HI Everyone, sorry for the newbie question but I'm kinda stuck here and didn't find a good way to work around the problem.
Basically I'm just trying to create a DataFrame from a SQL query but I keep getting an error:
val url = "jdbc:postgresql://localhost/test"
val props = Properties()
props.setProperty("user", "test")
props.setProperty("password", "test")
val conn: Connection = DriverManager.getConnection(url, props)
val rs = conn.createStatement().executeQuery("select 1 from homes")
val df = DataFrame.of(rs)
println(df)and the error:
Exception in thread "main" java.lang.IllegalArgumentException: No enum constant java.sql.JDBCType.int4
at java.base/java.lang.Enum.valueOf(Enum.java:240)
at java.sql/java.sql.JDBCType.valueOf(JDBCType.java:34)
at smile.data.type.DataTypes.struct(DataTypes.java:168)
at smile.data.type.DataTypes.struct(DataTypes.java:158)
at smile.data.DataFrame.of(DataFrame.java:1261)I'm using org.postgresql:postgresql:42.2.12
10 replies
Hi hifeng, I tried to use MultivariateGaussianMixture but have the following exception:
Warning: Could not load Loader: java.lang.UnsatisfiedLinkError: no jnijavacpp in java.library.path
Exception in thread "main" java.lang.UnsatisfiedLinkError: no jniopenblas_nolapack in java.library.path
I import the dependency in maven like this:
<dependency>
<groupId>com.github.haifengl</groupId>
<artifactId>smile-core</artifactId>
<version>2.5.3</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.bytedeco/javacpp -->
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>javacpp</artifactId>
<version>1.5.3</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.bytedeco/openblas -->
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>openblas</artifactId>
<version>0.3.9-1.5.3</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.bytedeco/arpack-ng -->
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>arpack-ng</artifactId>
<version>3.7.0-1.5.3</version>
</dependency>
new_col = lambda each_row: math.sqrt((3*each_row.first_column)**4 + (3*each_row.second_column)**4+ (3*each_row.third_column)**4)
my_dataframe.apply(new_col)
import smile.data.formula.Formula;
import static smile.data.formula.Terms.*;df.stream().map(row -> { or something?
I have a situation:
1.) I've trained a random forest with a DataFrame object (target, feature 1, ... , feature 5).
2.) I would like to use RandomForest::predict with a new sample (production), that I get as double[5] {1,2,3,4,5}.
3.) I generate a Tuple t with schema (feature 1, ..., feature 5) and data [1,2,3,4,5]
4.) When I run predict(t) an array out of bounds exception occures. The problem is, that it tries to access feature 5 with index 5. This was correct in the schema of the dataset but not for the schema of the tuple t.
I would have expected, that in predict, it would access the data for Feature 5 from the tuple t on index 4.
Is there something I'm doing fundamentally wrong or might there be some inconsistencies? I couldn't help myself, since all cases I found in some sort of documentation predict on a tuple coming from the original DataFrame.
Thank you :-)
1.) Bind the new schema (feature 1, ... , feature 5).I've tried this, but since "response != null" in the RF's formula I get a NPE. So, is there any support for execution samples without targets?
2.) I artificially change my sample to include some dummy labels, s.t. the targets have a value aswell and the schema would correspond again to the training case.
The response variable Classification doesn't exist in the schema [...]. Should I pay attention to this message? How do I get rid of it?
sterr = true
smile.validation.metric package. The classification algorithms report posteriori probabilities if they are SoftClassifier. Some algorithms also have a score() method, which is not necessarily probabilities though.
again, SMILE is awesome ML library! clean dataframe implementation, classification framework which enable to expand other algorithm easliy. so, I am thinking to implement missing algorithms like a factorization machine, field aware FM, ngboost, and so on