107 lines
3.2 KiB
Text
107 lines
3.2 KiB
Text
/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_orig.py:550: SettingWithCopyWarning:
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A value is trying to be set on a copy of a slice from a DataFrame
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See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
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mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
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1.22.4
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1.4.1
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aaindex_df contains non-numerical data
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Total no. of non-numerial columns: 2
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Selecting numerical data only
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PASS: successfully selected numerical columns only for aaindex_df
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Now checking for NA in the remaining aaindex_cols
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Counting aaindex_df cols with NA
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ncols with NA: 4 columns
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Dropping these...
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Original ncols: 127
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Revised df ncols: 123
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Checking NA in revised df...
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PASS: cols with NA successfully dropped from aaindex_df
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Proceeding with combining aa_df with other features_df
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PASS: ncols match
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Expected ncols: 123
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Got: 123
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Total no. of columns in clean aa_df: 123
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Proceeding to merge, expected nrows in merged_df: 271
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PASS: my_features_df and aa_df successfully combined
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nrows: 271
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ncols: 269
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count of NULL values before imputation
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or_mychisq 256
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log10_or_mychisq 256
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dtype: int64
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count of NULL values AFTER imputation
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mutationinformation 0
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or_rawI 0
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logorI 0
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dtype: int64
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PASS: OR values imputed, data ready for ML
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Total no. of features for aaindex: 123
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No. of numerical features: 168
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No. of categorical features: 7
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index: 0
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ind: 1
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Mask count check: True
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index: 1
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ind: 2
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Mask count check: True
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Original Data
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Counter({0: 7, 1: 1}) Data dim: (8, 175)
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-------------------------------------------------------------
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Successfully split data: ORIGINAL training
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actual values: training set
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imputed values: blind test set
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Train data size: (8, 175)
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Test data size: (263, 175)
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y_train numbers: Counter({0: 7, 1: 1})
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y_train ratio: 7.0
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y_test_numbers: Counter({0: 262, 1: 1})
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y_test ratio: 262.0
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-------------------------------------------------------------
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Simple Random OverSampling
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Counter({0: 7, 1: 7})
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(14, 175)
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Simple Random UnderSampling
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Counter({0: 1, 1: 1})
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(2, 175)
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Simple Combined Over and UnderSampling
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Counter({0: 7, 1: 7})
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(14, 175)
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Traceback (most recent call last):
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File "/home/tanu/git/LSHTM_analysis/scripts/ml/./alr_orig.py", line 19, in <module>
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setvars(gene,drug)
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File "/home/tanu/git/LSHTM_analysis/scripts/ml/ml_data_orig.py", line 701, in setvars
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X_smnc, y_smnc = sm_nc.fit_resample(X, y)
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File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/imblearn/base.py", line 83, in fit_resample
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output = self._fit_resample(X, y)
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File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/imblearn/over_sampling/_smote/base.py", line 533, in _fit_resample
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X_resampled, y_resampled = super()._fit_resample(X_encoded, y)
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File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/imblearn/over_sampling/_smote/base.py", line 324, in _fit_resample
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nns = self.nn_k_.kneighbors(X_class, return_distance=False)[:, 1:]
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File "/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neighbors/_base.py", line 749, in kneighbors
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raise ValueError(
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ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6
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