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fix: use is_string_dtype to check for object/string types

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Matthias
2026-04-12 19:33:28 +02:00
parent f6f0180fc1
commit 79ea1ba1d5
3 changed files with 7 additions and 7 deletions
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freqtrade/freqai/data_drawer.py
+1 -1
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@@ -361,7 +361,7 @@ class FreqaiDataDrawer:
label_loc = df.columns.get_loc(label) label_loc = df.columns.get_loc(label)
pred_label_loc = predictions.columns.get_loc(label) pred_label_loc = predictions.columns.get_loc(label)
df.iloc[-1, label_loc] = predictions.iloc[-1, pred_label_loc] df.iloc[-1, label_loc] = predictions.iloc[-1, pred_label_loc]
if df[label].dtype == object: if pd.api.types.is_string_dtype(df[label].dtype):
continue continue
label_mean_loc = df.columns.get_loc(f"{label}_mean") label_mean_loc = df.columns.get_loc(f"{label}_mean")
label_std_loc = df.columns.get_loc(f"{label}_std") label_std_loc = df.columns.get_loc(f"{label}_std")
freqtrade/freqai/data_kitchen.py
+3 -3
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@@ -435,7 +435,7 @@ class FreqaiDataKitchen:
for label in predictions.columns: for label in predictions.columns:
append_dict[label] = predictions[label] append_dict[label] = predictions[label]
if predictions[label].dtype == object: if pd.api.types.is_string_dtype(predictions[label].dtype):
continue continue
if "labels_mean" in self.data and label in self.data["labels_mean"]: if "labels_mean" in self.data and label in self.data["labels_mean"]:
append_dict[f"{label}_mean"] = self.data["labels_mean"][label] append_dict[f"{label}_mean"] = self.data["labels_mean"][label]
@@ -879,7 +879,7 @@ class FreqaiDataKitchen:
self.data["labels_mean"], self.data["labels_std"] = {}, {} self.data["labels_mean"], self.data["labels_std"] = {}, {}
for label in self.data_dictionary["train_labels"].columns: for label in self.data_dictionary["train_labels"].columns:
if self.data_dictionary["train_labels"][label].dtype == object: if pd.api.types.is_string_dtype(self.data_dictionary["train_labels"][label].dtype):
continue continue
f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label]) f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1] self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
@@ -905,7 +905,7 @@ class FreqaiDataKitchen:
self.find_labels(dataframe) self.find_labels(dataframe)
for key in self.label_list: for key in self.label_list:
if dataframe[key].dtype == object: if pd.api.types.is_string_dtype(dataframe[key].dtype):
self.unique_classes[key] = dataframe[key].dropna().unique() self.unique_classes[key] = dataframe[key].dropna().unique()
if self.unique_classes: if self.unique_classes:
freqtrade/freqai/freqai_interface.py
+3 -3
View File
@@ -676,7 +676,7 @@ class IFreqaiModel(ABC):
self.set_start_dry_live_date(strat_df) self.set_start_dry_live_date(strat_df)
for label in hist_preds_df.columns: for label in hist_preds_df.columns:
if hist_preds_df[label].dtype == object: if pd.api.types.is_string_dtype(hist_preds_df[label].dtype):
continue continue
hist_preds_df[f"{label}_mean"] = 0 hist_preds_df[f"{label}_mean"] = 0
hist_preds_df[f"{label}_std"] = 0 hist_preds_df[f"{label}_std"] = 0
@@ -706,7 +706,7 @@ class IFreqaiModel(ABC):
num_candles = self.freqai_info.get("fit_live_predictions_candles", 100) num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
dk.data["labels_mean"], dk.data["labels_std"] = {}, {} dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
for label in full_labels: for label in full_labels:
if self.dd.historic_predictions[dk.pair][label].dtype == object: if pd.api.types.is_string_dtype(self.dd.historic_predictions[dk.pair][label].dtype):
continue continue
f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles)) f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1] dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
@@ -896,7 +896,7 @@ class IFreqaiModel(ABC):
] ]
self.fit_live_predictions(self.dk, self.dk.pair) self.fit_live_predictions(self.dk, self.dk.pair)
for label in label_columns: for label in label_columns:
if dk.full_df[label].dtype == object: if pd.api.types.is_string_dtype(dk.full_df[label].dtype):
continue continue
if "labels_mean" in self.dk.data: if "labels_mean" in self.dk.data:
dk.full_df.at[index, f"{label}_mean"] = self.dk.data["labels_mean"][ dk.full_df.at[index, f"{label}_mean"] = self.dk.data["labels_mean"][