removed tqdm and switched over to rich (#137)

* removed tqdm and switched over to rich

* fixed issue with rich progressbar

Author: manujosephv

src/pytorch_tabular/categorical_encoders.py

@@ -12,8 +12,8 @@
 
 import numpy as np
 import pandas as pd
+from rich.progress import track
 from sklearn.base import BaseEstimator, TransformerMixin
-from tqdm.autonotebook import tqdm
 
 from pytorch_tabular.utils import get_logger
 
@@ -198,9 +198,9 @@ def transform(self, X: pd.DataFrame, y=None) -> pd.DataFrame:
         assert all(c in X.columns for c in self.cols)
 
         X_encoded = X.copy(deep=True)
-        for col, mapping in tqdm(
+        for col, mapping in track(
             self._mapping.items(),
-            desc="Encoding the data...",
+            description="Encoding the data...",
             total=len(self._mapping.values()),
         ):
             for dim in range(mapping[self.NAN_CATEGORY].shape[0]):

src/pytorch_tabular/feature_extractor.py

@@ -4,8 +4,8 @@
 from collections import defaultdict
 
 import pandas as pd
+from rich.progress import track
 from sklearn.base import BaseEstimator, TransformerMixin
-from tqdm.autonotebook import tqdm
 
 from pytorch_tabular.models import NODEModel, TabNetModel
 from pytorch_tabular.models.mixture_density import MDNModel
@@ -57,7 +57,7 @@ def transform(self, X: pd.DataFrame, y=None) -> pd.DataFrame:
         self.tabular_model.model.eval()
         inference_dataloader = self.tabular_model.datamodule.prepare_inference_dataloader(X_encoded)
         logits_predictions = defaultdict(list)
-        for batch in tqdm(inference_dataloader, desc="Generating Features..."):
+        for batch in track(inference_dataloader, description="Generating Features..."):
             for k, v in batch.items():
                 if isinstance(v, list) and (len(v) == 0):
                     # Skipping empty list

src/pytorch_tabular/tabular_model.py

@@ -22,10 +22,10 @@
 from pytorch_lightning.callbacks import RichProgressBar
 from pytorch_lightning.callbacks.gradient_accumulation_scheduler import GradientAccumulationScheduler
 from pytorch_lightning.utilities.model_summary import summarize
+from rich.progress import track
 from sklearn.base import TransformerMixin
 from sklearn.preprocessing import LabelEncoder
 from torch import nn
-from tqdm.autonotebook import tqdm
 
 from pytorch_tabular.config import (
     DataConfig,
@@ -1084,7 +1084,7 @@ def predict(
         quantile_predictions = []
         logits_predictions = defaultdict(list)
         is_probabilistic = hasattr(self.model.hparams, "_probabilistic") and self.model.hparams._probabilistic
-        for batch in tqdm(inference_dataloader, desc="Generating Predictions..."):
+        for batch in track(inference_dataloader, description="Generating Predictions..."):
             for k, v in batch.items():
                 if isinstance(v, list) and (len(v) == 0):
                     # Skipping empty list