,125,78,31,0,27.6,0.565,49,1
|
||||
1,168,88,29,0,35.0,0.905,52,1
|
||||
2,129,0,0,0,38.5,0.304,41,0
|
||||
4,110,76,20,100,28.4,0.11800000000000001,27,0
|
||||
6,80,80,36,0,39.8,0.177,28,0
|
||||
10,115,0,0,0,0.0,0.261,30,1
|
||||
2,127,46,21,335,34.4,0.17600000000000002,22,0
|
||||
9,164,78,0,0,32.8,0.14800000000000002,45,1
|
||||
2,93,64,32,160,38.0,0.674,23,1
|
||||
3,158,64,13,387,31.2,0.295,24,0
|
||||
5,126,78,27,22,29.6,0.439,40,0
|
||||
10,129,62,36,0,41.2,0.441,38,1
|
||||
0,134,58,20,291,26.4,0.35200000000000004,21,0
|
||||
3,102,74,0,0,29.5,0.121,32,0
|
||||
7,187,50,33,392,33.9,0.826,34,1
|
||||
3,173,78,39,185,33.8,0.97,31,1
|
||||
10,94,72,18,0,23.1,0.595,56,0
|
||||
1,108,60,46,178,35.5,0.415,24,0
|
||||
5,97,76,27,0,35.6,0.37799999999999995,52,1
|
||||
4,83,86,19,0,29.3,0.317,34,0
|
||||
1,114,66,36,200,38.1,0.289,21,0
|
||||
1,149,68,29,127,29.3,0.349,42,1
|
||||
5,117,86,30,105,39.1,0.251,42,0
|
||||
1,111,94,0,0,32.8,0.265,45,0
|
||||
4,112,78,40,0,39.4,0.23600000000000002,38,0
|
||||
1,116,78,29,180,36.1,0.496,25,0
|
||||
0,141,84,26,0,32.4,0.433,22,0
|
||||
2,175,88,0,0,22.9,0.326,22,0
|
||||
2,92,52,0,0,30.1,0.141,22,0
|
||||
3,130,78,23,79,28.4,0.32299999999999995,34,1
|
||||
8,120,86,0,0,28.4,0.259,22,1
|
||||
2,174,88,37,120,44.5,0.6459999999999999,24,1
|
||||
2,106,56,27,165,29.0,0.426,22,0
|
||||
2,105,75,0,0,23.3,0.56,53,0
|
||||
4,95,60,32,0,35.4,0.284,28,0
|
||||
0,126,86,27,120,27.4,0.515,21,0
|
||||
8,65,72,23,0,32.0,0.6,42,0
|
||||
2,99,60,17,160,36.6,0.45299999999999996,21,0
|
||||
1,102,74,0,0,39.5,0.293,42,1
|
||||
11,120,80,37,150,42.3,0.785,48,1
|
||||
3,102,44,20,94,30.8,0.4,26,0
|
||||
1,109,58,18,116,28.5,0.21899999999999997,22,0
|
||||
9,140,94,0,0,32.7,0.7340000000000001,45,1
|
||||
13,153,88,37,140,40.6,1.1740000000000002,39,0
|
||||
12,100,84,33,105,30.0,0.488,46,0
|
||||
1,147,94,41,0,49.3,0.358,27,1
|
||||
1,81,74,41,57,46.3,1.0959999999999999,32,0
|
||||
3,187,70,22,200,36.4,0.408,36,1
|
||||
6,162,62,0,0,24.3,0.17800000000000002,50,1
|
||||
4,136,70,0,0,31.2,1.182,22,1
|
||||
1,121,78,39,74,39.0,0.261,28,0
|
||||
3,108,62,24,0,26.0,0.223,25,0
|
||||
0,181,88,44,510,43.3,0.222,26,1
|
||||
8,154,78,32,0,32.4,0.44299999999999995,45,1
|
||||
1,128,88,39,110,36.5,1.057,37,1
|
||||
7,137,90,41,0,32.0,0.391,39,0
|
||||
0,123,72,0,0,36.3,0.258,52,1
|
||||
1,106,76,0,0,37.5,0.19699999999999998,26,0
|
||||
6,190,92,0,0,35.5,0.278,66,1
|
||||
2,88,58,26,16,28.4,0.7659999999999999,22,0
|
||||
9,170,74,31,0,44.0,0.40299999999999997,43,1
|
||||
9,89,62,0,0,22.5,0.142,33,0
|
||||
10,101,76,48,180,32.9,0.171,63,0
|
||||
2,122,70,27,0,36.8,0.34,27,0
|
||||
5,121,72,23,112,26.2,0.245,30,0
|
||||
1,126,60,0,0,30.1,0.349,47,1
|
||||
1,93,70,31,0,30.4,0.315,23,0
|
||||
|
|
|
@ -1,88 +0,0 @@
|
|||
PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
|
||||
904,1,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23.0,1,0,21228,82.2667,B45,S
|
||||
906,1,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47.0,1,0,W.E.P. 5734,61.175,E31,S
|
||||
916,1,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48.0,1,3,PC 17608,262.375,B57 B59 B63 B66,C
|
||||
918,1,1,"Ostby, Miss. Helene Ragnhild",female,22.0,0,1,113509,61.9792,B36,C
|
||||
920,0,1,"Brady, Mr. John Bertram",male,41.0,0,0,113054,30.5,A21,S
|
||||
926,0,1,"Mock, Mr. Philipp Edmund",male,30.0,1,0,13236,57.75,C78,C
|
||||
936,1,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45.0,1,0,11753,52.5542,D19,S
|
||||
938,0,1,"Chevre, Mr. Paul Romaine",male,45.0,0,0,PC 17594,29.7,A9,C
|
||||
940,1,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60.0,0,0,11813,76.2917,D15,C
|
||||
942,0,1,"Smith, Mr. Lucien Philip",male,24.0,1,0,13695,60.0,C31,S
|
||||
945,1,1,"Fortune, Miss. Ethel Flora",female,28.0,3,2,19950,263.0,C23 C25 C27,S
|
||||
949,0,3,"Abelseth, Mr. Olaus Jorgensen",male,25.0,0,0,348122,7.65,F G63,S
|
||||
951,1,1,"Chaudanson, Miss. Victorine",female,36.0,0,0,PC 17608,262.375,B61,C
|
||||
956,0,1,"Ryerson, Master. John Borie",male,13.0,2,2,PC 17608,262.375,B57 B59 B63 B66,C
|
||||
960,0,1,"Tucker, Mr. Gilbert Milligan Jr",male,31.0,0,0,2543,28.5375,C53,C
|
||||
961,1,1,"Fortune, Mrs. Mark (Mary McDougald)",female,60.0,1,4,19950,263.0,C23 C25 C27,S
|
||||
965,0,1,"Ovies y Rodriguez, Mr. Servando",male,28.5,0,0,PC 17562,27.7208,D43,C
|
||||
966,1,1,"Geiger, Miss. Amalie",female,35.0,0,0,113503,211.5,C130,C
|
||||
967,0,1,"Keeping, Mr. Edwin",male,32.5,0,0,113503,211.5,C132,C
|
||||
969,1,1,"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)",female,55.0,2,0,11770,25.7,C101,S
|
||||
973,0,1,"Straus, Mr. Isidor",male,67.0,1,0,PC 17483,221.7792,C55 C57,S
|
||||
984,1,1,"Davidson, Mrs. Thornton (Orian Hays)",female,27.0,1,2,F.C. 12750,52.0,B71,S
|
||||
988,1,1,"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)",female,76.0,1,0,19877,78.85,C46,S
|
||||
992,1,1,"Stengel, Mrs. Charles Emil Henry (Annie May Morris)",female,43.0,1,0,11778,55.4417,C116,C
|
||||
1001,0,2,"Swane, Mr. George",male,18.5,0,0,248734,13.0,F,S
|
||||
1004,1,1,"Evans, Miss. Edith Corse",female,36.0,0,0,PC 17531,31.6792,A29,C
|
||||
1006,1,1,"Straus, Mrs. Isidor (Rosalie Ida Blun)",female,63.0,1,0,PC 17483,221.7792,C55 C57,S
|
||||
1009,1,3,"Sandstrom, Miss. Beatrice Irene",female,1.0,1,1,PP 9549,16.7,G6,S
|
||||
1010,0,1,"Beattie, Mr. Thomson",male,36.0,0,0,13050,75.2417,C6,C
|
||||
1014,1,1,"Schabert, Mrs. Paul (Emma Mock)",female,35.0,1,0,13236,57.75,C28,C
|
||||
1023,0,1,"Gracie, Col. Archibald IV",male,53.0,0,0,113780,28.5,C51,C
|
||||
1034,0,1,"Ryerson, Mr. Arthur Larned",male,61.0,1,3,PC 17608,262.375,B57 B59 B63 B66,C
|
||||
1042,1,1,"Earnshaw, Mrs. Boulton (Olive Potter)",female,23.0,0,1,11767,83.1583,C54,C
|
||||
1048,1,1,"Bird, Miss. Ellen",female,29.0,0,0,PC 17483,221.7792,C97,S
|
||||
1050,0,1,"Borebank, Mr. John James",male,42.0,0,0,110489,26.55,D22,S
|
||||
1058,0,1,"Brandeis, Mr. Emil",male,48.0,0,0,PC 17591,50.4958,B10,C
|
||||
1069,0,1,"Stengel, Mr. Charles Emil Henry",male,54.0,1,0,11778,55.4417,C116,C
|
||||
1070,1,2,"Becker, Mrs. Allen Oliver (Nellie E Baumgardner)",female,36.0,0,3,230136,39.0,F4,S
|
||||
1071,1,1,"Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)",female,64.0,0,2,PC 17756,83.1583,E45,C
|
||||
1073,0,1,"Compton, Mr. Alexander Taylor Jr",male,37.0,1,1,PC 17756,83.1583,E52,C
|
||||
1074,1,1,"Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)",female,18.0,1,0,113773,53.1,D30,S
|
||||
1076,1,1,"Douglas, Mrs. Frederick Charles (Mary Helene Baxter)",female,27.0,1,1,PC 17558,247.5208,B58 B60,C
|
||||
1088,0,1,"Spedden, Master. Robert Douglas",male,6.0,0,2,16966,134.5,E34,C
|
||||
1094,0,1,"Astor, Col. John Jacob",male,47.0,1,0,PC 17757,227.525,C62 C64,C
|
||||
1100,1,1,"Rosenbaum, Miss. Edith Louise",female,33.0,0,0,PC 17613,27.7208,A11,C
|
||||
1107,0,1,"Head, Mr. Christopher",male,42.0,0,0,113038,42.5,B11,S
|
||||
1110,1,1,"Widener, Mrs. George Dunton (Eleanor Elkins)",female,50.0,1,1,113503,211.5,C80,C
|
||||
1114,1,2,"Cook, Mrs. (Selena Rogers)",female,22.0,0,0,W./C. 14266,10.5,F33,S
|
||||
1126,0,1,"Cumings, Mr. John Bradley",male,39.0,1,0,PC 17599,71.2833,C85,C
|
||||
1128,0,1,"Warren, Mr. Frank Manley",male,64.0,1,0,110813,75.25,D37,C
|
||||
1131,1,1,"Douglas, Mrs. Walter Donald (Mahala Dutton)",female,48.0,1,0,PC 17761,106.425,C86,C
|
||||
1134,0,1,"Spedden, Mr. Frederic Oakley",male,45.0,1,1,16966,134.5,E34,C
|
||||
1137,0,1,"Kenyon, Mr. Frederick R",male,41.0,1,0,17464,51.8625,D21,S
|
||||
1144,0,1,"Clark, Mr. Walter Miller",male,27.0,1,0,13508,136.7792,C89,C
|
||||
1162,0,1,"McCaffry, Mr. Thomas Francis",male,46.0,0,0,13050,75.2417,C6,C
|
||||
1164,1,1,"Clark, Mrs. Walter Miller (Virginia McDowell)",female,26.0,1,0,13508,136.7792,C89,C
|
||||
1179,0,1,"Snyder, Mr. John Pillsbury",male,24.0,1,0,21228,82.2667,B45,S
|
||||
1185,0,1,"Dodge, Dr. Washington",male,53.0,1,1,33638,81.8583,A34,S
|
||||
1197,1,1,"Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead)",female,64.0,1,1,112901,26.55,B26,S
|
||||
1198,0,1,"Allison, Mr. Hudson Joshua Creighton",male,30.0,1,2,113781,151.55,C22 C26,S
|
||||
1200,0,1,"Hays, Mr. Charles Melville",male,55.0,1,1,12749,93.5,B69,S
|
||||
1206,1,1,"White, Mrs. John Stuart (Ella Holmes)",female,55.0,0,0,PC 17760,135.6333,C32,C
|
||||
1208,0,1,"Spencer, Mr. William Augustus",male,57.0,1,0,PC 17569,146.5208,B78,C
|
||||
1213,0,3,"Krekorian, Mr. Neshan",male,25.0,0,0,2654,7.2292,F E57,C
|
||||
1214,0,2,"Nesson, Mr. Israel",male,26.0,0,0,244368,13.0,F2,S
|
||||
1218,1,2,"Becker, Miss. Ruth Elizabeth",female,12.0,2,1,230136,39.0,F4,S
|
||||
1223,0,1,"Dulles, Mr. William Crothers",male,39.0,0,0,PC 17580,29.7,A18,C
|
||||
1227,0,1,"Maguire, Mr. John Edward",male,30.0,0,0,110469,26.0,C106,S
|
||||
1235,1,1,"Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)",female,58.0,0,1,PC 17755,512.3292,B51 B53 B55,C
|
||||
1242,1,1,"Greenfield, Mrs. Leo David (Blanche Strouse)",female,45.0,0,1,PC 17759,63.3583,D10 D12,C
|
||||
1247,0,1,"Julian, Mr. Henry Forbes",male,50.0,0,0,113044,26.0,E60,S
|
||||
1248,1,1,"Brown, Mrs. John Murray (Caroline Lane Lamson)",female,59.0,2,0,11769,51.4792,C101,S
|
||||
1256,1,1,"Harder, Mrs. George Achilles (Dorothy Annan)",female,25.0,1,0,11765,55.4417,E50,C
|
||||
1263,1,1,"Wilson, Miss. Helen Alice",female,31.0,0,0,16966,134.5,E39 E41,C
|
||||
1264,0,1,"Ismay, Mr. Joseph Bruce",male,49.0,0,0,112058,0.0,B52 B54 B56,S
|
||||
1266,1,1,"Dodge, Mrs. Washington (Ruth Vidaver)",female,54.0,1,1,33638,81.8583,A34,S
|
||||
1270,0,1,"Hipkins, Mr. William Edward",male,55.0,0,0,680,50.0,C39,S
|
||||
1282,0,1,"Payne, Mr. Vivian Ponsonby",male,23.0,0,0,12749,93.5,B24,S
|
||||
1283,1,1,"Lines, Mrs. Ernest H (Elizabeth Lindsey James)",female,51.0,0,1,PC 17592,39.4,D28,S
|
||||
1287,1,1,"Smith, Mrs. Lucien Philip (Mary Eloise Hughes)",female,18.0,1,0,13695,60.0,C31,S
|
||||
1289,1,1,"Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli)",female,48.0,1,1,13567,79.2,B41,C
|
||||
1292,1,1,"Bonnell, Miss. Caroline",female,30.0,0,0,36928,164.8667,C7,S
|
||||
1296,0,1,"Frauenthal, Mr. Isaac Gerald",male,43.0,1,0,17765,27.7208,D40,C
|
||||
1297,0,2,"Nourney, Mr. Alfred (Baron von Drachstedt"")""",male,20.0,0,0,SC/PARIS 2166,13.8625,D38,C
|
||||
1299,0,1,"Widener, Mr. George Dunton",male,50.0,1,1,113503,211.5,C80,C
|
||||
1303,1,1,"Minahan, Mrs. William Edward (Lillian E Thorpe)",female,37.0,1,0,19928,90.0,C78,Q
|
||||
1306,1,1,"Oliva y Ocana, Dona. Fermina",female,39.0,0,0,PC 17758,108.9,C105,C
|
||||
|
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