Course Name: Portfolio Management using Machine Learning: Hierarchical Risk Parity, Section No: 12, Unit No: 8, Unit type: Notebook
Error when run until [In7]
Hi,
I am not able to replicate the issue. It is working fine on my local system. It would be great if you could please share the error that you are getting while running the code, so that we can help debug the same.
Thanks,
Akshay
TypeError Traceback (most recent call last)
Cell In[7], line 2
1 # Scale the data using StandardScaler
----> 2 train_dataset_scaled = pd.DataFrame(StandardScaler().fit_transform(train_dataset),
3 columns=[train_dataset.columns],
4 index=train_dataset.index)
File ~\anaconda3\Lib\site-packages\sklearn\utils\_set_output.py:140, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
138 @wraps(f)
139 def wrapped(self, X, *args, **kwargs):
--> 140 data_to_wrap = f(self, X, *args, **kwargs)
141 if isinstance(data_to_wrap, tuple):
142 # only wrap the first output for cross decomposition
143 return_tuple = (
144 _wrap_data_with_container(method, data_to_wrap[0], X, self),
145 *data_to_wrap[1:],
146 )
File ~\anaconda3\Lib\site-packages\sklearn\base.py:915, in TransformerMixin.fit_transform(self, X, y, **fit_params)
911 # non-optimized default implementation; override when a better
912 # method is possible for a given clustering algorithm
913 if y is None:
914 # fit method of arity 1 (unsupervised transformation)
--> 915 return self.fit(X, **fit_params).transform(X)
916 else:
917 # fit method of arity 2 (supervised transformation)
918 return self.fit(X, y, **fit_params).transform(X)
File ~\anaconda3\Lib\site-packages\sklearn\preprocessing\_data.py:837, in StandardScaler.fit(self, X, y, sample_weight)
835 # Reset internal state before fitting
836 self._reset()
--> 837 return self.partial_fit(X, y, sample_weight)
File ~\anaconda3\Lib\site-packages\sklearn\base.py:1151, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
1144 estimator._validate_params()
1146 with config_context(
1147 skip_parameter_validation=(
1148 prefer_skip_nested_validation or global_skip_validation
1149 )
1150 ):
-> 1151 return fit_method(estimator, *args, **kwargs)
File ~\anaconda3\Lib\site-packages\sklearn\preprocessing\_data.py:873, in StandardScaler.partial_fit(self, X, y, sample_weight)
841 """Online computation of mean and std on X for later scaling.
842
843 All of X is processed as a single batch. This is intended for cases
(...)
870 Fitted scaler.
871 """
872 first_call = not hasattr(self, "n_samples_seen_")
--> 873 X = self._validate_data(
874 X,
875 accept_sparse=("csr", "csc"),
876 dtype=FLOAT_DTYPES,
877 force_all_finite="allow-nan",
878 reset=first_call,
879 )
880 n_features = X.shape[1]
882 if sample_weight is not None:
File ~\anaconda3\Lib\site-packages\sklearn\base.py:579, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)
508 def _validate_data(
509 self,
510 X="no_validation",
(...)
515 **check_params,
516 ):
517 """Validate input data and set or check the `n_features_in_` attribute.
518
519 Parameters
(...)
577 validated.
578 """
--> 579 self._check_feature_names(X, reset=reset)
581 if y is None and self._get_tags()["requires_y"]:
582 raise ValueError(
583 f"This {self.__class__.__name__} estimator "
584 "requires y to be passed, but the target y is None."
585 )
File ~\anaconda3\Lib\site-packages\sklearn\base.py:440, in BaseEstimator._check_feature_names(self, X, reset)
420 """Set or check the `feature_names_in_` attribute.
421
422 .. versionadded:: 1.0
(...)
436 should set `reset=False`.
437 """
439 if reset:
--> 440 feature_names_in = _get_feature_names(X)
441 if feature_names_in is not None:
442 self.feature_names_in_ = feature_names_in
File ~\anaconda3\Lib\site-packages\sklearn\utils\validation.py:2021, in _get_feature_names(X)
2019 # mixed type of string and non-string is not supported
2020 if len(types) > 1 and "str" in types:
-> 2021 raise TypeError(
2022 "Feature names are only supported if all input features have string names, "
2023 f"but your input has {types} as feature name / column name types. "
2024 "If you want feature names to be stored and validated, you must convert "
2025 "them all to strings, by using X.columns = X.columns.astype(str) for "
2026 "example. Otherwise you can remove feature / column names from your input "
2027 "data, or convert them all to a non-string data type."
2028 )
2030 # Only feature names of all strings are supported
2031 if len(types) == 1 and types[0] == "str":
TypeError: Feature names are only supported if all input features haveHi,
The error might be due to a difference in a library version on the Quantra portal and your local system. One way to resolve it is to set up the Quantra Python environment on your system and then try running the notebook on the quantra environment. You can refer to this link to learn more about setting up the Python environment on your system. Please do let us know if you face any issues.
Thanks,
Akshay