Merge pull request #4 from moritzwilksch/fixes

fixes + env

.gitattributes

@@ -0,0 +1,3 @@
+# GitHub syntax highlighting
+pixi.lock linguist-language=YAML
+

.gitignore

@@ -128,3 +128,6 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+# pixi environments
+.pixi
+

pixi.toml

@@ -0,0 +1,24 @@
+[project]
+name = "pyfin-sentiment"
+version = "0.1.0"
+description = "Add a short description here"
+authors = ["Moritz Wilksch <[email protected]>"]
+channels = ["conda-forge"]
+platforms = ["linux-64"]
+
+[tasks]
+postinstall = "pip install --no-build-isolation -e ." 
+
+[dependencies]
+pip = ">=24.0,<25"
+polars = ">=0.20.15,<0.21"
+numpy = ">=1.26.4,<1.27"
+scikit-learn = ">=1.4.1.post1,<1.4.2"
+
+
+[feature.dev.dependencies]
+pytest = ">=7.2.0,<8.2"
+twine = ">=5.0.0,<5.1"
+
+[environments]
+dev = ["dev"]

requirements.txt

@@ -4,4 +4,4 @@ isort
 toml
 joblib
 scikit-learn
-polars==0.12.0
+polars==0.20.*

src/pyfin_sentiment/model.py

@@ -137,7 +137,7 @@ def predict(self, texts: Union[list, np.ndarray]) -> np.ndarray:
         if len(texts) == 0:
             raise ValueError(f"Please provide at least one text. Got {texts}")
 
-        df = pl.DataFrame(texts, columns=["text"])
+        df = pl.DataFrame(texts, schema=["text"])
         df = self.preprocessor.process(df)
 
         return self.model.predict(df["text"].to_list())
@@ -157,7 +157,7 @@ def predict_proba(self, texts: Union[list, np.ndarray]) -> np.ndarray:
                 f"Please provide a list or np.ndarray of texts. Got {type(texts)}."
             )
 
-        df = pl.DataFrame(texts, columns=["text"])
+        df = pl.DataFrame(texts, schema=["text"])
         df = self.preprocessor.process(df)
 
-        return self.model.predict_proba(df["text"].to_list())
+        return self.model.predict_proba(df["text"].to_list())

src/pyfin_sentiment/preprocessing.py

@@ -25,34 +25,34 @@ def __init__(
         self.multi_spaces = multi_spaces
 
     def fix_symbols(self, df: pl.DataFrame) -> pl.DataFrame:
-        return df.with_column(
+        return df.with_columns(
             pl.col(self.TEXTCOL)
             .str.replace_all(r">", ">")
             .str.replace_all(r"&lt;", "<")
             .str.replace_all(r"&amp;", "&")
         )
 
     def prep_numbers(self, df: pl.DataFrame) -> pl.DataFrame:
-        return df.with_column(pl.col(self.TEXTCOL).str.replace_all(r"\d", "9"))
+        return df.with_columns(pl.col(self.TEXTCOL).str.replace_all(r"\d", "9"))
 
     def prep_cashtags(self, df: pl.DataFrame) -> pl.DataFrame:
-        return df.with_column(
+        return df.with_columns(
             pl.col(self.TEXTCOL).str.replace_all(self.CASHTAG_REGEX, "TICKER")
         )
 
     def prep_mentions(self, df: pl.DataFrame) -> pl.DataFrame:
-        return df.with_column(
+        return df.with_columns(
             pl.col(self.TEXTCOL).str.replace_all(self.MENTION_REGEX, "@USER")
         )
 
     def prep_lowercase(self, df: pl.DataFrame) -> pl.DataFrame:
-        return df.with_column(pl.col(self.TEXTCOL).str.to_lowercase())
+        return df.with_columns(pl.col(self.TEXTCOL).str.to_lowercase())
 
     def prep_newlines(self, df: pl.DataFrame) -> pl.DataFrame:
-        return df.with_column(pl.col(self.TEXTCOL).str.replace_all("\n", " "))
+        return df.with_columns(pl.col(self.TEXTCOL).str.replace_all("\n", " "))
 
     def prep_multi_spaces(self, df: pl.DataFrame) -> pl.DataFrame:
-        return df.with_column(pl.col(self.TEXTCOL).str.replace_all(r"\s+", " "))
+        return df.with_columns(pl.col(self.TEXTCOL).str.replace_all(r"\s+", " "))
 
     def process(self, df: pl.DataFrame) -> pl.DataFrame:
         if self.symbols: