convert_input_array

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "perpetual"
-version = "0.7.6"
+version = "0.7.7"
 edition = "2021"
 authors = ["Mutlu Simsek <[email protected]>"]
 homepage = "https://perpetual-ml.com"

python-package/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "py-perpetual"
-version = "0.7.6"
+version = "0.7.7"
 edition = "2021"
 authors = ["Mutlu Simsek <[email protected]>"]
 homepage = "https://perpetual-ml.com"
@@ -19,7 +19,7 @@ crate-type = ["cdylib", "rlib"]
 
 [dependencies]
 pyo3 = { version = "0.22.6", features = ["extension-module"] }
-perpetual_rs = {package="perpetual", version = "0.7.6", path = "../" }
+perpetual_rs = {package="perpetual", version = "0.7.7", path = "../" }
 numpy = "0.22.1"
 ndarray = "0.16.1"
 serde_plain = { version = "1.0" }

python-package/examples/openml.ipynb

@@ -120,7 +120,7 @@
    "source": [
     "from perpetual.utils import convert_input_frame\n",
     "\n",
-    "features_, df_flat, rows, cols, categorical_features_, cat_mapping = convert_input_frame(df, \"auto\")\n",
+    "features_, df_flat, rows, cols, categorical_features_, cat_mapping = convert_input_frame(df, \"auto\", 1000)\n",
     "\n",
     "pd.Series(df_flat).to_csv(\"../../resources/christine_flat.csv\", index=False, header=False)\n",
     "pd.Series(y).to_csv(\"../../resources/christine_y.csv\", index=False, header=False)"

python-package/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "maturin"
 
 [project]
 name = "perpetual"
-version = "0.7.6"
+version = "0.7.7"
 description = "A self-generalizing gradient boosting machine which doesn't need hyperparameter optimization"
 license = { file = "LICENSE" }
 keywords = [

python-package/python/perpetual/utils.py

@@ -37,15 +37,15 @@ def convert_input_array(x, objective) -> np.ndarray:
 
     if type(x).__module__.split(".")[0] == "numpy":
         if len(x.shape) == 2:
-            classes_, x_, *_ = convert_input_frame(x, None)
+            classes_, x_, *_ = convert_input_frame(x, None, 1000)
         else:
             x_ = x
     elif type_series(x) == "pandas_series":
         x_ = x.to_numpy()
     elif type_series(x) == "polars_series":
         x_ = x.to_numpy(allow_copy=False)
     elif type_df(x) == "polars_df" or type_df(x) == "pandas_df":
-        classes_, x_, *_ = convert_input_frame(x, None)
+        classes_, x_, *_ = convert_input_frame(x, None, 1000)
     else:
         x_ = x.to_numpy()